Abstract Background Chronic kidney disease (CKD) is a globally prevalent chronic condition characterized by progressive renal function decline, imposing significant economic and psychological burdens on patients. Metabolic syndrome (MetS), characterized by obesity, hypertension, hyperglycemia, and dyslipidemia, is a significant risk factor for CKD. A strong epidemiological association exists between CKD and MetS. This study explores the genetic connections between MetS-related diseases and CKD, focusing on identifying shared risk loci, key tissues, and underlying genetic mechanisms. Methods We performed a cross-trait pleiotropy analysis using summary-level GWAS data from ten MetS-related diseases and CKD obtained from the IEU database to detect shared pleiotropic loci and genes. Functional annotation and tissue-specific analyses were conducted to reveal potential associations between CKD and MetS. Additionally, we used metabolite colocalization methods to explore the metabolic perspective of these diseases’ associations. Finally, Mendelian randomization (MR) was employed for further association analysis. Results The study identified shared genetic mechanisms between mental disorders and prostatitis, revealing 1,437 pleiotropic loci at genome-wide significance. Forty-four dominant risk SNP loci were annotated, with 11 loci confirmed through causal colocalization analysis. Further gene-level analysis identified eight unique pleiotropic genes, including APOC1, APOE, BICC1, and PDILT. Pathway analysis identified the significant involvement of the Metabolism of Fat-Soluble Vitamins, Positive Regulation of Plasma Membrane-Bounded Cell Projection Assembly, and Positive Regulation of RNA Metabolic Process pathways in these diseases. Tissue enrichment analyses at the SNP and gene levels indicated that pleiotropic mechanisms play crucial roles in the Adipose Visceral Omentum, Brain Cerebellum, and Testis. Ultimately, phenotypic-level metabolite colocalization analysis revealed a metabolic intermediary mechanism linking MetS-related diseases and CKD. Conclusion This study uncovers the complex genetic interactions between CKD and MetS-related diseases, identifying shared genetic loci and biological pathways, providing novel insights for future therapeutic strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-025-00472-7. Introduction Chronic kidney disease (CKD) affects about 13.4% of the global population, impacting an estimated 850 million people worldwide [[36]1]. CKD can advance to end-stage renal disease (ESRD), necessitating dialysis or kidney transplantation for patients. These treatments are not only costly but also remain inaccessible in many regions, posing a significant burden on global healthcare systems [[37]2]. CKD impacts various organ systems, heightening the risk of complications like cardiovascular disease, stroke, and cognitive impairment [[38]3, [39]4]. Cardiovascular disease, in particular, remains the leading cause of mortality among CKD patients [[40]5]. Therefore, CKD has become a critical issue in the field of global public health. Metabolic syndrome (MetS), encompassing obesity, hypertension, hyperglycemia, and dyslipidemia, is a recognized risk factor for CKD. Studies have shown that individuals with metabolic syndrome have a significantly higher risk of developing CKD, with approximately 22.7% of those with MetS eventually progressing to CKD [[41]6]. MetS contributes to kidney damage through multiple mechanisms, with hypertension, a key component of the syndrome, being closely linked to the onset of CKD. Elevated triglyceride and fasting blood glucose levels are significantly linked to CKD prevalence in hypertensive individuals [[42]7]. Moreover, MetS-induced inflammatory response and oxidative stress are key mechanisms in kidney injury. Oxidative stress can lead to endothelial dysfunction, thereby exacerbating damage to the renal tubules and glomeruli [[43]8]. Genetic factors significantly influence the link between MetS and CKD. Genetic predisposition can impact the severity of MetS characteristics, thereby influencing CKD onset and progression [[44]9]. Therefore, MetS is not only a risk factor for CKD but also closely related to the pathogenesis of CKD through several pathological pathways. High-definition likelihood (HDL) analysis, utilizing summary data from genome-wide association studies (GWAS) and linkage disequilibrium score regression (LDSC) methods, has been extensively used in recent years to identify genetic correlations between complex traits [[45]10, [46]11]. However, it remains unclear whether these genetic correlations are driven by a few key loci or by widespread polygenic effects. Systematic studies on the genetic overlap, shared susceptibility genes, and potential effects between metabolic syndrome-related diseases and CKD remain limited. Analyzing correlations of GWAS signals across traits is effective in identifying shared loci between diseases or traits [[47]12, [48]13]. These pleiotropic loci are potential therapeutic targets and may provide insights into the mechanisms of related diseases or aid in their prevention. Recently, a novel approach called “PLACO” was proposed to identify pleiotropic genetic loci at the Single Nucleotide Polymorphism (SNP) level [[49]14]. We employed PLACO in our study to identify shared SNP-level pleiotropic loci between CKD and MetS-related diseases. Furthermore, we performed a batch two-sample bidirectional Mendelian randomization analysis to explore potential causal associations between CKD and MetS traits. Identifying genetic variants or loci responsible for genome-wide correlations and exploring the shared genetic basis of complex diseases is of substantial research importance. A flowchart of our study is presented in Fig. [50]1. Fig. 1. [51]Fig. 1 [52]Open in a new tab Flowchart of the entire research process Materials and methods GWAS summary data sources and quality control This study utilized summary-level GWAS data for metabolic syndrome-related diseases from the IEU GWAS database. All included GWAS studies had sample sizes exceeding 300,000, and the most recent available versions of the datasets were selected from the database. The metabolic syndrome-related diseases studied include the following ten conditions: atrial fibrillation, coronary atherosclerosis, cardiac arrhythmias, diabetes, hypertension, hyperlipidemia, ischemic stroke, obesity, venous thromboembolism, and hypercholesterolemia. For CKD, we used the most recent N14_CHRONKIDNEYDIS GWAS dataset from the Finnish r9 version database, which includes 11,265 cases and 436,208 controls. All GWAS data used in this study were based on European ancestry samples to ensure population consistency across analyses. Table [53]S1 in the supplementary material details the sources and characteristics of all datasets. Furthermore, all included GWAS data underwent a consistent quality control process. The specific quality control steps are described in the “GWAS Data Quality Control” section of the supplementary material. All GWAS data were directly downloaded from publicly available online databases without any restrictions or permissions required. Genome-wide genetic association tendencies This study utilized the LDSC method [[54]11] to evaluate the common genetic architecture between metabolic syndrome-related diseases and CKD. The LDSC analysis utilized LD scores derived from common SNP genotypes of European ancestry samples in the 1000 Genomes Project [[55]15]. The Jackknife method in LDSC was used to estimate standard errors (SE) and correct for potential attenuation bias. Additionally, the LDSC intercept was used to evaluate the potential population overlap across different studies. Given that the GWAS data for CKD and metabolic syndrome used in this study were sourced from different databases and did not involve any population overlap, this characteristic effectively minimized biases arising from population overlap, thereby enhancing the reliability of the study’s conclusions. In our analysis, approximately 1.2 million common SNPs overlapped across the CKD and MetS GWAS datasets. Moreover, all LDSC intercept values were near zero (|intercept| < 0.05), confirming negligible sample overlap bias. To confirm the robustness of the LDSC findings, the study utilized the HDL method, a likelihood-based genetic association analysis tool. HDL improves the estimation of genetic correlations by more efficiently utilizing summary statistics from GWAS. Compared to LDSC, HDL uses Maximum Likelihood Estimation (MLE), which significantly reduces the variance in genetic association estimates under the same data conditions. This variance reduction by half (compared to LDSC) greatly improves the precision and robustness of the analysis [[56]10]. The validation through HDL further strengthened the credibility of the genome-wide genetic overlap analysis in this study. Organ-specific association hierarchical analysis To further investigate the extent of association between metabolic syndrome-related diseases and CKD across different tissues and organs, this study analyzed the SNP-based genetic enrichment in specific cells and tissues. We utilized Stratified Linkage Disequilibrium Score Regression (S-LDSC) to evaluate genetic enrichment significance across various cell and tissue types. We obtained data for 54 human tissues from the Genotype-Tissue Expression (GTEx) database [[57]16] to assess SNP-based genetic enrichment across diverse tissues and cells. S-LDSC analysis revealed significant genetic enrichment in specific tissues and organs, offering insights into the biological mechanisms linking metabolic syndrome-related diseases with CKD. Gene-level exploration analysis To further investigate the shared biological mechanisms involved in the identified pleiotropic loci, this study precisely mapped the genes near each locus based on the leading SNP at each site. We utilized the Multi-Marker Analysis of Genomic Annotation (MAGMA) to analyze GWAS data for identifying potential biological functions of pleiotropic loci. Using MAGMA gene analysis, we identified pleiotropy-associated genes by accounting for linkage disequilibrium between markers and assessing their combined multi-marker effects. We applied a Bonferroni correction based on the number of genes tested (18,345), setting the significance threshold at P < 0.05/18,345 ≈ 2.73 × 10⁻⁶. We performed MAGMA gene set analysis to investigate the biological roles of the leading SNPs. We tested 10,678 gene sets from the Molecular Signatures Database (MSigDB), encompassing curated gene sets (c2.al) and Gene Ontology (GO) terms (c5.bp, c5.cc, c5.mf).To avoid false positive results, all tested gene sets were subjected to Bonferroni correction (P < 0.05/10,678 = 4.68 × 10^− 6).We utilized the Metascape web tool (metascape.org) for pathway enrichment analysis to systematically determine the functions of mapped genes, referencing the MSigDB database [[58]17]. SNP-level exploration analysis This study utilized pleiotropy analysis under the composite null hypothesis (PLACO) to systematically identify SNP-level genetic associations between metabolic syndrome and chronic kidney disease [[59]14]. PLACO is a statistical tool specifically designed to detect gene pleiotropy, capable of identifying genetic variants shared between multiple phenotypes. This method provides an important approach for uncovering common genetic mechanisms between different diseases. In our study, SNPs achieving genome-wide significance (P < 5 × 10^− 8) were identified as pleiotropic variants. These SNPs showed significant genetic links to various phenotypes, indicating their potential importance in disease onset and progression. Identifying pleiotropic variants is essential for uncovering the shared genetic foundation between metabolic syndrome-related diseases and chronic kidney disease. To further validate the biological functions of these pleiotropic SNPs, we performed genomic region localization analysis using a functional mapping and annotation tool (FUMA) [[60]18]. FUMA enables precise localization of SNPs to specific genomic regions, providing support for a deeper understanding of the functional significance of these variants. Additionally, we conducted a Bayesian colocalization analysis to further assess the credibility of the metabolic syndrome-related diseases and major shared risk loci, thus providing a robust genetic basis for the study. Potential exploration of drug targets in European populations This study employed the summary-based Mendelian randomization (SMR) approach, combining GWAS summary data with eQTL data, to detect gene expression levels linked to complex traits through pleiotropy. eQTL denotes genetic variations linked to gene expression levels. By revealing the association between specific SNPs and gene expression levels, eQTL studies can determine which genetic variants may regulate differences in gene expression. In our study, the SMR method combined eQTL and GWAS data to explore the potential relationship between pleiotropic SNPs associated with chronic kidney disease and metabolic syndrome-related diseases, and gene expression levels. Using SMR and the Heterogeneity in Dependent Instrument (HEIDI) test, we evaluated whether there is a pleiotropic association between gene expression levels and complex traits. SMR aims to determine if an SNP’s impact on a phenotype is mediated by alterations in gene expression. An SNP linked to both gene expression and complex traits implies a pleiotropic mechanism, suggesting the gene’s significant role in the genetic foundation of these traits. The HEIDI test assesses whether the association results from colocalization by determining if an SNP’s influence on gene expression and complex traits originates from the same causal variant. If the HEIDI test reveals that this association is driven by colocalization between different loci, rather than a single pleiotropic effect, it allows for a more precise interpretation of the genetic mechanisms underlying these complex traits. Through the SMR approach, we identified key genes with pleiotropy between chronic kidney disease and metabolic syndrome-related diseases, and uncovered the regulatory mechanisms between genetic variants and phenotypes. These findings provide important clues for exploring new drug targets and lay a solid foundation for understanding the genetic basis of the related diseases. Metabolic colocalization analysis Building on the previously proposed polytrait colocalization hypothesis-prioritization method, this study integrates extensive blood metabolite GWAS data to explore the potential associations between metabolic syndrome-related diseases and chronic kidney disease. This study utilized a blood metabolite database containing GWAS data for 1,400 metabolites and their related ratios. We focused on lipid, energy metabolism, and carbohydrate metabolites, selecting GWAS data for 425 metabolites, and developed a novel metabolic colocalization method. This improved approach demonstrated significant advantages in accurately pinpointing the role of blood metabolites in complex diseases, while also effectively identifying and validating potential mediation models for blood metabolites. This method offers a new perspective for understanding the regulatory mechanisms of blood metabolites in metabolic syndrome-related diseases and chronic kidney disease. The blood metabolite data utilized in this study are accessible to the public (GWAS catalog numbers GCST90199621-GCST90201020), with comprehensive methodologies outlined in the Supplementary Methods and Figures. After identifying key blood metabolites that mediate the relationship between metabolic syndrome-related diseases and chronic kidney disease, we standardized the names of the metabolites using the PubChem database. Pathway enrichment analysis was performed with MetaboAnalyst to identify key metabolic pathways associated with the comorbidity of metabolic syndrome and chronic kidney disease. Association analysis This study employed bidirectional two-sample Mendelian randomization analysis to assess the causal impact of diseases related to metabolic syndrome on chronic kidney disease. We conducted bidirectional two-sample Mendelian randomization (MR) analyses in batches, using metabolic syndrome-related diseases and chronic kidney disease as exposure and outcome respectively. We employed the “clumping” method in PLINK 1.9 to identify independent significant SNPs (P < 5 × 10^− 8) linked to metabolic syndrome-related diseases. To maintain the independence of instrumental variables (IVs), an r² threshold of 0.001 and a clumping window of 10,000 kb were established. The calculation of r² was based on the data from the third phase of the 1000 Genomes Project, which served as the reference panel. To assess the strength of the instrumental variables, we used the F-statistic (F > 10) as the selection criterion. For validating causal relationships, we employed five Mendelian Randomization (MR) analysis methods on each set of instrumental variables: Inverse Variance Weighted (IVW), Weighted Median, Weighted Mode, Simple Mode, and MR-Egger regression. To test for horizontal pleiotropy, we further performed a global test using the intercept from the MR-Egger regression. Detailed analysis workflows and parameter settings can be found in the Supplementary Methods and Figures. All Mendelian randomization (MR) studies were conducted in strict adherence to the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) guidelines. The detailed STROBE-MR checklist is provided in the Supplementary Material section. Software and packages The main statistical analysis utilized R (version 4.3.3). LDSC and S-LDSC analyses were performed using version 1.0.1 of the “LDSC” software [[61]13]. PLACO was implemented using the “PLACO” package [[62]14]. Bayesian colocalization analysis was performed using the “coloc” package (version 5.2.1) [[63]14], while HyPrColoc employed the “hyprcoloc” package (v1.0) [[64]19]. Functional analysis was performed using the FUMA web tool [[65]18]. MAGMA software facilitated both gene and gene-set analyses [[66]20]. Mendelian randomization analyses were performed using the “MendelianRandomization” (v0.9.0) [[67]21] and “mr.raps” (v0.4.1) [[68]22] packages. Result Shared genetic architecture between CKD and MetS-related diseases We initially assessed the genetic correlations to explore the common genetic architecture between CKD and MetS-related diseases. The findings demonstrated strong consistency between the LDSC and HDL analyses (refer to Table [69]1). Specifically, using LDSC, we identified seven MetS-related diseases that exhibited significant genetic correlations with CKD, including coronary artery disease (CAD), diabetes (DB), essential hypertension (EH), hyperlipidemia (HL), ischemic stroke (IS), obesity (OB), and hypercholesterolemia (HC). In the HDL analysis, we observed not only significant genetic correlations between the seven aforementioned diseases and CKD but also identified atrial fibrillation (AF) as another disease showing a genetic correlation with CKD. To enhance the robustness of subsequent analyses, we focused on the intersection of positive findings from both methods, ultimately including CAD, DB, EH, HL, IS, OB, and HC for further investigation. Notably, the genetic correlations between DB, EH, IS, OB, and HC with CKD were highly significant (P < 0.0001), indicating that these diseases may have a stronger association with CKD due to a shared genetic basis. Table 1. Genetic correlation between chronic kidney disease and metabolic syndrome-related disorders Trait pairs LDSC HDL r[g] (SE) P r[g] (SE) P CKD&AF 0.216(0.115) 0.059 0.246 (0.117) 0.036 CKD&CAD 0.223(0.069) 0.001 0.297 (0.083) 3.29*10^− 4 CKD&CAR -0.376(0.467) 0.420 0.041 (0.107) 0.700 CKD&DB 0.585(0.089) 5.27*10^− 11 0.698 (0.118) 3.02*10^− 9 CKD&EH 0.47(0.065) 6.14*10^− 13 0.576 (0.086) 2.60*10^− 11 CKD&HC 0.407(0.086) 1.92*10^− 6 0.563 (0.121) 3.03*10^− 6 CKD&HL 0.525(0.195) 0.007 0.455 (0.133) 6.24*10^− 4 CKD&IS 0.477(0.104) 4.56*10^− 6 0.884 (0.279) 0.002 CKD&OB 0.534(0.119) 7.66*10^− 6 0.514 (0.121) 2.09*10^− 5 CKD&VTE 0.123(0.119) 0.298 0.131 (0.142) 0.356 [70]Open in a new tab LDSC linkage disequilibrium score regression, HDL high-definition likelihood, SE standard error, AF atrial fibrillation, CAD coronary artery disease, CAR cardiac arrhythmias, DB diabetes, EH hypertension, HL hyperlipidaemia, IS ischemic stroke, OB Obesity, VTE venous thromboembolism, HC hypercholesterolaemia Organ-level association results The study utilized the S-LDSC method to assess SNP heritability enrichment for MetS and CKD in particular tissues and organs. S-LDSC utilized summary GWAS data from multiple tissues and organs to evaluate the genetic enrichment significance of specific traits across these tissues. We obtained data for 54 human tissues from the GTEx database. The significance of SNP heritability enrichment across tissues and cell types was assessed using Z-scores and P-values of regression coefficients, after adjusting for the baseline model and all gene sets. The results revealed significant SNP heritability enrichment in cardiovascular-related regions, such as the coronary artery (Artery_Coronary), aorta (Artery_Aorta), and tibial artery (Artery_Tibial). Interestingly, the genetic enrichment of MetS-related diseases and CKD was found to be lowest in the nervous system-related organs, suggesting that the nervous system may contribute less to the genetic basis of these two disease categories. For specific analysis results and significant differences, please refer to Fig. [71]2, with detailed statistical results provided in Table [72]S2. Fig. 2. [73]Fig. 2 [74]Open in a new tab Heatmap depicting the correlation results between traits and various organs calculated via sLDSC. Abbreviations: AF atrial fibrillation; CAD: Coronary Artery Disease; CAR cardiac arrhythmias; CKD chronic kidney disease; DB diabetes; EH hypertension; HL hyperlipidaemia; IS ischemic stroke; OB Obesity; HC hypercholesterolaemia; VTE venous thromboembolism Identification and evaluation of pleiotropic loci for MetS and CKD Leveraging the genetic connections between CKD and MetS-related diseases identified through LDSC and HDL methods, this study utilized the pleiotropy analysis tool, PLACO, to systematically detect potential pleiotropic loci linked to these eight diseases. A total of 1,437 novel pleiotropic SNP loci (P < 5 × 10^− 8) associated with CKD and MetS-related diseases were identified. Detailed results of the PLACO analysis are shown in Supplementary Figure [75]S1. Based on the PLACO findings, we further utilized the FUMA tool to filter 44 pleiotropic genomic risk loci associated with CKD and MetS-related diseases. In subsequent colocalization analysis, 11 potential pleiotropic loci with higher colocalization probabilities (PP.H4 > 0.7) were confirmed, including: rs429358, rs1649068, rs11039162, rs557675, rs71384446, rs201284854, rs1341344, rs7451008, rs10224210, and rs10776752. Notably, rs429358 exhibited higher colocalization probabilities in both CKD & CAD and CKD & DB. Detailed analysis of these loci is presented in Table [76]2, with the visualized colocalization results shown in Figures [77]S2-[78]5, and the full colocalization results are provided in Supplementary Table [79]S3. Table 2. Eleven colocalized loci were identified through a colocalization analysis conducted on 44 pleiotropic loci (PP.H4 > 0.7) Trait pairs Chr Locus boundary Region Lead SNP P [PLACO] PP.H4.abf CKD&CAD 19 45,392,254–45,428,234 19q13.32 rs429358 1.825E-12 0.8855 CKD&DB 19 45,392,254–45,424,351 19q13.32 rs429358 6.643E-10 0.9338 CKD&EH 10 60,253,364–60,374,898 10q21.1 rs1649068 2.161E-12 0.8044 CKD&EH 11 46,949,601–48,675,371 11p11.2 rs11039162 7.856E-13 0.7897 CKD&EH 11 65,501,060–65,566,719 11q13.1 rs557675 1.406E-12 0.9628 CKD&EH 16 20,347,156–20,414,776 16p12.3 rs71384446 1.121E-22 0.9998 CKD&EH 1 113,022,154–113,247,563 1p13.2 rs201284854 4.64E-11 0.7434 CKD&EH 1 56,756,907–56,761,001 1p32.2 rs1341344 3.128E-08 0.8378 CKD&EH 6 20,652,717–20,703,952 6p22.3 rs7451008 2.155E-08 0.8812 CKD&EH 7 151,402,852–151,415,536 7q36.1 rs10224210 1.167E-14 0.9939 CKD&IS 1 113,038,761–113,055,752 1p13.2 rs10776752 1.756E-08 0.8181 [80]Open in a new tab Notes: Lead SNP was the SNP with minimum P values within the corresponding locus. PP.H4 was the posterior probability of H4 calculated by coloc analysis; the Locus boundary was defined as “start–end” Abbreviations: PP.H4 the posterior probability of H4, CAD coronary artery disease, DB diabetes, EH hypertension, IS ischemic stroke Additionally, this study identified several pleiotropic regions that appeared repeatedly across multiple trait pairs, including 6q25.3, 10q25.2, 19q13.32, 16p12.3, 16q12.2, 1p13.2, 12q24.12, and 17q21.33. Notably, the 19q13.32 region exhibited significant shared genetic mechanisms between CKD and four other MetS-related diseases (HC, HL, DB, and CAD), as detailed in Table [81]S4. MAGMA gene-level enrichment analysis Through MAGMA gene enrichment analysis performed using the FUMA tool, this study identified 4,582 genes significantly enriched in MetS-related diseases and CKD. After FDR correction, 234 genes were identified as unique, and following Bonferroni correction, 51 genes showed statistical significance. Detailed results are presented in Supplementary Figure [82]S6. Further analysis revealed that these genes are involved in several key biological pathways (see Fig. [83]3A and Table [84]S5). After Bonferroni correction, several pathways remained significant, including: Metabolism of Fat-Soluble Vitamins, Positive Regulation of Plasma Membrane-Bounded Cell Projection Assembly, Positive Regulation of RNA Metabolic Process, Regulation of Ras Protein Signal Transduction, and cAMP Response Element Binding Protein Binding. Fig. 3. [85]Fig. 3 [86]Open in a new tab Bar chart visualizing MAGMA results: (A) pathway enrichment analysis and (B) tissue and organ enrichment analysis. Notes: The red dotted line indicates the significance threshold of 0.05 after multiple corrections, while the blue line represents the uncorrected significance threshold of 0.05. Abbreviations: CAD: Coronary Artery Disease; CKD chronic kidney disease; DB diabetes; EH hypertension; HL hyperlipidaemia; IS ischemic stroke; OB Obesity; HC hypercholesterolaemia Additionally, gene set analysis highlighted the critical roles of these genes in the nervous system, endocrine system, and urogenital system. Subsequent organ-specific analysis showed that the enrichment in certain organs remained significant even after Bonferroni correction, such as Adipose Visceral Omentum, Brain Cerebellum, and Testis (visualized in Fig. [87]3B, with detailed statistical results in Table [88]S6). These findings reveal shared genetic mechanisms between MetS-related diseases and CKD, providing important insights for further investigation of their molecular regulation and potential therapeutic targets. Drug targets in European populations This study employed the SMR method to identify 12,036 potential drug targets from complex genetic signals, with criteria of p_SMR < 0.05 and p_HEIDI > 0.05. To enhance the precision of these targets, we combined PLACO analysis and integrated the results from FUMA, MAGMA, and SMR, ultimately identifying a set of key genes significantly associated with multiple traits (see Table [89]S7 and Fig. [90]4). These genes showed notable genetic signals across different traits, indicating their potential key roles in various diseases. Moreover, eQTL and SMR analyses further validated the pleiotropic nature of these genes and provided precise annotations of their chromosomal locations. These findings offer valuable insights for exploring potential drug targets related to CKD and MetS-associated diseases. The detailed information on these genes is provided in Table [91]S8. We conducted GO enrichment analysis to further elucidate the functions of these key genes. The findings indicate that NR1H3 and NR1H2 significantly regulate gene expression associated with cholesterol transport and efflux, synaptic transmission, cholinergic activity, and cellular homeostasis (Fig. [92]5A). To explore the relationships between GO terms, we conducted network visualization using Cytoscape. Terms with a similarity score greater than 0.3 were connected by edges, with edge thickness representing the strength of similarity. The “force-directed” layout was applied, and edge bundling was used to enhance the clarity of the network. The analysis revealed a particularly strong association between synaptic transmission, cholinergic, cellular homeostasis, and positive regulation of catabolic processes (Fig. [93]5B). Further enrichment analysis of top-level GO biological process terms revealed significant associations with localization, biological regulation, regulation of biological processes, and homeostatic processes (Fig. [94]5C). Additionally, a deeper functional annotation using the DisGeNET database showed highly significant statistical associations (P < 1 × 10⁻¹⁰) with clinical traits, including diastolic blood pressure, triglyceride levels, alcohol consumption, systolic blood pressure, serum HDL cholesterol levels, mean blood pressure, and high-density lipoprotein levels (Fig. [95]5D). In summary, these findings provide important insights into the prioritization of drug targets and offer valuable clues for understanding the molecular mechanisms underlying CKD and MetS-related diseases, thereby laying a solid foundation for future research. Fig. 4. [96]Fig. 4 [97]Open in a new tab Overview of pleiotropic genes associated with metabolic syndrome-related diseases and CKD. Notes: The signals represent hits of genes across different trait pairs. Abbreviations: eQTL expression quantitative trait loci; SMR summary-based Mendelian randomization; CKD chronic kidney disease Fig. 5. [98]Fig. 5 [99]Open in a new tab Enrichment Analysis of Pleiotropic Genes: (A) Pathway enrichment analysis of the identified pleiotropic genes, including KEGG, GO, and WikiPathways; (B) Network visualization of enriched terms; (C) Enrichment analysis of top-level Gene Ontology biological processes; (D) Enrichment analysis based on the DisGeNET database. Notes: Nodes are colored according to cluster ID, with nodes sharing the same cluster ID being positioned in close proximity Metabolic colocalization analysis The genetic mechanisms common to tissues like visceral adipose omentum, aorta, coronary artery, and tibial artery indicate that blood metabolites could significantly influence disease pathogenesis. We utilized the HyPrColoc method to identify blood metabolites showing colocalization signals at pleiotropic loci for further investigation of this mechanism. Through multivariate colocalization analysis using HyPrColoc, we identified key blood metabolites (see Table [100]S9). The results showed that four pleiotropic loci (rs429358, rs769449, rs77924615, and rs557675) supported the role of 16 distinct blood metabolites in the shared causal variants between MetS-related diseases and CKD. Subsequently, we standardized the names of these 16 metabolites using the PubChem database (corrected results are shown in Table [101]S10). Based on the corrected names, we conducted pathway enrichment analysis using the MetaboAnalyst tool. The analysis identified a notable enrichment in the Glycerophospholipid metabolism pathway. This finding further highlights the potential regulatory role of blood metabolites across multiple diseases and their biological significance (enrichment results are detailed in Table [102]S11). Association analysis In the end, a total of 690 SNPs were used as instrumental variables for the subsequent MR analysis. The detailed information of the instrumental variables is presented in Table [103]S12. In this study, MR analysis using the IVW method was employed to investigate the causal effects of MetS-related diseases (DB, EH, OB) on the incidence of CKD. The forward MR analysis revealed a significant positive correlation between DB, EH, OB, and CKD risk, indicating that increased risks of DB, EH, and OB elevate CKD risk. Specifically, the effect sizes estimated by the IVW method were as follows: DB: OR = 44.583, 95% CI = 18.955–104.860, P = 3.254 × 10^− 18; EH: OR = 2.604–3.484, 95% CI = 1.946, P = 1.184 × 10^− 10. The MR-Egger method’s slope results aligned with those of the IVW method, reinforcing the analysis’s robustness. However, in the MR analysis between OB and CKD, only two instrumental variables were identified due to the stringent P-value threshold applied during tool variable selection. The limited number of instrumental variables led to an overestimated odds ratio (OR), and thus the interpretation of the results should be made with caution. In the reverse MR analysis, the results showed that CKD had a significant positive causal effect only on EH (OR = 1.050, 95% CI = 1.033–1.067, P = 4.457 × 10^− 9). The forest plot for the IVW method in the MR analysis is shown in Fig. [104]6, and the detailed analysis results are presented in Table [105]S13. Furthermore, the scatter plot and funnel plot excluded potential outliers, enhancing the reliability of the findings. Fig. 6. [106]Fig. 6 [107]Open in a new tab Association analysis results derived from Mendelian randomization: (A) IVW results from forward MR analysis; (B) IVW results from reverse MR analysis. Abbreviations: AF atrial fibrillation; CAD: Coronary Artery Disease; CAR cardiac arrhythmias; CKD chronic kidney disease; DB diabetes; EH hypertension; HL hyperlipidaemia; IS ischemic stroke; OB Obesity; HC hypercholesterolaemia; VTE venous thromboembolism Discussion Epidemiological studies suggest that there may be a complex interaction between CKD and MetS-related diseases. Previous research has shown that early kidney injury can trigger the onset of hypertension, creating a vicious cycle of kidney damage and blood pressure dysregulation [[108]23]. Additionally, CKD patients are at an increased risk for metabolic syndrome-related diseases, such as CAD [[109]24] and DB [[110]25]. Furthermore, studies have indicated that metabolic syndrome-related conditions, including EH [[111]26] and DB [[112]27], are not only closely linked to the onset of CKD but also contribute to its progression. These diseases often coexist and exacerbate kidney function deterioration through mechanisms such as hyperfiltration, inflammation, and oxidative stress [[113]28]. This study utilized various genetic methods to investigate the genetic correlation between CKD and diseases related to metabolic syndrome. Our genetic correlation analysis using the LDSC method revealed significant genetic correlations between CKD and seven metabolic syndrome-related diseases, including CAD, DB, EH, HL, IS, OB, and HC. To further validate this finding, we applied the HDL method, which confirmed that the epidemiological correlation between CKD and metabolic syndrome-related diseases is likely due to their complex genetic relationships. To further investigate the mechanisms underlying these genetic correlations, we first conducted PLACO to identify potential pleiotropic loci. These loci were then filtered using the FUMA tool, which allowed us to pinpoint risk-related genetic variants. We identified several risk loci, such as rs429358 and rs10774624, that showed significant associations across multiple phenotypes. Prior research has emphasized the significant impact of these SNPs on the development and progression of CKD and diseases related to metabolic syndrome. For example, rs429358 is a major variant located within the APOE gene locus [[114]29]. The APOE gene is closely associated with various metabolic syndrome-related diseases, with APOE ε2 and ε3 alleles being recognized as risk factors for diabetes mellitus type 2 (T2DM) [[115]30, [116]31], while the APOE ε4 allele serves as an independent risk factor for both T2DM and CAD [[117]32]. Additionally, some studies have suggested that ApoE ε2 and ε4 alleles may also be linked to an increased risk of T2DM and diabetic nephropathy [[118]33]. APOE is thought to exert its effects by promoting the transformation of macrophages from a pro-inflammatory M1 phenotype to an anti-inflammatory M2 phenotype through VLDL-R or apoER2 receptors. This shift enhances macrophage resistance to NF-κB and STAT1, transcription factors activated by pro-inflammatory agents commonly found in atherosclerotic environments. The M2 macrophage phenotype may thus represent a protective mechanism that counters vascular inflammation and atherosclerosis [[119]34]. In CKD patients, APOE genotype is closely linked to kidney function, and APOE concentrations, as well as its distribution across lipoprotein categories, correlate with kidney function changes [[120]35]. To explore the organ-specific mechanisms underlying these genetic correlations, we next performed sLDSC analysis using organ tissue datasets from the GTEx database. This analysis revealed that cardiovascular-related regions showed significantly stronger genetic correlations compared to other regions. This finding was further supported by MAGMA organ-specific enrichment analysis. Notably, we also observed significant genetic enrichment in visceral fat mesentery, which aligns with its critical role in metabolic-related diseases. Research has identified B cells, particularly B-1a cells, within mesenteric adipose tissue, which function as novel immune modulators and play an important role in maintaining metabolic homeostasis. IgM produced by B-1b cells can modulate the inflammatory response of M1 macrophages, and both cell types are closely associated with insulin resistance [[121]36, [122]37]. Additionally, B cells and their IgM production in perivascular adipose tissue are also known to play a protective role in CAD [[123]38, [124]39]. Furthermore, the activation state of the mesentery is characterized by an increase in stem-like or progenitor cells. Animal studies have shown that interaction between the mesentery and injured kidneys can slow the progression of CKD, likely mediated by mesenteric stem cells and their secreted products [[125]40, [126]41]. To further explore the genetic mechanisms underlying CKD and metabolic syndrome-related diseases, we performed MAGMA pathway enrichment analysis, which identified five pathways with P-values meeting the Bonferroni correction threshold (P < 0.05/10,678 = 4.68 × 10^− 6). This result indicates that all the enriched pathways shown in the figure are associated with CKD and metabolic syndrome-related diseases, with five pathways exhibiting significant correlations. We will focus on the pathway with the highest correlation — the cAMP response element-binding protein (CREB) pathway. Previous studies have shown that the ERK/CREB signaling pathway affects vascular pathology by regulating the expression of OPN and PAI-1, genes associated with reduced cell surface fibrinolytic activity [[127]42], increased proliferation of vascular smooth muscle cells [[128]43], and vascular calcification [[129]44], thereby influencing HL and CAD [[130]45]. Additionally, CREB has been found to regulate the transcription of the G-protein-coupled receptor signaling protein 2 in vascular smooth muscle cells in response to angiotensin II, which is an important negative feedback mechanism for blood pressure homeostasis [[131]46]. These findings suggest that CREB not only plays a pivotal role in vascular pathology but may also regulate blood pressure and cardiovascular function, thus influencing the progression of CKD and related diseases. Furthermore, studies have indicated that the activity of CREB is closely linked to the inhibition of glycogen synthase kinase 3β (GSK3β). Inhibition of GSK3β significantly enhances CREB activity, leading to the suppression of the TGF-β1/Smad signaling pathway, which reduces the pro-fibrotic plasticity of renal tubular epithelial cells and alleviates interstitial fibrosis and tubular atrophy in CKD [[132]47]. This suggests that CREB could be a potential therapeutic target for CKD, particularly in the context of fibrosis and kidney dysfunction. In addition to pathway analysis, we conducted a rigorous screening for potential drug targets, among which BICC1 caught our attention. BICC1 is composed of tandem repeats of heterogeneous nuclear ribonucleoprotein K homology (KH) and KH-like (KHL) domains at its N-terminus, separated from the C-terminal sterile alpha motif domain by a serine-glycine-rich sequence [[133]48]. BICC1 is involved not only in RNA processing during cellular homeostasis but also in response to cellular stress [[134]49]. Research has shown that intronic variants of BICC1 are significantly associated with the slope of estimated glomerular filtration rate (eGFR), with each additional A allele slowing the eGFR slope by 0.13% [[135]50]. Additionally, BICC1 is linked to blood urea nitrogen levels [[136]51]. Loss of BICC1 function has also been implicated in the development of polycystic kidney disease [[137]52, [138]53]. Interestingly, recent studies have suggested that BICC1 functions downstream of ONECUT1 and plays a role in the control of NEUROG3(+) endocrine cell differentiation and duct morphogenesis, pathways that may be associated with diabetes [[139]54]. Therefore, BICC1 not only plays a significant role in the progression of CKD and metabolic syndrome-related diseases but could also serve as a potential target for drug development aimed at treating both conditions simultaneously. Research has shown that hyperglycemia suppresses the AMPK-MTOR-PIK3C3 pathway, reducing autophagic degradation of caveolin-1 (CAV1). This results in CAV1 accumulation and the formation of caveolae, which in turn promotes the transcytosis of LDL across endothelial cells, accelerating the development of atherosclerosis [[140]55]. Additionally, miR-369-3p has been found to regulate the succinate-GPR91 signaling pathway, effectively ameliorating oxLDL-induced mitochondrial stress and inflammasome activation, thus inhibiting the progression of diabetes-associated atherosclerosis [[141]56]. These findings highlight the crucial role of metabolites in the pathogenesis of atherosclerosis and related diseases. In this context, a large observational study emphasized the cumulative LDL-C burden as a major driver of CAD in patients with familial hypercholesterolemia [[142]57]. A retrospective cohort study further underscored the importance of strict control of LDL-C levels in patients with low HDL-C to prevent CAD [[143]58]. Moreover, research has indicated that low HDL-C levels have a long-term protective effect on the kidneys [[144]59], while high LDL-C levels are associated with a decline in eGFR and an increased risk of CKD in healthy men over the next decade [[145]60]. Furthermore, the TG/HDL-C ratio is considered a positive risk factor for CKD [[146]61]. These studies collectively demonstrate the significant role of blood metabolites in the development of metabolic syndrome and related diseases, prompting us to explore a novel metabolite colocalization approach to better understand these interactions. In our study, we conducted KEGG enrichment analysis of blood metabolites that were colocalized, and identified glycerophospholipid metabolism as a key pathway in the interaction between CKD and metabolic syndrome-related diseases. Studies have shown that cardiolipin, a lipid found in the mitochondria, plays a pivotal role in atherosclerosis by promoting oxidative stress, inflammatory responses, and immune cell activation [[147]62]. Other research has shown that trimethylamine-N-oxide, a byproduct of phosphatidylcholine metabolism, enhances cholesterol accumulation in macrophages and foam cell formation in the arterial walls, thereby exacerbating the progression of atherosclerosis [[148]63, [149]64]. These findings suggest that glycerophospholipid metabolism not only plays a key role in atherosclerosis but may also be implicated in CKD through disturbances in lipid metabolism. Furthermore, lipid metabolism dysregulation, including glycerophospholipid metabolism, may manifest years before CKD onset, potentially serving as a critical biomarker for identifying individuals with early renal dysfunction [[150]65]. Therefore, further investigation into the role of glycerophospholipid metabolism could offer novel insights for the early diagnosis and intervention of both CKD and metabolic syndrome-related diseases. This study employed MR to investigate the genetic relationship between MetS-related diseases and CKD. To ensure robust results, we applied strict criteria for selecting instrumental variables: a P-value threshold of < 5 × 10^− 8, r² < 0.001, a 10,000 kb physical distance window, and F-statistic > 10. These standards effectively eliminated weak instruments and collinearity issues, though they also reduced the number of eligible variables. For instance, the analysis between obesity and CKD included only one instrument, resulting in a large OR that limited statistical inference and clinical significance. Therefore, while some results are biologically plausible, the limited number of instruments warrants cautious interpretation of their generalizability. Bidirectional MR analysis suggested a potential positive causal relationship between metabolic syndrome-related diseases (e.g., diabetes, hypertension, obesity) and CKD. Conversely, CKD was found to elevate the risk of hypertension. This finding is consistent with clinical observations, indicating a complex causal interplay between metabolic syndrome and CKD. Obesity contributes to renal dysfunction by increasing sodium reabsorption and compensatory hyperfiltration, exacerbating renal burden [[151]66]. Hyperglycemia-induced kidney damage is a key mechanism in the development of CKD [[152]67]. Hypertension not only serves as an independent risk factor for CKD but also accelerates its progression [[153]68]. Additionally, CKD progression involves sodium retention and elevated blood pressure, which together form a vicious cycle, further damaging the kidneys [[154]69]. Core features of metabolic syndrome, such as obesity, insulin resistance, and dyslipidemia, are closely linked to CKD onset and progression, with these factors synergistically contributing to renal damage [[155]70]. However, due to the inherent limitations of MR, we cannot fully exclude confounding factors, and the results should be viewed as exploratory evidence rather than definitive causal conclusions. Previous MR studies have reported similar findings. For example, Alisa D. Kjaergaard et al. found a significant association between BMI and decreased eGFR [[156]71], and Jie Zheng et al. validated the causal effects of obesity, hypertension, and diabetes on CKD [[157]72].In conclusion, this study provides preliminary evidence of a genetic association between metabolic syndrome and CKD, with bidirectional analysis suggesting a potential causal role of metabolic syndrome-related diseases in CKD development. Given the limitations of MR, causal inferences should be made cautiously. Future studies should further validate these findings and explore their underlying biological mechanisms and clinical implications. It is important to recognize the limitations of our study. First, similar to many other genetic studies, we utilized summary-level data rather than individual-level data, which restricts our ability to perform more detailed population stratification, such as by sex, age, or other demographic factors. This limitation may affect the precision of our findings in specific subgroups. Second, the sample size for the blood metabolite GWAS used in this study was relatively small, which may compromise the robustness of our conclusions regarding the role of blood metabolites in the context of CKD and MetS-related diseases. Consequently, caution is needed when interpreting these findings. Third, since our analysis focused solely on individuals of European ancestry, the findings may not be fully generalizable to other populations or ethnic groups. Fourth, although we utilized the largest available GWAS datasets for each trait (with total sample sizes typically > 30,000), some traits had relatively small case numbers (for example, the CKD GWAS included only ~ 11,265 cases, and certain MetS trait GWAS such as CAD and HL also had fewer cases), potentially reducing the statistical power of our analyses. We did not perform an explicit power or sample size calculation or additional sensitivity analyses, which further underscores the need for caution when interpreting these results. Future studies should incorporate formal power assessments and additional robustness checks to validate our findings. Conclusion Our study elucidates the complex relationships between MetS-related diseases and CKD, particularly CAD, DB, EH, HL, IS, OB, and HC. The identification of shared pleiotropic risk loci (6q25.3, 10q25.2, 19q13.32, 16p12.3, 16q12.2, 1p13.2, 12q24.12, and 17q21.33) and genes (e.g., APOC1, APOE, BICC1, PDILT, SLC22A2, UMOD, WNT2B, and ZNF652) highlights common mechanisms, such as the Metabolism of Fat-Soluble Vitamins, Positive Regulation of Plasma Membrane-Bounded Cell Projection Assembly, and Positive Regulation of RNA Metabolic Process as potential triggers. Through metabolite colocalization analysis, we identified 16 distinct blood metabolites that play crucial roles in the shared pathogenesis of MetS-related diseases and CKD. Enrichment analysis of these metabolites revealed significant statistical associations with the Glycerophospholipid Metabolism pathway. Furthermore, our findings clarify the causal relationships between MetS-related diseases (DB, EH, and OB) and CKD, providing a deeper understanding of their shared genetic architecture and potential molecular mechanisms. These insights offer a valuable foundation for future research into targeted therapeutic interventions. Supplementary Information Below is the link to the electronic supplementary material. [158]Supplementary Material 1^ (279.5KB, xlsx) [159]Supplementary Material 2^ (1.4MB, docx) [160]Supplementary Material 3^ (44KB, docx) Acknowledgements