title: Inverse association between fruit and vegetable intake and BMI even after controlling for demographic, socioeconomic and lifestyle factors.
abstract: OBJECTIVE
  To estimate fruit and vegetable (FV) intake levels of US adult population and evaluate the association between FV intake and BMI status after controlling for confounding demographic, socioeconomic and lifestyle factors. We also sought to identify moderating factors.
  
  
  METHODS
  We used 2007 Behavior Risk Factors Surveillance System (N > 400,000) data. FV intake was dichotomized as ≥5 servings (FV5+) versus <5 servings/ day. BMI status was categorized as normal, overweight, and obese. Identification of moderators was performed by testing interactions between BMI status and other variables using bivariate analyses followed by multiple logistic regression analysis incorporating complex survey sampling design features.
  
  
  RESULTS
  Only 24.6% of US adults consumed ≥5 servings per day and less than 4% consumed 9 or more servings. Overweight (% FV5+ = 23.9%) and obese (21.9%) groups consumed significantly less FV than the normal-weight (27.4%) group (p < 0.0001). This inverse association remained significant even after controlling for potential confounding factors. Multivariate analysis identified five significant moderators (p < 0.0001) after controlling for all evaluated variables: race, sex, smoking status, health coverage, and physical activity. Notably, physically inactive obese males tended to consume the least FV (% FV5+ = 14.7%).
  
  
  CONCLUSIONS
  Current US population FV intake level is below recommended levels. The inverse association between FV intake and obesity was significant and was moderated by demographic, socioeconomic status, and lifestyle factors. These factors should be considered when developing policies and interventions to increase FV intake.
authors.affiliation: Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA. moonseong.heo@einstein.yu.edu
authors.forename: Moonseong
authors.initials: M
authors.lastname: Heo
authors.forename: Ryung S
authors.initials: RS
authors.lastname: Kim
authors.forename: Judith
authors.initials: J
authors.lastname: Wylie-Rosett
authors.forename: David B
authors.initials: DB
authors.lastname: Allison
authors.forename: Steve B
authors.initials: SB
authors.lastname: Heymsfield
authors.forename: Myles S
authors.initials: MS
authors.lastname: Faith
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015992:Body Mass Index; D015986:Confounding Factors, Epidemiologic; D003430:Cross-Sectional Studies; D004032:Diet / Q000592:standards; D004034:Diet Surveys; D015444:Exercise; D005247:Feeding Behavior; D005260:Female; D005638:Fruit; D015438:Health Behavior; D006801:Humans; D008019:Life Style; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009765:Obesity / Q000453:epidemiology / Q000209:etiology*; D050177:Overweight / Q000453:epidemiology / Q000209:etiology*; D015995:Prevalence; D012307:Risk Factors; D012737:Sex Factors; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology; D014675:Vegetables; D055815:Young Adult