Factors Associated with Glycemic Variability in Patients with Type 2 Diabetes: Focus on Oral Hypoglycemic Agents and Cardiovascular Risk Factors

Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning

BACKGROUND

Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adverse outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). However, the prognostic significance of combined SHR-GV evaluation for ASCVD mortality remains unclear. This study examines associations of SHR, GV, and their synergistic effects with mortality in patients with ASCVD across different glucose metabolic states, incorporating machine learning (ML) to identify critical risk factors influencing mortality.

METHODS

Patients with ASCVD were screened in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and stratified into normal glucose regulation (NGR), pre-diabetes mellitus (Pre-DM), and diabetes mellitus (DM) groups based on glucose metabolic status. The primary endpoint was 28-day mortality, with 90-day mortality as the secondary outcome. SHR and GV levels were categorized into tertiles. Associations with mortality were analyzed using Kaplan-Meier(KM) curves, Cox proportional hazards models, restricted cubic splines (RCS), receiver operating characteristic (ROC) curves, landmark analyses, and subgroup analyses. Five ML algorithms were employed for mortality risk prediction, with SHapley Additive exPlanations (SHAP) applied to identify critical predictors.

RESULTS

A total of 2807 patients were included, with a median age of 71 years, and 58.78% were male. Overall, 483 (23.14%) and 608 (29.13%) patients died within 28 and 90 days of ICU admission, respectively. In NGR and Pre-DM subgroups, combined SHR-GV assessment demonstrated superior predictive performance for 28-day mortality versus SHR alone [NGR: AUC 0.688 (0.636-0.739) vs. 0.623 (0.568-0.679), P = 0.028; Pre-DM: 0.712 (0.659-0.764) vs. 0.639 (0.582-0.696), P = 0.102] and GV alone [NGR: 0.688 vs. 0.578 (0.524-0.633), P < 0.001; Pre-DM: 0.712 vs. 0.593 (0.524-0.652), P < 0.001]. Consistent findings were observed for 90-day mortality prediction. However, in the DM subgroup, combined assessment improved prediction only for 90-day mortality vs. SHR alone [AUC 0.578 (0.541-0.616) vs. 0.560 (0.520-0.599), P = 0.027], without significant advantages in other comparisons.

CONCLUSIONS

Combined SHR and GV assessment serves as a critical prognostic tool for ASCVD mortality, providing enhanced predictive accuracy compared to individual metrics, particularly in NGR and Pre-DM patients. This integrated approach could inform personalized glycemic management strategies, potentially improving clinical outcomes.

Prognostic value of longitudinal HbA1c variability in predicting the development of diabetic sensorimotor polyneuropathy among patients with type 2 diabetes mellitus: A prospective cohort observational study

AIMS/INTRODUCTION

This prospective cohort study aims to identify the optimal measure of glycated hemoglobin (HbA1c) variability and to explore its relationship with the development of new diabetic sensorimotor polyneuropathy (DSPN) in individuals with type 2 diabetes mellitus, building upon previous cross-sectional studies that highlighted a significant association between HbA1c visit-to-visit variability and DSPN.

MATERIALS AND METHODS

In a prospective study, 321 participants diagnosed with type 2 diabetes mellitus underwent comprehensive clinical assessments, neurophysiologic studies, and laboratory evaluations at enrollment and follow-up. Various indices, including HbA1c standard deviation (HbA1c SD), coefficient of variation (HbA1c CV), HbA1c change score (HbA1c HVS), and average real variability (HbA1c ARV), were employed to calculate the visit-to-visit variability HbA1c based on 3 month intervals. The investigation focused on examining the associations between these indices and the development of new DSPN.

RESULTS

The average follow-up duration was 16.9 ± 6.9 months. The Cox proportional hazards model identified age (P = 0.001), diabetes duration (P = 0.024), and HbA1C ARV (P = 0.031) as the sole factors associated with the development of new DSPN. Furthermore, the cumulative risk of developing DSPN over 1 year demonstrated a significant association with HbA1C ARV (P = 0.03, log-rank test).

CONCLUSIONS

Apart from age and diabetes duration, HbA1c variability emerged as a robust predictor for the occurrence of new DSPN. Among the various measures of HbA1c variability evaluated, HbA1c ARV demonstrated the highest potential as a reliable indicator for anticipating the onset of new DSPN.

Comparative Effectiveness of Oral Hypoglycemic Agents for Glycemic Control and Glycemic Variability in Patients with Type 2 Diabetes Mellitus: Using Flash Glucose Monitoring

AIM

The study aimed to compare the effectiveness of oral hypoglycemic agents (OHAs) as monotherapy, dual and quadruple therapy for glycemic control (GC) and glycemic variability (GV) in patients with type 2 diabetes (T2DM) using flash glucose monitoring system (FGM).

BACKGROUND

Diabetes management largely relies on HbA1c monitoring. Glycemic variability has been an evolving glycemic target for preventing complications related to type 2 diabetes mellitus.

OBJECTIVE

The purpose of the study was to compare glycemic control measures and glycemic variability measures among study groups and to study the relationships between GC and GV indices.

METHODS

Retrospectively, FGM data were collected from 50 T2DM patients. The patients were classified based on prescribed number of OHAs as monotherapy [group 1: Dipeptidyl peptidase- 4 (DPP-4) inhibitors (n=10), group 2: Sodium-glucose co-transporter-2 (SGLT2) inhibitors (n=10), group 3: Sulphonylureas (n=10), group 4: Dual therapy (n=10), and group 5: Quadruple therapy (n=10)]. Measures of GC and GV were evaluated.

RESULTS

Significant differences between study groups were observed in GC and GV measurements. The SGLT2 inhibitors monotherapy group demonstrated optimal GC [eA1c (%): 6.5 ± 2.2; MBG: 140.80 ± 63.94; TIR: 60.60 ± 19.96] and GV (SD: 42.38 ± 34.57; CV: 27.85 ± 6.68; MAGE: 96.76 ± 52.47; MODD: 33.96 ± 22.91) in comparison to other study groups. On using Pearson correlation analysis, mean blood glucose (MBG) and mean amplitude of glycemic excursion (MAGE) showed moderate correlation (r = 0.742)(r2 = 0.551), depicting distinct glucose variabilities at the same mean blood glucose levels.

CONCLUSION

The monotherapy group of SGLT2 inhibitors demonstrated glucose-lowering effects with reduced glycemic variability. Hence, optimum glycemic control is associated with decreased glycemic variability.

Glycemic Variability in Subjects with Diabetes and Hypogonadism during Testosterone Replacement Treatment: A Pilot Study

Background: This is a proof of concept, as a pilot study, with the aim to evaluate continuous glucose monitoring metrics (CGM) in subjects with type 2 diabetes (T2DM), treated with nutritional therapy and metformin, before and after testosterone replacement therapy (TRT). Methods: In this longitudinal observational study, subjects affected by T2DM and starting TRT for documented ED and hypogonadism were enrolled. All subjects mounted a CGM system during the v0 visit, one week before the beginning of the TRT (week−1), during v2, four weeks after the start of TRT (week 4), and v4 (week 12). CGM was worn for about 144 h after each visit. Results: A total of seven patients, referring to our clinic for erectile dysfunction (ED), were studied (aged 63.3 ± 2.3 years). Mean (± standard deviation) total testosterone level was 2.3 ± 0.6 ng/mL at baseline. After TRT, total testosterone level was 4.6 ± 3.04 ng/mL at week 4 and 3.93 ± 4.67 ng/mL at week 12. No significant differences were observed in TIR, TAR, TBR, estimated HbA1c, AUC below, and AUC above limit during the intervention period. Conclusions: This is the first study evaluating the effects of TRT on daily glucose excursions in subjects with T2DM and hypogonadism. Though we did not find any significant difference in key CGM metrics during the 12 weeks of TRT, this study confirms the glycometabolic safety of the TRT even on the most novel standardized glycemic targets.

Influence of dipeptidyl peptidase-4 inhibitors on glycemic variability in patients with type 2 diabetes: A meta-analysis of randomized controlled trials

OBJECTIVE

The influence of dipeptidyl peptidase-4 (DPP4) inhibitors on glycemic variability compared to other oral antidiabetic drugs (OADs), measured based on the mean amplitude of glycemic excursions (MAGE), has not been comprehensively analyzed. The aim of the study was to perform a meta-analysis to compare the effects of DPP4 inhibitors on MAGE with other OADs in type 2 diabetes mellitus (T2DM) patients without concurrent insulin treatments.

METHODS

The Medline (PubMed), Embase (Ovid), and CENTER (Cochrane Library) databases were searched for relevant randomized controlled trials (RCTs). Study characteristics and outcome data were independently extracted by two authors. A random-effect model was used to combine the results.

RESULTS

Fourteen studies with 855 patients were included. Compared to other OADs, DPP4 inhibitors significantly reduced MAGE (mean difference [MD]: -0.69 mmol/L, 95% confidence interval [CI]: -0.95 to -0.43, P<0.001) with mild heterogeneity (I^{2 =} 28%). Predefined subgroup analyses suggested that DPP4 inhibitors were more effective in reducing MAGE compared to insulin secretagogues (MD: -0.92 mmol/L, P<0.001) and non-secretagogues (MD: -0.43 mmol/L, P=0.02), as well as compared to sulfonylureas (MD: -0.91 mmol/L, P<0.001) and sodium glucose cotransporter 2 inhibitors (MD: -0.67 mmol/L, P=0.03).

CONCLUSIONS

DPP4 inhibitors may significantly reduce glycemic variability compared to other oral anti-diabetic drugs, as evidenced by MAGE in T2DM patients with no concurrent insulin treatment.

SYSTEMATIC REVIEW REGISTRATION

INPLASY, registration number: INPLASY2021120113.

Prevalence of glycemic variability and factors associated with the glycemic arrays among end-stage kidney disease patients on chronic hemodialysis

Glycemic variability (GV) confers a significantly higher risk of diabetic-related complications, especially cardiovascular. Despite extensive research in this area, data on end-stage kidney disease (ESKD) patients on chronic hemodialysis are scarce. This study aims to determine the magnitude of GV among ESKD (diabetic vs nondiabetic) patients and its associated factors on hemodialysis days (HDD) and non-hemodialysis days (NHDD) where postulation of a higher GV observed among diabetic on HDD.We recruited 150 patients on hemodialysis, 93 patients with type 2 diabetic (DM-ESKD), and 57 with nondiabetic (NDM-ESKD). The GV indices (standard deviation [SD] and percentage coefficient variant [%CV]) were obtained from 11-point and 7-point self-monitoring blood glucose (fasting to post-meal) (SMBG) profiles on HDD and NHDD. The GV indices and its associated factors of both DM-ESKD and NDM-ESKD were analyzed to compare HDD vs NHDD.Mean blood glucose on HDD was 9.33 [SD 2.7, %CV 30.6%] mmol/L in DM-ESKD compared with 6.07 [SD 0.85, %CV 21.3%] mmol/L in NDM-ESKD (P = <.01). The DM-ESKD group experienced significantly above target GV indices compared to NDM-ESKD on both HDD and NHDD, particularly in the subgroup with HbA1c 8-10% (P = <.01). Presence of diabetes, older age, hyperlipidemia, HbA1c, ferritin levels, and albumin were identified as factors associated with GV.DM-ESKD patients have above-target GV indices, especially on HDD, therefore increasing their risk of developing future complications. We identified high HbA1c, older age group, presence of hyperlipidemia, ferritin levels, and albumin as factors associated with GV indices that may be used as surrogate markers for GV. Since these groups of patients are vulnerable to CVD mortality, urgent attention is needed to rectify it.

New Insights into the Role of Visit-to-Visit Glycemic Variability and Blood Pressure Variability in Cardiovascular Disease Risk

PURPOSE OF REVIEW

There is evidence from epidemiologic studies that variability in cardiovascular risk factors influences risk of cardiovascular disease. We review new studies and novel findings in the relationship between visit-to-visit glycemic variability and blood pressure variability and risk of adverse outcomes.

RECENT FINDINGS

Visit-to-visit glycemic variability is consistently linked to macrovascular disease. This relationship has been observed in both clinical trials and retrospective studies of electronic health records. Long-term blood pressure variability also predicts cardiovascular outcomes, and the association appears stronger in those with lower levels of systolic and diastolic function. As epidemiologic evidence increases in support of a role for metabolic risk factor variability in cardiovascular risk, there is a corresponding rise in interest in applying this information toward improving risk factor prediction and treatment. Future investigation of underlying mechanisms for these associations as well as implications for therapy is also warranted. The potential additive contribution of variability of multiple parameters also merits additional scrutiny. As our technology for capturing risk factor variability continues to improve, this will only enhance our understanding of its links with vascular disease and how to best utilize this information to reduce cardiovascular outcomes.

Dipeptidyl-Peptidase-IV Inhibitors, Imigliptin and Alogliptin, Improve Beta-Cell Function in Type 2 Diabetes

OBJECTS

Imigliptin is a novel dipeptidyl peptidase-4 inhibitor. In the present study, we aimed to evaluate the effects of imigliptin and alogliptin on insulin resistance and beta-cell function in Chinese patients with type-2 diabetes mellitus (T2DM).

METHODS

A total of 37 Chinese T2DM patients were randomized to receive 25 mg imigliptin, 50 mg imigliptin, placebo, and 25 mg alogliptin (positive drug) for 13 days. Oral glucose tolerance tests were conducted at baseline and on day 13, followed by the oral minimal model (OMM).

RESULTS

Imigliptin or alogliptin treatment, compared with their baseline or placebo, was associated with higher beta-cell function parameters (φ_s and φ_tot) and lower glucose area under the curve (AUC) and postprandial glucose levels. The changes in the AUC for the glucose appearance rate between 0 and 120 min also showed a decrease in imigliptin or alogliptin groups. However, the insulin resistance parameter, fasting glucose, was not changed. For the homeostatic model assessment (HOMA-β and HOMA-IR) parameters or secretory units of islets in transplantation index (SUIT), no statistically significant changes were found both within treatments and between treatments.

CONCLUSIONS

After 13 days of treatment, imigliptin and alogliptin could decrease glycemic levels by improving beta-cell function. By comparing OMM with HOMA or SUIT results, glucose stimulation might be more sensitive for detecting changes in beta-cell function.

HbA1C variability and hypoglycemia hospitalization in adults with type 1 and type 2 diabetes: A nested case-control study

AIMS

To determine association between HbA_1C variability and hypoglycemia requiring hospitalization (HH) in adults with type 1 diabetes (T1D) and type 2 diabetes (T2D).

METHODS

Using nested case-control design in electronic health record data in England, one case with first or recurrent HH was matched to one control who had not experienced HH in incident T1D and T2D adults. HbA_1C variability was determined by standard deviation of ≥3 HbA_1C results. Conditional logistic models were applied to determine association of HbA_1C variability with first and recurrent HH.

RESULTS

In T1D, every 1.0% increase in HbA_1C variability was associated with 90% higher first HH risk (95% CI, 1.25-2.89) and 392% higher recurrent HH risk (95% CI, 1.17-20.61). In T2D, a 1.0% increase in HbA_1C variability was associated with 556% higher first HH risk (95% CI, 3.88-11.08) and 573% higher recurrent HH risk (95% CI,1.59-28.51). In T2D for first HH, the association was the strongest in non-insulin non-sulfonylurea users (P<0.0001); for recurrent HH, the association was stronger in insulin users than sulfonylurea users (P=0.07). The HbA_1C variability-HH association was stronger in more recent years in T2D (P≤0.004).

CONCLUSIONS

HbA_1C variability is a strong predictor for HH in T1D and T2D.

Glucose fluctuation and the resultant endothelial injury are correlated with pancreatic β cell dysfunction in patients with coronary artery disease

AIMS

We evaluated whether glucose fluctuation (GF) causes vascular endothelial injury and affects glucometabolic factors during lengthy oral glucose tolerance test (OGTT).

METHODS

We enrolled consecutive 116 patients with coronary artery disease (CAD) who were performed coronary angiography and 4-h OGTT. Blood samples were collected before and 4h after glucose load to measure endothelial injury factor [von Willebrand factor (vWF) and vWF/a disintegrin-like and metalloproteinase with thrombospondin type-1 motifs 13 (ADAMTS-13) ratio]. GF was defined as maximum - minimum blood glucose levels during 4-h OGTT. We estimated the relationship between GF and glucometabolic factors.

RESULTS

vWV and vWF/ADAMTS-13 ratio were significantly correlated with GF during 4-h OGTT. GF was significantly correlated with homeostasis model to assess insulin resistance (HOMA-IR) (R=0.262), Matsuda index (R=-0.405), insulinogenic index (R=-0.336), HbA1c (R=0.281) and disposition index (R=-0.672). When dividing patients into impaired and preserved category groups according to the average value of GF (122mg/dL), adjusted to age, sex, HOMA-β, insulinogenic index, HOMA-IR, Matsuda index and HbA1c, disposition index was an independent risk factor for impaired GF [odds ratio (95% confidence interval): 2.87 (1.70-4.83), P<0.001].

CONCLUSION

Pancreatic β cell dysfunction is associated with GF and causes endothelial injury in CAD patients.

Efficacy of lifestyle interventions in patients with type 2 diabetes: A systematic review and meta-analysis

BACKGROUND

The current meta-analysis evaluated the outcomes of various lifestyle interventions, including diet modifications (DIET), physical activity (PA), and patient education (EDU) in reducing the risk of cardiovascular disease in patients with type 2 diabetes.

METHODS

Randomized clinical trials comparing lifestyle intervention with "usual care" (control) in type 2 diabetes patients were hand-searched from medical databases by two independent reviewers using the terms "diabetes, cardiovascular risk, lifestyle, health education, dietary, exercise/physical activities, and behavior intervention".

RESULTS

Of the 235 studies identified, 17 were chosen for the meta-analysis. The average age of patients ranged from 50-67.3 years. Results reveal no significant difference between the groups, with respect to BMI, while PA and DIET yielded a greater reduction in HbA1c. Significant reduction in both systolic and diastolic pressures in the DIET group, and diastolic pressure in the PA group, was observed. HDL-c in the DIET group was significantly higher than the control group, while no change in LDL-c levels, was seen in all three intervention subtypes. There was no difference between the EDU vs. the control group in terms of HbA1c, blood pressure or HDL-c and LDL-c.

CONCLUSION

DIET intervention showed an improvement in HbA1c, systolic/diastolic blood pressure and HDL-c, with an exception of LDL-c and BMI, suggesting that nutritional intervention had a significant impact on the quality of life by reducing the cardiovascular risk in type 2 diabetes patients.