Prediction of treatment response in patients with newly diagnosed type 2 diabetes: the Skaraborg diabetes register

Predicting the HbA1c level following glucose-lowering interventions in individuals with HbA1c-defined prediabetes: a post-hoc analysis from the randomized controlled PRE-D trial

PURPOSE

To investigate whether the prediction of post-treatment HbA_1c levels can be improved by adding an additional biomarker of the glucose metabolism in addition to baseline HbA_1c.

METHODS

We performed an exploratory analysis based on data from 112 individuals with prediabetes (HbA_1c 39-47 mmol) and overweight/obesity (BMI ≥ 25 kg/m²), who completed 13 weeks of glucose-lowering interventions (exercise, dapagliflozin, or metformin) or control (habitual living) in the PRE-D trial. Seven prediction models were tested; one basic model with baseline HbA_1c as the sole glucometabolic marker and six models each containing one additional glucometabolic biomarker in addition to baseline HbA_1c. The additional glucometabolic biomarkers included: 1) plasma fructosamine, 2) fasting plasma glucose, 3) fasting plasma glucose × fasting serum insulin, 4) mean glucose during a 6-day free-living period measured by a continuous glucose monitor 5) mean glucose during an oral glucose tolerance test, and 6) mean plasma glucose × mean serum insulin during the oral glucose tolerance test. The primary outcome was overall goodness of fit (R²) from the internal validation step in bootstrap-based analysis using general linear models.

RESULTS

The prediction models explained 46-50% of the variation (R²) in post-treatment HbA1c with standard deviations of the estimates of ~2 mmol/mol. R² was not statistically significantly different in the models containing an additional glucometabolic biomarker when compared to the basic model.

CONCLUSION

Adding an additional biomarker of the glucose metabolism did not improve the prediction of post-treatment HbA_1c in individuals with HbA_1c-defined prediabetes.

An Updated Insight Into Molecular Mechanism of Hydrogen Sulfide in Cardiomyopathy and Myocardial Ischemia/Reperfusion Injury Under Diabetes

Cardiovascular diseases are the most common complications of diabetes, and diabetic cardiomyopathy is a major cause of people death in diabetes. Molecular, transcriptional, animal, and clinical studies have discovered numerous therapeutic targets or drugs for diabetic cardiomyopathy. Within this, hydrogen sulfide (H₂S), an endogenous gasotransmitter alongside with nitric oxide (NO) and carbon monoxide (CO), is found to play a critical role in diabetic cardiomyopathy. Recently, the protective roles of H₂S in diabetic cardiomyopathy have attracted enormous attention. In addition, H₂S donors confer favorable effects in myocardial infarction, ischaemia-reperfusion injury, and heart failure under diabetic conditions. Further studies have disclosed that multiplex molecular mechanisms are responsible for the protective effects of H₂S against diabetes-elicited cardiac injury, such as anti-oxidative, anti-apoptotic, anti-inflammatory, and anti-necrotic properties. In this review, we will summarize the current findings on H₂S biology and pharmacology, especially focusing on the novel mechanisms of H₂S-based protection against diabetic cardiomyopathy. Also, the potential roles of H₂S in diabetes-aggravated ischaemia-reperfusion injury are discussed.

Distinct trajectories of HbA1c in newly diagnosed Type 2 diabetes from the DPV registry using a longitudinal group-based modelling approach

AIM

To identify groups of heterogeneous HbA_1c trajectories over time in newly diagnosed Type 2 diabetes.

METHODS

The study comprised 6355 adults with newly diagnosed Type 2 diabetes (55% men, median age 62 years, baseline BMI 31 kg/m² ) from the Diabetes Patienten Verlaufsdokumentation (DPV) prospective multicentre diabetes registry (Germany, Austria). Individuals were assessed during the first 5 years after diabetes diagnosis if they had ≥ 3 aggregated HbA_1c measurements during follow-up. Latent class growth modelling was used to determine distinct subgroups that followed similar longitudinal HbA_1c patterns (SAS: Proc Traj). Multinomial logistic regression models were used to investigate which variables were associated with the respective HbA_1c trajectory groups.

RESULTS

Four distinct longitudinal HbA_1c trajectory (glycaemic control) groups were found. The largest group (56% of participants) maintained stable good glycaemic control (HbA_1c 42-45 mmol/mol). Twenty-six percent maintained stable moderate glycaemic control (HbA_1c 57-62 mmol/mol). A third group (12%) initially showed severe hyperglycaemia (HbA_1c 97 mmol/mol) but reached good glycaemic control within 1 year. The smallest group (6%) showed stable poor glycaemic control (HbA_1c 79-88 mmol/mol). Younger age at diabetes diagnosis, male sex, and higher BMI were associated with the stable moderate or poor glycaemic control groups. Insulin therapy was strongly associated with the highly improved glycaemic control group.

CONCLUSIONS

Four subgroups with distinct HbA_1c trajectories were determined in newly diagnosed Type 2 diabetes using a group-based modelling approach. Approximately one-third of people with newly diagnosed Type 2 diabetes need either better medication adherence or earlier intensification of glucose-lowering therapy.