Background: Multiple laboratory tests are used in the diagnosis and management of patients with diabetes mellitus The quality of the scientific evidence supporting the use of these assays varies substantially

Approach: An expert committee drafted evidence-based recommendations for the use of laboratory analysis in patients with diabetes An external panel of experts reviewed a draft of the guidelines, which were modified in response to the reviewers’ suggestions A revised draft was posted on the Internet and was presented at the AACC Annual Meeting in July, 2000 The recommendations were modified again in response to oral and written comments The guidelines were reviewed by the Professional Practice Committee of the American Diabetes Association

Content: Measurement of plasma glucose remains the sole diagnostic criterion for diabetes Monitoring of glycemic control is performed by the patients, who measure their own plasma or blood glucose with meters, and by laboratory analysis of glycated hemoglobin The potential roles of noninvasive glucose monitoring, genetic testing, autoantibodies, microalbumin, proinsulin, C-peptide, and other analytes are addressed

Summary: The guidelines provide specific recommendations based on published data or derived from expert consensus Several analytes are of minimal clinical value at the present time, and measurement of them is not recommended

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Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

The National Cholesterol Education Program’s Adult Treatment Panel III (ATP III) report [1] added the metabolic syndrome, a multidimensional risk factor for cardiovascular disease (CVD), as a coequal partner of elevated low-density lipoprotein (LDL) cholesterol for risk-reduction therapies. Interest in the metabolic syndrome has increased greatly since the publication of the ATP III report. There are several aspects to the metabolic syndrome that deserve consideration for clinical practice: 1) major clinical outcomes, 2) metabolic components, 3) pathogenesis, 4) clinical criteria for diagnosis, 5) risk for clinical outcomes, and 6) therapeutic interventions.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(05)66378-7.pdf

The Metabolic Syndrome

Objective To assist health professionals who counsel patients with overweight and obesity, a systematic review was undertaken to determine types of weight-loss interventions that contribute to successful outcomes and to define expected weight-loss outcomes from such interventions. Design A search was conducted for weight-loss–focused randomized clinical trials with ≥1-year follow-up. Eighty studies were identified and are included in the evidence table. Outcomes measures The primary outcomes were a measure of weight loss at 6, 12, 24, 36, and 48 months. Eight types of weight-loss interventions—diet alone, diet and exercise, exercise alone, meal replacements, very-low-energy diets, weight-loss medications (orlistat and sibutramine), and advice alone—were identified. By using simple pooling across studies, subjects mean amount of weight loss at each time point for each intervention was determined. Statistical analyses performed Efficacy outcomes were calculated by meta-analysis and provide support for the pooled data. Hedges' gu was combined across studies to obtain an average effect size (and confidence level). Results A mean weight loss of 5 to 8.5 kg (5% to 9%) was observed during the first 6 months from interventions involving a reduced-energy diet and/or weight-loss medications with weight plateaus at approximately 6 months. In studies extending to 48 months, a mean 3 to 6 kg (3% to 6%) of weight loss was maintained with none of the groups experiencing weight regain to baseline. In contrast, advice-only and exercise-alone groups experienced minimal weight loss at any time point. Conclusions Weight-loss interventions utilizing a reduced-energy diet and exercise are associated with moderate weight loss at 6 months. Although there is some regain of weight, weight loss can be maintained. The addition of weight-loss medications somewhat enhances weight-loss maintenance.

Weight-loss Outcomes: A Systematic Review and Meta-Analysis of Weight-Loss Clinical Trials With a Minimum 1-year Follow-Up

BACKGROUND The value of continuous glucose monitoring in the management of type 1 diabetes mellitus has not been determined. METHODS In a multicenter clinical trial, we randomly assigned 322 adults and children who were already receiving intensive therapy for type 1 diabetes to a group with continuous glucose monitoring or to a control group performing home monitoring with a blood glucose meter. All the patients were stratified into three groups according to age and had a glycated hemoglobin level of 7.0 to 10.0%. The primary outcome was the change in the glycated hemoglobin level at 26 weeks. RESULTS The changes in glycated hemoglobin levels in the two study groups varied markedly according to age group (P=0.003), with a significant difference among patients 25 years of age or older that favored the continuous-monitoring group (mean difference in change, -0.53%; 95% confidence interval [CI], -0.71 to -0.35; P<0.001). The between-group difference was not significant among those who were 15 to 24 years of age (mean difference, 0.08; 95% CI, -0.17 to 0.33; P=0.52) or among those who were 8 to 14 years of age (mean difference, -0.13; 95% CI, -0.38 to 0.11; P=0.29). Secondary glycated hemoglobin outcomes were better in the continuous-monitoring group than in the control group among the oldest and youngest patients but not among those who were 15 to 24 years of age. The use of continuous glucose monitoring averaged 6.0 or more days per week for 83% of patients 25 years of age or older, 30% of those 15 to 24 years of age, and 50% of those 8 to 14 years of age. The rate of severe hypoglycemia was low and did not differ between the two study groups; however, the trial was not powered to detect such a difference. CONCLUSIONS Continuous glucose monitoring can be associated with improved glycemic control in adults with type 1 diabetes. Further work is needed to identify barriers to effectiveness of continuous monitoring in children and adolescents. (ClinicalTrials.gov number, NCT00406133.)

Continuous glucose monitoring and intensive treatment of type 1 diabetes.

Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.

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International Consensus on Use of Continuous Glucose Monitoring

OBJECTIVE Obesity is an important risk factor for type 2 diabetes. Weight loss in patients with type 2 diabetes is associated with improved glycemic control and reduced cardiovascular disease risk factors, but weight loss is notably difficult to achieve and sustain with caloric restriction and exercise. The purpose of this study was to assess the impact of treatment with orlistat, a pancreatic lipase inhibitor, on weight loss, glycemic control, and serum lipid levels in obese patients with type 2 diabetes on sulfonylurea medications. RESEARCH DESIGN AND METHODS In a multicenter 57-week randomized double-blind placebo-controlled study, 120 mg orlistat or placebo was administered orally three times a day with a mildly hypocaloric diet to 391 obese men and women with type 2 diabetes who were aged > 18 years, had a BMI of 28–40 kg/m 2 , and were clinically stable on oral sulfonylureas. Changes in body weight, glycemic control, lipid levels, and drug tolerability were measured. RESULTS After 1 year of treatment, the orlistat group lost 6.2 ± 0.45% (mean ± SEM) of initial body weight vs. 4.3 ± 0.49% in the placebo group ( P P 1c ( P P P P P P P P CONCLUSIONS Orlistat is an effective treatment modality in obese patients with type 2 diabetes with respect to clinically meaningful weight loss and maintenance of weight loss, improved glycemic control, and improved lipid profile.

Role of Orlistat in the Treatment of Obese Patients With Type 2 Diabetes: A 1-year randomized double-blind study

RESEARCH DESIGN AND METHODS — A total of 91 insulin-requiring patients with type 1 (n 75) and type 2 (n 16) diabetes were enrolled in this multicenter randomized study. Subjects wore a transcutaneous, 3-day, continuous glucose-sensing system for three consecutive 72-h periods. Subjects were randomly assigned (1:1 ratio) to either a control group (continuous glucose data not provided) or a display group (continuous glucose data not provided during period 1 but displayed during periods 2 and 3). During periods 2 and 3, patients in the display group had real-time access to sensor glucose values, could review glucose trends over the preceding 1, 3, and 9 h, and were provided with high (200 mg/dl) and low (80 mg/dl) alerts and a low (55 mg/dl) alarm. Sensors were inserted by patients, and both groups used (or wore) the system during daily activities. Device accuracy was assessed by comparing continuous glucose values to paired self-monitoring of blood glucose (SMBG) meter readings. Clinical effectiveness was evaluated by analyzing between-group (control vs. display, periods 2 and 3) and within-group (display, period 1 vs. period 3) differences in time spent in high, low, and target (81–140 mg/dl) glucose zones. RESULTS — When prospective, real-time sensor values were compared with SMBG values, 95.4% of 6,767 paired glucose values fell within Clarke error grid A and B zones. Pearson’s correlation coefficient was 0.88, and mean and median absolute relative differences were 21.2 and 15.9%, respectively. No systematic bias was detected at any of the prespecified glucose levels (50, 80, 100, 150, and 200 mg/dl). When compared with control subjects, the display group spent 21% less time as hypoglycemic (55 mg/dl), 23% less time as hyperglycemic (240 mg/dl), and 26% more time in the target (81–140 mg/dl) glucose range (P 0.001 for each comparison). Nocturnal (10:00 P.M. to 6:00 A.M.) hypoglycemia, as assessed at two thresholds, was also reduced by 38% (55 mg/dl; P 0.001) and 33% (55– 80 mg/dl; P 0.001) in the display group compared with control subjects. CONCLUSIONS — We conclude that real-time continuous glucose monitoring for periods up to 72 h is accurate and safe in insulin-requiring subjects with type 1 and type 2 diabetes. This study demonstrates that availability of realtime, continuously measured glucose levels can significantly improve glycemic excursions by reducing exposure to hyperglycemia without increasing the risk of hypoglycemia, which may reduce long-term diabetes complications and their associated economic costs. Diabetes Care 29:44–50, 2006

https://care.diabetesjournals.org/content/diacare/29/1/44.full.pdf

Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial.

Objective We evaluated effects of unmasking of continuous display of continuous glucose monitoring (CGM) on quality of glycemic control and glycemic variability. Methods We reanalyzed CGM data from 85 patients using a 7-day glucose sensor. Glucose values were "masked" during the first week but "unmasked" during the next 2 weeks. We evaluated 48 criteria for quality of glycemic control, including mean glucose, SD, percentage of values within-, above- or below- specified ranges, Schlichtkrull's M(100) index, mean amplitude of glycemic excursion (MAGE), mean of daily differences (MODD), the J index, "Index of Glycemic Control" (IGC), Hyperglycemia Index, Hypoglycemia Index, High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI), average daily risk range (ADRR), GRADE scores, and CONGA(n). We calculated SD values between daily means, between days-within time points, within days, between time points (for the average glucose profile for several days), and within series for time segments of arbitrary length. Results Unmasking CGM displays resulted in rapid, highly statistically significant improvement in 29 indices, including percentage within, percentage above, and percentage below target range, mean glucose, SD, SD of daily means, MODD, M(100), IGC, GRADE, HBGI, and J index. Both hyperglycemia and hypoglycemia improved during the first week after unmasking; further improvement in hypoglycemia was seen during the following week. Results obtained using multiple criteria were consistent and highly correlated. Conclusions Continuous access to display of CGM sensors dramatically improved 29 indices of glycemic control and glycemic variability.

Improved Quality of Glycemic Control and Reduced Glycemic Variability with Use of Continuous Glucose Monitoring

Objectives: To cluster participants with prediabetes with five type 2 diabetes (T2D)-related partitioned polygenetic risk scores (pPRSs) and examine the risk of incident diabetes and the benefit of adherence to healthy lifestyle across clusters. Design: Prospective cohort study Setting: Hispanic Community Health Study/Study of Latinos (HCHS/SOL), US; UK Biobank (UKBB), UK. Participants: 7,227 US Hispanic/Latinos without diabetes from HCHS/SOL, including 3,677 participants with prediabetes. 400,149 non-Hispanic whites without diabetes from UKBB, including 16,284 participants with prediabetes. Main outcome measures: Prediabetes was defined by fasting plasma glucose (fasting glucose) between 100-125 mg/dL, 2-hour oral glucose tolerance test (OGTT 2h glucose) between 140-199 mg/dL, or hemoglobin A1c (HbA1c) between 5.7% and 6.5%. Diabetes was defined by fasting glucose levels [≥]126 mg/dL, 2h glucose after OGTT [≥]200 mg/dL, HbA1c [≥]6.5%, current use of anti-diabetic medications, or medical record. Five pPRSs representing various pathways related to T2D were calculated based on 94 T2D-related genetic variants. Health lifestyle score was assessed with five modifiable risk factors, including BMI, smoking, alcohol drinking, physical activity, and diet for T2D. Results: Using clustering on five pRPSs, six clusters of individuals with prediabetes with distinctive patterns of pPRSs and corresponding metabolic traits were identified in HCHS/SOL, with each cluster presenting disparate patterns of pPRSs and different patterns of metabolic traits. Except cluster 3 which was not detected, the other five clusters were conformed in participants with prediabetes in UKBB, with each cluster showing the similar patterns of pPRSs as their corresponding cluster in HCHS/SOL. At baseline, proportion of impaired glucose tolerance (IGT)/impaired fasting glucose (IFG) and glycemic traits in HCHS/SOL (fasting glucose, OGTT 2h glucose, and HbA1c) were not significantly different across six clusters (P=0.13, P=0.62, P=0.35, P=0.96, respectively). In UKBB, random glucose and HbA1c at baseline did not show significant difference across five clusters (P=0.43, P=0.71, respectively). Although baseline glycemic traits were similar across clusters, cluster 6, which featured a very low proinsulin score, exhibited elevated risk of incident T2D in both cohorts (risk ratio [RR]=1.39, 95% confidence interval [95% CI]=[1.10, 1.76] vs. cluster 1 in HCHS/SOL; Hazard ratio [HR]=1.29, 95% CI=[1.00, 1.69] vs. cluster 1 in UKBB; Combined RR/HR=1.34 [1.13, 1.60]). To explain the elevated risk of incident T2D in cluster 6, interactions between proinsulin score and other three pPRSs (Beta-cell score, Lipodystrophy-like score, Liver-lipid score) and sum score were detected (P for interaction=0.001, 0.04, 0.02 and 0.002, respectively). Cluster 5 showed an increased risk of incident T2D in UKBB (HR=1.35 [1.05, 1.75] vs. cluster 1) and in the combined analysis with HCHS/SOL (RR/HR=1.29 [1.08, 1.53]), although its risk of T2D was not significantly different from cluster 1 in HCHS/SOL (RR=1.23 [0.96, 1.57]). Inverse associations between the lifestyle score and risk of T2D were observed across different clusters, with a suggestively stronger association in Cluster 5 compared to Cluster 1, in both cohorts. Cluster 5 showed reduced risk of incident diabetes caused by healthy lifestyle score (RR=0.65 [0.47, 0.89], HR=0.71 [0.62, 0.81], respectively. Combined RR/HR=0.70 [0.62, 0.79]). Among individuals with a healthy lifestyle, those in Cluster 5 had a similar risk of T2D compared to those in Cluster 1 (combined RR/HR=1.03 [0.91-1.18]). Conclusions: This study identified genetic subtypes of prediabetes which may differ in risk of progression to T2D and in benefits from healthy lifestyle.

Roy Kaplan

Papers

Role of Orlistat in the Treatment of Obese Patients With Type 2 Diabetes: A 1-year randomized double-blind study

Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial.

Improved Quality of Glycemic Control and Reduced Glycemic Variability with Use of Continuous Glucose Monitoring

Genetic subtypes of prediabetes, healthy lifestyle, and risk of type 2 diabetes: Prospective cohort study