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BackgroundOne challenge for primary care providers caring for patients with nonalcoholic fatty liver disease is to identify those at the highest risk for clinically significant liver disease.AimTo derive a risk stratification tool using variables from structured electronic health record (EHR) data for use in populations which are disproportionately affected with obesity and diabetes.MethodsWe used data from 344 participants who underwent Fibroscan examination to measure liver fat and liver stiffness measurement [LSM]. Using two approaches, multivariable logistic regression and random forest classification, we assessed risk factors for any hepatic fibrosis (LSM > 7 kPa) and significant hepatic fibrosis (> 8 kPa). Possible predictors included data from the EHR for age, gender, diabetes, hypertension, FIB-4, body mass index (BMI), LDL, HDL, and triglycerides.ResultsOf 344 patients (56.4% women), 34 had any hepatic fibrosis, and 15 significant hepatic fibrosis. Three variables (BMI, FIB-4, diabetes) were identified from both approaches. When we used variable cut-offs defined by Youden's index, the final model predicting any hepatic fibrosis had an AUC of 0.75 (95% CI 0.67-0.84), NPV of 91.5% and PPV of 40.0%. The final model with variable categories based on standard clinical thresholds (i.e., BMI ≥ 30 kg/m2; FIB-4 ≥ 1.45) had lower discriminatory ability (AUC 0.65), but higher PPV (50.0%) and similar NPV (91.3%). We observed similar findings for predicting significant hepatic fibrosis.ConclusionsOur results demonstrate that standard thresholds for clinical risk factors/biomarkers may need to be modified for greater discriminatory ability among populations with high prevalence of obesity and diabetes.

Background & aimsA recent panel of international experts proposed the disease acronym metabolic (dysfunction)-associated fatty liver disease (MAFLD) in lieu of nonalcoholic fatty liver disease (NAFLD). We aimed to estimate the burden of and risk factors for NAFLD and MAFLD, and to examine the concordance between definitions in a Veterans population.MethodsWe conducted a cross-sectional study among randomly selected patients within primary care at the Houston Veterans Affairs (VA) facility. Participants completed a survey, provided blood, and underwent Fibroscan. In the absence of heavy alcohol, hepatitis C virus and hepatitis B virus, a controlled attenuation parameter median ≥290 dB/m was used to define NAFLD, whereas MAFLD was defined as controlled attenuation parameter median ≥290 dB/m and either body mass index ≥25 kg/m2 or diabetes, or 2 or more of the following: hypertension, high triglycerides, low high-density lipoprotein cholesterol, and high low-density lipoprotein cholesterol.ResultsThe mean age of participants was 50.9 years, 55.4% were women, 42.8% were white, and 43.8% were Black. The prevalence of NAFLD was 40.6% (82/202). All 82 patients with NAFLD had a body mass index ≥25 kg/m2, and therefore met our criteria for MAFLD (ie, 100% concordance). Compared with patients with no metabolic trait, patients with ≥3 traits had a 48-fold (adjusted odds ratio, 47.6; 95% confidence interval, 11.3-200) higher risk of NAFLD/MAFLD. Overall, 19 participants (9.4% of the total, 15.9% of those with NAFLD) had at least moderate fibrosis.ConclusionsNAFLD was present in 40% of Veterans registered in primary care; 9.4% of veterans had at least moderate hepatic fibrosis, with most having concurrent NAFLD. There was perfect concordance between NAFLD and the alternative MAFLD definition.

BackgroundDiet is associated with metabolic (dysfunction)-associated fatty liver disease (MAFLD), but the dietary composition associated with MAFLD risk has not been well-examined.AimThe purpose of this study was to assess the association of two healthy eating indices with the presence and severity of MAFLD in a sample of Veterans in a primary care setting.MethodsThis was a single center cross-sectional study using a random stratified sample of Veterans enrolled in primary care. Participants underwent a Fibroscan and completed an interviewer-administered Diet History Questionnaire II from which we calculated the Healthy Eating Index-2015 and Alternate Mediterranean Diet Score. We used multivariable logistic regression models to assess associations of dietary quality with MAFLD.ResultsWe analyzed data from 187 participants, 53.5% of whom were female. On average, participants were 50.2 years of age (SD, 12.3 years) with an average BMI of 31.7 kg/m2. MAFLD was detected in 78 (42%) and at least moderate fibrosis in 12 (6%) participants. We found that the Alternate Mediterranean Diet Score was inversely associated with MAFLD (adjusted OR = 0.85, 95%CI 0.72-1.00), but controlling for BMI and total energy intake attenuated the association (adjusted OR = 0.92, 95%CI 0.74-1.15). We found no statistically significant associations between the Healthy Eating Index-2015 and MAFLD or advanced fibrosis.DiscussionWe found that the Alternate Mediterranean Diet Score was significantly associated with lower MAFLD risk in Veterans; however, the association was mediated by BMI and total energy intake. A Mediterranean-style diet could potentially help reduce the risk of MAFLD, particularly if it helps control total energy intake and weight.

Eosinophilic esophagitis (EoE) is a chronic inflammatory condition of the esophagus. Many new studies have been reported recently that describe EoE management. An expert panel was convened by the American Gastroenterological Association Institute and the Joint Task Force on Allergy-Immunology Practice Parameters to provide a technical review to be used as the basis for an updated clinical guideline. This technical review was developed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework. Eighteen focused EoE management questions were considered, with 15 answered using the GRADE framework and 3 with a narrative summary. There is moderate certainty in the evidence that topical glucocorticosteroids effectively reduce esophageal eosinophil counts to

Background: Early detection of hepatocellular carcinoma (HCC) is crucial for improving patient outcomes, but we lack robust clinical biomarkers. This study aimed to identify a metabolite and/or lipid panel for early HCC detection. Methods: We developed a high-resolution liquid chromatography mass spectrometry (LC-MS)-based profiling platform and evaluated differences in the global metabolome and lipidome between 28 pre-diagnostic serum samples from patients with cirrhosis who subsequently developed HCC (cases) and 30 samples from patients with cirrhosis and no HCC (controls). We linked differentially expressed metabolites and lipids to their associated genes, proteins, and transcriptomic signatures in publicly available datasets. We used machine learning models to identify a minimal panel to distinguish between cases and controls. Results: Among cases compared with controls, 124 metabolites and 246 lipids were upregulated, while 208 metabolites and 73 lipids were downregulated. The top upregulated metabolites were glycoursodeoxycholic acid, 5-methyltetrahydrofolic acid, octanoyl-coenzyme A, and glycocholic acid. Elevated lipids comprised glycerol lipids, cardiolipin, and phosphatidylethanolamine, whereas suppressed lipids included oxidized phosphatidylcholine and lysophospholipids. There was an overlap between differentially expressed metabolites and lipids and previously published transcriptomic signatures, illustrating an association with liver disease severity. A panel of 12 metabolites that distinguished between cases and controls with an area under the receiver operating curve of 0.98 for the support vector machine (interquartile range, 0.9– 1). Conclusion: Using prediagnostic serum samples, we identified a promising metabolites panel that accurately identifies patients with cirrhosis who progressed to HCC. Further validation of this panel is required. [ABSTRACT FROM AUTHOR]

BackgroundObservational studies suggest greater height is associated with increased ovarian cancer risk, but cannot exclude bias and/or confounding as explanations for this. Mendelian randomisation (MR) can provide evidence which may be less prone to bias.MethodsWe pooled data from 39 Ovarian Cancer Association Consortium studies (16,395 cases; 23,003 controls). We applied two-stage predictor-substitution MR, using a weighted genetic risk score combining 609 single-nucleotide polymorphisms. Study-specific odds ratios (OR) and 95% confidence intervals (CI) for the association between genetically predicted height and risk were pooled using random-effects meta-analysis.ResultsGreater genetically predicted height was associated with increased ovarian cancer risk overall (pooled-OR (pOR) = 1.06; 95% CI: 1.01-1.11 per 5 cm increase in height), and separately for invasive (pOR = 1.06; 95% CI: 1.01-1.11) and borderline (pOR = 1.15; 95% CI: 1.02-1.29) tumours.ConclusionsWomen with a genetic propensity to being taller have increased risk of ovarian cancer. This suggests genes influencing height are involved in pathways promoting ovarian carcinogenesis.