Genetic study uncovers key drivers of metabolic syndrome with brain tissue links

By Dr. Sushama R. Chaphalkar, PhD.Reviewed by Susha Cheriyedath, M.Sc.Oct 1 2024

New research uncovers over 1,300 genetic loci driving metabolic syndrome, revealing links to brain tissues and offering insights for tailored health strategies across populations.

Study: Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome. Image Credit: Lightspring / Shutterstock

In a recent study published in the journal Nature Genetics, researchers conducted a large-scale multivariate genome-wide association study (GWAS) of metabolic syndrome (MetS) in Europe, analyzing genetic correlations between MetS components.

They identified 1,650 lead SNPs across 939 genetic loci, of which 1,307 genetic loci were linked to MetS, enriched in brain tissues, and discovered 11 key genes. Among these loci, 82 SNPs were independent of MetS component GWAS results, suggesting unique genetic contributions.

Further, they demonstrated that polygenic risk scores (PRS) showed strong predictive power, with individuals in the top decile of the PRS having a 2.21-fold higher risk of developing MetS compared to those in the lowest decile. This was shown in both European and East Asian populations.

Background

MetS is a cluster of risk factors, including central obesity, dyslipidemia, hypertension, and impaired glucose tolerance, that increase the risk of cardiovascular disease and type 2 diabetes (T2D). These traits often run in families, indicating a shared genetic basis, with heritability estimates between 0.10 and 0.61. While GWAS have identified genetic variants linked to individual MetS components, the genetics underlying the shared risk across the components, the genetic basis of the shared risk across these components has been less well understood.

Previous efforts, like clustering and colocalization analyses, focused primarily on T2D variants, but these methods fall short in explaining the shared genetic basis of all MetS traits. Traditional GWAS approaches categorized MetS based on meeting specific criteria, which may introduce variability and limit understanding of its genetic architecture. Multivariate approaches, such as genomic structural equation modeling (SEM), provide greater flexibility and precision in uncovering common genetic pathways.

In the present study, researchers analyzed the shared genetic architecture of MetS in a large European cohort, identifying key genetic links through genomic SEM, gene prioritization, and polygenic risk score (PRS) analysis across multiple populations while also exploring associations with various health outcomes.

About the study

The researchers examined MetS through a multi-component genetic analysis, selecting seven key traits: body mass index (BMI), waist circumference (WC), T2D, fasting glucose (FG), hypertension (HTN), high-density lipoprotein (HDL), and triglycerides (TG). The effective sample sizes were between 151,188 and 1,253,277.

To prevent overlap between discovery and validation datasets, a UK Biobank (UKB)-excluded cohort was created for post-GWAS analyses. A fixed-effects meta-analysis was conducted for T2D and HTN, using GWAS summary statistics from UKB, FinnGen, MVP, and others. Quality control included filtering SNPs based on allele types, minor allele frequency, and imputation quality.

Exploratory and confirmatory factor analyses (EFA and CFA) identified latent factors explaining MetS components. Three factors were identified—obesity, insulin resistance/HTN, and dyslipidemia—together accounting for 70.2% of the variance in MetS traits. Genetic correlations and SNP-based heritability were estimated.

Additionally, a multivariate GWAS was performed to uncover SNP associations with MetS factors, and gene prioritization was achieved through functional mapping. Finally, PRS and two-sample Mendelian randomization (TSMR) were applied to assess causal relationships between MetS and other health outcomes, supported by a cross-population comparison using East Asian data.

Results and discussion

Three latent factors, obesity, insulin resistance/HTN, and dyslipidemia, together accounted for 70.2% of the variance in the seven MetS components. This correlated three-factor model provided good fit indices, reinforcing the understanding that MetS is a multi-dimensional trait rather than one that can be explained by a single factor.

A multivariate GWAS identified 1,650 lead SNPs across 939 genetic loci. A total of 1,307 of these SNPs were deemed independent from previously reported signals. Notably, 82 SNPs were independent of MetS component GWAS results, suggesting unique genetic contributions.

Genetic correlation analysis revealed significant associations between MetS and 82 external traits after correction, with substantial overlaps identified with cardiovascular diseases, nonalcoholic fatty liver disease, and cognitive functions. Heritability was particularly enriched in conserved genomic regions and brain tissues, highlighting the importance of brain-related mechanisms in MetS.

Multiple significant associations with MetS were identified across various tissues. A total of 11 target genes were identified, including BCL7B, AMHR2, FEZ2, HM13, MLXIPL, MYO1F, MED23, RBM6, SP1, RFT1, and STRA13. The PRS analysis demonstrated an increasing odds ratio for MetS risk with higher PRS deciles, with a notable association between the MetS PRS and cardiovascular disease incidence.

Furthermore, significant causal associations were established between MetS and 29 health outcomes, including ischemic heart disease, renal failure, and nonalcoholic fatty liver disease, indicating the extensive impact of MetS on various bodily systems and health conditions.

This study demonstrates the transferability of genetic insights from European populations to East Asian populations, providing valuable insights for the development of targeted prevention strategies and personalized treatment approaches. However, the study is limited by its focus on European ancestry, potential variation in SNP effects across populations, reliance on genetic covariance for the factor model, periodic updates to MetS definitions, and limited GWAS data for non-European populations.

Conclusion

In conclusion, the study identified shared genetic factors influencing MetS components and established significant associations with various health outcomes, highlighting the importance of brain tissues in these pathways.

The findings also suggest that the genetic insights gained from European populations could apply to East Asian populations, indicating a broader relevance for future research and potential interventions.