XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

OBJECTIVE To identify and define clinically meaningful type 1 diabetes outcomes beyond hemoglobin A 1c (HbA 1c ) based upon a review of the evidence, consensus from clinical experts, and input from researchers, people with type 1 diabetes, and industry. Priority outcomes include hypoglycemia, hyperglycemia, time in range, diabetic ketoacidosis (DKA), and patient-reported outcomes (PROs). While priority outcomes for type 1 and type 2 diabetes may overlap, type 1 diabetes was the focus of this work. RESEARCH AND METHODS A Steering Committee—comprising representatives from the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange—was the decision-making body for the Type 1 Diabetes Outcomes Program. Their work was informed by input from researchers, industry, and people with diabetes through Advisory Committees representing each stakeholder group. Stakeholder surveys were used to identify priority outcomes. The outcomes prioritized in the surveys were hypoglycemia, hyperglycemia, time in range, DKA, and PROs. To develop consensus on the definitions of these outcomes, the Steering Committee relied on published evidence, their clinical expertise, and feedback from the Advisory Committees. RESULTS The Steering Committee developed definitions for hypoglycemia, hyperglycemia, time in range, and DKA in type 1 diabetes. The definitions reflect their assessment of the outcome’s short- and long-term clinical impact on people with type 1 diabetes. Knowledge gaps to be addressed by future research were identified. The Steering Committee discussed PROs and concluded that further type 1 diabetes–specific development is needed. CONCLUSIONS The Steering Committee recommends use of the defined clinically meaningful outcomes beyond HbA 1c in the research, development, and evaluation of type 1 diabetes therapies.

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Standardizing Clinically Meaningful Outcome Measures Beyond HbA 1c for Type 1 Diabetes: A Consensus Report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange.

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.

Distal symmetric sensorimotor polyneuropathy (DSPN) is the most common neurological manifestation in diabetes. Major risk factors of DSPN include diabetes duration, hyperglycemia, and age, followed by prediabetes, hypertension, dyslipidemia, and obesity. Height, smoking, insulin resistance, hypoinsulinemia, and others represent an additional risk. Importantly, hyperglycemia, hypertension, dyslipidemia, obesity, and smoking are modifiable. Stringent glycemic control has been shown to be effective in type 1, but not to the same extent in type 2 diabetes. Antilipidemic treatment, especially with fenofibrate, and multi-factorial intervention have produced encouraging results, but more experience is necessary. The major comorbidities of DSPN are depression, autonomic neuropathy, peripheral arterial disease, cardiovascular disease, nephropathy, retinopathy, and medial arterial calcification. Knowledge of risk factors and comorbidities has the potential to enrich the therapeutic strategy in clinical practice as part of the overall medical care for patients with neuropathy. This article provides an updated overview of DSPN risk factors and comorbidities.

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Risk Factors and Comorbidities in Diabetic Neuropathy: An Update 2015.

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.

Diabetic peripheral neuropathy (DPN) is one of the most common microvascular complications of diabetes. Glycemic variability could be an independent risk factor for diabetes complications in addition to average glucose. Type 2 diabetes with well-controlled glycosylated hemoglobin A1c (HbA1c) may have different terms of glycemic variability and vascular complication consequences. The aim of the study is to investigate the relationship between glycemic variability and DPN in type 2 diabetes with well-controlled HbA1c (HbA1c   0.05). Univariate analysis showed DPN was closely associated with BMI (OR 0.82, CI 0.72–0.94, p = 0.005), TC (OR 0.63, CI 0.42–0.93, p = 0.02), LDLC (OR 0.4, CI 0.20–0.80, p = 0.009), SDBG (OR 2.95, CI 1.55–5.61, p = 0.001), MODD (OR 4.38, CI 1.48–12.93, p = 0.008), MAGE (OR 2.18, CI 1.47–3.24, p < 0.001). Multivariate logistic regression analysis showed that MAGE (OR 2.05, CI 1.36–3.09, p = 0.001) and BMI (OR 0.85, CI 0.73–0.99, p = 0.033) were significantly correlating with DPN. Glycemic variability, evaluated by MAGE, was the most significantly independent risk factor for DPN. There was a close relationship between glycemic variability evaluated by MAGE and DPN in type 2 diabetes with well-controlled HbA1c.

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The relationship between glycemic variability and diabetic peripheral neuropathy in type 2 diabetes with well-controlled HbA1c

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.

Women with previous gestational diabetes mellitus (pGDM) and postpartum normal glucose tolerance (NGT) may carry impaired islet β cell secretion, insulin resistance and subsequent altered glucose homeostasis. And certain normoglycemic groups at risks of diabetes were presented with elevated glycemic variability. The aim of study was to investigate the glycemic variability in NGT women with pGDM. Total 48 NGT women with pGDM (pGDM group) and 48 age- and BMI-matched NGT women without pGDM (control group) were recruited in the study. Integrated β cell function was assessed with the Insulin Secretion-Sensitivity Index-2 (ISSI-2) derived from oral glucose tolerance test. All subjects were monitored using the continuous glucose monitoring system for consecutive 72 h. The multiple parameters of glycemic variability included the mean blood glucose (MBG), standard deviation of blood glucose (SDBG), mean of daily differences (MODD), mean amplitude of glycemic excursions (MAGE) and the incremental areas above preprandial glucose values (AUCpp). The pGDM group had a higher MBG (6.5 ± 0.9 vs. 5.9 ± 0.8 mmol/L, p < 0.05), SDBG (1.3 ± 0.3 vs. 0.9 ± 0.2 mmol/L, p < 0.05), MODD (1.4 ± 0.3 vs. 1.1 ± 0.2 mmol/L, p < 0.05), MAGE (2.7 ± 0.4 vs. 1.8 ± 0.5 mmol/L, p < 0.05), and AUCpp (26.8 ± 3.4 vs. 19.2 ± 3.2 mmol/L·h, p < 0.05), when compared to the control group, and the differences remained significant after adjusting for anthropometric indices and metabolic risk factors. Islet β cell function index ISSI-2 in the pGDM group was lower than in the control group (p < 0.05). MBG, SDBG, MODD, MAGE and AUCpp were all negatively associated with ISSI-2 in the pGDM group (r = −0.31, −0.30, −0.34, −0.48 and −0.54, respectively, p < 0.05), and the correlations remained significant after adjusting for anthropometric indices and metabolic risk factors. Normal glucose tolerance women with pGDM were presented with elevated glycemic variability, which may be associated with impaired islet β cell function.

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Li-hua Zhao

Papers

The relationship between glycemic variability and diabetic peripheral neuropathy in type 2 diabetes with well-controlled HbA1c

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.

Glycemic variability in normal glucose tolerance women with the previous gestational diabetes mellitus.