Deciphering the Causal Influence of BMI and related Metabolic, Inflammatory, and Cardiovascular Factors on Brain Structure: A Mendelian Randomization Study

Deciphering the Causal Influence of BMI and related Metabolic, Inflammatory, and Cardiovascular Factors on Brain Structure: A Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deciphering the Causal Influence of BMI and related Metabolic, Inflammatory, and Cardiovascular Factors on Brain Structure: A Mendelian Randomization Study Nils Opel, Jodie Painter, Alexander Refisch, Moritz Rau, Martin Walther, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4365189/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Mar, 2026 Read the published version in Molecular Psychiatry → Version 1 posted 10 You are reading this latest preprint version Abstract Obesity is a highly prevalent metabolic risk factor that commonly coincides with additional metabolic, cardiovascular, and inflammatory abnormalities. Obesity has frequently been shown to affect brain physiology at multiple levels, and to increase the risk for the development of neuropsychiatric disorders such as major depression and dementia. Previous large-scale neuroimaging research has consistently shown overlapping brain structural alterations in obesity and neuropsychiatric disorders, with the most pronounced alterations being lower cortical thickness in the frontal and temporal cortex. Yet, the direction of association, and the potential causal effect of obesity on brain structural decline, remains unclear. Moreover, it is imperative to determine which of the multifaceted biological systems impacted by obesity, encompassing metabolic, cardiovascular, and inflammatory aspects, may be implicated in the link between obesity and brain structural decline. In this study, we employed univariate and multivariate Mendelian randomization (MR) as an instrumental variable (IV) approach to clarify the causal direction of the relationship between body mass index (BMI) and brain structure and to disentangle the metabolic, cardiovascular, and inflammatory factors that might underlie this relationship. We found evidence for a potential causal influence of elevated BMI on lower cortical thickness, with most prominent effects in frontal and temporal regions. We furthermore found a concurrent association of the inflammatory serum marker CRP and visceral adipose tissue (VAT) with lower cortical thickness, both globally and regionally across brain regions, largely overlapping with those associated with increased BMI. In contrast, very few associations with cortical thickness emerged for blood pressure or metabolic serum markers. Our findings thus corroborate the notion of a causal effect of BMI on lower cortical thickness and indicate low-grade inflammation as a potential candidate mechanism implicated in this relationship. Future research should aim to delineate if and how the BMI related effect on brain structural decline conveys an increased risk for the development of neuropsychiatric disorders. Biological sciences/Neuroscience Health sciences/Biomarkers/Diagnostic markers Figures Figure 1 Figure 2 Introduction Obesity is a hereditary condition 1 that commonly coincides with a spectrum of metabolic, cardiovascular, and inflammatory disturbances. Specifically, elevated blood pressure, impaired glucose metabolism, excess visceral fat accumulation, and alterations in serum triglycerides and cholesterol levels are prevalent comorbid health issues linked to obesity, collectively characterizing the metabolic syndrome 2 , 3 . Furthermore, adipose tissue secretion of pro-inflammatory cytokines is known to induce low-grade inflammation in the context of obesity. In addition to its impact on body-wide metabolic, cardiovascular, and inflammatory systems, accumulating evidence underscores obesity as a significant determinant of brain health, with frequent associations observed between obesity and substantial and widespread brain structural abnormalities 4 – 8 . Recent work by the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and others has consistently demonstrated that increased BMI (weight [kg]/height 2 [m]), a proxy for increases in fat mass, is associated with brain structural abnormalities in healthy controls as well as in psychiatric patients, primarily with lower fronto-temporal cortical thickness 5 , 6 , 9 , 10 . As the majority of previous neuroimaging studies investigating the association between obesity and the brain were based on cross-sectional designs, the direction of associations between increased BMI and brain structural alterations is, however, unknown. Most importantly it is unclear whether brain structural alterations precede and predispose to higher BMI or if higher BMI induces changes in brain structure. As previous reports have found that genetic variants associated with BMI are highly expressed in the central nervous system (CNS) 11 , it appears possible that genetically determined alterations in brain structure might precede weight gain and hence mediate the effect of genetic risk on the development of obesity. This notion is supported by findings from prospective functional MRI studies reporting that changes in reward system signalling might predict future weight gain 12 , 13 . In an attempt to address this open research question, previous imaging genetic research has revealed associations between polygenic risk for BMI and alterations in brain structure, particularly prefrontal grey matter volume reductions and reduced occipital surface area 6 , 14 . However, the previously observed associations between polygenic risk for obesity and brain structure are of small effect size and limited to specific brain regions such as the medial prefrontal cortex 6 . In contrast, the replicated phenotypic associations between BMI and brain structure, which have been extensively validated, are broadly distributed and have effect sizes that exceed those observed for common neuropsychiatric disorders 6 . A large proportion of the BMI-related variation in brain structure thus cannot be directly traced back to genetic variants. Furthermore, previous longitudinal studies have reported BMI to be associated with decreased grey matter volume and cortical thickness decline over time, thus underpinning the notion of potential BMI-related atrophic or neurodegenerative processes 15 , 16 . This is in line with recent reports on accelerated brain ageing in individuals with overweight or obesity 17 , 18 . With regard to the potential biological mechanisms underlying the association between BMI and brain structural alterations, a growing body of data suggests chronic low-grade inflammation to represent a fundamental common biological mechanism linking obesity and neuropsychiatric disorders 19 . Since adipose tissue expansion upon a high-fat diet results in a disruption of an anti-inflammatory milieu with increased differentiation and recruitment of pro-inflammatory immune cells 20 , 21 , chronic low-grade inflammation may be attributed to increased BMI. By promoting oxidative stress and dysregulation in energy-regulating neuroendocrine metabolic pathways, chronic low-grade inflammation contributes significantly to impaired energy supply, to which the CNS is highly vulnerable 22 . Beyond that, emerging evidence indicates a cross-talk between systemic inflammatory processes and inflammation in the CNS, further exacerbating neuronal injury as a potential source for the manifestation of neuropsychiatric symptoms 23 . One possibility to investigate the direction of associations between BMI and brain structure are instrumental variable (IV) based methods such as Mendelian Randomization (MR). Utilizing summary statistics from large-scale genome-wide association studies (GWAS), MR offers the possibility to investigate whether observational associations between exposures and outcomes are consistent with causal effects, and has previously been applied to clarify causal relationships between BMI and observationally-associated phenotypes 24 – 26 . Multivariate MR analyses provide a valuable tool for unravelling complex relationships among a multitude of associated phenotypes, particularly essential in the context of obesity and its multifaceted interactions with metabolic, cardiovascular, and inflammatory factors. Using summary statistics from large-scale GWAS for BMI 11 , related metabolic, cardiovascular and inflammatory phenotypes and cortical structure 27 , here we present a MR study as a hypothesis-driven instrumental variable approach to clarify the causality and direction of the relationship between BMI and cortical thickness across the entire brain and to disentangle the metabolic, cardiovascular, and inflammatory factors that might underlie this relationship. Based on the frequently replicated observational association between higher BMI and lower cortical thickness, particularly in the frontal and temporal cortex, our principal hypothesis posits a putative causal effect of higher BMI on lower cortical thickness primarily in the frontal and temporal cortex. Second, we hypothesize that indicated low-grade inflammation in addition to further metabolic and systemic parameters could be associated with BMI related cortical thickness alterations. Methods Mendelian Randomization analyses Associations between BMI, related cardiovascular, metabolic and inflammatory phenotypes, and cortical thickness measured globally and at 34 cortical regions of interest (ROI; see below) were evaluated by Two-Sample MR. In an MR analysis, genetic variants significantly associated with an exposure or risk factor of interest in a large GWAS meta-analysis are used as instrumental variables (IV) to evaluate the relationship with a phenotypic outcome (here cortical thickness). As genetic variants are fixed at conception, and unlikely to be impacted by confounding or reverse causation, MR enables causal inferences to be drawn regarding exposure-outcome relationships. GWAS summary statistics for cortical thickness, body mass index and related metabolic, cardiovascular and inflammatory phenotypes GWAS summary statistics for cortical thickness measures as the outcomes were taken from the ENIGMA consortium GWAS meta-analyses of Grasby et al. 27 (Supplementary Tables 1 and 2). Using genetic and brain MRI data for up to 23,183 individuals of European ancestry 27 , these meta-analyses were conducted for the average thickness of the entire cortex (global average thickness) and for hemisphere-averaged cortical thickness for the 34 ROIs as defined by the Desikan-Killiany cortical atlas 28 . The variant effect sizes utilized in the current study were taken from the meta-analyses including only ENIGMA cohorts ( i.e. , excluding the UK Biobank), and run with no genomic-control or correction for global average thickness for the regional analyses 27 . This is in line with observational analyses of directly measured cortical thickness that do not apply global corrections, and allowed us to minimize potential bias due to overlapping samples (primarily from the UK Biobank) in outcome and exposure GWAS 29 . The full ENIGMA data are available upon request ( http://enigma.usc.edu/research/download-enigma-gwas-results/ ). The GWAS summary statistics for BMI as the primary exposure were taken from the combined Genetic Investigation of Anthropometric Traits (GIANT) Consortium 11 and UK Biobank meta-analysis that included up to 681,275 individuals of European descent 30 . Summary statistics for the additional exposures investigated here were taken from GWAS for visceral adipose tissue (VAT), as a measure of fat stored around internal organs 31 , C-reactive protein (CRP), as a signature marker for chronic inflammation 32 , blood pressure measures (systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP) 33 ), and serum fasting glucose (FG) 34 and lipids (HDL and triglycerides (TG) 35 , 36 ) Instrumental variable construction Each instrumental variable (IV) was constructed using only variants reaching the genome-wide significant threshold (P < 5.0 x 10 − 08 ) in the respective GWAS meta-analysis for each trait (see above). Variants with allele mismatches between outcome and exposure summary data, and with palindromic alleles with minor allele frequencies > 0.45, were excluded. Linkage disequilibrium between genetic variants was determined to ensure only independent variants (r 2 10, and close to 1, respectively) for each IV. Details of the variant composition and effect sizes for each exposure and outcomes per IV are shown in Supplementary Tables 1 and 2. Statistical analyses The univariate MR analyses were conducted using the TwoSampleMR program 39 . For each exposure-outcome analysis, association effect sizes were calculated using the inverse variance weighted (IVW) MR method as the primary analysis. As IVW-MR assumes all of the BMI-associated variants are valid IVs, we also performed sensitivity analyses using methods that allow for the presence of some invalid variants, namely the weighted median 40 , weighted mode 41 and MR Egger 42 methods. Heterogeneity between BMI and cortical thickness effect sizes was investigated using Cochrane’s Q, the direction of causality was inferred using MR Steiger 39 , and the presence of directional horizontal pleiotropy was assessed with the MR Egger intercept 43 . To ensure the results were not driven by undetected horizontal pleiotropy, analyses were re-run for all cortical regions associated with BMI, and for which pleiotropy or heterogeneity was detected, using the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO; 44 ) method, which removes variants with outlying effect sizes that may indicate associations with confounders. Multivariable MR (MVMR) was conducted to test the direct effect of associated exposures using the MVMR package 45 . For all pairwise analyses conducted, SNPs associated with one exposure that were not present in the available dataset for the other exposure were replaced by proxy SNPs where possible (r 2 > 0.08, non-palindromic SNPs only). As this resulted in IVs comprising fewer SNPs than were included in the univariate analyses for most traits, F-statistics and I 2 GX were recalculated to test the strength of these smaller instruments. The analytical protocol was divided into four steps, which were performed as followed: First, to test our hypothesis of a putative causal effect of increased BMI on lower global cortical thickness as suggested by observational studies, we performed the MR analyses for average thickness of the entire cortex. Second, complementary to the general analyses, we conducted MR analyses to assess the putative direction of effect between BMI and 34 specific cortical ROIs defined according to the Desikan-Killiany cortical atlas 28 . Third, we performed further univariate MR analyses to test whether additional obesity-related metabolic, cardiovascular, and inflammatory phenotypes were associated with cortical thickness globally and regionally. Lastly, multivariable MR was performed for all brain regions nominally associated with BMI and any cardiovascular, metabolic and/or inflammatory trait to determine the independence of these associations. Multiple testing correction We used matrix spectral decomposition 46 to account for correlation between the cortical regions and estimate the number of independent variables tested. As this method requires individual level phenotypic data, we used the cortical region phenotypic data from the UK Biobank cohort that contributed to the main (all-cohorts) cortical GWAS meta-analysis presented in Grasby et al 27 . To be consistent with the GWAS meta-analysis, the cortical regions were residualised for biological sex, age, and ancestry. Across the 35 cortical regions we have included in our study the effective number of traits was estimated to be 29, hence we applied a significance threshold of P ≤ 1.7 x 1 Results BMI and average thickness of the entire cortex Our MR analyses revealed increased BMI to be nominally associated with a decrease in average cortical thickness globally (beta = -0.0089, 95% CI = -0.0152 - -0.0027, P = 5.07 x 10 − 3 ); Fig. 1 ; Supplementary Table 3). This relationship held in the sensitivity analyses (MR_PRESSO outlier-corrected beta = -0.0082, 95% CI = -0.0142 - -0.0021, P = 8.47 x 10 − 3 ; Supplementary Table 4), although it did not surpass the significance threshold taking multiple testing into account. BMI and cortical regions of interest throughout the brain MR analyses of associations between BMI and hemisphere-averaged cortical thickness for the 34 cortical ROIs suggested that causal relationships may underlie the genetic correlations detected between increased BMI and decreased cortical thickness at multiple, particularly frontal and temporal, regions. As for the entire cortex, the MR Steiger results supported the direction of effect of BMI impacting cortical thickness (Supplementary Table 3) in all brain regions. For the frontal cortex, increased BMI was associated with decreased cortical thickness in five of the 13 regions tested: caudal middle frontal (beta = -0.0086, 95% CI = -0.0170 - -0.0001, P = 4.74 x 10 − 2 ), frontal pole (beta = -0.0149, 95% CI = -0.0295 - -0.0003, P = 4.51 x 10 − 2 ), paracentral (beta = -0.0121, 95% CI = -0.0206 - -0.0036, P = 5.33 x 10 − 3 ), precentral (beta = -0.0141, 95% CI = -0.0223 - -0.0058, P = 8.66 x 10 − 4 ) and superior frontal (beta = -0.0130, 95% CI = -0.0217 - -0.0004, P = 3.16 x 10 − 3 ) (Fig. 1 , Supplementary Table 3). All associations were supported by the sensitivity analyses accounting for potential confounding due to pleiotropy, with similar effect estimates in the MR-PRESSO analyses accounting for outliers (see Supplementary Table 4), although only the precentral association signal surpassed the threshold for multiple testing. For the temporal cortex, increased BMI was associated with decreased cortical thickness in seven of the 10 regions tested: entorhinal (beta = -0.0219, 95% CI = -0.0423 - -0.0015, P = 3.56 x 10 − 2 ), fusiform (beta = -0.0162, 95% CI = -0.0423 - -0.0015, P = 7.55 x 10 − 05 ), inferior temporal (beta = -0.0115, 95% CI = -0.0203 - -0.0027, P = 1.04 x 10 − 02 ), middle temporal (beta = -0.0093, 95% CI = -0.0190 - -0.0008, P = 3.28 x 10 − 02 ), superior temporal (beta = -0.0099, 95% CI = -0.0198 - -0.0005, P = 4.89 x 10 − 02 ), temporal pole (beta = -0.0282, 95% CI = -0.0462 - -0.0101, P = 2.22 x 10 − 03 ) and transverse temporal (beta = -0.0186, 95% CI = -0.0314 - -0.0058, P = 4.52 x 10 − 03 ) regions (Fig. 1 , Supplementary Table 3). Here, only the association with the fusiform region remained significant accounting for multiple testing. Sensitivity analyses to account for potential pleiotropy detected outliers for all BMI-associated temporal regions except the entorhinal and fusiform regions, with similar effect sizes in the outlier-corrected MR-PRESSO analyses (Supplementary Table 4). MR analyses of BMI and cortical thickness in the remaining brain regions indicated increased BMI to be nominally associated with lower thickness in one of six parietal regions and two of four occipital regions: precuneus (beta = -0.0086, 95% CI = -0.0163 - -0.0009, P = 2.81 x 10 − 02 ), cuneus (beta = -0.0083, 95% CI = -0.0158 - -0.0008, P = 3.06 x 10 − 02 ) and lingual regions (beta = -0.00826, 95% CI = -0.0151 - -0.0014, P = 1.87 x 10 − 02 ). The effect sizes for these assocations were similar in outlier corrected analyses (Supplementary Table 4), although none of these regions surpassed our significance threshold taking multiple testing into account. Relationship between metabolic, cardiovascular and inflammatory phenotypes with BMI-associated cortical thickness Our follow-up MR analyses of additional metabolic, cardiovascular and inflammatory traits identified associations between cortical thickness and both VAT and CRP globally and regionally across the cortex. Increased VAT was associated with lower cortical thickness globally and in 17 of the 34 cortical regions, mostly overlapping those regions also associated with BMI (11/16) and with broadly similar effect sizes (Fig. 2 , Supplementary Table 5). Increased CRP was also associated with lower cortical thickness globally, and in 14 of the 34 cortical regions, the majority of which (8/16) were also associated with BMI, VAT or both adiposity measures (Fig. 2 , Supplementary Table 5). As for BMI, associations between these measures and reduced cortical thickness were notable across the temporal region, surpassing multiple testing correction in the fusiform region for both VAT (beta = -0.019, 95% CI -0.029- -0.009, P = 1.18 x 10 − 4 ) and CRP (beta = -0.012, 95% CI -0.018- -0.005, P = 3.35 x 10 − 4 ). Significant associations were also observed for VAT in the inferior temporal region (beta = -0.0189, 95% CI -0.0302- -0.0070, P = 9.76 x 10 − 4 ) and for CRP in the lateral occipital region (beta = -0.0112, 95% CI -0.0180- -0.0045, P = 1.14 x 10 − 3 ). Associations between cortical thickness and the remaining metabolic and cardiovascular phenotypes were nominally suggested in only a small number of regions (Supplementary Table 5): three regions in the frontal and parietal cortex for HDL, four regions in the frontal, temporal and parietal cortex for FG, and one region for both DBP and PP in the temporal and occipital cortex, respectively. Multivariable MR Multivariate MR (MVMR) analyses were conducted for all regions associated with BMI and at least one metabolic, cardiovascular or inflammatory trait. We first tested for shared effects between BMI and all additional tested traits in the two regions where reduced cortical thickness was most significantly associated with increasing BMI, the precentral and fusiform regions (Supplementary Table 6), followed by analyses including all other regions associated with BMI and at least one other trait (Supplementary Table 7). For all regions associated with both BMI and VAT, the BMI effects were attenuated in the multivariable analyses whilst VAT maintained nominally significant negative effects in 6/12 regions tested (Supplementary Tables 6 and 7), suggesting that despite the high genetic correlation between these traits there may be a unique direct effect of VAT above that of BMI per se on cortical thickness regionally at these nominal levels of association. Conversely, for all regions associated with both BMI and CRP the effects of both traits attenuated in the multivariable analyses, with the negative direct effects of BMI remaining nominally significant in 4/8 regions, suggesting that CRP impacts are shared with those of BMI. No other tested trait was seen to impact the direct effect of increased BMI on reduced cortical thickness, also seen in the minimal impacts on instrument strength in the conditional Fstat analyses (Supplementary Tables 6 and 7) Discussion In the present study, using MR, we provide evidence for a putative causal effect of BMI on cortical thickness across the human brain, with increased BMI associated with lower cortical thickness. The most prominent effects were observed in the frontal and temporal regions, notably the precentral and fusiform gyri. We furthermore found a concurrent association of CRP and VAT with lower cortical thickness, both globally and regionally across brain regions, largely overlapping with those associated with increased BMI. Our finding of an association between increased BMI and lower cortical thickness particularly in the temporal cortex is in line with findings from latest large-scale multicenter analyses 10 , 47 that congruently report a relationship between increased BMI and lower temporal cortical thickness. In fact, the most up-to-date and largest mega-analysis from the ENIGMA consortium based on 6,420 participants indicated the strongest regional effect sizes for the association between increased BMI and lower cortical thickness in the fusiform gyrus 6 , which was supported by the findings of our MR analyses. More recent large-scale studies by the ENIGMA consortium and others further corroborate the association between increased BMI and lower temporal thickness 9 , 48 Two separate MR studies have also recently presented evidence for an impact of obesity on cortical thickness 49 , 50 . Limitations in both studies, however, resulted in smaller numbers of associations seen across the brain, and in the case of Chen et al. 2023b 50 there is evidence for both decreased and increased cortical thickness with increasing BMI. Both studies focused only on adiposity and included regional thickness data that had been corrected for global thickness; in the current study we used summary statistics that had not undergone such correction, in line with analyses in observational studies, to interrogate region-specific impacts 6 . Incorporating these considerations, it is apparent that such differences in methodology would have led to very different interpretations. Additionally, Chen et al. used GWAS summary statistics for both the adiposity and cortical measures that were generated including individuals from the UK Biobank, which is likely to have biased their results due to the large number of overlapping samples in the exposure and outcome datasets 50 . The main findings of our hypothesis-driven study hold several relevant implications for future research. First, our genetic results support the hypothesis of a causal effect of BMI on brain structural decline. Importantly, this is consistent with previous findings from longitudinal human neuroimaging studies reporting BMI related brain structural atrophy over time 15 as well as with recent reports on accelerated brain ageing in obesity 5 , 51 . Our finding of a putative causal effect of increased BMI on lower cortical thickness raises the question of the underlying biological mechanisms that mediate the effect of BMI on cortical structural decline. In this regard, our finding of a parallel association of VAT and CRP with changes in brain structure, that largely correspond to BMI-associated changes appears noteworthy. These findings align with prior research linking obesity, inflammation, and altered brain physiology globally and in cortical regions in particular 52 – 54 . Notably, past studies have emphasized the role of VAT in generating pro-inflammatory cytokines, potentially exerting adverse effects on the CNS. More specifically, pro-inflammatory molecules released by lymphocytes and M1 macrophages residing in the VAT were shown to induce apoptosis trough microglia stimulation inside of the CNS, a possible mechanism for cortical thinning 55 – 57 . Our results underscore the need for future mechanistic investigations in this domain. Chronic low-grade inflammation, which has been shown to be present in multiple neuropsychiatric disorders and obesity, may represent a substantial shared underlying biological mechanism. With the discoveries of meningeal lymphatic vessels surrounding the brain and signalling cascades activated by peripheral cytokines that result in an increased permeability of the blood brain barrier, it is only recently that researchers are starting to uncover pathways by which systemic inflammation affects brain function 58 , 59 . Moreover, there is a bidirectional relationship between chronic low-grade inflammation and dysregulated mitochondrial function 60 , 61 . Since mitochondrial integrity is essential to ensure extraordinary energy demands during synaptic transmission and plasticity 62 , this might be another potential mechanism by which obesity related peripheral maladjustments affect brain structure. In this regard, future studies should furthermore take into account the microbiome, more specifically the role of the gut-brain axis in the context of obesity. Gut dysbiosis can lead to an unbalanced immune response and influence systemic energy regulation 63 , 64 . Overall, the biological correlations in combination with the data we have presented are promising for possibly establishing a link between obesity, energy balance, neuroinflammation and cortical changes in the brain. Thus, chronic low-grade inflammation and dysregulated energy balance, inherent to BMI, ermerge as plausible mechanisms underlying the observed reduction in cortical thickness attributed to BMI 65 . In addition to the findings for CRP and VAT, it should be noted that none of the other examined factors, including glucose metabolism, serum triglycerides, lipoproteins and blood pressure, exhibited a consistent pattern of associations with brain structure. This finding is striking considering previous biological research suggesting a potential connection between the vascular system and brain integrity including findings of associations between higher BMI and white matter hyperintensities, which are known to be related to cerebrovascular diseases 65 . While clarification of the mechanistic underpinnings between the putative causal link of BMI and brain structure will rely on preclinical research (e.g., animal models), future translational research should take advantage of methodological progress in neuroimaging to clarify the relationship between BMI and brain structural decline: Investigation of perivascular spaces and glymphatic clearance through ultra-fast magnetic encephalography represent promising approaches for the investigation of intermediate phenotypes related to neuroinflammation and neurovascular changes in obesity in-vivo 66 , 67 . In summary, our study supports the notion that VAT and low-grade inflammation, rather than shifts in metabolic serum markers as previous beliefs linking obesity primarily with vascular factors have underestimated its direct impact on cognition. Recent findings challenge this view, suggesting that obesity contributes not only to vascular dementia but also, notably, to Alzheimer's dementia 68 While the overwhelming relevance of obesity and related metabolic dysregulation as a cardiovascular risk factor has long been recognized by research and clinical practice, our results advocate for the relevance of increased BMI as a risk factor for the development of neurodegenerative disorders. Increased BMI has been shown to act as an important risk factor for several neurodegenerative and psychiatric disorders and has recently been stated to be the most important modifiable risk factor for dementia in the US 69 , 70 . The putative causal effect of increased BMI on cortical thickness decline observed in the present study appears to be in line with the aforementioned reports. It could thus be speculated that lower cortical thickness might act as a relevant mediator in the association between adverse metabolic conditions and neuropsychiatric disorders. Thus, atrophy of brain regions located in the temporal lobe has long been known to be associated with the subsequent development of cognitive impairment and dementia 71 – 73 . Future studies should thus aim to further clarify the role of BMI related thickness decline in the fusiform temporal and precentral frontal gyrus as the most prominent findings in the present study, in the development of neuropsychiatric disorders. This study has a number of strengths and limitations. Strengths include the application of MR analyses to test hypotheses based on previous observational studies, using genetic information from large-scale meta-analyses of BMI as well as a comprehensive investigation of genetic determinants influencing cortical thickness 27 . These data allowed us to investigate both global as well as regional specific associations between BMI and cortical thickness, with the finding of causal associations between BMI and brain structure complementing previous cross-sectional and longitudinal neuroimaging studies. The regional specific analyses allowed us to confirm a putative causal effect of BMI particularly in the temporal cortex, thus providing candidate brain regions for subsequent mechanistic studies on underlying biological mediators. The inclusion of a variety of relevant related phenotypes covering a broad spectrum of metabolic, cardiovascular and inflammatory traits represents another strength as it allowed to disentangle and identify potential candidate systems for future mechanistic research on the impact of BMI on brain health. Limitations to our study should also be acknowledged. First, while we were able to investigate associations between BMI and cortical thickness, we could not investigate whether the thickness of the cortex globally or in particular regions influence BMI due to the current paucity of cortical thickness-associated genetic variants. Further, current data do not allow us to infer the exact timing of potential causal effects of BMI on brain structure. It thus remains unclear during which developmental stages and at which speed increased BMI might cause brain structural decline. Future GWAS meta-analyses and longitudinal cohort studies are warranted to address these open research questions. Collectively, the present study corroborates the notion of a putative causal effect of BMI on brain structural volume decline. Our observation of a causal effect of increased BMI on lower temporal and frontal cortical thickness calls for increased attention towards the relevance of obesity and related metabolic conditions as modifiable risk factors for brain health. Moreover, our findings suggest that visceral adipose tissue and low-grade inflammation may be critically linked phenotypes in understanding the impact of BMI on brain structure. These findings highlight the need for future experimental investigations aimed at unravelling the potential cascade of mechanisms and identifying intervention opportunities within the intricate connection between weight gain, adipose tissue, inflammation, and structural brain changes. Future research and preventive efforts should aim to further explore the biological mechanisms through which BMI might influence brain structural decline and clarify the relationship between BMI related brain structural impairment and specific domains of neurocognitive functioning. Declarations Acknowledgements and Funding The present study was funded through the BMBF – German Centre for Mental Health JE5 01EE2305A to NO. SM was supported by National Health and Medical Research Council (NHMRC) Fellowships APP1158127 and APP1172917. KG receives funding from the NHMRC Fellowship APP1173025. JNP was supported by the NHMRC Project Grant APP1163040. TH was supported by CIHR grants 142255, 180449, 186254 Author Contributions TH suggested the study. JNP and NO conceptualized the study and drafted the manuscript. JNP performed all statistical analyses. AR, MR, MW, JL contributed to the drafting and editing of the manuscript. TH, SM, PMT assisted in the conceptualization of the study and interpretation of the results, as well as editing of the manuscript. JNP, KLG, SEM contributed to the conceptualization and design of the study, interpretation of the results and editing of the manuscript. 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Perivascular spaces in the brain: anatomy, physiology and pathology. Nat Rev Neurol 16, 137–153, doi: 10.1038/s41582-020-0312-z (2020). Slomski, A. Obesity Is Now the Top Modifiable Dementia Risk Factor in the US. JAMA 328, 10–10, doi: 10.1001/jama.2022.11058 (2022). Albanese, E. et al. Body mass index in midlife and dementia: Systematic review and meta-regression analysis of 589,649 men and women followed in longitudinal studies. Alzheimers Dement (Amst) 8, 165–178, doi: 10.1016/j.dadm.2017.05.007 (2017). Slomski, A. Obesity Is Now the Top Modifiable Dementia Risk Factor in the US. Jama 328, 10, doi: 10.1001/jama.2022.11058 (2022). Bastos-Leite, A. J. et al. The contribution of medial temporal lobe atrophy and vascular pathology to cognitive impairment in vascular dementia. Stroke 38, 3182–3185, doi: 10.1161/strokeaha.107.490102 (2007). Visser, P. J., Verhey, F. R., Hofman, P. A., Scheltens, P. & Jolles, J. Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry 72, 491–497, doi: 10.1136/jnnp.72.4.491 (2002). Chauveau, L. et al. Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study. Front Aging Neurosci 13, 750154, doi: 10.3389/fnagi.2021.750154 (2021). 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Medland","email":"","orcid":"https://orcid.org/0000-0003-1382-380X","institution":"QIMR Berghofer Medical Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Medland","suffix":""}],"badges":[],"createdAt":"2024-05-03 16:26:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4365189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4365189/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41380-026-03501-x","type":"published","date":"2026-03-09T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60101805,"identity":"787ab0d4-8dd3-496e-a502-90dadc164d8a","added_by":"auto","created_at":"2024-07-11 19:55:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1046969,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization testing relationships between body mass index and cortical thickness across all cortical regions in addition to global average thickness.\u003cstrong\u003e \u003c/strong\u003eAs an effect size, the beta values of the calculated Inverse weighted variance were used. Green colour indicates a weaker effect on the respective cortical area, while red colour indicates a stronger effect. Linear transformed scaled values and their original effect sizes can be found in Supplementary Table 8. STS = Superior Temporal Sulcus, IVW = Inverted Variance Weighted.\u003cstrong\u003e \u003c/strong\u003eCreated with BrainPainter\u003c/p\u003e","description":"","filename":"ForestPlotBetaValues.png","url":"https://assets-eu.researchsquare.com/files/rs-4365189/v1/9be35bec1a6e25dad2bcbfb4.png"},{"id":60101808,"identity":"aa96d0a1-996a-4c22-acca-c051ad7cdc8b","added_by":"auto","created_at":"2024-07-11 19:55:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4841461,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization testing relationships between additional phenotypes and cortical thickness across cortical regions showing significant negative effects. As an effect size, the beta values of the calculated Inverse weighted variance were used. Green colour indicates a weaker effect on the respective cortical area, while red colour indicates a stronger effect. Linear transformed scaled values and their original effect sizes can be found in Supplementary Table 9. BMI = Body mass index.\u003cstrong\u003e \u003c/strong\u003eCreated with BrainPainter\u003c/p\u003e","description":"","filename":"Fig.2Painteretal.final.png","url":"https://assets-eu.researchsquare.com/files/rs-4365189/v1/0673146d7be43fe02f922659.png"},{"id":104292674,"identity":"03d9781c-914f-4b7b-ac24-6a6acc84565f","added_by":"auto","created_at":"2026-03-10 07:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7364776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4365189/v1/e4a4f716-ee81-4c9a-bfbf-88e55bcde4ec.pdf"},{"id":60101806,"identity":"b8a85de8-0450-4147-9155-0144087fd894","added_by":"auto","created_at":"2024-07-11 19:55:25","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3582868,"visible":true,"origin":"","legend":"","description":"","filename":"CorticalthicknessBMIMRSuppTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365189/v1/73b2f6398e906d53c4c910e5.xlsx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Deciphering the Causal Influence of BMI and related Metabolic, Inflammatory, and Cardiovascular Factors on Brain Structure: A Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a hereditary condition \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e that commonly coincides with a spectrum of metabolic, cardiovascular, and inflammatory disturbances. Specifically, elevated blood pressure, impaired glucose metabolism, excess visceral fat accumulation, and alterations in serum triglycerides and cholesterol levels are prevalent comorbid health issues linked to obesity, collectively characterizing the metabolic syndrome \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, adipose tissue secretion of pro-inflammatory cytokines is known to induce low-grade inflammation in the context of obesity. In addition to its impact on body-wide metabolic, cardiovascular, and inflammatory systems, accumulating evidence underscores obesity as a significant determinant of brain health, with frequent associations observed between obesity and substantial and widespread brain structural abnormalities \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Recent work by the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and others has consistently demonstrated that increased BMI (weight [kg]/height\u003csup\u003e2\u003c/sup\u003e [m]), a proxy for increases in fat mass, is associated with brain structural abnormalities in healthy controls as well as in psychiatric patients, primarily with lower fronto-temporal cortical thickness \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. As the majority of previous neuroimaging studies investigating the association between obesity and the brain were based on cross-sectional designs, the direction of associations between increased BMI and brain structural alterations is, however, unknown. Most importantly it is unclear whether brain structural alterations precede and predispose to higher BMI or if higher BMI induces changes in brain structure.\u003c/p\u003e \u003cp\u003eAs previous reports have found that genetic variants associated with BMI are highly expressed in the central nervous system (CNS) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, it appears possible that genetically determined alterations in brain structure might precede weight gain and hence mediate the effect of genetic risk on the development of obesity. This notion is supported by findings from prospective functional MRI studies reporting that changes in reward system signalling might predict future weight gain \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In an attempt to address this open research question, previous imaging genetic research has revealed associations between polygenic risk for BMI and alterations in brain structure, particularly prefrontal grey matter volume reductions and reduced occipital surface area \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the previously observed associations between polygenic risk for obesity and brain structure are of small effect size and limited to specific brain regions such as the medial prefrontal cortex \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In contrast, the replicated phenotypic associations between BMI and brain structure, which have been extensively validated, are broadly distributed and have effect sizes that exceed those observed for common neuropsychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A large proportion of the BMI-related variation in brain structure thus cannot be directly traced back to genetic variants. Furthermore, previous longitudinal studies have reported BMI to be associated with decreased grey matter volume and cortical thickness decline over time, thus underpinning the notion of potential BMI-related atrophic or neurodegenerative processes \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This is in line with recent reports on accelerated brain ageing in individuals with overweight or obesity \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith regard to the potential biological mechanisms underlying the association between BMI and brain structural alterations, a growing body of data suggests chronic low-grade inflammation to represent a fundamental common biological mechanism linking obesity and neuropsychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Since adipose tissue expansion upon a high-fat diet results in a disruption of an anti-inflammatory milieu with increased differentiation and recruitment of pro-inflammatory immune cells \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, chronic low-grade inflammation may be attributed to increased BMI. By promoting oxidative stress and dysregulation in energy-regulating neuroendocrine metabolic pathways, chronic low-grade inflammation contributes significantly to impaired energy supply, to which the CNS is highly vulnerable \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Beyond that, emerging evidence indicates a cross-talk between systemic inflammatory processes and inflammation in the CNS, further exacerbating neuronal injury as a potential source for the manifestation of neuropsychiatric symptoms \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne possibility to investigate the direction of associations between BMI and brain structure are instrumental variable (IV) based methods such as Mendelian Randomization (MR). Utilizing summary statistics from large-scale genome-wide association studies (GWAS), MR offers the possibility to investigate whether observational associations between exposures and outcomes are consistent with causal effects, and has previously been applied to clarify causal relationships between BMI and observationally-associated phenotypes \u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Multivariate MR analyses provide a valuable tool for unravelling complex relationships among a multitude of associated phenotypes, particularly essential in the context of obesity and its multifaceted interactions with metabolic, cardiovascular, and inflammatory factors.\u003c/p\u003e \u003cp\u003eUsing summary statistics from large-scale GWAS for BMI \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, related metabolic, cardiovascular and inflammatory phenotypes and cortical structure \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, here we present a MR study as a hypothesis-driven instrumental variable approach to clarify the causality and direction of the relationship between BMI and cortical thickness across the entire brain and to disentangle the metabolic, cardiovascular, and inflammatory factors that might underlie this relationship. Based on the frequently replicated observational association between higher BMI and lower cortical thickness, particularly in the frontal and temporal cortex, our principal hypothesis posits a putative causal effect of higher BMI on lower cortical thickness primarily in the frontal and temporal cortex. Second, we hypothesize that indicated low-grade inflammation in addition to further metabolic and systemic parameters could be associated with BMI related cortical thickness alterations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMendelian Randomization analyses\u003c/h2\u003e \u003cp\u003eAssociations between BMI, related cardiovascular, metabolic and inflammatory phenotypes, and cortical thickness measured globally and at 34 cortical regions of interest (ROI; see below) were evaluated by Two-Sample MR. In an MR analysis, genetic variants significantly associated with an exposure or risk factor of interest in a large GWAS meta-analysis are used as instrumental variables (IV) to evaluate the relationship with a phenotypic outcome (here cortical thickness). As genetic variants are fixed at conception, and unlikely to be impacted by confounding or reverse causation, MR enables causal inferences to be drawn regarding exposure-outcome relationships.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGWAS summary statistics for cortical thickness, body mass index and related metabolic, cardiovascular and inflammatory phenotypes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGWAS summary statistics for cortical thickness measures as the outcomes were taken from the ENIGMA consortium GWAS meta-analyses of Grasby et al. \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (Supplementary Tables\u0026nbsp;1 and 2). Using genetic and brain MRI data for up to 23,183 individuals of European ancestry \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, these meta-analyses were conducted for the average thickness of the entire cortex (global average thickness) and for hemisphere-averaged cortical thickness for the 34 ROIs as defined by the Desikan-Killiany cortical atlas \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The variant effect sizes utilized in the current study were taken from the meta-analyses including only ENIGMA cohorts (\u003cem\u003ei.e.\u003c/em\u003e, excluding the UK Biobank), and run with no genomic-control or correction for global average thickness for the regional analyses \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This is in line with observational analyses of directly measured cortical thickness that do not apply global corrections, and allowed us to minimize potential bias due to overlapping samples (primarily from the UK Biobank) in outcome and exposure GWAS \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The full ENIGMA data are available upon request (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://enigma.usc.edu/research/download-enigma-gwas-results/\u003c/span\u003e\u003cspan address=\"http://enigma.usc.edu/research/download-enigma-gwas-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe GWAS summary statistics for BMI as the primary exposure were taken from the combined Genetic Investigation of Anthropometric Traits (GIANT) Consortium \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and UK Biobank meta-analysis that included up to 681,275 individuals of European descent \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Summary statistics for the additional exposures investigated here were taken from GWAS for visceral adipose tissue (VAT), as a measure of fat stored around internal organs \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, C-reactive protein (CRP), as a signature marker for chronic inflammation \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, blood pressure measures (systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e), and serum fasting glucose (FG) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and lipids (HDL and triglycerides (TG) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variable construction\u003c/h2\u003e \u003cp\u003eEach instrumental variable (IV) was constructed using only variants reaching the genome-wide significant threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e) in the respective GWAS meta-analysis for each trait (see above). Variants with allele mismatches between outcome and exposure summary data, and with palindromic alleles with minor allele frequencies\u0026thinsp;\u0026gt;\u0026thinsp;0.45, were excluded. Linkage disequilibrium between genetic variants was determined to ensure only independent variants (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, minimum distance 10,000 Kb) were included in each IV. Instrument strength was determined by calculating F-statistics \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003csub\u003eGX\u003c/sub\u003e \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e (with strong instruments shown by values\u0026thinsp;\u0026gt;\u0026thinsp;10, and close to 1, respectively) for each IV. Details of the variant composition and effect sizes for each exposure and outcomes per IV are shown in Supplementary Tables\u0026nbsp;1 and 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe univariate MR analyses were conducted using the TwoSampleMR program \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. For each exposure-outcome analysis, association effect sizes were calculated using the inverse variance weighted (IVW) MR method as the primary analysis. As IVW-MR assumes all of the BMI-associated variants are valid IVs, we also performed sensitivity analyses using methods that allow for the presence of some invalid variants, namely the weighted median \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, weighted mode \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and MR Egger \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e methods. Heterogeneity between BMI and cortical thickness effect sizes was investigated using Cochrane\u0026rsquo;s Q, the direction of causality was inferred using MR Steiger \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and the presence of directional horizontal pleiotropy was assessed with the MR Egger intercept \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. To ensure the results were not driven by undetected horizontal pleiotropy, analyses were re-run for all cortical regions associated with BMI, and for which pleiotropy or heterogeneity was detected, using the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO; \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e) method, which removes variants with outlying effect sizes that may indicate associations with confounders.\u003c/p\u003e \u003cp\u003eMultivariable MR (MVMR) was conducted to test the direct effect of associated exposures using the MVMR package \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. For all pairwise analyses conducted, SNPs associated with one exposure that were not present in the available dataset for the other exposure were replaced by proxy SNPs where possible (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.08, non-palindromic SNPs only). As this resulted in IVs comprising fewer SNPs than were included in the univariate analyses for most traits, F-statistics and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003csub\u003eGX\u003c/sub\u003e were recalculated to test the strength of these smaller instruments.\u003c/p\u003e \u003cp\u003eThe analytical protocol was divided into four steps, which were performed as followed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFirst, to test our hypothesis of a putative causal effect of increased BMI on lower global cortical thickness as suggested by observational studies, we performed the MR analyses for average thickness of the entire cortex.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSecond, complementary to the general analyses, we conducted MR analyses to assess the putative direction of effect between BMI and 34 specific cortical ROIs defined according to the Desikan-Killiany cortical atlas \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThird, we performed further univariate MR analyses to test whether additional obesity-related metabolic, cardiovascular, and inflammatory phenotypes were associated with cortical thickness globally and regionally.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLastly, multivariable MR was performed for all brain regions nominally associated with BMI and any cardiovascular, metabolic and/or inflammatory trait to determine the independence of these associations.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMultiple testing correction\u003c/h2\u003e \u003cp\u003eWe used matrix spectral decomposition \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e to account for correlation between the cortical regions and estimate the number of independent variables tested. As this method requires individual level phenotypic data, we used the cortical region phenotypic data from the UK Biobank cohort that contributed to the main (all-cohorts) cortical GWAS meta-analysis presented in Grasby et al \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. To be consistent with the GWAS meta-analysis, the cortical regions were residualised for biological sex, age, and ancestry. Across the 35 cortical regions we have included in our study the effective number of traits was estimated to be 29, hence we applied a significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1.7 x 1\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBMI and average thickness of the entire cortex\u003c/h2\u003e \u003cp\u003eOur MR analyses revealed increased BMI to be nominally associated with a decrease in average cortical thickness globally (beta = -0.0089, 95% CI = -0.0152 - -0.0027, P\u0026thinsp;=\u0026thinsp;5.07 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e); Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table\u0026nbsp;3). This relationship held in the sensitivity analyses (MR_PRESSO outlier-corrected beta = -0.0082, 95% CI = -0.0142 - -0.0021, P\u0026thinsp;=\u0026thinsp;8.47 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e; Supplementary Table\u0026nbsp;4), although it did not surpass the significance threshold taking multiple testing into account.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBMI and cortical regions of interest throughout the brain\u003c/h2\u003e \u003cp\u003eMR analyses of associations between BMI and hemisphere-averaged cortical thickness for the 34 cortical ROIs suggested that causal relationships may underlie the genetic correlations detected between increased BMI and decreased cortical thickness at multiple, particularly frontal and temporal, regions. As for the entire cortex, the MR Steiger results supported the direction of effect of BMI impacting cortical thickness (Supplementary Table\u0026nbsp;3) in all brain regions.\u003c/p\u003e \u003cp\u003eFor the frontal cortex, increased BMI was associated with decreased cortical thickness in five of the 13 regions tested: caudal middle frontal (beta = -0.0086, 95% CI = -0.0170 - -0.0001, P\u0026thinsp;=\u0026thinsp;4.74 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), frontal pole (beta = -0.0149, 95% CI = -0.0295 - -0.0003, P\u0026thinsp;=\u0026thinsp;4.51 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), paracentral (beta = -0.0121, 95% CI = -0.0206 - -0.0036, P\u0026thinsp;=\u0026thinsp;5.33 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), precentral (beta = -0.0141, 95% CI = -0.0223 - -0.0058, P\u0026thinsp;=\u0026thinsp;8.66 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and superior frontal (beta = -0.0130, 95% CI = -0.0217 - -0.0004, P\u0026thinsp;=\u0026thinsp;3.16 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;3). All associations were supported by the sensitivity analyses accounting for potential confounding due to pleiotropy, with similar effect estimates in the MR-PRESSO analyses accounting for outliers (see Supplementary Table\u0026nbsp;4), although only the precentral association signal surpassed the threshold for multiple testing.\u003c/p\u003e \u003cp\u003eFor the temporal cortex, increased BMI was associated with decreased cortical thickness in seven of the 10 regions tested: entorhinal (beta = -0.0219, 95% CI = -0.0423 - -0.0015, P\u0026thinsp;=\u0026thinsp;3.56 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), fusiform (beta = -0.0162, 95% CI = -0.0423 - -0.0015, P\u0026thinsp;=\u0026thinsp;7.55 x 10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e), inferior temporal (beta = -0.0115, 95% CI = -0.0203 - -0.0027, P\u0026thinsp;=\u0026thinsp;1.04 x 10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e), middle temporal (beta = -0.0093, 95% CI = -0.0190 - -0.0008, P\u0026thinsp;=\u0026thinsp;3.28 x 10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e), superior temporal (beta = -0.0099, 95% CI = -0.0198 - -0.0005, P\u0026thinsp;=\u0026thinsp;4.89 x 10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e), temporal pole (beta = -0.0282, 95% CI = -0.0462 - -0.0101, P\u0026thinsp;=\u0026thinsp;2.22 x 10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e) and transverse temporal (beta = -0.0186, 95% CI = -0.0314 - -0.0058, P\u0026thinsp;=\u0026thinsp;4.52 x 10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e) regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;3). Here, only the association with the fusiform region remained significant accounting for multiple testing. Sensitivity analyses to account for potential pleiotropy detected outliers for all BMI-associated temporal regions except the entorhinal and fusiform regions, with similar effect sizes in the outlier-corrected MR-PRESSO analyses (Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eMR analyses of BMI and cortical thickness in the remaining brain regions indicated increased BMI to be nominally associated with lower thickness in one of six parietal regions and two of four occipital regions: precuneus (beta = -0.0086, 95% CI = -0.0163 - -0.0009, P\u0026thinsp;=\u0026thinsp;2.81 x 10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e), cuneus (beta = -0.0083, 95% CI = -0.0158 - -0.0008, P\u0026thinsp;=\u0026thinsp;3.06 x 10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e) and lingual regions (beta = -0.00826, 95% CI = -0.0151 - -0.0014, P\u0026thinsp;=\u0026thinsp;1.87 x 10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e). The effect sizes for these assocations were similar in outlier corrected analyses (Supplementary Table\u0026nbsp;4), although none of these regions surpassed our significance threshold taking multiple testing into account.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between metabolic, cardiovascular and inflammatory phenotypes with BMI-associated cortical thickness\u003c/h2\u003e \u003cp\u003eOur follow-up MR analyses of additional metabolic, cardiovascular and inflammatory traits identified associations between cortical thickness and both VAT and CRP globally and regionally across the cortex. Increased VAT was associated with lower cortical thickness globally and in 17 of the 34 cortical regions, mostly overlapping those regions also associated with BMI (11/16) and with broadly similar effect sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table\u0026nbsp;5). Increased CRP was also associated with lower cortical thickness globally, and in 14 of the 34 cortical regions, the majority of which (8/16) were also associated with BMI, VAT or both adiposity measures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table\u0026nbsp;5). As for BMI, associations between these measures and reduced cortical thickness were notable across the temporal region, surpassing multiple testing correction in the fusiform region for both VAT (beta = -0.019, 95% CI -0.029- -0.009, P\u0026thinsp;=\u0026thinsp;1.18 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and CRP (beta = -0.012, 95% CI -0.018- -0.005, P\u0026thinsp;=\u0026thinsp;3.35 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Significant associations were also observed for VAT in the inferior temporal region (beta = -0.0189, 95% CI -0.0302- -0.0070, P\u0026thinsp;=\u0026thinsp;9.76 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and for CRP in the lateral occipital region (beta = -0.0112, 95% CI -0.0180- -0.0045, P\u0026thinsp;=\u0026thinsp;1.14 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eAssociations between cortical thickness and the remaining metabolic and cardiovascular phenotypes were nominally suggested in only a small number of regions (Supplementary Table\u0026nbsp;5): three regions in the frontal and parietal cortex for HDL, four regions in the frontal, temporal and parietal cortex for FG, and one region for both DBP and PP in the temporal and occipital cortex, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable MR\u003c/h2\u003e \u003cp\u003eMultivariate MR (MVMR) analyses were conducted for all regions associated with BMI and at least one metabolic, cardiovascular or inflammatory trait. We first tested for shared effects between BMI and all additional tested traits in the two regions where reduced cortical thickness was most significantly associated with increasing BMI, the precentral and fusiform regions (Supplementary Table\u0026nbsp;6), followed by analyses including all other regions associated with BMI and at least one other trait (Supplementary Table\u0026nbsp;7). For all regions associated with both BMI and VAT, the BMI effects were attenuated in the multivariable analyses whilst VAT maintained nominally significant negative effects in 6/12 regions tested (Supplementary Tables\u0026nbsp;6 and 7), suggesting that despite the high genetic correlation between these traits there may be a unique direct effect of VAT above that of BMI \u003cem\u003eper se\u003c/em\u003e on cortical thickness regionally at these nominal levels of association. Conversely, for all regions associated with both BMI and CRP the effects of both traits attenuated in the multivariable analyses, with the negative direct effects of BMI remaining nominally significant in 4/8 regions, suggesting that CRP impacts are shared with those of BMI. No other tested trait was seen to impact the direct effect of increased BMI on reduced cortical thickness, also seen in the minimal impacts on instrument strength in the conditional Fstat analyses (Supplementary Tables\u0026nbsp;6 and 7)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, using MR, we provide evidence for a putative causal effect of BMI on cortical thickness across the human brain, with increased BMI associated with lower cortical thickness. The most prominent effects were observed in the frontal and temporal regions, notably the precentral and fusiform gyri. We furthermore found a concurrent association of CRP and VAT with lower cortical thickness, both globally and regionally across brain regions, largely overlapping with those associated with increased BMI.\u003c/p\u003e \u003cp\u003eOur finding of an association between increased BMI and lower cortical thickness particularly in the temporal cortex is in line with findings from latest large-scale multicenter analyses \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e that congruently report a relationship between increased BMI and lower temporal cortical thickness. In fact, the most up-to-date and largest mega-analysis from the ENIGMA consortium based on 6,420 participants indicated the strongest regional effect sizes for the association between increased BMI and lower cortical thickness in the fusiform gyrus \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, which was supported by the findings of our MR analyses. More recent large-scale studies by the ENIGMA consortium and others further corroborate the association between increased BMI and lower temporal thickness \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTwo separate MR studies have also recently presented evidence for an impact of obesity on cortical thickness \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Limitations in both studies, however, resulted in smaller numbers of associations seen across the brain, and in the case of Chen et al. 2023b \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e there is evidence for both decreased and increased cortical thickness with increasing BMI. Both studies focused only on adiposity and included regional thickness data that had been corrected for global thickness; in the current study we used summary statistics that had not undergone such correction, in line with analyses in observational studies, to interrogate region-specific impacts \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Incorporating these considerations, it is apparent that such differences in methodology would have led to very different interpretations. Additionally, Chen et al. used GWAS summary statistics for both the adiposity and cortical measures that were generated including individuals from the UK Biobank, which is likely to have biased their results due to the large number of overlapping samples in the exposure and outcome datasets \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe main findings of our hypothesis-driven study hold several relevant implications for future research. First, our genetic results support the hypothesis of a causal effect of BMI on brain structural decline. Importantly, this is consistent with previous findings from longitudinal human neuroimaging studies reporting BMI related brain structural atrophy over time \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e as well as with recent reports on accelerated brain ageing in obesity \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur finding of a putative causal effect of increased BMI on lower cortical thickness raises the question of the underlying biological mechanisms that mediate the effect of BMI on cortical structural decline. In this regard, our finding of a parallel association of VAT and CRP with changes in brain structure, that largely correspond to BMI-associated changes appears noteworthy. These findings align with prior research linking obesity, inflammation, and altered brain physiology globally and in cortical regions in particular \u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Notably, past studies have emphasized the role of VAT in generating pro-inflammatory cytokines, potentially exerting adverse effects on the CNS. More specifically, pro-inflammatory molecules released by lymphocytes and M1 macrophages residing in the VAT were shown to induce apoptosis trough microglia stimulation inside of the CNS, a possible mechanism for cortical thinning \u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Our results underscore the need for future mechanistic investigations in this domain. Chronic low-grade inflammation, which has been shown to be present in multiple neuropsychiatric disorders and obesity, may represent a substantial shared underlying biological mechanism. With the discoveries of meningeal lymphatic vessels surrounding the brain and signalling cascades activated by peripheral cytokines that result in an increased permeability of the blood brain barrier, it is only recently that researchers are starting to uncover pathways by which systemic inflammation affects brain function \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Moreover, there is a bidirectional relationship between chronic low-grade inflammation and dysregulated mitochondrial function \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Since mitochondrial integrity is essential to ensure extraordinary energy demands during synaptic transmission and plasticity \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, this might be another potential mechanism by which obesity related peripheral maladjustments affect brain structure. In this regard, future studies should furthermore take into account the microbiome, more specifically the role of the gut-brain axis in the context of obesity. Gut dysbiosis can lead to an unbalanced immune response and influence systemic energy regulation \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Overall, the biological correlations in combination with the data we have presented are promising for possibly establishing a link between obesity, energy balance, neuroinflammation and cortical changes in the brain. Thus, chronic low-grade inflammation and dysregulated energy balance, inherent to BMI, ermerge as plausible mechanisms underlying the observed reduction in cortical thickness attributed to BMI \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the findings for CRP and VAT, it should be noted that none of the other examined factors, including glucose metabolism, serum triglycerides, lipoproteins and blood pressure, exhibited a consistent pattern of associations with brain structure. This finding is striking considering previous biological research suggesting a potential connection between the vascular system and brain integrity including findings of associations between higher BMI and white matter hyperintensities, which are known to be related to cerebrovascular diseases \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. While clarification of the mechanistic underpinnings between the putative causal link of BMI and brain structure will rely on preclinical research (e.g., animal models), future translational research should take advantage of methodological progress in neuroimaging to clarify the relationship between BMI and brain structural decline: Investigation of perivascular spaces and glymphatic clearance through ultra-fast magnetic encephalography represent promising approaches for the investigation of intermediate phenotypes related to neuroinflammation and neurovascular changes in obesity in-vivo \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. In summary, our study supports the notion that VAT and low-grade inflammation, rather than shifts in metabolic serum markers as previous beliefs linking obesity primarily with vascular factors have underestimated its direct impact on cognition. Recent findings challenge this view, suggesting that obesity contributes not only to vascular dementia but also, notably, to Alzheimer's dementia \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile the overwhelming relevance of obesity and related metabolic dysregulation as a cardiovascular risk factor has long been recognized by research and clinical practice, our results advocate for the relevance of increased BMI as a risk factor for the development of neurodegenerative disorders. Increased BMI has been shown to act as an important risk factor for several neurodegenerative and psychiatric disorders and has recently been stated to be the most important modifiable risk factor for dementia in the US \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The putative causal effect of increased BMI on cortical thickness decline observed in the present study appears to be in line with the aforementioned reports. It could thus be speculated that lower cortical thickness might act as a relevant mediator in the association between adverse metabolic conditions and neuropsychiatric disorders. Thus, atrophy of brain regions located in the temporal lobe has long been known to be associated with the subsequent development of cognitive impairment and dementia \u003csup\u003e\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Future studies should thus aim to further clarify the role of BMI related thickness decline in the fusiform temporal and precentral frontal gyrus as the most prominent findings in the present study, in the development of neuropsychiatric disorders.\u003c/p\u003e \u003cp\u003eThis study has a number of strengths and limitations. Strengths include the application of MR analyses to test hypotheses based on previous observational studies, using genetic information from large-scale meta-analyses of BMI as well as a comprehensive investigation of genetic determinants influencing cortical thickness \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These data allowed us to investigate both global as well as regional specific associations between BMI and cortical thickness, with the finding of causal associations between BMI and brain structure complementing previous cross-sectional and longitudinal neuroimaging studies. The regional specific analyses allowed us to confirm a putative causal effect of BMI particularly in the temporal cortex, thus providing candidate brain regions for subsequent mechanistic studies on underlying biological mediators. The inclusion of a variety of relevant related phenotypes covering a broad spectrum of metabolic, cardiovascular and inflammatory traits represents another strength as it allowed to disentangle and identify potential candidate systems for future mechanistic research on the impact of BMI on brain health.\u003c/p\u003e \u003cp\u003eLimitations to our study should also be acknowledged. First, while we were able to investigate associations between BMI and cortical thickness, we could not investigate whether the thickness of the cortex globally or in particular regions influence BMI due to the current paucity of cortical thickness-associated genetic variants. Further, current data do not allow us to infer the exact timing of potential causal effects of BMI on brain structure. It thus remains unclear during which developmental stages and at which speed increased BMI might cause brain structural decline. Future GWAS meta-analyses and longitudinal cohort studies are warranted to address these open research questions.\u003c/p\u003e \u003cp\u003eCollectively, the present study corroborates the notion of a putative causal effect of BMI on brain structural volume decline. Our observation of a causal effect of increased BMI on lower temporal and frontal cortical thickness calls for increased attention towards the relevance of obesity and related metabolic conditions as modifiable risk factors for brain health. Moreover, our findings suggest that visceral adipose tissue and low-grade inflammation may be critically linked phenotypes in understanding the impact of BMI on brain structure. These findings highlight the need for future experimental investigations aimed at unravelling the potential cascade of mechanisms and identifying intervention opportunities within the intricate connection between weight gain, adipose tissue, inflammation, and structural brain changes. Future research and preventive efforts should aim to further explore the biological mechanisms through which BMI might influence brain structural decline and clarify the relationship between BMI related brain structural impairment and specific domains of neurocognitive functioning.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was funded through the BMBF \u0026ndash; German Centre for Mental Health JE5 01EE2305A to NO. SM was supported by National Health and Medical Research Council (NHMRC) Fellowships APP1158127 and APP1172917. KG receives funding from the NHMRC Fellowship APP1173025. \u0026nbsp;JNP was supported by the NHMRC Project Grant APP1163040. TH was supported by CIHR grants\u0026nbsp;142255, 180449, 186254\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTH suggested the study. JNP and NO conceptualized the study and drafted the manuscript. JNP performed all statistical analyses. AR, MR, MW, JL contributed to the drafting and editing of the manuscript. TH, SM, PMT assisted in the conceptualization of the study and interpretation of the results, as well as editing of the manuscript. JNP, KLG, SEM contributed to the conceptualization and design of the study, interpretation of the results and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all other authors, the corresponding author states that there is no conflict of interest and nothing to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBouchard, C. Genetics of Obesity: What We Have Learned Over Decades of Research. 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Obesity has frequently been shown to affect brain physiology at multiple levels, and to increase the risk for the development of neuropsychiatric disorders such as major depression and dementia. Previous large-scale neuroimaging research has consistently shown overlapping brain structural alterations in obesity and neuropsychiatric disorders, with the most pronounced alterations being lower cortical thickness in the frontal and temporal cortex. Yet, the direction of association, and the potential causal effect of obesity on brain structural decline, remains unclear. Moreover, it is imperative to determine which of the multifaceted biological systems impacted by obesity, encompassing metabolic, cardiovascular, and inflammatory aspects, may be implicated in the link between obesity and brain structural decline. In this study, we employed univariate and multivariate Mendelian randomization (MR) as an instrumental variable (IV) approach to clarify the causal direction of the relationship between body mass index (BMI) and brain structure and to disentangle the metabolic, cardiovascular, and inflammatory factors that might underlie this relationship. We found evidence for a potential causal influence of elevated BMI on lower cortical thickness, with most prominent effects in frontal and temporal regions. We furthermore found a concurrent association of the inflammatory serum marker CRP and visceral adipose tissue (VAT) with lower cortical thickness, both globally and regionally across brain regions, largely overlapping with those associated with increased BMI. In contrast, very few associations with cortical thickness emerged for blood pressure or metabolic serum markers. Our findings thus corroborate the notion of a causal effect of BMI on lower cortical thickness and indicate low-grade inflammation as a potential candidate mechanism implicated in this relationship. Future research should aim to delineate if and how the BMI related effect on brain structural decline conveys an increased risk for the development of neuropsychiatric disorders.\u003c/p\u003e","manuscriptTitle":"Deciphering the Causal Influence of BMI and related Metabolic, Inflammatory, and Cardiovascular Factors on Brain Structure: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-11 19:55:20","doi":"10.21203/rs.3.rs-4365189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-08-26T09:06:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-08-08T19:43:47+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-21T18:03:45+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-24T10:52:53+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-24T07:09:14+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-06-21T04:54:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-08T13:49:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-08T10:09:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2024-05-08T07:09:56+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-05-07T11:23:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b64d6e63-928f-4b07-a936-0cc419ec5fc5","owner":[],"postedDate":"July 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31678400,"name":"Biological sciences/Neuroscience"},{"id":31678401,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2026-03-10T07:12:19+00:00","versionOfRecord":{"articleIdentity":"rs-4365189","link":"https://doi.org/10.1038/s41380-026-03501-x","journal":{"identity":"molecular-psychiatry","isVorOnly":false,"title":"Molecular Psychiatry"},"publishedOn":"2026-03-09 04:00:00","publishedOnDateReadable":"March 9th, 2026"},"versionCreatedAt":"2024-07-11 19:55:20","video":"","vorDoi":"10.1038/s41380-026-03501-x","vorDoiUrl":"https://doi.org/10.1038/s41380-026-03501-x","workflowStages":[]},"version":"v1","identity":"rs-4365189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4365189","identity":"rs-4365189","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}