Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children
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 clinica...
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BMJ Publishing Group
2022-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/6/e058857.full |
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| author | Gilles Paradis Melanie Henderson Lisa Kakinami Angelo Tremblay Anna Smyrnova |
| author_facet | Gilles Paradis Melanie Henderson Lisa Kakinami Angelo Tremblay Anna Smyrnova |
| author_sort | Gilles Paradis |
| collection | DOAJ |
| 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 studySetting 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. |
| format | Article |
| id | doaj-art-6038f3d6fb24465b8f23c26b6f50029c |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-6038f3d6fb24465b8f23c26b6f50029c2025-02-01T14:35:09ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2021-058857Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of childrenGilles Paradis0Melanie Henderson1Lisa Kakinami2Angelo Tremblay3Anna Smyrnova4Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, CanadaDepartment of Pediatrics, Université de Montréal, Montreal, QC, CanadaPERFORM Centre, Concordia University, Montreal, Québec, CanadaDépartement de kinésiologie, Université Laval, Quebec City, Quebec, CanadaDepartment of Mathematics and Statistics, Concordia University, Montreal, Québec, CanadaObjectives 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 studySetting 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.https://bmjopen.bmj.com/content/12/6/e058857.full |
| spellingShingle | Gilles Paradis Melanie Henderson Lisa Kakinami Angelo Tremblay Anna Smyrnova Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children BMJ Open |
| title | Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children |
| title_full | Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children |
| title_fullStr | Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children |
| title_full_unstemmed | Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children |
| title_short | Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children |
| title_sort | comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children |
| url | https://bmjopen.bmj.com/content/12/6/e058857.full |
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