title: Metabolic syndrome, its components, and mortality in the elderly.
abstract: OBJECTIVE
  The metabolic syndrome (MetS) has been shown to predict mortality in the middle-aged, but less is known on the impact of MetS and its components on mortality risk in the elderly. Our objectives were 1) to examine the association of MetS with the risk of all-cause and cause-specific mortality in a French elderly community-dweller cohort and 2) to determine the main components driving these associations.
  
  
  METHODS
  Prospective analyses were carried out on 7118 men and women aged 65 yr and over from the Three-City cohort. Association between MetS (defined using the National Cholesterol Education Program Adult Treatment Panel III criteria) and mortality risk over the 7-yr follow-up was assessed using Cox proportional hazards models.
  
  
  RESULTS
  After adjusting for sociodemographic variables, health behaviors, and health status, a 50% increased risk for all-cause mortality was observed in participants with MetS at baseline compared with those without, with a hazard ratio of 1.54 [95% confidence interval (CI) = 1.24-1.92]. Elevated fasting blood glucose, high triglycerides, and low high-density lipoprotein cholesterol were the major contributors to this association, acting synergistically on mortality risk. For coronary heart disease mortality and cancer mortality, the hazard ratios associated with MetS were 2.21 (95% CI = 1.07-4.55) and 1.49 (95% CI = 1.04-2.14), respectively.
  
  
  CONCLUSIONS
  By showing that an elevated fasting blood glucose potentiates the excess mortality risk associated with lipid abnormality, our study supports the status of MetS as a risk factor for mortality in the elderly. Our findings emphasize the importance of MetS screening and managing dyslipidemia and hyperglycemia in older persons in general practice.
authors.affiliation: Institut National de la Santé et de la Recherche Médicale (INSERM) Unité (U)888, Université Montpellier, Montpellier, France. tasnime.akbaraly@inserm.fr
authors.forename: Tasnime N
authors.initials: TN
authors.lastname: Akbaraly
authors.forename: Mika
authors.initials: M
authors.lastname: Kivimaki
authors.forename: Marie-Laure
authors.initials: ML
authors.lastname: Ancelin
authors.forename: Pascale
authors.initials: P
authors.lastname: Barberger-Gateau
authors.forename: Thibault
authors.initials: T
authors.lastname: Mura
authors.forename: Christophe
authors.initials: C
authors.lastname: Tzourio
authors.forename: Jacques
authors.initials: J
authors.lastname: Touchon
authors.forename: Karen
authors.initials: K
authors.lastname: Ritchie
authors.forename: Claudine
authors.initials: C
authors.lastname: Berr
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001786:Blood Glucose; D008078:Cholesterol, LDL / Q000097:blood; D003327:Coronary Disease / Q000097:blood / Q000209:etiology / Q000401:mortality*; D005260:Female; D015438:Health Behavior; D006304:Health Status; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000150:complications / Q000401:mortality*; D009369:Neoplasms / Q000097:blood / Q000209:etiology / Q000401:mortality*; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012306:Risk; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood
chemical_list: D001786:Blood Glucose; D008078:Cholesterol, LDL; D014280:Triglycerides
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