Role of TCF7L2 and PPARG2 Gene Polymorphisms in Renal and Cardiovascular Complications among Patients with Type 2 Diabetes: A Cohort Study

Genetic variants related to insulin metabolism are associated with gestational diabetes mellitus

The current study sought to investigate the associations of common genetic risk variants with gestational diabetes mellitus (GDM) risk in the north Indian population and to evaluate their utility in identifying GDM cases. A case-control study, including 300 pregnant women, was included, and clinical and pathological information was collected. The amplification-refractory mutation system (ARMS) was used for genotyping four single nucleotide polymorphisms (SNPs), namely FTO (rs9939609), PPARG2 (rs1801282), SLC30A8 (rs13266634), and TCF7L2 (rs12255372). The odds ratio and confidence interval were determined for each SNP in different genetic models. Further, attributable risk, population penetrance, and relative risk were also calculated. The risk allele A of FTO (rs9939609) poses a two times higher risk of GDM (p = 0.02, OR = 2.5). The CG and GG genotypes of PPARG2 (rs1801282) have half a lower risk of GDM. In SLC30A8 (rs13266634), the recessive model analysis showed a two times higher risk of having GDM, while the recessive model (TT vs. GG + GT) analysis in TCF7L2 (rs12255372) indicates a lower risk of GDM. Finally, the relative risk, population penetrance, and attributable risk for risk allele in all four variants was higher in GDM mothers. All four polymorphisms were found to be significantly associated with BMI, HbA1c, and insulin. Our study first time confirmed a significant association with GDM for four variants, FTO, PPARG2, SLC30A8, and TCF7L2, in the North Indian population.

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.

PPARγ Gene Polymorphisms, Metabolic Disorders, and Coronary Artery Disease

Being activated by endogenous and exogenous ligands, nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) enhances insulin sensitivity, promotes adipocyte differentiation, stimulates adipogenesis, and has the properties of anti-atherosclerosis, anti-inflammation, and anti-oxidation. The Human PPARγ gene (PPARG) contains thousands of polymorphic loci, among them two polymorphisms (rs10865710 and rs7649970) in the promoter region and two polymorphisms (rs1801282 and rs3856806) in the exonic region were widely reported to be significantly associated with coronary artery disease (CAD). Mechanistically, PPARG polymorphisms lead to abnormal expression of PPARG gene and/or dysfunction of PPARγ protein, causing metabolic disorders such as hypercholesterolemia and hypertriglyceridemia, and thereby increasing susceptibility to CAD.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.