Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study

Insulin resistance-related circulating predictive markers in the metabolic syndrome: a systematic review in the Iranian population

Background

Specific biomarkers for metabolic syndrome (MetS) may improve diagnostic specificity for clinical information. One of the main pathophysiological mechanisms of MetS is insulin resistance (IR). This systematic review aimed to summarize IR-related biomarkers that predict MetS and have been investigated in Iranian populations.

Methods

An electronic literature search was done using the PubMed and Scopus databases up to June 2022. The risk of bias was assessed for the selected articles using the instrument suggested by the Joanna Briggs Institute (JBI). This systematic review protocol was registered with PROSPERO (registration number CRD42022372415).

Results

Among the reviewed articles, 46 studies investigated the association between IR biomarkers and MetS in the Iranian population. The selected studies were published between 2009 and 2022, with the majority being conducted on adults and seven on children and adolescents. The adult treatment panel III (ATP III) was the most commonly used criteria to define MetS. At least four studies were conducted for each IR biomarker, with LDL-C being the most frequently evaluated biomarker. Some studies have assessed the diagnostic potency of markers using the area under the curve (AUC) with sensitivity, specificity, and an optimal cut-off value. Among the reported values, lipid ratios and the difference between non-HDL-C and LDL-C levels showed the highest AUCs (≥ 0.80) for predicting MetS.

Conclusions

Considering the findings of the reviewed studies, fasting insulin, HOMA-IR, leptin, HbA1c, and visfatin levels were positively associated with MetS, whereas adiponectin and ghrelin levels were negatively correlated with this syndrome. Among the investigated IR biomarkers, the association between adiponectin levels and components of MetS was well established.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01347-6.

APOA1/C3/A4/A5 Gene Cluster at 11q23.3 and Lipid Metabolism Disorders: From Epigenetic Mechanisms to Clinical Practices

The APOA1/C3/A4/A5 cluster is an essential component in regulating lipoprotein metabolism and maintaining plasma lipid homeostasis. A genome-wide association analysis and Mendelian randomization have revealed potential associations between genetic variants within this cluster and lipid metabolism disorders, including hyperlipidemia and cardiovascular events. An enhanced understanding of the complexity of gene regulation has led to growing recognition regarding the role of epigenetic variation in modulating APOA1/C3/A4/A5 gene expression. Intensive research into the epigenetic regulatory patterns of the APOA1/C3/A4/A5 cluster will help increase our understanding of the pathogenesis of lipid metabolism disorders and facilitate the development of new therapeutic approaches. This review discusses the biology of how the APOA1/C3/A4/A5 cluster affects circulating lipoproteins and the current progress in the epigenetic regulation of the APOA1/C3/A4/A5 cluster.

Consumption of dietary fiber and APOA5 genetic variants in metabolic syndrome: baseline data from the Korean Medicine Daejeon Citizen Cohort Study

BACKGROUND

Consumption of dietary fiber has been suggested as an important aspect of a healthy diet to reduce the risk of metabolic syndrome (MetS), including cardiovascular disease. The role of fiber intake in MetS might differ by individual genetic susceptibility. APOA5 encodes a regulator of plasma triglyceride levels, which impacts the related mechanisms of MetS. This study investigated the association between dietary fiber and the risk of MetS, assessing their associations according to APOA5 genetic variants.

METHODS

A total of 1985 participants aged 30-55 years were included from a cross-sectional study based on the Korean Medicine Daejeon Citizen Cohort study at baseline (2017-2019). Dietary fiber intake was measured using a semiquantitative food frequency questionnaire. The APOA5 polymorphisms (rs2266788 A > G, rs662799 A > G, and rs651821 T > C) were genotyped using the Asia Precision Medicine Research Array. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs).

RESULTS

A higher consumption of dietary fiber was associated with a lower prevalence of MetS (P = 0.025). Among the components of MetS, an inverse association with dietary fiber was observed in increased waist circumference (OR, 95% CI = 0.60, 0.41-0.88, P for trend = 0.009) and elevated triglycerides (OR, 95% CI = 0.69, 0.50-0.96, P for trend = 0.012). Regarding the interaction with APOA5 genetic variants, a stronger association with dietary fiber intake was shown in G allele carriers of rs662799 than in A/A carriers (OR, 95% CI = 2.34, 1.59-3.44, P for interaction = 0.024) and in C allele carriers of rs651821 than in T/T carriers (OR, 95% CI = 2.35, 1.59-3.46, P for interaction = 0.027).

CONCLUSIONS

The findings of this study suggest that the benefits of dietary fiber on the risk of MetS could be modified by genetic variants of the APOA5 gene, providing a more effective strategy for preventing MetS.

Nomogram developed with APOA5 genetic variant rs662799 and clinical characteristics predicting risk of essential hypertension in a Chinese population

Background

The apolipoprotein A5 (APOA5) gene has been identified as a key regulatory factor in triglyceride (TG) metabolism and plasma lipid levels. Genetic polymorphisms of APOA5 have been linked to an elevated risk of atherosclerosis, metabolic syndrome, stroke, and coronary artery disease. The rs662799 variant is a single nucleotide polymorphism (SNP) that occurs at a specific position within the APOA5 gene. However, the association between rs662799 polymorphism and essential hypertension (EHT) remains unclear. The study aimed to comprehensively examine the potential correlation between the rs662799 polymorphism and the susceptibility to EHT in a Chinese population using a systematic analysis.

Methods

In a case study conducted at the First Affiliated Hospital of Xinjiang Medical University between Jan 2019 and Dec 2021, we examined a total of 700 cases of EHT along with 700 corresponding controls. The serum concentrations of various lipid parameters were measured by enzymatic method, while the genotyping of the SNP was performed using the improved multiplex ligation detection reaction (iMLDR) method. The independent risk factors of EHT were identified from multivariable logistic regression analysis. The nomogram prediction model that incorporated the APOA5 genetic variations and clinical variables was constructed. In addition, receiver operating characteristic (ROC) curve and Hosmer-Lemeshow test were conducted to determine the performance of the nomogram model. The optimal threshold was calculated based on Youden index.

Results

Our study revealed a higher prevalence of the G allele of the rs662799 variant in individuals diagnosed with EHT compared to the control group. Logistic regression analysis indicated that with the adjustment of other confounders, the observed difference between the two groups remained statistically significant [odds ratio (OR) =1.519; 95% confidence interval (CI): 1.203-1.917; P<0.001]. Based on 8 independent risk factors including APOA5 rs662799 G allele, age, body mass index (BMI), smoking, diabetes, education, low-density lipoprotein cholesterol (LDL-C), and TG, we constructed a novel risk evaluation nomogram of EHT. The area under the ROC curve of the nomogram was 0.722 (95% CI: 0.693-0.752; P<0.001) and 0.747 (95% CI: 0.690-0.804; P<0.001) for the training and validation set, respectively. Furthermore, the Hosmer-Lemeshow test indicated excellent calibration performance, yielding P values of 0.969 for the training set and 0.761 for the validation set.

Conclusions

In our study, the rs662799 variant of the APOA5 gene was significantly associated with susceptibility to EHT. A nomogram for the early prediction of EHT in in the Chinese population was successfully constructed and validated. The nomogram can provide a visual assessment of the risk of EHT for clinical consultation.

Genetic variations in IKZF3, LET7-a2, and CDKN2B-AS1: Exploring associations with metabolic syndrome susceptibility and clinical manifestations

BACKGROUND AND AIM

Metabolic syndrome (MetS) increases the risk of atherosclerosis and diabetes, but there are no approved predictive markers. This study assessed the role of specific genetic variations in MetS susceptibility and their impact on clinical manifestations.

METHOD

In this study, a genotype-phenotype assessment was performed for IKZF3 (rs907091), microRNA-let-7a-2 (rs1143770), and lncRNA-CDKN2B-AS1 (rs1333045).

RESULTS

Analyses indicate that while rs907091 and rs1143770 may have potential associations with MetS susceptibility and an increased risk of atherosclerosis and diabetes, there is an observed trend suggesting that the rs1333045 CC genotype may be associated with a decreased risk of MetS. The genotypes and allele frequencies of rs1333045 were significantly different between studied groups (OR = 0.56, 95% CI 0.38-0.81, p = 0.002, and OR = 0.71, 95% CI 0.55-0.92, p = 0.008), with the CC genotype displaying increased levels of HDL. Furthermore, the rs907091 TT genotype was associated with increased triglyceride, cholesterol, and HOMA index in MetS patients. Subjects with the CC genotype for rs1143770 had higher HbA1c and BMI. In silico analyses illustrated that rs907091 C remarkably influences the secondary structure and the target site of a broad spectrum of microRNAs, especially hsa-miR-4497. Moreover, rs1333045 creates a binding site for seven different microRNAs.

CONCLUSION

Further studies on other populations may help confirm these SNPs as useful predictive markers in assessing the MetS risk.

A Genome-Wide Association Study of Metabolic Syndrome in the Taiwanese Population

The purpose of this study was to investigate genetic factors associated with metabolic syndrome (MetS) by conducting a large-scale genome-wide association study (GWAS) in Taiwan, addressing the limited data on Asian populations compared to Western populations. Using data from the Taiwan Biobank, comprehensive clinical and genetic information from 107,230 Taiwanese individuals was analyzed. Genotyping data from the TWB1.0 and TWB2.0 chips, including over 650,000 single nucleotide polymorphisms (SNPs), were utilized. Genotype imputation using the 1000 Genomes Project was performed, resulting in more than 9 million SNPs. MetS was defined based on a modified version of the Adult Treatment Panel III criteria. Among all participants (mean age: 50 years), 23% met the MetS definition. GWAS analysis identified 549 SNPs significantly associated with MetS, collectively mapping to 10 genomic risk loci. Notable risk loci included rs1004558, rs3812316, rs326, rs4486200, rs2954038, rs10830963, rs662799, rs62033400, rs183130, and rs34342646. Gene-set analysis revealed 22 associated genes: CETP, LPL, APOA5, SIK3, ZPR1, APOC1, BUD13, MLXIPL, TOMM40, GCK, YKT6, RPS6KB1, FTO, VMP1, TUBD1, BCL7B, C19orf80 (ANGPTL8), SIDT2, SENP7, PAFAH1B2, DOCK6, and FOXA2. This study identified genomic risk loci for MetS in a large Taiwanese population through a comprehensive GWAS approach. These associations provide novel insights into the genetic basis of MetS and hold promise for the potential discovery of clinical biomarkers.

Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study

OBJECTIVE

Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of GCKR, BUD13 and APOA5, and environmental risk factors.

MATERIALS AND METHODS

A cohort study was conducted on 2,346 cases and 2,203 controls from eligible Tehran Cardiometabolic Genetic Study (TCGS) participants whose data were collected from 1999 to 2017. We used different regularization approaches [least absolute shrinkage and selection operator (LASSO), ridge regression (RR), elasticnet (ENET), adaptive LASSO (aLASSO), and adaptive ENET (aENET)] and a classical logistic regression (LR) model to classify MetS and select influential variables that predict MetS. Demographics, clinical features, and common polymorphisms in the GCKR, BUD13 and APOA5 genes of eligible participants were assessed to classify TCGS participant status in MetS development. The models' performance was evaluated by 10-repeated 10-fold crossvalidation. Various assessment measures of sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC-ROC) and AUC-precision-recall (AUC-PR) curves were used to compare the models.

RESULTS

During the follow-up period, 50.38% of participants developed MetS. The groups were not similar in terms of baseline characteristics and risk variants. MetS was significantly associated with age, gender, schooling years, body mass index (BMI), and alternate alleles in all the risk variants, as indicated by LR. A comparison of accuracy, AUCROC, and AUC-PR metrics indicated that the regularization models outperformed LR. Regularized machine learning models provided comparable classification performances, whereas the aLASSO model was more parsimonious and selected fewer predictors.

CONCLUSION

Regularized machine learning models provided more accurate and parsimonious MetS classifying models. These high-performing diagnostic models can lay the foundation for clinical decision support tools that use genetic and demographical variables to locate individuals at high risk for MetS.

Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture

BACKGROUND

Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D.

METHOD

A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets.

RESULTS

We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × [Formula: see text]). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × [Formula: see text]). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy).

CONCLUSION

The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways.