医学
2型糖尿病
内科学
糖尿病
前瞻性队列研究
疾病
人口
生命银行
冠状动脉疾病
腰围
肥胖
内分泌学
生物信息学
环境卫生
生物
作者
Kai Zhu,Frank Qian,Qi Lu,Rui Li,Zixin Qiu,Lin Li,Ruyi Li,Hancheng Yu,Yulei Deng,Kun Yang,Oscar H. Franco,Gang Liu
标识
DOI:10.2337/figshare.24830766
摘要
<p dir="ltr">Objectives: To prospectively evaluate the association between modifiable lifestyle factors and peripheral artery disease (PAD) among individuals with type 2 diabetes (T2D). Research design and methods: We included 14,543 individuals with T2D from the UK Biobank. We defined a weighted healthy lifestyle score using non-smoking, regular physical activity, high-quality diet, moderate alcohol consumption, optimal waist-to-hip ratio, and adequate sleep duration, and categorized into unfavorable, intermediate, and favorable lifestyle. We created a genetic risk score (GRS) using 19 SNPs previously found to be associated with PAD. We modeled the association between lifestyle score and PAD, overall and stratified by PAD genetic susceptibility. Results: After a median 13.5 years of follow-up, 628 incident cases of PAD were documented. A linear inverse association between the weighted lifestyle score and PAD was observed, with a HR (95% CI) of 0.27 (0.19, 0.38) for favorable compared to unfavorable lifestyle (Ptrend<0.0001). An estimated 58.3% (45.0%, 69.1%) of PAD in this population could be potentially avoidable if all participants attained a favorable lifestyle. Moreover, the PAD GRS was associated with increased PAD risk [HR (95%CI) per-SD increment: 1.13 (1.03, 1.23)]. A favorable lifestyle was able to partially mitigate the excess risk of PAD associated with higher GRS, albeit non-significant interaction. Several biomarkers in the lipid metabolism, hepatic/renal function, and systemic inflammation pathways collectively explained 13.3% (8.5%, 20.1%) of the association between weighted lifestyle score and PAD. Conclusion: A favorable lifestyle was associated with lower risk of PAD among individuals with T2D, independent of genetic predisposition to PAD.</p>
科研通智能强力驱动
Strongly Powered by AbleSci AI