title: Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study.
abstract: OBJECTIVE
  Despite some epidemiologic research demonstrating a positive relationship between serum uric acid (SUA) levels and the prevalence of metabolic syndrome (MetS), prospective data on SUA as a predictor of MetS incidence are limited.
  
  
  METHODS
  The authors examined SUA as a risk marker for incident MetS in a prospective study of 3857 subjects who were free of MetS at baseline recruitment. Hyperuricemia was defined as SUA ≥7.7 mg/dL for men and ≥6.6 mg/dL for women. The MetS was defined according to a unified criteria set by several major organizations.
  
  
  RESULTS
  During a mean follow-up of 5.41 years, 476 participants developed MetS. A significantly stepwise increase in the incidence of MetS across tertiles of SUA was observed in the whole group (p for trend <0.001). Among women, this association was more robust than in men. After adjustment for age, variations of blood pressure, triglycerides, HDL-C, glucose, and waist circumference, females in the middle and upper tertiles of SUA had significantly higher risk of developing MetS when compared with subjects in the lowest tertile [adjusted-HR (95% CI) was 1.67 (1.12-2.49) and 3.18 (2.20-4.60), respectively; p for trend <0.001]. Overall, hyperuricemia was a significantly independent risk determinant for MetS in women, but it was a non-significant factor for MetS mediating waist circumference and serum triglycerides in men.
  
  
  CONCLUSIONS
  SUA concentration is more closely associated with MetS in females than in males. Future investigations are needed to explore the underlying mechanisms involved in the sex-related association between SUA concentration and MetS risk.
authors.affiliation: Department of Health Business Administration, Meiho University, Pingtung County, Taiwan.
authors.forename: Tsan
authors.initials: T
authors.lastname: Yang
authors.forename: Chi-Hong
authors.initials: CH
authors.lastname: Chu
authors.forename: Chyi-Huey
authors.initials: CH
authors.lastname: Bai
authors.forename: San-Lin
authors.initials: SL
authors.lastname: You
authors.forename: Yu-Ching
authors.initials: YC
authors.lastname: Chou
authors.forename: Wan-Yun
authors.initials: WY
authors.lastname: Chou
authors.forename: Kuo-Liong
authors.initials: KL
authors.lastname: Chien
authors.forename: Lee-Ching
authors.initials: LC
authors.lastname: Hwang
authors.forename: Ta-Chen
authors.initials: TC
authors.lastname: Su
authors.forename: Chin-Hsiao
authors.initials: CH
authors.lastname: Tseng
authors.forename: Chien-An
authors.initials: CA
authors.lastname: Sun
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D005260:Female; D006801:Humans; D033461:Hyperuricemia / Q000097:blood / Q000453:epidemiology*; D015994:Incidence; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000453:epidemiology*; D008875:Middle Aged; D015995:Prevalence; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013624:Taiwan / Q000453:epidemiology; D013997:Time Factors; D014527:Uric Acid / Q000097:blood*; D055815:Young Adult
chemical_list: D015415:Biomarkers; D014527:Uric Acid