HbA1c variability predicts cardiovascular complications in type 2 diabetes regardless of being at glycemic target

HbA1c variability and all-cause mortality in type 1 and type 2 diabetes: A population-based cohort study using electronic health records

AIMS

To investigate associations between HbA1c variability and all-cause mortality in individuals with diabetes, accounting for average HbA1c level.

METHODS

Mean HbA1c and variability score (HVS) were estimated for people aged 31-90 with diabetes (type 1 = 20,347, type 2 = 409,821) with 4 + HbA1c measurements recorded in the Clinical Practice Research Datalink in 2011-14 and alive on 1/1/2015. Cox models estimated hazard ratios (HR) for all-cause mortality, ascertained from national linked mortality data during 2015-17. HbA1c level and variability were mutually adjusted for each other and other measured confounders.

RESULTS

Greater HbA1c variability was associated with younger age, non-white ethnicities (type 1 only), obesity, co-morbidities, and living in deprived areas. During follow-up, 1,043 (5.1 %) individuals with type 1 diabetes and 40,723 (9.9 %) individuals with type 2 diabetes died. In those with the most HbA1c variability compared to the least (HVS = 80-100 vs 0-20), the estimated adjusted HRs for mortality were 2.78 (95 %CI 2.15, 3.60) in type 1 diabetes and 1.91 (1.83, 1.99) in type 2 diabetes.

CONCLUSIONS

Variability in HbA1c was associated with greater subsequent mortality among people living with diabetes, independent from average HbA1c. Future research should investigate whether reducing HbA1c variability over time in selected patients lowers mortality risk independent of HbA1c level improvements.

Analysis glycemic variability in pregnant women with various type of hyperglycemia

OBJECTIVE

The study primarily aims to compare alterations in the daily patterns of glucose fluctuations across individuals with different kinds of diabetes in pregnancy and secondly investigate influencing factors that may react with glucose variations.

METHODS

We conducted a retrospective cohort study of 776 pregnant women in Shanghai General Hospital. We grouped participants who were exposed to gestational hyperglycemia into 5 sub-groups [Type 1 diabetes (T1DM), Type 2 diabetes (T2DM), Overt diabetes, Gestational diabetes (GDMA1 and GDMA2). Demographic variables and GV parameters were compared among 5 groups through ANOVA-test and Chi-square test. We estimated odd ratios (ORs) for the association between glucose coefficient of variation (CV) and possible influencing variables.

RESULTS

A final total of 776 pregnant women were analyzed. The proportion of pregnant women with pre-gestational diabetes was 31.83% (T1DM: 3.35%,T2DM: 28.48%), ODM 26.68%, and GDM was 41.49% (GDMA1:18.04%, GDMA2: 23.45%). T1DM group performed greatest glucose fluctuations with a CV value 35.02% whereas the number in all the other groups was no more than 22.82% (ODM group). In terms of achieving glycemic control target, only 57.70% participants hit the goal while all the other groups achieved the standard with at least a percentage of 94.20% (ODM group). Other parameters (GMI < 6.0%, GA < 15.70% and HbA1c < 6.0%) showed similar trends in each group. On multivariate logistic regression analysis of possible factors influencing CV, only body mass index (BMI) (OR: 0.754, 95% CI: 0.585-0.971; P = 0.029), HOMA- β (OR:0.969, 95%CI: 0.959-0.976; P = 0.037) and fasting plasma glucose (FPG) (OR: 1.832, 95% CI: 1.170-2.870; P = 0.008) reached statistical significance.

CONCLUSIONS

Pregnant women with type 1 or type 2 diabetes exhibit significantly greater glycemic variability compared to those with gestational diabetes, with the ODM group showing intermediate variability, and BMI, HOMA-β, and FPG identified as independent risk factors for unstable glucose variability.

Decoding Health: Exploring Essential Biomarkers Linked to Metabolic Dysfunction-Associated Steatohepatitis and Type 2 Diabetes Mellitus

The investigation of biomarkers for metabolic diseases such as type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatohepatitis (MASH) reveals their potential for advancing disease treatment and addressing their notable overlap. The connection between MASH, obesity, and T2DM highlights the need for an integrative management approach addressing mechanisms like insulin resistance and chronic inflammation. Obesity contributes significantly to the development of MASH through lipid dysregulation, insulin resistance, and chronic inflammation. Selective biomarker targeting offers a valuable strategy for detecting these comorbidities. Biomarkers such as CRP, IL-6, and TNF-α serve as indicators of inflammation, while HOMA-IR, fasting insulin, and HbA1c are essential for evaluating insulin resistance. Additionally, triglycerides, LDL, and HDL are crucial for comprehending lipid dysregulation. Despite the growing importance of digital biomarkers, challenges in research methodologies and sample variability persist, necessitating further studies to validate diagnostic tools and improve health interventions. Future opportunities include developing non-invasive biomarker panels, using multiomics, and using machine learning to enhance prognoses for diagnostic accuracy and therapeutic outcomes.

Dose-Response Relationship of Glucagon-like Peptide-1 Receptor Agonists on HbA1c and Body Weight in Type 2 Diabetes Mellitus: A Systematic Review and Network Meta-Analysis

OBJECTIVE

To explore the dose-response relationship of GLP-1 RAs in reducing glycated hemoglobin (HbA1c), body weight, and incidence of adverse events among type 2 diabetes mellitus (T2DM) patients.

METHODS

This systematic review and network meta-analysis followed the PRISMA guidelines. We conducted a systematic search of PubMed, Medline, Embase, Cochrane Central Register of Controlled Trials, and Web of Science for articles published up to October 20, 2024. Selected studies were randomized controlled trials focusing on adult T2DM patients treated with GLP-1 RAs. Primary outcomes included changes in HbA1c, body weight, and incidence of adverse events. Data extraction was performed by 2 independent researchers. Model-Based Network Meta-Analysis employing a random-effects Bayesian approach was used to synthesize the data.

RESULTS

The analysis included 62 trials with 17 140 participants. The study revealed a nonlinear dose-response relationship for various GLP-1 RAs, indicating significant reductions in HbA1c and body weight. Tirzepatide (10 mg/wk) was found to be particularly effective, reducing HbA1c by -1.76% (95% credible intervals: -2.10 to -1.41) and body weight by -8.63 kg (95% credible intervals: -9.84 to -7.39) without a significant increase in adverse events, highlighting its optimal balance between efficacy and safety. Other GLP-1 RAs also showed significant efficacy, underscoring the overall benefits of this class of medications in managing T2DM.

CONCLUSION

Our findings indicate a nonlinear dose-response relationship for GLP-1 RAs in managing T2DM. Tirzepatide at a dose of 10 mg/wk is identified as an optimal clinical dose offering a balance between efficacy and safety, contributing to refining T2DM management strategies and potentially enhancing patient outcomes.

A Narrative Review: Relationship Between Glycemic Variability and Emerging Complications of Diabetes Mellitus

A growing body of evidence emphasizes the role of glycemic variability (GV) in the development of conventional diabetes-related complications. Furthermore, advancements in diabetes management and increased life expectancy have led to the emergence of new complications, such as cancer, liver disease, fractures, infections, and cognitive dysfunction. GV is considered to exacerbate oxidative stress and inflammation, acting as a major mechanism underlying these complications. However, few reviews have synthesized the association between GV and these emerging complications or examined their underlying mechanisms. Hence, this narrative review provides a comprehensive discussion of the burden, risks, and mechanisms of GV in these complications, offering further evidence supporting GV as a potential therapeutic target for diabetes management.

When Does Metabolic Memory Start? Insights From the Association of Medical Diabetologists Annals Initiative on Stringent HbA1c Targets

Early, intensive glycemic control in patients with type 2 diabetes (T2D) is associated with long-term benefits in cardiovascular disease (CVD) development. Evidence on benefits of achieving HbA1c targets close to normal values is scant. Individuals with newly diagnosed T2D, without CVD at baseline, were identified in an Italian clinical registry (n = 251,339). We adopted three definitions of early exposure periods (0-1, 0-2, and 0-3 years). Mean HbA1c was categorized into HbA1c <5.7%, 5.7-6.4%, 6.5-7.0%, 7.1-8.0%, and >8.0%. The outcome was the incidence of major cardiovascular events. After a mean follow-up of 4.6 ± 2.9 years, at multivariate Cox regression analysis, compared with mean HbA1c <5.7% during the first year after diagnosis, the increase in the risk of CVD was 24%, 42%, 49%, and 56% for patients with HbA1c of 5.7-6.4%, 6.5-7.0%, 7.1-8.0%, and >8.0%, respectively. The same trend was documented in all exposure periods. In conclusion, our data support that an early achievement of stringent targets of HbA1c <5.7% is worthy for CVD prevention.

ARTICLE HIGHLIGHTS

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.

A Narrative Review of the Role of Blood Biomarkers in the Risk Prediction of Cardiovascular Diseases

Cardiovascular disease (CVD) is a global health crisis and a leading cause of morbidities and mortalities. Biomarkers whose evaluation would allow the detection of CVD at an early stage of development are actively sought. Biomarkers are objectively measured as indicators of health, disease, or response to an exposure or intervention, including therapeutic interventions. Hence, this review aims to identify biomarkers that can help predict CVD risk in the healthy population. This helps with risk prediction and is crucial for advancing preventive cardiology and improving clinical outcomes in a wide range of patient populations. Biomarkers such as atherogenic lipoproteins, fibrinogen, homocysteine, and thyroid-stimulating hormone (TSH) have been linked to CVD risk factors, including dyslipidemia, hypertension, diabetes, and obesity. When combined with conventional biomarkers, inflammatory markers such as C-reactive protein (CRP) can enhance risk prediction. However, biomarkers such as high-sensitivity troponin T (hsTnT) and N-terminal proBNP (NT-proBNP) are widely used as diagnostic biomarkers for heart failure (HF) and cardiac dysfunction, as they are released only after one to two hours of cardiovascular event occurrence. Myeloperoxidase (MPO) and procalcitonin (PCT) have developed into promising new biomarkers for the early detection of systemic bacterial infections as inflammatory markers, which are better diagnostic tools than screening. Combining biomarkers can improve test accuracy, but the best combinations for diagnosis or prognosis must be identified.

Alterations in aortic elasticity indices among type 2 diabetes patients in a low and middle income country using M-mode echocardiography: A cross-sectional comparative study

BACKGROUND

Diabetes is one of the leading causes of noncommunicable diseases worldwide. It is known to induce cardiovascular remodeling, which can result in a variety of complications, including a considerable increase in aortic stiffness. While studies in Western populations have explored these effects, data on Asians, mainly Vietnamese, are limited. This study aimed to assess aortic elasticity in type 2 diabetes mellitus (T2DM) patients compared to healthy individuals.

METHODS

This quantitative, cross-sectional study compared aortic elasticity indices between individuals with T2DM and healthy controls in Vietnam. Aortic elasticity indices were assessed for all participants using M-mode echocardiography.

RESULTS

A comparison between the healthy and T2DM groups revealed substantial differences in aortic elasticity indices. The aortic stiffness index (ASI) was significantly greater in the T2DM group than in the control group, with median values of 6.10 (3.64-12.47) and 3.79 (2.40-8.50), respectively (p = 0.003). Aortic strain (AS) was substantially lower in the T2DM group than in the control group, with median values of 8.21% (4.24-13.07) and 10.66% (6.01-18.23), respectively (p = 0.039). Furthermore, the median aortic compliance (AC, 10-2mm/mmHg) and aortic distensibility (AD, 10-3mmHg-1) in individuals with T2DM were 4.07 (2.28-7.44) and 3.08 (1.57-5.26), respectively, lower than those in the control group, with median values of 6.40 (3.08-10.75) and 5.33 (2.80-9.79). A longer diabetes duration was linked to a greater ASI (r = 0.43, p < 0.05), while the AS decreased (r = -0.37, p < 0.05).

CONCLUSIONS

Substantial variations in aorta elasticity indices were found in patients with T2DM using M-mode echocardiography. These differences highlight the impact of T2DM on vascular health. More research is needed to investigate the consequences of these discrepancies and their significance for clinical purposes.

Clinical implications and pharmacological considerations of glycemic variability in patients with type 2 diabetes mellitus

Glycemic variability (GV), independently of glycemic control, has emerged as a prognostic marker in patients with type 2 diabetes mellitus (DM). In this study, we assessed the prognostic value of long-term GV for predicting major adverse cardiovascular events (MACE) in our local population. We also assessed its prognostic value for diabetic microvascular complications (DMC) and its relationships with antidiabetic medications. This was a retrospective cohort study that recruited 680 patients with type 2 DM across 2015-2017. MACE were defined as: the composite of; total death, myocardial infarction (MI), stroke, hospitalization due to heart failure, and revascularization. GV was calculated for two glycemic control markers: glycated hemoglobin (G-Hb) and fasting blood sugar (FBS); via three metrics- standard deviation (SD), coefficient of variation (CV), and variability independent from the mean (VIM). Cox proportional hazard models and Kruskal-Wallis tests were used in the statistical analysis. 105 events classified as MACE were identified in 86 patients and 104 DMC in 98 patients in an average follow-up period of 78.43 months. Long-term GV was found to be an independent predictor of MACE, particularly for FBS-CV but not a predictor of DMC. FBS-CV ≥ 17.51% as compared with < 17.51% was a significant and independent predictor of MACE, with HR 1.589 (95% CI; 1.022, 2.472) (P = 0.040). DMC were predicted mainly by the duration of type 2 DM, and by the glycemic control; similarly represented by G-Hb and FBS. Patients on metformin, and dipeptidyl peptidase (DPP) 4 inhibitors, had the lowest GV, as compared with patients whose treatments included insulin/sulphonylureas (P < 0.001). In our population, long-term GV predicted MACE: with FBS-CV superior to the "gold standard" glycemic control marker G-Hb. Further, GV may be explained, partially at least, by the choice of antidiabetic medications: this finding might contribute to the cardiovascular protection attributed to one class rather than another.

Glycemic Variability as a Predictor of Graft Failure Following Infrainguinal Bypass for Peripheral Arterial Disease: A Retrospective Cohort Study

BACKGROUND

Glycemic variability (GV), measured as the change in visit-to-visit glycated hemoglobin (HbA1c), increases the risk of multiple adverse outcomes. However, the impact of GV on graft patency following infrainguinal bypass (IIB) is unknown. A retrospective cohort study was undertaken to assess the impact of GV on graft patency.

METHODS

A 3-year single-center retrospective case notes analysis of all people undergoing IIB between 2017 and 2019. Rutherford stage, graft conduit, level of bypass, procedure details, baseline demographics, comorbidities, and GV were assessed. Time to reintervention, ipsilateral amputation, or death was recorded to determine primary patency (PP).

RESULTS

One hundred six IIB outcomes were analyzed: mean (± standard deviation) age 68.0 (9.2) years; 69 (65.1%) male, 37 (33.9%), 75 (70.8%) had diabetes mellitus; and 46 (43.4%) underwent elective procedures. GV > 9.1% was associated with significantly lower median PP than GV < 9.1%, 198 (97-753.5) vs. 713 (166.5-1,044.5) days (P = 0.045). On univariate analysis, GV > 9.1% vs. < 9.1% was significantly associated with PP (hazard ratio [HR] 1.85 [confidence interval {CI} 1.091-3.136], P = 0.022). Bypass level was also a univariate predictor, with below knee bypasses (HR 2.31 [CI 1.164-4.564], P = 0.017), and tibial (HR 2.00 [CI 1.022-3.090], P < 0.043) having lower PP than above knee bypasses. On multivariate adjustment, GV > 9.1% and level of bypass remained independent predictors of PP, HR 1.96 (95% CI: 1.12-3.42, P = 0.018) and HR 2.54 (95% CI: 1.24-5.22, P = 0.011), respectively.

CONCLUSIONS

GV is an independent predictor of PP following infrainguinal bypass, thus optimizing GV should be a therapeutic target.

Relationship Between Glycosylated Hemoglobin Variability and the Severity of Coronary Artery Disease in Patients With Type 2 Diabetes Mellitus

Background: Glycosylated hemoglobin (HbA1c) variability is a risk factor for cardiovascular complications in patients with Type 2 diabetes mellitus (T2DM), but its relationship with the severity of coronary artery disease (CAD) is unclear. Methods: Patients with T2DM who underwent coronary angiography due to angina were enrolled. HbA1c variability was expressed as coefficient of variation (CV), standard deviation (SD), variability independent of mean (VIM), and time in range (TIR). The severity of CAD was expressed by the number of involved vessels and Gensini score. Multivariate regression models were constructed to test the relationship between HbA1c variability, number of involved vessels, and the Gensini score, followed by linear regression analysis. Results: A total of 147 patients were included. In multivariate analysis, VIM-HbA1c (OR = 2.604; IQR: 1.15, 5.90; r = 0.026) and HbA1cTIR (OR = 0.13; IQR: 0.04, 0.41; r < 0.001) were independent risk factors for the number of involved vessels. After adjustment, HbA1cTIR (OR = 0.01; IQR: 0.002, 0.04; r < 0.001), SD-HbA1c (OR = 4.12, IQR: 1.64, 10.35; r = 0.001), CV-HbA1c (OR = 1.41, IQR: 1.04, 1.92; r = 0.007), and VIM-HbA1c (OR = 3.26; IQR: 1.43, 7.47; r = 0.003) were independent risk factors for the Gensini score. In the linear analysis, the Gensini score was negatively correlated with HbA1cTIR (β = -0.629; r < 0.001) and positively correlated with SD-HbA1c (β = 0.271; r = 0.001) and CV-HbA1c (β = 0.176; r = 0.033). After subgroup analysis, HbA1cTIR was a risk factor for the number of involved vessels. The Gensini score was negatively correlated with HbA1cTIR and positively correlated with SD-HbA1c at subgroups of subjects with a mean HbA1c ≤ 7%. Conclusions: Our analysis indicates that HbA1c variability, especially HbA1cTIR, plays a role for the severity of CAD in patients with T2DM. HbA1c variability may provide additional information and require management even at the glycemic target. Translational Aspects: Studies have shown that HbA1c variability is related to cardiovascular complications. Further, we explore the correlation between HbA1c variability and the severity of CAD. HbA1c variability is a risk factor for coronary stenosis in T2DM. It may be a potential indicator reflecting glycemic control for the prevention and treatment of cardiovascular complications.

BMI variability and cardiovascular outcomes within clinical trial and real-world environments in type 2 diabetes: an IMI2 SOPHIA study

BACKGROUND

BMI variability has been associated with increased cardiovascular disease risk in individuals with type 2 diabetes, however comparison between clinical studies and real-world observational evidence has been lacking. Furthermore, it is not known whether BMI variability has an effect independent of HbA1c variability.

METHODS

We investigated the association between BMI variability and 3P-MACE risk in the Harmony Outcomes trial (n = 9198), and further analysed placebo arms of REWIND (n = 4440) and EMPA-REG OUTCOME (n = 2333) trials, followed by real-world data from the Tayside Bioresource (n = 6980) using Cox regression modelling. BMI variability was determined using average successive variability (ASV), with first major adverse cardiovascular event of non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death (3P-MACE) as the primary outcome.

RESULTS

After adjusting for cardiovascular risk factors, a + 1 SD increase in BMI variability was associated with increased 3P-MACE risk in Harmony Outcomes (HR 1.12, 95% CI 1.08-1.17, P < 0.001). The most variable quartile of participants experienced an 87% higher risk of 3P-MACE (P < 0.001) relative to the least variable. Similar associations were found in REWIND and Tayside Bioresource. Further analyses in the EMPA-REG OUTCOME trial did not replicate this association. BMI variability's impact on 3P-MACE risk was independent of HbA1c variability.

CONCLUSIONS

In individuals with type 2 diabetes, increased BMI variability was found to be an independent risk factor for 3P-MACE across cardiovascular outcome trials and real-world datasets. Future research should attempt to establish a causal relationship between BMI variability and cardiovascular outcomes.

Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts

This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic complication in these patients. We used data from two cohorts, namely the discovery (one hospital; n = 12,809) and validation (two hospitals; n = 2019) cohorts, recruited between 2008 and 2022. The outcome of interest was the presence or absence of CVD at 3 years. We selected various ML-based models with hyperparameter tuning in the discovery cohort and performed area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. CVD was observed in 1238 (10.2%) patients in the discovery cohort. The random forest (RF) model exhibited the best overall performance among the models, with an AUROC of 0.830 (95% confidence interval [CI] 0.818-0.842) in the discovery dataset and 0.722 (95% CI 0.660-0.783) in the validation dataset. Creatinine and glycated hemoglobin levels were the most influential factors in the RF model. This study introduces a pioneering ML-based model for predicting CVD in Korean patients with T2DM, outperforming existing prediction tools and providing a groundbreaking approach for early personalized preventive medicine.

Effects of long-term HbA1c variability on serious infection risks in patients with type 2 diabetes and the influence of age, sex and ethnicity: A cohort study of primary care data

AIMS

Long-term HbA1c (glycated haemoglobin) variability is associated with micro- and macrovascular complications in Type 2 diabetes (T2D). We explored prospective associations between HbA1c variability and serious infections, and how these vary by HbA1c level, age, sex and ethnicity.

METHODS

411,963 T2D patients in England, aged 18-90, alive on 01/01/2015 in the Clinical Practice Research Datalink with ≥ 4 HbA1c measurements during 2011-14. Poisson regression estimated incidence rate ratios (IRRs) for infections requiring hospitalisation during 2015-19 by HbA1c variability score (HVS) and average level, adjusting for confounders, and stratified by age, sex, ethnicity and average level. Attributable risk fractions (AF) were calculated using reference categories for variability (HVS < 20) and average level (42-48 mmol/mol).

RESULTS

An increased infection risk (IRR > 1.2) was seen with even modest variability (HVS ≥ 20, 73 % of T2D patients), but only at higher average levels (≥64 mmol/mol, 27 % patients). Estimated AFs were markedly greater for variability than average level (17.1 % vs. 4.1 %). Associations with variability were greater among older patients, and those with lower HbA1c levels, but not observed among Black ethnicities.

CONCLUSIONS

HbA1c variability between T2D patients' primary care visits appears to be associated with more serious infections than average level overall. Well-designed trials could test whether these associations are causal.

Advances in secondary prevention mechanisms of macrovascular complications in type 2 diabetes mellitus patients: a comprehensive review

Type 2 diabetes mellitus (T2DM) poses a significant global health burden. This is particularly due to its macrovascular complications, such as coronary artery disease, peripheral vascular disease, and cerebrovascular disease, which have emerged as leading contributors to morbidity and mortality. This review comprehensively explores the pathophysiological mechanisms underlying these complications, protective strategies, and both existing and emerging secondary preventive measures. Furthermore, we delve into the applications of experimental models and methodologies in foundational research while also highlighting current research limitations and future directions. Specifically, we focus on the literature published post-2020 concerning the secondary prevention of macrovascular complications in patients with T2DM by conducting a targeted review of studies supported by robust evidence to offer a holistic perspective.

Factors associated with diabetic foot ulcers and lower limb amputations in type 1 and type 2 diabetes supported by real-world data from the German/Austrian DPV registry

AIMS

Diabetic foot ulcer (DFU) is a leading cause of lower limb amputations in people with diabetes. This study was aimed to retrospectively analyze factors affecting DFU using real-world data from a large, prospective central-European diabetes registry (DPV [Diabetes-Patienten-Verlaufsdokumentation]).

MATERIALS AND METHODS

We matched adults with type 1 (T1D) or type 2 diabetes (T2D) and DFU to controls without DFU by diabetes type, age, sex, diabetes duration, and treatment year to compare possible risk factors. Cox regression was used to calculate hazard ratios for amputation among those with DFU.

RESULTS

In our cohort (N = 63 464), male sex, taller height, and diabetes complications such as neuropathy, peripheral artery disease, nephropathy, and retinopathy were associated with DFU (all p < .001). Glycated hemoglobin (HbA1c) was related to DFU only in T1D (mean with 95% confidence interval [CI]: 7.8 [6.9-9.0] % vs 7.5 [6.8-8.5] %, p < .001). High triglycerides and worse low-density lipoprotein/high-density lipoprotein ratio were also associated with DFU in T1D, whereas smoking (14.7% vs 13.1%) and alcohol abuse (6.4% vs 3.8%, both p < .001) were associated with DFU in T2D. Male sex, higher Wagner grades, and high HbA1c in both diabetes types and insulin use in T2D were associated with increased hazard ratios for amputations.

CONCLUSIONS

Sex, body height, and diabetes complications were associated DFU risk in adults with T1D and T2D. Improvement in glycemic control and lipid levels in T1D and reduction of smoking and drinking in T2D may be appropriate interventions to reduce the risk for DFU or amputations.

Evolution of insulin therapy in the light of innovative technologies of the 21^st century

The advent of insulin biosimilars has ushered in a new era in the treatment of diabetes mellitus, promising increased accessibility and affordability of this life-saving medication. This comprehensive review explores the evolving landscape of insulin biosimilars, focusing on their therapeutic equivalence, regulatory considerations, and clinical implications. Biosimilars are biological drugs produced using innovative technologies that replicate the structure and action of the reference drug in a very close way and are not inferior in effectiveness and other characteristics to the original, but are more affordable in pricing. Assessing their therapeutic equivalence requires a multifaceted approach including physicochemical and biological characterization, preclinical studies, and performance testing. The introduction of biosimilar drugs into clinical practice is gradually gaining importance for global health, especially in the treatment of oncological, autoimmune, endocrine diseases and, in particular, diabetes. The process of their registration and introduction into patient treatment practice has already been standardized in developed countries, international documents necessary for implementation have been issued, and convincing and clear confirmation of the positive results obtained during preclinical and clinical studies proving the bioequivalence and interchangeability of the biosimilar and the reference drug is required. The clinical implications of insulin biosimilars are of paramount importance, impacting patient outcomes, healthcare costs, and overall diabetes management. This review synthesizes existing evidence on the efficacy, safety, and immunogenicity of insulin biosimilars, providing insights into their potential role in diabetes therapy. Insulin biosimilars represent a promising avenue for expanding access to insulin therapy while addressing the economic burden of diabetes care. This review underscores the importance of continued research, robust regulatory oversight, and informed clinical decision-making to maximize the benefits of insulin biosimilars for patients and healthcare systems worldwide.

Glycated hemoglobin variability and the risk of cardiovascular events in patients with prediabetes and type 2 diabetes mellitus: A post-hoc analysis of a prospective and multicenter study

AIMS/INTRODUCTION

High glycated hemoglobin (HbA1c) variability has been reported to be linked with cardiovascular events in type 2 diabetes patients. Only a few studies have been carried out on Asian patients. This study aimed to investigate the association of prediabetes and type 2 diabetes in Asian patients by performing a post-hoc analysis of a multicenter, prospective, observational study.

MATERIALS AND METHODS

Data for prediabetes and type 2 diabetes patients were retrieved from a multicenter national registry entitled "CORE-Thailand study." The primary outcome was 4P-MACE (major adverse cardiovascular events, including non-fatal myocardial infarction, heart failure hospitalization, non-fatal stroke and all-cause death). Patients were stratified according to quartiles of HbA1c standard deviation. The Cox proportional hazards regression model was used to estimate the association of HbA1c variability with incident cardiovascular disease.

RESULTS

A total of 3,811 patients with prediabetes and type 2 diabetes were included. The median follow-up duration was 54 months. In the fully adjusted model, the highest quartile of HbA1c variability showed a statistically significant association with 4P-MACE (hazard ratio [HR] 2.77, 95% confidence interval [CI] 1.77-4.35), fatal and non-fatal myocardial infarction (HR 6.91, 95% CI 1.90-25.12), hospitalization for heart failure (HR 3.34, 95% CI 1.20-9.26) and all-cause death (HR 3.10, 95% CI 1.72-5.57). All these outcomes were statistically significantly different among four quartiles of HbA1c (log-rank P-value <0.05). Fatal and non-fatal stroke showed no statistically significant association with high HbA1c variability.

CONCLUSION

High HbA1c variability in the highest quartile showed a statistically significant association with multiple adverse cardiovascular events in an Asian population. Minimizing HbA1c fluctuation during long-term follow up should be another important objective for type 2 diabetes patients.

Factors Associated with Diabetic Complication Index among Type 2 Diabetes Patients: Focusing on Regular Outpatient Follow-up and HbA1c Variability

PURPOSE

Preventing diabetic complications involves regular outpatient follow-up and maintaining low variability in hemoglobin A1c (HbA1c) levels. This study investigated the factors associated with diabetic complications, with a specific focus on the impact of regular outpatient follow-up and HbA1c variability, among patients with type 2 diabetes.

METHODS

The study design was secondary data analysis of electronic medical records from a university hospital in Korea. It included patients aged 40-79 with type 2 diabetes who were prescribed diabetes medication within three months of their first HbA1c test by an endocrinologist and were followed up for at least five years. Follow-up regularity, adjusted standard deviation of HbA1c levels, and diabetic complication indices were collected. Data were analyzed using the Chi-square test, independent t-test, repeated measures analysis of variance, and multiple regression analysis.

RESULTS

The study included 1566 patients. Lower follow-up regularity was observed in patients of older age, with comorbidities, diabetic complications, insulin treatment, a history of hospitalization, lower baseline estimated glomerular filtration rate (eGFR) and total cholesterol (TC), and higher HbA1c variability. Higher HbA1c variability was observed in younger patients without comorbidity but with insulin treatment, a history of hospitalization, higher baseline blood glucose (BG), HbA1c, TC, and triglyceride levels. HbA1c variability had the strongest influence on BG and HbA1c levels at the five-year follow-up. Baseline eGFR and TC were the most influential factors for their respective levels at the five-year follow-up. Follow-up regularity significantly affected BG, HbA1c, eGFR, and TC at five-year follow-up.

CONCLUSIONS

It has been shown that several variables besides regular follow-up and HbA1c variability have an influence. However, these are the two that can be corrected through nursing intervention and are important, so intervention on these is important.

Diabetes knowledge predicts HbA1c levels of people with type 2 diabetes mellitus in rural China: a ten-month follow-up study

Improving diabetes self-management (DSM) is facing real-world challenges among people with type 2 diabetes mellitus (T2DM) who have a low education level in resource-limited areas. This study aimed to investigate whether diabetes knowledge could predict glycemic levels in people with T2DM in rural China. This analytical cross-sectional study recruited 321 people with T2DM from eight villages by purposive sampling at baseline. After 10 months, 206 patients completed the follow-up survey and HbA1c tests, with a response rate of 64.17% (206/321). Multiple regression analysis was employed to explore the correlation between diabetes knowledge and HbA1c levels. The patient's diabetes knowledge was significantly negatively correlated with HbA1c levels before and after controlling for covariates in both hierarchical multiple regression and multiple logistic regression (p < 0.01). In addition, other influencing factors, including sex, age, marital status, employment status, income, and HbA1c levels at baseline, were also identified. Diabetes knowledge could predict HbA1c levels significantly among patients with low education levels in rural China. Therefore, interventions on improving diabetes knowledge need to be strengthened for patients in rural China so that they can improve their health outcomes and reduce the disease burden.

Association Between Visceral Fat Area and Glycated Hemoglobin in Type 2 Diabetics: A Retrospective Study

Introduction

Obesity is a risk factor for the development of type 2 diabetes (T2DM) as well as its associated metabolic complications. Central obesity, characterized by an increased visceral fat area (VFA), is contributed to the development of T2DM. However, the relationship between VFA and HbA1c is not particularly clear.

Methods

A total of 3173 patients with T2DM participated in the study at the Metabolic Management Center (MMC), with anthropometric and biochemical measurements recorded. To examine the association between HbA1c and VFA, fitting curves were plotted, facilitating a comprehensive observation of their relationship.

Results

HbA1c was inversely associated with VFA (β -1.79, 95% CI -2.34~-1.24, P < 0.001). The fitted curve shows that VFA increased with the increase of HbA1c when it was less than 8.62%. When it was greater than 8.62%, VFA decreased as HbA1c increased. Using linear inflection point analysis, we found that its inflection point interval falls within 8.36%~8.88%.

Conclusion

VFA was positively associated with HbA1c in individuals with T2DM. Furthermore, the relationship between the two variables was an inverted U-shaped association.

Fotagliptin monotherapy with alogliptin as an active comparator in patients with uncontrolled type 2 diabetes mellitus: a randomized, multicenter, double-blind, placebo-controlled, phase 3 trial

BACKGROUND

Dipeptidyl peptidase-4 inhibitors (DPP-4i) have become firmly established in treatment algorithms and national guidelines for improving glycemic control in type 2 diabetes mellitus (T2DM).To report the findings from a multicenter, randomized, double-blind, placebo-controlled phase 3 clinical trial, which was designed to assess the efficacy and safety of a novel DPP-4 inhibitor fotagliptin in treatment-naive patients with T2DM.

METHODS

Patients with T2DM were randomized to receive fotagliptin (n = 230), alogliptin (n = 113) or placebo (n = 115) at a 2:1:1 ratio for 24 weeks of double-blind treatment period, followed by an open-label treatment period, making up a total of 52 weeks. The primary efficacy endpoint was to determine the superiority of fotagliptin over placebo in the change of HbA1c from baseline to Week 24. All serious or significant adverse events were recorded.

RESULTS

After 24 weeks, mean decreases in HbA1c from baseline were -0.70% for fotagliptin, -0.72% for alogliptin and -0.26% for placebo. Estimated mean treatment differences in HbA1c were -0.44% (95% confidence interval [CI]: -0.62% to -0.27%) for fotagliptin versus placebo, and -0.46% (95% CI: -0.67% to -0.26%) for alogliptin versus placebo, and 0.02% (95%CI: -0.16% to 0.19%; upper limit of 95%CI < margin of 0.4%) for fotagliptin versus alogliptin. So fotagliptin was non-inferior to alogliptin. Compared with subjects with placebo (15.5%), significantly more patients with fotagliptin (37.0%) and alogliptin (35.5%) achieved HbA1c < 7.0% after 24 weeks of treatment. During the whole 52 weeks of treatment, the overall incidence of hypoglycemia was low for both of the fotagliptin and alogliptin groups (1.0% each). No drug-related serious adverse events were observed in any treatment group.

CONCLUSIONS

In summary, the study demonstrated improvement in glycemic control and a favorable safety profile for fotagliptin in treatment-naive patients with T2DM.

TRIAL REGISTRATION

ClinicalTrail.gov NCT05782192.

Intensified glycemic control by HbA1c for patients with coronary heart disease and Type 2 diabetes: a review of findings and conclusions

The occurrence and development of coronary heart disease (CHD) are closely linked to fluctuations in blood glucose levels. While the efficacy of intensified treatment guided by HbA1c levels remains uncertain for individuals with diabetes and CHD, this review summarizes the findings and conclusions regarding HbA1c in the context of CHD. Our review showed a curvilinear correlation between regulated level of HbA1c and therapeutic effectiveness of intensified glycemic control among patients with type 2 diabetes and coronary heart disease. It is necessary to optimize the dynamic monitoring indicators of HbA1c, combine genetic profiles, haptoglobin phenotypes for example and select more suitable hypoglycemic drugs to establish more appropriate glucose-controlling guideline for patients with CHD at different stage of diabetes.

Hemoglobin A1c Variability Metrics Predict Coronary Artery Calcium and Cardiovascular Events in Type 1 Diabetes: The CACTI Study

CONTEXT

Interventions that decrease mean glucose have reduced rates of micro- and macrovascular complications in type 1 diabetes (T1D). However, the difference in cardiovascular risk between people with T1D and the general population endures, suggesting that factors beyond hemoglobin A1C (HbA1c) normalization drive cardiovascular outcomes.

OBJECTIVE

To determine whether various HbA1c metrics predict anatomic cardiovascular disease (CVD) risk factors and/or CVD events in people with T1D.

METHODS

We used linear regression to analyze the relationship of several HbA1c metrics to anatomic CVD risk factors and then used Cox regression to model their relationship to incident CVD events in the CACTI Study (ClinicalTrials.gov Identifier: NCT00005754).

RESULTS

In linear regression models adjusted for age, sex, and T1D duration, baseline Hba1c (b = 0.3998, P = 0.0236), mean HbA1c (b = 0.5385, P = 0.0109), and HbA1c SD (b = 1.1521, P = 0.0068) were each positively associated with square root transformed coronary artery calcium volume. Conversely, only mean HbA1c (b = 1.659, P = 0.0048) positively associated with pericardial adipose tissue volume. In survival models adjusted for age, sex, and T1D duration, baseline HbA1c [hazard ratio (HR): 1.471, 95% CI: 1.257-1.721], mean HbA1c (HR: 1.850, 95% CI: 1.511-2.264), time-varying HbA1c (HR: 1.500, 95% CI: 1.236-1.821), and HbA1c SD (HR: 1.665, 95% CI: 1.022-2.711) each independently predicted CVD events over 14.3 ± 5.2 person-years of follow-up.

CONCLUSIONS/INTERPRETATION

We found that various HbA1c metrics positively correlated with CAC volume and independently predicted incident CVD events in the CACTI T1D cohort. These associations with CVD events persisted for baseline HbA1c, mean HbA1c, and time-varying HbA1c even after adjustment for numerous CVD risk factors.

Risk factor variability and cardiovascular risk among patients with diabetes: a nationwide observational study

AIMS

Cardiovascular risk factor control fluctuates, tends to change over time, and is potentially impacted by multifactorial interactions. Currently, the presence of risk factors, rather than their variability or interplay with one another, is taken into account to define the population at risk. The association between variability of risk factors and cardiovascular morbidity and mortality risk among patients with Type 2 diabetes mellitus (T2DM) remains debatable.

METHODS AND RESULTS

Using registry-derived data, we identified 29 471 people with T2DM, without cardiovascular disease (CVD) at baseline, and with at least five measurements of risk factors. Variability for each variable was expressed as quartiles of the standard deviation during 3 years (exposure). The incidence of myocardial infarction, stroke, and all-cause mortality was assessed during 4.80 (2.40-6.70) years following the exposure phase. The association between the measures of variability and the risk of developing the outcome was investigated through multivariable Cox proportional-hazards regression analysis with stepwise variable selection. Then, the recursive partitioning and amalgamation (RECPAM) algorithm was used to explore the interaction among the variability of risk factors associated with the outcome. An association between the variability of HbA1c, body weight, systolic blood pressure, and total cholesterol with the outcome considered was found. Among the six classes of risk identified by RECPAM, patients with a high variability of both body weight and blood pressure had the highest risk [Class 6, hazard ratio (HR) = 1.81; 95% confidence interval (CI) 1.61-2.05] compared with patients with low variability of both body weight and total cholesterol (Class 1, reference), despite a progressive reduction in the mean level of risk factors during successive visits. Individuals with high weight variability but low-moderate systolic blood pressure variability (Class 5, HR = 1.57; 95% CI 1.28-1.68), patients with moderate/high weight variability associated with high/very high HbA1c variability (Class 4, HR = 1.33; 95% CI 1.20-1.49), subjects with moderate/high weight variability and with low/moderate HbA1c variability (Class 3, HR = 1.12; 95% CI 1.00-1.25), as well as those with low weight variability associated with high/very high total cholesterol variability (Class 2, HR = 1.14; 95% CI 1.00-1.30) also showed a significant increase in the risk of an event.

CONCLUSION

Combined high variability of two risk factors, particularly body weight and blood pressure, is associated with cardiovascular risk among patients with T2DM. These findings highlight the importance of continuous balancing of multiple risk factors.

Glycemic dispersion: a new index for screening high glycemic variability

OBJECTIVE

For patients with diabetes, high-frequency and -amplitude glycemic variability may be more harmful than continuous hyperglycemia; however, there is still a lack of screening indicators that can quickly and easily assess the level of glycemic variability. The aim of this study was to investigate whether the glycemic dispersion index is effective for screening high glycemic variability.

METHODS

A total of 170 diabetes patients hospitalized in the Sixth Affiliated Hospital of Kunming Medical University were included in this study. After admission, the fasting plasma glucose, 2-hour postprandial plasma glucose, and glycosylated hemoglobin A1c were measured. The peripheral capillary blood glucose was measured seven times in 24 h, before and after each of three meals and before bedtime. The standard deviation of the seven peripheral blood glucose values was calculated, and a standard deviation of > 2.0 was used as the threshold of high glycemic variability. The glycemic dispersion index was calculated and its diagnostic efficacy for high glycemic variability was determined by the Mann-Whitney U test, receiver operating characteristic (ROC) curve and, Pearson correlation analysis.

RESULTS

The glycemic dispersion index of patients with high glycemic variability was significantly higher than that of those with low glycemic variability (p < 0.01). The best cutoff value of the glycemic dispersion index for screening high glycemic variability was 4.21. The area under the curve (AUC) was 0.901 (95% CI: 0.856-0.945) and had a sensitivity of 0.781 and specificity of 0.905. It was correlated with the standard deviation of blood glucose values (r = 0.813, p < 0.01).

CONCLUSIONS

The glycemic dispersion index had good sensitivity and specificity for screening high glycemic variability. It was significantly associated with the standard deviation of blood glucose concentration and is simple and easy to calculate. It was an effective screening indicator of high glycemic variability.

Higher glucose fluctuation is associated with a higher risk of cardiovascular disease: Insights from pooled results among patients with diabetes

BACKGROUND

The relationship between glucose fluctuation and the risk of cardiovascular disease (CVD) in patients with diabetes remains elusive. Glycated hemoglobin (HbA1c) variability is a key parameter of glucose fluctuation.

METHODS

PubMed, Cochrane Library, Web of Science, and Embase were searched up to 1 July 2022. Studies reporting associations of HbA1c variability (HbA1c-SD), coefficient of variation of HbA1c (HbA1c-CV), and HbA1c variability score [HVS] with the risk of CVD among patients with diabetes were included. We used three different insights (a high-low value meta-analysis, a study-specific meta-analysis, and a non-linear dose-response meta-analysis) to explore the relationship between HbA1c variability and CVD risk. A subgroup analysis was also performed to screen the potential confounding factors.

RESULTS

A total of 14 studies with 254 017 patients with diabetes were eligible. The highest HbA1c variability was significantly associated with increased risks of CVD (HbA1c-SD, risk ratio [RR] 1.45; HbA1c-CV, RR 1.74; HVS, RR 2.46; all p < .001) compared to the lowest HbA1c variability. The RRs of CVD for per HbA1c variability were significantly >1 (all p < .001). The subgroup analysis for per HbA1c-SD found a significant exposure-covariate interaction in the types of diabetes (p = .003 for interaction). The dose-response analysis showed a positive association between HbA1c-CV and CVD risk (P for nonlinearity <.001).

CONCLUSIONS

Our study suggests that the higher glucose fluctuation is significantly associated with the higher CVD risk in diabetes patients based on HbA1c variability. The CVD risk associated with per HbA1c-SD might be higher among patients type 1 diabetes than patients with type 2 diabetes.

Weight variability and diabetes complications

Body weight is a recognized risk factor for cardiovascular diseases (CVD). More recently, weight variability, i.e. the oscillation of body weight over time, has also been suggested to be independently associated with development of CVD and mortality in subjects without diabetes and in people with both type 1 and type 2 diabetes (T2D). In T2D, weight variability emerged as one of the most relevant risk factors for CVD and it was suggested to interact with the variability of other risk factors to identify people at high cardiovascular risk. In addition, weight variability seems also to confer a higher risk for microvascular complications in people with T2D. While the exact mechanism linking weight variability to CVD is unknown, evidence from experimental models suggests that weight cycling promote an enduring pro-inflammatory program in the adipose tissue. Here we review the clinical evidence relative to the association of weight variability with CVD and microvascular complications of diabetes. We then briefly summarize the alterations proposed to explain this association. Finally, we synthesize the possible strategies, e.g. specific dietetic regimens and available glucose-lowering drugs, to minimize weight fluctuations.

Cardiorenal diseases in type 2 diabetes mellitus: clinical trials and real-world practice

Patients with type 2 diabetes mellitus (T2DM) can have multiple comorbidities and premature mortality due to atherosclerotic cardiovascular disease, hospitalization with heart failure and/or chronic kidney disease. Traditional drugs that lower glucose, such as metformin, or that treat high blood pressure and blood levels of lipids, such as renin-angiotensin-system inhibitors and statins, have organ-protective effects in patients with T2DM. Amongst patients with T2DM treated with these traditional drugs, randomized clinical trials have confirmed the additional cardiorenal benefits of sodium-glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1RA) and nonsteroidal mineralocorticoid receptor antagonists. The cardiorenal benefits of SGLT2i extended to patients with heart failure and/or chronic kidney disease without T2DM, whereas incretin-based therapy (such as GLP1RA) reduced cardiovascular events in patients with obesity and T2DM. However, considerable care gaps exist owing to insufficient detection, therapeutic inertia and poor adherence to these life-saving medications. In this Review, we discuss the complex interconnections of cardiorenal-metabolic diseases and strategies to implement evidence-based practice. Furthermore, we consider the need to conduct clinical trials combined with registers in specific patient segments to evaluate existing and emerging therapies to address unmet needs in T2DM.

Long-term HbA1c variability and macro-/micro-vascular complications in type 2 diabetes mellitus: a meta-analysis update

AIMS

The aim of the present study was to evaluate, by means of a meta-analysis approach, whether new available data, appeared on qualified literature, can support the effectiveness of an association of HbA1c variability with the risk of macro- and/or micro-vascular complications in type 2 diabetes mellitus (T2DM).

METHODS

The meta-analysis was conducted according to PRISMA Statement guidelines and considered published studies on T2DM, presenting HbA1c variability as standard deviation (SD) or its derived coefficient of variation (CV). Literature search was performed on PubMed in the time range 2015-July 2022, with no restrictions of language.

RESULTS

Twenty-three selected studies fulfilled the aims of the present investigation. Overall, the analysis of the risk as hazard ratios (HR) indicated a significant association between the HbA1c variability, expressed either as SD or CV, and the complications, except for neuropathy. Macro-vascular complications were all significantly associated with HbA1c variability, with HR 1.40 (95%CI 1.31-1.50, p < 0.0001) for stroke, 1.30 (95%CI 1.25-1.36, p < 0.0001) for transient ischaemic attack/coronary heart disease/myocardial infarction, and 1.32 (95%CI 1.13-1.56, p = 0.0007) for peripheral arterial disease. Micro-vascular complications yielded HR 1.29 (95%CI 1.22-1.36, p < 0.0001) for nephropathy, 1.03 (95%CI 0.99-1.08, p = 0.14) for neuropathy, and 1.15 (95%CI 1.08-1.24, p < 0.0001) for retinopathy. For all-cause mortality, HR was 1.33 (95%CI 1.27-1.39, p < 0.0001), and for cardiovascular mortality 1.25 (95%CI 1.17-1.34, p < 0.0001).

CONCLUSIONS

Our meta-analysis on HbA1c variability performed on the most recent published data since 2015 indicates positive association between HbA1c variability and macro-/micro-vascular complications, as well as mortality events, in T2DM, suggesting that this long-term glycaemic parameter merits further attention as a predictive, independent risk factor for T2DM population.

Associations Between Mean HbA1c, HbA1c Variability, and Both Mortality and Macrovascular Complications in Patients with Diabetes Mellitus: A Registry-Based Cohort Study

Background

We investigate the association between mean HbA1c, HbA1c variability, and all-cause mortality and diabetes-related macrovascular complications in patients with diabetes.

Methods

We performed a retrospective cohort study using patients present in the Singapore Health Services diabetes registry (SDR) during 2013 to 2014. We assessed mean HbA1c using three models: a baseline mean HbA1c for 2013-14, the mean across the whole follow-up period, and a time-varying yearly updated mean. We assessed HbA1c variability at baseline using the patient's HbA1c variability score (HVS) for 2013-14. The association between mean HbA1c, HVS, and 6 outcomes were assessed using Cox proportional hazard models.

Results

We included 43,837-53,934 individuals in the analysis; 99.3% had type 2 diabetes mellitus. The data showed a J-shaped distribution in adjusted hazard ratios (HRs) for all-cause mortality, ischemic heart disease, acute myocardial infarction, peripheral arterial disease, and ischemic stroke, with an increased risk of developing these outcomes at HbA1c <6% (42 mmol/mol) and ≥8% (64 mmol/mol). With the addition of HVS, the J-shaped distribution was maintained for the above outcomes, but HRs were greater at HbA1c <6.0% (42 mmol/mol) and reduced at HbA1c ≥8.0% (64 mmol/mol) when compared to models without HVS. The risk for all outcomes increased substantially with increasing glycaemic variability.

Conclusion

Both low (<6.0% [42 mmol/mol]) and high (≥8.0% [64 mmol/mol]) levels of glycaemic control are associated with increased all-cause mortality and diabetes-related macrovascular complications. Glycaemic variability is independently associated with increased risk for these outcomes. Therefore, patients with stable glycaemic level of 6-8% (42-64mmol/mol) are at lowest risk of all-cause mortality and diabetes-related macrovascular complications.

Association of visit-to-visit HbA1c variability with cardiovascular diseases in type 2 diabetes within or outside the target range of HbA1c

Background

Although a growing attention has been recently paid to the role of HbA1c variability in the risk of diabetic complications, the impact of HbA1c variability on cardiovascular diseases (CVD) in type 2 diabetes is still debated. The aim of the study is to investigate the association of HbA1c variability with CVD in individuals within or outside the target range of HbA1c.

Methods

Using data from Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation (ADVANCE), we enrolled 855 patients with type 2 diabetes in China. The primary outcomes included major macrovascular events and major microvascular events. Visit-to-visit HbA1c variability was expressed as the coefficient of variation (CV) of five measurements of HbA1c taken 3-24 months after treatment. Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR).

Results

Among 855 patients in the intensive glucose treatment group, 563 and 292 patients were assigned to the group of "within the target range of HbA1c" (WTH) (updated mean HbA1c ≤ 7.0%) and "outside the target range of HbA1c" (OTH) (updated mean HbA1c > 7.0%), respectively. HbA1c variability was positively associated with the risk of major microvascular events in all patients and both the subgroups during a median follow-up period of 4.8 years. Particularly, the risk related to HbA1c variability was higher in patients in WTH group for the new or worsening nephropathy [aHR: 3.35; 95% confidence interval (CI): 1.05-10.74; P = 0.042].

Conclusions

This retrospective cohort study confirmed the positive correlation between HbA1c variability and major microvascular events, especially in subjects in WTH or OTH.

Glycaemic variability and risk of adverse cardiovascular events in acute coronary syndrome

OBJECTIVE

The relationship between different glycaemic variability (GV) indexes and adverse cardiovascular outcomes is not well understood. This study aims to determine whether GV is related to the occurrence of adverse cardiovascular events in patients with acute coronary syndrome (ACS).

METHODS

PubMed, EMBASE, and Web of Science were comprehensively searched from the establishment of databases to 29 June 2022. The relationship between two important GV indexes, including the mean amplitude of glycemic excursion (MAGE) and standard deviation (SD), and the adverse cardiovascular events in ACS patients were evaluated, respectively.

RESULTS

A total of 11 studies with 3709 ACS patients were included. Pooled results showed that patients with higher GV had significantly increased risk of adverse cardiovascular events, including MAGE (relative risk [RR] = 1.76, 95% CI: 1.40 to 2.22, p < 0.001, I² = 25%) and SD (RR = 2.14, 95% CI: 1.73 to 2.66, p < 0.001, I² = 0%).

CONCLUSIONS

Increased GV is related to the poor prognosis in patients with ACS. Additionally, more well-designed studies comparing different indicators of GV with adverse cardiovascular events in ACS patients are still warranted.