Pathway Analysis of Metabolic Syndrome Using a Genome-Wide Association Study of Korea Associated Resource (KARE) Cohorts

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential. Various machine learning approaches have been employed to predict MetS, such as logistic regression, support vector machines, and several boosting techniques. However, these methods use MetS as a binary status and do not consider that MetS comprises five components. Therefore, a method that focuses on these characteristics of MetS is needed. In this study, we propose a multi-task deep learning model designed to predict MetS and its five components simultaneously. The benefit of multi-task learning is that it can manage multiple tasks with a single model, and learning related tasks may enhance the model's predictive performance. To assess the efficacy of our proposed method, we compared its performance with that of several single-task approaches, including logistic regression, support vector machine, CatBoost, LightGBM, XGBoost and one-dimensional convolutional neural network. For the construction of our multi-task deep learning model, we utilized data from the Korean Association Resource (KARE) project, which includes 352,228 single nucleotide polymorphisms (SNPs) from 7729 individuals. We also considered lifestyle, dietary, and socio-economic factors that affect chronic diseases, in addition to genomic data. By evaluating metrics such as accuracy, precision, F1-score, and the area under the receiver operating characteristic curve, we demonstrate that our multi-task learning model surpasses traditional single-task machine learning models in predicting MetS.

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.

Gamma-glutamyl-leucine levels are causally associated with elevated cardio-metabolic risks

Objective

Gamma-glutamyl dipeptides are bioactive peptides involved in inflammation, oxidative stress, and glucose regulation. Gamma-glutamyl-leucine (Gamma-Glu-Leu) has been extensively reported to be associated with the risk of cardio-metabolic diseases, such as obesity, metabolic syndrome, and type 2 diabetes. However, the causality remains to be uncovered. The aim of this study was to explore the causal-effect relationships between Gamma-Glu-Leu and metabolic risk.

Materials and methods

In this study, 1,289 subjects were included from a cross-sectional survey on metabolic syndrome (MetS) in eastern China. Serum Gamma-Glu-Leu levels were measured by untargeted metabolomics. Using linear regressions, a two-stage genome-wide association study (GWAS) for Gamma-Glu-Leu was conducted to seek its instrumental single nucleotide polymorphisms (SNPs). One-sample Mendelian randomization (MR) analyses were performed to evaluate the causality between Gamma-Glu-Leu and the metabolic risk.

Results

Four SNPs are associated with serum Gamma-Glu-Leu levels, including rs12476238, rs56146133, rs2479714, and rs12229654. Out of them, rs12476238 exhibits the strongest association (Beta = -0.38, S.E. = 0.07 in discovery stage, Beta = -0.29, S.E. = 0.14 in validation stage, combined P-value = 1.04 × 10^-8). Each of the four SNPs has a nominal association with at least one metabolic risk factor. Both rs12229654 and rs56146133 are associated with body mass index, waist circumference (WC), the ratio of WC to hip circumference, blood pressure, and triglyceride (5 × 10^-5 < P < 0.05). rs56146133 also has nominal associations with fasting insulin, glucose, and insulin resistance index (5 × 10^-5 < P < 0.05). Using the four SNPs serving as the instrumental SNPs of Gamma-Glu-Leu, the MR analyses revealed that higher Gamma-Glu-Leu levels are causally associated with elevated risks of multiple cardio-metabolic factors except for high-density lipoprotein cholesterol and low-density lipoprotein cholesterol (P > 0.05).

Conclusion

Four SNPs (rs12476238, rs56146133, rs2479714, and rs12229654) may regulate the levels of serum Gamma-Glu-Leu. Higher Gamma-Glu-Leu levels are causally linked to cardio-metabolic risks. Future prospective studies on Gamma-Glu-Leu are required to explain its role in metabolic disorders.

Disentangling Genetic Risks for Metabolic Syndrome

A quarter of the world's population is estimated to meet the criteria for metabolic syndrome (MetS), a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type 2 diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with MetS components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations of fasting glucose, HDL cholesterol, systolic blood pressure, triglycerides, and waist circumference was used, which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on MetS to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more MetS components, indicating that MetS is a complex, heterogeneous disorder. Associated loci harbor genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the MetS factor GWAS predicts 5.9% of the variance in MetS. These results provide mechanistic insights into the genetics of MetS and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple MetS components.

Transethnic meta-analysis of exome-wide variants identifies new loci associated with male-specific metabolic syndrome

BACKGROUND

Metabolic syndrome (MetS) is a group of very common human conditions promoting strong understand the impact of rare variants, beyond exome-wide association studies, to potentially discover causative variants, across different ethnic populations.

OBJECTIVE

We performed transethnic, exome-wide MetS association studies on MetS in men.

METHODS

We analyzed genotype data of 5302 European subjects (2658 cases and 2644 controls), in the discovery stage of the European METabolic Syndrome In Men study, generated from exome chips, and 2481 subjects (714 cases and 1767 controls), in the replication stage, across 6 independent cohorts of 5 ancestries (T2D-GENES consortium), using whole-exome sequencing. We therefore evaluated gene-level and variant-level associations, of rare variants for MetS, using logistic regression (LR) and multivariate analyses (MulA).

RESULTS

Gene-based association found the gene for the cholesteryl ester transfer protein (CETP) (from MulA, p value = 4.67 × 10-9; from LR, p value = 0.009) to well associate with MetS. At two missense variants, from 8 rare variants in CETP, Ala390Pro (rs5880) (from MulA, p value = 1.28 × 10-7; from LR, p value = 1.34 × 10-4) and Arg468Gln (rs1800777) (from MulA, p value = 2.40 × 10-5; from LR, p value = 1.49 × 10-3) significantly associated with MetS across five ancestries.

CONCLUSIONS

Our findings highlight novel rare variants of genes that confer MetS susceptibility, in Europeans, that are shared with diverse populations, emphasizing an opportunity to further understand the biological target or genes that underlie MetS, across populations.

The Causal Relationship of Circulating Triglyceride and Glycated Hemoglobin: A Mendelian Randomization Study

CONTEXT

The association between circulating triglyceride (TG) and glycated hemoglobin A1c (HbA1c), a biomarker for type 2 diabetes, has been widely addressed, but the causal direction of the relationship is still ambiguous.

OBJECTIVE

To confirm the causal relationship between TG and HbA1c by using bidirectional and 2-step Mendelian randomization (MR) approaches.

METHODS

We carried out a bidirectional MR approach using the summarized results from the public database to examine any potential causal effects between serum TG and HbA1c in 16 000 individuals of the Taiwan Biobank cohort. We used the MR estimate and the MR inverse variance-weighted method to reveal that relationship between TG and HbA1c. To further determine whether the DNA methylation at specific sequences mediate the causal pathway between TG and HbA1c, using the 2-step MR approach.

RESULTS

We identified that a single-unit increase in TG measured via log transformation of mg/dL data was associated with a significant increase of 10 units of HbA1c (95% CI = 1.05-18.95, P = 0.029). In contrast, the genetic determinants of HbA1c do not contribute to the amount of circulating TG (beta = 1.75, 95% CI = -11.50 to 14.90). Sensitivity analyses, included the weighted-median approach and MR-Egger regression, were performed to confirm no pleiotropic effect among these instrumental variables. Furthermore, we identified the genetic variant, rs1823200, is associated with both methylation of the CpG site adjacent to CADPS gene and HbA1c level.

CONCLUSION

Our study suggests that higher circulating TG can have an affect on genomic methylation status, ultimately causing elevated level of circulating HbA1c.

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network

BACKGROUND

Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms.

METHODS

We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category.

RESULTS

A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks.

CONCLUSION

This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.

Genetic Variants Related to Cardiometabolic Traits Are Associated to B Cell Function, Insulin Resistance, and Diabetes Among AmeriCan Indians: The Strong Heart Family Study

Background: Genetic research may inform underlying mechanisms for disparities in the burden of type 2 diabetes mellitus among American Indians. Our objective was to assess the association of genetic variants in cardiometabolic candidate genes with B cell dysfunction via HOMA-B, insulin resistance via HOMA-IR, and type 2 diabetes mellitus in the Strong Heart Family Study (SHFS).

Methods and Results: We examined the association of variants, previously associated with cardiometabolic traits (∼200,000 from Illumina Cardio MetaboChip), using mixed models of HOMA-B residuals corrected for HOMA-IR (cHOMA-B), log transformed HOMA-IR, and incident diabetes, adjusted for age, sex, population stratification, and familial relatedness. Center-specific estimates were combined using fixed effect meta-analyses. We used Bonferroni correction to account for multiple testing (P < 4.13 × 10⁻⁷). We also assessed the association between variants in candidate diabetes genes with these metabolic traits. We explored the top SNPs in an independent, replication sample from Southwestern Arizona. We identified significant associations with cHOMA-B for common variants at 26 loci of which 8 were novel (PRSS7, FCRL5, PEL1, LRP12, IGLL1, ARHGEF10, PARVA, FLJ16686). The most significant variant association with cHOMA-B was observed on chromosome 5 for an intergenic variant near PARP8 (rs2961831, P = 6.39 × 10⁻⁹). In the replication study, we found a signal at rs4607517 near GCK/YKT6 (P = 0.01). Variants near candidate diabetes genes (especially GCK and KCNQ1) were also nominally associated with HOMA-IR and cHOMA-B.

Conclusion: We identified variants at novel loci and confirmed those at known candidate diabetes loci associations for cHOMA-B. This study also provided evidence for association of variants at KCNQ2, CTNAA2, and KCNQ1with cHOMA-B among American Indians. Further studies are needed to account for the high heritability of diabetes among the American Indian participants of the SHFS cohort.

Identification of polymorphisms in 12q24.1, ACAD10, and BRAP as novel genetic determinants of blood pressure in Japanese by exome-wide association studies

We performed exome-wide association studies to identify genetic variants that influence systolic or diastolic blood pressure or confer susceptibility to hypertension in Japanese. The exome-wide association studies were performed with the use of Illumina HumanExome-12 DNA Analysis BeadChip or Infinium Exome-24 BeadChip arrays and with 14,678 subjects, including 8215 individuals with hypertension and 6463 controls. The relation of genotypes of 41,843 single nucleotide polymorphisms to systolic or diastolic blood pressure was examined by linear regression analysis. After Bonferroni's correction, 44 and eight polymorphisms were significantly (P < 1.19 × 10⁻⁶) associated with systolic or diastolic blood pressure, respectively, with six polymorphisms (rs12229654, rs671, rs11066015, rs2074356, rs3782886, rs11066280) being associated with both systolic and diastolic blood pressure. Examination of the relation of allele frequencies to hypertension with Fisher's exact test revealed that 100 of the 41,843 single nucleotide polymorphisms were significantly (P < 1.19 × 10⁻⁶) associated with hypertension. Subsequent multivariable logistic regression analysis with adjustment for age and sex showed that five polymorphisms (rs150854849, rs202069030, rs139012426, rs12229654, rs76974938) were significantly (P < 1.25 × 10⁻⁴) associated with hypertension. The polymorphism rs12229654 was thus associated with both systolic and diastolic blood pressure and with hypertension. Six polymorphisms (rs12229654 at 12q24.1, rs671 of ALDH2, rs11066015 of ACAD10, rs2074356 and rs11066280 of HECTD4, and rs3782886 of BRAP) were found to be associated with both systolic and diastolic blood pressure, with those at 12q24.1 or in ACAD10 or BRAP being novel determinants of blood pressure in Japanese.

Common dysregulated pathways in obese adipose tissue and atherosclerosis

Background

The metabolic syndrome is becoming increasingly prevalent in the general population that is at simultaneous risk for both type 2 diabetes and cardiovascular disease. The critical pathogenic mechanisms underlying these diseases are obesity-driven insulin resistance and atherosclerosis, respectively. To obtain a better understanding of molecular mechanisms involved in pathogenesis of the metabolic syndrome as a basis for future treatment strategies, studies considering both inherent risks, namely metabolic and cardiovascular, are needed. Hence, the aim of this study was to identify pathways commonly dysregulated in obese adipose tissue and atherosclerotic plaques.

Methods

We carried out a gene set enrichment analysis utilizing data from two microarray experiments with obese white adipose tissue and atherosclerotic aortae as well as respective controls using a combined insulin resistance-atherosclerosis mouse model.

Results

We identified 22 dysregulated pathways common to both tissues with p values below 0.05, and selected inflammatory response and oxidative phosphorylation pathways from the Hallmark gene set to conduct a deeper evaluation at the single gene level. This analysis provided evidence of a vast overlap in gene expression alterations in obese adipose tissue and atherosclerosis with Il7r, C3ar1, Tlr1, Rgs1 and Semad4d being the highest ranked genes for the inflammatory response pathway and Maob, Bckdha, Aldh6a1, Echs1 and Cox8a for the oxidative phosphorylation pathway.

Conclusions

In conclusion, this study provides extensive evidence for common pathogenic pathways underlying obesity-driven insulin resistance and atherogenesis which could provide a basis for the development of novel strategies to simultaneously prevent type 2 diabetes and cardiovascular disease in patients with metabolic syndrome.

Electronic supplementary material

The online version of this article (doi:10.1186/s12933-016-0441-2) contains supplementary material, which is available to authorized users.

Interactions of Environmental Factors and APOA1-APOC3-APOA4-APOA5 Gene Cluster Gene Polymorphisms with Metabolic Syndrome

OBJECTIVE

The present study investigated the prevalence and risk factors for Metabolic syndrome. We evaluated the association between single nucleotide polymorphisms (SNPs) in the apolipoprotein APOA1/C3/A4/A5 gene cluster and the MetS risk and analyzed the interactions of environmental factors and APOA1/C3/A4/A5 gene cluster polymorphisms with MetS.

METHODS

A study on the prevalence and risk factors for MetS was conducted using data from a large cross-sectional survey representative of the population of Jilin Province situated in northeastern China. A total of 16,831 participations were randomly chosen by multistage stratified cluster sampling of residents aged from 18 to 79 years in all nine administrative areas of the province. Environmental factors associated with MetS were examined using univariate and multivariate logistic regression analyses based on the weighted sample data. A sub-sample of 1813 survey subjects who met the criteria for MetS patients and 2037 controls from this case-control study were used to evaluate the association between SNPs and MetS risk. Genomic DNA was extracted from peripheral blood lymphocytes, and SNP genotyping was determined by MALDI-TOF-MS. The associations between SNPs and MetS were examined using a case-control study design. The interactions of environmental factors and APOA1/C3/A4/A5 gene cluster polymorphisms with MetS were assessed using multivariate logistic regression analysis.

RESULTS

The overall adjusted prevalence of MetS was 32.86% in Jilin province. The prevalence of MetS in men was 36.64%, which was significantly higher than the prevalence in women (29.66%). MetS was more common in urban areas (33.86%) than in rural areas (31.80%). The prevalence of MetS significantly increased with age (OR = 8.621, 95%CI = 6.594-11.272). Mental labor (OR = 1.098, 95%CI = 1.008-1.195), current smoking (OR = 1.259, 95%CI = 1.108-1.429), excess salt intake (OR = 1.252, 95%CI = 1.149-1.363), and a fruit and dairy intake less than 2 servings a week were positively associated with MetS (P<0.05). A family history of diabetes (OR = 1.630, 95%CI = 1.484-1.791), cardiovascular disease or cerebral diseases (OR = 1.297, 95%CI = 1.211-1.389) was associated with MetS. APOA1 rs670, APOA5 rs662799 and rs651821 revealed significant differences in genotype distributions between the MetS patients and control subjects. The minor alleles of APOA1 rs670, APOA5 rs662799 and rs651821, and APOA5 rs2075291 were associated with MetS (P<0.0016). APOA1 rs5072 and APOC3 rs5128, APOA5 rs651821 and rs662799 were in strong linkage disequilibrium to each other with r2 greater than 0.8. Five haplotypes were associated with an increased risk of MetS (OR = 1.23, 1.58, 1.80, 1.90, and 1.98). When we investigated the interactions of environmental factors and APOA1/C3/A4/A5 gene cluster gene polymorphisms, we found that APOA5 rs662799 had interactions with tobacco use and alcohol consumption (PGE<0.05).

CONCLUSIONS

There was a high prevalence of MetS in the northeast of China. Male gender, increasing age, mental labor, family history of diabetes, cardiovascular disease or cerebral diseases, current smoking, excess salt intake, fruit and dairy intake less than 2 servings a week, and drinking were associated with MetS. The APOA1/C3/A4/A5 gene cluster was associated with MetS in the Han Chinese. APOA5 rs662799 had interactions with the environmental factors associated with MetS.