Variant rs10911021 that associates with coronary heart disease in type 2 diabetes, is associated with lower concentrations of circulating HDL cholesterol and large HDL particles but not with amino acids

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Massive Loss of Transcription Factors Promotes the Initial Diversification of Placental Mammals

As one of the most successful group of organisms, mammals occupy a variety of niches on Earth as a result of macroevolution. Transcription factors (TFs), the fundamental regulators of gene expression, may also have evolved. To examine the relationship between TFs and mammalian macroevolution, we analyzed 140,821 de novo-identified TFs and their birth and death histories from 96 mammalian species. Gene tree vs. species tree reconciliation revealed that placental mammals experienced an upsurge in TF losses around 100 million years ago (Mya) and also near the Cretaceous–Paleogene boundary (K–Pg boundary, 66 Mya). Early Euarchontoglires, Laurasiatheria and marsupials appeared between 100 and 95 Mya and underwent initial diversification. The K-Pg boundary was associated with the massive extinction of dinosaurs, which lead to adaptive radiation of mammals. Surprisingly, TF loss decelerated, rather than accelerated, molecular evolutionary rates of their target genes. As the rate of molecular evolution is affected by the mutation rate, the proportion of neutral mutations and the population size, the decrease in molecular evolution may reflect increased functional constraints to survive target genes.

Systems epidemiology of metabolomics measures reveals new relationships between lipoproteins and other small molecules

INTRODUCTION

The study of lipoprotein metabolism at the population level can provide valuable information for the organisation of lipoprotein related processes in the body. To use this information towards interventional hypotheses generation and testing, we need to be able to identify the mechanistic connections among the large number of observed correlations between the measured components of the system.

OBJECTIVES

To use population level metabolomics information to gain insight on their biochemical networks and metabolism.

METHODS

Genetic and metabolomics information for 230 metabolic measures, predominately lipoprotein related, from a targeted nuclear magnetic resonance approach, in two samples of an established European cohort, totalling more than 9400 individuals analysed using phenotypic and genetic correlations, as well as Mendelian Randomisation.

RESULTS

More than 20,500 phenotypic correlations were identified in the data, with almost 2000 also showing evidence of strong genetic correlation. Mendelian randomisation, provided evidence for a causal effect between 9496 pairs of metabolic measures, mainly between lipoprotein traits. The results provide insights on the organisation of lipoproteins in three distinct classes, the heterogeneity between HDL particles, and the association, or lack of, between CLA, glycolysis markers, such as glucose and citrate, and glycoproteins with lipids subfractions. Two examples for the use of the approach in systems biology of lipoproteins are presented.

CONCLUSIONS

Genetic variation can be used to infer the underlying mechanisms for the associations between lipoproteins for hypothesis generation and confirmation, and, together with biological information, to map complex biological processes.

Genetics of macrovascular complications in type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a metabolic disorder that currently affects more than 400 million worldwide and is projected to cause 552 million cases by the year 2030. Long-term vascular complications, such as coronary artery disease, myocardial infarction, stroke, are the leading causes of morbidity and mortality among diabetic patients. The recent advances in genome-wide technologies have given a powerful impetus to the study of risk markers for multifactorial diseases. To date, the role of genetic and epigenetic factors in modulating susceptibility to T2DM and its vascular complications is being successfully studied that provides the accumulation of genomic knowledge. In the future, this will provide an opportunity to reveal the pathogenetic pathways in the development of the disease and allow to predict the macrovascular complications in T2DM patients. This review is focused on the evidence of the role of genetic variants and epigenetic changes in the development of macrovascular pathology in diabetic patients.

ACE2 polymorphisms associated with cardiovascular risk in Uygurs with type 2 diabetes mellitus

Background

Type 2 diabetes mellitus (T2D), rapidly increasing to epidemic proportions, globally escalates cardiovascular disease risk. Although intensive interventions and comprehensive management of environmental risks factors for T2D are associated with reduced cardiovascular disease, such approaches are limited for individuals with high genetic T2D risk. In this study we investigated possible associations of ACE2 polymorphisms and cardiovascular risks in Uygur patients with T2D.

Methods

275 Uygur T2D patients and 272 non-diabetic Uygur individuals were enrolled as study participants. 14 ACE2 polymorphisms were genotyped by Matrix-assisted laser desorption ionization time-of-flight mass spectrometry.

Results

ACE2 SNP rs1978124, rs2048683, rs2074192, rs233575, rs4240157, rs4646156, rs4646188 and rs879922 were associated with T2D (all P < 0.05). The 8 diabetic risk related ACE2 SNPs were further associated with diabetic related cardiovascular complications or events but exhibited heterogeneity as fellows: firstly, almost all diabetic risk related ACE2 SNPs (all P < 0.05) were associated with increased SBP except rs1978124 and rs2074192, while rs2074192, rs4646188 and rs879922 were associated elevated DBP (all P < 0.05). Secondly, SNP rs4646188 was not correlated with any type of dyslipidemia (TRIG, HDL-C, LDL-C or CHOL), and the other 7 diabetic risk related loci were at least correlated with one type of dyslipidemia (all P < 0.05). In particular, rs879922 were simultaneously correlated with four type of dyslipidemia (all P < 0.05). Thirdly, ACE2 SNP rs2074192 and rs879922 were associated with carotid arteriosclerosis stenosis (CAS) ≥ 50% (both P < 0.05). Fourthly, ACE2 SNP rs2074192, rs4240157, rs4646188 and 879922 were associated with increased MAU (all P < 0.05). In addition, ACE2 SNP rs2048683, rs4240157, rs4646156, rs4646188 and rs879922 were linked to heavier LVMI (all P < 0.05), but only rs4240157, rs4646156 and rs4646188 were associated with lower LVEF (all P < 0.05).

Conclusion

ACE2 SNP rs879922 may be a common genetic loci and optimal genetic susceptibility marker for T2D and T2D related cardiovascular risks in Uygurs.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0771-3) contains supplementary material, which is available to authorized users.

Type 2 Diabetes Mellitus and Cardiovascular Disease: Genetic and Epigenetic Links

Type 2 diabetes mellitus (DM) is a common metabolic disorder predisposing to diabetic cardiomyopathy and atherosclerotic cardiovascular disease (CVD), which could lead to heart failure through a variety of mechanisms, including myocardial infarction and chronic pressure overload. Pathogenetic mechanisms, mainly linked to hyperglycemia and chronic sustained hyperinsulinemia, include changes in metabolic profiles, intracellular signaling pathways, energy production, redox status, increased susceptibility to ischemia, and extracellular matrix remodeling. The close relationship between type 2 DM and CVD has led to the common soil hypothesis, postulating that both conditions share common genetic and environmental factors influencing this association. However, although the common risk factors of both CVD and type 2 DM, such as obesity, insulin resistance, dyslipidemia, inflammation, and thrombophilia, can be identified in the majority of affected patients, less is known about how these factors influence both conditions, so that efforts are still needed for a more comprehensive understanding of this relationship. The genetic, epigenetic, and environmental backgrounds of both type 2 DM and CVD have been more recently studied and updated. However, the underlying pathogenetic mechanisms have seldom been investigated within the broader shared background, but rather studied in the specific context of type 2 DM or CVD, separately. As the precise pathophysiological links between type 2 DM and CVD are not entirely understood and many aspects still require elucidation, an integrated description of the genetic, epigenetic, and environmental influences involved in the concomitant development of both diseases is of paramount importance to shed new light on the interlinks between type 2 DM and CVD. This review addresses the current knowledge of overlapping genetic and epigenetic aspects in type 2 DM and CVD, including microRNAs and long non-coding RNAs, whose abnormal regulation has been implicated in both disease conditions, either etiologically or as cause for their progression. Understanding the links between these disorders may help to drive future research toward an integrated pathophysiological approach and to provide future directions in the field.

Circulating sortilin level as a potential biomarker for coronary atherosclerosis and diabetes mellitus

Context

A previous genome-wide association study showed that a genetic variant of sortilin was associated with the risk of coronary artery disease (CAD). However, the role of circulating sortilin is still unknown. We investigated the potential role of plasma sortilin as a biomarker for CAD and diabetes mellitus.

Methods

We enrolled statin-naïve subjects with CAD (n = 31) who underwent coronary artery bypass surgery and control subjects (n = 116) who were free from CAD as evaluated by coronary CT angiography. The presence of diabetes mellitus was evaluated and plasma sortilin levels were measured with a commercial ELISA kit.

Results

Plasma sortilin levels were higher in subjects with CAD and subjects with diabetes mellitus than in those without CAD or diabetes mellitus. Subjects in the highest sortilin tertile group were older and had higher glucose and HbA1c levels, but lipid profiles in the three tertile groups were comparable. Multivariable logistic regression analysis revealed that sortilin levels were independently associated with CAD. In addition, the receiver operating characteristic curve analysis showed that plasma sortilin levels could identify the presence of CAD or diabetes mellitus.

Conclusions

Elevated circulating sortilin levels are associated with CAD and diabetes mellitus and can be used as a biomarker of both diseases in statin-naïve subjects.

Mechanistic insights from combining genomics with metabolomics

PURPOSE OF REVIEW

Metabolomics directly measure substrates and products of biological processes and pathways. Based on instrumentation and throughput advances, the use of metabolomics has only recently become feasible at the population level. This has led to an intense interest in using the new information in combination with genomics, and other omics technologies, to give biological context to the rapidly accumulating associations between genes and diseases or their risk factors.

RECENT FINDINGS

The use of metabolomics-genomic associations for the metabolic characterization of genes of interest has confirmed known pathways and permitted the identification of new ones. These include the unknown metabolite X12063 linking statins to myopathies, the role of glycerophospholipids in cholesterol metabolism, the structure of lipoprotein (a), the lipoprotein lipase-independent effect of Apolipoprotein C-III coding and the role of branched chain amino acids in the antagonistic coregulation of levels of HDLs and triglyceride.

SUMMARY

The findings reviewed illustrate the importance of integrating metabolomics and genomics for the greater understanding of biological mechanisms. The limitations of the current approaches are also discussed together with approaches that will be required to make the most of the current multiomics data available.

Genetic risk analysis of coronary artery disease in Pakistani subjects using a genetic risk score of 21 variants

BACKGROUND AND AIMS

Conventional coronary artery disease (CAD) risk factors like age, gender, blood lipids, hypertension and smoking have been the basis of CAD risk prediction algorithms, but provide only modest discrimination. Genetic risk score (GRS) may provide improved discrimination over and above conventional risk factors. Here we analyzed the genetic risk of CAD in subjects from Pakistan, using a GRS of 21 variants in 18 genes and examined whether the GRS is associated with blood lipid levels.

METHODS

625 (405 cases and 220 controls) subjects were genotyped for variants, NOS3 rs1799983, SMAD3 rs17228212, APOB rs1042031, LPA rs3798220, LPA rs10455872, SORT1 rs646776, APOE rs429358, GLUL rs10911021, FTO rs9939609, MIA3 rs17465637, CDKN2Ars10757274, DAB2IP rs7025486, CXCL12 rs1746048, ACE rs4341, APOA5 rs662799, CETP rs708272, MRAS rs9818870, LPL rs328, LPL rs1801177, PCSK9 rs11591147 and APOE rs7412 by TaqMan and KASPar allele discrimination techniques.

RESULTS

Individually, the single SNPs were not associated with CAD except APOB rs1042031 and FTO rs993969 (p = 0.01 and 0.009 respectively). However, the combined GRS of 21 SNPs was significantly higher in cases than controls (19.37 ± 2.56 vs. 18.47 ± 2.45, p = 2.9 × 10^-5), and compared to the bottom quintile, CAD risk in the top quintile of the GRS was 2.96 (95% CI 1.71-5.13). Atherogenic blood lipids showed significant positive association with GRS.

CONCLUSIONS

The GRS was quantitatively associated with CAD risk and showed association with blood lipid levels, suggesting that the mechanism of these variants is likely to be, in part at least, through creating an atherogenic lipid profile in subjects carrying high numbers of risk alleles.