Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits.
Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits.
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DOI：
10.1038/s41598-022-08050-1
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发表时间：
2022-03-08
影响因子：
4.6
通讯作者：
Franks PW
中科院分区：
文献类型：
--
作者：
Poveda A;Pomares-Millan H;Chen Y;Kurbasic A;Patel CJ;Renström F;Hallmans G;Johansson I;Franks PW
本研究评估了约300种生活方式暴露与9种心脏代谢特征的时间相关性，以确定可能为降低心脏代谢疾病风险的生活方式干预提供信息的暴露/暴露组。分析是在一个纵向样本中进行的，该样本包括生活在瑞典北方的31，000名成年人。线性混合模型用于评估生活方式暴露的平均相关性，线性回归模型用于测试与心脏代谢特征10年变化的相关性。在评估生活方式变量平均关联的分析中，“身体活动”和“一般健康”是包含最多“暂定信号”的暴露类别，而“烟草使用”是10年变化关联分析的首要类别。11个可变变量显示了大多数心脏代谢特征之间一致的平均关联。这些变量属于以下领域：（i）吸烟，（ii）饮料（过滤咖啡），（iii）身体活动，（iv）酒精摄入量，以及（v）与北欧生活方式相关的特定变量（休闲时间狩猎/钓鱼和煮咖啡消费）。我们使用了一种不可知的、数据驱动的方法来评估广泛的心血管代谢疾病的既定和新的风险因素。我们的研究结果突出了关键变量，沿着了它们各自的影响估计，这些变量可能会优先用于随后的预测模型和生活方式干预。
The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising > 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits. ‘Physical activity’ and ‘General Health’ were the exposure categories containing the highest number of ‘tentative signals’ in analyses assessing the average association of lifestyle variables, while ‘Tobacco use’ was the top category for the 10-year change association analyses. Eleven modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to the domains: (i) Smoking, (ii) Beverage (filtered coffee), (iii) physical activity, (iv) alcohol intake, and (v) specific variables related to Nordic lifestyle (hunting/fishing during leisure time and boiled coffee consumption). We used an agnostic, data-driven approach to assess a wide range of established and novel risk factors for cardiometabolic disease. Our findings highlight key variables, along with their respective effect estimates, that might be prioritised for subsequent prediction models and lifestyle interventions.
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