Association of obesity measures and multimorbidity in Pakistan: findings from the IMPACT study.

Obesity is a major factor leading to multimorbidity. However, the relative importance of obesity measures, including body mass index (BMI), body fat percentage (BF%) and visceral fat (VF), in relation to multimorbidity has not been extensively studied in Asia. Therefore, the objective of this study was to examine the relation of these measures of obesity with multimorbidity in a representative community sample in Pakistan. This is a community-based cross-sectional study. This study was conducted among residents of Gulshan town, Karachi, Pakistan. Data on healthy individuals and individuals with chronic conditions were recorded. All self-reported chronic conditions were further assessed by physical examination, medical history of the participants and laboratory findings. Multivariate logistic regression and receiver operating characteristic (ROC) curve analyses of BMI, BF% and VF as predictors of obesity were used to examine the association with multimorbidity. A total of 1500 participants (738 men and 762 women) were recruited, with a median age of 54.5 years. Multivariate logistic regression showed a significant association of BMI (odds ratio [OR] = 1.28, 95% confidence interval [CI] = 1.00-1.81, P-value 0.049) and BF% (OR = 2.28, 95% CI = 1.50-3.45, P-value <0.001) with multimorbidity. However, the ROC analysis for BMI, BF% and VF showed very similar results, even when the analysis was stratified by gender. In this exploratory analysis, increasing age and female gender were significantly associated with multimorbidity compared with their counterparts. Adult populations with high BF% levels carry a higher risk of multimorbidity than those with high BMI scores. In a population with differing metabolic characteristics, BMI might be less precise than direct adiposity measurements. Additional studies are needed to confirm the potential use of measuring the anatomical location and metabolic characteristics of lean and fat mass to identify risk of diseases.

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