Dataset Information

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

ABSTRACT: Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

SUBMITTER: McWhinney SR

PROVIDER: S-EPMC8760047 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

McWhinney Sean R SR Abé Christoph C Alda Martin M Benedetti Francesco F Bøen Erlend E Del Mar Bonnin Caterina C Borgers Tiana T Brosch Katharina K Canales-Rodríguez Erick J EJ Cannon Dara M DM Dannlowski Udo U Díaz-Zuluaga Ana M AM Elvsåshagen Torbjørn T Eyler Lisa T LT Fullerton Janice M JM Goikolea Jose M JM Goltermann Janik J Grotegerd Dominik D Haarman Bartholomeus C M BCM Hahn Tim T Howells Fleur M FM Ingvar Martin M Kircher Tilo T J TTJ Krug Axel A Kuplicki Rayus T RT Landén Mikael M Lemke Hannah H Liberg Benny B Lopez-Jaramillo Carlos C Malt Ulrik F UF Martyn Fiona M FM Mazza Elena E McDonald Colm C McPhilemy Genevieve G Meier Sandra S Meinert Susanne S Meller Tina T Melloni Elisa M T EMT Mitchell Philip B PB Nabulsi Leila L Nenadic Igor I Opel Nils N Ophoff Roel A RA Overs Bronwyn J BJ Pfarr Julia-Katharina JK Pineda-Zapata Julian A JA Pomarol-Clotet Edith E Raduà Joaquim J Repple Jonathan J Richter Maike M Ringwald Kai G KG Roberts Gloria G Salvador Raymond R Savitz Jonathan J Schmitt Simon S Schofield Peter R PR Sim Kang K Stein Dan J DJ Stein Frederike F Temmingh Henk S HS Thiel Katharina K van Haren Neeltje E M NEM Gestel Holly Van HV Vargas Cristian C Vieta Eduard E Vreeker Annabel A Waltemate Lena L Yatham Lakshmi N LN Ching Christopher R K CRK Andreassen Ole O Thompson Paul M PM Hajek Tomas T

Molecular psychiatry 20210416 11

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tes ...[more]

PMID: 33863996

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Project description:ImportanceNeurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are increasingly common. Individuals with NDDs have heightened obesity risks, but long-term data on body mass index (BMI) trends over time in this population are lacking.ObjectiveTo assess secular BMI changes from 2004 to 2020 among children with NDDs compared with those without NDDs.Design, setting, and participantsThis repeated cross-sectional study used data from the Child and Adolescent Twin Study in Sweden. Children born between January 1, 1992, and December 31, 2010, were screened for neurodevelopmental symptoms using the Autism-Tics, ADHD, and Other Comorbidities inventory between July 2004 and April 2020 when they were 9 or 12 years of age. Data analysis was conducted between September 27, 2023, and January 30, 2024.Main outcomes and measuresBMI percentiles (15th, 50th, and 85th) were modeled using quantile regression and compared between youths with and without NDDs. Secular changes in BMI percentiles over time spanning 2004 to 2020 were evaluated and stratified by NDD subtype.ResultsThe cohort included 24 969 Swedish twins (12 681 [51%] boys) born between 1992 and 2010, with mean (SD) age of 9 (0.6) years. Of these, 1103 (4%) screened positive for 1 or more NDDs, including ADHD, ASD, and/or learning disability. Results indicated that at the 85th BMI percentile, there was a greater increase in BMI from 2004 to 2020 among youths with NDDs compared with those without NDDs (β for interaction [βint] between NDD status and time, 1.67; 95% CI, 0.39-2.90). The greatest divergence was seen for ASD (βint, 2.12; 95% CI, 1.26-3.70) and learning disability (βint, 1.92; 95% CI, 0.65-3.82). Within the latest cohort (2016-2020), the 85th BMI percentile was 1.99 (95% CI, 1.08-2.89) points higher among children with NDDs compared with those without NDDs.Conclusions and relevanceIn this repeated cross-sectional study, at the higher end of the BMI distribution, children with NDDs had significantly greater increases in BMI compared with peers without NDDs over a 16-year period, highlighting an increasing risk of overweight over time in youths with NDDs compared with those without NDDs. Targeted obesity prevention efforts for this high-risk population are needed.

Project description:BackgroundPatients with schizophrenia and bipolar disorder have an overlapping polygenic architecture and clinical similarities, although the 2 disorders are distinct diagnoses with clinical dissimilarities. It remains unclear whether there are specific differences in subcortical volumes between schizophrenia and bipolar disorder, and whether the subcortical differences are affected by any clinical characteristics. We investigated differences in subcortical volumes bilaterally among patients with schizophrenia, patients with bipolar disorder and healthy controls. We also investigated the influences of clinical characteristics on specific subcortical volumes in these patient groups.MethodsWe collected 3 T T 1-weighted MRI brain scans from 413 participants (157 with schizophrenia, 51 with bipolar disorder and 205 controls) with a single scanner at a single institute. We used FreeSurfer version 6.0 for processing the T 1-weighted images to segment the following subcortical brain volumes: thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala and nucleus accumbens. Differences in the 7 subcortical volumes were investigated among the groups. We also evaluated correlations between subcortical volumes and clinical variables in these patient groups.ResultsOf 7 subcortical regions, patients with schizophrenia had significantly smaller volumes in the left thalamus (Cohen d = -0.29, p = 5.83 × 10-3), bilateral hippocampi (left, d = -0.36, p = 8.85 × 10-4; right, d = -0.41, p = 1.15 × 10-4) and left amygdala (d = -0.31, p = 4.02 × 10-3) than controls. Compared with controls, patients with bipolar disorder had bilateral reductions only in the hippocampal volumes (left, d = -0.52, p = 1.12 × 10-3; right, d = -0.58, p = 0.30 × 10-4). We also found that patients with schizophrenia had significantly smaller volumes in the bilateral amygdalae (left, d = -0.43, p = 4.22 × 10-3; right, d = -0.45, p = 4.56 × 10-3) than patients with bipolar disorder. We did not find any significant volumetric differences in the other 6 subcortical structures between patient groups (p > 0.05). Smaller left amygdalar volumes were significantly correlated with younger onset age only in patients with schizophrenia (r = 0.22, p = 5.78 × 10-3).LimitationsWe did not evaluate the differences in subcortical volumes between patients stratified based on clinical bipolar disorder subtype and a history of psychotic episodes because our sample size of patients with bipolar disorder was limited.ConclusionOur findings suggest that volumetric differences in the amygdala between patients with schizophrenia and those with bipolar disorder may be a putative biomarker for distinguishing 2 clinically similar diagnoses.

Project description:Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Dataset Information

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

Publications

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

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