TY - JOUR
T1 - Alzheimer's Imaging Consortium
AU - Raji, Cyrus A.
AU - Meysami, Somayeh
AU - Lee, Soojin
AU - Garg, Saurabh
AU - Akbari, Nasrin
AU - Pompa, Rodrigo Solis
AU - Gouda, Ahmed
AU - Nguyen, Thanh Duc
AU - Basar, Saqib
AU - Chodakiewitz, Yosef Gavriel
AU - Merrill, David A.
AU - Patel, Amar
AU - Durand, Daniel J.
AU - Hashemi, Sam
N1 - Publisher Copyright:
© 2025 The Alzheimer's Association. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
PY - 2025/12/1
Y1 - 2025/12/1
N2 - BACKGROUND: Brain age - an image derived measure from structural brain images on T1 weighted scans may reveal information on Alzheimer's risk. We have previously shown that increased abdominal adipose tissue relates to brain atrophy. We evaluated the links between abdominal adipose tissue and brain age. METHOD: A total of 1,164 healthy participants from four sites (mean chronological age 55.17 ± 12.37 years, 52% women; 48% men; 39% non-white) were scanned on 1.5T MR machines with a whole-body protocol. Whole body sequences utilized in the quantitative analyses of abdominal fat were coronal T1 were used to segment visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) segmentation. In this process, a nnU-Net model was used for fully supervised segmentation and ITK-SNAP was used for manual annotation. Brain age was computed using a regression-based 3D Simple Fully Convolutional Network. The model was trained on in-house T1-weighted MRI scans collected from 5,500 healthy individuals, aged 18 to 89 years. Brain age gap (BAG) was computed by subtracting chronological age from brain age. Bivariate correlations between VAT and SAT to chronological and brain age were done with partial correlations adjusted for sex with brain age. VAT and SAT were normalized to total abdominal body fat volume. Chronological age was not adjusted for in brain age models to avoid collinearity. RESULT: Mean brain age exceeded chronological age (mean brain age = 56.04 ± 12.65, mean BAG = 0.69) and were highly correlated (r=0.94, p <.001). VAT and SAT were positively related to increased chronological age (VAT: r=0.2780, p = 5.477e-20; r=0.0924, p = 0.002817) and increased brain age (VAT: r=0.2806, p = 2.42e-20; SAT: r=0.0947, p = 0.002189) with VAT being more closely linked to age than SAT. This did not change when adjusting for sex in separate partial correlations between VAT and SAT for brain age (VAT: rp = r=0.2948, p = 2.247e-22; SAT: r=0.1070, p = 0.0005353). No statistically significant link was noted with VAT, SAT, and BAG. CONCLUSION: Both VAT and SAT are linked to chronological and brain age with VAT being more strongly linked. VAT may be a key target for modifying brain age and Alzheimer's risk.
AB - BACKGROUND: Brain age - an image derived measure from structural brain images on T1 weighted scans may reveal information on Alzheimer's risk. We have previously shown that increased abdominal adipose tissue relates to brain atrophy. We evaluated the links between abdominal adipose tissue and brain age. METHOD: A total of 1,164 healthy participants from four sites (mean chronological age 55.17 ± 12.37 years, 52% women; 48% men; 39% non-white) were scanned on 1.5T MR machines with a whole-body protocol. Whole body sequences utilized in the quantitative analyses of abdominal fat were coronal T1 were used to segment visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) segmentation. In this process, a nnU-Net model was used for fully supervised segmentation and ITK-SNAP was used for manual annotation. Brain age was computed using a regression-based 3D Simple Fully Convolutional Network. The model was trained on in-house T1-weighted MRI scans collected from 5,500 healthy individuals, aged 18 to 89 years. Brain age gap (BAG) was computed by subtracting chronological age from brain age. Bivariate correlations between VAT and SAT to chronological and brain age were done with partial correlations adjusted for sex with brain age. VAT and SAT were normalized to total abdominal body fat volume. Chronological age was not adjusted for in brain age models to avoid collinearity. RESULT: Mean brain age exceeded chronological age (mean brain age = 56.04 ± 12.65, mean BAG = 0.69) and were highly correlated (r=0.94, p <.001). VAT and SAT were positively related to increased chronological age (VAT: r=0.2780, p = 5.477e-20; r=0.0924, p = 0.002817) and increased brain age (VAT: r=0.2806, p = 2.42e-20; SAT: r=0.0947, p = 0.002189) with VAT being more closely linked to age than SAT. This did not change when adjusting for sex in separate partial correlations between VAT and SAT for brain age (VAT: rp = r=0.2948, p = 2.247e-22; SAT: r=0.1070, p = 0.0005353). No statistically significant link was noted with VAT, SAT, and BAG. CONCLUSION: Both VAT and SAT are linked to chronological and brain age with VAT being more strongly linked. VAT may be a key target for modifying brain age and Alzheimer's risk.
UR - https://www.scopus.com/pages/publications/105025739667
U2 - 10.1002/alz70862_110302
DO - 10.1002/alz70862_110302
M3 - Article
C2 - 41433548
AN - SCOPUS:105025739667
SN - 1552-5260
VL - 21
SP - e110302
JO - Alzheimer's & dementia : the journal of the Alzheimer's Association
JF - Alzheimer's & dementia : the journal of the Alzheimer's Association
ER -