Genome-wide association study of metabolic syndrome in Korean populations

Type 2 diabetes and cardiovascular conditions prediction in individuals with metabolic syndrome-associated lipoprotein lipase gene (LPL) single nucleotide polymorphisms (SNPs)

OBJECTIVE

Metabolic syndrome (MetS) is predictive of increased risk of type 2 diabetes (T2D) and cardiovascular conditions (CVC). Lipoprotein lipase gene (LPL) single nucleotide polymorphisms (SNPs) may be of importance to the eventual diagnosis of T2D and CVC. This study aimed to predict the diagnosis of T2D and CVC amongst individuals with LPL SNPs rs268, rs11542065, rs116403115, rs118204057, rs118204061, rs144466625, and rs547644955.

METHODS

This is a retrospective study using the UK Biobank data. Variables associated with MetS, T2D and CVC were selected from the data set. The total number of subjects in the cohort was 12,872 (mean age 56 years ± 8.1, 90.0 % were of British ethnicity, and 53.9 % were females). Logistic regression was used to assess whether the T2D and CVC can be predicted based on the presence of LPL SNPs and some of the clinical measures.

RESULTS

Prediction models using clinical parameters showed good area under the curve (AUC) for prediction of T2D and CVC diagnosis (in receiver operating characteristic (ROC) analysis, area under the curve (AUC) = 0.959 for T2D, AUC = 0.772 for CVC). The addition of Polygenic Risk Scores (PRS/s) showed an improvement for diagnosis of both (AUC = 0.961 and 0.790 for TD and CVC, respectively). Further addition of SNPs showed more increase in AUC (AUC = 0.965 and 0.837 for T2D and CVC, respectively). The additive effect of the PRSs and LPL SNPs was more pronounced in the CVC than in the T2D model. The variant that had major significance for both T2D and CVC diagnoses was rs547644955 (AUC 1.0 and 0.910, respectively). The SNPs rs116403115 and rs118204057 both had an AUC of 1.0 for T2D diagnosis.

CONCLUSION

The prediction of T2D and CVC diagnoses with the use of clinically available factors may be enhanced with the addition of PRSs and SNPs, including LPL SNPs, which may have implications for stratified or personalised approaches for disease prevention or treatment.

Optimization of multi-ancestry polygenic risk score disease prediction models

Polygenic risk scores (PRS) have ushered in a new era in genetic epidemiology, offering insights into individual predispositions to a wide range of diseases. However, despite recent marked enhancements in predictive power, PRS-based models still need to overcome several hurdles before they can be broadly applied in the clinic. Chiefly, they need to achieve sufficient accuracy, easy interpretability and portability across diverse populations. Leveraging trans-ancestry genome-wide association study (GWAS) meta-analysis, we generated novel, diverse summary statistics for 30 medically-related traits and benchmarked the performance of six existing PRS algorithms using UK Biobank. We built an ensemble model using logistic regression to combine outputs of top-performing algorithms and validated it on the diverse eMERGE and PAGE MEC cohorts. It surpassed current state-of-the-art PRS models, with minimal performance drops in external cohorts, indicating good calibration. To enhance predictive accuracy for clinical application, we incorporated easily-accessible clinical characteristics such as age, gender, ancestry and risk factors, creating disease prediction models intended as prospective diagnostic tests, with easily interpretable positive or negative outcomes. After adding clinical characteristics, 12 out of 30 models surpassed 80% AUC. Further, 25 traits exceeded the diagnostic odds ratio (DOR) of five, and 19 traits exceeded DOR of 10 for all ancestry groups, indicating high predictive value. Our PRS model for coronary artery disease identified 55-80 times more true coronary events than rare pathogenic variant models, reinforcing its clinical potential. The polygenic component modulated the effect of high-risk rare variants, stressing the need to consider all genetic components in clinical settings. These findings show that newly developed PRS-based disease prediction models have sufficient accuracy and portability to warrant consideration of being used in the clinic.

Multi-omics integration analysis and association study reveal the potential of ADIPOQ function in gestational diabetes mellitus

AIM

To investigate the role of ADIPOQ gene in gestational diabetes mellitus (GDM).

METHODS

We genotyped single nucleotide polymorphisms (SNPs) rs266729 and rs1501299 within the ADIPOQ gene in a cohort of 1157 pregnant women of north Chinese Han ethnicity. This cohort comprised 560 pregnant women diagnosed with GDM and 597 pregnant women who exhibited normal oral glucose tolerance test at 24-28 weeks' gestation. All participants were recruited from the Department of Obstetrics and Gynecology at the Second Affiliated Hospital of Harbin Medical University. Additionally, we used conventional bioinformatics analysis methods to conduct multi-omics analysis (transcriptome, epigenome, and single-cell level) of ADIPOQ-regulated GDM.

RESULTS

The systolic blood flow velocity/diastolic blood flow velocity (S/D) ratio of the umbilical artery in GDM patients with CC genotype of rs266729 and GG genotype of rs1501299 was higher than control. Single-cell analysis suggested that ADIPOQ was expressed in extravillous trophoblast (EVT), T cell, monocytes, myelocyte, NK cell and syncytiotrophoblast (SCT). Functional enrichment analysis showed ADIPOQ gene was associated with response to nutrient levels, fat cell differentiation.

CONCLUSION

The findings of our study indicate a correlation between SNPs of ADIPOQ in GDM patients, and ADIPOQ is involved in the transcriptional regulation of GDM.

Longitudinal Association of Changes in Metabolic Syndrome with Cognitive Function: 12-Year Follow-up of the Guangzhou Biobank Cohort Study

BACKGRUOUND

The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies.

METHODS

MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function.

RESULTS

Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores.

CONCLUSION

Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Exploring lipodystrophy gene expression in adipocytes: unveiling insights into the pathogenesis of insulin resistance, type 2 diabetes, and clustering diseases (metabolic syndrome) in Asian Indians

Background

Studying the molecular mechanisms of lipodystrophy can provide valuable insights into the pathophysiology of insulin resistance (IR), type 2 diabetes (T2D), and other clustering diseases [metabolic syndrome (MetS)] and its underlying adipocentric disease (MetS disease).

Methods

A high-confidence lipodystrophy gene panel comprising 50 genes was created, and their expressions were measured in the visceral and subcutaneous (both peripheral and abdominal) adipose depots of MetS and non-MetS individuals at a tertiary care medical facility.

Results

Most lipodystrophy genes showed significant downregulation in MetS individuals compared to non-MetS individuals in both subcutaneous and visceral depots. In the abdominal compartment, all the genes showed relatively higher expression in visceral depot as compared to their subcutaneous counterpart, and this difference narrowed with increasing severity of MetS. Their expression level shows an inverse correlation with T2D, MetS, and HOMA-IR and with other T2D-related intermediate traits. Results also demonstrated that individualization of MetS patients could be done based on adipose tissue expression of just 12 genes.

Conclusion

Adipose tissue expression of lipodystrophy genes shows an association with MetS and its intermediate phenotypic traits. Mutations of these genes are known to cause congenital lipodystrophy syndromes, whereas their altered expression in adipose tissue contributes to the pathogenesis of IR, T2D, and MetS.

Interaction between genetic risk score and dietary fat intake on lipid-related traits in Brazilian young adults

The occurrence of dyslipidaemia, which is an established risk factor for cardiovascular diseases, has been attributed to multiple factors including genetic and environmental factors. We used a genetic risk score (GRS) to assess the interactions between genetic variants and dietary factors on lipid-related traits in a cross-sectional study of 190 Brazilians (mean age: 21 ± 2 years). Dietary intake was assessed by a trained nutritionist using three 24-h dietary recalls. The high GRS was significantly associated with increased concentration of TAG (beta = 0·10 mg/dl, 95 % CI 0·05-0·16; P < 0·001), LDL-cholesterol (beta = 0·07 mg/dl, 95 % CI 0·04, 0·11; P < 0·0001), total cholesterol (beta = 0·05 mg/dl, 95 % CI: 0·03, 0·07; P < 0·0001) and the ratio of TAG to HDL-cholesterol (beta = 0·09 mg/dl, 95 % CI: 0·03, 0·15; P = 0·002). Significant interactions were found between the high GRS and total fat intake on TAG:HDL-cholesterol ratio (Pinteraction = 0·03) and between the high GRS and SFA intake on TAG:HDL-cholesterol ratio (Pinteraction = 0·03). A high intake of total fat (>31·5 % of energy) and SFA (>8·6 % of energy) was associated with higher TAG:HDL-cholesterol ratio in individuals with the high GRS (beta = 0·14, 95 % CI: 0·06, 0·23; P < 0·001 for total fat intake; beta = 0·13, 95 % CI: 0·05, 0·22; P = 0·003 for SFA intake). Our study provides evidence that the genetic risk of high TAG:HDL-cholesterol ratio might be modulated by dietary fat intake in Brazilians, and these individuals might benefit from limiting their intake of total fat and SFA.

Multipartite network analysis to identify environmental and genetic associations of metabolic syndrome in the Korean population

Network analysis has become a crucial tool in genetic research, enabling the exploration of associations between genes and diseases. Its utility extends beyond genetics to include the assessment of environmental factors. Unipartite network analysis is commonly used in genomics to visualize initial insights and relationships among variables. Syndromic diseases, such as metabolic syndrome, are characterized by the simultaneous occurrence of various signs, symptoms, and clinicopathological features. Metabolic syndrome encompasses hypertension, diabetes, obesity, and dyslipidemia, and both genetic and environmental factors contribute to its development. Given that relevant data often consist of distinct sets of variables, a more intuitive visualization method is needed. This study applied multipartite network analysis as an effective method to understand the associations among genetic, environmental, and disease components in syndromic diseases. We considered three distinct variable sets: genetic factors, environmental factors, and disease components. The process involved projecting a tripartite network onto a two-mode bipartite network and then simplifying it into a one-mode network. This approach facilitated the visualization of relationships among factors across different sets and within individual sets. To transition from multipartite to unipartite networks, we suggest both sequential and concurrent projection methods. Data from the Korean Association Resource (KARE) project were utilized, including 352,228 SNPs from 8840 individuals, alongside information on environmental factors such as lifestyle, dietary, and socioeconomic factors. The single-SNP analysis step filtered SNPs, supplemented by reference SNPs reported in a genome-wide association study catalog. The resulting network patterns differed significantly by sex: demographic factors and fat intake were crucial for women, while alcohol consumption was central for men. Indirect relationships were identified through projected bipartite networks, revealing that SNPs such as rs4244457, rs2156552, and rs10899345 had lifestyle interactions on metabolic components. Our approach offers several advantages: it simplifies the visualization of complex relationships among different datasets, identifies environmental interactions, and provides insights into SNP clusters sharing common environmental factors and metabolic components. This framework provides a comprehensive approach to elucidate the mechanisms underlying complex diseases like metabolic syndrome.

Genome-wide association study for metabolic syndrome reveals APOA5 single nucleotide polymorphisms with multilayered effects in Koreans

BACKGROUND AND PURPOSE

Genome-wide association studies (GWAS) of metabolic syndrome (MetS) have predominantly focused on non-Asian populations, with limited representation from East Asian cohorts. Moreover, previous GWAS analyses have primarily emphasized the significance of top single nucleotide polymorphisms (SNPs), poorly explaining other SNP signals in linkage disequilibrium. This study aimed to reveal the interaction between rs651821 and rs2266788, the principal variants of apolipoprotein A5 (APOA5), within the most significant loci identified through GWAS on MetS.

METHODS

GWAS on MetS and its components was conducted using the data from the Korean Genome and Epidemiology Study (KoGES) city cohort comprising 58,600 individuals with available biochemical, demographic, lifestyle factors, and the most significant APOA5 locus was analyzed further in depth.

RESULTS

According to GWAS of MetS and its diagnostic components, a significant association between the APOA5 SNPs rs651821/rs2266788 and MetS/triglycerides/high-density lipoprotein phenotypes was revealed. However, a conditional analysis employing rs651821 unveiled a reversal in the odds ratio for rs2266788. Therefore, rs651821 and rs2266788 emerged as independent and opposing signals in the extended GWAS analysis, i.e., the multilayered effects. Further gene-environment interaction analyses regarding lifestyle factors such as smoking, alcohol consumption, and physical activity underscored these multilayered effects.

CONCLUSION

This study unveils the intricate interplay between rs651821 and rs2266788 derived from MetS GWAS. Removing the influence of lead SNP reveals an independent protective signal associated with rs2266788, suggesting a multilayered effect between these SNPs. These findings underline the need for novel perspectives in future MetS GWAS.

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.

Identification of shared genetic risks underlying metabolic syndrome and its related traits in the Korean population

Introduction: Observational studies have demonstrated strong correlations between metabolic syndrome (MetS) and its related traits. To gain insight into the genetic architecture and molecular mechanism of MetS, we investigated the shared genetic basis of MetS and its related traits and further tested their causal relationships. Methods: Using summary statistics from genome-wide association analyses of about 72,000 subjects from the Korean Genome and Epidemiological Study (KoGES), we conducted genome-wide multi-trait analyses to quantify the overall genetic correlation and Mendelian randomization analyses to infer the causal relationships between traits of interest. Results: Genetic correlation analyses revealed a significant correlation of MetS with its related traits, such as obesity traits (body mass index and waist circumference), lipid traits (triglyceride and high-density lipoprotein cholesterol), glycemic traits (fasting plasma glucose and hemoglobin A1C), and blood pressure (systolic and diastolic). Mendelian randomization analyses further demonstrated that the MetS-related traits showing significant overall genetic correlation with MetS could be genetically determined risk factors for MetS. Discussion: Our study suggests a shared genetic basis of MetS and its related traits and provides novel insights into the biological mechanisms underlying these complex traits. Our findings further inform public health interventions by supporting the important role of the management of metabolic risk factors such as obesity, unhealthy lipid profiles, diabetes, and high blood pressure in the prevention of MetS.

Association between APOA5 polymorphisms and susceptibility to metabolic syndrome: a systematic review and meta-analysis

BACKGROUND

The association between Apolipoprotein A5 (APOA5) genetic polymorphisms and susceptibility to metabolic syndrome (MetS) has been established by many studies, but there have been conflicting results from the literature. We performed a meta-analysis of observational studies to evaluate the association between APOA5 gene polymorphisms and the prevalence of MetS.

METHODS

PubMed, Web of Science, Embase, and Scopus were searched up to April 2024. The random effects model was used to estimate the odds ratios (ORs) and 95% confidence intervals (CI) of the association between APOA5 gene polymorphisms and the prevalence of MetS development. The potential sources of heterogeneity were evaluated by subgroup analyses and sensitivity analyses.

RESULTS

A total of 30 studies with 54,986 subjects (25,341 MetS cases and 29,645 healthy controls) were included. The presence of rs662799 and rs651821 polymorphisms is associated with an approximately 1.5-fold higher likelihood of MetS prevalence (OR = 1.42, 95% CI: 1.32, 1.53, p < 0.001; I2 = 67.1%; P-heterogeneity < 0.001; and OR = 1.50, 95% CI: 1.36-1.65, p < 0.001), respectively. MetS is also more prevalent in individuals with the genetic variants rs3135506 and rs2075291. There was no evidence of a connection with rs126317.

CONCLUSION

The present findings suggest that polymorphisms located in the promoter and coding regions of the APOA5 gene are associated with an increased prevalence of MetS in the adult population. Identifying individuals with these genetic variations could lead to early disease detection and the implementation of preventive strategies to reduce the risk of MetS and its related health issues. However, because the sample size was small and there was evidence of significant heterogeneity for some APOA5 gene polymorphisms, these results need to be confirmed by more large-scale and well-designed studies.

Genetic variations in ACE2 gene associated with metabolic syndrome in southern China: a case-control study

Metabolic syndrome (MetS) is closely related to cardiovascular and cerebrovascular diseases, and genetic predisposition is one of the main triggers for its development. To identify the susceptibility genes for MetS, we investigated the relationship between angiotensin-converting enzyme 2 (ACE2) single nucleotide polymorphisms (SNPs) and MetS in southern China. In total, 339 MetS patients and 398 non-MetS hospitalized patients were recruited. Four ACE2 polymorphisms (rs2074192, rs2106809, rs879922, and rs4646155) were genotyped using the polymerase chain reaction-ligase detection method and tested for their potential association with MetS and its related components. ACE2 rs2074192 and rs2106809 minor alleles conferred 2.485-fold and 3.313-fold greater risks of MetS in women. ACE2 rs2074192 and rs2106809 variants were risk factors for obesity, diabetes, and low-high-density lipoprotein cholesterolemia. However, in men, the ACE2 rs2074192 minor allele was associated with an approximately 0.525-fold reduction in MetS prevalence. Further comparing the components of MetS, ACE2 rs2074192 and rs2106809 variants reduced the risk of obesity and high triglyceride levels. In conclusion, ACE2 rs2074192 and rs2106809 SNPs were independently associated with MetS in a southern Chinese population and showed gender heterogeneity, which can be partially explained by obesity. Thus, these SNPs may be utilized as predictive biomarkers and molecular targets for MetS. A limitation of this study is that environmental and lifestyle differences, as well as genetic heterogeneity among different populations, were not considered in the analysis.

Genome-wide association study of metabolic dysfunction-associated fatty liver disease in a Korean population

Genome-wide association studies have identified several genetic variants associated with nonalcoholic fatty liver disease. To emphasize metabolic abnormalities in fatty liver, metabolic (dysfunction)-associated fatty liver disease (MAFLD) has been introduced; thus, we aimed to investigate single-nucleotide polymorphisms related to MAFLD and its subtypes. A genome-wide association study was performed to identify genetic factors related to MAFLD. We used a Korean population-based sample of 2282 subjects with MAFLD and a control group of 4669. We replicated the results in a validation sample which included 639 patients with MAFLD and 1578 controls. Additionally, we categorized participants into three groups, no MAFLD, metabolic dysfunction (MD)-MAFLD, and overweight/obese-MAFLD. After adjusting for age, sex, and principal component scores, rs738409 [risk allele G] and rs3810622 [risk allele T], located in the PNPLA3 gene, showed significant associations with MAFLD (P-values, discovery set = 1.60 × 10-15 and 4.84 × 10-10; odds ratios, 1.365 and 1.284, validation set = 1.39 × 10-4, and 7.15 × 10-4, odds ratios, 1.299 and 1.264, respectively). An additional SNP rs59148799 [risk allele G] located in the GATAD2A gene showed a significant association with MAFLD (P-values, discovery set = 2.08 × 10-8 and validation set = 0.034, odds ratios, 1.387 and 1.250). rs738409 was significantly associated with MAFLD subtypes ([overweight/obese-MAFLD; odds ratio (95% confidence interval), P-values, 1.515 (1.351-1.700), 1.43 × 10-12 and MD-MAFLD: 1.300 (1.191-1.416), 2.90 × 10-9]. There was a significant relationship between rs3810622 and overweight/obese-MAFLD and MD-MAFLD [odds ratios (95% confidence interval), P-values, 1.418 (1.258, 1.600), 1.21 × 10-8 and 1.225 (1.122, 1.340), 7.06 × 10-6, respectively]; the statistical significance remained in the validation set. PNPLA3 was significantly associated with MAFLD and MAFLD subtypes in the Korean population. These results indicate that genetic factors play an important role in the pathogenesis of MAFLD.

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.

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.

Exploring the role of genetic variations in NAFLD: implications for disease pathogenesis and precision medicine approaches

Non-alcoholic fatty liver disease (NAFLD) is one of the leading causes of chronic liver diseases, affecting more than one-quarter of people worldwide. Hepatic steatosis can progress to more severe forms of NAFLD, including NASH and cirrhosis. It also may develop secondary diseases such as diabetes and cardiovascular disease. Genetic and environmental factors regulate NAFLD incidence and progression, making it a complex disease. The contribution of various environmental risk factors, such as type 2 diabetes, obesity, hyperlipidemia, diet, and sedentary lifestyle, to the exacerbation of liver injury is highly understood. Nevertheless, the underlying mechanisms of genetic variations in the NAFLD occurrence or its deterioration still need to be clarified. Hence, understanding the genetic susceptibility to NAFLD is essential for controlling the course of the disease. The current review discusses genetics' role in the pathological pathways of NAFLD, including lipid and glucose metabolism, insulin resistance, cellular stresses, and immune responses. Additionally, it explains the role of the genetic components in the induction and progression of NAFLD in lean individuals. Finally, it highlights the utility of genetic knowledge in precision medicine for the early diagnosis and treatment of NAFLD patients.

CETP gene polymorphisms and haplotypes are explanatory variables for HDL cholesterol level in sickle cell disease

Variations in lipid profile have been observed in sickle cell disease (SCD) and understanding their relationship with disease severity is crucial. This study aimed to investigate the association of polymorphisms of the CETP gene and laboratory markers of disease severity with lipid profile in a pediatric population with SCD. Biochemical and anthropometric analyses and CETP and alpha-thalassemia genotyping were performed. The study included 133 children and adolescents with sickle cell anemia (SCA) or hemoglobin SC disease (SCC), in steady-state. The SCA and no hydroxyurea (no HU) groups had higher values of ApoB, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) compared to the SCC and HU groups. However, there were no significant differences in ApoA1 and HDL-C levels between the groups based on genotype. Furthermore, the groups with altered levels of ApoA1, HDL-C, and the triglyceride/HDL ratio exhibited lower hemoglobin (Hb) levels and higher white blood cell counts. Hb level was associated to HDL-C levels. Analysis of CETP gene variants showed that the minor alleles of rs3764261 (C>A), rs247616 (C>T), and rs183130 (C>T), as well as the TTA haplotype, are explanatory variables for HDL-C levels. These findings suggested that dyslipidemia in SCD, specifically related to HDL-C levels, may be influenced by individual genetic background. Additionally, further investigation is needed to determine if clinical manifestations are impacted by CETP gene variants.

Emerging Perspectives on the Set of Conditions That Lead to the Emergence of Metabolic Syndrome

Metabolic syndrome, as a medical condition, presents multifactorial complexity that is characterized by the resulting damage from genetic, environmental, and lifestyle factors (presence or absence of physical activity, food choices). Thus, metabolic syndrome qualifies unequivocally as a medical condition in which there are, simultaneously, several independent metabolic risk factors, namely, abdominal obesity, high triglyceride level, low HDL cholesterol level, arterial hypertension, and high glycemic level. Although age, sex, socio-economic status, and the precise definition of metabolic syndrome all influence the prevalence and risk of developing the condition, clinical and epidemiological studies clearly show that central obesity, as measured by an increased abdominal circumference, is the main risk factor. Thus, due to the growing global incidence of obesity, there has been an increase in the incidence of metabolic syndrome. Starting with obesity, all other metabolic risk factors are influenced: for example, as a result of insulin resistance with hyperglycemia, diabetes is linked to an increased risk of cardiovascular disease due to increased abdominal circumference. Through this review, we aimed to highlight the latest research studies and dietary nutritional interventions useful in the prevention of this disease but also implementation strategies for primary prevention among the healthy population.

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.

GCKR and ADIPOQ gene polymorphisms in women with gestational diabetes mellitus

AIMS

To investigate the associations of GCKR and ADIPOQ variants with the risk of gestational diabetes mellitus (GDM) in Chinese women.

METHODS

GCKR rs1260326, ADIPOQ rs266729, and rs1501299 were selected and genotyped in 519 GDM patients and 498 controls. Candidate SNPs were genotyped using multiplex polymerase chain reaction (PCR) combined with next-generation sequencing methods, and the association of these SNPs with GDM was analyzed.

RESULTS

We found that GCKR rs1260326 was significantly associated with an increased risk of GDM in the allele model, the codominant model (CC vs. TT), the dominant model, the recessive model, and the genotypic model distributions (p = 0.0029, p = 0.0022, p = 0.0402, p = 0.0038, and p = 0.0028, respectively). The rs1260326 polymorphism was shown to be associated with 1 h-OGTT level and gravidity in GDM patients (CC vs. TT: p = 0.0475 and p = 0.0220, respectively). Diastolic blood pressure (DBP) was significantly higher in the GDM patients with the rs266729 GG genotype compared to those with the CC or CG genotype (p = 0.0444 and p = 0.0339, respectively). The DBP of the GDM patients with the rs1501299 GT genotype was lower than that of those with the GG genotype (p = 0.0197). There was a weak linkage disequilibrium value between the GCKR and ADIPOQ SNPs.

CONCLUSIONS

The genes GCKR and ADIPOQ may be involved in the pathophysiology of GDM.

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.

Coagulation Dysfunctions in Non-Alcoholic Fatty Liver Disease-Oxidative Stress and Inflammation Relevance

Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases. Its incidence is progressively rising and it is possibly becoming a worldwide epidemic. NAFLD encompasses a spectrum of diseases accounting for the chronic accumulation of fat within the hepatocytes due to various causes, excluding excessive alcohol consumption. In this study, we aimed to focus on finding evidence regarding the implications of oxidative stress and inflammatory processes that form the multifaceted pathophysiological tableau in relation to thrombotic events that co-occur in NAFLD and associated chronic liver diseases. Recent evidence on the pathophysiology of NAFLD suggests that a complex pattern of multidirectional components, such as prooxidative, proinflammatory, and prothrombotic components, better explains the multiple factors that promote the mechanisms underlying the fatty acid excess and subsequent processes. As there is extensive evidence on the multi-component nature of NAFLD pathophysiology, further studies could address the complex interactions that underlie the development and progression of the disease. Therefore, this study aimed to describe possible pathophysiological mechanisms connecting the molecular impairments with the various clinical manifestations, focusing especially on the interactions among oxidative stress, inflammation, and coagulation dysfunctions. Thus, we described the possible bidirectional modulation among coagulation homeostasis, oxidative stress, and inflammation that occurs in the various stages of NAFLD.

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.

A Pilot Genome-Wide Association Study Identifies Novel Markers of Metabolic Syndrome in Patients with Psoriasis

BACKGROUND

Recent studies have reported that psoriasis is associated with the development of metabolic syndrome. Genome-wide association studies have been used to discover gene variant markers that occur frequently in case group in relation to specific diseases.

OBJECTIVE

The aim of the present study was to investigate the variants of specific genes involved in metabolic syndrome associated with psoriasis.

METHODS

A total of 95 psoriasis patients were recruited and divided into two groups: one with metabolic syndrome (38 patients) and the other without (57 patients). After genotyping, imputation, and quality checking, the association between the several single nucleotide polymorphisms and metabolic syndrome in psoriasis was tested, followed by gene set enrichment analysis.

RESULTS

We found 76 gene polymorphisms that conferred an increased risk for metabolic syndrome in patients with psoriasis. Four single nucleotide polymorphisms (rs17154774 of FRMD4A, rs77498336 of GPR116, rs75949580 and rs187682251 of MAPK4) showed the strongest association between metabolic syndrome and psoriasis. The epidermal growth factor receptor protein was located at the center of the protein interactions for the gene polymorphisms.

CONCLUSION

This study identified several previously unknown polymorphisms associated with metabolic syndrome in psoriasis. These results highlight the potential for future genetic studies to elucidate the development, and ultimately prevent the onset, of metabolic syndrome in patients with psoriasis.

High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile

Healthy metabolic measures in humans are associated with longevity. Dysregulation leads to metabolic syndrome (MetS) and negative health outcomes. Recent exceptional longevity (EL) genome wide association studies have facilitated estimation of an individual's polygenic risk score (PRS) for EL. We tested the hypothesis that individuals with high ELPRS have a low prevalence of MetS. Participants were from five cohorts of middle-aged to older adults. The primary analyses were performed in the UK Biobank (UKBB) (n = 407,800, 40-69 years). Replication analyses were undertaken using three Australian studies: Hunter Community Study (n = 2122, 55-85 years), Older Australian Twins Study (n = 539, 65-90 years) and Sydney Memory and Ageing Study (n = 925, 70-90 years), as well as the Swedish Gothenburg H70 Birth Cohort Studies (n = 2273, 70-93 years). MetS was defined using established criteria. Regressions and meta-analyses were performed with the ELPRS and MetS and its components. Generally, MetS prevalence (22-30%) was higher in the older cohorts. In the UKBB, high EL polygenic risk was associated with lower MetS prevalence (OR = 0.94, p = 1.84 × 10^-42) and its components (p < 2.30 × 10^-8). Meta-analyses of the replication cohorts showed nominal associations with MetS (p = 0.028) and 3 MetS components (p < 0.05). This work suggests individuals with a high polygenic risk for EL have a healthy metabolic profile promoting longevity.

The mechanism of BUD13 m6A methylation mediated MBNL1-phosphorylation by CDK12 regulating the vasculogenic mimicry in glioblastoma cells

Vasculogenic mimicry (VM) is an endothelium-independent tumor microcirculation that provides adequate blood supply for tumor growth. The presence of VM greatly hinders the treatment of glioblastoma (GBM) with anti-angiogenic drugs. Therefore, targeting VM formation may be a feasible therapeutic strategy for GBM. The research aimed to evaluate the roles of BUD13, CDK12, MBNL1 in regulating VM formation of GBM. BUD13 and CDK12 were upregulated and MBNL1 was downregulated in GBM tissues and cells. Knockdown of BUD13, CDK12, or overexpression of MBNL1 inhibited GBM VM formation. METTL3 enhanced the stability of BUD13 mRNA and upregulated its expression through m6A methylation. BUD13 enhanced the stability of CDK12 mRNA and upregulated its expression. CDK12 phosphorylated MBNL1, thereby regulating VM formation of GBM. The simultaneous knockdown of BUD13, CDK12, and overexpression of MBNL1 reduced the volume of subcutaneously transplanted tumors in nude mice and prolonged the survival period. Thus, the BUD13/CDK12/MBNL1 axis plays a crucial role in regulating VM formation of GBM and provides a potential target for GBM therapy.

Nephrolithiasis: A Red Flag for Cardiovascular Risk

Epidemiological evidence shows that nephrolithiasis is associated with cardiovascular (CV) morbidities. The association between nephrolithiasis and CV disease is not surprising because both diseases share conditions that facilitate their development. Metabolic conditions, encompassed in the definition of metabolic syndrome (MS), and habits that promote nephrolithiasis by altering urine composition also promote clinical manifestations of CV disease. By inducing oxidative stress, these conditions cause endothelial dysfunction and increased arterial stiffness, which are both well-known predictors of CV disease. Furthermore, the subtle systemic metabolic acidosis observed in stone formers with CV disease may have a pathogenic role by increasing bone turnover and leading to reduced mineral content and osteoporosis/osteopenia. Heart valves and/or coronary artery and aortic calcifications are frequently associated with reduced mineral density. This is known as the 'calcification paradox' in osteoporosis and has also been observed in subjects with calcium nephrolithiasis. Evidence supports the hypothesis that osteoporosis/osteopenia is an independent risk factor for the development of CV calcifications. In the long term, episodes of renal stones may occur from the onset of metabolic derangements/MS to arterial stiffness/atherosclerosis and CV morbidities. These episodes should be considered a warning sign of an ongoing and silent atherosclerotic process. The evaluation of cardiometabolic risk factors and MS components should be routine in the assessment of renal stone formers. This would allow for treatment and prevention of the development of CV complications, which are much more severe for the patient and for public health.

Identification of genetic variants related to metabolic syndrome by next-generation sequencing

BACKGROUND

Metabolic syndrome (MetS) is a cluster of conditions associated with glucose intolerance, hypertension, abdominal obesity, dyslipidemia, and insulin resistance that increase the risk of cardiovascular diseases (CVD) and type 2 diabetes (T2D). Since MetS is known as a complex symptom with a high incidence of genetic factors, it is important to identify genetic variants for each clinical characteristic of MetS.

METHODS

We performed targeted next-generation sequencing (NGS) to identify genetic variants related to obesity, blood glucose, triacylglycerol (TG), and high-density lipoprotein (HDL)-cholesterol level, and hypertension in 48 subjects with MetS and in 48 healthy subjects.

RESULTS

NGS analysis revealed that 26 of 48 subjects (54.2%) with MetS had putative non-synonymous variants related to the clinical features of MetS. Of the subjects with MetS, 8 (16.7%) had variants in 4 genes (COL6A2, FTO, SPARC, and MTHFR) related to central obesity, 17 (35.4%) had variants in 6 genes (APOB, SLC2A2, LPA, ABCG5, ABCG8, and GCKR) related to hyperglycemia, 3 (6.3%) had variants in 4 genes (APOA1, APOC2, APOA4, and LMF1) related to hypertriglyceridemia, 8 (16.7%) had variants in 4 genes (ABCA1, CETP, SCARB1, and LDLR) related to low HDL-cholesterolemia, and 5 (10.4%) had variants in ADD1 related to hypertension.

CONCLUSIONS

Our findings may contribute to broadening the genetic spectrum of risk variants related to the development of MetS.

Genetic Variants Associated with Elevated Plasma Ceramides in Individuals with Metabolic Syndrome

Metabolic syndrome (MetS) is a complex condition of metabolic disorders and shows a steady onset globally. Ceramides are known as intracellular signaling molecules that influence key metabolism through various pathways such as MetS and insulin resistance. Therefore, it is important to identify novel genetic factors related to increased plasma ceramides in subjects with MetS. Here we first measured plasma ceramides levels in 37 subjects with MetS and in 38 healthy subjects by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Specifically, levels of C16 ceramide (Cer-16), C18 ceramide (Cer-18), C20 ceramide (Cer-20), C18 dihydroceramide (DhCer-18), C24 dihydroceramide (DhCer-24), and C24:1 dihydroceramide (DhCer-24:1) were significantly increased in MetS group (p < 5.0 × 10−2). We then performed single nucleotide polymorphism (SNP) genotyping to identify variants associated with elevated plasma ceramides in MetS group using Axiom® Korea Biobank Array v1.1 chip. We also performed linear regression analysis on genetic variants involved in ceramide synthesis and significantly elevated plasma ceramides and dihydroceramides. Ten variants (rs75397325, rs4246316, rs80165332, rs62106618, rs12358192, rs11006229, rs10826014, rs149162405, rs6109681, and rs3906631) across six genes (ACER1, CERS3, CERS6, SGMS1, SPTLC2, and SPTLC3) functionally involved in ceramide biosynthesis showed significant associations with the elevated levels of at least one of the ceramide species in MetS group at a statistically significant threshold of false discovery rate (FDR)-adjusted p < 5.0 × 10−2. Our findings suggest that the variants may be genetic determinants associated with increased plasma ceramides in individuals with MetS.

Impact of Lipid Genetic Risk Score and Saturated Fatty Acid Intake on Central Obesity in an Asian Indian Population

Abnormalities in lipid metabolism have been linked to the development of obesity. We used a nutrigenetic approach to establish a link between lipids and obesity in Asian Indians, who are known to have a high prevalence of central obesity and dyslipidaemia. A sample of 497 Asian Indian individuals (260 with type 2 diabetes and 237 with normal glucose tolerance) (mean age: 44 ± 10 years) were randomly chosen from the Chennai Urban Rural Epidemiological Study (CURES). Dietary intake was assessed using a previously validated questionnaire. A genetic risk score (GRS) was constructed based on cholesteryl ester transfer protein (CETP) and lipoprotein lipase (LPL) genetic variants. There was a significant interaction between GRS and saturated fatty acid (SFA) intake on waist circumference (WC) (Pinteraction = 0.006). Individuals with a low SFA intake (≤23.2 g/day), despite carrying ≥2 risk alleles, had a smaller WC compared to individuals carrying <2 risk alleles (Beta = −0.01 cm; p = 0.03). For those individuals carrying ≥2 risk alleles, a high SFA intake (>23.2 g/day) was significantly associated with a larger WC than a low SFA intake (≤23.2 g/day) (Beta = 0.02 cm, p = 0.02). There were no significant interactions between GRS and other dietary factors on any of the measured outcomes. We conclude that a diet low in SFA might help reduce the genetic risk of central obesity confirmed by CETP and LPL genetic variants. Conversely, a high SFA diet increases the genetic risk of central obesity in Asian Indians.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

The association between rs1260326 with the risk of NAFLD and the mediation effect of triglyceride on NAFLD in the elderly Chinese Han population

Background: Accumulated studies have pointed out the striking association between variants in or near APOC3, GCKR, PNPLA3, and nonalcoholic fatty liver disease (NAFLD) at various ages from multiple ethnic groups. This association remained unclear in the Chinese Han elderly population, and whether this relationship correlated to any clinical parameters was also unclear.

Objectives: This study aims to decipher the complex relevance between gene polymorphisms, clinical parameters, and NAFLD by association study and mediation analysis.

Methods: Eight SNPs (rs2854116, rs2854117, rs780093, rs780094, rs1260362, rs738409, rs2294918, and rs2281135) within APOC3, GCKR, and PNPLA3 were genotyped using the MassARRAY® platform in a large Chinese Han sample comprising of 733 elderly NAFLD patients and 824 age- and ethnic-matched controls. Association and mediation analysis were employed by R.

Results: The genotypic frequencies of rs1260326 and rs780094 were significantly different between NAFLD and control (rs1260326: P=0.004, P_corr=0.020, OR [95%CI]= 0.69 [0.54-0.89]; rs780094: P=0.005, P_corr=0.025, OR [95%CI]= 0.70 [0.55-0.90]). Particularly, an increased triglyceride level was observed in carriers of rs1260326 T allele (1.94±1.19 mmol/L) compared with non-carriers (1.73±1.05 mmol/L).no significant results were observed in rs780094. Notably, triglyceride levels had considerably indirect impacts on association between NAFLD and rs1260326 (β =0.01, 95% CI: 0.01–0.02), indicating that 12.7% of the association of NAFLD with rs1260326 was mediated by triglyceride levels.

Conclusions: Our results identified a prominent relationship between GCKR rs1260326 and NAFLD, and highlighted the mediated effect of triglyceride levels on the that association in the Chinese Han elderly.

Delving into the Nutraceutical Benefits of Purple Carrot against Metabolic Syndrome and Cancer: A Review

Metabolic syndrome (MetS) constitutes a group of risk factors that may increase the risk of cancer and other health problems. Nowadays, researchers are focusing on food compounds that could prevent many chronic diseases. Thus, people are shifting from dietary supplements towards healthy nutritional approaches. As a nutritious and natural food source, purple carrot (Daucus carota spp. Sativus var. atrorubens Alef.) roots could have an important role in the prevention of MetS as well as cancer. This review provides deep insight into the role of purple carrot’s main bioactive compounds and their effectiveness against MetS and cancer. Phenolic compounds, such as anthocyanin, present in purple carrot roots may be especially productive in avoiding or delaying the onset of cardiovascular disease (CVDs), obesity, diabetes, and cancer. Anthocyanins and other phenolics are successful in reducing metabolic changes and inflammation by inhibiting inflammatory effects. Many researchers have made efforts to employ this vegetable in the prevention and treatment of MetS and cancer. However, more advanced studies are required for the identification of its detailed role, effectiveness, suitable intake, and the effect of its bioactive compounds against these diseases.

A Nutrigenetic Update on CETP Gene–Diet Interactions on Lipid-Related Outcomes

Purpose of Review

An abnormal lipid profile is considered a main risk factor for cardiovascular diseases and evidence suggests that single nucleotide polymorphisms (SNPs) in the cholesteryl ester transfer protein (CETP) gene contribute to variations in lipid levels in response to dietary intake. The objective of this review was to identify and discuss nutrigenetic studies assessing the interactions between CETP SNPs and dietary factors on blood lipids.

Recent Findings

Relevant articles were obtained through a literature search of PubMed and Google Scholar through to July 2021. An article was included if it examined an interaction between CETP SNPs and dietary factors on blood lipids. From 49 eligible nutrigenetic studies, 27 studies reported significant interactions between 8 CETP SNPs and 17 dietary factors on blood lipids in 18 ethnicities. The discrepancies in the study findings could be attributed to genetic heterogeneity, and differences in sample size, study design, lifestyle and measurement of dietary intake. The most extensively studied ethnicities were those of Caucasian populations and majority of the studies reported an interaction with dietary fat intake. The rs708272 (TaqIB) was the most widely studied CETP SNP, where ‘B1’ allele was associated with higher CETP activity, resulting in lower high-density lipoprotein cholesterol and higher serum triglycerides under the influence of high dietary fat intake.

Summary

Overall, the findings suggest that CETP SNPs might alter blood lipid profiles by modifying responses to diet, but further large studies in multiple ethnic groups are warranted to identify individuals at risk of adverse lipid response to diet.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11883-022-00987-y.

Membrane Melatonin Receptors Activated Cell Signaling in Physiology and Disease

The pineal hormone melatonin has attracted great scientific interest since its discovery in 1958. Despite the enormous number of basic and clinical studies the exact role of melatonin in respect to human physiology remains elusive. In humans, two high-affinity receptors for melatonin, MT1 and MT2, belonging to the family of G protein-coupled receptors (GPCRs) have been cloned and identified. The two receptor types activate Gi proteins and MT2 couples additionally to Gq proteins to modulate intracellular events. The individual effects of MT1 and MT2 receptor activation in a variety of cells are complemented by their ability to form homo- and heterodimers, the functional relevance of which is yet to be confirmed. Recently, several melatonin receptor genetic polymorphisms were discovered and implicated in pathology-for instance in type 2 diabetes, autoimmune disease, and cancer. The circadian patterns of melatonin secretion, its pleiotropic effects depending on cell type and condition, and the already demonstrated cross-talks of melatonin receptors with other signal transduction pathways further contribute to the perplexity of research on the role of the pineal hormone in humans. In this review we try to summarize the current knowledge on the membrane melatonin receptor activated cell signaling in physiology and pathology and their relevance to certain disease conditions including cancer.

Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

Background

Metabolic syndrome (MetS) is a cluster of multiple cardiometabolic risk factors that increase the risk of type 2 diabetes and cardiovascular diseases. Identifying novel biomarkers of MetS and their genetic associations could provide insights into the mechanisms of cardiometabolic diseases.

Methods

Potential MetS-associated metabolites were screened and internally validated by untargeted metabolomics analyses among 693 patients with MetS and 705 controls. External validation was conducted using two well-established targeted metabolomic methods among 149 patients with MetS and 253 controls. The genetic associations of metabolites were determined by linear regression using our previous genome-wide SNP data. Causal relationships were assessed using a one-sample Mendelian Randomization (MR) approach.

Findings

Nine metabolites were ultimately found to be associated with MetS or its components. Five metabolites, including LysoPC(14:0), LysoPC(15:0), propionyl carnitine, phenylalanine, and docosapentaenoic acid (DPA) were selected to construct a metabolite risk score (MRS), which was found to have a dose-response relationship with MetS and metabolic abnormalities. Moreover, MRS displayed a good ability to differentiate MetS and metabolic abnormalities. Three SNPs (rs11635491, rs7067822, and rs1952458) were associated with LysoPC(15:0). Two SNPs, rs1952458 and rs11635491 were found to be marginally correlated with several MetS components. MR analyses showed that a higher LysoPC(15:0) level was causally associated with the risk of overweight/obesity, dyslipidaemia, high uric acid, high insulin and high HOMA-IR.

Interpretation

We identified five metabolite biomarkers of MetS and three SNPs associated with LysoPC(15:0). MR analyses revealed that abnormal LysoPC metabolism may be causally linked the metabolic risk.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2017YFC0907004).

Association Between Change in Metabolic Syndrome Status and Risk of Incident Atrial Fibrillation: A Nationwide Population-Based Study

Backgroud There is a paucity of information on whether changes in metabolic syndrome (MetS) status affect the risk of new-onset atrial fibrillation (AF). We aimed to evaluate whether changes in MetS status and components of MetS affect AF risk using data from a nationwide observational cohort. Methods and Results A total of 7 565 531 adults without prevalent AF (mean age, 47±14 years) who underwent 2 serial health examinations by the Korean National Health Insurance Cooperation were identified. The patients were categorized into 4 groups according to the change in MetS status in serial evaluations, as follows: patients with persistent MetS (n=1 388 850), healthy patients newly diagnosed with MetS in the second evaluation (n=608 158), patients with MetS who were healthy in the second evaluation (n=798 555), and persistently healthy individuals (n=4 769 968). During a mean 7.9-year follow-up, incident AF was diagnosed in 139 305 (1.8%) patients. After multivariable adjustment, the AF risk was higher by 31% in the patients with persistent MetS , 26% in the patients with MetS who were healthy in the second evaluation, and 16% in the healthy patients newly diagnosed with MetS in the second evaluation compared with the persistently healthy individuals. Regardless of the MetS component type, the AF risk correlated with changes in the number of components. The risk of AF was strongly correlated with MetS status changes in the young and middle-age groups (20-39 years and 40-64 years, respectively) than in the elderly group (≥65 years). Conclusions Dynamic changes in MetS status and persistent MetS were associated with an increased risk of AF in a large-scale Asian population.

Type 2 diabetes is associated with the MTNR1B gene, a genetic bridge between circadian rhythm and glucose metabolism, in a Turkish population

Type 2 diabetes (T2D) is a complicated public health problem in Turkey as well as worldwide. Genome-wide approaches have been guiding in very challenging situations, such as the elucidation of genetic variations underlying complex diseases such as T2D. Despite intensive studies worldwide, few studies have determined the genetic susceptibility to T2D in Turkish populations. In this study, we investigated the effect of genes that are strongly associated with T2D in genome-wide association (GWA) studies, including MTNR1B, CDKAL1, THADA, ADAMTS9 and ENPP1, on T2D and its characteristic traits in a Turkish population. In 824 nonobese individuals (454 T2D patients and 370 healthy individuals), prominent variants of these GWA genes were genotyped by real-time PCR using the LightSNiP Genotyping Assay System. The SNP rs1387153 C/T, which is located 28 kb upstream of the MTNR1B gene, was significantly associated with T2D and fasting blood glucose levels (P < 0.05). The intronic SNP rs10830963 C/G in the MTNR1B gene was not associated with T2D, but it was associated with fasting blood glucose, HbA1C and LDL levels (P < 0.05). The other important GWA loci investigated in our study were not found to be associated with T2D or its traits. Only the SNP rs1044498 (A/C variation) in the ENPP1 gene was determined to be related to fasting blood glucose (P < 0.05). Our study suggests, consistent with the literature, that the MTNR1B locus, which has a prominent role in glucose regulation, is associated with T2D development by affecting blood glucose levels in our population.

A Genome-Wide Association Study of Novel Genetic Variants Associated With Anthropometric Traits in Koreans

Most previous genome-wide association studies (GWAS) have identified genetic variants associated with anthropometric traits. However, most of the evidence were reported in European populations. Anthropometric traits such as height and body fat distribution are significantly affected by gender and genetic factors. Here we performed GWAS involving 64,193 Koreans to identify the genetic factors associated with anthropometric phenotypes including height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip ratio. We found nine novel single-nucleotide polymorphisms (SNPs) and 59 independent genetic signals in genomic regions that were reported previously. Of the 19 SNPs reported previously, eight genetic variants at RP11-513I15.6 and one genetic variant at the RP11-977G19.10 region and six Asian-specific genetic variants were newly found. We compared our findings with those of previous studies in other populations. Five overlapping genetic regions (PAN2, ANKRD52, RNF41, HGMA1, and C6orf106) had been reported previously but none of the SNPs were independently identified in the current study. Seven of the nine newly found novel loci associated with height in women revealed a statistically significant skeletal expression of quantitative trait loci. Our study provides additional insight into the genetic effects of anthropometric phenotypes in East Asians.

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

Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role in the pathogenesis of MetS. The present study aimed to assess the association between 18 single nucleotide polymorphisms (SNPs) located at the BUD13, ZPR1, and APOA5 genes with MetS in the Tehran Cardio-metabolic Genetics Study (TCGS). In 5421 MetS affected and non-affected participants, we analyzed the data using two models. The first model (MetS model) examined SNPs' association with MetS. The second model (HTg-MetS Model) examined the association of SNPs with MetS affection participants who had a high plasma triglyceride (TG). The four-gamete rules were used to make SNP sets from correlated nearby SNPs. The kernel machine regression models and single SNP regression evaluated the association between SNP sets and MetS. The kernel machine results showed two sets over three sets of correlated SNPs have a significant joint effect on both models (p < 0.0001). Also, single SNP regression results showed that the odds ratios (ORs) for both models are almost similar; however, the p-values had slightly higher significance levels in the HTg-MetS model. The strongest ORs in the HTg-MetS model belonged to the G allele in rs2266788 (MetS: OR = 1.3, p = 3.6 × 10^–7; HTg-MetS: OR = 1.4, p = 2.3 × 10^–11) and the T allele in rs651821 (MetS: OR = 1.3, p = 2.8 × 10^–7; HTg-MetS: OR = 1.4, p = 3.6 × 10^–11). In the present study, the kernel machine regression models could help assess the association between the BUD13, ZPR1, and APOA5 gene variants (11p23.3 region) with lipid-related traits in MetS and MetS affected with high TG.

Systematic review of genome-wide association studies of abdominal aortic aneurysm

BACKGROUND AND AIMS

Abdominal aortic aneurysm (AAA) is an important cause of death worldwide and has an estimated heritability between 70 and 77%. Genome-wide association studies (GWAS) are an established way to discover genetic risk variants. The aim of this study was to systematically review the findings and quality of previous AAA GWAS.

METHODS

The Medline, PubMed, Web of Science and relevant genetic databases were searched to identify previous AAA GWAS. A framework was developed to grade the methodological quality of the GWAS. Data from included studies were extracted to assess methods and findings.

RESULTS

Eight case-control studies were included. Thirty-three of the 38 total single nucleotide polymorphisms (SNPs) previously reported were associated with AAA diagnosis at genome-wide significance (p < 5.0 × 10^-8). The CDKN2B antisense RNA-1 gene had the most significant association with AAA diagnosis (p = 6.94 × 10^-29 and p = 1.54 × 10^-33 for rs4007642 and rs10757274 respectively). Age, sex and smoking history were not reported for the complete cohort in any of the included studies, although five of the eight studies adjusted or matched for at least two confounding variables. All included studies had important design limitations including lack of sample size estimation, inconsistent case and control ascertainment and limited phenotyping of the AAAs. AAA growth was assessed in one GWAS, however, no significant associations with the reported SNPs were found.

CONCLUSIONS

This systematic review identified 33 SNPs associated with AAA diagnosis at genome-wide significance previously validated in multiple cohorts. The association between SNPs and AAA growth was not adequately examined. Previous GWAS have a number of design limitations.

Chromosomal regions strongly associated with waist circumference and body mass index in metabolic syndrome in a family-based study

Obesity is the most crucial phenotype in metabolic syndrome (MetS), and waist circumference (WC) and body mass index (BMI) are two common indexes to define obesity. It is an accepted fact that genetic and environmental interaction influence obesity and MetS. Microsatellites are a subcategory of tandem repeats with a length of 1 to 10 nucleotides. Tandem repeats make up repetitive genomic regions. Differences in the number of tandem repeats or their variation (alleles) result in microsatellite polymorphisms. Thus, we attempted to find microsatellite variation associated with WC and BMI in a family-based study. Twelve microsatellite markers were selected to investigate possible genes or chromosomal regions in 91 families with at least one affected MetS. The cut-off values for BMI and WC were considered 25 kg/m² and 90 cm, respectively. In all members of the families, the strongest association was observed between the marker D11S1304 (allele 1) with both WC and BMI, independently, by the biallelic model in the family-based association test analysis (P < 0.05). Besides, when we compared high- and low-level groups in members with MetS, the markers D8S1743 and D11S1304 (allele 1) showed a strong association with WC (P = 0.0080) and BMI (P = 0.0074), respectively. When the simultaneous detection of the high WC and MetS status was used as a trait, the strongest association was observed with the marker D8S1743 (P = 0.0034). Moreover, when BMI with the high MetS status was used as a trait, the strongest association was observed with the marker D8S1743 (allele 4) (P = 0.0034). The obtained results showed a relationship between obesity and MetS with markers on the selected regions on chromosomes 8 and 11, and to a lesser degree, on chromosome 12.

Risk of Metabolic Syndrome in Kidney Stone Formers: A Comparative Cohort Study with a Median Follow-Up of 19 Years

Background: Kidney stone formers (SF) are more likely to develop diabetes mellitus (DM), but there is no study examining risk of metabolic syndrome (MetS) in this population. We aimed to describe the risk of MetS in SF compared to non-SF. Methods and Materials: SF referred to a tertiary referral metabolic centre in Southern England from 1990 to 2007, comparator patients were age, sex, and period (first stone) matched with 3:1 ratio from the same primary care database. SF with no documentation or previous MetS were excluded. Ethical approval was obtained and MetS was defined using the modified Association of American Clinical Endocrinologists (AACE) criteria. Analysis with cox proportional hazard regression. Results: In total, 828 SF were included after 1000 records were screened for inclusion, with 2484 age and sex matched non-SF comparators. Median follow-up was 19 years (interquartile range—IQR: 15–22) for both stone formers and stone-free comparators. SF were at significantly increased risk of developing MetS (hazard ratio—HR: 1.77; 95% confidence interval—CI: 1.55–2.03, p < 0.001). This effect was robust to adjustment for pre-existing components (HR: 1.91; 95% CI: 1.66–2.19, p < 0.001). Conclusions: Kidney stone formers are at increased risk of developing metabolic syndrome. Given the pathophysiological mechanism, the stone is likely a ‘symptom’ of an underlying metabolic abnormality, whether covert or overt. This has implications the risk of further stone events and cardiovascular disease.

GCKR common functional polymorphisms are associated with metabolic syndrome and its components: a 10-year retrospective cohort study in Iranian adults

BACKGROUND

Previous studies reported that common functional variants (rs780093, rs780094, and rs1260326) in the glucokinase regulator gene (GCKR) were associated with metabolic syndrome despite the simultaneous association with the favorable and unfavorable metabolic syndrome components. We decided to evaluate these findings in a cohort study with a large sample size of Iranian adult subjects, to our knowledge for the first time. We investigated the association of the GCKR variants with incident MetS in mean follow-up times for nearly 10 years.

METHODS

Analysis of this retrospective cohort study was performed among 5666 participants of the Tehran Cardiometabolic Genetics Study (TCGS) at 19-88 years at baseline. Linear and logistic regression analyses were used to investigate the metabolic syndrome (JIS criteria) association and its components with rs780093, rs780094, and rs1260326 in an additive genetic model. Cox regression was carried out to peruse variants' association with the incidence of metabolic syndrome in the TCGS cohort study.

RESULTS

In the current study, we have consistently replicated the association of the GCKR SNPs with higher triglyceride and lower fasting blood sugar levels (p < 0.05) in Iranian adults. The CT genotype of the variants was associated with lower HDL-C levels. The proportional Cox adjusted model regression resulted that TT carriers of rs780094, rs780093, and rs1260326 were associated with 20%, 23%, and 21% excess risk metabolic syndrome incidence, respectively (p < 0.05).

CONCLUSIONS

Elevated triglyceride levels had the strongest association with GCKR selected variants among the metabolic syndrome components. Despite the association of these variants with decreased fasting blood sugar levels, T alleles of the variants were associated with metabolic syndrome incidence; so whether individuals are T allele carriers of the common functional variants, they have a risk factor for the future incidence of metabolic syndrome.

The Relationship of Dietary Pattern and Genetic Risk Score with the Incidence Dyslipidemia: 14-Year Follow-Up Cohort Study

This study was conducted to investigate the relationship between dietary pattern and genetic risk score (GRS) for dyslipidemia risk among Korean adults. Hypercholesterolemia and hypertriglyceridemia defined as total cholesterol ≥240 mg/dL and triglyceride ≥200 mg/dL or use dyslipidemia medication. The GRS was calculated by summing the risk alleles of the selected seven single-nucleotide polymorphisms related to dyslipidemia. Dietary patterns were identified by principal component analysis based on the frequency of 36 food groups, "whole grain and soybean products" pattern, "meat, fish and vegetables" pattern, and "bread and noodle" pattern were identified. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using the multivariate Cox proportional hazards regression model. High intake of a "whole grain and soybean products" pattern decreased risks of hypercholesterolemia (HR: 0.82, 95% CI: 0.72-0.93, p for trend = 0.0006) and hypertriglyceridemia (HR: 0.85, 95% CI: 0.75-0.97, p for trend = 0.0344). In the highest tertile of GRS, the "whole grain and soybean products" pattern was inversely related to hypercholesterolemia risk. Therefore, for people with genotypes that can cause hypercholesterolemia, eating whole grains and soybean products may have a meaningful response, these results could be utilized for genome-based nutrition management.