You are in the accessibility menu

Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/74386
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDe Oliveira, Erick Prado-
dc.contributor.authorMoreto, Fernando-
dc.contributor.authorSilveira, Liciana Vaz de Arruda-
dc.contributor.authorBurini, Roberto Carlos-
dc.date.accessioned2014-05-27T11:28:10Z-
dc.date.accessioned2016-10-25T18:42:45Z-
dc.date.available2014-05-27T11:28:10Z-
dc.date.available2016-10-25T18:42:45Z-
dc.date.issued2013-01-16-
dc.identifierhttp://dx.doi.org/10.1186/1475-2891-12-11-
dc.identifier.citationNutrition Journal, v. 12, n. 1, 2013.-
dc.identifier.issn1475-2891-
dc.identifier.urihttp://hdl.handle.net/11449/74386-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/74386-
dc.description.abstractBackground: High plasma uric acid (UA) is a prerequisite for gout and is also associated with the metabolic syndrome and its components and consequently risk factors for cardiovascular diseases. Hence, the management of UA serum concentrations would be essential for the treatment and/or prevention of human diseases and, to that end, it is necessary to know what the main factors that control the uricemia increase. The aim of this study was to evaluate the main factors associated with higher uricemia values analyzing diet, body composition and biochemical markers. Methods. 415 both gender individuals aged 21 to 82 years who participated in a lifestyle modification project were studied. Anthropometric evaluation consisted of weight and height measurements with later BMI estimation. Waist circumference was also measured. The muscle mass (Muscle Mass Index - MMI) and fat percentage were measured by bioimpedance. Dietary intake was estimated by 24-hour recalls with later quantification of the servings on the Brazilian food pyramid and the Healthy Eating Index. Uric acid, glucose, triglycerides (TG), total cholesterol, urea, creatinine, gamma-GT, albumin and calcium and HDL-c were quantified in serum by the dry-chemistry method. LDL-c was estimated by the Friedewald equation and ultrasensitive C-reactive protein (CRP) by the immunochemiluminiscence method. Statistical analysis was performed by the SAS software package, version 9.1. Linear regression (odds ratio) was performed with a 95% confidence interval (CI) in order to observe the odds ratio for presenting UA above the last quartile (♂UA > 6.5 mg/dL and ♀ UA > 5 mg/dL). The level of significance adopted was lower than 5%. Results: Individuals with BMI ≥ 25 kg/m§ssup§2§esup§ OR = 2.28(1.13-4.6) and lower MMI OR = 13.4 (5.21-34.56) showed greater chances of high UA levels even after all adjustments (gender, age, CRP, gamma-gt, LDL, creatinine, urea, albumin, HDL-c, TG, arterial hypertension and glucose). As regards biochemical markers, higher triglycerides OR = 2.76 (1.55-4.90), US-CRP OR = 2.77 (1.07-7.21) and urea OR = 2.53 (1.19-5.41) were associated with greater chances of high UA (adjusted for gender, age, BMI, waist circumference, MMI, glomerular filtration rate, and MS). No association was found between diet and UA. Conclusions: The main factors associated with UA increase were altered BMI (overweight and obesity), muscle hypotrophy (MMI), higher levels of urea, triglycerides, and CRP. No dietary components were found among uricemia predictors. © 2013 de Oliveira et al.; licensee BioMed Central Ltd.en
dc.language.isoeng-
dc.sourceScopus-
dc.subjectBody composition-
dc.subjectDiet-
dc.subjectInflammation-
dc.subjectMetabolic syndrome components-
dc.subjectUric acid-
dc.subjectalbumin-
dc.subjectbiochemical marker-
dc.subjectC reactive protein-
dc.subjectcalcium-
dc.subjectcholesterol-
dc.subjectcreatinine-
dc.subjectgamma glutamyltransferase-
dc.subjectglucose-
dc.subjecthigh density lipoprotein cholesterol-
dc.subjectlow density lipoprotein cholesterol-
dc.subjecttriacylglycerol-
dc.subjecturea-
dc.subjecturic acid-
dc.subjectadult-
dc.subjectaged-
dc.subjectalbumin blood level-
dc.subjectanalytic method-
dc.subjectanthropometry-
dc.subjectbioimpedance-
dc.subjectblood chemistry-
dc.subjectblood sampling-
dc.subjectbody composition-
dc.subjectbody fat-
dc.subjectbody height-
dc.subjectbody mass-
dc.subjectbody weight-
dc.subjectcalcium blood level-
dc.subjectchemiluminescence immunoassay-
dc.subjectcholesterol blood level-
dc.subjectclinical article-
dc.subjectclinical assessment tool-
dc.subjectcontrolled study-
dc.subjectcreatinine blood level-
dc.subjectdata analysis software-
dc.subjectdiagnostic test accuracy study-
dc.subjectdiet-
dc.subjectdietary intake-
dc.subjectdry chemistry method-
dc.subjectfemale-
dc.subjectfood chain-
dc.subjectgamma glutamyl transferase blood level-
dc.subjectgender-
dc.subjectglucose blood level-
dc.subjectHealthy Eating Index-
dc.subjecthuman-
dc.subjecthyperuricemia-
dc.subjectimmunochemiluminiscence-
dc.subjectimpedance-
dc.subjectlifestyle modification-
dc.subjectmale-
dc.subjectmetabolic syndrome X-
dc.subjectmuscle mass-
dc.subjectMuscle Mass Index-
dc.subjectobesity-
dc.subjectrisk factor-
dc.subjectsensitivity and specificity-
dc.subjectserum-
dc.subjecttriacylglycerol blood level-
dc.subjecturea blood level-
dc.subjecturic acid blood level-
dc.subjectwaist circumference-
dc.subjectAdult-
dc.subjectAged-
dc.subjectAged, 80 and over-
dc.subjectBiological Markers-
dc.subjectBlood Glucose-
dc.subjectBlood Pressure-
dc.subjectBody Composition-
dc.subjectBody Mass Index-
dc.subjectC-Reactive Protein-
dc.subjectCalcium-
dc.subjectCardiovascular Diseases-
dc.subjectCholesterol, HDL-
dc.subjectCholesterol, LDL-
dc.subjectCreatinine-
dc.subjectCross-Sectional Studies-
dc.subjectElectric Impedance-
dc.subjectEnergy Intake-
dc.subjectFatty Acids, Unsaturated-
dc.subjectFemale-
dc.subjectHumans-
dc.subjectLife Style-
dc.subjectMale-
dc.subjectMetabolic Syndrome X-
dc.subjectMiddle Aged-
dc.subjectOdds Ratio-
dc.subjectSerum Albumin-
dc.subjectTriglycerides-
dc.subjectUrea-
dc.subjectUric Acid-
dc.subjectWaist Circumference-
dc.subjectYoung Adult-
dc.titleDietary, anthropometric, and biochemical determinants of uric acid in free-living adultsen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationCenter for Exercise Metabolism and Nutrition (CeMENutri) Department of Public Health Botucatu School of Medicine (UNESP), Botucatu-
dc.description.affiliationDepartment of Pathology Botucatu School of Medicine (UNESP), Botucatu-
dc.description.affiliationDepartment of Bioestatistic Bioscience Institute (UNESP), Botucatu-
dc.description.affiliationCeMENutri Departamento de Saúde Pública Faculdade de Medicina UNESP, Distrito de Rubião Jr. s/n, Botucatu, SP 18.618-970-
dc.description.affiliationUnespCenter for Exercise Metabolism and Nutrition (CeMENutri) Department of Public Health Botucatu School of Medicine (UNESP), Botucatu-
dc.description.affiliationUnespDepartment of Pathology Botucatu School of Medicine (UNESP), Botucatu-
dc.description.affiliationUnespDepartment of Bioestatistic Bioscience Institute (UNESP), Botucatu-
dc.description.affiliationUnespCeMENutri Departamento de Saúde Pública Faculdade de Medicina UNESP, Distrito de Rubião Jr. s/n, Botucatu, SP 18.618-970-
dc.identifier.doi10.1186/1475-2891-12-11-
dc.identifier.wosWOS:000315383600001-
dc.rights.accessRightsAcesso aberto-
dc.identifier.file2-s2.0-84872151884.pdf-
dc.relation.ispartofNutrition Journal-
dc.identifier.scopus2-s2.0-84872151884-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

There are no files associated with this item.
 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.