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Objectives: To evaluate the association between percent body fat (%BF) and body mass index (BMI) among BMI-defined non-obese individuals between 40 and 69 years of age using a population-based Canadian sample., Data and Methods: Cross-sectional data from the Canadian Health Measures Survey (2007 and 2009) was used to select all middle-aged individuals with BMI < 30 kg/m2 (n = 2,656). %BF was determined from anthropometric skinfolds and categorized according to sex-specific equations. Association of other anthropometry measures and metabolic markers were evaluated across different %BF categories. Significance of proportions was evaluated using chi-squared and Bonferroni-adjusted Wald test. Diagnostic performance measures of BMI-defined overweight categories compared to those defined by %BF were reported., Results: The majority (69%) of the sample was %BF-defined overweight/obese, while 55% were BMI-defined overweight. BMI category was not concordant with %BF classification for 30% of the population. The greatest discordance between %BF and BMI was observed among %BF-defined overweight/obese women (32%). Sensitivity and specificity of BMI-defined overweight compared to %BF-defined overweight/obese were (58%, 94%) among females and (82%, 59%) among males respectively. According to the estimated negative predictive value, if an individual is categorized as BMI-defined non-obese, he/she has a 52% chance of being in the %BF-defined overweight/obese category., Conclusion: Middle-aged individuals classified as normal by BMI may be overweight/obese based on measures of %BF. These individuals may be at risk for chronic diseases, but would not be identified as such based on their BMI classification. Quantifying %BF in this group could inform targeted strategies for disease prevention.

Background: Osteoarthritis (OA) is a chronic degenerative joint disease. While it is well-established that obesity affects OA through increased axial loading on the joint cartilage, the indirect effect of obesity through metabolic processes among the body mass index (BMI)-defined non-obese population, i.e., BMI < 30 kg/m2, is less known. Our goal was to evaluate the association of metabolic markers including body fat percentage (BF%), waist circumference, maximum weight gain during adulthood and serum creatinine with self-reported OA to establish if such measures offer additional information over BMI among the non-obese population between 40 and 65 years of age. Methods: Cross-sectional data from two cycles of the Canadian Health Measures Survey (CHMS) in 2007–2009 and 2009–2011 were analyzed. Sex-specific logistic regression models were developed to evaluate the association of self-reported OA with metabolic markers. Models were separately adjusted for age, BMI categories and serum creatinine, and a stratified analysis across BM categories was performed. In a secondary analysis, we evaluated the association of self-reported OA, cardiovascular diseases and hypertension across BF% categories. Results: Of 2462 individuals, 217 (8.8%) self-reported OA. After adjusting for age and BMI, those within BF%-defined overweight/obese category had 2.67 (95% CI: 1.32–3.51) and 2.11(95% CI: 1.38–3.21) times higher odds of reporting self-reported OA compared to those within BF%-defined athletic/acceptable category for females and males, respectively. BF% was also significantly associated with self-reported OA after adjusting for age and serum creatinine only among females (OR: 1.47, 95%CI: 1.12–1.84). Furthermore, among the BMI-defined overweight group, the age-adjusted odds of self-reported OA was significantly higher for overweight/obese BF% compared to athletic/acceptable BF% in both females and males. In a secondary analysis, we showed that the association of self-reported OA and hypertension/cardiovascular diseases is significantly higher among BF% overweight/obese (OR: 1.37, 95%CI: 1.19–3.09) compared to BF% athletic/acceptable (OR: 1.13, 95%CI: 0.87–2.82). Conclusion: Our results provide corroborating evidence for a relationship between body fat and OA in a population-based study, while no significant independent correlates were found between other metabolic markers and OA prevalence. Future investigation on the longitudinal relationship between BF and OA among this sub-population may inform targeted prevention opportunities. [ABSTRACT FROM AUTHOR]