University of Alberta, Department of Psychiatry, Edmonton, Alberta, Canada,
University of Alberta, Department of Psychiatry, Edmonton, Alberta, Canada.
Gerontology. 2023;69(12):1394-1403. doi: 10.1159/000534015. Epub 2023 Sep 19.
An aging population will bring a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors, and chronic disease conditions.
We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA). The BioAge gap, which is the difference between BioAge index and chronological age, was used to quantify the differential aging, i.e., the difference between biological and chronological age, of the CLSA participants. We further investigated the associations between the BioAge gap and lifestyle, environmental factors, and current and future health conditions.
BioAge gap had strong associations with existing adverse health conditions (e.g., cancers, cardiovascular diseases, diabetes, and kidney diseases) and future disease onset (e.g., Parkinson's disease, diabetes, and kidney diseases). We identified that frequent consumption of processed meat, pork, beef, and chicken, poor outcomes in nutritional risk screening, cigarette smoking, exposure to passive smoking are associated with positive BioAge gap ("older" BioAge than expected). We also identified several modifiable factors, including eating fruits, legumes, vegetables, related to negative BioAge gap ("younger" BioAge than expected).
Our study shows that a BioAge index based on easily accessible blood tests has the potential to quantify the differential biological aging process that can be associated with current and future adverse health events. The identified risk and protective factors for differential aging indicated by BioAge gap are informative for future research and guidelines to promote healthy longevity.
人口老龄化将给医疗保健系统带来紧迫挑战。通过量化生物衰老过程并了解可改变的生活方式和环境因素以及慢性疾病状况的作用，可以深入了解促进健康长寿的方法。
我们基于加拿大老龄化纵向研究（CLSA）中易于获取的血液检测数据，应用多种最先进的机器学习模型开发了一种生物年龄（BioAge）指数。生物年龄差距（BioAge gap）用于量化 CLSA 参与者的差异老化，即生物年龄与实际年龄之间的差异。我们进一步研究了 BioAge gap 与生活方式、环境因素以及当前和未来健康状况之间的关联。
BioAge gap 与现有的不良健康状况（例如癌症、心血管疾病、糖尿病和肾脏疾病）以及未来疾病的发病（例如帕金森病、糖尿病和肾脏疾病）密切相关。我们发现，经常食用加工肉、猪肉、牛肉和鸡肉，营养风险筛查结果不佳，吸烟，暴露于被动吸烟与正向 BioAge gap（即生物年龄比预期的大）相关。我们还确定了几个可改变的因素，包括食用水果、豆类、蔬菜，与负向 BioAge gap（即生物年龄比预期的小）相关。
我们的研究表明，基于易于获取的血液测试的 BioAge 指数具有量化可与当前和未来不良健康事件相关的差异生物衰老过程的潜力。BioAge gap 所识别的差异老化的风险和保护因素为促进健康长寿的未来研究和指南提供了信息。
