Carolyn T Bramante1,2, Elise F Palzer3, Kyle D Rudser4,3, Justin R Ryder4,5, Claudia K Fox4,5, Eric M Bomberg4,5, Megan O Bensignor4,5, Amy C Gross4,5, Nancy E Sherwood6, Aaron S Kelly4,6. 1. Center for Pediatric Obesity Medicine, University of Minnesota Medical School, Minneapolis, MN, USA. bramante@umn.edu. 2. Division of General Internal Medicine, University of Minnesota Medical School, Minneapolis, MN, USA. bramante@umn.edu. 3. School of Public Health and Biostatistical Design & Analysis Center, University of Minnesota, Minneapolis, MN, USA. 4. Center for Pediatric Obesity Medicine, University of Minnesota Medical School, Minneapolis, MN, USA. 5. Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA. 6. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Abstract
BACKGROUND: There are limited data comparing the relative associations of various BMI metrics with adiposity and cardiometabolic risk factors in youth. OBJECTIVE: Examine correlations of 7 different BMI metrics with adiposity, cardiometabolic risk factors, and biomarkers (i.e. blood pressure, waist circumference, cholesterol, leptin, insulin, high molecular weight adiponectin, high-sensitivity c-reactive protein (hsCRP)). METHODS: This was a cross-sectional analysis of youth in all BMI categories. BMI metrics: BMI z-score (BMIz), extended BMIz (ext.BMIz), BMI percentile (BMIp), percent of the BMI 95th percentile (%BMIp95), percent of the BMI median (%BMIp50), triponderal mass index (TMI), and BMI (BMI). Correlations between these BMI metrics and adiposity, visceral adiposity, cardiometabolic risk factors and biomarkers were summarized using Pearson's correlations. RESULTS: Data from 371 children and adolescents ages 8-21 years old were included in our analysis: 52% were female; 20.2% with Class I obesity, 20.5% with Class II, and 14.3% with Class III obesity. BMIp consistently demonstrated lower correlations with adiposity, risk factors, and biomarkers (r = 0.190-0.768) than other BMI metrics. The %BMIp95 and %BMIp50 were marginally more strongly correlated with measures of adiposity as compared to other BMI metrics. The ext.BMIz did not meaningfully outperform BMIz. CONCLUSION: Out of all the BMI metrics evaluated, %BMIp95 and %BMIp50 were the most strongly correlated with measures of adiposity. %BMIp95 has the benefit of being used currently to define obesity and severe obesity in both clinical and research settings. BMIp consistently had the lowest correlations. Future research should evaluate the longitudinal stability of various BMI metrics and their relative associations with medium to long-term changes in adiposity and cardiometabolic outcomes in the context of intervention trials.
BACKGROUND: There are limited data comparing the relative associations of various BMI metrics with adiposity and cardiometabolic risk factors in youth. OBJECTIVE: Examine correlations of 7 different BMI metrics with adiposity, cardiometabolic risk factors, and biomarkers (i.e. blood pressure, waist circumference, cholesterol, leptin, insulin, high molecular weight adiponectin, high-sensitivity c-reactive protein (hsCRP)). METHODS: This was a cross-sectional analysis of youth in all BMI categories. BMI metrics: BMI z-score (BMIz), extended BMIz (ext.BMIz), BMI percentile (BMIp), percent of the BMI 95th percentile (%BMIp95), percent of the BMI median (%BMIp50), triponderal mass index (TMI), and BMI (BMI). Correlations between these BMI metrics and adiposity, visceral adiposity, cardiometabolic risk factors and biomarkers were summarized using Pearson's correlations. RESULTS: Data from 371 children and adolescents ages 8-21 years old were included in our analysis: 52% were female; 20.2% with Class I obesity, 20.5% with Class II, and 14.3% with Class III obesity. BMIp consistently demonstrated lower correlations with adiposity, risk factors, and biomarkers (r = 0.190-0.768) than other BMI metrics. The %BMIp95 and %BMIp50 were marginally more strongly correlated with measures of adiposity as compared to other BMI metrics. The ext.BMIz did not meaningfully outperform BMIz. CONCLUSION: Out of all the BMI metrics evaluated, %BMIp95 and %BMIp50 were the most strongly correlated with measures of adiposity. %BMIp95 has the benefit of being used currently to define obesity and severe obesity in both clinical and research settings. BMIp consistently had the lowest correlations. Future research should evaluate the longitudinal stability of various BMI metrics and their relative associations with medium to long-term changes in adiposity and cardiometabolic outcomes in the context of intervention trials.
Authors: Aaron S Kelly; Sarah E Barlow; Goutham Rao; Thomas H Inge; Laura L Hayman; Julia Steinberger; Elaine M Urbina; Linda J Ewing; Stephen R Daniels Journal: Circulation Date: 2013-09-09 Impact factor: 29.690
Authors: Robert J Kuczmarski; Cynthia L Ogden; Shumei S Guo; Laurence M Grummer-Strawn; Katherine M Flegal; Zuguo Mei; Rong Wei; Lester R Curtin; Alex F Roche; Clifford L Johnson Journal: Vital Health Stat 11 Date: 2002-05