Genetic Pleiotropy Underlying Obesity and Autoimmunity Disorders: A Large-Scale Cross-Trait Genome-wide Association Analysis

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Using Stratified Pleiotropic Locus Mapping (PLACO), we resolve 10,324 pleiotropic SNPs mapping to 52 risk loci, with Bayesian colocalization confirming nine causal variants. Multivariate gene annotation reveals 133 unique pleiotropic genes—including CLN3, SH2B1, ATP2A1 and MMEL1—enriched in hematopoietic cell differentiation and immune homeostasis pathways. Tissue-specific heritability concentrates in spleen, whole blood, and EBV-transformed lymphocytes, while immune co-localization implicates six IgD + CD38- %B cell-related traits as pathological conduits. Drug-target prioritization nominates 92 candidates, establishing core mechanisms for comorbidity. obesity genome-wide association autoimmune diseases genetic effect pleiotropy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Obesity is a chronic metabolic disorder marked by abnormal fat distribution or an excessive accumulation of body fat. The mechanisms underlying obesity are complex, including intricate interactions among genetics, hormones and the environment. This condition poses substantial medical challenges and is accompanied by an increased risk of complications and mortality rates [ 1 ]. Autoimmune diseases are defined by a loss of self-tolerance, leading to pathological alterations and clinical manifestations caused by an immune response directed against self-components [ 2 ]. Obesity is increasingly acknowledged as a major risk factor contributing to the development and progression of autoimmune diseases. The relationship is intricate and involves various mechanisms, such as chronic inflammation, hormonal imbalances, dysbiosis of gut flora, and metabolic disorders. Conversely, autoimmune diseases can also contribute to the development of obesity through various mechanisms [ 3 ]. Compared to normal people, obese people have a 40% increased risk of rheumatoid arthritis [ 4 ], and a 30–50% elevated risk of psoriasis [ 5 ]. One study found that the odds ratio for the association between obesity and hypothyroidism was approximately 2.45, indicating a strong correlation between increased body mass index (BMI) and the likelihood of developing hypothyroidism [ 6 ]. Currently, discussions on obesity and autoimmune diseases mainly focus on molecular mechanisms, such as inflammation, hormonal changes, gut microbiota, and metabolic disorders. However, there has been little exploration from the perspective of genome-wide association studies. This highlights a significant gap within this domain and underscores the urgent need to pinpoint common risk loci linking obesity to autoimmune diseases. It is also important to recognize that traditional clinical or epidemiologic studies may face difficulties in maintaining the statistical validity of their findings. High-definition likelihood (HDL) based on GWAS summary data [ 7 ], along with linkage disequilibrium (LD) score regression (LDSC) methods [ 8 ], has recently been created to determine if obesity and these autoimmune diseases are genetically correlated. At present, it remains unclear whether the entire genome or only a small number of loci are responsible for this genetic association. The genetic correlation, common susceptibility genes, and possible effector linkages between obesity and autoimmune illness have not yet been thoroughly explored in much of research. Cross-trait analysis using GWAS signal correlation has been proven to correctly identify common loci between disorders. Pleiotropic loci can serve as therapeutic targets, offering opportunities for simultaneous prevention and deeper insight into these diseases. A new method, “PLACO”, has also been introduced to identify pleiotropic loci at the SNP level [ 9 ]. Therefore, identifying specific genetic variants or loci underlying genome-wide genetic correlations is crucial for understanding the common genetic etiologies of these complex diseases. The study flowchart is shown in Fig. 1 . Methods GWAS summary data source We curated the most recent European ancestry GWAS summary data for 17 major autoimmune disorders from the FinnGen study: autoimmune thyroiditis (AIT), hypothyroidism (HT), primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC), inflammatory bowel disease (IBD), crohn’s disease (CD), ulcerative colitis (UC), multiple sclerosis (MS), systemic sclerosis (SS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), celiac disease (CeD), irritable bowel syndrome (IBS), myasthenia gravis (MG), psoriasis (PsO) and vitiligo [ 10 ]. The GWAS summary statistics for obesity were obtained from the IEU, which includes a total of 32858 cases and 65839 controls of European descen [ 11 ]. The same quality control procedure was applied across all studies. The association between obesity status and SNP genotypes in each study was assessed using logistic regression, with genetic principal components included as covariates. Risk estimates were ultimately combined through fixed-effects inverse variance weighted (IVW) meta-analysis [ 12 ]. Data sources and detailed descriptions are summarized in Additional file 1: Table S1 . Quality control Rigorous quality assurance protocols were deployed to safeguard GWAS data accuracy and reliability. To mitigate confounding effects from low-frequency variants [ 13 ], we employed a minor allele frequency (MAF) threshold > 1%. This deliberate focus on common variants amplifies statistical power while dramatically curtailing false positives—fortifying result robustness. Stringent QC measures acted as dual-filtering mechanism for samples and markers: Only samples surpassing 95% call rates and SNPs exceeding 99% call rates were retained; all substandard entries were discarded. This strategic synthesis of MAF filtering with uncompromising QC ensured exclusive analysis of high-fidelity data, effectively neutralizing bias and spurious associations. Genome-wide association study We selected the LDSC approach to investigate the shared genetic architecture between obesity and the autoimmune diseases [ 8 ]. The LDSC method leverages LD scores computed from common SNP genotypes in the European ancestry subset of the 1000 Genomes Project [ 14 ]. This approach offers significant utility in elucidating genetic relationships between traits, thereby providing a more direct window into the potential overlap of distinct genetic factors. A critical aspect of LDSC analysis involves the computation of the standard error (SE) of estimates using a jackknife method for bias correction. This step is paramount, as it addresses pervasive attenuation bias in genetic analyses; failure to correct for this bias can confound the results. Furthermore, the LDSC intercept furnishes valuable insight into potential population stratification between the two studies. As revealed by the intercept, this additional information delineates the genetic similarities of the investigated populations with greater precision, introducing an extra layer of validation [ 8 ]. To further bolster these robust findings, we employed HDL methodology as an additional validation tool. HDL is grounded in likelihood theory and was specifically engineered to enhance the performance of GWAS summary data. A salient advantage of HDL over LDSC lies in its capacity to substantially reduce the variance in estimates of genetic correlation, achieving reductions of up to 60%. This sharpening of variance not only refines precision but also augments the reliability of the genetic overlap estimates [ 8 ]. Employing both LDSC and HDL methodologies in tandem allowed us to scrutinize our findings from complementary perspectives. This dual-layered strategy served as a rigorous safeguard, ensuring the empirical robustness and dependability of the genome-wide genetic overlap analysis results. Tissue-related hierarchical analysis We investigated herein the association of obesity with the autoimmune diseases and, importantly, tested such associations across a wide range of tissues and organs. Furthermore, we sought to investigate enrichment of SNP heritability for obesity and the four diseases within specific cells and tissues. We applied Stratified-LDSC (S-LDSC) to test genetic enrichment for specific cell and tissue types. To this end, we used the GTEx database [ 15 ] to make an estimation of the SNP heritability enrichment in a dataset comprising 54 human tissues, including various tissue and cell types. We aimed in this study to investigate genetic relationships of obesity with the autoimmune diseases, the key focus being placed on investigating such associations across the wide range of tissues and organs. By merging these data into one analytical frame, the approach allowed the further investigation of the possible biological pathways that may link these conditions through the study of the variability with which genetic factors could express in different tissue types. The current study focused primarily on the SNP heritability enrichment assessment with regard to obesity and four major diseases in single cells and tissues. Genetic enrichments were estimated using the S-LDSC approach [ 16 ]. It is uniquely suited to assess the genetic contribution of particular cell and tissue types, enabling a nuanced understanding of how SNP heritability might be distributed across the genome. We utilized the comprehensive dataset from the GTEx database [ 15 ], including data from 54 human tissues. Such wealth of this resource has enabled us to investigate SNP heritability enrichment not only at a broad tissue level but even at specific cell-type resolution for a finer view of the genetic underpinning of obesity and its associated diseases. By using both S-LDSC and the GTEx dataset in our study, we could observe specific patterns of genetic enrichments across tissues and cell types which have shed new light on how obesity may influence the development of these diseases through different genetic effects in various tissues. Gene-level exploratory analysis Our approach in the study was to attempt to find common genetic mechanisms between obesity and the associated loci of the autoimmune diseases. We mapped the leading SNPs from each locus to their surrounding genes with the intention of finding the putative causal genes. In investigating the functional mechanism behind such shared loci, the MAGMA method was employed, an advanced technique to conduct a multi-marker effect analysis on GWAS data [ 17 ]. MAGMA enabled us to further investigate the functional roles of the identified loci by accounting for LD between markers and detecting multi-marker effects with a significance threshold of P < 0.05/17644 = 2.83×10 − 6 . This approach indeed appeared to be useful in identifying pleiotropic genes influencing multiple traits at once and further demonstrated the highly complex genetic architecture of these diseases [ 18 ]. We further extended our set of findings with a MAGMA gene set analysis, enabling investigation into the biological functions of the leading SNPs associated with the investigated traits. In total, 17004 gene sets from the Molecular Signatures Database (MSigDB)were tested, including curated gene sets (c2.all) and Gene Ontology (GO) terms involving biological processes (c5.bp), cellular components (c5.cc), and molecular functions (c5.mf) [ 19 ]. These gene sets were very broad in scope, thus providing a very rich framework to investigate the biological functions that are attached to our variants of interest. We used a Bonferroni correction for multiple testing, adjusting the significance threshold to P < 0.05/17004 = 2.94×10⁻ 6 to minimize the risk of a false positive result. For a more functional characteristic investigation of the mapped genes, pathway enrichment analysis using the Metascape web tool (metascape.org) was conducted. It enabled the mapping of genes into pathways in the MSigDB database [ 19 ] and gave a better overview of how these loci may influence the greater biological landscape. Complementing the above analyses, we have further applied a genome-wide tissue-specific enrichment analysis of PLACO polygenic results to 54 tissues from the GTEx dataset as an important procedure in understanding how tissues genetic influences contribute toward the traits studied. Specifically, for all identified polygenic genes in each given tissue, we extracted and calculated the expression levels, averaging across them after log2 transformations. This transformation had identified differentially expressed genes (DEGs) through which there was a gained detailed mapping of the regulated genes in tissues. The direction of regulation-specific to tissues may be assessed based on the sign of the t-statistic for these DEGs, thus granularity of genetic effects across the tissues. Hierarchical exploratory analysis of the SNP A systematic investigation of genetic associations between autoimmune diseases and obesity at the SNP level is undertaken by using pleiotropic analysis under a composite null hypothesis (PLACO) [ 9 ]. PLACO is one of the powerful statistical methods designed to find gene pleiotropy, which allows identification of shared genetic variation across multiple phenotypes. This was particularly helpful in identifying those SNPs which were significantly associated with multiple diseases, thus providing a deeper insight into the genetic relationship between obesity and autoimmune conditions. We defined pleiotropic variants for the analysis as those SNPs reaching a genome-wide significance threshold of P < 5×10⁻⁸. They show robust genetic association signals at numerous conditions, thus reinforcing these genetic factors in the cause of obesity and most of the examined autoimmune diseases. Identification of the said pleiotropic variants is central towards resolving shared genetic pathways connecting obesity and autoimmune diseases by an ability to offer, much clearly now, explanations as to why such conditions happen together because of a common gene. To further substantiate the biological significance of the identified pleiotropic SNPs, we utilized a functional mapping and annotation tool (FUMA) [ 20 ]. This tool allowed us to narrow the identified risk variants down to specific genomic regions, which, for simplicity, we will call "risk loci". Mapping those SNPs back to their respective loci provided us with important knowledge about the functional consequences these variants could take in affecting either gene expression itself or protein functionality. Finally, we used Bayesian colocalization [ 21 ] to further probe the shared genetic architecture. This approach identified shared genetic risk regions between obesity and autoimmune diseases, providing a deeper understanding of the genetic interplay between the two. These findings from analysis are of utmost importance in identifying specific locations that can explain why such complex conditions often occur together. Exploration of potential drug targets in the European population Summary-based Mendelian randomization (SMR) [ 22 ] is a newly developed advanced analytical approach integrating two sources of GWAS data results and expression quantitative trait loci (eQTL)data in search of pleiotropic gene expression level associated with the complex trait. eQTLs are the genetic variants that are significantly associated with the gene expression level to thereby provide some explanations for the individual variation of the gene expression [ 23 ]. By detecting associations between individual single nucleotide polymorphisms (SNPs) and gene expression, eQTL studies allow the identification of genetic variants that could affect gene expression levels and complex traits. The SMR approach utilizes both summary data from eQTL and GWAS to investigate the possible impact of SNPs on complex traits for a better understanding of the genetic etiology of diseases such as obesity and the autoimmune disorders. SMR is most often used in combination with the Heterogeneity in Dependent Instrument HEIDI test to detect pleiotropic relationships between gene expression and complex traits. The main idea of SMR is to see whether changes in gene expression represent the causal mechanism behind the effect of a SNP on a specific trait. The association of an SNP with gene expression and a complex trait in the presence of pleiotropic effects will suggest that the gene has a substantial role in the genetic basis of a particular trait under consideration. Furthermore, the HEIDI test interrogates whether this association of SNP, gene expression, and complex traits is due to colocation—if the effects of a SNP on gene expression and the trait are emanating from the same variation. If the HEIDI test is passed, it implies that the observed association is the result of colocation between different loci, providing more accurate insights into the genetic mechanisms at work. This means that the association is likely driven by distinct causal variants, rather than a single polygenic effect. In sum, we applied the SMR method not only to help identify genes associated with obesity and autoimmune diseases but also to shed light on the regulatory mechanisms linking genetic variation to phenotype, thereby offering essential clues for the discovery of potential therapeutic targets. Immuno-co-localization analysis We developed a novel immune co-localization method, building on a prior multi-trait co-localization hypothesis prioritization method, and incorporating extensive immune GWAS data encompassing 731 distinct immune cell types [ 24 ]. This approach offers significant advantages in precisely localizing the role of immune traits in complex diseases, while it can help us effectively validating potential immune mediation models. By integrating these advancements, the method provides new insights into the regulatory mechanisms of the immune system in autoimmune diseases and obesity. Immune cell data are available under accession numbers GCST0001391-GCST0002121 in the GWAS catalog. Results Shared genetic mechanisms link obesity and autoimmune diseases First, we assessed the genetic correlations link obesity and autoimmune diseases. The results obtained from LDSC and HDL were highly consistent (Table 1 and Additional file 1: Table S2 ). Specifically, the LDSC method identified genetic associations between all traits and obesity. Similarly, HDL confirmed significant genetic correlations between these conditions. Using the LDSC approach, seven traits exhibited significant genetic correlations with OB: HT, PSC, CD, RA, T1D, CeD, and PsO. Implementation of HDL analysis identified five traits with significant genetic links to OB: HT, CD, MS, RA, and PsO. Integration of both methods yielded eight obesity-associated traits: HT, PSC, CD, MS, RA, T1D, CeD, and PsO. Notably, RA, HT and PsO demonstrated robust genetic associations with OB in both methods, surviving multiple testing correction (P_ HDL and P_LDSC < 0.05/17 = 2.94 × 10⁻³). Table 1 Genetic correlation between obesity and autoimmune diseases Trait Pairs LDSC HDL r g (SE) P r g (SE) P OB&AIT -0.09685 (0.1213) 0.4246 / / OB&HT 0.07529 (0.02967) 0.01116 0.1185 (0.0261) 5.65E-06 OB&PBC -0.0408 (0.08041) 0.6118 0.0904 (0.2765) 7.44E-01 OB&PSC 0.2174 (0.1046) 0.0377 / / OB&IBD -0.05842 (0.03795) 0.1237 -0.0294 (0.0286) 3.04E-01 OB&CD 0.1556 (0.05519) 0.004799 0.1658 (0.0694) 1.69E-02 OB&UC -0.02432 (0.07235) 0.7367 -0.0352 (0.0326) 2.81E-01 OB&MS 0.02243 (0.03284) 0.4946 0.0644 (0.0275) 1.94E-02 OB&SS 0.05646 (0.1373) 0.681 / / OB&RA 0.1529 (0.04901) 0.001807 0.1807 (0.0515) 4.47E-04 OB&SLE 0.04703 (0.04271) 0.2708 / / OB&Vitiligo 0.07235 (0.113) 0.5221 / / OB&T1D -0.1506 (0.07103) 0.03403 -0.0505 (0.0326) 1.21E-01 OB&CeD -0.1534 (0.04821) 0.001464 -0.1198 (0.0774) 1.22E-01 OB&IBS 0.006604 (0.04748) 0.8894 -0.0539 (0.0494) 2.75E-01 OB&MG 0.09386 (0.1024) 0.3594 / / OB&PsO 0.2539 (0.04) 2.17E-10 0.3096 (0.0407) 2.95E-14 LDSC linkage disequilibrium score regression, HDL high-definition likelihood, SE standard error, OB obesity, AIT autoimmune thyroiditis, HT hypothyroidism, PBC primary biliary cirrhosis, PSC primary sclerosing cholangitis, IBD inflammatory bowel disease, CD Crohn’s disease, UC ulcerative colitis, MS multiple sclerosis, SS systemic sclerosis, RA rheumatoid arthritis, SLE systemic lupus erythematosus, T1D type 1 diabetes, CeD celiac disease, IBS irritable bowel syndrome, MG myasthenia gravis, PsO psoriasis Tissue enrichment Using S-LDSC, we assessed SNP heritability enrichment for obesity and autoimmune diseases across specific cells and tissues. This method was utilized to GWAS summary data from various tissues and organs to evaluate whether there was significant genetic enrichment for specific traits in these tissues. The GTEx dataset, which contains expression data for 54 human tissues, served as the basis for this analysis. We computed the regression coefficient Z-score and the corresponding p-value for each tissue and cell type to evaluate the extent of SNP heritability enrichment. All analyses were performed after adjusting for the baseline model and gene sets to control for potential confounding factors. Further tissue-specific analysis revealed that SNP loci associated with obesity and autoimmune diseases were enriched in several tissues, notably including the spleen, Cells EBV-transformed lymphocytes and Lung (Fig. 2 and Additional file 1: Table S9). MAGMA gene-level enrichment analysis We conducted MAGMA gene enrichment analysis using the FUMA tool to identify genes significantly associated with autoimmune diseases and obesity. In total, 199 significantly enriched genes were identified, including 133 unique genes (Additional file 1: Table S7). MAGMA analysis detected 42 pleiotropic genes recurrently observed across different trait-trait pairs, with CLN3 identified for eight pairs, followed by AGER (5), MMEL1 (4), and RNF5 (4). A comprehensive analysis of these genes revealed their involvement in pivotal biological pathways, such as hematopoietic cell differentiation, immune homeostasis regulation, metabolic-immune interactions and cellular stress response mechanisms. (Fig. 3 A and Additional file 1: Table S5). To gain deeper insights into their characteristics, we performed tissue-specific analysis. The results indicated that these enriched genes were significantly expressed in spleen、lung、brain cerebellum、brain cerebellar hemisphere and cells EBV-transformed lymphocytes, providing clear evidence of the genetic mechanisms shared by autoimmune diseases and obesity. (Fig. 3 B and Additional file 1: Table S6). Further enrichment analysis of the GO biological processes associated with these genes revealed significant enrichment in hematopoietic cell differentiation, immune homeostasis regulation, metabolic-immune interactions, and cellular stress response mechanisms. These processes play pivotal roles in immune dysregulation mediated by the NF-κB signaling pathway, underlying the pathogenesis of obesity and autoimmune diseases. Assessment and validation of polygenic loci linked to obesity and autoimmune diseases Given the obesity and autoimmune diseases share genetic mechanisms identified through LDSC and HDL methods, we applied a novel polygenic analysis method, PLACO, to identify potential pleiotropic SNPs associated with both conditions (Additional file 2: Fig. S1 ). The QQ plots exhibited no evidence of systematic deviation between observed and expected values, effectively excluding population stratification artifacts. (Additional file 2: Fig. S2 ). In total, we identified 10,324 potential SNP loci associated with obesity and autoimmune diseases, among which 758 passed the Bonferroni correction. Based on the PLACO results, we utilized the FUMA tool to pinpoint 52 polygenic risk loci associated with both obesity and autoimmune diseases (P 0.7) (Table 2 ). The pairwise phenotypic correlation patterns are visualized in Additional file 2: Fig. S3 ~ S9. Notably, some genomic regions are shared across different trait pairs. For instance, regions such as 16p11.2 and 6p22.1 have been implicated in multiple traits (Additional file 1: Table S4). Table 2 9 Colocalized Loci from 52 Pleiotropic Loci in Obesity & Autoimmune Diseases (PP.H4.abf>0.7). Trait pairs Locus boundary Region LeadSNPs p PP.H4.abf OB&PsO 11:27541623–27742447 11p14.1 rs10767664 1.88E-11 0.767382 OB&PsO 18:57730096–58017249 18q21.32 rs10871777;rs17700633 2.21E-14 0.851882 OB&T1D 16:28338039–29001460 16p11.2 rs12446550 2.31E-10 0.967535 OB&RA 16:28338039–28955702 16p11.2 rs6565259 5.38E-12 0.968924 OB&MS 11:122496771–122553139 11q24.1 rs6589939 2.37E-08 0.871188 OB&CD 2:25074874–25238282 2p23.3 rs916485 1.61E-12 0.925674 OB&HT 1:74977277–75014538 1p31.1 rs3843262 2.85E-08 0.842608 OB&HT 1:110078255–110225084 1p13.3 rs17024393 1.67E-08 0.982337 OB&HT 17:45766846–45823227 17q21.32 rs1808192 5.43E-11 0.848686 Lead SNP was the SNP with minimum P values within the corresponding locus.; PP.H4.abf was the posterior probability of H4 calculated using the Approximate Bayes Factor; Locus boundary was “chromosome: start-end” OB obesity, PsO psoriasis, T1D type 1 diabetes, RA rheumatoid arthritis, MS multiple sclerosis, CD Crohn’s disease, HT hypothyroidism OB obesity, HT hypothyroidism, PSC primary sclerosing cholangitis, CD Crohn’s disease, MS multiple sclerosis, RA rheumatoid arthritis, T1D type 1 diabetes, CeD celiac disease, PsO psoriasis. Drug target in the European population We utilized the SMR method to identify 92 potential drug targets within complex signals (p_SMR 0.05), then rigorously refined target criteria through comprehensive integration of PLACO analysis with FUMA, MAGMA and SMR methodologies; this multidimensional strategy pinpointed a gene set demonstrating significant associations with multiple traits (Fig. 5 and Additional file 7) that revealed robust genetic signatures across diverse tissues, while subsequent eQTL and SMR analyses jointly validated these genes' pleiotropic effects across various traits enabling precise chromosomal annotations—through cross-tissue integrative analysis (Fig. 5 ) we systematically mapped pleiotropic genes, wherein key loci including CLN3, SH2B1, ATP2A1 and MMEL1 exhibited consistent evidence across methodological frameworks, with CLN3 notably emerging as a recurrent pleiotropic hub showing significant associations for obesity comorbid with type 1 diabetes (OB-T1D) and rheumatoid arthritis (OB-RA) while maintaining conserved regulatory architecture in whole blood. Immuno-co-localization analysis The shared mechanisms of affected tissues, including spleen、lung、brain cerebellum、brain cerebellar hemisphere and cells EBV-transformed lymphocytes,, highlight the important role of immune mechanisms across various diseases. HyPrColoc was used for multi-trait colocalization analysis to identify key immune cells (Additional file 1: Table S8). Our results support the critical influence of IgD+ %B cell, IgD + CD38br AC, IgD + CD38dim %B cell, and IgD + CD38dim AC on different cells. Notably, a total of six IgD + CD38- %B cell-related immune traits were observed, including: IgD + CD38- AC on Unsw mem %B cell, IgD + CD38- AC on Unsw mem AC, IgD + CD38- AC on IgD- CD27- %B cell, IgD + CD38- AC on IgD + AC, IgD + CD38- AC on IgD- CD27- AC, and IgD + CD38- AC on IgD + CD38- %B cell. Discussion Obesity is related to autoimmune diseases through many factors. In this study, the genetic correlation of obesity with a range of autoimmune diseases has been approached from a broad genetic point of view. LDSC and HDL analyses uncovered significant genetic overlap between obesity and multiple autoimmune disorders (HT, PSC, CD, MS, RA, T1D, CeD, PsO), implying shared genetic susceptibility. Epidemiologically, obesity amplifies susceptibility to these conditions. Key evidence includes: Large cohort studies demonstrate a 3–12% increased risk of RA [ 25 ]and elevated HT risk per 5 kg/m² rise in body mass index (BMI) [ 26 ], accelerated T1D onset in children with obesity [ 27 ], and intensified PsO severity [ 28 ]. Childhood obesity operates as an established environmental trigger for MS [ 29 – 30 ]. While most associations demonstrate increased disease risk, an intriguing counterpoint emerges in RA—termed the "obesity paradox"—where obesity correlates with attenuated radiographic joint damage progression despite heightened susceptibility [ 31 – 33 ]. Proposed mechanisms center on obesity-fueled chronic inflammation, characterized by adipose tissue overproduction of cytokines (e.g., IL-6, TNF-α) and adipokine imbalance (e.g., elevated leptin, suppressed adiponectin), collectively eroding immune homeostasis and disrupting endocrine-metabolic axes [ 34 – 36 ]. We identified genetic risk loci for obesity and autoimmune diseases, including 16p11.2 and 6p22.1. Previous studies demonstrate their dual roles: The 16p11.2 locus harbors SH2B1, variants in which impair leptin/insulin signaling for energy homeostasis and lead to NF-κB-mediated immune dysregulation [ 37 – 39 ]. Additionally, CLN3 at this locus, when perturbed, disrupts lysosome-dependent inflammatory pathways, potentially involving NF-κB [ 40 – 41 ]. GWAS confirms the association of 16p11.2 with both obesity and autoimmunity. For 6p22.1, MMEL1 dysfunction alters NF-κB-driven cytokine production [ 42 ], and the enhancer variant rs13089078 alters chromatin conformation to dysregulate NF-κB signaling, disrupting immune-metabolic balance. [ 43 ] We searched the GWAS catalog (Additional file 1: Table S10) and found that the 1p31.3 locus (reported in 138 studies) is associated with multiple autoimmune disorders, including OB [ 44 ], HT [ 45 – 46 ], CD [ 47 – 48 ], MS [ 49 ], RA [ 50 – 51 ], T1D [ 52 – 53 ], CeD [ 54 – 55 ], and PsO [ 56 – 57 ].This locus harbors IL23R, PGM1, NFIA, and JAK1, variants in which are involved in disrupting IL-23/Th17 pathway modulation impacting barrier immunity and adipocyte inflammation, glycogen metabolism linking to insulin resistance and antibody glycosylation, adipogenesis regulation and immune cell differentiation, and cytokine signaling central to both metabolic and autoimmune pathologies [ 58 – 62 ]. Our study demonstrates that genes with obesity pathogenesis roles established in prior literature now show autoimmune disease associations: ATXN2L variants (regulating hypothalamic leptin signaling through RNA processing [ 63 ]) correlate with HT and T1D; BCL7C dysfunction (mediating adipocyte differentiation via SWI/SNF chromatin remodeling complexes [ 64 ]) and ZNHIT3 perturbations (modulating PPARγ transcriptional activity and adipogenesis [ 65 ]) exhibit autoimmune linkages. Conversely, literature-confirmed autoimmune-related genes reveal novel connections: BTN3A1 variants (butyrophilin family member regulating γδ T-cell activation and cytokine production in CD/T1D [ 66 – 67 ]) reconfigure adipose tissue macrophage polarization; DENND1A perturbations (GEF protein controlling clathrin-mediated endocytosis of immune receptors in CD/ CeD [ 68 ]) disrupt adipocyte leptin receptor trafficking; DOC2A polymorphisms (Ca²⁺-sensor modulating synaptic vesicle exocytosis in MS [ 69 ]) alter hypothalamic neuropeptide release—mechanisms potentially contributing to obesity pathogenesis through chronic inflammation, leptin resistance, and energy balance dysregulation respectively. Analysis of shared genetic architecture revealed common mechanisms between obesity and autoimmune diseases (including HT, PSC, CD, MS, RA, T1D, CeD, and PsO). Identified biological processes included hematopoietic cell differentiation, immune homeostasis regulation, metabolic-immune interactions, and cellular stress responses. For each disease pair, we observed significant enrichment of pleiotropy in the spleen, lung, brain cerebellum, brain cerebellar hemisphere, and EBV-transformed lymphocytes. Notably, multi-trait colocalization analysis revealed a substantial enrichment of immune signatures associated with specific B cell subsets, including IgD+ %B cell, IgD + CD38br AC, Unsw mem %B cell, Sw mem AC, PB/PC %B cell, Memory B cell %B cell, Naive-mature B cell AC, and IgD- CD38br %B cell. These subsets represent key compartments of B cell heterogeneity and homeostasis. These findings collectively underscore the pivotal role played by transitional, naive, and memory B cell subsets in autoimmune disorders and obesity. We propose that IL23R variants, SH2B1 defects, CLN3 deficiency, and ADCY3 variants play crucial roles in this context. IL23R variants dysregulate the IL-23/Th17 axis, driving inflammation in barrier tissues like the gut (linking to CD and CeD) and adipose tissue; this Th17 polarization exacerbates metabolic inflammation and tissue damage [ 70 – 71 ]. SH2B1 defects disrupt leptin signaling centrally to drive obesity and peripherally impair Treg/Th17 balance, increasing MS risk [ 72 ]. CLN3 deficiency disrupts lysosomal enzyme trafficking and autophagic reformation, promoting autoantibody production under metabolic stress [ 73 ]. Furthermore, our results highlighted the critical role of metabolic-antigen presentation crosstalk as a shared trigger. Multi-tissue analyses revealed that obesity-associated chronic inflammation drives tissue-specific risks in the spleen, lung, cerebellum, and EBV-transformed lymphocytes. Dysregulation of IL23R enhances Th17-mediated inflammation, breaking peripheral tolerance. JAK1 dysfunction impairs cytokine signaling critical for immune cell homeostasis. In B cells, SH2B1 loss impairs JAK2/STAT3-mediated leptin signaling, while ADCY3 variants reduce cAMP-dependent immunoregulation. Additionally, DOC2A-mediated vesicle release imbalance in the cerebellum exacerbates neuroinflammation, synergizing with systemic metabolic dysfunction to propagate CNS autoimmunity. Our study delineates a shared metabo-immunological axis wherein obesity propagates autoimmunity through disrupted antigen presentation, B cell dysregulation, and neuroimmune crosstalk. Limitations Our study has several limitations. First, as with many other studies, we relied on aggregate-level data instead of individual-level data, which constrains our ability to further stratify the population (e.g., by sex, age, or other demographic factors). Second, the small immune cell GWAS sample size reduces the robustness of our conclusions, highlighting the need for caution when interpreting the findings. Third, our analysis was conducted to individuals of European ancestry, which may limit the generalizability of our results to other populations or ancestry groups. Moreover, the relatively small sample size of the primary traits in our study may have diminished statistical power, further emphasizing the need for careful interpretation of the findings. Conclusions Our study established a robust genetic link between obesity and eight autoimmune diseases (HT, PSC, CD, MS, RA, T1D, CeD, PsO). We identified pleiotropic risk loci (e.g., 16p11.2 harboring SH2B1/CLN3) and 133 shared genes enriched in hematopoietic differentiation and immune-metabolic crosstalk. Tissue-specific heritability highlighted roles of spleen, whole blood, and EBV-transformed lymphocytes, while immuno-co-localization implicated IgD + CD38- B-cell subsets as key mediators. SMR prioritized 92 druggable targets (e.g., CLN3, ADCY3). These findings elucidate shared pathogenesis through chronic inflammation, lysosomal dysfunction, and B-cell dysregulation, providing a foundation for targeted therapeutics. Declarations Conflict of interest There are no conflicts of interest that directly relate to the subject matter of this article for the writers. Funding This work was supported by the Shenzhen Hospital of Traditional Chinese Medicine 3030 Programme [grant no. 28]. Sanming Project of Medicine in Shenzhen [No. SZZYSM202411016]. Author Contributions XJ created the study's concept and design, authored the article, and created the tables and figures. SL, SZ and JL helped analyse data. HZ and DL made revisions to this work. The final manuscript has been read and approved by all writers. Acknowledgments The authors sincerely thank all relevant researchers for sharing and publishing the data. References Chandrasekaran P, Weiskirchen R. The role of obesity in type 2 diabetes mellitus—An overview[J]. 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Proceedings of the National Academy of Sciences 120.31 (2023): e2308750120. Su B, et al. Janus kinase and signal transducer and activator of transcription inhibitors in type 1 diabetes and immune checkpoint–related diabetes: current status and future perspectives. Front Immunol. 2025;16:1571247. Banerjee S et al. JAK–STAT signaling as a target for inflammatory and autoimmune diseases: current and future prospects. Drugs 77.5 (2017): 521–546. Dre’Von A, Dobson, et al. Novel genetic regulators of fibrinogen synthesis identified by an in vitro experimental platform. J Thromb Haemost. 2023;21(3):522–33. Dietrich N et al. BRG1 HSA domain interactions with BCL7 proteins are critical for remodeling and gene expression. Life Sci Alliance 6.5 (2023). Rahman M, Lutfur et al. New ZNHIT3 Variants disrupting snoRNP assembly cause prenatal PEHO syndrome with isolated hydrops. MedRxiv (2024). Latha T, Sree, et al. γδ T cell-mediated immune responses in disease and therapy. Front Immunol. 2014;5:571. Karunakaran MM. The Vγ9Vδ2 T cell antigen receptor and butyrophilin-3 A1: models of interaction, the possibility of co-evolution, and the case of dendritic epidermal T cells. Front Immunol. 2014;5:648. Dutta D, Julie G, Donaldson. Sorting of clathrin-independent cargo proteins depends on Rab35 delivered by clathrin‐mediated endocytosis. Traffic 16.9 (2015): 994–1009. Groffen, Alexander JA, et al. DOC2A and DOC2B are sensors for neuronal activity with unique calcium-dependent and kinetic properties. J Neurochem. 2006;97(3):818–33. Xu W-D, et al. Association of Interleukin-23 receptor gene polymorphisms with susceptibility to Crohn’s disease: A meta-analysis. Sci Rep. 2015;5(1):18584. Schnell A, Littman DR, Kuchroo VK. TH17 cell heterogeneity and its role in tissue inflammation. Nat Immunol. 2023;24(1):19–29. Rui L. SH2B1 regulation of energy balance, body weight, and glucose metabolism. World J diabetes. 2014;5:511. Hersrud SL, Attila D, Kovács. Pearce. Antigen presenting cell abnormalities in the Cln3–/– mouse model of juvenile neuronal ceroid lipofuscinosis. Biochim et Biophys Acta (BBA)-Molecular Basis Disease. 2016;1862(7):1324–36. Supplementary Files Additionalfile1.xlsx Additionalfile2.docx Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 25 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 13 Aug, 2025 First submitted to journal 11 Aug, 2025 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7345356","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501766656,"identity":"0b202284-ef15-4770-9500-256f73425997","order_by":0,"name":"Xin Jiang","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-6945-3393","institution":"Guangzhou University of Traditional Chinese Medicine: Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Jiang","suffix":""},{"id":501766657,"identity":"f093268f-708c-4d72-8795-58bf4360ce68","order_by":1,"name":"Shunqing Li","email":"","orcid":"","institution":"Guangzhou University of Traditional Chinese Medicine: Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shunqing","middleName":"","lastName":"Li","suffix":""},{"id":501766658,"identity":"952d6c65-c684-47bd-a7c0-c77036944a03","order_by":2,"name":"Shimao Zhang","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shimao","middleName":"","lastName":"Zhang","suffix":""},{"id":501766659,"identity":"53737b9a-7f3d-473b-b6ff-7e0a9cf5bc26","order_by":3,"name":"Juntong Li","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juntong","middleName":"","lastName":"Li","suffix":""},{"id":501766660,"identity":"377bcfb0-9570-4de1-9054-95301019f475","order_by":4,"name":"Deliang Liu","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Deliang","middleName":"","lastName":"Liu","suffix":""},{"id":501766661,"identity":"819e72b8-df83-4493-ac00-731bd2f1d455","order_by":5,"name":"Hengxia Zhao","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hengxia","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-08-11 10:29:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7345356/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7345356/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-025-07422-1","type":"published","date":"2025-12-19T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89955025,"identity":"b04ae150-e359-46d8-a9a1-ce5cdd26cbdb","added_by":"auto","created_at":"2025-08-26 21:20:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384382,"visible":true,"origin":"","legend":"\u003cp\u003eStudy workflow\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/47dd4abff8e96a74ddf7ef53.jpg"},{"id":89955316,"identity":"44ddaaf4-4393-40c6-ae18-59075eeac3b2","added_by":"auto","created_at":"2025-08-26 21:28:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":779472,"visible":true,"origin":"","legend":"\u003cp\u003eTissue enrichment results based on S-LDSC. OB obesity, HT hypothyroidism, PSC primary sclerosing cholangitis, CD Crohn’s disease, MS multiple sclerosis, RA rheumatoid arthritis, T1D type 1 diabetes, CeD celiac disease, PsO psoriasis.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/d66da703cc156cd6533c3949.jpg"},{"id":89955021,"identity":"e6cf7074-b44b-4c8a-9c30-52b549ed2a7e","added_by":"auto","created_at":"2025-08-26 21:20:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2471589,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot of MAGMA gene-set(A) and tissue-specific(B) analysis for genome-wide pleiotropic results. OB obesity, HT hypothyroidism, PSC primary sclerosing cholangitis, CD Crohn’s disease, MS multiple sclerosis, RA rheumatoid arthritis, T1D type 1 diabetes, CeD celiac disease, PsO psoriasis.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/72cd447e75f34e34d5d73c15.jpg"},{"id":89955319,"identity":"9387a898-6235-4fe1-87a0-dae0e064802c","added_by":"auto","created_at":"2025-08-26 21:28:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20504043,"visible":true,"origin":"","legend":"\u003cp\u003eThe circular diagram presents pleiotropic loci and genes identified by PLACO among the trait pairs. Note: Shared loci (PP.H4.abf \u0026gt; 0.7) are highlighted in orange; shared genes are highlighted in blud.\u003c/p\u003e\n\u003cp\u003eOB obesity, HT hypothyroidism, PSC primary sclerosing cholangitis, CD Crohn’s disease, MS multiple sclerosis, RA rheumatoid arthritis, T1D type 1 diabetes, CeD celiac disease, PsO psoriasis.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/34eb6ab3b31dabed2f680b96.jpg"},{"id":89955050,"identity":"99fd4286-e57e-4600-b7c9-67b01567bcfc","added_by":"auto","created_at":"2025-08-26 21:20:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":630613,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of pleiotropic genes for obesity and the autoimmune disorders. eQTL expression quantitative trait loci, SMR summary-based Mendelian randomization.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/2f38cab1e537c0df31886883.jpg"},{"id":89955033,"identity":"0856895b-ede1-4abd-a1d5-ad82fee9a005","added_by":"auto","created_at":"2025-08-26 21:20:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":535782,"visible":true,"origin":"","legend":"\u003cp\u003ePosterior probability distribution graph(A), Scatter plot of posterior probabilities of candidate SNPs and interpretations(B), Biaxial plot of the relationship between the regional probability and the posterior probability(C) and Bar chart showing the relationship between genetic loci and traits(D)\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/a087e11b714815ed5628cd52.jpg"},{"id":98815114,"identity":"32ff5baa-5a15-4667-a3d1-1ad8b4143dba","added_by":"auto","created_at":"2025-12-22 16:13:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5718374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/197f20f2-c3bf-4af5-8a8c-5e261ead955b.pdf"},{"id":89955029,"identity":"2cb9edfa-fcb7-4ca1-81dd-2830695feb37","added_by":"auto","created_at":"2025-08-26 21:20:47","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":2770744,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/5adb2087f98174a82af9b109.xlsx"},{"id":89955317,"identity":"d58068fe-b9f5-45e9-bdad-962fe2ee68b3","added_by":"auto","created_at":"2025-08-26 21:28:47","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1155533,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7345356/v1/54ae47f56095230547a8ef78.docx"}],"financialInterests":"","formattedTitle":"Genetic Pleiotropy Underlying Obesity and Autoimmunity Disorders: A Large-Scale Cross-Trait Genome-wide Association Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eObesity is a chronic metabolic disorder marked by abnormal fat distribution or an excessive accumulation of body fat. The mechanisms underlying obesity are complex, including intricate interactions among genetics, hormones and the environment. This condition poses substantial medical challenges and is accompanied by an increased risk of complications and mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Autoimmune diseases are defined by a loss of self-tolerance, leading to pathological alterations and clinical manifestations caused by an immune response directed against self-components [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Obesity is increasingly acknowledged as a major risk factor contributing to the development and progression of autoimmune diseases. The relationship is intricate and involves various mechanisms, such as chronic inflammation, hormonal imbalances, dysbiosis of gut flora, and metabolic disorders. Conversely, autoimmune diseases can also contribute to the development of obesity through various mechanisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Compared to normal people, obese people have a 40% increased risk of rheumatoid arthritis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and a 30\u0026ndash;50% elevated risk of psoriasis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. One study found that the odds ratio for the association between obesity and hypothyroidism was approximately 2.45, indicating a strong correlation between increased body mass index (BMI) and the likelihood of developing hypothyroidism [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrently, discussions on obesity and autoimmune diseases mainly focus on molecular mechanisms, such as inflammation, hormonal changes, gut microbiota, and metabolic disorders. However, there has been little exploration from the perspective of genome-wide association studies. This highlights a significant gap within this domain and underscores the urgent need to pinpoint common risk loci linking obesity to autoimmune diseases. It is also important to recognize that traditional clinical or epidemiologic studies may face difficulties in maintaining the statistical validity of their findings.\u003c/p\u003e\u003cp\u003eHigh-definition likelihood (HDL) based on GWAS summary data [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], along with linkage disequilibrium (LD) score regression (LDSC) methods [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], has recently been created to determine if obesity and these autoimmune diseases are genetically correlated. At present, it remains unclear whether the entire genome or only a small number of loci are responsible for this genetic association. The genetic correlation, common susceptibility genes, and possible effector linkages between obesity and autoimmune illness have not yet been thoroughly explored in much of research. Cross-trait analysis using GWAS signal correlation has been proven to correctly identify common loci between disorders. Pleiotropic loci can serve as therapeutic targets, offering opportunities for simultaneous prevention and deeper insight into these diseases. A new method, \u0026ldquo;PLACO\u0026rdquo;, has also been introduced to identify pleiotropic loci at the SNP level [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, identifying specific genetic variants or loci underlying genome-wide genetic correlations is crucial for understanding the common genetic etiologies of these complex diseases. The study flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGWAS summary data source\u003c/h2\u003e\u003cp\u003eWe curated the most recent European ancestry GWAS summary data for 17 major autoimmune disorders from the FinnGen study: autoimmune thyroiditis (AIT), hypothyroidism (HT), primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC), inflammatory bowel disease (IBD), crohn\u0026rsquo;s disease (CD), ulcerative colitis (UC), multiple sclerosis (MS), systemic sclerosis (SS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), celiac disease (CeD), irritable bowel syndrome (IBS), myasthenia gravis (MG), psoriasis (PsO) and vitiligo [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The GWAS summary statistics for obesity were obtained from the IEU, which includes a total of 32858 cases and 65839 controls of European descen [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The same quality control procedure was applied across all studies. The association between \u003cb\u003eobesity\u003c/b\u003e status and SNP genotypes in each study was assessed using logistic regression, with genetic principal components included as covariates. Risk estimates were ultimately combined through \u003cb\u003efixed-effects inverse variance weighted (IVW) meta-analysis\u003c/b\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Data sources and detailed descriptions are summarized in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQuality control\u003c/h3\u003e\n\u003cp\u003eRigorous quality assurance protocols were deployed to safeguard GWAS data accuracy and reliability. To mitigate confounding effects from low-frequency variants [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], we employed a minor allele frequency (MAF) threshold\u0026thinsp;\u0026gt;\u0026thinsp;1%. This deliberate focus on common variants amplifies statistical power while dramatically curtailing false positives\u0026mdash;fortifying result robustness. Stringent QC measures acted as dual-filtering mechanism for samples and markers: Only samples surpassing 95% call rates and SNPs exceeding 99% call rates were retained; all substandard entries were discarded. This strategic synthesis of MAF filtering with uncompromising QC ensured exclusive analysis of high-fidelity data, effectively neutralizing bias and spurious associations.\u003c/p\u003e\n\u003ch3\u003eGenome-wide association study\u003c/h3\u003e\n\u003cp\u003eWe selected the LDSC approach to investigate the shared genetic architecture between obesity and the autoimmune diseases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The LDSC method leverages LD scores computed from common SNP genotypes in the European ancestry subset of the 1000 Genomes Project [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This approach offers significant utility in elucidating genetic relationships between traits, thereby providing a more direct window into the potential overlap of distinct genetic factors. A critical aspect of LDSC analysis involves the computation of the standard error (SE) of estimates using a jackknife method for bias correction. This step is paramount, as it addresses pervasive attenuation bias in genetic analyses; failure to correct for this bias can confound the results. Furthermore, the LDSC intercept furnishes valuable insight into potential population stratification between the two studies. As revealed by the intercept, this additional information delineates the genetic similarities of the investigated populations with greater precision, introducing an extra layer of validation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To further bolster these robust findings, we employed HDL methodology as an additional validation tool. HDL is grounded in likelihood theory and was specifically engineered to enhance the performance of GWAS summary data. A salient advantage of HDL over LDSC lies in its capacity to substantially reduce the variance in estimates of genetic correlation, achieving reductions of up to 60%. This sharpening of variance not only refines precision but also augments the reliability of the genetic overlap estimates [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Employing both LDSC and HDL methodologies in tandem allowed us to scrutinize our findings from complementary perspectives. This dual-layered strategy served as a rigorous safeguard, ensuring the empirical robustness and dependability of the genome-wide genetic overlap analysis results.\u003c/p\u003e\n\u003ch3\u003eTissue-related hierarchical analysis\u003c/h3\u003e\n\u003cp\u003eWe investigated herein the association of obesity with the autoimmune diseases and, importantly, tested such associations across a wide range of tissues and organs. Furthermore, we sought to investigate enrichment of SNP heritability for obesity and the four diseases within specific cells and tissues. We applied Stratified-LDSC (S-LDSC) to test genetic enrichment for specific cell and tissue types. To this end, we used the GTEx database [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] to make an estimation of the SNP heritability enrichment in a dataset comprising 54 human tissues, including various tissue and cell types. We aimed in this study to investigate genetic relationships of obesity with the autoimmune diseases, the key focus being placed on investigating such associations across the wide range of tissues and organs. By merging these data into one analytical frame, the approach allowed the further investigation of the possible biological pathways that may link these conditions through the study of the variability with which genetic factors could express in different tissue types. The current study focused primarily on the SNP heritability enrichment assessment with regard to obesity and four major diseases in single cells and tissues. Genetic enrichments were estimated using the S-LDSC approach [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It is uniquely suited to assess the genetic contribution of particular cell and tissue types, enabling a nuanced understanding of how SNP heritability might be distributed across the genome. We utilized the comprehensive dataset from the GTEx database [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], including data from 54 human tissues. Such wealth of this resource has enabled us to investigate SNP heritability enrichment not only at a broad tissue level but even at specific cell-type resolution for a finer view of the genetic underpinning of obesity and its associated diseases. By using both S-LDSC and the GTEx dataset in our study, we could observe specific patterns of genetic enrichments across tissues and cell types which have shed new light on how obesity may influence the development of these diseases through different genetic effects in various tissues.\u003c/p\u003e\n\u003ch3\u003eGene-level exploratory analysis\u003c/h3\u003e\n\u003cp\u003eOur approach in the study was to attempt to find common genetic mechanisms between obesity and the associated loci of the autoimmune diseases. We mapped the leading SNPs from each locus to their surrounding genes with the intention of finding the putative causal genes. In investigating the functional mechanism behind such shared loci, the MAGMA method was employed, an advanced technique to conduct a multi-marker effect analysis on GWAS data [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. MAGMA enabled us to further investigate the functional roles of the identified loci by accounting for LD between markers and detecting multi-marker effects with a significance threshold of \u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05/17644\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2.83\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e. This approach indeed appeared to be useful in identifying pleiotropic genes influencing multiple traits at once and further demonstrated the highly complex genetic architecture of these diseases [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We further extended our set of findings with a MAGMA gene set analysis, enabling investigation into the biological functions of the leading SNPs associated with the investigated traits. In total, \u003cb\u003e17004\u003c/b\u003e gene sets from the Molecular Signatures Database (MSigDB)were tested, including curated gene sets (c2.all) and Gene Ontology (GO) terms involving biological processes (c5.bp), cellular components (c5.cc), and molecular functions (c5.mf) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These gene sets were very broad in scope, thus providing a very rich framework to investigate the biological functions that are attached to our variants of interest. We used a Bonferroni correction for multiple testing, adjusting the significance threshold to \u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05/17004\u0026thinsp;=\u0026thinsp;2.94\u0026times;10⁻\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e to minimize the risk of a false positive result. For a more functional characteristic investigation of the mapped genes, pathway enrichment analysis using the Metascape web tool (metascape.org) was conducted. It enabled the mapping of genes into pathways in the MSigDB database [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and gave a better overview of how these loci may influence the greater biological landscape. Complementing the above analyses, we have further applied a genome-wide tissue-specific enrichment analysis of PLACO polygenic results to 54 tissues from the GTEx dataset as an important procedure in understanding how tissues genetic influences contribute toward the traits studied. Specifically, for all identified polygenic genes in each given tissue, we extracted and calculated the expression levels, averaging across them after log2 transformations. This transformation had identified differentially expressed genes (DEGs) through which there was a gained detailed mapping of the regulated genes in tissues. The direction of regulation-specific to tissues may be assessed based on the sign of the t-statistic for these DEGs, thus granularity of genetic effects across the tissues.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eHierarchical exploratory analysis of the SNP\u003c/h2\u003e\u003cp\u003eA systematic investigation of genetic associations between autoimmune diseases and obesity at the SNP level is undertaken by using pleiotropic analysis under a composite null hypothesis (PLACO) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. PLACO is one of the powerful statistical methods designed to find gene pleiotropy, which allows identification of shared genetic variation across multiple phenotypes. This was particularly helpful in identifying those SNPs which were significantly associated with multiple diseases, thus providing a deeper insight into the genetic relationship between obesity and autoimmune conditions. We defined pleiotropic variants for the analysis as those SNPs reaching a genome-wide significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻⁸. They show robust genetic association signals at numerous conditions, thus reinforcing these genetic factors in the cause of obesity and most of the examined autoimmune diseases. Identification of the said pleiotropic variants is central towards resolving shared genetic pathways connecting obesity and autoimmune diseases by an ability to offer, much clearly now, explanations as to why such conditions happen together because of a common gene. To further substantiate the biological significance of the identified pleiotropic SNPs, we utilized a functional mapping and annotation tool (FUMA) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This tool allowed us to narrow the identified risk variants down to specific genomic regions, which, for simplicity, we will call \"risk loci\". Mapping those SNPs back to their respective loci provided us with important knowledge about the functional consequences these variants could take in affecting either gene expression itself or protein functionality. Finally, we used Bayesian colocalization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to further probe the shared genetic architecture. This approach identified shared genetic risk regions between obesity and autoimmune diseases, providing a deeper understanding of the genetic interplay between the two. These findings from analysis are of utmost importance in identifying specific locations that can explain why such complex conditions often occur together.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExploration of potential drug targets in the European population\u003c/h3\u003e\n\u003cp\u003eSummary-based Mendelian randomization (SMR) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] is a newly developed advanced analytical approach integrating two sources of GWAS data results and expression quantitative trait loci (eQTL)data in search of pleiotropic gene expression level associated with the complex trait. eQTLs are the genetic variants that are significantly associated with the gene expression level to thereby provide some explanations for the individual variation of the gene expression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. By detecting associations between individual single nucleotide polymorphisms (SNPs) and gene expression, eQTL studies allow the identification of genetic variants that could affect gene expression levels and complex traits. The SMR approach utilizes both summary data from eQTL and GWAS to investigate the possible impact of SNPs on complex traits for a better understanding of the genetic etiology of diseases such as obesity and the autoimmune disorders. SMR is most often used in combination with the Heterogeneity in Dependent Instrument HEIDI test to detect pleiotropic relationships between gene expression and complex traits. The main idea of SMR is to see whether changes in gene expression represent the causal mechanism behind the effect of a SNP on a specific trait. The association of an SNP with gene expression and a complex trait in the presence of pleiotropic effects will suggest that the gene has a substantial role in the genetic basis of a particular trait under consideration. Furthermore, the HEIDI test interrogates whether this association of SNP, gene expression, and complex traits is due to colocation\u0026mdash;if the effects of a SNP on gene expression and the trait are emanating from the same variation. If the HEIDI test is passed, it implies that the observed association is the result of colocation between different loci, providing more accurate insights into the genetic mechanisms at work. This means that the association is likely driven by distinct causal variants, rather than a single polygenic effect. In sum, we applied the SMR method not only to help identify genes associated with obesity and autoimmune diseases but also to shed light on the regulatory mechanisms linking genetic variation to phenotype, thereby offering essential clues for the discovery of potential therapeutic targets.\u003c/p\u003e\n\u003ch3\u003eImmuno-co-localization analysis\u003c/h3\u003e\n\u003cp\u003eWe developed a novel immune co-localization method, building on a prior multi-trait co-localization hypothesis prioritization method, and incorporating extensive immune GWAS data encompassing 731 distinct immune cell types [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This approach offers significant advantages in precisely localizing the role of immune traits in complex diseases, while it can help us effectively validating potential immune mediation models. By integrating these advancements, the method provides new insights into the regulatory mechanisms of the immune system in autoimmune diseases and obesity. Immune cell data are available under accession numbers GCST0001391-GCST0002121 in the GWAS catalog.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eShared genetic mechanisms link obesity and autoimmune diseases\u003c/h2\u003e\u003cp\u003eFirst, we assessed the genetic correlations link obesity and autoimmune diseases. The results obtained from LDSC and HDL were highly consistent (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Specifically, the LDSC method identified genetic associations between all traits and obesity. Similarly, HDL confirmed significant genetic correlations between these conditions. Using the LDSC approach, seven traits exhibited significant genetic correlations with OB: HT, PSC, CD, RA, T1D, CeD, and PsO. Implementation of HDL analysis identified five traits with significant genetic links to OB: HT, CD, MS, RA, and PsO. Integration of both methods yielded eight obesity-associated traits: HT, PSC, CD, MS, RA, T1D, CeD, and PsO. Notably, RA, HT and PsO demonstrated robust genetic associations with OB in both methods, surviving multiple testing correction (P_ HDL and P_LDSC\u0026thinsp;\u0026lt;\u0026thinsp;0.05/17\u0026thinsp;=\u0026thinsp;2.94 \u0026times; 10⁻\u0026sup3;).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic correlation between obesity and autoimmune diseases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTrait Pairs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLDSC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003csub\u003eg\u003c/sub\u003e(SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u003csub\u003eg\u003c/sub\u003e(SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;AIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.09685\u003c/p\u003e\u003cp\u003e(0.1213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;HT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07529\u003c/p\u003e\u003cp\u003e(0.02967)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1185\u003c/p\u003e\u003cp\u003e(0.0261)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.65E-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;PBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0408\u003c/p\u003e\u003cp\u003e(0.08041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0904\u003c/p\u003e\u003cp\u003e(0.2765)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.44E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;PSC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2174\u003c/p\u003e\u003cp\u003e(0.1046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;IBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.05842\u003c/p\u003e\u003cp\u003e(0.03795)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0294\u003c/p\u003e\u003cp\u003e(0.0286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.04E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;CD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1556\u003c/p\u003e\u003cp\u003e(0.05519)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1658\u003c/p\u003e\u003cp\u003e(0.0694)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.69E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;UC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.02432\u003c/p\u003e\u003cp\u003e(0.07235)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0352\u003c/p\u003e\u003cp\u003e(0.0326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.81E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02243\u003c/p\u003e\u003cp\u003e(0.03284)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0644\u003c/p\u003e\u003cp\u003e(0.0275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.94E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;SS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05646\u003c/p\u003e\u003cp\u003e(0.1373)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;RA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1529\u003c/p\u003e\u003cp\u003e(0.04901)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1807\u003c/p\u003e\u003cp\u003e(0.0515)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.47E-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;SLE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04703\u003c/p\u003e\u003cp\u003e(0.04271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;Vitiligo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07235\u003c/p\u003e\u003cp\u003e(0.113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;T1D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1506\u003c/p\u003e\u003cp\u003e(0.07103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0505\u003c/p\u003e\u003cp\u003e(0.0326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.21E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;CeD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1534\u003c/p\u003e\u003cp\u003e(0.04821)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1198\u003c/p\u003e\u003cp\u003e(0.0774)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.22E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;IBS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.006604\u003c/p\u003e\u003cp\u003e(0.04748)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0539\u003c/p\u003e\u003cp\u003e(0.0494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.75E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;MG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09386\u003c/p\u003e\u003cp\u003e(0.1024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;PsO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2539\u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.17E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3096\u003c/p\u003e\u003cp\u003e(0.0407)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.95E-14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLDSC linkage disequilibrium score regression, HDL high-definition likelihood, SE standard error, OB obesity, AIT autoimmune thyroiditis, HT hypothyroidism, PBC primary biliary cirrhosis, PSC primary sclerosing cholangitis, IBD inflammatory bowel disease, CD Crohn\u0026rsquo;s disease, UC ulcerative colitis, MS multiple sclerosis, SS systemic sclerosis, RA rheumatoid arthritis, SLE systemic lupus erythematosus, T1D type 1 diabetes, CeD celiac disease, IBS irritable bowel syndrome, MG myasthenia gravis, PsO psoriasis\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTissue enrichment\u003c/h2\u003e\u003cp\u003eUsing S-LDSC, we assessed SNP heritability enrichment for obesity and autoimmune diseases across specific cells and tissues. This method was utilized to GWAS summary data from various tissues and organs to evaluate whether there was significant genetic enrichment for specific traits in these tissues. The GTEx dataset, which contains expression data for 54 human tissues, served as the basis for this analysis. We computed the regression coefficient Z-score and the corresponding p-value for each tissue and cell type to evaluate the extent of SNP heritability enrichment. All analyses were performed after adjusting for the baseline model and gene sets to control for potential confounding factors. Further tissue-specific analysis revealed that SNP loci associated with obesity and autoimmune diseases were enriched in several tissues, notably including the spleen, Cells EBV-transformed lymphocytes and Lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Additional file 1: Table S9).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMAGMA gene-level enrichment analysis\u003c/h2\u003e\u003cp\u003eWe conducted MAGMA gene enrichment analysis using the FUMA tool to identify genes significantly associated with autoimmune diseases and obesity. In total, 199 significantly enriched genes were identified, including 133 unique genes (Additional file 1: Table S7). MAGMA analysis detected 42 pleiotropic genes recurrently observed across different trait-trait pairs, with CLN3 identified for eight pairs, followed by AGER (5), MMEL1 (4), and RNF5 (4).\u003c/p\u003e\u003cp\u003eA comprehensive analysis of these genes revealed their involvement in pivotal biological pathways, such as hematopoietic cell differentiation, immune homeostasis regulation, metabolic-immune interactions and cellular stress response mechanisms. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Additional file 1: Table S5). To gain deeper insights into their characteristics, we performed tissue-specific analysis. The results indicated that these enriched genes were significantly expressed in spleen、lung、brain cerebellum、brain cerebellar hemisphere and cells EBV-transformed lymphocytes, providing clear evidence of the genetic mechanisms shared by autoimmune diseases and obesity. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Additional file 1: Table S6).\u003c/p\u003e\u003cp\u003eFurther enrichment analysis of the GO biological processes associated with these genes revealed significant enrichment in hematopoietic cell differentiation, immune homeostasis regulation, metabolic-immune interactions, and cellular stress response mechanisms. These processes play pivotal roles in immune dysregulation mediated by the NF-κB signaling pathway, underlying the pathogenesis of obesity and autoimmune diseases.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAssessment and validation of polygenic loci linked to obesity and autoimmune diseases\u003c/h2\u003e\u003cp\u003eGiven the obesity and autoimmune diseases share genetic mechanisms identified through LDSC and HDL methods, we applied a novel polygenic analysis method, PLACO, to identify potential pleiotropic SNPs associated with both conditions (Additional file 2: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The QQ plots exhibited no evidence of systematic deviation between observed and expected values, effectively excluding population stratification artifacts. (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In total, we identified 10,324 potential SNP loci associated with obesity and autoimmune diseases, among which 758 passed the Bonferroni correction. Based on the PLACO results, we utilized the FUMA tool to pinpoint 52 polygenic risk loci associated with both obesity and autoimmune diseases (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Additional file: Table S3 and Additional file: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Subsequent co-localization analysis ultimately identified 9 potential pleiotropic loci (PP.H4.abf\u0026thinsp;\u0026gt;\u0026thinsp;0.7) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The pairwise phenotypic correlation patterns are visualized in Additional file 2: Fig. S3\u0026thinsp;~\u0026thinsp;S9. Notably, some genomic regions are shared across different trait pairs. For instance, regions such as 16p11.2 and 6p22.1 have been implicated in multiple traits (Additional file 1: Table S4).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e9 Colocalized Loci from 52 Pleiotropic Loci in Obesity \u0026amp; Autoimmune Diseases (PP.H4.abf\u0026gt;0.7).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait pairs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus boundary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeadSNPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePP.H4.abf\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;PsO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c5\"\u003e\u003cp\u003e2.21E-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.851882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;T1D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16:28338039\u0026ndash;29001460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16p11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ers12446550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.31E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.967535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;RA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16:28338039\u0026ndash;28955702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16p11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ers6565259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.38E-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.968924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11:122496771\u0026ndash;122553139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11q24.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ers6589939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.37E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.871188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;CD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2:25074874\u0026ndash;25238282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2p23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ers916485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.61E-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.925674\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;HT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1:74977277\u0026ndash;75014538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1p31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ers3843262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.85E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.842608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;HT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1:110078255\u0026ndash;110225084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1p13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ers17024393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.67E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.982337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB\u0026amp;HT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:45766846\u0026ndash;45823227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17q21.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ers1808192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.43E-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.848686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eLead SNP was the SNP with minimum P values within the corresponding locus.; PP.H4.abf was the posterior probability of H4 calculated using the Approximate Bayes Factor; Locus boundary was \u0026ldquo;chromosome: start-end\u0026rdquo;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOB obesity, PsO psoriasis, T1D type 1 diabetes, RA rheumatoid arthritis, MS multiple sclerosis, CD Crohn\u0026rsquo;s disease, HT hypothyroidism\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOB obesity, HT hypothyroidism, PSC primary sclerosing cholangitis, CD Crohn\u0026rsquo;s disease, MS multiple sclerosis, RA rheumatoid arthritis, T1D type 1 diabetes, CeD celiac disease, PsO psoriasis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDrug target in the European population\u003c/h2\u003e\u003cp\u003eWe utilized the SMR method to identify 92 potential drug targets within complex signals (p_SMR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p_HEIDI\u0026thinsp;\u0026gt;\u0026thinsp;0.05), then rigorously refined target criteria through comprehensive integration of PLACO analysis with FUMA, MAGMA and SMR methodologies; this multidimensional strategy pinpointed a gene set demonstrating significant associations with multiple traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Additional file 7) that revealed robust genetic signatures across diverse tissues, while subsequent eQTL and SMR analyses jointly validated these genes' pleiotropic effects across various traits enabling precise chromosomal annotations\u0026mdash;through cross-tissue integrative analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) we systematically mapped pleiotropic genes, wherein key loci including CLN3, SH2B1, ATP2A1 and MMEL1 exhibited consistent evidence across methodological frameworks, with CLN3 notably emerging as a recurrent pleiotropic hub showing significant associations for obesity comorbid with type 1 diabetes (OB-T1D) and rheumatoid arthritis (OB-RA) while maintaining conserved regulatory architecture in whole blood.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eImmuno-co-localization analysis\u003c/h2\u003e\u003cp\u003eThe shared mechanisms of affected tissues, including spleen、lung、brain cerebellum、brain cerebellar hemisphere and cells EBV-transformed lymphocytes,, highlight the important role of immune mechanisms across various diseases. HyPrColoc was used for multi-trait colocalization analysis to identify key immune cells (Additional file 1: Table S8). Our results support the critical influence of IgD+ %B cell, IgD\u0026thinsp;+\u0026thinsp;CD38br AC, IgD\u0026thinsp;+\u0026thinsp;CD38dim %B cell, and IgD\u0026thinsp;+\u0026thinsp;CD38dim AC on different cells. Notably, a total of six IgD\u0026thinsp;+\u0026thinsp;CD38- %B cell-related immune traits were observed, including: IgD\u0026thinsp;+\u0026thinsp;CD38- AC on Unsw mem %B cell, IgD\u0026thinsp;+\u0026thinsp;CD38- AC on Unsw mem AC, IgD\u0026thinsp;+\u0026thinsp;CD38- AC on IgD- CD27- %B cell, IgD\u0026thinsp;+\u0026thinsp;CD38- AC on IgD\u0026thinsp;+\u0026thinsp;AC, IgD\u0026thinsp;+\u0026thinsp;CD38- AC on IgD- CD27- AC, and IgD\u0026thinsp;+\u0026thinsp;CD38- AC on IgD\u0026thinsp;+\u0026thinsp;CD38- %B cell.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eObesity is related to autoimmune diseases through many factors. In this study, the genetic correlation of obesity with a range of autoimmune diseases has been approached from a broad genetic point of view.\u003c/p\u003e\u003cp\u003eLDSC and HDL analyses uncovered significant genetic overlap between obesity and multiple autoimmune disorders (HT, PSC, CD, MS, RA, T1D, CeD, PsO), implying shared genetic susceptibility. Epidemiologically, obesity amplifies susceptibility to these conditions. Key evidence includes: Large cohort studies demonstrate a 3\u0026ndash;12% increased risk of RA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]and elevated HT risk per 5 kg/m\u0026sup2; rise in body mass index (BMI) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], accelerated T1D onset in children with obesity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and intensified PsO severity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Childhood obesity operates as an established environmental trigger for MS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While most associations demonstrate increased disease risk, an intriguing counterpoint emerges in RA\u0026mdash;termed the \"obesity paradox\"\u0026mdash;where obesity correlates with attenuated radiographic joint damage progression despite heightened susceptibility [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Proposed mechanisms center on obesity-fueled chronic inflammation, characterized by adipose tissue overproduction of cytokines (e.g., IL-6, TNF-α) and adipokine imbalance (e.g., elevated leptin, suppressed adiponectin), collectively eroding immune homeostasis and disrupting endocrine-metabolic axes [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe identified genetic risk loci for obesity and autoimmune diseases, including 16p11.2 and 6p22.1. Previous studies demonstrate their dual roles: The 16p11.2 locus harbors SH2B1, variants in which impair leptin/insulin signaling for energy homeostasis and lead to NF-κB-mediated immune dysregulation [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, CLN3 at this locus, when perturbed, disrupts lysosome-dependent inflammatory pathways, potentially involving NF-κB [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. GWAS confirms the association of 16p11.2 with both obesity and autoimmunity. For 6p22.1, MMEL1 dysfunction alters NF-κB-driven cytokine production [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and the enhancer variant rs13089078 alters chromatin conformation to dysregulate NF-κB signaling, disrupting immune-metabolic balance. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eWe searched the GWAS catalog (Additional file 1: Table S10) and found that the 1p31.3 locus (reported in 138 studies) is associated with multiple autoimmune disorders, including OB [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], HT [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], CD [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], MS [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], RA [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], T1D [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], CeD [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and PsO [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].This locus harbors IL23R, PGM1, NFIA, and JAK1, variants in which are involved in disrupting IL-23/Th17 pathway modulation impacting barrier immunity and adipocyte inflammation, glycogen metabolism linking to insulin resistance and antibody glycosylation, adipogenesis regulation and immune cell differentiation, and cytokine signaling central to both metabolic and autoimmune pathologies [\u003cspan additionalcitationids=\"CR59 CR60 CR61\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study demonstrates that genes with obesity pathogenesis roles established in prior literature now show autoimmune disease associations: ATXN2L variants (regulating hypothalamic leptin signaling through RNA processing [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]) correlate with HT and T1D; BCL7C dysfunction (mediating adipocyte differentiation via SWI/SNF chromatin remodeling complexes [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]) and ZNHIT3 perturbations (modulating PPARγ transcriptional activity and adipogenesis [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]) exhibit autoimmune linkages. Conversely, literature-confirmed autoimmune-related genes reveal novel connections: BTN3A1 variants (butyrophilin family member regulating γδ T-cell activation and cytokine production in CD/T1D [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]) reconfigure adipose tissue macrophage polarization; DENND1A perturbations (GEF protein controlling clathrin-mediated endocytosis of immune receptors in CD/ CeD [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]) disrupt adipocyte leptin receptor trafficking; DOC2A polymorphisms (Ca\u0026sup2;⁺-sensor modulating synaptic vesicle exocytosis in MS [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]) alter hypothalamic neuropeptide release\u0026mdash;mechanisms potentially contributing to obesity pathogenesis through chronic inflammation, leptin resistance, and energy balance dysregulation respectively.\u003c/p\u003e\u003cp\u003eAnalysis of shared genetic architecture revealed common mechanisms between obesity and autoimmune diseases (including HT, PSC, CD, MS, RA, T1D, CeD, and PsO). Identified biological processes included hematopoietic cell differentiation, immune homeostasis regulation, metabolic-immune interactions, and cellular stress responses. For each disease pair, we observed significant enrichment of pleiotropy in the spleen, lung, brain cerebellum, brain cerebellar hemisphere, and EBV-transformed lymphocytes.\u003c/p\u003e\u003cp\u003eNotably, multi-trait colocalization analysis revealed a substantial enrichment of immune signatures associated with specific B cell subsets, including IgD+ %B cell, IgD\u0026thinsp;+\u0026thinsp;CD38br AC, Unsw mem %B cell, Sw mem AC, PB/PC %B cell, Memory B cell %B cell, Naive-mature B cell AC, and IgD- CD38br %B cell. These subsets represent key compartments of B cell heterogeneity and homeostasis. These findings collectively underscore the pivotal role played by transitional, naive, and memory B cell subsets in autoimmune disorders and obesity.\u003c/p\u003e\u003cp\u003eWe propose that IL23R variants, SH2B1 defects, CLN3 deficiency, and ADCY3 variants play crucial roles in this context. IL23R variants dysregulate the IL-23/Th17 axis, driving inflammation in barrier tissues like the gut (linking to CD and CeD) and adipose tissue; this Th17 polarization exacerbates metabolic inflammation and tissue damage [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. SH2B1 defects disrupt leptin signaling centrally to drive obesity and peripherally impair Treg/Th17 balance, increasing MS risk [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. CLN3 deficiency disrupts lysosomal enzyme trafficking and autophagic reformation, promoting autoantibody production under metabolic stress [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, our results highlighted the critical role of metabolic-antigen presentation crosstalk as a shared trigger. Multi-tissue analyses revealed that obesity-associated chronic inflammation drives tissue-specific risks in the spleen, lung, cerebellum, and EBV-transformed lymphocytes. Dysregulation of IL23R enhances Th17-mediated inflammation, breaking peripheral tolerance. JAK1 dysfunction impairs cytokine signaling critical for immune cell homeostasis. In B cells, SH2B1 loss impairs JAK2/STAT3-mediated leptin signaling, while ADCY3 variants reduce cAMP-dependent immunoregulation. Additionally, DOC2A-mediated vesicle release imbalance in the cerebellum exacerbates neuroinflammation, synergizing with systemic metabolic dysfunction to propagate CNS autoimmunity.\u003c/p\u003e\u003cp\u003eOur study delineates a shared metabo-immunological axis wherein obesity propagates autoimmunity through disrupted antigen presentation, B cell dysregulation, and neuroimmune crosstalk.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eOur study has several limitations. First, as with many other studies, we relied on aggregate-level data instead of individual-level data, which constrains our ability to further stratify the population (e.g., by sex, age, or other demographic factors). Second, the small immune cell GWAS sample size reduces the robustness of our conclusions, highlighting the need for caution when interpreting the findings. Third, our analysis was conducted to individuals of European ancestry, which may limit the generalizability of our results to other populations or ancestry groups. Moreover, the relatively small sample size of the primary traits in our study may have diminished statistical power, further emphasizing the need for careful interpretation of the findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study established a robust genetic link between obesity and eight autoimmune diseases (HT, PSC, CD, MS, RA, T1D, CeD, PsO). We identified pleiotropic risk loci (e.g., 16p11.2 harboring SH2B1/CLN3) and 133 shared genes enriched in hematopoietic differentiation and immune-metabolic crosstalk. Tissue-specific heritability highlighted roles of spleen, whole blood, and EBV-transformed lymphocytes, while immuno-co-localization implicated IgD\u0026thinsp;+\u0026thinsp;CD38- B-cell subsets as key mediators. SMR prioritized 92 druggable targets (e.g., CLN3, ADCY3). These findings elucidate shared pathogenesis through chronic inflammation, lysosomal dysfunction, and B-cell dysregulation, providing a foundation for targeted therapeutics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThere are no conflicts of interest that directly relate to the subject matter of this article for the writers.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Shenzhen Hospital of Traditional Chinese Medicine 3030 Programme [grant no. 28]. Sanming Project of Medicine in Shenzhen [No. SZZYSM202411016].\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eXJ created the study's concept and design, authored the article, and created the tables and figures. SL, SZ and JL helped analyse data. HZ and DL made revisions to this work. The final manuscript has been read and approved by all writers.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors sincerely thank all relevant researchers for sharing and publishing the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChandrasekaran P, Weiskirchen R. The role of obesity in type 2 diabetes mellitus\u0026mdash;An overview[J]. Int J Mol Sci. 2024;25(3):1882.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbarbar B, Aga H. A Review on Autoimmune Diseases: Recent Advances and Future Perspectives[J]. AlQalam J Med Appl Sci, 2024: 718\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsigalou C, Vallianou N, Dalamaga M. Autoantibody production in obesity: is there evidence for a link between obesity and autoimmunity? Curr Obes Rep. 2020;9(3):245\u0026ndash;54. [EB/OL].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorge MD, Baker JF. 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Biochim et Biophys Acta (BBA)-Molecular Basis Disease. 2016;1862(7):1324\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"obesity, genome-wide association, autoimmune diseases, genetic effect, pleiotropy","lastPublishedDoi":"10.21203/rs.3.rs-7345356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7345356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe interrogate the joint genetic architecture of obesity and 17 autoimmune disorders through integrated cross-trait analysis, identifying 8 conditions with significant genetic correlations to obesity. Using Stratified Pleiotropic Locus Mapping (PLACO), we resolve 10,324 pleiotropic SNPs mapping to 52 risk loci, with Bayesian colocalization confirming nine causal variants. Multivariate gene annotation reveals 133 unique pleiotropic genes\u0026mdash;including CLN3, SH2B1, ATP2A1 and MMEL1\u0026mdash;enriched in hematopoietic cell differentiation and immune homeostasis pathways. Tissue-specific heritability concentrates in spleen, whole blood, and EBV-transformed lymphocytes, while immune co-localization implicates six IgD\u0026thinsp;+\u0026thinsp;CD38- %B cell-related traits as pathological conduits. Drug-target prioritization nominates 92 candidates, establishing core mechanisms for comorbidity.\u003c/p\u003e","manuscriptTitle":"Genetic Pleiotropy Underlying Obesity and Autoimmunity Disorders: A Large-Scale Cross-Trait Genome-wide Association Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 21:20:42","doi":"10.21203/rs.3.rs-7345356/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-25T07:41:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-18T07:37:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T15:25:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-08-11T06:28:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d746c5a2-5c1d-428b-bc7f-5b9f7eaac239","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:10:25+00:00","versionOfRecord":{"articleIdentity":"rs-7345356","link":"https://doi.org/10.1186/s12967-025-07422-1","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2025-12-19 15:57:13","publishedOnDateReadable":"December 19th, 2025"},"versionCreatedAt":"2025-08-26 21:20:42","video":"","vorDoi":"10.1186/s12967-025-07422-1","vorDoiUrl":"https://doi.org/10.1186/s12967-025-07422-1","workflowStages":[]},"version":"v1","identity":"rs-7345356","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7345356","identity":"rs-7345356","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}