虚弱与心血管指标和行为特征的遗传和表型关联。

IF 11.4 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Journal of Advanced Research Pub Date : 2025-05-01 DOI:10.1016/j.jare.2024.06.012

Yihan Chen , Siying Lin , Shuangyu Yang , Mengling Qi , Yu Ren , Chong Tian , Shitian Wang , Yuedong Yang , Jianzhao Gao , Huiying Zhao

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引用次数: 0

摘要

简介虚弱指数（FI）是衡量虚弱程度的常用指标，许多指南都主张将其作为常规临床检测项目。FI与心血管指标（CIs）和行为特征（BCs）的遗传和表型关系尚不清楚，这妨碍了利用易于收集的数据监测FI的能力：本研究旨在调查虚弱与心血管指标和行为特征的遗传和表型关联，并进一步构建预测虚弱的模型：方法：通过跨性状 LD 评分回归法（LDSC）和孟德尔随机法（MR）评估了 FI 与 288 个 CI 和 90 个 BC 的遗传关系。这些CIs和BCs的表型数据与一个机器学习模型相结合，以预测英国生物库中个体的FI。预测的FI与2型糖尿病（T2D）和神经退行性疾病风险的关系通过Kaplan-Meier估计器和Cox比例危险模型进行了检验：MR揭示了7个CI和8个BC对FI的推定因果效应。综合这些 CI 和 BC，建立了预测 FI 的模型。预测的 FI 与观察到的 FI 显著相关（皮尔逊相关系数 = 0.660，P 值 = 4.96 × 10-62）。预测模型显示，"通常的步行速度 "对预测的贡献最大。被预测为高 FI 的患者患 T2D（HR = 2.635，P -16）和神经退行性疾病（HR = 2.307，P = 1.62 × 10-3）的风险明显高于其他患者：本研究从遗传和表型角度支持 FI 与 CIs 和 BCs 的关联。通过整合易于收集的 CIs 和 BCs 数据来预测 FI 的模型具有监测疾病风险的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics

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Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics

Introduction

Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data.

Objectives

This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI.

Method

Genetic relationships of FI with 288 CIs and 90 BCs were assessed by the cross-trait LD score regression (LDSC) and Mendelian randomization (MR). The phenotypic data of these CIs and BCs were integrated with a machine-learning model to predict FI of individuals in UK-biobank. The relationships of the predicted FI with risks of type 2 diabetes (T2D) and neurodegenerative diseases were tested by the Kaplan-Meier estimator and Cox proportional hazards model.

Results

MR revealed putative causal effects of seven CIs and eight BCs on FI. These CIs and BCs were integrated to establish a model for predicting FI. The predicted FI is significantly correlated with the observed FI (Pearson correlation coefficient = 0.660, P-value = 4.96 × 10^-62). The prediction model indicated “usual walking pace” contributes the most to prediction. Patients who were predicted with high FI are in significantly higher risk of T2D (HR = 2.635, P < 2 × 10^-16) and neurodegenerative diseases (HR = 2.307, P = 1.62 × 10^-3) than other patients.

Conclusion

This study supports associations of FI with CIs and BCs from genetic and phenotypic perspectives. The model that is developed by integrating easily collected CIs and BCs data in predicting FI has the potential to monitor disease risk.

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来源期刊

Journal of Advanced Research Multidisciplinary-Multidisciplinary

CiteScore

21.60

自引率

0.90%

发文量

280

审稿时长

12 weeks

期刊介绍： Journal of Advanced Research (J. Adv. Res.) is an applied/natural sciences, peer-reviewed journal that focuses on interdisciplinary research. The journal aims to contribute to applied research and knowledge worldwide through the publication of original and high-quality research articles in the fields of Medicine, Pharmaceutical Sciences, Dentistry, Physical Therapy, Veterinary Medicine, and Basic and Biological Sciences. The following abstracting and indexing services cover the Journal of Advanced Research: PubMed/Medline, Essential Science Indicators, Web of Science, Scopus, PubMed Central, PubMed, Science Citation Index Expanded, Directory of Open Access Journals (DOAJ), and INSPEC.