Two human brain systems micro-structurally associated with obesity.


Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
20 Oct 2023
Historique:
received: 26 11 2022
accepted: 05 10 2023
medline: 1 12 2023
pubmed: 20 10 2023
entrez: 20 10 2023
Statut: epublish

Résumé

The relationship between obesity and human brain structure is incompletely understood. Using diffusion-weighted MRI from ∼30,000 UK Biobank participants, we test the hypothesis that obesity (waist-to-hip ratio, WHR) is associated with regional differences in two micro-structural MRI metrics: isotropic volume fraction (ISOVF), an index of free water, and intra-cellular volume fraction (ICVF), an index of neurite density. We observed significant associations with obesity in two coupled but distinct brain systems: a prefrontal/temporal/striatal system associated with ISOVF and a medial temporal/occipital/striatal system associated with ICVF. The ISOVF~WHR system colocated with expression of genes enriched for innate immune functions, decreased glial density, and high mu opioid (MOR) and other neurotransmitter receptor density. Conversely, the ICVF~WHR system co-located with expression of genes enriched for G-protein coupled receptors and decreased density of MOR and other receptors. To test whether these distinct brain phenotypes might differ in terms of their underlying shared genetics or relationship to maps of the inflammatory marker C-reactive Protein (CRP), we estimated the genetic correlations between WHR and ISOVF (r People with obesity are at greater risk of cardiovascular diseases and metabolic conditions such as type 2 diabetes. More recently obesity has also been linked to changes in the brain that are associated with age-related dementia and cognitive decline. This includes a thinner cortex (the brain’s outer layer) and lower volume of grey matter which is where cognitive processes, such as learning, take place. However, questions remain about how obesity and grey matter are connected. For instance, it is unclear whether the change in volume is due to there being fewer cells (and thus more water between them) or fewer connections between cells in these brain areas. It is also unknown whether the reduced volume of grey matter is a cause or consequence of obesity. To address these questions, Kitzbichler et al. analysed 30,000 MRI scans of the human brain which are stored in the UK Biobank. This revealed two characteristics in grey matter that were linked to obesity: higher amounts of water between cells in some areas, and a lower density of connections between neurons in others. The areas with higher levels of free water are known to have more glial cells which provide support to neurons. They also have more receptors that bind to fatty acids (which are often raised in people with obesity) and more receptors for molecules and cells involved in the immune response. In contrast, the areas with a lower density of connections between neurons usually were more closely associated with genetic risk factors associated with obesity, and fewer receptors involved in feeding, appetite and energy use. The findings of Kitzblicher et al. suggest that differences in the density of connections between neurons may contribute to obesity. High water content in grey matter, on the other hand, may be a consequence of obesity that occurs as a result of immune receptors becoming activated. This provides new insights in to how obesity and grey matter in the brain are connected.

Autres résumés

Type: plain-language-summary (eng)
People with obesity are at greater risk of cardiovascular diseases and metabolic conditions such as type 2 diabetes. More recently obesity has also been linked to changes in the brain that are associated with age-related dementia and cognitive decline. This includes a thinner cortex (the brain’s outer layer) and lower volume of grey matter which is where cognitive processes, such as learning, take place. However, questions remain about how obesity and grey matter are connected. For instance, it is unclear whether the change in volume is due to there being fewer cells (and thus more water between them) or fewer connections between cells in these brain areas. It is also unknown whether the reduced volume of grey matter is a cause or consequence of obesity. To address these questions, Kitzbichler et al. analysed 30,000 MRI scans of the human brain which are stored in the UK Biobank. This revealed two characteristics in grey matter that were linked to obesity: higher amounts of water between cells in some areas, and a lower density of connections between neurons in others. The areas with higher levels of free water are known to have more glial cells which provide support to neurons. They also have more receptors that bind to fatty acids (which are often raised in people with obesity) and more receptors for molecules and cells involved in the immune response. In contrast, the areas with a lower density of connections between neurons usually were more closely associated with genetic risk factors associated with obesity, and fewer receptors involved in feeding, appetite and energy use. The findings of Kitzblicher et al. suggest that differences in the density of connections between neurons may contribute to obesity. High water content in grey matter, on the other hand, may be a consequence of obesity that occurs as a result of immune receptors becoming activated. This provides new insights in to how obesity and grey matter in the brain are connected.

Identifiants

pubmed: 37861301
doi: 10.7554/eLife.85175
pii: 85175
pmc: PMC10688972
doi:
pii:

Substances chimiques

Water 059QF0KO0R

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 104025/Z/14/Z
Pays : United Kingdom

Informations de copyright

© 2023, Kitzbichler et al.

Déclaration de conflit d'intérêts

MK, DM, RB, RD, RR, VW, JS, OD, FT, MC, EB, NH No competing interests declared

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Auteurs

Manfred G Kitzbichler (MG)

Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.

Daniel Martins (D)

Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Richard A I Bethlehem (RAI)

Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.

Richard Dear (R)

Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.

Rafael Romero-Garcia (R)

Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
Department of Medical Physiology and Biophysics, Instituto deBiomedicina de Sevilla (IBiS) HUVR/CSIC Universidad de Sevilla/CIBERSAM, ISCIII, Sevilla, Spain.

Varun Warrier (V)

Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
Department of Psychology, University of Cambridge, Cambridge, United States.

Jakob Seidlitz (J)

Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, United States.
Department of Child and Adolescent Psychiatry and Behavioral Science,The Children's Hospital of Philadelphia, Philadelphia, United States.
Department of Psychiatry, University of Pennsylvania, Philadelphia, United States.

Ottavia Dipasquale (O)

Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Federico Turkheimer (F)

Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Mara Cercignani (M)

Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom.

Edward T Bullmore (ET)

Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.

Neil A Harrison (NA)

Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom.

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