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Fujiyoshi A, Kohsaka S, Hata J, Hara M, Kai H, Masuda D, Miyamatsu N, Nishio Y, Ogura M, Sata M, Sekiguchi K, Takeya Y, Tamura K, Wakatsuki A, Yoshida H, Fujioka Y, Fukazawa R, Hamada O, Higashiyama A, Kabayama M, Kanaoka K, Kawaguchi K, Kosaka S, Kunimura A, Miyazaki A, Nii M, Sawano M, Terauchi M, Yagi S, Akasaka T, Minamino T, Miura K, Node K. JCS 2023 Guideline on the Primary Prevention of Coronary Artery Disease. Circ J 2024; 88:763-842. [PMID: 38479862 DOI: 10.1253/circj.cj-23-0285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Affiliation(s)
| | - Shun Kohsaka
- Department of Cardiology, Keio University School of Medicine
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University
| | - Mitsuhiko Hara
- Department of Health and Nutrition, Wayo Women's University
| | - Hisashi Kai
- Department of Cardiology, Kurume Univeristy Medical Center
| | | | - Naomi Miyamatsu
- Department of Clinical Nursing, Shiga University of Medical Science
| | - Yoshihiko Nishio
- Department of Diabetes and Endocrine Medicine, Kagoshima University Graduate School of Medical and Dental Sciences
| | - Masatsune Ogura
- Department of General Medical Science, Chiba University School of Medicine
- Department of Metabolism and Endocrinology, Eastern Chiba Medical Center
| | - Masataka Sata
- Department of Cardiovascular Medicine, Tokushima University Graduate School of Biomedical Sciences
| | | | - Yasushi Takeya
- Division of Helath Science, Osaka University Gradiate School of Medicine
| | - Kouichi Tamura
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine
| | | | - Hiroshi Yoshida
- Department of Laboratory Medicine, The Jikei University Kashiwa Hospital
| | - Yoshio Fujioka
- Division of Clinical Nutrition, Faculty of Nutrition, Kobe Gakuin University
| | | | - Osamu Hamada
- Department of General Internal Medicine, Takatsuki General Hospital
| | | | - Mai Kabayama
- Division of Health Sciences, Osaka University Graduate School of Medicine
| | - Koshiro Kanaoka
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center
| | - Kenjiro Kawaguchi
- Division of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University
| | | | | | | | - Masaki Nii
- Department of Cardiology, Shizuoka Children's Hospital
| | - Mitsuaki Sawano
- Department of Cardiology, Keio University School of Medicine
- Yale New Haven Hospital Center for Outcomes Research and Evaluation
| | | | - Shusuke Yagi
- Department of Cardiovascular Medicine, Tokushima University Hospital
| | - Takashi Akasaka
- Department of Cardiovascular Medicine, Nishinomiya Watanabe Cardiovascular Cerebral Center
| | - Tohru Minamino
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Meidicine
| | - Katsuyuki Miura
- Department of Preventive Medicine, NCD Epidemiology Research Center, Shiga University of Medical Science
| | - Koichi Node
- Department of Cardiovascular Medicine, Saga University
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Yang Q, Han X, Ye M, Jiang T, Wang B, Zhang Z, Li F. Association of genetically predicted 486 blood metabolites on the risk of Alzheimer's disease: a Mendelian randomization study. Front Aging Neurosci 2024; 16:1372605. [PMID: 38681667 PMCID: PMC11047179 DOI: 10.3389/fnagi.2024.1372605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 03/27/2024] [Indexed: 05/01/2024] Open
Abstract
Background Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention. Methods We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary two-sample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings. Results After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data. Conclusion By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD.
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Affiliation(s)
- Qiqi Yang
- Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
- The First Clinical Medical School, Anhui University of Chinese Medicine, Hefei, China
| | - Xinyu Han
- The First Clinical Medical School, Anhui University of Chinese Medicine, Hefei, China
| | - Min Ye
- The First Clinical Medical School, Anhui University of Chinese Medicine, Hefei, China
| | - Tianxin Jiang
- Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Baoguo Wang
- Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Zhenfeng Zhang
- Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Fei Li
- Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
- Intelligent Manufacturing Institute, Hefei University of Technology, Hefei, China
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Ndrepepa G, Kufner S, Cassese S, Joner M, Xhepa E, Wiebe J, Sager HB, Kessler T, Laugwitz KL, Schunkert H, Kastrati A. A Ten-Year Follow-Up Study of the Association Between Uric Acid and Adverse Cardiovascular Events in Patients With Coronary Artery Disease. Am J Cardiol 2024; 216:19-26. [PMID: 38336081 DOI: 10.1016/j.amjcard.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/09/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024]
Abstract
The association between uric acid (UA) and long-term mortality in patients with coronary artery disease is poorly investigated. We assessed the association between UA and 10-year mortality after percutaneous coronary intervention (PCI) in 3,998 patients who underwent PCI. Patients were categorized in groups according to UA tertiles: tertile 1 (UA <5.80 mg/100 ml, n = 1,347), tertile 2 (UA 5.80 to 7.04 mg/100 ml, n = 1,340), and tertile 3 (UA >7.94 mg/100 ml, n = 1,311). The primary outcome was 10-year all-cause mortality. All-cause deaths occurred in 1,200 patients: 320 deaths (26.5%) in patients with UA in the first tertile, 325 deaths (26.9%) in patients with UA in the second tertile, and 555 deaths (46.0%) in patients with UA in the third tertile (adjusted hazard ratio 1.22, 95% confidence interval 1.17 to 1.27, p <0.001) for 1 mg/100 ml increment in UA level. Cardiac deaths occurred in 748 patients: 194 deaths (16.5%) in patients with UA in the first tertile, 202 deaths (17.0%) in patients with UA in the second tertile, and 352 deaths (29.7%) in patients with UA in the third tertile (adjusted hazard ratio 1.24 [1.17 to 1.32], p <0.001) for 1 mg/100 ml increment in the UA level. The 10-year rates of target lesion revascularization, target vessel revascularization, or nontarget vessel revascularization did not differ significantly according to the UA level. In conclusion, in patients with coronary artery disease treated with PCI, increased UA level was associated with higher 10-year mortality. Increased UA level was not associated with the progression of atherosclerosis in nontreated coronary vessels or progression of intimal hyperplasia in stented lesions requiring intervention.
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Affiliation(s)
- Gjin Ndrepepa
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.
| | - Sebastian Kufner
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Salvatore Cassese
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Michael Joner
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Erion Xhepa
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Jens Wiebe
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Hendrik B Sager
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Thorsten Kessler
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Karl-Ludwig Laugwitz
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany; Medizinische Klinik und Poliklinik Innere Medizin I (Kardiologie, Angiologie, Pneumologie), Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Adnan Kastrati
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
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Silva S, Fatumo S, Nitsch D. Mendelian randomization studies on coronary artery disease: a systematic review and meta-analysis. Syst Rev 2024; 13:29. [PMID: 38225600 PMCID: PMC10790478 DOI: 10.1186/s13643-023-02442-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. We aimed to summarize what is currently known with regard to causal modifiable risk factors associated with CAD in populations of diverse ancestries through conducting a systematic review and meta-analysis of Mendelian randomization (MR) studies on CAD. METHODS The databases Embase, Medline, Cochrane Library and Web of Science were searched on the 19th and 20th of December 2022 for MR studies with CAD as a primary outcome; keywords of the search strategy included "coronary artery disease" and "mendelian randomization". Studies were included if they were published in the English language, included only human participants, employed Mendelian randomization as the primary methodology and studied CAD as the outcome of interest. The exclusion criteria resulted in the removal of studies that did not align with the predefined inclusion criteria, as well as studies which were systematic reviews themselves, and used the same exposure and outcome source as another study. An ancestry-specific meta-analysis was subsequently conducted on studies which investigated either body mass index, lipid traits, blood pressure or type 2 diabetes as an exposure variable. Assessment of publication bias and sensitivity analyses was conducted for risk of bias assessment in the included studies. RESULTS A total of 1781 studies were identified through the database searches after de-duplication was performed, with 47 studies included in the quantitative synthesis after eligibility screening. Approximately 80% of all included study participants for MR studies on CAD were of European descent irrespective of the exposure of interest, while no study included individuals of African ancestry. We found no evidence of differences in terms of direction of causation between ancestry groups; however, the strength of the respective relationships between each exposure and CAD were different, with this finding most evident when blood pressure was the exposure of interest. CONCLUSIONS Findings from this review suggest that patterns regarding the causational relationship between modifiable risk factors and CAD do not differ in terms of direction when compared across diverse ancestry populations. Differences in the observed strengths of the respective relationships however are indicative of the value of increasing representation in non-European populations, as novel genetic pathways or functional SNPs relating to CAD may be uncovered through a more global analysis. SYSTEMATIC REVIEW REGISTRATION The protocol for this systematic review was registered to the International Prospective Register of Systematic Reviews (PROSPERO) and is publicly available online (CRD42021272726).
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Affiliation(s)
- Sarah Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda.
| | - Segun Fatumo
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda.
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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Yang B, Ma K, Xiang R, Yang G, Luo Y, Wu F, Mao M. Uric acid and evaluate the coronary vascular stenosis gensini score correlation research and in gender differences. BMC Cardiovasc Disord 2023; 23:546. [PMID: 37940848 PMCID: PMC10634079 DOI: 10.1186/s12872-023-03581-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/26/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND AND AIMS Recent studies have shown that the negative effect of uric acid (UA) on coronary arteries determines the severity of atherosclerotic disease. This study aims to explore the relationship between serum UA level and Gensini score, which reflects the severity of coronary artery disease. METHODS A total of 860 patients with suspected coronary heart disease who were admitted to hospital due to angina pectoris or myocardial ischemia related symptoms and received coronary angiography were selected. Based on the findings of the angiography, they were categorized into two groups: the coronary heart disease (CHD) group (n = 625) and the control group (n = 235). The uric acid levels and other clinical data were compared between these groups. Additionally, the prevalence of coronary heart disease and Gensini score were compared between the groups, considering gender-specific quartiles of uric acid levels. The clinical baseline data were analyzed using appropriate statistical methods, and multivariate logistic regression analysis was conducted to identify independent risk factors for coronary heart disease. RESULTS Of 860 patients (mean age, 63.97 ± 11.87 years), 528 were men (mean age, 62.06 ± 11.5 years) and 332 were women (mean age, 66.99 ± 10.11 years). The proportion of smoking, diabetes, hypertension, and hyperlipidemia in the coronary heart disease group was higher than that in the control group (P < 0.05). HbA1C, Gensini score, BMI, TG and hsCRP in the coronary heart disease group were higher than those in the control group (P < 0.05), and HDL-C was lower than that in the control group (P < 0.05). There were no significant differences in age, heart rate, Cr, TC and LDL-C between the two groups (P > 0.05).Multivariate logistic regression analysis showed that age, hypertension, hsCRP and SUA levels increased the risk of coronary heart disease, and the difference was statistically significant(OR = 1.034,95%CI 1.016-1.052, P = 0.001; OR = 1.469,95%CI 1.007-2.142, P = 0.046;OR = 1.064,95%CI 1.026-1.105, P = 0.001; OR = 1.011,95%CI 1.008-1.014, P < 0.001). CONCLUSION Serum uric acid is positively correlated with Gensini score in patients with coronary heart disease, which is an independent factor for evaluating the degree of coronary artery stenosis and has a predictive effect.
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Affiliation(s)
- Bao Yang
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400010, China
| | - Kanghua Ma
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400010, China
| | - Rui Xiang
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400010, China
| | - Guoli Yang
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400010, China
| | - Yue Luo
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400010, China
| | - Fan Wu
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400010, China
| | - Min Mao
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400010, China.
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2023; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.3] [Citation(s) in RCA: 240] [Impact Index Per Article: 240.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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Ji L, Shu P. A Mendelian randomization study of serum uric acid with the risk of venous thromboembolism. Arthritis Res Ther 2023; 25:122. [PMID: 37468959 PMCID: PMC10354911 DOI: 10.1186/s13075-023-03115-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Observational studies have linked hyperuricemia with venous thromboembolism (VTE). We aimed to investigate whether there are causal relationships between uric acid levels and VTE and its subtypes, including deep venous thrombosis (DVT) of the lower extremities and pulmonary embolism (PE). METHODS We utilized Mendelian randomization (MR) analysis to estimate the causal association in European individuals. We extracted two sets of polygenic instruments strongly associated (p < 5 × 10-8) with uric acid from the CKDGen consortium and UK biobank, respectively. Genetic associations with the risk of VTE, DVT, and PE were obtained from the FinnGen biobank. We used the inverse-variance weighted method as the preliminary estimate. Additionally, we employed MR-Egger, weighted median, and Mendelian randomization pleiotropy residual sum and outlier method as complementary assessments. Sensitivity analyses were performed to test for pleiotropic bias. RESULTS The genetically instrumented serum uric acid levels had no causal effects on VTE, DVT, and PE. Two sets of polygenic instruments used for exposure, along with three complementary MR methods, also yielded no significant association. CONCLUSIONS Our MR analysis provided no compelling evidence for a causal relationship of serum uric acid with the risk of VTE. This suggests that uric acid-lowering therapies in patients with hyperuricemia may not be effective in reducing the likelihood of developing VTE.
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Affiliation(s)
- Lixian Ji
- Department of Rheumatology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, 322000, China
| | - Peng Shu
- Department of Orthopedic Surgery, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, 322000, China.
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Uric acid and risk of pre-eclampsia: results from a large case-control study and meta-analysis of prospective studies. Sci Rep 2023; 13:3018. [PMID: 36810371 PMCID: PMC9944921 DOI: 10.1038/s41598-023-29651-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 02/08/2023] [Indexed: 02/23/2023] Open
Abstract
To quantify the association between maternal uric acid levels and pre-eclampsia risk in a large collection of primigravid women. A case-control study (1365 cases of pre-eclampsia and 1886 normotensive controls) was conducted. Pre-eclampsia was defined as blood pressure ≥ 140/90 mmHg and proteinuria ≥ 300 mg/24 h. Sub-outcome analysis included early, intermediate, and late pre-eclampsia. Multivariable analysis for pre-eclampsia and its sub-outcomes was conducted using binary and multinomial logistic regression, respectively. Additionally, a systematic review and meta-analysis of cohort studies measuring uric acid levels < 20 weeks of gestation was performed to rule out reverse causation. There was a positive linear association between increasing uric acid levels and presence of pre-eclampsia. Adjusted odds ratio of pre-eclampsia was 1.21 (95%CI 1.11-1.33) for every one standard deviation increase in uric acid levels. No differences in the magnitude of association were observed between early and late pre-eclampsia. Three studies with uric acid measured < 20 weeks' gestation were identified, with a pooled OR for pre-eclampsia of 1.46 (95%CI 1.22-1.75) for a top vs. bottom quartile comparison. Maternal uric acid levels are associated with risk of pre-eclampsia. Mendelian randomisation studies would be helpful to further explore the causal role of uric acid in pre-eclampsia.
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Hu Y, Li J, Yin C, Xu L, Li S, Chen Y, Wang Y, Cheng Z, Bai Y. Mediating effect of metabolic diseases on the relationship between hyperuricemia and coronary heart disease. Nutr Metab Cardiovasc Dis 2023; 33:315-322. [PMID: 36599782 DOI: 10.1016/j.numecd.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/11/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND AIMS Studies have shown that elevated serum uric acid (SUA) may increase the risk of coronary heart disease (CHD). However, it is still disputable how mediate effects between metabolic diseases and hyperuricemia affect the incidence of CHD. This study aimed to explore whether metabolic diseases may mediate the connection from hyperuricemia at baseline to the elevated incidence risk of CHD during follow-ups. METHODS AND RESULTS Based on the Jinchang cohort, 48 001 subjects were followed for 9 years between June 2011 and December 2019. Multivariate-adjusted Cox regression models were applied to estimate hazard ratios (HRs) of CHD with 95% confidence intervals (CIs). Significantly increased risks of CHD were observed in hyperuricemia (HR:1.46, 95%CI:1.28, 1.67) when compared with normouricemia population. The mediating effect model further demonstrated that metabolic diseases could mediate the association between hyperuricemia and CHD pathogenesis, partially for the combined metabolic diseases with mediation effects of 45.12%, 25.24% for hypertension, 28.58% for overweight or obese status, 29.05% for hypertriglyceridemia, 6.70% for hypercholesterolemia, 3.52% for low high density lipoprotein cholesterol (HDL-C), and 6.51% for high low density lipoprotein cholesterol (LDL-C), respectively. CONCLUSIONS Hyperuricemia significantly increased the risk of incident CHD, and this association was partly mediated by metabolic diseases.
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Affiliation(s)
- Yujia Hu
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Street, Lanzhou, Gansu 730000, China
| | - Jing Li
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Street, Lanzhou, Gansu 730000, China
| | - Chun Yin
- Workers' Hospital of Jinchuan Group Co., Ltd., Jinchang, Gansu, China
| | - Lulu Xu
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Street, Lanzhou, Gansu 730000, China
| | - Siyu Li
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Street, Lanzhou, Gansu 730000, China
| | - Yarong Chen
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Street, Lanzhou, Gansu 730000, China
| | - Yufeng Wang
- Workers' Hospital of Jinchuan Group Co., Ltd., Jinchang, Gansu, China
| | - Zhiyuan Cheng
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Street, Shenzhen, 518055, China
| | - Yana Bai
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Street, Lanzhou, Gansu 730000, China.
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Campbell S, Wiemer H, Fitzpatrick R, Carriere C, Teed S, Hico P, Snook A. A Pilot Study of Inhaled Low-dose Methoxyflurane to Support Cunningham Reduction of Anterior Shoulder Dislocation. EURASIAN JOURNAL OF EMERGENCY MEDICINE 2022. [DOI: 10.4274/eajem.galenos.2022.03206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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11
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Zhang Q, Fang H, Zhu Z. NRBP1 modulates uric acid transporter ABCG2 expression by activating the Wnt/β-catenin pathway in HK-2 cells. Nefrologia 2022:S2013-2514(22)00140-7. [PMID: 36437206 DOI: 10.1016/j.nefroe.2022.11.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 09/04/2021] [Indexed: 06/16/2023] Open
Abstract
BACKGROUND Nuclear receptor binding protein 1 (NRBP1) and ATP-binding cassette subfamily G member 2 (ABCG2) was the gout risk gene and high-capacity urate exporter respectively. However, the relationship between NRBP1 and ABCG2 and the underlying molecular mechanism contributing to these associations are unknown. METHODS Firstly, the efficiency of the overexpression and knockdown of NRBP1 was confirmed by western blot. Next, the effect of NRBP1 overexpression and knockdown on the expression of ABCG2, organic anion transporter 1 (OAT1), glucose transporter 9 (GLUT9) and urate transporter 1 (URAT1) was detected by qRT-PCR and western blot. At the same time, the cellular location of ABCG2 and its expression after NRBP1 overexpression and knockdown was tested by immunofluorescence (IF) staining. Then, the mechanism of NRBP1 modulates ABCG2 expression was evaluated by western blot with or without the β-catenin inhibitor (21H7). RESULTS The lentivirus system was used to generate stable NRBP1 overexpression, while the plasmids carrying a NRBP1 siRNA was generated to knockdown NRBP1 expression in HK-2 cells. Meanwhile, the overexpression of NRBP1 significantly decreased the mRNAs and proteins expression of GLUT9 and URAT1, while the knockdown of NRBP1 increased the mRNAs and proteins expression of ABCG2 significantly. In addition, the NRBP1 modulates the expression of ABCG2 was by ctivating the Wnt/β-catenin pathway in HK-2 cells according to the IF and western blot results. CONCLUSION Taken together, our study demonstrated that NRBP1 inhibition played an essential role in attenuating hyperuricemia and gout by upregulation of ABCG2 via Wnt/β-catenin signaling pathway in HK-2 cells.
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Affiliation(s)
- Qiankun Zhang
- Division of Nephrology, Lishui Central Hospital and Fifth Affiliated Hospital of Wenzhou Medical College, Lishui 323000, China
| | - Hang Fang
- Division of Nephrology, Lishui Central Hospital and Fifth Affiliated Hospital of Wenzhou Medical College, Lishui 323000, China; Institute of Nephrology, Zhejiang University, Hangzhou 310003, China
| | - Zaihua Zhu
- Division of Rheumatology and Immunology, Huashan Hospital Fudan University, Shanghai 200000, China.
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Cheremushkina EV, Eliseev MS. Hyperuricemia and gout: effects on bone and articular cartilage (literature review). OBESITY AND METABOLISM 2022. [DOI: 10.14341/omet12894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Gout is a disease characterized by deposition of sodium monourate crystals in tissues which is the reason of inflammation among persons with hyperuricemia (HU). The prevalence of HU, which can be considered the first stage of gout formation, varies in different countries. Despite this, only a small number of persons with HU have been shown to develop symptoms of gout. Recent data suggest that HU is an independent risk factor for cartilage and bone damage. UA, both in the form of crystals and in a dissolved form, activates damage and potentiates cell death by releasing reactive oxygen species, activating the necroptosis pathway, neutrophil traps, synthesis of pro-inflammatory cytokines, and other pathogenetic mechanisms that cause the negative effects of HU and gout on articular cartilage and subchondral bone. The association of HU and osteoarthritis (OA) is well known and based on the common pathogenesis, but the direction of this relationship is still a debatable issue. The accumulated data suggest the need for a deeper study of the relationship of gout and asymptomatic HU with pathological processes leading to the development and progression of OA and disorders of bone metabolism.
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Correlation between serum uric acid and coronary collateral circulation in patients with coronary chronic total occlusion. CARDIOLOGY PLUS 2022. [DOI: 10.1097/cp9.0000000000000033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
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14
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Kanbay M, Xhaard C, Le Floch E, Dandine‐Roulland C, Girerd N, Ferreira JP, Boivin J, Wagner S, Bacq‐Daian D, Deleuze J, Zannad F, Rossignol P. Weak Association Between Genetic Markers of Hyperuricemia and Cardiorenal Outcomes: Insights From the STANISLAS Study Cohort With a 20-Year Follow-Up. J Am Heart Assoc 2022; 11:e023301. [PMID: 35470676 PMCID: PMC9238600 DOI: 10.1161/jaha.121.023301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 02/04/2022] [Indexed: 11/16/2022]
Abstract
Background Hyperuricemia is associated with poor cardiovascular outcomes, although it is uncertain whether this relationship is causal in nature. This study aimed to: (1) assess the heritability of serum uric acid (SUA) levels, (2) conduct a genome-wide association study on SUA levels, and (3) investigate the association between certain single-nucleotide polymorphisms and target organ damage. Methods and Results The STANISLAS (Suivi Temporaire Annuel Non-Invasif de la Santé des Lorrains Assurés Sociaux) study cohort is a single-center longitudinal cohort recruited between 1993 and 1995 (visit 1), with a last visit (visit 4 [V4]) performed ≈20 years apart. Serum lipid profile, SUA, urinary albumin/creatinine ratio, estimated glomerular filtration rate, 24-hour ambulatory blood pressure monitoring, transthoracic echocardiography, pulse wave velocity, and genotyping for each participant were assessed at V4. A total of 1573 participants were included at V4, among whom 1417 had available SUA data at visit 1. Genome-wide association study results highlighted multiple single-nucleotide polymorphisms on the SLC2A9 gene linked to SUA levels. Carriers of the most associated mutated SLC2A9 allele (rs16890979) had significantly lower SUA levels. Although SUA level at V4 was highly associated with diabetes, prediabetes, higher body mass index, CRP (C-reactive protein) levels, estimated glomerular filtration rate variation (visit 1-V4), carotid intima-media thickness, and pulse wave velocity, rs16890979 was only associated with higher carotid intima-media thickness. Conclusions Our findings demonstrate that rs16890979, a genetic determinant of SUA levels located on the SLC2A9 gene, is associated with carotid intima-media thickness despite significant associations between SUA levels and several clinical outcomes, thereby lending support to the hypothesis of a link between SUA and cardiovascular disease.
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Affiliation(s)
- Mehmet Kanbay
- Division of NephrologyDepartment of MedicineKoc University School of MedicineIstanbulTurkey
| | - Constance Xhaard
- Université de LorraineINSERM CIC‐P 1433CHRU de NancyINSERM U1116F‐CRIN INI‐CRCT (Cardiovascular and Renal Clinical Trialists)NancyFrance
| | - Edith Le Floch
- Centre National de Recherche en Génomique HumaineInstitut François JacobCEAUniversité Paris‐SaclayEvryFrance
| | - Claire Dandine‐Roulland
- Centre National de Recherche en Génomique HumaineInstitut François JacobCEAUniversité Paris‐SaclayEvryFrance
| | - Nicolas Girerd
- Université de LorraineINSERM CIC‐P 1433CHRU de NancyINSERM U1116F‐CRIN INI‐CRCT (Cardiovascular and Renal Clinical Trialists)NancyFrance
| | - João Pedro Ferreira
- Université de LorraineINSERM CIC‐P 1433CHRU de NancyINSERM U1116F‐CRIN INI‐CRCT (Cardiovascular and Renal Clinical Trialists)NancyFrance
| | - Jean‐Marc Boivin
- Université de LorraineINSERM CIC‐P 1433CHRU de NancyINSERM U1116F‐CRIN INI‐CRCT (Cardiovascular and Renal Clinical Trialists)NancyFrance
| | - Sandra Wagner
- Université de LorraineINSERM CIC‐P 1433CHRU de NancyINSERM U1116F‐CRIN INI‐CRCT (Cardiovascular and Renal Clinical Trialists)NancyFrance
| | - Delphine Bacq‐Daian
- Centre National de Recherche en Génomique HumaineInstitut François JacobCEAUniversité Paris‐SaclayEvryFrance
| | - Jean‐François Deleuze
- Centre National de Recherche en Génomique HumaineInstitut François JacobCEAUniversité Paris‐SaclayEvryFrance
| | - Faiez Zannad
- Université de LorraineINSERM CIC‐P 1433CHRU de NancyINSERM U1116F‐CRIN INI‐CRCT (Cardiovascular and Renal Clinical Trialists)NancyFrance
| | - Patrick Rossignol
- Université de LorraineINSERM CIC‐P 1433CHRU de NancyINSERM U1116F‐CRIN INI‐CRCT (Cardiovascular and Renal Clinical Trialists)NancyFrance
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15
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Differential effect of a xanthine oxidase inhibitor on arterial stiffness and carotid atherosclerosis: a subanalysis of the PRIZE study. Hypertens Res 2022; 45:602-611. [PMID: 35169280 DOI: 10.1038/s41440-022-00857-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 01/10/2023]
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De Luca L, Gulizia MM, Gabrielli D, Meessen J, Mattei L, D'Urbano M, Colivicchi F, Temporelli PL, Borghi C, Desideri G. Impact of serum uric acid levels on cardiovascular events and quality of life in patients with chronic coronary syndromes: Insights from a contemporary, prospective, nationwide registry. Nutr Metab Cardiovasc Dis 2022; 32:393-401. [PMID: 34893417 DOI: 10.1016/j.numecd.2021.09.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/05/2021] [Accepted: 09/30/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND AIMS Hyperuricemia is a metabolic disorder that has been associated with adverse cardiovascular (CV) events. Using the data from a nationwide, prospective registry on patients with chronic coronary syndromes (CCS), we assessed the impact of serum uric acid (SUA) levels on quality of life (QoL) and major adverse CV events (MACE), a composite of CV death and hospitalization for myocardial infarction, heart failure (HF), angina or revascularization at 1-year. METHODS AND RESULTS Among the 5070 consecutive CCS patients enrolled in the registry, levels of SUA were available for 2394 (47.2%). Patients with SUA levels available at baseline were grouped as low tertile (n = 860; 4.3 [3.7-4.7] mg/dL), middle tertile (n = 739; 5.6 [5.3-5.9] mg/dL) and high tertile (n = 795; 7.1 [6.7-7.9] mg/dL). At 1 year, the incidence of MACE was 3.7%, 4.1% and 6.8% for low, middle and high tertiles, respectively (p = 0.005 for low vs high tertile). Patients in the high tertile of SUA had a significantly higher rate of CV mortality (1.4% vs 0.4%; p = 0.05) and hospital admission for HF (2.8% vs 1.6%; p = 0.03) compared to the low tertile. However, hyperuricemia did not result as an independent predictor of MACE at multivariable analysis [hazard ratio: 1.27; 95% confidence intervals: 0.81-2.00; p = 0.3]. CONCLUSIONS In this contemporary, large cohort of CCS, those in the high tertile of SUA had a greater burden of CV disease and worse QoL. However, SUA did not significantly influence the higher rate of CV mortality, hospitalization for HF and MACE observed in these patients during 1-year follow-up.
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Affiliation(s)
- Leonardo De Luca
- Department of Cardiosciences, Azienda Ospedaliera San Camillo-Forlanini, Roma, Italy.
| | | | - Domenico Gabrielli
- Department of Cardiosciences, Azienda Ospedaliera San Camillo-Forlanini, Roma, Italy
| | - Jennifer Meessen
- Department of Cardiovascular Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Luisa Mattei
- Division of Cardiology, Ospedale Monfalcone-Gorizia, Italy
| | | | | | - Pier L Temporelli
- Division of Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Gattico-Veruno, Novara, Italy
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Giovambattista Desideri
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
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Choi HK, McCormick N, Yokose C. Excess comorbidities in gout: the causal paradigm and pleiotropic approaches to care. Nat Rev Rheumatol 2022; 18:97-111. [PMID: 34921301 DOI: 10.1038/s41584-021-00725-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2021] [Indexed: 02/07/2023]
Abstract
Gout is a common hyperuricaemic metabolic condition that leads to painful inflammatory arthritis and a high comorbidity burden, especially cardiometabolic-renal (CMR) conditions, including hypertension, myocardial infarction, stroke, obesity, hyperlipidaemia, type 2 diabetes mellitus and chronic kidney disease. Substantial advances have been made in our understanding of the excess CMR burden in gout, ranging from pathogenesis underlying excess CMR comorbidities, inferring causal relationships from Mendelian randomization studies, and potentially discovering urate crystals in coronary arteries using advanced imaging, to clinical trials and observational studies. Despite many studies finding an independent association between blood urate levels and risk of incident CMR events, Mendelian randomization studies have largely found that serum urate is not causal for CMR end points or intermediate risk factors or outcomes (such as kidney function, adiposity, metabolic syndrome, glycaemic traits or blood lipid concentrations). Although limited, randomized controlled trials to date in adults without gout support this conclusion. If imaging studies suggesting that monosodium urate crystals are deposited in coronary plaques in patients with gout are confirmed, it is possible that these crystals might have a role in the inflammatory pathogenesis of increased cardiovascular risk in patients with gout; removing monosodium urate crystals or blocking the inflammatory pathway could reduce this excess risk. Accordingly, data for CMR outcomes with these urate-lowering or anti-inflammatory therapies in patients with gout are needed. In the meantime, highly pleiotropic CMR and urate-lowering benefits of sodium-glucose cotransporter 2 (SGLT2) inhibitors and key lifestyle measures could play an important role in comorbidity care, in conjunction with effective gout care based on target serum urate concentrations according to the latest guidelines.
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Affiliation(s)
- Hyon K Choi
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston, MA, USA.
- Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Arthritis Research Canada, Vancouver, British Columbia, Canada.
| | - Natalie McCormick
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston, MA, USA
- Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Arthritis Research Canada, Vancouver, British Columbia, Canada
| | - Chio Yokose
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston, MA, USA
- Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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18
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Al Shanableh Y, Hussein YY, Saidwali AH, Al-Mohannadi M, Aljalham B, Nurulhoque H, Robelah F, Al-Mansoori A, Zughaier SM. Prevalence of asymptomatic hyperuricemia and its association with prediabetes, dyslipidemia and subclinical inflammation markers among young healthy adults in Qatar. BMC Endocr Disord 2022; 22:21. [PMID: 35031023 PMCID: PMC8760639 DOI: 10.1186/s12902-022-00937-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 12/27/2021] [Indexed: 11/27/2022] Open
Abstract
AIM The aim of this study is to investigate the prevalence of asymptomatic hyperuricemia in Qatar and to examine its association with changes in markers of dyslipidemia, prediabetes and subclinical inflammation. METHODS A cross-sectional study of young adult participants aged 18 - 40 years old devoid of comorbidities collected between 2012 and 2017. Exposure was defined as uric acid level, and outcomes were defined as levels of different blood markers. De-identified data were collected from Qatar Biobank. T-tests, correlation tests and multiple linear regression were all used to investigate the effects of hyperuricemia on blood markers. Statistical analyses were conducted using STATA 16. RESULTS The prevalence of asymptomatic hyperuricemia is 21.2% among young adults in Qatar. Differences between hyperuricemic and normouricemic groups were observed using multiple linear regression analysis and found to be statistically and clinically significant after adjusting for age, gender, BMI, smoking and exercise. Significant associations were found between uric acid level and HDL-c p = 0.019 (correlation coefficient -0.07 (95% CI [-0.14, -0.01]); c-peptide p = 0.018 (correlation coefficient 0.38 (95% CI [0.06, 0.69]) and monocyte to HDL ratio (MHR) p = 0.026 (correlation coefficient 0.47 (95% CI [0.06, 0.89]). CONCLUSIONS Asymptomatic hyperuricemia is prevalent among young adults and associated with markers of prediabetes, dyslipidemia, and subclinical inflammation.
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Affiliation(s)
| | - Yehia Y Hussein
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar
| | | | | | - Budoor Aljalham
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar
| | - Hamnah Nurulhoque
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar
| | - Fahad Robelah
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar
| | - Areej Al-Mansoori
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar
| | - Susu M Zughaier
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar.
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, PO Box 2713, Doha, Qatar.
- College of Medicine, Qatar University, PO Box 2713, Doha, Qatar.
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Han R, Zhang Y, Jiang X. Relationship Between Four Non-Insulin-Based Indexes of Insulin Resistance and Serum Uric Acid in Patients with Type 2 Diabetes: A Cross-Sectional Study. Diabetes Metab Syndr Obes 2022; 15:1461-1471. [PMID: 35591906 PMCID: PMC9113036 DOI: 10.2147/dmso.s362248] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/16/2022] [Indexed: 12/15/2022] Open
Abstract
AIM The aim of this study was to investigate the association between serum uric acid (SUA) levels and four insulin resistance surrogates in patients with type 2 diabetes (T2DM). The four non-insulin-based indexes of insulin resistance (IR) include the glucose and triglycerides index (TyG), TyG index with body mass index (TyG-BMI), ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL-c) and metabolic score for insulin resistance (METS-IR). METHODS A total of 687 patients with T2DM were enrolled in the current study. Patients were stratified into three groups according to their levels of SUA. Spearman correlation was used to analyze the correlation between SUA and clinical variables. Multiple linear regression analysis was used to assess the association between SUA and the four insulin resistance surrogates. Receiver operating characteristic (ROC) analyses and the area under the ROC curve (AUC) were then used to assess the ability of TyG, TyG-BMI, TG/HDL-c, and METS-IR to discriminate hyperuricemia (HUA) in T2DM. RESULTS SUA in T2DM was significantly positively correlated with TyG (r 0.406 P < 0.01), TyG-BMI (r 0.272 P < 0.01), TG/HDL-c (r 0.493 P < 0.01), and METS-IR (r 0.238 P < 0.01). Furthermore, higher values of the four insulin resistance surrogates were independently correlated with higher SUA levels in T2DM patients (P < 0.01 for all) after adjusting for confounding factors. TyG, TyG-BMI, TG/HDL-c, and METS-IR all had a significant discriminative ability for HUA in patients with T2DM. The AUC values were 0.693 (95% CI 0.645-0.741), 0.649 (95% CI 0.599-0.699), 0.768 (95% CI 0.726-0.811), and 0.660 (95% CI 0.609-0.710), respectively. CONCLUSION The present study suggests that TyG, TyG-BMI, TG/HDL-c and METS-IR had a significant correlation with SUA in T2DM. TG/HDL-c was the best marker among the four insulin resistance surrogates for the identification of HUA in T2DM.
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Affiliation(s)
- Rongfeng Han
- Department of Endocrinology, Tianjin First Center Hospital, Tianjin, 300192, People’s Republic of China
- Correspondence: Rongfeng Han, Department of Endocrinology, Tianjin First Center Hospital, Tianjin, 300192, People’s Republic of China, Email
| | - Yang Zhang
- Department of Endocrinology, Tianjin First Center Hospital, Tianjin, 300192, People’s Republic of China
| | - Xia Jiang
- Department of Endocrinology, Tianjin First Center Hospital, Tianjin, 300192, People’s Republic of China
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Lee WJ, Peng LN, Lin MH, Lin CH, Chen LK. Clinical Efficacy of Multidomain Interventions among Multimorbid Older People Stratified by the Status of Physio-Cognitive Declines: A Secondary Analysis from the Randomized Controlled Trial for Healthy Aging. J Nutr Health Aging 2022; 26:909-917. [PMID: 36259579 DOI: 10.1007/s12603-022-1843-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
OBJECTIVES To investigate the clinical efficacy of integrated multidomain intervention among community-living older adults with multimorbidity and physio-cognitive decline syndrome (PCDS). DESIGN, SETTING AND PARTICIPANTS This is the secondary analysis from a randomized controlled trial that data of 340 participants with Montreal Cognitive Assessment (MoCA) scores≥18 were excerpted for analysis. INTERVENTION Sixteen 2-hour sessions per year were provided for participants, including physical exercise, cognitive training, dietician education and individualized integrated care for multimorbidity. MEASUREMENTS Handgrip strength, 6-m walking speed, MoCA (total score and sub-domains), Cardiovascular Health Study (CHS) frailty score, quality of life, and serum biochemistry biomarkers. RESULTS Overall, 96/340 (28.2%) of all participants have PCDS, and the integrated multidomain intervention significantly improved global cognitive performance (overall difference 1.1, 95% CI 0.4 - 1.8, p=0.003), and domains of concentration (overall difference 0.3, 95%CI 0.1 - 0.5, p=0.011), language (overall difference 0.2, 95%CI 0.1 - 0.3, p=0.006), abstract thinking (overall difference 0.1, 95%CI 0.0 - 0.3, p=0.027), and orientation(overall difference 0.2, 95%CI 0.0 - 0.4, p=0.013) across all timepoints among those with PCDS. Besides, interventions also significantly reduced frailty score among those with cognitive impairment no dementia (overall difference -0.3, 95%CI -0.5 - -0.1, p=0.011) and mobility impairment no disability (overall difference -0.3, 95%CI -0.4 - -0.1, p=0.004). and improved quality of life at domain of physical role limitation among those with PCDS (overall difference 5.3, 95%CI 0.3 - 10.4, p=0.038). CONCLUSIONS The integrated multidomain lifestyle intervention plus multimorbidity management significantly improved cognitive function, and enhanced quality of life among older adults with multimorbidity and PCDS in the communities.
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Affiliation(s)
- W-J Lee
- Prof. Liang-Kung Chen, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shih-Pai Rd., Taipei 11217, Taiwan,
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Modulation of Vascular Smooth Muscle Cell Multiplication, Apoptosis, and Inflammatory Damage by miR-21 in Coronary Heart Disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6942699. [PMID: 34873417 PMCID: PMC8643245 DOI: 10.1155/2021/6942699] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/28/2021] [Accepted: 11/03/2021] [Indexed: 12/21/2022]
Abstract
This study is aimed at exploring the role and potential molecular mechanism of microRNA-21 (miR-21) in coronary heart disease (CHD). RT-qPCR analysis was conducted to detect the expression of miR-21, Sprouty 1 (SPRY1), and connexin 43 (CX43). The protein expression of SPRY1 and CX43 was measured by western blot. ELISA was performed for measuring inflammatory factors, including intercellular adhesion molecule-1 (ICAM-1) and interleukin-1 beta (IL-1β). The target relationship between miR-21 and SPRY1 was determined by dual-luciferase reporter assay. Cell multiplication and apoptosis were detected using CCK-8 assay and flow cytometry analysis, respectively. Our results indicated that miR-21, CX43, and the level of inflammatory cytokines including ICAM-1 and IL-1β were upregulated, while SPRY1 was downregulated in blood samples from CHD patients compared with the controls. Besides, miR-21 directly targeted SRPY-1. miR-21 could suppress SPRY1 expression and enhance CX43 expression in VSMCs. Moreover, miR-21 accelerated cell multiplication and attenuated cell apoptosis in VSMCs. Collectively, these findings suggested that miR-21 could effectively elevate VSMC multiplication and repress apoptosis by targeting SPRY1 in CHD, providing a potential target for therapeutic strategy of CHD.
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NRBP1 modulates uric acid transporter ABCG2 expression by activating the Wnt/β-catenin pathway in HK-2 cells. Nefrologia 2021. [DOI: 10.1016/j.nefro.2021.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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McCormick N, O’Connor MJ, Yokose C, Merriman TR, Mount DB, Leong A, Choi HK. Assessing the Causal Relationships Between Insulin Resistance and Hyperuricemia and Gout Using Bidirectional Mendelian Randomization. Arthritis Rheumatol 2021; 73:2096-2104. [PMID: 33982892 PMCID: PMC8568618 DOI: 10.1002/art.41779] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/16/2021] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Hyperuricemia is closely associated with insulin resistance syndrome (and its many cardiometabolic sequelae); however, whether they are causally related has long been debated. We undertook this study to investigate the potential causal nature and direction between insulin resistance and hyperuricemia, along with gout, by using bidirectional Mendelian randomization (MR) analyses. METHODS We used genome-wide association data (n = 288,649 for serum urate [SU] concentration; n = 763,813 for gout risk; n = 153,525 for fasting insulin) to select genetic instruments for 2-sample MR analyses, using multiple MR methods to address potential pleiotropic associations. We then used individual-level, electronic medical record-linked data from the UK Biobank (n = 360,453 persons of European ancestry) to replicate our analyses via single-sample MR analysis. RESULTS Genetically determined SU levels, whether inferred from a polygenic score or strong individual loci, were not associated with fasting insulin concentrations. In contrast, genetically determined fasting insulin concentrations were positively associated with SU levels (0.37 mg/dl per log-unit increase in fasting insulin [95% confidence interval (95% CI) 0.15, 0.58]; P = 0.001). This persisted in outlier-corrected (β = 0.56 mg/dl [95% CI 0.45, 0.67]) and multivariable MR analyses adjusted for BMI (β = 0.69 mg/dl [95% CI 0.53, 0.85]) (P < 0.001 for both). Polygenic scores for fasting insulin were also positively associated with SU level among individuals in the UK Biobank (P < 0.001). Findings for gout risk were bidirectionally consistent with those for SU level. CONCLUSION These findings provide evidence to clarify core questions about the close association between hyperuricemia and insulin resistance syndrome: hyperinsulinemia leads to hyperuricemia but not the other way around. Reducing insulin resistance could lower the SU level and gout risk, whereas lowering the SU level (e.g., allopurinol treatment) is unlikely to mitigate insulin resistance and its cardiometabolic sequelae.
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Affiliation(s)
- Natalie McCormick
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA
- Department of Medicine, Harvard Medical School, Boston MA USA
- Arthritis Research Canada, Richmond BC Canada
| | - Mark J. O’Connor
- Endocrine Division, Massachusetts General Hospital, Boston MA USA
| | - Chio Yokose
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA
- Department of Medicine, Harvard Medical School, Boston MA USA
| | - Tony R. Merriman
- Biochemistry Department, University of Otago, Dunedin, New Zealand
- Division of Rheumatology and Clinical Immunology, University of Alabama, Birmingham AL
| | - David B. Mount
- Department of Medicine, Harvard Medical School, Boston MA USA
- Brigham and Women’s Hospital and VA Boston Healthcare System, Harvard Medical School, Boston MA USA
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston MA USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston MA USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge MA USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston MA USA
| | - Hyon K. Choi
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA
- Department of Medicine, Harvard Medical School, Boston MA USA
- Arthritis Research Canada, Richmond BC Canada
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24
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Pluimakers VG, van Santen SS, Fiocco M, Bakker MCE, van der Lelij AJ, van den Heuvel-Eibrink MM, Neggers SJCMM. Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review. Obes Rev 2021; 22:e13312. [PMID: 34258851 PMCID: PMC8596408 DOI: 10.1111/obr.13312] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/11/2021] [Accepted: 06/09/2021] [Indexed: 12/26/2022]
Abstract
Childhood cancer survivors (CCS) are at increased risk to develop metabolic syndrome (MetS), diabetes, and cardiovascular disease. Common criteria underestimate adiposity and possibly underdiagnose MetS, particularly after abdominal radiotherapy. A systematic literature review and meta-analysis on the diagnostic and predictive value of nine newer MetS related biomarkers (adiponectin, leptin, uric acid, hsCRP, TNF-alpha, IL-1, IL-6, apolipoprotein B (apoB), and lipoprotein(a) [lp(a)]) in survivors and adult non-cancer survivors was performed by searching PubMed and Embase. Evidence was summarized with GRADE after risk of bias evaluation (QUADAS-2/QUIPS). Eligible studies on promising biomarkers were pooled. We identified 175 general population and five CCS studies. In the general population, valuable predictive biomarkers are uric acid, adiponectin, hsCRP and apoB (high level of evidence), and leptin (moderate level of evidence). Valuable diagnostic biomarkers are hsCRP, adiponectin, uric acid, and leptin (low, low, moderate, and high level of evidence, respectively). Meta-analysis showed OR for hyperuricemia of 2.94 (age-/sex-adjusted), OR per unit uric acid increase of 1.086 (unadjusted), and AUC for hsCRP of 0.71 (unadjusted). Uric acid, adiponectin, hsCRP, leptin, and apoB can be alternative biomarkers in the screening setting for MetS in survivors, to enhance early identification of those at high risk of subsequent complications.
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Affiliation(s)
| | - Selveta S van Santen
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Department of Medicine, Endocrinology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Marta Fiocco
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Medical Statistics, Department of Biomedical Data Science, Leiden UMC, Leiden, Netherlands.,Mathematical Institute, Leiden University, Leiden, Netherlands
| | - Marie-Christine E Bakker
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Department of Medicine, University Medical Center Utrecht, Netherlands
| | - Aart J van der Lelij
- Department of Medicine, Endocrinology, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Sebastian J C M M Neggers
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands.,Department of Medicine, Endocrinology, Erasmus Medical Center, Rotterdam, Netherlands
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25
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Effects of incorporating multidomain interventions into integrated primary care on quality of life: a randomised controlled trial. THE LANCET. HEALTHY LONGEVITY 2021; 2:e712-e723. [DOI: 10.1016/s2666-7568(21)00248-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/13/2021] [Accepted: 09/13/2021] [Indexed: 01/07/2023] Open
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26
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Ching SC, Wen LJ, Ismail NIM, Looi I, Kooi CW, Peng LS, Mui LS, Tamibmaniam J, Muninathan P, Hooi OB, Ali SMM, Hassan MRA, Mohamad MS, Griffiths LR, Wei LK. SLC17A3 rs9379800 and Ischemic Stroke Susceptibility at the Northern Region of Malaysia. J Stroke Cerebrovasc Dis 2021; 30:105908. [PMID: 34384670 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105908] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/24/2021] [Accepted: 05/19/2021] [Indexed: 10/20/2022] Open
Abstract
OBJECTIVES The relationships of Paired Like Homeodomain 2 (PITX2), Ninjurin 2 (NINJ2), TWIST-Related Protein 1 (TWIST1), Ras Interacting Protein 1 (Rasip1), Solute Carrier Family 17 Member 3 (SLC17A3), Methylmalonyl Co-A Mutase (MUT) and Fer3 Like BHLH Transcription Factor (FERD3L) polymorphisms and gene expression with ischemic stroke have yet to be determined in Malaysia. Hence, this study aimed to explore the associations of single nucleotide polymorphisms (SNPs) and gene expression with ischemic stroke risk among population who resided at the Northern region of Malaysia. MATERIALS AND METHODS Study subjects including 216 ischemic stroke patients and 203 healthy controls were recruited upon obtaining ethical clearance. SNP genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism assays. Gene expression levels were quantified by real-time polymerase chain reaction assays. Statistical and genetic analyses were conducted with SPSS version 22.2, PLINK version 1.07 and multifactor dimensionality reduction software. RESULTS Study subjects with G allele, CG or GG genotypes of SLC17A3 rs9379800 demonstrated increased risk of ischemic stroke with the odds ratios ranging from 1.76-fold to 3.14-fold (p<0.05). When stratified study subjects according to the ethnicity, SLC17A3 rs9379800 G allele and CG genotype contributed to 2.14- and 2.96-fold of ischemic stroke risk among Malay population significantly, in the multivariate analysis (p<0.05). However, no significant associations were observed for PITX2, NINJ2, TWIST1, Rasip1, and MUT polymorphisms with ischemic stroke risk in the multivariate analysis for the pooled cases and controls as well as when stratified them according to the ethnicity. Lower mRNA expression levels of Rasip1, SLC17A3, MUT and FERD3L were observed among cases (p<0.05). After FDR adjustment, the mRNA level of SLC17A3 remained significantly associated with ischemic stroke among Malay population (q=0.034). CONCLUSION In conclusion, this study suggests that SLC17A3 rs9379800 polymorphism and its gene expression contribute to significant ischemic stroke risk among Malaysian population, particularly the Malay who resided at the Northern Region of the country. Our findings can provide useful information for the future diagnosis, management and treatment of ischemic stroke patients.
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Affiliation(s)
- Shu Chai Ching
- Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia
| | - Lim Jing Wen
- Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia
| | - Nor Ismaliza Mohd Ismail
- Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia
| | - Irene Looi
- Clinical Research Centre, Seberang Jaya Hospital, Ministry of Health, Penang, Malaysia
| | - Cheah Wee Kooi
- Clinical Research Centre, Taiping Hospital, Jalan Tamingsari, Taiping, Perak, Malaysia
| | - Long Soo Peng
- Clinical Research Centre, Seberang Jaya Hospital, Ministry of Health, Penang, Malaysia
| | - Lee Soon Mui
- Clinical Research Centre, Seberang Jaya Hospital, Ministry of Health, Penang, Malaysia
| | | | - Prema Muninathan
- Clinical Research Centre, Taiping Hospital, Jalan Tamingsari, Taiping, Perak, Malaysia
| | - Ong Beng Hooi
- Clinical Research Centre, Hospital Sultanah Bahiyah, Kedah, Malaysia
| | | | | | - Mohd Saberi Mohamad
- Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, United Arab Emirates
| | - Lyn R Griffiths
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Loo Keat Wei
- Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia.
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27
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Lukkunaprasit T, Rattanasiri S, Ongphiphadhanakul B, McKay GJ, Attia J, Thakkinstian A. Causal Associations of Urate With Cardiovascular Risk Factors: Two-Sample Mendelian Randomization. Front Genet 2021; 12:687279. [PMID: 34306027 PMCID: PMC8297413 DOI: 10.3389/fgene.2021.687279] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/10/2021] [Indexed: 12/22/2022] Open
Abstract
Background Mendelian Randomization (MR) studies show conflicting causal associations of genetically predicted serum urate with cardiovascular risk factors (i.e., hypertension, diabetes, lipid profile, and kidney function). This study aimed to robustly investigate a causal relationship between urate and cardiovascular risk factors considering single nucleotide polymorphisms (SNPs) as instrumental variables using two-sample MR and various sensitivity analyses. Methods Data on SNP-urate associations were taken from the Global Urate Genetics Consortium and data on SNP-cardiovascular risk factor associations were taken from various consortia/UK Biobank. SNPs were selected by statistically and biologically driven approaches as instrumental variables. Various sensitivity analyses were performed using different MR methods including inverse variance weighted, MR-Egger, weighted median/mode, MR-PRESSO, and the contamination mixture method. Results The statistically driven approach showed significant causal effects of urate on HDL-C and triglycerides using four of the six MR methods, i.e., every 1 mg/dl increase in genetically predicted urate was associated with 0.047 to 0.103 SD decrease in HDL-C and 0.034 to 0.207 SD increase in triglycerides. The biologically driven approach to selection of SNPs from ABCG2, SLC2A9, SLC17A1, SLC22A11, and SLC22A12 showed consistent causal effects of urate on HDL-C from all methods with 0.038 to 0.057 SD decrease in HDL-C per 1 mg/dl increase of urate, and no evidence of horizontal pleiotropy was detected. Conclusion Our study suggests a significant and robust causal effect of genetically predicted urate on HDL-C. This finding may explain a small proportion (7%) of the association between increased urate and cardiovascular disease but points to urate being a novel cardiac risk factor.
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Affiliation(s)
- Thitiya Lukkunaprasit
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Department of Pharmacology, College of Pharmacy, Rangsit University, Pathum Thani, Thailand
| | - Sasivimol Rattanasiri
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Gareth J McKay
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - John Attia
- Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, and Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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28
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Coneys R, Storm CS, Kia DA, Almramhi M, Wood N. Mendelian Randomisation Finds No Causal Association between Urate and Parkinson's Disease Progression. Mov Disord 2021; 36:2182-2187. [PMID: 34056740 DOI: 10.1002/mds.28662] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 04/20/2021] [Accepted: 05/06/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a common neurodegenerative movement disorder. Observational studies suggest higher levels of plasma urate may protect against Parkinson's risk and progression; however, causality cannot be established. OBJECTIVES This study set out to determine whether there is a true causal association between urate levels and PD age at onset (AAO) and progression severity using recently released PD AAO and progression genome-wide association study (GWAS) data. METHODS A large two-sample Mendelian randomization design was employed, using genetic variants underlying urate levels and the latest GWAS data for PD outcomes. RESULTS This study found no causal association between urate levels and Parkinson's risk, AAO, or progression severity. CONCLUSIONS Our results predict increasing urate levels as a therapeutic strategy is unlikely to benefit PD patients. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Rachel Coneys
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Catherine S Storm
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Demis A Kia
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Mona Almramhi
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - NicholasW Wood
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
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29
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Uric Acid-An Emergent Risk Marker for Thrombosis? J Clin Med 2021; 10:jcm10102062. [PMID: 34065792 PMCID: PMC8150596 DOI: 10.3390/jcm10102062] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/07/2021] [Accepted: 05/11/2021] [Indexed: 12/22/2022] Open
Abstract
Hyperuricemia is nowadays an established cardiovascular risk factor. Experimental studies linked elevated serum uric acid (SUA) levels with endothelial dysfunction (ED), inflammation, and prothrombotic state. The purpose of this review is to summarize the current evidence that emphasizes the possible role of uric acid as a biomarker for a prothrombotic state. A large number of clinical trials correlated SUA levels with both incident and recurrent cases of venous thromboembolism (VTE), independent of other confounding risk factors. Moreover, increased SUA levels may be an important tool for the risk stratification of patients with pulmonary embolism (PE). Left atrial thrombosis was correlated with high SUA levels in several studies and its addition to classical risk scores improved their predictive abilities. In patients with acute myocardial infarction (MI), hyperuricemia was associated with increased mortality, and the idea that hyperuricemia may be able to act as a surrogate to unstable coronary plaques was advanced. Finally, SUA was correlated with an increased risk of thromboembolic events in different systemic diseases. In conclusion, uric acid has been considered a marker of a thrombotic milieu in several clinical scenarios. However, this causality is still controversial, and more experimental and clinical data is needed.
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30
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Dedov II, Shestakova MV, Melnichenko GA, Mazurina NV, Andreeva EN, Bondarenko IZ, Gusova ZR, Dzgoeva FK, Eliseev MS, Ershova EV, Zhuravleva MV, Zakharchuk TA, Isakov VA, Klepikova MV, Komshilova KA, Krysanova VS, Nedogoda SV, Novikova AM, Ostroumova OD, Pereverzev AP, Rozhivanov RV, Romantsova TI, Ruyatkina LA, Salasyuk AS, Sasunova AN, Smetanina SA, Starodubova AV, Suplotova LA, Tkacheva ON, Troshina EA, Khamoshina MV, Chechelnitskaya SM, Shestakova EA, Sheremet’eva EV. INTERDISCIPLINARY CLINICAL PRACTICE GUIDELINES "MANAGEMENT OF OBESITY AND ITS COMORBIDITIES". OBESITY AND METABOLISM 2021; 18:5-99. [DOI: 10.14341/omet12714] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | - M. S. Eliseev
- Research Institute of Rheumatogy named after V.A. Nasonova
| | | | | | | | - V. A. Isakov
- Federal Research Center of Nutrition, Biotechnology and Food Safety
| | - M. V. Klepikova
- Russian Medical Academy of Continuous Professional Education
| | | | | | | | - A. M. Novikova
- Research Institute of Rheumatogy named after V.A. Nasonova
| | - O. D. Ostroumova
- A.I. Yevdokimov Moscow State University of Medicine and Dentistry
| | - A. P. Pereverzev
- Russian National Research Medical University named after N.I. Pirogov
| | | | | | | | | | - A. N. Sasunova
- Federal Research Center of Nutrition, Biotechnology and Food Safety
| | | | | | | | - O. N. Tkacheva
- Russian National Research Medical University named after N.I. Pirogov
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31
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Gill D, Cameron AC, Burgess S, Li X, Doherty DJ, Karhunen V, Abdul-Rahim AH, Taylor-Rowan M, Zuber V, Tsao PS, Klarin D, Evangelou E, Elliott P, Damrauer SM, Quinn TJ, Dehghan A, Theodoratou E, Dawson J, Tzoulaki I. Urate, Blood Pressure, and Cardiovascular Disease: Evidence From Mendelian Randomization and Meta-Analysis of Clinical Trials. Hypertension 2021; 77:383-392. [PMID: 33356394 PMCID: PMC7803439 DOI: 10.1161/hypertensionaha.120.16547] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 11/25/2020] [Indexed: 02/07/2023]
Abstract
Serum urate has been implicated in hypertension and cardiovascular disease, but it is not known whether it is exerting a causal effect. To investigate this, we performed Mendelian randomization analysis using data from UK Biobank, Million Veterans Program and genome-wide association study consortia, and meta-analysis of randomized controlled trials. The main Mendelian randomization analyses showed that every 1-SD increase in genetically predicted serum urate was associated with an increased risk of coronary heart disease (odds ratio, 1.19 [95% CI, 1.10-1.30]; P=4×10-5), peripheral artery disease (1.12 [95% CI, 1.03-1.21]; P=9×10-3), and stroke (1.11 [95% CI, 1.05-1.18]; P=2×10-4). In Mendelian randomization mediation analyses, elevated blood pressure was estimated to mediate approximately one-third of the effect of urate on cardiovascular disease risk. Systematic review and meta-analysis of randomized controlled trials showed a favorable effect of urate-lowering treatment on systolic blood pressure (mean difference, -2.55 mm Hg [95% CI, -4.06 to -1.05]; P=1×10-3) and major adverse cardiovascular events in those with previous cardiovascular disease (odds ratio, 0.40 [95% CI, 0.22-0.73]; P=3×10-3) but no significant effect on major adverse cardiovascular events in all individuals (odds ratio, 0.67 [95% CI, 0.44-1.03]; P=0.07). In summary, these Mendelian randomization and clinical trial data support an effect of higher serum urate on increasing blood pressure, which may mediate a consequent effect on cardiovascular disease risk. High-quality trials are necessary to provide definitive evidence on the specific clinical contexts where urate lowering may be of cardiovascular benefit.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
- Department of Medicine, Centre for Pharmacology and Therapeutics, Hammersmith Campus (D.G.), Imperial College London, United Kingdom
- Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.)
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, United Kingdom (D.G.)
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom (D.G.)
| | - Alan C. Cameron
- Institute of Cardiovascular and Medical Sciences (A.C.C., D.J.D., M.T.-R., T.J.Q., J.D.), University of Glasgow, United Kingdom
| | - Stephen Burgess
- MRC Biostatistics Unit, Cambridge Institute of Public Health, United Kingdom (S.B., V.Z.)
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (S.B.)
| | - Xue Li
- Centre for Global Health, Usher Institute (X.L., E.T.), University of Edinburgh, United Kingdom
- School of Public Health, Zhejiang University, Hangzhou, China (X.L.)
| | - Daniel J. Doherty
- Institute of Cardiovascular and Medical Sciences (A.C.C., D.J.D., M.T.-R., T.J.Q., J.D.), University of Glasgow, United Kingdom
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
| | - Azmil H. Abdul-Rahim
- Institute of Neuroscience and Psychology (A.H.A.-R.), University of Glasgow, United Kingdom
| | - Martin Taylor-Rowan
- Institute of Cardiovascular and Medical Sciences (A.C.C., D.J.D., M.T.-R., T.J.Q., J.D.), University of Glasgow, United Kingdom
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
- MRC Biostatistics Unit, Cambridge Institute of Public Health, United Kingdom (S.B., V.Z.)
| | - Philip S. Tsao
- Department of Medicine, Stanford University School of Medicine, CA (P.S.T.)
| | - Derek Klarin
- Malcom Randall VA Medical Center, Gainesville, FL (D.K.)
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, MA (D.K.)
- Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL (D.K.)
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Greece (E.E., I.T.)
| | - VA Million Veteran Program
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
- Department of Medicine, Centre for Pharmacology and Therapeutics, Hammersmith Campus (D.G.), Imperial College London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health (P.E., A.D., I.T.), Imperial College London, United Kingdom
- British Heart Foundation Centre of Research Excellence (P.E.), Imperial College London, United Kingdom
- Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.)
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, United Kingdom (D.G.)
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom (D.G.)
- Institute of Cardiovascular and Medical Sciences (A.C.C., D.J.D., M.T.-R., T.J.Q., J.D.), University of Glasgow, United Kingdom
- Institute of Neuroscience and Psychology (A.H.A.-R.), University of Glasgow, United Kingdom
- MRC Biostatistics Unit, Cambridge Institute of Public Health, United Kingdom (S.B., V.Z.)
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (S.B.)
- Centre for Global Health, Usher Institute (X.L., E.T.), University of Edinburgh, United Kingdom
- Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine (E.T.), University of Edinburgh, United Kingdom
- School of Public Health, Zhejiang University, Hangzhou, China (X.L.)
- VA Palo Alto Health Care System, CA (P.S.T.)
- Department of Medicine, Stanford University School of Medicine, CA (P.S.T.)
- Malcom Randall VA Medical Center, Gainesville, FL (D.K.)
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, MA (D.K.)
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, MA (D.K.)
- Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL (D.K.)
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Greece (E.E., I.T.)
- UK Dementia Research Institute at Imperial College London, United Kingdom (P.E., A.D., I.T.)
- Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, United Kingdom (P.E., I.T.)
- Health Data Research UK, London (P.E.)
- Corporal Michael J. Crescenz VA Medical Center, PA (S.M.D.)
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia (S.M.D.)
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health (P.E., A.D., I.T.), Imperial College London, United Kingdom
- British Heart Foundation Centre of Research Excellence (P.E.), Imperial College London, United Kingdom
| | - Scott M. Damrauer
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
- Department of Medicine, Centre for Pharmacology and Therapeutics, Hammersmith Campus (D.G.), Imperial College London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health (P.E., A.D., I.T.), Imperial College London, United Kingdom
- British Heart Foundation Centre of Research Excellence (P.E.), Imperial College London, United Kingdom
- Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.)
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, United Kingdom (D.G.)
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom (D.G.)
- Institute of Cardiovascular and Medical Sciences (A.C.C., D.J.D., M.T.-R., T.J.Q., J.D.), University of Glasgow, United Kingdom
- Institute of Neuroscience and Psychology (A.H.A.-R.), University of Glasgow, United Kingdom
- MRC Biostatistics Unit, Cambridge Institute of Public Health, United Kingdom (S.B., V.Z.)
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (S.B.)
- Centre for Global Health, Usher Institute (X.L., E.T.), University of Edinburgh, United Kingdom
- Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine (E.T.), University of Edinburgh, United Kingdom
- School of Public Health, Zhejiang University, Hangzhou, China (X.L.)
- VA Palo Alto Health Care System, CA (P.S.T.)
- Department of Medicine, Stanford University School of Medicine, CA (P.S.T.)
- Malcom Randall VA Medical Center, Gainesville, FL (D.K.)
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, MA (D.K.)
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, MA (D.K.)
- Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL (D.K.)
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Greece (E.E., I.T.)
- UK Dementia Research Institute at Imperial College London, United Kingdom (P.E., A.D., I.T.)
- Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, United Kingdom (P.E., I.T.)
- Health Data Research UK, London (P.E.)
- Corporal Michael J. Crescenz VA Medical Center, PA (S.M.D.)
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia (S.M.D.)
| | - Terence J. Quinn
- Institute of Cardiovascular and Medical Sciences (A.C.C., D.J.D., M.T.-R., T.J.Q., J.D.), University of Glasgow, United Kingdom
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health (P.E., A.D., I.T.), Imperial College London, United Kingdom
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute (X.L., E.T.), University of Edinburgh, United Kingdom
- Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine (E.T.), University of Edinburgh, United Kingdom
| | - Jesse Dawson
- Institute of Cardiovascular and Medical Sciences (A.C.C., D.J.D., M.T.-R., T.J.Q., J.D.), University of Glasgow, United Kingdom
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health (D.G., V.K., V.Z., E.E., P.E., A.D., I.T.), Imperial College London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health (P.E., A.D., I.T.), Imperial College London, United Kingdom
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Weisman A, Tomlinson GA, Lipscombe LL, Garg AX, Perkins BA, Cherney DZI, Hawker GA. Allopurinol and Renal Outcomes in Adults With and Without Type 2 Diabetes: A Retrospective, Population-Based Cohort Study and Propensity Score Analysis. Can J Diabetes 2021; 45:641-649.e4. [PMID: 33714662 DOI: 10.1016/j.jcjd.2021.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/13/2020] [Accepted: 01/12/2021] [Indexed: 10/22/2022]
Abstract
OBJECTIVES Elevated uric acid (UA) is common in diabetes and is implicated in the pathogenesis of chronic kidney disease (CKD). Lowering UA with allopurinol may delay CKD progression. We assessed the association between allopurinol and renal outcomes in older adults both with and without diabetes, and whether this differed by diabetes status. METHODS We conducted a population-based, retrospective cohort study of older adults ≥66 years of age with a gout flare using administrative data from Ontario, Canada. The primary outcome was doubling of creatinine or kidney failure. Secondary outcomes were a composite of death or kidney failure, decline in estimated glomerular filtration rate by >30%, death and kidney failure. New allopurinol users were compared with nonusers using Cox proportional hazards models and inverse probability of treatment weighting (IPTW). An interaction between allopurinol use and presence or absence of diabetes was assessed. RESULTS Among 5,937 older adults with a gout flare (1,911 with diabetes), 1,304 (22%) were newly treated with allopurinol. Median follow-up time was 1.11 (interquartile range, 0.33 to 3.21) years for allopurinol users and 3.38 (interquartile range, 1.42 to 4.43) years for nonusers. There was no association between allopurinol use and the primary outcome (IPTW-adjusted hazard ratio, 0.97; 95% confidence interval, 0.72 to 1.31), and this did not differ by diabetes status. Allopurinol use was not associated with any of the secondary outcomes. CONCLUSIONS Allopurinol use was not associated with renal outcomes in older adults with or without diabetes. This supports the interpretation of UA as a biomarker of CKD risk rather than a modifiable target for prevention or treatment of CKD.
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Affiliation(s)
- Alanna Weisman
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; ICES, Toronto, Ontario, Canada; Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada.
| | - George A Tomlinson
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Medicine, University Health Network/Mt Sinai Hospital, Toronto, Ontario, Canada
| | - Lorraine L Lipscombe
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; ICES, Toronto, Ontario, Canada; Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada
| | - Amit X Garg
- ICES, Toronto, Ontario, Canada; Department of Medicine, Epidemiology & Biostatistics, Western University Hospital Research Institute, Toronto, Ontario, Canada
| | - Bruce A Perkins
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - David Z I Cherney
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Medicine, Division of Nephrology, University Health Network, Toronto General, Toronto, Ontario, Canada
| | - Gillian A Hawker
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; ICES, Toronto, Ontario, Canada; Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada
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Liu J, Chen L, Yuan H, Huang K, Li G, Sun N, Huo Y. Survey on uric acid in Chinese subjects with essential hypertension (SUCCESS): a nationwide cross-sectional study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:27. [PMID: 33553320 PMCID: PMC7859747 DOI: 10.21037/atm-20-3458] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Background Hyperuricemia (HUA) is associated with hypertension and increased cardiovascular risk. Current data regarding the prevalence of HUA in Chinese hypertensive patients are lacking. Our study aims to explore the prevalence and determinants of HUA in Chinese hypertensive adults. Methods Treatment-naive hypertensive adults or those taking single antihypertensive agent were included in a nationwide cross-sectional study. Basic demographics, antihypertensive medications, serum uric acid (UA), and other parameters were documented. Results The overall prevalence rate of HUA was 38.7% among 33,785 valid cases, 35.1% for males (UA >420 µmol/L), and 45.2% for females (UA >360 µmol/L). A multiple logistic regression analysis, adjusted for demographic and clinical factors (model 1), revealed that female sex [odds ratio (OR), 95% CI, 1.43, 1.36–1.51], age of ≥65 years (1.12, 1.05–1.19), low evaluated glomerular filtration rate [eGFR; 2.06, 1.91–2.23, the lowest [Q1] vs. the highest quartile (Q4)], unmarried (1.58, 1.10–2.27), Western China residency (3.21, 3.33–3.91), longer hypertension duration (1.97, 1.78–2.12, Q4 vs. Q1) and aspirin use (1.21, 1.14–1.29) were associated with HUA. In a multiple logistic regression analysis adjusted for clinical and metabolic parameters (model 2), female sex (OR, 95% CI, 1.34, 1.27–1.41), age of ≥65 years (1.09, 1.03–1.16), low eGFR (2.35, 2.19–2.52, Q1 vs. Q4), new–onset hypertension (2.01, 1.73–2.33), higher quartile of fasting blood glucose (FBG), triglyceride (TG), low density lipoprotein cholesterol (LDL-C) levels, and body mass index (BMI) were associated with higher risk of HUA (1.89, 1.76–2.03; 2.15, 1.99–2.31; 2.86, 2.67–3.06; 1.27, 1.27–1.36, respectively, Q4 vs. Q1). Losartan, valsartan, and nifedipine were associated with lower risk of HUA (OR, 95% CI, 0.77, 0.67–0.88, 0.68, 0.60–0.77; 0.87, 0.77–0.99, 0.79, 0.70–0.89 and 0.80, 0.70–0.91, 0.82, 0.72–0.92), respectively, in models 1 and 2. Conclusions The prevalence rate of HUA in Chinese hypertensive patients was 38.7%. Female sex, aging (≥65 years), and low eGFR were independent predictors of HUA. HUA was lower among the patients who were taking losartan, valsartan, and nifedipine. Western region residents, new-onset hypertension, longer hypertension duration, aspirin use, higher FBG, TG, LDL-C levels and BMI were potential risk factors for HUA.
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Affiliation(s)
- Jing Liu
- Department of Cardiology, Peking University People's Hospital, Beijing, China
| | - Luyuan Chen
- Department of Cardiology, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Hong Yuan
- Department of Cardiology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Kai Huang
- Department of Cardiology, Union Hospital Affiliated with Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Guangping Li
- Department of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Ningling Sun
- Department of Cardiology, Peking University People's Hospital, Beijing, China
| | - Yong Huo
- Department of Cardiology, Peking University First Hospital, Beijing, Chin
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Zhu J, Zeng Y, Zhang H, Qu Y, Ying Z, Sun Y, Hu Y, Chen W, Yang H, Yang J, Song H. The Association of Hyperuricemia and Gout With the Risk of Cardiovascular Diseases: A Cohort and Mendelian Randomization Study in UK Biobank. Front Med (Lausanne) 2021; 8:817150. [PMID: 35400029 PMCID: PMC8985123 DOI: 10.3389/fmed.2021.817150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/29/2021] [Indexed: 12/22/2022] Open
Abstract
Background The association between hyperuricemia/gout with cardiovascular diseases (CVD) have been investigated. However, whether the magnitude of associations differs between hyperuricemia and gout, and the causality of these associations, remains inconclusive. Methods Based on UK Biobank, we conducted a cohort analysis including 431,967 participants, who were categorized as gout, hyperuricemia, and normal groups at recruitment, and followed up for CVD until December 2019. The phenotypic association of hyperuricemia/gout with CVD was estimated by Cox regression, adjusting for multiple confounders. Further exploration on the causality of such links was performed using Mendelian Randomization (MR) analysis, where we selected exclusive genetic variants for hyperuricemia and for gout based on summary GWAS data from independent populations. Results During mean 10.20 years of follow-up, hyperuricemia patients were associated with increased CVD (HR = 1.33, 95% CI: 1.29-1.36), compared to individuals who were free of hyperuricemia/gout. The risk elevation was even higher for gout patients (HR = 1.54, 95% CI: 1.48-1.62). Furthermore, we found significantly positive association between genetic liability for hyperuricemia and CVD in both one-sample (OR = 1.06, 95% CI: 1.02-1.11) and two-sample (OR = 1.09, 95% CI: 1.03-1.16) MR analysis. However, genetic liability for gout was not associated with CVD (OR = 0.89, 95% CI: 0.79-1.01 in one-sample, and OR = 0.92, 95% CI: 0.82-1.21 in two-sample MR analysis). Conclusion Individuals with hyperuricemia/gout were at increased risk of various types of CVD. As the MR analyses suggest a causal effect of hyperuricemia, but not gout, on CVD, these results indicate the possible effects of other gout-associated factors on the development of CVD, in addition to the uric acid pathway.
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Affiliation(s)
- Jianwei Zhu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Hanyue Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuanyuan Qu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Zhiye Ying
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yajing Sun
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yao Hu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Wenwen Chen
- Division of Nephrology, Kidney Research Institute, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Huazhen Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jing Yang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China.,Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
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Chen B, Lu C, Gu HQ, Li Y, Zhang G, Lio J, Luo X, Zhang L, Hu Y, Lan X, Chen Z, Xie Q, Pan H. Serum Uric Acid Concentrations and Risk of Adverse Outcomes in Patients With COVID-19. Front Endocrinol (Lausanne) 2021; 12:633767. [PMID: 34025575 PMCID: PMC8134697 DOI: 10.3389/fendo.2021.633767] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/07/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Although hyperuricemia frequently associates with respiratory diseases, patients with severe coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) can show marked hypouricemia. Previous studies on the association of serum uric acid with risk of adverse outcomes related to COVID-19 have produced contradictory results. The precise relationship between admission serum uric acid and adverse outcomes in hospitalized patients is unknown. METHODS Data of patients affected by laboratory-confirmed COVID-19 and admitted to Leishenshan Hospital were retrospectively analyzed. The primary outcome was composite and comprised events, such as intensive care unit (ICU) admission, mechanical ventilation, or mortality. Logistic regression analysis was performed to explore the association between serum concentrations of uric acid and the composite outcome, as well as each of its components. To determine the association between serum uric acid and in-hospital adverse outcomes, serum uric acid was also categorized by restricted cubic spline, and the 95% confidence interval (CI) was used to estimate odds ratios (OR). RESULTS The study cohort included 1854 patients (mean age, 58 years; 52% women). The overall mean ± SD of serum levels of uric acid was 308 ± 96 µmol/L. Among them, 95 patients were admitted to ICU, 75 patients received mechanical ventilation, and 38 died. In total, 114 patients reached composite end-points (have either ICU admission, mechanical ventilation or death) during hospitalization. Compared with a reference group with estimated baseline serum uric acid of 279-422 µmol/L, serum uric acid values ≥ 423 µmol/L were associated with an increased risk of composite outcome (OR, 2.60; 95% CI, 1.07- 6.29) and mechanical ventilation (OR, 3.01; 95% CI, 1.06- 8.51). Serum uric acid ≤ 278 µmol/L was associated with an increased risk of the composite outcome (OR, 2.07; 95% CI, 1.18- 3.65), ICU admission (OR, 2.18; 95% CI, 1.17- 4.05]), and mechanical ventilation (OR, 2.13; 95% CI, 1.06- 4.28), as assessed by multivariate analysis. CONCLUSIONS This study shows that the association between admission serum uric acid and composite outcome of COVID-19 patients was U-shaped. In particular, we found that compared with baseline serum uric acid levels of 279-422 µmol/L, values ≥ 423 µmol/L were associated with an increased risk of composite outcome and mechanical ventilation, whereas levels ≤ 278 µmol/L associated with increased risk of composite outcome, ICU admission and mechanical ventilation.
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Affiliation(s)
- Bo Chen
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
| | - Chenyang Lu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
| | - Hong-Qiu Gu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Center for Healthcare Quality Management in Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yang Li
- Department of Endocrinology, West China Hospital, Sichuan University, Chengdu, China
| | - Guqin Zhang
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jonathan Lio
- Internal Medicine, University of Chicago, Chicago, IL, United States
| | - Xiongyan Luo
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
| | - Lingshu Zhang
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
| | - Yidan Hu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaomeng Lan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zerong Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Qibing Xie
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Qibing Xie, ; Huaqin Pan,
| | - Huaqin Pan
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Critical Care Medicine, Leishenshan Hospital, Wuhan, China
- Clinical Research Center of Hubei Critical Care Medicine, Wuhan, China
- *Correspondence: Qibing Xie, ; Huaqin Pan,
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Saito Y, Tanaka A, Node K, Kobayashi Y. Uric acid and cardiovascular disease: A clinical review. J Cardiol 2020; 78:51-57. [PMID: 33388217 DOI: 10.1016/j.jjcc.2020.12.013] [Citation(s) in RCA: 142] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 12/18/2020] [Indexed: 01/12/2023]
Abstract
Uric acid, the end-product of purine metabolism in humans, is not only a cause of gout, but also may play roles in developing cardiovascular diseases such as hypertension, atrial fibrillation, chronic kidney disease, heart failure, coronary artery disease, and cardiovascular death. Several clinical investigations have reported serum uric acid as a predictive marker for cardiovascular outcomes. Although the causal relationship of hyperuricemia to cardiovascular diseases remains controversial, there has been a growing interest in uric acid because of the increased prevalence of hyperuricemia worldwide. This review article summarizes current evidence concerning the relation between hyperuricemia and cardiovascular diseases.
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Affiliation(s)
- Yuichi Saito
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba 260-8677, Japan.
| | - Atsushi Tanaka
- Department of Cardiovascular Medicine, Saga University, Saga, Japan
| | - Koichi Node
- Department of Cardiovascular Medicine, Saga University, Saga, Japan
| | - Yoshio Kobayashi
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba 260-8677, Japan
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Mackenzie IS, Ford I, Nuki G, Hallas J, Hawkey CJ, Webster J, Ralston SH, Walters M, Robertson M, De Caterina R, Findlay E, Perez-Ruiz F, McMurray JJV, MacDonald TM. Long-term cardiovascular safety of febuxostat compared with allopurinol in patients with gout (FAST): a multicentre, prospective, randomised, open-label, non-inferiority trial. Lancet 2020; 396:1745-1757. [PMID: 33181081 DOI: 10.1016/s0140-6736(20)32234-0] [Citation(s) in RCA: 174] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND Febuxostat and allopurinol are urate-lowering therapies used to treat patients with gout. Following concerns about the cardiovascular safety of febuxostat, the European Medicines Agency recommended a post-licensing study assessing the cardiovascular safety of febuxostat compared with allopurinol. METHODS We did a prospective, randomised, open-label, blinded-endpoint, non-inferiority trial of febuxostat versus allopurinol in patients with gout in the UK, Denmark, and Sweden. Eligible patients were 60 years or older, already receiving allopurinol, and had at least one additional cardiovascular risk factor. Those who had myocardial infarction or stroke in the previous 6 months or who had severe congestive heart failure or severe renal impairment were excluded. After a lead-in phase in which allopurinol dose was optimised towards achieving a serum urate concentration of less than 0·357 mmol/L (<6 mg/dL), patients were randomly assigned (1:1, with stratification according to previous cardiovascular events) to continue allopurinol (at the optimised dose) or start febuxostat at 80 mg/day, increasing to 120 mg/day if necessary to achieve the target serum urate concentration. The primary outcome was a composite of hospitalisation for non-fatal myocardial infarction or biomarker-positive acute coronary syndrome; non-fatal stroke; or cardiovascular death. The hazard ratio (HR) for febuxostat versus allopurinol in a Cox proportional hazards model (adjusted for the stratification variable and country) was assessed for non-inferiority (HR limit 1·3) in an on-treatment analysis. This study is registered with the EU Clinical Trials Register (EudraCT 2011-001883-23) and ISRCTN (ISRCTN72443728) and is now closed. FINDINGS From Dec 20, 2011, to Jan 26, 2018, 6128 patients (mean age 71·0 years [SD 6·4], 5225 [85·3%] men, 903 [14·7%] women, 2046 [33·4%] with previous cardiovascular disease) were enrolled and randomly allocated to receive allopurinol (n=3065) or febuxostat (n=3063). By the study end date (Dec 31, 2019), 189 (6·2%) patients in the febuxostat group and 169 (5·5%) in the allopurinol group withdrew from all follow-up. Median follow-up time was 1467 days (IQR 1029-2052) and median on-treatment follow-up was 1324 days (IQR 870-1919). For incidence of the primary endpoint, on-treatment, febuxostat (172 patients [1·72 events per 100 patient-years]) was non-inferior to allopurinol (241 patients [2·05 events per 100 patient-years]; adjusted HR 0·85 [95% CI 0·70-1·03], p<0·0001). In the febuxostat group, 222 (7·2%) of 3063 patients died and 1720 (57·3%) of 3001 in the safety analysis set had at least one serious adverse event (with 23 events in 19 [0·6%] patients related to treatment). In the allopurinol group, 263 (8·6%) of 3065 patients died and 1812 (59·4%) of 3050 had one or more serious adverse events (with five events in five [0·2%] patients related to treatment). Randomised therapy was discontinued in 973 (32·4%) patients in the febuxostat group and 503 (16·5%) patients in the allopurinol group. INTERPRETATION Febuxostat is non-inferior to allopurinol therapy with respect to the primary cardiovascular endpoint, and its long-term use is not associated with an increased risk of death or serious adverse events compared with allopurinol. FUNDING Menarini, Ipsen, and Teijin Pharma Ltd.
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Affiliation(s)
- Isla S Mackenzie
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Ian Ford
- The Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - George Nuki
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | | | | | - John Webster
- Clinical Pharmacology Unit, University of Aberdeen, Aberdeen, UK
| | - Stuart H Ralston
- Molecular Medicine Centre, University of Edinburgh, Edinburgh, UK
| | - Matthew Walters
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Michele Robertson
- The Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Raffaele De Caterina
- University of Pisa, Pisa University Hospital, and Fondazione VillaSerena per la Ricerca, CittàSant'Angelo, Pescara, Italy
| | - Evelyn Findlay
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Fernando Perez-Ruiz
- Osakidetza, OSI EE-Cruces, Cruces University Hospital-Rheumatology Division, Biskay, Spain
| | - John J V McMurray
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Thomas M MacDonald
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK.
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Grant AJ, Burgess S. An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting. Biostatistics 2020; 23:609-625. [PMID: 33155035 PMCID: PMC9007434 DOI: 10.1093/biostatistics/kxaa045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 09/14/2020] [Accepted: 09/26/2020] [Indexed: 01/21/2023] Open
Abstract
Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method that uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can be used where there are any number of potential pleiotropic covariates up to the number of genetic variants less one. We show the results of simulation studies that demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.
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Affiliation(s)
- Andrew J Grant
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK and Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
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Fatima T, Nilsson PM, Turesson C, Dehlin M, Dalbeth N, Jacobsson LTH, Kapetanovic MC. The absolute risk of gout by clusters of gout-associated comorbidities and lifestyle factors-30 years follow-up of the Malmö Preventive Project. Arthritis Res Ther 2020; 22:244. [PMID: 33066806 PMCID: PMC7566061 DOI: 10.1186/s13075-020-02339-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/02/2020] [Indexed: 12/11/2022] Open
Abstract
Background Gout is predicted by a number of comorbidities and lifestyle factors. We aimed to identify discrete phenotype clusters of these factors in a Swedish population-based health survey. In these clusters, we calculated and compared the incidence and relative risk of gout. Methods Cluster analyses were performed to group variables with close proximity and to obtain homogenous clusters of individuals (n = 22,057) in the Malmö Preventive Project (MPP) cohort. Variables clustered included obesity, kidney dysfunction, diabetes mellitus (DM), hypertension, cardiovascular disease (CVD), dyslipidemia, pulmonary dysfunction (PD), smoking, and the use of diuretics. Incidence rates and hazard ratios (HRs) for gout, adjusted for age and sex, were computed for each cluster. Results Five clusters (C1–C5) were identified. Cluster C1 (n = 16,063) was characterized by few comorbidities. All participants in C2 (n = 750) had kidney dysfunction (100%), and none had CVD. In C3 (n = 528), 100% had CVD and most participants were smokers (74%). C4 (n = 3673) had the greatest fractions of obesity (34%) and dyslipidemia (74%). In C5 (n = 1043), proportions with DM (51%), hypertension (54%), and diuretics (52%) were highest. C1 was by far the most common in the population (73%), followed by C4 (17%). These two pathways included 86% of incident gout cases. The four smaller clusters (C2–C5) had higher incidence rates and a 2- to 3-fold increased risk for incident gout. Conclusions Five distinct clusters based on gout-related comorbidities and lifestyle factors were identified. Most incident gout cases occurred in the cluster of few comorbidities, and the four comorbidity pathways had overall a modest influence on the incidence of gout.
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Affiliation(s)
- Tahzeeb Fatima
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. .,Lund Arthritis Research Group, Lund University, Lund, Sweden.
| | - Peter M Nilsson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Carl Turesson
- Rheumatology, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Department of Rheumatology, Skåne University Hospital, Malmö, Sweden
| | - Mats Dehlin
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Lennart T H Jacobsson
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Meliha C Kapetanovic
- Department of Clinical Sciences Lund, Section of Rheumatology, Lund University and Skåne University Hospital, Lund, Sweden
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40
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Panevin TS, Eliseev MS, Shestakova MV, Nasonov EL. [Advantages of therapy with sodium glucose cotransporter type 2 inhibitors in patients with type 2 diabetes mellitus in combination with hyperuricemia and gout]. TERAPEVT ARKH 2020; 92:110-118. [PMID: 32598783 DOI: 10.26442/00403660.2020.05.000633] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Indexed: 12/27/2022]
Abstract
Currently, only two drugs for reducing uric acid (UA), allopurinol and febuxostat, are registered in the Russian Federation, but their use does not allow to achieve the target level of UA in all cases. According to the results of numerous randomized trials, hyperuricemia and gout are associated with the corresponding components of the metabolic syndrome, including diabetes mellitus. The influence of factors is due to the need to search for new drugs that have a complex effect on several components of metabolic syndrome at once. Potentially attractive in this regard is a new group of drugs for the treatment of type 2 diabetes mellitus inhibitors of the sodium-glucose cotransporter of type 2, which, in addition to the main hypoglycemic actions, showed positive effects on the cardiovascular system, kidneys, as well as lowering UA.
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Affiliation(s)
- T S Panevin
- Nasonova Research Institute of Rheumatology.,National Medical Research Center for Endocrinology
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41
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Cheng D, Hu C, Du R, Qi H, Lin L, Wu X, Ma L, Peng K, Li M, Xu M, Xu Y, Bi Y, Wang W, Chen Y, Lu J. Serum uric acid and risk of incident diabetes in middle-aged and elderly Chinese adults: prospective cohort study. Front Med 2020; 14:802-810. [PMID: 32350764 DOI: 10.1007/s11684-019-0723-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 09/09/2019] [Indexed: 01/08/2023]
Abstract
The association between serum uric acid and the risk of incident diabetes in Chinese adults remains unknown. This study aimed to investigate this association in a community-dwelling population aged ≥ 40 years in Shanghai, China. Oral glucose tole3rance test was conducted during baseline and follow-up visits. Relative risk regression was utilized to examine the associations between baseline gender-specific serum uric acid levels and incident diabetes risk. A total of 613 (10.3%) incident diabetes cases were identified during the follow-up visit after 4.5 years. Fasting plasma glucose, postload glucose, and glycated hemoglobin A1c during the follow-up visit progressively increased across the sex-specific quartiles of serum uric acid (all Ps < 0.05). The incidence rate of diabetes increased across the quartiles of serum uric acid (7.43%, 8.77%, 11.47%, and 13.43%). Multivariate adjusted regression analysis revealed that individuals in the highest quartile had 1.36-fold increased risk of diabetes compared with those in the lowest quartile of serum uric acid (odds ratio (95% confidence interval) = 1.36 (1.06-1.73)). Stratified analysis indicated that the association was only observed in women. Accordingly, serum uric acid was associated with the increased risk of incident diabetes among middle-aged and elderly Chinese women.
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Affiliation(s)
- Di Cheng
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chunyan Hu
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Rui Du
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Hongyan Qi
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lin Lin
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Department of Endocrine and Metabolic Diseases, Ruijin Hospital North, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201821, China
| | - Xueyan Wu
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lina Ma
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Kui Peng
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital North, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201821, China.
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2020; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.2] [Citation(s) in RCA: 365] [Impact Index Per Article: 91.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 01/01/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework. Nat Commun 2020; 11:1010. [PMID: 32081875 PMCID: PMC7035387 DOI: 10.1038/s41467-020-14452-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 12/10/2019] [Indexed: 12/18/2022] Open
Abstract
In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we develop MR-TRYX, a framework that exploits horizontal pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure-outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We develop a multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses.
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44
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Joosten LAB, Crişan TO, Bjornstad P, Johnson RJ. Asymptomatic hyperuricaemia: a silent activator of the innate immune system. Nat Rev Rheumatol 2020; 16:75-86. [PMID: 31822862 PMCID: PMC7075706 DOI: 10.1038/s41584-019-0334-3] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/29/2019] [Indexed: 12/22/2022]
Abstract
Asymptomatic hyperuricaemia affects ~20% of the general population in the USA, with variable rates in other countries. Historically, asymptomatic hyperuricaemia was considered a benign laboratory finding with little clinical importance in the absence of gout or kidney stones. Yet, increasing evidence suggests that asymptomatic hyperuricaemia can predict the development of hypertension, obesity, diabetes mellitus and chronic kidney disease and might contribute to disease by stimulating inflammation. Although urate has been classically viewed as an antioxidant with beneficial effects, new data suggest that both crystalline and soluble urate activate various pro-inflammatory pathways. This Review summarizes what is known about the role of urate in the inflammatory response. Further research is needed to define the role of asymptomatic hyperuricaemia in these pro-inflammatory pathways.
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Affiliation(s)
- Leo A B Joosten
- Department of Medical Genetics, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
- Department of Internal Medicine and Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Tania O Crişan
- Department of Medical Genetics, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Petter Bjornstad
- Department of Medicine of the University of Colorado School of Medicine of the University Hospital, Aurora, CO, USA
| | - Richard J Johnson
- Department of Medicine of the University of Colorado School of Medicine of the University Hospital, Aurora, CO, USA.
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45
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Serum uric acid levels and cardiovascular mortality in a general Japanese population: the Hisayama Study. Hypertens Res 2020; 43:560-568. [DOI: 10.1038/s41440-019-0390-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 11/13/2019] [Accepted: 12/15/2019] [Indexed: 02/04/2023]
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46
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2019; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.1] [Citation(s) in RCA: 585] [Impact Index Per Article: 117.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2019] [Indexed: 12/20/2022] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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Lee CL, Wang JS. Effects of hyperuricemia on incident renal replacement therapy and all-cause mortality among patients with chronic kidney disease stages 3-5: a retrospective cohort study. SAO PAULO MED J 2019; 137:523-529. [PMID: 32159639 PMCID: PMC9754277 DOI: 10.1590/1516-3180.2019.0406211019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Findings regarding the effects of hyperuricemia on renal function and mortality have been inconsistent. OBJECTIVES To investigate the effects of hyperuricemia on incident renal replacement therapy and all-cause mortality among patients with chronic kidney disease (CKD). DESIGN AND SETTING Retrospective cohort study conducted in a medical center in Taiwan. METHODS Patients with CKD in stages 3-5, without histories of renal replacement therapy, were consecutively recruited from 2007 to 2013. Their medical history, laboratory and medication data were collected from hospital records. The mean uric acid level in the first year of follow-up was used for analyses. Hyperuricemia was defined as mean uric acid level ≥ 7.0 mg/dl in men or ≥ 6.0 mg/dl in women. The primary outcomes were incident renal replacement therapy and all-cause mortality, and these data were retrospectively collected from hospital records until the end of 2015. RESULTS A total of 4,381 patients were analyzed (mean age 71.0 ± 14.8 years; males 62.7%), and the median follow-up period was 2.5 years. Patients with hyperuricemia were at increased risk of incident renal replacement therapy and all-cause mortality, especially those with CKD in stages 4 or 5. Compared with patients with CKD in stage 3 and normouricemia, patients with CKD in stages 4 or 5 presented significantly higher risk of all-cause mortality only if they had hyperuricemia. CONCLUSIONS In patients with CKD in stages 3-5, hyperuricemia was associated with higher risk of incident renal replacement therapy and all-cause mortality. Whether treatment with uric acid-lowering drugs in these patients would improve their outcomes merits further investigation.
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Affiliation(s)
- Chia-Lin Lee
- MD, PhD. Assistant Professor, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
| | - Jun-Sing Wang
- MD, PhD. Assistant Professor, Department of Internal Medicine, Division of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan.
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Genetically determined serum urate levels and cardiovascular and other diseases in UK Biobank cohort: A phenome-wide mendelian randomization study. PLoS Med 2019; 16:e1002937. [PMID: 31626644 PMCID: PMC6799886 DOI: 10.1371/journal.pmed.1002937] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 09/17/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance. METHODS AND FINDINGS A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40-69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10-4). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization. CONCLUSION In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.
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Tin A, Marten J, Halperin Kuhns VL, Li Y, Wuttke M, Kirsten H, Sieber KB, Qiu C, Gorski M, Yu Z, Giri A, Sveinbjornsson G, Li M, Chu AY, Hoppmann A, O'Connor LJ, Prins B, Nutile T, Noce D, Akiyama M, Cocca M, Ghasemi S, van der Most PJ, Horn K, Xu Y, Fuchsberger C, Sedaghat S, Afaq S, Amin N, Ärnlöv J, Bakker SJL, Bansal N, Baptista D, Bergmann S, Biggs ML, Biino G, Boerwinkle E, Bottinger EP, Boutin TS, Brumat M, Burkhardt R, Campana E, Campbell A, Campbell H, Carroll RJ, Catamo E, Chambers JC, Ciullo M, Concas MP, Coresh J, Corre T, Cusi D, Felicita SC, de Borst MH, De Grandi A, de Mutsert R, de Vries APJ, Delgado G, Demirkan A, Devuyst O, Dittrich K, Eckardt KU, Ehret G, Endlich K, Evans MK, Gansevoort RT, Gasparini P, Giedraitis V, Gieger C, Girotto G, Gögele M, Gordon SD, Gudbjartsson DF, Gudnason V, Haller T, Hamet P, Harris TB, Hayward C, Hicks AA, Hofer E, Holm H, Huang W, Hutri-Kähönen N, Hwang SJ, Ikram MA, Lewis RM, Ingelsson E, Jakobsdottir J, Jonsdottir I, Jonsson H, Joshi PK, Josyula NS, Jung B, Kähönen M, Kamatani Y, Kanai M, Kerr SM, Kiess W, Kleber ME, Koenig W, Kooner JS, Körner A, Kovacs P, Krämer BK, Kronenberg F, Kubo M, Kühnel B, La Bianca M, Lange LA, Lehne B, Lehtimäki T, Liu J, Loeffler M, Loos RJF, Lyytikäinen LP, Magi R, Mahajan A, Martin NG, März W, Mascalzoni D, Matsuda K, Meisinger C, Meitinger T, Metspalu A, Milaneschi Y, O'Donnell CJ, Wilson OD, Gaziano JM, Mishra PP, Mohlke KL, Mononen N, Montgomery GW, Mook-Kanamori DO, Müller-Nurasyid M, Nadkarni GN, Nalls MA, Nauck M, Nikus K, Ning B, Nolte IM, Noordam R, O'Connell JR, Olafsson I, Padmanabhan S, Penninx BWJH, Perls T, Peters A, Pirastu M, Pirastu N, Pistis G, Polasek O, Ponte B, Porteous DJ, Poulain T, Preuss MH, Rabelink TJ, Raffield LM, Raitakari OT, Rettig R, Rheinberger M, Rice KM, Rizzi F, Robino A, Rudan I, Krajcoviechova A, Cifkova R, Rueedi R, Ruggiero D, Ryan KA, Saba Y, Salvi E, Schmidt H, Schmidt R, Shaffer CM, Smith AV, Smith BH, Spracklen CN, Strauch K, Stumvoll M, Sulem P, Tajuddin SM, Teren A, Thiery J, Thio CHL, Thorsteinsdottir U, Toniolo D, Tönjes A, Tremblay J, Uitterlinden AG, Vaccargiu S, van der Harst P, van Duijn CM, Verweij N, Völker U, Vollenweider P, Waeber G, Waldenberger M, Whitfield JB, Wild SH, Wilson JF, Yang Q, Zhang W, Zonderman AB, Bochud M, Wilson JG, Pendergrass SA, Ho K, Parsa A, Pramstaller PP, Psaty BM, Böger CA, Snieder H, Butterworth AS, Okada Y, Edwards TL, Stefansson K, Susztak K, Scholz M, Heid IM, Hung AM, Teumer A, Pattaro C, Woodward OM, Vitart V, Köttgen A. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels. Nat Genet 2019; 51:1459-1474. [PMID: 31578528 PMCID: PMC6858555 DOI: 10.1038/s41588-019-0504-x] [Citation(s) in RCA: 225] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 08/27/2019] [Indexed: 12/22/2022]
Abstract
Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.
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Affiliation(s)
- Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Welch Centre for Prevention, Epidemiology and Clinical Research, Baltimore, MD, USA.
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Yong Li
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Karsten B Sieber
- Target Sciences-Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | - Chengxiang Qiu
- Department of Medicine and Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mathias Gorski
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Zhi Yu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics & Gynecology, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | | | - Man Li
- Department of Medicine, Division of Nephrology and Hypertension, University of Utah, Salt Lake City, UT, USA
| | | | - Anselm Hoppmann
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Luke J O'Connor
- Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Bram Prins
- Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Teresa Nutile
- Institute of Genetics and Biophysics Adriano Buzzati-Traverso-CNR, Naples, Italy
| | - Damia Noce
- Eurac Research, Institute for Biomedicine, Bolzano, Italy
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Centre for Integrative Medical Sciences, Yokohama (Kanagawa), Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Massimiliano Cocca
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
| | - Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Yizhe Xu
- Department of Medicine, Division of Nephrology and Hypertension, University of Utah, Salt Lake City, UT, USA
| | | | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Saima Afaq
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
- Institute of Public Health & Social Sciences, Khyber Medical University, Peshawar, Pakistan
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
- School of Health and Social Studies, Dalarna University, Falun, Sweden
| | - Stephan J L Bakker
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | | | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ginevra Biino
- Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy
| | - Eric Boerwinkle
- Human Genetics Centre, University of Texas Health Science Centre, Houston, TX, USA
| | - Erwin P Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thibaud S Boutin
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Marco Brumat
- University of Trieste, Department of Medicine, Surgery and Health Sciences, Trieste, Italy
| | - Ralph Burkhardt
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Eric Campana
- University of Trieste, Department of Medicine, Surgery and Health Sciences, Trieste, Italy
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eulalia Catamo
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Cardiology, Ealing Hospital, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Marina Ciullo
- Institute of Genetics and Biophysics Adriano Buzzati-Traverso-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Tanguy Corre
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Daniele Cusi
- Institute of Biomedical Technologies, Italy National Research Council, Milano, Italy
- Bio4Dreams, Milano, Italy
| | | | - Martin H de Borst
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Aiko P J de Vries
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Centre, Leiden, the Netherlands
| | - Graciela Delgado
- Fifth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Ayşe Demirkan
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands
| | - Olivier Devuyst
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Katalin Dittrich
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Centre for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Georg Ehret
- Cardiology, Geneva University Hospitals, Geneva, Switzerland
| | - Karlhans Endlich
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Ron T Gansevoort
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Paolo Gasparini
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
- University of Trieste, Department of Medicine, Surgery and Health Sciences, Trieste, Italy
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Molecular Geriatrics, Uppsala University, Uppsala, Sweden
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Giorgia Girotto
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
- University of Trieste, Department of Medicine, Surgery and Health Sciences, Trieste, Italy
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine, Bolzano, Italy
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Vilmundur Gudnason
- Icelandic Heart Association, Kópavogur, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pavel Hamet
- Montreal University Hospital Research Centre, Centre Hospitalier de lUniversité de Montréal, Montreal, Quebec, Canada
- Medpharmgene, Montreal, Quebec, Canada
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Andrew A Hicks
- Eurac Research, Institute for Biomedicine, Bolzano, Italy
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Hilma Holm
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Centre, Shanghai, China
- Shanghai Industrial Technology Institute, Shanghai, China
| | - Nina Hutri-Kähönen
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Pediatrics, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA
- The Centre for Population Studies, National Heart, Lung, and Blood Institute, Framingham, MA, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Raychel M Lewis
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Johanna Jakobsdottir
- Icelandic Heart Association, Kópavogur, Iceland
- The Centre of Public Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Helgi Jonsson
- Landspitalinn University Hospital, Reykjavík, Iceland
- University of Iceland, Reykjavík, Iceland
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Navya Shilpa Josyula
- Geisinger Research, Biomedical and Translational Informatics Institute, Rockville, MD, USA
| | - Bettina Jung
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Centre for Integrative Medical Sciences, Yokohama (Kanagawa), Japan
- Kyoto-McGill International Collaborative School in Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masahiro Kanai
- Laboratory for Statistical Analysis, RIKEN Centre for Integrative Medical Sciences, Yokohama (Kanagawa), Japan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shona M Kerr
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Wieland Kiess
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Centre for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Marcus E Kleber
- Fifth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Biostatistics, University of Ulm, Ulm, Germany
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, 323 School of Public Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Antje Körner
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Centre for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Peter Kovacs
- Integrated Research and Treatment Centre Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Bernhard K Krämer
- Fifth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Michiaki Kubo
- RIKEN Centre for Integrative Medical Sciences, Yokohama (Kanagawa), Japan
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
| | - Martina La Bianca
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver-Anschutz Medical Campus, Aurora, CO, USA
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jun Liu
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Reedik Magi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Winfried März
- Fifth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | | | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
- Ludwig-Maximilians-Universität München at UNIKA-T Augsburg, Augsburg, Germany
| | - Thomas Meitinger
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Christopher J O'Donnell
- VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Otis D Wilson
- Vanderbilt University Medical Centre, Division of Nephrology & Hypertension, Nashville, TN, USA
| | - J Michael Gaziano
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center, VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands
| | - Martina Müller-Nurasyid
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University Munich, Munich, Germany
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Boting Ning
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Centre, Leiden, the Netherlands
| | | | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Thomas Perls
- Department of Medicine, Geriatrics Section, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Munich, Germany
| | - Mario Pirastu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, UOS of Sassari, Sassari, Italy
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Giorgio Pistis
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
- Gen-info Ltd, Zagreb, Croatia
| | - Belen Ponte
- Nephrology Service, Department of Specialties in Internal Medicine, University Hospitals of Geneva, Geneva, Switzerland
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Tanja Poulain
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ton J Rabelink
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Centre, Leiden, the Netherlands
- Einthoven Laboratory of Experimental Vascular Research, Leiden University Medical Centre, Leiden, the Netherlands
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Rainer Rettig
- Institute of Physiology, University Medicine Greifswald, Karlsburg, Germany
| | - Myriam Rheinberger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Federica Rizzi
- Department of Health Sciences, University of Milan, Milano, Italy
- ePhood Scientific Unit, ePhood SRL, Milano, Italy
| | - Antonietta Robino
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Alena Krajcoviechova
- Center for Cardiovascular Prevention, First Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czech Republic
- Thomayer Hospital, Prague, Czech Republic
| | - Renata Cifkova
- Center for Cardiovascular Prevention, First Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czech Republic
- Department of Medicine II, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics Adriano Buzzati-Traverso-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Kathleen A Ryan
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yasaman Saba
- Molecular Biology and Biochemistry, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Erika Salvi
- Department of Health Sciences, University of Milan, Milano, Italy
- Neurology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Christian M Shaffer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Albert V Smith
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | | | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Michael Stumvoll
- Division of Endocrinology, Nephrology and Rheumatology, University of Leipzig, Leipzig, Germany
| | | | - Salman M Tajuddin
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Andrej Teren
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Centre Leipzig, Leipzig, Germany
| | - Joachim Thiery
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Chris H L Thio
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | | | - Anke Tönjes
- Department of Endocrinology and Nephrology, University of Leipzig, Leipzig, Germany
| | - Johanne Tremblay
- Montreal University Hospital Research Centre, Centre Hospitalier de lUniversité de Montréal, Montreal, Quebec, Canada
- Centre de Recherche du CHUM, Montreal, Quebec, Canada
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Simona Vaccargiu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, UOS of Sassari, Sassari, Italy
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Durrer Centre for Cardiovascular Research, the Netherlands Heart Institute, Utrecht, the Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics plc, Oxford, UK
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Peter Vollenweider
- Internal Medicine, Department of Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Gerard Waeber
- Internal Medicine, Department of Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Neuherberg, Germany
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Munich, Germany
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sarah H Wild
- Centre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - James F Wilson
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London, UK
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Murielle Bochud
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Centre, Jackson, MS, USA
| | - Sarah A Pendergrass
- Geisinger Research, Biomedical and Translational Informatics Institute, Danville, PA, USA
| | - Kevin Ho
- Kidney Health Research Institute, Geisinger, Danville, PA, USA
- Department of Nephrology, Geisinger, Danville, PA, USA
| | - Afshin Parsa
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Department of Epidemiology, Department of Health Service, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern AG, Traunstein, Germany
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Adam S Butterworth
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Centre for Integrative Medical Sciences, Osaka, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Centre, Nashville, TN, USA
- Department of Veterans Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | | | - Katalin Susztak
- Department of Medicine and Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Adriana M Hung
- Vanderbilt University Medical Centre, Division of Nephrology & Hypertension, Nashville, TN, USA
- Department of Veterans Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | | | - Owen M Woodward
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany.
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Liang L, Hou X, Bainey KR, Zhang Y, Tymchak W, Qi Z, Li W, Banh HL. The association between hyperuricemia and coronary artery calcification development: A systematic review and meta-analysis. Clin Cardiol 2019; 42:1079-1086. [PMID: 31571239 PMCID: PMC6837029 DOI: 10.1002/clc.23266] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 09/04/2019] [Accepted: 09/06/2019] [Indexed: 12/14/2022] Open
Abstract
Hyperuricemia coincides with coronary artery calcification (CAC) development, but the role of serum uric acid (SUA) as a risk factor for CAC remains unclear. The objective of this study was to gain an insight into the association between SUA and CAC in adults by performing a meta-analysis. MEDLINE, EMBASE, the Cochrane Library, and EBSCO (CINAHL) were searched for relevant observational studies published until 2 June 2019. Studies were included only if they reported data on CAC presence (Agatston score > 0) or progression related to hyperuricemia in subclinical adult patients. The pooled estimates of crude and adjusted odds ratios (ORs) and 95% confidence interval (CI) were calculated to evaluate the association between CAC presence or progression and hyperuricemia. A total of 11 studies were identified involving 11 108 adults. The pooled OR based on the frequency of CAC presence showed that patients in the high SUA group had 1.806-fold risk for developing CAC (95% CI: 1.491-2.186) under the minimal threshold of hyperuricemia (more than 6 mg/dL or 357 μmoL/L). When SUA levels were analyzed as categorical variables, the pooled estimate of adjusted ORs was 1.48 (95% CI: 1.23-1.79) for CAC presence. Additionally, for each increase of 1 mg/dL of SUA level, the risk of CAC progression was increased by 31% (95% CI: 1.15-1.49) with an average follow-up duration ranged from 4.6 to 6.1 years. Hyperuricemia is closely associated with increased risk of CAC development and CAC progression in asymptomatic patients.
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Affiliation(s)
- Ling Liang
- Department of Cardiology, The First Affiliated Hospital of Xiamen University, Xiamen, China.,Department of Cardiology, First Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Xianghua Hou
- Department of Nephrology, The First Affiliated Hospital of Xiamen University, Xiamen, China.,Department of Nephrology, First Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Kevin R Bainey
- Division of Cardiology, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Yanlin Zhang
- Department of Nephrology, The First Affiliated Hospital of Xiamen University, Xiamen, China.,Department of Nephrology, First Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Wayne Tymchak
- Division of Cardiology, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Zhongquan Qi
- Institute of Organ Transplantation, Xiamen University, Xiamen, China
| | - Weihua Li
- Department of Cardiology, The First Affiliated Hospital of Xiamen University, Xiamen, China.,Department of Cardiology, First Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Hoan Linh Banh
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
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