Disentangling Genetic Risks for Metabolic Syndrome

Systems genetics of metabolic health in the BXD mouse genetic reference population

Susceptibility to metabolic syndrome (MetS) is dependent on genetics, environment, and gene-by-environment interactions, rendering the study of underlying mechanisms challenging. The majority of experiments in model organisms do not incorporate genetic variation and lack specific evaluation criteria for MetS. Here, we derived a continuous metric, the metabolic health score (MHS), based on standard clinical parameters and defined its molecular signatures in the liver and circulation. In human UK Biobank, the MHS associated with MetS status and was predictive of future disease incidence, even in individuals without MetS. Using quantitative trait locus analyses in mice, we found two MHS-associated genetic loci and replicated them in unrelated mouse populations. Through a prioritization scheme in mice and human genetic data, we identified TNKS and MCPH1 as candidates mediating differences in the MHS. Our findings provide insights into the molecular mechanisms sustaining metabolic health across species and uncover likely regulators. A record of this paper's transparent peer review process is included in the supplemental information.

Causal relationship between metabolic syndrome and hidradenitis suppurativa: A two-sample bidirectional Mendelian randomization study

Observational studies have suggested an association between metabolic syndrome (MetS) and hidradenitis suppurativa (HS), but whether this relationship is causal remains unclear. Elucidating the causal direction could provide insights into disease mechanisms and potential interventions. We performed bidirectional two-sample Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) of MetS and HS. For validation, we replicated the MetS analysis using data from an independent GWAS. We applied multiple MR methods, primarily inverse variance-weighted (IVW) regression, and conducted sensitivity analyses to assess heterogeneity and pleiotropy. The MR analysis demonstrated MetS causally increased HS risk (IVW odds ratio [OR], 1.428 [95% CI, 1.193-1.710]; p < 0.001), with consistent evidence from sensitivity analyses. However, HS did not appear to causally influence MetS risk (IVW OR, 1.008 [95% CI, 0.988-1.028]; p = 0.438). This study provides evidence that MetS causally increases the risk of developing HS. However, we found no evidence for a causal relationship in the reverse direction from HS to MetS. Further research is warranted to elucidate the mechanisms underlying the identified causal association between MetS and subsequent HS development.

Multiple genetic polymorphisms are associated with the risk of metabolic syndrome, fatty liver, and airflow limitation: A Taiwan Biobank study

BACKGROUND

Links have been reported between the airflow limitation and both metabolic syndrome (MetS) and fatty liver (FL). Additionally, associations between genetic factors and risks of MetS, FL, and airflow limitation have been identified separately in different studies. Our study aims to simultaneously explore the association between specific single nucleotide polymorphisms (SNPs) of certain genes and the risk of the three associated diseases.

METHODS

In this retrospective cross-sectional nationwide study, 150,709 participants from the Taiwan Biobank (TWB) were enrolled. We conducted a genotype-phenotype association analysis of nine SNPs on seven genes (ApoE-rs429358, MBOAT7-rs641738, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, TM6SF2-rs58542926, and IFNL4-rs368234815) using data from the TWB1.0 and TWB2.0 genotype dataset. Participants underwent a series of assessments including questionnaires, blood examinations, abdominal ultrasounds, and spirometry examinations.

RESULTS

MetS was associated with FL and airflow limitation. ApoE-rs429358, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, and TM6SF2-rs58542926 were significantly associated with the risk of MetS. The cumulative impact of T alleles of ApoE-rs429358 and TM6SF2-rs58542926 on the risk of FL was observed (p-value for trend < 0.001). Individuals without MetS and airflow limitation carrying LEPR-rs1805096 G_G genotype exhibited a reduction in the forced expiratory volume in 1 s percentage prediction (Coefficient -35, 95 % confidence interval (CI) -69.7- -0.4), low forced vital capacity percentage prediction (Coefficient -41.6, 95 % CI -82.6- -0.6), and low vital capacity percentage prediction (Coefficient -42.2, 95 % CI -84.2- -0.1).

CONCLUSIONS

MetS significantly correlated with FL and airflow limitation. Multiple SNPs were notably associated with MetS. Specifically, T alleles of ApoE-rs429358 and TM6SF2-rs58542926 cumulatively increased the risk of FL. LEPR-rs1805096 shows a trend-wise association with pulmonary function, which is significant in patients without MetS or airflow limitation.

Genetic variations of low-density lipoprotein cholesterol on metabolic disorders in obstructive sleep apnea

BACKGROUND

The study aimed to explore the relationship between low-density lipoprotein cholesterol (LDL-C) genetic variants and obstructive sleep apnea (OSA) and its complications, including cardiovascular diseases (CVD), insulin resistance (IR), and metabolic syndrome (MS).

METHOD

4329 individuals with suspected OSA who underwent a comprehensive assessment of anthropometric, biochemical, and polysomnography (PSG) data, along with 30 LDL-C single nucleotide polymorphisms (SNPs) were enrolled. The 10-year Framingham CVD risk score (FRS), IR and MS were evaluated for each subject. Linear regression and logistic regression were utilized to examine the correlations among these variables.

RESULTS

After the Benjamini-Hochberg correction, linear regression results indicated positive correlations between variants rs3741297 and rs629301 with FRS (β = 0.031, PBH=0.002; β = 0.026, PBH=0.015). Logistic regression revealed that rs3741297 increased MS risk among total subjects [OR = 1.67 (95% CI:1.369-2.038), PBH=1.32 × 10- 5] and increased IR risk in females [OR = 3.475 (95% CI:1.653-7.307), PBH=0.03]. In males, rs2642438 decreased MS risk [OR = 0.81 (95% CI:0.703-0.933), PBH=0.045].

CONCLUSIONS

The rs3741297 variant correlated with susceptibility to CVD, IR, and MS in the OSA population. OSA, CVD, IR and MS share a potentially common genetic background, which may promote precision medicine.

CINICAL TRIAL REGISTRATION

The study protocol was registered with the Chinese Clinical Trial Registry (ChiCTR1900025714).

Genetic predisposition and antipsychotic treatment effect on metabolic syndrome in schizophrenia: a ten-year follow-up study using the Estonian Biobank

Background

Schizophrenia (SCZ) patients exhibit 30% higher prevalence of metabolic syndrome (MetS) compared to the general population with its suboptimal management contributing to increased mortality. Large-scale studies providing real-world evidence of the underlying causes remain limited.

Methods

To address this gap, we used real-world health data from the Estonian Biobank, spanning a median follow-up of ten years, to investigate the impact of genetic predisposition and antipsychotic treatment on the development of MetS in SCZ patients. Specifically, we set out to characterize antipsychotic treatment patterns, genetic predisposition of MetS traits, MetS prognosis, and body mass index (BMI) trajectories, comparing SCZ cases (n = 677) to age- and sex-matched controls (n = 2708).

Findings

SCZ cases exhibited higher genetic predisposition to SCZ (OR = 1.75, 95% CI 1.58-1.94), but lower polygenic burden for increased BMI (OR = 0.88, 95% CI 0.88-0.96) and C-reactive protein (OR = 0.88, 95% CI 0.81-0.97) compared to controls. While SCZ cases showed worse prognosis of MetS (HR 1.95, 95% CI 1.54-2.46), higher antipsychotic adherence within the first treatment year was associated with reduced long-term MetS incidence. Linear mixed modelling, incorporating multiple BMI timepoints, underscored the significant contribution of both, antipsychotic medication, and genetic predisposition to higher BMI, driving the substantially upward trajectory of BMI in SCZ cases.

Interpretation

These findings contribute to refining clinical risk prediction and prevention strategies for MetS among SCZ patients and emphasize the significance of incorporating genetic information, long-term patient tracking, and employing diverse perspectives when analyzing real-world health data.

Funding

EU Horizon 2020, Swedish Research Council, Estonian Research Council, Estonian Ministry of Education and Research, University of Tartu.

Genetic prediction of the relationship between metabolic syndrome and colorectal cancer risk: a Mendelian randomization study

BACKGROUND

Despite a growing body of observational studies indicating a potential link between metabolic syndrome and colorectal cancer, a definitive causal relationship has yet to be established. This study aimed to elucidate the causal relationship between metabolic syndrome and colorectal cancer through Mendelian randomization.

METHODS

We screened for instrumental variables associated with metabolic syndrome and its diagnostic components and with colorectal cancer through the use of a genome-wide association study database, and conducted a preliminary Mendelian randomization analysis. To corroborate the dependability of our conclusions, an additional dataset was used for replication analysis in a Mendelian randomization method, which was further integrated with a meta-analysis.

RESULTS

Preliminary analysis using the inverse variance weighted method revealed positive correlations between metabolic syndrome (OR [95% CI] = 1.37[1.15-1.63], P = 5.02 × 10-4) and waist circumference (OR [95% CI] = 1.39[1.21-1.61], P = 7.38 × 10-6) and the risk of colorectal cancer. Replication analysis also revealed the same results: metabolic syndrome (OR [95% CI] = 1.24[1.02-1.51], P = 0.030) and waist circumference (OR [95% CI] = 1.23[1.05-1.45], P = 0.013). The meta-analysis results further confirmed the associations between metabolic syndrome (OR [95% CI] = 1.31[1.15-1.49], P < 0.001) and waist circumference (OR [95% CI] = 1.32[1.18-1.47], P < 0.001) and colorectal cancer.

CONCLUSION

Our study indicated that metabolic syndrome increases the risk of CRC, particularly in patients with abdominal obesity.

Mediating effect of metabolic syndrome in the association of educational attainment with intervertebral disc degeneration and low back pain

The causal association of educational attainment (EA) with intervertebral disc degeneration (IVDD) or low back pain (LBP), and the mediating effect of metabolic syndrome (MetS) in this association, is not studied to date. In this study, using summary statistics of genome-wide association studies primarily conducted in the individuals of European ancestry, Mendelian randomization (MR) analyses were performed to investigate: (1) the total and direct effects of EA on IVDD and LBP, (2) bidirectional associations of EA with MetS or the components of MetS, (3) causal effects of MetS or its components on IVDD and LBP, and (4) mediating effects of MetS or its components on the causal associations of EA with IVDD and LBP. Univariable MR analysis demonstrated that genetically proxied EA was inversely associated with IVDD (ORIVW: 0.90; 95 % CI: 0.87-0.92) and LBP (ORIVW: 0.86; 95 % CI: 0.84-0.89). Consistent results were obtained after adjusting for potential confounders (cognition, economic level, smoking traits, and metabolic factors). Mediation analysis proved that the effect of EA on IVDD mediated by MetS, waist circumference, and high-density lipoprotein cholesterol was 11.38 %, 9.22 %, and 2.17 %, respectively. Besides, MetS mediated 8.42 % and waist circumference mediated 5.81 % of the EA effects on LBP, respectively. Our findings provided support for MetS mediating the causal protective effects of EA on IVDD and LBP, which provided causal evidence to the etiology and intervention targets of IVDD and LBP.

The causal relationship between metabolic syndrome and its components and cardiovascular disease: A mendelian randomization study

AIM

To investigate the causal relationship between metabolic syndrome (MetS) and its components and 14 cardiovascular diseases using Mendelian randomization (MR).

METHODS

We used summary statistics from large-scale genome-wide association studies of MetS, its components, and cardiovascular diseases. We performed a two-sample MR analysis using the inverse-variance weighted method and other sensitivity methods. We also performed multivariate MR to adjust for potential risk factors.

RESULTS

Our study found that MetS was causally associated with an increased risk of ischemic stroke, abdominal aortic aneurysm, pulmonary embolism, coronary heart disease, heart failure, and peripheral artery disease. Waist circumference was causally associated with an increased risk of 6 cardiovascular diseases. Type 2 diabetes mellitus, diastolic blood pressure, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol were all causally associated with coronary heart disease, with varying causal relationships with the remaining 5 cardiovascular diseases. Multivariate MR showed that, except for ischaemic stroke, waist circumference remained causally associated with the remaining five cardiovascular diseases after adjusting for potential confounders.

CONCLUSION

Our study provides evidence that metabolic syndrome is causally associated with 6 cardiovascular diseases. Waist circumference is the most important component of these relationships. These findings have implications for the prevention and management of metabolic syndrome and cardiovascular diseases.

Causal effects of sleep traits on metabolic syndrome and its components: a Mendelian randomization study

PURPOSE

Previous observational studies have suggested an association between sleep disturbance and metabolic syndrome (MetS). However, it remains unclear whether this association is causal. This study aims to investigate the causal effects of sleep-related traits on MetS using Mendelian randomization (MR).

METHODS

Single-nucleotide polymorphisms strongly associated with daytime napping, insomnia, chronotype, short sleep, and long sleep were selected as genetic instruments from the corresponding genome-wide association studies (GWAS). Summary-level data for MetS were obtained from two independent GWAS datasets. Univariable and multivariable MR analyses were conducted to investigate and verify the causal effects of sleep traits on MetS.

RESULTS

The univariable MR analysis demonstrated that genetically predicted daytime napping and insomnia were associated with increased risk of MetS in both discovery dataset (OR daytime napping = 1.630, 95% CI 1.273, 2.086; OR insomnia = 1.155, 95% CI 1.108, 1.204) and replication dataset (OR daytime napping = 1.325, 95% CI 1.131, 1.551; OR insomnia = 1.072, 95% CI 1.046, 1.099). For components, daytime napping was positively associated with triglycerides (beta = 0.383, 95% CI 0.160, 0.607) and waist circumference (beta = 0.383, 95% CI 0.184, 0.583). Insomnia was positively associated with hypertension (OR = 1.101, 95% CI 1.042, 1.162) and waist circumference (beta = 0.067, 95% CI 0.031, 0.104). The multivariable MR analysis indicated that the adverse effect of daytime napping and insomnia on MetS persisted after adjusting for BMI, smoking, drinking, and another sleep trait.

CONCLUSION

Our study supported daytime napping and insomnia were potential causal factors for MetS characterized by central obesity, hypertension, or elevated triglycerides.

Effects of Metabolic Syndrome and Its Components on Chronic Kidney Disease and Renal Function: A Two-Sample Mendelian Randomization Study

Objective: The association of metabolic syndrome (MetS) and its components with chronic kidney disease (CKD) and renal function remains controversial in observational studies. To comprehensively investigate the association between MetS and its components with CKD and renal function, a Mendelian randomization (MR) study was performed. Methods: The inverse variance weighting (IVW) of random effects was used as the main estimation method, while MR-Egger and weighted median analysis results were used for auxiliary judgments. Cochran's Q test, MR-Egger intercept test, leave-one-out analysis, and funnel plots were used to assess heterogeneity and pleiotropy. Results: The MR analyses of genetically predicted MetS and its components' association with CKD risk and renal function showed the following causal associations: hypertension with CKD risk; MetS and obesity with increased blood urea nitrogen and decreased estimated glomerular filtration rate based on cystatin C; hypertension and diabetes with increased urine albumin-creatinine ratio and increased risk of microalbuminuria; and CKD with increased triglyceride. Conclusion: Based on genetic data, this study demonstrated an association between hypertension and CKD risk and a causal association between other MetS components and renal function. The early diagnosis and prevention of MetS and its components might be essential for CKD management.

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Genetic evidence for the causal relations between metabolic syndrome and psychiatric disorders: a Mendelian randomization study

Emerging evidence reveals associations between metabolic syndrome (MetS) and psychiatric disorders (PDs), although causality remains uncertain. Consequently, we conducted Mendelian randomization (MR) to systematically evaluate the causality between MetS and PDs. Linkage disequilibrium score regression estimated the heritability of PDs and their genetic correlations with MetS. In primary analyses, the main model employed inverse variance weighting method, with sensitivity analyses using various MR models to ensure robustness. Replication MR analyses, involving cohorts distinct from those in the primary analyses, were performed to validate the generalizability of the findings. Multivariable MR analyses were carried out to account for genetically predicted body mass index (BMI). As a result, genetic correlations of MetS with attention-deficit/hyperactivity disorder(ADHD), anorexia nervosa(ANO), major depressive disorder(MDD), and schizophrenia were identified. Causal effects of MetS on ADHD (OR: 1.59 [95% CI:1.45-1.74]), ANO (OR: 1.42 [95% CI:1.25-1.61]), MDD(OR: 1.23 [95% CI: 1.13-1.33]), and the effects of ADHD (OR: 1.03 [95% CI: 1.02-1.04]) and ANO (OR: 1.01 [95% CI: 1.01-1.02]) on MetS were observed in primary analyses. Results from sensitivity analyses and replication analyses were generally consistent with the primary analyses, confirming the robustness and generalizability of the findings. Associations between MetS and ADHD, as well as ANO persisted after adjusting for BMI, whereas the statistical significance of the association between MetS and MDD was no longer observable. These results contribute to a deeper understanding of the mechanisms underlying PDs, suggesting potential modifiable targets for public prevention and clinical intervention in specific PDs related to metabolic pathways.

Unraveling phenotypic variance in metabolic syndrome through multi-omics

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics' roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene-diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook.

Causal association between metabolic syndrome and cholelithiasis: a Mendelian randomization study

Background

Metabolic syndrome (MetS) has been associated with digestive system diseases, and recent observational studies have suggested an association between MetS and cholelithiasis. However, the causal relationship between them remains unclear. This study aimed to assess the causal effect of MetS on cholelithiasis using Mendelian randomization (MR) analysis.

Methods

Single nucleotide polymorphisms (SNPs) of MetS and its components were extracted from the public genetic variation summary database. The inverse variance weighting method (IVW), weighted median method, and MR-Egger regression were used to evaluate the causal relationship. A sensitivity analysis was performed to ensure the stability of the results.

Results

IVW showed that MetS increased the risk of cholelithiasis (OR = 1.28, 95% CI = 1.13-1.46, P = 9.70E-05), and the weighted median method had the same result (OR = 1.49, 95% CI = 1.22-1.83, P = 5.68E-05). In exploring the causal relationship between MetS components and cholelithiasis, waist circumference (WC) was significantly associated with cholelithiasis. IVW analysis (OR = 1.48, 95% CI = 1.34-1.65, P = 1.15E-13), MR-Egger regression (OR = 1.62, 95% CI = 1.15-2.28, P = 0.007), and weighted median (OR = 1.73, 95% CI = 1.47-2.04, P = 1.62E-11) all found the same results.

Conclusion

Our study indicated that MetS increases the incidence of cholelithiasis, especially in MetS patients with abdominal obesity. Control and treatment of MetS can effectively reduce the risk of gallstone formation.