Claudio Borghi1, Fernando Rodriguez-Artalejo2, Guy De Backer3, Jean Dallongeville4, Jesús Medina5, Javier Nuevo5, Eliseo Guallar6, Joep Perk7, José R Banegas8, Florence Tubach9, Carine Roy10, Julian P Halcox11. 1. Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy. Electronic address: claudio.borghi@unibo.it. 2. Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/IdiPaz, Madrid, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; IMDEA-Food Institute, Madrid, Spain; CEI UAM+CSIC, Madrid, Spain. 3. Department of Public Health, University of Ghent, Ghent, Belgium. 4. INSERM U 744, Institut Pasteur de Lille, Université Lille-Nord de France, Lille, France. 5. Medical Evidence and Observational Research, Global Medical Affairs, AstraZeneca, Madrid, Spain. 6. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Welch Center of Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 7. School of Health and Caring Sciences, Linnaeus University, Kalmar, Sweden. 8. Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/IdiPaz, Madrid, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain. 9. Département de Biostatistique, Santé Publique et Information Médicale, Centre de Pharmacoépidémiologie (Cephepi), AP-HP, Hôpital Pitié-Salpétrière, Paris, France; INSERM CIC-EC 1425, ECEVE, UMR 1123, Paris, France; Université Pierre et Marie Curie, Sorbonne Universités, Paris, France. 10. Département d'Epidémiologie et Recherche Clinique, Centre de Pharmacoépidémiologie (Cephepi), Assistance Publique - Hôpitaux de Paris, Hôpital Bichat, Paris, France. 11. Institute of Life Sciences 2, Swansea University Medical School, Singleton Park, Swansea, UK.
Abstract
BACKGROUND: Reports are conflicting on whether serum uric acid (sUA) levels are independently associated with increased cardiovascular (CV) death risk. METHODS: This post hoc analysis assessed the relationship between sUA levels and CV death risk score in 7531 patients from the cross-sectional, multinational EURIKA study (NCT00882336). Patients had at least one CV risk factor but no clinical CV disease. Ten-year risk of CV death was estimated using SCORE-HDL and SCORE algorithms, categorized as low (<1%), intermediate (1% to <5%), high (≥5% to <10%) or very high (≥10%). RESULTS: Mean serum sUA levels increased significantly with increasing CV death risk category in the overall population and in subgroups stratified by diuretics use or renal function (all P<0.0001). Multivariate ordinal logistic regression analyses, adjusted for factors significantly associated with CV death risk in univariate analyses (study country, body mass index, number of CV risk factors and comorbidities, use of lipid lowering therapies, antihypertensives and antidiabetics), showed a significant association between sUA levels and SCORE-HDL category in the overall population (OR: 1.39 [95% CI: 1.34-1.44]) and all subgroups (using diuretics: 1.32 [1.24-1.40]; not using diuretics: 1.46 [1.39-1.53]; estimated glomerular filtration rate [eGFR]<60ml/min/1.73m2: 1.30 [1.22-1.38]; eGFR≥60ml/min/1.73m2: 1.44 [1.38-1.51]; all P<0.0001). Similar results were obtained when using SCORE. CONCLUSIONS: Higher sUA levels are associated with progressively higher 10-year CV death risk score in patients with at least one CV risk factor but no CV disease.
BACKGROUND: Reports are conflicting on whether serum uric acid (sUA) levels are independently associated with increased cardiovascular (CV) death risk. METHODS: This post hoc analysis assessed the relationship between sUA levels and CV death risk score in 7531 patients from the cross-sectional, multinational EURIKA study (NCT00882336). Patients had at least one CV risk factor but no clinical CV disease. Ten-year risk of CV death was estimated using SCORE-HDL and SCORE algorithms, categorized as low (<1%), intermediate (1% to <5%), high (≥5% to <10%) or very high (≥10%). RESULTS: Mean serum sUA levels increased significantly with increasing CV death risk category in the overall population and in subgroups stratified by diuretics use or renal function (all P<0.0001). Multivariate ordinal logistic regression analyses, adjusted for factors significantly associated with CV death risk in univariate analyses (study country, body mass index, number of CV risk factors and comorbidities, use of lipid lowering therapies, antihypertensives and antidiabetics), showed a significant association between sUA levels and SCORE-HDL category in the overall population (OR: 1.39 [95% CI: 1.34-1.44]) and all subgroups (using diuretics: 1.32 [1.24-1.40]; not using diuretics: 1.46 [1.39-1.53]; estimated glomerular filtration rate [eGFR]<60ml/min/1.73m2: 1.30 [1.22-1.38]; eGFR≥60ml/min/1.73m2: 1.44 [1.38-1.51]; all P<0.0001). Similar results were obtained when using SCORE. CONCLUSIONS: Higher sUA levels are associated with progressively higher 10-year CV death risk score in patients with at least one CV risk factor but no CV disease.
Keywords:
Cardiovascular risk; European Study on Cardiovascular Risk Prevention and Management in Usual Daily Practice (EURIKA); Serum uric acid; Systematic COronary Risk Evaluation (SCORE); Systematic COronary Risk Evaluation algorithm including high-density lipoprotein cholesterol (SCORE-HDL)
Authors: Alena Krajčoviechová; Peter Wohlfahrt; Jan Bruthans; Pavel Šulc; Věra Lánská; Lenka Eremiášová; Jan Pudil; Aleš Linhart; Jan Filipovský; Otto Mayer; Jiří Widimský; Milan Blaha; Claudio Borghi; Renata Cífková Journal: J Clin Hypertens (Greenwich) Date: 2020-04-09 Impact factor: 3.738
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