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 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.Author summary: Many people experience multiple chronic diseases at the same time, such as heart disease, diabetes, and mental health conditions, which makes their health harder to predict and manage. This challenge contributes to health disparities, as individuals with multiple conditions often receive less accurate risk assessments and have limited access to tailored care. Research suggests that experiences early in life, from prenatal development to childhood, can shape long-term health, but these factors are rarely included in disease prediction models. In this study, we used machine learning to understand how ELFs contribute to predicting the risk of heart disease and diabetes, especially in people with mental health conditions. Our results show that including ELFs makes risk predictions more accurate, particularly for people affected by multiple diseases. We also visualize the influence of these factors in health risk prediction, helping to better understand the connection between early-life experiences and later disease. This research highlights the importance of considering life-course factors in disease prevention, paving the way for more personalized and fair approaches to healthcare.