title: Pathophysiologic mechanisms of obesity and related metabolic disorders: an epidemiologic study using questionnaire and serologic biomarkers.
abstract: BACKGROUND
  It is still unclear whether individuals with the same degree of obesity but different weight histories since young adulthood have different insulin concentration, prevalence of metabolic syndrome components and their clustering.
  
  
  METHODS
  A cross-sectional study was conducted on 3,399 (for weight difference analysis) and 1,879 (for weight fluctuation analysis) Japanese men aged 40-59 years. Weight difference was calculated by subtracting the recalled weight at about 25 years old from the current weight. The root mean square error around the slope of weight on age (weight-RMSE) was calculated by a simple linear regression model, in which the subject's actual weights at ages 20, 25, 30, 40 years and 5 years prior to the study, as well as current weight, were dependent variables against the subject's age as the independent variable. Each metabolic syndrome component was defined as follows: serum triglycerides > or =150 mg/dL; HDL-cholesterol <40 mg/dL; fasting glucose > or =110 mg/dL; and blood pressure > or =140/90 mm Hg.
  
  
  RESULTS
  Those who gained <10%, <20%, or 20% or more in weight had a significantly higher than unity odds ratio of having two or more metabolic syndrome components in relation to those whose weight remained stable: 1.28 (95% confidence interval: 0.95-1.73), 2.49 (1.91-3.24), and 5.30 (3.97-7.07), respectively. Weight-RMSE was significantly and positively associated with fasting insulin concentration independent of current weight, weight-slope or other lifestyle-related factors.
  
  
  CONCLUSIONS
  Metabolic syndrome components would likely tend to cluster more in individuals with large weight gain on a physiologic basis characterized by high fasting insulin concentration. Furthermore, weight fluctuation was suggested to increase the risk of fasting hyperinsulinemia.
authors.affiliation: Department of Public Health/Health Information Dynamics, Field of Social Life Science, Program in Health and Community Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan. h828@med.nagoya-u.ac.jp
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Yatsuya
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose; D001794:Blood Pressure; D015992:Body Mass Index; D008076:Cholesterol, HDL / Q000097:blood; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D015438:Health Behavior; D006801:Humans; D007564:Japan / Q000453:epidemiology; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology*; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology* / Q000378:metabolism; D015995:Prevalence; D011795:Surveys and Questionnaires; D014280:Triglycerides / Q000097:blood; D015430:Weight Gain