Genomic structural equation modelling provides insights into the shared multivariate genetic architecture of cardio-kidney-metabolic syndrome components

Genomic structural equation modelling provides insights into the shared multivariate genetic architecture of cardio-kidney-metabolic syndrome components | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomic structural equation modelling provides insights into the shared multivariate genetic architecture of cardio-kidney-metabolic syndrome components Zhonghai Wang, Xin Chen, Han Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7187218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cardiovascular-kidney-metabolic (CKM) syndrome has placed a substantial burden on society both socially and economically. Although many genome-wide association studies (GWASs) of single phenotypes have been conducted, little is currently known about the genetic architecture of CKM syndrome. Methods A multivariate GWAS of CKM syndrome (mvCKM) in Europe was performed via genomic structural equation modelling (gSEM). A subsequent series of post-GWAS analyses elucidated novel loci and functional mechanisms of mvCKM. Cell-gene-pathway-Mendelian disease analysis further revealed the enrichment status of mvCKM. We particularly focused on various genomic loci and chromosomal regions related to CKM syndrome to explore potential targets. Results A total of 261 novel SNPs were identified and 92 causal SNPs (posterior probability > 0.95) were estimated independent of single phenotypes. Furthermore, we employed multiple transcriptome-wide association analysis approaches to explore 10 susceptible genes. One of these genes, B3GNT7, was also identified via the MAGMA method. The multi-marker analysis for genome annotation at the cellular level demonstrated that mvCKM was primarily enriched in metabolic cells, organs, and associated pathways. Partitioned heritability analysis revealed that conserved regions may make substantial genomic contributions. Polygenic risk scores indicated high genetic contributions from regions on chromosomes 4, 6, 1, and 9. Conclusions This study provides an essential understanding of the genetic architecture of CKM syndrome via mvCKM in Europeans, offering new viewpoints for precision medicine and public health initiatives. Cardiovascular-kidney-metabolic syndrome Genomic structural equation modeling Single nucleotide polymorphism Polygenic risk score Genomic element Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cardiovascular-Kidney-Metabolic syndrome (CKM), as a new paradigm for cross-system diseases, reveals the complex pathophysiological network among cardiovascular diseases, chronic kidney diseases, and metabolic disorders [ 1 ]. The introduction of this concept marked a breakthrough in our understanding of multiorgan interactions. Accumulating evidence indicates that individuals with obesity, type 2 diabetes (T2D), and chronic kidney disease (CKD) face a 3 to 5-fold increased risk of cardiovascular events compared with the general population [ 2 ]. Moreover, these conditions act synergistically through a complex pathophysiological network, exacerbating multiorgan dysfunction. Currently, genome-wide association studies (GWASs) have revealed the genetic basis of individual metabolic phenotypes [ 3 – 7 ]. However, CKM syndrome has been proven to have significant clinical heterogeneity, and its overall genetic architecture as a multisystem comorbidity has not been fully resolved. Current research in dissecting CKM syndrome faces several challenges: First, understanding the complex causal relationships in CKM syndrome is difficult from the traditional single-disease viewpoint. In recent years, advances in large-scale GWASs and genomic structural equation modelling (gSEM) have provided new insights for dissecting the shared genetic mechanisms of complex traits. Notably, integrated GWAS data from seven metabolic components; they utilized gSEM to construct a three-factor model encompassing obesity, insulin resistance/hypertension, and dyslipidemia [ 8 ]. These findings highlight the critical role of multiomics integration and cross-organ network analysis in deciphering the genetic basis of comorbid diseases. Therefore, our study innovatively introduced gSEM and integrated the summary data of GWASs from three major systems of CKM syndrome [ 9 ]. By analysing these data, we explored the associations between these SNPs and the potential CKM syndrome phenotypes, and then conducted a GWAS study on the potential CKM syndrome phenotypes (mvCKM) that have never been directly measured. Furthermore, we employed various post - GWAS methods to identify the functional mechanisms of mvCKM. In particular, we focused on various genomic loci and chromosomal regions related to CKM syndrome to explore the potential targets [ 10 , 11 ]. In general, this study not only broadens our understanding of CKM syndrome but also provides theoretical and practical support for intervention strategies to reduce the global burden of chronic diseases. Materials and methods Univariate input GWAS data The univariate input data for the mvCKM were derived from six summarized GWASs related to CKM syndrome, encompassing traits including heart failure (HF) [3], coronary heart disease (CHD) [4], stroke [5], T2D [6], obesity (https://gwas.mrci eu.ac.uk/), and CKD [7]. All input GWASs obtained ethical approval from their respective Institutional Review Boards, and written informed consent was secured from all participants. The summary GWAS data for HF and CHD were obtained from separate GWAS meta-analyses: HF data originated from 11 cohorts comprising 977,323 individuals of European ancestry, and CHD data were derived from 13 European cohorts involving 141,217 European participants. Additionally, stroke GWAS data were extracted from a meta-analysis encompassing 18 European cohorts. Data for T2D GWAS were sourced from Bonàs-Guarch and colleagues, who included 70,127 European individuals, and those for the obesity GWAS were acquired from the UK Biobank, with 463,010 European participants. Additionally, the summarized GWAS for CKD involved 482,858 European participants, including data from UK Biobank and FinnGen. Detailed information about these GWAS data is provided in Table S1. An outline of our study design is illustrated in Figure 1. Quality control of univariate input GWAS and sample overlap The quality control standards for univariable input GWAS were as follows: 1) Samples with > 5% missing data were excluded. 2) SNPs within the major histocompatibility complex (MHC) region were removed. Additionally, we constructed the mvCKM model via default parameters. The study utilized univariate input GWAS derived from a range of independent genomic information repositories, with separate participants in each dataset. This implies that in the GWAS analysis, we thoroughly accounted for sample overlap across various cohorts to guarantee the precision and applicability of the findings. Genomic structural equation modelling Using the GenomicSEM package, we conducted multivariate GWASs on HF, CHD, stroke, T2D, obesity, and CKD to investigate the shared genetic architecture underlying these phenotypes. GSEM, a novel multivariate analytical method, enables systematic exploration of genetic correlation networks among complex diseases. This approach has two advantages: it corrects statistical biases from sample overlap and size differences via weighted covariance matrices, and identifies genetic variants affecting specific phenotypic subsets [9]. The GSEM operates through two sequential stages. In the initial stage, the genetic covariance matrix is constructed. This begins with the standardization of univariate GWAS datasets. A multivariate extension of cross-trait linkage disequilibrium (LD) score regression was subsequently implemented to derive an empirical genetic covariance matrix across six phenotypic traits. This matrix is then utilized as the foundational input for the common factor model within the SEM framework [12]. In the second stage, the gSEM is specified to fit the empirical genetic covariance matrix in the first stage. The objective of this stage is to minimize the difference between the estimated genetic covariance matrix and the empirical genetic covariance matrix to estimate the parameters of the SEM. In our study, our aim was to identify the genetic characteristics underlying six CKM syndrome-associated traits; thus, we opted to test a single-factor model [13]. The model's goodness-of-fit was rigorously assessed through four key indicators: SRMR, χ², AIC, and CFI. Shared covariance analysis results across six input GWASs were created via gSEM. Q SNP heterogeneity To assess the adequacy of CKM-related SNPs within the gSEM framework, we quantified SNP heterogeneity. Our study tested the hypothesis that the SNP associations detected in univariate GWASs are entirely mediated by mvCKM. The significant Q SNP signals (P < 0.05) for mvCKM suggest that these SNPs might influence traits through pathways independent of the shared mechanisms captured in the mvCKM model [8]. Multilevel genome-wide analysis The multiple-tier quality control was implemented for the constructed mvCKM. This framework first involves adjusting the thresholds for correlation tests (P < 5 x 10 -16 ) to analyse novel SNP loci detectable at different significance levels [8]. This strategy provides dual benefits: effectively controlling the false positive rate through a streamlined statistical framework while enabling genome-wide locus screening and validation of mvCKM-identified locus novelty, with demonstrated utility in prior post-GWAS studies. Two-step LD score regression We applied two-step LD score regression to distinguish genetic signals from confounding effects and quantify heritability. The workflow included three stages: Retaining all SNPs (including missing values), preserving low-quality variants (INFO < 0.9 or MAF < 0.01), and keeping valid P-value SNPs without strand exclusion, removing partition LD scores with zero variance [12]. Identification of novel genetic variants We used FUMA to identify genomic risk loci [14] and selected lead SNPs associated with mvCKM (r² < 0.1, P < 5 × 10 -8 ). If the distance between a lead SNP locus and an identified locus in the univariable input GWAS data exceeded 1 Mb, it was considered a novel locus. To determine whether the leading SNPs in mvCKM had pleiotropic associations, we referred to the relevant information of the published significant associations (P < 5 × 10 -8 ) in the GWAS Catalog [15]. Subsequently, we analyzed the relevant output files using Multi-marker Analysis of GenoMic Annotation (MAGMA) [16]. The significance threshold for the MAGMA analysis was set at a false discovery rate (FDR) P-value < 0.05. SUSIE and FINEMAP To identify the causal variants most likely associated with mvCKM, we used SUSIE and FINEMAP for fine-mapping analysis (echolocatoR v.2.0.3) [17, 18]. We defined lead SNP-associated regions using a 250 kb window, calculated causal inference probabilities for all SNPs within them, and classified SNPs with SUSIE/FINEMAP posterior probabilities >0.95 as potential causal variants. Notably, echolocatoR defines a “consensus SNP”, which refers to the variants that appear in both the SUSIE and FINEMAP results. For these consensus SNPs, the tool calculates their average posterior probability and determines the average credible set on the base of the probability results. Transcriptome-wide association study We referred to the 37920 expression quantitative trait loci (eQTLs) in the GTEx v.8 dataset and performed FUSION to conduct a comprehensive transcriptome-wide association study (TWAS) analysis, prioritizing mvCKM-associated genes [19]. Notably, mvCKM in our TWAS analysis contained sufficient variation information, allowing us to analyse 36,149 features. On the base of additional evidence for colocalization and fine mapping, we prioritized ‘high-confidence’ mvCKM-associated genes identified by FUSION. Finally, we considered TWAS-significant genes associated with mvCKM that also demonstrated colocalization (PP.H4 >0.75) and were likely to be causal (FOCUS posterior inclusion probability >0.90) [19]. Gene set pathways enrichment and cell annotation analysis To investigate the core genes and related pathways of mvCKM, we used data from MAGMA and FUMA to conduct gene set enrichment analysis (GSEA) [14]. Also, we used MendelVar to explore if mvCKM is enriched in Mendelian diseases and related pathways [20]. For the cell annotation analysis, we employed the CELLECT method [21], which is based on the Tabula Muris dataset [22], and includes transcriptome data from 100,000 cells and 20 different organs and tissues in mice. After preprocessing and standardization, gene expression specificity scores were calculated. Cell-type specificity analysis and classification were conducted via LDSC, with an FDR threshold of < 0.05. Heritability partition We employed S-LDSC to estimate genomic partitioning heritability [10], which quantifies the genetic contributions of distinct genomic regions to phenotypic heritability. By combining phenotypic genetic data, weighted LD matrices, genotype frequencies, and summary statistics, S-LDSC decomposes phenotypic variance into genomic segments, revealing the specific roles and proportions of each region in the total heritability. Polygenic risk scores We constructed polygenic risk scores (PRS) from GWAS summary data to analyse the genetic influences of distinct chromosomal regions on mvCKM. By combining GWAS summary data with LD reference panels, we applied PRS-CS software to estimate posterior effect sizes for SNPs [11]. These estimates are used to compute the PRS, which quantifies the cumulative effect of genetic variants on the mvCKM phenotype. Results Statistical indicators for structural equation modeling LD score regression analysis indicated that when six univariate GWASs, namely, HF, CHD, stroke, T2D, obesity, and CKD, were input into the novel GWAS, their heritability contributions were 6.1%, 13.7%, 6.0%, 21.5%, 1.1%, and 9.5% respectively. The detailed genetic parameters are shown in Fig. 2 and Table S1 . The common factor model of the genetic covariance matrix and the empirical covariance matrix of the six univariate GWASs fit well (χ²=28.66, df = 9, χ²/df = 3.18, CFI = 0.97, SRMR = 0.08; Table S3). This result suggests shared genetic factors in gSEM. Stratified evaluation of mvCKM Using gSEM, we obtained an indirectly measured GWAS to explore the links between 4,498,214 SNPs and mvCKM. Among these genomic loci, we identified 1,338 genomic loci under more stringent conditions (P < 5 × 10 − 16 ) (Table S4). Overall, more than 2,000 loci in mvCKM are newly discovered and differ from those identified by the six univariate GWAS, highlighting the enhanced capabilities of gSEM. These lead SNPs are typically enriched in associations with cardiometabolic systems, cancer, neural functions, and inflammation and immunity. In general, our study revealed that these newly discovered SNPs are often closely related to important components of mvCKM. Genome-wide quality control via LD score regression A total of 4,497,771 SNPs were initially identified, and 961,631 valid SNPs were retained after stringent parameter controls were applied via the LD score regression. LD score regression analysis revealed a mean χ² of 1.183 (λGC = 1.095), with 493 significant SNPs and a maximum χ² of 913.763. Heritability estimates showed h² = 8.37 x 10 − 4 (genetic-environmental ratio: 0.134), which was supported by nonsignificant heterogeneity (P > 0.05) and a regression intercept of 0.975. Multiple estimates indicate that the potential inflation in gSEM is due to polygenic heritability signals, rather than population stratification bias or pleiotropic parameter effects. Identification of novel lead SNP FUMA software was employed to assess mvCKM, leading to the identification of 1599 risk loci (Fig. 2 , Table S5) and 816 genes that are likely linked to mvCKM (FDR < 0.05). Most of the 2029 annotated lead SNP loci were located in introns and intergenic regions (Table S6). Among them, a total of 22 lead SNPs were identified at the GWAS-subtracted loci. Among the 2029 lead SNPs, 261 were novel compared with the lead SNPs from the six input GWASs. These novel lead SNPs were significantly associated with mainly cardiometabolic health, neural function, inflammation and immunity, bone and body composition, and cancer and autoimmune diseases. SUSIE and FINEMAP By combining SUSIE and FINEMAP, 92 causal variation loci were identified (mean PP > 0.95), especially on chromosomes 2, 3, 5 and 13. These loci could be mapped to 85 potential genes, revealing the potential pleiotropy of cardiometabolic traits. The regional map showed obvious peaks at these loci, and other credible set variations also provided evidence of associations (Fig. 3 , Table S8). Fourteen loci were found to be colocalized and possibly causal signals with mvCKM, as shown by the colocalization test. Transcriptome-wide association study A total of 2,747 genes were associated with mvCKM, 31 of which genes met the criteria for Bonferroni correction (Fig. 4 , Table S9). We further tested these genes, via colocalization and FOCUS analysis. The results revealed that 59 genes passed the Bayesian colocalization test (PPH4 > 0.75), and 63 genes presented potentially causal signals with mvCKM (PIP > 0.9). By integrating the results of all methods, these “high-confidence” gene-level associations involve the following genes, which can be divided into two categories according to the TWASZ scores: one category with TWASZ scores > 0, including ACY1, AGMAT, BPTF, CDK6, RP11-680H20.1, and RPL37A; and the other category with TWASZ scores < 0, including B3GNT7, PDE6D, RDH14, and RPSAP36 (Fig. 4 , Table S9). Cell-gene-pathway-Mendelian disease enrichment analysis Using MAGMA for genome mapping, we identified 37 genes, which were then used for gene set analysis, revealing their enrichment in GSEA entries. (Table S10, S11). Most of the gene sets were linked to metabolism, including T2D, metabolic syndrome, blood glucose, obesity, and body measurements, and the diseases mapped by MendelVar enrichment received partial support from GSEA entries (e.g., abnormal glucose metabolism, cardiovascular diseases) (Fig. 5 a, Table S12). The relevant pathway enrichment also involved metabolism and cardiac abnormalities (Fig. 5 a Table S13). Cell-type enrichment analysis revealed that four cell types exceeded the significance threshold, namely, pancreatic PP cells, pancreatic α-cells, bone marrow macrophages, and splenic macrophages (Table S14). The first two cell types are closely related to insulin/glucagon in the pancreas, whereas the latter two, macrophages, play crucial roles in inflammation regulation and autoimmunity (Fig. 5 b). Enrichment analysis of mvCKM revealed enrichment in one and two tissues, primarily in the pancreas and fetal blood tissue, respectively, suggesting that the pathogenesis of CKM syndrome involves metabolic regulation during early developmental programming and multiorgan interactions in adulthood. These findings provide a theoretical basis for early-life interventions and the targeting metabolic-inflammatory pathways (Tables S15, S16). In the enrichment analysis of multi-tissue chromatin, we observed significant enrichment of histone modifications (e.g., H3K4me1) and regulatory element region markers, primarily in the pancreas, gastrointestinal tract, and T cells (Table S17). These chromatin signatures unveil the multisystem epigenetic regulatory network of CKM syndrome, encompassing abnormal activation/inhibition of genes associated with metabolism, immunity, and organ development. Partitioned heritability We performed partitioned heritability analysis via S-LDSC to assess the contribution of various functional genomic annotations to the heritability of mvCKM. As shown in Table S18, the results of the conserved regions remained significant after multiple-test correction (FDR < 0.05). Although the conserved regions accounted for only approximately 2.6% of the whole-genome SNPs, they explained about 69.2% of the heritability, indicating that evolutionarily conserved noncoding regulatory elements play a key role in CKM syndrome. At a nominal significance threshold, coding regions (Coding_UCSC/extend.500) and enhancers marked by H3K4me1/H3K27ac also showed moderate to high heritability enrichment. However, these annotation signals failed to pass the multiple-test correction, suggesting that their effects may depend on specific biological contexts or require a larger sample size for verification. Polygenic risk scores Our findings revealed significant differences in the genetic contributions of different chromosomal regions to mvCKM. In particular, in regions of chromosomes 1, 4, 6, and 9, we observed relatively high genetic contributions. These regions may contain important genes and regulatory elements that influence the susceptibility to CKM syndrome (Table S19). Discussion This study systematically investigated the genetic basis of six CKM syndrome-related traits: HF, CHD, stroke, T2D, obesity, and CKD. By integrating multidimensional methodologies including gSEM, fine-mapping, TWAS, multitiered genetic contribution assessment (PRS-CS/S-LDSC), and cell-gene-pathway-Mendelian disease enrichment analysis, we identified novel genetic signals through joint analysis of these complex traits. Our findings demonstrated that genetic factors distributed across distinct genomic/chromosomal regions not only drive CKM syndrome development but also may exert lifelong impacts on individual health through cross-scale biological mechanisms. Our study employed gSEM to reveal the genetic covariance among six related indicators of CKM syndrome, indicating the existence of shared genetic factors among these phenotypes. In the confirmatory factor analysis of common factor models, HF, which explained approximately 76.6% of the variance, exhibited the strongest association with the common factor, suggesting that it may be highly driven by shared genetic mechanisms. HF is one of the core symptoms of stage IV CKM syndrome, where patients exhibit concurrent cardiac structural and functional abnormalities, metabolic disorders and renal damage, ultimately progressing to end-organ damage [ 23 ]. Compared with GWASs of HF and other traits, mvCKM can play a more extensive role. The SEM further confirms the complex genetic links among the six traits, indicating that they do not exist in isolation but are intertwined together. These findings provide important insights for the implementation of future precision medicine and public health interventions. Using the subsequent FUMA method, we identified 261 novel lead SNPs. These SNPs are mainly associated with cardiometabolic health, cancer, and autoimmune diseases. Most of the newly discovered SNPs are located in genomic regulatory regions (introns and intergenic regions), indicating that such SNPs may produce extensive effects through regulatory pathways. In addition, they can regulate the expression levels of nearby or distant genes by disrupting the activities of enhancers, promoters, or insulators [ 24 ]. Several new and interesting SNPs have attracted our attention. These SNPs are related to traits such as cognition, skeletal muscle, and autoimmune diseases. For example, rs35771425, rs57109420, rs10808026, rs7131535, and rs147571544 are associated with Alzheimer’s disease [ 15 ]. However, few empirical studies have explored the functions and significance of these loci. This study identified 92 elementary SNPs through stratified analysis and fine-mapping. These SNPs are located in gene regions associated with cardiometabolic diseases, immune and inflammatory responses, and neurological and cognitive functions. These findings align with those of previous studies demonstrating that critical loci for cardiometabolic diseases often exhibit strong correlations with specific SNPs [ 25 , 26 ]. For instance, the rs12061508 locus anchored in the KCN2 gene significantly associated with metabolic and inflammatory markers, suggesting its potential role as a causal variant [ 27 ]. The identification of these SNPs enables refined characterization of how these genes modulate health status and disease susceptibility through metabolic, inflammatory, and other pivotal biological mechanisms. Notably, we combined the findings of FUSION, FOCUS, and colocalization. These “high-confidence” gene-level associations involve possible risk genes, such as ACY1, AGMAT, and BPTF, and potential protective genes, including B3GNT7, PDE6D, RDH14, and RPSAP36. CDK6 can play a pivotal role in atherosclerosis, myocardial injury, obesity, and insulin resistance by integrating cell cycle regulation with metabolic reprogramming [ 28 ]. Targeting the kinase activity of CDK6 or related pathways (such as RUNX1 and CHREBP) may provide new strategies for the treatment of cardiometabolic syndrome. The encoded protein has been implicated in the onset and progression of various diseases, including autoimmune disorders, cancers, neurodevelopmental conditions, musculoskeletal issues, and metabolic diseases [ 29 ]. The key pathological processes involving B3GNT7 may involve metabolic diseases by influencing cell signal transduction, inflammatory response, or insulin sensitivity [ 30 ]. B3GNT7 may be an important gene for the precise treatment of CKM syndrome in the future. We conducted a series of enrichment analyses, including cell-type, gene, tissue, chromosome, pathway, and enrichment analysis. Generally, the findings of cell-type enrichment analysis showed that pancreatic cells and macrophages exceeded the significance criterion. MAGMA gene enrichment was related mainly to metabolism, heart, neurodevelopment, and immunity. MendelVar-enriched diseases were partially supported by GSEA (e.g., abnormal glucose metabolism and cardiovascular conditions). The related pathways also involved metabolism, endocrine and cardiac abnormalities. In addition, tissue enrichment was mainly concentrated in adipose tissue and brain tissue. Significant enrichment of histone modifications (H3K4me1) and regulatory element region markers was also found mainly in the pancreas. Numerous studies have shown that metabolic abnormalities can drive the progression of CKM syndrome through insulin resistance, chronic low-grade inflammation, oxidative stress, endothelial dysfunction, and neuroendocrine imbalance [ 31 ]. For example, metabolic abnormalities induce cardiomyocyte apoptosis and coronary atherosclerosis through lipotoxicity and glucotoxicity [ 32 ]. Moreover, the accumulation of extracardiac adipose tissue directly compresses the myocardium, aggravating diastolic dysfunction [ 31 ]. In addition, it can also lead to glomerular hyperfiltration and podocyte injury, whereas lipid metabolism disorders accelerate interstitial fibrosis through tubular lipid deposition. By analysing the whole-genome data of mvCKM, we identified multiple chromosomal risk regions associated with mvCKM, many of which are located in noncoding regions [ 33 – 35 ]. We also found genetic markers in the coding region and the regions extending 500 bp upstream and downstream, which not only directly reflect gene function but also may indirectly affect phenotypes through regulatory pathways [ 36 , 37 ]. Additionally, we also found that in some chromosomal risk regions, epigenetic markers of histone modification are closely related to disease susceptibility. These findings indicate that these regions not only affect gene expression but also suggest that the pathogenicity of genetic risk loci may be achieved through epigenetic mechanisms. This study has several limitations. First, the study population was primarily from Europe. Future studies should expand the sample range and conduct verification in populations of different races and from different regions. Second, even though we have identified multiple SNPs/genes related to CKM syndrome via fine mapping and transcriptomics analysis, how to link these genes with specific biological mechanisms remains an urgent problem. Moreover, while studies have revealed the significant role of genetic factors in CKM syndrome, the impact of environmental factors should not be overlooked. Conclusions In summary, we provide new insights into the GWAS of CKM syndrome based on mvCKM. By combining gSEM, fine mapping and transcriptomics analysis, we identified multiple novel genetic loci and revealed how these loci are genetically linked to complex traits by affecting gene expression. Our findings not only increase the comprehension of the mechanism of mvCKM, but also provide novel perspectives for precision medicine and public health interventions. Abbreviations CKM, Cardiovascular-kidney-metabolic mvCKM, Multivariate GWAS of CKM Syndrome gSEM, Genomic Structural Equation Modelling HF, Heart Failure CHD, Coronary Heart Disease T2D, Type 2 Diabetes CKD, Chronic Kidney Disease SNP, Single Nucleotide Polymorphism GWAS, Genome-wide Association Study FUMA, Functional Mapping and Annotation of Genome-Wide Association Studies MAGMA, Multi-marker Analysis of GenoMic Annotation LD, Linkage Disequilibrium SUSIE, Sum of Single Effects GSEA, Gene Set Enrichment Analysis TWAS, Transcriptome-wide Association Study eQTL, expression Quantitative Trait Loci PIP, Posterior Inclusion Probabilities CELLECT, Cell-type Expression-specific Integration for Complex Traits S-LDSC, Stratified Linkage Disequilibrium Score Regression PRS-CS Polygenic Risk Score with Continuous Shrinkage Declarations Acknowledgements We would like to acknowledge the participants, researchers, and institutions (including UKB, MRC-IEU, and FinnGen) whose studies were cited in this research —without their contributions, this work would not have been possible. Author contributions ZHW: Writing – original draft, Software, Resources, Project administration, Methodology, Conceptualization. XC: Visualization, Methodology, Investigation, Formal analysis, Data curation. HW: Writing – review & editing, Software. A final version of the manuscript was approved by all authors. Funding This study was supported by The Third People's Hospital of Chengdu Foundation (CSY-YN-03-2024-023) Availability of data and materials Publicly accessible datasets served as the foundation for all research evaluations. Summary-level statistics for HF are available at the GWAS Catalog (GCST009541); CHD (GCST003116); stroke (GCST005838); T2D (GCST005413), CKD (GCST90018822), and OBESITY (ukb-b-15541). GTEx weights for FUSION analyses are available at https://gusevlab.org/projects/fusion/. Single-cell gene expression data from the Tabula Muris study are available at https://tabula-muris.ds.czbiohub.org/. Summary-level statistics used for Mendelian randomization analyses are accessible in the IEU Open GWAS Project at https://gwas.mrcieu.ac.uk/ using the IEU Open GWAS Project ID. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethics and consent to participate declarations Not applicable. This study utilized publicly available datasets that were fully anonymized and compliant with their original ethical guidelines. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Authors' information 1 Department of Cardiology, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China. 2 North Sichuan Medical College, Nanchong, Sichuan, China. 3 Department of Laboratory Medicine, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China References Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. 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Schilder BM, Humphrey J, Raj T. echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline. Bioinformatics. 2022;38(2):536-9. doi: 10.1093/bioinformatics/btab658. Feng H, Mancuso N, Gusev A, Majumdar A, Major M, Pasaniuc B, et al. Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies. PLoS Genet. 2021;17(4):e1008973. doi: 10.1371/journal.pgen.1008973. Sobczyk MK, Gaunt TR, Paternoster L. MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes. Bioinformatics. 2021;37(1):1-8. doi: 10.1093/bioinformatics/btaa1096. Timshel PN, Thompson JJ, Pers TH. Genetic mapping of etiologic brain cell types for obesity. Elife. 2020;9: e55851. doi: 10.7554/eLife.55851. The Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature. 2018;562(7727):367-72. doi: 10.1038/s41586-018-0590-4. Lassen MCH, Ostrominski JW, Claggett BL, Packer M, Zile M, Desai AS, et al. Cardiovascular-kidney-metabolic overlap in heart failure with preserved ejection fraction: Cardiac structure and function, clinical outcomes, and response to sacubitril/valsartan in PARAGON-HF. Eur J Heart Fail. 2024;26(8):1762-1774. doi: 10.1002/ejhf.3304 Tseng CC, Wong MC, Liao WT, Chen CJ, Lee SC, Yen JH, et al. genetic variants in transcription factor binding sites in humans: triggered by natural selection and triggers of diseases. Int J Mol Sci. 2021;22(8):4187. doi: 10.3390/ijms22084187 Lee Y, Cho EJ, Choe EK, Kwak MS, Yang JI, Oh SW, et al. Genome-wide association study of metabolic dysfunction-associated fatty liver disease in a Korean population. Sci Rep. 2024;14(1):9753. doi: 10.1038/s41598-024-60152-0 Zuo FW, Liu ZY, Wang MW, Du JY, Ding PZ, Zhang HR, et al. CCDC92 promotes podocyte injury by regulating PA28α/ABCA1/cholesterol efflux axis in type 2 diabetic mice. Acta Pharmacol Sin. 2024;45(5):1019-31. doi: 10.1038/s41401-023-01213-4. Konieczny MJ, Omarov M, Zhang L, Malik R, Richardson TG, Baumeister SE, et al. The genomic architecture of circulating cytokine levels points to drug targets for immune-related diseases. Commun Biol. 2025; 8 (1): 34. doi: 10.1038/s42003-025-07453-w. Hu AJ, Li W, Dinh C, Zhang Y, Hu JK, Daniele SG, et al. CDK6 inhibits de novo lipogenesis in white adipose tissues but not in the liver. Nat Commun. 2024;15(1):1091. doi: 10.1038/s41467-024-45294-z. Wu D, Robinson CV. Understanding glycoprotein structural heterogeneity and interactions: Insights from native mass spectrometry. Curr Opin Struct Biol. 2022;74:102351. doi: 10.1016/j.sbi.2022.102351. Xie A, Wang J, Liu Y, Li G, Yang N. Impacts of β-1, 3-N-acetylglucosaminyltransferases (B3GNTs) in human diseases. Mol Biol Rep. 2024;51(1):476. doi: 10.1007/s11033-024-09405-9. Song M, Wang L, Tian J, Qin Y, Zhang W, Chen S, et al. Photosensitive biomimetic nanomedicine-mediated recombination of adipose microenvironments for antiobesity therapy. Adv Mater. 2025:e2417377. doi: 10.1002/adma.202417377. Luong TVT, Yang S, Kim J. Lipotoxicity as a therapeutic target in the type 2 diabetic heart. J Mol Cell Cardiol. 2025;201:105-121. doi: 10.1016/j.yjmcc.2025.02.010. Pazos F. Range of adiposity and cardiorenal syndrome. World J Diabetes. 2020;11(8):322-350. doi: 10.4239/wjd.v11.i8.322. Zhu X, Ma S, Wong WH. Genetic effects of sequence-conserved enhancer-like elements on human complex traits. Genome Biol. 2024;25(1):1. doi: 10.1186/s13059-023-03142-1 Krefting J, Andrade-Navarro MA, Ibn-Salem J. Evolutionary stability of topologically associating domains is associated with conserved gene regulation. BMC Biol. 2018;16(1):87. doi: 10.1186/s12915-018-0556-x. Xia Y, Brewer A, Bell JT. DNA methylation signatures of incident coronary heart disease: findings from epigenome-wide association studies. Clin Epigenetics. 2021;13(1):186. doi: 10.1186/s13148-021-01175-6. Li M, Zhao JV, Kwok MK, Schooling CM. Age and sex specific effects of APOE genotypes on ischemic heart disease and its risk factors in the UK Biobank. Sci Rep. 2021;11(1):9229. doi: 10.1038/s41598-021-88256-x. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7187218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490137989,"identity":"14c2e71e-4dbd-4570-aa8a-4d9461a32eb9","order_by":0,"name":"Zhonghai Wang","email":"","orcid":"","institution":"The Third People's Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Zhonghai","middleName":"","lastName":"Wang","suffix":""},{"id":490137990,"identity":"568043cb-15ff-44e0-8547-3c9806d402b6","order_by":1,"name":"Xin Chen","email":"","orcid":"","institution":"The Third People's Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""},{"id":490137991,"identity":"bed1eed7-f821-48e6-b0c0-b430c1c75c39","order_by":2,"name":"Han Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYJACZiCWsz/e//EBkCHDwMBGnBZjhjMHjA0YGAx4iNaS2HAjwUyCKC0Gx88efl1QcyexsSEhreJj2x8efva2BIYfFdtwazmTl2Y949gz42aGA8duzmwz4JHsOXaAsefMbZxazA7kmBnzsB2WbWNsbLvNC9RicCO9gZmxDY+W82+AWv4dZuxhZmYrJk7LjRzjx7xthxVnsLGxMUO0pB3Aq8X+xhszZt6+w8YGPDzMkjPOGYP8knAQn18k+3OMP/N8OyxnIP+G8cOHMjk5YIgZPvhRgVsLELBJYAgdwKceCJg/EFAwCkbBKBgFIx0AAMUwV1RT5fanAAAAAElFTkSuQmCC","orcid":"","institution":"The Third People's Hospital of Chengdu","correspondingAuthor":true,"prefix":"","firstName":"Han","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-22 12:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7187218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7187218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87590723,"identity":"baec47c6-d7a8-4372-8a58-15695816e2c2","added_by":"auto","created_at":"2025-07-25 14:49:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":368010,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy overview. \u003c/strong\u003eAn overview of this study’s analysis flow and methodology. Created with Drawio. HF, Heart failure; CHD, Coronary heart disease; T2D, Type 2 diabetes; CKD, Chronic kidney disease. FUMA, Functional Mapping and Annotation; SUSIE/FINEMAP, sum of single effects/fine mapping of causal variants; TWAS, Transcriptome-wide association study; FOCUS, Fine-mapping Of Causal Variants Using Summary statistics; MAGMA, Multi-marker Analysis of Genomic Annotation; GSEA, gene set enrichment analysis; CELLECT, CELL-type expression-specific integration for Complex Traits; IVW, inverse variance weighted; PRS, polygenic risk score; S-LDSC, stratified linkage disequilibrium score regression.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7187218/v1/6c27cde94d482185dfac8d8e.png"},{"id":87591976,"identity":"44f4de74-b445-484e-bba4-37c616f46217","added_by":"auto","created_at":"2025-07-25 14:57:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10647981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariate cardiovascular-kidney-metabolic syndrome GWAS (mvCKM) modeled with gSEM. \u003c/strong\u003e(a) Genetic correlations for SEM with gSEM, displaying pairwise LD score genetic correlation estimates for the six univariate phenotypes. (b) LocusZoom plot of the lead SNP rs271948-associated locus on chromosome 5 by FUMA. (c) Manhattan plot showing gene associations (−log10(P-value)) with mvCKM, ordered by chromosome. The red dashed line indicates the threshold for conventional genome-wide significance (P = 5 × 10\u003csup\u003e−8\u003c/sup\u003e). P values are derived from two-sided Wald tests for each SNP on mvCKM.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7187218/v1/e3791a6a3d2754a6c317ca79.png"},{"id":87590724,"identity":"6f0c10e8-b13a-4551-9cb9-c6e39f6dfe23","added_by":"auto","created_at":"2025-07-25 14:49:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1704281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrates GWAS significance, fine-mapping (FINEMAP/SUSIE) probabilities, and transcript annotation. \u003c/strong\u003eThe purple-to-red color gradient annotates the -log10(p) values of GWAS results, indicating the significance of the association between SNPs and the phenotype; solid circles and error bars represent the fine-mapping posterior probability (FINEMAP PP), while the triangle symbols correspond to the results from the SUSIE method. The significant locus rs2306536 exhibits high confidence in both methods (PP \u0026gt; 0.8).\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7187218/v1/e5979c5c1bb2eebe41c6d2b6.png"},{"id":87590728,"identity":"b4f58bba-4eed-49d7-9956-3cfa661d90b7","added_by":"auto","created_at":"2025-07-25 14:49:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12649077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTWAS-FUSION and FOCUS reveals causal variants and genes.\u003c/strong\u003e (a) TWAS-FUSION results showing chromatin interaction profiles (circular plot) across chromosomal regions. (b) FOCUS Fine-Mapping Analysis of Chromosome: The y-axis represents -log10(p) values, and the x-axis represents different genes or regions. The darker the color, the higher the PIP value, indicating a higher importance of the gene or region in the model. (c) Gene-level Manhattan plot of transcriptome-wide association study. TWAS Z-scores are plotted. The expression quantitative trait loci (eQTL) data are derived from the GTEx V8 sparse canonical correlation analysis. The blue lines represent Z = ± 4.69, corresponding to the Bonferroni adjusted P-value threshold = 1.38 × 10\u003csup\u003e−6\u003c/sup\u003e (0.05/36,149 sCCA features available for analysis). Red points and labels indicate genes (Ensembl gene IDs) surpassing the threshold. TWAS, transcriptome-wide association study; sCCA, sparse canonical correlation analysis. FOCUS, fine-mapping of causal variants using summary statistics.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7187218/v1/f4c70756f848ac9ae339ad2d.png"},{"id":87590740,"identity":"ac8baef2-ba5c-48e6-a81b-04bd10534364","added_by":"auto","created_at":"2025-07-25 14:49:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1411604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis and partitioning heritability. \u003c/strong\u003e(a) MendelVar enrichment analysis reveals disease-specific gene overlap patterns. This scatterplot illustrates gene overlap enrichment across 47 heritable disorders. The y-axis lists phenotypes, while the x-axis indicates the number of shared genes. Point color reflects empirical P-values, and point size represents the overlap ratio. (b) Spatial mapping of CELLECT-Based cell-type enrichment across anatomical regions. This schematic illustrates results from CELLECT analysis, highlighting cell types significantly enriched for genetic associations with the target phenotype.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7187218/v1/e7570fda961fc35d950299a6.png"},{"id":88403768,"identity":"1c78c91c-45dc-47e3-9517-1fb832c72548","added_by":"auto","created_at":"2025-08-06 07:24:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":34289808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7187218/v1/2777ca2d-af63-41d7-b12e-5fb4584161a0.pdf"},{"id":87591980,"identity":"1f01f578-3c12-4918-a87b-30ac8bb8f52c","added_by":"auto","created_at":"2025-07-25 14:57:26","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11164937,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7187218/v1/57534269ae83815f7bdd8732.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic structural equation modelling provides insights into the shared multivariate genetic architecture of cardio-kidney-metabolic syndrome components","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular-Kidney-Metabolic syndrome (CKM), as a new paradigm for cross-system diseases, reveals the complex pathophysiological network among cardiovascular diseases, chronic kidney diseases, and metabolic disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The introduction of this concept marked a breakthrough in our understanding of multiorgan interactions. Accumulating evidence indicates that individuals with obesity, type 2 diabetes (T2D), and chronic kidney disease (CKD) face a 3 to 5-fold increased risk of cardiovascular events compared with the general population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, these conditions act synergistically through a complex pathophysiological network, exacerbating multiorgan dysfunction. Currently, genome-wide association studies (GWASs) have revealed the genetic basis of individual metabolic phenotypes [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, CKM syndrome has been proven to have significant clinical heterogeneity, and its overall genetic architecture as a multisystem comorbidity has not been fully resolved.\u003c/p\u003e\u003cp\u003eCurrent research in dissecting CKM syndrome faces several challenges: First, understanding the complex causal relationships in CKM syndrome is difficult from the traditional single-disease viewpoint. In recent years, advances in large-scale GWASs and genomic structural equation modelling (gSEM) have provided new insights for dissecting the shared genetic mechanisms of complex traits. Notably, integrated GWAS data from seven metabolic components; they utilized gSEM to construct a three-factor model encompassing obesity, insulin resistance/hypertension, and dyslipidemia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These findings highlight the critical role of multiomics integration and cross-organ network analysis in deciphering the genetic basis of comorbid diseases. Therefore, our study innovatively introduced gSEM and integrated the summary data of GWASs from three major systems of CKM syndrome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. By analysing these data, we explored the associations between these SNPs and the potential CKM syndrome phenotypes, and then conducted a GWAS study on the potential CKM syndrome phenotypes (mvCKM) that have never been directly measured. Furthermore, we employed various post - GWAS methods to identify the functional mechanisms of mvCKM. In particular, we focused on various genomic loci and chromosomal regions related to CKM syndrome to explore the potential targets [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In general, this study not only broadens our understanding of CKM syndrome but also provides theoretical and practical support for intervention strategies to reduce the global burden of chronic diseases.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eUnivariate input GWAS data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate input data for the mvCKM were derived from six summarized GWASs related to CKM syndrome, encompassing traits including heart failure (HF) [3], coronary heart disease (CHD) [4], stroke [5], T2D [6], obesity (https://gwas.mrci eu.ac.uk/), and CKD [7]. All input GWASs obtained ethical approval from their respective Institutional Review Boards, and written informed consent was secured from all participants. The summary GWAS data for HF and CHD were obtained from separate GWAS meta-analyses: HF data originated from 11 cohorts comprising 977,323 individuals of European ancestry, and CHD data were derived from 13 European cohorts involving 141,217 European participants. Additionally, stroke GWAS data were extracted from a meta-analysis encompassing 18 European cohorts. Data for T2D GWAS were sourced from Bonàs-Guarch and colleagues, who included 70,127 European individuals, and those for the obesity GWAS were acquired from the UK Biobank, with 463,010 European participants. Additionally, the summarized GWAS for CKD involved 482,858 European participants, including data from UK Biobank and FinnGen. Detailed information about these GWAS data is provided in Table S1. An outline of our study design is illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality control of univariate input GWAS and sample overlap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quality control standards for univariable input GWAS were as follows: 1) Samples with \u0026gt; 5% missing data were excluded. 2) SNPs within the major histocompatibility complex (MHC) region were removed. Additionally, we constructed the mvCKM model via default parameters. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study utilized univariate input GWAS derived from a range of independent genomic information repositories, with separate participants in each dataset. This implies that in the GWAS analysis, we thoroughly accounted for sample overlap across various cohorts to guarantee the precision and applicability of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic structural equation modelling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the GenomicSEM package, we conducted multivariate GWASs on HF, CHD, stroke, T2D, obesity, and CKD to investigate the shared genetic architecture underlying these phenotypes. GSEM, a novel multivariate analytical method, enables systematic exploration of genetic correlation networks among complex diseases. This approach has two advantages: it corrects statistical biases from sample overlap and size differences via weighted covariance matrices, and identifies genetic variants affecting specific phenotypic subsets [9].\u003c/p\u003e\n\u003cp\u003eThe GSEM operates through two sequential stages. In the initial stage, the genetic covariance matrix is constructed. This begins with the standardization of univariate GWAS datasets. A multivariate extension of cross-trait linkage disequilibrium (LD) score regression was subsequently implemented to derive an empirical genetic covariance matrix across six phenotypic traits. This matrix is then utilized as the foundational input for the common factor model within the SEM framework [12].\u003c/p\u003e\n\u003cp\u003eIn the second stage, the gSEM is specified to fit the empirical genetic covariance matrix in the first stage. The objective of this stage is to minimize the difference between the estimated genetic covariance matrix and the empirical genetic covariance matrix to estimate the parameters of the SEM. In our study, our aim was to identify the genetic characteristics underlying six CKM\u0026nbsp;syndrome-associated traits; thus, we opted to test a single-factor model [13]. The model's goodness-of-fit was rigorously assessed through four key indicators: SRMR, χ², AIC, and CFI. Shared covariance analysis results across six input GWASs were created via gSEM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ\u003csub\u003eSNP\u003c/sub\u003e heterogeneity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the adequacy of CKM-related SNPs within the gSEM framework, we quantified SNP heterogeneity. Our study tested the hypothesis that the SNP associations detected in univariate GWASs are entirely mediated by mvCKM. The significant Q\u003csub\u003eSNP\u003c/sub\u003e signals (P \u0026lt; 0.05) for mvCKM suggest that these SNPs might influence traits through pathways independent of the shared mechanisms captured in the mvCKM model [8].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultilevel genome-wide analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multiple-tier quality control was implemented for the constructed mvCKM. This framework first involves adjusting the thresholds for correlation tests (P \u0026lt; 5 x 10\u003csup\u003e-16\u003c/sup\u003e) to analyse novel SNP loci detectable at different significance levels [8]. This strategy provides dual benefits: effectively controlling the false positive rate through a streamlined statistical framework while enabling genome-wide locus screening and validation of mvCKM-identified locus novelty, with demonstrated utility in prior post-GWAS studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTwo-step LD score regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied two-step LD score regression to distinguish genetic signals from confounding effects and quantify heritability. The workflow included three stages: Retaining all SNPs (including missing values), preserving low-quality variants (INFO \u0026lt; 0.9 or MAF \u0026lt; 0.01), and keeping valid P-value SNPs without strand exclusion, removing partition LD scores with zero variance [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of novel genetic variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used FUMA to identify genomic risk loci [14] and selected lead SNPs associated with mvCKM (r² \u0026lt; 0.1, P \u0026lt; 5 × 10\u003csup\u003e-8\u003c/sup\u003e). If the distance between a lead SNP locus and an identified locus in the univariable input GWAS data exceeded 1 Mb, it was considered a novel locus. To determine whether the leading SNPs in mvCKM had pleiotropic associations, we referred to the relevant information of the published significant associations (P \u0026lt; 5 × 10\u003csup\u003e-8\u003c/sup\u003e) in the GWAS Catalog [15]. Subsequently, we analyzed the relevant output files using Multi-marker Analysis of GenoMic Annotation (MAGMA) [16]. The significance threshold for the MAGMA analysis was set at a false discovery rate (FDR) P-value \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSUSIE and FINEMAP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the causal variants most likely associated with mvCKM, we used SUSIE and FINEMAP for fine-mapping analysis (echolocatoR v.2.0.3) [17, 18]. We defined lead SNP-associated regions using a 250 kb window, calculated causal inference probabilities for all SNPs within them, and classified SNPs with SUSIE/FINEMAP posterior probabilities \u0026gt;0.95 as potential causal variants. Notably, echolocatoR defines a “consensus SNP”, which refers to the variants that appear in both the SUSIE and FINEMAP results. For these consensus SNPs, the tool calculates their average posterior probability and determines the average credible set on the base of the probability results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome-wide association study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe referred to the 37920 expression quantitative trait loci (eQTLs) in the GTEx v.8 dataset and performed FUSION to conduct a comprehensive transcriptome-wide association study (TWAS) analysis, prioritizing mvCKM-associated genes [19]. Notably, mvCKM in our TWAS analysis contained sufficient variation information, allowing us to analyse 36,149 features. On the base of additional evidence for colocalization and fine mapping, we prioritized ‘high-confidence’ mvCKM-associated genes identified by FUSION. Finally, we considered TWAS-significant genes associated with mvCKM that also demonstrated colocalization (PP.H4 \u0026gt;0.75) and were likely to be causal (FOCUS posterior inclusion probability \u0026gt;0.90) [19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set pathways enrichment and cell annotation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the core genes and related pathways of mvCKM, we used data from MAGMA and FUMA to conduct gene set enrichment analysis (GSEA) [14]. Also, we used MendelVar to explore if mvCKM is enriched in Mendelian diseases and related pathways [20].\u003c/p\u003e\n\u003cp\u003eFor the cell annotation analysis, we employed the CELLECT method [21], which is based on the Tabula Muris dataset [22], and includes transcriptome data from 100,000 cells and 20 different organs and tissues in mice. After preprocessing and standardization, gene expression specificity scores were calculated. Cell-type specificity analysis and classification were conducted via LDSC, with an FDR threshold of \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeritability partition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed S-LDSC to estimate genomic partitioning heritability [10], which quantifies the genetic contributions of distinct genomic regions to phenotypic heritability. By combining phenotypic genetic data, weighted LD matrices, genotype frequencies, and summary statistics, S-LDSC decomposes phenotypic variance into genomic segments, revealing the specific roles and proportions of each region in the total heritability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolygenic risk scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe constructed polygenic risk scores (PRS) from GWAS summary data to analyse the genetic influences of distinct chromosomal regions on mvCKM. By combining GWAS summary data with LD reference panels, we applied PRS-CS software to estimate posterior effect sizes for SNPs [11]. These estimates are used to compute the PRS, which quantifies the cumulative effect of genetic variants on the mvCKM phenotype.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eStatistical indicators for structural equation modeling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLD score regression analysis indicated that when six univariate GWASs, namely, HF, CHD, stroke, T2D, obesity, and CKD, were input into the novel GWAS, their heritability contributions were 6.1%, 13.7%, 6.0%, 21.5%, 1.1%, and 9.5% respectively. The detailed genetic parameters are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The common factor model of the genetic covariance matrix and the empirical covariance matrix of the six univariate GWASs fit well (χ\u0026sup2;=28.66, df\u0026thinsp;=\u0026thinsp;9, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.18, CFI\u0026thinsp;=\u0026thinsp;0.97, SRMR\u0026thinsp;=\u0026thinsp;0.08; Table S3). This result suggests shared genetic factors in gSEM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStratified evaluation of mvCKM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing gSEM, we obtained an indirectly measured GWAS to explore the links between 4,498,214 SNPs and mvCKM. Among these genomic loci, we identified 1,338 genomic loci under more stringent conditions (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e) (Table S4). Overall, more than 2,000 loci in mvCKM are newly discovered and differ from those identified by the six univariate GWAS, highlighting the enhanced capabilities of gSEM. These lead SNPs are typically enriched in associations with cardiometabolic systems, cancer, neural functions, and inflammation and immunity. In general, our study revealed that these newly discovered SNPs are often closely related to important components of mvCKM.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenome-wide quality control via LD score regression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 4,497,771 SNPs were initially identified, and 961,631 valid SNPs were retained after stringent parameter controls were applied via the LD score regression. LD score regression analysis revealed a mean χ\u0026sup2; of 1.183 (λGC\u0026thinsp;=\u0026thinsp;1.095), with 493 significant SNPs and a maximum χ\u0026sup2; of 913.763. Heritability estimates showed h\u0026sup2; = 8.37 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (genetic-environmental ratio: 0.134), which was supported by nonsignificant heterogeneity (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and a regression intercept of 0.975. Multiple estimates indicate that the potential inflation in gSEM is due to polygenic heritability signals, rather than population stratification bias or pleiotropic parameter effects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of novel lead SNP\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFUMA software was employed to assess mvCKM, leading to the identification of 1599 risk loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S5) and 816 genes that are likely linked to mvCKM (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Most of the 2029 annotated lead SNP loci were located in introns and intergenic regions (Table S6). Among them, a total of 22 lead SNPs were identified at the GWAS-subtracted loci. Among the 2029 lead SNPs, 261 were novel compared with the lead SNPs from the six input GWASs. These novel lead SNPs were significantly associated with mainly cardiometabolic health, neural function, inflammation and immunity, bone and body composition, and cancer and autoimmune diseases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSUSIE and FINEMAP\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBy combining SUSIE and FINEMAP, 92 causal variation loci were identified (mean PP\u0026thinsp;\u0026gt;\u0026thinsp;0.95), especially on chromosomes 2, 3, 5 and 13. These loci could be mapped to 85 potential genes, revealing the potential pleiotropy of cardiometabolic traits. The regional map showed obvious peaks at these loci, and other credible set variations also provided evidence of associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S8). Fourteen loci were found to be colocalized and possibly causal signals with mvCKM, as shown by the colocalization test.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTranscriptome-wide association study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 2,747 genes were associated with mvCKM, 31 of which genes met the criteria for Bonferroni correction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table S9). We further tested these genes, via colocalization and FOCUS analysis. The results revealed that 59 genes passed the Bayesian colocalization test (PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.75), and 63 genes presented potentially causal signals with mvCKM (PIP\u0026thinsp;\u0026gt;\u0026thinsp;0.9). By integrating the results of all methods, these \u0026ldquo;high-confidence\u0026rdquo; gene-level associations involve the following genes, which can be divided into two categories according to the TWASZ scores: one category with TWASZ scores\u0026thinsp;\u0026gt;\u0026thinsp;0, including ACY1, AGMAT, BPTF, CDK6, RP11-680H20.1, and RPL37A; and the other category with TWASZ scores\u0026thinsp;\u0026lt;\u0026thinsp;0, including B3GNT7, PDE6D, RDH14, and RPSAP36 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table S9).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell-gene-pathway-Mendelian disease enrichment analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing MAGMA for genome mapping, we identified 37 genes, which were then used for gene set analysis, revealing their enrichment in GSEA entries. (Table S10, S11). Most of the gene sets were linked to metabolism, including T2D, metabolic syndrome, blood glucose, obesity, and body measurements, and the diseases mapped by MendelVar enrichment received partial support from GSEA entries (e.g., abnormal glucose metabolism, cardiovascular diseases) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Table S12). The relevant pathway enrichment also involved metabolism and cardiac abnormalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea Table S13).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCell-type enrichment analysis revealed that four cell types exceeded the significance threshold, namely, pancreatic PP cells, pancreatic α-cells, bone marrow macrophages, and splenic macrophages (Table S14). The first two cell types are closely related to insulin/glucagon in the pancreas, whereas the latter two, macrophages, play crucial roles in inflammation regulation and autoimmunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Enrichment analysis of mvCKM revealed enrichment in one and two tissues, primarily in the pancreas and fetal blood tissue, respectively, suggesting that the pathogenesis of CKM syndrome involves metabolic regulation during early developmental programming and multiorgan interactions in adulthood. These findings provide a theoretical basis for early-life interventions and the targeting metabolic-inflammatory pathways (Tables S15, S16). In the enrichment analysis of multi-tissue chromatin, we observed significant enrichment of histone modifications (e.g., H3K4me1) and regulatory element region markers, primarily in the pancreas, gastrointestinal tract, and T cells (Table S17). These chromatin signatures unveil the multisystem epigenetic regulatory network of CKM syndrome, encompassing abnormal activation/inhibition of genes associated with metabolism, immunity, and organ development.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePartitioned heritability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed partitioned heritability analysis via S-LDSC to assess the contribution of various functional genomic annotations to the heritability of mvCKM. As shown in Table S18, the results of the conserved regions remained significant after multiple-test correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although the conserved regions accounted for only approximately 2.6% of the whole-genome SNPs, they explained about 69.2% of the heritability, indicating that evolutionarily conserved noncoding regulatory elements play a key role in CKM syndrome. At a nominal significance threshold, coding regions (Coding_UCSC/extend.500) and enhancers marked by H3K4me1/H3K27ac also showed moderate to high heritability enrichment. However, these annotation signals failed to pass the multiple-test correction, suggesting that their effects may depend on specific biological contexts or require a larger sample size for verification.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolygenic risk scores\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur findings revealed significant differences in the genetic contributions of different chromosomal regions to mvCKM. In particular, in regions of chromosomes 1, 4, 6, and 9, we observed relatively high genetic contributions. These regions may contain important genes and regulatory elements that influence the susceptibility to CKM syndrome (Table S19).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically investigated the genetic basis of six CKM syndrome-related traits: HF, CHD, stroke, T2D, obesity, and CKD. By integrating multidimensional methodologies including gSEM, fine-mapping, TWAS, multitiered genetic contribution assessment (PRS-CS/S-LDSC), and cell-gene-pathway-Mendelian disease enrichment analysis, we identified novel genetic signals through joint analysis of these complex traits. Our findings demonstrated that genetic factors distributed across distinct genomic/chromosomal regions not only drive CKM syndrome development but also may exert lifelong impacts on individual health through cross-scale biological mechanisms.\u003c/p\u003e\u003cp\u003eOur study employed gSEM to reveal the genetic covariance among six related indicators of CKM syndrome, indicating the existence of shared genetic factors among these phenotypes. In the confirmatory factor analysis of common factor models, HF, which explained approximately 76.6% of the variance, exhibited the strongest association with the common factor, suggesting that it may be highly driven by shared genetic mechanisms. HF is one of the core symptoms of stage IV CKM syndrome, where patients exhibit concurrent cardiac structural and functional abnormalities, metabolic disorders and renal damage, ultimately progressing to end-organ damage [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Compared with GWASs of HF and other traits, mvCKM can play a more extensive role. The SEM further confirms the complex genetic links among the six traits, indicating that they do not exist in isolation but are intertwined together. These findings provide important insights for the implementation of future precision medicine and public health interventions.\u003c/p\u003e\u003cp\u003eUsing the subsequent FUMA method, we identified 261 novel lead SNPs. These SNPs are mainly associated with cardiometabolic health, cancer, and autoimmune diseases. Most of the newly discovered SNPs are located in genomic regulatory regions (introns and intergenic regions), indicating that such SNPs may produce extensive effects through regulatory pathways. In addition, they can regulate the expression levels of nearby or distant genes by disrupting the activities of enhancers, promoters, or insulators [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Several new and interesting SNPs have attracted our attention. These SNPs are related to traits such as cognition, skeletal muscle, and autoimmune diseases. For example, rs35771425, rs57109420, rs10808026, rs7131535, and rs147571544 are associated with Alzheimer\u0026rsquo;s disease [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, few empirical studies have explored the functions and significance of these loci.\u003c/p\u003e\u003cp\u003eThis study identified 92 elementary SNPs through stratified analysis and fine-mapping. These SNPs are located in gene regions associated with cardiometabolic diseases, immune and inflammatory responses, and neurological and cognitive functions. These findings align with those of previous studies demonstrating that critical loci for cardiometabolic diseases often exhibit strong correlations with specific SNPs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For instance, the rs12061508 locus anchored in the KCN2 gene significantly associated with metabolic and inflammatory markers, suggesting its potential role as a causal variant [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The identification of these SNPs enables refined characterization of how these genes modulate health status and disease susceptibility through metabolic, inflammatory, and other pivotal biological mechanisms.\u003c/p\u003e\u003cp\u003eNotably, we combined the findings of FUSION, FOCUS, and colocalization. These \u0026ldquo;high-confidence\u0026rdquo; gene-level associations involve possible risk genes, such as ACY1, AGMAT, and BPTF, and potential protective genes, including B3GNT7, PDE6D, RDH14, and RPSAP36. CDK6 can play a pivotal role in atherosclerosis, myocardial injury, obesity, and insulin resistance by integrating cell cycle regulation with metabolic reprogramming [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Targeting the kinase activity of CDK6 or related pathways (such as RUNX1 and CHREBP) may provide new strategies for the treatment of cardiometabolic syndrome. The encoded protein has been implicated in the onset and progression of various diseases, including autoimmune disorders, cancers, neurodevelopmental conditions, musculoskeletal issues, and metabolic diseases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The key pathological processes involving B3GNT7 may involve metabolic diseases by influencing cell signal transduction, inflammatory response, or insulin sensitivity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. B3GNT7 may be an important gene for the precise treatment of CKM syndrome in the future.\u003c/p\u003e\u003cp\u003eWe conducted a series of enrichment analyses, including cell-type, gene, tissue, chromosome, pathway, and enrichment analysis. Generally, the findings of cell-type enrichment analysis showed that pancreatic cells and macrophages exceeded the significance criterion. MAGMA gene enrichment was related mainly to metabolism, heart, neurodevelopment, and immunity. MendelVar-enriched diseases were partially supported by GSEA (e.g., abnormal glucose metabolism and cardiovascular conditions). The related pathways also involved metabolism, endocrine and cardiac abnormalities. In addition, tissue enrichment was mainly concentrated in adipose tissue and brain tissue. Significant enrichment of histone modifications (H3K4me1) and regulatory element region markers was also found mainly in the pancreas. Numerous studies have shown that metabolic abnormalities can drive the progression of CKM syndrome through insulin resistance, chronic low-grade inflammation, oxidative stress, endothelial dysfunction, and neuroendocrine imbalance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For example, metabolic abnormalities induce cardiomyocyte apoptosis and coronary atherosclerosis through lipotoxicity and glucotoxicity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, the accumulation of extracardiac adipose tissue directly compresses the myocardium, aggravating diastolic dysfunction [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, it can also lead to glomerular hyperfiltration and podocyte injury, whereas lipid metabolism disorders accelerate interstitial fibrosis through tubular lipid deposition. By analysing the whole-genome data of mvCKM, we identified multiple chromosomal risk regions associated with mvCKM, many of which are located in noncoding regions [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe also found genetic markers in the coding region and the regions extending 500 bp upstream and downstream, which not only directly reflect gene function but also may indirectly affect phenotypes through regulatory pathways [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, we also found that in some chromosomal risk regions, epigenetic markers of histone modification are closely related to disease susceptibility. These findings indicate that these regions not only affect gene expression but also suggest that the pathogenicity of genetic risk loci may be achieved through epigenetic mechanisms.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the study population was primarily from Europe. Future studies should expand the sample range and conduct verification in populations of different races and from different regions. Second, even though we have identified multiple SNPs/genes related to CKM syndrome via fine mapping and transcriptomics analysis, how to link these genes with specific biological mechanisms remains an urgent problem. Moreover, while studies have revealed the significant role of genetic factors in CKM syndrome, the impact of environmental factors should not be overlooked.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, we provide new insights into the GWAS of CKM syndrome based on mvCKM. By combining gSEM, fine mapping and transcriptomics analysis, we identified multiple novel genetic loci and revealed how these loci are genetically linked to complex traits by affecting gene expression. Our findings not only increase the comprehension of the mechanism of mvCKM, but also provide novel perspectives for precision medicine and public health interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKM, Cardiovascular-kidney-metabolic\u003c/p\u003e\n\u003cp\u003emvCKM, Multivariate GWAS of CKM Syndrome\u003c/p\u003e\n\u003cp\u003egSEM, Genomic Structural Equation Modelling\u003c/p\u003e\n\u003cp\u003eHF, Heart Failure\u003c/p\u003e\n\u003cp\u003eCHD, Coronary Heart Disease\u003c/p\u003e\n\u003cp\u003eT2D, Type 2 Diabetes\u003c/p\u003e\n\u003cp\u003eCKD, Chronic Kidney Disease\u003c/p\u003e\n\u003cp\u003eSNP, Single Nucleotide Polymorphism\u003c/p\u003e\n\u003cp\u003eGWAS, Genome-wide Association Study\u003c/p\u003e\n\u003cp\u003eFUMA, Functional Mapping and Annotation of Genome-Wide Association Studies\u003c/p\u003e\n\u003cp\u003eMAGMA, Multi-marker Analysis of GenoMic Annotation\u003c/p\u003e\n\u003cp\u003eLD, Linkage Disequilibrium\u003c/p\u003e\n\u003cp\u003eSUSIE, Sum of Single Effects\u003c/p\u003e\n\u003cp\u003eGSEA, Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eTWAS, Transcriptome-wide Association Study\u003c/p\u003e\n\u003cp\u003eeQTL, expression Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003ePIP, Posterior Inclusion Probabilities\u003c/p\u003e\n\u003cp\u003eCELLECT, Cell-type Expression-specific Integration for Complex Traits\u003c/p\u003e\n\u003cp\u003eS-LDSC, Stratified Linkage Disequilibrium Score Regression\u003c/p\u003e\n\u003cp\u003ePRS-CS \u0026nbsp; \u0026nbsp; \u0026nbsp; Polygenic Risk Score with Continuous Shrinkage\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the participants, researchers, and institutions (including UKB, MRC-IEU, and FinnGen) whose studies were cited in this research \u0026nbsp;\u0026mdash;without their contributions, this work would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZHW: Writing \u0026ndash; original draft, Software, Resources, Project administration, Methodology, Conceptualization. XC: Visualization, Methodology, Investigation, Formal analysis, Data curation. HW: Writing \u0026ndash; review \u0026amp; editing, Software. A final version of the manuscript was approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by The Third People\u0026apos;s Hospital of Chengdu Foundation (CSY-YN-03-2024-023)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly accessible datasets served as the foundation for all research evaluations. Summary-level statistics for HF are available at the GWAS Catalog (GCST009541); CHD (GCST003116); stroke (GCST005838); T2D (GCST005413), CKD (GCST90018822), and OBESITY (ukb-b-15541). GTEx weights for FUSION analyses are available at https://gusevlab.org/projects/fusion/. Single-cell gene expression data from the Tabula Muris study are available at https://tabula-muris.ds.czbiohub.org/. Summary-level statistics used for Mendelian randomization analyses are accessible in the IEU Open GWAS Project at https://gwas.mrcieu.ac.uk/ using the IEU Open GWAS Project ID. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and consent to participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study utilized publicly available datasets that were fully anonymized and compliant with their original ethical guidelines. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Cardiology, The Third People\u0026apos;s Hospital of Chengdu, Chengdu, Sichuan, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eNorth Sichuan Medical College, Nanchong, Sichuan, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Laboratory Medicine, The Third People\u0026apos;s Hospital of Chengdu, Chengdu, Sichuan, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNdumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. 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Sci Rep. 2021;11(1):9229. doi: 10.1038/s41598-021-88256-x. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular-kidney-metabolic syndrome, Genomic structural equation modeling, Single nucleotide polymorphism, Polygenic risk score, Genomic element","lastPublishedDoi":"10.21203/rs.3.rs-7187218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7187218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCardiovascular-kidney-metabolic (CKM) syndrome has placed a substantial burden on society both socially and economically. Although many genome-wide association studies (GWASs) of single phenotypes have been conducted, little is currently known about the genetic architecture of CKM syndrome.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA multivariate GWAS of CKM syndrome (mvCKM) in Europe was performed via genomic structural equation modelling (gSEM). A subsequent series of post-GWAS analyses elucidated novel loci and functional mechanisms of mvCKM. Cell-gene-pathway-Mendelian disease analysis further revealed the enrichment status of mvCKM. We particularly focused on various genomic loci and chromosomal regions related to CKM syndrome to explore potential targets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 261 novel SNPs were identified and 92 causal SNPs (posterior probability\u0026thinsp;\u0026gt;\u0026thinsp;0.95) were estimated independent of single phenotypes. Furthermore, we employed multiple transcriptome-wide association analysis approaches to explore 10 susceptible genes. One of these genes, B3GNT7, was also identified via the MAGMA method. The multi-marker analysis for genome annotation at the cellular level demonstrated that mvCKM was primarily enriched in metabolic cells, organs, and associated pathways. Partitioned heritability analysis revealed that conserved regions may make substantial genomic contributions. Polygenic risk scores indicated high genetic contributions from regions on chromosomes 4, 6, 1, and 9.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study provides an essential understanding of the genetic architecture of CKM syndrome via mvCKM in Europeans, offering new viewpoints for precision medicine and public health initiatives.\u003c/p\u003e","manuscriptTitle":"Genomic structural equation modelling provides insights into the shared multivariate genetic architecture of cardio-kidney-metabolic syndrome components","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 14:49:21","doi":"10.21203/rs.3.rs-7187218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae191c41-c0f5-42fd-a10d-ea5c1104f363","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-07T12:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 14:49:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7187218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7187218","identity":"rs-7187218","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}