Long-term glycemic variability and risk of stroke in patients with diabetes: a meta-analysis

The association of haemoglobin A1c variability with adverse outcomes in patients with atrial fibrillation prescribed anticoagulants

AIMS

The association of haemoglobin A1c (HbA1c) variability with the risk of adverse outcomes in patients with atrial fibrillation (AF) prescribed anticoagulants remains unclear. This study aimed to evaluate the association of HbA1c variability with the risk of ischaemic stroke (IS)/systemic embolism (SE) and all-cause mortality among patients with non-valvular AF prescribed anticoagulants.

METHODS AND RESULTS

Patients newly diagnosed with AF from 2013 to 2018 were included. Variability in HbA1c, indexed by the coefficient of variation (CV), was determined for those with at least three HbA1c measurements available from the time of study enrolment to the end of follow-up. To evaluate whether prevalent diabetes would modify the relationship between HbA1c variability and outcomes, participants were divided into diabetes and non-diabetes groups. The study included 8790 patients (mean age 72.7% and 48.5% female). Over a median follow-up of 5.5 years (interquartile range 5.2, 5.8), the incident rate was 3.74 per 100 person-years for IS/SE and 4.89 for all-cause mortality in the diabetes group. The corresponding incident rates in the non-diabetes group were 2.41 and 2.42 per 100 person-years. In the diabetes group, after adjusting for covariates including mean HbA1c, greater HbA1c variability was significantly associated with increased risk of IS/SE [hazard ratio (HR) = 1.65, 95% confidence interval (CI): 1.27-2.13) and all-cause mortality (HR = 1.24, 95% CI: 1.05-1.47) compared with the lowest CV tertile. A similar pattern was evident in the non-diabetes group (IS/SE: HR = 1.58, 95% CI: 1.23-2.02; all-cause mortality: HR = 1.35, 95% CI: 1.10-1.64).

CONCLUSION

Greater HbA1c variability was independently associated with increased risk of IS/SE and all-cause mortality among patients with AF, regardless of diabetic status.

Impact of acute glycemic variability on short-term outcomes in patients with ST-segment elevation myocardial infarction: a multicenter population-based study

BACKGROUND

Given the increasing attention to glycemic variability (GV) and its potential implications for cardiovascular outcomes. This study aimed to explore the impact of acute GV on short-term outcomes in Chinese patients with ST-segment elevation myocardial infarction (STEMI).

METHODS

This study enrolled 7510 consecutive patients diagnosed with acute STEMI from 274 centers in China. GV was assessed using the coefficient of variation of blood glucose levels. Patients were categorized into three groups according to GV tertiles (GV1, GV2, and GV3). The primary outcome was 30-day all-cause death, and the secondary outcome was major adverse cardiovascular events (MACEs). Cox regression analyses were conducted to determine the independent correlation between GV and the outcomes.

RESULTS

A total of 7136 patients with STEMI were included. During 30-days follow-up, there was a significant increase in the incidence of all-cause death and MACEs with higher GV tertiles. The 30-days mortality rates were 7.4% for GV1, 8.7% for GV2 and 9.4% for GV3 (p = 0.004), while the MACEs incidence rates was 11.3%, 13.8% and 15.8% for the GV1, GV2 and GV3 groups respectively (p < 0.001). High GV levels during hospitalization were significantly associated with an increased risk of 30-day all-cause mortality and MACEs. When analyzed as a continuous variable, GV was independently associated with a higher risk of all-cause mortality (hazard ratio [HR] 1.679, 95% confidence Interval [CI] 1.005-2.804) and MACEs (HR 2.064, 95% CI 1.386-3.074). Additionally, when analyzed as categorical variables, the GV3 group was found to predict an increased risk of MACEs, irrespective of the presence of diabetes mellitus (DM).

CONCLUSION

Our study findings indicate that a high GV during hospitalization was significantly associated with an increased risk of 30-day all-cause mortality and MACE in Chinese patients with STEMI. Moreover, acute GV emerged as an independent predictor of increased MACEs risk, regardless of DM status.

Glycemic Variability and the Risk of Diabetic Peripheral Neuropathy: A Meta-Analysis

Glycemic variability (GV) has been related to complications in patients with diabetes. The aim of the systematic review and meta-analysis was to investigate whether GV is also associated with the incidence of diabetic peripheral neuropathy (DPN). A systematic search of Medline, Web of Science, Embase, and Cochrane Library database was conducted to identify relevant observational studies with longitudinal follow-up. The Newcastle-Ottawa Scale was used for study quality evaluation. A random-effects model was utilized to pool the results, accounting for heterogeneity. Ten observational studies including 72 565 patients with diabetes were included. The quality score was 8-9, indicating generally good quality of the included studies. With a mean follow-up duration of 7.1 years, 11 532 patients (15.9%) were diagnosed as DPN. Compared to patients with low GV, patients with high GV were associated with an increased risk incidence of DPN (risk ratio: 1.51, 95% confidence interval: 1.23 to 1.85, p<0.001; I2=78%). In addition, subgroup analysis showed consistent results in patients with type 1 and type 2 diabetes, and in studies evaluating the short-term and long-term GV (p for subgroup difference=0.82 and 0.53). Finally, results of subgroup analysis also suggested that the association between GV and risk of DPN were not significantly affected by study design, follow-up durations, diagnostic methods for DPN, adjustment of mean glycated hemoglobin A1c, or study quality scores (p for subgroup difference all>0.05). A high GV may be associated with an increased incidence of DPN.

Comparative Effect of Glucose-Lowering Drugs for Type 2 Diabetes Mellitus on Stroke Prevention: A Systematic Review and Network Meta-Analysis

BACKGRUOUND

There is still a lack of research on which diabetic drugs are more effective in preventing stroke. Our network metaanalysis aimed to compare cerebrovascular benefits among glucose-lowering treatments.

METHODS

We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and the ClinicalTrials.gov registry for clinical trials from inception through May 25, 2021. We included both prespecified cerebrovascular outcomes and cerebrovascular events reported as severe adverse events. Subgroup analyses were conducted by stroke subtype, publication type, age of patients, baseline glycosylated hemoglobin (HbA1c), duration of type 2 diabetes mellitus, and cardiovascular risks.

RESULTS

Of 2,861 reports and 1,779 trials screened, 79 randomized controlled trials comprising 206,387 patients fulfilled the inclusion criteria. In the pairwise meta-analysis, the use of glucagon-like peptide-1 (GLP-1) agonist was associated with a lower risk of total stroke compared with placebo (relative risk [RR], -0.17; 95% confidence interval [CI], -0.27 to -0.07). In the network meta- analysis, only the use of sodium-glucose cotransporter-2 (SGLT-2) inhibitor was associated with a reduction of total stroke, compared with placebo (RR, 0.81; 95% CI, 0.67 to 0.98). In the subgroup analyses, the use of SGLT-2 inhibitor and GLP-1 agonist was associated with a lower risk of stroke in those with high HbA1c (≥8.0) and low-risk of cardiovascular disease, respectively.

CONCLUSION

SGLT-2 inhibitors and GLP-1 agonists were shown to be beneficial for stroke prevention in patients with type 2 diabetes mellitus.

Pramlintide an Adjunct to Insulin Therapy: Challenges and Recent Progress in Delivery

Dysregulation of various glucoregulatory hormones lead to failure of insulin monotherapy in patients with diabetes mellitus due to various reasons, including severe hypoglycemia, glycemic hypervariability, and an increased risk of microvascular complications. However, pramlintide as an adjunct to insulin therapy enhances glucagon suppression and thereby offers improved glycemic control. Clinical studies have shown that pramlintide improves glycemic control, reduces postprandial glucose excursions, and promotes weight loss in patients with type 1 and type 2 diabetes. Although clinical benefits of pramlintide are well reported, there still exists a high patient resistance for the therapy, as separate injections for pramlintide and insulin must be administered. Although marketed insulin formulations generally demonstrate a peak action in 60-90 minutes, pramlintide elicits its peak concentration at around 20-30 minutes after administration. Thus, owing to the significant differences in pharmacokinetics of exogenously administered pramlintide and insulin, the therapy fails to elicit its action otherwise produced by the endogenous hormones. Hence, strategies such as delaying the release of pramlintide by using inorganic polymers like silica, synthetic polymers like polycaprolactone, and lipids have been employed. Also, approaches like noncovalent conjugation, polyelectrolyte complexation, and use of amphiphilic excipients for codelivery of insulin and pramlintide have been explored to address the issues with pramlintide delivery and improve patient adherence to the therapy. This approach may usher in a new era of diabetes management, offering patients multiple options to tailor their treatment and improve their quality of life. SIGNIFICANCE STATEMENT: To our knowledge, this is the first report that summarizes various challenges in insulin and pramlintide codelivery and strategies to overcome them. The paper also provides deeper insights into various novel formulation strategies for pramlintide that could further broaden the reader's understanding of peptide codelivery.

Glycemic variability and the risk of atrial fibrillation: a meta-analysis

Background

Glycemic variability (GV) has been associated with vascular complications in patients with diabetes. However, the relationship between GV and risk of atrial fibrillation (AF) remains not fully determined. We therefore conducted a systematic review and meta-analysis to evaluate the above association.

Methods

Medline, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure were searched for longitudinal follow-up studies comparing the incidence of AF between patients with higher versus lower GV. A random-effects model incorporating the potential heterogeneity was used to pool the results.

Results

Nine cohort studies with 6,877,661 participants were included, and 36,784 (0.53%) participants developed AF during follow-up. Pooled results showed that a high GV was associated with an increased risk of AF (risk ratio [RR]: 1.20, 95% confidence interval [CI]: 1.11 to 1.30, p < 0.001, I² = 20%). Subgroup analyses suggested consistent association between GV and AF in prospective (RR: 1.29, 95% CI: 1.05 to 1.59, p = 0.01) and retrospective studies (RR: 1.18, 95% CI: 1.08 to 1.29, p = 0.002), in diabetic (RR: 1.24, 95% CI: 1.03 to 1.50, p = 0.03) and non-diabetic subjects (RR: 1.13, 95% CI: 1.00 to 1.28, p = 0.05), in studies with short-term (RR: 1.25, 95% CI: 1.11 to 1.40, p < 0.001) and long-term GV (RR: 1.18, 95% CI: 1.05 to 1.34, p = 0.006), and in studies with different quality scores (p for subgroup difference all > 0.05).

Conclusion

A high GV may predict an increased risk of AF in adult population.

Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes

Purpose

To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors.

Patients and Methods

Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually.

Results

Three HbA1c trajectories were distinguished over six years: "stable, adequate" (86.5%), "improving, but inadequate" (7.3%), and "fluctuating, inadequate" (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels.

Conclusion

Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient's unique risk factors offering a practical tool for clinicians to support treatment planning.

Acute glycemic variability and mortality of patients with acute stroke: a meta-analysis

BACKGROUND

Increased glucose fluctuation has been related to poor prognosis in patients with critical illnesses, while its prognostic role in patients with acute stroke remains unknown. The meta-analysis aimed to evaluate the association between the acute glycemic variation (GV) and mortality risk in patients with acute stroke.

METHODS

Cohort studies were obtained by searching Medline, Web of Science, Embase, Wanfang and CNKI databases. A random-effect model which incorporates the intra-study heterogeneity was chosen to pool the results.

RESULTS

Ten cohort studies with 1433 patients were included, and 280 (19.5%) of them died within 90 days of disease onset. Results of the meta-analyses showed that a higher acute GV was associated with an increased risk of early mortality in patients with acute stroke, as indicated by GV measured with the coefficient of variation of blood glucose (CVBG, odds ratio [OR]: 2.24, 95% CI 1.40 to 3.58, p < 0.001, I2 = 73%), the standard deviation of blood glucose (SDBG, OR: 2.31, 95% CI 1.70 to 3.13, p < 0.001, I2 = 50%), and the mean amplitude of glycemic excursion (OR: 3.57, 95% CI 1.44 to 8.85, p = 0.006, I2 = 23%). For acute GV measured with CVBG and SDBG, subgroup analyses showed consistent results in patients with acute ischemic and hemorrhagic stroke, and for studies reporting 28-day and 90-day all-cause mortality (p for subgroup analysis all > 0.05).

CONCLUSIONS

Higher acute GV may be an independent risk factor of early mortality in patients with acute stroke.