Cross-Trait Genome-Wide Association Studies Identify Shared Genetic Risk Loci Between Psoriasis and Metabolic Syndrome and Their Associated Traits

Abstract

Background:

Psoriasis (PsO) demonstrates frequent co-occurrence with metabolic syndrome (MetS). Nevertheless, the shared genetic architecture underlying both pathological conditions remains incompletely characterized. This investigation sought to examine genetic correlations between PsO and multiple MetS-associated traits, and to identify shared genetic risk loci and genes contributing to their coexistence.

Methods:

Genome-wide association study data for PsO, MetS, and related traits in European populations were integrated to evaluate genetic associations between traits and to identify shared loci. Bayesian colocalization analysis was applied to determine whether association signals for different traits at the same locus were attributable to a common causal variant. Functional annotation and gene mapping were conducted for shared loci, followed by functional classification and pathway enrichment analyses of pleiotropic gene sets. In addition, summary data-based Mendelian randomization and transcriptome-wide association study analyses were applied to prioritize candidate genes with potential therapeutic relevance.

Results:

Significant genetic associations were observed between PsO and five metabolic traits, including body mass index, high-density lipoprotein cholesterol, triglycerides, waist circumference, and type 2 diabetes mellitus, while MetS, as a composite trait, also exhibited a genetic association with PsO. Pleiotropic Analysis under composite null hypothesis (PLACO) analysis revealed a total of 141 shared risk loci, with 22 loci substantiated by Bayesian colocalization analysis findings (PP.H4 ≥ 0.75). Multimarker analysis of genomic annotation analysis identified 195 distinct pleiotropic genes. The pathway enrichment analysis indicated that these genes were predominantly involved in immune and inflammatory pathways, transcriptional and epigenetic regulation, autophagy, and lipid–cholesterol metabolism, indicating that such biological processes may contribute to the shared genetic background of PsO and MetS-related traits. Through integrative evidence from multiple analytical approaches, three candidate therapeutic target genes, namely, KAT8, STX4, and VKORC1, were prioritized.

Conclusions:

Shared genetic loci, pleiotropic genes, and core biological pathways between PsO and multiple MetS-related traits were identified, and potential intervention targets were highlighted, providing genetic evidence to support subsequent functional investigations.

Keywords

psoriasis metabolic syndrome genome-wide association studies linkage disequilibrium score regression pleiotropic analysis under composite null hypothesis

Introduction

Psoriasis (PsO) constitutes an immune-mediated chronic inflammatory condition that impacts roughly 2%–3% of the global population. The pathogenesis of this condition exhibits complexity, constituting a multifactorial disorder arising from the synergistic influence of genetic and environmental determinants, encompassing multiple pathological processes including immune dysregulation, excessive keratinocyte proliferation, and angiogenesis.¹ At the pathophysiological level, PsO is distinguished by multilevel interactions between innate and adaptive immune systems.^2,3 Although PsO was formerly regarded as an inflammatory disorder confined to cutaneous tissues, contemporary investigations have established its systemic nature,⁴ with frequent coexistence along with various co-morbidities, including hypertension, metabolic syndrome (MetS), cardiovascular disease, gastrointestinal disorders, and mood disorders.^4,5

Among the numerous co-morbidities, the relationship between PsO and MetS demonstrates particular proximity. MetS constitutes a clinical syndrome distinguished by abdominal obesity, hypertension, dyslipidemia, and insulin (INS) resistance, fundamentally representing a pathological condition that facilitates thrombotic and inflammatory responses within the organism.⁶ The pathogenesis of this syndrome encompasses interactions between genetic and acquired determinants (e.g., high-calorie diet), which collectively result in enhanced inflammatory cytokine activity and subsequently initiate systemic inflammation.⁷ Research has established that PsO and MetS exhibit substantial commonalities in risk determinants, genetic foundations, and pathogenic mechanisms.^8,9 Within these, dysregulation of the interleukin-17 (IL-23)/T helper 17 cells (Th17) immune-inflammatory signaling cascade has been identified as a shared central pathological mechanism, whereas additional potential mechanisms encompass endoplasmic reticulum stress, pro-inflammatory cytokine secretion, excessive reactive oxygen species production, modified adipokine concentrations, and gut microbiota imbalance.¹⁰ Nevertheless, contemporary evidence concerning their correlation has been predominantly obtained from observational investigations,^11–15 and comprehensive genome-wide studies to systematically evaluate their pleiotropic effects remain insufficient. Furthermore, investigations utilizing advanced cross-trait methodologies for comprehensive exploration are comparatively limited. This knowledge deficiency restricts the thorough comprehension of the molecular mechanisms underlying PsO and MetS co-morbidity, while simultaneously constraining the advancement of associated therapeutic approaches.

In recent years, methodologies including linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) developed from genome-wide association studies (GWAS) summary statistics^16–18 have furnished efficient approaches for evaluating comprehensive genetic correlations across diverse complex phenotypes. Nevertheless, the determination of whether such comprehensive genetic correlations are propelled by limited strong-effect pleiotropic variants or arise from the aggregation of multiple weak-effect variants requires further clarification. To date, systematic assessment of genetic overlap between PsO and various MetS-associated phenotypes, characterization of shared susceptibility genes, and investigation of their potential causal pathways remain inadequately addressed. Within this context, cross-phenotype analysis of GWAS signals for identifying pleiotropic variants has emerged as an efficient approach for elucidating shared genetic architectures between disorders and precisely mapping shared genetic variants. This approach not only facilitates enhanced comprehension of disease mechanisms but also establishes significant pathways for investigating common therapeutic targets across multiple associated disorders. Progressive developments in analytical methodologies have continuously improved the effectiveness and accuracy of such investigations. The newly established pleiotropic analysis under the composite null hypothesis (PLACO) methodology represents compelling evidence, as it enables more accurate identification of pleiotropic genetic variants at the single-nucleotide polymorphism (SNP) level.^19,20 Therefore, this investigation seeks to employ sophisticated genetic analysis methods to examine the shared genetic structure between PsO and MetS, including related phenotypic characteristics; identify essential risk-associated genes and biological pathways; and explore their underlying mechanistic connections, thus providing fresh genetic insights into understanding this intricate co-morbid relationship. The comprehensive analytical framework of this investigation is presented in Figure 1.

FIG. 1.

Flowchart of the study design.

Materials and Methods

Sources of GWAS summary data

GWAS summary statistics for MetS and 10 associated traits investigated in this analysis, encompassing MetS, body mass index (BMI), waist circumference (WC), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), fasting glucose (GLU), INS, systolic blood pressure (SBP), and diastolic blood pressure (DBP), were procured from the publicly accessible IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/). GWAS data for PsO were acquired from the most recent FinnGen R12 release (https://www.finngen.fi/en), comprising 12,760 cases and 482,181 controls. To minimize potential confounding effects arising from variations in allelic frequencies and linkage disequilibrium patterns across population groups, the analysis was restricted to individuals of European ancestry. Detailed information regarding data sources and summary statistics for individual datasets can be found in Supplementary Table S1. A thorough explanation of quality control protocols is available in Supplementary Data S1.

This study utilized only publicly available, deidentified GWAS summary statistics. Therefore, ethical review and approval were waived by the Institutional Review Board of The Affiliated Hospital of Jiangxi University of Chinese Medicine.

Genome-wide genetic correlation analysis

Genetic associations between PsO and each metabolic trait, including MetS, were estimated using LDSC.²¹ To enhance result robustness, significant associations were further examined using HDL methods.

Pleiotropic risk loci identification and colocalization analysis

Shared genetic loci influencing PsO, MetS, and related traits were identified using the PLACO framework. Independent validation of these loci was conducted through cross-phenotype association analysis (CPASSOC).^22,23 Genomic mapping and functional annotation were carried out with the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) platform.²⁴ Bayesian colocalization analysis was subsequently applied to determine whether association signals across phenotypes originated from a common causal variant, with PP.H4 ≧ 0.75 defined as strong evidence of colocalization.²⁵

Functional annotation of pleiotropic loci and genes

Pleiotropic genes were identified from PLACO-derived loci using multimarker analysis of genomic annotation (MAGMA), with gene-level significance determined at PBon < 0.05.²⁶ MAGMA was further applied for gene set enrichment and tissue-specific expression enrichment analyses. Functional classification and pathway enrichment of pleiotropic genes were performed using the Metascape platform.²⁷

Summary data-based Mendelian randomization analysis

To prioritize genes potentially associated with disease susceptibility, summary data-based Mendelian randomization (SMR) was conducted. This approach integrated GWAS summary statistics with expression quantitative trait loci (eQTL) data.²⁸ Linkage-related heterogeneity was excluded using the Heterogeneity in Dependent Instruments (HEIDI) test (PHEIDI > 0.05), and statistical significance was assessed following Bonferroni correction (PBon < 0.05).

Transcriptome-wide association study

To provide additional validation for prioritized candidate genes, a transcriptome-wide association study (TWAS) was conducted using the Functional Summary-based Imputation (FUSION) method.^29,30

Detailed methodological descriptions are provided in Supplementary Data S2 (S1: LDSC/HDL; S2: PLACO/CPASSOC/COLOC; S3: MAGMA/Metascape; S4: SMR/HEIDI; S5: TWAS/FUSION).

Results

Genetic correlations between PsO and MetS and related traits

LDSC and HDL were applied to evaluate genetic associations between PsO and MetS, BMI, WC, HDL-C, TG, T1DM, T2DM, GLU, INS, SBP, and DBP. Consistent results were obtained across both analytical approaches (Table 1). LDSC analysis demonstrated significant positive genetic correlations of PsO with BMI, WC, TG, T2DM, and MetS at the nominal significance threshold (P < 0.05), whereas a significant inverse genetic correlation was observed with HDL-C. HDL analysis not only corroborated findings from the LDSC assessment but also revealed genetic correlations between PsO and T1DM, in addition to SBP. Notably, following False Discovery Rate (FDR) adjustment via the Benjamini–Hochberg procedure, genetic correlations between PsO and MetS, along with five associated traits detected through LDSC analysis, maintained statistical robustness (P_FDR < 0.05; Fig. 2 and Supplementary Table S2). To establish methodological rigor for subsequent mechanistic investigations, further analyses within this investigation concentrated on trait pairs between PsO and MetS with its associated traits that concurrently satisfied both LDSC analytical criteria (P_FDR < 0.05) and HDL analytical criteria (P < 0.05).

FIG. 2.

Genetic associations between psoriasis and metabolic syndrome and related traits. BMI, body mass index; DBP, diastolic blood pressure; GLU, fasting serum glucose; HDL-C, high-density lipoprotein cholesterol; INS, fasting serum insulin; MetS, metabolic syndrome; PsO, psoriasis; SBP, systolic blood pressure; T1DM, type 1 diabetes; T2DM, type 2 diabetes; TG, triglycerides; WC, waist circumference.

Table 1.

Genetic Associations Between Psoriasis and Metabolic Syndrome and Related Traits

Trait pairs	LDSC		HDL
Trait pairs	rg (SE)	P	rg (SE)	P
PsO&MetS	0.269 (0.039)	4.42E-12	0.312 (0.044)	8.21E-13
PsO&BMI	0.223 (0.028)	8.56E-16	0.289 (0.03)	1.50E-21
PsO&WC	0.237 (0.024)	1.95E-22	0.302 (0.032)	1.57E-21
PsO&HDL-C	−0.17 (0.028)	8.47E-10	−0.217 (0.051)	2.22E-05
PsO&TG	0.159 (0.033)	1.34E-06	0.175 (0.037)	2.66E-06
PsO&T1DM	0.147 (0.073)	0.044	0.154 (0.063)	0.015
PsO&T2DM	0.285 (0.037)	2.65E-14	0.307 (0.037)	6.15E-17
PsO&GLU	−0.074 (0.04)	0.064	−0.027 (0.044)	0.549
PsO&INS	0.03 (0.041)	0.464	0.088 (0.045)	0.054
PsO&SBP	0.027 (0.029)	0.354	0.061 (0.029)	0.034
PsO&DBP	0.023 (0.032)	0.479	0.039 (0.027)	0.146

BMI, body mass index; DBP, diastolic blood pressure; GLU, fasting serum glucose; HDL, high-definition likelihood; HDL-C, high-density lipoprotein cholesterol; INS, fasting serum insulin; LDSE, linkage disequilibrium score regression; MetS, metabolic syndrome; PsO, psoriasis; SBP, systolic blood pressure; T1DM, type 1 diabetes; T2DM, type 2 diabetes; TG, triglycerides; WC, waist circumference.

Pleiotropic genetic loci shared between PsO and MetS and related traits

After the identification of cross-trait genetic correlations, specific shared genetic variants between PsO and MetS and related traits were subsequently elucidated. The PLACO method was employed to identify pleiotropic SNPs across the six identified trait pairs, yielding 2373 genome-wide significant loci (P < 5 × 10⁻⁸; Supplementary Table S3). Among these, 1865 pleiotropic SNPs were independently validated using the CPASSOC method (Supplementary Table S4). Integration of PLACO findings with FUMA annotation identified 141 pleiotropic genomic loci shared between PsO and MetS and 5 related traits (Supplementary Table S5). The distribution comprised 18 loci shared between PsO and MetS, 14 with BMI, 43 with HDL-C, 35 with TG, 12 with WC, and 19 with T2DM. Among the 141 pleiotropic loci, 22 exhibited strong colocalization signals (PP.H4 ≥ 0.75; Fig. 3, Table 2, and Supplementary Table S5). All 22 strong colocalization signal pleiotropic loci were independently validated through CPASSOC analysis, confirming the robustness and cross-method reproducibility of these findings (Supplementary Tables S5 and S6). Additionally, loci at 6q21, 11p14.1, and 11q13.1 were associated with a minimum of five trait pairs, indicating that these genomic regions may constitute pivotal genetic convergence points in the shared pathogenesis of PsO and MetS (Supplementary Table S7).

FIG. 3.

Colocalized loci identified by colocalization analysis performed on 141 pleiotropic loci (PP.H4 ≥ 0.75).

Table 2.

Twenty-Two Colocalized Loci Identified by Colocalization Analysis Performed on 141 Pleiotropic Loci (PP.H4 > 0.75)

Trait pairs	Locus boundary	Region	Lead SNPs	P	PP.H4
PsO&MetS	2:27189063–28268742	2p23.2	rs74446114	2.50E-10	0.823
PsO&MetS	6:139824992–139844268	6q24.1	rs4896469	1.31E-13	0.996
PsO&MetS	15:31637569–31660799	15q13.3	rs28624578	3.34E-10	0.991
PsO&MetS	17:47322746–47454515	17q21.32	rs17709919	1.79E-09	0.911
PsO&MetS	18:57730096–58017249	18q21.32	rs10871777	8.14E-14	0.808
PsO&BMI	12:111826477–112906415	12q24.13	rs17696736	5.72E-10	0.956
PsO&HDL-C	2:162992004–163373152	2q24.2	rs1990760	4.82E-12	0.999
PsO&HDL-C	6:139805476–139844429	6q24.1	rs2908519	1.53E-13	0.964
PsO&HDL-C	11:109938758–110033291	11q22.3	rs653169	9.59E-17	0.919
PsO&HDL-C	12:111708458–112928596	12q24.13	rs7978923	3.98E-09	0.999
PsO&HDL-C	15:31637569–31660799	15q13.3	rs28624578	1.84E-09	0.993
PsO&HDL-C	18:57732418–58017249	18q21.32	rs619825	1.18E-08	0.780
PsO&TG	2:27069117–27990955	2p23.3	rs35656364	2.18E-09	0.939
PsO&TG	5:156370829–156484648	5q33.3	rs10044267	2.30E-09	0.791
PsO&TG	6:139805476–139842181	6q24.1	rs10872516	3.00E-13	0.971
PsO&TG	12:111826477–112906415	12q24.12	rs653178	1.65E-09	0.771
PsO&TG	15:31637569–31660799	15q13.3	rs28624578	3.35E-09	0.994
PsO&TG	17:47303456–47475549	17q21.33	rs12951873	1.78E-12	0.907
PsO&TG	18:57730096–57912226	18q21.32	rs489693	3.29E-09	0.781
PsO&T2DM	2:27548038–27752871	2p23.3	rs780096	2.31E-09	0.938
PsO&T2DM	10:75409967–75694826	10q22.2	rs2633310	1.35E-12	0.974
PsO&T2DM	18:57730096–57913434	18q21.32	rs8089364	3.21E-10	0.788

SNPs, single-nucleotide polymorphisms.

To investigate the biological significance of these pleiotropic loci, MAGMA gene set enrichment analysis was conducted. The findings demonstrated significant enrichment of these pleiotropic genes in biological processes associated with immune and inflammatory regulation. Concurrently, pathway enrichment analysis revealed gene expression hierarchies centered on transcriptional regulation. The analytical results also markedly implicated the regulation of lipid and cholesterol homeostasis. These findings collectively established a biological modular framework encompassing epigenetic-transcriptional regulation, immune inflammation, and lipid metabolism as central components (Supplementary Table S8). Tissue-specific analysis revealed that enrichment signals were predominantly concentrated within multiple central nervous system regions, including the cerebellum, cerebral cortex, frontal cortex, and anterior cingulate cortex. Significant enrichment was also observed in the terminal ileum of the digestive tract, suggesting potential involvement of the neuro–endocrine axis and gut–brain axis (Supplementary Table S9). Collectively, these findings suggest that immune inflammation, transcriptional and epigenetic regulation, and lipid-cholesterol metabolic mechanisms may serve important roles in the co-morbidity of PsO and MetS.

Identification and functional characterization of pleiotropic genes

Candidate genes corresponding to pleiotropic SNPs were identified through gene mapping via the FUMA platform, with gene-level analysis being conducted utilizing MAGMA. A total of 319 pleiotropic genes were identified across the six trait pairs, encompassing 195 unique genes (Supplementary Table S10). Notably, 40.5% (79/195) of these genes were observed across ≥2 trait pairs. Among the significant pleiotropic genes identified, RGS17 and STX4 were found to be shared across five trait pairs, whereas ATP6V0A1, BCKDK, FBXL19, KAT8, RP11-196G11.1, STX1B, TUBG2, VKORC1, ZNF646, and ZSWIM8 were shared across four trait pairs. eQTL analysis demonstrated that RGS17, STX4, ATP6V0A1, BCKDK, KAT8, STX1B, TUBG2, VKORC1, and ZNF646 exhibited significant expression in cis-eQTL and whole blood (Fig. 4 and Supplementary Table S11). Through integrative analysis using SMR, HEIDI, and TWAS, KAT8, STX4, and VKORC1 exhibited robust signals as candidate therapeutic targets in the PsO–BMI and PsO–WC associations. In addition, KAT8 and VKORC1 were also identified as significant candidate targets in the PsO–TG association (Fig. 4 and Supplementary Table S12).

FIG. 4.

Summary of eQTL and SMR analysis results across multiple tissues for pleiotropic genes identified by MAGMA. eQTL, expression quantitative trait loci; MAGMA, multimarker analysis of genomic annotation; SMR, summary data-based Mendelian randomization.

Functional characterization and assessment of pleiotropic genes as therapeutic targets

To comprehensively characterize the functional roles of the identified genes, rigorous Gene Ontology (GO) enrichment analysis was performed utilizing stringent significance criteria (P_FDR < 0.05). The analysis revealed that candidate genes exhibited significant enrichment across multiple immune regulatory networks, encompassing lymphocyte miRNA targeting mechanisms, Signal Transducer and Activator of Transcription (STAT), and Activator Protein 1 signaling cascades. Substantial enrichment was additionally observed within metabolism-associated pathways, including carbohydrate response mechanisms, INS secretion and signal transduction processes, leptin–INS interactions, and aberrant lipid signaling networks. Moreover, the analysis demonstrated enrichment patterns associated with neuro–immune interactions and stress repair mechanisms. Collectively, these findings highlighted the synergistic dysregulation among immune regulatory networks, metabolic pathways, and neuro–immune systems, which may collectively contribute to the genetic predisposition toward PsO and MetS-related phenotypes (Fig. 5A). Additionally, Protein-Protein Interaction (PPI) network analysis revealed an autophagy-associated interaction cluster consisting of SOCS3, AMBRA1, PABPC4, HGFAC, DNMT3B, MTA2, EEF1G, ATG13, and additional components (Fig. 5B), indicating the potentially significant contribution of autophagy signaling mechanisms in the co-morbidity between PsO and MetS.

FIG. 5.

Enrichment analysis of pleiotropic genes. (A) Displays the associated GO biological processes. (B) Presents the protein–protein interaction network constructed from the identified pleiotropic genes.

Discussion

This investigation utilized comprehensive genetic analytical approaches to elucidate substantial genetic correlations and shared pleiotropic loci between PsO and MetS along with multiple associated traits. These discoveries furnish novel perspectives for comprehending the common genetic foundation underlying these pathological conditions. In contrast to preceding investigations that predominantly concentrated on clinical and epidemiological associations, this investigation constitutes the initial systematic genetic evidence for the co-morbidity phenomenon between these disorders. This genetic convergence facilitates the explanation of the clinically documented elevated co-morbidity rates and establishes a framework for identifying potential therapeutic targets. The genetic association analysis indicated positive correlations of PsO with BMI, WC, TG, and T2DM, along with a negative correlation with HDL-C. Given the phenotypic complexity and heterogeneity of MetS, it was examined as an integrated trait, and a significant positive correlation with PsO was observed. These results are in alignment with multiple prior investigations. A large retrospective cohort study involving 2480, 489 individuals with T2DM reported associations between obesity-related indices and PsO risk, with BMI demonstrating a J-shaped relationship and WC exhibiting a linear increase in risk [highest category hazard ratio (HR) = 1.123, 95% confidence interval (CI): 1.091–1.156].³¹ Meta-analyses further indicated that PsO was significantly associated with an elevated prevalence of diabetes [odds ratio (OR) = 1.59, 95% CI: 1.38–1.83], and that the incidence of new-onset diabetes was increased among patients with PsO (relative risk = 1.27, 95% CI: 1.16–1.40); this association was more significant in individuals with severe PsO (OR = 1.97, 95% CI: 1.48–2.62).³² In addition, observational evidence suggested that a twofold increase in plasma triglyceride levels corresponded to a higher risk of PsO (HR = 1.26, 95% CI: 1.15–1.39).³³ Genetic analyses based on the UK Biobank also supported a positive genetic correlation between PsO and T2DM (rg = 0.19, P = 3 × 10⁻³).³⁴

Through PLACO and FUMA analyses, 141 significant pleiotropic genetic loci shared between PsO and MetS, and related traits were identified. Among these, the genomic regions 6q21, 11p14.1, and 11q13.1 were repeatedly observed across multiple trait pairs, including PsO with WC, TG, HDL-C, and MetS, consistent with prior reports describing these loci and adjacent genes. At the 6q21 locus, TRAF3 Interacting Protein 2 (TRAF3IP2) has been shown in animal models to inhibit endothelial INS signaling and impair vasodilation when ectopically expressed, indicating a potential role in obesity-related metabolic dysregulation³⁵; moreover, cellular studies have demonstrated effects of TRAF3IP2 on hyperglycemia-induced cardiomyocyte inflammation and apoptosis.³⁶ In addition, TRAF3IP2 has been identified as a susceptibility gene for PsO, with its encoded NF-κB activator 1 protein contributing to psoriatic immunopathogenesis through the mediation of IL-17 signaling.^37,38 Within the 11p14.1 region, brain-derived neurotropic factor (BDNF) has been implicated in the regulation of energy homeostasis by modulating feeding behavior, physical activity, and peripheral glucose metabolism.^39,40 Animal studies have demonstrated that, in a MetS model induced by high-fat diet intervention—characterized by significant elevations in fasting blood glucose, triglycerides/ultra-low-density lipoprotein cholesterol, total cholesterol, and the atherosclerotic index—relative BDNF expression was reduced to approximately 0.3-fold of that observed in control groups.⁴¹ Furthermore, clinical investigations have reported significantly lower plasma BDNF levels in patients with PsO compared with healthy controls,⁴² indicating a dual involvement of BDNF in psoriatic pathology and metabolic regulation. With respect to the 11q13.1 region, searches of the GWAS Catalog have linked this locus to PsO⁴³ as well as to obesity- and diabetes-related phenotypes.^44–48 Within this region, Lysine Acetyltransferase 5 (KAT5) (TIP60) could promote lipin-1 translocation to the endoplasmic reticulum via acetylation, thereby accelerating triglyceride synthesis.⁴⁹ In addition, KAT5-mediated Ras Homolog Enriched in Brain acetylation has been implicated in connecting dietary palmitic acid exposure to excessive Mechanistic Target of Rapamycin Complex 1 activation and systemic INS resistance.⁵⁰ KAT5 has also been reported to influence Th17/regulatory T cell differentiation and tissue infiltration through the regulation of Forkhead Box P3, contributing to psoriatic immunopathology.⁵¹ Collectively, the repeated identification of these pleiotropic loci and the functional attributes of neighboring genes support, at the genetic level, a shared biological foundation linking PsO-associated immune inflammation with metabolic dysregulation.

A major strength of this study is the integration of PLACO, MAGMA, FUMA, SMR, and TWAS to establish a multilayered validation framework, through which three pleiotropic genes—KAT8, STX4, and VKORC1—were identified as candidate therapeutic targets with high confidence. In the PsO–BMI and PsO–WC trait combinations, all three genes demonstrated robust target signals, whereas KAT8 and VKORC1 also emerged as significant candidates in the PsO–TG combination. Substantial evidence from prior investigations supports the targets identified through this multidimensional analytical strategy. For example, KAT8 encodes a histone acetyltransferase and serves as an important regulator of early adipocyte differentiation.^52,53 Genetic studies have demonstrated significant associations between variants at the KAT8 locus and obesity susceptibility as well as WC.^54,55 In addition, increased KAT8 expression and activity can catalyze Histone H4 Lysine 16 acetylation modification, epigenetically enhancing the transcription of pathogenic chemokines such as C-X-C Motif Chemokine Ligand 2 and C-C Motif Chemokine Ligand 3 and thereby promoting the neutrophil-dominated inflammatory cascade characteristic of PsO.⁵⁶ Similarly, STX4 has been reported to be significantly associated with BMI in a multiomics integrative analysis.⁵⁷ Experimental evidence from animal models has indicated that STX4 knockout results in impaired brown adipose tissue function, disrupted energy homeostasis, and accelerated obesity development.⁵⁸ At the cellular level, the loss of STX4 could attenuate INS-stimulated glucose uptake in adipocytes and suppress adiponectin secretion.⁵⁹ A case–control study reported significantly reduced STX4 expression in the peripheral blood of patients with PsO compared with healthy controls,⁶⁰ indicating potential involvement in the biological processes underlying PsO and obesity-related traits and supporting its candidacy as an intervention target. With respect to VKORC1, recent evidence has demonstrated its role in regulating lipid metabolism-related gene expression and lipid storage in hepatocytes, with genetic variants linked to multiple metabolic phenotypes, including hepatic fat content, obesity, and lipid profiles.⁶¹ Although direct mechanistic evidence connecting VKORC1 to PsO pathogenesis remains limited, Mendelian randomization analyses have suggested an association with PsO risk.⁶² Integrated analyses using MAGMA, GO, and PPI highlighted the enrichment of the IL-17/STAT signaling pathway, cholesterol and broader lipid metabolism pathways, and autophagy-related processes. Tissue-specific enrichment analyses indicated broad involvement of the central nervous system regions, including the cerebellum, cerebral cortex, frontal cortex, and anterior cingulate gyrus, as well as the nucleus accumbens, pituitary gland, and terminal ileum, implicating neuroendocrine regulation and the gut–brain axis in PsO–MetS co-morbidity. Overall, these candidate genes converge on interconnected regulatory layers, including immune and inflammatory signaling, epigenetic and transcriptional control, and energy and lipid metabolic networks.

Several limitations warrant consideration. First, the analyses were based on publicly available summary-level data, and potential heterogeneity may have arisen from differences in phenotype definitions, cohort composition, genotyping platforms, quality control procedures, and statistical models across contributing studies. Second, the absence of individual-level data precluded stratified analyses by sex or age, factors that may modify genetic effects. Third, the datasets were predominantly derived from populations of European ancestry, which may limit the generalizability of the results to other ethnic groups; validation in cohorts with diverse ancestral backgrounds remains necessary. Last, although the identification and characterization of shared genetic loci provide insight into genetic associations between PsO and MetS-related traits, causal direction and complete mechanistic pathways cannot yet be established. Future investigations integrating functional genomics with experimental models may enable functional validation of key loci and genes, as well as clarification of potential causal relationships and their direction between PsO and MetS-related traits.

Conclusions

A complex shared genetic structure between PsO and multiple MetS-related traits, particularly BMI, WC, HDL-C, TG, and T2DM, is delineated in this study. Through the identification of pleiotropic loci, functional genes, and shared genetic pathways linking PsO with MetS-related traits, a refined framework for understanding this co-morbidity is provided, offering genetic evidence to support subsequent functional studies and target validation, and outlining potential directions for testable therapeutic intervention.

Authors’ Contributions

X.J.: Conceptualization, methodology, formal analysis, data curation, and writing—original draft. P.Z.: Data curation and visualization. Q.Z.: Data curation and visualization. W.W.: Supervision and funding acquisition. D.W.: Review and editing and funding acquisition.

Footnotes

Acknowledgments

The authors sincerely thank the GWAS platforms for providing access to the data. They also gratefully acknowledge Bullet Edits Ltd. for professional language editing and proofreading of the article.

Author Disclosure Statement

The authors declare that they have no competing interests in relation to this study.

Funding Information

This work was supported by the Natural Science Foundation of Jiangxi Province (Grant No. 20212BAB206063) and the Technology Innovation Team Development Program of Jiangxi University of Traditional Chinese Medicine (Grant No. 22009).

Supplemental Material

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