{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bramante CT"],"funding":["NCATS NIH HHS","NCRR NIH HHS","NIDDK NIH HHS","NHLBI NIH HHS"],"pagination":["359-365"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8926007"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["46(2)"],"pubmed_abstract":["<h4>Background</h4>There are limited data comparing the relative associations of various BMI metrics with adiposity and cardiometabolic risk factors in youth.<h4>Objective</h4>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)).<h4>Methods</h4>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 (%BMI<sub>p95</sub>), percent of the BMI median (%BMI<sub>p50</sub>), 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.<h4>Results</h4>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 %BMI<sub>p95</sub> and %BMI<sub>p50</sub> were marginally more strongly correlated with measures of adiposity as compared to other BMI metrics. The ext.BMIz did not meaningfully outperform BMIz.<h4>Conclusion</h4>Out of all the BMI metrics evaluated, %BMI<sub>p95</sub> and %BMI<sub>p50</sub> were the most strongly correlated with measures of adiposity. %BMI<sub>p95</sub> 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."],"journal":["International journal of obesity (2005)"],"pubmed_title":["BMI metrics and their association with adiposity, cardiometabolic risk factors, and biomarkers in children and adolescents."],"pmcid":["PMC8926007"],"funding_grant_id":["K23 DK124654","UL1 TR002494","KL2 TR002492","R01 HL110957","K23 DK129721","UL1 TR000114","UL1 RR033183","K23 DK125668"],"pubmed_authors":["Bensignor MO","Kelly AS","Gross AC","Sherwood NE","Ryder JR","Bramante CT","Palzer EF","Bomberg EM","Rudser KD","Fox CK"],"additional_accession":[]},"is_claimable":false,"name":"BMI metrics and their association with adiposity, cardiometabolic risk factors, and biomarkers in children and adolescents.","description":"<h4>Background</h4>There are limited data comparing the relative associations of various BMI metrics with adiposity and cardiometabolic risk factors in youth.<h4>Objective</h4>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)).<h4>Methods</h4>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 (%BMI<sub>p95</sub>), percent of the BMI median (%BMI<sub>p50</sub>), 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.<h4>Results</h4>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 %BMI<sub>p95</sub> and %BMI<sub>p50</sub> were marginally more strongly correlated with measures of adiposity as compared to other BMI metrics. The ext.BMIz did not meaningfully outperform BMIz.<h4>Conclusion</h4>Out of all the BMI metrics evaluated, %BMI<sub>p95</sub> and %BMI<sub>p50</sub> were the most strongly correlated with measures of adiposity. %BMI<sub>p95</sub> 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.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2024-11-20T22:33:14.197Z","creation":"2024-11-20T22:33:14.197Z"},"accession":"S-EPMC8926007","cross_references":{"pubmed":["34718333"],"doi":["10.1038/s41366-021-01006-x"]}}