title: The value of the metabolic syndrome concept in elderly adults: is it worth less than the sum of its parts?
abstract: OBJECTIVE
  To determine whether the metabolic syndrome (MetS) or its components were more closely associated with disease states and inflammation in elderly adults.
  
  
  METHODS
  Sydney Memory and Ageing Study. Cross-sectional, observational cohort.
  
  
  METHODS
  Population-derived, community-dwelling elderly adults.
  
  
  METHODS
  Nine hundred thirty individuals aged 70 to 90.
  
  
  METHODS
  Age- and sex-adjusted odds ratios (ORs) for disease states; fasting circulating inflammatory markers and oxidative metabolism byproducts.
  
  
  RESULTS
  MetS was associated with diabetes mellitus (OR = 4.1, P < .001) and bowel cancer (OR = 9.1, P = .03) but not in analyses that controlled for component conditions. Models containing component conditions had the strongest associations with heart disease. Disease associations were improved after addition of component conditions to the MetS model. The reverse did not hold: disease associations were not improved when MetS was added to the components model. Low high-density lipoprotein cholesterol (HDL-C) was independently associated with myocardial infarction (OR = 2.32) and angina pectoris (OR = 2.59) (both P < .008). Waist circumference was independently associated with cancer (OR = 1.82, P = .008). Although MetS was associated with higher C-reactive protein, vascular cell adhesion molecule, interleukin-6, amyloid A, homocysteine, and malondialdehyde, it explained less than half of the variance of models containing its components.
  
  
  CONCLUSIONS
  The observation that MetS is associated with disease states and markers of circulating inflammation in the elderly is explained mainly by abdominal obesity and low HDL-C. Longitudinal data will further clarify these cross-sectional findings that MetS appears to be less than the sum of its parts in elderly adults.
authors.affiliation: Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia. k.samaras@garvan.org.au
authors.forename: Katherine
authors.initials: K
authors.lastname: Samaras
authors.forename: John
authors.initials: J
authors.lastname: Crawford
authors.forename: Bernard T
authors.initials: BT
authors.lastname: Baune
authors.forename: Lesley V
authors.initials: LV
authors.lastname: Campbell
authors.forename: Evelyn
authors.initials: E
authors.lastname: Smith
authors.forename: Ora
authors.initials: O
authors.lastname: Lux
authors.forename: Henry
authors.initials: H
authors.lastname: Brodaty
authors.forename: Julian N
authors.initials: JN
authors.lastname: Trollor
authors.forename: Perminder
authors.initials: P
authors.lastname: Sachdev
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D015415:Biomarkers / Q000378:metabolism; D002318:Cardiovascular Diseases / Q000453:epidemiology; D003430:Cross-Sectional Studies; D003920:Diabetes Mellitus / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology* / Q000378:metabolism; D009369:Neoplasms / Q000453:epidemiology; D009517:New South Wales / Q000453:epidemiology; D015995:Prevalence; D012044:Regression Analysis; D012307:Risk Factors; D018709:Statistics, Nonparametric
chemical_list: D015415:Biomarkers