Jillian Ashley-Martin 1 , Regina Ensenauer 2 , Bryan Maguire 3 , Stefan Kuhle 1 . Show Affiliations »
Abstract
OBJECTIVE: To model the development of the tri-ponderal mass index (TMI, kg/m3) throughout childhood and adolescence and to compare the utility of the TMI with that of the body mass index (BMI, kg/m2) to predict cardiometabolic risk in a population-based sample of Canadian children and youth. METHODS: We used data from the Canadian Health Measures Survey to model TMI from 6 to 19 years of age. Percentile curves were developed using the LMS method. Logistic regression was used to predict abnormal levels of cardiometabolic markers; predictive accuracy was assessed using the area under the ROC curve (AUC). RESULTS: Mean TMI was relatively stable from ages 6 to 19 years for both sexes, but variability increased with age. There was no notable difference in AUC values for prediction models based on BMI z-score compared with TMI for any of the outcomes. For both BMI z-score and TMI, prediction accuracy was good for homeostasis model assessment insulin resistance and having ≥3 abnormal tests (AUC>0.80), fair for C-reactive protein and poor for the remainder of the outcomes. CONCLUSIONS: The use of a single sex-specific TMI cut-off for overweight or obesity is hampered by the increasing variability of the measure with age. Weight-for-height indices likely have only limited ability to predict cardiometabolic marker levels, and changing the scaling power of height is unlikely to improve predictive accuracy. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.
OBJECTIVE: To model the development of the tri-ponderal mass index (TMI, kg/m3) throughout childhood and adolescence and to compare the utility of the TMI with that of the body mass index (BMI, kg/m2) to predict cardiometabolic risk in a population-based sample of Canadian children and youth. METHODS: We used data from the Canadian Health Measures Survey to model TMI from 6 to 19 years of age. Percentile curves were developed using the LMS method. Logistic regression was used to predict abnormal levels of cardiometabolic markers; predictive accuracy was assessed using the area under the ROC curve (AUC). RESULTS: Mean TMI was relatively stable from ages 6 to 19 years for both sexes, but variability increased with age. There was no notable difference in AUC values for prediction models based on BMI z-score compared with TMI for any of the outcomes. For both BMI z-score and TMI, prediction accuracy was good for homeostasis model assessment insulin resistance and having ≥3 abnormal tests (AUC>0.80), fair for C-reactive protein and poor for the remainder of the outcomes. CONCLUSIONS: The use of a single sex-specific TMI cut-off for overweight or obesity is hampered by the increasing variability of the measure with age. Weight-for-height indices likely have only limited ability to predict cardiometabolic marker levels, and changing the scaling power of height is unlikely to improve predictive accuracy. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.
Entities: Disease
Gene
Species
Keywords:
epidemiology; growth; metabolic; obesity; statistics
Year: 2019
PMID: 30655268 DOI: 10.1136/archdischild-2018-316028
Source DB: PubMed Journal: Arch Dis Child ISSN: 0003-9888 Impact factor: 3.791