Dang Vien Ngoc, Cecil Charlotte, Pariante Carmine M, Hernández-González Jerónimo, Lekadir Karim
Departament de Matemàtiques i Informàtica, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
PLOS Digit Health. 2025 Aug 18;4(8):e0000982. doi: 10.1371/journal.pdig.0000982. eCollection 2025 Aug.
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.
近期证据表明，心理 - 心血管 - 代谢（PCM）共病起源于早期生活因素（ELF）的暴露，因此探索这种关联对于理解和有效管理这些复杂的健康问题至关重要。此外，当前临床指南推荐的心血管疾病（CVD）和糖尿病风险预测模型，在这些ELF可能起重要作用的临床相关亚人群中，通常表现出次优性能。我们的方法研究了ELF对基于机器学习的共病群体风险预测模型的贡献，纳入了广泛的早期生活和近端变量，特别关注产前和产后的ELF。为解决整合不同早期生活和近端因素的复杂性，我们利用能够处理高维、异构数据源的模型，以提高复杂临床群体的预测准确性。通过所学习的模型评估ELF的长期预测能力及其对模型决策的影响，全局和局部模型无关的解释技术使我们能够阐明一些导致共病的相互作用。本研究的数据来自英国生物银行，展示了利用单一大规模数据库进行此类研究的固有优势和局限性。我们的结果表明，在同时患有心理健康问题（抑郁症或焦虑症）和糖尿病的个体中，CVD的预测性能得到增强（AUC - ROC：+7.9%，Acc：+14.7%，Cohen's d：1.5）。同样，我们证明在同时患有心理健康问题和CVD的人群中，糖尿病风险预测得到改善（AUC - ROC：+12.3%，Acc：+13.5%，Cohen's d：2.5）。对这些整合了大量ELF和其他预测因子（包括7个核心弗明汉和英国糖尿病变量）的模型进行检查，提供了关键信息，可能有助于更深入地理解心理 - 心血管 - 代谢共病。我们的研究结果强调了将生命历程因素纳入风险模型的实用性。在共病背景下，整合各种生理、心理和ELF变得尤为重要。
