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Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.

ABSTRACT: Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = -0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

SUBMITTER: Opel N

PROVIDER: S-EPMC8589644 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.

Opel Nils N Thalamuthu Anbupalam A Milaneschi Yuri Y Grotegerd Dominik D Flint Claas C Leenings Ramona R Goltermann Janik J Richter Maike M Hahn Tim T Woditsch Georg G Berger Klaus K Hermesdorf Marco M McIntosh Andrew A Whalley Heather C HC Harris Mathew A MA MacMaster Frank P FP Walter Henrik H Veer Ilya M IM Frodl Thomas T Carballedo Angela A Krug Axel A Nenadic Igor I Kircher Tilo T Aleman Andre A Groenewold Nynke A NA Stein Dan J DJ Soares Jair C JC Zunta-Soares Giovana B GB Mwangi Benson B Wu Mon-Ju MJ Walter Martin M Li Meng M Harrison Ben J BJ Davey Christopher G CG Cullen Kathryn R KR Klimes-Dougan Bonnie B Mueller Bryon A BA Sämann Philipp G PG Penninx Brenda B Nawijn Laura L Veltman Dick J DJ Aftanas Lyubomir L Brak Ivan V IV Filimonova Elena A EA Osipov Evgeniy A EA Reneman Liesbeth L Schrantee Anouk A Grabe Hans J HJ Van der Auwera Sandra S Wittfeld Katharina K Hosten Norbert N Völzke Henry H Sim Kang K Gotlib Ian H IH Sacchet Matthew D MD Lagopoulos Jim J Hatton Sean N SN Hickie Ian I Pozzi Elena E Thompson Paul M PM Jahanshad Neda N Schmaal Lianne L Baune Bernhard T BT Dannlowski Udo U

Molecular psychiatry 20200528 9

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced ef ...[more]

PMID: 32467648

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Project description:Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

Project description:BackgroundObservational studies have shown associations between psychiatric disorders and osteoarthritis (OA). However, the causal impact of different psychiatric disorder types on specific sites of osteoarthritis remains unclear. This study aimed to comprehensively understand the potential causal associations between psychiatric disorders and osteoarthritis using Mendelian randomization (MR) analysis.MethodsWe collected data from genome-wide association studies of knee osteoarthritis (KOA) (n = 403,124), hip osteoarthritis (HOA) (n = 393,873), osteoarthritis of the knee or hip (KHOA) (n = 417,596), as well as three psychiatric disorders: bipolar disorder (n = 41,917), major depressive disorder (n = 170,756), and schizophrenia (n = 76,755) among European populations. We applied bidirectional univariate and multivariate MR analyses, including inverse variance weighted, Mendelian randomization-Egger, weighted median, simple mode, and weighted mode. We considered p < .05 as a criterion for identifying potential evidence of association. Bonferroni correction was used for multiple tests.ResultsOur univariate MR analysis results demonstrated that bipolar disorder is a protective factor for KOA (OR = 0.90, 95% CI = 0.83 to 0.97, p = 0.0048) and may also be protective for KHOA (p = 0.02). Conversely, major depression has a positive causal effect on both KOA (OR = 1.27; 95% CI = 1.08 to 1.49; p = 0.0036) and KHOA (OR = 1.24; 95% CI = 1.12 to 1.37; p = 3.62×10-05 ). Furthermore, our analysis suggested that KHOA may be a risk factor for major depression (OR = 1.06; 95% CI = 1.00 to 1.12; p = 0.0469) in reverse MR. After adjusting smoking (OR = 1.46; 95% CI = 1.19 to 1.65; p = 0.0032) and body mass index (OR = 1.44; 95% CI = 1.09 to 1.81; p = 8.56×10-04 ), the casual association between major depression and KHOA remained.ConclusionOur study indicates that major depression is a great risk factor for KHOA, increasing the likelihood of their occurrence. However, further in-depth studies will be required to validate these results and elucidate the underlying molecular mechanisms.

Project description:There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.

Dataset Information

Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.

Publications

Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.

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