ANGPTL8 Gene Polymorphism rs2278426 Is Related to Carotid Intima-Media Thickness in T2DM

Angiopoietin-Like Proteins: Cardiovascular Biology and Therapeutic Targeting for the Prevention of Cardiovascular Diseases

Despite the best pharmacologic tools available, cardiovascular diseases (CVDs) remain a major cause of morbidity and mortality in developed countries. After 2 decades of research, new therapeutic targets, such as angiopoietin-like proteins (ANGPTLs), are emerging. ANGPTLs belong to a family of 8 members, from ANGPTL1 to ANGPTL8; they have structural homology with angiopoietins and are secreted in the circulation. ANGPTLs display a multitude of physiological and pathologic functions; they contribute to inflammation, angiogenesis, cell death, senescence, hematopoiesis, and play a role in repair, maintenance, and tissue homeostasis. ANGPTLs-particularly the triad ANGPTL3, 4, and 8-have an established role in lipid metabolism through the regulation of triacylglycerol trafficking according to the nutritional status. Some ANGPTLs also contribute to glucose metabolism. Therefore, dysregulation in ANGPTL expression associated with abnormal circulating levels are linked to a plethora of CVD and metabolic disorders including atherosclerosis, heart diseases, diabetes, but also obesity and cancers. Because ANGPTLs bind to different receptors according to the cell type, antagonists are therapeutically inadequate. Recently, direct inhibitors of ANGPTLs, mainly ANGPTL3, have been developed, and specific monoclonal antibodies and antisense oligonucleotides are currently being tested in clinical trials. The aim of the current review is to provide an up-to-date preclinical and clinical overview on the function of the 8 members of the ANGPTL family in the cardiovascular system, their contribution to CVD, and the therapeutic potential of manipulating some of them.

Inflammation and endothelial function-related gene polymorphisms are associated with carotid atherosclerosis-A study of community population in Southwest China

OBJECTIVES

To investigate the relationships between 18 single nucleotide polymorphisms with carotid atherosclerosis and whether interactions among these genes were associated with an increased risk of carotid atherosclerosis.

METHODS

Face-to-face surveys were conducted with individuals aged 40 or older in eight communities. A total of 2377 individuals were included in the study. Ultrasound was used to detect carotid atherosclerosis in the included population. 18 loci of 10 genes associated with inflammation and endothelial function were detected. Gene-gene interactions were analyzed using generalized multifactor dimensionality reduction (GMDR).

RESULTS

Among the 2377 subjects, 445 (18.7%) subjects had increased intima-media thickness in the common carotid artery (CCA-IMT), and 398 (16.7%) subjects were detected with vulnerable plaque. In addition, NOS2A rs2297518 polymorphism was associated with increased CCA-IMT, IL1A rs1609682, and HABP2 rs7923349 polymorphisms were associated with vulnerable plaque. Besides, GMDR analysis showed significant gene-gene interactions among TNFSF4 rs1234313, IL1A rs1609682, TLR4 rs1927911, ITGA2 rs1991013, NOS2A rs2297518, IL6R rs4845625, ITGA2 rs4865756, HABP2 rs7923349, NOS2A rs8081248, HABP2 rs932650.

CONCLUSION

The prevalences of increased CCA-IMT and vulnerable plaque were high in Southwestern China's high-risk stroke population. Furthermore, inflammation and endothelial function-related gene polymorphisms were associated with carotid atherosclerosis.

SLC2A9 rs1014290 Polymorphism is Associated with Prediabetes and Type 2 Diabetes

PURPOSE

To investigate the association of the A/G rs1014290 polymorphism in SLC2A9 with type 2 diabetes (T2DM) and prediabetes mellitus (pre-DM). Patients and Methods. We enrolled 1058 patients who attended the Hebei General Hospital, Shijiazhuang, Hebei Province, China. The patients underwent general testing and oral glucose tolerance tests and were divided into three groups: 352 patients newly diagnosed with T2DM, 358 patients with pre-DM, and 348 healthy controls. The single nucleotide polymorphism (SNP) was detected by ligase detection reactions. The χ ² test, one-way ANOVA, and binary logistic regression analysis were used to analyze the results.

RESULTS

In the T2DM group, the GG genotype frequency at the rs1014290 locus was significantly lower (14.8%) than it was in the healthy controls. Furthermore, the GG genotype group was associated with a reduced risk of T2DM in unadjusted and confounder-adjusted models compared with the risk in the AA genotype group. The G allele in the SLC2A9 rs1014290 locus decreased susceptibility to T2DM. In the pre-DM group, the GG and AG genotype groups had no significant correlation with the risk of pre-DM in any of the models. In the T2DM group, the uric acid level was significantly lower in the GG genotype group. In the T2DM and pre-DM groups, the HOMA-β levels were significantly higher in the GA (P  < 0.001) and GG (P  < 0.001) genotype groups than it was in the AA genotype group, and HOMA-IR was significantly lower in the GA (P  < 0.001) and GG (P  < 0.001) genotype groups than it was in the AA genotype group.

CONCLUSION

The A/G (rs1014290) SNP in SLC2A9 is closely related to the occurrence and development of diabetes.

Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning

Background

Arterial stiffness assessed by pulse wave velocity is a major risk factor for cardiovascular diseases. The incidence of cardiovascular events remains high in diabetics. However, a clinical prediction model for elevated arterial stiffness using machine learning to identify subjects consequently at higher risk remains to be developed.

Methods

Least absolute shrinkage and selection operator and support vector machine-recursive feature elimination were used for feature selection. Four machine learning algorithms were used to construct a prediction model, and their performance was compared based on the area under the receiver operating characteristic curve metric in a discovery dataset (n = 760). The model with the best performance was selected and validated in an independent dataset (n = 912) from the Dryad Digital Repository (https://doi.org/10.5061/dryad.m484p). To apply our model to clinical practice, we built a free and user-friendly web online tool.

Results

The predictive model includes the predictors: age, systolic blood pressure, diastolic blood pressure, and body mass index. In the discovery cohort, the gradient boosting-based model outperformed other methods in the elevated arterial stiffness prediction. In the validation cohort, the gradient boosting model showed a good discrimination capacity. A cutoff value of 0.46 for the elevated arterial stiffness risk score in the gradient boosting model resulted in a good specificity (0.813 in the discovery data and 0.761 in the validation data) and sensitivity (0.875 and 0.738, respectively) trade-off points.

Conclusion

The gradient boosting-based prediction system presents a good classification in elevated arterial stiffness prediction. The web online tool makes our gradient boosting-based model easily accessible for further clinical studies and utilization.

ANGPTL8 in metabolic homeostasis: more friend than foe?

ANGPTL8 is an important cytokine, which is significantly increased in type 2 diabetes mellitus (T2DM), obesity and metabolic syndrome. Many studies have shown that ANGPTL8 can be used as a bio-marker of these metabolic disorders related diseases, and the baseline ANGPTL8 level has also been found to be positively correlated with retinopathy and all-cause mortality in patients with T2DM. This may be related to the inhibition of lipoprotein lipase activity and the reduction of circulating triglyceride (TG) clearance by ANGPTL8. Consistently, inhibition of ANGPTL8 seems to prevent or improve atherosclerosis. However, it is puzzling that ANGPTL8 seems to have a directing function for TG uptake in peripheral tissues; that is, ANGPTL8 specifically enhances the reserve and buffering function of white adipose tissue, which may alleviate the ectopic lipid accumulation to a certain extent. Furthermore, ANGPTL8 can improve insulin sensitivity and inhibit hepatic glucose production. These contradictory results lead to different opinions on the role of ANGPTL8 in metabolic disorders. In this paper, the correlation between ANGPTL8 and metabolic diseases, the regulation of ANGPTL8 and the physiological role of ANGPTL8 in the process of glucose and lipid metabolism were summarized, and the physiological/pathological significance of ANGPTL8 in the process of metabolic disorder was discussed.

Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning

Background

Arterial stiffness assessed by pulse wave velocity is a major risk factor for cardiovascular diseases. The incidence of cardiovascular events remains high in diabetics. However, a clinical prediction model for elevated arterial stiffness using machine learning to identify subjects consequently at higher risk remains to be developed.

Methods

Least absolute shrinkage and selection operator and support vector machine-recursive feature elimination were used for feature selection. Four machine learning algorithms were used to construct a prediction model, and their performance was compared based on the area under the receiver operating characteristic curve metric in a discovery dataset (n = 760). The model with the best performance was selected and validated in an independent dataset (n = 912) from the Dryad Digital Repository (https://doi.org/10.5061/dryad.m484p). To apply our model to clinical practice, we built a free and user-friendly web online tool.

Results

The predictive model includes the predictors: age, systolic blood pressure, diastolic blood pressure, and body mass index. In the discovery cohort, the gradient boosting-based model outperformed other methods in the elevated arterial stiffness prediction. In the validation cohort, the gradient boosting model showed a good discrimination capacity. A cutoff value of 0.46 for the elevated arterial stiffness risk score in the gradient boosting model resulted in a good specificity (0.813 in the discovery data and 0.761 in the validation data) and sensitivity (0.875 and 0.738, respectively) trade-off points.

Conclusion

The gradient boosting-based prediction system presents a good classification in elevated arterial stiffness prediction. The web online tool makes our gradient boosting-based model easily accessible for further clinical studies and utilization.