Interactions between age, sex and visceral adipose tissue on brain ageing.

body composition cohort study database research elderly weight control

Journal

Diabetes, obesity & metabolism
ISSN: 1463-1326
Titre abrégé: Diabetes Obes Metab
Pays: England
ID NLM: 100883645

Informations de publication

Date de publication:
20 Jun 2024
Historique:
revised: 03 06 2024
received: 18 02 2024
accepted: 04 06 2024
medline: 20 6 2024
pubmed: 20 6 2024
entrez: 20 6 2024
Statut: aheadofprint

Résumé

To examine the associations between visceral adipose tissue (VAT) and brain structural measures at midlife and explore how these associations may be affected by age, sex and cardiometabolic factors. We used abdominal and brain magnetic resonance imaging data from a population-based cohort of people at midlife in the UK Biobank. Regression modelling was applied to study associations of VAT volume with total brain volume (TBV), grey matter volume (GMV), white matter volume, white matter hyperintensity volume (WMHV) and total hippocampal volume (THV), and whether these associations were altered by age, sex or cardiometabolic factors. Complete data were available for 17 377 participants (mean age 63 years, standard deviation = 12, 53% female). Greater VAT was associated with lower TBV, GMV and THV (P < .001). We found an interaction between VAT and sex on TBV (P < .001), such that the negative association of VAT with TBV was greater in men (β = -2.89, 95% confidence interval [CI] -2.32 to -10.15) than in women (β = -1.32, 95% CI -0.49 to -3.14), with similar findings for GMV. We also found an interaction between VAT and age (but not sex) on WMHV (P < .001). The addition of other cardiometabolic factors or measures of physical activity resulted in little change to the models. VAT volume is associated with poorer brain health in midlife and this relationship is greatest in men and those at younger ages.

Identifiants

pubmed: 38899555
doi: 10.1111/dom.15727
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Health and Medical Research Council (NHMRC)
ID : 1137837
Organisme : NIH HHS
ID : R01DK129320
Pays : United States

Informations de copyright

© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

Références

Li J, Joshi P, Ang TFA, et al. Mid‐ to late‐life body mass index and dementia risk: 38 years of follow‐up of the Framingham study. Am J Epidemiol. 2021;190:2503‐2510.
Pedditzi E, Peters R, Beckett N. The risk of overweight/obesity in mid‐life and late life for the development of dementia: a systematic review and meta‐analysis of longitudinal studies. Age Ageing. 2016;45:14‐21.
Gustafson D, Rothenberg E, Blennow K, Steen B, Skoog I. An 18‐year follow‐up of overweight and risk of Alzheimer disease. Arch Intern Med. 2003;163:1524‐1528.
Fitzpatrick AL, Kuller LH, Lopez OL, et al. Midlife and late‐life obesity and the risk of dementia: cardiovascular health study. Arch Neurol. 2009;66:336‐342.
Floud S, Simpson RF, Balkwill A, et al. Body mass index, diet, physical inactivity, and the incidence of dementia in 1 million UK women. Neurology. 2020;94:e123‐e132.
Singh‐Manoux A, Dugravot A, Shipley M, et al. Obesity trajectories and risk of dementia: 28 years of follow‐up in the Whitehall II study. Alzheimers Dement. 2018;14:178‐186.
Whitmer RA, Gunderson EP, Barrett‐Connor E, Quesenberry CP Jr, Yaffe K. Obesity in middle age and future risk of dementia: a 27 year longitudinal population based study. BMJ. 2005;330:1360.
Morys F, Dadar M, Dagher A. Association between midlife obesity and its metabolic consequences, cerebrovascular disease, and cognitive decline. J Clin Endocrinol Metab. 2021;106:e4260‐e4274.
Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its metabolic consequences, including diabetes and cardiovascular disease. Front Cardiovasc Med. 2020;7:22.
Fox CS, Massaro JM, Hoffmann U, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham heart study. Circulation. 2007;116:39‐48.
Liu J, Fox CS, Hickson DA, et al. Impact of abdominal visceral and subcutaneous adipose tissue on cardiometabolic risk factors: the Jackson heart study. J Clin Endocrinol Metab. 2010;95:5419‐5426.
Giralt M, Villarroya F. White, brown, beige/brite: different adipose cells for different functions? Endocrinology. 2013;154:2992‐3000.
Kajimura S. Adipose tissue in 2016: advances in the understanding of adipose tissue biology. Nat Rev Endocrinol. 2017;13:69‐70.
Ibrahim MM. Subcutaneous and visceral adipose tissue: structural and functional differences. Obes Rev. 2010;11:11‐18.
Hamer M, Batty GD. Association of body mass index and waist‐to‐hip ratio with brain structure: UK biobank study. Neurology. 2019;92:e594‐e600.
Jagust W, Harvey D, Mungas D, Haan M. Central obesity and the aging brain. Arch Neurol. 2005;62:1545‐1548.
Debette S, Beiser A, Hoffmann U, et al. Visceral fat is associated with lower brain volume in healthy middle‐aged adults. Ann Neurol. 2010;68:136‐144.
Mulugeta A, Lumsden A, Hypponen E. Unlocking the causal link of metabolically different adiposity subtypes with brain volumes and the risks of dementia and stroke: a Mendelian randomization study. Neurobiol Aging. 2021;102:161‐169.
Raji CA, Meysami S, Hashemi S, et al. Visceral and subcutaneous abdominal fat predict brain volume loss at midlife in 10,001 individuals. Aging Dis. 2023;19.
Onat A, Avci GS, Barlan MM, et al. Measures of abdominal obesity assessed for visceral adiposity and relation to coronary risk. Int J Obes Relat Metab Disord. 2004;28:1018‐1025.
Seidell JC, Oosterlee A, Deurenberg P, Hautvast JG, Ruijs JH. Abdominal fat depots measured with computed tomography: effects of degree of obesity, sex, and age. Eur J Clin Nutr. 1988;42:805‐815.
Arnoldussen IAC, Gustafson DR, Leijsen EMC, de Leeuw FE, Kiliaan AJ. Adiposity is related to cerebrovascular and brain volumetry outcomes in the RUN DMC study. Neurology. 2019;93:e864‐e878.
Cho J, Seo S, Kim WR, Kim C, Noh Y. Association between visceral fat and brain cortical thickness in the elderly: a neuroimaging study. Front Aging Neurosci. 2021;13:694629.
Shuster A, Patlas M, Pinthus JH, Mourtzakis M. The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol. 2012;85:1‐10.
Chaudry O, Grimm A, Friedberger A, et al. Magnetic resonance imaging and bioelectrical impedance analysis to assess visceral and abdominal adipose tissue. Obesity (Silver Spring). 2020;28:277‐283.
Browning LM, Mugridge O, Dixon AK, Aitken SW, Prentice AM, Jebb SA. Measuring abdominal adipose tissue: comparison of simpler methods with MRI. Obes Facts. 2011;4:9‐15.
Doherty A, Jackson D, Hammerla N, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study. PLoS One. 2017;12:e0169649.
Alfaro‐Almagro F, Jenkinson M, Bangerter NK, et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK biobank. Neuroimage. 2018;166:400‐424.
Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross‐sectional brain change analysis. Neuroimage. 2002;17:479‐489.
West J, Dahlqvist Leinhard O, Romu T, et al. Feasibility of MR‐based body composition analysis in large scale population studies. PLoS One. 2016;11:e0163332.
Leinhard OD, Johansson A, Rydell J, et al. Quantitative abdominal fat estimation using MRI. 2008 19th International Conference on Pattern Recognition; IEEE; 2008:1‐4.
Romu T, Borga M, Od L. MANA—Multi scale adaptive normalized averaging. IEEE International Symposium on Biomedical Imaging: from Nano to Macro; IEEE; 2011:361‐364.
Karlsson A, Rosander J, Romu T, et al. Automatic and quantitative assessment of regional muscle volume by multi‐atlas segmentation using whole‐body water‐fat MRI. J Magn Reson Imaging. 2015;41:1558‐1569.
Borga M, Thomas EL, Romu T, et al. Validation of a fast method for quantification of intra‐abdominal and subcutaneous adipose tissue for large‐scale human studies. NMR Biomed. 2015;28:1747‐1753.
Than S, Moran C, Beare R, et al. Interactions between age, sex, menopause, and brain structure at midlife: a UK biobank study. J Clin Endocrinol Metab. 2021;106:410‐420.
Nedungadi TP, Clegg DJ. Sexual dimorphism in body fat distribution and risk for cardiovascular diseases. J Cardiovasc Transl Res. 2009;2:321‐327.
Cannavale CN, Bailey M, Edwards CG, et al. Systemic inflammation mediates the negative relationship between visceral adiposity and cognitive control. Int J Psychophysiol. 2021;165:68‐75.
Subramaniapillai S, Suri S, Barth C, et al. Sex‐ and age‐specific associations between cardiometabolic risk and white matter brain age in the UK biobank cohort. Hum Brain Mapp. 2022;43:3759‐3774.
Clegg D, Hevener AL, Moreau KL, et al. Sex hormones and Cardiometabolic health: role of estrogen and estrogen receptors. Endocrinology. 2017;158:1095‐1105.
Than S, Moran C, Collyer TA, et al. Associations of sex, age, and Cardiometabolic risk profiles with brain structure and cognition: a UK biobank latent class analysis. Neurology. 2022;99:e1853‐e1865.
Lampe L, Zhang R, Beyer F, et al. Visceral obesity relates to deep white matter hyperintensities via inflammation. Ann Neurol. 2019;85:194‐203.
Wang S, Ren J. Obesity paradox in aging: from prevalence to pathophysiology. Prog Cardiovasc Dis. 2018;61:182‐189.
Chapman IM. Obesity paradox during aging. Interdiscip Top Gerontol. 2010;37:20‐36.
Haley AP, Oleson S, Pasha E, et al. Phenotypic heterogeneity of obesity‐related brain vulnerability: one‐size interventions will not fit all. Ann N Y Acad Sci. 2018;1428:89‐102.
Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. Lancet. 2017;390:2673‐2734.
Gelman A, Hill J, Vehtari A. Regression and Other Stories. Cambridge University Press; 2021.

Auteurs

Chris Moran (C)

Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Australia.
Department of Geriatric Medicine, Peninsula Health, Mornington, Australia.
National Centre for Healthy Ageing, Frankston, Australia.
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Department of Home, Acute and Community, Alfred Health, Caulfield, Australia.

Jarin Herson (J)

Department of Geriatric Medicine, Peninsula Health, Mornington, Australia.

Stephanie Than (S)

Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Australia.
Department of Geriatric Medicine, Peninsula Health, Mornington, Australia.
National Centre for Healthy Ageing, Frankston, Australia.
Department of Geriatric Medicine, Western Health, Footscray, Australia.

Taya Collyer (T)

Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Australia.
National Centre for Healthy Ageing, Frankston, Australia.

Richard Beare (R)

Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Australia.
National Centre for Healthy Ageing, Frankston, Australia.
Developmental Imaging, Murdoch Children's Research Institute, Parkville, Australia.

Sarah Syed (S)

Department of Home, Acute and Community, Alfred Health, Caulfield, Australia.

Velandai Srikanth (V)

Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Australia.
Department of Geriatric Medicine, Peninsula Health, Mornington, Australia.
National Centre for Healthy Ageing, Frankston, Australia.

Classifications MeSH