Computational Bioscience Program, Department of Pharmacology (L.M.S.), University of Colorado Anschutz Medical School, Aurora.
Institute for Precision Cardiovascular Medicine at the American Heart Association, Dallas, TX (L.M.S., J.L.H.).
Circ Heart Fail. 2021 Feb;14(2):e006799. doi: 10.1161/CIRCHEARTFAILURE.119.006799. Epub 2021 Feb 9.
Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke.
We used machine learning feature selection based on random forest analysis to identify potential risk factors associated with coronary heart disease, stroke, and HF in FHS. We evaluated the significance of selected variables using univariable and multivariable Cox proportional hazards analysis adjusted for known cardiovascular risks. Findings from FHS were then validated using CHS and ARIC.
We identified multiple dietary and behavioral risk factors for cardiovascular disease outcomes including marital status, red meat consumption, whole milk consumption, and coffee consumption. Among these dietary variables, increasing coffee consumption was associated with decreasing long-term risk of HF congruently in FHS, ARIC, and CHS.
Higher coffee intake was found to be associated with reduced risk of HF in all three studies. Further study is warranted to better define the role, possible causality, and potential mechanism of coffee consumption as a potential modifiable risk factor for HF.
冠心病、心力衰竭（HF）和中风是具有多种表型的复杂疾病。尽管这些疾病的许多风险因素已广为人知，但对尚未确定的风险因素进行研究可能会改善风险评估和患者对预防指南的依从性。我们在弗雷明汉心脏研究（FHS）、心血管心脏研究（CHS）和社区动脉粥样硬化风险研究（ARIC）中调查了饮食领域，以确定与冠心病、HF 和中风相关的潜在生活方式和行为因素。
我们使用基于随机森林分析的机器学习特征选择来识别与 FHS 中的冠心病、中风和 HF 相关的潜在风险因素。我们使用单变量和多变量 Cox 比例风险分析评估选定变量的显著性，调整了已知的心血管风险。然后使用 CHS 和 ARIC 验证了 FHS 的结果。
我们确定了多种与心血管疾病结局相关的饮食和行为风险因素，包括婚姻状况、红肉类消费、全脂牛奶消费和咖啡消费。在这些饮食变量中，咖啡摄入量的增加与 FHS、ARIC 和 CHS 中 HF 长期风险的降低一致相关。
在所有三项研究中，较高的咖啡摄入量与 HF 风险降低相关。需要进一步研究以更好地定义咖啡作为 HF 的潜在可改变风险因素的作用、可能的因果关系和潜在机制。
