Association Between Body Composition Patterns, Brain Diseases and Injuries and Risk of Epilepsy: a Prospective Cohort Study

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We aimed to prospectively examine the associations between body composition patterns, specific measurements and epilepsy risk, while exploring the mediating role of brain-related injuries. Methods: We constructed a large-scale cohort study within the UK Biobank (UKB), deriving 7 body composition patterns via principal component analysis (PCA) that captured variation in muscle strength, bone density, lean mass and fat distribution. Multivariable Cox proportional hazards models were employed to assess associations between these patterns, individual body measurements and epilepsy risk. We performed stratified analyses by polygenic risk score (PRS) and mediation analyses to evaluate the indirect contributions of brain injuries. Results: Among the 475,960 participants, 3,026 epilepsy cases were identified over an average follow-up of 10.9 years. Patterns of “lean mass”, “muscle strength”, “bone density” and “leg-dominant fat distribution” were associated with reduced risk of epilepsy (hazard ratios [HRs]: 0.68-0.97), whereas “fat-to-lean mass”, “central obesity” and “arm-dominant fat distribution” patterns were associated with increased incidence (HRs: 1.01-1.34). Similar trends were noted for corresponding individual body measurements. These associations were consistent across PRS strata. Falls, stroke, and neurodegenerative diseases mediated 17.9%, 27.2%, and 31.0% of effects for “muscle strength,” “bone density,” and “arm-dominant fat distribution”, respectively. Conclusions: Body composition patterns involving muscle strength, bone density, and fat distribution show robust associations with epilepsy, partly mediated by neurological disorders. Optimizing body composition and preventing neurological insults may help reduce epilepsy risk in middle-aged and older adults. Epidemiology Neurology Epilepsy Body composition Body fat distribution Polygenetic risk score Mediation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Epilepsy is a noncommunicable neurological disorder that affects over 50 million people worldwide 1 , with its incidence exhibiting a bimodal age distribution, peaking in infants and the elderly as well as mounting steadily beyond age 50 2 . Genetic abnormalities have been historically acknowledged as a culprit of generalized epilepsy 3 , while other common risk factors vary by age group. Cerebrovascular and neurodegenerative diseases are prevalent risk factors among older adults, whereas epilepsy associated with traumatic brain injuries, infections and tumors might develop at any age 2 , 4 . Still, approximately 50% of cases report an unknown cause, probably resulting from complex interactions among genetic predispositions, environmental and behavioral factors 4 . Cross-sectional studies have revealed that patients with epilepsy tend to be more sedentary, higher in fat 5 and at increased risk for sarcopenia 6 , osteoporosis and fractures 7 , 8 . However, it is unclear whether these features serve as underlying risk factors preceding diagnosis or emerge as adverse effects of antiepileptic drugs (AEDs) use and lifestyle shifts. Notably, limited studies exploring the association between body mass index (BMI) and epilepsy have yielded inconsistent findings 9 – 11 . Although BMI is easy to measure and widely used, it neither distinguishes between various body components, such as fat, lean mass and skeletal structure, nor captures the fat accumulation in different depots, which may drive diverse health outcomes 12 . Leveraging comprehensive body measurements data from the UK Biobank (UKB), we sought to clarify the associations between predefined patterns of body composition representing muscle strength, bone density, lean mass (LM) and fat mass (FM) storage in different depots, specific body measurements and risk of epilepsy. Additionally, we examined the modification effect of common variants quantified by polygenetic risk score (PRS), and whether relevant brain disorders and accidental injuries, separately or as a group, play a potential mediating role. 2 Methods 2.1 Study design and population The present study was derived from the UKB, a large-scale survey that enrolled over 500,000 residents aged 40–69 years across England, Scotland and Wales. Individual information on sociodemographic, lifestyle, body metrics and genetic factors was collected through touchscreen questionnaires, physical measurements and biological samples at baseline. The UKB study was approved by the North West Multicenter Research Ethical Committee and all participants provided informed consent. For our cohort, we excluded individuals who withdrew consent (n = 1,298), had a documented or self-reported history of epilepsy before recruitment (n = 5,256), and those with missing or extreme body measurements (i.e., exceeding ± 5 standard derivations) or key covariates (n = 19,897), resulting in a study population of 475,960 participants ( S1 Figure ). 2.2 Body Composition Body composition was assessed by trained staff following a standard protocol. Height, weight, waist and hip circumference were obtained manually. Regional (i.e., arm, leg, and trunk) and total FM and LM were evaluated through the Tanita BC-418MA body composition analyzer. Grip strength was measured using the Jamar J00105 hydraulic hand dynamometer. Bone density was conducted at the calcaneus using the Norland McCue Contact Ultrasound Bone Analyzer. These measurements and their derivations, a total of 28 items, were analyzed using principal component analysis (PCA), as described in previous studies 13 , 14 . Seven principal components with eigenvalues > 1.0 were retained, accounting for over 90% of the total variance ( S2 Figure ). In light of the substantial similarity of these patterns between sexes, we applied male-derived loading coefficients to both sexes to ensure consistency and comparability. The resulting components were interpreted and labeled based on dominant loading variables: “fat-to-lean mass” (indicating FM relative to LM), “lean mass,” “central obesity,” “muscle strength,” “bone density,” “arm-dominant fat distribution,” (highlighting FM primarily distributed in the arms) and “leg-dominant fat distribution” (underscoring FM mainly storage in the legs). 2.3 Ascertainment of Epilepsy Epilepsy cases in this study were identified based on self-reported medical conditions during subsequent visits, as well as linked data from primary care records, hospital admissions and death registry records, according to the corresponding International Classification of Diseases codes (ICD) ( S3 Table ). The follow-up duration was calculated from the recruitment date to the date of first diagnosis, death, or December 31, 2019, whichever came first. 2.4 PRS Construction Details of genotyping and imputation for UKB samples have been described elsewhere 15 . We curated 20 single-nucleotide polymorphisms (SNPs) with minor allele frequency > 0.01 and P < 5 × 10 − 8 from the latest multi-ancestry genome-wide association study (GWAS) by International League Against Epilepsy Consortium (ILAE) 16 ( S4 Table ). We extracted published effect estimates (β i ) of each variant and recoded the SNP i as additive risk allele counts (0, 1, 2). The PRS was calculated as the weighted sum of risk alleles using the following formula, where n denotes the number of selected SNPs: $$\:\text{P}\text{R}\text{S}={\sum\:}_{\text{i}}^{\text{n}}{{\beta\:}}_{\text{i}}{\times\:\text{S}\text{N}\text{P}}_{\text{i}}$$ 2.5 Covariates and Mediators The covariates included age at recruitment, sex, ethnicity, educational level (college/university degree or others), Townsend deprivation index (TDI), smoking status (never, former or current smoker), average weekly alcohol intake (none, low-risk or high-risk drinking), physical activity (low, moderate or high level) and comorbidities (i.e., hypertension, diabetes, disorders of lipoprotein metabolism and other lipidaemia) ( S5 Table ). In this study, we considered the occurrence of stroke, neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease and Huntington's disease), inflammatory encephalopathies (i.e., meningitis, encephalitis, myelitis and encephalomyelitis), and hospitalizations attributable to falls or traffic accidents after enrollment and within five years before epilepsy diagnosis as potential mediators of the studied associations. This was based on the premise that most cases of secondary epilepsy typically emerge within a few years after the primary condition 17 – 19 . 2.6 Statistical analyses Baseline demographic characteristics were presented according to the ultimate epilepsy diagnosis. Categorical variables were exhibited as counts and proportions, whereas continuous variables were summarized as means and SD. We conducted sex-stratified analyses to examine the associations between identified body composition patterns, individual components and epilepsy risk with Cox proportional hazard models. Exposures were initially modeled as continuous variables using restricted cubic splines with knots at the 5th, 35th, 65th and 95th percentiles to flexibly characterize the shape of associations. Nonlinearity was assessed via likelihood ratio tests comparing models with linear terms only versus models including both linear and cubic terms. In light of the observed nonlinearity in most associations, we further categorized exposures into sex-specific tertiles for phenotypic associations. Cox models with attained age as the time scale were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for epilepsy incidence per 1 standard deviation (SD) increase in continuous variables, and by tertile for categorical variables (referenced to the lowest tertile). All models were adjusted for sex, ethnicity, age at recruitment, TDI, smoking status, average weekly alcohol intake, physical activity and comorbidities. To disentangle whether associations varied by genetic susceptibility to epilepsy, we constructed PRS based on common variants and performed stratified analyses by PRS tertiles. We employed single-mediator models with and without exposure-mediator interaction terms to evaluate the separate intermediary role and introduced a multi-mediator model to assess the combined contributions with the R package “CMAverse” 20 . Parametric bootstrapping (n = 400 times) was used to calculate 95% CIs and p values. We excluded participants who self-reported taking antiepileptic drugs and applied a 2-year lag time to minimize reverse causality. Furthermore, we investigated the associations between body composition patterns and two main epilepsy subtypes, generalized (GE) and focal epilepsy (FE), indexed by ICD codes. To address the potential influence of ethnicity on body composition and genetics, we limited our analyses to solely European descent. Lastly, Fine-Grey models accounting for the competing risk of all-cause mortality were constructed. All analyses were performed using R (version 4.3.2, R Foundation for Statistical Computing, Vienna, Austria), with two-sided P values < 0.05 considered statistically significant. 3 Results 3.1 Baseline characteristics Among the 475,960 participants included in this study, 3,026 incident cases of epilepsy were identified during an average follow-up of 10.9 years. Compared with non-cases, those who developed epilepsy were more likely to be male, older, less educated, exhibit adverse health behaviors and present a higher prevalence of common metabolic comorbidities ( S6 Table ). 3.2 Phenotypic Associations of Body Composition and Epilepsy Restricted cubic spline models revealed an L-shaped association between the “bone density” pattern and epilepsy risk, and largely linear associations for “lean mass,” “central obesity” and “arm-dominant fat distribution” patterns across sexes (Fig. 1 ). Alternatively, associations for “fat-to-lean mass,” “muscle strength” and “leg-dominant fat distribution” patterns differed between men and women. In survival analyses, the “muscle strength,” “bone density” and “arm-dominant fat distribution” patterns showed robust associations with epilepsy risk, regardless of whether they were modeled as continuous (Fig. 2 , S7 Table ) or categorical variables (Fig. 3 ), with highly comparable estimates across sexes. Compared with the first tertiles, the higher level of “lean mass” (HR for high level: 0.91 [0.83, 1.00]; HR for continuous: 0.97 [0.95, 0.99]), “muscle strength” (HR for moderate level: 0.77 [0.70, 0.83]; HR for high level: 0.68 [0.62, 0.75]; HR for continuous: 0.89 [0.87, 0.91]), “bone density” (HR for moderate level: 0.87 [0.79, 0.94]; HR for high level: 0.87 [0.79, 0.95]; HR for continuous: 0.96 [0.93, 0.99]) and “leg-dominant fat distribution” (HR for moderate level: 0.90 [0.82, 0.99]) patterns were associated with lower incidence of epilepsy, whereas “fat-to-lean mass” (HR for continuous: 1.01 [1.00, 1.02]), “central obesity” (HR for high level: 1.13 [1.04, 1.24]; HR for continuous: 1.04 [1.02, 1.07]) and “arm-dominant fat distribution” (HR for high level: 1.34 [1.22, 1.47]; HR for continuous: 1.12 [1.09, 1.16]) patterns were associated with increased risk. These associations were directionally consistent across sexes, except for the “central obesity” pattern, which exhibited a notable inverse correlation in males at a moderate level (HR: 0.88 [0.77, 1.00]) versus a positive association in females at a high level (HR: 1.25 [1.10, 1.43]). As for individual components of body composition with risk of epilepsy, partial similarity to the findings of the above patterns was noted ( S8 Table ). Specifically, the leading central obesity indicators were positively correlated with epilepsy onset, while measures of muscle strength and bone density presented inverse associations. Particularly, higher “arm FM/LM” (HR for continuous: 1.06 [1.03,1.10]) and “arm/whole FM” (HR for high level: 1.14 [1.05,1.25]; HR for continuous: 1.04 [1.01,1.08]) were associated with elevated epilepsy risk, collaborating the finding of “arm-dominant fat distribution” pattern. 3.3 Modification Role of Polygenic Risk Score Epilepsy incidence was found to increase with higher PRS strata across all exposure categories (Fig. 4 ). Associations between body composition patterns and epilepsy were reasonably comparable across PRS tertiles, indicating limited modification effect. 3.4 Mediation Analyses of Brain Diseases and Injuries Associations of “bone density,” “muscle strength,” and “arm-dominant fat distribution” with epilepsy were partially mediated by brain disorders and injuries (Fig. 5 , S9 Table ). The effect of “bone density” was exclusively mediated by falls, with the mediating effect accounting for 23.5%, which increased to 27.2% when incorporating the interactions of all mediators. “Muscle strength” and “arm-dominant fat distribution” patterns were found to be mediated through multiple conditions encompassing falls, stroke and neurodegenerative diseases, among which stroke took a predominant role (Proportion mediated: 9.1% for “muscle strength”; 31.6% for “arm-dominant fat distribution”), contributing to combined effects of 6.7% and 31.4%, respectively. 3.5 Sensitive analyses These observed associations generally persisted after excluding potentially undiagnosed patients ( S10 Table ), among the white only ( S11 Table ) and when adjusting for the competing risks of all-cause mortality ( S12 Table ), but were markedly attenuated in specific epilepsy subtypes, probably attributable to reduced cases (GE: 874; FE: 868 VS All epilepsy: 3,026) and wider CIs ( S13-14 Table ). 4 Discussion Utilizing a prospective cohort of 475,960 participants with an average follow-up of 10.9 years, we investigated the associations between 7 patterns of body composition extracted by PCA, 28 individual body components and the risk of epilepsy. Analyses of the identified patterns, modeled as either continuous or categorical variables, consistently showed that “muscle strength” and “bone density” were associated with reduced risk of epilepsy, while “arm-dominant fat distribution” patterns were associated with increased incidence, partly supported by the findings of individual measurements. These associations persisted across strata of genetic susceptibility and were significantly mediated by brain dysfunction and traumatic injuries, particularly falls, stroke and neurodegenerative diseases. Our findings largely aligned with previous studies delving into the relationship between body composition and epilepsy, nevertheless, most of them lacked prospective designs and solely concentrated on separate body measurements, disregarding the interactions within various components 10 , 21 – 24 . The prospective design of our study enables temporal inference, providing novel insights that low lean mass, reduced grip strength and decreased bone density may contribute to epileptogenesis or reflect early premorbid changes, rather than merely serving as comorbidities. Furthermore, our study expanded existing knowledge by demonstrating that these associations were independent of genetic susceptibility of common variants. It is complicated to assess the impact of adiposity by differing consequences of central or abdominal, and peripheral or subcutaneous obesity 25 , 26 , as the former is an established risk factor of metabolic syndrome 27 and cardiovascular diseases 28 , whereas gluteal-femoral fat may confer protective effects. A previous Mendelian randomization study reported a positive association between hip circumference, waist-to-hip ratio and juvenile myoclonic epilepsy, which was in line with our observation that “central obesity” pattern was associated with elevated epilepsy risk 29 . We further identified gender differences in this association, characterized by an inverse relationship at moderate levels in males versus a positive association at high levels in females, indicating underlying sex-specific susceptibility that merits further investigation. Notably, our study sheds first light on the role of peripheral adiposity in epilepsy by identifying significant associations between “arm-dominant fat distribution”, “leg-dominant fat distribution” patterns and epileptogenesis, highlighting that fat and muscle distribution play a prominent role in predicting and potentially mitigating epilepsy risk. Preferential lower-body fat deposition probably benefits from lowering lipid overflow and ectopic fat, guarding against insulin resistance and systemic inflammation 30 , 31 , diminishing the risk of metabolic and cardiovascular diseases 32 . Conversely, the “arm-dominant fat distribution” pattern was found to potentially exacerbate epilepsy risk, collaborating with findings of component “arm FM/LM” and “arm/whole FM” in our study. This pattern, in which fat is stored in the arms and lean tissue is distributed in the hips and legs, tended to exhibit a higher abdomen fat ratio and increased muscle fat infiltration and thus was considered as a passive loading effect of excessive adiposity 13 . Taken together, our findings underscore the critical role of body composition in epilepsy among middle-aged and older adults. Promoting optimal body composition through lifestyle interventions such as regular physical activity, adequate sunlight exposure and balanced nutrition, may pose a modifiable strategy to alleviate epilepsy risk. The underlying mechanisms of body composition patterns with epilepsy have not been clarified, but our study gives a hint that brain insults play an important mediating role. It is well-established that stroke and traumatic brain injuries are common causes of acquired epilepsy, with post-stroke epilepsy accounting for approximately 50% of cases in the elderly 33 and post-traumatic epilepsy contributing to 20% of the cases in general population 34 . People with neurodegenerative diseases also reported a 7.5-fold increased risk of developing epilepsy later in life 35 . It is plausible that body composition affects epilepsy risk by altering susceptibility to these mediating conditions, either by promoting secondary seizures or contributing to cumulative neurological damage. Additionally, bone density and muscle strength, as measured by grip strength, are acknowledged protective factors against falls 36 , fall-related fractures 37 and subsequent injuries 38 , 39 . Growing evidence supports the predictive value of grip strength for a range of adverse aging-related health outcomes 40 , and interventions of resistance and strength training have shown favorable impacts on brain volume 41 , inflammatory markers 42 , and risk of stroke 38 , 43 and Alzheimer's disease 44 . The “arm-dominant fat distribution” pattern, characterized by increased abdomen fat ratio and muscle fat infiltration, is linked to the production of adipocytokines and pro-inflammatory cytokines 30 . These factors trigger insulin resistance, neuroinflammation and the development of amyloid beta plaques 45 , resulting in cerebrovascular 30 and neurodegenerative diseases 46 , which are key contributors to epilepsy. Our mediation analyses validated the intermediatory role of falls, stroke and neurodegenerative diseases, highlighting the importance of early intervention to prevent brain injuries and mitigate long-term epilepsy risk. This study has several notable strengths. First, we leveraged a large, nationally representative cohort with robust longitudinal health data, facilitating a prospective investigation of these associations, which greatly reduces the likelihood of reverse causality and strengthens the identification of mediators. Additionally, through PCA, we generated 7 patterns representing muscle strength, bone density, lean mass and fat accumulated in different depots. This approach moves beyond traditional metrics such as BMI and enables a more nuanced investigation of the relationship between various body components, interactions among them, and epilepsy risk, with direct implications for public health. However, fat mass and lean mass were measured at baseline via bioelectrical impedance, which may introduce greater variability compared with the quantitative MRI and dual-energy X-ray absorptiometry (DXA). We were also unable to cross-validate the associations with body measurements from diverse sources. Moreover, despite conducting subtype-specific sensitivity analyses for GE and FE indexed by ICD codes, we lacked sufficient clinical detail to distinguish spontaneous and provoked seizures or to classify finer subtypes. Finally, due to the transient nature of seizures, which can sometimes present with subtle or focal manifestations, some cases may have been missed, leading to incomplete case exclusion. To address this, we conducted lagged analyses excluding cases diagnosed within the first 2 years of follow-up, and the associations remained robust. 5 Conclusions In this large prospective cohort, higher levels of body composition patterns characterized by “muscle strength” and “bone density” were associated with reduced risk of epilepsy, while the “arm-dominant fat distribution” pattern was linked to elevated incidence. These associations were independent of genetic susceptibility and were partially mediated by falls, stroke and neurodegenerative diseases. Our findings highlight the importance of optimizing body composition and implementing early interventions targeting brain injuries as effective strategies to reduce epilepsy risk in middle-aged and older adults. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was derived from the UKB study, which was ethically approved by the North West Multicenter Research Ethics Committee. Informed consent was obtained from all individual participants included in the study. Funding This research was supported by High-performance Computing Public Platform (Shenzhen Campus) of SUN YAT-SEN UNIVERSITY. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions QY and YQ conceptualized and designed the study. QY performed the statistical analyses. QY, LQ, XY, XG and QQ extracted the data and performed the validation. QY drafted and RQ, DC, YZ, XW and YQ revised the manuscript. XW, YZ and YQ supervised the data analysis and interpretation. All authors provided feedback and approved the final version of the manuscript submitted for publication. Acknowledgements This research was conducted using the UK Biobank Resource (Application Number: 78559). 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J Gerontol Biol Sci Med Sci 79(3):glad224. 10.1093/gerona/glad224 de Boleti A, de Cardoso AP, Frihling PHF, Silva BEE, de Moraes PS, Migliolo LFRN (2023) Adipose tissue, systematic inflammation, and neurodegenerative diseases. Neural Regen Res 18(1):38–46. 10.4103/1673-5374.343891 Al-Kuraishy HM, Al-Gareeb AI, Alsayegh AA et al (2023) A potential link between visceral obesity and risk of alzheimer’s disease. Neurochem Res 48(3):745–766. 10.1007/s11064-022-03817-4 Additional Declarations The authors declare no competing interests. Supplementary Files ZhaoBodycompositionandEpilepsySupplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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08:55:14","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118994,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/666f23e8206439395397eebb.html"},{"id":93569957,"identity":"cfacdfea-3693-4a6c-8a43-fc4872fabcf5","added_by":"auto","created_at":"2025-10-15 08:55:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":717878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable adjusted hazard ratios for epilepsy risk according to identified body composition patterns on a continuous scale, with separate analyses for females (red) and males (blue).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe solid lines represent the multivariate adjusted HRs, and the shaded areas indicate the 95% CIs. Reference lines for no association are marked by the dashed lines at a hazard ratio of 1.0. Restricted cubic splines were constructed with 4 knots at the 5th, 35th, 65th, and 95th percentiles for each pattern. Analyses were adjusted for sex, ethnicity, age at recruitment, Townsend deprivation index, smoking status, average weekly alcohol intake, physical activity and comorbidities (denoted as “P overall”). We tested the potential nonlinearity by using the likelihood ratio test comparing models with linear terms only versus models including both linear and cubic terms (denoted as “P nonlinearity”). Abbreviations: HR, hazard ratio; CI, confidence interval; PC, principal component.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/3ce8a05fa4f7bfd4fc62c70a.png"},{"id":93569967,"identity":"2c9748bb-508e-4951-8326-6f3143f0a1c5","added_by":"auto","created_at":"2025-10-15 08:55:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":628066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios for incident epilepsy according to identified patterns, individual components of body compositionon a continuous scale in multivariable Cox proportional hazards models.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations between identified patterns, individual components of body composition and epilepsy were evaluated using Cox proportional hazards models with attained age as the timescale after adjusting for sex, ethnicity, age at recruitment, Townsend deprivation index, smoking status, average weekly alcohol intake, physical activity and comorbidities. The spots represent the multivariate adjusted hazard ratios, and the lines indicate the 95% confidence intervals. * Significant at P\u0026lt;0.05. Abbreviations: PC, principal component; WC, waist circumference; HC, hip circumference; WTH, waist-to-hip ratio; WTHR, waist-to-height ratio; WWI, waist-to-weight ratio; BSI, body shape index; FM, fat mass; LM, lean mass; Grip, grip strength; BMI, body mass index; BMD, bone mineral density.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/61a46f6152cc21ba00fe2784.png"},{"id":93569954,"identity":"e6586b9c-60d8-4a16-b3cc-6a1562cf45f7","added_by":"auto","created_at":"2025-10-15 08:55:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3945154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios for incident epilepsy according to body composition patterns as categorical variables in multivariable Cox proportional hazards models.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIRs were calculated as the number of cases per 100,000 person-years. HRs and 95% CIs were derived from Cox proportional hazards models with attained age as the timescale after adjusting for sex, ethnicity, age at recruitment, Townsend deprivation index, smoking status, average weekly alcohol intake, physical activity, and comorbidities. Abbreviations: IR, incidence rate; HR, hazard ratio; CI, confidence interval; PC, principal component.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/0c4c1ebca6a4c54d6ff4a628.jpeg"},{"id":93569951,"identity":"93884e82-4c04-4fd7-b324-8c116492391f","added_by":"auto","created_at":"2025-10-15 08:55:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1350732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios for incident epilepsy according to body composition patterns as categorical variables across polygenetic risk score tertiles.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIRs were calculated as the number of cases per 100,000 person-years. HRs and 95% CIs were derived from Cox proportional hazards models with attained age as the timescale after adjusting for sex, ethnicity, age at recruitment, Townsend deprivation index, smoking status, average weekly alcohol intake, physical activity, and comorbidities. Abbreviations: IR, incidence rate; HR, hazard ratio; CI, confidence interval; PC, principal component.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/ad7d44b16a90ee52370446de.png"},{"id":93569963,"identity":"137a4cab-a5f2-47d3-8821-e344560929c4","added_by":"auto","created_at":"2025-10-15 08:55:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":241762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediating effect of brain diseases and injuries on the associations between body composition patterns and epilepsy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results that reached statistical significance (i.e., p\u0026lt;0.05 using the false discovery rate for adjustment of multiple testing) are marked with color, with blue denoting inverse associations and red denoting positive associations. Abbreviations: PC, principal component; ns = not statistically significant.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/b7840b5b40bd4cbb2e3e71c6.png"},{"id":93571237,"identity":"dfe6aa9f-98ff-4e43-b500-bfcf859af094","added_by":"auto","created_at":"2025-10-15 09:03:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7855679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/6255aa6f-a64f-4d65-baee-305e33729619.pdf"},{"id":93569934,"identity":"0e512333-21a4-4c59-a11f-5e1ce96b8886","added_by":"auto","created_at":"2025-10-15 08:55:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1634827,"visible":true,"origin":"","legend":"","description":"","filename":"ZhaoBodycompositionandEpilepsySupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7804320/v1/8a6e1ead18dc288ed8ca3c15.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssociation Between Body Composition Patterns, Brain Diseases and Injuries and Risk of Epilepsy: a Prospective Cohort Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEpilepsy is a noncommunicable neurological disorder that affects over 50\u0026nbsp;million people worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, with its incidence exhibiting a bimodal age distribution, peaking in infants and the elderly as well as mounting steadily beyond age 50\u003csup\u003e2\u003c/sup\u003e. Genetic abnormalities have been historically acknowledged as a culprit of generalized epilepsy\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, while other common risk factors vary by age group. Cerebrovascular and neurodegenerative diseases are prevalent risk factors among older adults, whereas epilepsy associated with traumatic brain injuries, infections and tumors might develop at any age\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Still, approximately 50% of cases report an unknown cause, probably resulting from complex interactions among genetic predispositions, environmental and behavioral factors\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCross-sectional studies have revealed that patients with epilepsy tend to be more sedentary, higher in fat\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and at increased risk for sarcopenia\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, osteoporosis and fractures\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, it is unclear whether these features serve as underlying risk factors preceding diagnosis or emerge as adverse effects of antiepileptic drugs (AEDs) use and lifestyle shifts. Notably, limited studies exploring the association between body mass index (BMI) and epilepsy have yielded inconsistent findings\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Although BMI is easy to measure and widely used, it neither distinguishes between various body components, such as fat, lean mass and skeletal structure, nor captures the fat accumulation in different depots, which may drive diverse health outcomes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLeveraging comprehensive body measurements data from the UK Biobank (UKB), we sought to clarify the associations between predefined patterns of body composition representing muscle strength, bone density, lean mass (LM) and fat mass (FM) storage in different depots, specific body measurements and risk of epilepsy. Additionally, we examined the modification effect of common variants quantified by polygenetic risk score (PRS), and whether relevant brain disorders and accidental injuries, separately or as a group, play a potential mediating role.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and population\u003c/h2\u003e\u003cp\u003eThe present study was derived from the UKB, a large-scale survey that enrolled over 500,000 residents aged 40\u0026ndash;69 years across England, Scotland and Wales. Individual information on sociodemographic, lifestyle, body metrics and genetic factors was collected through touchscreen questionnaires, physical measurements and biological samples at baseline. The UKB study was approved by the North West Multicenter Research Ethical Committee and all participants provided informed consent.\u003c/p\u003e\u003cp\u003eFor our cohort, we excluded individuals who withdrew consent (n\u0026thinsp;=\u0026thinsp;1,298), had a documented or self-reported history of epilepsy before recruitment (n\u0026thinsp;=\u0026thinsp;5,256), and those with missing or extreme body measurements (i.e., exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;5 standard derivations) or key covariates (n\u0026thinsp;=\u0026thinsp;19,897), resulting in a study population of 475,960 participants (\u003cb\u003eS1 Figure\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Body Composition\u003c/h2\u003e\u003cp\u003eBody composition was assessed by trained staff following a standard protocol. Height, weight, waist and hip circumference were obtained manually. Regional (i.e., arm, leg, and trunk) and total FM and LM were evaluated through the Tanita BC-418MA body composition analyzer. Grip strength was measured using the Jamar J00105 hydraulic hand dynamometer. Bone density was conducted at the calcaneus using the Norland McCue Contact Ultrasound Bone Analyzer.\u003c/p\u003e\u003cp\u003eThese measurements and their derivations, a total of 28 items, were analyzed using principal component analysis (PCA), as described in previous studies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Seven principal components with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1.0 were retained, accounting for over 90% of the total variance (\u003cb\u003eS2 Figure\u003c/b\u003e). In light of the substantial similarity of these patterns between sexes, we applied male-derived loading coefficients to both sexes to ensure consistency and comparability. The resulting components were interpreted and labeled based on dominant loading variables: \u0026ldquo;fat-to-lean mass\u0026rdquo; (indicating FM relative to LM), \u0026ldquo;lean mass,\u0026rdquo; \u0026ldquo;central obesity,\u0026rdquo; \u0026ldquo;muscle strength,\u0026rdquo; \u0026ldquo;bone density,\u0026rdquo; \u0026ldquo;arm-dominant fat distribution,\u0026rdquo; (highlighting FM primarily distributed in the arms) and \u0026ldquo;leg-dominant fat distribution\u0026rdquo; (underscoring FM mainly storage in the legs).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Ascertainment of Epilepsy\u003c/h2\u003e\u003cp\u003eEpilepsy cases in this study were identified based on self-reported medical conditions during subsequent visits, as well as linked data from primary care records, hospital admissions and death registry records, according to the corresponding International Classification of Diseases codes (ICD) (\u003cb\u003eS3 Table\u003c/b\u003e). The follow-up duration was calculated from the recruitment date to the date of first diagnosis, death, or December 31, 2019, whichever came first.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 PRS Construction\u003c/h2\u003e\u003cp\u003eDetails of genotyping and imputation for UKB samples have been described elsewhere\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. We curated 20 single-nucleotide polymorphisms (SNPs) with minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.01 and P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e from the latest multi-ancestry genome-wide association study (GWAS) by International League Against Epilepsy Consortium (ILAE) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eS4 Table\u003c/b\u003e). We extracted published effect estimates (β\u003csub\u003ei\u003c/sub\u003e) of each variant and recoded the SNP\u003csub\u003ei\u003c/sub\u003e as additive risk allele counts (0, 1, 2). The PRS was calculated as the weighted sum of risk alleles using the following formula, where n denotes the number of selected SNPs:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{R}\\text{S}={\\sum\\:}_{\\text{i}}^{\\text{n}}{{\\beta\\:}}_{\\text{i}}{\\times\\:\\text{S}\\text{N}\\text{P}}_{\\text{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Covariates and Mediators\u003c/h2\u003e\u003cp\u003eThe covariates included age at recruitment, sex, ethnicity, educational level (college/university degree or others), Townsend deprivation index (TDI), smoking status (never, former or current smoker), average weekly alcohol intake (none, low-risk or high-risk drinking), physical activity (low, moderate or high level) and comorbidities (i.e., hypertension, diabetes, disorders of lipoprotein metabolism and other lipidaemia) (\u003cb\u003eS5 Table\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we considered the occurrence of stroke, neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease and Huntington's disease), inflammatory encephalopathies (i.e., meningitis, encephalitis, myelitis and encephalomyelitis), and hospitalizations attributable to falls or traffic accidents after enrollment and within five years before epilepsy diagnosis as potential mediators of the studied associations. This was based on the premise that most cases of secondary epilepsy typically emerge within a few years after the primary condition\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analyses\u003c/h2\u003e\u003cp\u003eBaseline demographic characteristics were presented according to the ultimate epilepsy diagnosis. Categorical variables were exhibited as counts and proportions, whereas continuous variables were summarized as means and SD.\u003c/p\u003e\u003cp\u003eWe conducted sex-stratified analyses to examine the associations between identified body composition patterns, individual components and epilepsy risk with Cox proportional hazard models. Exposures were initially modeled as continuous variables using restricted cubic splines with knots at the 5th, 35th, 65th and 95th percentiles to flexibly characterize the shape of associations. Nonlinearity was assessed via likelihood ratio tests comparing models with linear terms only versus models including both linear and cubic terms. In light of the observed nonlinearity in most associations, we further categorized exposures into sex-specific tertiles for phenotypic associations. Cox models with attained age as the time scale were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for epilepsy incidence per 1 standard deviation (SD) increase in continuous variables, and by tertile for categorical variables (referenced to the lowest tertile). All models were adjusted for sex, ethnicity, age at recruitment, TDI, smoking status, average weekly alcohol intake, physical activity and comorbidities. To disentangle whether associations varied by genetic susceptibility to epilepsy, we constructed PRS based on common variants and performed stratified analyses by PRS tertiles. We employed single-mediator models with and without exposure-mediator interaction terms to evaluate the separate intermediary role and introduced a multi-mediator model to assess the combined contributions with the R package \u0026ldquo;CMAverse\u0026rdquo;\u003csup\u003e20\u003c/sup\u003e. Parametric bootstrapping (n\u0026thinsp;=\u0026thinsp;400 times) was used to calculate 95% CIs and p values.\u003c/p\u003e\u003cp\u003eWe excluded participants who self-reported taking antiepileptic drugs and applied a 2-year lag time to minimize reverse causality. Furthermore, we investigated the associations between body composition patterns and two main epilepsy subtypes, generalized (GE) and focal epilepsy (FE), indexed by ICD codes. To address the potential influence of ethnicity on body composition and genetics, we limited our analyses to solely European descent. Lastly, Fine-Grey models accounting for the competing risk of all-cause mortality were constructed.\u003c/p\u003e\u003cp\u003eAll analyses were performed using R (version 4.3.2, R Foundation for Statistical Computing, Vienna, Austria), with two-sided P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e\u003cp\u003eAmong the 475,960 participants included in this study, 3,026 incident cases of epilepsy were identified during an average follow-up of 10.9 years. Compared with non-cases, those who developed epilepsy were more likely to be male, older, less educated, exhibit adverse health behaviors and present a higher prevalence of common metabolic comorbidities (\u003cb\u003eS6 Table\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Phenotypic Associations of Body Composition and Epilepsy\u003c/h2\u003e\u003cp\u003eRestricted cubic spline models revealed an L-shaped association between the \u0026ldquo;bone density\u0026rdquo; pattern and epilepsy risk, and largely linear associations for \u0026ldquo;lean mass,\u0026rdquo; \u0026ldquo;central obesity\u0026rdquo; and \u0026ldquo;arm-dominant fat distribution\u0026rdquo; patterns across sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Alternatively, associations for \u0026ldquo;fat-to-lean mass,\u0026rdquo; \u0026ldquo;muscle strength\u0026rdquo; and \u0026ldquo;leg-dominant fat distribution\u0026rdquo; patterns differed between men and women.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn survival analyses, the \u0026ldquo;muscle strength,\u0026rdquo; \u0026ldquo;bone density\u0026rdquo; and \u0026ldquo;arm-dominant fat distribution\u0026rdquo; patterns showed robust associations with epilepsy risk, regardless of whether they were modeled as continuous (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S7 \u003cb\u003eTable\u003c/b\u003e) or categorical variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with highly comparable estimates across sexes. Compared with the first tertiles, the higher level of \u0026ldquo;lean mass\u0026rdquo; (HR for high level: 0.91 [0.83, 1.00]; HR for continuous: 0.97 [0.95, 0.99]), \u0026ldquo;muscle strength\u0026rdquo; (HR for moderate level: 0.77 [0.70, 0.83]; HR for high level: 0.68 [0.62, 0.75]; HR for continuous: 0.89 [0.87, 0.91]), \u0026ldquo;bone density\u0026rdquo; (HR for moderate level: 0.87 [0.79, 0.94]; HR for high level: 0.87 [0.79, 0.95]; HR for continuous: 0.96 [0.93, 0.99]) and \u0026ldquo;leg-dominant fat distribution\u0026rdquo; (HR for moderate level: 0.90 [0.82, 0.99]) patterns were associated with lower incidence of epilepsy, whereas \u0026ldquo;fat-to-lean mass\u0026rdquo; (HR for continuous: 1.01 [1.00, 1.02]), \u0026ldquo;central obesity\u0026rdquo; (HR for high level: 1.13 [1.04, 1.24]; HR for continuous: 1.04 [1.02, 1.07]) and \u0026ldquo;arm-dominant fat distribution\u0026rdquo; (HR for high level: 1.34 [1.22, 1.47]; HR for continuous: 1.12 [1.09, 1.16]) patterns were associated with increased risk. These associations were directionally consistent across sexes, except for the \u0026ldquo;central obesity\u0026rdquo; pattern, which exhibited a notable inverse correlation in males at a moderate level (HR: 0.88 [0.77, 1.00]) versus a positive association in females at a high level (HR: 1.25 [1.10, 1.43]).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs for individual components of body composition with risk of epilepsy, partial similarity to the findings of the above patterns was noted (\u003cb\u003eS8 Table\u003c/b\u003e). Specifically, the leading central obesity indicators were positively correlated with epilepsy onset, while measures of muscle strength and bone density presented inverse associations. Particularly, higher \u0026ldquo;arm FM/LM\u0026rdquo; (HR for continuous: 1.06 [1.03,1.10]) and \u0026ldquo;arm/whole FM\u0026rdquo; (HR for high level: 1.14 [1.05,1.25]; HR for continuous: 1.04 [1.01,1.08]) were associated with elevated epilepsy risk, collaborating the finding of \u0026ldquo;arm-dominant fat distribution\u0026rdquo; pattern.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Modification Role of Polygenic Risk Score\u003c/h2\u003e\u003cp\u003eEpilepsy incidence was found to increase with higher PRS strata across all exposure categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Associations between body composition patterns and epilepsy were reasonably comparable across PRS tertiles, indicating limited modification effect.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Mediation Analyses of Brain Diseases and Injuries\u003c/h2\u003e\u003cp\u003eAssociations of \u0026ldquo;bone density,\u0026rdquo; \u0026ldquo;muscle strength,\u0026rdquo; and \u0026ldquo;arm-dominant fat distribution\u0026rdquo; with epilepsy were partially mediated by brain disorders and injuries (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S9 \u003cb\u003eTable\u003c/b\u003e). The effect of \u0026ldquo;bone density\u0026rdquo; was exclusively mediated by falls, with the mediating effect accounting for 23.5%, which increased to 27.2% when incorporating the interactions of all mediators. \u0026ldquo;Muscle strength\u0026rdquo; and \u0026ldquo;arm-dominant fat distribution\u0026rdquo; patterns were found to be mediated through multiple conditions encompassing falls, stroke and neurodegenerative diseases, among which stroke took a predominant role (Proportion mediated: 9.1% for \u0026ldquo;muscle strength\u0026rdquo;; 31.6% for \u0026ldquo;arm-dominant fat distribution\u0026rdquo;), contributing to combined effects of 6.7% and 31.4%, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Sensitive analyses\u003c/h2\u003e\u003cp\u003eThese observed associations generally persisted after excluding potentially undiagnosed patients (\u003cb\u003eS10 Table\u003c/b\u003e), among the white only (\u003cb\u003eS11 Table\u003c/b\u003e) and when adjusting for the competing risks of all-cause mortality (\u003cb\u003eS12 Table\u003c/b\u003e), but were markedly attenuated in specific epilepsy subtypes, probably attributable to reduced cases (GE: 874; FE: 868 VS All epilepsy: 3,026) and wider CIs (\u003cb\u003eS13-14 Table\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eUtilizing a prospective cohort of 475,960 participants with an average follow-up of 10.9 years, we investigated the associations between 7 patterns of body composition extracted by PCA, 28 individual body components and the risk of epilepsy. Analyses of the identified patterns, modeled as either continuous or categorical variables, consistently showed that \u0026ldquo;muscle strength\u0026rdquo; and \u0026ldquo;bone density\u0026rdquo; were associated with reduced risk of epilepsy, while \u0026ldquo;arm-dominant fat distribution\u0026rdquo; patterns were associated with increased incidence, partly supported by the findings of individual measurements. These associations persisted across strata of genetic susceptibility and were significantly mediated by brain dysfunction and traumatic injuries, particularly falls, stroke and neurodegenerative diseases.\u003c/p\u003e\u003cp\u003eOur findings largely aligned with previous studies delving into the relationship between body composition and epilepsy, nevertheless, most of them lacked prospective designs and solely concentrated on separate body measurements, disregarding the interactions within various components\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The prospective design of our study enables temporal inference, providing novel insights that low lean mass, reduced grip strength and decreased bone density may contribute to epileptogenesis or reflect early premorbid changes, rather than merely serving as comorbidities. Furthermore, our study expanded existing knowledge by demonstrating that these associations were independent of genetic susceptibility of common variants.\u003c/p\u003e\u003cp\u003eIt is complicated to assess the impact of adiposity by differing consequences of central or abdominal, and peripheral or subcutaneous obesity\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, as the former is an established risk factor of metabolic syndrome\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, whereas gluteal-femoral fat may confer protective effects. A previous Mendelian randomization study reported a positive association between hip circumference, waist-to-hip ratio and juvenile myoclonic epilepsy, which was in line with our observation that \u0026ldquo;central obesity\u0026rdquo; pattern was associated with elevated epilepsy risk\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. We further identified gender differences in this association, characterized by an inverse relationship at moderate levels in males versus a positive association at high levels in females, indicating underlying sex-specific susceptibility that merits further investigation. Notably, our study sheds first light on the role of peripheral adiposity in epilepsy by identifying significant associations between \u0026ldquo;arm-dominant fat distribution\u0026rdquo;, \u0026ldquo;leg-dominant fat distribution\u0026rdquo; patterns and epileptogenesis, highlighting that fat and muscle distribution play a prominent role in predicting and potentially mitigating epilepsy risk. Preferential lower-body fat deposition probably benefits from lowering lipid overflow and ectopic fat, guarding against insulin resistance and systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, diminishing the risk of metabolic and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Conversely, the \u0026ldquo;arm-dominant fat distribution\u0026rdquo; pattern was found to potentially exacerbate epilepsy risk, collaborating with findings of component \u0026ldquo;arm FM/LM\u0026rdquo; and \u0026ldquo;arm/whole FM\u0026rdquo; in our study. This pattern, in which fat is stored in the arms and lean tissue is distributed in the hips and legs, tended to exhibit a higher abdomen fat ratio and increased muscle fat infiltration and thus was considered as a passive loading effect of excessive adiposity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Taken together, our findings underscore the critical role of body composition in epilepsy among middle-aged and older adults. Promoting optimal body composition through lifestyle interventions such as regular physical activity, adequate sunlight exposure and balanced nutrition, may pose a modifiable strategy to alleviate epilepsy risk.\u003c/p\u003e\u003cp\u003eThe underlying mechanisms of body composition patterns with epilepsy have not been clarified, but our study gives a hint that brain insults play an important mediating role. It is well-established that stroke and traumatic brain injuries are common causes of acquired epilepsy, with post-stroke epilepsy accounting for approximately 50% of cases in the elderly\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and post-traumatic epilepsy contributing to 20% of the cases in general population\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. People with neurodegenerative diseases also reported a 7.5-fold increased risk of developing epilepsy later in life\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. It is plausible that body composition affects epilepsy risk by altering susceptibility to these mediating conditions, either by promoting secondary seizures or contributing to cumulative neurological damage. Additionally, bone density and muscle strength, as measured by grip strength, are acknowledged protective factors against falls\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, fall-related fractures\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and subsequent injuries\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Growing evidence supports the predictive value of grip strength for a range of adverse aging-related health outcomes\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and interventions of resistance and strength training have shown favorable impacts on brain volume\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, inflammatory markers\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, and risk of stroke\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and Alzheimer's disease\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The \u0026ldquo;arm-dominant fat distribution\u0026rdquo; pattern, characterized by increased abdomen fat ratio and muscle fat infiltration, is linked to the production of adipocytokines and pro-inflammatory cytokines\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These factors trigger insulin resistance, neuroinflammation and the development of amyloid beta plaques\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, resulting in cerebrovascular\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and neurodegenerative diseases\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, which are key contributors to epilepsy. Our mediation analyses validated the intermediatory role of falls, stroke and neurodegenerative diseases, highlighting the importance of early intervention to prevent brain injuries and mitigate long-term epilepsy risk.\u003c/p\u003e\u003cp\u003eThis study has several notable strengths. First, we leveraged a large, nationally representative cohort with robust longitudinal health data, facilitating a prospective investigation of these associations, which greatly reduces the likelihood of reverse causality and strengthens the identification of mediators. Additionally, through PCA, we generated 7 patterns representing muscle strength, bone density, lean mass and fat accumulated in different depots. This approach moves beyond traditional metrics such as BMI and enables a more nuanced investigation of the relationship between various body components, interactions among them, and epilepsy risk, with direct implications for public health. However, fat mass and lean mass were measured at baseline via bioelectrical impedance, which may introduce greater variability compared with the quantitative MRI and dual-energy X-ray absorptiometry (DXA). We were also unable to cross-validate the associations with body measurements from diverse sources. Moreover, despite conducting subtype-specific sensitivity analyses for GE and FE indexed by ICD codes, we lacked sufficient clinical detail to distinguish spontaneous and provoked seizures or to classify finer subtypes. Finally, due to the transient nature of seizures, which can sometimes present with subtle or focal manifestations, some cases may have been missed, leading to incomplete case exclusion. To address this, we conducted lagged analyses excluding cases diagnosed within the first 2 years of follow-up, and the associations remained robust.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn this large prospective cohort, higher levels of body composition patterns characterized by \u0026ldquo;muscle strength\u0026rdquo; and \u0026ldquo;bone density\u0026rdquo; were associated with reduced risk of epilepsy, while the \u0026ldquo;arm-dominant fat distribution\u0026rdquo; pattern was linked to elevated incidence. These associations were independent of genetic susceptibility and were partially mediated by falls, stroke and neurodegenerative diseases. Our findings highlight the importance of optimizing body composition and implementing early interventions targeting brain injuries as effective strategies to reduce epilepsy risk in middle-aged and older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003e This study was derived from the UKB study, which was ethically approved by the North West Multicenter Research Ethics Committee. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by High-performance Computing Public Platform (Shenzhen Campus) of SUN YAT-SEN UNIVERSITY. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eQY and YQ conceptualized and designed the study. QY performed the statistical analyses. QY, LQ, XY, XG and QQ extracted the data and performed the validation. QY drafted and RQ, DC, YZ, XW and YQ revised the manuscript. XW, YZ and YQ supervised the data analysis and interpretation. All authors provided feedback and approved the final version of the manuscript submitted for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis research was conducted using the UK Biobank Resource (Application Number: 78559). 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Neurochem Res 48(3):745\u0026ndash;766. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11064-022-03817-4\u003c/span\u003e\u003cspan address=\"10.1007/s11064-022-03817-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sun Yat-sen University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epilepsy, Body composition, Body fat distribution, Polygenetic risk score, Mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-7804320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7804320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAims: \u003c/strong\u003eDifferent body compositions may exert varying effects on epilepsy. We aimed to prospectively examine the associations between body composition patterns, specific measurements and epilepsy risk, while exploring the mediating role of brain-related injuries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe constructed a large-scale cohort study within the UK Biobank (UKB), deriving 7 body composition patterns via principal component analysis (PCA) that captured variation in muscle strength, bone density, lean mass and fat distribution. Multivariable Cox proportional hazards models were employed to assess associations between these patterns, individual body measurements and epilepsy risk. We performed stratified analyses by polygenic risk score (PRS) and mediation analyses to evaluate the indirect contributions of brain injuries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong the 475,960 participants, 3,026 epilepsy cases were identified over an average follow-up of 10.9 years. Patterns of “lean mass”, “muscle strength”, “bone density” and “leg-dominant fat distribution” were associated with reduced risk of epilepsy (hazard ratios [HRs]: 0.68-0.97), whereas “fat-to-lean mass”, “central obesity” and “arm-dominant fat distribution” patterns were associated with increased incidence (HRs: 1.01-1.34). Similar trends were noted for corresponding individual body measurements. These associations were consistent across PRS strata. Falls, stroke, and neurodegenerative diseases mediated 17.9%, 27.2%, and 31.0% of effects for “muscle strength,” “bone density,” and “arm-dominant fat distribution”, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eBody composition patterns involving muscle strength, bone density, and fat distribution show robust associations with epilepsy, partly mediated by neurological disorders. Optimizing body composition and preventing neurological insults may help reduce epilepsy risk in middle-aged and older adults.\u003c/p\u003e","manuscriptTitle":"Association Between Body Composition Patterns, Brain Diseases and Injuries and Risk of Epilepsy: a Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 08:54:49","doi":"10.21203/rs.3.rs-7804320/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38e8640a-aec2-4662-b3ea-7d7eb29a43d6","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55937892,"name":"Epidemiology"},{"id":55937893,"name":"Neurology"}],"tags":[],"updatedAt":"2025-10-15T08:54:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 08:54:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7804320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7804320","identity":"rs-7804320","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}