Genome-wide screen for metabolic syndrome susceptibility Loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits

Genetic Links Between Metabolic Syndrome and Osteoarthritis: Insights From Cross-Trait Analysis

CONTEXT

Previous observational studies have indicated a bidirectional association between metabolic syndrome (MetS) and osteoarthritis (OA). However, it remains unclear whether these bidirectional associations reflect causal relationships or shared genetic factors, and the underlying biological mechanisms of this association are not fully understood.

OBJECTIVE

We aimed to explore the genetic connection between MetS and OA using genome-wide association study (GWAS) summary data.

METHODS

Leveraging summary statistics from GWAS conducted by the UK Biobank and the Glucose and Insulin-related Traits Consortium (MAGIC), we performed global genetic correlation analyses, genome-wide cross-trait meta-analyses, and a bidirectional two-sample Mendelian randomization analyses using summary statistics from GWAS to comprehensively assess the relationship of MetS and OA.

RESULTS

We first detected an extensive genetic correlation between MetS and OA (rg = 0.393, P = 1.52 × 10-18), which was consistent in 4 MetS components, including waist circumference, triglycerides, hypertension, and high-density lipoprotein cholesterol and OA with rg ranging from -0.229 to 0.490. We then discovered 32 variants jointly associated with MetS and OA through Multi-Trait Analysis of GWAS (MTAG). Co-localization analysis found 12 genes shared between MetS and OA, with functional implications in several biological pathways. Finally, Mendelian randomization analysis suggested genetic liability to MetS significantly increased the risk of OA, but no reverse causality was found.

CONCLUSION

Our results illustrate a common genetic architecture, pleiotropic loci, as well as causality between MetS and OA, potentially enhancing our knowledge of high comorbidity and genetic processes that overlap between the 2 disorders.

Polygenic Score Approach to Predicting Risk of Metabolic Syndrome

BACKGROUND/OBJECTIVES

Metabolic syndrome (MetS) is a complex condition linking obesity, diabetes, and hypertension, representing a major challenge in clinical care. Its rising global prevalence, driven by urbanization, sedentary lifestyles, and dietary changes, underscores the need for effective management. This study aims to explore the genetic mechanisms behind MetS, including adiposity, inflammation, neurotransmitters, and β-cell function, to develop a prognostic tool for MetS risk.

METHODS

We genotyped 40 genetic variants across these pathways in 279 MetS patients and 397 healthy individuals. Using logistic regression, we evaluated the prognostic capability of a polygenic score model for MetS risk, both independently and with other factors like sex and age.

RESULTS

Logistic regression analysis identified 18 genetic variants significantly associated with MetS. The optimal predictive model used polygenic scores calculated with weights assigned to the 18 loci (AUC 82.5%, 95% CI 79.4-85.6%), with age and sex providing a minimal, non-significant improvement (AUC 83.3%, 95% CI 80.2-86.3%). The addition of the polygenic score significantly improved net reclassification (NRI = 1.03%, p = 3.42 × 10-50). Including all 40 variants did not enhance prediction (NRI = -0.11, p = 0.507).

CONCLUSIONS

Polygenic scores could aid in predicting MetS risk and health outcomes, emphasizing the need for diagnostic tools tailored to specific populations. Additional research is warranted to corroborate these conclusions and explore the molecular mechanisms of MetS.

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.

Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank

Insulin resistance (IR) is a well-established risk factor for metabolic disease. The ratio of triglycerides to high-density lipoprotein cholesterol (TG:HDL-C) is a surrogate marker of IR. We conducted a genome-wide association study of the TG:HDL-C ratio in 402,398 Europeans within the UK Biobank. We identified 369 independent SNPs, of which 114 had a false discovery rate-adjusted P value < 0.05 in other genome-wide studies of IR making them high-confidence IR-associated loci. Seventy-two of these 114 loci have not been previously associated with IR. These 114 loci cluster into five groups upon phenome-wide analysis and are enriched for candidate genes important in insulin signaling, adipocyte physiology and protein metabolism. We created a polygenic-risk score from the high-confidence IR-associated loci using 51,550 European individuals in the Michigan Genomics Initiative. We identified associations with diabetes, hyperglyceridemia, hypertension, nonalcoholic fatty liver disease and ischemic heart disease. Collectively, this study provides insight into the genes, pathways, tissues and subtypes critical in IR.

Unraveling phenotypic variance in metabolic syndrome through multi-omics

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

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

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

Maternal pre-pregnancy BMI, offspring epigenome-wide DNA methylation, and childhood obesity: findings from the Boston Birth Cohort

BACKGROUND

Maternal pre-pregnancy obesity is an established risk factor for childhood obesity. Investigating epigenetic alterations induced by maternal obesity during fetal development could gain mechanistic insight into the developmental origins of childhood obesity. While obesity disproportionately affects underrepresented racial and ethnic mothers and children in the USA, few studies investigated the role of prenatal epigenetic programming in intergenerational obesity of these high-risk populations.

METHODS

This study included 903 mother-child pairs from the Boston Birth Cohort, a predominantly urban, low-income minority birth cohort. Mother-infant dyads were enrolled at birth and the children were followed prospectively to age 18 years. Infinium Methylation EPIC BeadChip was used to measure epigenome-wide methylation level of cord blood. We performed an epigenome-wide association study of maternal pre-pregnancy body mass index (BMI) and cord blood DNA methylation (DNAm). To quantify the degree to which cord blood DNAm mediates the maternal BMI-childhood obesity, we further investigated whether maternal BMI-associated DNAm sites impact birthweight or childhood overweight or obesity (OWO) from age 1 to age 18 and performed corresponding mediation analyses.

RESULTS

The study sample contained 52.8% maternal pre-pregnancy OWO and 63.2% offspring OWO at age 1-18 years. Maternal BMI was associated with cord blood DNAm at 8 CpG sites (genome-wide false discovery rate [FDR] < 0.05). After accounting for the possible interplay of maternal BMI and smoking, 481 CpG sites were discovered for association with maternal BMI. Among them 123 CpGs were associated with childhood OWO, ranging from 42% decrease to 87% increase in OWO risk for each SD increase in DNAm. A total of 14 identified CpG sites showed a significant mediation effect on the maternal BMI-child OWO association (FDR < 0.05), with mediating proportion ranging from 3.99% to 25.21%. Several of these 14 CpGs were mapped to genes in association with energy balance and metabolism (AKAP7) and adulthood metabolic syndrome (CAMK2B).

CONCLUSIONS

This prospective birth cohort study in a high-risk yet understudied US population found that maternal pre-pregnancy OWO significantly altered DNAm in newborn cord blood and provided suggestive evidence of epigenetic involvement in the intergenerational risk of obesity.

Current Data and New Insights into the Genetic Factors of Atherogenic Dyslipidemia Associated with Metabolic Syndrome

Atherogenic dyslipidemia plays a critical role in the development of metabolic syndrome (MetS), being one of its major components, along with central obesity, insulin resistance, and hypertension. In recent years, the development of molecular genetics techniques and extended analysis at the genome or exome level has led to important progress in the identification of genetic factors (heritability) involved in lipid metabolism disorders associated with MetS. In this review, we have proposed to present the current knowledge related to the genetic etiology of atherogenic dyslipidemia, but also possible challenges for future studies. Data from the literature provided by candidate gene-based association studies or extended studies, such as genome-wide association studies (GWAS) and whole exome sequencing (WES,) have revealed that atherogenic dyslipidemia presents a marked genetic heterogeneity (monogenic or complex, multifactorial). Despite sustained efforts, many of the genetic factors still remain unidentified (missing heritability). In the future, the identification of new genes and the molecular mechanisms by which they intervene in lipid disorders will allow the development of innovative therapies that act on specific targets. In addition, the use of polygenic risk scores (PRS) or specific biomarkers to identify individuals at increased risk of atherogenic dyslipidemia and/or other components of MetS will allow effective preventive measures and personalized therapy.

Risk prediction of the metabolic syndrome using TyG Index and SNPs: a 10-year longitudinal prospective cohort study

TyG (triglyceride and glucose) index using triglyceride and fasting blood glucose is recommended as a useful marker for insulin resistance. To clarify whether the TyG index is a marker for predicting metabolic syndrome (MetS) and to investigate the importance of single-nucleotide polymorphisms (SNPs) in MetS diagnosis. From 2001 to 2014, a longitudinal prospective cohort study of 3580 adults aged 40-70 years was conducted. The area under the receiver operating characteristic curves (AUROC) and Youden index (YI) was calculated to assess the diagnostic value. During the 14-year follow-up, 1270 subjects developed MetS. Five SNPs in four genes (BUD13 rs10790162, ZPR1 rs2075290, APOA5 rs2266788, APOA5 rs2075291, and MKL1 rs4507196) significantly correlated with susceptibility to MetS (p < 0.00005). The areas under the curve of TyG index and HOMA-IR were 0.854 (95% confidence interval [CI], 0.841-0.867) and 0.702 (95% CI, 0.684-0.721), respectively. Despite no statistical significance, AUROC and YI were increased when MetS was diagnosed using TyG index and the five SNPs. TyG index might be useful for identifying individuals at high risk of developing MetS. The combination of TyG index and SNPs showed better diagnostic accuracy than TyG index alone, indicating the potential value of novel SNPs for MetS diagnosis.

Pleiotropic Effects of APOB Variants on Lipid Profiles, Metabolic Syndrome, and the Risk of Diabetes Mellitus

Apolipoprotein B (ApoB) plays a crucial role in lipid and lipoprotein metabolism. The effects of APOB locus variants on lipid profiles, metabolic syndrome, and the risk of diabetes mellitus (DM) in Asian populations are unclear. We included 1478 Taiwan Biobank participants with whole-genome sequence (WGS) data and 115,088 TWB participants with Axiom genome-wide CHB array data and subjected them to genotype-phenotype analyses using APOB locus variants. Five APOB nonsynonymous mutations, including Asian-specific rs144467873 and rs13306194 variants, were selected from participants with the WGS data. Using a combination of regional association studies, a linkage disequilibrium map, and multivariate analysis, we revealed that the APOB locus variants rs144467873, rs13306194, and rs1367117 were independently associated with total, low-density lipoprotein (LDL), and non-high-density lipoprotein (non-HDL) cholesterol levels; rs1318006 was associated with HDL cholesterol levels; rs13306194 and rs35131127 were associated with serum triglyceride levels; rs144467873, rs13306194, rs56213756, and rs679899 were associated with remnant cholesterol levels; and rs144467873 and rs4665709 were associated with metabolic syndrome. Mendelian randomization (MR) analyses conducted using weighted genetic risk scores from three or two LDL-cholesterol-level-associated APOB variants revealed significant association with prevalent DM (p = 0.0029 and 8.2 × 10^-5, respectively), which became insignificant after adjustment for LDL-C levels. In conclusion, these results indicate that common and rare APOB variants are independently associated with various lipid levels and metabolic syndrome in Taiwanese individuals. MR analyses supported APOB variants associated with the risk of DM through their associations with LDL cholesterol levels.

Genetic Variants at the APOE Locus Predict Cardiometabolic Traits and Metabolic Syndrome: A Taiwan Biobank Study

Several apolipoprotein genes are located at the APOE locus on chromosome 19q13.32. This study explored the genetic determinants of cardiometabolic traits and metabolic syndrome at the APOE locus in a Taiwanese population. A total of 81,387 Taiwan Biobank (TWB) participants were enrolled to undergo genotype−phenotype analysis using data from the Axiom Genome-Wide CHB arrays. Regional association analysis with conditional analysis revealed lead single-nucleotide variations (SNVs) at the APOE locus: APOE rs7412 and rs429358 for total, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) cholesterol levels; CLPTM1 rs3786505 and rs11672748 for LDL and HDL cholesterol levels; and APOC1 rs438811 and APOE-APOC1 rs439401 for serum triglyceride levels. Genotype−phenotype association analysis revealed a significant association of rs429358 and rs438811 with metabolic syndrome and of rs7412, rs438811, and rs439401 with serum albumin levels (p < 0.0015). Stepwise regression analysis indicated that CLPTM1 variants were independently associated with LDL and HDL cholesterol levels (p = 3.10 × 10−15 for rs3786505 and p = 1.48 × 10−15 for rs11672748, respectively). APOE rs429358 and APOC1 rs438811 were also independently associated with metabolic syndrome (p = 2.29 × 10−14) and serum albumin levels (p = 3.80 × 10−6), respectively. In conclusion, in addition to APOE variants, CLPTM1 is a novel candidate locus for LDL and HDL cholesterol levels at the APOE gene region in Taiwan. Our data also indicated that APOE and APOC1 variants were independently associated with metabolic syndrome and serum albumin levels, respectively. These results revealed the crucial role of genetic variants at the APOE locus in predicting cardiometabolic traits and metabolic syndrome.

Towards harnessing the value of organokine crosstalk to predict the risk for cardiovascular disease in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Importantly, NAFLD increases the risk for cardiovascular disease (CVD). A causal relationship has been substantiated. Given the pandemic proportions of NAFLD, a reliable scoring system for predicting the risk of NAFLD-associated CVD is an urgent medical need. We here review cumulative evidence suggesting that systemically released organokines - especially certain adipokines, hepatokines, and cardiokines - may serve this purpose. The underlying rationale is that these signalers directly communicate between white adipose tissue, liver, and heart as key players in the pathogenesis of NAFLD and resultant CVD events. Moreover, evidence suggests that these organ-specific cytokines are secreted in a biologically predetermined, cascade-like pattern. Consequently, upon pinpointing organokines of relevance, we sketch requirements to establish an algorithm predictive of the CVD risk in patients with NAFLD. Such an algorithm, as to be consolidated in the form of an applicable equation, may be improved continuously by machine learning. To the best of our knowledge, such an option has not yet been considered. Establishing and implementing a reliable algorithm for determining the NAFLD-associated CVD risk has the potential to save many NAFLD patients from life-threatening CVD events.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

“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.

Genetic and environmental correlational structure among metabolic syndrome endophenotypes

Metabolic syndrome (MetS) is diagnosed by the presence of high scores on three or more metabolic traits, including systolic and diastolic blood pressure (SBP, DBP), glucose and insulin levels, cholesterol and triglyceride (TG) levels, and central obesity. A diagnosis of MetS is associated with increased risk of cardiovascular disease and type 2 diabetes. The components of MetS have long been demonstrated to have substantial genetic components, but their genetic overlap is less well understood. The present paper takes a multi-prong approach to examining the extent of this genetic overlap. This includes the quantitative genetic and additive Bayesian network modeling of the large TwinsUK project and examination of the results of genome-wide association study (GWAS) of UK Biobank data through use of LD score regression and examination of the number of genes and pathways identified in the GWASes which overlap across MetS traits. Results demonstrate a modest genetic overlap, and the genetic correlations obtained from TwinsUK and UK Biobank are nearly identical. However, these correlations imply more genetic dissimilarity than similarity. Furthermore, examination of the extent of overlap in significant GWAS hits, both at the gene and pathway level, again demonstrates only modest but significant genetic overlap. This lends support to the idea that in clinical treatment of MetS, treating each of the components individually may be an important way to address MetS.

Genetics of murine type 2 diabetes and comorbidities

ABSTRAC

Type 2 diabetes (T2D) is a polygenic and multifactorial complex disease, defined as chronic metabolic disorder. It's a major global health concern with an estimated 463 million adults aged 20-79 years with diabetes and projected to increase up to 700 million by 2045. T2D was reported to be one of the four leading causes of non-communicable disease (NCD) deaths in 2012. Environmental factors play a part in the development of polygenic forms of diabetes. Polygenic forms of diabetes often run-in families. Fortunately, T2D, which accounts for 90-95% of the entire four types of diabetes including, Type 1 diabetes (T1D), T2D, monogenic diabetes syndromes (MGDS), and Gestational diabetes mellitus, can be prevented or delayed through nutrition and lifestyle changes as well as through pharmacologic interventions. Typical symptom of the T2D is high blood glucose levels and comprehensive insulin resistance of the body, producing an impaired glucose tolerance. Impaired glucose tolerance of T2D is accompanied by extensive health complications, including cardiovascular diseases (CVD) that vary in morbidity and mortality among populations. The pathogenesis of T2D varies between populations and/or ethnic groupings and is known to be attributed extremely by genetic components and environmental factors. It is evident that genetic background plays a critical role in determining the host response toward certain environmental conditions, whether or not of developing T2D (susceptibility versus resistant). T2D is considered as a silent disease that can progress for years before its diagnosis. Once T2D is diagnosed, many metabolic malfunctions are observed whether as side effects or as independent comorbidity. Mouse models have been proven to be a powerful tool for mapping genetic factors that underline the susceptibility to T2D development as well its comorbidities. Here, we have conducted a comprehensive search throughout the published data covering the time span from early 1990s till the time of writing this review, for already reported quantitative trait locus (QTL) associated with murine T2D and comorbidities in different mouse models, which contain different genetic backgrounds. Our search has resulted in finding 54 QTLs associated with T2D in addition to 72 QTLs associated with comorbidities associated with the disease. We summarized the genomic locations of these mapped QTLs in graphical formats, so as to show the overlapping positions between of these mapped QTLs, which may suggest that some of these QTLs could be underlined by sharing gene/s. Finally, we reviewed and addressed published reports that show the success of translation of the identified mouse QTLs/genes associated with the disease in humans.

Dissecting genome-wide studies for microbiome-related metabolic diseases

Despite the meteoric rise in genome-wide association studies for metabolic diseases (MetD) over the last few years, our understanding of the pathogenesis of these diseases is still far from complete. Recent developments have established that MetD arises from complex interactions between host genetics, the gut microbiome and the environment. However, our knowledge of the genetic and microbiome components involved and the underlying molecular mechanisms remains limited. Here, we review and summarize recent studies investigating the genetic and microbiome basis of MetD. Then, given the critical importance of study-individual's ancestry in these studies, we leverage 4932 whole-genome sequence samples from 18 worldwide ethnic groups to examine genetic diversity in currently reported variants associated with MetD. The analyses show marked differences in gene-specific proportion of pathogenic single-nucleotide polymorphisms (SNPs) and gene-specific SNPs MAFs across ethnic groups, highlighting the importance of population- and ethnic-specific investigations in pinpointing the causative factors for MetD. We conclude with a discussion of research areas where further investigation on interactions between host genetics, microbiome and the environment is needed.

Analysis of the Potential Genetic Links between Psoriasis and Cardiovascular Risk Factors

Cardiovascular risk factors are one of the most common comorbidities in psoriasis. A higher prevalence of hypertension, insulin resistance and type 2 diabetes, dyslipidemia, obesity, metabolic syndrome, depression, as well as cardiovascular disease was confirmed in psoriatic patients in comparison to the general population. Data suggest that psoriasis and systemic inflammatory disorders may originate from the pleiotropic interactions with many genetic pathways. In this review, the authors present the current state of knowledge on the potential genetic links between psoriasis and cardiovascular risk factors. The understanding of the processes linking psoriasis with cardiovascular risk factors can lead to improvement of psoriasis management in the future.

Ancestry specific associations of a genetic risk score, dietary patterns and metabolic syndrome: a longitudinal ARIC study

BACKGROUND

Associations have been observed among genetic variants, dietary patterns, and metabolic syndrome (MetS). A gap in knowledge is whether a genetic risk score (GRS) and dietary patterns interact to increase MetS risk among African Americans. We investigated whether MetS risk was influenced by interaction between a GRS and dietary patterns among Whites and African Americans. A secondary aim examined if molecular genetic clusterings differed by racial ancestry.

METHODS

We used longitudinal data over 4-visits (1987-1998) that included 10,681 participants aged 45-64y at baseline from the Atherosclerosis Risk in Communities study (8451 Whites and 2230 African Americans). We constructed a simple-count GRS as the linear weighted sum of high-risk alleles (0, 1, 2) from cardiovascular disease polymorphisms from the genome-wide association studies catalog associated with MetS risk. Three dietary patterns were determined by factor analysis of food frequency questionnaire data: Western, healthy, and high-fat dairy. MetS was defined according to the 2016 National Cholesterol Education Program Adult Treatment Panel III criteria but used 2017 American Heart Association/American College of Cardiology criteria for elevated blood pressure. Analyses included generalized linear model risk ratios (RR), 95% confidence intervals (CI), and Bonferroni correction for multiple testing.

RESULTS

The Western dietary pattern was associated with higher risk for MetS across increasing GRS tertiles among Whites (p < 0.017). The high-fat dairy pattern was protective against MetS, but its impact was most effective in the lowest GRS tertile in Whites (RR = 0.62; CI: 0.52-0.74) and African Americans (RR = 0.67; CI: 0.49-0.91). Among each racial group within GRS tertiles, the Western dietary pattern was associated with development and cycling of MetS status between visits, and the high-fat dairy pattern with being free from MetS (p < 0.017). The healthy dietary pattern was associated with higher risk of MetS among African Americans which may be explained by higher sucrose intake (p < 0.0001). Fewer genes, but more metabolic pathways for obesity, body fat distribution, and lipid and carbohydrate metabolism were identified in African Americans than Whites. Some polymorphisms were linked to the Western and high-fat dairy patterns.

CONCLUSION

The influence of dietary patterns on MetS risk appears to differ by genetic predisposition and racial ancestry.

Adulthood blood levels of hsa-miR-29b-3p associate with preterm birth and adult metabolic and cognitive health

Preterm birth (PTB) is associated with increased risk of type 2 diabetes and neurocognitive impairment later in life. We analyzed for the first time the associations of PTB with blood miRNA levels in adulthood. We also investigated the relationship of PTB associated miRNAs and adulthood phenotypes previously linked with premature birth. Blood MicroRNA profiling, genome-wide gene expression analysis, computer-based cognitive testing battery (CANTAB) and serum NMR metabolomics were performed for Young Finns Study subjects (aged 34-49 years, full-term n = 682, preterm n = 84). Preterm birth (vs. full-term) was associated with adulthood levels of hsa-miR-29b-3p in a fully adjusted regression model (p = 1.90 × 10-4, FDR = 0.046). The levels of hsa-miR-29b-3p were down-regulated in subjects with PTB with appropriate birthweight for gestational age (p = 0.002, fold change [FC] = - 1.20) and specifically in PTB subjects with small birthweight for gestational age (p = 0.095, FC = - 1.39) in comparison to individuals born full term. Hsa-miR-29b-3p levels correlated with the expressions of its target-mRNAs BCL11A and CS and the gene set analysis results indicated a target-mRNA driven association between hsa-miR-29b-3p levels and Alzheimer's disease, Parkinson's disease, Insulin signaling and Regulation of Actin Cytoskeleton pathway expression. The level of hsa-miR-29b-3p was directly associated with visual processing and sustained attention in CANTAB test and inversely associated with serum levels of VLDL subclass component and triglyceride levels. In conlcusion, adult blood levels of hsa-miR-29b-3p were lower in subjects born preterm. Hsa-miR-29b-3p associated with cognitive function and may be linked with adulthood morbidities in subjects born preterm, possibly through regulation of gene sets related to neurodegenerative diseases and insulin signaling as well as VLDL and triglyceride metabolism.

Adaptive and maladaptive roles for ChREBP in the liver and pancreatic islets

Excessive sugar consumption is a contributor to the worldwide epidemic of cardiometabolic disease. Understanding mechanisms by which sugar is sensed and regulates metabolic processes may provide new opportunities to prevent and treat these epidemics. Carbohydrate Responsive-Element Binding Protein (ChREBP) is a sugar-sensing transcription factor that mediates genomic responses to changes in carbohydrate abundance in key metabolic tissues. Carbohydrate metabolites activate the canonical form of ChREBP, ChREBP-alpha, which stimulates production of a potent, constitutively active ChREBP isoform called ChREBP-beta. Carbohydrate metabolites and other metabolic signals may also regulate ChREBP activity via posttranslational modifications including phosphorylation, acetylation, and O-GlcNAcylation that can affect ChREBP’s cellular localization, stability, binding to cofactors, and transcriptional activity. In this review, we discuss mechanisms regulating ChREBP activity and highlight phenotypes and controversies in ChREBP gain- and loss-of-function genetic rodent models focused on the liver and pancreatic islets.

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.

Cross-talk between ANGPTL4 gene SNP Rs1044250 and weight management is a risk factor of metabolic syndrome

Background

The prevalence of metabolic syndrome (Mets) is closely related to an increased incidence of cardiovascular events. Angiopoietin-like protein 4 (ANGPTL4) is contributory to the regulation of lipid metabolism, herein, may provide a target for gene-aimed therapy of Mets. This observational case control study was designed to elucidate the relationship between ANGPTL4 gene single nucleotide polymorphism (SNP) rs1044250 and the onset of Mets, and to explore the interaction between SNP rs1044250 and weight management on Mets.

Methods

We have recruited 1018 Mets cases and 1029 controls in this study. The SNP rs1044250 was genotyped with blood samples, base-line information and Mets-related indicators were collected. A 5-year follow-up survey was carried out to track the lifestyle interventions and changes in Mets-related indicators.

Results

ANGPTL4 gene SNP rs1044250 is an independent risk factor for increased waist circumference (OR 1.618, 95% CI [1.119–2.340]; p = 0.011), elevated blood pressure (OR 1.323, 95% CI [1.002–1.747]; p = 0.048), and Mets (OR 1.875, 95% CI [1.363–2.580]; p < 0.001). The follow-up survey shows that rs1044250 CC genotype patients with weight gain have an increased number of Mets components (M [Q1, Q3]: CC 1 (0, 1), CT + TT 0 [− 1, 1]; p = 0.021); The interaction between SNP rs1044250 and weight management is a risk factor for increased systolic blood pressure (β = 0.075, p < 0.001) and increased diastolic blood pressure (β = 0.097, p < 0.001), the synergistic effect of weight management and SNP rs1044250 is negative (S < 1).

Conclusion

ANGPTL4 gene SNP rs1044250 is an independent risk factor for increased waist circumference and elevated blood pressure, therefore, for Mets. However, patients with wild type SNP 1044250 are more likely to have Mets when the body weight is increased, mainly due to elevated blood pressure.

Epigenetic Associations With Estimated Glomerular Filtration Rate Among Men With Human Immunodeficiency Virus Infection

BACKGROUND

People living with human immunodeficiency virus (HIV) infection have higher risk for chronic kidney disease (CKD), defined by a reduced estimated glomerular filtration rate (eGFR). Previous studies have implicated epigenetic changes related to CKD; however, the mechanism of HIV-related CKD has not been thoroughly investigated.

METHODS

We conducted an epigenome-wide association study of eGFR among 567 HIV-positive and 117 HIV-negative male participants in the Veterans Aging Cohort Study to identify epigenetic signatures of kidney function.

RESULTS

By surveying more than 400 000 cytosine guanine dinucleotide (CpG) sites measured from peripheral blood mononuclear cells, we identified 15 sites that were significantly associated with eGFR (false discovery rate Q value < 0.05) among HIV-positive participants. The most significant CpG sites, located at MAD1L1, TSNARE1/BAI1, and LTV1, were all negatively associated with eGFR (cg06329547, P = 5.25 × 10-9; cg23281907, P = 1.37 × 10-8; cg18368637, P = 5.17 × 10-8). We also replicated previously reported eGFR-associated CpG sites including cg17944885 (P = 2.5 × 10-5) located between ZNF788 and ZNF20 on chromosome 19 in the pooled population.

CONCLUSIONS

In this study we uncovered novel epigenetic associations with kidney function among people living with HIV and suggest potential epigenetic mechanisms linked with HIV-related CKD risk.

An epigenome-wide association study of metabolic syndrome and its components

The role of metabolic syndrome (MetS) as a preceding metabolic state for type 2 diabetes and cardiovascular disease is widely recognised. To accumulate knowledge of the pathological mechanisms behind the condition at the methylation level, we conducted an epigenome-wide association study (EWAS) of MetS and its components, testing 1187 individuals of European ancestry for approximately 470 000 methylation sites throughout the genome. Methylation site cg19693031 in gene TXNIP —previously associated with type 2 diabetes, glucose and lipid metabolism, associated with fasting glucose level (P = 1.80 × 10⁻⁸). Cg06500161 in gene ABCG1 associated both with serum triglycerides (P = 5.36 × 10⁻⁹) and waist circumference (P = 5.21 × 10⁻⁹). The previously identified type 2 diabetes–associated locus cg08309687 in chromosome 21 associated with waist circumference for the first time (P = 2.24 × 10⁻⁷). Furthermore, a novel HDL association with cg17901584 in chromosome 1 was identified (P = 7.81 × 10⁻⁸). Our study supports previous genetic studies of MetS, finding that lipid metabolism plays a key role in pathology of the syndrome. We provide evidence regarding a close interplay with glucose metabolism. Finally, we suggest that in attempts to identify methylation loci linking separate MetS components, cg19693031 appears to represent a strong candidate.

The Variant rs1784042 of the SIDT2 Gene is Associated with Metabolic Syndrome through Low HDL-c Levels in a Mexican Population

The Mexican population has one of the highest prevalences of metabolic syndrome (MetS) worldwide. The aim of this study was to investigate the association of single-nucleotide polymorphisms (SNPs) with MetS and its components. First, we performed a pilot Genome-wide association study (GWAS) scan on a sub-sample derived from the Health Workers Cohort Study (HWCS) (n = 411). Based on GWAS results, we selected the rs1784042 and rs17120425 SNPs in the SIDT1 transmembrane family member 2 (SIDT2) gene for replication in the entire cohort (n = 1963), using predesigned TaqMan assays. We observed a prevalence of MetS in the HWCS of 52.6%. The minor allele frequency for the variant rs17120425 was 10% and 29% for the rs1784042. The SNP rs1784042 showed an overall association with MetS (OR = 0.82, p = 0.01) and with low levels of high-density lipoprotein (HDL-c) (odds ratio (OR) = 0.77, p = 0.001). The SNP rs17120425 had a significant association with type 2 diabetes (T2D) risk in the overall population (OR = 1.39, p = 0.033). Our results suggest an association of the rs1784042 and rs17120425 variants with MetS, through different mechanisms in the Mexican population. Further studies in larger samples and other populations are required to validate these findings and the relevance of these SNPs in MetS.

Association of Candidate Gene Polymorphism with Metabolic Syndrome among Mongolian Subjects: A Case-Control Study

Metabolic syndrome (MetS) is complex and determined by the interaction between genetic and environmental factors and their influence on obesity, insulin resistance, and related traits associated with diabetes and cardiovascular disease risk. Some dynamic markers, including adiponectin (ADIPOQ), brain-derived neurotrophic factor (BDNF), and lipoprotein lipase (LPL), are implicated in MetS; however, the influence of their genetic variants on MetS susceptibility varies in racial and ethnic groups. We investigated the association of single nucleotide polymorphism (SNP)-SNP interactions among nine SNPs in six genes with MetS's genetic predisposition in Mongolian subjects. A total of 160 patients with MetS for the case group and 144 healthy individuals for the control group were selected to participate in this study. Regression analysis of individual SNPs showed that the ADIPOQ + 45GG (odds ratio (OR) = 2.09, p = 0.011) and P⁺P⁺ of LPL PvuII (OR = 2.10, p = 0.038) carriers had an increased risk of MetS. Conversely, G allele of LPL S447X (OR = 0.45, p = 0.036) and PGC-1α 482Ser (OR = 0.26, p = 0.001) allele were estimated as protective factors, respectively. Moreover, a haplotype containing the G-P⁺-G combination was related to MetS. Significant loci were also related to body mass index (BMI), systolic blood pressure (SBP), serum high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and fasting blood glucose (FBG), adipokines, and insulin as well as insulin resistance (p < 0.05). Our results confirm that ADIPOQ + 45T > G, LPL PvII, and PGC-1α Gly482Ser loci are associated with MetS in Mongolian subjects.

Cardiometabolic risk profile and diet quality among internal migrants in Brazil: a population-based study

PURPOSE

Studies of migrants can improve understanding of the environmental influence on the risk of chronic diseases. In continental countries, internal migration has been associated with changes in diet and health status. The objective of this study is to assess differences in diet quality and the cardiometabolic risk profile between migrants and the host population.

METHODS

A cross-sectional, population-based study was conducted in the city of São Paulo. The study population included internal migrants, defined as individuals born outside São Paulo city who had lived in the city for ten years or longer. The final population (n = 537) was divided into three groups: natives of São Paulo (45.5%), migrants from the Southeast (26.9%) and migrants from the Northeast (27.5%). The joint interim statement consensus criteria were used for diagnosing MetSyn. Diet quality was estimated using the revised version of the Brazilian Healthy Eating Index (BHEI-R). Comparisons between the data of BHEI-R, cardiometabolic risk factors and MetSyn in migrants and natives were performed using generalized linear models adjusted for confounding factors, respectively.

RESULTS

Southeastern and Northeastern migrants younger than 60 years had a higher average of for whole fruit and oil components, respectively. Northeastern migrants older than 60 years had higher systolic and diastolic blood pressure, atherogenic ratio concentrations, lower HDL-C and were more likely to present metabolic syndrome compared to those born in São Paulo of the same age group.

CONCLUSION

Native and internal migrants from Brazil resident in São Paulo exhibited differences in diet quality and cardiometabolic risk factors.

Mitochondrial dysfunction in metabolic syndrome

Metabolic syndrome is co-occurrence of obesity, insulin resistance, atherogenic dyslipidemia (high triglyceride, low high density lipoprotein cholesterol), and hypertension. It is a global health problem. An estimated 20%-30% of adults of the world have metabolic syndrome. Metabolic syndrome is associated with increased risk of type 2 diabetes mellitus, nonalcoholic fatty liver disease, myocardial infarction, and stroke. Thus, it is a major cause of morbidity and mortality worldwide. However, molecular pathogenesis of metabolic syndrome is not well known. Recently, there has been interest in the role of mitochondria in pathogenesis of metabolic problems such as obesity, metabolic syndrome, and type 2 diabetes mellitus. Mitochondrial dysfunction contributes to the oxidative stress and systemic inflammation seen in metabolic syndrome. Role of mitochondria in the pathogenesis of metabolic syndrome is intriguing but far from completely understood. However, a better understanding will be very rewarding as it may lead to novel approaches to control this major public health problem. This brief review explores pathogenesis of metabolic syndrome from a mitochondrial perspective.

Nutritional genomics, inflammation and obesity

The Human Genome Project has significantly broadened our understanding of the molecular aspects regulating the homeostasis and the pathophysiology of different clinical conditions. Consequently, the field of nutrition has been strongly influenced by such improvements in knowledge - especially for determining how nutrients act at the molecular level in different conditions, such as obesity, type 2 diabetes, cardiovascular disease, and cancer. In this manner, characterizing how the genome influences the diet and vice-versa provides insights about the molecular mechanisms involved in chronic inflammation-related diseases. Therefore, the present review aims to discuss the potential application of Nutritional Genomics to modulate obesity-related inflammatory responses. Arch Endocrinol Metab. 2020;64(3):205-22.

Common genetic variation in obesity, lipid transfer genes and risk of Metabolic Syndrome: Results from IDEFICS/I.Family study and meta-analysis

As the prevalence of metabolic syndrome (MetS) in children and young adults is increasing, a better understanding of genetics that underlie MetS will provide critical insights into the origin of the disease. We examined associations of common genetic variants and repeated MetS score from early childhood to adolescence in a pan-European, prospective IDEFICS/I.Family cohort study with baseline survey and follow-up examinations after two and six years. We tested associations in 3067 children using a linear mixed model and confirmed the results with meta-analysis of identified SNPs. With a stringent Bonferroni adjustment for multiple comparisons we obtained significant associations(p < 1.4 × 10⁻⁴) for 5 SNPs, which were in high LD (r² > 0.85) in the 16q12.2 non-coding intronic chromosomal region of FTO gene with strongest association observed for rs8050136 (effect size(β) = 0.31, p_Wald = 1.52 × 10⁻⁵). We also observed a strong association of rs708272 in CETP with increased HDL (p = 5.63 × 10⁻⁴⁰) and decreased TRG (p = 9.60 × 10⁻⁵) levels. These findings along with meta-analysis advance etiologic understanding of childhood MetS, highlighting that genetic predisposition to MetS is largely driven by genes of obesity and lipid metabolism. Inclusion of the associated genetic variants in polygenic scores for MetS may prove to be fundamental for identifying children and subsequently adults of the high-risk group to allow earlier targeted interventions.

Genetic Determinants of Clustering of Cardiometabolic Risk Factors in U.K. Biobank

Objective: The metabolic syndrome (MetS) is a description of a clustering of cardiometabolic risk factors in the same individual. This study searched for genetic loci associated with all five prespecified components of MetS to find a common pathophysiological link for this risk factor clustering. Methods: Using data from 291,107 individuals in the U.K. biobank, a genome-wide association study (GWAS) was performed versus each of the five components of the syndrome as continuous variables (glucose, systolic blood pressure, triglycerides, waist circumference, and high-density lipoprotein-cholesterol). Results: Using false discovery rate <0.05, three loci were related to all five MetS components (rs7575523; nearest gene LINC0112, rs3936511; intron of C5orf67, and rs111970447; intron of GIP). Of those, C5orf67 seems the most interesting candidate for clustering of risk factors, since previous GWASs in other samples have identified this locus as being related to all five risk factors. Also, genetic loci being related to the different combinations of four or three MetS components were presented. Generally, each MetS component combination was related to a unique genetic profile, and the genetic overlap between these combinations was low. Conclusion: A genetic locus was discovered being related to each of the five MetS components, being a candidate for a common pathophysiological link for risk factor clustering. In addition, genetic loci being related to different combinations of four or three MetS components were presented, and the genetic overlap between those combinations of MetS was low.

Whole Genome Resequencing Reveals Selection Signatures Associated With Important Traits in Ethiopian Indigenous Goat Populations

Ethiopia is considered as the main gateway for the introduction of livestock species, including goat, to the African continent. Ethiopian goats are characterized by their unique adaptive ability, and different physical characteristics in terms of morphology, body size, coat colors, and other important traits. The comparative population genomic analysis provides useful genomic information associated with important traits. Whole-genome resequencing of 44 Ethiopian indigenous goats produced 16 million single-nucleotide polymorphisms (SNPs) as well as 123,577 insertions and deletions. Specifically, 11,137,576, 10,760,581, 10,833,847, 12,229,657 and 10,749,996 putative SNPs were detected in Abergelle, Afar, Begait, Central Highland and Meafure goat populations, respectively. In this study, we used population differentiation (F_ST) and pooled heterozygosity (H_P) Cbased approaches. From the F_ST analysis, we identified 480 outlier windows. The H_P approach detected 108 and 205 outlier windows for Abergelle, and Begait, respectively. About 11 and 5 genes under selective signals were common for both approaches that were associated with important traits. After genome annotation, we found 41 Gene ontology (GO) terms (12 in biological processes, 8 in cellular components and 11 in the molecular function) and 10 Kyoto Encyclopedia of Genes and Genomes pathways. Several of the candidate genes are involved in the reproduction, body weight, fatty acids, and disease related traits. Our investigation contributes to deliver valuable genetic information and paves the way to design conservation strategy, breed management, genetic improvement, and utilization programs. The genomic resources generated in the study will offer an opportunity for further investigations.

Genome-Wide Association Analyses of Equine Metabolic Syndrome Phenotypes in Welsh Ponies and Morgan Horses

Equine metabolic syndrome (EMS) is a complex trait for which few genetic studies have been published. Our study objectives were to perform within breed genome-wide association analyses (GWA) to identify associated loci in two high-risk breeds, coupled with meta-analysis to identify shared and unique loci between breeds. GWA for 12 EMS traits identified 303 and 142 associated genomic regions in 264 Welsh ponies and 286 Morgan horses, respectively. Meta-analysis demonstrated that 65 GWA regions were shared across breeds. Region boundaries were defined based on a fixed-size or the breakdown of linkage disequilibrium, and prioritized if they were: shared between breeds or across traits (high priority), identified in a single GWA cohort (medium priority), or shared across traits with no SNPs reaching genome-wide significance (low priority), resulting in 56 high, 26 medium, and seven low priority regions including 1853 candidate genes in the Welsh ponies; and 39 high, eight medium, and nine low priority regions including 1167 candidate genes in the Morgans. The prioritized regions contained protein-coding genes which were functionally enriched for pathways associated with inflammation, glucose metabolism, or lipid metabolism. These data demonstrate that EMS is a polygenic trait with breed-specific risk alleles as well as those shared across breeds.

Genome-Wide Association Study of the Metabolic Syndrome in UK Biobank

Background: The metabolic syndrome (MetS) is a description of a clustering of cardiometabolic risk factors in the same individual. Previous genome-wide association studies (GWASs) have identified 29 independent genetic loci linked to MetS as a binary trait. This study used data from UK biobank to search for additional loci. Methods: Using data from 291,107 individuals in the UK biobank, a GWAS was performed versus the binary trait MetS (harmonized NCEP criteria). Results: In a GWAS of MetS (binary) we found 93 independent loci with P < 5 × 10^-8, of which 80 were not identified in previous GWASs of MetS. However, the majority of those loci have previously been associated with one or more of the five MetS components. Of particular interest are the genes being related to MetS (binary) in this study, but not to any of the MetS components in past studies, such as WDR48, KLF14, NAADL1, GADD45G, and OR5R1, as well as the two loci that have been associated with all five MetS components in past studies, SNX10 and C5orf67. A pathway analysis of the 93 independent loci showed the immune system, transportation of small molecules, and metabolism to be enriched. Conclusion: This GWAS of the MetS in UK biobank identified several new loci being associated with MetS. Most of those have previously been found to be associated with different components of MetS, but several loci were found not previously linked to cardiometabolic disease.

Impact of demography and population dynamics on the genetic architecture of human longevity

The study of the genetics of longevity has been mainly addressed by GWASs that considered subjects from different populations to reach higher statistical power. The "price to pay" is that population-specific evolutionary histories and trade-offs were neglected in the investigation of gene-environment interactions. We propose a new “diachronic” approach that considers processes occurred at both evolutionary and lifespan timescales. We focused on a well-characterized population in terms of evolutionary history (i.e. Italians) and we generated genome-wide data for 333 centenarians from the peninsula and 773 geographically-matched healthy individuals. Obtained results showed that: (i) centenarian genomes are enriched for an ancestral component likely shaped by pre-Neolithic migrations; (ii) centenarians born in Northern Italy unexpectedly clustered with controls from Central/Southern Italy suggesting that Neolithic and Bronze Age gene flow did not favor longevity in this population; (iii) local past adaptive events in response to pathogens and targeting arachidonic acid metabolism became favorable for longevity; (iv) lifelong changes in the frequency of several alleles revealed pleiotropy and trade-off mechanisms crucial for longevity. Therefore, we propose that demographic history and ancient/recent population dynamics need to be properly considered to identify genes involved in longevity, which can differ in different temporal/spatial settings.

Genome-Wide Association Study of Metabolic Syndrome Reveals Primary Genetic Variants at CETP Locus in Indians

Indians, a rapidly growing population, constitute vast genetic heterogeneity to that of Western population; however they have become a sedentary population in past decades due to rapid urbanization ensuing in the amplified prevalence of metabolic syndrome (MetS). We performed a genome-wide association study (GWAS) of MetS in 10,093 Indian individuals (6,617 MetS and 3,476 controls) of Indo-European origin, that belong to our previous biorepository of The Indian Diabetes Consortium (INDICO). The study was conducted in two stages-discovery phase (N = 2,158) and replication phase (N = 7,935). We discovered two variants within/near the CETP gene-rs1800775 and rs3816117-associated with MetS at genome-wide significance level during replication phase in Indians. Additional CETP loci rs7205804, rs1532624, rs3764261, rs247617, and rs173539 also cropped up as modest signals in Indians. Haplotype association analysis revealed GCCCAGC as the strongest haplotype within the CETP locus constituting all seven CETP signals. In combined analysis, we perceived a novel and functionally relevant sub-GWAS significant locus-rs16890462 in the vicinity of SFRP1 gene. Overlaying gene regulatory data from ENCODE database revealed that single nucleotide polymorphism (SNP) rs16890462 resides in repressive chromatin in human subcutaneous adipose tissue as characterized by the enrichment of H3K27me3 and CTCF marks (repressive gene marks) and diminished H3K36me3 marks (activation gene marks). The variant displayed active DNA methylation marks in adipose tissue, suggesting its likely regulatory activity. Further, the variant also disrupts a potential binding site of a key transcription factor, NRF2, which is known for involvement in obesity and metabolic syndrome.

Post genome-wide gene-environment interaction study: The effect of genetically driven insulin resistance on breast cancer risk using Mendelian randomization

PURPOSE

The role of insulin resistance (IR) in developing postmenopausal breast cancer has not been thoroughly resolved and may be confounded by lifestyle factors such as obesity. We examined whether genetically determined IR is causally associated with breast cancer risk.

METHODS

We conducted Mendelian randomization (MR) analyses using individual-level data from our previous meta-analysis of a genome-wide association study (GWAS) (n = 11,109 non-Hispanic white postmenopausal women). Four single-nucleotide polymorphisms were associated with fasting glucose (FG), 2 with fasting insulin (FI), and 6 with homeostatic model assessment-IR (HOMA-IR) but were not associated with obesity. We used this GWAS to employ hazard ratios (HRs) for breast cancer risk by adjusting for potential confounding factors.

RESULTS

No direct association was observed between comprising 12 IR genetic instruments and breast cancer risk (HR = 0.93, 95% CI: 0.76-1.14). In phenotype-specific analysis, genetically elevated FG was associated with reduced risk for breast cancer (main contributor of this MR-effect estimate: G6PC2 rs13431652; HR = 0.59, 95% CI: 0.35-0.99). Genetically driven FI and HOMA-IR were not significantly associated. Stratification analyses by body mass index, exercise, and dietary fat intake with combined phenotypes showed that genetically elevated IR was associated with greater breast cancer risk in overall obesity and inactive subgroups (single contributor: MTRR/LOC729506 rs13188458; HR = 2.21, 95% CI: 1.03-4.75).

CONCLUSIONS

We found complex evidence for causal association between IR and risk of breast cancer, which may support the potential value of intervention trials to lower IR and reduce breast cancer risk.

Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

Background

Metabolic syndrome (MetS), defined as a cluster of metabolic risk factors including dyslipidemia, insulin-resistance, and elevated blood pressure, has been known as partly heritable. MetS effects the lives of many people worldwide, yet females have been reported to be more vulnerable to this cluster of risks.

Methods

To elucidate genetic variants underlying MetS specifically in females, we performed a genome-wide association study (GWAS) for MetS as well as its component traits in a total of 9932 Korean female subjects (including 2276 MetS cases and 1692 controls). To facilitate the prediction of MetS in females, we calculated a genetic risk score (GRS) combining 14 SNPs detected in our GWA analyses specific for MetS.

Results

GWA analyses identified 14 moderate signals (P_meta < 5X10^− 5) specific to females for MetS. In addition, two genome-wide significant female-specific associations (P_meta < 5X10^− 8) were detected for rs455489 in DSCAM for fasting plasma glucose (FPG) and for rs7115583 in SIK3 for high-density lipoprotein cholesterol (HDLC). Logistic regression analyses (adjusted for area and age) between the GRS and MetS in females indicated that the GRS was associated with increased prevalence of MetS in females (P = 5.28 × 10^− 14), but not in males (P = 3.27 × 10^− 1). Furthermore, in the MetS prediction models using GRS, the area under the curve (AUC) of the receiver operating characteristics (ROC) curve was higher in females (AUC = 0.85) than in males (AUC = 0.57).

Conclusion

This study highlights new female-specific genetic variants associated with MetS and its component traits and suggests that the GRS of MetS variants is a likely useful predictor of MetS in females.

Electronic supplementary material

The online version of this article (10.1186/s12881-019-0830-y) contains supplementary material, which is available to authorized users.

Exome chip-driven association study of lipidemia in >14,000 Koreans and evaluation of genetic effect on identified variants between different ethnic groups

Lipid levels in blood are widely used to diagnose and monitor chronic diseases. It is essential to identify the genetic traits involved in lipid metabolism for understanding chronic diseases. However, the influence of genetic traits varies depending on race, sex, age, and ethnicity. Therefore, research focusing on populations of individual countries is required, and the results can be used as a basis for comparison of results of other studies at the cross-racial and cross-country levels. In the present study, we selected lipid-related variants and evaluated their effects on lipid-related diseases in more than 14,000 subjects of three cohorts using the Illumina Human Exome Beadchip. A genome-wide association study was conducted using EPACTs after adjusting for age, sex, and recruitment area. A genome-wide significance cutoff was defined as p < 5E-08 in all the three cohorts. Sixteen variants represented the lipid traits and were classified as vulnerable to borderline hypertriglyceridemia, hyper-LDL-cholesterolemia, or hypo-HDL-cholesterolemia. Moreover, we compared the genetic effects of the 16 variants between ethnic groups and identified the missense variants in apolipoprotein A-V, cholesterol ester transfer protein, and apolipoprotein E as Asian-specific. Our study provides candidate genes as markers for chronic diseases through the evaluation of genetic effects.

Altered gene expression in antipsychotic-induced weight gain

Antipsychotic drugs are one of the largest types of prescribed drugs. However, antipsychotic-induced weight gain (AIWG) is a major problem for the patients. AIWG increases cardiovascular and cerebrovascular morbidity and mortality, and reduces quality of life and drug compliance. To characterize changes in gene expression related to AIWG, we sequenced total messenger RNA from the blood samples of two groups of schizophrenia patients before and after 3 months of treatment with antipsychotics. The "weight gain" group was defined by an increase of body mass index (BMI) >1.5 points (18 patients; median BMI increase = 2.69) and the "no weight gain" group was defined by a change of BMI between <1.0 and >-1.0 points (18 patients; median BMI increase = 0.26). We found 115 genes with significant differential expression in the weight gain group before and after medication and 156 in the no weight gain group before and after medication. The weight gain group was significantly enriched with genes related to "obesity" and "BMI" (Fisher; p = 0.0002 and 0.01, respectively) according to the Gene Reference into Function (GeneRIF) database. In the no weight gain group, the enrichment was much smaller (Fisher; p = 0.02 and 0.79). This study is a first step toward detecting genetic factors that cause AIWG and to generating prediction tests in future studies with larger data sets.

Dissecting features of epigenetic variants underlying cardiometabolic risk using full-resolution epigenome profiling in regulatory elements

Sparse profiling of CpG methylation in blood by microarrays has identified epigenetic links to common diseases. Here we apply methylC-capture sequencing (MCC-Seq) in a clinical population of ~200 adipose tissue and matched blood samples (Ntotal~400), providing high-resolution methylation profiling (>1.3 M CpGs) at regulatory elements. We link methylation to cardiometabolic risk through associations to circulating plasma lipid levels and identify lipid-associated CpGs with unique localization patterns in regulatory elements. We show distinct features of tissue-specific versus tissue-independent lipid-linked regulatory regions by contrasting with parallel assessments in ~800 independent adipose tissue and blood samples from the general population. We follow-up on adipose-specific regulatory regions under (1) genetic and (2) epigenetic (environmental) regulation via integrational studies. Overall, the comprehensive sequencing of regulatory element methylomes reveals a rich landscape of functional variants linked genetically as well as epigenetically to plasma lipid traits.

A decade in psychiatric GWAS research

After more than 10 years of accumulated efforts, genome-wide association studies (GWAS) have led to many findings, most of which have been deposited into the GWAS Catalog. Between GWAS's inception and March 2017, the GWAS Catalog has collected 2429 studies, 1818 phenotypes, and 28,462 associated SNPs. We reclassified the psychology-related phenotypes into 217 reclassified phenotypes, which accounted for 514 studies and 7052 SNPs. In total, 1223 of the SNPs reached genome-wide significance. Of these, 147 were replicated for the same psychological trait in different studies. Another 305 SNPs were replicated within one original study. The SNPs rs2075650 and rs4420638 were linked to the most replications within a single reclassified phenotype or very similar reclassified phenotypes; both were associated with Alzheimer's disease (AD). Schizophrenia was associated with 74 within-phenotype SNPs reported in independents studies. Alzheimer's disease and schizophrenia were both linked to some physical phenotypes, including cholesterol and body mass index, through common GWAS signals. Alzheimer's disease also shared risk SNPs with age-related phenotypes such as age-related macular degeneration and longevity. Smoking-related SNPs were linked to lung cancer and respiratory function. Alcohol-related SNPs were associated with cardiovascular and digestive system phenotypes and disorders. Two separate studies also identified a shared risk SNP for bipolar disorder and educational attainment. This review revealed a list of reproducible SNPs worthy of future functional investigation. Additionally, by identifying SNPs associated with multiple phenotypes, we illustrated the importance of studying the relationships among phenotypes to resolve the nature of their causal links. The insights within this review will hopefully pave the way for future evidence-based genetic studies.

Nuclear genetic regulation of the human mitochondrial transcriptome

Mitochondria play important roles in cellular processes and disease, yet little is known about how the transcriptional regime of the mitochondrial genome varies across individuals and tissues. By analyzing >11,000 RNA-sequencing libraries across 36 tissue/cell types, we find considerable variation in mitochondrial-encoded gene expression along the mitochondrial transcriptome, across tissues and between individuals, highlighting the importance of cell-type specific and post-transcriptional processes in shaping mitochondrial-encoded RNA levels. Using whole-genome genetic data we identify 64 nuclear loci associated with expression levels of 14 genes encoded in the mitochondrial genome, including missense variants within genes involved in mitochondrial function (TBRG4, MTPAP and LONP1), implicating genetic mechanisms that act in trans across the two genomes. We replicate ~21% of associations with independent tissue-matched datasets and find genetic variants linked to these nuclear loci that are associated with cardio-metabolic phenotypes and Vitiligo, supporting a potential role for variable mitochondrial-encoded gene expression in complex disease.

Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease.

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.

An in vivo screen for neuronal genes involved in obesity identifies Diacylglycerol kinase as a regulator of insulin secretion

Objective

Obesity is a complex disorder involving many genetic and environmental factors that are required to maintain energy homeostasis. While studies in human populations have led to significant progress in the generation of an obesity gene map and broadened our understanding of the genetic basis of common obesity, there is still a large portion of heritability and etiology that remains unknown. Here, we have used the genetically tractable fruit fly, Drosophila melanogaster, to identify genes/pathways that function in the nervous system to regulate energy balance.

Methods

We performed an in vivo RNAi screen in Drosophila neurons and assayed for obese or lean phenotypes by measuring changes in levels of stored fats (in the form of triacylglycerides or TAG). Three rounds of screening were performed to verify the reproducibility and specificity of the adiposity phenotypes. Genes that produced >25% increase in TAG (206 in total) underwent a second round of screening to verify their effect on TAG levels by retesting the same RNAi line to validate the phenotype. All remaining hits were screened a third time by testing the TAG levels of additional RNAi lines against the genes of interest to rule out any off-target effects.

Results

We identified 24 genes including 20 genes that have not been previously associated with energy homeostasis. One identified hit, Diacylglycerol kinase (Dgk), has mammalian homologues that have been implicated in genome-wide association studies for metabolic defects. Downregulation of neuronal Dgk levels increases TAG and carbohydrate levels and these phenotypes can be recapitulated by reducing Dgk levels specifically within the insulin-producing cells that secrete Drosophila insulin-like peptides (dILPs). Conversely, overexpression of kinase-dead Dgk, but not wild-type, decreased circulating dILP2 and dILP5 levels resulting in lower insulin signalling activity. Despite having higher circulating dILP levels, Dgk RNAi flies have decreased pathway activity suggesting that they are insulin-resistant.

Conclusion

Altogether, we have identified several genes that act within the CNS to regulate energy homeostasis. One of these, Dgk, acts within the insulin-producing cells to regulate the secretion of dILPs and energy homeostasis in Drosophila.

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RNAi screen in neurons identifies 24 regulators of energy homeostasis.

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One of the hits, Dgk, affects lipid and carbohydrate homeostasis.

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Dgk acts within the IPCs to regulate dILP secretion and insulin signalling activity.