White Matter Integrity Abnormalities in Healthy Overweight Individuals Revealed by Whole Brain Meta-Analysis of Diffusion Tensor Imaging Studies

Cheng, Xiaodong; Wang, Wenchang; Sun, Chen; Sun, Yana; Zhou, Cong

doi:https://doi.org/10.1155/2023/7966540

Journal of Obesity

On this page

Abstract Introduction Methods Results Discussion Conclusion Data Availability Ethical Approval Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Review Article | Open Access

Volume 2023 | Article ID 7966540 | https://doi.org/10.1155/2023/7966540

White Matter Integrity Abnormalities in Healthy Overweight Individuals Revealed by Whole Brain Meta-Analysis of Diffusion Tensor Imaging Studies

Xiaodong Cheng,¹Wenchang Wang,²Chen Sun,²Yana Sun,³and Cong Zhou^2,4

Academic Editor: Aron Weller

Received10 Jul 2023

Revised24 Sept 2023

Accepted03 Oct 2023

Published23 Oct 2023

Abstract

Objective. This study aimed to conduct a coordinate-based meta-analysis (CBMA) to investigate white matter (WM) abnormalities in healthy individuals with overweight or obesity. Methods. A systematic literature search using Web of Science and PubMed datasets was performed. Original investigations that used diffusion tensor imaging (DTI) to explore fractional anisotropy (FA) differences between healthy overweight/obese individuals and normal weight controls were collected. The meta-analysis was conducted using the seed-based d mapping (SDM) software, employing stringent thresholds for significance. Sensitivity analyses and meta-regression analysis were also performed to examine the robustness of the results and explore potential associations with age and body mass index (BMI). Results. The analysis included five studies comprising 232 overweight/obese individuals and 219 healthy normal weight controls. The findings showed that overweight/obese individuals exhibited reduced fractional anisotropy (FA) in specific regions, namely, the right superior longitudinal fasciculus (SLF), the splenium of the corpus callosum (CC), and the right median network, cingulum. Meta-regression analysis further revealed that these FA reductions were associated with age. Conclusion. These findings provided insights into the potential impact of overweight/obesity on cognition, emotion, and neural functions and highlighted the significance of early prevention and intervention for overweight on the basis of neuroimaging.

1. Introduction

Overweight as well as obesity is closely associated with 21 diseases, including cardiovascular, metabolic, digestive, respiratory, neurological, musculoskeletal, and infectious diseases [1, 2]. Moreover, there is a dose-response relationship between the degree of obesity and the risk of developing complex comorbidities. The higher the degree of obesity, the greater the risk of developing complex comorbidities [1]. Specially, obesity is related with increased risk of accelerated cognitive decline and emotional distress [3, 4], suggesting neurobiological changes.

One recent meta-analysis of structural MRI in obesity exhibited abnormalities in gray matter volume (GMV) in regional brain regions such as the frontal lobe, temporal lobe, precentral gyrus, and occipital lobe [5]. The study by Caunca and colleagues indicated that greater BMI and waist circumference were significantly associated with thinner cortices, particularly in individuals aged less than 65 years. This suggested that obesity might also be linked to alterations in cortical thickness, which is another key aspect of brain structure [6]. Diffusion tensor imaging (DTI) is a well-established MRI technique that has been extensively used to explore white matter (WM) properties at a microstructural level [7]. Previous neuroimaging evidence indicated that elevated body mass index (BMI) is also associated with reduced white matter integrity [8, 9]. Some portion of the corpus callosum (CC) and other vital neural tracts, such as the cingulate bundle, the superior longitudinal fasciculus (SLF), the inferior longitudinal fasciculus (ILF), and the uncinate fasciculus (UF), are particularly susceptible to the effects of body weight [10–12]. This could potentially contribute to impairments in reward-related behavior, cognitive and inhibitory control, and memory and decision-making processes among individuals who are overweight or obese. One systematic review [11] examined 31 cross-sectional studies comparing individuals with obesity to normal weight controls. The majority of these studies reported decreased fractional anisotropy (FA) and increased mean diffusivity (MD) in WM, suggesting altered properties. However, the pattern of alterations varied across studies. The inconsistency in findings may be attributed to the substantial heterogeneity among the cohorts included in the study.

Meta-analysis is a valuable technique that allows researchers to consolidate and analyze data from multiple studies, providing a more robust and applicable estimate of the effect size. By combining data from various sources, meta-analysis increases the sample size and statistical power, making it easier to detect even small effects and reducing the risk of erroneous findings, such as false positives or false negatives [13]. Coordinate-based meta-analysis (CBMA) has gained prominence as a method to address the discrepancies observed in various studies exploring neuroimaging results. One recent CBMA aimed to identify consistent white matter alterations in obesity [10]. The researchers performed a meta-analysis using FA datasets and found a reduction in FA in the genu of the CC in individuals with obesity. This finding was further validated using an independent sample, supporting the association between obesity and reduced WM integrity in this specific brain region involved in executive function. The study highlighted the need for further research on the mechanisms linking obesity and white matter integrity loss. A lately published CBMA [12] found reduced FA in the genu and splenium of the CC, middle cerebellar peduncles, anterior thalamic radiation, corticospinal projections, and cerebellum. The region of interest meta-analysis replicated the associations between obesity and lower FA in the genu and splenium of the CC and middle cerebellar peduncles. The effect size of obesity-related brain changes was small to medium. The study suggested that obesity-related WM alterations were localized rather than diffuse, and understanding these brain correlates may help identify risk factors and targets for prevention or treatment.

The aforementioned meta-analysis considered obese cohorts with comorbidities such as type 2 diabetes (T2DM), but there is a lack of research specifically examining WM characteristics in healthy individuals who are overweight or obese. Herein, we intended to conduct a CBMA solely on healthy overweight or obese individuals compared with healthy individuals with a normal weight. Building on previous findings from published articles, our hypothesis was that the overweight/obese group would exhibit alterations in WM microstructures, particularly in central nerve tracts such as the SLF and the CC.

2. Methods

2.1. Literature Search Strategy

We registered our CBMA protocol with PROSPERO (https://www.crd.york.ac.uk/PROSPERO) under registration number CRD42022303123 and adhered to PRISMA guidelines [14–16] in conducting this meta-analysis. We conducted a systematic search of Web of Science and PubMed datasets for peer-reviewed articles published until February 28, 2023, using advanced search functions and the following keywords: (“obesity” or “obese” or “overweight” or “body mass index” or “waist circumference” or “waist-to-hip ratio”) and ((“diffusion tensor imaging” or “DTI” or “diffusion magnetic resonance imaging”) or (“fractional anisotropy”) or (“tract‐based spatial statistics” or “TBSS”)).

2.2. Study Selection

We included only English-language original investigations published in peer-reviewed journals that used the whole brain approach to explore FA differences between healthy overweight/obese individuals and healthy normal weight controls, reporting their results in Montreal Neurological Institute (MNI) or Talairach coordinates and using a threshold for significance. We excluded case reports, reviews, meta-analyses, and tractography-based only studies, as well as studies with no direct between-group comparison or from which peak coordinates or parametric maps could not be obtained.

2.3. Quality Assessment and Data Extraction

Following the guidelines for neuroimaging meta-analyses [17], two authors (XC and WW) independently searched the literature, assessed the quality of the retrieved articles, and extracted and cross-checked data from eligible articles. Both authors also separately checked the quality of the final studies. For each study, we recorded the first author, cohort size, demographics (gender and age), BMI, and data for SDM calculations, including the coordinates of main findings and values related to effect size (Z-statistics, t-statistics, and value).

2.4. SDM Meta-Analysis

The SDM software (https://www.sdmproject.com) [18, 19] was utilized to conduct a comprehensive meta-analysis of regional FA differences between individuals classified as healthy overweight/obese and those of normal weight. The standardized SDM tutorial and previous meta-analysis studies were referred during the analysis. In brief, the SDM technique employs effect sizes in conjunction with peak coordinates extracted from databases of statistical parametric maps to generate maps depicting the original effect size of FA differences between healthy overweight/obese individuals and healthy normal weight controls [20]. We applied more stringent thresholds including , a peak height threshold of Z = 1.00, and a cluster size threshold of 10 voxels.

2.5. Jackknife Sensitivity Analyses

To examine the robustness of our findings, we performed a jackknife sensitivity analysis. This approach entailed repeating the primary analysis for a total of n times (where n corresponds to the number of datasets included) and systematically excluding one study at a time. We considered a brain region highly reliable if it remained significant after performing jackknife sensitivity in all or most of the combinations of studies [18].

2.6. Meta-Regression Analysis

To minimize the detection of potential correlations between demographics and neuroimaging, a more stringent threshold of was employed. This threshold was consistent with previous meta-analyses and the recommendations of the SDM creators [18]. We explored the associations between FA alterations and mean age and BMI. The aforementioned procedures were only implemented in brain regions identified in the main analysis.

3. Results

3.1. Characteristics of the Included Studies

A total of 366 articles were initially identified. Based on the inclusion and exclusion criteria, a total of 5 articles were finally included [21–25]. The final sample comprised 232 overweight individuals and 219 healthy controls. The basic characteristics of the included studies are shown in Table 1, and the study selection processes are presented in Figure 1.

3.2. Regional FA Differences in Overweight/Obese Individuals

Overweight/obese individuals exhibited FA reductions in the right superior longitudinal fasciculus (SLF), the splenium of the corpus callosum (CC), and the right median network, cingulum (Figure 2 and Table 2). There were no observed increases in FA. The whole brain jackknife approach confirmed the reliability of our findings as the decreased FA values persisted across all combinations (Table 2).

3.3. Meta-Regression Analysis

Our meta-regression analysis indicated negative correlations between age and FA in all above three clusters, with statistical significance defined by the strict threshold of (Table 3). That is, with age increasing, the FA value of these regions would get lower.

4. Discussion

Our study aimed to investigate WM abnormalities in healthy individuals with overweight/obesity through conducting a CBMA. The findings revealed that overweight/obese individuals exhibited reduced FA in specific regions, including the right SLF, the splenium of the CC, and the right median network, cingulum. Importantly, meta-regression analysis further revealed that these FA reductions showed a significant association with age, suggesting age-related changes in these WM regions among overweight/obese individuals. The findings provided insights into the potential impact of overweight on cognition, emotion, and neural system functions. Additionally, this study can contribute to supplementing more specific evidence to drive the development of prevention and intervention strategies for overweight/obesity-related brain structural changes and might serve as an early warning sign for cognitive disorders associated with obesity and provide guidance for relevant treatments and rehabilitation.

Consistent with previous studies [10, 12], our results indicated that overweight/obesity is associated with decreased FA in vital neural bundles. The SLF plays a crucial role in connecting the frontal and parietal lobes, which are involved in executive functions such as attention and working memory [26]. The CC is responsible for interhemispheric communication, and alterations in its microstructure have been linked to cognitive deficits and emotional distress [10]. The cingulum connects various brain regions involved in emotion regulation and has been implicated in mood disorders [27]. Therefore, the observed FA reductions in these regions might be related to cognitive and emotional impairments commonly observed in overweight/obese individuals. Besides, the aberrant WM microstructure within these brain regions might be responsible for individuals’ abnormal eating behavior and could also be a consequence of overweight. Our meta-regression analysis further revealed a negative correlation between age and FA in the identified brain regions. This was consistent with previous research suggesting that older age was associated with decreased FA in various brain regions [28] and also in accord with the structural MRI findings that brain microstructures of obese individuals might further deteriorate with aging [6]. The relationship between age and FA in overweight/obese individuals may be a result of the cumulative effects of obesity-related factors such as chronic inflammation and oxidative stress, which accelerate brain aging processes [29]. However, more research is needed to elucidate the underlying mechanisms and explore potential interactions between obesity, age, and brain white matter integrity.

Limitations of our study include the relatively small number of included studies and heterogeneity in participant characteristics, such as differences in age, sex, and BMI. Future studies should address these limitations by including larger sample sizes and controlling for potential confounding factors. Additionally, longitudinal studies are needed to determine the temporal relationship between obesity and alterations in brain WM microstructure. Furthermore, additional DTI parameters such as MD, axial diffusivity (AD), and radial diffusivity (RD) would offer valuable insights into characterizing microstructural WM alterations in healthy overweight individuals. Regrettably, we encountered limitations in conducting meta-analyses involving these DTI metrics due to the inadequacy of available datasets. Lastly, we only utilized peak coordinates and effect sizes from published studies, rather than the original statistical brain maps, which might result in partial loss of original information.

5. Conclusion

In conclusion, our study found that healthy overweight/obese individuals exhibited age-related decreased FA in the right SLF, the splenium of the CC, and the right median network, cingulum. These findings provide valuable insights into the potential impact of overweight/obesity on cognition, emotion, and neural system functions. The study also highlights the importance of developing prevention and intervention strategies for brain structural changes associated with overweight/obesity and suggests that these changes may serve as early warning signs for cognitive disorders in this population.

Data Availability

The dataset analyzed during the current study is available from the corresponding author on reasonable request.

Ethical Approval

This article is a meta-analysis with all analyses based on previously published studies, and thus no ethical approval and patient consent are required.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

CZ designed the study and revised the manuscript. XC and WW collected the data, undertook the statistical analysis, and wrote the initial manuscript. CS and YS contributed to data collection and interpretation, conducted statistical analysis, and modified the paper. All authors have reviewed and approved the final manuscript.

Acknowledgments

This study was supported by the Medical and Health Science and Technology Development Plan of Shandong Province (202003061210), the Key Research and Development Plan of Jining City (2021YXNS024), the Cultivation Plan of High-Level Scientific Research Projects of Jining Medical University (JYGC2021KJ006), and the Supporting Fund for Teachers’ Research of Jining Medical University (600903001).

References

M. Kivimaki, T. Strandberg, J. Pentti et al., “Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study,” Lancet Diabetes & Endocrinology, vol. 10, no. 4, pp. 253–263, 2022.
View at: Publisher Site | Google Scholar
The Gbd 2015 Obesity Collaborators, “Health effects of overweight and obesity in 195 countries over 25 years,” New England Journal of Medicine, vol. 377, no. 1, pp. 13–27, 2017.
View at: Publisher Site | Google Scholar
I. A. Dekkers, P. R. Jansen, and H. J. Lamb, “Obesity, brain volume, and white matter microstructure at MRI: a cross-sectional UK biobank study,” Radiology, vol. 291, no. 3, pp. 763–771, 2019.
View at: Publisher Site | Google Scholar
P. V. Aguiar, W. Á D. S. Dionisio, E. Souza et al., “Binge eating, depressive symptoms and suicidal ideation in obese candidates for bariatric surgery,” Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity, vol. 28, no. 1, p. 12, 2023.
View at: Publisher Site | Google Scholar
M. J. Herrmann, A. K. Tesar, J. Beier, M. Berg, and B. Warrings, “Grey matter alterations in obesity: a meta-analysis of whole-brain studies,” Obesity Reviews, vol. 20, no. 3, pp. 464–471, 2019.
View at: Publisher Site | Google Scholar
M. R. Caunca, H. Gardener, M. Simonetto et al., “Measures of obesity are associated with MRI markers of brain aging: the Northern Manhattan Study,” Neurology, vol. 93, no. 8, pp. e791–e803, 2019.
View at: Publisher Site | Google Scholar
D. Le Bihan, J. F. Mangin, C. Poupon et al., “Diffusion tensor imaging: concepts and applications,” Journal of Magnetic Resonance Imaging, vol. 13, no. 4, pp. 534–546, 2001.
View at: Publisher Site | Google Scholar
K. M. Stanek, S. M. Grieve, A. M. Brickman et al., “Obesity is associated with reduced white matter integrity in otherwise healthy adults,” Obesity, vol. 19, no. 3, pp. 500–504, 2011.
View at: Publisher Site | Google Scholar
J. Repple, N. Opel, S. Meinert et al., “Elevated body-mass index is associated with reduced white matter integrity in two large independent cohorts,” Psychoneuroendocrinology, vol. 91, pp. 179–185, 2018.
View at: Publisher Site | Google Scholar
J. Daoust, J. Schaffer, Y. Zeighami, A. Dagher, I. Garcia-Garcia, and A. Michaud, “White matter integrity differences in obesity: a meta-analysis of diffusion tensor imaging studies,” Neuroscience & Biobehavioral Reviews, vol. 129, pp. 133–141, 2021.
View at: Publisher Site | Google Scholar
L. Okudzhava, M. Heldmann, and T. F. Munte, “A systematic review of diffusion tensor imaging studies in obesity,” Obesity Reviews: An Official Journal of the International Association for the Study of Obesity, vol. 23, no. 3, Article ID 13388, 2022.
View at: Publisher Site | Google Scholar
L. M. F. Dietze, S. R. McWhinney, J. Radua, and T. Hajek, “Extended and replicated white matter changes in obesity: voxel-based and region of interest meta-analyses of diffusion tensor imaging studies,” Frontiers in Nutrition, vol. 10, Article ID 1108360, 2023.
View at: Publisher Site | Google Scholar
M. W. Cheung and R. Vijayakumar, “A guide to conducting a meta-analysis,” Neuropsychology Review, vol. 26, no. 2, pp. 121–128, 2016.
View at: Publisher Site | Google Scholar
D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, and The Prisma Group, “Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement,” Journal of Clinical Epidemiology, vol. 62, no. 10, pp. 1006–1012, 2009.
View at: Publisher Site | Google Scholar
A. Liberati, D. G. Altman, J. Tetzlaff et al., “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration,” British Medical Journal, vol. 339, no. 1, p. 2700, 2009.
View at: Publisher Site | Google Scholar
D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, and The Prisma Group, “Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement,” PLoS Medicine, vol. 6, no. 7, Article ID 1000097, 2009.
View at: Publisher Site | Google Scholar
V. I. Muller, E. C. Cieslik, A. R. Laird et al., “Ten simple rules for neuroimaging meta-analysis,” Neuroscience & Biobehavioral Reviews, vol. 84, pp. 151–161, 2018.
View at: Publisher Site | Google Scholar
J. Radua and D. Mataix-Cols, “Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder,” British Journal of Psychiatry, vol. 195, no. 5, pp. 393–402, 2009.
View at: Publisher Site | Google Scholar
A. Albajes-Eizagirre, A. Solanes, E. Vieta, and J. Radua, “Voxel-based meta-analysis via permutation of subject images (PSI): theory and implementation for SDM,” NeuroImage, vol. 186, pp. 174–184, 2019.
View at: Publisher Site | Google Scholar
J. Radua, D. Mataix-Cols, M. L. Phillips et al., “A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps,” European Psychiatry, vol. 27, no. 8, pp. 605–611, 2012.
View at: Publisher Site | Google Scholar
M. E. Shott, M. A. Cornier, V. A. Mittal et al., “Orbitofrontal cortex volume and brain reward response in obesity,” International Journal of Obesity, vol. 39, no. 2, pp. 214–221, 2015.
View at: Publisher Site | Google Scholar
I. Papageorgiou, L. G. Astrakas, V. Xydis et al., “Abnormalities of brain neural circuits related to obesity: a Diffusion Tensor Imaging study,” Magnetic Resonance Imaging, vol. 37, pp. 116–121, 2017.
View at: Publisher Site | Google Scholar
J. Ottino-Gonzalez, M. A. Jurado, I. Garcia-Garcia et al., “Allostatic load and disordered white matter microstructure in overweight adults,” Scientific Reports, vol. 8, no. 1, Article ID 15898, 2018.
View at: Publisher Site | Google Scholar
A. Samara, T. Murphy, J. Strain et al., “Neuroinflammation and white matter alterations in obesity assessed by diffusion basis spectrum imaging,” Frontiers in Human Neuroscience, vol. 13, p. 464, 2019.
View at: Publisher Site | Google Scholar
N. M. Estella, L. G. Sanches, M. F. Maranhao et al., “Brain white matter microstructure in obese women with binge eating disorder,” European Eating Disorders Review, vol. 28, no. 5, pp. 525–535, 2020.
View at: Publisher Site | Google Scholar
F. Janelle, C. Iorio-Morin, S. D’Amour, and D. Fortin, “Superior longitudinal fasciculus: a review of the anatomical descriptions with functional correlates,” Frontiers in Neurology, vol. 13, Article ID 794618, 2022.
View at: Publisher Site | Google Scholar
E. J. Bubb, C. Metzler-Baddeley, and J. P. Aggleton, “The cingulum bundle: anatomy, function, and dysfunction,” Neuroscience & Biobehavioral Reviews, vol. 92, pp. 104–127, 2018.
View at: Publisher Site | Google Scholar
C. Lebel and C. Beaulieu, “Longitudinal development of human brain wiring continues from childhood into adulthood,” Journal of Neuroscience, vol. 31, no. 30, pp. 10937–10947, 2011.
View at: Publisher Site | Google Scholar
J. Gunstad, R. H. Paul, R. A. Cohen, D. F. Tate, and E. Gordon, “Obesity is associated with memory deficits in young and middle-aged adults,” Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity, vol. 11, no. 1, pp. e15–e19, 2006.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2023 Xiaodong Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

219

Downloads

354

Citations