Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction.
Recent evidence suggests that psycho-cardio-metabolic (PCM) multimorbidity finds its origins in exposure to early-life factors (ELFs), making the exploration of this association crucial for understanding and effective management of these complex health issues. Moreover, risk prediction models for ca...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-08-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000982 |
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| author | Vien Ngoc Dang Charlotte Cecil Carmine M Pariante Jerónimo Hernández-González Karim Lekadir |
| author_facet | Vien Ngoc Dang Charlotte Cecil Carmine M Pariante Jerónimo Hernández-González Karim Lekadir |
| author_sort | Vien Ngoc Dang |
| collection | DOAJ |
| description | Recent evidence suggests that psycho-cardio-metabolic (PCM) multimorbidity finds its origins in exposure to early-life factors (ELFs), making the exploration of this association crucial for understanding and effective management of these complex health issues. Moreover, risk prediction models for cardiovascular diseases (CVD) and diabetes, as recommended by current clinical guidelines, typically demonstrate sub-optimal performance in clinically relevant sub-populations where these ELFs may play a substantial role. Our methodological approach investigates the contribution of ELFs to machine-learning-based risk prediction models for comorbid populations, incorporating a wide set of early-life and proximal variables, with a special focus on prenatal and postnatal ELFs. To address the complexity of integrating diverse early-life and proximal factors, we leverage models capable of handling high-dimensional, heterogeneous data sources to enhance prediction accuracy in complex clinical populations. The long-term predictive ability of ELFs, along with their influence on model decisions, is assessed with the learned models, and global and local model-agnostic interpretative techniques allow us to elucidate some interactions leading to multimorbidity. The data for this study is derived from the UK Biobank, showcasing both the strengths and limitations inherent in utilizing a single, large-scale database for such research. Our results show enhanced predictive performance for CVD (AUC-ROC: +7.9%, Acc: +14.7%, Cohen's d: 1.5) among individuals with concurrent mental health issues (depression or anxiety) and diabetes. Similarly, we demonstrate improved diabetes risk prediction (AUC-ROC: +12.3%, Acc: +13.5%, Cohen's d: 2.5) in those with concurrent mental health conditions and CVD. The inspection of these models, which integrate a large set of ELFs and other predictors (including the 7-core Framingham and UKDiabetes variables), provides key information that could lead to a more profound understanding of psycho-cardio-metabolic multimorbidity. Our findings highlight the utility of incorporating life-course factors into risk models. Integrating a diverse range of physiological, psychological, and ELFs becomes particularly pertinent in the context of multimorbidity. |
| format | Article |
| id | doaj-art-5a0f8fb2bac8467dad8adfc1d8fe8c21 |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-5a0f8fb2bac8467dad8adfc1d8fe8c212025-08-23T05:33:15ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-08-0148e000098210.1371/journal.pdig.0000982Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction.Vien Ngoc DangCharlotte CecilCarmine M ParianteJerónimo Hernández-GonzálezKarim LekadirRecent evidence suggests that psycho-cardio-metabolic (PCM) multimorbidity finds its origins in exposure to early-life factors (ELFs), making the exploration of this association crucial for understanding and effective management of these complex health issues. Moreover, risk prediction models for cardiovascular diseases (CVD) and diabetes, as recommended by current clinical guidelines, typically demonstrate sub-optimal performance in clinically relevant sub-populations where these ELFs may play a substantial role. Our methodological approach investigates the contribution of ELFs to machine-learning-based risk prediction models for comorbid populations, incorporating a wide set of early-life and proximal variables, with a special focus on prenatal and postnatal ELFs. To address the complexity of integrating diverse early-life and proximal factors, we leverage models capable of handling high-dimensional, heterogeneous data sources to enhance prediction accuracy in complex clinical populations. The long-term predictive ability of ELFs, along with their influence on model decisions, is assessed with the learned models, and global and local model-agnostic interpretative techniques allow us to elucidate some interactions leading to multimorbidity. The data for this study is derived from the UK Biobank, showcasing both the strengths and limitations inherent in utilizing a single, large-scale database for such research. Our results show enhanced predictive performance for CVD (AUC-ROC: +7.9%, Acc: +14.7%, Cohen's d: 1.5) among individuals with concurrent mental health issues (depression or anxiety) and diabetes. Similarly, we demonstrate improved diabetes risk prediction (AUC-ROC: +12.3%, Acc: +13.5%, Cohen's d: 2.5) in those with concurrent mental health conditions and CVD. The inspection of these models, which integrate a large set of ELFs and other predictors (including the 7-core Framingham and UKDiabetes variables), provides key information that could lead to a more profound understanding of psycho-cardio-metabolic multimorbidity. Our findings highlight the utility of incorporating life-course factors into risk models. Integrating a diverse range of physiological, psychological, and ELFs becomes particularly pertinent in the context of multimorbidity.https://doi.org/10.1371/journal.pdig.0000982 |
| spellingShingle | Vien Ngoc Dang Charlotte Cecil Carmine M Pariante Jerónimo Hernández-González Karim Lekadir Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. PLOS Digital Health |
| title | Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. |
| title_full | Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. |
| title_fullStr | Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. |
| title_full_unstemmed | Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. |
| title_short | Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. |
| title_sort | characterizing the role of early life factors in machine learning based multimorbidity risk prediction |
| url | https://doi.org/10.1371/journal.pdig.0000982 |
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