Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
J Affect Disord. 2024 Nov 1;364:188-193. doi: 10.1016/j.jad.2024.08.078. Epub 2024 Aug 13.
Multiple epidemiological studies have observed the connection between aging and brain volumes. The concept of accelerated biological aging (BA) is more powerful for observing the degree of aging of an individual than chronologic age (CA). The objective of this study is to explore the relationship between BA and brain volumes.
BA was measured from clinical traits using two blood-chemistry algorithms, the Klemera-Doubal method (KDM) and the PhenoAge. The two age acceleration biomarkers were calculated by the residuals from regressing CA, termed "KDM-acceleration" and "PhenoAge-acceleration". Brain volumes were from brain magnetic resonance imaging (MRI) data. After adjustment for confounding factors, general linear regression models were used to examine associations between KDM-acceleration and PhenoAge-acceleration and brain volumes, respectively. Additionally, we stratified participants by sex, age, and the four quartiles of the Townsend Deprivation Index (TDI) for extra subgroup analysis.
14,725 participants with available information were enrolled. After full adjustment, we observed negative associations between KDM-acceleration and brain volumes, such as gray matter (β = -0.029), white matter (β = -0.021), gray and white matter (β = -0.026), and hippocampus (β = -0.011 for left and β = -0.014 for right). There were also negative associations between PhenoAge-acceleration and brain volumes, such as white matter (β = -0.008), gray and white matter (β = -0.010), thalamus (β = -0.012 for left and β = -0.012 for right). In the subgroup analysis stratified by sex, age, and the four quartiles of TDI, the association between KDM-acceleration and PhenoAge-acceleration and brain volumes still existed. In subgroup analyses, the variation in associations suggests that socioeconomic and biological factors may differentially influence brain aging.
Our research indicated that more advanced BA was associated with less brain tissue.
多项流行病学研究观察到衰老与大脑体积之间的联系。与实际年龄（CA）相比，生物年龄加速（BA）的概念更能观察个体的衰老程度。本研究旨在探讨 BA 与大脑体积之间的关系。
使用两种血液化学算法（Klemera-Doubal 法（KDM）和 PhenoAge）从临床特征测量 BA。通过回归 CA 的残差计算出两个年龄加速生物标志物，分别称为“KDM-加速”和“PhenoAge-加速”。大脑体积来自脑磁共振成像（MRI）数据。在调整混杂因素后，使用线性回归模型分别检验 KDM-加速和 PhenoAge-加速与大脑体积之间的关联。此外，我们按性别、年龄和 Townsend 剥夺指数（TDI）的四个四分位数对参与者进行分层，进行额外的亚组分析。
纳入了 14725 名有可用信息的参与者。经过完全调整后，我们观察到 KDM-加速与大脑体积呈负相关，例如灰质（β=-0.029）、白质（β=-0.021）、灰质和白质（β=-0.026）和海马体（β=-0.011 左和β=-0.014 右）。PhenoAge-加速与大脑体积也呈负相关，例如白质（β=-0.008）、灰质和白质（β=-0.010）、丘脑（β=-0.012 左和β=-0.012 右）。在按性别、年龄和 TDI 的四个四分位数分层的亚组分析中，KDM-加速和 PhenoAge-加速与大脑体积之间的关联仍然存在。在亚组分析中，关联的变化表明社会经济和生物学因素可能以不同的方式影响大脑衰老。
我们的研究表明，BA 越先进，脑组织越少。
