Leveraging Machine Learning and Genetic Risk Scores for the Prediction of Metabolic Syndrome in Children with Obesity

Aguilera, Concepción M. ; Bustos-Aibar, Mireia ; Anguita-Ruiz, Augusto ; Torres-Martos, Álvaro ; Bueno, Gloria (Universidad de Zaragoza) ; Leis, Rosaura ; Alcalá-Fernández, Jesús

Resumen: Background and objectives: Obesity is a growing global epidemic, associated with increased
cardiometabolic disorders. Metabolic syndrome (MS) is defined by altered insulin, blood pressure,
glucose, and lipid levels. Pubertal children with obesity are highly susceptible to developing MS,
necessitating its early identification. This study aims to compute phenotype-specific genetic risk
scores for MS-related biochemical markers and evaluate their clinical utility using machine learning-
based models. Methods: Longitudinal data from the PUBMEP Spanish cohort were analyzed,
including 138 children (71 girls and 67 boys) at two time points, spanning from prepuberty to
puberty. Clinical, endogenous, environmental, and omics variables were measured. Genetic risk
scores were generated using GWAS data and PRSice-2 software. These scores, alongside non-genetic
prepubertal data (e.g., biochemical, anthropometric, and physical activity data), were integrated into
predictive models using machine learning techniques to forecast the MS status during puberty. Linear
models explored interactions between environmental factors, genetic risk scores, and disease risk.
Results: Strong associations were observed between each genetic risk score and its corresponding
phenotypic biomarker. Notably, certain scores related to obesity and high-density lipoprotein levels
exhibited significant interactions with environmental factors, such as sedentary lifestyle, modulating
disease effects. The predictive machine learning models incorporating prepubertal genetics, high-
density lipoprotein, and sedentary lifestyle achieved reasonable performance in predicting pubertal
obesity (AUC, accuracy, and sensitivity of 0.89). These models strike a favorable balance between
risk scores derived from genetic factors and clinical variables. However, when individual risk
scores were considered in isolation, limited predictive results were observed for MS and associated
altered components. Discussion: This study demonstrates the importance of the early identification
of at-risk children for MS. The integration of genetic risk scores, clinical variables, and machine
learning techniques offers promising avenues for predicting pubertal MS. While individual risk scores
have limitations in isolation, polygenic risk scores serve as valuable tools for investigating gene–
environment interactions. Following our results, polygenic risk scores lacked sufficient predictive ability in most clinical traits, limiting their clinical application. Nevertheless, they remain valuable
analytical tools for exploring the association with the environment, by consolidating the effects of
multiple single nucleotide polymorphisms into a single variable.

Idioma: Inglés

DOI: 10.3390/proceedings2023091377

Año: 2024

Publicado en: Proceedings (MDPI) 91 (2024), 377 [2 pp.]
ISSN: 2504-3900

Financiación: info:eu-repo/grantAgreement/ES/AEI/FJC2021-046952-I
Financiación: info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101080219/EU/Preventing lifetime obesity by early risk-factor identification, prognosis and intervention/eprObes
Financiación: info:eu-repo/grantAgreement/ES/ISCIII-FEDER/PI20-00563
Financiación: info:eu-repo/grantAgreement/ES/ISCIII-FEDER/PI20-00924
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI20-00563
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI20-00711
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI20-00988
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI23-00028
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI23-00129
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI23-00165
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI23-00191
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI23-01032

Tipo y forma: Congress (Published version)

Área (Departamento): Área Pediatría (Dpto. Microb.Ped.Radio.Sal.Pú.)