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1. Combination of computed tomography angiography with coronary artery calcium score for improved diagnosis of coronary artery disease: a collaborative meta-analysis of stable chest pain patients referred for invasive coronary angiography.

Author

Mohamed, Mahmoud, Bosserdt, Maria, Wieske, Viktoria, Dubourg, Benjamin, Alkadhi, Hatem, Garcia, Mario, Leschka, Sebastian, Zimmermann, Elke, Shabestari, Abbas, Nørgaard, Bjarne, Meijs, Matthijs, Øvrehus, Kristian, Diederichsen, Axel, Knuuti, Juhani, Halvorsen, Bjørn, Mendoza-Rodriguez, Vladymir, Wan, Yung-Liang, Bettencourt, Nuno, Martuscelli, Eugenio, Buechel, Ronny, Mickley, Hans, Sun, Kai, Muraglia, Simone, Kaufmann, Philipp, Herzog, Bernhard, Tardif, Jean-Claude, Schütz, Georg, Laule, Michael, Newby, David, Achenbach, Stephan, Haase, Robert, Biavati, Federico, Mézquita, Aldo, Schlattmann, Peter, Dewey, Marc, and Budoff, Matthew

Subjects
Computed tomography angiography, Coronary angiography, Coronary artery disease, Humans, Female, Male, Coronary Artery Disease, Coronary Angiography, Computed Tomography Angiography, Calcium, Predictive Value of Tests, Tomography, X-Ray Computed, Coronary Stenosis, Chest Pain
Abstract

OBJECTIVES: Coronary computed tomography angiography (CCTA) has higher diagnostic accuracy than coronary artery calcium (CAC) score for detecting obstructive coronary artery disease (CAD) in patients with stable chest pain, while the added diagnostic value of combining CCTA with CAC is unknown. We investigated whether combining coronary CCTA with CAC score can improve the diagnosis of obstructive CAD compared with CCTA alone. METHODS: A total of 2315 patients (858 women, 37%) aged 61.1 ± 10.2 from 29 original studies were included to build two CAD prediction models based on either CCTA alone or CCTA combined with the CAC score. CAD was defined as at least 50% coronary diameter stenosis on invasive coronary angiography. Models were built by using generalized linear mixed-effects models with a random intercept set for the original study. The two CAD prediction models were compared by the likelihood ratio test, while their diagnostic performance was compared using the area under the receiver-operating-characteristic curve (AUC). Net benefit (benefit of true positive versus harm of false positive) was assessed by decision curve analysis. RESULTS: CAD prevalence was 43.5% (1007/2315). Combining CCTA with CAC improved CAD diagnosis compared with CCTA alone (AUC: 87% [95% CI: 86 to 89%] vs. 80% [95% CI: 78 to 82%]; p

Published
2024

2. MEFIB‐Index and MAST‐Score in the assessment of hepatic decompensation in metabolic dysfunction‐associated steatosis liver disease—Individual participant data meta‐analyses

Author

Noureddin, Nabil, Ajmera, Veeral, Bergstrom, Jaclyn, Bettencourt, Richele, Huang, Daniel Q, Siddiqi, Harris, Majzoub, Abdul M, Nayfeh, Tarek, Tamaki, Nobuharu, Izumi, Namiki, Nakajima, Atsushi, Idilman, Ramzan, Gumussoy, Mesut, Oz, Digdem Kuru, Erden, Ayse, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Digestive Diseases, Liver Disease, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Humans, Female, Adult, Middle Aged, Aged, Male, Retrospective Studies, Liver Cirrhosis, Esophageal and Gastric Varices, Hepatic Encephalopathy, Fatty Liver, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundThere are limited data regarding the longitudinal association between MEFIB-Index (MRE combined with FIB-4) versus MAST-Score (MRI-aspartate aminotransferase) and hepatic decompensation.AimTo examine the longitudinal association between MEFIB-Index versus MAST-Score in predicting hepatic decompensation in patients with metabolic dysfunction-associated steatotic liver disease (MASLD).MethodsThis was a longitudinal, retrospective analysis of subjects from United States, Japan, and Turkey who underwent a baseline MRE and MRI-PDFF and were followed for hepatic decompensation. Cox-proportional hazard analyses were used to assess the association between MEFIB-Index versus MAST-Score with a composite primary outcome (hepatic decompensation) defined as ascites, hepatic encephalopathy, and varices needing treatment.ResultsThis meta-analysis of individual participants (IPDMA) included 454 patients (58% women) with a mean (±SD) age of 56.0 (±13.5) years. The MEFIB-Index (MRE ≥3.3 kPa + FIB 4 ≥1.6) and MAST-Score (>0.242) were positive for 34% and 9% of the sample, respectively. At baseline, 23 patients met criteria for hepatic decompensation. Among 297 patients with available longitudinal data with a median (IQR) of 4.2 (5.0) years of follow-up, 25 incident cases met criteria for hepatic decompensation. A positive MEFIB-Index [HR = 49.22 (95% CI: 6.23-388.64, p

Published
2023

3. Ramipril for the Treatment of COVID-19: RAMIC, a Randomized, Double-Blind, Placebo-Controlled Clinical Trial

Author

Huang, Daniel Q, Ajmera, Veeral, Tomaszewski, Christian, LaFree, Andrew, Bettencourt, Ricki, Thompson, Wesley K, Smith, Davey M, Malhotra, Atul, Mehta, Ravindra L, Tolia, Vaishal, Yin, Jeffrey, Insel, Paul A, Leachman, Stone, Jung, Jinho, Collier, Summer, Richards, Lisa, Woods, Kristin, Amangurbanova, Maral, Bhatt, Archana, Zhang, Xinlian, Penciu, Oana M, Zarich, Stuart, Retta, Tamrat, Harkins, Michelle S, Teixeira, J Pedro, Chinnock, Brian, Utay, Netanya S, Lake, Jordan E, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Lung, Clinical Trials and Supportive Activities, Infectious Diseases, Emerging Infectious Diseases, Clinical Research, Coronaviruses, 6.1 Pharmaceuticals, Good Health and Well Being, Humans, Female, Male, COVID-19, Ramipril, SARS-CoV-2, Retrospective Studies, Prospective Studies, Angiotensin-Converting Enzyme Inhibitors, Double-Blind Method, Treatment Outcome, Coronavirus, Therapy, Pharmacology and Pharmaceutical Sciences, General Clinical Medicine, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

IntroductionRetrospective studies report that angiotensin-converting enzyme inhibitors (ACEIs) may reduce the severity of COVID-19, but prospective data on de novo treatment with ACEIs are limited. The RAMIC trial was a randomized, multicenter, placebo-controlled, double-blind, allocation-concealed clinical trial to examine the efficacy of de novo ramipril versus placebo for the treatment of COVID-19.MethodsEligible participants were aged 18 years and older with a confirmed diagnosis of SARS-CoV-2 infection, recruited from urgent care clinics, emergency departments, and hospital inpatient wards at eight sites in the USA. Participants were randomly assigned to daily ramipril 2.5 mg or placebo orally in a 2:1 ratio, using permuted block randomization. Analyses were conducted on an intention-to-treat basis. The primary outcome was a composite of mortality, intensive care unit (ICU) admission, or invasive mechanical ventilation by day 14.ResultsBetween 27 May 2020 and 19 April 2021, a total of 114 participants (51% female) were randomized to ramipril (n = 79) or placebo (n = 35). The overall mean (± SD) age and BMI were 45 (± 15) years and 33 (± 8) kg/m2. Two participants in the ramipril group required ICU admission and one died, compared with none in the placebo group. There were no significant differences between ramipril and placebo in the primary endpoint (ICU admission, mechanical ventilation, or death) (3% versus 0%, p = 1.00) or adverse events (27% versus 29%, p = 0.82). The study was terminated early because of a low event rate and subsequent Emergency Use Authorization of therapies for COVID-19.ConclusionDe novo ramipril was not different compared with placebo in improving or worsening clinical outcomes from COVID-19 but appeared safe in non-critically ill patients with COVID-19.Trial registrationClinicaltrials.gov NCT04366050.

Published
2023

4. Type 2 diabetes, hepatic decompensation, and hepatocellular carcinoma in patients with non-alcoholic fatty liver disease: an individual participant-level data meta-analysis

Author

Huang, Daniel Q, Noureddin, Nabil, Ajmera, Veeral, Amangurbanova, Maral, Bettencourt, Ricki, Truong, Emily, Gidener, Tolga, Siddiqi, Harris, Majzoub, Abdul M, Nayfeh, Tarek, Tamaki, Nobuharu, Izumi, Namiki, Yoneda, Masato, Nakajima, Atsushi, Idilman, Ramazan, Gumussoy, Mesut, Oz, Digdem Kuru, Erden, Ayse, Allen, Alina M, Noureddin, Mazen, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Diabetes, Chronic Liver Disease and Cirrhosis, Clinical Research, Rare Diseases, Liver Disease, Cancer, Digestive Diseases, Liver Cancer, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Adult, Humans, Female, Adolescent, Middle Aged, Male, Carcinoma, Hepatocellular, Non-alcoholic Fatty Liver Disease, Diabetes Mellitus, Type 2, Liver Neoplasms, Esophageal and Gastric Varices, Gastrointestinal Hemorrhage, Clinical sciences
Abstract

BackgroundData are scarce regarding the development of hepatic decompensation in patients with non-alcoholic fatty liver disease (NAFLD) with and without type 2 diabetes. We aimed to assess the risk of hepatic decompensation in people with NAFLD with and without type 2 diabetes.MethodsWe did a meta-analysis of individual participant-level data from six cohorts in the USA, Japan, and Turkey. Included participants had magnetic resonance elastography between Feb 27, 2007, and June 4, 2021. Eligible studies included those with liver fibrosis characterisation by magnetic resonance elastography, longitudinal assessment for hepatic decompensation and death, and included adult patients (aged ≥18 years) with NAFLD, for whom data were available regarding the presence of type 2 diabetes at baseline. The primary outcome was hepatic decompensation, defined as ascites, hepatic encephalopathy, or variceal bleeding. The secondary outcome was the development of hepatocellular carcinoma. We used competing risk regression using the Fine and Gray subdistribution hazard ratio (sHR) to compare the likelihood of hepatic decompensation in participants with and without type 2 diabetes. Death without hepatic decompensation was a competing event.FindingsData for 2016 participants (736 with type 2 diabetes; 1280 without type 2 diabetes) from six cohorts were included in this analysis. 1074 (53%) of 2016 participants were female with a mean age of 57·8 years (SD 14·2) years and BMI of 31·3 kg/m2 (SD 7·4). Among 1737 participants (602 with type 2 diabetes and 1135 without type 2 diabetes) with available longitudinal data, 105 participants developed hepatic decompensation over a median follow-up time of 2·8 years (IQR 1·4-5·5). Participants with type 2 diabetes had a significantly higher risk of hepatic decompensation at 1 year (3·37% [95% CI 2·10-5·11] vs 1·07% [0·57-1·86]), 3 years (7·49% [5·36-10·08] vs 2·92% [1·92-4·25]), and 5 years (13·85% [10·43-17·75] vs 3·95% [2·67-5·60]) than participants without type 2 diabetes (p

Published
2023

5. A prospective study on the prevalence of NAFLD, advanced fibrosis, cirrhosis and hepatocellular carcinoma in people with type 2 diabetes

Author

Ajmera, Veeral, Cepin, Sandra, Tesfai, Kaleb, Hofflich, Heather, Cadman, Karen, Lopez, Scarlett, Madamba, Egbert, Bettencourt, Ricki, Richards, Lisa, Behling, Cynthia, Sirlin, Claude B, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Women's Health, Cost Effectiveness Research, Chronic Liver Disease and Cirrhosis, Biomedical Imaging, Prevention, Aging, Hepatitis, Rare Diseases, Digestive Diseases, Diabetes, Clinical Research, Health Services, Obesity, Liver Disease, 4.2 Evaluation of markers and technologies, 2.1 Biological and endogenous factors, Metabolic and endocrine, Oral and gastrointestinal, Good Health and Well Being, Humans, Female, Aged, Male, Non-alcoholic Fatty Liver Disease, Prospective Studies, Diabetes Mellitus, Type 2, Carcinoma, Hepatocellular, Prevalence, Liver Neoplasms, Liver Cirrhosis, Liver, Elasticity Imaging Techniques, Fibrosis, nonalcoholic fatty liver disease, diabetes, screening, Public Health and Health Services, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background & aimsThere are limited prospective data on patients with type 2 diabetes mellitus (T2DM) specifically enrolled and systematically assessed for advanced fibrosis or cirrhosis due to non-alcoholic fatty liver disease (NAFLD). Therefore, we aimed to evaluate the prevalence of advanced fibrosis and cirrhosis in a prospectively recruited cohort of adults with T2DM.MethodsThis prospective study enrolled adults aged ≥50 years with T2DM, recruited from primary care or endocrinology clinics. Participants underwent a standardized clinical research visit with MRI-proton density fat fraction (MRI-PDFF), magnetic resonance elastography (MRE), vibration-controlled transient elastography (VCTE) and controlled-attenuation parameter. NAFLD was defined as MRI-PDFF ≥5% after exclusion of other liver diseases. Advanced fibrosis and cirrhosis were defined by established liver stiffness cut-off points on MRE or VCTE if MRE was not available.ResultsOf 524 patients screened, 501 adults (63% female) with T2DM met eligibility. The mean age and BMI were 64.6 (±8.1) years and 31.4 (±5.9) kg/m2, respectively. The prevalence of NAFLD, advanced fibrosis and cirrhosis was 65%, 14% and 6%, respectively. In multivariable adjusted models, adjusted for age and sex, obesity and insulin use were associated with increased odds of advanced fibrosis (odds ratio 2.50; 95% CI 1.38-4.54; p = 0.003 and odds ratio 2.71; 95% CI 1.33-5.50; p = 0.006, respectively). Among 29 patients with cirrhosis, two were found to have hepatocellular carcinoma and one patient had gallbladder adenocarcinoma.ConclusionUtilizing a uniquely well-phenotyped prospective cohort of patients aged ≥50 years with T2DM, we found that the prevalence of advanced fibrosis was 14% and that of cirrhosis was 6%. These data underscore the high risk of advanced fibrosis/cirrhosis in adults aged ≥50 years with T2DM.Impact and implicationsNon-alcoholic fatty liver disease (NAFLD) is common in patients with type 2 diabetes (T2DM), however, there are limited prospective data characterizing the prevalence of advanced fibrosis and cirrhosis using the most accurate non-invasive biomarkers of liver fat and fibrosis. We show that 14% of older adults with T2DM have advanced fibrosis and 6% have cirrhosis, which places them at risk for liver failure and liver cancer. Accurate prevalence rates and comparative analysis regarding the diagnostic accuracy of non-invasive tests in this population will guide the optimal screening strategy and future cost-effectiveness analyses. These results will inform future Hepatology and Endocrinology practice guidelines regarding NAFLD screening programs in older adults with T2DM.

Published
2023

6. Prevalence and factors associated with liver fibrosis among first‐degree relatives of Mexican Americans with hepatocellular carcinoma

Author

Sharpton, Suzanne, Shan, Kuangda, Bettencourt, Ricki, Lee, Miryoung, McCormick, Joseph B, Fisher‐Hoch, Susan P, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Liver Disease, Chronic Liver Disease and Cirrhosis, Prevention, Liver Cancer, Nutrition, Rare Diseases, Cancer, Diabetes, Clinical Research, Digestive Diseases, Adult, Humans, Female, Middle Aged, Male, Carcinoma, Hepatocellular, Mexican Americans, Non-alcoholic Fatty Liver Disease, Cohort Studies, Prospective Studies, Prevalence, Cross-Sectional Studies, Liver Neoplasms, Liver Cirrhosis, Elasticity Imaging Techniques, Liver, elastography, familial risk, fatty liver, fibrosis, liver stiffness, Clinical Sciences, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

Background and aimsWhether hepatocellular carcinoma (HCC) increases the familial risk for hepatic fibrosis has not been thoroughly explored, particularly in Mexican Americans who are disproportionately affected by obesity and metabolic syndrome. We evaluated the risk of significant hepatic fibrosis in first-degree relatives of Mexican American adults with HCC.MethodsWe performed a cross-sectional analysis of a prospective cohort of Mexican American probands with HCC and first-degree relatives enrolled in the Hispanic Liver Cancer Cohort study. We evaluated the prevalence of hepatic fibrosis in first-degree relatives, defined by liver stiffness measurement (LSM) >= 7.0 kPa with transient elastography (TE). Secondary outcomes included the prevalence of definite hepatic steatosis, defined by controlled attenuation parameter >=288 dB/m.ResultsWe identified 70 probands diagnosed with HCC; 47% were female and the mean age was 62 years (±13 years). Among 112 first-degree relatives with a mean age of 43 years (±14 years), 19 (17%) had significant fibrosis and 47 (42%) had definite hepatic steatosis, respectively. The prevalence of significant fibrosis was 20% in first-degree relatives 40 years of age or older. Regression analysis revealed that diabetes (OR 3.2, 95% CI: 1.1-9.2, p = 0.03) and aspartate aminotransferase >=30 units/L (OR 4.0, 95% CI: 1.4-11.7, p = 0.01) were predictors of significant fibrosis in first-degree relatives.ConclusionsUsing a well-phenotyped familial cohort, we found that the prevalence of significant fibrosis and definite hepatic steatosis are high in first-degree relatives of Mexican Americans with HCC, particularly those with diabetes, suggesting that this population may benefit from screening for liver disease.

Published
2023

7. Direct Comparison of Quantitative US versus Controlled Attenuation Parameter for Liver Fat Assessment Using MRI Proton Density Fat Fraction as the Reference Standard in Patients Suspected of Having NAFLD.

Author

Jung, Jinho, Han, Aiguo, Madamba, Egbert, Bettencourt, Ricki, Loomba, Rohan R, Boehringer, Andrew S, Andre, Michael P, Erdman, John W, O'Brien, William D, Fowler, Kathryn J, Sirlin, Claude B, and Loomba, Rohit

Subjects
Liver, Humans, Protons, Magnetic Resonance Imaging, Prospective Studies, Reference Standards, Middle Aged, Female, Male, Elasticity Imaging Techniques, Non-alcoholic Fatty Liver Disease, Digestive Diseases, Liver Disease, Cancer, Biomedical Imaging, Clinical Research, Chronic Liver Disease and Cirrhosis, Inflammatory and immune system, Medical and Health Sciences, Nuclear Medicine & Medical Imaging
Abstract

Background MRI-derived proton density fat fraction (PDFF) is an accurate, reliable, and safe biologic marker for use in the noninvasive diagnosis of hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD). Because of the cost and limited availability of MRI, it is necessary to develop an accurate method to diagnose NAFLD with potential point-of-care access. Purpose To compare the diagnostic accuracy of the quantitative US (QUS) fat fraction (FF) estimator with that of the controlled attenuation parameter (CAP) in the diagnosis of NAFLD using contemporaneous MRI-derived PDFF as the reference standard. Materials and Methods Participants with or suspected of having NAFLD were prospectively recruited at the NAFLD Research Center between July 2015 and July 2019. All participants underwent MRI-derived PDFF measurement, transient elastography with CAP measurement, and QUS. QUS FF was derived using computed QUS parameters from the acquired radiofrequency US data using a calibrated reference phantom. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of QUS FF and CAP in the diagnosis of hepatic steatosis (defined as MRI-derived PDFF ≥ 5%). AUCs were compared using the DeLong test. Results A total of 123 participants were included (mean age, 52 years ± 13 [SD]; 67 [54%] women). Of these participants, 100 (81%) had MRI-derived PDFF of 5% or more. QUS FF had a significantly higher AUC for diagnosis of NAFLD than did CAP (0.92 [95% CI: 0.87, 0.98] vs 0.79 [95% CI: 0.67, 0.90], P = .03). QUS FF had a sensitivity of 98% (98 of 100) and a specificity of 48% (11 of 23). CAP had a sensitivity of 87% (87 of 100) and a specificity of 57% (13 of 23). Conclusion The quantitative US fat fraction estimator is more accurate than the controlled attenuation parameter in the diagnosis of hepatic steatosis in patients with or suspected of having nonalcoholic fatty liver disease. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ito in this issue.

Published
2022

8. Comparison of clinical prediction rules for ruling out cirrhosis in nonalcoholic fatty liver disease (NAFLD)

Author

Brandman, Danielle, Boyle, Marie, McPherson, Stuart, Van Natta, Mark L, Sanyal, Arun J, Kowdley, Kris, Neuschwander‐Tetri, Brent, Chalasani, Naga, Abdelmalek, Manal F, Terrault, Norah A, McCullough, Art, Bettencourt, Ricki, Caussy, Cyrielle, Kleiner, David E, Behling, Cynthia, Tonascia, James, Anstee, Quentin M, Loomba, Rohit, and Network, Members of the Nonalcoholic Steatohepatitis Clinical Research

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Chronic Liver Disease and Cirrhosis, Liver Disease, Digestive Diseases, Hepatitis, Oral and gastrointestinal, Good Health and Well Being, Adult, Biopsy, Clinical Decision Rules, Cross-Sectional Studies, Female, Fibrosis, Humans, Liver, Liver Cirrhosis, Non-alcoholic Fatty Liver Disease, cirrhosis, nonalcoholic fatty liver disease, noninvasive assessment, Members of the Nonalcoholic Steatohepatitis Clinical Research Network, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

Background and aimsPatients with nonalcoholic fatty liver disease (NAFLD) cirrhosis benefit from referral to subspecialty care. While several clinical prediction rules exist to identify advanced fibrosis, the cutoff for excluding cirrhosis due to NAFLD is unclear. This analysis compared clinical prediction rules for excluding biopsy-proven cirrhosis in NAFLD.MethodsAdult patients were enrolled in the NASH Clinical Research Network (US) and the Newcastle Cohort (UK). Clinical and laboratory data were collected at enrolment, and a liver biopsy was taken within 1 year of enrolment. Optimal cutoffs for each score (eg, FIB-4) to exclude cirrhosis were derived from the US cohort, and sensitivity, specificity, positive predictive value, negative predictive value and AUROC were calculated. The cutoffs were evaluated in the UK cohort.Results147/1483 (10%) patients in the US cohort had cirrhosis. All prediction rules had similarly high NPV (0.95-0.97). FIB-4 and NAFLD fibrosis scores were the most accurate in characterising patients as having cirrhosis (AUROC 0.84-0.86). 59/494 (12%) patients in the UK cohort had cirrhosis. Prediction rules had high NPV (0.92-0.96), and FIB-4 and NAFLD fibrosis score the most accurate in the prediction of cirrhosis in the UK cohort (AUROC 0.87-0.89).ConclusionsThis cross-sectional analysis of large, multicentre international datasets shows that current clinical prediction rules perform well in excluding cirrhosis with appropriately chosen cutoffs. These clinical prediction rules can be used in primary care to identify patients, particularly those who are white, female, and

Published
2022

9. Comparative efficacy of an optimal exam between ultrasound versus abbreviated MRI for HCC screening in NAFLD cirrhosis: A prospective study

Author

Huang, Daniel Q, Fowler, Kathryn J, Liau, Joy, Cunha, Guilherme M, Louie, Ashley L, An, Julie Y, Bettencourt, Ricki, Jung, Jinho, Gitto, Zachary, Hernandez, Christie, Lopez, Scarlett J, Gupta, Hersh, Sirlin, Claude B, Marks, Robert M, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Hepatitis, Cancer, Clinical Research, Liver Disease, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Liver Cancer, Rare Diseases, Biomedical Imaging, Oral and gastrointestinal, Carcinoma, Hepatocellular, Contrast Media, Female, Humans, Liver Cirrhosis, Liver Neoplasms, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Prospective Studies, Retrospective Studies, abbreviated MRI, cirrhosis, hepatocellular carcinoma, NAFLD, surveillance, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundRetrospective studies report that visualisation of the liver may be severely limited using ultrasound (US), potentially contributing to diminished sensitivity for detection of hepatocellular carcinoma (HCC) among patients with nonalcoholic fatty liver disease (NAFLD) and cirrhosis, but there are limited prospective data.AimsTo compare liver visualisation scores prospectively for US and abbreviated hepatobiliary phase (HBP) magnetic resonance imaging (AMRI) in a cohort of participants with NAFLD cirrhosis and a clinical indication for HCC surveillance.MethodsThis prospective multicenter study included 54 consecutive participants (67% women) with NAFLD cirrhosis who underwent contemporaneous US as well as HBP-AMRI with gadoxetic acid. Primary outcome was the proportion of imaging examinations with severe limitations in liver visualisation (visualisation score C) compared head-to-head between US and AMRI.ResultsThe mean (± standard deviation) age was 63.3 years (±8.4) and body mass index was 32.0 kg/m2 (±6.0). Nineteen participants (35%) had severe visualisation limitations on US, compared with 10 (19%) with AMRI, p

Published
2022

10. MRE combined with FIB-4 (MEFIB) index in detection of candidates for pharmacological treatment of NASH-related fibrosis

Author

Jung, Jinho, Loomba, Rohan R, Imajo, Kento, Madamba, Egbert, Gandhi, Sanil, Bettencourt, Ricki, Singh, Seema, Hernandez, Carolyn, Valasek, Mark A, Behling, Cynthia, Richards, Lisa, Fowler, Katie, Sirlin, Claude B, Nakajima, Atsushi, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Hepatitis, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Liver Disease, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Oral and gastrointestinal, Good Health and Well Being, Aged, California, Cross-Sectional Studies, Elasticity Imaging Techniques, Female, Humans, Japan, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Predictive Value of Tests, Prospective Studies, fatty liver, hepatic fibrosis, liver function test, liver imaging, Paediatrics and Reproductive Medicine, Gastroenterology & Hepatology, Clinical sciences, Nutrition and dietetics
Abstract

ObjectivePatients with non-alcoholic fatty liver disease (NAFLD) with ≥stage 2 fibrosis are at increased risk for liver-related mortality and are candidates for pharmacological therapies for treatment of NAFLD. The aim of this prospective cohort study is to examine the diagnostic accuracy of MR elastography (MRE) combined with fibrosis-4 (FIB-4) in diagnosing ≥stage 2 fibrosis (candidates for pharmacological therapies).DesignThis is a cross-sectional analysis of a prospective cohort (University of California at San Diego (UCSD)-NAFLD) including 238 consecutive patients with contemporaneous MRE and biopsy-proven NAFLD. Non-alcoholic steatohepatitis-Clinical Research Network-Histologic Scoring System was used to assess histology. The radiologist and pathologist were blinded to clinical, pathological and imaging data, respectively. Receiver operating characteristics (ROCs) were determined to examine the diagnostic accuracy of MRE and FIB-4 for diagnosis of ≥stage 2 fibrosis in NAFLD. We then validated these findings in an independent validation cohort derived from Yokohama City University in Japan (Japan-NAFLD Cohort; N=222 patients).ResultsIn the UCSD-NAFLD (training) Cohort, MRE demonstrated a clinically significant diagnostic accuracy for the detection of ≥stage 2 fibrosis with an area under the ROC curve (AUROC) of 0.93 (95% CI 0.90 to 0.97) vs FIB-4 with an AUROC of 0.78 (95% CI 0.71 to 0.85), which was both clinically and statistically significant (p

Published
2021

11. The impact of genetic risk on liver fibrosis in non‐alcoholic fatty liver disease as assessed by magnetic resonance elastography

Author

Ajmera, Veeral, Liu, Amy, Bettencourt, Ricki, Dhar, Debanjan, Richards, Lisa, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Liver Disease, Clinical Research, Genetics, Digestive Diseases, Prevention, Chronic Liver Disease and Cirrhosis, Biomedical Imaging, Hepatitis, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Cross-Sectional Studies, Elasticity Imaging Techniques, Female, Fibrosis, Humans, Liver, Liver Cirrhosis, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundVariants in multiple genetic loci modify the risk of non-alcoholic fatty liver disease (NAFLD) and cirrhosis but there are limited data on the quantitative impact of variant copies on liver fibrosis.AimTo investigate the effect of PNPLA3, TM6SF2, MBOAT7, GCKR and HSD17B13 genotype on liver fibrosis assessed by magnetic resonance elastography (MRE), a reproducible, accurate, continuous biomarker of liver fibrosis.MethodsThis is a cross-sectional analysis derived from a well-characterised cohort at risk for NAFLD who underwent genotyping and MRE assessment. Liver stiffness (LS) was estimated using MRE and advanced fibrosis was defined as liver stiffness ≥3.63 kilopascals (kPa). Univariable and multivariable linear and logistic regression analysis, were used to assess the association between genotype and MRE.ResultsTwo hundred sixty-four patients (63% women) with a mean age 53 (±17) years, and 31% Hispanic ethnicity with genotyping and MRE were included. The odds of advanced fibrosis were 3.1 (95% CI: 1.1-8.9, P = 0.04) for CG and 6.5 (95% CI: 2.2-18.9, P

Published
2021

12. Optimal Threshold of Controlled Attenuation Parameter for Detection of HIV-Associated NAFLD With Magnetic Resonance Imaging as the Reference Standard

Author

Ajmera, Veeral H, Cachay, Edward R, Ramers, Christian B, Bassirian, Shirin, Singh, Seema, Bettencourt, Richele, Richards, Lisa, Hamilton, Gavin, Middleton, Michael, Fowler, Katie, Sirlin, Claude, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Hepatitis, Infectious Diseases, HIV/AIDS, Sexually Transmitted Infections, Clinical Research, Liver Disease, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Women's Health, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Infection, Oral and gastrointestinal, Good Health and Well Being, Biopsy, Elasticity Imaging Techniques, Female, HIV Infections, Humans, Liver, Magnetic Resonance Imaging, Male, Non-alcoholic Fatty Liver Disease, Prospective Studies, ROC Curve, Reference Standards, NASH, steatosis, MRI-PDFF, Biological Sciences, Medical and Health Sciences, Microbiology, Clinical sciences
Abstract

BackgroundControlled attenuation parameter (CAP) is an ultrasound-based point-of-care method to quantify liver fat; however, the optimal threshold for CAP to detect pathologic liver fat among persons living with human immunodeficiency virus (HIV; PLWH) is unknown. Therefore, we aimed to identify the diagnostic accuracy and optimal threshold of CAP for the detection of liver-fat among PLWH with magnetic resonance imaging proton-density fat fraction (MRI-PDFF) as the reference standard.MethodsPatients from a prospective single-center cohort of PLWH at risk for HIV-associated nonalcoholic fatty liver disease (NAFLD) who underwent contemporaneous MRI-PDFF and CAP assessment were included. Subjects with other forms of liver disease including viral hepatitis and excessive alcohol intake were excluded. Receiver operatic characteristic (ROC) curve analysis were performed to identify the optimal threshold for the detection of HIV-associated NAFLD (liver fat ≥ 5%).ResultsSeventy PLWH (90% men) at risk for NAFLD were included. The mean (± standard deviation) age and body mass index were 48.6 (±10.2) years and 30 (± 5.3) kg/m2, respectively. The prevalence of HIV-associated NAFLD (MRI-PDFF ≥ 5%) was 80%. The M and XL probes were used for 56% and 44% of patients, respectively. The area under the ROC curve of CAP for the detection of MRI-PDFF ≥ 5% was 0.82 (0.69-0.95) at the cut-point of 285 dB/m. The positive predictive value of CAP ≥ 285 dB/m was 93.2% in this cohort with sensitivity of 73% and specificity of 78.6%.ConclusionsThe optimal cut-point of CAP to correctly identify HIV-associated NAFLD was 285 dB/m, is similar to previously published cut-point for primary NAFLD and may be incorporated into routine care to identify patients at risk of HIV-associated NAFLD.

Published
2021

13. A Universal Gut-Microbiome-Derived Signature Predicts Cirrhosis

Author

Oh, Tae Gyu, Kim, Susy M, Caussy, Cyrielle, Fu, Ting, Guo, Jian, Bassirian, Shirin, Singh, Seema, Madamba, Egbert V, Bettencourt, Ricki, Richards, Lisa, Yu, Ruth T, Atkins, Annette R, Huan, Tao, Brenner, David A, Sirlin, Claude B, Downes, Michael, Evans, Ronald M, and Loomba, Rohit

Subjects
Microbiology, Biological Sciences, Biomedical and Clinical Sciences, Medical Biochemistry and Metabolomics, Clinical Research, Liver Disease, Microbiome, Digestive Diseases, Human Genome, Hepatitis, Machine Learning and Artificial Intelligence, Genetics, Chronic Liver Disease and Cirrhosis, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Oral and gastrointestinal, Adult, Aged, Aged, 80 and over, Aspartate Aminotransferases, Cohort Studies, Feces, Female, Gastrointestinal Microbiome, Humans, Liver Cirrhosis, Male, Metabolome, Metagenome, Middle Aged, Non-alcoholic Fatty Liver Disease, Serum Albumin, Human, NAFLD, NASH, biomarker, cirrhosis, fatty liver, liver fibrosis, metabolomics, metagenomics, microbiome, microbiota, non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, Biochemistry and Cell Biology, Endocrinology & Metabolism, Biochemistry and cell biology, Medical biochemistry and metabolomics
Abstract

Dysregulation of the gut microbiome has been implicated in the progression of non-alcoholic fatty liver disease (NAFLD) to advanced fibrosis and cirrhosis. To determine the diagnostic capacity of this association, we compared stool microbiomes across 163 well-characterized participants encompassing non-NAFLD controls, NAFLD-cirrhosis patients, and their first-degree relatives. Interrogation of shotgun metagenomic and untargeted metabolomic profiles by using the random forest machine learning algorithm and differential abundance analysis identified discrete metagenomic and metabolomic signatures that were similarly effective in detecting cirrhosis (diagnostic accuracy 0.91, area under curve [AUC]). Combining the metagenomic signature with age and serum albumin levels accurately distinguished cirrhosis in etiologically and genetically distinct cohorts from geographically separated regions. Additional inclusion of serum aspartate aminotransferase levels, which are increased in cirrhosis patients, enabled discrimination of cirrhosis from earlier stages of fibrosis. These findings demonstrate that a core set of gut microbiome species might offer universal utility as a non-invasive diagnostic test for cirrhosis.

Published
2020

14. Clinical Utility of an Increase in Magnetic Resonance Elastography in Predicting Fibrosis Progression in Nonalcoholic Fatty Liver Disease

Author

Ajmera, Veeral H, Liu, Amy, Singh, Seema, Yachoa, Georg, Ramey, Matthew, Bhargava, Meera, Zamani, Ava, Lopez, Scarlett, Mangla, Neeraj, Bettencourt, Ricki, Rizo, Emily, Valasek, Mark, Behling, Cynthia, Richards, Lisa, Sirlin, Claude, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Hepatitis, Liver Disease, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Biomedical Imaging, Clinical Research, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Adult, Aged, Body Mass Index, Disease Progression, Elasticity Imaging Techniques, Female, Humans, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Prospective Studies, Medical Biochemistry and Metabolomics, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background and aimsCross-sectional studies have shown that magnetic resonance elastography (MRE) is accurate in the noninvasive detection of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD). However, there are limited data on the longitudinal association between an increase in liver stiffness on MRE and fibrosis progression in NAFLD. Therefore, using a well-characterized prospective cohort of patients with biopsy-proven NAFLD, we aimed to examine the longitudinal association between a 15% increase in liver stiffness on MRE and fibrosis progression in NAFLD.Approach and resultsThis prospective cohort study included 102 patients (62.7% women) with biopsy-proven NAFLD who underwent contemporaneous MRE and liver biopsy at baseline followed by a repeat paired liver biopsy and MRE assessment. The primary outcome was odds of fibrosis progression by one or more stage as assessed by the Nonalcoholic Steatohepatitis Clinical Research Network histologic scoring system. The mean (±SD) of age and body mass index (BMI) were 52 (±14) years and 32.6 (±5.3) kg/m2 , respectively. The median time interval between the two paired assessments was 1.4 years (interquartile range 2.15 years). The number of patients with fibrosis stages 0, 1, 2, 3, and 4 was 27, 36, 12, 17, and 10, respectively. In unadjusted analysis, a 15% increase in MRE was associated with increased odds of histologic fibrosis progression (odds ratio [OR], 3.56; 95% confidence interval [CI], 1.17-10.76; P = 0.0248). These findings remained clinically and statistically significant even after multivariable adjustment for age, sex, and BMI (adjusted OR, 3.36; 95% CI, 1.10-10.31; P = 0.0339). A 15% increase in MRE was the strongest predictor of progression to advanced fibrosis (OR, 4.90; 95% CI, 1.35-17.84; P = 0.0159).ConclusionsA 15% increase in liver stiffness on MRE may be associated with histologic fibrosis progression and progression from early fibrosis to advanced fibrosis.

Published
2020

15. MRI Assessment of Treatment Response in HIV‐associated NAFLD: A Randomized Trial of a Stearoyl‐Coenzyme‐A‐Desaturase‐1 Inhibitor (ARRIVE Trial)

Author

Ajmera, Veeral H, Cachay, Edward, Ramers, Christian, Vodkin, Irine, Bassirian, Shirin, Singh, Seema, Mangla, Neeraj, Bettencourt, Richele, Aldous, Jeannette L, Park, Daniel, Lee, Daniel, Blanchard, Jennifer, Mamidipalli, Adrija, Boehringer, Andrew, Aslam, Saima, Leinhard, Olof Dahlqvist, Richards, Lisa, Sirlin, Claude, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Biomedical Imaging, Clinical Trials and Supportive Activities, Chronic Liver Disease and Cirrhosis, Liver Disease, Digestive Diseases, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Oral and gastrointestinal, Adult, Cholic Acids, Double-Blind Method, Elasticity Imaging Techniques, Female, HIV Infections, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Medical Biochemistry and Metabolomics, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

Aramchol, an oral stearoyl-coenzyme-A-desaturase-1 inhibitor, has been shown to reduce hepatic fat content in patients with primary nonalcoholic fatty liver disease (NAFLD); however, its effect in patients with human immunodeficiency virus (HIV)-associated NAFLD is unknown. The aramchol for HIV-associated NAFLD and lipodystrophy (ARRIVE) trial was a double-blind, randomized, investigator-initiated, placebo-controlled trial to test the efficacy of 12 weeks of treatment with aramchol versus placebo in HIV-associated NAFLD. Fifty patients with HIV-associated NAFLD, defined by magnetic resonance imaging (MRI)-proton density fat fraction (PDFF) ≥5%, were randomized to receive either aramchol 600 mg daily (n = 25) or placebo (n = 25) for 12 weeks. The primary endpoint was a change in hepatic fat as measured by MRI-PDFF in colocalized regions of interest. Secondary endpoints included changes in liver stiffness using magnetic resonance elastography (MRE) and vibration-controlled transient elastography (VCTE), and exploratory endpoints included changes in total-body fat and muscle depots on dual-energy X-ray absorptiometry (DXA), whole-body MRI, and cardiac MRI. The mean (± standard deviation) of age and body mass index were 48.2 ± 10.3 years and 30.7 ± 4.6 kg/m2 , respectively. There was no difference in the reduction in mean MRI-PDFF between the aramchol group at -1.3% (baseline MRI-PDFF 15.6% versus end-of-treatment MRI-PDFF 14.4%, P = 0.24) and the placebo group at -1.4% (baseline MRI-PDFF 13.3% versus end-of-treatment MRI-PDFF 11.9%, P = 0.26). There was no difference in the relative decline in mean MRI-PDFF between the aramchol and placebo groups (6.8% versus 1.1%, P = 0.68). There were no differences in MRE-derived and VCTE-derived liver stiffness and whole-body (fat and muscle) composition analysis by MRI or DXA. Compared to baseline, end-of-treatment aminotransferases were lower in the aramchol group but not in the placebo arm. There were no significant adverse events. Conclusion: Aramchol, over a 12-week period, did not reduce hepatic fat or change body fat and muscle composition by using MRI-based assessment in patients with HIV-associated NAFLD (clinicaltrials.gov ID:NCT02684591).

Published
2019

16. Do effects of early life interventions on linear growth correspond to effects on neurobehavioural development? A systematic review and meta-analysis

Author

Prado, Elizabeth L, Larson, Leila M, Cox, Katherine, Bettencourt, Kory, Kubes, Julianne N, and Shankar, Anuraj H

Subjects
Public Health, Health Sciences, Clinical Research, Mental Health, Prevention, Clinical Trials and Supportive Activities, Pediatric, Nutrition, Mental health, Good Health and Well Being, Child, Child Development, Child, Preschool, Dietary Supplements, Early Intervention, Educational, Female, Humans, Infant, Infant, Newborn, Nutritional Status, Pregnancy, Microbiology, Public Health and Health Services, Epidemiology, Health services and systems, Public health
Abstract

BackgroundFaltering in linear growth and neurobehavioural development during early childhood are often assumed to have common causes because of their consistent association. This notion has contributed to a global focus on the promotion of nutrition during pregnancy and childhood to improve both conditions. Our aim was to assess whether effects of interventions on linear growth are associated with effects on developmental scores and to quantify these associations.MethodsIn this systematic review and meta-analysis, we included randomised trials done during pregnancy and in children aged 0-5 years that reported effects of any intervention on length-for-age or height-for-age Z scores (LAZ or HAZ) and on any of the following outcomes: motor, cognitive or mental, language, and social-emotional or behavioural development. We searched MEDLINE (Ovid), CINAHL (EBSCO), and PsycINFO (EBSCO) from database inception to June 25, 2019. Study-level data were extracted and, when required, authors were contacted for missing information. We calculated weighted meta-regression coefficients of the association between standardised effect sizes of interventions on LAZ or HAZ and developmental outcome scores and calculated pooled effect sizes for different types of intervention.FindingsOf the 7207 studies identified, we included 75 studies with 122 comparisons between intervention and control groups and outcomes reported for 72 275 children. Across all interventions, effect sizes on LAZ or HAZ were significantly associated with effect sizes on social-emotional scores (β 0·23, 95% CI 0·05 to 0·41; p=0·02), but not on cognitive (0·18, -0·36 to 0·72; p=0·51), language (0·12, -0·07 to 0·31; p=0·21), or motor development scores (0·23, -0·05 to 0·50; p=0·11). In studies that provided nutritional supplements, we observed positive significant pooled effect sizes on all five outcomes of LAZ or HAZ (effect size 0·05, 95% CI 0·01-0·09; p=0·01; n=50), cognitive or mental (0·06, 0·03-0·10; p

Published
2019

17. Collagen Formation Assessed by N‐Terminal Propeptide of Type 3 Procollagen Is a Heritable Trait and Is Associated With Liver Fibrosis Assessed by Magnetic Resonance Elastography

Author

Caussy, Cyrielle, Bhargava, Meera, Villesen, Ida F, Gudmann, Natasja S, Leeming, Diana J, Karsdal, Morten A, Faulkner, Claire, Bao, Denny, Liu, Amy, Lo, Min‐Tzu, Bettencourt, Ricki, Bassirian, Shirin, Richards, Lisa, Brenner, David A, Chen, Chi‐Hua, Sirlin, Claude B, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Immunology, Chronic Liver Disease and Cirrhosis, Liver Disease, Digestive Diseases, Clinical Research, Biomedical Imaging, Hepatitis, Genetics, 2.1 Biological and endogenous factors, Adult, Aged, Collagen Type III, Elasticity Imaging Techniques, Epidemiologic Studies, Extracellular Matrix, Female, Humans, Liver Cirrhosis, Male, Membrane Proteins, Middle Aged, Non-alcoholic Fatty Liver Disease, Quantitative Trait, Heritable, Medical Biochemistry and Metabolomics, Gastroenterology & Hepatology, Clinical sciences
Abstract

N-terminal propeptide of type 3 procollagen (PRO-C3) is a biomarker of liver fibrosis in nonalcoholic fatty liver disease (NAFLD). This study examines the association between PRO-C3 concentration and liver fibrosis assessed by magnetic resonance elastography (MRE)-measured stiffness (MRE-stiffness) and the heritability of PRO-C3 concentration in a cohort of twins and families with and without NAFLD. We performed a cross-sectional analysis of a well-characterized prospective cohort of 306 participants, including 44 probands with NAFLD-cirrhosis and their 72 first-degree relatives, 24 probands with NAFLD without advanced fibrosis and their 24 first-degree relatives, and 72 controls without NAFLD and their 72 first-degree relatives. Liver steatosis was assessed by magnetic resonance imaging proton density fat fraction, and liver fibrosis was assessed by MRE-stiffness. Serum PRO-C3 was assessed by competitive, enzyme-linked immunosorbent assay. We assessed the familial correlation of PRO-C3 concentration, the shared gene effects between PRO-C3 concentration and liver steatosis and fibrosis, and the association between PRO-C3 concentration and genetic variants in the patatin-like phospholipase domain-containing 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), membrane-bound O-acyltransferase domain-containing (MBOAT), and glucokinase regulator (CGKR) genes. In multivariable-adjusted models including age, sex, body mass index, and ethnicity, serum PRO-C3 correlated strongly with liver fibrosis (r2  = 0.50, P

Published
2019

18. A gut microbiome signature for cirrhosis due to nonalcoholic fatty liver disease.

Author

Caussy, Cyrielle, Tripathi, Anupriya, Humphrey, Greg, Bassirian, Shirin, Singh, Seema, Faulkner, Claire, Bettencourt, Ricki, Rizo, Emily, Richards, Lisa, Xu, Zhenjiang Z, Downes, Michael R, Evans, Ronald M, Brenner, David A, Sirlin, Claude B, Knight, Rob, and Loomba, Rohit

Subjects
Feces, Humans, Bacteria, Liver Cirrhosis, ROC Curve, Family, Female, Male, Non-alcoholic Fatty Liver Disease, Gastrointestinal Microbiome
Abstract

The presence of cirrhosis in nonalcoholic-fatty-liver-disease (NAFLD) is the most important predictor of liver-related mortality. Limited data exist concerning the diagnostic accuracy of gut-microbiome-derived signatures for detecting NAFLD-cirrhosis. Here we report 16S gut-microbiome compositions of 203 uniquely well-characterized participants from a prospective twin and family cohort, including 98 probands encompassing the entire spectrum of NAFLD and 105 of their first-degree relatives, assessed by advanced magnetic-resonance-imaging. We show strong familial correlation of gut-microbiome profiles, driven by shared housing. We report a panel of 30 features, including 27 bacterial features with discriminatory ability to detect NAFLD-cirrhosis using a Random Forest classifier model. In a derivation cohort of probands, the model has a robust diagnostic accuracy (AUROC of 0.92) for detecting NAFLD-cirrhosis, confirmed in a validation cohort of relatives of proband with NAFLD-cirrhosis (AUROC of 0.87). This study provides evidence for a fecal-microbiome-derived signature to detect NAFLD-cirrhosis.

Published
2019

19. Magnetic Resonance vs Transient Elastography Analysis of Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review and Pooled Analysis of Individual Participants

Author

Hsu, Cynthia, Caussy, Cyrielle, Imajo, Kento, Chen, Jun, Singh, Siddharth, Kaulback, Kellee, Le, Minh-Da, Hooker, Jonathan, Tu, Xin, Bettencourt, Ricki, Yin, Meng, Sirlin, Claude B, Ehman, Richard L, Nakajima, Atsushi, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Liver Disease, Biomedical Imaging, Oral and gastrointestinal, Good Health and Well Being, Adult, Aged, Aged, 80 and over, Diagnostic Tests, Routine, Elasticity Imaging Techniques, Female, Humans, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, ROC Curve, Magnetic Resonance Elastography, Fibroscan, Transient Elastography, NAFLD, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background & aimsMagnetic resonance elastography (MRE) and transient elastography (TE) are noninvasive techniques for detection of liver fibrosis. Single-center studies have compared the diagnostic performance of MRE vs TE in patients with nonalcoholic fatty liver disease (NAFLD). We conducted a pooled analysis of individual participant data from published studies to compare the diagnostic performance of MRE vs TE for staging of liver fibrosis in patients with NAFLD, using liver biopsy as reference.MethodsWe performed a systematic search of publication databases, from 2005 through 2017. We identified 3 studies of adults with NAFLD who were assessed by MRE, TE, and liver biopsy. In a pooled analysis, we calculated the cluster-adjusted area under the curve (AUROC) of MRE and TE for the detection of each stage of fibrosis. AUROC comparisons between MRE and TE were performed using the Delong test.ResultsOur pooled analysis included 230 participants with biopsy-proven NAFLD with mean age of 52.2±13.9 years and a body mass index of 31.9±7.5 kg/m2. The proportions of patients with fibrosis of stages 0, 1, 2, 3, and 4 were: 31.7%, 27.8%, 15.7%, 13.9%, and 10.9%, respectively. The AUROC of TE vs MRE for detection of fibrosis stages ≥1 was 0.82 (95% CI, 0.76-0.88) vs 0.87 (95% CI, 0.82-0.91) (P=.04); for stage≥ 2 was 0.87 (95% CI, 0.82-0.91) vs 0.92 (95% CI, 0.88-0.96) (P=.03); for stage ≥3 was 0.84 (95% CI, 0.78-0.90) vs 0.93 (95% CI, 0.89-0.96) (P=.001); for stage ≥ 4 was 0.84 (95% CI, 0.73-0.94) vs 0.94 (95% CI, 0.89-0.99) (P=.005).ConclusionIn a pooled analysis of data from individual participants with biopsy-proven NAFLD, we found MRE to have a statistically significantly higher diagnostic accuracy than TE in detection of each stage of fibrosis. MRE and TE each have roles in detection of fibrosis in patients with NAFLD, depending upon the level of accuracy desired.

Published
2019

20. Serum bile acid patterns are associated with the presence of NAFLD in twins, and dose‐dependent changes with increase in fibrosis stage in patients with biopsy‐proven NAFLD

Author

Caussy, Cyrielle, Hsu, Cynthia, Singh, Seema, Bassirian, Shirin, Kolar, James, Faulkner, Claire, Sinha, Nikhil, Bettencourt, Ricki, Gara, Naveen, Valasek, Mark A, Schnabl, Bernd, Richards, Lisa, Brenner, David A, Hofmann, Alan F, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Digestive Diseases, Hepatitis, Liver Disease, Chronic Liver Disease and Cirrhosis, Oral and gastrointestinal, Good Health and Well Being, Adult, Aged, Bile Acids and Salts, Biomarkers, Biopsy, Cohort Studies, Cross-Sectional Studies, Diseases in Twins, Female, Humans, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundThe fasting-state serum bile acid profile in nonalcoholic fatty liver disease (NAFLD) has been reported to differ when nonalcoholic steatohepatitis is compared to nonalcoholic fatty liver. However, there are few data comparing changes in NAFLD vs non-NAFLD, or whether the bile acid profile differs according to the degree of fibrosis.AimTo examine the serum bile acid profile across the entire spectrum of NAFLD.MethodsWe performed a cross-sectional analysis of two complementary cohorts: a Twin and Family cohort of 156 participants, and a biopsy-proven-NAFLD cohort of 156 participants with fasting bile acid profiling using liquid chromatography/mass spectrometry.ResultsIn the Twin and Family cohort (mean age 46.3 years and body mass index (BMI) 26.6 kg/m2 ), 36 (23%) participants had NAFLD (magnetic resonance imaging proton density fat fraction ≥ 5%). Higher chenodeoxycholyl conjugates (9.0% vs 6.5%, P = 0.019) and lower glycohyocholate (1.2% vs 3.6%, P

Published
2019

21. Association Between Obesity and Discordance in Fibrosis Stage Determination by Magnetic Resonance vs Transient Elastography in Patients With Nonalcoholic Liver Disease

Author

Caussy, Cyrielle, Chen, Jun, Alquiraish, Mosab H, Cepin, Sandra, Nguyen, Phirum, Hernandez, Carolyn, Yin, Meng, Bettencourt, Ricki, Cachay, Edward R, Jayakumar, Saumya, Fortney, Lynda, Hooker, Jonathan, Sy, Ethan, Valasek, Mark A, Rizo, Emily, Richards, Lisa, Brenner, David, Sirlin, Claude B, Ehman, Richard L, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Obesity, Clinical Research, Liver Disease, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Oral and gastrointestinal, Adolescent, Adult, Aged, Aged, 80 and over, Biopsy, Body Mass Index, Cross-Sectional Studies, Elasticity Imaging Techniques, Female, Humans, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Young Adult, Liver Fibrosis, Nonalcoholic Fatty Liver Disease, Magnetic Resonance Elastography, Transient Elastography, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background & aimsMagnetic resonance elastography (MRE) and transient elastography (TE) are noninvasive techniques used to detect liver fibrosis in nonalcoholic fatty liver disease. MRE detects fibrosis more accurately than TE, but MRE is more expensive, and the concordance between MRE and TE have not been optimally assessed in obese patients. It is important to determine under which conditions TE and MRE produce the same readings, so that some patients can simply undergo TE evaluation to detect fibrosis. We aimed to assess the association between body mass index (BMI) and discordancy between MRE and TE findings, using liver biopsy as the reference, and validated our findings in a separate cohort.MethodsWe performed a cross-sectional study of 119 adults with nonalcoholic fatty liver disease who underwent MRE, TE with M and XL probe, and liver biopsy analysis from October 2011 through January 2017 (training cohort). MRE and TE results were considered to be concordant if they found patients to have the same stage fibrosis as liver biopsy analysis. We validated our findings in 75 adults with nonalcoholic fatty liver disease who underwent contemporaneous MRE, TE, and liver biopsy at a separate institution from March 2010 through May 2013. The primary outcome was rate of discordance between MRE and TE in determining stage of fibrosis (stage 2-4 vs 0-1). Secondary outcomes were the rate of discordance between MRE and TE in determining dichotomized stage of fibrosis (1-4 vs 0, 3-4 vs 0-2, and 4 vs 0-3).ResultsIn the training cohort, there was 43.7% discordance in findings from MRE versus TE. BMI associated significantly with discordance in findings from MRE versus TE (odds ratio, 1.69; 95% confidence interval, 1.15-2.51; P = .008) after multivariable adjustment by age and sex. The findings were confirmed in the validation cohort: there was 45.3% discordance in findings from MRE versus TE. BMI again associated significantly with discordance in findings from MRE versus TE (odds ratio, 1.52; 95% confidence interval, 1.04-2.21; P = .029) after multivariable adjustment by age and sex.ConclusionsWe identified and validated BMI as a factor significantly associated with discordance of findings from MRE versus TE in assessment of fibrosis stage. The degree of discordancy increases with BMI.

Published
2018

22. Link between gut‐microbiome derived metabolite and shared gene‐effects with hepatic steatosis and fibrosis in NAFLD

Author

Caussy, Cyrielle, Hsu, Cynthia, Lo, Min‐Tzu, Liu, Amy, Bettencourt, Ricki, Ajmera, Veeral H, Bassirian, Shirin, Hooker, Jonathan, Sy, Ethan, Richards, Lisa, Schork, Nicholas, Schnabl, Bernd, Brenner, David A, Sirlin, Claude B, Chen, Chi‐Hua, Loomba, Rohit, and Consortium, Genetics of NAFLD in Twins

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Chronic Liver Disease and Cirrhosis, Women's Health, Digestive Diseases, Microbiome, Biomedical Imaging, Clinical Research, Liver Disease, Genetics, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Adult, Aged, Cross-Sectional Studies, Female, Gastrointestinal Microbiome, Humans, Liver Cirrhosis, Male, Metformin, Middle Aged, Non-alcoholic Fatty Liver Disease, Phenylpropionates, Proof of Concept Study, Genetics of NAFLD in Twins Consortium, Clinical Sciences, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

Previous studies have shown that gut-microbiome is associated with nonalcoholic fatty liver disease (NAFLD). We aimed to examine if serum metabolites, especially those derived from the gut-microbiome, have a shared gene-effect with hepatic steatosis and fibrosis. This is a cross-sectional analysis of a prospective discovery cohort including 156 well-characterized twins and families with untargeted metabolome profiling assessment. Hepatic steatosis was assessed using magnetic-resonance-imaging proton-density-fat-fraction (MRI-PDFF) and fibrosis using MR-elastography (MRE). A twin additive genetics and unique environment effects (AE) model was used to estimate the shared gene-effect between metabolites and hepatic steatosis and fibrosis. The findings were validated in an independent prospective validation cohort of 156 participants with biopsy-proven NAFLD including shotgun metagenomics sequencing assessment in a subgroup of the cohort. In the discovery cohort, 56 metabolites including 6 microbial metabolites had a significant shared gene-effect with both hepatic steatosis and fibrosis after adjustment for age, sex and ethnicity. In the validation cohort, 6 metabolites were associated with advanced fibrosis. Among them, only one microbial metabolite, 3-(4-hydroxyphenyl)lactate, remained consistent and statistically significantly associated with liver fibrosis in the discovery and validation cohort (fold-change of higher-MRE versus lower-MRE: 1.78, P < 0.001 and of advanced versus no advanced fibrosis: 1.26, P = 0.037, respectively). The share genetic determination of 3-(4-hydroxyphenyl)lactate with hepatic steatosis was RG :0.57,95%CI:0.27-0.80, P < 0.001 and with fibrosis was RG :0.54,95%CI:0.036-1, P = 0.036. Pathway reconstruction linked 3-(4-hydroxyphenyl)lactate to several human gut-microbiome species. In the validation cohort, 3-(4-hydroxyphenyl)lactate was significantly correlated with the abundance of several gut-microbiome species, belonging only to Firmicutes, Bacteroidetes and Proteobacteria phyla, previously reported as associated with advanced fibrosis. Conclusion: This proof of concept study provides evidence of a link between the gut-microbiome and 3-(4-hydroxyphenyl)lactate that shares gene-effect with hepatic steatosis and fibrosis. (Hepatology 2018).

Published
2018

23. Magnetic Resonance Imaging Proton Density Fat Fraction Associates With Progression of Fibrosis in Patients With Nonalcoholic Fatty Liver Disease

Author

Ajmera, Veeral, Park, Charlie C, Caussy, Cyrielle, Singh, Seema, Hernandez, Carolyn, Bettencourt, Ricki, Hooker, Jonathan, Sy, Ethan, Behling, Cynthia, Xu, Ronghui, Middleton, Michael S, Valasek, Mark A, Faulkner, Claire, Rizo, Emily, Richards, Lisa, Sirlin, Claude B, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Liver Disease, Clinical Research, Digestive Diseases, Hepatitis, Biomedical Imaging, Chronic Liver Disease and Cirrhosis, 4.1 Discovery and preclinical testing of markers and technologies, Oral and gastrointestinal, Adipose Tissue, Adult, Aged, Biomarkers, Biopsy, Disease Progression, Female, Follow-Up Studies, Humans, Liver, Liver Cirrhosis, Liver Function Tests, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Protons, Steatosis, Risk Factor, Biomarker, NASH, Neurosciences, Paediatrics and Reproductive Medicine, Gastroenterology & Hepatology, Clinical sciences, Nutrition and dietetics
Abstract

Markers are needed to predict progression of nonalcoholic fatty liver disease (NAFLD). The proton density fat fraction, measured by magnetic resonance imaging (MRI-PDFF), provides an accurate, validated marker of hepatic steatosis; however, it is not clear whether the PDFF identifies patients at risk for NAFLD progression. We performed a follow-up study of 95 well-characterized patients with biopsy-proven NAFLD and examined the association between liver fat content and fibrosis progression. MRI-PDFF measurements were made at study entry (baseline). Biopsies were collected from patients at baseline and after a mean time period of 1.75 years. Among patients with no fibrosis at baseline, a higher proportion of patients in the higher liver fat group (MRI-PDFF ≥15.7%) had fibrosis progression (38.1%) than in the lower liver fat group (11.8%) (P = .067). In multivariable-adjusted logistic regression models (adjusted for age, sex, ethnicity, and body mass index), patients in the higher liver fat group had a significantly higher risk of fibrosis progression (multivariable-adjusted odds ratio 6.7; 95% confidence interval 1.01-44.1; P = .049). Our findings associate higher liver fat content, measured by MRI-PDFF, with fibrosis progression.

Published
2018

24. Optimal threshold of controlled attenuation parameter with MRI‐PDFF as the gold standard for the detection of hepatic steatosis

Author

Caussy, Cyrielle, Alquiraish, Mosab H, Nguyen, Phirum, Hernandez, Carolyn, Cepin, Sandra, Fortney, Lynda E, Ajmera, Veeral, Bettencourt, Ricki, Collier, Summer, Hooker, Jonathan, Sy, Ethan, Rizo, Emily, Richards, Lisa, Sirlin, Claude B, and Loomba, Rohit

Subjects
Digestive Diseases, Chronic Liver Disease and Cirrhosis, Biomedical Imaging, Liver Disease, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Adult, Area Under Curve, Cross-Sectional Studies, Elasticity Imaging Techniques, Female, Humans, Liver, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Prospective Studies, ROC Curve, Medical Biochemistry and Metabolomics, Clinical Sciences, Immunology, Gastroenterology & Hepatology
Abstract

The optimal threshold of controlled attenuation parameter (CAP) for the detection of hepatic steatosis using both M and XL probe is unknown in nonalcoholic fatty liver disease (NAFLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) is an accurate and precise method of detecting the presence of hepatic steatosis that is superior to CAP. Thus, the aim of this study was to evaluate the diagnostic accuracy and optimal threshold of CAP for the detection of hepatic steatosis as defined by MRI-PDFF ≥ 5%. This prospective cross-sectional study included 119 adults (59% women) with and without NAFLD who underwent MRI-PDFF and CAP using either M or XL probe when indicated within a 6-month period at the NAFLD Research Center, University of California, San Diego. The mean ( ± standard deviation) age and body mass index were 52.4 (±15.2) years and 29.9 (±5.5) kg/m2 , respectively. The prevalence of NAFLD (MRI-PDFF ≥ 5%) and MRI-PDFF ≥ 10% was 70.6% and 47.1%, respectively. The area under the receiver operating characteristic (AUROC) of CAP for the detection of MRI-PDFF ≥ 5% was 0.80 (95% confidence interval [CI], 0.70-0.90) at the cut-point of 288 dB/m and of MRI-PDFF ≥ 10% was 0.87 (95% CI, 0.80-0.94) at the cut-point of 306 dB/m. When stratified by the interquartile range (IQR) of CAP, we observed that an IQR below the median (30 dB/m) had a robust AUROC compared with an IQR above the median (0.92 [95% CI, 0.85-1.00] versus 0.70 [95% CI, 0.56-0.85]; P = 0.0117), and these differences were statistically and clinically significant.ConclusionThe cut-point of CAP for presence of hepatic steatosis (MRI-PDFF ≥ 5%) was 288 dB/m. The diagnostic accuracy of CAP for the detection of hepatic steatosis is more reliable when the IQR of CAP is

Published
2018

25. Increased severity of liver fat content and liver fibrosis in non-alcoholic fatty liver disease correlate with epicardial fat volume in type 2 diabetes: A prospective study

Author

Brouha, Sharon S, Nguyen, Phirum, Bettencourt, Ricki, Sirlin, Claude B, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Liver Disease, Heart Disease, Clinical Research, Chronic Liver Disease and Cirrhosis, Heart Disease - Coronary Heart Disease, Cardiovascular, Digestive Diseases, Diabetes, Oral and gastrointestinal, Metabolic and endocrine, Adipose Tissue, Coronary Disease, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Elasticity Imaging Techniques, Female, Humans, Liver, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Pericardium, Prospective Studies, Severity of Illness Index, Non-alcoholic fatty liver disease, Liver fibrosis, Magnetic resonance imaging, Coronary disease, Multidetector computed tomography, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

ObjectivesTo determine whether severity of non-alcoholic fatty liver disease (NAFLD) and liver fibrosis quantitatively assessed in individuals with diabetes mellitus (DM)-2 correlate with increased coronary heart disease (CHD) risk using non-invasive markers.MethodsWe conducted a single-centre, prospective, cross-sectional study in 100 consecutive diabetic individuals without known CHD recruited between March 2013 and September 2014. History, physical examination, serum markers, cardiac computed tomography (CT), magnetic resonance (MR) imaging-estimated proton density fat fraction (PDFF) and MR elastography (MRE) were obtained for 95 participants. Written informed consent was provided. Institutional review board approved this study. Spearman rank correlation was performed to assess for correlations. Multiple linear regression model determined independent predictors of epicardial adipose tissue (EAT) volume.ResultsA p value < 0.05 determined statistical significance. The EAT volume was higher in the NAFLD group, defined as MR-imaging PDFF ≥ 5 %, compared to the non-NAFLD group (126.5 ml (IQR 80.9) versus 85.4 ml (IQR 44.7), p=0.002). MR imaging-PDFF correlated with EAT (r=0.42, p < 0.0001). MR imaging-PDFF and liver fibrosis were independently associated with EAT.ConclusionsHigher liver fat content and liver fibrosis may portend worse cardiovascular risk in diabetics.Key points• EAT volume is higher in diabetic individuals with NAFLD. • Liver fat content is positively correlated with EAT. • Liver fat content and liver fibrosis were independently associated with EAT. • Higher liver fat content and fibrosis may adversely affect cardiovascular risk.

Published
2018

26. Nonalcoholic fatty liver disease with cirrhosis increases familial risk for advanced fibrosis

Author

Caussy, Cyrielle, Soni, Meera, Cui, Jeffrey, Bettencourt, Ricki, Schork, Nicholas, Chen, Chi-Hua, Ikhwan, Mahdi Al, Bassirian, Shirin, Cepin, Sandra, Gonzalez, Monica P, Mendler, Michel, Kono, Yuko, Vodkin, Irine, Mekeel, Kristin, Haldorson, Jeffrey, Hemming, Alan, Andrews, Barbara, Salotti, Joanie, Richards, Lisa, Brenner, David A, Sirlin, Claude B, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Hepatitis, Liver Disease, Biomedical Imaging, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Prevention, Clinical Research, Oral and gastrointestinal, Adult, Aged, Family, Female, Humans, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Prospective Studies, Risk Factors, Familial NAFLD Cirrhosis Research Consortium, Medical and Health Sciences, Immunology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundThe risk of advanced fibrosis in first-degree relatives of patients with nonalcoholic fatty liver disease and cirrhosis (NAFLD-cirrhosis) is unknown and needs to be systematically quantified. We aimed to prospectively assess the risk of advanced fibrosis in first-degree relatives of probands with NAFLD-cirrhosis.MethodsThis is a cross-sectional analysis of a prospective cohort of 26 probands with NAFLD-cirrhosis and 39 first-degree relatives. The control population included 69 community-dwelling twin, sib-sib, or parent-offspring pairs (n = 138), comprising 69 individuals randomly ascertained to be without evidence of NAFLD and 69 of their first-degree relatives. The primary outcome was presence of advanced fibrosis (stage 3 or 4 fibrosis). NAFLD was assessed clinically and quantified by MRI proton density fat fraction (MRI-PDFF). Advanced fibrosis was diagnosed by liver stiffness greater than 3.63 kPa using magnetic resonance elastography (MRE).ResultsThe prevalence of advanced fibrosis in first-degree relatives of probands with NAFLD-cirrhosis was significantly higher than that in the control population (17.9% vs. 1.4%, P = 0.0032). Compared with controls, the odds of advanced fibrosis among the first-degree relatives of probands with NAFLD-cirrhosis were odds ratio 14.9 (95% CI, 1.8-126.0, P = 0.0133). Even after multivariable adjustment by age, sex, Hispanic ethnicity, BMI, and diabetes status, the risk of advanced fibrosis remained both statistically and clinically significant (multivariable-adjusted odds ratio 12.5; 95% CI, 1.1-146.1, P = 0.0438).ConclusionUsing a well-phenotyped familial cohort, we demonstrated that first-degree relatives of probands with NAFLD-cirrhosis have a 12 times higher risk of advanced fibrosis. Advanced fibrosis screening may be considered in first-degree relatives of NAFLD-cirrhosis patients.Trial registrationUcsd irb140084.FundingNational Institute of Diabetes and Digestive and Kidney Diseases and National Institute of Environmental Health Sciences, NIH.

Published
2017

27. Gut Microbiome-Based Metagenomic Signature for Non-invasive Detection of Advanced Fibrosis in Human Nonalcoholic Fatty Liver Disease

Author

Loomba, Rohit, Seguritan, Victor, Li, Weizhong, Long, Tao, Klitgord, Niels, Bhatt, Archana, Dulai, Parambir Singh, Caussy, Cyrielle, Bettencourt, Richele, Highlander, Sarah K, Jones, Marcus B, Sirlin, Claude B, Schnabl, Bernd, Brinkac, Lauren, Schork, Nicholas, Chen, Chi-Hua, Brenner, David A, Biggs, William, Yooseph, Shibu, Venter, J Craig, and Nelson, Karen E

Subjects
Microbiology, Biological Sciences, Liver Disease, Microbiome, Genetics, Clinical Research, Chronic Liver Disease and Cirrhosis, Hepatitis, Human Genome, Digestive Diseases, Women's Health, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Oral and gastrointestinal, Good Health and Well Being, Adult, Aged, Bacteria, Feces, Female, Gastrointestinal Microbiome, Humans, Liver Cirrhosis, Male, Metagenomics, Middle Aged, Non-alcoholic Fatty Liver Disease, Prognosis, Prospective Studies, NASH, biomarker, cirrhosis, fatty liver, fibrosis, hepatic steatosis, hepatitis, liver disease, microbiome, non-invasive, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Endocrinology & Metabolism, Biochemistry and cell biology, Medical biochemistry and metabolomics
Abstract

The presence of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is the most important predictor of liver mortality. There are limited data on the diagnostic accuracy of gut microbiota-derived signature for predicting the presence of advanced fibrosis. In this prospective study, we characterized the gut microbiome compositions using whole-genome shotgun sequencing of DNA extracted from stool samples. This study included 86 uniquely well-characterized patients with biopsy-proven NAFLD, of which 72 had mild/moderate (stage 0-2 fibrosis) NAFLD, and 14 had advanced fibrosis (stage 3 or 4 fibrosis). We identified a set of 40 features (p < 0.006), which included 37 bacterial species that were used to construct a Random Forest classifier model to distinguish mild/moderate NAFLD from advanced fibrosis. The model had a robust diagnostic accuracy (AUC 0.936) for detecting advanced fibrosis. This study provides preliminary evidence for a fecal-microbiome-derived metagenomic signature to detect advanced fibrosis in NAFLD.

Published
2017

28. Smartphone-Based Geofencing to Ascertain Hospitalizations

Author

Nguyen, Kaylin T, Olgin, Jeffrey E, Pletcher, Mark J, Ng, Madelena, Kaye, Leanne, Moturu, Sai, Gladstone, Rachel A, Malladi, Chaitanya, Fann, Amy H, Maguire, Carol, Bettencourt, Laura, Christensen, Matthew A, and Marcus, Gregory M

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Clinical Research, Health Services, Networking and Information Technology R&D (NITRD), Patient Safety, Adult, Aged, Appointments and Schedules, Attitude to Computers, Cardiac Catheterization, Electronic Health Records, Electrophysiologic Techniques, Cardiac, Feasibility Studies, Female, Geographic Information Systems, Hospitalization, Humans, Male, Middle Aged, Mobile Applications, Patient Satisfaction, Smartphone, Surveys and Questionnaires, Telemedicine, Time Factors, United States, fast food, hospitalization, internet, pharmacies, smartphone, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Public health
Abstract

BackgroundAscertainment of hospitalizations is critical to assess quality of care and the effectiveness and adverse effects of various therapies. Smartphones, mobile geolocators that are ubiquitous, have not been leveraged to ascertain hospitalizations. Therefore, we evaluated the use of smartphone-based geofencing to track hospitalizations.Methods and resultsParticipants aged ≥18 years installed a mobile application programmed to geofence all hospitals using global positioning systems and cell phone tower triangulation and to trigger a smartphone-based questionnaire when located in a hospital for ≥4 hours. An in-person study included consecutive consenting patients scheduled for electrophysiology and cardiac catheterization procedures. A remote arm invited Health eHeart Study participants who consented and engaged with the study via the internet only. The accuracy of application-detected hospitalizations was confirmed by medical record review as the reference standard. Of 22 eligible in-person patients, 17 hospitalizations were detected (sensitivity 77%; 95% confidence interval, 55%-92%). The length of stay according to the application was positively correlated with the length of stay ascertained via the electronic medical record (r=0.53; P=0.03). In the remote arm, the application was downloaded by 3443 participants residing in all 50 US states; 243 hospital visits at 119 different hospitals were detected through the application. The positive predictive value for an application-reported hospitalization was 65% (95% confidence interval, 57%-72%).ConclusionsMobile application-based ascertainment of hospitalizations can be achieved with modest accuracy. This first proof of concept may ultimately be applicable to geofencing other types of prespecified locations to facilitate healthcare research and patient care.

Published
2017

29. Assessment of treatment response in non‐alcoholic steatohepatitis using advanced magnetic resonance imaging

Author

Lin, SC, Heba, E, Bettencourt, R, Lin, GY, Valasek, MA, Lunde, O, Hamilton, G, Sirlin, CB, and Loomba, R

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Chronic Liver Disease and Cirrhosis, Biomedical Imaging, Liver Disease, Clinical Trials and Supportive Activities, Hepatitis, Digestive Diseases, Clinical Research, 6.1 Pharmaceuticals, Adult, Aged, Anticholesteremic Agents, Biomarkers, Cross-Sectional Studies, Diet, Fat-Restricted, Double-Blind Method, Ezetimibe, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Treatment Outcome, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundMagnetic resonance imaging-derived measures of liver fat and volume are emerging as accurate, non-invasive imaging biomarkers in non-alcoholic steatohepatitis (NASH). Little is known about these measures in relation to histology longitudinally.AimTo examine any relationship between MRI-derived proton-density fat-fraction (PDFF), total liver volume (TLV), total liver fat index (TLFI), vs. histology in a NASH trial.MethodsThis is a secondary analysis of a 24-week randomised, double-blind, placebo-controlled trial of 50 patients with biopsy-proven NASH randomised to oral ezetimibe 10 mg daily (n = 25) vs. placebo (n = 25). Baseline and post-treatment anthropometrics, biochemical profiling, MRI and biopsies were obtained.ResultsBaseline mean PDFF correlated strongly with TLFI (Spearman's ρ = 0.94, n = 45, P < 0.0001) and had good correlation with TLV (ρ = 0.57, n = 45, P < 0.0001). Mean TLV correlated strongly with TLFI (ρ = 0.78, n = 45, P < 0.0001). After 24 weeks, PDFF remained strongly correlated with TLFI (ρ = 0.94, n = 45, P < 0.0001), maintaining good correlation with TLV (ρ = 0.51, n = 45, P = 0.0004). TLV remained strongly correlated with TLFI (ρ = 0.74, n = 45, P < 0.0001). Patients with Grade 1 vs. 3 steatosis had lower PDFF, TLV, and TLFI (P < 0.0001, P = 0.0003, P < 0.0001 respectively). Regression analysis of changes in MRI-PDFF vs. TLV indicates that 10% reduction in MRI-PDFF predicts 257 mL reduction in TLV.ConclusionsThe MRI-PDFF and TLV strongly correlated with TLFI. Decreases in steatosis were associated with an improvement in hepatomegaly. Lower values of these measures reflect lower histologic steatosis grades. MRI-derived measures of liver fat and volume may be used as dynamic and more responsive imaging biomarkers in a NASH trial, than histology.

Published
2017

30. Magnetic Resonance Elastography vs Transient Elastography in Detection of Fibrosis and Noninvasive Measurement of Steatosis in Patients With Biopsy-Proven Nonalcoholic Fatty Liver Disease

Author

Park, Charlie C, Nguyen, Phirum, Hernandez, Carolyn, Bettencourt, Ricki, Ramirez, Kimberly, Fortney, Lynda, Hooker, Jonathan, Sy, Ethan, Savides, Michael T, Alquiraish, Mosab H, Valasek, Mark A, Rizo, Emily, Richards, Lisa, Brenner, David, Sirlin, Claude B, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Hepatitis, Clinical Research, Liver Disease, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Oral and gastrointestinal, Good Health and Well Being, Adult, Aged, Area Under Curve, Biopsy, Cross-Sectional Studies, Elasticity Imaging Techniques, Female, Humans, Liver, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Prospective Studies, ROC Curve, Ultrasonography, Noninvasive, Assessment, Comparative, Biomarker, Neurosciences, Paediatrics and Reproductive Medicine, Gastroenterology & Hepatology, Clinical sciences, Nutrition and dietetics
Abstract

Background & aimsMagnetic resonance imaging (MRI) techniques and ultrasound-based transient elastography (TE) can be used in noninvasive diagnosis of fibrosis and steatosis in patients with nonalcoholic fatty liver disease (NAFLD). We performed a prospective study to compare the performance of magnetic resonance elastography (MRE) vs TE for diagnosis of fibrosis, and MRI-based proton density fat fraction (MRI-PDFF) analysis vs TE-based controlled attenuation parameter (CAP) for diagnosis of steatosis in patients undergoing biopsy to assess NAFLD.MethodsWe performed a cross-sectional study of 104 consecutive adults (56.7% female) who underwent MRE, TE, and liver biopsy analysis (using the histologic scoring system for NAFLD from the Nonalcoholic Steatohepatitis Clinical Research Network Scoring System) from October 2011 through May 2016 at a tertiary medical center. All patients received a standard clinical evaluation, including collection of history, anthropometric examination, and biochemical tests. The primary outcomes were fibrosis and steatosis. Secondary outcomes included dichotomized stages of fibrosis and nonalcoholic steatohepatitis vs no nonalcoholic steatohepatitis. Receiver operating characteristic curve analyses were used to compare performances of MRE vs TE in diagnosis of fibrosis (stages 1-4 vs 0) and MRI-PDFF vs CAP for diagnosis of steatosis (grades 1-3 vs 0) with respect to findings from biopsy analysis.ResultsMRE detected any fibrosis (stage 1 or more) with an area under the receiver operating characteristic curve (AUROC) of 0.82 (95% confidence interval [CI], 0.74-0.91), which was significantly higher than that of TE (AUROC, 0.67; 95% CI, 0.56-0.78). MRI-PDFF detected any steatosis with an AUROC of 0.99 (95% CI, 0.98-1.00), which was significantly higher than that of CAP (AUROC, 0.85; 95% CI, 0.75-0.96). MRE detected fibrosis of stages 2, 3, or 4 with AUROC values of 0.89 (95% CI, 0.83-0.96), 0.87 (95% CI, 0.78-0.96), and 0.87 (95% CI, 0.71-1.00); TE detected fibrosis of stages 2, 3, or 4 with AUROC values of 0.86 (95% CI, 0.77-0.95), 0.80 (95% CI, 0.67-0.93), and 0.69 (95% CI, 0.45-0.94). MRI-PDFF identified steatosis of grades 2 or 3 with AUROC values of 0.90 (95% CI, 0.82-0.97) and 0.92 (95% CI, 0.84-0.99); CAP identified steatosis of grades 2 or 3 with AUROC values of 0.70 (95% CI, 0.58-0.82) and 0.73 (95% CI, 0.58-0.89).ConclusionsIn a prospective, cross-sectional study of more than 100 patients, we found MRE to be more accurate than TE in identification of liver fibrosis (stage 1 or more), using biopsy analysis as the standard. MRI-PDFF is more accurate than CAP in detecting all grades of steatosis in patients with NAFLD.

Published
2017

31. Novel candidate blood‐based transcriptional biomarkers of machado‐joseph disease

Author

Raposo, Mafalda, Bettencourt, Conceição, Maciel, Patrícia, Gao, Fuying, Ramos, Amanda, Kazachkova, Nadiya, Vasconcelos, João, Kay, Teresa, Rodrigues, Ana João, Bettencourt, Bruno, Bruges-Armas, Jácome, Geschwind, Daniel, Coppola, Giovanni, and Lima, Manuela

Subjects
Genetics, Brain Disorders, Clinical Research, Neurodegenerative, Neurosciences, Rare Diseases, Biotechnology, 2.1 Biological and endogenous factors, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Aetiology, Neurological, Adult, Aged, Aged, 80 and over, Azores, Biomarkers, Female, Humans, Machado-Joseph Disease, Male, Middle Aged, Transcriptome, Up-Regulation, Young Adult, spinocerebellar ataxia type 3, polyglutamine disease, gene expression, ataxin-3, microarray, Clinical Sciences, Human Movement and Sports Sciences, Neurology & Neurosurgery
Abstract

BackgroundMachado-Joseph disease (or spinocerebellar ataxia type 3) is a late-onset polyglutamine neurodegenerative disorder caused by a mutation in the ATXN3 gene, which encodes for the ubiquitously expressed protein ataxin-3. Previous studies on cell and animal models have suggested that mutated ataxin-3 is involved in transcriptional dysregulation. Starting with a whole-transcriptome profiling of peripheral blood samples from patients and controls, we aimed to confirm abnormal expression profiles in Machado-Joseph disease and to identify promising up-regulated genes as potential candidate biomarkers of disease status.MethodsThe Illumina Human V4-HT12 array was used to measure transcriptome-wide gene expression in peripheral blood samples from 12 patients and 12 controls. Technical validation and validation in an independent set of samples were performed by quantitative real-time polymerase chain reaction (PCR).ResultsBased on the results from the microarray, twenty six genes, found to be up-regulated in patients, were selected for technical validation by quantitative real-time PCR (validation rate of 81% for the up-regulation trend). Fourteen of these were further tested in an independent set of 42 patients and 35 controls; 10 genes maintained the up-regulation trend (FCGR3B, CSR2RA, CLC, TNFSF14, SLA, P2RY13, FPR2, SELPLG, YIPF6, and GPR96); FCGR3B, P2RY13, and SELPLG were significantly up-regulated in patients when compared with controls.ConclusionsOur findings support the hypothesis that mutated ataxin-3 is associated with transcription dysregulation, detectable in peripheral blood cells. Furthermore, this is the first report suggesting a pool of up-regulated genes in Machado-Joseph disease that may have the potential to be used for fine phenotyping of this disease. © 2015 International Parkinson and Movement Disorder Society.

Published
2015

32. Shared genetic effects between hepatic steatosis and fibrosis: A prospective twin study.

Author

Cui, Jeffrey, Chen, Chi-Hua, Lo, Min-Tzu, Schork, Nicholas, Bettencourt, Ricki, Gonzalez, Monica P, Bhatt, Archana, Hooker, Jonathan, Shaffer, Katherine, Nelson, Karen E, Long, Michelle T, Brenner, David A, Sirlin, Claude B, Loomba, Rohit, and For The Genetics Of Nafld In Twins Consortium

Subjects
Humans, Fatty Liver, Liver Cirrhosis, Diseases in Twins, Risk Factors, Prospective Studies, Cross-Sectional Studies, Middle Aged, Female, Male, Biomedical Imaging, Chronic Liver Disease and Cirrhosis, Liver Disease, Prevention, Genetics, Nutrition, Digestive Diseases, Clinical Research, 2.1 Biological and endogenous factors, Diagnostic Radiology, Gastroenterology & Hepatology, Clinical Sciences, Medical Biochemistry and Metabolomics, Immunology
Abstract

Nonalcoholic fatty liver disease is associated with metabolic risk factors including hypertension and dyslipidemia and may progress to liver fibrosis. Studies have shown that hepatic steatosis and fibrosis are heritable, but whether they have a significant shared gene effect is unknown. This study examined the shared gene effects between hepatic steatosis and fibrosis and their associations with metabolic risk factors. This was a cross-sectional analysis of a prospective cohort of well-characterized, community-dwelling twins (45 monozygotic, 20 dizygotic twin pairs, 130 total subjects) from southern California. Hepatic steatosis was assessed with magnetic resonance imaging-proton density fat fraction and hepatic fibrosis with magnetic resonance elastography. A standard bivariate twin additive genetics and unique environment effects model was used to estimate the proportion of phenotypic variance between two phenotypes accounted for by additive genetic effects and individual-specific environmental effects. Genetic correlations estimated from this model represent the degree to which the genetic determinants of two phenotypes overlap. Mean (± standard deviation) age and body mass index were 47.1 (±21.9) years and 26.2 (±5.8) kg/m2 , respectively. Among the cohort, 20% (26/130) had hepatic steatosis (magnetic resonance imaging-proton density fat fraction ≥5%), and 8.2% (10/122) had hepatic fibrosis (magnetic resonance elastography ≥3 kPa). Blood pressure (systolic and diastolic), triglycerides, glucose, homeostatic model assessment of insulin resistance, insulin, hemoglobin A1c, and low high-density lipoprotein had significant shared gene effects with hepatic steatosis. Triglycerides, glucose, homeostatic model assessment of insulin resistance, insulin, hemoglobin A1c, and low high-density lipoprotein had significant shared gene effects with hepatic fibrosis. Hepatic steatosis and fibrosis had a highly significant shared gene effect of 0.756 (95% confidence interval 0.716-1, P < 0.0001).ConclusionsGenes involved with steatosis pathogenesis may also be involved with fibrosis pathogenesis. (Hepatology 2016;64:1547-1558).

Published
2016

33. Association of Nonalcoholic Fatty Liver Disease With Visceral Adiposity but Not Coronary Artery Calcification in the Elderly

Author

Jacobs, Kathleen, Brouha, Sharon, Bettencourt, Ricki, Barrett-Connor, Elizabeth, Sirlin, Claude, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Aging, Prevention, Liver Disease, Clinical Research, Heart Disease, Cardiovascular, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Heart Disease - Coronary Heart Disease, Aged, Aged, 80 and over, Calcium, California, Coronary Artery Disease, Coronary Vessels, Cross-Sectional Studies, Female, Humans, Intra-Abdominal Fat, Longitudinal Studies, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Obesity, Prospective Studies, Tomography, X-Ray Computed, Metabolic Syndrome, Cardiac Computed Tomography, Coronary Artery Calcium, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background & aimsNonalcoholic fatty liver disease (NAFLD) is related closely to risk factors for coronary artery disease, but it is unclear whether NAFLD independently contributes to atherosclerosis. We investigated the association between NAFLD and coronary artery calcium (CAC) scores, determined based on noncontrast cardiac computed tomography data, in an elderly cohort.MethodsWe conducted a longitudinal, cross-sectional study of data from 250 participants (mean age, 67.6 ± 7.0 y; 43.2% men) in the Rancho Bernardo Study-a prospective population-based study of mostly white adults in suburban Southern California. We compared CAC scores, liver-to-spleen attenuation ratio, and volumes of visceral adipose tissue (VAT) at baseline and after a 5-year follow-up period.ResultsWe assigned participants to groups based on CAC scores (0, 0-10, 11-100, 101-400, and >400). Among groups, the liver-to-spleen attenuation ratio did not vary significantly, but VAT increased with CAC score (median and interquartile range values were as follows: 50.0 [33.3-77.4] cm(3), 63.0 [33.9-93.1] cm(3), 66.1 [48.2-80.2] cm(3), 69.1 [48.1-85.0] cm(3), 76.1 [53.1-108.5] cm(3) for CAC groups; P = .0054). In multivariable regression analysis, NAFLD at baseline was not associated with an increased risk of a CAC score greater than 0. Longitudinal analysis showed that visceral fat, but not hepatic steatosis, increased in participants with increasing CAC scores (interquartile range 57.1-92.4) vs 55.2 cm(3) in patients without (interquartile range 36.5-81.1, P = .0401). The proportion of patients with NAFLD decreased after the 5-year follow-up period (from 29.3% before to 14.1% afterward; P = .0081), despite increased mean CAC scores and VAT volume in patients.ConclusionsIn adults age 67.6 ± 7.0 years, the proportion with NAFLD decreased despite increasing CAC score and VAT with age. There was no clear association between NAFLD and CAC score. However, VAT was associated with baseline and increasing CAC scores. Visceral adiposity therefore might be a risk factor for coronary artery disease.

Published
2016

34. Magnetic resonance elastography identifies fibrosis in adults with alpha‐1 antitrypsin deficiency liver disease: a prospective study

Author

Kim, RG, Nguyen, P, Bettencourt, R, Dulai, PS, Haufe, W, Hooker, J, Minocha, J, Valasek, MA, Aryafar, H, Brenner, DA, Sirlin, CB, and Loomba, R

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Chronic Liver Disease and Cirrhosis, Liver Disease, Biomedical Imaging, Chronic Obstructive Pulmonary Disease, Clinical Research, Rare Diseases, Lung, Emphysema, Digestive Diseases, 4.2 Evaluation of markers and technologies, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Adult, Aged, Biopsy, Case-Control Studies, Elasticity Imaging Techniques, Female, Genotype, Humans, Liver Cirrhosis, Male, Middle Aged, Pilot Projects, Prospective Studies, Sensitivity and Specificity, alpha 1-Antitrypsin Deficiency, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundLimited data exist on the clinical presentation and non-invasive detection of liver fibrosis in adults with homozygous Z genotype alpha-1 antitrypsin (AAT) deficiency.AimsTo compare demographic, biochemical, histological and imaging data of AAT deficient patients to normal-control and biopsy-proven non-alcoholic fatty liver disease (NAFLD) patients, and to assess the diagnostic accuracy of magnetic resonance elastography (MRE) in detecting fibrosis in AAT deficiency.MethodsStudy includes 33 participants, 11 per group, who underwent clinical research evaluation, liver biopsy (AAT and NAFLD groups), and MRE. Histological fibrosis was quantified using a modified Ishak 6-point scale and liver stiffness by MRE. Diagnostic performance of MRE in detecting fibrosis was assessed by receiver operating characteristic (ROC) analysis.ResultsMean (±s.d.) of age and BMI of normal-control, AAT and NAFLD groups was 57 (±19), 57 (±18), and 57 (±13) years, and 22.7 (±2.5), 24.8 (±4.0) and 31.0 (±5.1) kg/m(2) respectively. Serum ALT [mean ± s.d.] was similar within normal-control [16.4 ± 4.0] and AAT groups [23.5 ± 10.8], but was significantly lower in AAT than NAFLD even after adjustment for stage of fibrosis (P < 0.05, P = 0.0172). For fibrosis detection, MRE-estimated stiffness had an area under the ROC curve of 0.90 (P < 0.0001); an MRE threshold of ≥3.0 kPa provided 88.9% accuracy, with 80% sensitivity and 100% specificity to detect presence of any fibrosis (stage ≥1).ConclusionsThis pilot prospective study suggests magnetic resonance elastography may be accurate for identifying fibrosis in patients with alpha-1 antitrypsin deficiency. Larger validation studies are warranted.

Published
2016

35. Non‐invasive screening of diabetics in primary care for NAFLD and advanced fibrosis by MRI and MRE

Author

Doycheva, I, Cui, J, Nguyen, P, Costa, EA, Hooker, J, Hofflich, H, Bettencourt, R, Brouha, S, Sirlin, CB, and Loomba, R

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Hepatitis, Obesity, Rare Diseases, Health Services, Clinical Research, Diabetes, Liver Disease, Digestive Diseases, Chronic Liver Disease and Cirrhosis, 4.2 Evaluation of markers and technologies, Oral and gastrointestinal, Metabolic and endocrine, Aged, Body Mass Index, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Elasticity Imaging Techniques, Female, Humans, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Prevalence, Primary Health Care, Prospective Studies, Waist Circumference, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundCurrent guidelines do not recommend screening for non-alcoholic fatty liver disease (NAFLD) or advanced fibrosis. Patients with type 2 diabetes mellitus (T2DM) are known to be at increased risk for NAFLD and advanced fibrosis.AimTo assess the feasibility in diabetics in a primary care setting of screening for NAFLD and advanced fibrosis, by using non-invasive magnetic resonance imaging (MRI) to estimate the hepatic proton density fat fraction (MRI-PDFF) and magnetic resonance elastography (MRE) to estimate hepatic stiffness.MethodsWe performed a cross-sectional analysis of a prospective study that included 100 (53% men) consecutively enrolled diabetics who did not have any other aetiology of liver disease. All patients underwent a standardised research visit, laboratory tests, MRI-PDFF, and MRE.ResultsMean (±s.d.) age and body mass index (BMI) was 59.7 (±11.2) years and 30.8 (±6.5) kg/m(2) , respectively. The prevalence of NAFLD (defined as MRI-PDFF ≥5%) and advanced fibrosis (defined as MRE ≥3.6 kPa) was 65% and 7.1%, respectively. One patient with advanced fibrosis had definite hepatocellular carcinoma. When compared to those without NAFLD, patients with NAFLD were younger (P = 0.028) and had higher mean BMI (P = 0.0008), waist circumference (P < 0.0001) and prevalence of metabolic syndrome (84.6% vs. 40.0%, P < 0.0001). Only 26% of those with NAFLD had elevated alanine aminotransferase.ConclusionsThis proof-of-concept study demonstrates that T2DM has significant rates of both NAFLD and advanced fibrosis. Concomitant screening for NAFLD and advanced fibrosis by using MRI-proton density fat fraction and magnetic resonance elastography in T2DM is feasible and may be considered after validation in a larger cohort.

Published
2016

36. Heritability of Hepatic Fibrosis and Steatosis Based on a Prospective Twin Study.

Author

Loomba, Rohit, Schork, Nicholas, Chen, Chi-Hua, Bettencourt, Ricki, Bhatt, Ana, Ang, Brandon, Nguyen, Phirum, Hernandez, Carolyn, Richards, Lisa, Salotti, Joanie, Lin, Steven, Seki, Ekihiro, Nelson, Karen E, Sirlin, Claude B, Brenner, David, and Genetics of NAFLD in Twins Consortium

Subjects
Genetics of NAFLD in Twins Consortium, Humans, Liver Cirrhosis, Genetic Predisposition to Disease, Magnetic Resonance Imaging, Multivariate Analysis, Risk Factors, Chi-Square Distribution, Prospective Studies, Cross-Sectional Studies, Heredity, Twins, Dizygotic, Twins, Monozygotic, Phenotype, Adult, Aged, Middle Aged, California, Female, Male, Elasticity Imaging Techniques, Non-alcoholic Fatty Liver Disease, Fatty Liver, Genetic Factors, NAFLD, NASH, Chronic Liver Disease and Cirrhosis, Clinical Research, Biomedical Imaging, Liver Disease, Digestive Diseases, Aetiology, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Clinical Sciences, Neurosciences, Paediatrics and Reproductive Medicine, Gastroenterology & Hepatology
Abstract

Background & aimsLittle is known about the heritability of hepatic fibrosis, and the heritability of hepatic steatosis has not been assessed systematically in adults. We investigated the heritability of hepatic fibrosis and steatosis in a community-dwelling twin cohort.MethodsWe performed a cross-sectional analysis of a cohort of well-characterized twins residing in Southern California including 60 pairs of twins (42 monozygotic and 18 dizygotic; average age, 45.7 ± 22.1 y; average body mass index, 26.4 ± 5.7 kg/m(2)). We collected data on medical history, physical examinations, fasting laboratory test results, and liver health; all participants underwent an advanced magnetic resonance imaging (MRI) examination of the liver from January 2012 through January 2015. Hepatic steatosis was quantified noninvasively by MRI and determined based on the proton-density fat fraction (MRI-PDFF); liver fibrosis was measured based on stiffness measured by magnetic resonance elastography.ResultsTwenty-six of the 120 subjects (21.7%) had nonalcoholic fatty liver disease (defined as MRI-PDFF ≥ 5% after exclusion of other causes of hepatic steatosis). The presence of hepatic steatosis correlated between monozygotic twins (r(2) = 0.70; P < .0001) but not between dizygotic twins (r(2) = 0.36; P = .2). The level of liver fibrosis also correlated between monozygotic twins (r(2) = 0.48; P

Published
2015

37. Novel association between serum pentraxin‐2 levels and advanced fibrosis in well‐characterised patients with non‐alcoholic fatty liver disease

Author

Verna, EC, Patel, J, Bettencourt, R, Nguyen, P, Hernandez, C, Valasek, MA, Kisselva, T, Brenner, DA, and Loomba, R

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Liver Disease, Clinical Research, Inflammatory and immune system, Oral and gastrointestinal, Good Health and Well Being, Adult, Aged, Biomarkers, Blood Proteins, Cross-Sectional Studies, Female, Humans, Liver Cirrhosis, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Severity of Illness Index, Pharmacology and Pharmaceutical Sciences, Gastroenterology & Hepatology, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundPentraxin-2 (PTX-2), a serum protein, inhibits inflammation and fibrosis, and recombinant PTX-2 is being tested as an anti-fibrotic agent.AimTo evaluate the association between serum PTX-2 levels and fibrosis stage in patients with non-alcoholic fatty liver disease (NAFLD).MethodsSerum pentraxin-2 levels were compared between four groups of well-characterised patients including NAFLD with no fibrosis, NAFLD with mild-moderate fibrosis (stage 1-2), NAFLD with advanced fibrosis (stage 3-4), and age-sex matched non-NAFLD controls.ResultsSixty subjects were included in the study. The mean age was 58.9 years, 68% were male and 58% were Caucasian. In univariate analysis, serum PTX-2 levels significantly decreased from non-NAFLD controls to mild NAFLD with no fibrosis, to NAFLD with mild-moderate fibrosis and were lowest in patients with NAFLD and advanced fibrosis, in a dose-dependent manner (P

Published
2015

38. Perceptions, Information Sources, and Behavior Regarding Alcohol and Heart Health

Author

Whitman, Isaac R, Pletcher, Mark J, Vittinghoff, Eric, Imburgia, Kourtney E, Maguire, Carol, Bettencourt, Laura, Sinha, Tuhin, Parsnick, Todd, Tison, Geoffrey H, Mulvanny, Christopher G, Olgin, Jeffrey E, and Marcus, Gregory M

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease, Substance Misuse, Basic Behavioral and Social Science, Behavioral and Social Science, Alcoholism, Alcohol Use and Health, Clinical Research, Cardiovascular, 3.1 Primary prevention interventions to modify behaviours or promote wellbeing, Good Health and Well Being, Adult, Age Factors, Aged, Alcohol Drinking, Alcoholic Beverages, Cardiovascular Diseases, Cohort Studies, Cross-Sectional Studies, Female, Health Knowledge, Attitudes, Practice, Health Status, Humans, Internet, Male, Middle Aged, Sex Factors, Socioeconomic Factors, United States, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

Despite the equipoise regarding alcohol's cardiovascular effects and absence of relevant rigorous controlled trials, the lay press frequently portrays alcohol as "heart healthy." The public perception of alcohol's heart effects, the sources of those perceptions, and how they may affect behavior are unknown. We performed a cross-sectional analysis of data obtained from March 2013 to September 2014 from consecutive participants enrolled in the Health eHeart Study. Of 5,582 participants, 1,707 (30%) viewed alcohol as heart healthy, 2,157 (39%) viewed it as unhealthy, and 1,718 (31%) were unsure. Of those reporting alcohol as heart healthy, 80% cited lay press as a source of their knowledge. After adjustment, older age (odds ratio 1.11), higher education (odds ratio 1.37), higher income (odds ratio 1.07), US residence (odds ratio 1.63), and coronary artery disease (odds ratio 1.51) were associated with perception of alcohol as heart healthy (all p 5,000 consecutive Health eHeart participants, approximately 1/3 believed alcohol to be heart healthy, and the majority cited the lay press as the origin of that perception. Those with a perception of alcohol as heart healthy drink substantially more alcohol.

Published
2015

39. Association Between Quantity of Liver Fat and Cardiovascular Risk in Patients With Nonalcoholic Fatty Liver Disease Independent of Nonalcoholic Steatohepatitis

Author

Arulanandan, Ahilan, Ang, Brandon, Bettencourt, Ricki, Hooker, Jonathan, Behling, Cynthia, Lin, Grace Y, Valasek, Mark A, Ix, Joachim H, Schnabl, Bernd, Sirlin, Claude B, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Hepatitis, Obesity, Nutrition, Clinical Research, Liver Disease, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Oral and gastrointestinal, Adult, Aged, California, Case-Control Studies, Cross-Sectional Studies, Female, Humans, Liver, Magnetic Resonance Imaging, Male, Metabolic Syndrome, Middle Aged, Non-alcoholic Fatty Liver Disease, Risk Assessment, Noninvasive, Heart and Vascular Disease, Detection, Prognostic Factor, Nonalcoholic Steatohepatitis, Cardiovascular Risk, Cardiovascular Disease, Liver Fat, Noninvasive Biomarker, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background & aimsThe association between quantity of liver fat and the presence of metabolic syndrome needs to be assessed systematically. We aimed to determine the association between the quantity of liver fat and the presence of the metabolic syndrome in patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD), independent of nonalcoholic steatohepatitis (NASH).MethodsWe recruited 146 patients with well-characterized biopsy-proven NAFLD and 50 individuals without NAFLD (controls) to participate in a case-control study at the NAFLD Translational Research Unit at the University of California San Diego. Liver fat was quantified in patients with NAFLD and controls using an advanced magnetic resonance imaging-based biomarker, the proton-density-fat-fraction (MRI-PDFF). Patients with NAFLD were divided into groups based on whether they were above or below the median MRI-PDFF value (15.4% in patients with NAFLD); the MRI-PDFF value for controls was less than 5%. The primary outcome was the presence of the metabolic syndrome using Adult Treatment Panel III criteria without and with adjustment for the presence of NASH.ResultsCompared with NAFLD patients with MRI-PDFF values below the median, and compared with controls, NAFLD patients with MRI-PDFF values above the median were more likely to have abdominal obesity (P < .0001), lower levels of high-density cholesterol (P < .0001), higher levels of triglycerides (P < .0001), and higher fasting glucose levels (P < .001). Compared with NAFLD patients with MRI-PDFF values below the median, NAFLD patients with MRI-PDFF above the median were more likely to have the metabolic syndrome (60.3% vs. 44.4%; P < .04), independent of biopsy-detected NASH.ConclusionsIncreased liver fat content in patients with NAFLD is associated with increased rates of the metabolic syndrome, independent of NASH. There appears to be an association between the quantity of liver fat and the risk for cardiovascular disease in patients with NAFLD.

Published
2015

40. Ezetimibe for the treatment of nonalcoholic steatohepatitis: Assessment by novel magnetic resonance imaging and magnetic resonance elastography in a randomized trial (MOZART trial)

Author

Loomba, Rohit, Sirlin, Claude B, Ang, Brandon, Bettencourt, Ricki, Jain, Rashmi, Salotti, Joanie, Soaft, Linda, Hooker, Jonathan, Kono, Yuko, Bhatt, Archana, Hernandez, Laura, Nguyen, Phirum, Noureddin, Mazen, Haufe, William, Hooker, Catherine, Yin, Meng, Ehman, Richard, Lin, Grace Y, Valasek, Mark A, Brenner, David A, Richards, Lisa, and Consortium, San Diego Integrated NAFLD Research

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Clinical Trials and Supportive Activities, Hepatitis, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Clinical Research, Liver Disease, 6.1 Pharmaceuticals, Oral and gastrointestinal, Anticholesteremic Agents, Azetidines, Double-Blind Method, Elasticity Imaging Techniques, Ezetimibe, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, San Diego Integrated NAFLD Research Consortium, Medical Biochemistry and Metabolomics, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

UnlabelledEzetimibe inhibits intestinal cholesterol absorption and lowers low-density lipoprotein cholesterol. Uncontrolled studies have suggested that it reduces liver fat as estimated by ultrasound in nonalcoholic steatohepatitis (NASH). Therefore, we aimed to examine the efficacy of ezetimibe versus placebo in reducing liver fat by the magnetic resonance imaging-derived proton density-fat fraction (MRI-PDFF) and liver histology in patients with biopsy-proven NASH. In this randomized, double-blind, placebo-controlled trial, 50 patients with biopsy-proven NASH were randomized to either ezetimibe 10 mg orally daily or placebo for 24 weeks. The primary outcome was a change in liver fat as measured by MRI-PDFF in colocalized regions of interest within each of the nine liver segments. Novel assessment by two-dimensional and three-dimensional magnetic resonance elastography was also performed. Ezetimibe was not significantly better than placebo at reducing liver fat as measured by MRI-PDFF (mean difference between the ezetimibe and placebo arms -1.3%, P = 0.4). Compared to baseline, however, end-of-treatment MRI-PDFF was significantly lower in the ezetimibe arm (15%-11.6%, P < 0.016) but not in the placebo arm (18.5%-16.4%, P = 0.15). There were no significant differences in histologic response rates, serum alanine aminotransferase and aspartate aminotransferase levels, or longitudinal changes in two-dimensional and three-dimensional magnetic resonance elastography-derived liver stiffness between the ezetimibe and placebo arms. Compared to histologic nonresponders (25/35), histologic responders (10/35) had a significantly greater reduction in MRI-PDFF (-4.35 ± 4.9% versus -0.30 ± 4.1%, P < 0.019).ConclusionsEzetimibe did not significantly reduce liver fat in NASH. This trial demonstrates the application of colocalization of MRI-PDFF-derived fat maps and magnetic resonance elastography-derived stiffness maps of the liver before and after treatment to noninvasively assess treatment response in NASH.

Published
2015

41. Higher dose corticosteroids in patients admitted to hospital with COVID-19 who are hypoxic but not requiring ventilatory support (RECOVERY)

Author

Obbina Abani, Ali Abbas, Fatima Abbas, Joshua Abbas, Kasim Abbas, Mustafa Abbas, Sadia Abbasi, Hakam Abbass, Alfie Abbott, Alison Abbott, Nabeel Abdallah, Ammar Abdelaziz, Ashraf Abdelaziz, Mohamed Abdelfattah, Bushra Abdelqader, Audrey Abdul, Basir Abdul, Siddiqui Abdul, Althaf Abdul Rasheed, Ajibode Abdulakeem, Rezan Abdul-Kadir, Abdullah Abdullah, Abdulfatahi Abdulmumeen, Rasheed Abdul-Raheem, Niyaz Abdulshukkoor, Kula Abdusamad, Yazeed Abed El Khaleq, Mai Abedalla, Abeer Abeer Ul Amna, Lynn Abel, Katrina Abernethy, Movin Abeywickrema, Chandra Abhinaya, Affyarsyah Abidin, Adebanke Aboaba, Abigail Aboagye-Odei, Christopher Aboah, Heba Aboelela, Hani Abo-Leyah, Karim Abouelela, Ahmed Abou-Haggar, Mahmoud Abouibrahim, Ahmed Abousamra, Mona Abouzaid, Miriam Abraham, Tizzy Abraham, Abraheem Abraheem, Judith Abrams, Rebecca Abrams, Hyacinth-John Abu, Ahmed Abu-Arafeh, Syed M Abubacker, Akata Abung, Yousuf Abusamra, Yaa Aceampong, Amaka Achara, Devikumar Acharya, Faustina Acheampong, Prince Acheampong, Sarah Acheampong, Janet Acheson, Shiella Achieng, Andres Acosta, Rebecca Acquah, Catherine Acton, Jacqueline Adabie-Ankrah, Paul Adair, Fiona Adam, Matthew Adam, Huzaifa Adamali, Marta Adamczyk, Carol Adams, Charlotte Adams, Daniel Adams, Kate Adams, Laura Adams, Nikkita Adams, Richard Adams, Tim Adams, Laura Adamu-Ikeme, Krishma Adatia, Kirsty Adcock, Lawrence Addai-Boampong, Afua Addo, Oluwatobi Adeagbo, Ade Adebiyi, Ogunlana Adedeji, Yewande Adegeye, Ken Adegoke, Vicki Adell, Sherna Adenwalla, Femi W Adeoye, Oluwasegun A Adesemoye, Emmanuel O Adewunmi, Adedamola Adeyanju, Joyce Adeyemi, Tenifayo Adeyemo, Binay Adhikari, Shanti A Adhikari, Rina Adhikary, Adhikarla Aditya, Patience Adjepong, Gabrielle Adkins, Adnan Adnan, Marguerite Adriaanse, John Aeron-Thomas, Debbie Affleck, Carmel Afnan, Muhammad Afridi, Patricia Afrim, Felicia Akua Afriyie, Zainab A Aftab, Meenakshi Agarwal, Rachel Agbeko, Chris Agbo, Sunil Aggarwal, Arameh Aghababaie, Laura Aguilar Jimenez, Jacqueline Afrakomah Agyekum, Kwame Agyen, Shafana Ahamed Sadiq, Mohamed H Ahammed Nazeer, Mohammad Ahmad, Syed Ahmad, Afshan Ahmed, Ashar Ahmed, Asim Ahmed, Basheer A R Ahmed, Bilal Ahmed, Forizuddin Ahmed, Hamze Ahmed, Hanad Ahmed, Irshad Ahmed, Khaled Ahmed, Khalil Ahmed, Liban Ahmed, Mahin Ahmed, Maria C Ahmed, Muhammad S Ahmed, Naseer Ahmed, Nausheen Ahmed, Osama Ahmed, Rajia A Ahmed, Rawya Ahmed, Rizwan Ahmed, Saif Ahmed, Sammiya Ahmed, Sana G Ahmed, Syed Ahmed, Syed H Ahmed, Roa Ahmed Ali, Bilal Ahmed Mohamud, Sana Ahmed, Sana Ahmer, Augustine Ahonia, Christine Aiken, Dhiraj Ail, Mark Ainsworth, Myriam Aissa, Lindianne Aitken, Bini Ajay, Abdulakeem Ajibode, Ayesha Ajmi, Nasim Akhtar, Nauman Akhtar, Suha Akili, Bolutito Akinbiyi, Oludoyinsola Akindolie, Yinka Akinfenwa, Olugbenga Akinkugbe, Ibrahim Akinpelu, Mohammad Akram, Olugbenro Aktinade, Uzayr Akudi, Ahmad S A R Al Aaraj, Asma Al Balushi, Majd Al Dakhola, Aladdin Al Swaifi, Eslam Al-Abadi, Adegoke Alabi, Narendra Aladangady, Mohamed Alafifi, Ayaz Alam, Sajid Alam, Abbas Al-Asadi, Kyriaki Alatzoglou, Paul Albert, Albertus Albertus, Lorraine Albon, Angela Alcala, Gemma Alcorn, Stephen Alcorn, Aggie Aldana, David Alderdice, Abdullah Aldesouki, Rayan Aldouri, Jonathan Aldridge, Nicolas Aldridge, Ram Maya Ale, Ana Alegria, Alison Alexander, Courtney Alexander, John Alexander, Peter D G Alexander, Julyan Al-fori, Laith Alghazawi, Osama Alhabsha, Bahij Al-Hakim, Rafil Alhameed, Mohammed Al-Hayali, Shams Al-Hity, Ali Ali, Amanda Ali, Asad Ali, Fawzia R Ali, Jawad Ali, Mariam Ali, Mohamed Ali, Mohammad Ali, Muhammad S Ali, Nayab Ali, Oudai Ali, Ramla Ali, Sakina Ali, Syed Ali, Emilio Aliberti, Jade Alin, Abid Alina, Analyn Alipustain, Bachti Alisjahbana, Fine Aliyuda, Katrin Alizadeh, Maithem Al-Jibury, Saba Al-Juboori, Majid Al-Khalil, Abdullah Alkhudhayri, Moutaz Alkhusheh, Fiona Allan, Nataliya Allan, Alison Allanson, Robert Allcock, Eireann Allen, Jade Allen, Kerry Allen, Louise Allen, Poppy Allen, Rebecca Allen, Sam Allen, Sharon Allen, Simon Allen, Tania Allen, Abiola Alli, Kathryn Allison, Bethan Allman, Helen K Allsop, Lynne Allsop, David Allsup, Amal F T Almahroos, Hassan Al-Moasseb, Magda Al-Obaidi, Lina Alomari, Akram Al-Rabahi, Bahar Al-Ramadhani, Zayneb Al-Saadi, Riyam Al-Sammarraie, Inji Alshaer, Rustam Al-Shahi Salman, Warkaq Al-Shamkhani, Fadia Alsheikh, Bashar Al-Sheklly, Sara Altaf, Amanda Alty, Mary Alvarez, Maria Alvarez Corral, Edward Alveyn, Maysaa Alzetani, Susan Amamou, Noor Amar, Sakkarai Ambalavanan, Robert Ambrogetti, Chris Ambrose, Amir Ameen, Amel Amelia Ganefianty, Katharine Ames, Maria R Amezaga, Allison Amin, Amina Amin, Kanish Amin, Syed Amin, Tara Amin, Badshah Amit, Amjad Amjad, Neelma Amjad, James Amoah-Dankwa, Victoria Amosun, Muhammad Amsal, Khaled Amsha, John Amuasi, Noelia Amutio Martin, Pugh Amy, Ann Anada, Atul Anand, Samantha Anandappa, Satya D Anantapatnaikuni, Ellen Anderson, Julie Anderson, Laura Anderson, Marina Anderson, Michelle Anderson, Nicola Anderson, Rachel Anderson, Rebecca Anderson, Rory Anderson, Samantha Anderson, Wendy Anderson, Prematie Andreou, Angela Andrews, Antonette Andrews, Jill Andrews, Joyann Andrews, Kanayochukwu Aneke, Andrew Ang, Wan Wei Ang, Tammy Angel, Aramburo Angela, Paola Angelini, Lazarus Anguvaa, Oleg Anichtchik, Millicent Anim-Somuah, Krishnan Aniruddhan, Jessica Annett, Louise Anning, Maldwyn Ansah, Patrick J Anstey, Rebekah Anstey, Alpha Anthony, Aaron Anthony-Pillai, Philip Antill, Zhelyazkova Antonina, Varghese Anu, Muhammad Anwar, Elena Apetri, Aristeidis Apostolopoulos, Sarah Appleby, Diane Appleyard, Maia F Aquino, Bianca Araba, Samuel Aransiola, Mariana Araujo, Ann Archer, Denise Archer, Samuel Archer, Simon Archer, Daniele Arcoria, Christian Ardley, Ana-Maria Arias, Oluwatoyin Aribike, Ryoki Arimoto, Charlotte Arkley, Charlotte Armah, Ilianna Armata, Jennifer Armistead, Adam Armitage, Ceri Armstrong, Maureen Armstrong, Sonia Armstrong, Wendy Armstrong, Philippa Armtrong, Heike Arndt, Clare Arnison-Newgass, David Arnold, Rachael Arnold, Andrew Arnott, Dhawal Arora, Kavan Arora, Pardeep Arora, Rishi Arora, Arslam Arter, Armena Arumaithurai, Ayush Arya, Rita Arya, Diptesh Aryal, Denisa Asandei, Adeeba Asghar, Malik Asghar, Arif Ashab, Catherine Ashbrook-Raby, Helen Ashby, Jan Ashcroft, John Ashcroft, Samuel Ashcroft, Georgia Asher, Zakariya Ashfak, Ayesha Ashfaq, Abdul Ashish, Dawn Ashley, Sally Ashman-Flavell, Sundar Ashok, Abd-El-Aziz Ashour, Muhammad Z Ashraf, Saima Ashraf, Mohammad B Ashraq, Deborah Ashton, Susan Ashton, Andrew Ashworth, Fiona J Ashworth, Rebecca Ashworth, Arshia Aslam, I Aslam, Suhail Aslam, Laura Aslett, Harshini Asogan, Atif Asrar, Omar Assaf, Raine Astin-Chamberlain, Deborah Athorne, Billie Atkins, Christopher Atkins, Stacey Atkins, John Atkinson, Vicki Atkinson, Abolaji Atomode, Brygitta Atraskiewicz, Abdul A Attia, Eva Attubato, Marie Attwood, Paula Aubrey, Zoltan Auer, Avinash Aujayeb, Aye Chan Thar Aung, Hnin Aung, Hnin Wint Wint Aung, Ko Ko Aung, Kyaw Thu Aung, Nwe Aung, Yin Aung, Zaw Myo Aung, Emily Austin, Karen Austin, Abdusshakur Auwal, Malcolm Avari, Miriam Avery, Nicholas Aveyard, Joanne Avis, Georgina Aviss, Cristina Avram, Paula Avram, Abuzar Awadelkareem, Gabriel Awadzi, Mahmoud Awaly, Atia Awan, Aszad Aya, Eman Ayaz, John Maximilian Ayerh, Amanda Ayers, Jawwad Azam, Ahmed Azeem, Mohammed Azharuddin, Ammar Aziz, Ghazala Aziz, Imran Aziz, N Aziz, Ali Azkoul, Ashaari Azman Shah, Giada Azzopardi, Hocine Azzoug, Fiyinfoluwa Babatunde, Melvin Babi, Babiker Babiker, Gayna Babington, Matthew Babirecki, Marta Babores, Adetona O Babs-Osibodu, Sammy Bacciarelli, Roudi Bachar, Michel-Elie Bachour, Alisjahbana Bachti, Gina Bacon, Jenny Bacon, Bibi Badal, Aysha Badat, Maryuma Bader, Gurpreet R Badhan, Shreya Badhrinarayanan, Joseph P Bae, Alice Baggaley, Amy Baggott, Graham Bagley, Dinesh Bagmane, Lynsey Bagshaw, Kasra Bahadori, Angela Bailey, James Bailey, Katie Bailey, Lindsey Bailey, Liz Bailey, Lucy Bailey, Maria A Bailey, Morgan Bailey, Pippa Bailey, Sarah Bailey, Hamish Baillie, J Kenneth Baillie, Jennifer Bain, Vikram Bains, David Baird, Eilidh Baird, Kevin Baird, Susan Baird, Tracy Baird, Yolanda Baird, Aiysha Bajandouh, Danielle C Baker, Evelyn Baker, Johanne Baker, Josephine Baker, Kenneth Baker, Mark Baker, Rebecca Baker, Terri-Anne Baker, Victoria Baker, Hugh Bakere, Nawar Bakerly, Michelle Baker-Moffatt, Ameet Bakhai, Nauman Bakhtiar, Panos Bakoulas, Dhanalakshmi Bakthavatsalam, Niranjan Balachandran, Andrea Balan, Prasath Balasingam, Theodosios Balaskas, Madhu Balasubramaniam, Nicola Balatoni, Alison Balcombe, Alexander Baldwin, Ashley Baldwin, Caron Baldwin, Danielle Baldwin, Fiona Baldwin, Rebekah Baldwin-Jones, Nichola Bale, James Balfour, Matthew Ball, Robert Ball, K Ballard, Ismael Balluz, Craig Balmforth, Emese Balogh, Abeir Baltmr, Amir Baluwala, Gabby Bambridge, Alasdair Bamford, Amy Bamford, Peter Bamford, Adefunke Bamgboye, Elizabeth Bancroft, Hollie Bancroft, Joyce Banda, Krishna Bandaru, Srini Bandi, Nageswar Bandla, Somaditya Bandyopadhyam, Amit Banerjee, Ritwik Banerjee, Santosh Baniya, Omar Bani-Saad, Harrison Banks, Luke Banks, Paul Banks, Claudia Bann, Helen Bannister, Oliver Bannister, Laura Banton, Tran Bao, Mariamma Baptist, Tanya Baqai, Ananya M Baral, Sharad Chandra Baral, Desislava Baramova, Russel Barber, Emma Barbon, Monica Barbosa, Jamie Barbour, Alexander Barclay, Amanda Barclay, Claire Barclay, George Bardsley, Stephanie Bareford, Shahedal Bari, Morris Barimbing, Amy Barker, Debbie Barker, Erandi Barker, Helen Barker, Joseph Barker, Leon Barker, Lesley Barker, Oliver Barker, Kerry Barker-Williams, Sinha Barkha, Juliana Barla, Gavin Barlow, Richard Barlow, Valerie Barlow, James Barnacle, Alex Barnard, Debi Barnes, Nicky Barnes, Rory Barnes, Theresa Barnes, Calum Barnetson, Amy Barnett, Ashton Barnett-Vanes, Prince Gyenti Barning, William Barnsley, Andrew Barr, David Barr, James Barr, Charlotte Barr, Nina Barratt, Shaney Barratt, Manuella Barrera, Amy Barrett, Fiona Barrett, Jessica Barrett, Sue Barrett, Esther Barrow, Jazz Bartholomew, Claire Bartlett, Georgina Bartlett, James Bartlett, Louise Bartlett, Shauna Bartley, Sandra Bartolmeu-Pires, Anna Barton, Greg Barton, Jill Barton, Lorna Barton, Rachael Barton, Rosaleen Baruah, Sonia Baryschpolec, Hannah Bashir, Archana Bashyal, Betsy Basker, Sudha Basnet, Buddha Basnyat, Ayten Basoglu, Alexander Basran, John Bassett, G Bassett, Chris Bassford, Bengisu Bassoy, Victoria Bastion, Anup Bastola, Anupam Basumatary, Precious Basvi, Jireh A Batac, Vipula R Bataduwaarachchi, Tristan Bate, Harry J Bateman, Kathryn Bateman, Vhairi Bateman, Eleanor Bates, Hayley Bates, Michelle Bates, Simon Bates, Sally Batham, Ana Batista, Amit Batla, Dushyant Batra, Harry Batty, Thomas Batty, Alice Batty, Miranda Baum, Rachel Baumber, Carina Bautista, Fareha Bawa, Tanveer Bawa, Fatima S Bawani, Simon Bax, Matt Baxter, Nicola Baxter, Zachary Baxter, Hannah Bayes, Lee-Ann Bayo, Farid Bazari, Rohit Bazaz, Ahmad Bazli, Laura Beacham, Wendy Beadles, Kirsten Beadon, Philip Beak, Andy Beale, Kathy Beard, Jack Bearpark, Alanna Beasley, Sandee Beattie, Karen Beaumont, Dawn Beaumont-Jewell, Theresa Beaver, Sarah Beavis, Christy Beazley, Sarah Beck, Virginia Beckett, Rosie Beckitt, Sarah Beckley, Heidi Beddall, Seonaid Beddows, Deborah Beeby, Sophia Beeby, Gail Beech, Michelle Beecroft, Nigel Beer, Sally Beer, Jane Beety, Gabriela Bega, Alison Begg, Susan Begg, Sara Beghini, Ayesha Begum, Salman Begum, Selina Begum, Teresa Behan, Roya Behrouzi, Jon Beishon, Claire Beith, James Belcher, Holly Belfield, Katherine Belfield, Ajay Belgaumkar, Dina Bell, Gareth Bell, Gillian Bell, Jessica Bell, Lauren Bell, Louise Bell, Nicholas Bell, Pippa Bell, Stephanie Bell, Jennifer Bellamu, Mary Bellamy, Tammy Bellamy, Arianna Bellini, Amanda Bellis, Fionn Bellis, Lesley Bendall, Naveena Benesh, Nicola Benetti, Leonie Benham, Guy Benison-Horner, Sarah Benkenstein, Tracey Benn, Ann Bennett, Caroline Bennett, Denise Bennett, Gillian Bennett, Kaytie Bennett, Kristopher Bennett, Lorraine Bennett, Miriam R Bennett, Sara Bennett, Karen Bennion, Gautham Benoy, Vivienne Benson, Andrew Bentley, James Bentley, Ian Benton, Eva Beranova, Matthew Beresford, Colin Bergin, Malin Bergstrom, Jolanta Bernatoniene, Thomas Berriman, Zoe Berry, Frida Best, Kimberley Best, Ans-Mari Bester, Yvonne Beuvink, Emily Bevan, Sarah Bevins, Tom Bewick, Andrew Bexley, Sonay Beyatli, Fenella Beynon, Arjun Bhadi, Sanjay Bhagani, Shiv Bhakta, Rekha Bhalla, Khushpreet Bhandal, Kulbinder Bhandal, Ashwin Bhandari, Lila Nath Bhandari, Sangam Bhandari, Jennifer Bhanich Supapol, Aashutosh Bhanot, Ravina Bhanot, Swati Bhasin, Abdul Bhat, Prashanth Bhat, Rahul Bhatnagar, Karan Bhatt, Janki Bhayani, Deepika Bhojwani, Parminder Bhuie, M S Bhuiyan, Salimuzzaman Bhuiyan, Anna Bibby, Fatima Bibi, Naheeda Bibi, Salma Bibi, Tihana Bicanic, Sarah Bidgood, Julie Bigg, Sarah Biggs, Alphonsa Biju, Andras Bikov, Sophie Billingham, Jessica Billings, Prof Binh, Alice Binns, Muhammad BinRofaie, Oliver Bintcliffe, Catherine Birch, Janine Birch, Jenny Birch, Katherine Birchall, Sam Bird, Sumedha Bird, Sarah Bird, Mark Birt, Claire Bishop, Kilanalei Bishop, Linda Bishop, Lisa Bishop, Karen Bisnauthsing, Nibedan Biswas, Mustapha Bittaye, Sahar Biuk, Karen Blachford, Ethel Black, Helen Black, Karen Black, Mairead Black, Polly Black, Virginia Black, Hayley Blackgrove, Bethan Blackledge, Joanne Blackler, Samantha Blackley, Helen Blackman, Caroline Blackstock, Cameron Blair, Francesca Blakemore, Helen Blamey, Alison Bland, Sujata Blane, Simon Blankley, Parry Blaxill, Katie Blaylock, Jane Blazeby, Natalie Blencowe, Ben Bloom, Jack Bloomfield, Angela Bloss, Alex Blowers, Samuel Blows, Hannah Bloxham, Susan Blrd, Louise Blundell, Andrew Blunsum, Mark Blunt, Tadhg Blunt, Ian Blyth, Kevin Blyth, Andrew Blythe, Karen Blythe, Kwabena Appiah Boahen, Marilyn Boampoaa, Sarah Board, Boniface Bobie, Karen Bobruk, 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Zain Chaudhry, Nazia Chaudhuri, Muhammad Chaudhury, Anoop Chauhan, Ruchi S Chauhan, Alexander Chavasse, Nicola Chavasse, Vipal Chawla, Lindsay Cheater, James Cheaveau, Charlotte Cheeld, Michelle Cheeseman, Fang Chen, Hui Min Chen, Terence Chen, Floria Cheng, Lok Yin Cheng, Zhihang Cheng, Helen Chenoweth, Chun How Cheong, Shiney Cherian, Mary Cherrie, Helen Cheshire, Chee Kay Cheung, Elaine Cheung, Kayan Cheung, Michelle Cheung, Claire Cheyne, Swati Chhabra, Wei Ling Chia, Eric Chiang, Angela Chiapparino, Rosavic Chicano, Moses Chikungwa, Zviedzo A Chikwanha, Gemma Chilcott, Sam Chilcott, Alison Chilvers, Phillipa Chimbo, KokWai Chin, Wen Jie Chin, Rumbidzai Chineka, Amol Chingale, Ezomike Chinonso, Chloe Chin-Saad, Margaret Chirgwin, Heather Chisem, Claire Chisenga, Catherine Chisholm, Ben Chisnall, Carolyn Chiswick, Sunder Chita, Nihil Chitalia, Matthew Chiu, Laura Chiverton, Brenda Chivima, Catherine Chmiel, Soha Choi, Willy Choon Kon Yune, Meena Chopra, Michelle Chopra, Vandana 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Tak-Yan Ellis, Yvette Ellis, Alison C Ellwood, Rahma Elmahdi, Einas Elmahi, Hannah-May Elmasry, Mohammed El-Naggar, Najla Elndari, Omer Elneima, Mohamed Elokl, Ahmed Elradi, Mohamed Elsaadany, Mohammed A S A Elsayed, Sally El-Sayeh, Hana El-Sbahi, Mohammad Elsebaei, Tarek Elsefi, Karim El-Shakankery, Alguili Elsheikh, Hosni El-Taweel, Sarah Elyoussfi, Jonathan Emberey, Jonathan R Emberson, John Emberton, Anna Emery, Julian Emmanuel, Ingrid Emmerson, Michael Emms, Florence Emond, Marieke Emonts, Nicu Enachi, Dickson Enenche, Angila Engden, Katy English, Emma Entwistle, Hene Enyi, Marios Erotocritou, Peter Eskander, Hanif Esmail, Fatheha Essa, Brynach Evans, Chris Evans, Debra Evans, E Evans, Emrys Evans, Gail Evans, Gareth Evans, Isabel Evans, Jack Evans, Jacqueline Evans, Jennifer Evans, John Evans, Jolanta Evans, Lisa Evans, Lynn Evans, Mim Evans, Morgan Evans, Ranoromanana Evans, Sharon Evans, Sian Evans, Teriann Evans, Tomos Evans, Caroline Everden, Serenydd Everden, Lynette Every, 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Flanagan, Charles Flanders, Nikolaos Flaris, Gail Fleming, Julie Fleming, Lucy Fleming, Paul Fleming, William Flesher, Alison Fletcher, Anna Fletcher, Jonathan Fletcher, Lucy Fletcher, Simon Fletcher, Sophie Fletcher, Fiona Flett, Karen Flewitt, Sarah Flockhart, Christopher Flood, Ian Floodgate, Jonathan Flor, Vincent Florence, Mary Flowerdew, Sharon Floyd, Margaret J Flynn, Rachel Flynn, Claire Foden, Adama Fofana, Georgina Fogarty, Paul Foley, Linda Folkes, Tracey Fong, Daniela M Font, Aiwyne Foo, Jane Foo, Andrew Foot, Helen R Foot, Jayne Foot, Jane Forbes, Amber Ford, Jamie Ford, Imogen Fordham, Jennifer Foreman, Matthew Forester, Mohammad Forkan, Caroline Fornolles, Adam Forrest, Ellie Forsey, Miranda Forsey, Thomas Forshall, Elliot Forster, Abigail Forsyth, Julian Forton, Conrad Foster, Emily Foster, Joseph Foster, Rachel A Foster, Tracy Foster, Angela Foulds, Ian Foulds, Folakemi Fowe, Natasha Fowkes, Emily Fowler, Robert Fowler, Stephen Fowler, Amy Fox, Claire Fox, Heather Fox, Jonathan Fox, Laura Fox, Lauren Fox, Natalie Fox, Olivia Fox, Simon Fox, Sarah-Jane Foxton, Eva Fraile, Rebecca Frake, Alex Francioni, Olesya Francis, Rebecca Francis, Sarah Francis, Theodora Francis-Bacon, Helen Frankland, Jessica Franklin, Siobhan Franklin, Catherine Fraser, Andi Fratila, Sharon Frayling, Martyn Fredlund, Anna Freeman, Carol Freeman, Elaine Freeman, Hannah Freeman, Nicola Freeman, Clare Freer, Eleanor French, Thomas French, Karen Freshwater, Matthew Frise, Renate Fromson, Adam Frosh, John Frost, Victoria Frost, Oliver Froud, Rachel Frowd, Arun Fryatt, Andrzej Frygier, Bridget Fuller, Liz Fuller, Lucy Fuller, Tracy Fuller, Duncan Fullerton, Carrie Fung, Gayle Fung, Sarah Funnell, John Furness, Andrew Fyfe, Nytianandan G, Elizabeth Gabbitas, Claire Gabriel, Zoë Gabriel, Hadiza Gachi, Sophie Gaffarena, Sarah Gage, Joshua Gahir, Sarveen Gajebasia, Katarzyna Gajewska-Knapik, Babita Gajmer, Zacharoula Galani, Christopher Gale, Hugo Gale, Linda Gale, Rebecca Gale, Swetha Gali, Bernadette Gallagher, Jude Gallagher, Rosie Gallagher, William Gallagher, Feroze Gallam, Joanne Galliford, Catherine Galloway, Chris Galloway, Emma Galloway, Jacqui Galloway, James Galloway, Anna Galvin, Victoria Galvis, Gareth Gamble, Laura Gamble, Liz Gamble, Brian Gammon, Chien N Gan, Muhammad B Ganaie, Jaikumar Ganapathi, Ramesh Ganapathy, Kaminiben Gandhi, Sarah Gandhi, Usha Ganesh, Thenugan Ganeshanathan, Saibal Ganguly, Abrar Gani, Pauline Ganley, Ukraina Garcia, Emma-James Garden, Antoni D Gardener, Emma Gardiner, Michael Gardiner, Phil Gardiner, Siobhan Gardiner, Caroline Gardiner-Hill, Jonathan Gardner, Louis Gardner, Mark Garfield, Atul Garg, Isha Garg, Nathan Garlick, Daniel Garner, Justin Garner, Lucie Garner, Zoe Garner, Rosaline Garr, Kathryn A Garrero, Manisha Gartaula, Florence Garty, Rachel Gascoyne, Hyeriju Gashau, Aoife Gatenby, Erin Gaughan, Alok Gaurav, Mariana Gavrila, Jane Gaylard, Sophie Gayle, Catherine Geddie, Ian Gedge, Sarah Gee, Fartuun Geele, Kalaichelvi Geerthan, Minnie Gellamucho, Karzan Gelly, Leila Gelmon, Sandra Gelves-Zapata, Gemma Genato, Neal Gent, Susan Gent, Natalie Geoghegan, Aparna George, Bini George, Sam George, Susan George, Tina George, Valerie P George, Simon Georges, Domonique Georgiou, Peter Gerard, Leigh Gerdes, Louise Germain, Helen Gerrish, Abel Getachew, Louise Gethin, Siobhan Gettings, Hisham Ghanayem, Bijan Ghavami Kia, Shaista Ghazal, Anca Gherman, Alison Ghosh, Dipansu Ghosh, Justin Ghosh, Subhamay Ghosh, Sarra Giannopoulou, Malick Gibani, Chris Gibb, Ben Gibbison, Kerry Gibbons, Alex Gibson, Bethan Gibson, Jamie Gibson, Kimberley Gibson, Kirsty Gibson, Sian Gibson, Mary Gigi, Cat Gilbert, Jeanette Gilbert, Kayleigh Gilbert, Benjamin Giles, James Gilham, Mandy Gill, Lynne Gill, Paul Gillen, Annelies Gillesen, Katherine Gillespie, Elizabeth Gillham, Andrew Gillian, Deborah Gilliland, Robert Gillott, Danielle Gilmour, Kate Gilmour, Lesley Gilmour, Louis Ginn, Franciscus Ginting, Theodora Giokanini-Royal, Anna Gipson, Barbie Giri, Joanna Girling, Rhian Gisby, Angelena Gkioni, Aikaterini Gkoritsa, Effrossyni Gkrania-Klotsas, Amy Gladwell, James Glanville, Jessica Glasgow, Susannah Glasgow, Jon Glass, Lynn Glass, Sharon Glaysher, Lisa Gledhill, Eleanor Glenday, Ana Glennon, Jodie Glossop, John Glover, Kyle Glover, Michelle Glover, Jan Glover Bengtsson, Deborah Glowski, Sharon Glynn, Chevanthy Gnanalingam, Julie Goddard, Wendy Goddard, Emily Godden, Jo Godden, Sarah Godlee, Emma Godson, Gillian Godwin, Sukanya Gogoi, Aiky Goh, Manjinder Gohel, Rebeca Goiriz, Sriya Gokaraju, Raphael Goldacre, Arthur Goldsmith, Portia Goldsmith, Darren Gomersall, Lucia Gomez, Raquel Gomez-Marcos, Ali Gondal, Celia Gonzalez, Jack Goodall, Vicky Goodall, Bob Goodenough, Anna Goodfellow, Laura Goodfellow, James Goodlife, Camelia Goodwin, Elizabeth Goodwin, Jayne Goodwin, Paula Goodyear, Rajiv Gooentilleke, Michelle Goonasekara, Sheila Gooseman, Shameer Gopal, Chris Gordon, Sally Gordon, Robin 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Hale, Jennie Hale, Carmel Halevy, Paul Halford, William Halford, Alaina Halim, Alistair Hall, Anthony Hall, Claire Hall, Elizabeth Hall, Fiona Hall, Helen Hall, Jennifer Hall, Kathryn Hall, Laura Hall, Jan Hallas, Kyle Hallas, Charles Hallett, Jackie Halliday, Akmal Hallman, Heather Halls, Maryam Hamdollah-Zadeh, Ifedadepo A Hamed-Adekale, Bilal Hameed, Mohammed Hameed, Raph L Hamers, Imran Hamid, Mohamad Hamie, Raymond Hamill, Bethany Hamilton, Fergus Hamilton, Gus Hamilton, Leigh Hamilton, Melanie Hamilton, Nicola Hamilton, Sara Hamilton, Ruth Hamlin, Eleanor Hamlyn, Beatrice Hammans, Shirley Hammersley, Kate Hammerton, Bev Hammond, Emily Hammond, Leah Hammond, Sally Hammond, Fiona Hammonds, Ibrahim Hamoodi, Karen Hampshire, James A Hampson, Jude Hampson, Lucy Hampson, Lisa Hamzah, Jenny Han, Ozan Hanci, Sadiyah Hand, Lala Handayani, Jasmine Handford, Soran Handrean, Nina K Handzewniak, Sarah Haney, Vu Thi Kim Hang, Sheharyar Hanif, E Hanison, Esther Hanison, Jennifer Hannah, Amy Hannington, Merhej Hannun, Aidan Hanrath, Helena Hanratty, Daniel Hansen, Anita Hanson, Ashley Hanson, Helen Hanson, Jane Hanson, Kathryn Hanson, Steve Hanson, Ala Haqiqi, Monjurul Haque, Henry Harcourt, Lesley Harden, Zoe Harding, Simon Hardman, Marc Hardwick, Gareth Hardy, Joanna Hardy, Yasmine Hardy, Kumar Haresh, Rachel Harford, Beverley Hargadon, James Hargraves, Carolyn Hargreaves, Alice Harin, Mohammed Haris, Edward Harlock, Paula Harman, Tracy Harman, Mark Harmer, Muhammad A Haroon, Charlie Harper, Heather Harper, James Harper, Peter Harper, Rosemary Harper, Sarah Harrhy, Kate Harrington, Sian Harrington, Yasmin Harrington-Davies, Jade Harris, Jess Harris, John Harris, Laura Harris, Marie-Clare Harris, Nichola Harris, Sophie Harris, David Harrison, Laura Harrison, Melanie Harrison, Olakunbi A Harrison, Rachael Harrison, Rowan Harrison, Susie Harrison, Thomas Harrison, Wendy Harrison, Elizabeth Harrod, Ciaran Hart, Dominic Hart, Jessica Hartley, Lisa Hartley, Rosemary Hartley, Ruth Hartley, Tom Hartley, William Hartrey, Phillipa Hartridge, Stuart Hartshorn, Alice Harvey, Angela Harvey, Anna Harvey, Max Harvey, Catherine Harwood, Helen Harwood, Zoe Harzeli, Brigitte Haselden, Kazi Hashem, Mohammed Hashimm, Tadaaki Hashimoto, Imranullah Hashmi, Jack Haslam, Zena Haslam, Grace Hasnip, Adil Hassan, Ali Hassan, Amro Hassan, Awab Hassan, Zeinab Hassan, Sapna Hassasing, Jane Hassell, Philip Hassell, Alex Hastings, Bethany Hastings, Janice Hastings, Sarah Hathaway-Lees, Jonathan Hatton, Jennifer Hau, May Havinden-Williams, Stefan Havlik, Daniel B Hawcutt, Kadean Hawes, Liz Hawes, Nicola Hawes, Lydia Hawker, Annie Hawkins, Joe Hawkins, Nancy Hawkins, W John Hawkins, Dan Hawley, Ed Hawley-Jones, Edward Haworth, Alasdair W Hay, Cathy Hay, Amna Hayat, Jamal Hayat, Mohamed-Riyal Hayathu, Anne Hayes, Jonas Hayes, Kate Hayes, Melony Hayes, Fiona Hayes, Patrick Hayle, Chloe Haylett, Antara Hayman, Melanie Hayman, Matthew Haynes, Richard Haynes, Rachel Hayre, Carole Hays, Sarah Haysom, James Hayward, Patrick Haywood, Hazel Haywood Hasford, Tracy Hazelton, Phoebe Hazenberg, Zhengmai He, Elizabeth Headon, Carrie Heal, Brendan Healy, Jessica L Healy, Amy Hearn, David Heasman, Angela Heath, Debbie Heath, Rowan Heath, Diane Heaton, Adam Heavens, Kerry Hebbron, Catherine Heckman, Gemma Hector, Sadie Heddon, Andy Hedges, Katrine Hedges, Cheryl Heeley, Elaine Heeney, Richard Heinink, Rajdeep Heire, Ingvild Helgesen, Joanne Hemingway, Ulla Hemmila, Beth Hemmings, Scott Hemphill, Deborah Hemsley, Abigail Henderson, Eilidh Henderson, Jennifer Henderson, Steven Henderson, Joanne Henry, Karol Henry, Lavinia Henry, Margo Henry, Natalie Henry, David Henshall, Gillian Herdman, Rosaleen Herdman-Grant, Morag Herkes, Lucas E Hermans, Fe Marie Hernandez, Emma Heron, Lisa Heron, William Herrington, Emilia Heselden, Peta Heslop, Tracey Heslop, Simon Hester, Emily Hetherington, Joseph Hetherington, Chamila Hettiarachchi, Pramodh Hettiarachchi, Hayley Hewer, John Hewertson, Anna Hewetson, Sue Hewins, Nicola Hewitson, Claire Hewitt, Davina Hewitt, Richard Hewitt, Samuel Hey, Robert S Heyderman, Mathis Heydtmann, Joseph Heys, Jonathan Heywood, Meg Hibbert, John Hickey, Naomi Hickey, Peter Hickey, Nicky Hickman, Alex Hicks, Jenny Hicks, Scott R Hicks, Phan Thi Thu Hien, Daniel Higbee, Lucy Higgins, Andrew Higham, Martin Highcock, Judith Highgate, Mondy Hikmat, Amanda Hill, Helen Hill, Joanne Hill, Lisa Hill, Lucy Hill, Phoebe Hill, Uta Hill, Annette Hilldrith, Catherine Hillman-Cooper, Jon Hilton, Zoe Hilton, Sarah Hinch, Andrew Hindle, Esther Hindley, Alice Hindmarsh, Paul Hine, Kim Hinshaw, Clare Hird, Claire Hirst, Lindsay Hirst, Jemma Hives, Htet M Hlaing, Benson Ho, David K K Ho, Rosemary Ho, Michaela Hoare, David Hobden, Gill Hobden, Maria Hobrok, Simon Hobson, Christopher Hodge, Simon Hodge, Lesley Hodgen, George Hodgetts, Holly Hodgkins, Sally Hodgkinson, David Hodgson, Helen Hodgson, Luke Hodgson, Sheila Hodgson, Gemma Hodkinson, Kenneth Hodson, Mike Hodson, Alison Hogan, Matthew Hogben, Lucy Hogg, Lee Hoggett, Abigail Holborow, Catherine Holbrook, Rebecca Holbrook, Catherine Holden, Jill Holden, Melinda Holden, Sophie Holden, Thomas Holder, Niels Holdhof, Hannah Holdsworth, Lisa Holland, Maureen Holland, Nicky Holland, Poppy Holland, Susan Holland, Marie L Hollands, Elizabeth Holliday, Kyra Holliday, Mark Holliday, Nina Holling, Laszlo Hollos, Nora Hollos, Leah Holloway, Simon Holloway, Max Hollowday, Marcus Hollyer, Amy Holman, Ann Holmes, Megan Holmes, Raphael Holmes, Rebecca Holmes, Kelly Holroyd, Beth Holroyd-Hind, Lyndsey Holt, Siobhan Holt, Susie Holt, Alexandra Holyome, Marie Home, Renate Homewood, Kate Hong, Louise Hoole, Clare Hooper, Samantha Hope, Susan Hope, Bridget Hopkins, Peter W Horby, Stephanie Horler, Anil Hormis, Daniel Hornan, Nicola Hornby, Thomas Horne, Zoey Horne, Rebecca Horner, Caroline Horrobin, Latoya Horsford, Megan Horsford, Mark Horsford, Valana Horsham, Alexander Horsley, Ashley Horsley, Elizabeth Horsley, Sarah Horton, Jane Hosea, Toby Hoskins, Muhammad S Hossain, Rashed Hossain, Maxine Hough, Sarah Hough, Catherine Houghton, Kathryn Houghton, Rebecca Houlihan, Kay Housely, Hamish Houston, Roseanna Hovvels, Lee How, Laura Howaniec, Joanne Howard, Kate Howard, Laura Howard, Linda Howard, Lucy Howard, Megan Howard, Sarah Howard, Stuart Howard, Richard Howard-Griffin, Louise Howard-Sandy, Serena Howe, Alice Howell, Mark Howells, Lyn Howie, Kerry Howlett, Sophie Howlett, Ruth Howman, Josh Hrycaiczuk, Hein Htet, Naing Z Htoon, Su Htwe, Ying Hu, Chiang O H Huah, Sylvia Huang, Kelly Hubbard, Abby Huckle, Shahzya Huda, Alison Hudak, Lisa Hudig, Heather Hudson, Suzanne Hudson, Oli Hudson, Alison Hufton, Connor Huggins, Alistair Hughes, Caitlin Hughes, Emma Hughes, Gareth Hughes, Heather Hughes, Luke Hughes, Martin Hughes, Rachel Hughes, Rebecca Hughes, Samantha Hughes, Stephen Hughes, Vikki Hughes, Wesley Hughes, Lukas Huhn, Ching Hui, Ruth Hulbert, Diana Hull, Grace Hull, Robert Hull, Amanda Hulme, Peter Hulme, Wendy Hulse, George Hulston, Ryan Hum, Megan Hume, Charlotte Humphrey, Alasdair Humphries, Amy Humphries, Joanne Humphries, Charlotte Hunt, Fiona Hunt, Jane Hunt, Jonquil Hunt, Kristen Hunt, Luke Hunt, Molly Hunt, Sophie Hunt, Alexandra Hunter, Catherine Hunter, Ewan Hunter, Karl Hunter, Neil Hunter, Ross Hunter, Sophie Hunter, George Huntington, Fuad Huq, Elizabeth Hurditch, Judith Hurdman, Cian Hurley, Katrina Hurley, Mohammed A Husain, Syeda Y Husaini, Coralie Huson, Afreen Hussain, Chreif Hussain, Ibraar Hussain, Ifza Hussain, Mohammad Hussain, Mohammed Hussain, Muhammad Hussain, Reda Hussain, Rizwana Hussain, Samia Hussain, Sanniah Hussain, Yasmin Hussain, Mohammed Hussam El-Din, Samahir F E M Hussein, Ziad Hussein, Rebecca Hussey, Adiel Haqiq Hussien, Anja Hutchinson, Camille Hutchinson, Dorothy Hutchinson, Elizabeth Hutchinson, John Hutchinson, Claire Hutsby, Paula Hutton, Nguyen Quang Huy, Thuong Huyen, Tran Bang Huyen, Nguyen Thi Huyen 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Jennings, Jacqui Jennings, Louise Jennings, Virginia Jennings, Ellen Jerome, Douglas Jerry, Greame Jervis, Ellen Jessup-Dunton, Jorge A Jesus Silva, Champa Jetha, Kishan Jethwa, Roshan Kumar Jha, Shaman Jhanji, Khoo Jian, Zhixin Jiao, Laura Jimenez, Ana Jimenez Gil, Jithin Jith, Teishel Joefield, Navraj Johal, Simran Johal, Karine Johannessen, Aisyah Johari, Annie John, Anu John, Maya John, Navin John, Emma Johns, Margaret Johns, Antoinette Johnson, Emma Johnson, Gillian Johnson, Kathryn Johnson, Katie Johnson, Luke Johnson, Mark Johnson, Nelsonseelan Johnson, Oliver Johnson, Rachel Johnson, Brogan Johnston, Claire Johnston, Janet Johnston, Susan Johnston, Victoria Johnston, Dawn Johnstone, Ed Johnstone, Janet Johnstone, Manohar Joishy, Adam Jones, Alistair Jones, Amy Jones, Angela Jones, Annabel Jones, Ben Jones, Bryony Jones, Carys Jones, Ceri Jones, Charlotte Jones, Christine E Jones, Debra Jones, Emily Jones, Gareth Jones, Geraldine Jones, Jac Jones, James Jones, Jonathon Jones, Julie Jones, Kate E Jones, Kimberly Jones, Laura Jones, Laura M Jones, Louise Jones, Mathew Jones, Nancy Jones, Nicola Jones, Olivia Jones, Paul Jones, Philip H Jones, Rhianna Jones, Ruth E Jones, Samantha Jones, Sophie Jones, Stefanie Jones, Steve Jones, Susan Jones, Taya Jones, Thomas Jones, Tim Jones, Tracey Jones, Ramya Jonnalagadda, Rebecca Jordache, Michele Jordan, Sarah Jordan, Abi Jose, Litty Jose, Louise Jose, Sanal Jose, Anna Joseph, Gigee Joseph, P Aiden Joseph, Rosane Joseph, Sibet Joseph, Dhaara Joshi, Mehul Joshi, Pratichi Joshi, Tushar Joshi, Benz Josiah, Lijo Joy, Tiffany Joyce, Hong Ju, Adriel Ju Wen Kwek, Andrew Judd, Edward Jude, Parminder Judge, Jessica Juhl, Sirisha Jujjavarapu, Mark Juniper, Edmund Juszczak, Deepthi Jyothish, Kasamu Kabiru Dawa, Mark Kacar, Nikhil Kadam, Nicole Kader, Ajai Kailey, Matthew Kain, Gail Kakoullis, Azad Kala Bhushan, Richard J K Kalayi, Roobala Kaliannan Periyasami, Efthymia Kallistrou, Thomas Kalmus Eliasz, Seika Kalsoom, Elisa Kam, 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Laybourne, Christine Laycock, Reina Layug, Maria Lazo, Thi Thao Le, Vietland Le, Amelia Lea, William Lea, Louisa E Leach, Ian Leadbitter, Thomas Leahy, Richard Lean, Lorna Leandro, Darren Leaning, Ruth Leary, Sandra Leason, Marie A Ledingham, Caroline Lee, Emma Lee, Hannah Lee, Irish Lee, Joshua Lee, Judith Lee, Sam Lee, Shannon Lee, Shi Han Lee, Simon Lee, Sindy Lee, Stephanie Lee, Tracey Lee, Xiang Lee, Robyn Lee, Diana Lees, Jennifer Lees, Helen Legge, Julian Leggett, Katie Leigh-Ellis, David Leitch, Nicky Leitch, Eleni Lekoudis, Petula Lemessy, Nicholas Lemoine, Rebecca Lenagh, Katy Leng, Katrina Lennon, Liz Lennon, Beth Leonard, Kelly Leonard, Wen Leong, Nicky Leopold, Oskar Lepiarczyk, Isla Leslie, Eva Noviani Lestari, Eleni Lester, Emma Levell, Chris Levett, Anastasia Levynska, Alice Lewin, Alison Lewis, Christopher Lewis, David Lewis, Dee Lewis, Heather Lewis, Joanne Lewis, Joseph Lewis, Kathryn Lewis, Keir Lewis, Leon Lewis, Mandy Lewis, Marissa Lewis, Nicolas Lewis, Robert 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Infection and Global Health Division, University of St Andrews. School of Medicine, and Group, RECOVERY Collaborative

Subjects
Adult, Male, Hypoxia/etiology, SARS-CoV-2, COVID-19, 3rd-DAS, General Medicine, Adrenal Cortex Hormones/therapeutic use, AC, Dexamethasone/therapeutic use, Hospitals, Oxygen, Treatment Outcome, SDG 3 - Good Health and Well-being, MCP, Humans, Female
Abstract

Background Low-dose corticosteroids have been shown to reduce mortality for patients with COVID-19 requiring oxygen or ventilatory support (non-invasive mechanical ventilation, invasive mechanical ventilation, or extracorporeal membrane oxygenation). We evaluated the use of a higher dose of corticosteroids in this patient group. Methods This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) is assessing multiple possible treatments in patients hospitalised for COVID-19. Eligible and consenting adult patients with clinical evidence of hypoxia (ie, receiving oxygen or with oxygen saturation Findings Between May 25, 2021, and May 13, 2022, 1272 patients with COVID-19 and hypoxia receiving no oxygen (eight [1%]) or simple oxygen only (1264 [99%]) were randomly allocated to receive usual care plus higher dose corticosteroids (659 patients) versus usual care alone (613 patients, of whom 87% received low-dose corticosteroids during the follow-up period). Of those randomly assigned, 745 (59%) were in Asia, 512 (40%) in the UK, and 15 (1%) in Africa. 248 (19%) had diabetes and 769 (60%) were male. Overall, 123 (19%) of 659 patients allocated to higher dose corticosteroids versus 75 (12%) of 613 patients allocated to usual care died within 28 days (rate ratio 1·59 [95% CI 1·20–2·10]; p=0·0012). There was also an excess of pneumonia reported to be due to non-COVID infection (64 cases [10%] vs 37 cases [6%]; absolute difference 3·7% [95% CI 0·7–6·6]) and an increase in hyperglycaemia requiring increased insulin dose (142 [22%] vs 87 [14%]; absolute difference 7·4% [95% CI 3·2–11·5]). Interpretation In patients hospitalised for COVID-19 with clinical hypoxia who required either no oxygen or simple oxygen only, higher dose corticosteroids significantly increased the risk of death compared with usual care, which included low-dose corticosteroids. The RECOVERY trial continues to assess the effects of higher dose corticosteroids in patients hospitalised with COVID-19 who require non-invasive ventilation, invasive mechanical ventilation, or extracorporeal membrane oxygenation. Funding UK Research and Innovation (Medical Research Council), National Institute of Health and Care Research, and Wellcome Trust.

Published
2023
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42. Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials

Author

Noureddin, Mazen, Lam, Jessica, Peterson, Michael R, Middleton, Michael, Hamilton, Gavin, Le, Thuy‐Anh, Bettencourt, Ricki, Changchien, Chris, Brenner, David A, Sirlin, Claude, and Loomba, Rohit

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Chronic Liver Disease and Cirrhosis, Biomedical Imaging, Liver Disease, Clinical Trials and Supportive Activities, Hepatitis, Digestive Diseases, Clinical Research, 4.1 Discovery and preclinical testing of markers and technologies, Oral and gastrointestinal, Adult, Biomarkers, Biopsy, Body Weight, Fatty Liver, Female, Humans, Liver, Longitudinal Studies, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male, Middle Aged, Non-alcoholic Fatty Liver Disease, Medical Biochemistry and Metabolomics, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

UnlabelledThe magnetic resonance imaging-estimated proton density fat fraction (MRI-PDFF) is a novel imaging-based biomarker that allows fat mapping of the entire liver, whereas the magnetic resonance spectroscopy-measured proton density fat fraction (MRS-PDFF) provides a biochemical measure of liver fat in small regions of interest. Cross-sectional studies have shown that MRI-PDFF correlates with MRS-PDFF. The aim of this study was to show the utility of MRI-PDFF in assessing quantitative changes in liver fat through a three-way comparison of MRI-PDFF and MRS-PDFF with the liver histology-determined steatosis grade at two time points in patients with nonalcoholic fatty liver disease (NAFLD). Fifty patients with biopsy-proven NAFLD who participated in a randomized trial underwent a paired evaluation with liver biopsy, MRI-PDFF, and MRS-PDFF at the baseline and 24 weeks. The mean age and body mass index were 47.8 ± 11.7 years and 30.7 ± 6.5 kg/m(2), respectively. MRI-PDFF showed a robust correlation with MRS-PDFF both at week 0 and at week 24 (r = 0.98, P < 0.0001 for both). Cross-sectionally, MRI-PDFF and MRS-PDFF increased with increases in the histology-determined steatosis grade both at week 0 and at week 24 (P < 0.05 for all). Longitudinally, patients who had a decrease (≥ 1%) or increase (≥ 1%) in MRI-PDFF (confirmed by MRS-PDFF) showed a parallel decrease or increase in their body weight and serum alanine aminotransferase and aspartate aminotransferase levels at week 24 (P < 0.05). This small increase or decrease in liver fat could not be quantified with histology.ConclusionIn this longitudinal study, MRI-PDFF correlated well with MRS-PDFF and was more sensitive than the histology-determined steatosis grade in quantifying increases or decreases in the liver fat content. Therefore, it could be used to quantify changes in liver fat in future clinical trials.

Published
2013

43. Feed intake, milk production and metabolism of Holstein, Gyr and Girolando-F1 heifers with high body condition score during the transition period

Author

Isabelle Damé Veber, Angelo, Sheila Cristina Bosco, Stivanin, Elissa Forgiarini, Vizzotto, Arthur Fernandes, Bettencourt, Matheus Gomes, Lopes, Marcio Nunes, Corrêa, Luiz Gustavo Ribeiro, Pereira, and Vivian, Fischer

Subjects
Eating, Milk, General Veterinary, Aryldialkylphosphatase, Postpartum Period, Animals, Lactation, Cattle, Female, Fatty Acids, Nonesterified, Energy Metabolism, Diet
Abstract

The transition period in taurine dairy cows is widely reported in the literature. However, little is known about the metabolism of zebu animals and their crossbreeding with taurine breeds during this phase. Considering the importance of these breeds in tropical and subtropical regions, this study aimed to evaluate the feed intake, milk production and hepatic metabolism in Holstein, Gyr and Girolando-F1 (½ Holstein × ½ Gyr) heifers presenting high body condition score (BCS) during the transition period (prepartum weeks -2 and -1 and postpartum weeks +1 and + 3). Twelve heifers of each genetic group were used, totaling 36 animals. Variables considered were blood metabolites related to liver function, dry matter intake (DMI), body condition score (BCS), body weight (BW), milk yield (MY), and fat and protein concentrations in milk. Gyr heifers had the lowest concentrations of paraoxonase in weeks -2 (43.13 U/mL), -1 (62.10 U/mL) and + 3 (77.89 U/mL), albumin in week -1 (3.07 g/dL), and the highest concentration of non-esterified fatty acids (NEFA) in weeks -2 (1.35 mmol/L) and -1 (1.19 mmol/L). β-hydroxybutyrate (BHB) and NEFA values were negatively correlated with prepartum DMI (-0.82 and -0.57, respectively), while paraoxonase was positively correlated to DMI (0.54). Gyr cows were more susceptible to inflammation despite having intermediate BCS and lower milk yield. Girolando-F1 animals showed the highest BCS among groups, although their hepatic metabolism had better results than Gyr cows. Holstein animals had lower lipomobilization and higher DMI and MY between breeds. These results suggest that the metabolism of zebu and crossbred cows does not react equally to the metabolism of Holstein cows concerning stress factors such as transition period and obesity. Therefore, the present study addresses an emerging theme that highlights the need for differentiated management during the transition period between the different breeds studied in order to ensure the maximum health and welfare of these animals.

Published
2022
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44. Direct Comparison of Quantitative US versus Controlled Attenuation Parameter for Liver Fat Assessment Using MRI Proton Density Fat Fraction as the Reference Standard in Patients Suspected of Having NAFLD

Author

Jinho Jung, Aiguo Han, Egbert Madamba, Ricki Bettencourt, Rohan R. Loomba, Andrew S. Boehringer, Michael P. Andre, John W. Erdman, William D. O’Brien, Kathryn J. Fowler, Claude B. Sirlin, and Rohit Loomba

Subjects
Male, Liver Disease, Inflammatory and immune system, Chronic Liver Disease and Cirrhosis, Middle Aged, Reference Standards, Medical and Health Sciences, Magnetic Resonance Imaging, Nuclear Medicine & Medical Imaging, Liver, Clinical Research, Non-alcoholic Fatty Liver Disease, Biomedical Imaging, Elasticity Imaging Techniques, Humans, Female, Radiology, Nuclear Medicine and imaging, Prospective Studies, Protons, Digestive Diseases, Cancer, Original Research
Abstract

BACKGROUND: MRI-derived proton density fat fraction (PDFF) is an accurate, reliable, and safe biologic marker for use in the noninvasive diagnosis of hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD). Because of the cost and limited availability of MRI, it is necessary to develop an accurate method to diagnose NAFLD with potential point-of-care access. PURPOSE: To compare the diagnostic accuracy of the quantitative US (QUS) fat fraction (FF) estimator with that of the controlled attenuation parameter (CAP) in the diagnosis of NAFLD using contemporaneous MRI-derived PDFF as the reference standard. MATERIALS AND METHODS: Participants with or suspected of having NAFLD were prospectively recruited at the NAFLD Research Center between July 2015 and July 2019. All participants underwent MRI-derived PDFF measurement, transient elastography with CAP measurement, and QUS. QUS FF was derived using computed QUS parameters from the acquired radiofrequency US data using a calibrated reference phantom. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of QUS FF and CAP in the diagnosis of hepatic steatosis (defined as MRI-derived PDFF ≥ 5%). AUCs were compared using the DeLong test. RESULTS: A total of 123 participants were included (mean age, 52 years ± 13 [SD]; 67 [54%] women). Of these participants, 100 (81%) had MRI-derived PDFF of 5% or more. QUS FF had a significantly higher AUC for diagnosis of NAFLD than did CAP (0.92 [95% CI: 0.87, 0.98] vs 0.79 [95% CI: 0.67, 0.90], P = .03). QUS FF had a sensitivity of 98% (98 of 100) and a specificity of 48% (11 of 23). CAP had a sensitivity of 87% (87 of 100) and a specificity of 57% (13 of 23). CONCLUSION: The quantitative US fat fraction estimator is more accurate than the controlled attenuation parameter in the diagnosis of hepatic steatosis in patients with or suspected of having nonalcoholic fatty liver disease. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ito in this issue.

Published
2022
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45. Comparative efficacy of an optimal exam between ultrasound versus abbreviated <scp>MRI</scp> for <scp>HCC</scp> screening in <scp>NAFLD</scp> cirrhosis: A prospective study

Author

Daniel Q. Huang, Kathryn J. Fowler, Joy Liau, Guilherme M. Cunha, Ashley L. Louie, Julie Y. An, Ricki Bettencourt, Jinho Jung, Zachary Gitto, Christie Hernandez, Scarlett J. Lopez, Hersh Gupta, Claude B. Sirlin, Robert M. Marks, and Rohit Loomba

Subjects
Liver Cirrhosis, Male, Carcinoma, Hepatocellular, Hepatology, Liver Neoplasms, Gastroenterology, Contrast Media, Middle Aged, Magnetic Resonance Imaging, Article, Non-alcoholic Fatty Liver Disease, Humans, Female, Pharmacology (medical), Prospective Studies, Retrospective Studies
Abstract

BACKGROUND: Retrospective studies report that visualisation of the liver may be severely limited using ultrasound (US), potentially contributing to diminished sensitivity for detection of hepatocellular carcinoma (HCC) among patients with nonalcoholic fatty liver disease (NAFLD) and cirrhosis, but there are limited prospective data. AIMS: To compare liver visualisation scores prospectively for US and abbreviated hepatobiliary phase (HBP) magnetic resonance imaging (AMRI) in a cohort of participants with NAFLD cirrhosis and a clinical indication for HCC surveillance. METHODS: This prospective multicenter study included 54 consecutive participants (67% women) with NAFLD cirrhosis who underwent contemporaneous US as well as HBP-AMRI with gadoxetic acid. Primary outcome was the proportion of imaging examinations with severe limitations in liver visualisation (visualisation score C) compared head-to-head between US and AMRI. RESULTS: The mean (± standard deviation) age was 63.3 years (±8.4) and body mass index was 32.0 kg/m(2) (±6.0). Nineteen participants (35%) had severe visualisation limitations on US, compared with 10 (19%) with AMRI, p < 0.0001. Nine (17%) participants had

Published
2022
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46. Outcomes of primary total hip arthroplasty following septic arthritis of the hip

Author

Jacob W, Bettencourt, Cody C, Wyles, Douglas R, Osmon, Arlen D, Hanssen, Daniel J, Berry, and Matthew P, Abdel

Subjects
Adult, Male, Reoperation, Prosthesis-Related Infections, Arthroplasty, Replacement, Hip, Kaplan-Meier Estimate, Disease-Free Survival, Osteoarthritis, Hip, Humans, Orthopedics and Sports Medicine, Aged, Proportional Hazards Models, Retrospective Studies, Aged, 80 and over, Arthritis, Infectious, Candidiasis, Bacterial Infections, Cryptococcosis, Middle Aged, Treatment Outcome, Case-Control Studies, Female, Hip Joint, Surgery, Hip Prosthesis, Follow-Up Studies
Abstract

Aims Septic arthritis of the hip often leads to irreversible osteoarthritis (OA) and the requirement for total hip arthroplasty (THA). The aim of this study was to report the mid-term risk of any infection, periprosthetic joint infection (PJI), aseptic revision, and reoperation in patients with a past history of septic arthritis who underwent THA, compared with a control group of patients who underwent THA for OA. Methods We retrospectively identified 256 THAs in 244 patients following septic arthritis of the native hip, which were undertaken between 1969 and 2016 at a single institution. Each case was matched 1:1, based on age, sex, BMI, and year of surgery, to a primary THA performed for OA. The mean age and BMI were 58 years (35 to 84) and 31 kg/m2 (18 to 48), respectively, and 100 (39%) were female. The mean follow-up was 11 years (2 to 39). Results The ten-year survival free of any infection was 91% and 99% in the septic arthritis and OA groups, respectively (hazard ratio (HR) = 13; p < 0.001). The survival free of PJI at ten years was 93% and 99% in the septic arthritis and OA groups, respectively (HR = 10; p = 0.002). There was a significantly higher rate of any infection at ten years when THA was undertaken within five years of the diagnosis of septic arthritis compared with those in whom THA was undertaken > five years after this diagnosis was made (14% vs 5%, respectively; HR = 3.1; p = 0.009), but there was no significant difference in ten-year survival free of aseptic revision (HR = 1.14; p = 0.485). The mean Harris Hip Scores at two and five years postoperatively were significantly lower in the septic arthritis group compared with the OA group (p = 0.001 for both). Conclusion There was a ten-fold increased risk of PJI in patients with a history of septic arthritis who underwent THA compared with those who underwent THA for OA with a ten-year cumulative incidence of 7%. The risk of any infection had a strong downward trend as the time interval between the diagnosis of septic arthritis and THA increased, highlighted by a 3.1-fold higher risk when THAs were performed within five years of the diagnosis being made. Cite this article: Bone Joint J 2022;104-B(2):227–234.

Published
2022
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47. Genetic defects are common in myopathies with tubular aggregates

Author

Qiang Gang, Conceição Bettencourt, Stefen Brady, Janice L. Holton, Estelle G. Healy, John McConville, Patrick J. Morrison, Michela Ripolone, Raffaella Violano, Monica Sciacco, Maurizio Moggio, Marina Mora, Renato Mantegazza, Simona Zanotti, Zhaoxia Wang, Yun Yuan, Wei‐wei Liu, David Beeson, Michael Hanna, and Henry Houlden

Subjects
Adult, Male, animal structures, Adolescent, Biopsy, General Neuroscience, Neurosciences. Biological psychiatry. Neuropsychiatry, Middle Aged, Pedigree, Young Adult, HEK293 Cells, Muscular Diseases, Exome Sequencing, Humans, Female, Neurology. Diseases of the nervous system, Neurology (clinical), RC346-429, Muscle, Skeletal, Research Articles, RC321-571, Research Article
Abstract

Objective A group of genes have been reported to be associated with myopathies with tubular aggregates (TAs). Many cases with TAs still lack of genetic clarification. This study aims to explore the genetic background of cases with TAs in order to improve our knowledge of the pathogenesis of these rare pathological structures. Methods Thirty‐three patients including two family members with biopsy confirmed TAs were collected. Whole‐exome sequencing was performed on 31 unrelated index patients and a candidate gene search strategy was conducted. The identified variants were confirmed by Sanger sequencing. The wild‐type and the mutant p.Ala11Thr of ALG14 were transfected into human embryonic kidney 293 cells (HEK293), and western blot analysis was performed to quantify protein expression levels. Results Eleven index cases (33%) were found to have pathogenic variant or likely pathogenic variants in STIM1, ORAI1, PGAM2, SCN4A, CASQ1 and ALG14. Among them, the c.764A>T (p.Glu255Val) in STIM1 and the c.1333G>C (p.Val445Leu) in SCN4A were novel. Western blot analysis showed that the expression of ALG14 protein was severely reduced in the mutant ALG14 HEK293 cells (p.Ala11Thr) compared with wild type. The ALG14 variants might be associated with TAs in patients with complex multisystem disorders. Interpretation This study expands the phenotypic and genotypic spectrums of myopathies with TAs. Our findings further confirm previous hypothesis that genes related with calcium signalling pathway and N‐linked glycosylation pathway are the main genetic causes of myopathies with TAs.

Published
2021
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48. Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

Author

Obbina Abani, Ali Abbas, Fatima Abbas, Mustafa Abbas, Sadia Abbasi, Hakam Abbass, Alfie Abbott, Nabeel Abdallah, Ashraf Abdelaziz, Mohamed Abdelfattah, Bushra Abdelqader, David Abdo, Basir Abdul, Althaf Abdul Rasheed, Ajibode Abdulakeem, Rezan Abdul-Kadir, Amina Abdulle, Abdulfatahi Abdulmumeen, Rasheed Abdul-Raheem, Niyaz Abdulshukkoor, Kula Abdusamad, Yazeed Abed El Khaleq, Mai Abedalla, Abeer Ul Amna Abeer Ul Amna, Katrina Abernethy, Adebanke Aboaba, Hani Abo-Leyah, Ahmed Abou-Haggar, Mahmoud Abouibrahim, Miriam Abraham, Tizzy Abraham, Abraheem Abraheem, Judith Abrams, Hyacinth-John Abu, Ahmed Abu-Arafeh, Syed M Abubacker, Akata Abung, Yaa Aceampong, Amaka Achara, Devikumar Acharya, Sarah Acheampong, Janet Acheson, Andres Acosta, Catherine Acton, Jacqueline Adabie-Ankrah, Sara Adair, Fiona Adam, Matthew Adam, Huzaifa Adamali, Carol Adams, Charlotte Adams, Kate Adams, Lisa Adams, Richard Adams, Tim Adams, Kirsty Adcock, Jemaimah Addai, Ade Adebiyi, Ken Adegoke, Vicki Adell, Debo Ademokun, Sherna Adenwalla, Oluwasegun A Adesemoye, Emmanuel O Adewunmi, Joyce Adeyemi, Rina Adhikary, Gabrielle Adkins, Adnan Adnan, John Aeron-Thomas, Debbie Affleck, Dominic Affron, Carmel Afnan, Muhammad Afridi, Zainab A Aftab, Meenakshi Agarwal, Rachel Agbeko, Chris Agbo, Penny Agent, Sunil Aggarwal, Arameh Aghababaie, Shafana Ahamed Sadiq, Mohamed H Ahammed Nazeer, Humayun Ahmad, Mohammad Ahmad, Syed Ahmad, Asim Ahmed, Bilal Ahmed, Forizuddin Ahmed, Hamze Ahmed, Iram Ahmed, Irshad Ahmed, Khaled Ahmed, Liban Ahmed, Mahin Ahmed, Maria C Ahmed, Muhammad S Ahmed, Naseer Ahmed, Nausheen Ahmed, Osama Ahmed, Rajia A Ahmed, Rizwan Ahmed, Saif Ahmed, Sammiya Ahmed, Sara Ahmed, Sophia Ahmed, Syed Ahmed, Syed Haris Ahmed, Roa Ahmed Ali, Sana Ahmed, Sana Ahmer, Dhiraj Ail, Mark Ainsworth, Giulia Airoldi, Myriam Aissa, Lindianne Aitken, Bini Ajay, Francis Ajeneye, Abdulakeem Ajibode, Ayesha Ajmi, Tahir Akbar, Naim Akhtar, Nasim Akhtar, Nauman Akhtar, Suha Akili, Oludoyinsola Akindolie, Yinka Akinfenwa, Olugbenga Akinkugbe, Ibrahim Akinpelu, Olajide Akinwumiju, Olugbenro Aktinade, Ahmad Al Aaraj, Asma Al Balushi, Majd Al Dakhola, Aladdin Al Swaifi, Eslam Al-Abadi, Narendra Aladangady, Ayaz Alam, Sajid Alam, Abbas Al-Asadi, Karina Al-Asadi, Kyriaki Alatzoglou, Manaf Al-Bayati, Paul Albert, Lorraine Albon, Gemma Alcorn, Stephen Alcorn, Aggie Aldana, David Alderdice, Rayan Aldouri, Jonathan Aldridge, Nicolas Aldridge, Ana Alegria, Alison Alexander, John Alexander, Peter D G Alexander, Charlotte Alford, Julyan Al-Fori, Laith Alghazawi, Bahij Al-Hakim, Shams Al-Hity, Ali Ali, Asad Ali, Fawzia R Ali, Hoodo Ali, Jawad Ali, Mariam Ali, Mohammad Ali, Nayab Ali, Oudai Ali, Sabira Ali, Sakina Ali, Syed Ali, Abid Alina, Fine Aliyuda, Katrin Alizadeh, Maithem Al-Jibury, Saba Al-Juboori, Majid Al-Khalil, Moutaz Alkhusheh, Allameddine Allameddine, Fiona Allan, Rachel Allan, Alison Allanson, Robert Allcock, Beverley Allen, Eireann Allen, Jess Allen, Kerry Allen, Laura Allen, Louise Allen, Poppy Allen, Rebecca Allen, Sam Allen, Sharon Allen, Simon Allen, Kathryn Allison, Bethan Allman, Lynne Allsop, Hassan Al-Moasseb, Magda Al-Obaidi, Lina Alomari, Akram Al-Rabahi, Bahar Al-Ramadhani, Zayneb Al-Saadi, Inji Alshaer, Rustam Al-Shahi Salman, Warkaq Al-Shamkhani, Bashar Al-Sheklly, Sara Altaf, Mary Alvarez, Balaal Alyas, Maysaa Alzetani, Susan Amamou, Noor Amar, Sakkarai Ambalavanan, Sarah-Jayne Ambler, Robert Ambrogetti, Chris Ambrose, Amir Ameen, Kenneth Amenyah, Maria R Amezaga, Allison Amin, Amina Amin, Kanish Amin, Syed Amin, Tara Amin, Amjad Amjad, Neelma Amjad, Mariam Ammoun, Victoria Amosun, Khaled Amsha, Pugh Amy, Atul Anand, Rekha Anand, Samantha Anandappa, Julie Anderson, Kevin Anderson, Laura Anderson, Michelle Anderson, Nicola Anderson, Rachel Anderson, Rory Anderson, Wendy Anderson, Prematie Andreou, Angela Andrews, Antonette Andrews, Jill Andrews, Susan Andrews, Gregory Andrikopoulos, Kanayochukwu Aneke, Andrew Ang, Wan Wei Ang, Tammy Angel, Aramburo Angela, Paola Angelini, Lazarus Anguvaa, Oleg Anichtchik, Millicent Anim-Somuah, Krishnan Aniruddhan, Jessica Annett, Patrick J Anstey, Rebekah Anstey, Alpha Anthony, Aaron Anthony-Pillai, Philip Antill, Zhelyazkova Antonina, Varghese Anu, Muhammad Anwar, George Apostolides, Aristeidis Apostolopoulos, Sarah Appleby, Diane Appleyard, Maia Far Aquino, Bianca Araba, Samuel Aransiola, Mariana Araujo, Emily Arbon, Ann Archer, Denise Archer, Simon Archer, Christian Ardley, Ana-Maria Arias, Ryoki Arimoto, Charlotte Arkley, Charlotte Armah, Ilianna Armata, Adam Armitage, Ceri Armstrong, Maureen Armstrong, Sonia Armstrong, Sylvia Armstrong-Fisher, Philippa Armtrong, Heike Arndt, Clare Arnison-Newgass, David Arnold, Rachael Arnold, Sarah Arnott, Dhawal Arora, Kavan Arora, Pardeep Arora, Rishi Arora, Andrea Arroyo, Arslam Arter, Ayush Arya, Rita Arya, Denisa Asandei, Adeeba Asghar, Catherine Ashbrook-Raby, Glen Ashby, Helen Ashby, Jan Ashcroft, John Ashcroft, Samuel Ashcroft, Deborah Asher, Ayesha Ashfaq, Ben Ashford, Suhail Ashgar, Abdul Ashish, Sally Ashman-Flavell, Sundar Ashok, Abd-El-Aziz Ashour, Muhammad Z Ashraf, Saima Ashraf, Mohammad B Ashraq, Deborah Ashton, Susan Ashton, Andrew Ashworth, Rebecca Ashworth, Arshia Aslam, Harshini Asogan, Abigail Asquith, Atif Asrar, Omar Assaf, Raine Astin-Chamberlain, Richard Athay-Hunt, Deborah Athorne, Billie Atkins, Christopher Atkins, Stacey Atkins, John Atkinson, Vicki Atkinson, Brygitta Atraskiewicz, Claire Atterbury, Abdul Ahmad Attia, Rita Atugonza, Paula Aubrey, Avinash Aujayeb, Aye Chan Thar Aung, Hnin Aung, Kyaw Thu Aung, Ni Aung, Yin Aung, Zaw Myo Aung, Emily Austin, Karen Austin, Abdusshakur Auwal, Miriam Avery, Joanne Avis, Georgina Aviss, Cristina Avram, Paula Avram, Gabriel Awadzi, Atia Awan, Aszad Aya, Eman Ayaz, Amanda Ayers, Jawwad Azam, Mohammed Azharuddin, Ghazala Aziz, N Aziz, Ali Azkoul, Ashaari Azman Shah, Giada Azzopardi, Hocine Azzoug, Fiyinfoluwa Babatunde, Melvin Babi, Babiker Babiker, Gayna Babington, Matthew Babirecki, Marta Babores, Adetona O Babs-Osibodu, Sammy Bacciarelli, Roudi Bachar, Gina Bacon, Jenny Bacon, Bibi Badal, Gurpreet R Badhan, Shreya Badhrinarayanan, Joseph P Bae, Sibel Bafekr Mishamandani, Alice Baggaley, Amy Baggott, Graham Bagley, Dinesh Bagmane, Lynsey Bagshaw, Kasra Bahadori, James Bailey, Katie Bailey, Lindsey Bailey, Liz Bailey, Morgan Bailey, Pippa Bailey, Sarah Bailey, Stephen Bailey, Hamish Baillie, J Kenneth Baillie, Jennifer Bain, Sanchia Baines, Vikram Bains, Aimi Baird, David Baird, Susan Baird, Tracy Baird, Yolanda Baird, Aiysha Bajandouh, Charles Baker, Emma Baker, Evelyn Baker, Johanne Baker, Josephine Baker, Kenneth Baker, Rebecca Baker, Terri-Anne Baker, Victoria Baker, Hugh Bakere, Nawar Bakerly, Michelle Baker-Moffatt, Nauman Bakhtiar, Panos Bakoulas, Julie Balaam, Niranjan Balachandran, Andrea Balan, Theodosios Balaskas, Madhu Balasubramaniam, Alison Balcombe, Alexander Baldwin, Ashley Baldwin, Caron Baldwin, Danielle Baldwin, Rebekah Baldwin-Jones, James Balfour, Gagan Bali, Sonya Balkee, Ceri Ball, Kasia Ballard, Amy Ballinger, Ismael Balluz, Craig Balmforth, Emese Balogh, Amir Baluwala, Gabby Bambridge, Alasdair Bamford, Amy Bamford, Peter Bamford, Adefunke Bamgboye, Elizabeth Bancroft, Hollie Bancroft, Tanya Bancroft, Joyce Banda, Krishna Bandaru, Srini Bandi, Nageswar Bandla, Somaditya Bandyopadhyam, Amit Banerjee, Millie Banerjee, Ritwik Banerjee, Harrison Banks, Lauren Banks, Luke Banks, Paul Banks, Oliver Bannister, Bharat Bansal, Robert Banthorpe, Laura Banton, Mariamma Baptist, Tanya Baqai, Ananya Mouli Baral, Desislava Baramova, Alex Barber, Russel Barber, Emma Barbon, Miriam Barbosa, Monica Barbosa, Jamie Barbour, Alexander Barclay, Claire Barclay, George Bardsley, Stephanie Bareford, Shahedal Bari, Amy Barker, Debbie Barker, Helen Barker, Joseph Barker, Leon Barker, Oliver Barker, Kerry Barker-Williams, Sinha Barkha, Juliana Barla, Gavin Barlow, Richard Barlow, Valerie Barlow, James Barnacle, Alex Barnard, Debi Barnes, Nicky Barnes, Theresa Barnes, Calum Barnetson, Amy Barnett, Matthew Barnett, Ashton Barnett-Vanes, William Barnsley, Andrew Barr, David Barr, Shaney Barratt, Manuella Barrera, Amy Barrett, Fiona Barrett, Jessica Barrett, Joe Barry, Jazz Bartholomew, Claire Bartlett, Georgina Bartlett, Greg Barton, Jill Barton, Lorna Barton, Rachael Barton, Rosaleen Baruah, Sonia Baryschpolec, Archana Bashyal, Betsy Basker, Ayten Basoglu, Gilda Bass, John Bassett, G Bassett, Chris Bassford, Pavinder Bassi, Betsy Bassis, Bengisu Bassoy, Victoria Bastion, Anupam Basumatary, Tristan Bate, Harry J Bateman, Ian Bateman, Kathryn Bateman, Vhairi Bateman, Eleanor Bates, Hayley Bates, Michelle Bates, Simon Bates, Sally Batham, Ana Batista, Amit Batla, Dushyant Batra, Donna Batty, Harry Batty, Thomas Batty, Peter Baughan, Miranda Baum, Carina Bautista, Fareha Bawa, Fatima S Bawani, Simon Bax, Lydia Baxter, Matt Baxter, Nicola Baxter, Zachary Baxter, Hannah Bayes, Charlotte Baylem, Lee Bayliss, Eileen Bays, Farid Bazari, Rohit Bazaz, Ahmad Bazli, Laura Beacham, Wendy Beadles, Philip Beak, Andy Beale, Kirk Beard, Jack Bearpark, Karen Beaumont, Dawn Beaumont-Jewell, Theresa Beaver, Sarah Beavis, Christy Beazley, Sarah Beck, Virginia Beckett, Rosie Beckitt, Heidi Beddall, Seonaid Beddows, Deborah Beeby, Gail Beech, Michelle Beecroft, Sally Beer, Jane Beety, Gabriela Bega, Alison Begg, Susan Begg, Sara Beghini, Ayesha Begum, Salman Begum, Selina Begum, Teresa Behan, Jasmine Beharry, Roya Behrouzi, Jon Beishon, Claire Beith, James Belcher, Holly Belfield, Katherine Belfield, Ajay Belgaumkar, Dina Bell, Gareth Bell, Gill Bell, Gillian Bell, Joshua Bell, Lauren Bell, Louise Bell, Nicholas Bell, Pippa Bell, Stephanie Bell, Jennifer L Bell, Jennifer Bellamu, Mary Bellamy, Arianna Bellini, Amanda Bellis, Fionn Bellis, Lesley Bendall, Naveena Benesh, Nicola Benetti, Leonie Benham, Guy Benison-Horner, Alexander Bennett, Ann Bennett, Caroline Bennett, Christopher Bennett, Gillian Bennett, Ian Bennett, Kristopher Bennett, Lorraine Bennett, Sara Bennett, Karen Bennion, Vivienne Benson, Jane Benstead, Andrew Bentley, Dionne Bentley, James Bentley, Ian Benton, Eva Beranova, Matthew Beresford, Colin Bergin, Malin Bergstrom, Jolanta Bernatoniene, Thomas Berriman, Zoe Berry, Marnie Berwick, Kimberley Best, Ans-Mari Bester, Yvonne Beuvink, Emily Bevan, Sarah Bevins, Tom Bewick, Andrew Bexley, Sonay Beyatli, Fenella Beynon, Arjun Bhadi, Sanjay Bhagani, Shiv Bhakta, Rekha Bhalla, Khushpreet Bhandal, Kulbinder Bhandal, Ashwin Bhandari, Sangam Bhandari, Aashutosh Bhanot, Ravina Bhanot, Sruti Bhaskara, Prashanth Bhat, Nikhil Bhatia, Rahul Bhatnagar, Karan Bhatt, Janki Bhayani, Deepika Bhojwani, Salimuzzaman Bhuiyan, Anna Bibby, Fatima Bibi, Naheeda Bibi, Salma Bibi, Tihana Bicanic, Sarah Bidgood, Julie Bigg, Sarah Biggs, Alphonsa Biju, Andras Bikov, Sophie Billingham, Jessica Billings, Carron Bilton, Teodirico Binas, Martin Binney, Alice Binns, Muhammad BinRofaie, Oliver Bintcliffe, Catherine Birch, Jenny Birch, Louann Birch, Janet Birchall, Katherine Birchall, Sam Bird, Sumedha Bird, Charndip Biring, Mark Birt, Raquel Bisa, Kilanalei Bishop, Linda Bishop, Lisa Bishop, Karen Bisnauthsing, Nibedan Biswas, Sahar Biuk, Karen Blachford, Ethel Black, Helen Black, Karen Black, Mairead Black, Polly Black, Hayley Blackgrove, Bethan Blackledge, Joanne Blackler, Samantha Blackley, Helen Blackman, Stuart Blackmore, Caroline Blackstock, Loraine Blackwood, Francesca Blakemore, Helen Blamey, Alison Bland, Sujata Blane, Simon Blankley, Mary Blanton, Parry Blaxill, Jenny Blaxland, Katie Blaylock, Jane Blazeby, Carol Blears, Natalie Blencowe, Donna Blofield, Ben Bloom, Jack Bloomfield, Angela Bloss, Hannah Bloxham, Louise Blundell, Andrew Blunsum, Mark Blunt, Ian Blyth, Kevin Blyth, Andrew Blythe, Karen Blythe, Marilyn Boampoaa, Boniface Bobie, Karen Bobruk, Pritesh Bodalia, Neena Bodasing, Tanya Bodenham, Sherin Bodh, Gabriele Boehmer, Marta Boffito, Kristyna Bohmova, Sumit Bokhandi, Maria Bokhar, Saba Bokhari, Sakina Bokhari, Syed Owais Bokhari, Ambrose Boles, Sarah Bollington, Sam Bolton, Charlotte Bomken, Charlotte Bond, Hayley Bond, Helena Bond, Stuart Bond, Thomas Bond, Alice Bone, Georgia Boniface, Wendy Bonnert, Lizzy Bonney, Leigh Boorman, Catherine Booth, Joanne Borbone, Cameron Borhani, Naomi Borman, Rachel Borrell, Mamu Boshir, Fiona Bottrill, Laura Bough, Hayley Boughton, Zoe Boult, Miriam Bourke, Stephen Bourke, Michelle Bourne, Rachel Bousfield, Lucy Boustred, Darren Bowen, Kaye Bowen, Alexandra Bowes, Amy Bowes, Philip Bowker, Louise Bowman, Simon Bowman, Rachel Bowmer, Angie Bowring, Geoff Bowyer, Helen Bowyer, Carmel Boyd, Jenny Boyd, Laura Boyd, Maxine Boyd, Namoi Boyle, Pauline Boyle, Rosalind Boyle, Louise Boyles, Osman Bozdag, Leanna Brace, David Brack, Charlotte Brackstone, Rob Bradburn, Jodie Bradder, Barry Bradley, Clare Jane Bradley, Pamela Bradley, Patrick Bradley, Paul Bradley, Joanne Bradley-Potts, Lynne Bradshaw, Sarah Bradshaw, Zena Bradshaw, Rebecca Brady, Shirin Brady, Denise Braganza, Suhail Brailsford, Jill Braithwaite, Marie Branch, Thomas Brankin-Frisby, Jamie Brannigan, Louise Brassington, Sophie Brattan, Fiona Bray, Nancy Bray, Angela Brazier, Manny Brazil, Lucy Brear, Tracy Brear, Stephen Brearey, Laura Bremner, Morwenna Brend, Giovanna Bretland, Chris Brewer, Hannah Bridge, Gavin Bridgwood, Hayley Briggs, Mark Briggs, Sara Brigham, John Bright, Chris Brightling, Lutece Brimfield, Elaine Brinkworth, Robin Brittain-Long, Vianne Britten, Terri Brittin, Lauren Broad, Sarah Broad, Rosie Broadhurst, Andrew Broadley, Marie Broadway, Christopher Brockelsby, Megan Brocken, Tomos Brockley, Andrew Broderick, Mary Brodsky, Fiona Brogan, David Broggio, Liz Brohan, Felicity Brokke, Jacob Brolly, David Bromley, Hannah Brooke-Ball, Verity Brooker, Ceri Brookes, Matthew Brookes, Alison Brooks, Karen Brooks, Nicole Brooks, Philip Brooks, Rachel Brooks, Sophie Brooks, Natalie Broomhead, Chloe Broughton, Nathaniel Broughton, Matt Brouns, Marie Browett, Alison Brown, Ammani Brown, Carly Brown, Catrin Brown, Chloe Brown, Elizabeth Brown, Ellen Brown, Heather Brown, Janet Brown, Louise Brown, Niall Brown, Pauline Brown, Rachel Brown, Richard Brown, Robert Brown, Steven Brown, Tom Brown, Wayne Brown, Bria Browne, Charlotte Browne, Duncan Browne, Mitchell Browne, Stephen Brownlee, Alba Brraka, David Bruce, Johanna Bruce, Michelle Bruce, Wojciech Brudlo, Nigel Brunskill, Alan Brunton, Margaret Brunton, Mandy Bryan, Meera Bryant, April Buazon, Maya Buch, Julie Buchan, Ruaridh Buchan, Alexis Buchanan, Ruaridh Buchanan, Danielle Buche, Amanda Buck, Matthew Buckland, Laura Buckley, Philip Buckley, Sarah Buckley, Carol Buckman, Kathleen Buckmire, George Bugg, Ramadan Bujazia, Marwan Bukhari, Shanze Bukhari, Richard Bulbulia, Alex Bull, Damian Bull, Rhian Bull, Thomas Bull, Naomi Bulteel, Kasun Bumunarachchi, Roneleeh Bungue-Tuble, Caroline Burchett, Dorota Burda, Christy Burden, Thomas G Burden, Mika Burgess, Paula Burgess, Richard Burgess, Sophia Burgess, Paula Burgett, Adrian Burman, Sara Burnard, Caroline Burnett, John Burnett, Amanda Burns, Amy Burns, Collette Burns, James Burns, Karen Burns, Samuel Burns, Sarah Burns, Daniel Burrage, Sadie Burrage, Kate Burrows, Claire Burston, Ben Burton, Fiona Burton, Matthew Burton, Angela Busby, Deborah Butcher, Aaron Butler, Jessica Butler, Joanne Butler, Joshua Butler, Lesley Butler, Peter Butler, Susan Butler, Al-Tahoor Butt, Mohammad M Butt, Sophia Butt, Caryl Butterworth, Nicola Butterworth-Cowin, Robert Buttery, Tom Buttle, Heather Button, Daniel Buttress, Jane Byrne, Wendy Byrne, Victoria Byrne-Watts, Eleanor Byworth, Amanda Cabandugama, Ruth Cade, Anthony Cadwgan, Donna Cairney, James Calderwood, Darren Caldow, Moira Caldwell, Giorgio Calisti, Debbie Callaghan, Jennifer Callaghan, Claire Callens, Donaldson Callum, Caroline Calver, Melissa Cambell-Kelly, Tracey Camburn, David R Cameron, Eleanor Cameron, Fraser Cameron, Sarah Cameron, Sheena Cameron, Christian Camm, Renee F D Cammack, Alison Campbell, Amy Campbell, Barbara Campbell, Bridget Campbell, Debbie Campbell, Helen Campbell, Hilary Campbell, Jonathan Campbell, Mark Campbell, Robyn Campbell, Wynny Campbell, Quentin Campbell Hewson, Julie Camsooksai, Ana Canabarro, Lisa Canclini, Shaula Mae Candido, Janie Candlish, Cielito Caneja, Johnathon Cann, Ruby Cannan, Emma Cannon, Michael Cannon, Petra Cannon, Vivienne Cannons, Jane Cantliff, Ben Caplin, Santino Capocci, Noemi Caponi, Angelika Capp, Anne Capps-Jenner, Thomas Capstick, Ishmael Carboo, Nuria Cardenas, Mary Cardwell, Rachel Carey, Simon Carley, Tammy Carlin, Andrew Carlton, Samantha Carmichael, Mandy Carnahan, Rebecca Carnegie, Charlotte Caroline, Emily Carpenter, Jodi Carpenter, David Carr, Sharon Carr, Anna Carrasco, Samantha Carrington, Zoe Carrington, Paul Carroll, Caroline Carron, Anne Carstairs, Jonathan Carter, Michael Carter, Moira Carter, Paul Carter, Penny Carter, Steven Carter, Simon Carter-Graham, Douglas Cartwright, Jo-Anne Cartwright, Claire Carty, Sinead Carty, Jaime Carungcong, Carolyn Carveth-Marshall, Susan Casey, Annie Cassells, Barbara Cassimon, Teresa Castiello, Gail Castle, Bridget Castles, Melanie Caswell, Ana Maria Catana, Heidi Cate, Susanne Cathcart, Katrina Cathie, Christine Catley, Laura Catlow, Matthew Caudwell, Jill Caulfield, Anna Cavazza, Luke Cave, Simon Cavinato, Frianne Cawa, Kathryn Cawley, Chloe Caws, Hankins Cendl, Hannah Century, Jeva Cernova, Mansur Cesay, Ed Cetti, Stephanie Chabane, Manish Chablani, Cathleen Chabo, David Chadwick, Julie Chadwick, Robert Chadwick, Ela Chakkarapani, Arup Chakraborty, Mallinath Chakraborty, Mollika Chakravorty, James Chalmers, Richard Chalmers, Georgina Chamberlain, Sarah Chamberlain, Emma Chambers, Jonathan Chambers, Lucy Chambers, Naomi Chambers, Alex Chan, Carmen Chan, Cheuk Chan, Evelyn Chan, Kayen Chan, Kimberley Chan, Ping Chan, Rebekah (Pui-Ching) Chan, Xin Hui Chan, Chris Chandler, Heidi Chandler, Kim J Chandler, Stuart Chandler, Zoe Chandler, Vikki Chandler-Vizard, Sumit Chandra, Navin Chandran, Badrinathan Chandrasekaran, Cherry Chang, Yvonne Chang, Josephine Chaplin, Graeme Chapman, John Chapman, Katie Chapman, Laura Chapman, Lianne Chapman, Polly Chapman, Timothy Chapman, Lucy Chappell, Linda Chapple, Amanda Charalambou, Bethan Charles, Dianne Charlton, Sally Charlton, Kevin Chatar, Calvin Chatha, Ritesh Chaube, Muhammad YN Chaudhary, Iram Chaudhry, Nazia Chaudhuri, Muhammad Chaudhury, Anoop Chauhan, Ruchi S Chauhan, Vipul Chauhan, Nicola Chavasse, Iknam Chaven, Rosanna Chavez, Vipal Chawla, Maria Cheadle, Lindsay Cheater, James Cheaveau, Charlotte Cheeld, Michelle Cheeseman, Fang Chen, Hui Min Chen, Terence Chen, Lok Yin Cheng, Zhihang Cheng, Helen Chenoweth, Chun How Cheong, Shiney Cherian, Suzanne Cherif, Mary Cherrie, Helen Cheshire, Barry Chesterson, Betty Cheung, Chee Kay Cheung, Elaine Cheung, Michelle Cheung, Claire Cheyne, Swati Chhabra, Wei Ling Chia, Eric Chiang, Angela Chiapparino, Rosavic Chicano, Zviedzo A Chikwanha, Sam Chilcott, Phillipa Chimbo, KokWai Chin, Wen Jie Chin, Rumbidzai Chineka, Amol Chingale, Karen Chinn, Vashira Chiroma, Heather Chisem, Claire Chisenga, Ben Chisnall, Carolyn Chiswick, Sunder Chita, Nihil Chitalia, Matthew Chiu, Brenda Chivima, Catherine Chmiel, Soha Choi, Willy Choon Kon Yune, Vandana Choudhary, Sarah Choudhury, Bing-Lun Chow, Fateha Chowdhury, Mahibbur Chowdhury, Shahid Chowdhury, Victoria Christenssen, Peter Christian, Alexander Christides, Fiona Christie, Daniel Christmas, Thereza Christopherson, Mark Christy, Paris Chrysostomou, Yunli Chua, Shabs Chucha, Dip Chudgar, Richard Chudleigh, Srikanth Chukkambotla, Michael E Chukwu, Izu Chukwulobelu, Chi Y Chung, Ben Church, Elaine Church, Sara R Church, David Churchill, Nicole Cianci, Nick Ciccone, Paola Cicconi, Paola Cinardo, Zdenka Cipinova, Bessie Cipriano, Sarah Clamp, Melanie Clapham, Edel Clare, Sarbjit Clare, Andrew Clark, Charlotte Clark, Diane Clark, Felicity Clark, Gabrielle Clark, James Clark, Katherine Clark, Kaylea Clark, Louise Clark, Lucy Clark, Matthew Clark, Patricia Clark, Richard Clark, Thomas Clark, Wendy Clark, Zoe Clark, Andrea Clarke, Heather Clarke, Mark Clarke, Paul Clarke, Robert Clarke, Roseanne Clarke, Samantha Clarke, Sarah Clarke, Sheron Clarke, Tracy Clarke, Andrew Clarkson, Alleyna Claxton, Kate Clay, Elizabeth Clayton, Olivia Clayton, Jill Clayton-Smith, Chris Cleaver, Carlota Clemente de la Torre, Jayne Clements, Suzanne Clements, Francesca Clemons, Lee Clifford, Lynne Clifford, Rachael Clifford, Sarah Clifford, Amelia Clive, Jonathan Clouston, Samantha Clueit, Andrea Clyne, Michelle Coakley, Peter G L Coakley, Tony Coates, Kathryn Cobain, Alexandra Cochrane, Patricia Cochrane, Maeve Cockerell, Helen Cockerill, Shirley Cocks, Rachel Codling, Adam Coe, Samantha Coetzee, David Coey, Danielle Cohen, Jonathan Cohen, Oliver Cohen, Mike Cohn, Louise Coke, Olutoyin Coker, Nicholas Colbeck, Roghan Colbert, Carol Cole, Esther Cole, Jade Cole, Joby Cole, Julie Cole, Richard Cole, Sue Cole, Garry Coleman, Matt Coleman, Holly Coles, Rebecca Coles, Macleod Colin, Alicia Colino-Acevedo, Julie Colley, Dawn Collier, Heather Collier, Paul Collini, Emma Collins, Jaimie Collins, Joanne Collins, Nicola Collins, Sally Collins, Vicky Collins, Andrew Collinson, Bernadette Collinson, Jennifer Collinson, Matthew Collis, Madeleine Colmar, Hayley E Colton, James Colton, Katie Colville, Carolyn Colvin, Ryan Colwell, Edward Combes, David Comer, Alison Comerford, Mike Comery, Dónal Concannon, Robin Condliffe, Lynne Connell, Natalie Connell, Karen Connelly, Gavin Connolly, Mireille Connolly, Emma Connor, Samantha Conran, Antonia Conroy, Veronica Conteh, Rory Convery, Camilla Conway, Francesca Conway, Grainne Conway, Rhiannon Conway, Jo-Anna Conyngham, Colette Cook, Eloise Cook, Gemma Cook, Helen Cook, Julie Cook, Danielle Cooke, Graham Cooke, Katrina Cooke, Soo Cooke, Tim Cooke, Adele Cooper, Alison Cooper, Chris Cooper, David Cooper, Helen Cooper, Jamie Cooper, Joanne Cooper, Joshua Cooper, Laura Cooper, Lauren Cooper, Nick Cooper, Rowena Cooper, Emma Cooperwaite, Thomas Cope, Sinead Corbet, Carolyn Corbett, John Corcoran, Chris Cordell, Jessica Cordle, Alasdair Corfield, John Corless, Alison Corlett, Joe Cornwell, Michael Cornwell, Diana Corogeanu, Mirella Corredera, Ruth Corrigan, Catherine Corry, Rita Corser, Denise Cosgrove, Tracey Cosier, Patricia Costa, Charlie Coston, Susannah Cotgrove, Zoe Coton, Lisa-Jayne Cottam, Rhiannon Cotter, Donna Cotterill, Alice Cotton, Caroline Cotton, Katy Cotton, Andrew Coull, James Coulson, David Counsell, David Counter, Cherry Coupland, Ellie Courtney, Julia Courtney, Rebecca Cousins, Alexander Cowan, Elena Cowan, Ruth Cowburn, Richard Cowell, Louise Cowen, Steve Cowman, Amanda Cowton, Debra Anne Cox, Ellie Cox, Giles Cox, Karina Cox, Miriam Cox, Karen Coy, Beverly Craig, Victoria Craig, Felicity Craighead, Matthew Cramp, Beverley Crane, Jacolene Crause, Adrian Crawford, Angie Crawford, Emma Crawford, Isobel Crawford, Richard Crawforth, Sarah Crawshaw, Ben Creagh-Brown, Andrew Creamer, Ryan Creighton, Joanne Cremona, Saveria Cremona, Janet Cresswell, Mark Cribb, Charles Crichton, Declan Crilly, Lauren Crisp, Nikki Crisp, Dominic Crocombe, Maria Croft, Ian Cron, Derrick Crook, Jennifer Crooks, Helen Croot, Harriet Crosby, Sarah Cross, Tim Cross, Amy Crothers, Stephen Crotty, Susan Crouch, Madeleine Crow, Amanda Crowder, Kate Crowley, Teresa Crowley, Rebecca Croysdill, Callum Cruickshank, Conor Cruickshank, Irena Cruickshank, James Cruise, Helen Crumley, Carina Cruz, Trino Cruz Cervera, Dominic Cryans, Guanguo Cui, Helen Cui, Donna Cullen, Lorraine Cullen, Gillian Cummings-Fosong, Marie Cundall, Victoria Cunliffe, Lorraine Cunningham, Neil Cunningham, Nicola Cunningham, Jason Cupitt, Hollie Curgenven, Debra Curley, Gerens Curnow, David Curran, Simon Curran, Craig Currie, Jacqueline Currie, Scarlett Currie, Abi Curtis, Becca Curtis, Jonathan Curtis, Katrina Curtis, Olivia Curtis, Thomas Curtis, Rebecca Cuthbertson, Sean Cutler, Marta Czekaj, Patrycja Czylok, Joana da Rocha, Andrew Dagens, Helen Daggett, Phil Daggett, Jacqui Daglish, Sandeep Dahiya, Helen Dakers-Black, Anne Dale, Katie Dale, Michaela Dale, Sam Dale, Jolyon Dales, Helen Dalgleish, Nikki Dallas, Helen Dallow, Dermot Dalton, Zoe Daly, Akila Danga, Amelia Daniel, Priya Daniel, Allison Daniels, Adela Dann, Sandra Danso-Bamfo, Nimo Daoud, Alex Darbyshire, Janet Darbyshire, Paul Dargan, Paul Dark, Tace Darling, Kate Darlington, Tom Darton, Guledew Darylile, Manjusha Das, Sukamal Das, Martin Daschel, Joanne Dasgin, Dibyendu Datta, Anna Daunt, Emily Davenport, Mark Davey, Michelle Davey, Miriam Davey, Molly Davey, Arlene David, Mini David, Alexander Davidson, Laura Davidson, Neil Davidson, Richard Davidson, Albert Davies, Alison Davies, Amanda Davies, Amy Davies, Angela Davies, Carolyn Davies, Catrin Davies, Cheryl Davies, Drew Davies, Elaine Davies, Ffyon Davies, Helen Davies, James Davies, Jane Davies, Jeni Davies, Jim Davies, Karen Davies, Kelly Davies, Kim Davies, Louisa Davies, Mark Davies, Matthew Davies, Michelle Davies, Nina Davies, Owen Davies, Patrick Davies, Rachel Davies, Rhys Davies, Ruth Davies, Sarah Davies, Simon Davies, Alison Davis, Gwyneth Davis, Illinos Davis, Julie-Ann Davis, Katherine Davis, Peter Davis, Alexander Davison, Sophia Davison, Mark Davy, Christine Dawe, H Dawe, Mark Dawkins, Danielle Dawson, Elizabeth Dawson, Joy Dawson, Susan Dawson, Tom Dawson, Andrew Daxter, Andrew Day, Helena Day, Jacob Day, Jeremy Day, Lynn Day, Jamie D'Costa, Parijat De, Duneesha de Fonseka, Toni de Freitas, Frederico De Santana Miranda, Eleanor de Sausmarez, Shanika de Silva, Thushan de Silva, Jessica De Sousa, Paulo De Sousa, James de Souza, Anthony de Soyza, Natasha de Vere, Johannes de Vos, Bethan Deacon, Sharon Dealing, Anna Dean, Julie Dean, Katrina Dean, Stephen Dean, Tessa Dean, Jill Deane, James Dear, Effie Dearden, Alison Deary, Catherine Deas, Samuel Debbie, Gabor Debreceni, Vashist Deelchand, Matthew Deeley, Joanne Deery, Emmanuel Defever, Raji Dehulia, Max Deighton, Manuela Del Forno, Arnold Dela Rosa, Lisa Delaney, Amanda Dell, Carrie Demetriou, David DeMets, Jane Democratis, Jacqueline Denham, Emmanuelle Denis, Laura Denley, Craig Denmade, Kathy Dent, Martin Dent, Elise Denton, Tom Denwood, Nishigandh Deole, Darshita Depala, Maria Depante, Randle Derbyshire, Susan Dermody, Amisha Desai, Asmita Desai, Purav Desai, Sanjeev Deshpande, Vai Deshpande, Brendan Devine, Sirjana Devkota, Nicola Dewland, Prakash Dey, Vishal Dey, Rogin Deylami, Jazz Dhaliwal, Kevin Dhaliwal, Jas Dhalliwal, Mandeep Dhanda, Sundip Dhani, Amandeep Dhanoa, Mili Dhar, Devesh Dhasmana, Aman Dhesi, Ekanjali Dhillon, Reiss Dhillon, Pamela Diamond, Priya Dias, Stephanie Diaz, Kayleigh Diaz-Pratt, Debbie Dickerson, Pamela Dicks, Stuart Dickson, Julie Dijo, Sean Dillane, Sarah Diment, Paul Dimitri, Maria Dineen, ThaiHa Dinh, Tri Dinh, Alex Dipper, Laura Dirmantaite, Lisa Ditchfield, Sarah Diver, Lavanya Diwakar, Caroline Dixon, Giles Dixon, Stephen Dixon-Mould, Brice Djeugam, Petr Dlouhy, Laurence Dobbie, Marinela Dobranszky Oroian, Charlotte Dobson, Lee Dobson, Marie Docherty, David Dockrell, James Dodd, Jackie Dodds, Rebecca Dodds, Steve Dodds, Richi Dogra, Erin Doherty, Warren Doherty, Yumiko Doi, Iain Doig, Eleanor Doke, Daniel Dolan, Mark Dolman, Rozzie Dolman, Lisa Donald, Callum Donaldson, Christopher Donaldson, Denise Donaldson, Gillian Donaldson, Kate Donaldson, Joanne Donnachie, Christopher Donnelly, Eilish Donnelly, Ronan Donnelly, Aravindhan Donohoe, Gemma Donohoe, Bryan Donohue, Sinead Donton, Emma Dooks, Grainne Doran, Kane Dorey, Sharon Dorgan, Amanda Dornan, Moonira Dosani, Davinder Dosanjh, Paula Dospinescu, Katie Douglas, Jonathan Douse, Lucy Dowden, Michelle Dower, Kerry Dowling, Sud Dowling, Nicola Downer, Charlotte Downes, Rob Downes, Thomas Downes, Damian Downey, Philippa Downey, Robert Downey, Louise Downs, Simon Dowson, Cornel Dragan, Cristina Dragomir, Cristina Dragos, Maire Drain, Chelsea Drake, Victoria Drew, Olivia Drewett, Celine Driscoll, Helena Drogan, Nikki Drogman, Graham Drummond, Ronald Druyeh, Simon Drysdale, An Du Thinh, Hazel Dube, Judith Dube, Ophias Dube, Stephen Duberley, Hayley Duckles-Leech, Nicola Duff, Emma Duffield, Sam Duff-Miller, Helen Duffy, Lionel Dufour, Annette Duggan, Helen Duggan, Parveen Dugh, Janice Duignan, Mick Duley, Simon Dummer, Andrew Duncan, Christopher Duncan, Fullerton Duncan, Gregory Duncan, Stephanie Dundas, Alessia Dunn, Charlotte Dunn, Damian Dunn, Laura Dunn, Paul Dunn, Charlene Dunne, Karen Dunne, Fiona Dunning, Aidan Dunphy, Venkat Duraiswamy, Beatriz Duran, Ingrid DuRand, Steve Durgacharan, Natalie Duric, Alison Durie, Emily Durie, Hannah Durrington, Haris Duvnjak, Akshay Dwarakanath, Laasya Dwarakanath, Ellen Dwyer, Claudia Dyball, Lee Dyble, Kristyn Dyer, Harvey Dymond, Tom Dymond, Chris Eades, Laura 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Elliott, Toby Elliott, Annie Ellis, Ann-Marie Ellis, Christine Ellis, Kay Ellis, Kaytie Ellis, Tak-Yan Ellis, Yvette Ellis, Megan Ellison, Rahma Elmahdi, Einas Elmahi, Hannah-May Elmasry, Mohammed Elmi, Najla Elndari, Omer Elneima, Mohamed Elokl, Ahmed Elradi, Mohamed Elsaadany, Sally El-Sayeh, Hana El-Sbahi, Tarek Elsefi, Karim El-Shakankery, Robert Elshaw, Hosni El-Taweel, Sarah Elyoussfi, Jonathan Emberey, Jonathan R Emberson, John Emberton, Julian Emmanuel, Ingrid Emmerson, Michael Emms, Florence Emond, Marieke Emonts, Nicu Enachi, Angila Engden, Katy English, Emma Entwistle, Hene Enyi, Marios Erotocritou, Helen Escreet, Peter Eskander, Hanif Esmail, Lise Estcourt, Amy Evans, Brynach Evans, Chris Evans, Daren Evans, Debra Evans, Gail Evans, Gareth Evans, Jennifer Evans, Lisa Evans, Lynn Evans, Margaret Evans, Michelle Evans, Mim Evans, Morgan Evans, Ranoromanana Evans, Ryan Evans, Teriann Evans, Terry John Evans, Tony Eve, Caroline Everden, Serenydd Everden, Hayley Evison, Lynsey Evison, Penny Eyton-Jones, Jacqueline Faccenda, Leila Fahel, Youstina Fahmay, Sara Fairbairn, Terry Fairbairn, Andy Fairclough, Louise Fairlie, Mark Fairweather, Anne Fajardo, Naomi Falcone, Euan Falconer, Jonathan Falconer, John Fallon, Andrea Fallow, David Faluyi, Victoria Fancois, Qayyum Farah, Novin Fard, Amr Farg, Margaret Farinto, Adam Farmer, Katie Farmer, Toni Farmery, Samantha Farnworth, Faiyaz Farook, Hadia Farooq, Sidrah Farooq, Fiona Farquhar, Karen Farrar, Aaron Farrell, Barbara Farrell, James Farthing, Syeda Farzana, Rahmatu Fasina, Azam Fatemi, Mina Fatemi, Nibah Fatimah, Maria Faulkner, Saul N Faust, Joe Fawke, Sinmidele Fawohunre, Abul Fazal, Kelly Feane, Simon Fearby, Alex Feben, Federico Fedel, Daria Fedorova, James Feely-Henderson, Christopher Fegan, Mae Felongco, Lynsey Felton, Tim Felton, Kate Fenlon, Andrea Fenn, Isabelle Fenner, Ciara Fenton, Melisa Fenton, Cameron Ferguson, Jenny Ferguson, Kathryn Ferguson, Katie Ferguson, Stephanie Ferguson, Susan Ferguson, Susie Ferguson, Victoria Ferguson, Denzil Fernandes, Candida Fernandez, Eduardo Fernandez, Maria Fernandez, Sonia Fernandez Lopez, Callum Jeevan Fernando, Ahmed Feroz, Pietro Ferranti, Thais Ferrari Gersogamo, Eleanor Ferrelly, Alexandra Ferrera, Emma Ferriman, Nicholas Fethers, Ben Field, Janet Field, Rebecca Field, Karen Fielder, Lindsey Fieldhouse, Andra Fielding, Julie Fielding, Len Fielding, Sarah Fielding, Asma Fikree, Sarah Ann Filson, Sarah Finbow, Debbie Finch, Joanne Finch, Laurie Finch, Joanne Finden, Natalie Fineman, Lauren Finlayson, Adam Finn, Joanne Finn, Clare Finney, Sofia Fiouni, Jo Fiquet, Dani Fisher, Emily Fisher, James Fisher, Neil Fisher, Meadow Fisher Crisp, Daniel Fishman, Krystofer Fishwick, Lorraine Fitzgerald, Chloe Fitzpatrick-Creamer, Jan Flaherty, Michael Flanagan, Charles Flanders, Cathy Flatters, Julie Fleming, Lucy Fleming, Paul Fleming, William Flesher, Alison Fletcher, Jonathan Fletcher, Lucy Fletcher, Simon Fletcher, Sophie Fletcher, Karen Flewitt, Christopher Flood, Ian Floodgate, Vincent Florence, Sharon Floyd, Kelly Flynn, Rachel Flynn, Sara Flynn, Claire Foden, Adama Fofana, Georgina Fogarty, Claire Foley, Paul Foley, Linda Folkes, Daniela Mock Font, Evodian Fonyonga, Aiwyne Foo, Jane Foo, Andrew Foot, Jayne Foot, Jane Forbes, Jamie Ford, Kathy Ford, Jennifer Foreman, Caroline Fornolles, Adam Forrest, Ellie Forsey, Miranda Forsey, Thomas Forshall, Elliot Forster, Julian Forton, Emily Foster, Joseph Foster, Rachel A Foster, Tracy Foster, Theodora Foukanelli, Angela Foulds, Ian Foulds, Folakemi Fowe, Emily Fowler, Robert Fowler, Stephen Fowler, Caroline Fox, Claire Fox, Daniel Fox, Heather Fox, Jonathan Fox, Lauren Fox, Natalie Fox, Olivia Fox, Simon Fox, Sarah-Jane Foxton, Yasin Fozdar, Rebecca Frake, Alex Francioni, Olesya Francis, Rebecca Francis, Sarah Francis, Theodora Francis-Bacon, Jason Frankcam, Helen Frankland, Gayle Franklin, Jessica Franklin, Darron Franks, Catherine Fraser, Laura Fraser, Sharon Frayling, Martyn Fredlund, Matthew Free, Carol Freeman, Elaine Freeman, Hannah Freeman, Nicola Freeman, Clare Freer, Ian Freestone, Eleanor French, Matthew Frise, Renate Fromson, Claire Froneman, Adam Frosh, John Frost, Victoria Frost, Oliver Froud, Rachel Frowd, Arun Fryatt, Jake Fryer, Janet Fu, Bridget Fuller, Liz Fuller, Neil Fuller, Tracy Fuller, Duncan Fullerton, Jenny Fullthorpe, Carrie Fung, Gayle Fung, Sarah Funnell, John Furness, Charlene Furtado, Andrew Fyfe, Nytianandan G, Elizabeth Gabbitas, Claire Gabriel, Diana Gabriel, Hadiza Gachi, Rose Gad, Joshua Gahir, Sarveen Gajebasia, Katarzyna Gajewska-Knapik, Zacharoula Galani, Christopher Gale, Hugo Gale, Rebecca Gale, Swetha Gali, Karen Galilee, Bernadette Gallagher, Jude Gallagher, Rosie Gallagher, William Gallagher, Joanne Galliford, Catherine Galloway, Chris Galloway, Emma Galloway, Jacqui Galloway, James Galloway, Laura Gamble, Liz Gamble, Brian Gammon, Jaikumar Ganapathi, Ramesh Ganapathy, Kaminiben Gandhi, Sarah Gandhi, Usha Ganesh, Abrar Gani, Emma-James Garden, Antoni D Gardener, Emma Gardiner, Jill Gardiner, Michael Gardiner, Phil Gardiner, Siobhan Gardiner, Caroline Gardiner-Hill, Jonathan Gardner, Mark Garfield, Atul Garg, Nathan Garlick, Justin Garner, Lucie Garner, Zoe Garner, Kimberley Garnett, Robert Garney, Rosaline Garr, Michael Garstka, Peter Gartan, Florence Garty, Rachel Gascoyne, Hyeriju Gashau, Noha Gasmalseed, Michaela Gaspar, Aoife Gatenby, Erin Gaughan, Alok Gaurav, Mariana Gavrila, Jane Gaylard, Emma Gaywood, Catherine Geddie, Alison Geddis, Ian Gedge, Sarah Gee, Minerva Gellamucho, Karzan Gelly, Leila Gelmon, Sandra Gelves-Zapata, Gemma Genato, Susan Gent, Natalie Geoghegan, Chloe George, Sam George, Tina George, Simon Georges, Domonique Georgiou, Peter Gerard, Leigh Gerdes, Louise Germain, Helen Gerrish, Abel Getachew, Louise Gethin, Hisham Ghanayem, Amardeep Ghattaoraya, Anca Gherman, Alison Ghosh, Justin Ghosh, Sudhamay Ghosh, Sarra Giannopoulou, Malick Gibani, Andrew Gibb, Ben Gibbison, Kerry Gibbons, Alex Gibson, Bethan Gibson, Kimberley Gibson, Kirsty Gibson, Sian Gibson, Cat Gilbert, Jeanette Gilbert, Joanne Gilbert, Kayleigh Gilbert, Sean Gilchrist, Benjamin Giles, Mandy Gill, Rose Gill, Lynne Gill, Paul Gillen, Annelies Gillesen, Katherine Gillespie, Matt Gillespie, Elizabeth Gillham, Andrew Gillian, Deborah Gilliland, Robert Gillott, Danielle Gilmour, Kate Gilmour, Theodora Giokanini-Royal, Anna Gipson, Joanna Girling, Rhian Gisby, Angelena Gkioni, Aikaterini Gkoritsa, Effrossyni Gkrania-Klotsas, Amy Gladwell, James Glanville, Jessica Glasgow, Susannah Glasgow, Jon Glass, Lynn Glass, Sharon Glaysher, Lisa Gledhill, Ana Glennon, John Glover, Kyle Glover, Jan Glover Bengtsson, Chevanthy Gnanalingam, Julie Goddard, Wendy Goddard, Emily Godden, Jo Godden, Gillian Godding, Emma Godson, Gerry Gogarty, Sukanya Gogoi, Aiky Goh, Rebeca Goiriz, Sriya Gokaraju, Philip Gold, Raphael Goldacre, Arthur Goldsmith, Portia Goldsmith, Darren Gomersall, Lucia Gomez, Raquel Gomez-Marcos, Ali Gondal, Celia Gonzalez, Jack Goodall, Bob Goodenough, Laura Goodfellow, James Goodlife, Camelia Goodwin, Elizabeth Goodwin, Jayne Goodwin, Paula Goodyear, Rajiv Gooentilleke, Sharif Goolam-Hossen, Michelle Goonasekara, Sheila Gooseman, Shameer Gopal, Peter Gordon, Sally Gordon, Hugh Gorick, Caitlin Gorman, Claire Gorman, Stuart Gormely, Diana Gorog, Jan Gorry, Michelle Gorst, Thomas Gorsuch, Jayshreebahen Gosai, Rebecca Gosling, Sally Gosling, Georgina Gosney, Vanessa Goss, Dzintars Gotham, Naomi Gott, Elizabeth Goudie, Amanda Gould, Angela Gould, Susan Gould, Lysander Gourbault, Anna Gouveia, Abha Govind, Sharon Gowans, Girish Gowda, Rohit Gowda, Pauline Gowdy, Hannah Gower, Thomas Gower, Pankaj Goyal, Sunil Goyal, Sushant Goyal, Beverley Graham, Clive Graham, Jane Graham, Jonathan Graham, Justin Graham, Libby Graham, Sharon Graham, Matthew Graham-Brown, Julia Grahamslaw, Gianluca Grana, Tracyanne Grandison, Louis Grandjean, Alison Grant, Ann Grant, David Grant, 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Griffiths, Yvonne Griffiths, Sofia Grigoriadou, Steph Grigsby, Paul Grist, Stephen Grist, Evelina Grobovaite, Clarissa Grondin, Rachel Groome, Liliana Grosu, Jenny Grounds, Margaret Grout, Helen Grover, Jayne Groves, Neil Grubb, Julie Grundy, Francesca Guarino, Sharada Gudur, Sharazeq Guettari, Shivang Gulati, Vikas Gulia, Pumali Gunasekera, Malin Gunawardena, Kirun Gunganah, Jessica Gunn, Emma Gunter, Alok Gupta, Atul Gupta, Rajeev Gupta, Richa Gupta, Rishi Gupta, Tarun Gupta, Vineet Gupta, Ankur Gupta-Wright, Victoria Guratsky, Alvyda Gureviciute, Sambasivarao Gurram, Bhawana Gurung, Shraddha Gurung, Hazel Guth, Ruth Habibi, Berkin Hack, Pamela Hackney, Christian Hacon, Aiman Haddad, Denise Hadfield, Michalis Hadjiandreou, Nikolaos Hadjisavvas, Leah Hadzik, Anna Haestier, Nauman Hafiz, Rana Hafiz-Ur-Rehman, Javed Hafsa, Samantha Hagan, Jack W Hague, Rosemary Hague, Kate Haigh, Christina Haines, Scott Hainey, Morton Hair, Brigid Hairsine, Juraj Hajnik, Anne Haldeos, Writaja Halder, Jennie Hale, Carmel Halevy, Paul Halford, William Halford, Alistair Hall, Anthony Hall, Claire Hall, Elizabeth Hall, Emma Hall, Fiona Hall, Helen Hall, Jennifer Hall, Kathryn Hall, Bill Hall, Jan Hallas, Kyle Hallas, Charles Hallett, Becky-Lee Halls, Heather Halls, Maryam Hamdollah-Zadeh, Bilal Hameed, Imran Hamid, Mohamad Hamie, Bethany Hamilton, Fergus Hamilton, Leigh Hamilton, Nicola Hamilton, Ruth Hamlin, Eleanor Hamlyn, Beatrice Hammans, Shirley Hammersley, Kate Hammerton, Bev Hammond, Leah Hammond, Sara Hammond, Fiona Hammonds, Ibrahim Hamoodi, Karen Hampshire, Elizabeth Hampson, Jude Hampson, Lucy Hampson, Ozan Hanci, Ian Hancock, Sadiyah Hand, Jasmine Handford, Soran Handrean, Sarah Haney, Sheharyar Hanif, E Hanison, Alison Hanlon, Jennifer Hannah, Amy Hannington, Merhej Hannun, Aidan Hanrath, Anita Hanson, Jane Hanson, Kathryn Hanson, Steve Hanson, Helen Hanwell, Mazhar Ul Haq, Ala Haqiqi, Monjurul Haque, Lesley Harden, Zoe Harding, Simon Hardman, Joanna Hardy, Kumar Haresh, Rachel Harford, Beverley Hargadon, Carolyn Hargreaves, Emily Hargreaves, James Hargreaves, Alice Harin, Mohammed Haris, Edward Harlock, Sandra Harlow, Paula Harman, Tracy Harman, Mark Harmer, Muhammad A Haroon, Charlie Harper, Fiona Harper, Heather Harper, Melanie Harper, Peter Harper, Rosemary Harper, Sarah Harrhy, Sian Harrington, Yasmin Harrington-Davies, Jade Harris, Jess Harris, John Harris, Laura Harris, Marie-Clare Harris, Naomi Harris, Nichola Harris, Sophie Harris, Alex Harrison, David Harrison, Julie Harrison, Laura Harrison, Melanie Harrison, Rowan Harrison, Susie Harrison, Thomas Harrison, Wendy Harrison, Elizabeth Harrod, Ciaran Hart, Dominic Hart, Lisa Hartley, Rosemary Hartley, Ruth Hartley, Tom Hartley, William Hartrey, Phillipa Hartridge, Stuart Hartshorn, Heli Harvala, Alice Harvey, Angela Harvey, Max Harvey, Catherine Harwood, Helen Harwood, Brigitte Haselden, Kazi Hashem, Mohammed Hashimm, Tadaaki Hashimoto, Imranullah Hashmi, Sarah Haskins, Zena Haslam, Adil Hassan, Ali Hassan, Wagae UI Hassan, Waqar Ul Hassan, Sapna Hassasing, Jane Hassell, Philip Hassell, Alex Hastings, Bethany Hastings, Janice Hastings, Stephanie Hatch, Jonathan Hatton, Sheryl Haviland, May Havinden-Williams, Stefan Havlik, Daniel B Hawcutt, Kadean Hawes, Liz Hawes, Nicola Hawes, Annie Hawkins, Catherine Hawkins, Nancy Hawkins, Tanya Hawkins, Dan Hawley, Ed Hawley-Jones, Edward Haworth, Cathy Hay, Amna Hayat, Jamal Hayat, Mohamed-Riyal Hayathu, Tamsin Haydon, Anne Hayes, Jonas Hayes, Kate Hayes, Melony Hayes, Vanessa Hayes, Fiona Hayes, Patrick Hayle, Chloe Haylett, Antara Hayman, Melanie Hayman, Matthew Haynes, Richard Haynes, Rachel Hayre, Sarah Haysom, James Hayward, Patrick Haywood, Tracy Hazelton, Phoebe Hazenberg, Zhengmai He, Elizabeth Headon, Carrie Heal, Brendan Healy, Amy Hearn, Angela Heath, Rowan Heath, Diane Heaton, Kerry Hebbron, Gemma Hector, Andy Hedges, Katrine Hedges, Cheryl Heeley, Elaine Heeney, Rajdeep Heire, Ulla Hemmila, Cassie Hemmings, Scott 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Susie Holt, Alexandra Holyome, Marie Home, Toni Home, Renate Homewood, Kate Hong, Laura Hontoria del Hoyo, Clare Hooper, Sarah Hoosdally, Samantha Hope, Susan Hope, Bridget Hopkins, Peter W Horby, Stephanie Horler, Anil Hormis, Daniel Hornan, Nicola Hornby, Zoey Horne, Latoya Horsford, Megan Horsford, Mark Horsford, Valana Horsham, Alexander Horsley, Ashley Horsley, Elizabeth Horsley, Sarah Horton, Nicola Horton-Turner, Jane Hosea, Toby Hoskins, Muhammad S Hossain, Rashed Hossain, Leanne Hostler, Maxine Hough, Sarah Hough, Brittany Houghton, Catherine Houghton, Iain Houghton, Kathryn Houghton, Rebecca Houlihan, Angela Houston, Hamish Houston, Tawedzegwa Hove, Roseanna Hovvels, Lee How, Laura Howaniec, Laura Howard, Linda Howard, Lucy Howard, Sarah Howard, Stuart Howard, Richard Howard-Griffin, Alison Howarth, Diane Howarth, Serena Howe, Mark Howells, Lyn Howie, Kerry Howlett, Sophie Howlett, Joanne Hoyle, Josh Hrycaiczuk, Naing Zaya Htoon, Su Htwe, Ying Hu, Chiang Ooi Huah Huah, Abby Huckle, Shahzya Huda, Alison Hudak, Lisa Hudig, Alex Hudson, Cara Hudson, Heather Hudson, Peter Hudson, Oli Hudson, Alison Hufton, Connor Huggins, Alistair Hughes, Eithne Hughes, Emma Hughes, Gareth Hughes, Heather Hughes, Luke Hughes, Rachel Hughes, Rebecca Hughes, Samantha Hughes, Stephen Hughes, Vikki Hughes, Wesley Hughes, Lukas Huhn, Ching Hui, Ruth Hulbert, Diana Hull, Grace Hull, Robert Hull, Amanda Hulme, Peter Hulme, Wendy Hulse, George Hulston, Ryan Hum, Laura Humber, Megan Hume, Charlotte Humphrey, Ismay Humphreys, Alasdair Humphries, Joanne Humphries, Lena Hunold, Fiona Hunt, Kristen Hunt, Luke Hunt, Sophie Hunt, Al Hunter, Alexandra Hunter, Isobel Hunter, Karl Hunter, Neil Hunter, George Huntington, Elizabeth Hurditch, Cian Hurley, Katrina Hurley, Mohammed A Husain, Syeda Yusra Husaini, Coralie Huson, Afreen Hussain, Ibraar Hussain, Ifza Hussain, Mohammad Hussain, Muhammad Hussain, Reda Hussain, Sajid Hussain, Samia Hussain, Sanniah Hussain, Wasim Hussain, Yasmin Hussain, Mohammed Hussam El-Din, Raheem Hussein, Rebecca Hussey, Camille Hutchinson, Dorothy Hutchinson, Elizabeth Hutchinson, John Hutchinson, Claire Hutsby, Paula Hutton, Daniella Hydes, Jamie Hyde-Wyatt, Niamh Hynes, Megan Hyslop, Mazen Ibraheim, Abdalla Ibrahim, Ahmed Ibrahim, Asil Ibrahim, Mohamed Ibrahim, Monzeer Ibrahim, Wadah Ibrahim, Adetokunbo I Idowu, Muhammad Idrees, Hina Iftikhar, Mawara Iftikhar, Chukwuemeka Igwe, Mohammad Ijaz, Amaju Ikomi, Clare Iles, Stamatina Iliodromiti, Mary Ilsley, Lorna Ilves, La'ali Imam-Gutierrez, Christopher Imray, Alison Imtiaz, Haider Imtiaz, Claire Ingall, Jack Ingham, Julie Ingham, Rory Ingham, Tejas Ingle, Jennifer Inglis, Anne Ingram, Luke Ingram, Peter Inns, Ken Inweregbu, Andreea A Ionescu, Ana Ionita, Ilian P Iordanov, Anil Ipe, Adil Iqbal, Madiha Iqbal, Mohammed Iqbal, Faisal Iqbal Sait, Jane Ireland, Robert Irons, Mohannad Irshad, Muhammad S Irshad, Janice Irvine, Val Irvine, Pamela Irving, Robert Irving, Mina Ishak, Erica Isherwood, Aminul Islam, Abdurrahman Islim, Ali Ismail, Omar Ismail, Caroline Ison, M'hamedi Israa, Sharon Isralls, Ali Issa, Monica Ivan, Catrina Ivel, Chineze Ivenso, Ashleigh Ivy, Sophie Iwanikiw, Karen Ixer, Menaka Iyer, Mia Iyer, Calum Jack, Amanda Jackson, Anthony Jackson, Ben Jackson, Beth Jackson, Douglas Jackson, Ella Jackson, Hayley Jackson, Helen Jackson, Jane Jackson, Julie Jackson, Karin Jackson, Lauren Jackson, Melanie Jackson, Nicola Jackson, Shane Jackson, Sharon Jackson, Nikita Jacob, Patricia Jacob, Reni Jacob, Nicola Jacques, Terri-Lisa Jacques-Brown, Anisa Jafar, Daniel Jafferji, Ali Jaffery, Chandrashekar Jagadish, Vijay Jagannathan, Sam Jaggard, Mandeep Jagpal, Fernandez R Jaime, Neemisha Jain, Seema Jain, Susan Jain, Sanjay Jaiswal, Danyal Jajbhay, Thomas Jaki, Bintou Jallow, Yusuf Jaly, Sabine Jamal, Zeba Jamal, Yasmin Jameel, Albie James, Christie James, Kate James, Lee James, Linda James, Mark James, Nicholas James, Olivia James, Rebecca James, Ruth James, Tracy James, Jack Jameson, Aaron Jamison, Phoebe Jane, Azara Janmohamed, Sabrina Jansz, Deepa Japp, Lorraine Jappy, Victor Jardim, Catherine Jardine, Emma Jarnell, Ellie Jarvie, Ann Jarvis, Claire Jarvis, Lisa Jarvis, Rosina Jarvis, Patrycja Jastrzebska, Hafsa Javed, Mays Jawad, Lona Jawaheer, Kauky Jawaid, Anu Jayachandran, Dinakaran Jayachandran, Angelina Jayakumar, Deepak Jayaram, Ravi Jayaram, Geeshath Jayasekera, Thilina Jayatilleke, Abi Jayebalan, Saman Jeddi, Vandana Jeebun, Mohammad S Jeelani, Zeynab Jeewa, Emma Jefferson, Katie Jeffery, Helen Jeffrey, Jenni Jeffrey, Rachel Jeffrey, Sue Jeffrey, Nathan Jeffreys, Benjamin Jeffs, Debbie Jegede, Taylor Jemima, Ifan Jenkin, Alison Jenkins, Christopher Jenkins, David Jenkins, Elinor Jenkins, Sarah Jenkins, Sian Jenkins, Stephen Jenkins, Jacqui Jennings, Louise Jennings, Rebecca Jennings, Virginia Jennings, Ellen Jerome, Douglas Jerry, Ellen Jessup-Dunton, Jorge Antonio Jesus Silva, Champa Jetha, Kishan Jethwa, Jeby Jeyachandran, Visuvanathan Jeyakumar, Dharshana Jeyapalan, Shaman Jhanji, Khoo Jian, Zhixin Jiao, Laura Jimenez, Ana Jimenez Gil, Jithin Jith, Teishel Joefield, Navraj Johal, Karine Johannessen, Aisyah Johari, Annie John, Anu John, Navin John, Sarah John, Emma Johns, Margaret Johns, Anne-Marie Johnson, Antoinette Johnson, David Johnson, Emma Johnson, Gillian Johnson, Kathryn Johnson, Katie Johnson, Luke Johnson, Mark Johnson, Nelsonseelan Johnson, Oliver Johnson, Rachel Johnson, Tracy Johnson, Zoe Johnson, Claire Johnston, Janet Johnston, Laura Johnston, Susan Johnston, Victoria Johnston, Dawn Johnstone, Ed Johnstone, Janet Johnstone, Manohar Joishy, Adam Jones, Alistair Jones, Annabel Jones, Ben Jones, Bryony Jones, Carys Jones, Ceri Jones, Charlotte Jones, Christine E Jones, Debra Jones, Emily Jones, Gareth Jones, Geraldine Jones, Hazel Jones, Jac Jones, James Jones, Jamie Jones, Jessica Jones, Jonathon Jones, Julie Jones, Karen Jones, Kate E Jones, Kevin Jones, Laura Jones, Laura M Jones, Lorna Jones, Louise Jones, Mathew Jones, Nicola Jones, Paul Jones, Rhianna Jones, Ruth E Jones, Samantha Jones, Sophie Jones, Stefanie Jones, Steve Jones, Taya Jones, Tim Jones, Tracey Jones, Ramya Jonnalagadda, Rebecca Jordache, Annette Jose, Sanal Jose, Anna Joseph, Joseph Joseph, Rosane Joseph, Sibet Joseph, Dhaara Joshi, Mehul Joshi, Pratichi Joshi, Revati Joshi, Benz Josiah, Tiffany Joyce, Adriel Ju Wen Kwek, Edward Jude, Parminder Judge, Jessica Juhl, Sirisha Jujjavarapu, Mark Juniper, Edmund Juszczak, Deepthi Jyothish, Kasamu Kabiru Dawa, Mark Kacar, Katarina Kacinova, Nikhil Kadam, Rebecca Kahari, Gail Kakoullis, Azad Kala Bhushan, Richard JK Kalayi, Roobala Kaliannan Periyasami, Efthymia Kallistrou, Seika Kalsoom, Elisa Kam, John Kamara, Mohamed Kamara, Ajay Kamath, Prakash Kamath, Ravindra Kamath, Siddharth Arun Kamerkar, Nick Kametas, Musaiwale Kamfose, Arul Kandaswamy, Leia Kane, Osei Kankam, Thogulava Kannan, Abhinav Kant, Vikas Kapil, Ritoo Kapoor, Sonal Kapoor, Sourjya Kar, Janaka Kara, Vasita Kara, Marina Karakantza, Rona Kark, Nicholas Karunaratne, Natashja Kasianczuk, Vidya Kasipandian, Rizwan Kassam, Janarth Kathirgamachelvam, Victoria Katsande, Kulbinder Kaul, Daljit Kaur, Dervinder Kaur, Jasmin Kaur, Jaspreet Kaur, Satvinder Kaur, Zunaira Kausar, Mohammad AA Kawser, Andrea Kay, Sarah Kay, Jossy N Kayappurathu, Callum Kaye, Ahemd Kazeem, Naved Kazi, Sharon Kaznica, Samantha Kearley, Rachel Kearns, Nichola Kearsley, Joanne Keating, John Keating, Liza Keating, Elizabeth Keddie-Gray, Katie Keen, Natalie Keenan, Jonathan Kefas, Stephen Kegg, Laura Keith, Uzoamaka Keke, Tosin Kelani, Joanne Kellett, Jeremy Kellington, Alison Kelly, Conor Kelly, David Kelly, Diane Kelly, Dominic Kelly, Emma Kelly, Laura Kelly, Martin Kelly, Michael Kelly, Rosalind Kelly, Sinead Kelly, Stephen Kelly, Thomas Kelly, Mary Kelly-Baxter, Marketa Keltos, Timothy Kemp, Kelly Kemsley, Alexandra Kendall-Smith, Sarah Kennard, Ann Kennedy, Caroline Kennedy, James Kennedy, Sophie Kennedy-Hay, Julia Kenny, Kelly Kent, Melanie Kent, Lynne Keogan, Alexander Keough, Clement Kerlin, A Kerr, Andrew Kerr, Maria Kerr, Caroline Kerrison, Anthony Kerry, Samantha Kershaw, Helen Kerslake, Ian Kerslake, Helen Kerss, Jocelyn Keshet-Price, Margaret Kevern, Georgina Keyte, Abdul Khadar, Ali Khalid, Muhammad U Khalid, Syed Khalid, Amir Khalil, Asma Khalil, Sijjad Khalil, Abubakar Khan, Ali Khan, Al-Imran Khan, Arham Khan, Asad Khan, Aurangzeb Khan, Burhan Khan, Camran Khan, Fatimah Khan, Kausik Khan, Malik Aamaz Khan, Marria Khan, Mehrunnisha Khan, Mohammad Khan, Mohammed Khan, Nayeem Khan, Omar Khan, Rahe Khan, Rahila Khan, Sabiya Khan, Shabana Khan, Shahul Khan, Shoaib Khan, Tasaduksultan Khan, Waseem Khan, Usman F Khatana, Jibran Khatri, Jyoti Khatri, Hafiza Khatun, Taslima Khatun, Mena Kheia, Jacyntha Khera, Htet Ei Khin, Najaf Khoja, Kiran Khokhar, Jayne Khorsandi, Chloe Khurana, Faith Kibutu, Andrew Kidd, Michelle Kidd, Joe Kidney, Shane Kidney, Will Kieffer, James Kilbane, Caroline Kilby, 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Michelle Lake, Nicki Lakeman, David Lalloo, Fiona Lalloo, Alison Lam, Fiona Lamb, Lucy Lamb, Thomas Lamb, Nick Lambe, Pauline Lambert, Claudia Lameirinhas, Mohammed KG Lami, Abigail Lamikanra, Holly Lamont, Michal Lamparski, Djillali Lamrani, Christine Lanaghan, Rebecca Lanaway, Ivone Lancona-Malcolm, Julia Lancut, Geraldine Landers, Martin J Landray, Matthew Lane, Nicholas Lane, Alidih Lang, Stephen Lang, Daniel Langer, Margaret Langley, Charles Langoya, Emily Langthorne, Taiya Large, Wojciech Lason, Anna Last, Scott Latham, John Latham-Mollart, Afzal Latheef, Darren Latimer, Nang Latt, Carly-Jane Lattimore, Dawn Lau, Eva Lau, Myra Laurenson, Hou Law, Jennifer Law, Jessica Law, Penny Law, Richard Law, Colin Lawler, Mark Lawley, Emma Lawrence, Jo Lawrence, Neil Lawrence, Ryan Lawrie, Jemima Lawson, Joanne Lawson, Louise Lawson, Rebecca Lawton, Michael Lay, Christine Laycock, Reina Layug, Maria Lazo, Vietland Le, Amelia Lea, William Lea, Ian Leadbitter, Thomas Leahy, Richard Lean, Lorna Leandro, Darren Leaning, Sandra Leason, Christina Leaver, Marie Anne Ledingham, Emma Lee, Hannah Lee, Irish Lee, Judith Lee, Sam Lee, Shi Han Lee, Simon Lee, Sindy Lee, Stephanie Lee, Tracey Lee, Xiang Lee, Diana Lees, Jennifer Lees, Helen Legge, Julian Leggett, Katie Leigh-Ellis, Kevan Leighton, Nicky Leitch, Eleni Lekoudis, Petula Lemessy, Nicholas Lemoine, Joana Lemos, Irina Lenchuk, Katy Leng, Katrina Lennon, Liz Lennon, Isabel Lentell, Kelly Leonard, Wen Leong, Nicky Leopold, Oskar Lepiarczyk, Isla Leslie, Eleni Lester, Joe Leung, Ullrich Leuschner, Emma Levell, Chris Levett, Alice Lewin, Michaela Lewin, Alison Lewis, David Lewis, Dee Lewis, Georgina Lewis, Gillian Lewis, Joanne Lewis, Joseph Lewis, Kathryn Lewis, Keir Lewis, Leon Lewis, Lisa Lewis, Marissa Lewis, Rob Lewis, Robert Lewis, Catherine Lewis-Clarke, Lorraine Lewis-Prosser, Katherine Lewiston, Adam Lewszuk, Penny Lewthwaite, Samantha Ley, Anna Li, Jenny Li, Angela Liao, Victoria Licence, David Lieberman, Susan 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Thushika Mahendiran, Siva Mahendran, Sarah Maher, Anistta Maheswaran, Shameera Maheswaran, Tina Maheswaran, Parisa Mahjoob-Afag, Ahmed Mahmood, Farhana Mahmood, Waheed Mahmood, Zahra Mahmood, Hager Mahmoud, Ewan Mahony, Luke Mair, Toluwani Majekdunmi, Kesson Majid, Rupert Major, Jaydip Majumdar, Mohammad KH Majumder, Stephen Makin, Marius Malanca, Hannah Malcolm, Flora Malein, Neeraj Malhan, Ayesha Malik, Gulshan Malik, Mohammed Maljk, Paul Mallett, Petrina Mallinder, Georgia Mallison, Louise Mallon, Edward Malone, Gracie Maloney, Edgar Malundas, Madhu Mamman, Irene Man, Kathy Man, Rossana Mancinelli, Marco Mancuso-Marcello, Tracy Manders, Lauren Manderson, Justin Mandeville, Roope Manhas, Carmen Maniero, Ravi Manikonda, Bobby Mann, Jonathan Manning, Lynne Mannion, Katherine Mansi, Katarina Manso, Dina Mansour, Isheunesu T Mapfunde, Predeesh Mappa, Hemant Maraj, Garikayi Marange, Lisa March, Clare Marchand, Neil Marcus, Maria Marecka, Gomathi Margabanthu, Jordi Margalef, Lavinia Margarit, Georgios Margaritopoulos, Mike Margarson, Fernandez M Maria del Rocio, Teresa Maria Pfyl, Victor Mariano, Helen Maria-Osborn, Ashleigh Maric, Grace Markham, John Markham, Maria Marks, Pamela Marks, Elisabeth Marouzet, Arran Marriott, Cheryl Marriott, Nemonie Marriott, Brian Marsden, Karen Marsden, Paul Marsden, Sarah Marsden, Tracy Marsden, Robyn Marsh, Adam Marshall, Andrew Marshall, Gail Marshall, Henry Marshall, Jaimie Marshall, James Marshall, Jenna Marshall, Nicola Marshall, Riley Marshall, Jennifer Marshall, Samantha Marston, Emmeline Martin, Hayley Martin, Hope Martin, Jane Martin, Karen Martin, Kate Martin, Laila Martin, Michael Martin, Noelia Martin, Tim Martin, Winston Martin, Sarah Martin, Tim Martindale, Marcus Martineau, Alexander Martinez, Lauren Martinez, Jose Carlos Martinez Garrido, Juan Martin-Lazaro, Olivia Martins, Lucas Martins Ferreira, Vijay Kumar Maruthamuthu, Gemma Maryan, Roman Mary-Genetu, Sam Maryosh, Vidan Masani, Diego Maseda, Sheila Mashate, 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Raj, Pradeep Rajagopalan, Nithy Rajaiah, Arvind Rajasekaran, Aylur Rajasri, Thurkka Rajeswaran, Jyothi Rajeswary, Jeyanthy Rajkanna, Gayathri Rajmohan, Ruth Rallan, David Ralphs, Katherine Ralston, Maximilian Ralston, Matsa Ram, Balaji Ramabhadran, Fathima Ramali, Mohamed Ramali, Athimalaipet Ramanan, Shashikira Ramanna, Maheshi Ramasamy, Dhanishta Ramdin, Jozel Ramirez, Mylah Ramirez, Geshwin Ramnarain, Lidia Ramos, Shanthi Ramraj, Alex Ramshaw, Aleem Rana, Ghulam F Rana, Rehman Rana, Abby Rand, James Rand, Helen Randall, Harpal Randheva, Poonam Ranga, Manmeet Rangar, Harini Rangarajan, Sameer Ranjan, Poormina Ranka, Rajesh Rankhelawon, Haley Ranton, Anita Rao, Sandhya Rao, Sanjay Rao, Deepak Rao, Anuja Rasarathnam, Alia Rashid, Khalid Rashid, Simbisai Ratcliff, Sam Ratcliffe, Sophy Ratcliffe, Sanjeev Rath, Mohmad I Rather, Selina Rathore, Aravinden Ratnakumar, Jonathan Ratoff, Deepa Rattehalli, Jason Raw, Hywel Rawlins, Gautam Ray, Michelle Ray, Adam Raymond-White, Dana Raynard, 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Anand Shah, Bhavni Shah, Momin Shah, Neil Shah, Pallav Shah, Priyank Shah, Qasim Shah, Sarfaraz H Shah, Snehal Shah, Suraj Shah, Syed Shah, Wajid Shah, Saarma Shahad, Sousan Shahi, Sipan Shahnazari, Muhammad Shahzeb, Aisha Shaibu, Zara Shaida, Amina Y Shaikh, Maliha Shaikh, Rajit Shail, Mariya Shaji, Muhammad Shakeel, Korah Shalan, Nadia Shamim, Kazi Shams, Alison Shanahan, Thomas Shanahan, Shaminie Shanmugaranjan, Hamed Sharaf, Muhammad Sharafat, Asir Sharif, Ajay Sharma, Akhilesh Sharma, Ash Sharma, Bhawna Sharma, Mona Sharma, Ojasvi Sharma, Poonam Sharma, Rajeev Sharma, Sanjeev Sharma, Sarkhara Sharma, Shriv Sharma, Sonal Sharma, Alexander Sharp, Charles Sharp, Gemma Sharp, Paula Sharratt, Phoebe Sharratt, Katherine Sharrocks, Emma Sharrod, Christopher Shaw, Daisy Shaw, David Shaw, Deborah Shaw, Joanne Shaw, Jonathan Shaw, Lisa Shaw, Tomos G Shaw, Anna Shawcross, Jill Shawe, Lou Shayler, Khuram Shazad, Sophy Shedwell, Jonathan Sheffield, Zak Shehata, Arshiya Sheik, Asif Sheikh, 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Harriet Smith, Hazel Smith, Helen Smith, Jacky Smith, Janice Smith, Jessica Smith, Juliet Smith, Karen Smith, Kate Smith, Kathryn Smith, Katie Smith, Kelly Smith, Kerry Smith, Lara Smith, Linda Smith, Lisa Smith, Loren Smith, Maria Smith, Mel Smith, Oliver Smith, Rachel Smith, Rebecca Smith, Richard Smith, Ruth Smith, Sally Smith, Samantha Smith, Stacey Smith, Stephanie Smith, Susan Smith, Imogen Smith, John Smith, Gemma Smithson, Sue Smolen, Sara Smuts, Naoise Smyth, Annette Snell, David Snell, Luke Snell, Beng So, Michelle Soan, Toluleyi Sobande, Alberto Sobrino Diaz, Basit Sohail, Bina Sohail, Herminder Sohal, Roy Soiza, Mary Sokolowski, Olajumoke Solademi, Krishma Solanki, Babak Soleimani, Amanda Solesbury, Reanne Solly, Louise Solomon, Subash Somalanka, Chandrashekaraiah Somashekar, Raj Sonia, Shiu-Ching Soo, Deepti Sood, Pavandeep Soor, Germanda Soothill, Jennifer Soren, Youssef Sorour, Apina Sothinathan, Pragalathan Sothirajah, Najwa Soussi, Donna Southam, David Southern, Iain 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Holt Walters, James Walters, Jocelyn Walters, Eileen Walton, Lucy Walton, Olivia Walton, Sharon Walton, Susan Walton, Mandy Wan, Thin Wan, Mary Wands, Rachel Wane, Frank Wang, Nick Wang, Ran Wang, Deborah Warbrick, Samantha Warburton, Deborah Ward, Emma Ward, Joanna Ward, Karen Ward, Luke Ward, Nicola Ward, Rachael Ward, Rebecca Ward, Thomas Ward, Tom Ward, Scott A Warden, Adele Wardle, Karen Wardle, Steve Wardle, Hassan Wardy, Scott Waring, Jenny Warmington, Ben Warner, Christian Warner, Lewis Warnock, Sarah Warran, Jade Warren, Lisa Warren, Yolanda Warren, Hannah Warren-Miell, Gill Warwick, Charlotte Washington, Helen Wassall, Hazel J Watchorn, Holly Waterfall, Abby Waters, Donald Waters, Mark Waterstone, Catherine Watkins, Catrin Watkins, Eleanor Watkins, Karen Watkins, Lynn Watkins, Nick Watkins, Abigail Watson, Adam JR Watson, Ekaterina Watson, Eleanor Watson, Paul Watson, Rebecca Watson, Robert Watson, Sandra Watson, Malcolm Watters, Donna Watterson, Daniel Watts, John Watts, Merlin Watts, Victoria Waugh, Emma Wayman, Akhlaq Wazir, Mark Weatherhead, Nick Weatherly, Paul Weaver, Hayley Webb, Kathryn Webb, Kylie Webb, Stephen Webb, Cheryl Websdale, Deborah Webster, Ian Webster, Tim Webster, Kathleen Wedgeworth, Ling Wee, Rebecca Weerakoon, Thanuja Weerasinghe, Janaka Weeratunga, Maria Weetman, Shuying Wei, Immo Weichert, Hugh Welch, James Welch, Leanne Welch, Steven Welch, Samantha Weller, Lucy Wellings, Brian Wells, Susan Wellstead, Berni Welsh, Richard Welsh, Ingeborg Welters, Rachael Welton, Lauren Wentworth, Kate Wesseldine, James Wesson, Jim Wesson, Adam West, Magdelena West, Raha West, Ruth West, Sophie West, Luke Western, Ruth Westhead, Heather Weston, Alice Westwood, Bill Wetherill, Sharon Wheaver, Helen Wheeler, Ben Whelan, Matthew Whelband, Amanda Whileman, Alison Whitcher, Abbie White, Andrew White, Benjamin White, Christopher White, Duncan White, Emily White, James White, Jonathan White, Katie White, Marie White, Nick White, Sarah White, Sonia White, Stephen White, Tracey White, Catherine Whitehead, Anne Whitehouse, Claire Whitehouse, Tony Whitehouse, Julia Whiteley, Sophie Whiteley, Victoria Whiteside, Drew Whitley, Kaitlyn Whitley, Gabriel Whitlingum, David Whitmore, Elizabeth Whittaker, Lindsay Whittam, Andrew Whittingham Hirst, Ashley Whittington, Helen Whittle, Robert Whittle, Suzanne Whyte, Eunice Wiafe, Lou Wiblin, John Widdrington, Jason Wieboldt, Hannah Wieringa, Cornelia Wiesender, Laura Wiffen, Andrew Wight, Christopher Wignall, Danielle Wilcock, Emma Wilcock, Louise Wilcox, Laura Wild, Stephen Wild, Michael Wilde, Peter Wilding, Ritchie Wildman, Tracey Wildsmith, Joe Wileman, Donna Wiles, Joy Wiles, Kate Wiles, Elva Wilhelmsen, Thomas Wiliams, Chloe Wilkes, Janet Wilkie, David Wilkin, Hannah Wilkins, Joy Wilkins, Suzanne Wilkins, Helen Wilkinson, Holly Wilkinson, Iain Wilkinson, Lesley Wilkinson, Martin Wilkinson, Nicola Wilkinson, Sophia Wilkinson, Susan Wilkinson, Tim Wilkinson, Sylvia Willetts, Aimee Williams, Alexandra Williams, Alison Williams, Angharad Williams, Ava Williams, Carl Williams, Caroline V Williams, Claire Williams, Dewi Williams, Gail Williams, Gemma Williams, Gina Williams, Hannah Williams, James Williams, Jayne Williams, Jennie Williams, John Williams, Joseph Williams, Karen Williams, Kathryn Williams, Marie Williams, Matthew Williams, Patricia Williams, Penny Williams, Rachael Williams, Rupert Williams, Samson Williams, Sarah Williams, Sophie Williams, Tamanna Williams, Annie Williamson, Cath Williamson, Catherine Williamson, Dawn Williamson, James D Williamson, Rachel Williamson, Helen Williamson, Bruce Willian, Elizabeth Willis, Emily Willis, Heather Willis, Herika Willis, Joanna Willis, Laura Willmott, Louise Wills, Lucy Willsher, Catherine Willshire, Francesca Willson, Alison Wilson, Andrea Wilson, Antoinette Wilson, Billy Wilson, Catherine Wilson, Eve Wilson, James Wilson, Karen Wilson, Kate Wilson, Lucinda Wilson, Mark Wilson, Matthew Wilson, Toni Wilson, Evie Wiltsher, Marlar Win, Tin Win, Wut Yee Win Win, Lucinda Winckworth, Laura Winder, Piers Winder, Phillip Windrum, Kerry Winham-Whyte, Helen Winmill, Simon Winn, Carmen Winpenny, Helen Winslow, Helen Winter, Jonathan Winter, Pascal Winter, Barbara Winter-Goodwin, Stephen Wisdom, Matthew Wise, Martin Wiselka, Rebecca Wiseman, Sophie Wiseman, Steven Wishart, Holly Wissett, Eric Witele, Nicholas Withers, Janet Wittes, Donna Wixted, Therese Wodehouse, Will Wolf, Nicola Wolff, Kirsten Wolffsohn, Rebecca Wolf-Roberts, Magda Wolna, Elena Wolodimeroff, Adam Wolstencroft, Alan Wong, Charlotte Wong, Chi-Hung Wong, Edwin Wong, Jessica Sue Yi Wong, Kit Y Wong, Lee Wong, Mei Yin Wong, Nick Wong, Sam Wong, Yun Man Wong, Amanda Wood, Caroline Wood, Carrie Anne Wood, Dianne Wood, Fiona Wood, Hannah Wood, Jennifer Wood, Joe Wood, Julia Wood, Kathryn Wood, Lisa Wood, Louise Wood, Michelle Wood, Stephen Wood, Tracy Wood, Ursula Wood, Katharine Woodall, Rebecca Woodfield, Christopher Woodford, Elizabeth Woodford, Jill Woodford, Luke Woodford, Louise Woodhead, Timothy Woodhead, Philip Woodland, Marc Woodman, Debra Woods, Jane Woods, Katherine Woods, Sarah Woods, Zoe Woodward, Rachel Wookey, Megan Woolcock, Gemma Wooldridge, Rebecca Woolf, Chris Woollard, Christopher Woollard, Louisa Woollen, Emma Woolley, Jade Woolley, Daniel Woosey, Dan Wootton, Joanne Wootton, Daniel Worley, Stephy Worton, Jonathan Wraight, Maria Wray, Tim Wreford-Bush, Joanne Wren, Kim Wren, Lynn Wren, Caroline Wrey Brown, Catherine Wright, Demi Wright, Francesca Wright, Imogen Wright, Lee Wright, Lianne Wright, Pete Wright, Rachel Wright, Rebecca Wright, Stephanie Wright, Tim Wright, Caroline Wroe, Hannah Wroe, Henry Wu, Peishan Wu, Pensee Wu, Jonathan Wubetu, Retno Wulandari, Craig Wyatt, Frederick Wyn-Griffiths, Inez Wynter, Bindhu Xavier, Arnold Xhikola, Zhongyang Xia, Huiyuan Xiao, Masseh Yakubi, May Yan, Freda Yang, Yingjia Yang, Michael Yanney, Woei Lin Yap, Nabil Yaqoob, Naairah Yaqub, Salima Yasmin, Bryan Yates, David Yates, Edward Yates, Helen Yates, Julie Yates, Mark Yates, Charlotte Yearwood Martin, Andrew Yeatman, Khin Yein, Fiona Yelnoorkar, Peter Yew, Kawai Yip, Laura Ylquimiche, Laura Ylquimiche Melly, Inez Ynter, H Yong, Jemma Yorke, Jasmine Youens, Abdel Younes Ibrahim, Eoin Young, Gail Young, Louise Young, Richard Young, Asfand Yousafzar, Sajeda Youssouf, Ahmed Yousuf, Chrissie Yu, Bernard Yung, Daniel Yusef, Said Yusef, Intekhab Yusuf, Anna-Sophia Zafar, Silvia Zagalo, Su Zaher, Aqsa Zahoor, Kareem Zaki, Nabhan Zakir, Kasia Zalewska, Ane Zamalloa, Mohsin Zaman, Raisa Zaman, Shakir Zaman, Julie Zamikula, Louise Zammit, Marie Zammit-Mangion, Lynn Zarb, Esther Zebracki, Daniel Zehnder, Lisa Zeidan, Marian Zelman, Xiaobei Zhao, Dongling Zheng, Doreen Zhu, Madiha Zia, Omar Zibdeh, Rabia Zill-E-Huma, Ei Thankt Zin, Veronica Zindonda, Eleanor Zinkin, Vivian Zinyemba, Christos Zipitis, Arkadiusz Zmierczak, Azam Zubir, Roslin Zuha, Naz Zuhra, Rasha Zulaikha, Sabrina Zulfikar, Carol Zullo, Ana Zuriaga-Alvaro, Will Zuurbier, Sheba Zyengi, and University of St Andrews. School of Medicine

Subjects
Male, Convalescent plasma, medicine.medical_treatment, 030204 cardiovascular system & hematology, Antibodies, Viral, Rate ratio, 0302 clinical medicine, RA0421, RA0421 Public health. Hygiene. Preventive Medicine, Medicine, Hospital Mortality, 030212 general & internal medicine, 11 Medical and Health Sciences, Aged, 80 and over, Medicine(all), Mortality rate, Covid19, Articles, 3rd-DAS, General Medicine, Middle Aged, Hospitals, Treatment Outcome, Female, Open label, Coronavirus Infections, Life Sciences & Biomedicine, RM, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), 03 medical and health sciences, Medicine, General & Internal, SDG 3 - Good Health and Well-being, General & Internal Medicine, Internal medicine, Humans, In patient, Pandemics, COVID-19 Serotherapy, Aged, Mechanical ventilation, Science & Technology, SARS-CoV-2, business.industry, Significant difference, Immunization, Passive, COVID-19, Length of Stay, NIS, R1, Respiration, Artificial, United Kingdom, RM Therapeutics. Pharmacology, RECOVERY Collaborative Group, business
Abstract

Background: Many patients with COVID-19 have been treated with plasma containing anti-SARS-CoV-2 antibodies. We aimed to evaluate the safety and efficacy of convalescent plasma therapy in patients admitted to hospital with COVID-19. Methods: This randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) is assessing several possible treatments in patients hospitalised with COVID-19 in the UK. The trial is underway at 177 NHS hospitals from across the UK. Eligible and consenting patients were randomly assigned (1:1) to receive either usual care alone (usual care group) or usual care plus high-titre convalescent plasma (convalescent plasma group). The primary outcome was 28-day mortality, analysed on an intention-to-treat basis. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings: Between May 28, 2020, and Jan 15, 2021, 11558 (71%) of 16287 patients enrolled in RECOVERY were eligible to receive convalescent plasma and were assigned to either the convalescent plasma group or the usual care group. There was no significant difference in 28-day mortality between the two groups: 1399 (24%) of 5795 patients in the convalescent plasma group and 1408 (24%) of 5763 patients in the usual care group died within 28 days (rate ratio 1·00, 95% CI 0·93–1·07; p=0·95). The 28-day mortality rate ratio was similar in all prespecified subgroups of patients, including in those patients without detectable SARS-CoV-2 antibodies at randomisation. Allocation to convalescent plasma had no significant effect on the proportion of patients discharged from hospital within 28 days (3832 [66%] patients in the convalescent plasma group vs 3822 [66%] patients in the usual care group; rate ratio 0·99, 95% CI 0·94–1·03; p=0·57). Among those not on invasive mechanical ventilation at randomisation, there was no significant difference in the proportion of patients meeting the composite endpoint of progression to invasive mechanical ventilation or death (1568 [29%] of 5493 patients in the convalescent plasma group vs 1568 [29%] of 5448 patients in the usual care group; rate ratio 0·99, 95% CI 0·93–1·05; p=0·79). Interpretation: In patients hospitalised with COVID-19, high-titre convalescent plasma did not improve survival or other prespecified clinical outcomes. Funding: UK Research and Innovation (Medical Research Council) and National Institute of Health Research.

Published
2021
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49. Glucose variability predicts 6-month mortality in patients hospitalized with acute heart failure

Author

Patrícia Lourenço, Paulo Bettencourt, Catarina Cidade-Rodrigues, Filipe M Cunha, Diana Oliveira, and Catarina Elias

Subjects
Blood Glucose, Male, medicine.medical_specialty, medicine.medical_treatment, Disease, 030204 cardiovascular system & hematology, 03 medical and health sciences, 0302 clinical medicine, Predictive Value of Tests, Diabetes mellitus, Internal medicine, Internal Medicine, medicine, Humans, In patient, Registries, 030212 general & internal medicine, Aged, Retrospective Studies, Aged, 80 and over, Heart Failure, business.industry, Insulin, Confounding, Mean age, Prognosis, medicine.disease, Hospitalization, Oxidative Stress, Logistic Models, Diabetes Mellitus, Type 2, Heart failure, Emergency Medicine, Cardiology, Female, business, Ischemic heart
Abstract

In diabetes mellitus (DM), glycaemic fluctuations associate with higher oxidative stress than sustained chronic hyperglycaemia and glucose variability increases the risk of chronic diabetic complications. Our hypothesis was that higher glucose variability would associate with mortality after an acute heart failure (HF) episode. We retrospectively analysed patients with DM hospitalized with acute HF between 2009 and 2010. Patients with 2 point-of-care glucose values/day were excluded. Glucose coefficient of variation (GCV) was defined as (glucose standard deviation/mean glucose) × 100. Patients were categorized according GCV ≤ 30.0 and 30.0%. Follow-up: 6-months. Endpoint: all-cause mortality. A Cox-regression analysis was used to study the association of glucose variability with 6-month mortality. We studied 214 diabetic patients with acute HF, 49.1% male, mean age 76 years. Mean glycaemia during hospitalization was 187 ± 50 mg/dL, hypoglycaemia ( 70 mg/dL) was reported in 21 patients and mean GCV was 28.3 ± 7.6%. Patients with GCV 30.0% had higher mean glycaemia, more hypoglycaemic episodes and higher HbA1c; they were also more often treated with insulin. Patients were similar concerning age, gender, comorbidities, left ventricular systolic dysfunction and ischemic heart disease. During the 6-month follow-up, 38 (17.8%) patients died. Patients with GCV 30.0% had a HR of 6-month mortality of 2.21 (95% CI: 1.16-4.21), p = 0.02. This association with more than twofold higher short-term mortality was independent of main confounders. Elevated glycaemic variability in acute HF admissions of patients with DM predicts short-term mortality. Patients with GCV 30.0% have an independent more than twofold higher risk of 6-month death after an acute HF hospitalization.

Published
2021
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50. Identification of multiple system atrophy mimicking Parkinson’s disease or progressive supranuclear palsy

Author

Yasuo Miki, Janice L. Holton, Zane Jaunmuktane, Adam Kenji Yamamoto, Helen Ling, Sandrine C. Foti, Conceição Bettencourt, Koichi Wakabayashi, Kate Strand, Marcos C B Oliveira, Eduardo De Pablo-Fernandez, Andrew J. Lees, Niall Quinn, Thomas T. Warner, Yasmine T. Asi, and Eiki Tsushima

Subjects
Male, Levodopa, Pediatrics, medicine.medical_specialty, Parkinson's disease, Disease, Apraxia, Progressive supranuclear palsy, Diagnosis, Differential, Orthostatic vital signs, Atrophy, medicine, Humans, Aged, Palsy, business.industry, Parkinson Disease, Middle Aged, Multiple System Atrophy, medicine.disease, eye diseases, nervous system diseases, Female, Supranuclear Palsy, Progressive, Neurology (clinical), business, medicine.drug
Abstract

We studied a subset of patients with autopsy-confirmed multiple system atrophy who presented a clinical picture that closely resembled either Parkinson’s disease or progressive supranuclear palsy. These mimics are not captured by the current diagnostic criteria for multiple system atrophy. Among 218 autopsy-proven multiple system atrophy cases reviewed, 177 (81.2%) were clinically diagnosed and pathologically confirmed as multiple system atrophy (i.e. typical cases), while the remaining 41 (18.8%) had received an alternative clinical diagnosis, including Parkinson’s disease (i.e. Parkinson’s disease mimics; n = 16) and progressive supranuclear palsy (i.e. progressive supranuclear palsy mimics; n = 17). We also reviewed the clinical records of another 105 patients with pathologically confirmed Parkinson’s disease or progressive supranuclear palsy, who had received a correct final clinical diagnosis (i.e. Parkinson’s disease, n = 35; progressive supranuclear palsy-Richardson syndrome, n = 35; and progressive supranuclear palsy-parkinsonism, n = 35). We investigated 12 red flag features that would support a diagnosis of multiple system atrophy according to the current diagnostic criteria. Compared with typical multiple system atrophy, Parkinson’s disease mimics more frequently had a good levodopa response and visual hallucinations. Vertical gaze palsy and apraxia of eyelid opening were more commonly observed in progressive supranuclear palsy mimics. Multiple logistic regression analysis revealed an increased likelihood of having multiple system atrophy [Parkinson’s disease mimic versus typical Parkinson’s disease, odds ratio (OR): 8.1; progressive supranuclear palsy mimic versus typical progressive supranuclear palsy, OR: 2.3] if a patient developed any one of seven selected red flag features in the first 10 years of disease. Severe autonomic dysfunction (orthostatic hypotension and/or urinary incontinence with the need for a urinary catheter) was more frequent in clinically atypical multiple system atrophy than other parkinsonian disorders (Parkinson’s disease mimic versus typical Parkinson’s disease, OR: 4.1; progressive supranuclear palsy mimic versus typical progressive supranuclear palsy, OR: 8.8). The atypical multiple system atrophy cases more frequently had autonomic dysfunction within 3 years of symptom onset than the pathologically confirmed patients with Parkinson’s disease or progressive supranuclear palsy (Parkinson’s disease mimic versus typical Parkinson’s disease, OR: 4.7; progressive supranuclear palsy mimic versus typical progressive supranuclear palsy, OR: 2.7). Using all included clinical features and 21 early clinical features within 3 years of symptom onset, we developed decision tree algorithms with combinations of clinical pointers to differentiate clinically atypical cases of multiple system atrophy from Parkinson’s disease or progressive supranuclear palsy.

Published
2021
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