Journal
NEUROBIOLOGY OF AGING
Volume 115, Issue -, Pages 60-69Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.neurobiolaging.2022.03.015
Keywords
Brain-age; Cognitive-age; Telomere; Resting-state functional connectivity; Structural connectivity; Subcortical gray matter; Cortical thickness
Categories
Funding
- Kwan Im Thong Hood Cho Temple
- Lee Kim Tah Holdings Pte Ltd, under the Mind Science Centre, Department of Psychological Medicine, National University of Singapore
- Nanyang Assistant Professorship
- Singapore Ministry of Education Tier 2
- ADNI (National Institutes of Health)
- DOD ADNI (Department of Defense)
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengi-neering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- Bio Clinica , Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd
- Genentech ,Inc.
- Fujirebio
- GE Healthcare [021080-0 0 0 01]
- IXICO Ltd. [MOE-T2EP30120-0016]
- Janssen Alzheimer Immuno therapy Research & Development, LLC. [U01 AG024904]
- Johnson & Johnson Pharmaceutical Research & Development LLC. [W81XWH-12-2-0012]
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Meso Scale Diagnostics , LLC
- Neuro Rx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
Ask authors/readers for more resources
The study found that telomere shortening may be associated with biological aging of the brain, and using machine learning models can predict the age of the brain and cognition, with a clear correlation between telomere length and age discrepancies.
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 mean = 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 mean = 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. (c) 2022 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available