Body fat impacts brain morphology, functional connectivity, and white matter microstructure in adult
Poster No:
932
Submission Type:
Abstract Submission
Authors:
Yingji Fu1, Die Zhang1, Anqi Qiu1,2,3
Institutions:
1the Hong Kong Polytechnic University, Hong Kong, China 2National University of Singapore, Singapore, Singapore 3Johns Hopkins University, Baltimore, United States
First Author:
Co-Author(s):
Introduction:
Obesity has profound implications for human health, yet general measures like BMI fail to capture the differential effects of body fat distribution (Harris, 2017). Regional fat accumulation influences health outcomes differently. For example, upper limb fat is linked with higher risks of cognitive decline(Forte et al., 2017) and cardiovascular diseases compared to lower limb fat(Piché et al., 2018). Visceral fat, through inflammatory factors, results in neuroinflammation(Kawai et al., 2021). Based on these findings, we hypothesize that body fat distribution may induce different effects on aging brain. This study aimed to systematically investigate the associations of body fat, including arm fat percentage (AFP), leg fat percentage (LFP), trunk fat percentage (TFP), and VAT mass, with brain morphology, functional connectivity (FC), and white matter microstructure in adults from the UK Biobank.
Methods:
From over 500,000 UK Biobank participants, 23,088 individuals with AFP, TFP, and LFP, and 18,886 with VAT mass, were included in this study. Brain imaging metrics included: (1) cortical thickness, volume, surface area, and subcortical volume from morphological analyses; (2) resting-state FC grouped by seven cortical networks, subcortical (SUB), cerebellar (CER), and brainstem (BST) networks; and (3) white matter microstructure parameters from Neurite Orientation Dispersion and Density Imaging (NODDI), including neurite density index (NDI), isotropic volume fraction (ISOVF), and orientation dispersion index. To account for general obesity, brain imaging metrics were adjusted for BMI, and the residualized imaging markers were used as dependent variables in linear regression models to examine their associations with AFP, TFP, LFP, and VAT mass as independent variables, while adjusting for multiple covariates. Statistical significance was set at p < 0.01 with multiple comparisons corrected using random field theory (RFT) for cortical morphology and white matter analyses, and false discovery rate (FDR) for FC analyses.
Results:
Across fat measurements, consistent abnormalities in subcortical volume and in FC of SUB-CER-BST circuits were observed (Fig. 1, 2a-d), along with associated white matter tract abnormalities, such as the corticospinal tract, cerebellar peduncle, and thalamic radiation (Fig. 2e-h). Additionally, higher fat percentages were associated with atrophy in the medial prefrontal, medial and lateral temporal, sensorimotor, and precuneus cortices, as well as subcortical structures (Fig. 1; PRFT < 0.01). These regions corresponded to decreased FC in the default mode network (DMN), sensorimotor network (SMN), and limbic network (LIM) (Fig. 2a-d; PFDR < 0.01). Furthermore, widespread abnormalities in white matter (Fig. 2e-g; PRFT < 0.01) were closely linked to these regions and networks. Specifically, increased AFP and TFP were associated with reduced cortical thickness in sensorimotor cortices and abnormal NODDI parameters in SMN-related tracts, such as the internal capsule (Fig. 2e-f; PRFT < 0.01). Elevated LFP was linked to decreased FC within the LIM (Fig. 2c; PFDR < 0.01), while higher VAT mass was associated with reduced cortical volume and surface area in DMN-related cortices, including the precuneus and prefrontal cortex (Fig. 1d; PRFT < 0.01). Notably, NODDI analysis revealed that VAT mass was uniquely associated with widespread white matter damage, reflected by decreased NDI and increased ISOVF (Fig. 2h; PRFT < 0.01).
·Figure 1. Associations of body fat distribution with cortical and subcortical morphometry.
·Figure 2. Associations of body fat distribution with functional connectivity, and NODDI metrics.
Conclusions:
We identified the potential common and unique impacts of body fat distribution on the brain. Consistent abnormalities were observed in the SUB-CER-BST circuits, while AFP and TFP were associated with abnormalities in the sensorimotor system, LFP with disruptions in the LIM network, and VAT mass with atrophy in the medial DMN cortices and selective impairments in white matter microstructure. These findings emphasize the importance of targeted interventions to address specific fat distribution in preserving brain health.
Lifespan Development:
Aging 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Keywords:
Aging
Other - obesity; fat distribution; brain health
1|2Indicates the priority used for review
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2. Harris, T. B. (2017). Weight and Body Mass Index in Old Age: Do They Still Matter? Journal of the American Geriatrics Society, 65(9), 1898.
3. Kawai, T., Autieri, M. V., & Scalia, R. (2021). Adipose tissue inflammation and metabolic dysfunction in obesity. American Journal of Physiology-Cell Physiology, 320(3), C375–C391.
4. Piché, M.-E., Poirier, P., Lemieux, I., & Després, J.-P. (2018). Overview of Epidemiology and Contribution of Obesity and Body Fat Distribution to Cardiovascular Disease: An Update. Progress in Cardiovascular Diseases, 61(2), 103–113.