OBJECTIVE: We performed a systematic review and meta-analysis of studies that assessed the performance of body mass index (BMI) to detect body adiposity. DESIGN: Data sources were MEDLINE, EMBASE, Cochrane, Database of Systematic Reviews, Cochrane CENTRAL, Web of Science, and SCOPUS. To be included, studies must have assessed the performance of BMI to measure body adiposity, provided standard values of diagnostic performance, and used a body composition technique as the reference standard for body fat percent (BF%) measurement. We obtained pooled summary statistics for sensitivity, specificity, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR). The inconsistency statistic (I2) assessed potential heterogeneity. RESULTS: The search strategy yielded 3341 potentially relevant abstracts, and 25 articles met our predefined inclusion criteria. These studies evaluated 32 different samples totaling 31 968 patients. Commonly used BMI cutoffs to diagnose obesity showed a pooled sensitivity to detect high adiposity of 0.50 (95% confidence interval (CI): 0.43-0.57) and a pooled specificity of 0.90 (CI: 0.86-0.94). Positive LR was 5.88 (CI: 4.24-8.15), I (2)=97.8%; the negative LR was 0.43 (CI: 0.37-0.50), I (2)=98.5%; and the DOR was 17.91 (CI: 12.56-25.53), I (2)=91.7%. Analysis of studies that used BMI cutoffs >or=30 had a pooled sensitivity of 0.42 (CI: 0.31-0.43) and a pooled specificity of 0.97 (CI: 0.96-0.97). Cutoff values and regional origin of the studies can only partially explain the heterogeneity seen in pooled DOR estimates. CONCLUSION: Commonly used BMI cutoff values to diagnose obesity have high specificity, but low sensitivity to identify adiposity, as they fail to identify half of the people with excess BF%.
OBJECTIVE: We performed a systematic review and meta-analysis of studies that assessed the performance of body mass index (BMI) to detect body adiposity. DESIGN: Data sources were MEDLINE, EMBASE, Cochrane, Database of Systematic Reviews, Cochrane CENTRAL, Web of Science, and SCOPUS. To be included, studies must have assessed the performance of BMI to measure body adiposity, provided standard values of diagnostic performance, and used a body composition technique as the reference standard for body fat percent (BF%) measurement. We obtained pooled summary statistics for sensitivity, specificity, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR). The inconsistency statistic (I2) assessed potential heterogeneity. RESULTS: The search strategy yielded 3341 potentially relevant abstracts, and 25 articles met our predefined inclusion criteria. These studies evaluated 32 different samples totaling 31 968 patients. Commonly used BMI cutoffs to diagnose obesity showed a pooled sensitivity to detect high adiposity of 0.50 (95% confidence interval (CI): 0.43-0.57) and a pooled specificity of 0.90 (CI: 0.86-0.94). Positive LR was 5.88 (CI: 4.24-8.15), I (2)=97.8%; the negative LR was 0.43 (CI: 0.37-0.50), I (2)=98.5%; and the DOR was 17.91 (CI: 12.56-25.53), I (2)=91.7%. Analysis of studies that used BMI cutoffs >or=30 had a pooled sensitivity of 0.42 (CI: 0.31-0.43) and a pooled specificity of 0.97 (CI: 0.96-0.97). Cutoff values and regional origin of the studies can only partially explain the heterogeneity seen in pooled DOR estimates. CONCLUSION: Commonly used BMI cutoff values to diagnose obesity have high specificity, but low sensitivity to identify adiposity, as they fail to identify half of the people with excess BF%.
Authors: Augustine W Kang; Carol Ewing Garber; Charles B Eaton; Patricia M Risica; Andrew G Bostom Journal: Med Sci Sports Exerc Date: 2019-06 Impact factor: 5.411
Authors: Sergey Rudnev; Jane S Burns; Paige L Williams; Mary M Lee; Susan A Korrick; Tatiana Denisova; Yuri Dikov; Gennady Kozupitsa; Russ Hauser; Oleg Sergeyev Journal: Clin Nutr ESPEN Date: 2019-11-16
Authors: Piyawan Kittiskulnam; Glenn M Chertow; George A Kaysen; Cynthia Delgado; Lorien S Dalrymple; Kirsten L Johansen Journal: Am J Kidney Dis Date: 2015-11-21 Impact factor: 8.860