Results determined for 3-year study of liraglutide versus placebo diabetes risk reduction and weight management in individuals with prediabetes.

With over 90 million people with prediabetes, it is important to understand the best options to prevent or delay the diagnosis of type 2 diabetes. In this study, they randomized 2,254 patients in a 2:1 ratio using a telephone or web-based system to receive either once-daily subcutaneous liraglutide (3.0 mg) or a matched placebo as an adjunct to a reduced-calorie diet and increased physical activity over 160 weeks. Individuals in the study were age 18 years or older and had prediabetes and a body mass index of at least 30 kg/m2 or at least 27 kg/m2 with comorbidities. All participants received standardized lifestyle intervention counseling approximately once a month throughout the trial. The trial was conducted at 191 clinical research sites in 27 countries and is registered with ClinicalTrials.gov, number NCT01272219.

The study ran between June 1, 2011, and March 2, 2015. 1,128 (50%) participants completed the study up to week 160, after withdrawal of 714 (47%) participants in the liraglutide group and 412 (55%) participants in the placebo group. By week 160, 26 (2%) of 1,472 individuals in the liraglutide group versus 46 (6%) of 738 in the placebo group were diagnosed with diabetes while on treatment. The mean time from randomization to diagnosis was 99 (SD 47) weeks for the 26 individuals in the liraglutide group versus 87 (47) weeks for the 46 individuals in the placebo group. Taking the different diagnosis frequencies between the treatment groups into account, the time to onset of diabetes over 160 weeks among all randomized individuals was 2·7 times longer with liraglutide than with placebo (p<0·0001), corresponding with a hazard ratio of 0·21. Liraglutide induced greater weight loss than placebo at week 160 (–6·1 [SD 7·3] vs −1·9% [6·3]; estimated treatment difference −4·3%, 95% CI −4·9 to −3·7, p<0·0001). Serious adverse events were reported by 227 (15%) of 1,501 randomized-treated individuals in the liraglutide group versus 96 (13%) of 747 individuals in the placebo group.

Results showed that after 160 weeks, 2 percent of individuals in the liraglutide group were diagnosed with diabetes while on treatment compared with 6 percent in the placebo group. The mean time from randomization to diagnosis for participants in the liraglutide group was approximately 99 weeks compared with 87 weeks in the placebo group. The liraglutide group also showed greater weight loss and improvements in glycemic control and cardiometabolic risk factors than the placebo group.

Liraglutide 3·0 mg was shown to reduce bodyweight and improve glucose metabolism after the 56-week period of this trial, one of four trials in the SCALE program. In the 3-year assessment of the SCALE Obesity and Prediabetes trial, they aimed to evaluate the proportion of individuals with prediabetes who were diagnosed with type 2 diabetes.

In this trial, 3 years of results were provided for this treatment, with the limitation that withdrawn individuals were not followed up after discontinuation. Liraglutide 3·0 mg might provide health benefits in terms of reduced risk of diabetes in individuals with obesity and prediabetes.

It was found that, “Liraglutide 3.0 mg, as a GLP-1 receptor agonist, provides a different treatment option for individuals with obesity or overweight, with or without type 2 diabetes, having direct glucose-dependent effects on insulin secretion and weight-loss mediated effects on improved insulin resistance,” the authors concluded.

In conclusion, it was found that liraglutide was associated with the lower risk of a diagnosis of type 2 diabetes in overweight or obese patients with prediabetes combined with diet and exercise along with sustained weight loss compared with placebo.

Practice Pearls:

• Liraglutide was associated with the lower risk of a diagnosis of type 2 diabetes.
• It was also found that the use of liraglutide provided sustained weight loss in overweight or obese patients.
• Future studies will determine if liraglutide will be recommended for the prevention of type 2 diabetes.

Lancet Volume 389, No. 10077, p1399–1409, 8 April 2017

New research suggests HbA1c may be the most effective method to identify patients with undiagnosed prediabetes.

Point-of-care testing enhances the screening ability in primary-care settings. The findings were published recently in the Annals of Family Medicine. According to the researchers, “Diabetes and prediabetes need to be on our radar as possible diagnoses. In the United States, we need to be thoughtful and aggressive in screening.”

The data suggest that for screening purposes, HbA1c is a better test than a fasting blood glucose because post-meal glucose spikes happen sooner in the course of developing type 2 diabetes than a high fasting blood sugar.

It could very well be that if we were using A1c tests, we could catch more people with prediabetes and diabetes. Plus, the use of a point-of-care HbA1c machine enabled detection of prediabetes and diabetes cases in a timelier manner compared with the use of an outside lab.

Dr. Heather Whitley published a previous article comparing the features of three currently available point-of-care HbA1c tests, one of which is a handheld device and the other two are bench-top models (Diabetes Spectr. 2015;28:201-208).

This prospective longitudinal study compares diabetes screenings between standard practices vs systematically offered point-of-care (POC) hemoglobin A1c (HbA1c) tests in patients age 45 years or older. Systematically screened participants (n = 164) identified 63% (n = 104) with unknown hyperglycemia and 53% (n = 88) in prediabetes. The standard practice (n = 324) screened 22% (n = 73), most commonly by blood glucose (96%); 8% (n = 6) and 33% (n = 24) were found to have diabetes and prediabetes, respectively. The association between screening outcome and screening method was statistically significant (P = 0.005) in favor of HbA1c. HbA1c may be the most effective method to identify patients unknowingly living in hyperglycemia. Point-of-care tests further facilitate screening evaluation in a timely and feasible fashion.

The latest study was done in a single family-medicine clinic from April 2013 through March 2014. A total of 689 patients were seen and evaluated for eligibility; 164 who met the American Diabetes Association’s screening age cutoff of 45 years (and older) and were without exclusion criteria were screened using a point-of-care HbA1c test.  Another 709 patients underwent usual clinic care. After exclusions, 324 were evaluated by chart review.

Most of the patients (87%) were white, 55% were female, mean age was 63 years, and mean body-mass index was 31.0 kg/m2.

In the active screening arm [HbA1c point-of-care test], just 37% of the patients had an HbA1c of 5.6% or below (normoglycemia). Over half (53%) met the HbA1c criteria for prediabetes (5.7%–6.4%), while 10% were in the diabetes range (≥6.5%).

In the standard-practice arm, 22% (73) of the 324 evaluated persons were tested, most often by blood glucose (96%, typically as part of a larger venipuncture chemistry panel and not always fasting). Only four individuals received HbA1c testing, and one got both.  Of these, 33% (24) were in the prediabetes range and 8% (six) tested in the diabetes range, while the majority (59%) tested euglycemic (n=43). The association between screening outcome and screening method was statistically significant in favor of HbA1c (P = .005).

In a post hoc analysis, Dr. Whitley and colleagues reanalyzed their data using the screening criteria of the United States Preventative Services Task Force (USPSTF) for overweight or obese patients ages 40 to 70 years.  Those recommendations reduced the number of people screened from 164 [using ADA criteria] to 104 and missed identifying 36 patients with prediabetes and six with diabetes HbA1c levels. “Regardless of guidelines used, the analysis shows that systematically screening patients is more effective than standard screening practices,” Dr. Whitley wrote.

Most important is that when using the point of care testing, you get the results in a few minutes; then you can implement something to improve that care during that visit.

Practice Pearls:

• Getting the results instantly can provide the time to discuss with the patient the importance of lifestyle.
• Early diagnosis of prediabetes can make a major difference in improving the quality of life of patients.
• The final results show the importance of early screening. Over half (53%) met the HbA1c criteria for prediabetes (5.7%–6.4%), while 10% were in the diabetes range (≥6.5%).

Ann Fam Med. 2017;15:162-164. Abstract

www.a1ctest.com

Results showed that the intervention will be cost effective.

In 2014, the U.S. Preventive Services Task Force (USPSTF) recommended behavioral counseling interventions for overweight or obese adults with the following known cardiovascular disease risk factors: impaired fasting glucose (IFG), hypertension, dyslipidemia, or metabolic syndrome. That led to accessing the long-term cost-effectiveness (CE) of implementing the recommended interventions in the U.S.

They used a disease progression model to simulate the 25-year CE of the USPSTF recommendation for eligible U.S. adults and subgroups defined by a combination of the risk factors. The baseline population was estimated using 2005–2012 National Health and Nutrition Examination Survey (NHANES). The cost and effectiveness of the intervention were obtained from systematic reviews. Incremental CE ratios (ICERs), measured in cost/quality-adjusted life-year (QALY), were used to assess the CE of the intervention compared with no intervention.

It was estimated that ∼98 million U.S. adults (44%) would be eligible for the recommended intervention. Compared with no intervention, the ICER of the intervention would be $13,900/QALY. CE varied widely among subgroups, ranging from a cost saving of $302 per capita for those who were obese with IFG, hypertension, and dyslipidemia to a cost of $103,200/QALY in overweight people without these conditions.

Conclusions from the survey recommended that intervention is cost effective based on the conventional cost effectiveness threshold. Considerable variation in CE across the recommended subpopulations suggests that prioritization based on risk level would yield larger total health gains per dollar spent.

From the results, it was estimated that under the new USPSTF recommendation on behavioral counseling for CVD prevention, 98 million Americans are eligible for the intervention, which would cost $64 billion if all were to participate. Applying the conventional “willingness-to-pay” cutoff of $50,000/QALY, the intervention is cost effective for the overall targeted population as well as for each age-group. However, CE varies substantially depending on the risk factor profile of the participants; the intervention is cost effective for overweight adults with IFG and for obese adults with at least one of three risk factors (dyslipidemia, hypertension, or IFG); these two groups account for 68% of the eligible population.

The intervention is cost saving if it was implemented in persons who are obese with IFG and hypertension, dyslipidemia, or both, 19.8 million or 20.2% of all eligible population. CE could be improved substantially by targeting these higher-risk subgroups and/or delivering the intervention in group settings. The results are consistent with those of previous studies that found intensive lifestyle interventions aimed at reducing the incidence of type 2 diabetes among people with prediabetes to be cost effective, with a median cost of approximately $14,000/QALY gained. Our risk group analysis was also consistent with a previous study of diabetes prevention interventions, in which those with higher levels of fasting plasma glucose or A1C had more favorable CE ratios than those at the lower end of the prediabetes spectrum. Applying the USPSTF recommendation to those with a relatively low risk (overweight rather than obese, and one additional risk factor rather than multiple) diminishes CE because, while the costs for implementing the intervention are the same, the number of cases of diabetes and CVD averted is smaller. Reductions in BMI and blood glucose levels have more impact on CE than reductions in blood pressure and lipid levels because of their greater risk reduction of diabetes. Previous studies have shown that behavioral counseling intervention can reduce the risk of type 2 diabetes by 38–60%. A recent study suggested that delaying or preventing type 2 diabetes for 10 years for a person at age 40 years might save more than $30,000 in lifetime medical spending. However, the effects of this intervention on other risk factors (i.e., lipid levels and blood pressure) are modest. The reductions in systolic/diastolic blood pressure reported by the USPSTF were 1–3 mmHg, and for LDL were 1.4–6 mg/dL. As expected, we found the recommended intervention to be more cost effective in the longer simulation horizon.

Chronic disease prevention typically provides more benefit over the long term than the short term. CVD events may not occur in the short term, and diabetes-related complications typically do not occur until years after diabetes onset. While policy makers and program planners are often interested in short-term results, it may be more appropriate to take a longer perspective when evaluating CVD and diabetes prevention.

Practice Pearls:

• 98 million Americans are eligible for the intervention, which would cost $64 billion if all were to participate.
• The intervention in preventing diabetes is cost effective for the overall targeted population as well as for each age-group.
• The recommended intervention is cost effective based on the conventional cost effectiveness threshold.

Diabetes Care 2017 May; 40(5): 640-646. https://doi.org/10.2337/dc16-1186

This article is featured in a podcast available at http://www.diabetesjournals.org/content/diabetes-core-update-podcasts.

Dental practices should focus on patients with severe periodontitis for screening of prediabetes

Diabetes has been an ongoing global health issue that has been associated with various other health conditions. Multiple studies have demonstrated the connection between diabetes and one such oral condition, periodontitis. Periodontitis is a severe gum infection that damages the soft tissue and destroys the supporting structures of the teeth. Its prevalence is 2X higher in those over 50 years old and 2-3X higher in patients with diabetes than in a normal healthy person. It is mainly associated with uncontrolled diabetes making them susceptible to infections and impaired wound healing.

Diabetes is a preventable disease, thus early diagnosis of prediabetes is essential for the prevention of diabetes and related complications. Studies suggest that periodontitis is an early complication of diabetes and it may be used as a tool to screen patients for diabetes early on. Although most dental practices lack equipment for blood biochemistry, measurements of glycated hemoglobin (HbA1c) may assist to screen for diabetes in patients with diabetes. The study aims to analyze HbA1c levels and affirm the presence of prediabetes in participants with or without periodontitis from a university dental clinic using analysis of dry blood spots. In a study, a consecutive series of patients from the Department of Periodontology of the Academic Centre of Dentistry Amsterdam (ACTA) who were diagnosed with periodontitis were to be enrolled for the treatment. A total of 313 participants were included, among which 126 patients had mild/moderate periodontitis, 78 patients had severe periodontitis and 109 subjects did not have periodontitis. HbA1c values were obtained by the analysis of dry blood spots. Moreover, differences in mean HbA1c values and the prevalence of prediabetes between the groups were analyzed. Patients were classified as suffering from periodontitis using the Centers for Disease Control and Prevention American Academy of Periodontology (CDC-AAP) case definition.

A pilot study determined the sample size, including 49 healthy subjects and 33 patients with periodontitis. Clinical relevance was demonstrated with a mean difference in the HbA1c of 0.7% (7 mmol/mol) between patients with periodontitis and control subjects. Sample size was calculated based on a two-sided student’s t-test of 5% with 80% power and the mean of HbA1c of both controlled and periodontitis groups (5.5% [37 mmol/mol] and 6.2% [44 mmol/mol]) respectively, and an overall study population-based SD of 1.3% (15 mmol/mol). A minimum of 61 subjects per group was required. According to the American Diabetes Association (ADA) guidelines, HbA1c values were classified as normal (<5.7%), prediabetes (5.7– 6.4%), or diabetes mellitus (≥ 6.5%). The parametric and nonparametric tests compared the general and diabetes-related characteristics of the study population, HbA1c levels, and prediabetes prevalence within the study population and were corrected for multiple testing (Bonferroni).

A significantly higher HbA1c value of 6.1% ±1.4% (43 mmol/mol±15 mmol/mol) in mild or moderate periodontitis groups resulted and 6.3% ±1.3% (45 mmol/mol±15 mmol/mol) in severe periodontitis groups compared to the control group of 5.7%±0.7% (39 mmol/mol±8 mmol/mol, p=0.003). Also, a noteworthy overrepresentation of suspected diabetes (23% and 14%) and prediabetes (47% and 46%) in the severe periodontitis group and mild/moderate periodontitis group respectively were shown, in comparison to the control group (10% and 37%, p=0.010). Overall, 18.1% of patients with suspected new diabetes were found among subjects with severe periodontitis compared with 9.9% and 8.5% among subjects with mild/moderate periodontitis and controls respectively (p=0.024).

In conclusion, the dental practices should focus on patients with severe periodontitis for screening for prediabetes because that would considerably decrease the number of suspected new diabetes cases. Since early detection is the key to prevention of diabetes, it adds on to prevent other severe complications along with the treatment of periodontitis. Normally, patients with periodontitis showed higher hbA1c levels compared with individuals without periodontitis. Among the study subjects with severe periodontitis with unknown metabolic status, there were 18% suspected new diabetes cases. Periodontitis as an early sign of diabetes mellitus serves as a valuable risk indicator and thus, dental offices that treat patients with periodontitis are suitable location for screening for diabetes by a simple finger stick and validated HbA1c dry spot analysis.

Practice Pearls:

• Early diagnosis of prediabetes mellitus is essential for the prevention of diabetes complications.
• Periodontitis may be a first sign of type 2 diabetes.
• On average, patients with periodontitis showed higher glycated hemoglobin (HbA1c) plasma levels compared with individuals without periodontitis.
• Among the subjects with severe periodontitis whose metabolic status was unknown, 18% of suspected new diabetes cases were identified.

References:

1. Davenport L. Dentists Note Severe Periodontitis ‘May Point to Early Diabetes. Medscape Log In. Medscape Medical News, 2 Mar. 2017. Web. 9 Mar. 2017.
2.  Lam DW, LeRoith D. The worldwide diabetes epidemic. Curr Opin Endocrinol Diabetes Obes.  2012;19:93–6.
3.  Mayo Clinic Staff. Periodontitis. Mayo Clinic. MFMER, 4 Feb. 2014. Web. 9 Mar. 2017.
4.  Teeuw WJ, Kosho MX, Poland DC, Gerdes VEA, and Loos BG. Periodontitis as a possible early sign of diabetes mellitus. BMJ Open Diabetes Research and Care.  2017;5:e000326.

Low serum magnesium partly mediated through insulin resistance.

Magnesium (Mg) is one of the important cations in the human body. It plays a critical role in the actions of enzymes and it is a co-factor in several pathways, including glucose transport, insulin sensitivity, and insulin secretion. Studies have found an inverse relationship between serum Mg levels and the incidence of diabetes. Although it is believed that there is an increased risk of acquiring diabetes with low serum Mg levels, it is a modifiable risk factor.

Contrarily, the association between Mg levels and diabetes risk might be due to reverse causation, where diabetes may cause urinary magnesium loss. Since, in patients with prediabetes, serum glucose levels are below the threshold for urinary Mg wasting, it is unlikely to influence serum Mg levels. Therefore, the purpose of this study is to analyze the association between serum Mg levels in patients with diabetes and with prediabetes. Also, Mg-regulating genes were examined to inspect their influence on the risk of acquiring diabetes through serum Mg levels. Moreover, the effect of insulin resistance was quantified in association between serum Mg levels and diabetes risk.

From 14,926 participants age 45 years old and above, a total of 8,555 participants (mean age, 64.7 years) with normal glucose levels (mean ± SD: 5.46 ± 0.58 mmol/l) at baseline were selected from the three prospective population-based cohort studies. They were followed up every 4-5 years (median follow-up, 5.7 years). Within the population-based Rotterdam Study, Cox models was used to adjust for age, sex, lifestyle factors, comorbidities, kidney function, serum levels of electrolytes and diuretic use. It was also used to study the association between serum Mg and prediabetes or diabetes participants. Moreover, two mediation analyses were performed in order to understand if common genetic variation in eight Mg-regulating genes influenced diabetes risk through serum Mg levels and to quantify the proportion of the effect of serum Mg levels by HOMA-IR on diabetes, which is mediated through insulin resistance.

Association between serum Mg levels and incident diabetes were identified in 806 cases out of 8,555 participants without diabetes at baseline over a median follow-up of 6.7 years. A decrease in serum Mg by 0.1mmol/l was associated with an increase in diabetes risk (HR 1.18 [95% CI 1.04, 1.33]), confirming findings from previous studies. On the other hand, association between serum Mg levels and incident prediabetes yield similar results (HR 1.12 [95% CI 1.01, 1.25]). Genetic risk factors that significantly influenced diabetes incident (p<0.05) were associated with gene variation in CLDN19, CNNM2, FXYD2, SLC41A2, and TRPM6. However, gene variation in CNNM2, FXYD2, SLC41A2 and TRPM6 were completely mediated by serum Mg levels. Overall, 29.1% of the serum Mg level’s effects on diabetes were mediated through insulin resistance, whereas for prediabetes, 13.4% was mediated through insulin resistance.

The study lacked unmeasured confounding factors between the mediator and the outcome, making it difficult to check unmeasured confounding factors, but the analyses were adjusted for many potential confounders to avoid unlikely association between serum Mg and diabetes risk due to residual confounding. Also, the study was unable to stratify based on ethnicity as the population was mainly of European descent, whereas previous studies showed lack of association between serum Mg and diabetes incidence in black subjects. Overall, the comprehensive assessment of this study reduces potential bias resulting from misclassifications and the mediation analysis allowed for a causal interpretation of data.

Serum Mg had already been associated with diabetes, but increase in renal Mg wasting could also lead to low serum Mg levels. Thus, if low Mg levels were the result of uncontrolled diabetes instead of a cause, then the effect of Mg supplement on diabetes risk would not be expected. This study provides evidence that Mg does influence diabetes and prediabetes risk, but the association with prediabetes is unlikely to be caused by reversed causation as glucose levels are not high to cause increased urinary Mg wasting.

In conclusion, low serum Mg levels were associated with an increased risk of prediabetes, as well as to increased risk of diabetes. The specific variations in Mg-regulating genes can change diabetes risk through serum Mg levels. The study concludes that the findings support a potential role of Mg levels in the development of diabetes, however the theory is partly mediated through insulin resistance.

Practice Pearls:

The effect of serum magnesium on prediabetes and diabetes risk is partly mediated through insulin resistance.
Serum magnesium and incident diabetes may be due to reverse causation in diabetes due to induced urinary magnesium loss. However, it is unlikely in prediabetes due to low serum glucose levels below the threshold for urinary magnesium wasting.
Common genetic variation in magnesium regulating genes TRPM6, CLDN19, SLC41A2, CNNM2 and FXYD2 can significantly modify the risk of diabetes through serum magnesium levels.

References:

1. Arpaci D, Tocoglu AG, Ergenc H, Korkmaz S, Ucar A, and Tamer A. Associations of serum Magnesium levels with diabetes mellitus and diabetic complications. Hippokratia. 2015 Apr-Jun;19(2):153-157.
2. Kieboom BCT, Ligthart S, Dehghan A, Kurstjens S, de Baaij JHF, Franco OH, et al. Serum magnesium and the risk of prediabetes: A Population-Based Cohort Study. Diabetologia. 2017 Feb 21.
3. Xu J, Xu W, Yao H, Sun W, Zhou Q, and Cai L. Associations of serum and urinary magnesium with the pre-diabetes, diabetes and diabetic complications in the Chinese Northeast population. PLoS One. 2013;8:e56750.