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多模态脑龄与实际年龄的差异与端粒缩短有关。

Differences between multimodal brain-age and chronological-age are linked to telomere shortening.

机构信息

Psychology, School of Social Sciences, National Technological University, 48 Nanyang Avenue, Singapore 639798.

Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 119559; Department of Genome Sciences, The John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2601.

出版信息

Neurobiol Aging. 2022 Jul;115:60-69. doi: 10.1016/j.neurobiolaging.2022.03.015. Epub 2022 Mar 31.

DOI:10.1016/j.neurobiolaging.2022.03.015
PMID:35472831
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9133148/
Abstract

Telomere shortening is theorized to accelerate biological aging, however, this has not been tested in the brain and cognitive contexts. We used machine learning age-prediction models to determine brain/cognitive age and quantified the degree of accelerated aging as the discrepancy between brain and/or cognitive and chronological ages (i.e., age gap). We hypothesized these age gaps are associated with telomere length (TL). Using healthy participants from the ADNI-3 cohort (N = 196, Age=70.7), we trained age-prediction models using 4 modalities of brain features and cognitive scores, as well as a 'stacked' model combining all brain modalities. Then, these 6 age-prediction models were applied to an independent sample diagnosed with mild cognitive impairment (N = 91, Age=71.3) to determine, for each subject, the model-specific predicted age and age gap. TL was most strongly associated with age gaps from the resting-state functional connectivity model after controlling for confounding variables. Overall, telomere shortening was significantly related to older brain but not cognitive age gaps. In particular, functional relative to structural brain-age gaps, were more strongly implicated in telomere shortening.

摘要

端粒缩短被认为会加速生物衰老,但这在大脑和认知环境中尚未得到验证。我们使用机器学习的年龄预测模型来确定大脑/认知年龄,并量化加速衰老的程度,即大脑和/或认知年龄与实际年龄之间的差距(即年龄差距)。我们假设这些年龄差距与端粒长度(TL)有关。使用 ADNI-3 队列中的健康参与者(N=196,年龄=70.7),我们使用 4 种大脑特征和认知评分模式以及结合所有大脑模式的“堆叠”模型来训练年龄预测模型。然后,将这 6 种年龄预测模型应用于独立的轻度认知障碍诊断样本(N=91,年龄=71.3),以确定每个受试者的特定模型预测年龄和年龄差距。在控制混杂变量后,静息态功能连接模型的年龄差距与 TL 最密切相关。总的来说,端粒缩短与大脑衰老而不是认知衰老差距显著相关。特别是,与结构脑年龄差距相比,功能脑年龄差距与端粒缩短的关系更为密切。

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