Search

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

Background: Cancer-related fatigue (CRF) occurs in nearly all patients with metastatic breast cancer (MBC)., Objectives: This real-world analysis aimed to describe the prevalence and importance of fatigue in patients with MBC within 3 months of treatment with single-agent taxane-based chemotherapy during the timeframe of 2020-2022 in the United States and Europe. It was also conducted to assess whether there was a difference in relapsed patients compared to patients diagnosed de novo ., Design: Electronic health records were analyzed from approximately 150 million patients to identify patients with MBC who underwent taxane treatment., Results: In 2021, 50,490 patients had MBC, of whom 16,170 were diagnosed de novo and 34,330 experienced relapse. The proportion of patients undergoing taxane-based chemotherapy was 7.5% ( n  = 1220) and 13.4% ( n  = 4590), respectively, and the prevalence of any fatigue and CRF was similar between the groups (24.6% versus 25.7% and 6.6% versus 5.4%, respectively)., Conclusion: At least one in four patients with MBC undergoing taxane-based treatment will experience fatigue. This highlights the importance of validating screening tools to identify CRF, which is necessary to advance clinical trials aimed at investigating treatment strategies to improve patient-centered outcomes for fatigue., Competing Interests: AF is a consultant for Dompé Farmaceutici S.p.A, participant on advisory boards for Roche, Novartis, Lilly, Pfizer, MSD, Pierre Fabre, Eisai, Epionpharma, Gilead, Seagen, Astra Zeneca, and Exact Science. EMG, RW, MCS, EB, MD, PAR, and MA are employees of Dompé Farmaceutici S.p.A. The other authors have no conflicts of interest to declare., (© The Author(s), 2024.)

The PERSEIDS study aimed to estimate incidence/prevalence of interstitial lung diseases (ILDs), fibrosing interstitial lung diseases (F-ILDs), idiopathic pulmonary fibrosis (IPF), systemic sclerosis-associated ILD (SSc-ILD), other non-IPF F-ILDs and their progressive-fibrosing (PF) forms in six European countries, as current data are scarce. This retrospective, two-phase study used aggregate data (2014-2018). In Phase 1, incident/prevalent cases of ILDs above were identified from clinical databases through an algorithm based on codes/keywords, and incidence/prevalence was estimated. For non-IPF F-ILDs, the relative percentage of subtypes was also determined. In Phase 2, a subset of non-IPF F-ILD cases was manually reviewed to determine the percentage of PF behaviour and usual interstitial pneumonia-like (UIP-like) pattern. A weighted mean percentage of progression was calculated for each country and used to extrapolate incidence/prevalence of progressive-fibrosing ILDs (PF-ILDs). In 2018, incidence/10 5 person-years ranged between 9.4 and 83.6 (ILDs), 7.7 and 76.2 (F-ILDs), 0.4 and 10.3 (IPF), 6.6 and 71.7 (non-IPF F-ILDs), and 0.3 and 1.5 (SSc-ILD); and prevalence/10 5 persons ranged between 33.6 and 247.4 (ILDs), 26.7 and 236.8 (F-ILDs), 2.8 and 31.0 (IPF), 22.3 and 205.8 (non-IPF F-ILDs), and 1.4 and 10.1 (SSc-ILD). Among non-IPF F-ILDs, sarcoidosis was the most frequent subtype. PF behaviour and UIP-like pattern were present in a third of non-IPF F-ILD cases each and hypersensitivity pneumonitis showed the highest percentage of progressive behaviour. Incidence of PF-ILDs ranged between 2.1 and 14.5/10 5 person-years, and prevalence between 6.9 and 78.0/10 5 persons. To our knowledge, PERSEIDS is the first study assessing incidence, prevalence and rate of progression of ILDs across several European countries. Still below the threshold for orphan diseases, the estimates obtained were higher and more variable than reported in previous studies, but differences in study design/population must be considered., Competing Interests: Conflict of interest: O. Hilberg has nothing to disclose. Conflict of interest: A-M. Hoffmann-Vold had a consultancy and medical writing relationship with Boehringer Ingelheim; received unrestricted grants from Bayer and Boehringer Ingelheim; received consulting fees from Actelion, Boehringer Ingelheim, ARXX and Medscape; received honoraria for lectures and presentations from Actelion, Boehringer Ingelheim, Roche, Merck Sharp & Dohme, Lilly and Medscape; received support for attending meetings and/or travel from Actelion, Boehringer Ingelheim, Roche and Medscape; and was board member of EUSTAR and Nordic PH group. Conflict of interest: V. Smith received grants to research support, as senior clinical investigator from Research Foundation Flanders and Boehringer Ingelheim, research grant from Belgian Fund for Scientific Research in Rheumatic Diseases and educational grant from Janssen-Cilag; received consultancy fees from Boehringer Ingelheim; received honoraria for lectures, presentations, and speaker fees from Accord Healthcare, UCB, Boehringer Ingelheim and Janssen-Cilag; received support for attending meetings and/or travel from Celgene and Boehringer Ingelheim; and was chair (unpaid) to EULAR Study group on Microcirculation in Rheumatic Diseases, co-chair (unpaid) to ACR Study Group on Microcirculation and SCTC working group, and steering committee member (unpaid) to ERN-ReCONNET. Conflict of interest: D. Bouros received consulting fees from Boehringer Ingelheim; received honoraria from Boehringer Ingelheim, Roche and AstraZeneca; received support for attending meetings and/or travel from Boehringer Ingelheim and Roche; and received other financial or nonfinancial interests from Chiesi and ELPEN. Conflict of interest: M. Kilpelainen has nothing to disclose. Conflict of interest: J. Guiot has nothing to disclose. Conflict of interest: A. Morais has nothing to disclose. Conflict of interest: S. Clemente received payment for lectures and presentations and manuscript writing from Boehringer Ingelheim; and was ad hoc expert member of EMEA (January 2020). Conflict of interest: Z. Daniil has nothing to disclose. Conflict of interest: D. Papakosta has nothing to disclose. Conflict of interest: H. Fretheim has nothing to disclose. Conflict of interest: S. Neves has nothing to disclose. Conflict of interest: T.M. Alfaro received consulting fees from Boehringer Ingelheim and Roche; received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Boehringer Ingelheim and Roche; received support for attending meetings and/or travel from Boehringer Ingelheim and Roche; and participated on a Data Safety Monitoring Board or Advisory Board of Boehringer Ingelheim and Roche. Conflict of interest: K.M. Antoniou received consulting fees from Boehringer Ingelheim and Roche; received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Boehringer Ingelheim and Roche; received support for attending meetings and/or travel from Boehringer Ingelheim and Roche; and had leadership or fiduciary role in ERS Assembly 12 Secretary (unpaid). Conflict of interest: N. Valveny received funding to TFS for study conduction and medical writing from Boehringer Ingelheim and is employee of TFS. Conflict of interest: G. Asijee is employee of Boehringer Ingelheim. Conflict of interest: S. Soulard is employee of Boehringer Ingelheim. Conflict of interest: W. Wuyts received grants from Boehringer Ingelheim, Roche and Galapagos., (Copyright ©The authors 2022.)

Objective: To characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients., Design and Setting: This is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020., Participants: Two non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days., Outcomes: Demographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported., Results: We identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension., Conclusions: COVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension., Competing Interests: Competing interests: SLD reports grants from Anolinx. MAS reports grants from US National Institutes of Health, grants from Department of Veterans Affairs, during the conduct of the study; grants from IQVIA, personal fees from Janssen Research and Development, grants from US Food and Drug Administration, personal fees from Private Health Management, outside the submitted work. GH reports grants from NIH, during the conduct of the study; and grants from Janssen Research, outside the submitted work. FN reports being an employee of AstraZeneca until 2019 and holds some AstraZeneca shares, outside the submitted work. KK reports personal fees from the National Institutes of Health, outside the submitted work, and at the time of data analysis and initial drafting of the manuscript. KK was an employee of IQVIA. CRei reports he is an employee of IQVIA. GdM is an employee of IOMED. NV is an employee of TFS. AGS reports personal fees from Janssen R&D, outside the submitted work, and is a full-time employee of Janssen R&D and a Johnson and Johnson shareholder. CB reports personal fees from Janssen R&D, outside the submitted work, and is a full-time employee of Janssen R&D and a Johnson and Johnson shareholder. JDP reports grants from the National Library of Medicine, during the conduct of the study. AG is an employee of Regeneron Pharmaceuticals and reports stocks from Regeneron Pharmaceuticals. PRR reports having received research group grants from Innovative Medicine Initiative and Janssen Research and Development. PR reports being an employee of Janssen Research and Development and a shareholder of Johnson & Johnson. ER, SF-B, NS, LMS, DP, SCY, MR, AP-U, HA, KEL, MEM, AO, CA, CRey, TD-S, TMA, OA, W-U-RA, ILM, JMR, LSR, DD, LL and AP have nothing to declare. No other relationships or activities could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: To evaluate the association between serum total IgE levels and disease severity in adult patients with persistent allergic asthma and to explore the main predictors of IgE levels., Methods: We performed a multicenter, retrospective, observational study including adult patients diagnosed ≥ 1 year previously with persistent allergic asthma who were positive to ≥ 1 allergen. Patients also had serum total IgE and spirometry results available from the previous 12 months. Inclusion was stratified by asthma severity according to the GEMA 2009 criteria., Results: We included 383 patients with allergic asthma (129 mild, 82 moderate, and 172 severe). Mean (SD) age was 38 (15), 46 (16), and 45 (15) years, respectively (P < 0.001). Serum total IgE levels varied markedly (coefficient of variation, 147%). No association was observed with forced expiratory volume in 1 second (FEV1) or asthma severity: mean (SD)/median (IQR) of 403 (616)/214 (108-409), 361 (516)/204 (126-361), and 473 (676)/211 (98-545) IU/mL in the mild, moderate, and severe subgroups, respectively (P = .951). The severe subgroup had a higher percentage of patients with > 400 IU/mL (36% vs 26.4% [mild] and 18.3% [moderate], P = .010). In a multivariate multiple regression model, the independent predictors of higher IgE were younger age (P = .004), sensitization to ≥ 2 allergens (P = .009), male gender (P = .025), and family history of asthma (P = .122)., Conclusion: Serum total IgE levels in adult patients with persistent allergic asthma were high (two-thirds with levels > 150 IU/mL) and extremely variable. We did not find a significant association between serum total IgE levels and asthma severity or airflow limitation, except for a higher percentage of patients with IgE > 400 IU/mL in the severe subgroup.

Aims: We aimed to compare mean and between subject variability in haemoglobin (Hb) and erythropoiesis-stimulating agents (ESA) dose across the ESA compounds used to treat anaemia in dialysis patients., Methods: We performed a meta-analysis of randomized trials evaluating ESA in adult patients with chronic kidney disease on dialysis (target Hb 9-13.5 g dl(-1)), and compared mean Hb and its standard deviation (SD), and ESA dose and its coefficient of variation (CV) between the different agents [rHuEPO alfa or beta, darbepoetin alfa, pegylated-epoetin beta (PEG-EPO) or other epoetins]. The effect of route and frequency of administration, frequency of dose adjustments, study blinding and type, baseline value, Hb target and sampling frequency were also assessed., Results: Among 4983 patients from 16 studies, pooled Hb mean and SD during the evaluation phase were 11.5 g dl(-1) (95% CI 11.3, 11.7) and 0.99 g dl(-1) (0.88, 1.09), respectively. The Hb mean and SD were not significantly influenced by the covariates tested. Only Hb SD was significantly lower in maintenance studies relative to correction studies. No differences in mean ESA dose and CV were found across the covariates, except that PEG-EPO monthly dose was 42% higher than the every 2 weeks dose and the rHuEPO i.v. dose was 32% higher than the s.c. dose., Conclusions: Between subject variability in haemoglobin and ESA dose in dialysis patients is not associated with the type of ESA, nor with the dosing interval or route of administration, except for higher dose requirements in PEG-EPO monthly administration relative to every 2 weeks or rHuEPO i.v. relative to s.c., (© 2012 Amgen, Inc.. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.)

Aim: The purpose of this study was to determine the allelic and genotypic frequency distribution of the C34T mutation in the muscle isoform of the adenosine monophosphate deaminase 1 (AMPD1) gene and of the missense substitution K153R in the myostatin (GDF8) gene in one Spanish and two North African populations., Method: One sample of 98 individuals was genotyped from the South of Spain (Alpujarra) and two samples from Morocco (77 Berbers and 78 Arabs)., Results: The frequency of the AMPD1 C34T mutation was lower in Berbers (0.071) compared with the Alpujarra cohort (0.153, p = 0.018). The GDF8 K153R substitution showed little variability among the three cohorts., Conclusions: Studies with larger cohorts and other ethnic groups are needed to corroborate that there does not exist any major variability in the genotype distribution of genes associated with muscle phenotypes in the South-Eastern Mediterranean area.

Hypertension is a main risk factor for atherosclerosis through vascular wall hyperplasia. A recent study reported a new polymorphism (E65 K) in the beta(1) subunit (KCNMB1) gene of the Ca(2+)-dependent potassium channel with a protective effect against the severity of diastolic hypertension, but further data have lead to conflicting results. In order to ascertain the involvement of the E65 K variant in cardiovascular system regulation, the potential association between this mutation and ischaemic heart disease was assessed through a family-based association study (n=302 individuals). Transmission disequilibrium analysis failed to detect any association between this polymorphism and ischaemic heart disease. Although a minor effect cannot be discarded, sample analytical power and negative results do not support a major role for E65 K polymorphism in atherogenic pathologies.

Background: Although intravenous calcitriol is useful for decreasing intact parathyroid hormone (iPTH) blood levels in patients with secondary hyperparathyroidism (SHP) undergoing hemodialysis, approximately half these patients remain refractory to this treatment. The current study measures the diagnostic utility of double-phase technetium Tc 99m-sestamibi (MIBI) scintigraphy in predicting the response to calcitriol treatment., Methods: Sixty hemodialysis patients with SHP with iPTH blood levels between 240 and 600 pg/mL (ng/L) were selected. Initial intravenous calcitriol pulse therapy was 6 microg/wk (for iPTH levels of 400 to 600 pg/mL [ng/L]) or 3 microg/wk (for iPTH levels of 240 to 400 pg/mL [ng/L]). MIBI scintigraphy was performed before the onset of calcitriol therapy and repeated 1 year later. Patients were injected intravenously with 740 MBq of MIBI. Images were obtained at 15 minutes (thyroid phase) and 2 hours (parathyroid phase) after radiotracer administration. Focal areas of increased MIBI uptake were considered pathological parathyroid glands., Results: Forty-eight patients completed the study. After 1 year, iPTH levels had decreased significantly in 95.2% (47 patients), whereas good control (iPTH < 240 pg/mL [ng/L]) was reached in 70.8% (34 patients) and only 4 patients had iPTH levels greater than 400 pg/mL (ng/L; all were patients with 3 MIBI-positive areas at baseline determination). At baseline, there were 30 patients (62.5%) with MIBI positive areas (1, 2, or 3 areas), which decreased to 14 patients (29%) at the end of the study period. No patient showed 4 positive areas at any time. The 18 patients (37.5%) with no MIBI-positive area at baseline remained unchanged., Conclusion: MIBI scintigraphy is a reliable exploratory tool in predicting the response to treatment with intravenous calcitriol in hemodialysis patients with SHP.

A screening of 22 DNA polymorphisms has been performed in western Mediterranean populations (Iberian Peninsula, Morocco, and Central Mediterranean Islands). The analyzed markers correspond to polymorphic sites in several candidate genes for cardiovascular disease including apolipopoteins and their receptors (APOA1, APOB, APOE, APOC1, APOC2, LPA, and LDLR), genes implied in the hemostasis regulation (Factor VII, alpha and beta-fibrinogen, alpha and beta platelet-integrin, tissue plasminogen activator, and plasminogen activator inhibitor-1), and the angiotensin converting enzyme gene. The results are presented of a partial analysis carried out in following population samples: 6 from the Iberian Peninsula, 2 from Morocco, and 3 from Central Islands. The degree of inter-population diversity was significant and consistent with data from other kind of genetic polymorphisms. The apportionment of the allele frequency variance supported a geographic structure into three main regions: Central Mediterranean Islands, the Iberia Peninsula and North Africa. The genetic distance pattern is compatible with a south-to-north North African influence in the Iberian Peninsula and a remarkable gene flow from sub-Saharan Africa into Morocco. Epidemiologically, North Africa is characterized by high frequencies of LPA PNR alleles with high number of repeats (protective for cardiovascular risk) and high frequencies of the APOE*E4 allele (risk factor) as compared with European populations.

Through the nitric oxide (NO) production in the vascular system, the endothelial nitric oxide synthase (eNOS or NOS3) is a key enzyme in blood pressure regulation and atherosclerosis control. Several previous studies have suggested an important role of eNOS as a genetic risk factor for cardiovascular diseases. In this context, a genetic association study was carried out between two eNOS polymorphisms (the ecNOS4a/b VNTR and the G894T substitution) in a sample of 101 nuclear families having one affected offspring of ischemic heart disease (IHD). Transmission disequilibrium test (TDT) revealed partial associations between the VNTR marker and IHD in patients with a type A behavior pattern (TABP) (P = 0.0325, RR = 3.67) and for the haplotype formed by variant b of the VNTR and the T mutation of the G894T substitution in the IHD-affected subgroup having body mass index (BMI) lower than 25 (P = 0.0348, RR = 0.22). However, once multiple testing correction was applied, the associations became nonsignificant. A significant effect of the haplotype b-G increasing high-density lipoprotein cholesterol (HDL-C) plasma levels was detected (P = 0.021 after Bonferroni correction). From a population point of view, frequencies found for G894T substitution in Spain were significantly different from other populations., (Copyright 2002 Wiley-Liss, Inc.)

Objective: To neuropsychologically and genetically compare age-associated memory impairment (AAMI) and mild cognitive impairment (MCI) entities and to determine what proportion of AAMI diagnosed individuals could also receive a MCI diagnosis. To compare the distribution of a previously known genetic risk factor for Alzheimer's disease (apolipoprotein E common polymorphism) associated with these two conditions with a sample of the normal aging., Design: Neuropsychological and genetic assessments in AAMI and MCI individuals. Genetic assessment in AAMI, MCI, and control subjects., Setting: General health centers and geriatric homes from northeastern Spain (Catalunya)., Participants: One hundred and four subjects presenting subjective memory complaints were selected and the AAMI and MCI criteria were applied. One hundred and twenty-four healthy Spanish subjects age 50 and older were defined as controls., Measurements: Memory, language, and frontal lobe functions were assessed using standard neuropsychological tests. The apolipoprotein E (apo E) polymorphism was obtained by using polymerase chain reaction (PCR) and HhaI restriction endonuclease., Results: Sixty-seven percent of previously diagnosed AAMI individuals could also be identified as MCI subjects. These MCI cases differed from those only-AAMI individuals both in neuropsychological and genetic analyses, performing worse not only on memory but also on language and frontal lobe tests and presenting high and low prevalences of the apo E epsilon 3/epsilon 4 and epsilon 3/epsilon 3 genotypes, respectively. The general AAMI sample of 93 individuals also differed from controls in the apo E genotype and allele distributions but these differences were no longer present after subtracting the MCI cases (63 subjects). These findings reflect that the differences between the memory impaired sample and the control sample regarding the apo E polymorphism were mainly attributable to MCI individuals and not to those who received only a diagnosis of AAMI alone., Conclusions: Our findings suggest that among AAMI subjects, those who also fulfill the MCI criteria present a neuropsychological and genetic profile closer to that previously related to Alzheimer's disease than those individuals only eligible for a diagnosis of AAMI. However, our findings also suggest that using only the AAMI criteria still appears to select a population that differs genetically from the normal older population.

Age-associated memory impairment (AAMI) is a clinical entity which was originally described to define memory problems linked to normal aging. Apolipoprotein E and ACE genes have both been associated with cognitive impairment in aging and dementia. The purpose of this study was to investigate memory and executive functions in AAMI according to the genetic background. We found that subjects carrying the Apo E epsilon4 allele exhibit lower memory performance on tests of both declarative and procedural memory. We did not find differences on frontal lobe tests. These findings give further support to the hypothesis concerning a genetic susceptibility for cognitive impairment in aging.

Data on six protein polymorphisms (19 alleles) from the human population of Tenerife are presented and discussed along with other classical markers in relation to the origin of the Canarians. Genetic influences from three population groups were considered: the Iberians, and the Berbers and non-Berbers (Arabs) from north Africa. The systems examined show the Tenerife population lies within the limits of variation described for various Iberian groups, with a slight tendency towards the characteristics of north African populations. When blood groups, red cell enzymes and serum protein data were considered, the similarity of the Canary population to Iberians seems strengthened (70% estimated contribution of Iberian peninsula genes to the present-day Canarian pool), while some relation with north African groups is shown. Genetic distances between Canarians and Arabs and Canarians and Berbers are lower than those between the two north African groups, indicating a relative and comparable contribution of each to the present-day gene pool of the Canarian population. The Arab contribution could be attributable to the slaves who were introduced to these islands after the conquest in the 15th century, while the Berber contribution could be the remnants of the extinct aboriginal peoples of the islands (Guanches) or a more recent immigration due to slavery. Genetic data do not allow us to distinguish between these two possibilities.