Utility of different glycemic control metrics for optimizing management of diabetes

Self-Reported Food Security in Adolescents with Type 1 Diabetes: Association with Hemoglobin A1c and Mental Health Symptoms Independent of Household Food Security

BACKGROUND

Typically, child exposure to food insecurity is assessed by caregiver reports of household food security. Child report has the potential for greater accuracy because it pertains only to the child whose experiences may differ from caregiver reports.

OBJECTIVE

We assessed if adolescent-reported food insecurity was associated with levels of hemoglobin A1c (HbA1c), acute diabetes-related complications, depressive symptoms, and disordered eating behaviors in adolescents with type 1 diabetes, independently from household food security.

METHODS

In a cross-sectional analysis of the multicenter SEARCH for Diabetes in Youth Cohort Study (phase 4, 2016-2019) including 601 adolescents aged 10-17 y with type 1 diabetes and their caregivers, household food security, and adolescent-reported food security were assessed using the 18-item Household Food Security Survey Module and the 6-item Child Food Security Assessment questionnaire. Age-stratified (10-13 and 14-17) regression models were performed to estimate independent associations, adjusting for sociodemographics, clinical factors, and household food security.

RESULTS

Food insecurity was reported by 13.1% (n = 79) of adolescents and 15.6% (n = 94) of caregivers. Among adolescent-caregiver dyads, 82.5% (n = 496) of reports were concordant and 17.5% (n = 105) discordant, Cohen's κ= 0.3. Adolescent-reported food insecurity was not independently associated with HbA1c, diabetic ketoacidosis, and severe hypoglycemia, including in age-stratified analyses. Adolescent-reported food insecurity was independently associated with elevated odds of depressive symptoms [odds ratio (OR): 3.6; 95% confidence interval (CI): 1.3, 10.3] and disordered eating behaviors (OR: 2.5, 95% CI: 1.4, 4.6) compared with adolescents reporting food security; these associations remained in both age groups for disordered eating behaviors and in the older group for depressive symptoms.

CONCLUSIONS

Adolescents with type 1 diabetes may experience food insecurity differently than caregivers. Adolescent-reported food insecurity was independently associated with depressive symptoms and disordered eating behaviors and thus may be an important attribute to assess in addition to household food security in adolescents with type 1 diabetes.

Characteristics, glycemic control and outcomes of adults with type-2 diabetes mellitus attending specialized clinics in primary healthcare centers in Bahrain-A cross-sectional study

Introduction

Diabetes mellitus is a global health challenge that requires continuous and multidisciplinary management. Suboptimal diabetes management results in serious complications that impose a huge burden on patients and the healthcare system. This study aimed to assess the characteristics, glycemic control and outcomes of patients with type-2 diabetes attending primary healthcare centers in Bahrain according to the new American Diabetes Association (ADA) guidelines.

Materials and Methods

A cross-sectional study was conducted among adult patients with type-2 diabetes mellitus attending diabetic clinics in Bahrain. A multi-stage sampling technique was adopted. The data collection tool consisted of three parts: baseline and sociodemographic data, the physical measures of the patients and the most recent laboratory results. An A1C of less than 7% was indicative of good glycemic control.

Results

A total of 721 patients with type-2 diabetes mellitus were included with an average age of 58.4 years. Most patients were hypertensive (n = 457, 63.4%), and half of them were hyperlipidemic (n = 373, 51.7%). Around 57% (n = 402) of the patients adopted lifestyle modifications, 14.8% adopted diet control measures and around half performed weekly regular exercises. More than 92% of the cohort were on metformin, 52.0% (n = 375) were on Sulphonylurea medications and 41% (n = 298) were on insulin formulations. While only 40% of the patients had controlled diabetes (n = 283, 39.3%) and hypertension (n = 298, 41.3%), most patients achieved adequate cholesterol and low-density lipoprotein levels (83.2% and 76.6%, respectively). Non-Bahraini (P ≤ 0.001), young (P = 0.027) and obese patients (P = 0.003) had lower glycemic control measures. Adequate cholesterol levels were seen more in patients with a controlled glycemic index (P = 0.015).

Conclusion

Considering the new glycemic targets, glycemic and hypertension control was poor among diabetic patients, especially non-Bahraini, obese and young patients. Urgent interventions by policymakers, physicians and caregivers are needed to improve the outcomes of diabetes.

Glycated Albumin Correlates With Time-in-Range Better Than HbA1c or Fructosamine

CONTEXT

Intermediate-term glycemic control metrics may represent a viable alternative to continuous glucose monitoring (CGM) in patients without access to CGM.

OBJECTIVE

This work aimed to compare the relationship between CGM parameters and glycated albumin (GA), glycated hemoglobin A1c (HbA1c), and fructosamine for 24 weeks.

METHODS

We conducted exploratory comparative analyses of CGM subgroup data from a previously published 24-week prospective study of assay performance in 8 US clinics. Participants included 34 individuals with type 1 (n = 18) and type 2 diabetes (n = 16) undergoing changes to improve glycemic control (n = 22; group 1) or with stable diabetes therapy (n = 12; group 2). Main outcome measures included Pearson correlations between CGM and glycemic indices and receiver operating characteristic (ROC) analysis of glycemic index values predictive of time in range (TIR) greater than 70%.

RESULTS

At weeks 4 and 8, GA correlations with TIR were higher than HbA1c correlations in group 1. In group 2, GA correlations with TIR were statistically significant, whereas HbA1c correlations were not. In both groups over the first 12 weeks, GA correlations with TIR were higher than fructosamine-TIR correlations. In the ROC analysis, GA predicted a TIR greater than 70% during weeks 2 to 24 (area under the curve >0.80); HbA1c was predictive during weeks 12 to 24. Cutoff values for TIR greater than 70% were 17.5% (sensitivity and specificity, 0.88) for GA and 7.3% (0.86) for HbA1c.

CONCLUSION

GA is the most accurate predictor of TIR over 8 weeks compared with other glycemic indices, which may assist in clinical evaluation of changes in treatment where CGM is not possible and it is too early to use HbA1c (NCT02489773).

Leptin and the rs2167270 Polymorphism Are Associated with Glycemic Control in Type Two Diabetes Mellitus Patients on Metformin Therapy

Background and Objectives: Type two diabetes mellitus (T2DM) is a chronic disease with debilitating complications and high mortality. Evidence indicates that good glycemic control delays disease progression and is hence a target of disease management protocols. Nonetheless, some patients cannot maintain glycemic control. This study aimed to investigate the association between serum leptin levels and several SNPs of the LEP gene with the lack of glycemic control in T2DM patients on metformin therapy. Materials and Methods: In a hospital-based case-control study, 170 patients with poor glycemic control and 170 patients with good glycemic control were recruited. Serum leptin was measured. Patients were genotyped for three SNPs in the LEP gene (rs7799039, rs2167270, and rs791620). Results: Serum leptin was significantly lower in T2DM patients with poor glycemic control (p < 0.05). In multivariate analysis, serum leptin levels significantly lowered the risk of having poor glycemic control (OR = 0.985; CI: 0.976-0.994; p = 0.002); moreover, the GA genotype of rs2167270 was protective against poor glycemic control compared to the GG genotype (OR = 0.417; CI: 0.245-0.712; p = 0.001). Conclusions: Higher serum leptin and the GA genotype of the rs2167270 SNP of the LEP gene were associated with good glycemic control in T2DM patients on metformin therapy. Further studies with a larger sample size from multiple institutions are required to validate the findings.

Serum total carbohydrates, conjugated carbohydrates and total protein glycation index in diabetes mellitus

BACKGROUND

Diabetes mellitus is defined according to fasting blood glucose and clinical signs. But, the markers of glycation have been used recently as a criterion to diagnose and monitor the therapy.

OBJECTIVES

To measure serum total- and conjugated- saccharides and to define the new marker as serum total protein glycation index (sTPGI ) for diabetes.

DESIGN AND METHODS

The study population consisted of 172 subjects who were divided to control and diabetic cases. Serum total and conjugated saccharides were measured and sTPGI was defined to discriminate serum glycosylated and glycated saccharides.

RESULTS

Patients with diabetes compared with the controls had increased levels of serum (free) glucose, HbA1c, serum total carbohydrates, total conjugated carbohydrates and sTPGI. All three indices of serum carbohydrates showed significant positive correlation with serum glucose, HbA1c and diabetes. The equations: sTPGI = 0.12 Glucose (mg/dL) + 12 and sTPGI = 3.5HbA1c (%) + 5, were deduced for the association of sTPGI with serum free glucose and HbA1c. In ROC analysis, both HbA1c (AUC = 0.965, p ≤ 0.001) and sTPGI (AUC = 0.734, p ≤ 0.001) had strong and significant efficiency to discriminate diabetic cases from control subjects.

CONCLUSIONS

The results confirm that sTPGI obtained by indirect assay has high significant efficiency comparable to HbA1c to diagnose diabetes. sTPGI relative to HbA1c indicates the mean level of glycaemia over a shorter period of about one month so it responds more quickly to changes in therapy.

Long-term fasting glucose variability and risk of cancer in patients with type 2 diabetes mellitus: A retrospective population-based cohort study in Shanghai

BACKGROUNDS

Fasting blood glucose (FBG) variability may make an impact on adverse events in patients with diabetes mellitus. However, the association between long-term changes in FBG and cancer remains unclear. We aimed to investigate this association in a large-scale longitudinal study.

METHODS

Data were collected from 46 761 patients with type 2 diabetes mellitus aged 20-80 years who participated in the Diabetes Standardized Management Program in Shanghai, China. We adopted four indicators, including standard deviation (SD), coefficient of variation (CV), variation independent of the mean (VIM), and average real variability (ARV) to describe FBG variability. Adjusted multivariable Cox regression analyses and restricted cubic splines were used to investigate the association between long-term FBG variability and cancer risk. We also determined the interactive effect of FBG variability with hypertension and FBG-mean with hypertension on cancer risk, respectively.

RESULTS

In this study, we confirmed 2218 cancer cases (51.1% male) over a median follow-up of 2.86 years. In the multivariable-adjusted models, participants in the highest quartile of FBG variability had an increased risk of cancer compared with those in the lowest quartile. The nonlinear association was found when using FBG-VIM, FBG-ARV, and FBG-SD in restricted cubic spline plots. There was a significant interaction effect of FBG variability with hypertension on cancer, whereas the effect of FBG-mean with hypertension did not attain significance.

CONCLUSIONS

Our retrospective cohort study demonstrated a positive association between the long-term changes in FBG and cancer risk in patients with type 2 diabetes mellitus. FBG variability may independently predict cancer incidence.

Effect of lactic acid fermented foods on glycemic control in diabetic adults: a systemic review and meta-analysis of randomized controlled trials

Lactic acid bacteria (LAB) fermented foods are reported to have potential in managing glycemic control. This systematic review aimed to evaluate the effectiveness of LAB-fermented foods on improving glycemic control in adults with prediabetics or type 2 diabetes mellitus (T2DM). Randomized controlled trials (RCTs) on LAB fermentation-related foods were searched on PubMed, Cochrane, Excerpta Medica database (EMBASE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science. Sixteen RCTs were included, and the results concluded LAB-fermented food had significant effects in HbA1c (Z = 6.24, MD = -0.05, CI: -0.07 to -0.04, p ≤ 0.00001), fasting plasma glucose (Z = 2.50, MD = -0.16, CI: -0.29 to -0.04, p = 0.01) and fasting serum insulin (Z = 2.51, MD = -0.20, CI: -0.35 to -0.04, p = 0.01). There were significant effects on lipid profile, inflammatory markers, and body mass index in secondary analyses. Subgroup analysis suggests LAB-fermented consumption with a longer duration, younger age group and adults with T2DM, had a larger effect size. Clinicians could offer LAB-fermented food as dietary recommendations for prediabetic and diabetic adults. Larger trials are warranted to verify LAB-fermented food benefits on glycemic control. Systematic Review Registration: PROSPERO Registration No. CRD42022295220.

Endothelial Dysfunction and Platelet Hyperactivation in Diabetic Complications Induced by Glycemic Variability

The development and progression of the complications of chronic diabetes mellitus are attributed not only to increased blood glucose levels but also to glycemic variability. Therefore, a deeper understanding of the role of glycemic variability in the development of diabetic complications may provide more insight into targeted clinical treatment strategies in the future. Previously, the mechanisms implicated in glycemic variability-induced diabetic complications have been comprehensively discussed. However, endothelial dysfunction and platelet hyperactivation, which are two newly recognized critical pathogenic factors, have not been fully elucidated yet. In this review, we first evaluate the assessment of glycemic variability and then summarise the roles of endothelial dysfunction and platelet hyperactivation in glycemic variability-induced complications of diabetes, highlighting the molecular mechanisms involved and their interconnections.

Should the Quality of Glycemic Control Guide Dental Implant Therapy in Patients with Diabetes? Focus on Implant Survival

INTRODUCTION

Optimal glycemic control is crucial to dental implant long-term functional and esthetic success. Despite HbA1c levels of 7% or lower used is as an indicator for good glycemic control, however, this level may not be attainable for all diabetic patients. Most dentists do not consider patients with poor glycemic control candidates for implant therapy due to higher implant failure, infection or other complications.

AIM

This review challenges the concept of one size fits all and aims to critically appraise the evidence for the success or failure rate of dental implants and peri-implant health outcomes in patients with less than optimal glycemic control.

DISCUSSION

Evidence suggests that estimating glycemic control from HbA1c measurement alone is misleading. Moreover, elevated preoperative HbA1c was not associated with increased mortality and morbidity after major surgical procedures. Literature for the survival or success of implants in diabetic patients is inconsistent due to a lack of standardized reporting of clinical data collection and outcomes. While a number of studies report that implant treatment in patients with well controlled diabetes has a similar success rate to healthy individuals, other studies suggest that the quality of glycemic control in diabetic patients does not make a difference in the implant failure rate or marginal bone loss. This discrepancy could indicate that risk factors other than hyperglycemia may contribute to the survival of implants in diabetic patients.

CONCLUSION

In the era of personalized medicine, the clinician should utilize individualized information and analyze all risk factors to provide the patient with evidence-based treatment options.

Hemoglobin Wayne causing a falsely elevated hemoglobin A1c

Hemoglobin A1c (HbA1c) is an important tool for diagnosis and management of patients with diabetes mellitus (DM). However, hemoglobin variants can interfere with laboratory assays and lead to inaccurate results. This study describes a patient who was found to have falsely elevated HbA1c values secondary to hemoglobin Wayne, a rare hemoglobin variant. Although hemoglobin Wayne is often clinically silent, falsely elevated HbA1c results could lead to unnecessary medical interventions that could cause patient harm. This variation in results highlights the importance of utilizing additional measurements such as glucose readings and evaluating for hemoglobin variants when results are discordant.

Perspectives of glycemic variability in diabetic neuropathy: a comprehensive review

Diabetic neuropathy is one of the most prevalent chronic complications of diabetes, and up to half of diabetic patients will develop diabetic neuropathy during their disease course. Notably, emerging evidence suggests that glycemic variability is associated with the pathogenesis of diabetic complications and has emerged as a possible independent risk factor for diabetic neuropathy. In this review, we describe the commonly used metrics for evaluating glycemic variability in clinical practice and summarize the role and related mechanisms of glycemic variability in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. In addition, we also address the potential pharmacological and non-pharmacological treatment methods for diabetic neuropathy, aiming to provide ideas for the treatment of diabetic neuropathy.

Zhang et al. describe metrics for evaluating glycaemic variability (GV) in clinical practice and summarize the role and related mechanisms of GV in diabetic neuropathy, including cardiovascular autonomic neuropathy, diabetic peripheral neuropathy and cognitive impairment. They aim to stimulate ideas for the treatment of diabetic neuropathy.

Dependence of Heart Rate Variability Indices on the Mean Heart Rate in Women with Well-Controlled Type 2 Diabetes

Heart rate variability (HRV) is a method used to evaluate the presence of cardiac autonomic neuropathy (CAN) because it is usually attributed to oscillations in cardiac autonomic nerve activity. Recent studies in other pathologies suggest that HRV indices are strongly related to mean heart rate, and this does not depend on autonomic activity only. This study aimed to evaluate the correlation between the mean heart rate and the HRV indices in women patients with well-controlled T2DM and a control group. HRV was evaluated in 19 T2DM women and 44 healthy women during basal supine position and two maneuvers: active standing and rhythmic breathing. Time-domain (SDNN, RMSSD, pNN20) and frequency-domain (LF, HF, LF/HF) indices were obtained. Our results show that meanNN, age, and the maneuvers are the main predictors of most HRV indices, while the diabetic condition was a predictor only for pNN20. Given the known reduced HRV in patients with T2DM, it is clinically important that much of the HRV indices are dependent on heart rate irrespective of the presence of T2DM. Moreover, the multiple regression analyses evidenced the multifactorial etiology of HRV.

Lens fluorescence and skin fluorescence in the Copenhagen Twin Cohort Eye Study: Covariates and heritability

Lens and skin fluorescence are related to the systemic accumulation of advanced glycation end products, which is accelerated in diabetes. We have examined lens fluorescence and skin fluorescence in healthy adult twins. The study enrolled twins aged median 59 years from a national population-based registry. Diabetic individuals were excluded from analysis. The interrelatedness between fluorescence parameters and relations between fluorescence and age, current HbA1c and smoking pack years were examined using correlation tests and mixed model linear regression analyses. Broad-sense heritability was analyzed and compared for lens fluorescence, skin fluorescence and HbA1c. Lens fluorescence and skin fluorescence were crudely interrelated (R = 0.38). In linear regression analyses, age explained a larger fraction of the variance in lens fluorescence (R2 = 32%) than in skin fluorescence (R2 = 20%), whereas HbA1c explained smaller variance fractions (R2 = 3% and 8%, respectively) followed by smoking pack years (4% and 3%, respectively). In multivariate analyses, age, HbA1c and smoking pack years combined explained more of the variance in lens fluorescence (R2 = 35%) than in skin fluorescence (R2 = 21%), but the influence of HbA1c on lens fluorescence was not statistically significant (p = .2). Age-adjusted broad-sense heritability was 85% for lens fluorescence, 53% for skin fluorescence and 71% for HbA1c in best fitting heritability models. Both fluorescence parameters increased with age, current glycemia and cumulative smoking. Lens fluorescence was found to be a predominantly heritable trait, whereas skin fluorescence was more influenced by environmental factors and closer related to current glycemia. The results suggest that skin fluorophores have a faster turn-over than lens fluorophores.

The Influence of Baseline Hemoglobin A1c on Digital Health Coaching Outcomes in Adults With Type 2 Diabetes: Real-World Retrospective Cohort Study

BACKGROUND

Digital health coaching is an increasingly common diabetes self-management support strategy for individuals with type 2 diabetes and has been linked to positive mental and physical health outcomes. However, the relationship between baseline risk and outcomes is yet to be evaluated in a real-world setting.

OBJECTIVE

The purpose of this real-world study was to evaluate trends in digital health coaching outcomes by baseline hemoglobin A1c (HbA1c) to better understand which populations may experience the greatest clinical and psychosocial benefit.

METHODS

A retrospective cohort study design was used to evaluate program effect in a convenience sample of participants in a 12-week digital health coaching program administered by Pack Health. Participants were referred through their health care provider, payer, or employer. The program included patient-centered lifestyle counseling and psychosocial support delivered via telephone, text, and/or email. Self-reported HbA1c and weight were collected at baseline and completion. Physical and mental health were assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health Short Form and the Diabetes Distress Scale-2. Changes in HbA1c, weight, BMI, and physical and mental health were analyzed within three participant cohorts stratified by baseline HbA1c level.

RESULTS

Participants with complete HbA1c data sets (n=226) were included in the analysis. The sample population was 71.7% (162/226) female, with 61.5% (139/226) identifying as white and 34.1% (77/226) as black. Most participants (184/226, 81.4%) reported a baseline HbA1c ≥7%, and 20.3% (46/226) were classified as high risk (HbA1c >9%). Across HbA1c cohorts, the mean baseline BMI was 35.83 (SD 7.79), and the moderate-risk cohort (7% ≤ HbA1c ≤ 9%) reported the highest mean value (36.6, SD 7.79). At 12 weeks, patients reported a significant decrease in HbAlc, and high-risk participants reduced their levels by the greatest margin (2.28 points; P<.001). Across cohorts, BMI improved by 0.82 (P<.001), with the moderate-risk cohort showing the greatest reduction (-0.88; P<.001). Overall, participants reported significant improvements for PROMIS scores, with the greatest change occurring in the high-risk cohort for whom physical health improved 3.84 points (P<.001) and mental health improved 3.3 points (P<.001). However, the lowest-risk cohort showed the greatest improvements in diabetes distress (-0.76; P=.005).

CONCLUSIONS

Acknowledging the limitations in this real-world study design, the results reported here suggest that adults with type 2 diabetes with a high baseline HbA1c or high BMI may benefit the most from patient-centered digital health coaching programs when compared to their lower risk counterparts. While all participants improved in physical and mental health categories, participants with high HbA1c experienced the greatest HbA1c reduction and individuals with the highest baseline BMI lost the most weight. These results may be used to inform referrals for patients who are more likely to benefit from digital health coaching.

Impact of preoperative glycometabolic status on outcomes in cardiac surgery: Systematic review and meta-analysis

BACKGROUND

Historically, impaired glucose metabolism has been associated with early and late complicated clinical outcomes after cardiac surgery; however, such a condition is not specific to subjects with diabetes mellitus and involves a larger patient population.

METHODS

Databases were screened (January 2000 to December 2020) to identify eligible articles; studies that evaluated the association between preoperative metabolic status, as assessed by glycosylated hemoglobin levels and clinical outcomes, were considered. The studies were stratified in thresholds by baseline glycosylated hemoglobin level (lower vs higher).

RESULTS

Thirty studies, involving 34,650 patients, were included in the review. In a meta-analysis stratified by glycosylated hemoglobin levels, early mortality was numerically reduced in each threshold comparison and yielded the highest reductions when less than 5.5% versus greater than 5.5% glycosylated hemoglobin levels were compared (risk ratio, 0.39; 95% confidence interval, 0.18-0.84; P = .02). Comparing higher glycosylated hemoglobin threshold values yielded comparable results. Late mortality was reduced with lower levels of glycosylated hemoglobin. Low preoperative glycosylated hemoglobin was associated with the lowest risk of sternal wound infections (risk ratio, 0.50; 95% confidence interval, 0.32-0.80; P = .003 and risk ratio, 0.53; 95% confidence interval, 0.39-0.70; P < .0001) for comparisons of less than 7.5% versus greater than 7.5% and less than 7.0% versus greater than 7.0% glycosylated hemoglobin thresholds, respectively. Additionally, levels of glycosylated hemoglobin lower than 7% were associated with reduced hospital stay, lower risk of stroke/transient ischemic attack (risk ratio 0.53; 95% confidence interval, 0.39-0.70; P < .0001), and acute kidney injury (risk ratio, 0.65; 95% confidence interval, 0.54-0.79; P < .0001).

CONCLUSIONS

Lower levels of glycosylated hemoglobin in patients undergoing cardiac surgery are associated with a lower risk of early and late mortality, as well as in the incidence of postoperative acute kidney injury, neurologic complications, and wound infection, compared with higher levels.

Comparison of Premixed Human Insulin 30/70 to Biphasic Aspart 30 in Well-Controlled Patients with Type 2 Diabetes Using Continuous Glucose Monitoring

AIM

To compare in terms of glycemic variability two premixed insulins, Premixed Human Insulin 30/70 (PHI) and Biphasic Aspart 30 (BiAsp30), using Continuous Glucose Monitoring (CGM) and to estimate the correlation of Glycated Albumin (GA) and Fructosamine (FA) with CGM data. Patients-Data: A total of 36 well-controlled patients with type 2 Diabetes Mellitus (T2DM) underwent 7-day CGM with PHI and subsequently with BiAsp30. GA and FA were measured at the first and last day of each week of CGM.

RESULTS

BiAsp30 was associated with lower Average Blood Glucose (ABG) during the 23:00-03:00 period (PHI: 135.08 ± 28.94 mg/dL, BiAsp30: 117.75 ± 21.24 mg/dL, p < 0.001) and the 00:00-06:00 period (PHI: 120.42 ± 23.13 mg/dL, BiAsp30: 111.17 ± 14.74 mg/dL, p = 0.008), as well as with more time below range (<70 mg/dL) (TBR) during the 23:00-03:00 period in the week (PHI: 3.65 ± 5.93%, BiAsp30: 11.12 ± 16.07%, p = 0.005). PHI was associated with lower ABG before breakfast (PHI: 111.75 ± 23.9 mg/dL, BiAsp30: 128.25 ± 35.9 mg/dL, p = 0.013). There were no differences between the two groups in ABG, Time In Range and Time Below Range during the entire 24-h period for 7 days, p = 0.502, p = 0.534, and p = 0.258 respectively, and in TBR for the 00:00-06:00 period p = 0.253. Total daily insulin requirements were higher for BiAsp30 (PHI: 47.92 ± 12.18 IU, BiAsp30: 49.58 ± 14.12 IU, p = 0.001). GA and FA correlated significantly with ABG (GA: r = 0.512, p = 0.011, FA: r = 0.555, p = 0.005).

CONCLUSIONS

In well-controlled patients with T2DM, BiAsp30 is an equally effective alternative to PHI.

Current concepts and clinical importance of glycemic variability

BACKGROUND AND AIMS

Evolving evidence indicate that variations in blood glucose levels are likely to be an important factor in developing diabetic complications. Monitoring glucose fluctuations in patients remains as a therapeutic challenge and more evidence needs to be created in order to bring GV into limelight. This review encapsulates the most important findings conducted and discusses on them to provide readers a better understanding on this emerging subject.

METHODS

Keyword-based comprehensive desktop search was conducted to gather the relevant literature. Triple-stage cascade type content analysis of the literature was conducted to draw relevant themes of discussions.

RESULTS

High glycemic variability is associated with an increased risk of development of diabetic complications especially in cardiac conditions. The widely used and accepted metrics to determine the variations in blood glucose are Standard deviation (SD), MAGE (Mean amplitude of glycemic excursions) and MODD (Mean of daily differences). Occurrence of blood glucose variations affects at a molecular level thereby causing more harm than the occurrence of hyperglycemia alone.

CONCLUSION

Available data suggest that Glycemic Variability should be used as an additional marker of glycemia. Additional research globally, and in India are required.

Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel

Diabetes mellitus is a global health concern associated with significant morbidity and mortality. Inadequate control of diabetes leads to chronic complications and higher mortality rates, which emphasizes the importance of achieving glycemic targets. Although glycated hemoglobin (HbA_1c) is the gold standard for measuring glycemic control, it has several limitations. Therefore, in recent years, along with the emergence of continuous glucose monitoring (CGM) technology, glycemic control modalities have moved beyond HbA_1c. They encompass modern glucometrics, such as glycemic variability (GV) and time-in-range (TIR). The key advantage of these newer metrics over HbA_1c is that they allow personalized diabetes management with person-centric glycemic control. Basal insulin analogues, especially second-generation basal insulins with properties such as longer duration of action and low risk of hypoglycemia, have demonstrated clinical benefits by reducing GV and improving TIR. Therefore, for more effective and accurate diabetes management, the development of an integrated approach with second-generation basal insulin and glucometrics involving GV and TIR is the need of the hour. With this objective, a multinational group of endocrinologists and diabetologists reviewed the existing recommendations on TIR, provided their clinical insights into the individualization of TIR targets, and elucidated on the role of the second-generation basal insulin analogues in addressing TIR.

Comparison of CGM-Derived Measures of Glycemic Variability Between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus

BACKGROUND

To compare glycemic variability (GV) indices between patients with fibrocalculous pancreatic diabetes (FCPD) and type 2 diabetes mellitus (T2D) using continuous glucose monitoring (CGM).

METHODS

We measured GV indices using CGM (iPro™2 Professional CGM, Medtronic, USA) data in 61 patients each with FCPD and T2D who were matched for glycated hemoglobin A1c (HbA1c) and duration of diabetes. GlyCulator2 software was used to estimate the CGM-derived measures of GV (SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD], M value, and coefficient of variance [%CV]), hypoglycemia (time spent below 70 mg/dL, AUC below 70 mg/dL, glycemic risk assessment diabetes equation hypoglycemia, Low Blood Glucose Index), and hyperglycemia (time spent above 180 mg/dL at night [TSA > 180], AUC above 180 mg/dL [AUC > 180], glycemic risk assessment diabetes equation hyperglycemia, High Blood Glucose Index [HBGI], and J index). The correlation of GV indices with HbA1c, duration of diabetes, and demographic and biochemical parameters were also assessed.

RESULTS

All the CGM-derived measures of GV (SD, MAGE, CONGA, MODD, and %CV), except M value, were significantly higher in the FCPD group than in the T2D group (P < 0.05). Measures of hyperglycemia (TSA >180, AUC >180, HBGI, and J index) were significantly higher in the FCPD group than in the T2D group (P < 0.05). The measures of hypoglycemia were not significantly different between the two groups. All the hyperglycemia indices showed a positive correlation with HbA1c in both groups.

CONCLUSIONS

FCPD is associated with higher GV than is T2D. The findings of higher postprandial glycemic excursions in patients with FCPD could have potential therapeutic implications.

Diabetes mellitus in chronic kidney disease: Biomarkers beyond HbA1c to estimate glycemic control and diabetes-dependent morbidity and mortality

Diabetes mellitus (DM) is the leading cause of chronic kidney disease (CKD). Optimal glycemic control contributes to improved outcomes in patients with DM, particularly for microvascular damage, but blood glucose levels are too variable to provide an accurate assessment and instead markers averaging long-term glycemic load are used. The most established glycemic biomarker of long-term glycemic control is HbA1c. Nevertheless, HbA1c has pitfalls that limit its accuracy to estimate glycemic control, including the presence of altered red blood cell survival, hemoglobin glycation and suboptimal performance of HbA1c assays. Alternative methods to evaluate glycemic control in patients with DM include glycated albumin, fructosamine, 1-5 anhydroglucitol, continuous glucose measurement, self-monitoring of blood glucose and random blood glucose concentration measurements. Accordingly, our aim was to review the advantages and pitfalls of these methods in the context of CKD.

The Changing Landscape of Glycemic Targets: Focus on Continuous Glucose Monitoring

Continuous glucose monitoring (CGM) provides comprehensive assessment of daily glucose measurements for patients with diabetes and can reveal high and low blood glucose values that may occur even when a patient's A1C is adequately controlled. Among the measures captured by CGM, the percentage of time in the target glycemic range, or "time in range" (typically 70-180 mg/dL), has emerged as one of the strongest indicators of good glycemic control. This review examines the shift to using CGM to assess glycemic control and guide diabetes treatment decisions, with a focus on time in range as the key metric of glycemic control.

Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes

Background: Glycemic variability is an important factor to consider in diabetes management. It can be assessed with multiple glycemic variability metrics and quality of control indices based on continuous glucose monitoring (CGM) recordings. For this, a robust repeatable calculation is important. A widely used tool for automated assessment is the EasyGV software. The aim of this work is to implement new methods of glycemic variability assessment in EasyGV and to validate implementation of each glucose metric in EasyGV against a reference implementation of the calculations. Methods: Validation data used came from the JDRF CGM study. Validation of the implementation of metrics that are available in EasyGV software v9 was carried out and the following new methods were added and validated: personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage. Reference values considered gold standard calculations were derived from MATLAB implementation of each metric. Results: The Pearson correlation coefficient was above 0.98 for all metrics, except for mean amplitude of glycemic excursion (r = 0.87) as EasyGV implements a fuzzy logic approach to assessment of variability. Bland-Altman plots demonstrated validation of the new software. Conclusions: The new freely available EasyGV software v10 (www.phc.ox.ac.uk/research/technology-outputs/easygv) is a validated robust tool for analyzing different glycemic variabilities and control metrics.

Continuous Glucose Monitoring in Adults with Type 1 Diabetes: Real-World Data from the German/Austrian Prospective Diabetes Follow-Up Registry

Background: To analyze key indicators of metabolic control in adults with type 1 diabetes (T1D) using real-time or intermittent scanning continuous glucose monitoring (rtCGM/iscCGM) during real-life care, based on the German/Austrian/Swiss Prospective Diabetes Follow-up (DPV) registry. Methods: Cross-sectional analysis including 233 adults with T1D using CGM. We assessed CGM metrics by gender, age group (18 to <30 years vs. ≥30 years), insulin delivery method (multiple daily injections vs. continuous subcutaneous insulin infusion [CSII]) and sensor type (iscCGM vs. rtCGM), working days versus weekends, and daytime versus night-time using multivariable linear regression models (adjusted for demographic variables) or Wilcoxon signed-rank test. Results: Overall, 79/21% of T1D patients used iscCGM/rtCGM. Those aged ≥30 years spent more time in range (TIR [70-180 mg/dL] 54% vs. 49%) and hypoglycemic range <70 mg/dL (7% vs. 5%), less time in hyperglycemic range >180 mg/dL (38% vs. 46%) and had a lower glucose variability (coefficient of variation [CV] 36% vs. 37%) compared with adults aged <30 years. We found no significant differences between genders. Multivariable regression models revealed the highest Time In Range (TIR) and lowest time with sensor glucose >250 mg/dL, CV and daytime-night-time differences in those treated with CSII and rtCGM. Glucose profiles were slightly more favorable on working days. Conclusions: In our real-world data, rtCGM versus iscCGM was associated with a higher percentage of TIR and improved metabolic stability. Differences in ambulatory glucose profiles on working and weekend days may indicate lifestyle habits affecting glycemic stability. Real-life CGM results should be included in benchmarking reports in addition to hemoglobin A1c (HbA1c) and history of hypoglycemia.

The relationship between glycaemic variability and cardiovascular autonomic dysfunction in patients with type 1 diabetes: A systematic review

Rigorous glycaemic control-reflected by low HbA1c goals-is of the utmost importance in the prevention and management of complications in patients with type 1 diabetes mellitus (T1DM). However, previous studies suggested that short-term glycaemic variability (GV) is also important to consider as excessive glucose fluctuations may have an additional impact on the development of diabetic complications. The potential relationship between GV and the risk of cardiovascular autonomic neuropathy (CAN), a clinical expression of cardiovascular autonomic dysfunction, is of increasing interest. This systematic review aimed to summarize existing evidence concerning the relationship between GV and cardiovascular autonomic dysfunction in T1DM. An electronic database search of Medline (PubMed), Web of Science and Embase was performed up to October 2019. There were no limits concerning year of publication. Methodological quality was evaluated using the Newcastle Ottawa Scale for observational studies. Six studies (four cross-sectional and two prospective cohorts) were included. Methodological quality of the studies varied from level C to A2. Two studies examined the association between GV and heart rate variability (HRV), and both found significant negative correlations. Regarding cardiovascular autonomic reflex tests (CARTs), two studies did not, while two other studies did find significant associations between GV parameters and CART scores. However, associations were attenuated after adjusting for covariates such as HbA1c, age and disease duration. In conclusion, this systematic review found some preliminary evidence supporting an association between GV and cardiovascular autonomic dysfunction in T1DM. Hence, uncertainty remains whether high GV can independently contribute to the onset or progression of CAN. The heterogeneity in the methodological approach made it difficult to compare different studies. Future studies should therefore use uniformly evaluated continuous glucose monitoring-derived parameters of GV, while standardized assessment of HRV, CARTs and other potential cardiac autonomic function parameters is needed for an unambiguous definition of CAN.

Association Between Baseline Glycemic Markers (HbA1c) and 8-Year Trajectories of Functional Disability

BACKGROUND

This study assessed whether baseline (i) HbA1c (low [<5.7%], intermediate [5.7%-6.4%], and high [≥6.5%]) and (ii) glycemic control (7% HbA1c cutoff) in participants with self-reported diabetes were associated with differential 8-year functional disability trajectories.

METHODS

We used data from the 2006-2014 waves of the Health and Retirement Study for adults aged 50 years and older. Latent class mixture modeling was used to identify distinct functional disability trajectory classes. Multinomial logistic regression analysis examined the association between the newly constructed trajectories and baseline HbA1c levels, and glycemic control, respectively.

RESULTS

All participants (N = 5,966) were classified into four functional disability trajectory classes (no disability, low disability, low-increasing, and high-increasing). Participants with elevated HbA1c were at greater risk of being classified into the high-increasing (relative risk ratios = 1.63, 95% confidence interval [CI] = 1.25-2.11) trajectory class. Results showed significant effect modification by age and race. Three functional disability trajectories (no disability, low-increasing, and high-increasing) were identified for participants with self-reported diabetes (n = 1,119). There was no significant association between glycemic control in adults with self-reported diabetes and functional disability trajectory classes.

CONCLUSIONS

Participants with intermediate HbA1c and elevated HbA1c were more likely to be classified into the trajectories with progressing disability over the study period. More research is needed to better understand the association between glycemic markers and functional disability trajectories. Such research may provide insights into improvements for clinical care, self-management, and public health interventions for both conditions.

Results of a Study Comparing Glycated Albumin to Other Glycemic Indices

CONTEXT

Intermediate-term glycemic control metrics fulfill a need for measures beyond hemoglobin A1C.

OBJECTIVE

Compare glycated albumin (GA), a 14-day blood glucose measure, with other glycemic indices.

DESIGN

24-week prospective study of assay performance.

SETTING

8 US clinics.

PARTICIPANTS

Subjects with type 1 (n = 73) and type 2 diabetes (n = 77) undergoing changes to improve glycemic control (n = 98) or with stable diabetes therapy (n = 52).

INTERVENTIONS

GA, fructosamine, and A1C measured at prespecified intervals. Mean blood glucose (MBG) calculated using weekly self-monitored blood glucose profiles.

MAIN OUTCOME MEASURES

Primary: Pearson correlation between GA and fructosamine. Secondary: magnitude (Spearman correlation) and direction (Kendall correlation) of change of glycemic indices in the first 3 months after a change in diabetes management.

RESULTS

GA was more concordant (60.8%) with changes in MBG than fructosamine (55.5%) or A1C (45.5%). Across all subjects and visits, the GA Pearson correlation with fructosamine was 0.920. Pearson correlations with A1C were 0.655 for GA and 0.515 for fructosamine (P < .001) and with MBG were 0.590 and 0.454, respectively (P < .001). At the individual subject level, Pearson correlations with both A1C and MBG were higher for GA than for fructosamine in 56% of subjects; only 4% of subjects had higher fructosamine correlations with A1C and MBG. GA had a higher Pearson correlation with A1C and MBG in 82% and 70% of subjects, respectively.

CONCLUSIONS

Compared with fructosamine, GA correlates significantly better with both short-term MBG and long-term A1C and may be more useful than fructosamine in clinical situations requiring monitoring of intermediate-term glycemic control (NCT02489773).

Effects of Glucose Fluctuation Targeted Intervention on the Prognosis of Patients with Type 2 Diabetes following the First Episode of Cerebral Infarction

OBJECTIVE

The purpose of this study was to assess the effects of glucose fluctuation targeted intervention on neurologic function, independent living skills, and quality of life in type 2 diabetes patients following the first episode of cerebral infarction (CI).

METHODS

This was a randomized control trial. Following confirmed cerebral infarction, 75 patients with type 2 diabetes were randomized into 2 groups: control group (n = 37) with usual care, focused on hemoglobin A1c (HbA1c) control, targeting A1c < 7%, and intervention group (n = 37) with usual care, focused on hemoglobin A1c (HbA1c) control, targeting A1c < 7%, and intervention group (.

RESULTS

After 6 months, data from 63 patients were analyzed (30 in the control group, 33 in the intervention group). There was no difference (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (P > 0.05) in the reduction of A1c between the 2 groups, but the reductions of LAGE (.

CONCLUSION

Glucose fluctuation targeted intervention can improve nerve function for patients with T2DM following the first CI episode. This trial is registered with NCT03932084.

Variation in Diabetes Management: A National Assessment of Primary Care Providers

BACKGROUND

Glucose control is monitored primarily through ordering HbA1c levels, which is problematic in patients with glycemic variability. Herein, we report on the management of these patients by board-certified primary care providers (PCPs) in the United States.

METHODS

We measured provider practice in a representative sample of 156 PCPs. All providers cared for simulated patients with diabetes presenting with symptoms of glycemic variability. Provider responses were reviewed by trained clinicians against evidence-based care standards and accepted standard of care protocols.

RESULTS

Care varied widely-overall quality of care averaged 51.3%±10.6%-with providers performing just over half the evidence-based practices necessary for their cases. More worryingly, provider identified the underlying etiology of the poor glycemic control only 36.3% of the time. HbA1c was routinely ordered in 91.3% of all cases but often (59.5%) inappropriately. Ordering other tests of glycemic control (done in 15% of cases) led to significant increases in identifying the etiology of the hyperglycemia. Correctly modifying their patient's treatment was more likely to occur if doctors first identified the underlying etiology (65.9% vs 49.0%, P<0.001). We conservatively estimated a US $65/patient/visit in unnecessary testing and US $389 annually in additional care costs when the etiology was missed, translating potentially into millions of dollars of wasteful spending.

CONCLUSION

Despite established evidence that HbA1c misses short-term changes in diabetes, we found PCPs consistently ordered HbA1c, rarely using other available blood tests. However, if the factors leading to poor glycemic control were recognized, PCPs were more likely to correctly alter their patient's hypoglycemic therapy.

Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics

Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24-hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two “pre-diabetic behaviours” (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.

A review of the influence of periodontal treatment in systemic diseases

The effects and consequences of periodontal diseases might not be confined to the oral cavity. A great body of evidence has arisen supporting the claim demonstrating an association with several systemic conditions and diseases. With different levels of evidence, an association between periodontal disease and cardiovascular disease, diabetes, psoriasis, rheumatoid arthritis, pregnancy outcomes and respiratory diseases has been established. However, the true nature of this association, if it is causal, still remains elusive. For a better understanding of the complex relationships linking different conditions, interventional studies now begin to focus on the possible outcomes of periodontal treatment in relation to the events, symptoms and biomarkers of several systemic disorders, assessing if periodontal treatment has any impact on them, hopefully reducing their severity or prevalence. Therefore, we proceeded to review the recent literature on the subject, attempting to present a brief explanation of the systemic condition or disease, what proposed mechanisms might give biological plausibility to its association with periodontal disease, and finally and more importantly, what data are currently available pertaining to the effects periodontal treatment may have. Raising awareness and discussing the possible benefits of periodontal treatment on overall systemic health is important, in order to change the perception that periodontal diseases are only limited to the oral cavity, and ultimately providing better and comprehensive care to patients.

Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program

Background: Variations in blood glucose levels over a given time interval is termed as glycemic variability (GV). Higher GV is associated with higher diabetes-related complications. The current study was done with the aim of detecting the sensitivity of various GV indices among individuals with type 2 diabetes mellitus of different glycemic control status. Methods: We performed a longitudinal study among individuals with type 2 diabetes mellitus (T2DM) who were participating in a two-week diabetes self-management education (DSME) program. Participants were categorized by their HbA1c as poor (≥8%), acceptable (7%–8%), and optimal control (<7%). Continuous glucose monitoring (CGM) sensors recorded interstitial glucose every 15 min from day 1. The evaluated GV measures include standard deviation (SD), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), continuous overlapping net glycemic action (CONGA), mean of daily difference for inter-day variation (MODD), high blood glucose index (HBGI), and low blood glucose index (LBGI). Results: A total of 41 study participants with 46347 CGM values were available for analysis. Of 41 participants, 20 (48.7%) were in the poor, 10 (24.3%) in the acceptable, and 11 (26.8%) in the optimal control group. The GV indices (SD; CV; MODD; MAGE; CONGA; HBGI) of poorly controlled (77.43; 38.02; 45.82; 216.63; 14.10; 16.62) were higher than acceptable (50.02; 39.32; 30.79; 138.01; 8.87; 5.56) and optimal (34.15; 29.46; 24.56; 126.15; 8.67; 3.13) control group. Glycemic variability was reduced in the poorly and acceptably controlled groups by the end of the 2-week period. There was a rise in LBGI in the optimally controlled group, indicating pitfalls of tight glycemic control. Conclusion: Indices of glycemic variability are useful complements, and changes in it can be demonstrated within short periods.

Update on biomarkers of glycemic control

Attaining and maintaining good glycemic control is a cornerstone of diabetes care. The monitoring of glycemic control is currently based on the self-monitoring of blood glucose (SMBG) and laboratory testing for hemoglobin A1c (HbA1c), which is a surrogate biochemical marker of the average glycemia level over the previous 2-3 mo period. Although hyperglycemia is a key biochemical feature of diabetes, both the level of and exposure to high glucose, as well as glycemic variability, contribute to the pathogenesis of diabetic complications and follow different patterns in type 1 and type 2 diabetes. HbA1c provides a valuable, standardized and evidence-based parameter that is relevant for clinical decision making, but several biological and analytical confounders limit its accuracy in reflecting true glycemia. It has become apparent in recent years that other glycated proteins such as fructosamine, glycated albumin, and the nutritional monosaccharide 1,5-anhydroglucitol, as well as integrated measures from direct glucose testing by an SMBG/continuous glucose monitoring system, may provide valuable complementary data, particularly in circumstances when HbA1c results may be unreliable or are insufficient to assess the risk of adverse outcomes. Long-term associations of these alternative biomarkers of glycemia with the risk of complications need to be investigated in order to provide clinically relevant cut-off values and to validate their utility in diverse populations of diabetes patients.

Cross-sectional and longitudinal assessments of risk factors associated with hypertension and moderately increased albuminuria comorbidity in patients with type 2 diabetes: a 9-year open cohort study

BACKGROUND

Moderately increased albuminuria (MIA) is strongly associated with hypertension (HTN) in patients with type 2 diabetic mellitus (T2DM). However, the association between risk factors and coexisting HTN and MIA remains unassessed.

OBJECTIVES

This study aimed to determine both cross-sectional and longitudinal associations of risk factors with HTN and MIA comorbidity in patients with T2DM.

METHODS

A total of 1,600 patients with T2DM were examined at baseline and longitudinal data were obtained from 1,337 T2DM patients with at least 2 follow-up visits to assess the presence of HTN alone (yes/no), MIA alone (yes/no) and the coexistence of both (yes/no) in a 9-year open cohort study between 2004 and 2013. Bivariate mixed-effects logistic regression with a Bayesian approach was employed to evaluate associations of risk factors with HTN and MIA‎ comorbidity in the longitudinal assessment.

RESULTS

After adjustment for age and BMI, patients with uncontrolled plasma glucose, as a combined index of the glucose profile, were more likely to have HTN [odds ratio (OR): 1.73 with 95% Bayesian credible intervals (BCI) 1.29-2.20] and MIA [OR: 1.34 (‎95% BCI 1.13-1.62)]. The risks of having HTN and MIA were increased by a one-year raise in diabetes duration [with 0.89 (95% BCI 0.84-0.96) and 0.81 (95% BCI 0.73-0.92) ORs, respectively] and a one-unit increase in non-high-density lipoprotein-cholesterol (Non-HDL-C) [with 1.30 (95% BCI 1.23-1.34) and 1.24 (95% BCI 1.14-1.33) ORs, respectively].

CONCLUSIONS

T2DM patients with HTN,‎ MIA, and the coexistence of both had uncontrolled plasma glucose, significantly higher Non-HDL-C, and shorter diabetes duration than the other T2DM patients. Duration of diabetes and uncontrolled plasma glucose index showed the stronger effects on HTN and MIA comorbidity than on each condition separately.

A Comparison of Biomarkers in the Assessment of Glycemic Control in Diabetes: Reviewing the Evidence

BACKGROUND

Diabetes Mellitus (DM) is a chronic life-long progressive multisystem heterogeneous metabolic disorder with complex pathogenesis.

INTRODUCTION

Hyperglycemia is not only one of the classical signs of DM, but it also serves as the pivotal prerequisite for the diagnosis of the disease. However, with the advancement in the field of analytical biochemistry, a number of alternative and specific biomarkers have been discovered which can be used for better diagnosis of the DM. In this review, we have discussed various aspects of DM and different biomarkers used in assessing glycemia.

METHODOLOGY

A thorough literature survey was conducted to identify various studies that reported the use of conventional and non-conventional markers for the assessment of glycemia in DM patients.

CONCLUSION

The accurate detection and hence diagnosis of DM has become easy and more specific with the use of various biomarkers.

Prevalence of postprandial hyperglycaemia in basal insulin-treated patients with type 2 diabetes mellitus with controlled fasting glycaemia and elevated glycosylated haemoglobin

OBJECTIVE

To study the prevalence of postprandial hyperglycaemia (PPH) in type 2 diabetes mellitus (T2DM) patients treated with basal insulin, having fasting glucose < 130 mg/dL but HbA1c > 7.0% (53 mmol/mol).

METHODS

This was an observational prospective multicentric study conducted in Spain. During 2 weeks, patients recorded a 6-point self-measured blood glucose profile (before and 2 h after eating) every 2 days. PPH was defined according to IDF and ADA guidelines (> 160 and > 180 mg/dL, respectively).

RESULTS

We included 98 patients (males: 56.1%; mean age: 64.3 ± 10.4 years) who were treated with basal insulin for at least 1 year at stable doses in the last 2 months, 88.8% of them received concomitant oral antidiabetic drugs. Overall, 95.7% (95% CI 91.6-99.8) and 93.5% (95% CI 88.6-98.5) of patients showed ≥ 1 episode of PPH according to IDF and ADA criteria respectively. PPH was more frequently observed after lunch and dinner. The proportion of patients with ≥ 40% readings in range of PPH was 59.1% (95% CI 49.1-69.1) and 40.9% (95% CI 30.9-50.9), according to IDF and ADA criteria, respectively.

CONCLUSIONS

PPH is very common and should be considered a priority target in basal insulin-treated T2DM patients with elevated HbA1c despite controlled fasting glucose.

The Glycaemic Index-Food-Frequency Questionnaire: Development and Validation of a Food Frequency Questionnaire Designed to Estimate the Dietary Intake of Glycaemic Index and Glycaemic Load: An Effort by the PREVIEW Consortium

Dietary glycaemic index (GI) and glycaemic load (GL) are indices used to quantify the effect of carbohydrate quality and quantity on postprandial glycaemia. GI/GL-health associations are widely studied but data on the validity of integrated GI/GL measurements are scarce. We evaluated the performance of a food-frequency questionnaire (FFQ) specifically developed to assess GI/GL. In total, 263 Dutch men and 212 women (aged 55 ± 11 years) completed a 58-item GI-FFQ, an 183-item general-FFQ and a 2-day 24 h-recall and donated blood for glycated haemoglobin (HbA1c) determination. The level of agreement between these methods was evaluated by (1) cross-classification, (2) correlations and (3) Bland and Altman plots. The three dietary assessment methods provided comparable mean intake estimates for total carbohydrates (range: 214–237 g/day), mono/disaccharides (100–107 g/day), polysaccharides (114–132 g/day), as well as bread, breakfast cereals, potatoes, pasta, rice, fruit, dairy, cakes/cookies and sweets. Mean (±SD) GI estimates were also comparable between the GI-FFQ (54 ± 3), general-FFQ (53 ± 4) and 24 h-recalls (53 ± 5). Mean (±SD) GI-FFQ GL (117 ± 37) was slightly lower than the general-FFQ GL (126 ± 38) and 24 h-recalls GL (127 ± 37). Classification of GI in quartiles was identical for the GI-FFQ and general-FFQ for 43% of the population (r = 0.58) and with 24 h-recalls for 35% of the population (de-attenuated r = 0.64). For GL, this was 48% (r = 0.65) and 44% (de-attenuated r = 0.74). Correlations between GI and HbA1c were low (r = −0.09 for GI-FFQ, r = −0.04 for general-FFQ and r = 0.07 for 24 h-recalls). In conclusion, compared to a general-FFQ and 24 h-recalls, the GI-FFQ showed a moderate to good relative validity for carbohydrates, carbohydrate-rich foods and GI/GL. No metric predicted HbA1c.

Reduced Glucose Variability With Glucose-Dependent Versus Glucose-Independent Therapies Despite Similar Glucose Control and Hypoglycemia Rates in a Randomized, Controlled Study of Older Patients With Type 2 Diabetes Mellitus

BACKGROUND

Few studies have evaluated continuous glucose monitoring (CGM) in older patients with type 2 diabetes mellitus (T2DM) not using injectable therapy. CGM is useful for investigating hypoglycemia and glycemic variability, which is associated with complications in T2DM.

METHODS

A CGM substudy of Individualized treatMent aPproach for oldER patIents in a randomized trial in type 2 diabetes Mellitus (IMPERIUM)) was conducted. Patients were vulnerable (moderately ill and/or frail) older (≥65 years) individuals with suboptimally controlled T2DM. Strategy A comprised glucose-dependent therapies (n = 26) with a nonsulfonylurea oral antihyperglycemic medication (OAM) and a glucagon-like peptide-1 receptor agonist as the first injectable. Strategy B comprised non-glucose-dependent therapies (n = 21) with sulfonylurea as the preferred OAM and insulin glargine as the first injectable. Primary endpoints were duration and percentage of time spent with blood glucose (BG) ≤70 mg/dL over 24 hours at week 24.

RESULTS

Duration and percentage of time spent with hypoglycemia at ≤70 mg/dL were similar for Strategy A and Strategy B; glycemic control improved similarly in both arms (LSM change in HbA1c at week 24; A = -1.2%, B = -1.4%). Duration and percentage time spent with euglycemia and hyperglycemia were also similar in both arms. However, Strategy A was associated with lower within-day (21.1 ± 1.2 vs 25.1 ± 1.4, P = .046) and between-day (5.4 ± 1.0 vs 9.1 ± 1.3, P = .038) BG variability (coefficient of variance [LSM ± SE]) at week 24.

CONCLUSIONS

This CGM substudy in older patients with T2DM showed lower within- and between-day BG variability with glucose-dependent therapies but similar HbA1c reductions and hypoglycemia duration with glucose-independent strategies.

The chicken or the egg? Does glycaemic control predict cognitive function or the other way around?

The association between type 2 diabetes and cognitive dysfunction is well established. Prevention of the development of type 2 diabetes and its complications, as well as cognitive dysfunction and dementia, are leading goals in these fields. Deciphering the causality direction of the interplay between type 2 diabetes and cognitive dysfunction, and understanding the timeline of disease progression, are crucial for developing efficient prevention strategies. The prevailing perception is that type 2 diabetes leads to cognitive dysfunction and dementia. There is substantial evidence showing that accelerated cognitive decline in type 2 diabetes starts in midlife (mean age 40-60 years) and that it may even begin at the prediabetes stage. However, in this issue of Diabetologia, Altschul et al (doi: https://doi.org/10.1007/s00125-018-4645-8 ) show evidence for the reverse causality hypothesis, i.e. that lower cognitive function precedes poor glycaemic control. They found that cognitive function at early adolescence (age 11 years) predicts both HbA_1c levels and cognitive function at age 70 years. Moreover, they found that lower cognitive function at age 70 is associated with an increase in HbA_1c from age 70 to 79 years. Based on these findings, future studies should explore whether developing prevention strategies that target young adolescents with lower cognitive function will result in prevention of type 2 diabetes, breaking the vicious cycle of type 2 diabetes and cognitive dysfunction.

Different Indexes of Glycemic Variability as Identifiers of Patients with Risk of Hypoglycemia in Type 2 Diabetes Mellitus

INTRODUCTION

Recent publications frequently introduce new indexes to measure glycemic variability (GV), quality of glycemic control, or glycemic risk; however, there is a lack of evidence supporting the use of one particular parameter, especially in clinical practice.

METHODS

A cohort of type 2 diabetes mellitus (T2DM) patients in ambulatory care were followed using continuous glucose monitoring sensors (CGM). Mean glucose (MG), standard deviation, coefficient of variation (CV), interquartile range, CONGA1, 2, and 4, MAGE, M value, J index, high blood glucose index, and low blood glucose index (LBGI) were estimated. Hypoglycemia incidence (<54 mg/dl) was calculated. Area under the curve (AUC) was determined for different indexes as identifiers of patients with risk of hypoglycemia (IRH). Optimal cutoff thresholds were determined from analysis of the receiver operating characteristic curves.

RESULTS

CGM data for 657 days from 140 T2DM patients (4.69 average days per patient) were analyzed. Hypoglycemia was present in 50 patients with 144 hypoglycemic events in total (incidence rate of 0.22 events per patient/day). In the multivariate analysis, both CV (OR 1.20, 95% CI 1.12-1.28, P < .001) and LBGI (OR 4.83, 95% CI 2.41-9.71, P < .001) were shown to have a statistically significant association with hypoglycemia. The highest AUC were for CV (0.84; 95% CI 0.77-0.91) and LBGI (0.95; 95% CI 0.92-0.98). The optimal cutoff threshold for CV as IRH was 34%, and 3.4 for LBGI.

CONCLUSION

This analysis shows that CV can be recommended as the preferred parameter of GV to be used in clinical practice for T2DM patients. LBGI is the preferred IRH between glycemic risk indexes.

The value of fast-acting insulin aspart compared with insulin aspart for patients with diabetes mellitus treated with bolus insulin from a UK health care system perspective

BACKGROUND

Fast-acting insulin aspart is a new formulation of the rapid-acting insulin analogue insulin aspart and represents an advancement over current rapid-acting insulin analogues in terms of onset of action and postprandial glucose control. The objective of the current analysis was to demonstrate the cost impact of prescribing fast-acting insulin aspart instead of insulin aspart, to highlight the value of fast-acting insulin aspart for the treatment of people with diabetes requiring mealtime insulin.

METHODS

A cost-impact analysis was conducted from the perspective of the UK National Health Service (NHS). The analysis excluded patients' out-of-pocket expenses, carers' costs and lost productivity. The time horizon of the analysis was 1 year, and no discounting was therefore applied.

RESULTS

The displacement of insulin aspart with fast-acting insulin aspart is cost neutral for the UK NHS. Fast-acting insulin aspart is at price parity to insulin aspart in terms of the vial and Penfill® cartridge and is available in the FlexTouch® pen at the same price as the insulin aspart FlexPen® (and thus cheaper than the insulin aspart FlexTouch® pen). Patients using the insulin aspart FlexPen® will be upgraded to the FlexTouch® pen device, which is preferred by patients and healthcare professionals, on switching to fast-acting insulin aspart, at no additional cost.

CONCLUSIONS

Fast-acting insulin aspart offers additional clinical benefit but at no additional cost when compared with insulin aspart, and thus provides value to the UK NHS.

Personal Health Coaching as a Type 2 Diabetes Mellitus Self-Management Strategy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

OBJECTIVE

Personal health coaching (PHC) programs have become increasingly utilized as a type 2 diabetes mellitus (T2DM) self-management intervention strategy. This article evaluates the impact of PHC programs on glycemic management and related psychological outcomes.

DATA SOURCES

Electronic databases (CINAHL, MEDLINE, PubMed, PsycINFO, and Web of Science).

STUDY INCLUSION AND EXCLUSION CRITERIA

Randomized controlled trials (RCT) published between January 1990 and September 2017 and focused on the effectiveness of PHC interventions in adults with T2DM.

DATA EXTRACTION

Using prespecified format guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework.

DATA SYNTHESIS

Quantitative synthesis for primary (ie, hemoglobin A1c [HbA1c]) and qualitative synthesis for selected psychological outcomes.

RESULTS

Meta-analyses of 22 selected publications showed PHC interventions favorably impact HbA1c levels in studies with follow-ups at ≤3 months (-0.32% [95% confidence interval, CI = -0.55 to -0.09%]), 4 to 6 months (-0.50% [95% CI = -0.65 to -0.35%], 7 to 9 months (-0.66% [95% CI = -1.04 to -0.28%]), and 12 to 18 months (-0.24% [95% CI = -0.38 to -0.10%]). Subsequent subgroup analyses led to no conclusive patterns, except for greater magnitude of effect size in studies with conventional (2-arm) RCT design.

CONCLUSIONS

The PHC appears effective in improving glycemic control. Further research is required to assess the effectiveness of specific program components, training, and supervision approaches and to determine the cost-effectiveness of PHC interventions.

Elevated Serum Xanthine Oxidase Activity Is Associated With the Development of Type 2 Diabetes: A Prospective Cohort Study

OBJECTIVE

We aimed to evaluate whether xanthine oxidase (XO), a key enzyme in uric acid (UA) metabolism and a major source of reactive oxygen species, plays a causal and important role in the development of type 2 diabetes mellitus (T2DM) in a large prospective cohort study.

RESEARCH DESIGN AND METHODS

A total of 4,412 diabetes-free adults (2,071 women and 2,341 men) aged 30-65 years at baseline in 2008 were involved. Participants were followed for incident change of glucose metabolism during an average of 4.7 years. At baseline, serum XO and UA, serum lipids, and glucose homeostasis indexes including fasting blood glucose (FBG), 2-h blood glucose (PBG), glycosylated hemoglobin A_1c (HbA_1c), and fasting insulin were tested for analysis.

RESULTS

During an average follow-up period of 4.7 years, 249 women and 360 men developed new-onset T2DM. Serum XO activity was positively associated with UA concentration (all P values <0.001). When XO activity and UA concentration were considered in the same model of the sex-specific analysis, only XO activity was significantly associated with the incidence of T2DM, with the hazard ratios from the bottom to the top quartile of XO activity being 1.00, 1.67 (95% CI 1.00-2.79), 1.86 (1.11-3.13), and 2.36 (1.43-3.90) in women and 1.00, 1.01 (0.68-1.52), 1.41 (0.98-2.03), and 1.90 (1.30-2.78) in men.

CONCLUSIONS

Elevated serum XO activity, but not UA concentration, was associated with an increased risk of developing T2DM in women and men with mutual adjustment for XO and UA. Further studies are needed to examine the underlying mechanisms.

Genetics of HbA1c: a case study in clinical translation

Glycated hemoglobin (HbA1c) measures the amount of glucose in the blood in the previous 2-3 months and is used to test whether an individual has diabetes (HbA1c≥6.5%), or how well they are managing their diabetes. Genome-wide association studies have successfully identified multiple genomic loci influencing HbA1c, through both glycemic (factors that affect the amount blood glucose levels) and erythrocytic (factors that affect the red blood cell) pathways. Inaccuracies in HbA1c, due to non-glycemic variants, could lead to suboptimal care or adverse health consequences. A recently published example is the erythrocytic variant (rs1050828) in G6PD, which leads to the artificial lowering of HbA1c and missed diagnosis of diabetes using current thresholds. In this review we will discuss recent insights into the genetic etiology of HbA1c, and how these can translate to the clinic.

Higher In vitro Proliferation Rate of Rhizopus oryzae in Blood of Diabetic Individuals in Chronic Glycaemic Control Compared with Non-diabetic Individuals

Metabolic control improves outcomes associated with mucormycosis. The aim of this study was to compare the in vitro proliferation of Rhizopus oryzae in blood of individuals with and without diabetes at different glycaemic levels. Ninety-five individuals were included. Blood samples from each participant were incubated with sporangiospores of R. oryzae. The germination, filamentation and growth of R. oryzae were compared at different time points. Four groups were defined, one without (group A, n = 30) and three with diabetes: group B (HbA1c ≤7%, N = 24), group C (HbA1c 7.1-9%, N = 20) and group D (HbA1c > 9%, N = 21). The percentage of germinated sporangiospores was higher in the group A after 6 h (group A 56% ± 3, group B 35% ± 4, group C 48% ± 4, group D 46% ± 1.4, p = 0.01), 12 h (group A 54% ± 1.4, group B 19% ± 4, group C 16% ± 1, group D 9.5% ± 5, p < 0.001) and 24 h (group A 29% ± 1, group B 12% ± 4, group C 13.5% ± 3.5, group D 12% ± 1, p < 0.01). The filamentation was higher in groups with diabetes. Group B showed higher filamentation grade than group A at 6 h (0.4 ± 0.04 vs 1 ± 0.09, p < 0.001) and 24 h (1.6 ± 0.05 vs 2.1 ± 0.1, p = 0.05). In conclusion, R. oryzae proliferation was higher among diabetic individuals, including good glycaemic control, than among non-diabetic individuals.

Ambulatory glucose profile analysis of the juvenile diabetes research foundation continuous glucose monitoring dataset-Applications to the pediatric diabetes population

BACKGROUND

Increased continuous glucose monitor (CGM) use presents both the benefit and burden of increased data for clinicians to rapidly analyze. The ambulatory glucose profile (AGP) is an evolving a universal software report for CGM data analysis.

OBJECTIVES/HYPOTHESES

We utilized the Juvenile Diabetes Research Foundation-CGM dataset to evaluate the AGP across a broad spectrum of patients to show how AGP can be used clinically to assist with CGM-related decision making. We hypothesized that AGP metrics would be different across age and HbA1c strata.

SUBJECTS

AGPs were generated from the JDRF-CGM trial dataset for all periods during which there were ≥10 days of CGM coverage in the 2 weeks adjacent to an HbA1c measurement yielding 1101 AGPs for 393 unique subjects.

METHODS

AGPs were stratified by age group (8-14, 15-24, and ≥25 years) and HbA1c (within or above target for age) and compared for between group differences in AGP metrics via two-factor ANOVA. Glycemic differences between time periods were analyzed via segmented regression analysis.

RESULTS

Glucose exposure (average and estimated A1c) and variability (standard deviation and interquartile range) were different between the low and high HbA1c levels. Within a given HbA1c level all age groups were significantly different from each other with older patients having lower averages with less variability than younger patients.

CONCLUSIONS

AGP analysis of the JDRF-CGM data highlights significant differences in glycemic profiles between pediatric and adult age groups and between well and less well-controlled patient populations.

Associations of blood glucose dynamics with antihyperglycemic treatment and glycemic variability in type 1 and type 2 diabetes

AIMS

The dynamical structure of glucose fluctuation has largely been disregarded in the contemporary management of diabetes.

METHODS

In a retrospective study of patients with diabetes, we evaluated the relationship between glucose dynamics, antihyperglycemic therapy, glucose variability, and glucose exposure, while taking into account potential determinants of the complexity index. We used multiscale entropy (MSE) analysis of continuous glucose monitoring data from 131 subjects with type 1 (n = 18), type 2 diabetes (n = 102), and 11 nondiabetic control subjects. We compared the MSE complexity index derived from the glucose time series among the treatment groups, after adjusting for sex, age, diabetes duration, body mass index, and carbohydrate intake.

RESULTS

In type 2 diabetic patients who were on a diet or insulin regimen with/without oral agents, the MSE index was significantly lower than in nondiabetic subjects but was lowest in the type 1 diabetes group (p < 0.001). The decline in the MSE complexity across the treatment groups correlated with increasing glucose variability and glucose exposure. Statistically, significant correlations existed between higher MSE complexity indices and better glycemic control. In multivariate regression analysis, the antidiabetic therapy was the most powerful predictor of the MSE (β = -0.940 ± 0.242, R ² = 0.306, p < 0.001), whereas the potential confounders failed to contribute.

CONCLUSIONS

The loss of dynamical complexity in glucose homeostasis correlates more closely with therapy modalities and glucose variability than with clinical measures of glycemia. Thus, targeting the glucoregulatory system by adequate therapeutic interventions may protect against progressive worsening of diabetes control.

Serum 1,5-Anhydroglucitol: Risk Factor of Acute Ischemic Stroke and Transient Ischemic Attack in Well-Controlled Diabetes

BACKGROUND

Serum 1,5-anhydroglucitol (1,5-AG) levels are a measure that provides information on daily glycemic variations. We evaluated whether 1,5-AG could be a possible marker of acute ischemic stroke (AIS) or transient ischemic attacks (TIA) in patients with diabetes mellitus (DM).

METHODS

We retrospectively reviewed electronic medical records of 5,294 AIS/TIA patients. Of the 5,294, 1,898 had diabetes and in 1,246, serum 1,5-AG levels were measured (group S). Group S was divided into 2 subgroups: hemoglobin A1c (HbA1c) <7% (S-low) and >7% (S-high). As controls, 394 outpatients with diabetes (group C) without AIS/TIA were likewise divided into subgroups, C-low and C-high according to HbA1c level. In each HbA1c subgroup, the association between serum 1,5-AG (≥14 vs. <14 µg/mL) and stroke was examined using multivariable logistic regression (MLR) with stepwise variable selection. In model 1, the OR and 95% CI was examined adjusted for age and gender. Known risk factors for stroke; hypertension, dyslipidemia, alcohol consumption, smoking, and estimated glomerular filtration rate were included in model 2.

RESULTS

Overall, serum 1,5-AG levels were lower in group S than in group C. Serum 1,5-AG levels were low in subgroups S-high and C-high, showing no differences in mean values. However, mean serum 1,5-AG levels in S-low was statistically lower than that in C-low. MLR analysis showed that the OR for low (<14 µg/mL) 1,5-AG for stroke was statistically significant only in well-controlled diabetes (OR [95% CI] 2.19 [1.54-3.10]) in model 1 and (2.26 [1.56-3.28]) model 2.

CONCLUSIONS

Low serum 1,5-AG levels could be a possible marker for AIS/TIA risk in patients with well-controlled DM.

Both the frequency of HbA1c testing and the frequency of self-monitoring of blood glucose predict metabolic control: A multicentre analysis of 15 199 adult type 1 diabetes patients from Germany and Austria

BACKGROUND

The objective of this study was to examine the association between metabolic control and frequency of haemoglobin A_1c (HbA_1c ) measurements and of self-monitoring of blood glucose, as well as the interaction of both.

METHODS

Data of 15 199 adult type 1 diabetes patients registered in a standardized electronic health record (DPV) were included. To model the association between metabolic control and frequency of HbA_1c testing or of self-monitoring of blood glucose, multiple hierarchic regression models with adjustment for confounders were fitted. Tukey-Kramer test was used to adjust P values for multiple comparisons. Vuong test was used to compare non-nested models.

RESULTS

The baseline variables of the study population were median age 19.9 [Q1; Q3: 18.4; 32.2] years and diabetes duration 10.4 [6.8; 15.7] years. Haemoglobin A_1c was 60.4 [51.5; 72.5] mmol/mol. Frequency of HbA_1c testing was 8.0 [5.0; 9.0] within 2 years, and daily self-monitoring of blood glucose frequency was 5.0 [4.0; 6.0]. After adjustment, a U-shaped association between metabolic control and frequency of HbA_1c testing was observed with lowest HbA_1c levels in the 3-monthly HbA_1c testing group. There was an inverse relationship between self-monitoring of blood glucose and HbA_1c with lower HbA_1c associated with highest frequency of testing (>6 daily measurements). Quarterly HbA_1c testing and frequent self-monitoring of blood glucose were associated with best metabolic control. The adjusted Vuong Z statistic suggests that metabolic control might be better explained by HbA_1c testing compared to self-monitoring of blood glucose (P < .0001).

CONCLUSION

This research reveals the importance of quarterly clinical HbA_1c monitoring together with frequent self-monitoring of blood glucose in diabetes management to reach and maintain target HbA_1c .