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Article Dans Une Revue Communications Medicine Année : 2024

Machine learning-based health environmental-clinical risk scores in European children

Alexandros Siskos
Amrit Kaur Sakhi
  • Fonction : Auteur
Barbara Heude
  • Fonction : Auteur
Eduard Sabidó
  • Fonction : Auteur
Eva Borràs
  • Fonction : Auteur
Hector Keun
  • Fonction : Auteur
John Wright
  • Fonction : Auteur
Jordi Julvez
  • Fonction : Auteur
Jose Urquiza
Kristine Bjerve Gützkow
  • Fonction : Auteur
Leda Chatzi
  • Fonction : Auteur
Maribel Casas
  • Fonction : Auteur
Mariona Bustamante
Mark Nieuwenhuijsen
  • Fonction : Auteur
Martine Vrijheid
  • Fonction : Auteur
Mónica López-Vicente
Montserrat de Castro Pascual
Nikos Stratakis
  • Fonction : Auteur
Oliver Robinson
  • Fonction : Auteur
Regina Grazuleviciene
  • Fonction : Auteur
Remy Slama
  • Fonction : Auteur
Silvia Alemany
Xavier Basagaña
Marc Plantevit
  • Fonction : Auteur

Résumé

Background Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data. Methods In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions. Results ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods. The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health. Conclusions Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

Dates et versions

hal-04602386 , version 1 (05-06-2024)

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Jean-Baptiste Guimbaud, Alexandros Siskos, Amrit Kaur Sakhi, Barbara Heude, Eduard Sabidó, et al.. Machine learning-based health environmental-clinical risk scores in European children. Communications Medicine, 2024, 4 (1), pp.98. ⟨10.1038/s43856-024-00513-y⟩. ⟨hal-04602386⟩
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