Glycemic variability (GV) is a critical factor in the development of diabetic sensorimotor polyneuropathy (DSPN). This study aimed to evaluate the association of long-term GV, measured by glycated hemoglobin (HbA1c) average real variability (ARV), and short-term GV, assessed by time-in-range (TIR) ARV, with large and small nerve fiber dysfunction in individuals with well-controlled Type 2 Diabetes (T2D).

A prospective study conducted at a tertiary hospital in Taiwan included 82 T2D participants. Long-term GV was assessed using HbA1c ARV from visit-to-visit measurements at three-month intervals over 1 year. Short-term GV was evaluated as TIR ARV from seven-day fingerstick data collected quarterly. Large and small nerve functions were assessed using the Toronto Clinical Neuropathy Score (TCNS), nerve conduction studies, quantitative thermal testing, and Sudoscan.

Linear regression analysis adjusted for age, diabetes duration, and renal function revealed strong correlations between HbA1c ARV, TIR ARV, and diabetes duration. At baseline, high HbA1c ARV and TIR ARV groups exhibited higher TCNS and composite nerve conduction amplitude scores but lower cold detection thresholds compared to the low median groups. At one-year follow-up, TCNS significantly increased in the high HbA1c ARV (P = 0.001) and TIR ARV (P = 0.003) groups compared to the low median groups.

Both long-term and short-term GV significantly contribute to small and large nerve fiber dysfunction in T2D, yielding similar neurological outcomes despite stable mean glucose levels. Combining GV minimization strategies with standard glycemic control may be essential in reducing DSPN risk.

Many clinical trials yielded inconsistent results regarding the effect of intensive glycated hemoglobin control on cardiovascular diseases in type 2 diabetes. We identified distinct HbA1c trajectories and their association with the recurrent hospitalization of heart failures (HHF) for patients with type 2 diabetes starting from the date of diabetes diagnosis.

In this study, we included 194,258 patients who entered the SingHealth Diabetes Registry from 2013 to 2020. Their diagnoses of type 2 diabetes spanned the years 1960-2020, encompassing HbA1c measurements, records of HHF, and other cardiovascular complications. Latent class growth models (LCGM) with splines were used to extract the subgroups with distinct HbA1c trajectories. The association between HbA1c trajectories and the recurrent risk of HHF was investigated by nonhomogeneous Poisson processes (NHPP).

Eight distinct HbA1c trajectories were identified as follows: low stable (LowS, 22.2%), moderate low ascending (ModLowA, 12.7%), moderate high ascending (ModHighA, 11.5%), moderate low descending (ModLowD, 17.2%), moderate high descending (ModHighD, 10.1%), moderate high volatility (ModHighV, 10.1%), high with a sharp decline (HighSD, 8.0%), and high volatility (HighV, 10.2%). Using the Class LowS as a reference, the hazard ratios for recurrent HHF for the other classes are as follows: 0.79 for ModLowA, 1.30 for ModHighA, 1.17 for ModLowD, 1.89 for ModHighD, 1.94 for ModHighV, 1.25 for HighSD, and 2.88 for HighV. Considering recurrent HHFs, our NHPP model demonstrated predictive capability for type 2 diabetes patients' future HHF events.

Low baseline HbA1c levels are associated with a lower risk of recurrent HHF, while poor glycemic control significantly increases this risk. Our application of LCGM with splines effectively captures flexible, long-term HbA1c trajectories, while the innovative use of the NHPP model allows for precise modeling of HHF recurrence risk. This approach provides a foundation for personalized risk predictions and future HF management by incorporating dynamically updated risk factors.

Identification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with diabetes have been studied extensively but the inclusion of measures of glucose variability (GVs) in prognostic models is largely lacking. This study aims to assess which GVs (i.e., coefficient of variation (CV), standard deviation (SD), and time-varying) are better in predicting diabetic complications, including cardiovascular disease (CVD), diabetic retinopathy (DR), and chronic kidney disease (CKD). The model performance between traditional statistical models (adjusting for covariates) and machine learning (ML) models were compared.

A retrospective cohort of type 2 diabetes (T2D) patients between 2010 and 2019 in Ramathibodi Hospital was created. Complete case analyses were used. Three GVs using HbA1c and fasting plasma glucose (FPG) were considered including CV, SD, and time-varying. Cox proportional hazard regression, ML random survival forest (RSF) and left-truncated, right-censored (LTRC) survival forest were compared in two different data formats (baseline and longitudinal datasets). Adjusted hazard ratios with 95% confidence intervals were used to report the association between three GVs and diabetes complications. Model performance was evaluated using C-statistics along with feature importance in ML models.

A total of 40,662 T2D patients, mostly female (61.7%), with mean age of 57.2 years were included. After adjusting for covariates, HbA1c-CV, HbA1c-SD, FPG-CV and FPG-SD were all associated with CVD, DR and CKD, whereas time-varying HbA1c and FPG were associated with DR and CKD only. The CPH and RSF for DR (C-indices: 0.748-0.758 and 0.774-0.787) and CKD models (C-indices: 0.734-0.750 and 0.724-0.740) had modestly better performance than CVD models (C-indices: 0.703-0.730 and 0.698-0.727). Based on RSF feature importance, FPG GV measures ranked higher than HbA1c GV, and both GVs were the most important for DR prediction. Both traditional and ML models had similar performance.

We found that GVs based on HbA1c and FPG had comparable performance. Thus, FPG GV may be used as a potential monitoring parameter when HbA1c is unavailable or less accessible.

In this cross-sectional analysis, we explored how fluctuations in glycemic levels impact executive functions and psychosocial outcomes in patients with type 2 diabetes mellitus (T2DM). The goal was to understand the relationship between glycemic control and both neuropsychological and psychosocial health. We stratified participants into well-controlled and poorly controlled groups based on glycated hemoglobin (HbA1c) levels and variability, including a healthy control group for comparison. The study consisted of neuropsychological tests and psychosocial assessments. Results indicated that the poorly controlled T2DM group experienced significant executive dysfunction and scored lower on the Tower of London, Wisconsin Card Sorting, and Digit Span Tests, reflecting a broader impact on quality of life and resilience. These findings support the importance of maintaining stable glycemic levels for better executive and psychosocial outcomes and highlight the need for regular neuropsychological and psychosocial assessments in diabetes care.

Observational studies suggested that early menarche was associated with type 2 diabetes mellitus (T2DM) and heart failure (HF), but the causal relationships remain uncertain. We aimed to examine the causal relationships of age at menarche (AAM) with T2DM, hemoglobin A1c (HbA1c) and HF using Mendelian randomization (MR).

A 2-sample MR analysis was conducted using genome-wide association study (GWAS) summary statistics for AAM (n = 279,470), T2D (n = 490,089), HbA1c (n = 389,889) and HF (n = 977,323), and all summary statistics are from European ancestry. Genome-wide significant (P < 5 × 10-8) single nucleotide polymorphisms (SNP) connected to AAM were considered as instrumental variables (IVs). Inverse variance weighted (IVW) method was used for primary analysis. Sensitivity analyses were performed to evaluate the robustness of the results. More than 200 SNPs significantly correlated with AAM were screened as IVs. Later genetically predicted AAM decreased risk of T2DM (odds ratio [OR], 0.881; 95%CI, 0.838-0.926; P = 6.92 × 10-7), HbA1c level (OR, 0.964; 95%CI, 0.948-0.980; P = 2.06 × 10-5), and risk of HF (OR, 0.907; 95%CI, 0.870-0.947; P = 7.98 × 10-6). After excluding SNPs associated with body mass index (BMI), there was still a causal relationship between AAM and outcomes (T2DM: OR [95%CI], 0.926 [0.888-0.966]; HbA1c: OR [95%CI], 0.975 [0.960-0.992]; HF: OR [95%CI], 0.933 [0.894-0.972]).

This MR study provided evidence supporting the causal effect of AAM on T2DM, HbA1c and HF. Routine evaluation of AAM may contribute to the clinical risk stratification and making prevention strategies.

To conduct a systematic review in order to better understand the association of glycaemic risk factors and diabetes duration with risk of heart failure (HF) in individuals with type 2 diabetes (T2D).

We identified longitudinal studies investigating the association of glycaemic factors (glycated haemoglobin [HbA1c], HbA1c variability, and hypoglycaemia) and diabetes duration with HF in individuals with T2D. Hazard ratios and odds ratios were extracted and meta-analysed using a random-effects model where appropriate. Risk of bias assessment was carried out using a modified Newcastle-Ottawa Scale. Egger's test along with the trim-and-fill method were used to assess and account for publication bias.

Forty studies representing 4 102 589 people met the inclusion criteria. The risk of developing HF significantly increased by 15% for each percentage point increase in HbA1c, by 2% for each additional year of diabetes duration, and by 43% for having a history of severe hypoglycaemia. Additionally, variability in HbA1c levels was associated with a 20%-26% increased risk of HF for each unit increase in the metrics of variability (HbA1c standard deviation, coefficient of variation, and average successive variability). All included studies scored high in the risk of bias assessment. Egger's test suggested publication bias, with trim-and-fill analyses revealing a significant 14% increased risk of HF per percentage point increase in HbA1c.

Glycaemic risk factors and diabetes duration significantly contribute to the heightened risk of HF among individuals with T2D. A reduction in risk of HF is anticipated with better management of glycaemic risk factors.

Elevated HbA1c, a marker of poor glycemic control, is associated with adverse cardiovascular outcomes and mortality. HbA1c influences outcomes through distinct mechanisms of vascular dysfunction and atherosclerosis in ischemic stroke, during atrial remodeling and thrombus formation in paroxysmal atrial fibrillation (PAF). Optimal HbA1c thresholds are generally below optimal levels, with levels above this being linked to higher mortality in both populations. At extremes of glycemic control in ischemic stroke, patients face recurrence and poor recovery, while PAF patients experience amplified thromboembolic risks. In patients with both conditions, poor HbA1c control synergistically raises mortality. This systematic review explores how HbA1c levels directly contribute to mortality in patients with ischemic stroke and PAF, aiming to establish a causal link between elevated HbA1c and increased mortality risk. This review includes a comprehensive analysis of four cross-sectional studies, five randomized controlled trials (RCTs), and 17 cohort studies, providing a diverse range of evidence on the topic. The inclusion of these study designs offers a well-rounded understanding of the impact and outcomes observed in the research. Mortality metrics include short-term mortality, such as 30-day or 90-day, and long-term mortality over one, three, or five years. Specific metrics, like cardiovascular mortality, focus on deaths from stroke; some studies link mortality to functional decline post-stroke, where complications from immobility or recurrent vascular events contribute to outcomes. Secondary outcomes include survival metrics, functional recovery metrics, and complications. Studies use narrative synthesis due to its ability to accommodate heterogeneity in study designs, populations, and outcome measures, enabling a nuanced interpretation of complex, context-dependent data. HbA1c levels' impact on stroke outcomes, considering age, gender, and severity, is also examined. Confounding factors, functional recovery, and complications are also considered. A narrative synthesis was chosen. The study emphasizes the importance of strict glycemic control in patients with ischemic stroke or PAF, especially those with elevated HbA1c levels. It supports clinical guidelines for individualized HbA1c targets, with most stroke patients having a target of <7%. Clinicians should prioritize close monitoring and tailor treatment plans to avoid extreme HbA1c levels, which could inform more personalized and effective treatment strategies. Tight control of HbA1c levels entails individualized targets based on patient characteristics, with an emphasis on personalized treatment strategies that may include lifestyle modifications, oral hypoglycemics, or insulin therapy to optimize glycemic control.

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.

We aimed to investigate the association between glycemic variability and quality of life (QOL) in patients with diabetes, which has not been studied previously.

Patients who were undergoing treatment at the Kyoto Prefectural University of Medicine Hospital and Kameoka City Hospital participated in the KAMOGAWA-DM study, and completed the diabetes therapy-related (DTR)-QOL questionnaire from January 2016 to July 2020 were included in this study. We used linear regression analyses to compare the association between DTR-QOL scores and glycemic variability.

We included a total of 635 patients in this analysis. The hemoglobin A1c (HbA1c) levels of these patients were measured at least four times during the 9-month period, before and after answering the questionnaire. Results showed that HbA1c variability, HbA1c mean and duration of diabetes were negatively associated with the total DTR-QOL score. Conversely, the body mass index and total DTR-QOL score were positively associated with HbA1c variability.

A small variation in HbA1c level was associated with higher total DTR-QOL scores and the scores for each factor. Reducing blood glucose variability is significant when we treat diabetes.

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.

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.

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.

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.

The US Department of Veterans Affairs (VA) and the US Department of Defense (DoD) approved a joint clinical practice guideline for the management of type 2 diabetes. This was the product of a multidisciplinary guideline development committee composed of clinicians from both the VA and the DoD and was overseen by the VA/DoD Evidence Based Practice Work Group. The development process conformed to the standards for trustworthy guidelines as established by the National Academy of Medicine. The guideline development committee developed 12 key questions to guide an evidence synthesis. An independent third party identified relevant randomized controlled trials and systematic reviews that were published from January 2016 through April 2022. This evidence synthesis served as the basis for drafting recommendations. Twenty-six recommendations were generated and rated by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. Two algorithms were developed to guide clinical decision-making. This synopsis summarizes key aspects of the VA/DoD Clinical Practice Guideline for diabetes in 5 areas: prediabetes, screening for co-occurring conditions, diabetes self-management education and support, glycemic treatment goals, and pharmacotherapy. The guideline is designed to help clinicians and patients make informed treatment decisions to optimize health outcomes and quality of life and to align with patient-centered goals of care.

Gestational diabetes mellitus (GDM) is a special type of diabetes that commonly occurs in women during pregnancy and involves impaired glucose tolerance and abnormal glucose metabolism; GDM is diagnosed for the first time during pregnancy and can affect fetal growth and development.

To investigate the associations of serum D-dimer (D-D) and glycosylated hemoglobin (HbA1c) levels with third-trimester fetal growth restriction (FGR) in GDM patients.

The clinical data of 164 pregnant women who were diagnosed with GDM and delivered at the Obstetrics and Gynecology Hospital of Fudan University from January 2021 to January 2023 were analyzed retrospectively. Among these women, 63 whose fetuses had FGR were included in the FGR group, and 101 women whose fetuses had normal body weights were included in the normal body weight group (normal group). Fasting venous blood samples were collected from the elbow at 28-30 wk gestation and 1-3 d before delivery to measure serum D-D and HbA1c levels for comparative analysis. The diagnostic value of serum D-D and HbA1c levels for FGR was evaluated by receiver operating characteristic analysis, and the influencing factors of third-trimester FGR in GDM patients were analyzed by logistic regression.

Serum fasting blood glucose, fasting insulin, D-D and HbA1c levels were significantly greater in the FGR group than in the normal group, while the homeostasis model assessment of insulin resistance values were lower (P < 0.05). Regarding the diagnosis of FGR based on serum D-D and HbA1c levels, the areas under the curves (AUCs) were 0.826 and 0.848, the cutoff values were 3.04 mg/L and 5.80%, the sensitivities were 81.0% and 79.4%, and the specificities were 88.1% and 87.1%, respectively. The AUC of serum D-D plus HbA1c levels for diagnosing FGR was 0.928, and the sensitivity and specificity were 84.1% and 91.1%, respectively. High D-D and HbA1c levels were risk factors for third-trimester FGR in GDM patients (P < 0.05).

D-D and HbA1c levels can indicate the occurrence of FGR in GDM patients in the third trimester of pregnancy to some extent, and their combination can be used as an important index for the early prediction of FGR.

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.

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.

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).

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.

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.

Nonalcoholic fatty liver disease (NAFLD) and liver cirrhosis are significant clinical concerns, especially among individuals with type 2 diabetes mellitus (T2DM). However, in China, there is a paucity of reliable evidence detailing the characteristics of NAFLD and liver cirrhosis in T2DM. Furthermore, the relationship between blood glucose levels and NAFLD prevalence remains unclear.

Data from the Shanghai Suburban Adult Cohort and Biobank were analyzed, including 6621 participants with T2DM. NAFLD was diagnosed by ultrasonography and liver cirrhosis was performed according to the health information systems. Logistic regression and restricted cubic spline analysis were used to explore the potential risk factors for NAFLD and liver cirrhosis.

The prevalence of NAFLD was 59.36%, and liver cirrhosis was 1.43% among T2DM patients. In these patients, factors like age, being female, marital status, and obesity significantly increased the risk of NAFLD. Specifically, obesity had a strong positive association with NAFLD (odds ratio [OR] = 4.70, 95% confidence interval [CI]: 4.13-5.34). The higher glycated hemoglobin (HbA1c) quartile was associated with a heightened NAFLD risk compared to the lowest quartile (all p < .001). The HbA1c-NAFLD relationship displayed a linear that mimicked an inverted L-shaped pattern. A significant positive association existed between HbA1c levels and NAFLD for HbA1c <8.00% (OR = 1.59, 95% CI: 1.44-1.75), but this was not observed for HbA1c >8.00% (OR = 1.03, 95% CI: 0.92-1.15).

Systematic screening for NAFLD is essential in T2DM patients, especially with poor glucose control and obesity in female.

Type 2 diabetes mellitus (T2DM) is one of the leading causes of cardiovascular disease and powerful predictor for new-onset heart failure (HF).

We focus on the relevant literature covering evidence of risk stratification based on imaging predictors and circulating biomarkers to optimize approaches to preventing HF in DM patients.

Multiple diagnostic algorithms based on echocardiographic parameters of cardiac remodeling including global longitudinal strain/strain rate are likely to be promising approach to justify individuals at higher risk of incident HF. Signature of cardiometabolic status may justify HF risk among T2DM individuals with low levels of natriuretic peptides, which preserve their significance in HF with clinical presentation. However, diagnostic and predictive values of conventional guideline-directed biomarker HF strategy may be non-optimal in patients with obesity and T2DM. Alternative biomarkers affecting cardiac fibrosis, inflammation, myopathy, and adipose tissue dysfunction are plausible tools for improving accuracy natriuretic peptides among T2DM patients at higher HF risk. In summary, risk identification and management of the patients with T2DM with established HF require conventional biomarkers monitoring, while the role of alternative biomarker approach among patients with multiple CV and metabolic risk factors appears to be plausible tool for improving clinical outcomes.

In this article, the epidemiology, molecular mechanism of occurrence and development, risk factors, and treatment of diabetic microvascular complications such as diabetic nephropathy, diabetic retinopathy, and diabetic peripheral neuropathy were discussed, providing the theoretical basis for more accurate elucidation of the pathogenesis and treatment of diabetic microvascular complications.

The electronic database of PubMed was searched, and retrieved papers were screened for eligibility by two independent reviewers. Data were extracted using a standardized data extraction form and the quality of included papers was assessed.

Thirty-eight articles were included. Diabetes nephropathy, diabetes peripheral neuropathy, and diabetes retinopathy are the most common and serious microvascular complications of diabetes in clinical patients. Renin-angiotensin system blockers, beta drugs, statins, antivascular endothelial growth factor drugs, and antioxidants can inhibit the occurrence of microvascular complications in diabetes.

However, there has been no breakthrough in the treatment of diabetic microvascular complications. Therefore, prevention of diabetic microvascular complications is more important than treatment.

Background: In this study, we investigated whether different levels of hemoglobin A1c (HbA1c) are associated with different short-term and 1-year mortality rates among diabetic patients undergoing percutaneous coronary intervention. Patients & methods: Clinical events including in-hospital, 1-month and 1-year mortality were compared between three groups based on HbA1c levels of patients (I: ≤5.6%, II: 5.7-6.4%, III: ≥6.5%). Results: Among 165 diabetic individuals, patients with abnormal HbA1c levels (≥6.5%) experienced significantly higher hospitalization days (7.65 ± 1.64 days) compared with those with normal HbA1c (4.94 ± 0.97 days) (p < 0.0001). In-hospital mortality was significantly higher in group III (14.5%) and II (5.5%) compared with group I (0%) (p = 0.008). Conclusion: HbA1c levels may be a reliable predictor of short-term clinical events in diabetic patients.

The variability in diabetes risk factors, such as uric acid and lipids, may influence the development of complications. This study aimed to investigate the influence of such variability on the occurrence of diabetic complications. A retrospective analysis of electronic medical records was conducted with type 2 diabetic patients who received treatment at a tertiary care hospital in Chengdu, Sichuan Province, between 2013 and 2022. The risk factor variability is presented as the standard deviation (SD). The associations between the variability and complications were examined using a binary logistic regression model. The study included 369 patients with type 2 diabetes. The findings revealed that outpatient special disease management served as a protective factor against the development of complications [OR = 0.53, 95% confidence interval (CI) (0.29-0.10)], particularly for the prevention of diabetic peripheral neuropathy [OR = 0.51, 95% CI (0.30-0.86)]. Variability in total cholesterol (TC-SD) was found to be a risk factor for the development of complications [OR = 2.42, 95% CI (1.18-4.97)] and acted as a risk factor for diabetic peripheral vasculopathy [OR = 2.50, 95% CI (1.25-5.02)]. TC-SD is a risk factor for the occurrence of diabetic peripheral neuropathy and diabetic peripheral vasculopathy, whereas outpatient special disease management functions as a protective factor against complications and diabetic peripheral neuropathy. Thus, in addition to glycaemic control, the regulation of lipid levels should be emphasized, particularly among patients without outpatient special disease management, to delay the onset of complications.

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.

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.

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).

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.

This article focuses on extracting a standard feature set for predicting the complications of diabetes mellitus by systematically reviewing the literature. It is conducted and reported by following the guidelines of PRISMA, a well-known systematic review and meta-analysis method. The research articles included in this study are extracted using the search engine "Web of Science" over eight years. The most common complications of diabetes, diabetic neuropathy, retinopathy, nephropathy, and cardiovascular diseases are considered in the study.

The features used to predict the complications are identified and categorised by scrutinising the standards of electronic health records.

Overall, 102 research articles have been reviewed, resulting in 59 frequent features being identified. Nineteen attributes are recognised as a standard in all four considered complications, which are age, gender, ethnicity, weight, height, BMI, smoking history, HbA1c, SBP, eGFR, DBP, HDL, LDL, total cholesterol, triglyceride, use of insulin, duration of diabetes, family history of CVD, and diabetes. The existence of a well-accepted and updated feature set for health analytics models to predict the complications of diabetes mellitus is a vital and contemporary requirement. A widely accepted feature set is beneficial for benchmarking the risk factors of complications of diabetes.

This study is a thorough literature review to provide a clear state of the art for academicians, clinicians, and other stakeholders regarding the risk factors and their importance.

To explore the relationship between long-term glycemic variability and anxiety and depression in patients with type 2 diabetes.

A cohort comprising 214 individuals diagnosed with type 2 diabetes participated in this study. Comprehensive demographic and laboratory information was gathered for them. The evaluation of anxiety relied on the 7-item Generalized Anxiety Disorder Scale (GAD-7), while depression was assessed utilizing the 9-item Health Questionnaire (PHQ-9). Based on the presence or absence of anxiety and depression, participants were categorized into either the mood disorder or control groups. Subsequently, univariate and stepwise multiple binary logistic regression analyses were conducted to investigate the potential correlations between factors and the presence of anxiety and depression.

The prevalence of anxiety disorders is 23%, and depression is 32%. The prevalence of smoking, diabetic autonomic neuropathy, stroke, and osteoporosis in the mood disorder group was significantly higher than that in the control group (P < 0.05), the glycated hemoglobin A1c variability score (HVS), mean hemoglobin A1c value, total cholesterol, urinary albumin/creatinine and systemic immune-inflammatory index (SII) were significantly higher in the control group (P < 0.05). The level of high-density lipoprotein in the mood disorder group was significantly lower than the control group (P < 0.05). In stepwise multiple binary logistic regression analyses, the main factors associated with anxiety were depression (P < 0.001, OR=117.581) and gender (P < 0.001, OR=9.466), and the main factors related to depression included anxiety (P < 0.001, OR=49.424), smoking (P=0.042, OR=2.728), HVS (P=0.004, OR=8.664), and SII (P=0.014, OR=1.002).

Persistent fluctuations in blood glucose levels have been linked to anxiety and depression. Consequently, maintaining an optimal level of glycemic control and minimizing fluctuations becomes imperative in the comprehensive management of diabetes.

Although recent guidelines advocate for HbA1c target individualization, a comprehensive criterion for patient categorization remains absent. This study aimed to categorize HbA1c variability levels and explore the relationship between glycemic control, cardiovascular outcomes, and mortality across different degrees of variability.

Action to Control Cardiovascular Risk in Diabetes study data were used. HbA1c variability was measured using the HbA1c variability score (HVS) and standard deviation (SD). K-means and K-medians clustering were used to combine the HVS and SD.

K-means clustering was the most stable algorithm with the lowest clustering similarities. In the low variability group, intensive glucose-lowering treatment significantly reduced the risk of adverse cardiovascular outcomes (HR: 0·78 [95% CI: 0·63, 0·97]) without increasing mortality risk (HR: 1·07 [0.81, 1·42]); the risk of adverse cardiovascular events (HR: 1·33 [1·14, 1·56]) and all-cause mortality (HR: 1·23 [1·01,1·51]) increased with increasing mean HbA1c. In the high variability group, treatment increased the risk of cardiovascular events (HR: 2.00 [1·54, 2·60]) and mortality (HR: 2·20 [1·66, 2·92]); a higher mean HbA1c (7·86%, [7·66%, 8·06%]) had the lowest mortality risk, when the mean HbA1c was < 7·86%, a higher mean HbA1c was associated with a lower mortality risk (HR: 0·63 [0·42, 0·95]). In the medium variability group, a mean HbA1c around 7·5% was associated with the lowest risk.

HbA1c variability can guide glycemic control targets for patients with type 2 diabetes. For patients with low variability, the lower the HbA1c, the lower the risk. For those with medium variability, controlling HbA1c at 7·5% provides the maximum benefit. For patients with high variability, a mean HbA1c of around 7·8% presents the lowest risk of all-cause mortality, a lower HbA1c did not provide cardiovascular benefits but instead increased the mortality risk. Further studies, especially those with patients that reflect the general population with type 2 diabetes undergoing the latest therapeutic approaches, are essential to validate the conclusions of this study.

To explore the association between thyroid parameters and subclinical atherosclerosis (AS) in hospitalised euthyroid patients with type 2 diabetes mellitus (T2DM).

A retrospective analysis was conducted involving 1245 inpatients with T2DM. Free triiodothyronine (FT3), free thyroxine (FT4), and thyroid stimulating hormone (TSH) levels were measured, and carotid artery ultrasonography was performed. Thyroid hormone (TH) sensitivity was evaluated using thyroid feedback quantile-based index (TFQI), TSH index (TSHI), thyrotropin thyroxine resistance index (TT4RI), and free triiodothyronine/free thyroxine ratio (FT3/FT4).

In inpatients with T2DM having normal thyroid function, the incidence of subclinical AS declined with increasing levels of FT3, FT4, and FT3/FT4 (P trend < 0.05). Logistic regression analysis revealed that FT4 (OR, 0.914; 95% CI, 0.845-0.989), FT3 (OR, 0.374; 95% CI, 0.277-0.504), and FT3/FT4 (OR, 0.036; 95% CI, 0.013-0.061) were independently associated with subclinical AS (P < 0.05). However, TSH, TFQI, TSHI, and TT4RI levels were not associated with subclinical AS (P > 0.05). FT3/FT4 demonstrated superior predictive accuracy for subclinical AS than that of FT3 or FT4 alone (P < 0.001), with a cutoff point of 0.25.

In euthyroid inpatients with T2DM, subclinical AS exhibited negative correlation with FT3, FT4, and FT3/FT4 levels, independent of other risk factors for AS. Additionally, FT3/FT4 ratio had a good predictive value for subclinical AS.

The prognosis of diabetic peritoneal dialysis patients is poor. HbA1c serves as a crucial indicator for monitoring blood glucose control in patients with diabetes. Nevertheless, the relationship between visit-to-visit HbA1c variability and prognosis in peritoneal dialysis with diabetes remains unclear.

All participants were categorized into 3 groups based on the HbA1c variability score (HVS), which is the frequency of 0.5% (5.5 mmol/mol) alter in visit-to-visit HbA1c values. Then, the hazard ratio to HVS with all-cause mortality was analyzed using the Cox hazard model, followed by the Fine-Gray competing risk model for major adverse cardiovascular events. Subgroup and sensitivity analysis were conducted to ascertain the robustness of the findings.

Eight hundred twenty patients with type 2 diabetes were finally enrolled in this study from 2,855 participants with a mean age of 56.9 ± 14.6 years and a median follow-up time of 44 months [IQR: 27-70], death occurred in 496 (60.2%) individuals. Compared with the lowest category (HVS < 1/3) after being adjusted by potential confounding factors, the hazard ratio for all-cause mortality was 4.59 (3.74-5.64) and the sub-distribution hazard ratio for major adverse cardiovascular events was 1.91 (1.46-2.51) of the highest category (HVS ≥ 2/3). Subgroup interaction and sensitivity analysis, including the adjustment for variables such as time-weighted average HbA1c, HbA1c measurement times and expansion, confirmed the reliability of the results.

The HVS is related to the risk of poor prognosis in peritoneal dialysis with type 2 diabetes mellitus, independently of clinical multiple variables, and is a novel indicator with clinical guidance.

To characterize associations between long-term visit-to-visit variability of hemoglobin A1c (HbA1c) and risk of adverse kidney outcomes in patients with diabetes.

Observational study.

93,598 adults with diabetes undergoing routine care in Stockholm, Sweden.

Categories of baseline and time-varying HbA1c variability score (HVS, the percentage of total HbA1c measures that vary by>0.5% [5.5mmol/mol] during a 3-year window): 0-20%, 21%-40%, 41%-60%, 61%-80%, and 81%-100%, with 0-20% as the reference group.

Chronic kidney disease (CKD) progression (composite of>50% estimated glomerular filtration rate [eGFR] decline and kidney failure), acute kidney disease (AKI by clinical diagnosis or transient creatinine elevations according to KDIGO criteria), and worsening of albuminuria.

Multivariable Cox proportional hazards regression.

Compared with persons showing low HbA1c variability (HVS 0-20%), any increase in variability was associated with a higher risk of adverse kidney outcomes beyond mean HbA1c. For example, for patients with a baseline HbA1c variability of 81%-100%, the adjusted HR was 1.6 (95% CI, 1.47-1.74) for CKD progression, 1.23 [1.16-1.3] for AKI, and 1.28 [1.21-1.36] for worsening of albuminuria. The results were consistent across subgroups (diabetes subtypes, baseline eGFR, or albuminuria categories), in time-varying analyses and in sensitivity analyses including time-weighted average HbA1c or alternative metrics of variability.

Observational study, limitations of claims data, lack of information on diet, body mass index, medication changes, and diabetes duration.

Higher long-term visit-to-visit HbA1c variability is consistently associated with the risks of CKD progression, AKI, and worsening of albuminuria.

The evidence for current guideline recommendations derives from clinical trials that focus on a single HbA1c as the definitive measure of efficacy of an intervention. However, long-term visit-to-visit fluctuations of HbA1c may provide additional value in the prediction of future kidney complications. We evaluated the long-term fluctuations in glycemic control in almost 100,000 persons with diabetes undergoing routine care in Stockholm, Sweden. We observed that higher long-term HbA1c fluctuation is consistently associated with the risks of chronic kidney disease progression, worsening of albuminuria and acute kidney injury. This finding supports a role for long-term glycemic variability in the development of kidney complications and illustrates the potential usefulness of this metric for risk stratification at the bedside beyond a single HbA1c test.

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

Diabetes mellitus is a metabolic disorder with a complex etiology in which glycemic dynamics are disturbed and the body is unable to maintain the process of glucose homeostasis through the pancreas. Persistent symptoms of high blood glucose or low blood glucose may lead to diabetic complications, such as neuropathy, nephropathy, retinopathy, and cardiovascular diseases. Glycemic variability which can represent the presence of excessive glycemic excursions is an indicator for evaluating glucose homoeostasis. Limiting glycemic variability has gradually become an emerging therapeutic target in improve diabetes metabolism and prevent associated complications. This article reviews the progress of research on the various quantifiable parameters of glycemic variability and their relationships with vascular lesions and mechanisms.

Glycemic variability plays an important role in the development of cardiovascular disease (CVD). This study aims to determine whether long-term visit-to-visit glycemic variability is associated with aortic stiffness progression in participants with type 2 diabetes (T2D).

Prospective data were obtained from 2115 T2D participants in the National Metabolic Management Center (MMC) from June 2017 to December 2022. Two brachial-ankle pulse wave velocity (ba-PWV) measurements were performed to assess aortic stiffness over a mean follow-up period of 2.6 years. A multivariate latent class growth mixed model was applied to identify trajectories of blood glucose. Logistic regression models were used to determine the odds ratio (OR) for aortic stiffness associated with glycemic variability evaluated by the coefficient of variation (CV), variability independent of the mean (VIM), average real variability (ARV), and successive variation (SV) of blood glucose.

Four distinct trajectories of glycated hemoglobin (HbA1c) or fasting blood glucose (FBG) were identified. In the U-shape class of HbA1c and FBG, the adjusted ORs were 2.17 and 1.21 for having increased/persistently high ba-PWV, respectively. Additionally, HbA1c variability (CV, VIM, SV) was significantly associated with aortic stiffness progression, with ORs ranging from 1.20 to 1.24. Cross-tabulation analysis indicated that the third tertile of the HbA1c mean and VIM conferred a 78% (95% confidence interval [CI] 1.23-2.58) higher odds of aortic stiffness progression. Sensitivity analysis demonstrated that the SD of HbA1c and the highest HbA1c variability score (HVS) were significantly associated with the adverse outcomes independent of the mean of HbA1c during the follow-up.

Long-term visit-to-visit HbA1c variability was independently associated with aortic stiffness progression, suggesting that HbA1c variability was a strong predictor of subclinical atherosclerosis in T2D participants.

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.

Background: Sodium-glucose cotransporter-2 (SGLT2) inhibitors have proven to be effective in improving glycemic control in patients with type 2 diabetes mellitus (T2DM). However, the risk of diabetic ketoacidosis (DKA) in patients remains unclear. The purpose of this study is to conduct this systematic review and network meta-analysis for the risk of DKA of SGLT2 inhibitors in patients with T2DM. Methods: We searched for randomized controlled trials (RCTs) concerning SGLT2 inhibitors in patients with T2DM in PubMed, EMBASE (Ovid SP), Cochrane Central Register of Controlled Trials (Ovid SP), and ClinicalTrials.gov from inception to January 2022. The primary outcomes were the risk of DKA. We assessed the sparse network with a fixed-effect model and consistency model in a frequentist framework with a graph-theoretical method by the netmeta package in R. We assessed the evidence quality of evidence of outcomes according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Results: In total, 36 studies involving 52,264 patients were included. The network showed that there was no significant difference observed among SGLT2 inhibitors, other active antidiabetic drugs, and placebo in the risk of DKA. There was no significant difference in the DKA risk between different doses of SGLT2 inhibitors. The certainty of the evidence ranged from very low to moderate. The probabilities of rankings and P-score showed that compared to placebo, SGLT2 inhibitors might increase the risk of DKA (P-score = 0.5298). Canagliflozin might have a higher DKA risk than other SGLT2 inhibitors (P-score = 0.7388). Conclusion: Neither SGLT2 inhibitors nor other active antidiabetic drugs were associated with an increased risk of DKA compared to placebo, and the risk of DKA with SGLT2 inhibitors was not found to be dose-dependent. In addition, the use of canagliflozin was less advisable than other SGLT2 inhibitors according to the rankings and P-score. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier PROSPERO, CRD42021297081.

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.

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.

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).

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.

Background Peripheral arterial disease (PAD) is a severe complication in patients with type 2 diabetes. Glycemic variability (GV) is associated with increased risks of developing microvascular and macrovascular diseases. However, few studies have focused on the association between GV and PAD. Methods and Results This cohort study used a database maintained by the National Taiwan University Hospital, a tertiary medical center in Taiwan. For each individual, GV parameters were calculated, including fasting glucose coefficient of variability (FGCV) and hemoglobin A1c variability score (HVS). Multivariate Cox regression models were constructed to estimate the relationships between GV parameters and composite scores for major adverse limb events (MALEs) and major adverse cardiovascular events (MACEs). Between 2014 and 2019, a total of 45 436 adult patients with prevalent type 2 diabetes were enrolled for analysis, and GV was assessed during a median follow-up of 64.4 months. The average number of visits and time periods were 13.38 and 157.87 days for the HVS group and 14.27 and 146.59 days for the FGCV group, respectively. The incidence rates for cardiac mortality, PAD, and critical limb ischemia (CLI) were 5.38, 20.11, and 2.41 per 1000 person-years in the FGCV group and 5.35, 20.32, and 2.50 per 1000 person-years in HVS group, respectively. In the Cox regression model with full adjustment, the highest FGCV quartile was associated with significantly increased risks of MALEs (hazard ratio [HR], 1.57 [95% CI, 1.40-1.76]; P<0.001) and MACEs (HR, 1.40 [95% CI, 1.25-1.56]; P<0.001). Similarly, the highest HVS quartile was associated with significantly increased risks of MALEs (HR, 1.44 [95% CI, 1.28-1.62]; P<0.001) and MACEs (HR, 1.28 [95% CI, 1.14-1.43]; P<0.001). The highest FGCV and HVS quartiles were both associated with the development of PAD and CLI (FGCV: PAD [HR, 1.57; P<0.001], CLI [HR, 2.19; P<0.001]; HVS: PAD [HR, 1.44; P<0.001], CLI [HR, 1.67; P=0.003]). The Kaplan-Meier analysis showed significantly higher risks of MALEs and MACEs with increasing GV magnitude (log-rank P<0.001). Conclusions Among individuals with diabetes, increased GV is independently associated with the development of MALEs, including PAD and CLI, and MACEs. The benefit of maintaining stable glycemic levels for improving clinical outcomes warrants further studies.

Visit-to-visit variability of glycated hemoglobin (HbA1c) is a marker of long-term glycemic fluctuation, which has been related to increased risk of macrovascular complications in patients with type 2 diabetes mellitus (T2DM). The association between HbA1c variability and retinopathy in patients with T2DM, however, has been inconsistent in previous studies. In order to fully evaluate the above association, we conducted a meta-analysis. Observational studies related to the aim of the meta-analysis were identified by search of PubMed, Web of Science, and Embase databases. Studies with HbA1c variability evaluated as the standard deviation (SD) and/or the coefficients of variation (CV) of HbA1c were included. The results were analyzed using a random-effects model that incorporated potential heterogeneity between studies. Twelve observational studies involving 44 662 T2DM patients contributed to the meta-analysis. Overall, 5150 (11.5%) patients developed retinopathy. Pooled results showed that compared to patients with lower HbA1c variability, T2DM patients with higher HbA1c-SD (relative risk [RR]: 1.48, 95% confidence interval [CI]: 1.24 to 1.78, p<0.001, I2=34%) and higher HbA1c-CV (RR: 1.29, 95% CI: 1.05 to 1.59, p=0.02, I2=0%) were both associated with higher risk of DR. For studies with HbA1c-SD, the association was not significantly affected by study characteristics such as country, study design, mean age, disease duration, adjustment of mean HbA1c, or quality scores (p for subgroup difference all>0.05). In conclusion, higher HbA1c variability may be associated with an increased risk of retinopathy in patients with T2DM.

The aim of our study is to explore the value of serum glycosylated hemoglobin A1c (HbA1c) in disease severity and clinical outcomes of acute pancreatitis (AP).

Patients with AP were included from January 2013 to December 2020, retrospectively, dividing into normal serum HbA1c level (N-HbA1c) group and high serum HbA1c level (H-HbA1c) group according to the criteria HbA1c <6.5%. We compared patient characteristics, biochemical parameters, disease severity, and clinical outcomes of patients with AP in two groups. Besides, we evaluated the efficacy of serum HbA1c to predict organ failure (OF) in AP patients by receiver operating curve (ROC).

We included 441 patients with AP, including 247 patients in N-HbA1c group and 194 patients in H-HbA1c group. Serum HbA1c level was positively correlated with Atlanta classification, systemic inflammatory response syndrome, local complication, and OF (all p<0.05). Ranson, BISAP (bedside index of severity in acute pancreatitis), and CT severity index scores in patients with H-HbA1c were markedly higher than those in patients with N-HbA1c (all p<0.01). ROC showed that the best critical point for predicting the development of OF in AP with serum HbA1c is 7.05% (area under the ROC curve=0.79). Logistic regression analysis showed H-HbA1c was the independent risk factor for the development of OF in AP. Interestingly, in patients with presence history of diabetes and HbA1c <6.5%, the severity of AP was significantly lower than that in H-HbA1c group. Besides, there was no significant difference between with and without history of diabetes in N-HbA1c group.

Generally known, diabetes is closely related to the development of AP, and strict control of blood glucose can improve the related complications. Thus, the level of glycemic control before the onset of AP (HbA1c as an indicator) is the key to poor prognosis of AP, rather than basic history of diabetes. Elevated serum HbA1c level can become the potential indicator for predicting the disease severity of AP.

It remains unclear whether a combination of glycemic variability and glycated hemoglobin (HbA1c) status leads to a higher incidence of cardiovascular disease (CVD). Therefore, to investigate CVD risk according to the glucose control status during early diabetes, we examined visit-to-visit HbA1c variability among patients with type 2 diabetes (T2DM).

In this 9-year retrospective study, we measured HbA1c levels at each visit and tracked the change in HbA1c levels for 3 years after the first presentation (observation window) in newly diagnosed T2DM patients. We later assessed the occurrence of CVD in the last 3 years (target outcome window) of the study period after allowing a 3-year buffering window. The HbA1c variability score (HVS; divided into quartiles, HVS_Q1-4) was used to determine visit-to-visit HbA1c variability.

Among 4,817 enrolled T2DM patients, the mean HbA1c level was < 7% for the first 3 years. The group with the lowest HVS had the lowest rate of CVD (9.4%; 104/1,109 patients). The highest incidence of CVD of 26.7% (8/30 patients) was found in HVS [≥ 9.0%]_Q3, which was significantly higher than that in HVS [6.0-6.9%]_Q1 (P = 0.006), HVS [6.0-6.9%]_Q2 (P = 0.013), HVS [6.0-6.9%]_Q3 (P = 0.018), and HVS [7.0-7.9%]_Q3 (P = 0.040).

To our knowledge, this is the first long-term study to analyze the importance of both HbA1c change and visit-to-visit HbA1c variability during outpatient visits within the first 3 years. Lowering glucose levels during early diabetes may be more critical than reducing visit-to-visit HbA1c variability.

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).

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.

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).

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.

Significance: Diabetic peripheral neuropathy (DPN) associated with a diabetic foot ulcer (DFU) is likely to be complicated with critical factors such as biofilm infection and compromised skin barrier function of the diabetic skin. Repaired skin with a history of biofilm infection is known to be compromised in barrier function. Loss of barrier function is also observed in the oxidative stress affected diabetic and aged skin. Recent Advances: Loss of barrier function makes the skin prone to biofilm infection and cellulitis, which contributes to chronic inflammation and vasculopathy. Hyperglycemia favors biofilm formation as glucose lowering led to reduction in biofilm development. While vasculopathy limits oxygen supply, the O2 cost of inflammation is high increasing hypoxia severity. Critical Issues: The host nervous system can be inhabited by bacteria. Because electrical impulses are a part of microbial physiology, polymicrobial colonization of the host's neural circuit is likely to influence transmission of action potential. The identification of perineural apatite in diabetic patients with peripheral neuropathy suggests bacterial involvement. DPN starts in both feet at the same time. Future Directions: Pair-matched studies of DPN in the foot affected with DFU (i.e., DFU-DPN) compared with DPN in the without ulcer, and intact skin barrier function, are likely to provide critical insight that would help inform effective care strategies. This review characterizes DFU-DPN from a translational science point of view presenting a new paradigm that recognizes the current literature in the context of factors that are unique to DFU-DPN.