Unlocking the causal link of metabolically different adiposity subtypes with brain volumes and the risks of dementia and stroke: A Mendelian randomization study

Neuropathological links between T2DM and LOAD: systematic review and meta-analysis

Recent decades have described parallel neuropathological mechanisms increasing the risk for developing late-onset Alzheimer's dementia (LOAD) in type 2 diabetes mellitus (T2DM); however, still little is known of the role of diabetic encephalopathy and brain atrophy in LOAD. The aim of this systematic review is to provide a comprehensive view on diabetic encephalopathy/cerebral atrophy, taking into account neuroimaging data, neuropathology, metabolic and endocrine mechanisms, amyloid formation, brain perfusion impairments, neuroimmunology, and inflammasome activation. Key switches were identified, to further meta-analyze genomic candidate loci and epigenetic modifications. For the qualitative meta-analysis of genomic bases extracted, human linkage studies were examined; for epigenetic mechanisms, data from both human and animal studies are described. For the systematic review of pathophysiological mechanisms, 1,259 publications were evaluated and 93 gene loci extracted for candidate risk linkages. Sixty-six publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight the insulin signaling system, vascular markers, inflammation and inflammasome pathways, amylin interactions, and glycosylation mechanisms. The protocol was registered with PROSPERO (ID: CRD42023440535).

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

AIM

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

VAT volume is associated with poorer brain health in midlife and this relationship is greatest in men and those at younger ages.

Evidence based on Mendelian randomization and colocalization analysis strengthens causal relationships between structural changes in specific brain regions and risk of amyotrophic lateral sclerosis

Background

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the degeneration of motor neurons in the brain and spinal cord with a poor prognosis. Previous studies have observed cognitive decline and changes in brain morphometry in ALS patients. However, it remains unclear whether the brain structural alterations contribute to the risk of ALS. In this study, we conducted a bidirectional two-sample Mendelian randomization (MR) and colocalization analysis to investigate this causal relationship.

Methods

Summary data of genome-wide association study were obtained for ALS and the brain structures, including surface area (SA), thickness and volume of subcortical structures. Inverse-variance weighted (IVW) method was used as the main estimate approach. Sensitivity analysis was conducted detect heterogeneity and pleiotropy. Colocalization analysis was performed to calculate the posterior probability of causal variation and identify the common genes.

Results

In the forward MR analysis, we found positive associations between the SA in four cortical regions (lingual, parahippocampal, pericalcarine, and middle temporal) and the risk of ALS. Additionally, decreased thickness in nine cortical regions (caudal anterior cingulate, frontal pole, fusiform, inferior temporal, lateral occipital, lateral orbitofrontal, pars orbitalis, pars triangularis, and pericalcarine) was significantly associated with a higher risk of ALS. In the reverse MR analysis, genetically predicted ALS was associated with reduced thickness in the bankssts and increased thickness in the caudal middle frontal, inferior parietal, medial orbitofrontal, and superior temporal regions. Colocalization analysis revealed the presence of shared causal variants between the two traits.

Conclusion

Our results suggest that altered brain morphometry in individuals with high ALS risk may be genetically mediated. The causal associations of widespread multifocal extra-motor atrophy in frontal and temporal lobes with ALS risk support the notion of a continuum between ALS and frontotemporal dementia. These findings enhance our understanding of the cortical structural patterns in ALS and shed light on potentially viable therapeutic targets.

Gender-specific moderating role of abdominal obesity in the relationship between handgrip strength and cognitive impairment

BACKGROUND & AIMS

Both low handgrip strength (HGS) and abdominal obesity (AO) are associated with cognitive impairment. However, it remains unclear whether low HGS and AO interact to affect cognition, and whether the synergistic effect varies by gender. This study aimed to examine whether the association between low HGS and incident cognitive impairment was moderated by AO among Chinese older men and women.

METHODS

We used the data of participants (≥60 years) from four waves (2011-2018) of the China Health and Retirement Longitudinal Study. We defined low HGS as the maximal HGS of <28 kg in men and <18 kg in women, and AO as waist circumference of ≥90 cm for men and ≥80 cm for women. Cognitive impairment was defined as a global cognitive score in the lowest 10th percentile. For each gender, we used subdistribution hazards model to estimate subdistribution hazard ratios (SHRs) for the association of low HGS and AO with incident cognitive impairment, treating mortality as the competing event and controlling for other covariates. Multiplicative interaction was assessed through a cross-product interaction term of low HGS and AO in the model. Additive interaction between low HGS and AO was evaluated by calculating the relative excess risk due to interaction (RERI) and attributable proportion due to interaction (AP).

RESULTS

We included 3704 participants (Mean age: 66.9 ± 5.81; 54.9% male). During the 7-year follow-up, 1133 events of interest occurred (731 cognitive impairments and 402 deaths). Incidence rates of cognitive impairment and mortality were 4.1 (95% CI: 3.8 to 4.4) and 2.2 (95% CI: 2.0 to 2.5) per 100 person-years. There were positive multiplicative (SHR for the product term = 1.974, 95% CI: 1.114 to 3.500) and additive interactions (RERI = 1.056, 95% CI: 0.027 to 2.086, AP = 0.454, 95% CI: 0.158 to 0.750) of low HGS and AO on the risk of cognitive impairment among older men. Male participants with both low HGS and AO showed an increased risk of cognitive impairment (SHR = 2.325, 95% CI: 1.498 to 3.609) compared with those without either. There was no evidence of interaction among older women (SHR for the product term = 1.151, 95% CI: 0.725 to 1.825; RERI = 0.044, 95% CI: -0.524 to 0.613; AP = 0.039, 95% CI: -0.458 to 0.536).

CONCLUSIONS

Low HGS and AO may interact to synergistically increase the risk of cognitive impairment among Chinese older men. Screening the highest-risk subpopulation, who may benefit most from neurocognitive prevention strategies, may maximize potential public health gains.

Obesity causally influencing brain cortical structure: a Mendelian randomization study

Obesity may lead to cognitive impairment and psychiatric disorders, which are associated with alterations in the brain cortical structure. However, the exact causality remains inconclusive. We aimed to conduct two-sample Mendelian randomization (MR) analysis to identify the causal associations of obesity [body mass index (BMI), waist-hip ratio (WHR), and waist-hip ratio adjusted for BMI ((WHRadjBMI)) and brain cortical structure (cortical thickness and cortical surface area). Inverse-variance weighted (IVW) method was used as the main analysis, whereas a series of sensitivity analyses were employed to assess heterogeneity and pleiotropy. The main MR results showed that higher BMI significantly increased the cortical surface area of the transverse temporal (β = 5.13 mm2, 95% confidence interval [CI]: 2.55-7.71, P = 9.9 × 10-5); higher WHR significantly decreased cortical surface area of the inferior temporal (β = -38.60, 95% CI: -56.67- -20.54, P = 1.2 × 10-5), but significantly increased cortical surface area of the isthmus cingulate (β = 14.25, 95% CI: 6.97-21.54, P = 1.2 × 10-4). No significant evidence of pleiotropy was found in the MR analyses. This study supports that obesity has a causal effect on the brain cortical structure. Further studies are warranted to understand the clinical outcomes caused by these effects.

Causal effects of COVID-19 on structural changes in specific brain regions: a Mendelian randomization study

BACKGROUND

Previous studies have found a correlation between coronavirus disease 2019 (COVID-19) and changes in brain structure and cognitive function, but it remains unclear whether COVID-19 causes brain structural changes and which specific brain regions are affected. Herein, we conducted a Mendelian randomization (MR) study to investigate this causal relationship and to identify specific brain regions vulnerable to COVID-19.

METHODS

Genome-wide association study (GWAS) data for COVID-19 phenotypes (28,900 COVID-19 cases and 3,251,161 controls) were selected as exposures, and GWAS data for brain structural traits (cortical thickness and surface area from 51,665 participants and volume of subcortical structures from 30,717 participants) were selected as outcomes. Inverse-variance weighted method was used as the main estimate method. The weighted median, MR-Egger, MR-PRESSO global test, and Cochran's Q statistic were used to detect heterogeneity and pleiotropy.

RESULTS

The genetically predicted COVID-19 infection phenotype was nominally associated with reduced cortical thickness in the caudal middle frontal gyrus (β = - 0.0044, p = 0.0412). The hospitalized COVID-19 phenotype was nominally associated with reduced cortical thickness in the lateral orbitofrontal gyrus (β = - 0.0049, p = 0.0328) and rostral middle frontal gyrus (β = - 0.0022, p = 0.0032) as well as with reduced cortical surface area of the middle temporal gyrus (β = - 10.8855, p = 0.0266). These causal relationships were also identified in the severe COVID-19 phenotype. Additionally, the severe COVID-19 phenotype was nominally associated with reduced cortical thickness in the cuneus (β = - 0.0024, p = 0.0168); reduced cortical surface area of the pericalcarine (β = - 2.6628, p = 0.0492), superior parietal gyrus (β = - 5.6310, p = 0.0408), and parahippocampal gyrus (β = - 0.1473, p = 0.0297); and reduced volume in the hippocampus (β = - 15.9130, p = 0.0024).

CONCLUSIONS

Our study indicates a suggestively significant association between genetic predisposition to COVID-19 and atrophy in specific functional regions of the human brain. Patients with COVID-19 and cognitive impairment should be actively managed to alleviate neurocognitive symptoms and minimize long-term effects.

Association Between Metabolically Different Adiposity Subtypes and Osteoarthritis: A Mendelian Randomization Study

OBJECTIVE

In this Mendelian randomization (MR) study, the objective was to investigate the causal effect of metabolically different adiposity subtypes on osteoarthritis.

METHODS

We performed 2-sample MR using summary-level data for osteoarthritis (10,083 cases and 40,425 controls) from a genome-wide association using the UK Biobank, and for site-specific osteoarthritis from the Arthritis Research UK Osteoarthritis Genetics consortium. We used 3 classes of genetic instruments, which all increase body mass index but are associated with different metabolic profiles (unfavorable, neutral, and favorable). Primary analysis was performed using inverse variance weight (IVW), with additional sensitivity analysis from different MR methods. We further applied a nonlinear MR using UK Biobank data to understand the nature of the adiposity-osteoarthritis relationship.

RESULTS

Greater metabolically unfavorable and metabolically neutral adiposity were associated with higher odds of osteoarthritis (IVW odds ratio [OR] 1.56 [95% confidence interval (95% CI) 1.31, 1.85] and OR 1.60 [95% CI 1.15, 2.23], respectively). The estimate for the association between metabolically favorable adiposity and osteoarthritis was similar, although with notable imprecision (OR 1.55 [95% CI 0.70, 3.41]). Using site-specific osteoarthritis, metabolically unfavorable, neutral, and favorable adiposity were all associated with higher odds of knee osteoarthritis (OR 1.44 [95% CI 1.04, 1.98], OR 2.28 [95% CI 1.04, 4.99], and OR 6.80 [95% CI 2.08, 22.19], respectively). We found generally consistent estimates with a wider confidence interval crossing the null from other MR methods. The nonlinear MR analyses suggested a nonlinear relationship between metabolically unfavorable adiposity and osteoarthritis (Pnonlinear  = 0.003).

CONCLUSION

Metabolic abnormalities did not explain the association between greater adiposity and the risk of osteoarthritis, which might suggest that the association is largely due to a mechanical effect on the joints.

The impact of obesity: a narrative review

Obesity is a disease with a major negative impact on human health. However, people with obesity may not perceive their weight to be a significant problem and less than half of patients with obesity are advised by their physicians to lose weight. The purpose of this review is to highlight the importance of managing overweight and obesity by discussing the adverse consequences and impact of obesity. In summary, obesity is strongly related to >50 medical conditions, with many of them having evidence from Mendelian randomisation studies to support causality. The clinical, social and economic burdens of obesity are considerable, with these burdens potentially impacting future generations as well. This review highlights the adverse health and economic consequences of obesity and the importance of an urgent and concerted effort towards the prevention and management of obesity to reduce the burden of obesity.

Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition

AIMS

To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.

MATERIALS AND METHODS

Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.

RESULTS

In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (βstandardized -0.09, 95% CI -0.13 to -0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized -0.15, 95% CI -0.16 to -0.14) and HV (βstandardized -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).

CONCLUSIONS

Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.

Obesity in Late-Life as a Protective Factor Against Dementia and Dementia-Related Mortality

OBJECTIVE

We estimated the conversion from cognitively normal to mild cognitive impairment (MCI) to probable dementia and death for underweight, normal, overweight, and obese older adults, where the timing of examinations is associated with the severity of dementia.

METHODS

We analyzed six waves of the National Health and Aging Trends Study (NHATS). Body mass (BMI) was computed from height and weight. Multi-state survival models (MSMs) examined misclassification probability, time-to-event ratios, and cognitive decline.

RESULTS

Participants (n = 6078) were 77 years old, 62% had overweight and/or obese BMI. After adjusting for the effects of cardiometabolic factors, age, sex, and race, obesity was protective against developing dementia (aHR=.44; 95%CI [.29-.67]) and dementia-related mortality (aHR=.63; 95%CI [.42-.95]).

DISCUSSION

We found a negative relationship between obesity and dementia and dementia-related mortality, a finding that has been underreported in the literature. The continuing obesity epidemic might complicate the diagnosis and treatment of dementia.

Healthy Lifestyle, Genetic Risk and Brain Health: A Gene-Environment Interaction Study in the UK Biobank

Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.

Causality of abdominal obesity on cognition: a trans-ethnic Mendelian randomization study

BACKGROUND

Obesity has been associated with cognition in observational studies; however, whether its effect is confounding or a reverse causality remains inconclusive. This study aimed to investigate the causal relationships of overall obesity, measured by body mass index (BMI), and abdominal adiposity, measured by waist-hip ratio adjusted for BMI (WHRadjBMI), and cognition across European and Asian populations using Mendelian randomization (MR) analysis.

METHODS

We used publicly available genome-wide association study (GWAS) summary data of European ancestry, including BMI (n = 322,154) and WHRadjBMI (n = 210,088) from the GIANT consortium, and cognition performance (n = 257,828) from the UK Biobank and COGENT consortium. Data for individuals of Asian ancestry were retrieved from Taiwan Biobank to perform GWAS for BMI (n = 65,689), WHRadjBMI (n = 65,683), and Mini-Mental State Examination (MMSE, n = 21,273). MR analysis was carried out using the inverse-variance weighted method for the main results. Further, we examined the overall pleiotropy by MR-Egger intercept, and detected and adjusted for possible outliers using MR PRESSO.

RESULTS

No causal effect of BMI on cognition performance (beta [95% CI] = 0.00 [-0.07, 0.07], p value = 0.91) was found for Europeans; however, a 1-SD increase in WHRadjBMI was associated with a 0.07 standardized score decrease in cognition performance (beta [95% CI] = -0.07 [-0.12, -0.02], p value = 0.006). Further, no causal effect of BMI on MMSE (beta [95% CI] = 0.01 [-0.08, 0.10], p = 0.91) was found for Asians; however, a 1-SD increase in WHRadjBMI was associated with a 0.17 standardized score decrease in MMSE (beta [95% CI] = -0.17 [-0.30, -0.03], p = 0.02). In both populations, overall pleiotropy was not detected, and outliers did not affect the robustness of the main findings.

CONCLUSIONS

This trans-ethnic MR study reveals that abdominal adiposity, as measured by WHR adjusted for BMI, impairs cognition, whereas weak evidence suggests that BMI impairs cognition.

Association of life course adiposity with risk of incident dementia: a prospective cohort study of 322,336 participants

Cohort studies report inconsistent associations between body mass index (BMI) and all-cause incident dementia. Furthermore, evidence on fat distribution and body composition measures are scarce and few studies estimated the association between early life adiposity and dementia risk. Here, we included 322,336 participants from UK biobank to investigate the longitudinal association between life course adiposity and risk of all-cause incident dementia and to explore the underlying mechanisms driven by metabolites, inflammatory cells and brain structures. Among the 322,336 individuals (mean (SD) age, 62.24 (5.41) years; 53.9% women) in the study, during a median 8.74 years of follow-up, 5083 all-cause incident dementia events occurred. The risk of dementia was 22% higher with plumper childhood body size (p < 0.001). A strong U-shaped association was observed between adult BMI and dementia. More fat and less fat-free mass distribution on arms were associated with a higher risk of dementia. Interestingly, similar U-shaped associations were found between BMI and four metabolites (i.e., 3-hydroxybutrate, acetone, citrate and polyunsaturated fatty acids), four inflammatory cells (i.e., neutrophil, lymphocyte, monocyte and leukocyte) and abnormalities in brain structure that were also related to dementia. The findings that adiposity is associated with metabolites, inflammatory cells and abnormalities in brain structure that were related to dementia risk might provide clues to underlying biological mechanisms. Interventions to prevent dementia should begin early in life and include not only BMI control but fat distribution and body composition.

Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort

OBJECTIVE

To determine whether there is a correlation between obesity-related variants and regional brain volumes.

METHODS

Based on a mixed linear model (MLM), we analyzed the association between 1,498 obesity-related SNPs in the GWAS Catalog and 164 regional brain volumes from 29,420 participants (discovery cohort N = 19,997, validation cohort N = 9,423) in UK Biobank. The statistically significant brain regions in association analysis were classified into 6 major neural networks (dopamine (DA) motive system, central autonomic network (CAN), cognitive emotion regulation, visual object recognition network, auditory object recognition network, and sensorimotor system). We summarized the association between obesity-related variants (metabolically healthy obesity variants, metabolically unhealthy obesity variants, and unclassified obesity-related variants) and neural networks.

RESULTS

From association analysis, we determined that 17 obesity-related SNPs were associated with 51 regional brain volumes. Several single SNPs (e.g., rs13107325-T (SLC39A8), rs1876829-C (CRHR1), and rs1538170-T (CENPW)) were associated with multiple regional brain volumes. In addition, several single brain regions (e.g., the white matter, the grey matter in the putamen, subcallosal cortex, and insular cortex) were associated with multiple obesity-related variants. The metabolically healthy obesity variants were mainly associated with the regional brain volumes in the DA motive system, sensorimotor system and cognitive emotion regulation neural networks, while metabolically unhealthy obesity variants were mainly associated with regional brain volumes in the CAN and total tissue volumes. In addition, unclassified obesity-related variants were mainly associated with auditory object recognition network and total tissue volumes. The results of MeSH (medical subject headings) enrichment analysis showed that obesity genes associated with brain structure pointed to the functional relatedness with 5-Hydroxytryptamine receptor 4 (5-HT4), growth differentiation factor 5 (GDF5), and high mobility group protein AT-hook 2 (HMGA2 protein).

CONCLUSION

In summary, we found that obesity-related variants were associated with different brain volume measures. On the basis of the multiple SNPs, we found that metabolically healthy and unhealthy obesity-related SNPs were associated with different brain neural networks. Based on our enrichment analysis, modifications of the 5-HT4 pathway might be a promising therapeutic strategy for obesity.

Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18-94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19-86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) - the difference between actual age and the prediction of the brain's biological age - at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.