Oxidative stress (OS) is defined as a disturbance in the prooxidant-antioxidant balance of the cell, in favor of the former, which results in the antioxidant capacity of the cell to be overpowered. Excess reactive oxygen species (ROS) production is very harmful to cell constituents, especially proteins, lipids, and DNA, thus causing damage to the cell. Oxidative stress has been associated with a variety of pathologic conditions, including diabetes mellitus (DM), cancer, atherosclerosis, neurodegenerative diseases, rheumatoid arthritis, ischemia/reperfusion injury, obstructive sleep apnea, and accelerated aging. Regarding DM specifically, previous experimental and clinical studies have pointed to the fact that oxidative stress probably plays a major role in the pathogenesis and development of diabetic complications. It is postulated that hyperglycemia induces free radicals and impairs endogenous antioxidant defense systems through several different mechanisms. In particular, hyperglycemia promotes the creation of advanced glycation end-products (AGEs), the activation of protein kinase C (PKC), and the hyperactivity of hexosamine and sorbitol pathways, leading to the development of insulin resistance, impaired insulin secretion, and endothelial dysfunction, by inducing excessive ROS production and OS. Furthermore, glucose variability has been associated with OS as well, and recent evidence suggests that also hypoglycemia may be playing an important role in favoring diabetic vascular complications through OS, inflammation, prothrombotic events, and endothelial dysfunction. The association of these diabetic parameters (i.e., hyperglycemia, glucose variability, and hypoglycemia) with oxidative stress will be reviewed here.

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Association of Glycemic Indices (Hyperglycemia, Glucose Variability, and Hypoglycemia) with Oxidative Stress and Diabetic Complications.

Purpose of Review. Describing the diverse molecular mechanisms (particularly immunological) involved in the death of the pancreatic beta cell in type 1 and type 2 diabetes mellitus. Recent Findings. Beta cell death is the final event in a series of mechanisms that, up to date, have not been entirely clarified; it represents the pathophysiological mechanism in the natural history of diabetes mellitus. These mechanisms are not limited to an apoptotic process only, which is characteristic of the immune-mediated insulitis in type 1 diabetes mellitus. They also include the action of proinflammatory cytokines, the production of reactive oxygen species, DNA fragmentation (typical of necroptosis in type 1 diabetic patients), excessive production of islet amyloid polypeptide with the consequent endoplasmic reticulum stress, disruption in autophagy mechanisms, and protein complex formation, such as the inflammasome, capable of increasing oxidative stress produced by mitochondrial damage. Summary. Necroptosis, autophagy, and pyroptosis are molecular mechanisms that modulate the survival of the pancreatic beta cell, demonstrating the importance of the immune system in glucolipotoxicity processes and the potential role for immunometabolism as another component of what once known as the “ominous octet.”

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Pancreatic Beta Cell Death: Novel Potential Mechanisms in Diabetes Therapy.

Glycemic variability (GV), defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice. Although it remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macrovascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes. In this review, we summarize the adverse clinical outcomes of GV and discuss the beneficial measures, including continuous glucose monitoring, drugs, dietary interventions and exercise training, to improve it, aiming at better addressing the challenging aspect of blood glucose management.

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Glycemic variability: adverse clinical outcomes and how to improve it?

Diabetic peripheral neuropathy (DPN), the most common chronic complication of diabetes, has become an important public health crisis worldwide. Given that DPN is extremely difficult to treat, determining its risk factors and controlling it at an early stage is critical to preventing its serious consequences and the burden of social disease. Current studies suggest that the risk factors for diabetic peripheral neuropathy are the duration of diabetes, age, glycosylated hemoglobin A1c (HbA1c), diabetic retinopathy (DR), smoking, and body mass Index (BMI). However, most of the aforementioned studies are cross-sectional, and the sample sizes are very limited, so the strength of causal reasoning is relatively low. The current study systematically evaluated DPN’s influencing factors in patients with type 2 diabetes using evidence-based medicine. Overall, 16 included studies (14 cross-sectional studies and 2 case-control studies including 12,116 cases) that conformed to the present criteria were included in the final analysis. The results suggested that the duration of diabetes (MD 2.5, 95% CI 1.71~3.29), age (MD 4.00, 95% CI 3.05~4.95), HbA1c (MD 0.48, 95% CI 0.33~0.64), and DR (OR 2.34, 95% CI 1.74~3.16) are associated with significantly increased risks of DPN among diabetic patients, while BMI, smoking, total triglyceride (TG), and total cholesterol (TC) did not indicate any risks of increasing DPN. The findings provide a scientific basis for a further understanding of the causes of type 2 diabetes complicated with peripheral neuropathy and the improvement of preventive strategies. The next step is to conduct further high-quality prospective cohort studies to validate this paper’s findings.

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The risk factors for diabetic peripheral neuropathy: A meta-analysis.

​Objective Compared with hemoglobin A1c (HbA1c), continuous glucose monitoring (CGM) may better capture risk of diabetes complications in patients with chronic kidney disease (CKD), including diabetic peripheral neuropathy (DPN). We hypothesized that glucose time in range (TIR), measured by CGM, is associated with DPN symptoms among participants with type 2 diabetes mellitus (type 2 DM) and moderate-to-severe CKD. ​Research design and methods We enrolled 105 people with type 2 DM treated with insulin or sulfonylurea, 81 participants with CKD (estimated glomerular filtration rate (eGFR) ​Results Participants with CKD had a mean age of 68 years, diabetes duration 20 years, eGFR 38 mL/min/1.73 m2 and HbA1c 7.8%, 61 mmol/mol. Sixty-two participants reported ≥2 DPN symptoms, 51 (63%) with CKD and 11 (46%) controls. Less TIR and higher GMI were associated with higher risk of MNSI questionnaire score ≥2 (OR 1.25 (95% CI 1.02 to 1.52) per 10% lower TIR, and OR 1.79 (95% CI 1.05 to 3.04) per 1% higher GMI, adjusting for age, gender and race). Similar results were observed when analyses were restricted to participants with CKD. In contrast, there was no significant association of HbA1c with DPN symptoms. ​Conclusions Symptoms of DPN were common among participants with long-standing type 2 DM and CKD. Lower TIR and higher GMI were associated with DPN symptoms.

https://drc.bmj.com/content/bmjdrc/8/1/e000991.full.pdf

Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease.

Diabetic complications may be associated with impaired time-dependent glycemic control. Therefore, long-term glycemic variability, assessed by variations in haemoglobin A1c (HbA1c), may be a potential risk factor for microvascular complications, such as diabetic peripheral neuropathy (DPN). We investigated the association of HbA1c variability with DPN in patients with type 2 diabetes. In this cross-sectional study, 563 type 2 diabetic patients who had been screened for DPN and undergone quarterly HbA1c measurements during the year preceding enrolment were recruited. DPN was confirmed in patients displaying both clinical manifestations of neuropathy and abnormalities in a nerve conduction evaluation. HbA1c variability was assessed by the coefficient of variation of HbA1c (CV-HbA1c), and the mean of HbA1c (M-HbA1c) was calculated. In addition, medical history and clinical data were collected. Among the recruited patients, 18.1% (n = 102) were found to have DPN, and these patients also presented with a higher CV-HbA1c than the patients without DPN (p < 0.001). The proportion of patients with DPN increased significantly from 6.9% in the first to 19.1% in the second and 28.5% in the third tertile of CV-HbA1c (p for trend < 0.001). After adjusting for initial HbA1c, M-HbA1c and other clinical factors via multiple logistic regression analysis, the odds ratios (ORs) for DPN in the second and third versus those in the first CV-HbA1c tertile were 3.61 (95% CI 1.62–8.04) and 6.48 (2.86–14.72), respectively. The area under the receiver operating characteristic (ROC) curve of CV-HbA1c was larger than that of M-HbA1c, at 0.711 (95% CI 0.659–0.763) and 0.662 (0.604–0.721), respectively. ROC analysis also revealed that the optimal cutoff value of CV-HbA1c to indicate DPN was 15.15%, and its corresponding sensitivity and specificity were 66.67% and 65.73%, respectively. Increased HbA1c variability is closely associated with DPN in type 2 diabetic patients and could be considered as a potent indicator for DPN in these patients.

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HbA1c variability and diabetic peripheral neuropathy in type 2 diabetic patients.

Diabetic peripheral neuropathy (DPN), a common microvascular complication of diabetes, is linked to glycaemic derangements. Glycaemic variability, as a pattern of glycaemic derangements, is a key risk factor for diabetic complications. We investigated the association of glycaemic variability with DPN in a large-scale sample of type 2 diabetic patients. In this cross-sectional study, we enrolled 982 type 2 diabetic patients who were screened for DPN and monitored by a continuous glucose monitoring (CGM) system between February 2011 and January 2017. Multiple glycaemic variability parameters, including the mean amplitude of glycaemic excursions (MAGE), mean of daily differences (MODD), standard deviation of glucose (SD), and 24-h mean glucose (24-h MG), were calculated from glucose profiles obtained from CGM. Other possible risks for DPN were also examined. Of the recruited type 2 diabetic patients, 20.1% (n = 197) presented with DPN, and these patients also had a higher MAGE, MODD, SD, and 24-h MG than patients without DPN (p < 0.001). Using univariate and multiple logistic regression analyses, MAGE and conventional risks including diabetic duration, HOMA-IR, and hemoglobin A1c (HbA1c) were found to be independent contributors to DPN, and the corresponding odds ratios (95% confidence interval) were 4.57 (3.48–6.01), 1.10 (1.03–1.17), 1.24 (1.09–1.41), and 1.33 (1.15–1.53), respectively. Receiver operating characteristic analysis indicated that the optimal MAGE cutoff value for predicting DPN was 4.60 mmol/L; the corresponding sensitivity was 64.47%, and the specificity was 75.54%. In addition to conventional risks including diabetic duration, HOMA-IR and HbA1c, increased glycaemic variability assessed by MAGE is a significant independent contributor to DPN in type 2 diabetic patients.

Association of glycaemic variability evaluated by continuous glucose monitoring with diabetic peripheral neuropathy in type 2 diabetic patients.

Objectives Prolonged heart rate-corrected QT(QTc) interval is related to ventricular arrhythmia and cardiovascular mortality, with considerably high prevalence of type 2 diabetes. Additionally, long-term glycaemic variability could be a significant risk factor for diabetic complications in addition to chronic hyperglycaemia. We compared the associations of long-term glycaemic variability versus sustained chronic hyperglycaemia with the QTc interval among type 2 diabetes patients. Methods In this cross-sectional study, 2904 type 2 diabetes patients were recruited who had undergone at least four fasting plasma glucose (FPG) and 2-hour postprandial plasma glucose (PPG) measurements (at least once for every 3 months, respectively) during the preceding year. Long-term glycaemic variabilities of FPG and 2-hour PPG were assessed by their standard deviations (SD-FPG and SD-PPG, respectively), and chronic fasting and postprandial hyperglycaemia were assessed by their means (M-FPG and M-PPG, respectively). HbA1c was also determined upon enrolment to assess current overall glycaemic control. QTc interval was estimated from resting 12-lead electrocardiograms, and more than 440 ms was considered abnormally prolonged. Results Patients with prolonged QTc interval (≥440 ms) had greater M-FPG, M-PPG, SD-PPG and HbA1c than those with normal QTc interval but comparable SD-FPG. QTc interval was correlated with M-FPG, M-PPG, SD-PPG and HbA1c (r = 0.133, 0.153, 0.245 and 0.207, respectively, p = 0.000) but not with SD-FPG (r = 0.024, p = 0.189). After adjusting for metabolic risk factors via multiple linear regression analysis, SD-PPG, M-PPG and HbA1c (t = 12.16, 2.69 and 10.16, respectively, p = 0.000) were the major independent contributors to the increased QTc interval. The proportion of prolonged QTc interval increased significantly from 10.9% to 14.2% to 26.6% for the first (T1) to second (T2) to third (T3) tertiles of SD-PPG. After adjusting via multiple logistic regression analysis, the odd ratios of prolonged QTc interval of the T2 and T3 versus the T1 of SD-PPG were 1.15 (95% CI, 0.82-1.60) and 2.62 (1.92-3.57), respectively. Conclusions Increased long-term variability of PPG is a strong independent risk factor for prolonged QTc interval in type 2 diabetes patients, in addition to long-term postprandial hyperglycaemia and current HbA1c.

The association of long-term glycaemic variability versus sustained chronic hyperglycaemia with heart rate-corrected QT interval in patients with type 2 diabetes.

Reduced insulin sensitivity not only contributes to the pathogenesis of type 2 diabetes but is also linked to multiple metabolic risk factors and cardiovascular diseases (CVD). A prolonged heart rate-corrected QT interval (QTc interval) is related to ventricular arrhythmias and CVD mortality and exhibits a high prevalence among type 2 diabetes patients. The aim of the study was to investigate the relationship between insulin sensitivity and the QTc interval in patients with type 2 diabetes. This cross-sectional observational study recruited 2927 patients with type 2 diabetes who visited the Affiliated Haian Hospital and Second Affiliated Hospital of Nantong University. The insulin sensitivity index (Matsuda index, ISIMatsuda) derived from 75-g OGTT and other metabolic risk factors were examined in all patients. The QTc interval was estimated using a resting 12-lead electrocardiogram, and an interval longer than 440 ms was considered abnormally prolonged. The QTc interval was significantly and negatively correlated with the ISIMatsuda (r = −0.296, p < 0.001), and when the multiple linear regression analysis was adjusted for anthropometric parameters, metabolic risk factors, and current antidiabetic treatments, the QTc interval remained significantly correlated with the ISIMatsuda (β = −0.23, t = −12.63, p < 0.001). The proportion of patients with prolonged QTc interval significantly increased from 12.1% to 17.9%, 25.6% and 37.9% from the fourth to third, second and first quartile of the ISIMatsuda, respectively. After adjusting for anthropometric parameters by multiple logistic regression analysis, the corresponding odd ratios (ORs) for prolonged QTc interval of the first, second and third quartiles versus the fourth quartile of ISIMatsuda were 3.11 (95% CI 2.23–4.34), 2.09 (1.51–2.88) and 1.53 (1.09–2.14), respectively, and p for trend was <0.001. Reduced insulin sensitivity is associated with an increase in the QTc interval in patients with type 2 diabetes.

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The relationship between insulin sensitivity and heart rate-corrected QT interval in patients with type 2 diabetes

Objective 
The insulin-pancreatic acinar axis may play a major role in pancreatic function. Amylase is an exocrine enzyme that is produced by pancreatic acinar cells, and low serum amylase levels may be associated with endocrine diseases, such as metabolic syndrome and diabetes. We hypothesized that low serum amylase levels may be associated with impaired islet β cell function in type 2 diabetes. Therefore, we investigated the relationship between the serum amylase levels and islet β cell function in patients with early type 2 diabetes.


Methods 
The cross-sectional study recruited 2327 patients with a mean of 1.71±1.62 years since their diagnosis of type 2 diabetes, and all participants were treated with lifestyle intervention alone. Serum amylase levels, the 75-g oral glucose tolerance test (OGTT) and metabolic risk factors were examined in all participants. The insulin sensitivity index (Matsuda index, ISIMatsuda) and insulin secretion index (ratio of total area-under-the-insulin-curve to glucose-curve, AUCins/glu) were derived from the OGTT. Integrated islet β cell function was assessed by the Insulin Secretion-Sensitivity Index-2 (ISSI-2) (ISIMatsuda multiplied by AUCins/glu).


Results 
Serum amylase levels in the normal range were significantly correlated with ISIMatsuda, AUCins/glu and ISSI-2 (r = 0.203, 0.246 and 0.413, respectively, p<0.001). The association of the serum amylase levels with ISSI-2 (adjusted r = 0.363, p<0.001) was closer than the association with ISIMatsuda (adjusted r = 0.191, p<0.001) and AUCins/glu (adjusted r = 0.174, p<0.001) after adjusting for the anthropometric indices, time since the diagnosis of diabetes, lipid profiles, uric acid levels, estimated glomerular filtration rate, HbA1c levels, smoking and drinking using the partial correlation test. After adjusting for these metabolic risk factors in the multivariate regression analysis with the amylase levels as the dependent variable, ISSI-2 was the major independent contributor to the serum amylase levels (β = 0.416, t = 21.72, p<0.001). Meanwhile, in a comparison of the groups with the highest and lowest quartiles of serum amylase levels, the mean difference in logISSI-2 was 0.902 (95% CI 0.823 to 0.982), and after adjusting for metabolic risk factors, the mean difference in logISSI-2 was 0.610 (0.537 to 0.683).


Conclusions 
Serum amylase levels in the normal range are positively associated with integrated islet β cell function in patients with early type 2 diabetes, as assessed by ISSI-2.

/pdf/serum-amylase-levels-in-relation-to-islet-b-cell-function-in-3meixwnmim.pdf

Xiu-lin Zhang

Papers

HbA1c variability and diabetic peripheral neuropathy in type 2 diabetic patients.

Association of glycaemic variability evaluated by continuous glucose monitoring with diabetic peripheral neuropathy in type 2 diabetic patients.

The association of long-term glycaemic variability versus sustained chronic hyperglycaemia with heart rate-corrected QT interval in patients with type 2 diabetes.

The relationship between insulin sensitivity and heart rate-corrected QT interval in patients with type 2 diabetes

Serum Amylase Levels in Relation to Islet β Cell Function in Patients with Early Type 2 Diabetes.