衰老的血液生物标志物（HemeAge）和心血管风险：两个队列的机器学习分析

IF 5.4 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

American journal of preventive cardiology Pub Date : 2026-04-01 Epub Date: 2026-02-01 DOI:10.1016/j.ajpc.2026.101460

Adi Siddharth , David Zidar , Budhaditya Bose , Rakesh Gullapelli , Juan C Nicholas , Khurram Nasir , Sadeer Al-Kindi

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引用次数: 0

摘要

背景：实足年龄不能充分反映年龄变异性和心血管风险。从常规全血细胞计数（CBC）参数得出的生物年龄可能提供一个更可行的标记。目的利用CBC数据（HemeAge）建立生物年龄的机器学习模型，并在两个大型队列中评估其与死亡率和主要不良心血管事件（MACE）的关联。方法对53,355名NHANES参与者（1999-2010）进行XGBoost模型训练，根据CBC参数预测实足年龄。该模型应用于109,844名休斯顿卫理公会心血管疾病登记处的患者，产生“delta年龄”（预测减去实际年龄）。将患者分为弹性组（delta < -10）、比例组（-10≤delta≤10）和加速组（delta > 10）。Cox模型评估死亡率和MACE风险，并根据人口统计学和临床因素进行调整。结果红细胞分布宽度、平均细胞体积和中性粒细胞计数是预测年龄的关键指标。与比例衰老相比，加速衰老与死亡风险（HR 3.05, 95% CI 2.41-3.85）和MACE （HR 1.37, 95% CI 1.24-1.51）增加相关。弹性衰老降低了死亡风险（HR 0.59, 95% CI 0.52-0.68）和MACE （HR 0.76, 95% CI 0.72-0.81）。相关性在中年（40-80岁）、死亡和心力衰竭结局中最强，并在年龄分层和连续模型中持续存在。结论sheemeage可独立预测超过实足年龄的死亡率和心血管风险。这些易于获得的血液学标志物可能会加强风险分层，并为有针对性的预防策略提供信息。

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Hematologic biomarkers of aging (HemeAge) and cardiovascular risk: a machine learning analysis in two cohorts

Background

Chronological age inadequately reflects aging variability and cardiovascular risk. Biological age derived from routine complete blood count (CBC) parameters may provide a more actionable marker.

Objective

To develop a machine learning model of biological age using CBC data (HemeAge) and evaluate associations with mortality and major adverse cardiovascular events (MACE) in two large cohorts.

Methods

An XGBoost model was trained on 53,355 NHANES participants (1999–2010) to predict chronological age from CBC parameters. The model was applied to 109,844 Houston Methodist CVD Registry patients, generating "delta age" (predicted minus chronological age). Patients were classified as Resilient (delta < –10), Proportionate (–10 ≤ delta ≤ 10), or Accelerated (delta > 10). Cox models assessed mortality and MACE risk, adjusting for demographics and clinical factors.

Results

Red cell distribution width, mean cell volume, and neutrophil count were key age predictors. Accelerated aging associated with increased mortality risk (HR 3.05, 95% CI 2.41–3.85) and MACE (HR 1.37, 95% CI 1.24–1.51) versus proportionate aging. Resilient aging conferred reduced risk for mortality (HR 0.59, 95% CI 0.52–0.68) and MACE (HR 0.76, 95% CI 0.72–0.81). Associations were strongest in midlife (ages 40–80) and for death and heart failure outcomes and persisted across age-stratified and continuous models.

Conclusions

HemeAge independently predicts mortality and cardiovascular risk beyond chronological age. These accessible hematologic markers may enhance risk stratification and inform targeted prevention strategies.

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