描述早期生活因素在基于机器学习的多疾病风险预测中的作用。

IF 7.7
PLOS digital health Pub Date : 2025-08-18 eCollection Date: 2025-08-01 DOI:10.1371/journal.pdig.0000982
Vien Ngoc Dang, Charlotte Cecil, Carmine M Pariante, Jerónimo Hernández-González, Karim Lekadir
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引用次数: 0

摘要

最近的证据表明,心理-心脏-代谢(PCM)多病发现其起源暴露于早期生活因素(ELFs),使得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,目前临床指南推荐的心血管疾病(CVD)和糖尿病的风险预测模型,在临床相关的亚群中通常表现不佳,在这些亚群中,这些elf可能发挥重要作用。我们的方法学方法研究了ELFs对基于机器学习的共病人群风险预测模型的贡献,包括一系列广泛的早期和近端变量,特别关注产前和产后ELFs。为了解决整合各种早期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来提高复杂临床人群的预测准确性。elf的长期预测能力,以及它们对模型决策的影响,通过学习模型进行评估,而全局和局部模型不可知的解释技术使我们能够阐明导致多病的一些相互作用。本研究的数据来自英国生物银行,展示了利用单一的大规模数据库进行此类研究的优势和局限性。我们的研究结果显示,在同时存在心理健康问题(抑郁或焦虑)和糖尿病的个体中,CVD的预测性能增强(AUC-ROC: +7.9%, Acc: +14.7%, Cohen’s d: 1.5)。同样,我们发现在同时患有精神健康状况和心血管疾病的患者中,糖尿病风险预测(AUC-ROC: +12.3%, Acc: +13.5%, Cohen’s d: 2.5)得到了改善。这些模型整合了大量的elf和其他预测因子(包括7核心Framingham和UKDiabetes变量),对这些模型的检验提供了关键信息,可以更深刻地理解心理-心脏-代谢多病。我们的发现强调了将生命过程因素纳入风险模型的效用。在多重疾病的背景下,整合各种生理、心理和elf变得尤为重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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