Edited by: Jianhua Chen, Shanghai Jiao Tong University, China
Reviewed by: Wenbin Guo, Second Xiangya Hospital, Central South University, China; Luca Piretti, University Hospital of Padua, Italy; Zhongchun Liu, Wuhan University, China
†These authors have contributed equally to this work and share first authorship
This article was submitted to Addictive Disorders, a section of the journal Frontiers in Psychiatry
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Obesity is a multi-systemic disease with complex etiology. And consistent evidence indicated obesity or overweight subjects render brain structure changes. Increasing evidence indicates these subjects have shown widespread structural brain gray matter volume (GMV) changes. However, results from other neuroimaging studies have been inconsistent. Consequently, the question remains whether body mass index (BMI), a gold standard to define obesity/overweight, is associated with brain structural changes.
This study will apply an updated meta-analysis of voxel-based GMV studies to compare GMV changes in overweight and obese subjects. Online databases were used to build on relevant studies published before May 2022. The updated Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) explores GMV changes in individuals with overweight and obesity and further examines the correlation between GMV and obesity-related variables, specifically body mass index (BMI).
This research included fourteen studies and provided a whole-brain analysis of GMV distribution in overweight and obese individuals. It revealed lower GMV in brain regions, including the left putamen and right precentral gyrus, in individuals with overweight and obesity compared to lean controls. Further, meta-regression analyses revealed GMV in the left middle occipital gyrus was negatively correlated with the BMI of the whole sample.
GMV decreased was reported in reward circuit processing areas and sensorimotor processing areas of individuals with overweight and obesity diagnoses, suggesting an underlying structural basis for reward processing and sensorimotor processing dysregulation in overweight and obese subjects. Our results also suggest that GMV in occipital gyrus, a key region for food visual and gustatory encoding, is negatively associated with BMI. These results provide further evidence for the dysregulated reward circuit in individuals with overweight and obesity.
The prevalence of obesity diagnoses has been rising rapidly, as an estimated of 650 million people worldwide are currently identified as medically overweight. Kowning that obesity is highly correlated with other issues, including an increased risk of type 2 diabetes, respiratory problems, cardiovascular disease, mood-related disorders, and negative effects on one’s quality of life, it is considered one of the leading factors of death (
Compensatory overeating might be one of the leading causes to obesity (maybe the most frequent). To understand the brain structural changes of compensatory overeating and BMI increase, neuroimaging has been used to compare subjects with obesity and healthy controls (HCs) in structural magnetic resonance imaging (MRI) analyses (
Consequently, different from the Anisotropic Effect-Size Seed-Based d Mapping (AES-SDM) (
In this meta-analysis, we will use the voxel-based meta-analysis via the novel algorithm (e.g., PSI) to identify morphometric changes in overweight and obese subjects compared with lean subjects. Secondly, we will analyze the correlation between GMV difference and BMI among the sample pool. With regard to changes in brain structure for individuals with overweight and obesity, we include voxel-based statistical analysis to compare local differences in GMV after spatial normalization is taken into account (
Systematic and comprehensive searches of the PubMed
The inclusion criteria are as follows: (1) the study included subjects with obesity vs. lean controls; (2) using VBM to analyze whole-brain GMV differences of subjects with overweight and obesity; (3) the results reported statistical parametric maps and peak coordinates of the GMV alterations which were normalized into the Montreal Neurological Institute (MNI) or Talairach space (TL); (4) peer-reviewed studies; (5) all subjects provided informed consent; (6) participants aged ≥ 18. In addition, the authors of published studies were contacted by email when necessary information was not provided in the studies.
The exclusion criteria are as follows: (1) studies deal with seed voxel–based analysis, region-of interest (ROI), white matter changes or cortical thickness evaluations only rather than MRI whole-brain VBM; (2) review articles, theoretical papers, meta-analysis or animal experimental studies; (3) without lean controls; (4) participants aged<18; (5) when t- or z-maps were unavailable, consistent statistical thresholds throughout the brain were not used or peak coordinates were not reported (
Flowchart of the selection of VBM studies in subjects with overweight and obesity for meta-analysis.
To evaluate the quality of perceived studies criteria was applied as follows: (1) lean controls compared with obesity/overweight subjects; (2) method of diagnosis; (3) demographic data; (4) samples size(When studies with sample size < 10, we scored as 0; sample size > 30, we scored as 2; the middle section is marked 1); (5) the use of GM volume covariates; (6) whole brain analysis; (7) MRI machines and smooth kernels (8) standard spatial coordinates (e.g., MNI coordinates or TL). Each criterion was independently estimated by two independent reviewers who scored as 0, 1 or 2 if the criteria were unfulfilled, partially met or fully met, respectively, and any study scoring > 8.0 was included in the meta-analysis. Although not specifically designed as an evaluation tool, this checklist provided an objective and strict indication of each study that included in our analysis (
Regional differences in GMV between patients with overweight and obesity and lean individuals were analyzed with SDM-PSI
The present research included 14 structural MRI studies on overweight and obesity based on the search strategy. In total, there were 361 subjects with overweight and obesity (males = 107; females = 254; mean age range: 15.0 – 70.0 years; BMI rang: 26.20 – 43.10) and 419 controls (male = 188; female = 231; mean age range: 16.1–70.0 years; BMI rang: 20.96–24.0).
Description of the demographic and clinical characteristics in overweight and obesity subjects and lean subjects in the meta-analysis.
Study | Overweight and obesity subjects |
Lean subjects |
Magnetic field | Software | Smooth |
||||||
Sample/ |
Age |
BMI | Hand (left/right) | Sample/ |
Age |
BMI | Hand (left/right) | ||||
Brooks et al. ( |
59/34 | 70 | 33.0(0.3) | NA | 97/52 | 70 | 22.5(0.2) | NA | 1.5 | SPM | 8 |
Haltia et al. ( |
30/18 | 37(12) | 33.0 (4.3) | NA | 16/8 | 37 (21) | 22.2 (1.6) | NA | 1.5 | SPM | 12 |
Honea et al. ( |
72/49 | 38.9 (8.2) | 35.6 (3.6) | NA | 22/18 | 36.8 (10.9) | 21.6 (1.6) | NA | 3.0 | SPM | 10 |
Jauch-Chara et al. ( |
15/0 | 24.7(0.66) | 36.3(1.04) | NA | 15/0 | 24.6(0.69) | 23.2(0.38) | NA | 3.0 | SPM | 8 |
Karlsson et al. ( |
23/18 | 47.30 (8.90) | 43.1(3.74) | NA | 22/15 | 46.45 (9.45) | 24.0(2.28) | NA | 1.5 | SPM | 10 |
Mathar et al. ( |
19/8 | 27.0 | 33.6 | (0/19) | 23/12 | 25.1 | 21.8 | (0/23) | 3.0 | SPM | 8 |
Nouwen et al. ( |
13/12 | 15.0(1.9) | NA | NA | 20/14 | 16.1(1.9) | NA | NA | 3.0 | SPM | 6 |
Pannacciulli et al. ( |
24/13 | 32(8) | 39.4(4.7) | NA | 36/11 | 33(9) | 22.7(2.2) | NA | 1.5 | SPM | 8 |
Schienle et al. ( |
21/21 | 22.90 (2.59) | 28.30 (3.40) | NA | 21/21 | 22.57 (2.69) | 22.34 (1.93) | NA | 3.0 | SPM | 8 |
Shott et al. ( |
18/18 | 28.67 (8.30) | 34.78(4.44) | NA | 24/24 | 27.42 (6.28) | 21.64(1.26) | NA | 3.0 | SPM | 8 |
Smucny et al. ( |
28/14 | 30.29(3.81) | 26.19(2.90) | NA | 25/12 | 31.32(3.45) | 20.96(1.99) | NA | 3.0 | SPM | 8 |
Tuulari et al. ( |
47/42 | 44.9 (9.0) | 42.2(4.0) | NA | 29/23 | 45.9 (11.8) | 23.2(2.8) | NA | 1.5 | SPM | 8 |
Wang et al. ( |
31/7 | 39.58 (1.93) | 34.38 (0.69) | (5/26) | 49/21 | 29.55 (1.41) | 21.87 (0.29) | (7/42) | 3.0 | SPM | 8 |
Zhang et al. ( |
20/0 | 20∼28 | 33.56(3.53) | NA | 20/0 | 20∼28 | 21.48(1.43) | NA | 3.0 | SPM | 8 |
BMI: body mass index; SPM: statistical parametric morphometry.
The pooled SDM-PSI meta-analysis map revealed significant lower GMV in subjects with overweight and obesity in the brain areas of the left putamen and right precentral gyrus (
Meta-analysis results.
Lower gray matter volume in subjects with overweight and obesity compared with controls in the meta-analysis.
Anatomical regions | MNI coordinates x, y, z | SDM-z value | Number of voxels | Jackknife sensitivity | |
obesity < controls | |||||
Left lenticular nucleus, putamen | −28,6,2 | −5.004 | 0.0019 | 182 | 14/14 |
Right precentral gyrus, BA 44 | 52,8,30 | −4.848 | 0.023 | 13 | 13/14 |
MNI = Montreal Neurological Institute; SDM = signed differential mapping; BA = Brodmann area; * The p-value was adjusted via threshold-free cluster enhancement(TFCE) (
The results of the meta-regression analysis revealed that GMV in the left middle occipital gyrus (MNI coordinate: −22, −98, 10; 180 peak voxels; SDM z = - 3.745;
As shown in
This meta-analysis study revealed GM reduction in overweight and obese individuals, using a novel meta method of SDM-PSI based on 14 VBM studies. The results indicated lower GMV in the left putamen and right precentral gyrus in overweight and obese individuals in comparison with lean subjects, which is partly consistent with previously published meta-analysis studies (
This study revealed the reduced GMV in overweight and obese individuals in the putamen and precentral gyrus, in comparison with lean subjects, which aligns with previously published reviews (
In addition to the dopamine reward circuit, the putamen also performs a key role in the highly salient information processing, and is also involved in the origination, generation, and sequencing of motor behaviors (
Moreover, the function of the putamen is suggested to be lateralized (
Additionally, this study reported consistent GMV loss in the right precentral gyrus (PCG). PCG controls motor activity and involves the execution of the elaborative motor activity (
This study identified deficits of GMV in the putamen and PCG, which is consistent with the previous meta-analysis studies (
In the meta-regression analysis section of this study, it was ascertained that GMV in the left middle occipital gyrus was negatively associated with the BMI of the whole sample, indicated that BMI was negatively related to the pooled effect size of the GMV difference between overweight/obese subjects compared to the lean subjects in the left middle occipital gyrus, suggesting that higher BMI was related to greater GMV reduction in the left middle occipital gyrus, which is similar to findings in previous studies (
It is important to highlight several limitations of this study. Firstly, the data is based on collated analysis which has been extracted from published studies, as opposed to the original data, which may result in less accurate results (
In summary, the present research reported the most robust structural reduced of the GMV in the putamen and precentral gyrus in overweight and obese individuals. Moreover, GMV in the left occipital gyrus was negatively associated with the BMI of our samples. Our results are replicated with previously published brain structural findings in overweight and obese subjects and suggest that these patients are accompanied with brain abnormalities.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
LL and HY were responsible for the study concept and design. HY, WW, YM, and M-LL collected the data, analyzed data, and interpreted the results. LL wrote the manuscript. MZ, SL, TL, and QW provided critical revision of the manuscript. All authors read and approved the final manuscript.
This study was financially supported by the NSFC (Grant Nos. 81771446 and 82171499 to QW), National Natural Science Foundation of China (Grant No. 82101598 to HY, Grant No. 82001410 to WW, and Grant No. 82071524 to M-LL), Post-doctor Research Project, West China Hospital, Sichuan University (Grant No. 2021HXBH034 to HY), and Science and Technology Project of Sichuan Province (Grant No. 2021YJ0238 to ML-L and Grant No. 2022YFS0183 to HY).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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