TY - JOUR
T1 - Sex-specific differences in the association between body mass index and brain aging in young adults
T2 - Findings from the human connectome project
AU - Chin Fatt, Cherise R.
AU - Jha, Manish K.
AU - Minhajuddin, Abu
AU - Mayes, Taryn
AU - Trivedi, Madhukar H.
N1 - Funding Information:
Data collection and sharing for the Human Connectome Project was provided by the MGH-USC Human Connectome Project (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). The HCP was supported by an NIH grant 1U54MH091657, funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research. HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. In addition this work was also funded in part by the Center for Depression Research and Clinical Care (PI: Madhukar Trivedi).
Funding Information:
Dr. Jha has received contract research grants from Acadia Pharmaceuticals and Janssen Research & Development, and honoraria for CME presentations from North American Center for Continuing Medical Education and Global Medical Education. Dr. Trivedi , is or has been an advisor/consultant and received fee from (lifetime disclosure): Abbott Laboratories, Inc.; Abdi Ibrahim; Akzo (Organon Pharmaceuticals Inc.); Alkermes; AstraZeneca; Axon Advisors; Bristol-Myers Squibb Company; Cephalon, Inc.; Cerecor; CME Institute of Physicians; Concert Pharmaceuticals, Inc.; Eli Lilly & Company; Evotec; Fabre Kramer Pharmaceuticals; Inc.; Forest Pharmaceuticals; GlaxoSmithKline; Janssen Global Services, LLC; Janssen Pharmaceutical Products, LP; Johnson & Johnson PRD; Libby; Lundbeck; Meade Johnson; MedAvante; Medtronic; Merck; Mitsubishi Tanabe Pharma Development America, Inc.; Naurex; Neuronetics; Otsuka Pharmaceuticals; Pamlab; Parke-Davis Pharmaceuticals, Inc.; Pfizer Inc.; PgxHealth; Phoenix Marketing Solutions; Rexahn Pharmaceuticals; Ridge Diagnostics; Roche Products Ltd.; Sepracor; SHIRE Development; Sierra; SK Life and Science; Sunovion; Takeda; Tal Medical/Puretech Venture; Targacept; Transcept; VantagePoint; Vivus; and Wyeth-Ayerst Laboratories. In addition, he has received grants/research support from: Agency for Healthcare Research and Quality (AHRQ); Cyberonics, Inc.; National Alliance for Research in Schizophrenia and Depression; National Institute of Mental Health and the National Institute on Drug Abuse. Drs. Chin Fatt, Minhajuddin, and Mrs. Mayes have no disclosures or potential conflicts of interest.
Funding Information:
Data collection and sharing for the Human Connectome Project was provided by the MGH-USC Human Connectome Project (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR) , the National Institute of Mental Health (NIMH) , and the National Institute of Neurological Disorders and Stroke (NINDS) . The HCP was supported by an NIH grant 1U54MH091657 , funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research. HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. In addition this work was also funded in part by the Center for Depression Research and Clinical Care (PI: Madhukar Trivedi).
Publisher Copyright:
© 2020
PY - 2021/2
Y1 - 2021/2
N2 - Background: This report evaluated sex-specific differences in the association between brain aging and body mass index (BMI) in young adults using the publicly available data from the Human Connectome Project (HCP). Methods: Participants of HCP with available structural imaging and BMI data were included [n = 1112; mean age = 28.80 (SD = 3.70); mean BMI = 26.53 (SD = 5.20); males n = 507, females n = 605]. Predicted brain age was generated using raw T1-weighted MRI scan and a Gaussian Processes regression model. The difference (Δ aging) between brain age predicted by structural imaging and chronological age was computed. A linear regression model was used with Δ aging as the dependent variable, and sex, BMI, and BMI-by-sex interaction as independent variables of interest, and race, ethnicity, income, and education as covariates. Results: There was a significant BMI-by-sex interaction for Δ aging (p = 0.041). Higher BMI was associated with greater brain aging in both sexes. However, this association was substantially stronger in males (β = 0.215; SE = 0.050; p < 0.0001) than in females (β = 0.122; SE = 0.035; p = 0.0005). Conclusion: We found evidence suggesting that higher BMI is associated with greater brain aging in adults. Furthermore, the association between higher BMI and greater brain aging was stronger in males than in females. Future studies are needed to explore the mechanistic pathways that link higher BMI to greater brain aging and whether weight-loss interventions, such as exercise, can reverse higher BMI-associated greater brain aging.
AB - Background: This report evaluated sex-specific differences in the association between brain aging and body mass index (BMI) in young adults using the publicly available data from the Human Connectome Project (HCP). Methods: Participants of HCP with available structural imaging and BMI data were included [n = 1112; mean age = 28.80 (SD = 3.70); mean BMI = 26.53 (SD = 5.20); males n = 507, females n = 605]. Predicted brain age was generated using raw T1-weighted MRI scan and a Gaussian Processes regression model. The difference (Δ aging) between brain age predicted by structural imaging and chronological age was computed. A linear regression model was used with Δ aging as the dependent variable, and sex, BMI, and BMI-by-sex interaction as independent variables of interest, and race, ethnicity, income, and education as covariates. Results: There was a significant BMI-by-sex interaction for Δ aging (p = 0.041). Higher BMI was associated with greater brain aging in both sexes. However, this association was substantially stronger in males (β = 0.215; SE = 0.050; p < 0.0001) than in females (β = 0.122; SE = 0.035; p = 0.0005). Conclusion: We found evidence suggesting that higher BMI is associated with greater brain aging in adults. Furthermore, the association between higher BMI and greater brain aging was stronger in males than in females. Future studies are needed to explore the mechanistic pathways that link higher BMI to greater brain aging and whether weight-loss interventions, such as exercise, can reverse higher BMI-associated greater brain aging.
KW - Body mass index
KW - Brain age
KW - Sex differences
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U2 - 10.1016/j.psyneuen.2020.105059
DO - 10.1016/j.psyneuen.2020.105059
M3 - Article
C2 - 33254060
AN - SCOPUS:85097236188
VL - 124
JO - Psychoneuroendocrinology
JF - Psychoneuroendocrinology
SN - 0306-4530
M1 - 105059
ER -