Abstract
Background
Despite the effectiveness of lifestyle multidisciplinary (LMD) weight loss interventions in pediatric obesity, outcomes remain variable between individuals. Machine learning (ML), capable of capturing complex relationships between variables, offer a promising avenue to better understand and predict this variability
Methods
This study aimed to identify baseline predictors of 9-month LMD success in adolescents with obesity. A pooled database of 471 adolescents (42.9% males, 13.6 ± 1.4 years, BMI z-score 3.06 ± 0.51) from 21 clinical trials was used. Five ML models (Random Forest (RFC), XGBoost, LightGBM, Logistic Regression, and Support Vector Machine) were developed and evaluated to predict treatment response using both binary and multiclass classification
Results
Mean BMI z-score reduction at 9 months was −0.53 ± 0.34. For binary classification, RFC achieved the highest performance (accuracy = 0.97; AUC-ROC = 0.97). For multiclass prediction, XGBoost performed best (accuracy = 0.80; AUC-ROC = 0.90)
Conclusion
The most influential predictors consistently belonged to two main domains: (1) functional aptitudes, (e.g. forced vital capacity, maximal aerobic power, vertical jump height, resting VO2) and (2) dietary profiles (e.g. total energy intake, protein intake, hunger/fullness scores). Metabolic markers (eg. Insulin, cholesterol) also contributed to multiclass prediction. ML shows promise for personalizing adolescent weight loss strategies and deserves further study
Impact
While many fields of healthcare utilize artificial intelligence-based (AI) methods to achieve personalized and patient-centered care, these methods remain underexplored in the field of pediatric obesity
Identifying baseline predictors of lifestyle multidisciplinary weight loss interventions (LMD) would further improve the effectiveness of such interventions, currently hindered by inter-individual variability in response to treatment
Machine learning models were successful in predicting LMD’s success in adolescents with obesity with high accuracy, and uncovered a novel, promising dietary factors
Machine learning is a promising tool for personalization of weight management strategies in the field of pediatric obesity
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Data availability
The datasets generated and analyzed during the current study and analytical code are available from the corresponding author on reasonable request
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Acknowledgements
The authors want to thank the participants who completed the study and the CMI Romagnat and SSR Tzanou pediatric clinics for their contribution
Funding
This research has been supported through the Clermont Auvergne University I-SITE CIR-3 grant
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Authors and Affiliations
Laboratory of the Metabolic Adaptations to Exercise under Physiological and Pathological Conditions (AME2P), Centre de Recherche en Nutrition Humaine, Clermont Auvergne University, Clermont-Ferrand, France
Andrea Gaucherot, Duane Beraud, Paul Lonjou, Laurie Isacco & David Thivel
Academic Center of Vitoria, Federal University of Pernambuco, Vitoria de Santo Antao, Brazil
VirgÃnia Carol Leandro Góis
CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France; Department of Sport Medicine, functional and respiratory rehabilitation, CHU Clermont-Ferrand, Clermont-Ferrand, France
Valérie Julian & Martine Duclos
CRNH, INRAE, Unité de Nutrition Humaine, Clermont Auvergne University, Auvergne, Clermont-Ferrand, France
Valérie Julian, Martine Duclos & Yves Boirie
International Research Chair Health in Motion, Clermont Auvergne University Foundation, Clermont-Ferrand, France
Martine Duclos, Christelle Guillet, Laurie Isacco & David Thivel
Department of Human Nutrition, CHU Clermont-Ferrand, Clermont-Ferrand, France
Yves Boirie
Biostatistics Unit, DRCI, CHU Clermont-Ferrand, Clermont-Ferrand, France
Bruno Pereira
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Contributions
T.D. and B.P. designed the study; P.L., A.G., and D.B. managed the database and data quality process; A.G., B.P., and T.d. analyzed the data; T.D., I.L., M.D., Y.B., and B.P. are investigators of the database; A.G. and T.D. edited the first draft; V.J., C.G.L., I.S., G.C., and D.M. reviewed the results and the manuscript. All authors significantly contributed to the realization of this work
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Gaucherot, A., Beraud, D., Lonjou, P. et al. Identification of weight loss predictors using machine learning approaches in adolescents with obesity.
Pediatr Res (2026). https://doi.org/10.1038/s41390-026-05359-9
Received:29 September 2025
Revised:18 March 2026
Accepted:07 July 2026
Published:13 August 2026
Version of record:13 August 2026
DOI
:https://doi.org/10.1038/s41390-026-05359-9
