Transporters, TBC1D4, and ARID5B Variants to Explain Glycated Hemoglobin Variability in Patients with Type 2 Diabetes

Association of haemoglobin glycation index with all-cause and cardiovascular disease mortality in diabetic kidney disease: a cohort study

BACKGROUND

While the high haemoglobin glycation index (HGI) has been extensively investigated in diabetic populations, its impact on patients with diabetic kidney disease (DKD) remains unclear.

METHODS

We examined data from the National Health and Nutrition Examination Surveys (NHANES) conducted between 1999 and 2018. HGI was determined using the formula recommended by Hempe et al., which calculates the difference between measured and predicted HbA1c. Predicted HbA1c was derived from the equation: 0.024 FPG + 3.1. National death index records up to December 31, 2019, were utilized to assess mortality outcomes. To estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for both all-cause and cardiovascular disease (CVD) mortality, we utilized Cox proportional hazard models. A restricted cubic spline analysis was performed to explore the potential nonlinear relationship between HGI levels and mortality.

RESULTS

Our cohort study comprised data from 1,057 participants with DKD (mean [SE] age, 61.61 [0.57] years; 48.24% female). The mean HGI level was 0.44 (SE 0.04). Over a median follow-up period of 6.67 years, we observed 381 deaths, including 140 due to CVD. Compared with participants in the second tertile of HGI levels (0.03-0.74), those in the lowest tertile of HGI (-5.29-0.02) exhibited an all-cause mortality hazard ratio of 1.39 (95% CI, 1.02-1.88) and a CVD mortality hazard ratio of 1.10 (95% CI, 0.67-1.81). Conversely, participants in the highest tertile (0.75-9.60) demonstrated an all-cause mortality hazard ratio of 1.48 (95% CI, 1.05-2.08) and a CVD mortality hazard ratio of 2.06 (95% CI, 1.13-3.77) after further adjusting for HbA1c and other important variables. Additionally, a restricted cubic spline analysis revealed a U-shaped relationship between HGI and all-cause mortality (P < 0.001 for nonlinearity) and a J-shaped relationship between HGI and CVD mortality (P = 0.044 for nonlinearity).

CONCLUSIONS

Our cohort study suggests that HGI in DKD populations exhibits a U-shaped association with all-cause mortality and a J-shaped association with CVD mortality, independent of HbA1c levels.

Association of triglyceride glucose-body mass index and hemoglobin glycation index with heart failure prevalence in hypertensive populations: a study across different glucose metabolism status

BACKGROUND

The Triglyceride glucose-body mass index (TyG-BMI) and hemoglobin glycation index (HGI) are well-established surrogate markers for insulin resistance. Nevertheless, the extent to which these markers offer additive predictive value for heart failure (HF) prevalence in hypertensive populations, and their predictive utility across various diabetic statuses, remains to be clarified. Consequently, this study aimed to explore the independent and synergistic effects of TyG-BMI and HGI on HF risk among individuals with different diabetic statuses.

METHODS

Data from the study population (n = 9847) were obtained from the National Health and Nutrition Examination Survey (NHANES). Multivariable logistic regression models were employed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) to assess the combined associations between TyG-BMI and HGI and the prevalence of HF across various diabetic statuses.

RESULTS

In the total population, compared to the reference group (low TyG-BMI and low HGI), the OR (95% CI) for HF prevalence was 1.30 (1.04, 1.64) for the combination of low TyG-BMI and high HGI, 2.40 (1.76, 3.29) for high TyG-BMI and low HGI, and 3.47 (2.41, 4.99) for high TyG-BMI and high HGI. Interestingly, among normoglycemic individuals, higher TyG-BMI and HGI did not significantly increase the prevalence of HF. Conversely, in the prediabetic population, the OR (95%CI) for HF prevalence was 2.42 (1.69, 3.48) for the combination of high TyG-BMI and low HGI, and 4.30 (2.45, 7.54) for high TyG-BMI and high HGI. Similarly, in the diabetic population, the OR (95%CI) for HF prevalence was 2.22 (1.43, 3.45) for low TyG-BMI and high HGI, 4.04 (2.43, 6.73) for high TyG-BMI and low HGI, and 4.13 (2.25, 7.59) for high TyG-BMI and high HGI, compared to low TyG-BMI and low HGI.

CONCLUSION

This study reveals that elevated TyG-BMI and HGI levels exert a synergistic impact on the prevalence of HF in hypertensive adults, especially in those with prediabetes and diabetes. Additionally, the presence of prediabetes and diabetes may amplify the detrimental combined effect of TyG-BMI and HGI on HF prevalence.

Association between hemoglobin glycation index and 5-year major adverse cardiovascular events: the REACTION cohort study

BACKGROUND

The hemoglobin glycation index (HGI) was developed to quantify glucose metabolism and individual differences and proved to be a robust measure of individual glycosylated hemoglobin (HbA1c) bias. Here, we aimed to explore the relationship between different HGIs and the risk of 5-year major adverse cardiovascular events (MACEs) by performing a large multicenter cohort study in China.

METHODS

A total of 9791 subjects from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a Longitudinal Study (the REACTION study) were divided into five subgroups (Q1-Q5) with the HGI quantiles (≤5th, >5th and ≤33.3th, >33.3th and ≤66.7th, >66.7th and ≤95th, and >95th percentile). A multivariate logistic regression model constructed by the restricted cubic spline method was used to evaluate the relationship between the HGI and the 5-year MACE risk. Subgroup analysis between the HGI and covariates were explored to detect differences among the five subgroups.

RESULTS

The total 5-year MACE rate in the nationwide cohort was 6.87% (673/9791). Restricted cubic spline analysis suggested a U-shaped correlation between the HGI values and MACE risk after adjustment for cardiovascular risk factors ( χ2 = 29.5, P <0.001). After adjustment for potential confounders, subjects with HGIs ≤-0.75 or >0.82 showed odds ratios (ORs) for MACE of 1.471 (95% confidence interval [CI], 1.027-2.069) and 2.222 (95% CI, 1.641-3.026) compared to subjects with HGIs of >-0.75 and ≤-0.20. In the subgroup with non-coronary heart disease, the risk of MACE was significantly higher in subjects with HGIs ≤-0.75 (OR, 1.540 [1.039-2.234]; P = 0.027) and >0.82 (OR, 2.022 [1.392-2.890]; P <0.001) compared to those with HGIs of ≤-0.75 or >0.82 after adjustment for potential confounders.

CONCLUSIONS

We found a U-shaped correlation between the HGI values and the risk of 5-year MACE. Both low and high HGIs were associated with an increased risk of MACE. Therefore, the HGI may predict the 5-year MACE risk.

Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children's cohort

AIMS/HYPOTHESIS

There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution.

METHODS

Genotyping was successful in 2825 children aged 2-14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15-P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin.

RESULTS

A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10-8). Two variants associated with low z-insulin (P15, p value <5×10-6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin.

CONCLUSIONS/INTERPRETATION

The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker-disease associations.

The Combination Clinical Value of Plasma Brain Natriuretic Peptide and Serum HbAlc in the Diagnosis of Chronic Pulmonary Heart Disease

Objective. To analyze the combination clinical value of plasma brain natriuretic peptide and serum glycated hemoglobin (HbAlc) in chronic pulmonary heart disease. Methods. A total of 200 patients with chronic pulmonary heart disease admitted to our hospital from January 2021 to January 2022 were selected as the observation group, and 200 healthy subjects were selected as the control group during the same period. All subjects were examined by an ECG vector map and plasma BNP, and HbAlc levels were detected to analyze the value and clinical significance of each index in single diagnosis and combined diagnosis. Results. Plasma BNP and HbAlc levels in the observation group were significantly higher than those in the control group ( $P<0.05$ ). There were 154 BNP positive, 146 HbAlc positive, 164 parallel combined diagnosis positive, and 132 serial combined diagnosis positive. Sensitivity of series combination diagnosis was significantly higher than other indexes ( $P<0.05$ ); especially, parallel combination diagnosis was significantly higher than other indexes ( $P<0.05$ ). Besides, area under the ROC curve of parallel combination diagnosis and series combination diagnosis was significantly higher than that of each index alone diagnosis ( $P<0.05$ ). Conclusion. In the diagnosis of chronic pulmonary heart disease, the combination of plasma BNP and HbAlc can effectively improve the diagnostic specificity and sensitivity, as well as improve the area under the ROC curve.