title: Body mass index versus dual energy x-ray absorptiometry-derived indexes: predictors of cardiovascular and diabetic disease risk factors.
abstract: OBJECTIVE
  The body mass index (BMI), a ratio of weight/height(2) , dominates estimation of adiposity in population studies. BMI, however, does not distinguish among fat, muscle, or bone mass. Accordingly, its usage to assess and manage obesity in the population is limited. This study compares the use of BMI with direct measures of fat- and lean-mass to predict established cardiovascular and diabetes risk factors: blood pressure, lipids, and glucose.
  
  
  METHODS
  The entire Buffalo Police Department was the object of recruitment to a baseline study of physiological and psychological stress. Four hundred nine officers constitute the sample for this analysis. Regression methods focusing on explained variance in blood pressure, high density lipoprotein (HDL) cholesterol, and blood glucose compare the use of BMI to that of fat- and lean-mass indexes derived from dual energy X-ray absorptiometry (DEXA).
  
  
  RESULTS
  DEXA indexes explain 1.6%-3.3% (P < 0.05, all risk factors) more variance than BMI. Fat mass drives the association for blood pressure, trunk lean mass for HDL cholesterol, and both for blood glucose. High degrees of multicollinearity complicate interpretation of predictive models jointly containing BMI and DEXA indexes.
  
  
  CONCLUSIONS
  In police officers, DEXA indexes are better predictors of cardiovascular disease and diabetes risk factors. However, populations with different distributions of fitness, diet, and health conditions may demonstrate differentfeatures. In contrast to BMI, DEXA-derived measurements suggest avenues to explore metabolic processes, which relate to an index's underlying association with risk and may suggest more effective intervention strategies.
authors.affiliation: Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Morgantown, WV 26505, USA. DSharp@cdc.gov
authors.forename: Dan S
authors.initials: DS
authors.lastname: Sharp
authors.forename: Michael E
authors.initials: ME
authors.lastname: Andrew
authors.forename: Cecil M
authors.initials: CM
authors.lastname: Burchfiel
authors.forename: John M
authors.initials: JM
authors.lastname: Violanti
authors.forename: Jean
authors.initials: J
authors.lastname: Wactawski-Wende
mesh_terms: D015502:Absorptiometry, Photon / Q000379:methods*; D000328:Adult; D000368:Aged; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging / Q000453:epidemiology* / Q000209:etiology; D008076:Cholesterol, HDL / Q000097:blood; D002947:Cities; D015331:Cohort Studies; D003920:Diabetes Mellitus / Q000000981:diagnostic imaging / Q000453:epidemiology* / Q000209:etiology; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009518:New York / Q000453:epidemiology; D016495:Police; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012307:Risk Factors
chemical_list: D001786:Blood Glucose; D008076:Cholesterol, HDL