Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, MD 20740, USA.
Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA.
Genes (Basel). 2024 Sep 30;15(10):1285. doi: 10.3390/genes15101285.
BACKGROUND/OBJECTIVES: Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals.
In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors.
We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging.
The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases.
背景/目的：人类大脑衰老过程是一个复杂的过程，影响大脑功能和结构的各个方面，增加了患神经和精神疾病的易感性。许多非遗传（例如环境和生活方式）和遗传风险因素被发现有助于个体大脑衰老速度的差异。
在本文中，我们在英国生物库中进行了外显子组全关联研究（XWAS）和全基因组关联研究（GWAS），揭示了大脑衰老的多因素性质。我们应用了机器学习算法，并利用来自弥散张量成像数据的各向异性分数测量值来预测白质脑年龄差距（BAG），并将其视为大脑衰老的标志物。对于 XWAS，我们纳入了 107 个变量，涵盖了可能影响大脑衰老的五个主要类别的可改变暴露因素，并进行了单变量和多变量分析，以选择最终的非遗传风险因素集。
我们发现当前吸烟、包括油性鱼类、牛肉、羊肉、谷物和咖啡摄入在内的饮食习惯、手机使用时间、使用紫外线防护剂以及日光浴/太阳灯使用频率与 BAG 相关。在遗传分析中，我们在染色体 3 上发现了几个与基因 IP6K1、GMNC、OSTN 和 SLC25A20 映射的 SNP 与 BAG 显著相关，表明人类大脑衰老具有高度的遗传性和多基因结构。
我们研究中确定的关键非遗传和遗传风险因素为白质大脑衰老与神经退行性疾病之间的因果关系提供了深入的了解。
