Fasting plasma glucose and HbA1c levels predict the risk of type 2 diabetes and diabetic retinopathy in a Thai high-risk population with prediabetes

Association between ideal cardiovascular health and abnormal glucose metabolism in the elderly: evidence based on real-world data

BACKGROUND

Limited information is available on the effect of ideal cardiovascular health (CVH) and abnormal glucose metabolism in elderly people. We aimed to analyze the prevalence of CVH behaviors, abnormal glucose metabolism, and their correlation in 65 and older people.

METHODS

In this study, randomized cluster sampling, multivariate logistic regression, and mediating effects analysis were used. Recruiting was carried out between January 2020 and December 2020, and 1984 participants aged 65 years or older completed the study.

RESULTS

The prevalence of abnormal glucose metabolism in this group was 26.7% (n = 529), among which the prevalence of impaired fasting glucose (IFG) was 9.5% (male vs. female: 8.7% vs 10.1%, P = 0.338), and the prevalence of type 2 diabetes mellitus (T2DM) was 19.0% (male vs. female: 17.8 vs. 19.8%, P = 0.256). The ideal CVH rate (number of ideal CVH metrics ≥ 5) was only 21.0%. The risk of IFG and T2DM decreased by 23% and 20% with each increase in one ideal CVH metrics, with OR (95%CI) of 0.77(0.65-0.92) and 0.80(0.71-0.90), respectively (P -trend < 0.001). TyG fully mediated the ideal CVH and the incidence of T2DM, and its mediating effect OR (95%CI) was 0.88(0.84-0.91).

CONCLUSIONS

Each increase in an ideal CVH measure may effectively reduce the risk of abnormal glucose metabolism by more than 20%.

Causal relationships between blood metabolites and diabetic retinopathy: a two-sample Mendelian randomization study

Background

Diabetic retinopathy (DR) is a microvascular complication of diabetes, severely affecting patients' vision and even leading to blindness. The development of DR is influenced by metabolic disturbance and genetic factors, including gene polymorphisms. The research aimed to uncover the causal relationships between blood metabolites and DR.

Methods

The two-sample mendelian randomization (MR) analysis was employed to estimate the causality of blood metabolites on DR. The genetic variables for exposure were obtained from the genome-wide association study (GWAS) dataset of 486 blood metabolites, while the genetic predictors for outcomes including all-stage DR (All DR), non-proliferative DR (NPDR) and proliferative DR (PDR) were derived from the FinnGen database. The primary analysis employed inverse variance weighted (IVW) method, and supplementary analyses were performed using MR-Egger, weighted median (WM), simple mode and weighted mode methods. Additionally, MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were also conducted to guarantee the accuracy and robustness of the results. Subsequently, we replicated the MR analysis using three additional datasets from the FinnGen database and conducted a meta-analysis to determine blood metabolites associated with DR. Finally, reverse MR analysis and metabolic pathway analysis were performed.

Results

The study identified 13 blood metabolites associated with All DR, 9 blood metabolites associated with NPDR and 12 blood metabolites associated with PDR. In summary, a total of 21 blood metabolites were identified as having potential causal relationships with DR. Additionally, we identified 4 metabolic pathways that are related to DR.

Conclusion

The research revealed a number of blood metabolites and metabolic pathways that are causally associated with DR, which holds significant importance for screening and prevention of DR. However, it is noteworthy that these causal relationships should be validated in larger cohorts and experiments.

Effectiveness of a cognitive behavioral therapy-integrated, hospital-based program for prediabetes: a matched cohort study

Intensive lifestyle interventions are effective in preventing T2DM, but evidence is lacking for high cardiometabolic individuals in hospital settings. We evaluated a hospital-based, diabetes prevention program integrating cognitive behavioral therapy (CBT) for individuals with prediabetes. This matched cohort assessed individuals with prediabetes receiving the prevention program, which were matched 1:1 with those receiving standard care. The year-long program included five in-person sessions and several online sessions covering prediabetes self-management, dietary and behavioral interventions. Kaplan-Meier and Cox regression models estimated the 60-month T2DM incidence rate. Of 192 patients, 190 joined the prevention program, while 190 out of 10,260 individuals were in the standard-care group. Both groups had similar baseline characteristics (mean age 58.9 ± 10.2 years, FPG 102.3 ± 8.2 mg/dL, HbA1c 5.9 ± 0.3%, BMI 26.2 kg/m2, metabolic syndrome 75%, and ASCVD 6.3%). After 12 months, the intervention group only showed significant decreases in FPG, HbA1c, and triglyceride levels and weight. At 60 months, the T2DM incidence rate was 1.7 (95% CI 0.9-2.8) in the intervention group and 3.5 (2.4-4.9) in the standard-care group. After adjusting for variables, the intervention group had a 0.46 times lower risk of developing diabetes. Therefore, healthcare providers should actively promote CBT-integrated, hospital-based diabetes prevention programs to halve diabetes progression.

The relationship between blood vitamin A levels and diabetic retinopathy: a population-based study

We assessed the relationship between blood vitamin A levels and the risk of diabetic retinopathy. The study was population-based epidemiological study for 11,727 participants aged 40 or older who participated in the Korean National Health and Nutrition Examination Survey. Vitamin A in the blood was classified into quartiles. Diabetic retinopathy was diagnosed by the Early Treatment for Diabetic Retinopathy Study. After adjusting confounding variables such as age, sex, smoking, cholesterol, diabetes prevalence period, glycated hemoglobin levels, and high blood pressure, the odd ratio (OR) of vitamin A at quartile level 4 for diabetic retinopathy was 0.32 (95% confidence interval [CI], 0.14-0.72, P for trend < 0.001). In male, the OR of quartile 3 level vitamin A for diabetic retinopathy was 0.11 (95% CI, 0.01-0.69, P for trend = 0.010). In adults under the age of 60, the OR of vitamin A at quartile level 3 for diabetic retinopathy was 0.10. (95% CI, 0.03-0.29, P for trend < 0.001). Serum vitamin A high levels are associated with low risk of diabetic retinopathy. Particularly, there is a more effective relationship in male and adults under the age of 60.

Prevalence and predictive modeling of undiagnosed diabetes and impaired fasting glucose in Taiwan: a Taiwan Biobank study

INTRODUCTION

We investigated the prevalence of undiagnosed diabetes and impaired fasting glucose (IFG) in individuals without known diabetes in Taiwan and developed a risk prediction model for identifying undiagnosed diabetes and IFG.

RESEARCH DESIGN AND METHODS

Using data from a large population-based Taiwan Biobank study linked with the National Health Insurance Research Database, we estimated the standardized prevalence of undiagnosed diabetes and IFG between 2012 and 2020. We used the forward continuation ratio model with the Lasso penalty, modeling undiagnosed diabetes, IFG, and healthy reference group (individuals without diabetes or IFG) as three ordinal outcomes, to identify the risk factors and construct the prediction model. Two models were created: Model 1 predicts undiagnosed diabetes, IFG_110 (ie, fasting glucose between 110 mg/dL and 125 mg/dL), and the healthy reference group, while Model 2 predicts undiagnosed diabetes, IFG_100 (ie, fasting glucose between 100 mg/dL and 125 mg/dL), and the healthy reference group.

RESULTS

The standardized prevalence of undiagnosed diabetes for 2012-2014, 2015-2016, 2017-2018, and 2019-2020 was 1.11%, 0.99%, 1.16%, and 0.99%, respectively. For these periods, the standardized prevalence of IFG_110 and IFG_100 was 4.49%, 3.73%, 4.30%, and 4.66% and 21.0%, 18.26%, 20.16%, and 21.08%, respectively. Significant risk prediction factors were age, body mass index, waist to hip ratio, education level, personal monthly income, betel nut chewing, self-reported hypertension, and family history of diabetes. The area under the curve (AUC) for predicting undiagnosed diabetes in Models 1 and 2 was 80.39% and 77.87%, respectively. The AUC for predicting undiagnosed diabetes or IFG in Models 1 and 2 was 78.25% and 74.39%, respectively.

CONCLUSIONS

Our results showed the changes in the prevalence of undiagnosed diabetes and IFG. The identified risk factors and the prediction models could be helpful in identifying individuals with undiagnosed diabetes or individuals with a high risk of developing diabetes in Taiwan.