title: The metabolic syndrome, incidence of diabetes and mortality among the elderly: the Italian Longitudinal Study of Ageing.
abstract: OBJECTIVE
  To investigate whether or not the metabolic syndrome (MetS) can predict the incidence of diabetes and all-cause mortality among elderly subjects.
  
  
  METHODS
  Analyses were based on data collected by the Italian Longitudinal Study on Aging (ILSA) that, between 1992 and 1996, enrolled 5632 participants aged 65 to 84 years. The analyses included 3081 participants for whom complete data were available. Logistic-regression models were designed to study the influence of the MetS on the incidence of diabetes, adjusting for individual MetS components and possible confounders. Data on mortality collected between baseline and the 1996 follow-up were also considered, and Cox's proportional hazards models were used to determine the death risk attributable to the synergistic relationship between the MetS and diabetes.
  
  
  RESULTS
  The MetS was strongly associated with an increased risk of diabetes (OR: 5.53, 95% CI: 2.89-10.60). After adjusting for its individual components and possible confounders, the MetS maintained an important role in predicting the incidence of diabetes (OR: 2.65, 95% CI: 0.97-7.24) together with the fasting glucose component (OR: 5.89, 95% CI: 2.89-11.98). Over the 4-year follow-up, participants with diabetes, but without the MetS, and subjects with the MetS, but without diabetes, had no significant risk of death compared with the reference group. Elderly subjects who had both the MetS and diabetes had almost double the risk of death vs the reference group (HR: 1.80, 95% CI: 1.04-3.12).
  
  
  CONCLUSIONS
  The MetS is associated with the incidence of diabetes, and the synergy between the MetS and diabetes is an important risk factor for all-cause mortality in elderly subjects.
authors.affiliation: CNR-Institute of Neuroscience, Padova, Via Giustiniani 2, Padua, Italy. marianna.noale@in.cnr.it
authors.forename: M
authors.initials: M
authors.lastname: Noale
authors.forename: S
authors.initials: S
authors.lastname: Maggi
authors.forename: S
authors.initials: S
authors.lastname: Zanoni
authors.forename: F
authors.initials: F
authors.lastname: Limongi
authors.forename: S
authors.initials: S
authors.lastname: Zambon
authors.forename: G
authors.initials: G
authors.lastname: Crepaldi
mesh_terms: D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D001786:Blood Glucose / Q000378:metabolism; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000209:etiology / Q000401:mortality; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000453:epidemiology* / Q000209:etiology / Q000401:mortality; D005260:Female; D005500:Follow-Up Studies; D015577:Geriatric Assessment; D006801:Humans; D015994:Incidence; D007558:Italy / Q000453:epidemiology; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000150:complications / Q000453:epidemiology* / Q000401:mortality; D016017:Odds Ratio; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012307:Risk Factors
chemical_list: D001786:Blood Glucose