Does lowering of blood glucose improve cardiovascular morbidity and mortality?

Gene-gene interactions lead to higher risk for development of type 2 diabetes in a Chinese Han population: a prospective nested case-control study

Background

The purpose of this study was to evaluate the effect of single-nucleotide polymorphisms (SNPs) of the GCKR and G6PC2 genes on risk for type 2 diabetes and the SNP-SNP and haplotype-based interactions between these genes.

Methods

Subjects of this nested case-control study were selected from a prospective cohort residing in the rural area of Luoyang city in China. Cases (n = 538) were individually matched with controls. Six SNPs in the GCKR and G6PC2 genes were selected and genotyped using an SNPscan™ kit. Stratified Cox proportional hazards regression models were used to generate odds ratios (ORs) and 95% confidence intervals (CI) for different genotype models for the risk of T2DM. Generalized multifactor dimensionality reduction (GMDR) was used to analyze the interactions between two genes with among six SNPs. The linkage disequilibrium (LD) analysis and the haplotype analysis were carried out by SHEsis online.

Results

We found that the C allele of rs780094 was associated with increased risk for T2DM in Han Chinese population. However, the rs492594-C allele in G6PC2 was associated with a decreased risk of T2DM. We also found a significant SNP-SNP interaction between rs2293572 and rs492594, and the CCCCGC and CGCCCA haplotypes significantly increased the risk of T2DM, however, the CCCCCA haplotype had lower susceptibility to T2DM.

Conclusion

The results suggest that the GCKR and G6PC2 genes may contribute to the risk of T2DM independently and/or in an interactive manner in the Han Chinese population.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0813-6) contains supplementary material, which is available to authorized users.

Meta-analyses of the association of G6PC2 allele variants with elevated fasting glucose and type 2 diabetes

Objective

To collectively evaluate the association of glucose-6-phosphatase catalytic unit 2 (G6PC2) allele variants with elevated fasting glucose (FG) and type 2 diabetes (T2D).

Design

Meta-analysis

Data sources

PubMed, Web of Knowledge and Embase databases.

Study selection

Full text articles of studies that identified an association of G6PC2 with T2D and elevated FG.

Patient involvement

There was no T2D patient involvement in the analyses on the association of FG with G6PC2, there were T2D patients and non-diabetes patient involvement in the analyses on the association of T2D with G6PC2.

Statistical analysis

Random-effects meta-analyses were used to calculate the pool effect sizes. I² metric and H² tests were used to calculate the heterogeneity. Begg's funnel plot and Egger’s linear regression test were done to assess publication bias.

Results

Of the 423 studies identified, 21 were eligible and included. Data on three loci (rs560887, rs16856187 and rs573225) were available. The G allele at rs560887 in three ethnicities, the C allele at rs16856187 and the A allele at rs573225 all had a positive association with elevated FG. Per increment of G allele at rs560887 and A allele at rs573225 resulted in a FG 0.070 mmol/l and 0.075 mmol/l higher (ß (95% CI) = 0.070 (0.060, 0.079), p = 4.635e-50 and 0.075 (0.065, 0.085), p = 5.856e-48, respectively). With regard to the relationship of rs16856187 and FG, an increase of 0.152 (95% CI: 0.034–0.270; p = 0.011) and 0.317 (95% CI: 0.193–0.442, p = 6.046e-07) was found in the standardized mean difference (SMD) of FG for the AC and CC genotypes, respectively, when compared with the AA reference genotype. However, the G-allele of rs560887 in Caucasians under the additive model and the C-allele of rs16856187 under the allele and dominant models were associated with a decreased risk of T2D (OR (95% CI) = 0.964 (0.947, 0.981), p = 0.570e-4; OR (95% CI) = 0.892 (0.832, 0.956), p = 0.001; and OR (95% CI) = 0.923(0.892, 0.955), p = 5.301e-6, respectively).

Conclusions

Our meta-analyses demonstrate that all three allele variants of G6PC2 (rs560887, rs16856187 and rs573225) are associated with elevated FG, with two variants (rs560887 in the Caucasians subgroup and rs16856187 under the allele and dominant model) being associated with T2D as well. Further studies utilizing larger sample sizes and different ethnic populations are needed to extend and confirm these findings.

Glycemic control: Can nurse practitioners on interprofessional collaborative practice teams enhance clinical outcomes?

BACKGROUND AND PURPOSE

Care coordination and specialized knowledge of prescriptive authority are fundamental to advanced nursing practice. Little research documents patient clinical outcomes in primary care when nurse practitioners are members of an interprofessional education and collaborative practice (IPECP) team. This cross-sectional study examined differences in glycemic control among Texas patients who received care by a Family Nurse Practitioner (FNP) and an IPECP team in one calendar year.

METHODS

A convenience sample of 120 adult volunteers with diabetes was followed in a community-based clinic by an FNP; of these, 34 received additional care by an IPECP team. Data on selected demographic indices and HgbA1c were derived from the health center's Electronic Medical Record using a retrospective review, and linkage with the federally funded IPECP Project database.

CONCLUSIONS

Patients with two or more FNP visits and two or more visits with the IPECP team had statistically significant reductions in HgbA1c levels at the end of 1 year.

IMPLICATIONS FOR PRACTICE

Actualizing point-of-care treatment adjustments is a particular strength of advanced practice nurses but potentially a missed opportunity on interprofessional teams. Further study is needed on clinical outcomes of nurse practitioner contributions to IPECP team care.

Aberrant NKG2D expression with IL-17 production of CD4+ T subsets in patients with type 2 diabetes

Type 2 diabetes (T2D) is a systemic inflammatory disease. Although the natural killer group 2, member D (NKG2D) receptor, was not expressed normally on CD4+ T cells, the aberrant expression was found in pathological conditions such as in auto-immune diseases. However, the involvement of NKG2D in pathogenesis of T2D is unclear. We hypothesize that there is an inflammatory CD4+ T cell subpopulation expressing NKG2D and producing interleukin (IL)-17 in T2D. NKG2D expression on CD4+ T cells and their subsets were analyzed by multi-color staining using flow cytometry. Lymphocytes were activated by phorbol-12-myristate-13-acetate (PMA) and ionomycin, and were stained for intracellular IL-17. To investigate the mechanism of IL-17 production, patients' lymphocytes were stimulated using specific anti-T cell receptor (TCR) alone, anti-NKG2D alone or a combination of the two antibodies. CD4+ T cells and particularly, CD4+CD28^nullT subset of T2D patients were highly expressed NKG2D and more prevalent compared to non-diabetic individuals (ND) (P=0.039 and P=0.022, respectively). Significantly higher percentages of CD4+CD28^nullNKG2D+T cells of patients produced IL-17 when compared to those of ND (P=0.024) and were positively correlated with the level of glycated hemoglobin A1c (HbA1c) (R²=0.386, P=0.041). Additionally, this cell population could be stimulated by specific monoclonal anti-NKG2D to produce IL-17. In conclusion, CD4+CD28^nullNKG2D+T cells were expanded in T2D, especially in patients with poor glycemic control. NKG2D may be one of the surrogate co-stimulatory receptors leading to irregular inflammatory function producing IL-17. An IL-17 producing CD4+CD28^nullNKG2D+T cells may potentially be involved in pathogenesis and drive severity of the disease with the glycemic dependence. This particular cell type could be targeted for prognostic or therapeutic purposes.

Better risk assessment with glycated hemoglobin instead of cholesterol in CVD risk prediction charts

Traditional risk charts for the prediction of cardiovascular disease (CVD) include cholesterol parameters. We evaluated how models predict fatal CVD when cholesterol is replaced by glucose parameters. We used data from NHANES III, a US survey conducted 1988-1994 (follow-up until 2006); 15,454 participants (1,716 CVD deaths) were included. Based on the ESC SCORE method, we used age, sex, blood pressure, smoking and either of the following: (1) total cholesterol, (2) total-to-HDL-cholesterol, (3) glucose, (4) glycated hemoglobin (A1C). Scaled Brier score (BS), Nagelkerke's R(2) (NR) and integrated discrimination improvement (IDI) were used for model comparison. The ranking (best to worst) was: A1C (BS = 11.62 %; NR = 0.0865; IDI = 0.0091), glucose (11.16 %; 0.0734; 0.0067), total-to-HDL-cholesterol (9.97 %; 0.0547; 0.0010), cholesterol (9.75 %; 0.0484; 0, reference). Differences between models with cholesterol and glucose or A1C were statistically significant. This study suggests the use of A1C instead of cholesterol parameters in charts to assess CVD risk.

Blood glucose may be an alternative to cholesterol in CVD risk prediction charts

Background

Established risk models for the prediction of cardiovascular disease (CVD) include blood pressure, smoking and cholesterol parameters. The use of total cholesterol for CVD risk prediction has been questioned, particularly for primary prevention. We evaluated whether glucose could be used instead of total cholesterol for prediction of fatal CVD using data with long follow-up.

Methods

We followed-up 6,095 men and women aged ≥16 years who participated 1977-79 in a community based health study and were anonymously linked with the Swiss National Cohort until the end of 2008. During follow-up, 727 participants died of CVD. Based on the ESC SCORE methodology (Weibull regression), we used age, sex, blood pressure, smoking, and fasting glucose or total cholesterol. The mean Brier score (BS), area under the receiver-operating characteristic curve (AUC) and integrated discrimination improvement (IDI) were used for model comparison. We validated our models internally using cross-validation and externally using another data set.

Results

In our models, the p-value of total cholesterol was 0.046, that of glucose was p < 0.001. The model with glucose had a slightly better predictive capacity (BS: 2216x10^-5 vs. 2232x10^-5; AUC: 0.9181 vs. 0.9169, IDI: 0.009 with p-value 0.026) and could well discriminate the overall risk of persons with high and low concentrations. The external validation confirmed these findings.

Conclusions

Our study suggests that instead of total cholesterol glucose can be used in models predicting overall CVD mortality risk.

Explanatory models of heart failure etiology

Chronic health failure is a leading cause of hospital readmissions and is reaching epidemic proportions in the United States. Explanatory models of illness can provide insight about how people with heart failure perceive their etiology of heart failure. Six themes were found in this descriptive, qualitative study to explore the perceived origin of heart failure in 50 participants. Forty percent of the people were unaware of why they had the diagnosis. Misconceptions and misinformation were common, including confusion about whether the symptoms themselves caused the disease.