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Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children

Authors: Kakinami LSmyrnova AParadis GTremblay AHenderson M


Affiliations

1 PERFORM Centre, Concordia University, Montreal, Québec, Canada lisa.kakinami@concordia.ca.
2 Department of Mathematics and Statistics, Concordia University, Montreal, Québec, Canada.
3 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
4 Département de kinésiologie, Université Laval, Quebec City, Quebec, Canada.
5 Department of Pediatrics, Université de Montréal, Montreal, Quebec, Canada.
6 Research Center of CHU Sainte Justine, Université de Montréal, Montreal, Quebec, Canada.

Description

Objectives: Severe obesity (SO) prevalence varies between reference curve-based definitions (WHO: =99th percentile, Centers for Disease Control and Prevention (CDC): >1.2×95th percentile). Whether SO definitions differentially predict cardiometabolic disease risk is critical for proper clinical care and management but is unknown.

Design: Prospective cohort study SETTING: SO definitions were applied at baseline (2005-2008, Mage=9.6 years, n=548), and outcomes (fasting lipids, glucose, homoeostatic model assessment (HOMA-IR) and blood pressure) were assessed at first follow-up (F1: 2008-2011, Mage=11.6 years) and second follow-up (2015-2017, Mage=16.8 years) of the Quebec Adipose and Lifestyle Investigation in Youth cohort in Montreal, Quebec.

Participants: Respondents were youth who had at least one biological parent with obesity.

Primary outcome measures: Unfavourable cardiometabolic levels of fasting blood glucose (=6.1 mmol/L), insulin resistance (HOMA-IR index =2.0), high-density lipoprotein <1.03 mmol/L, low-density lipoprotein =2.6 mmol/L and triglycerides >1.24 mmol/L. Unfavourable blood pressure was defined as =90th percentile for age-adjusted, sex-adjusted and height-adjusted systolic or diastolic blood pressure.

Analysis: Area under the receiver operating characteristic curve (AUC) and McFadden psuedo R2 for predicting F1 or F2 unfavourable cardiometabolic levels from baseline SO definitions were calculated. Agreement was assessed with kappas.

Results: Baseline SO prevalence differed (WHO: 18%, CDC: 6.7%). AUCs ranged from 0.52 to 0.77, with fair agreement (kappa=37%-55%). WHO-SO AUCs for detecting unfavourable HOMA-IR (AUC>0.67) and high-density lipoprotein (AUC>0.59) at F1 were statistically superior than CDC-SO (AUC>0.59 and 0.53, respectively; p<0.05). Only HOMA-IR and the presence of more than three risk factors had acceptable model fit. WHO-SO was not more predictive than WHO-obesity, but CDC-SO was statistically inferior to CDC-obesity.

Conclusion: WHO-SO is statistically superior at predicting cardiometabolic risk than CDC-SO. However, as most AUCs were generally uninformative, and obesity definitions were the same if not better than SO, the improvement may not be clinically meaningful.


Links

PubMed: https://pubmed.ncbi.nlm.nih.gov/35705336/

DOI: 10.1136/bmjopen-2021-058857