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Predicting cardiometabolic markers in children using tri-ponderal mass index: a cross-sectional study
  1. Jillian Ashley-Martin1,
  2. Regina Ensenauer2,
  3. Bryan Maguire3,
  4. Stefan Kuhle1
  1. 1 Perinatal Epidemiology Research Unit, Departments of Obstetrics and Gynaecology and Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
  2. 2 Experimental Pediatrics and Metabolism, University Children’s Hospital, Heinrich Heine University, Düsseldorf, Germany
  3. 3 Cancer Care Ontario, Toronto, Ontario, Canada
  1. Correspondence to Dr Stefan Kuhle, Perinatal Epidemiology Research Unit, IWK Health Centre, Halifax, NS B3K 6R8, Canada; stefan.kuhle{at}


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.

  • epidemiology
  • growth
  • metabolic
  • obesity
  • statistics

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  • Contributors JAM contributed to the design of the study, and wrote the initial manuscript draft. RE contributed to the design of the study. BM contributed to the analysis. SK conceptualised and designed the study, and contributed to the analysis. All authors reviewed and revised the manuscript, and approved the final version as submitted.

  • Funding JAM held an IWK Health Centre Research Associate award.

  • Competing interests None declared.

  • Ethics approval IWK Health Centre Research Ethics Board, Halifax, NS, Canada (file number 1014413).

  • Provenance and peer review Not commissioned; internally peer reviewed.

  • Data sharing statement The data that support the findings of this study are available from Statistics Canada through the Statistics Canada Research Data Centres Program to researchers who meet the criteria for access to confidential data. The application process is described at In brief, researchers submit an application form and project proposal to the Statistics Canada Research Data Centres Program. Upon approval they have to undergo a security check. Once completed, they get access to one of the Research Data Centres in Canada to analyse the data. Only aggregated data can be released, and all output produced at the centres must be vetted by a Statistics Canada analyst before release.

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