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Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity.

ABSTRACT: Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI) have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to predict a practical clinical score, body mass index (BMI). Connectivity analysis was applied to DTI and rs-fMRI. Significant regions and associated imaging features were identified based on group-wise differences between healthy weight and non-healthy weight subjects. Six DTI-driven connections and 10 rs-fMRI-driven connectivities were identified. DTI-driven connections better reflected group-wise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multi-modal imaging features in a partial least-square regression framework (percent error 15.0%). Our study identified brain regions and imaging features that can adequately explain BMI. We identified potentially good imaging biomarker candidates for obesity-related diseases.

SUBMITTER: Park BY

PROVIDER: S-EPMC4633033 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity.

Park Bo-yong BY Seo Jongbum J Yi Juneho J Park Hyunjin H

PloS one 20151104 11

Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI) have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to pre ...[more]

PMID: 26536135

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Project description:IntroductionAlthough the putamen has a significant role in reward-seeking and motivated behaviors, including eating and food-seeking, minorities' diminished returns (MDRs) suggest that individual-level risk and protective factors have weaker effects for Non-Hispanic Black than Non-Hispanic White individuals. However, limited research is available on the relevance of MDRs in terms of the role of putamen functional connectivity on body mass index (BMI).PurposeBuilding on the MDRs framework and conceptualizing race and socioeconomic status (SES) indicators as social constructs, we explored racial and SES differences in the associations between putamen functional connectivity to the salience network and children's BMI.MethodsFor this cross-sectional study, we used functional magnetic resonance imaging (fMRI) data of 6473 9-10-year-old Non-Hispanic Black and Non-Hispanic White children from the Adolescent Brain Cognitive Development (ABCD) study. The primary independent variable was putamen functional connectivity to the salience network, measured by fMRI. The primary outcome was the children's BMI. Age, sex, neighborhood income, and family structure were the covariates. Race, family structure, parental education, and household income were potential moderators. For data analysis, we used mixed-effect models in the overall sample and by race.ResultsHigher right putamen functional connectivity to the salience network was associated with higher BMI in Non-Hispanic White children. The same association was missing for Non-Hispanic Black children. While there was no overall association in the pooled sample, a significant interaction was found, suggesting that the association between right putamen functional connectivity to the salience network and children's BMI was modified by race. Compared to Non-Hispanic White children, Non-Hispanic Black children showed a weaker association between right putamen functional connectivity to the salience network and BMI. While parental education and household income did not moderate our association of interest, marital status altered the associations between putamen functional connectivity to the salience network and children's BMI. These patterns were observed for right but not left putamen. Other/Mixed Race children also showed a pattern similar to Non-Hispanic Black children.ConclusionsThe association between right putamen functional connectivity to the salience network and children's BMI may depend on race and marital status but not parental education and household income. While right putamen functional connectivity to the salience network is associated with Non-Hispanic White children's BMI, Non-Hispanic Black children' BMI remains high regardless of their putamen functional connectivity to the salience network. This finding is in line with MDRs, which attributes diminished effects of individual-risk and protective factors for Non-Hispanic Black children to racism, stratification, and segregation.

Project description:Highlights • Women with patellofemoral pain have higher body mass index and body fat and lower skeletal muscle mass compared to pain-free women.• Men with patellofemoral pain and both sexes combined have no difference in body mass index, body fat, or skeletal muscle mass compared to their respective pain-free control groups.• Body mass index and levels of body fat and skeletal muscle mass should be considered when assessing women with patellofemoral pain.• Future studies should not assess body composition measures in a mixed-sex population without discriminating between men and women. Background We compared body mass index (BMI), body fat, and skeletal muscle mass between (1) a mixed-sex nonathletic cohort of people with patellofemoral pain (PFP) and pain-free people, and (2) a nonathletic cohort of people with PFP and pain-free people subgrouped by sex (i.e., men and women with PFP vs. pain-free men and women). Methods This cross-sectional study included 114 people with PFP (71 women, 43 men) and 54 pain-free controls (32 women, 22 men). All participants attended a single testing session to assess body composition measures, which included BMI, percentage of body fat (%BFBioimpedance), and skeletal muscle mass (both assessed by bioelectrical impedance analysis), and percentage of body fat (%BFSkinfold) (assessed by skinfold caliper analysis). A one-way univariate analysis of covariance (age and physical activity levels as covariates) was used to compare body composition measures between groups (i.e., PFP vs. pain-free group; women with PFP vs. pain-free women; men with PFP vs. pain-free men). Results Women with PFP presented significantly higher BMI, %BFBioimpedance, and %BFSkinfold, and lower skeletal muscle mass compared to pain-free women (p ≤ 0.04; effect size : ‒0.47 to 0.85). Men with PFP and men and women combined had no differences in BMI, %BFBioimpedance, %BFSkinfold, and skeletal muscle mass compared to their respective pain-free groups (p > 0.05). Conclusion Our findings indicate that BMI and body composition measures should be considered as part of the evaluation and management of people with PFP, especially in women, who have demonstrated higher BMI and body fat and lower skeletal muscle mass compared to pain-free controls. Future studies should not assess body composition measures in a mixed-sex population without distinguishing men participants from women participants. Graphical abstract Image, graphical abstract

Project description:BackgroundObesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD.MethodsLinear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity.ResultsIn the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P < 0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P < 0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results.ConclusionsA GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.

Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.

Dataset Information

Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity.

Publications

Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity.

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