Association of metabolic syndrome with brain imaging and cognitive function in UK Biobank

Abstract Background Metabolic syndrome (MetS) has been previously associated with an increased risk of developing dementia. Exploring links between MetS, neuroimaging and cognitive function measures can offer insights into whether MetS adversely affects brain health prior to dementia onset. We sought to examine the association of MetS with brain structure and cognition. Methods Participants were selected from the UK Biobank multi‐modal imaging study (2014‐ongoing). MetS was defined as the presence of ≥3 of the following components: elevated waist circumference, triglycerides, blood pressure, HbA1c, or reduced HDL‐cholesterol. Neuroimaging outcomes included total brain, grey/white matter, white matter hyperintensity (WMH) and hippocampal volume from T1/T2‐weighted magnetic resonance imaging (MRI), and cognitive tests measuring executive function, reasoning, memory, and processing speed. Cross‐sectional associations were assessed using linear regression adjusted for age, sex, ethnicity, education, socioeconomic status, household income, smoking status, alcohol intake, physical activity, APOE ‐ε4 carrier status, and assessment centre. Neuroimaging analyses were further adjusted for imaging‐related confounders (head size, head position, head motion). Results Among 32,864 dementia‐free participants, 9,484 (29%) had MetS. MetS was significantly associated with lower total brain volume (β:‐0.06, 95%CI: ‐0.08, ‐0.04), grey matter volume (β: ‐0.09, 95%CI: ‐0.11, ‐0.07), hippocampal volume (left = β: ‐0.02, 95%CI: ‐0.05, ‐0.00; right = β: ‐0.03, 95%CI: ‐0.05, 0.00), and greater WMH volume (β: 0.10, 95%CI: 0.08, 0.12). MetS participants performed significantly worse on cognitive tests of working memory (β: ‐0.11, 95%CI: ‐0.14, ‐0.08), verbal declarative memory (β: ‐0.10, 95%CI: ‐0.12, ‐0.07), processing speed (β: ‐0.09, 95%CI: ‐0.12, ‐0.06), verbal and numerical reasoning (β: ‐0.06, 95%CI: ‐0.08, ‐0.03), executive function (β: 0.05, 95%CI: 0.02, 0.07), and non‐verbal reasoning (β: ‐0.04, 95%CI: ‐0.06, ‐0.01). We observed dose‐response relationships, with an increasing number of MetS components being associated with lower brain volume, greater WMH volume, and poorer cognitive function across all tests. Conclusion MetS was associated with lower brain volume, greater vascular pathology, and poorer performance across multiple domains of cognitive function. These findings demonstrate that MetS is related to poorer brain health in dementia‐free individuals and could represent a novel risk factor for future development of dementia.

Authors

Institutions

Publication Details

Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.085544
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association of metabolic syndrome with brain imaging and cognitive function in UK Biobank

Elżbieta Kuźma, Anya Topiwala, Naomi E. Allen, Thomas J. Littlejohns et al.
Alzheimer s & Dementia
Machine Learning in Healthcare
article

Association of metabolic syndrome with brain imaging and cognitive function in UK Biobank

Elżbieta Kuźma, Anya Topiwala, Naomi E. Allen, Thomas J. Littlejohns, Danial Qureshi, Md Shehab Uddin Al‐Abid
article en

Abstract

Abstract Background Metabolic syndrome (MetS) has been previously associated with an increased risk of developing dementia. Exploring links between MetS, neuroimaging and cognitive function measures can offer insights into whether MetS adversely affects brain health prior to dementia onset. We sought to examine the association of MetS with brain structure and cognition. Methods Participants were selected from the UK Biobank multi‐modal imaging study (2014‐ongoing). MetS was defined as the presence of ≥3 of the following components: elevated waist circumference, triglycerides, blood pressure, HbA1c, or reduced HDL‐cholesterol. Neuroimaging outcomes included total brain, grey/white matter, white matter hyperintensity (WMH) and hippocampal volume from T1/T2‐weighted magnetic resonance imaging (MRI), and cognitive tests measuring executive function, reasoning, memory, and processing speed. Cross‐sectional associations were assessed using linear regression adjusted for age, sex, ethnicity, education, socioeconomic status, household income, smoking status, alcohol intake, physical activity, APOE ‐ε4 carrier status, and assessment centre. Neuroimaging analyses were further adjusted for imaging‐related confounders (head size, head position, head motion). Results Among 32,864 dementia‐free participants, 9,484 (29%) had MetS. MetS was significantly associated with lower total brain volume (β:‐0.06, 95%CI: ‐0.08, ‐0.04), grey matter volume (β: ‐0.09, 95%CI: ‐0.11, ‐0.07), hippocampal volume (left = β: ‐0.02, 95%CI: ‐0.05, ‐0.00; right = β: ‐0.03, 95%CI: ‐0.05, 0.00), and greater WMH volume (β: 0.10, 95%CI: 0.08, 0.12). MetS participants performed significantly worse on cognitive tests of working memory (β: ‐0.11, 95%CI: ‐0.14, ‐0.08), verbal declarative memory (β: ‐0.10, 95%CI: ‐0.12, ‐0.07), processing speed (β: ‐0.09, 95%CI: ‐0.12, ‐0.06), verbal and numerical reasoning (β: ‐0.06, 95%CI: ‐0.08, ‐0.03), executive function (β: 0.05, 95%CI: 0.02, 0.07), and non‐verbal reasoning (β: ‐0.04, 95%CI: ‐0.06, ‐0.01). We observed dose‐response relationships, with an increasing number of MetS components being associated with lower brain volume, greater WMH volume, and poorer cognitive function across all tests. Conclusion MetS was associated with lower brain volume, greater vascular pathology, and poorer performance across multiple domains of cognitive function. These findings demonstrate that MetS is related to poorer brain health in dementia‐free individuals and could represent a novel risk factor for future development of dementia.

Alzheimer s & DementiaVol. 20(S7)
Universität Hamburg (DE), University of Oxford (GB)
Openalex Percentile: Top 77%
Machine Learning in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.