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. 2025 Dec 19;24:28. doi: 10.1186/s12967-025-07422-1

Genetic pleiotropy underlying obesity and autoimmune disorders: a large-scale cross-trait gwas analysis in European ancestry populations

Xin Jiang 1,2, Shunqing Li 1, Shimao Zhang 2, Juntong Li 2, Deliang Liu 2, Hengxia Zhao 2,✉
PMCID: PMC12777313  PMID: 41419940

Abstract

Background

Obesity and autoimmune disorders represent a significant comorbidity burden, yet their shared genetic architecture is not fully understood. Elucidating the pleiotropic genetic basis underlying both conditions is crucial for unraveling the mechanisms driving their co-occurrence and advancing therapeutic strategies.

Methods

We conducted a large-scale cross-trait analysis integrating genome-wide association study (GWAS) summary data for obesity and 17 autoimmune diseases. Genetic correlations were assessed using LD score regression and high-definition likelihood. Cross-trait pleiotropic analysis was performed using Stratified Pleiotropic Locus Mapping (PLACO) to identify shared loci, followed by Bayesian colocalization to confirm shared causal variants. Gene-level and tissue-specific heritability analyses were conducted, and drug targets were prioritized via summary-based Mendelian randomization (SMR). Finally, immune co-localization and bidirectional Mendelian randomization were employed to elucidate immunological mechanisms and causal relationships.

Results

Our analysis identified eight autoimmune diseases with significant genetic correlations to obesity. We discovered 10,324 pleiotropic SNPs, which mapped to 52 independent risk loci, with nine loci confirmed as shared causal variants by colocalization. Gene-level analysis revealed 133 unique pleiotropic genes, including CLN3, SH2B1, and MMEL1, enriched in pathways of hematopoietic cell differentiation and immune homeostasis. Tissue-specific heritability was most prominent in the spleen, whole blood, and EBV-transformed lymphocytes. Immuno-co-localization implicated six IgD+ CD38- %B cell-related traits as key pathological conduits. Bidirectional Mendelian randomization established a causal role of obesity in hypothyroidism, psoriasis, and multiple sclerosis, while revealing an inverse causal association of type 1 diabetes with obesity risk.

Conclusions

This study demonstrates a robust shared genetic foundation between obesity and multiple autoimmune diseases, pinpointing specific pleiotropic loci, genes, and immune cell subsets. Our findings provide a mechanistic framework for their comorbidity and highlight potential targets for therapeutic intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-025-07422-1.

Keywords: Obesity, Genome-wide association, Autoimmune diseases, Genetic effect, Pleiotropy

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. 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 [2]. Compared to normal people, obese people have a 40% increased risk of rheumatoid arthritis [3], and a 30–50% elevated risk of psoriasis [4]. 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 [5].

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 limited 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 [6] along with linkage disequilibrium (LD) score regression (LDSC) methods [7], 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, Pleiotropic Analysis under Composite Null (PLACO), has also been introduced to identify pleiotropic loci at the SNP level [8]. Therefore, identifying specific genetic variants or loci underlying genome-wide genetic correlations is crucial for understanding the common genetic etiologies of these complex diseases. To address these gaps, our study integrates multiple analytical strategies. In particular, the combined application of pleiotropy analysis via PLACO and novel immune-specific co-localization represents a novel framework for uncovering the shared genetic basis and pathological conduits between obesity and autoimmune diseases. Based on the established biological interplay between metabolism and immunity, we specifically hypothesize that pleiotropic loci between obesity and autoimmune diseases are enriched in immune-metabolic pathways and map to IgD+ B-cell subsets. The study flowchart is shown in Fig. 1.

Fig. 1.

Fig. 1

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Study workflow

Methods

Gwas summary data source

This research constitutes a secondary analysis of publicly available GWAS summary statistics. 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 cholangitis (PBC), primary sclerosing cholangitis (PSC), Crohn’s disease (CD), ulcerative colitis (UC), systemic sclerosis (SS), rheumatoid arthritis (RA), celiac disease (CeD), irritable bowel syndrome (IBS), myasthenia gravis (MG), psoriasis (PsO) and vitiligo [9]. In addition, inflammatory bowel disease (IBD) data were sourced from the IIBDGC [10], multiple sclerosis (MS) from the IMSGC [11], type 1 diabetes (T1D) from the T1DGC [12], and systemic lupus erythematosus (SLE) from IEU [13]. The GWAS summary statistics for obesity(OB) were obtained from the IEU, which includes a total of 32,858 cases and 65,839 controls of European descent [14]. 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 [15]. Data sources and detailed descriptions are summarized in Additional file 1: Table S1.

Quality control

Rigorous quality assurance protocols were implemented to safeguard GWAS data accuracy and reliability. To mitigate confounding effects from low-frequency variants [16], we employed a minor allele frequency (MAF) threshold > 1%. This deliberate focus on common variants amplifies statistical power while dramatically curtailing false positives—enhancing result robustness. Stringent QC measures acted as a 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 [7]. The LDSC method leverages LD scores computed from common SNP genotypes in the European ancestry subset of the 1000 Genomes Project [17]. 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 lead to confound 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 [7]. 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 [7]. 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. A Bonferroni-corrected significance threshold of p < 0.05/17 = 2.94 × 10− 3 was applied to account for multiple testing across the 17 trait pairs.

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 [18] 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 [19]. 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 [18], which includes 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 [20]. 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 [21]. 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) [22]. 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 [22] 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 enabled the identification of differentially expressed genes (DEGs), facilitating a detailed mapping of gene regulation across 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 was undertaken using PLACO [8]. PLACO was applied with the key parameter Z_sq set to 80. Pleiotropic variants were defined as SNPs reaching a genome-wide significance threshold of p < 5 × 10− 8. To ensure variant independence, clumping was performed using the 1000 Genomes Project European population as the LD reference panel, with primary parameters set to r2 < 0.001 and a window size of 10000kb. To evaluate robustness, we performed sensitivity analyses using two additional clumping parameter sets (Liberal: r2 < 0.1, window = 500 kb; Stringent: r2 < 0.0001, window = 5000 kb). To further substantiate the biological significance of the identified pleiotropic SNPs, we utilized a functional mapping and annotation tool (FUMA) [23]. This tool was used to map SNPs to specific genomic regions, hereafter referred to as“risk loci”, and to annotate their potential functional consequences. Finally, Bayesian colocalization [24] was employed to further probe the shared genetic architecture (Additional file 2: Supplementary Methods).

Exploration of potential drug targets in the European population

Summary-based Mendelian randomization (SMR) [25] 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 [26]. 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.

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 [27]. 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.

Causal association analysis

We conducted bidirectional two-sample Mendelian randomization (MR) to evaluate potential causal pathways linking obesity and autoimmune diseases. Genetic instruments were independently selected from genome-wide significant SNPs (p < 5 × 10− 8) after LD clumping (r2 < 0.001, window size = 10,000 kb) using the 1000 Genomes European reference panel. To fortify instrument robustness, only variants with F-statistics > 10 were retained. Our analytical framework incorporated multiple MR techniques: inverse-variance weighted (IVW) [28], MR-Egger [29], weighted median [30], weighted mode [31], simple mode [31], and MR pleiotropy residual sum and outlier(MR-PRESSO) [32]. Sensitivity analyses encompassed Cochran’s Q test for heterogeneity, the MR-Egger intercept test for directional pleiotropy, and the MR-PRESSO global test. To this end, a bidirectional framework was applied, contrasting the forward (autoimmune diseases to obesity) and reverse (obesity to autoimmune diseases) causal directions.

Results

Shared genetic mechanisms link obesity and autoimmune diseases

First, we assessed the genetic correlations that 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. 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
rg(SE) P rg(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

0.0728

(0.0457)

 0.1111 0.0711 (0.0438) 1.05E-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
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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 applied 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).

Fig. 2.

Fig. 2

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Tissue 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

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. 3A 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. 3B and Additional file 1: Table S6).

Fig. 3.

Fig. 3

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Bar 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

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 < 5 × 10− 8) (Fig. 4, Additional file: Table S3 and Additional file: Fig. S1). Subsequent co-localization analysis ultimately identified 9 potential pleiotropic loci (PP.H4.abf > 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).

Fig. 4.

Fig. 4

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The circular diagram presents pleiotropic loci and genes identified by PLACO among the trait pairs. Note: Shared loci (PP.H4.abf > 0.7) are highlighted in orange; shared genes are highlighted in blud. 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

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
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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

Drug target in the European population

We utilized the SMR method with stringent criteria (p_SMR < 0.05, p_HEIDI > 0.05) to identify potential drug targets(Additional file 1: Table S12), 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 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.

Fig. 5.

Fig. 5

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Overview of pleiotropic genes for obesity and the autoimmune disorders. eQTL expression quantitative trait loci, SMR summary-based Mendelian randomization

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 disease risk. 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 (Fig. 6).

Fig. 6.

Fig. 6

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Posterior 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)

MR Estimates of Causal Effects between Autoimmune Diseases and Obesity

Our bidirectional MR analysis revealed distinct and asymmetric causal relationships between obesity and autoimmune diseases. The instrument variables(IVs) for all diseases included in the MR analyses are provided in Additional file 3. Genetic predisposition to T1D demonstrated a significant protective effect against obesity risk (IVW: OR = 0.947, 95%CI = 0.912–0.982, p = 0.004). Sensitivity analysis corroborated the robustness of this causal inference, revealing no substantial evidence of directional pleiotropy (MR – Egger intercept p = 0.711) or heterogeneity (Cochran’s Q p = 0.176). (Additional file 1: Tables S13 and Additional file 3: Fig. S10). Genetic predisposition to obesity exerted significant causal effects on the risk of three autoimmune conditions: HT (IVW: OR = 1.072, 95%CI = 1.023–1.123, p = 0.003), MS (IVW: OR = 1.108, 95%CI = 1.010–1.215, p = 0.030), and PsO (IVW: OR = 1.133, 95%CI = 1.023–1.254, p = 0.017) (Additional file 1: Tables S14 and Additional file 2: Fig. S11).

Discussion

Obesity shares a complex etiological relationship with autoimmune diseases, influenced by a multitude of 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.

Bidirectional MR analysis revealed a distinct causal asymmetry. Genetic predisposition to obesity was established as a causal risk factor for HT, PsO and MS. Conversely, evidence for reverse causality was largely absent, with the notable exception of a robust protective effect of T1D genetic liability against obesity. This clear causal architecture underscores that the shared genetic basis manifests primarily through obesity driving specific autoimmune risks, while also uncovering a unique, inverse relationship with T1D.

Epidemiologically, obesity amplifies susceptibility to these conditions. Key evidence includes: Large cohort studies demonstrate a 3–12% increased risk of RA [33] and elevated HT risk per 5 kg/m2 rise in body mass index (BMI) [34], accelerated T1D onset in children with obesity [35], and intensified PsO severity [36]. Childhood obesity operates as an established environmental trigger for MS [37, 38]. 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 [39–41]. 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 [42–44].

Building upon these epidemiological and broad genetic associations, our study employed a multi-trait framework to dissect shared mechanisms. While numerous studies have established links between obesity and individual autoimmune disorders [45, 46], our cross-trait analysis of 17 conditions simultaneously provides a unified genetic landscape that reveals broader, shared mechanisms previously unrecognized. This multi-trait framework was uniquely powered to distinguish core, universal pathways from pair-specific mechanisms. For instance, prior GWAS have independently linked the 16p11.2 locus with obesity [47] and RA [48]. Our study not only confirms this but extends its relevance to a broader autoimmune spectrum, including T1D and PsO, thereby elevating SH2B1 and CLN3 from pair-specific risk factors to central players in a shared immunometabolic axis. This ability to systematically aggregate evidence transforms a locus from a candidate in several pairwise relationships into a validated core risk region for the obesity-autoimmune comorbidity spectrum.

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 [49–51]. Additionally, CLN3 at this locus, when perturbed, disrupts lysosome-dependent inflammatory pathways, potentially involving NF-κB [52, 53]. GWAS confirms the association of 16p11.2 with both obesity and autoimmunity. For 6p22.1, MMEL1 dysfunction alters NF-κB-driven cytokine production [54], and the enhancer variant rs13089078 alters chromatin conformation to dysregulate NF-κB signaling, disrupting immune-metabolic balance [55].

Our multi-trait framework was uniquely powered to identify and prioritize pleiotropic genes with effects spanning three or more conditions, such as CLN3 and MMEL1—a level of evidence for their central role that cannot be achieved by comparing two traits in isolation.

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 [56], HT [57, 58], CD [59, 60], MS [11], RA [61, 62], T1D [63, 64], CeD [65, 66], and PsO [67, 68]. 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 [69–73].

This finding underscores a key advantage of our approach: the ability to systematically aggregate evidence from multiple single-disease studies to formally confirm and quantify the pleiotropic nature of a known locus, transforming it from a candidate in several pairwise relationships into a validated core risk region for the obesity-autoimmune comorbidity spectrum.

Furthermore, our study demonstrates bidirectional validation across the obesity-autoimmune spectrum. Genes with established roles in obesity pathogenesis such as ATXN2L variants (regulating hypothalamic leptin signaling through RNA processing [74]) correlate with HT and T1D; BCL7C dysfunction (mediating adipocyte differentiation via SWI/SNF chromatin remodeling complexes [75]) and ZNHIT3 perturbations (modulating PPARγ transcriptional activity and adipogenesis [76]) 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 [77, 78] reconfigure adipose tissue macrophage polarization; DENND1A perturbations (GEF protein controlling clathrin-mediated endocytosis of immune receptors in CD/CeD [79]) disrupt adipocyte leptin receptor trafficking; DOC2A polymorphisms (Ca2 + -sensor modulating synaptic vesicle exocytosis in MS [80]) alter hypothalamic neuropeptide release. This bidirectional validation—where our findings both corroborate hypothesized links and generate novel, mechanistically plausible hypotheses—exemplifies the discovery potential of a multi-trait framework over siloed research efforts.

Notably, our immuno-co-localization data pinpoint IgD+ CD38− B cells—which exhibit phenotypic overlap with DN2/ABCs—as a critical pathological conduit. This is corroborated by functional studies demonstrating that the T-bet+ subset within this population undergoes clonal expansion in obesity, secretes pathogenic IgG autoantibodies, and fuels metabolic inflammation [81, 82]. Our genetic analyses further indicate that obesity-predisposing variants dysregulate this specific B-cell compartment, thereby lowering the activation threshold for autoimmune initiation.

This precise cellular resolution—pinpointing a specific B-cell subset as a common conduit—is a direct benefit of the increased statistical power and multi-trait integrative approach employed here [83].

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.

Crucially, the consistency of these pathway and tissue enrichments across multiple distinct autoimmune diseases points to a common etiological thread rather than disease-specific phenomena. This convergent evidence strongly suggests that obesity contributes to autoimmunity through a limited set of fundamental biological processes, a hypothesis that is difficult to formulate based on disjointed single-disease reports.

Furthermore, multi-trait colocalization analysis demonstrated marked enrichment specifically within B cell subsets—most notably those associated with IgD+ CD38− phenotypes—highlighting their central role in mediating this comorbidity.

We propose that genetic variants in IL23R, SH2B1, CLN3, and ADCY3 play crucial roles in this context, with several representing highly prioritizable drug targets. 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 [84, 85]. SH2B1 defects disrupt leptin signaling centrally to drive obesity and peripherally impair Treg/Th17 balance, increasing MS risk [86]. CLN3 deficiency disrupts lysosomal enzyme trafficking and autophagic reformation, promoting autoantibody production under metabolic stress [87]. Among these, CLN3 and ADCY3 represent particularly high-priority and druggable targets. CLN3‘s central role in lysosomal-autophagic pathways makes it a key node for therapeutic intervention using autophagy modulators [88]. Conversely, ADCY3 is a classic GPCR pathway target, and its inhibitors are currently under investigation in metabolic trials, highlighting its direct druggability and potential for repurposing in immuno-metabolic diseases [89].

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. This further underscores the functional impact of ADCY3 dysregulation in a key immune cell population relevant to autoimmunity [89]. 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.

In conclusion, by moving beyond the constraints of single-disease analyses, our study provides a genetically validated framework that defines the core shared etiological pathways between obesity and autoimmunity. We not only replicate previously suspected links but also uncover novel pleiotropic genes, prioritize causal immune cell types, and delineate a unified metabo-immunological axis. These findings offer a foundational roadmap for developing broader therapeutic strategies that target these shared mechanisms, potentially benefiting multiple conditions simultaneously.

However, it is important to note that the mechanistic insights proposed herein are derived primarily from in silico analyses of genetic data. While these computational approaches provide compelling evidence for shared genetic architecture and suggest potential biological pathways, they do not constitute functional validation. Future studies employing experimental models are essential to definitively establish causality. For instance, the proposed role of SH2B1 in adipocyte-immune communication could be tested by using CRISPR-Cas9 to edit this gene in adipocyte-immune cell co-culture systems, followed by assessment of impacts on cytokine secretion, immune cell activation, and metabolic parameters.

Limitations

Our study has several limitations. First, as with many other studies, our analysis is entirely based on publicly available GWAS summary data instead of individual-level data, This constraint limited our ability to further stratify the population (e.g., by sex, age, or other demographic factors) and lacks experimental validation from original functional studies to confirm the proposed mechanisms. Second, all genetic fine-mapping analyses (including PLACO and colocalization) were conducted using a single LD reference panel from the 1000 Genomes Project European population. Although this is a widely adopted standard in the field, we acknowledge that employing additional LD panels would further strengthen the robustness of our pleiotropic locus identification. Third, the small immune cell GWAS sample size reduces the robustness of our conclusions, highlighting the need for caution when interpreting the findings. Fourth, a key limitation is that our analysis was conducted exclusively in individuals of European ancestry, which critically limits the generalizability of our findings to other populations and underscores the need for future replication in diverse ancestries. Fifth, we focused on SMR analysis and did not incorporate TWAS, which could offer complementary perspectives for future research on gene regulation mechanisms. 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 analysis prioritized multiple druggable targets (e.g., CLN3, ADCY3). Critically, bidirectional MR analysis confirmed obesity as a causal risk factor for HT, PsO, and MS, and revealed an inverse relationship between the genetic liability to T1D and obesity. These findings elucidate shared pathogenesis through chronic inflammation, lysosomal dysfunction, and B-cell dysregulation, providing a foundation for targeted therapeutics.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (304.5MB, txt)
Supplementary Material 2 (343.2MB, txt)
Supplementary Material 3 (297.2MB, txt)
Supplementary Material 4 (314.2MB, txt)

Acknowledgements

The authors sincerely thank all the consortia and researchers for publicly sharing the data used in this study.

Author contributions

X.J. created the study’s concept and design, authored the article, and created the tables and figures. S.L., S.Z. and J.L. contributed to data analysis. H.Z. and D.L. critically revised the manuscript. The final manuscript has been read and approved by all writers.

Funding

This work was supported by the Shenzhen Hospital of Traditional Chinese Medicine 3030 Programme [28]. Sanming Project of Medicine in Shenzhen [SZZYSM202411016].

Data availability

All data sources utilized in this study, including detailed descriptions and download links for the GWAS summary statistics, are comprehensively listed in Additional file 1: Table S1.

Declarations

Ethics approval and consent to participate

Not applicable. This study is a secondary analysis of publicly available GWAS summary statistics. No ethical approval was required.

Consent for publication

Not applicable. This manuscript does not contain any individual person’s data.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (304.5MB, txt)
Supplementary Material 2 (343.2MB, txt)
Supplementary Material 3 (297.2MB, txt)
Supplementary Material 4 (314.2MB, txt)

Data Availability Statement

All data sources utilized in this study, including detailed descriptions and download links for the GWAS summary statistics, are comprehensively listed in Additional file 1: Table S1.

Articles from Journal of Translational Medicine are provided here courtesy of BMC

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