Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Geroscience. 2023 Feb;45(1):439-450. doi: 10.1007/s11357-022-00650-z. Epub 2022 Sep 2.
Machine learning methods have been applied to estimate measures of brain aging from neuroimages. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI-based measure of dementia risk, the Alzheimer's disease pattern similarity (AD-PS) scores, with measures used to calculate biological age. Participants were those from visit 5 of the Atherosclerosis Risk in Communities Study with cognitive status adjudication, proteomic data, and AD-PS scores available. The AD-PS score estimation is based on previously reported machine learning methods. We evaluated associations of the AD-PS score with all-cause mortality. Sensitivity analyses using only cognitively normal (CN) individuals were performed treating CNS-related causes of death as competing risk. AD-PS score was examined in association with 32 proteins measured, using a Somalogic platform, previously reported to be associated with age. Finally, associations with a deficit accumulation index (DAI) based on a count of 38 health conditions were investigated. All analyses were adjusted for age, race, sex, education, smoking, hypertension, and diabetes. The AD-PS score was significantly associated with all-cause mortality and with levels of 9 of the 32 proteins. Growth/differentiation factor 15 (GDF-15) and pleiotrophin remained significant after accounting for multiple-testing and when restricting the analysis to CN participants. A linear regression model showed a significant association between DAI and AD-PS scores overall. While the AD-PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging.
机器学习方法已被应用于从神经影像中估计脑老化的指标。然而，这些指标很少在生物年龄的背景下进行研究。在这里，我们研究了基于 MRI 的痴呆风险评估指标——阿尔茨海默病模式相似性（AD-PS）评分与用于计算生物年龄的指标之间的关联。参与者是动脉粥样硬化风险社区研究（ARIC）第 5 次访视的参与者，认知状态评估、蛋白质组学数据和 AD-PS 评分可用。AD-PS 评分的估计是基于先前报道的机器学习方法。我们评估了 AD-PS 评分与全因死亡率之间的关联。使用仅认知正常（CN）个体的敏感性分析将中枢神经系统相关原因的死亡视为竞争风险。使用 Somalogic 平台评估 AD-PS 评分与之前报道与年龄相关的 32 种蛋白质的关联，该平台测量了 32 种蛋白质。最后，研究了基于 38 种健康状况计数的缺陷积累指数（DAI）与 AD-PS 评分的关联。所有分析均调整了年龄、种族、性别、教育、吸烟、高血压和糖尿病。AD-PS 评分与全因死亡率以及 32 种蛋白质中的 9 种呈显著相关。在考虑了多重检验并将分析仅限于 CN 参与者后，生长/分化因子 15（GDF-15）和多效蛋白仍然具有显著意义。线性回归模型显示 DAI 与 AD-PS 评分之间存在显著关联。虽然 AD-PS 评分是作为痴呆风险的评估指标创建的，但我们的分析表明它们也可以捕捉大脑衰老。
