Predicting cardiometabolic markers in children using tri-ponderal mass index: a cross-sectional study. Issue 6 (17th January 2019)

• Record Type: Journal Article
• Authors: Ashley-Martin, Jillian; Ensenauer, Regina; Maguire, Bryan; Kuhle, Stefan
• Is Part Of: Archives of disease in childhood. Volume 104:Issue 6(2019)
• Page Start: 577
• Page End: 582
• Publication Date: 2019-01-17
• Subjects: epidemiology -- growth -- metabolic -- obesity -- statistics; Children -- Diseases -- Periodicals; Infants -- Diseases -- Periodicals; 618.920005
• DOI: 10.1136/archdischild-2018-316028
• Languages: English
• ISSNs: 0003-9888

Abstract

Abstract : Objective: To model the development of the tri-ponderal mass index (TMI, kg/m 3 ) throughout childhood and adolescence and to compare the utility of the TMI with that of the body mass index (BMI, kg/m 2 ) 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.