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Meeting Reports

American Diabetes Association's 73rd Scientific Sessions (ADA)
June 21 - 25, 2013
Chicago, IL


Kirstine Bell, APD, CDE, University of Sydney, Sydney, Australia, presented pooled data from six randomized controlled trials conducted over a 10-year period showing that carbohydrate counting had no significant effect on glycemic control.

Carbohydrate counting has become the gold standard for adjusting prandial insulin dose despite limited evidence to recommend it over other dietary interventions for improving glycemic control in type 1 diabetes (T1D). Most international guidelines have based their recommendations on narrative reviews and the results of the few available studies; however, carbohydrate counting does not take into account differing effects of carbohydrates on blood glucose levels and the many different factors associated with stimulation of insulin secretion.

The objective of this meta-analysis was to assess the efficacy of carbohydrate counting on glycemic control in adults and children with T1D. Six studies that assessed the management of T1D with and without carbohydrate counting were included [DAFNE Study Group. BMJ 2002; Gilbertson HR et al. Diabetes Care 2001; Kalergis M et al. Diabetes Obes Metab 2000; Laurenzi A et al. Diabetes Care 2011; Scavone G et al. Diabet Med 2010; Trento M et al. J Endocrinol Invest 2011]. The control groups received usual care, general nutrition advice, or low glycemic index dietary advice. All studies were of at least 3 months' duration; patients (503 adults; 104 children aged 8 to 13 years) could be on flexible or fixed insulin therapy.

The primary outcome was improvement in glycated hemogloblin (HbA1C). Secondary measures included the number and severity of hypoglycemic episodes, fasting plasma glucose, insulin dose required to maintain glycemic control, body weight, and quality of life.

There was no significant improvement in HbA1C in patients who practiced carbohydrate counting compared with those who did not (Figure 1). The overall change in HbA1C was -0.3% points (p=0.185). Four studies favored carbohydrate counting (range, -1.0% to +0.14%); two favored the control (range, -0.3% to +0.24%).

Figure 1. Carbohydrate Counting Does Not Significantly Improve HbA1C

Reproduced with permission from K Bell, APD, CDE.


Due to the low number of studies and inconsistencies in reporting metrics, the results for the secondary outcomes are weak. However, there were trends for reduced risk of hypoglycemia, improved quality of life, and no changes in insulin dose, weight, or fasting plasma glucose level. The literature shows a wide variation in carbohydrate counting ability with greater accuracy and precision associated with lower HbA1C levels. However, skill and compliance were not measured in these studies.

As this analysis shows, carbohydrate counting may not result in optimal blood glucose control and clinicians need to realize some patients may fail with this approach. Many people with T1D have difficulty managing postprandial blood glucose levels despite their best efforts. In addition, carbohydrate counting has been linked to unhealthy food beliefs, fats and protein intake that exceed nutritional recommendations, and increased reliance on packaged foods. Clinicians need to emphasize healthy eating with insulin matched to food choices rather than choosing foods to limit insulin or making dosing easier. Additional research is needed to support the use of carbohydrate counting in clinical practice, particularly in children and adolescents. Recent studies examining the effect of protein and fat on insulin requirements show promising results and could provide an alternative method for determining prandial insulin dose.


Education concerning hypoglycemia benefits patients with type 1 diabetes (T1D) with impaired awareness of hypoglycemia, regardless of the system used to deliver insulin and monitor blood glucose, according to Stuart Little, MBBS, Newcastle University, Newcastle, United Kingdom, who reported on the Prevention of Recurrent Severe Hypoglycaemia: A Definitive Randomized Controlled Trial Comparing Optimised MDI and CSII With or Without Adjunctive Real-time Continuous Glucose Monitoring [HypoCOMPaSS; EUCTR2009-015396-27].

Typically, ~25% of patients with T1D have an impaired awareness of hypoglycemia. This lack of knowledge can lead to a markedly higher risk of life-threatening episodes of severe hypoglycemia.

In this study, 96 T1D patients with impaired awareness of hypoglycemia were randomized to either the conventional multiple daily injections of insulin aspart or insulin glargine (n=50) or pump-mediated delivery of insulin aspart (n=46). In both groups, glucose was either episodically or continuously monitored. All participants received a standardized 2-hour information program concerning the recognition of risk factors and symptoms of hypoglycemia, which proved equally effective in reducing episodes of hypoglycemia. The primary endpoint was the difference in awareness of hypoglycemia as determined using the Gold score at 24 weeks. Secondary endpoints were measures of overall glycemic control and patient reported outcomes including fear of hypoglycemia and treatment satisfaction.

The participants had a mean age of 49 years (range, 18 to 74 years), mean duration of diabetes of 29 years, and were C-peptide negative. Their impaired awareness of hypoglycemia was ≥4, as measured using the Gold score, which queries knowledge of the onset of hypoglycemia, with the response ranked on a 7-point scale ranging from 1 ("always aware”) to 7 ("never aware”) [Gold AE et al. Diabetes Care 1994]. Two thirds of the participants were women, two thirds had retinopathy, one quarter had nephropathy, and one quarter had concomitant immune-treated thyroid disease. The mean HbA1C level was 8.2%.

By 4 weeks, biochemical hypoglycemia measured by continuous monitoring was significantly reduced from 53.3 minutes (3.7% of time) to 24.5 minutes (1.7% of time), ~30 minutes less each day (p<0.001 vs baseline). The reduction was sustained over the remaining 20 weeks. At 24 weeks, a statistically significant improvement in the median Gold score from 5 to 4 was evident for all participants regardless of the method of insulin, compared with the baseline score (p<0.001).

The number of episodes of severe hypoglycemia, defined by the American Diabetes Association as hypoglycemia that requires assistance for treatment, was reduced from 9 at baseline to <1 at 24 weeks (p<0.001), with the proportion of patients affected declining from 92% and 77% at 1 year and 6 months prior to the trial, respectively, to just 19% during the trial.

The mean number of insulin doses decreased significantly by about 8 units over the 24-week trial (p<0.001). Both treatment arms displayed similarities in HbA1C values, Gold score, number of episodes of severe hypoglycemia, mean insulin dose, and fear of hypoglycemia. Participants who received insulin via a pump expressed greater satisfaction with treatment.

Prof. Little and his colleagues concluded that impaired awareness of hypoglycemia can be improved and recurrent hypoglycemia can be prevented through strategies targeted at avoiding severe biochemical hypoglycemia in a high-risk population affected by diabetes for almost 30 years. This benefit of education is more influential than the technology of insulin delivery.


Cell-replacement strategies to preserve insulin production and closed-loop continuous glucose monitoring and insulin delivery systems are attractive options for exogenous insulin delivered through several daily injections for patients with type 1 diabetes (T1D).

Todd M. Brusko, PhD, University of Florida, Gainesville, Florida, USA, provided an update on advances in cellular-based therapies for T1D, noting that regulatory T cells (Tregs) are central in controlling T-cell activation and preventing the development of T1D, and could represent a new therapeutic paradigm because of the diversity of their functional capacity. Tregs have the ability not only to block autoreactive effector T cells, the cells that elicit b cell destruction, but also to limit inflammation. Therefore, harnessing Tregs and directing them to sites of inflammation could specifically target autoimmune disease.

The function of Tregs in patients with T1D is not defective, he said. Rather, a subset of patients has defects in the suppressor function of Tregs, thereby influencing the pathogenesis of T1D. Autologous umbilical cord blood, which contains a robust population of "naïve” highly functional Tregs, is being explored as a source of these immunomodulatory cells for the treatment of autoimmune diseases including T1D. A trial involving infusing Tregs into adults with T1D is underway led by Jeffrey Bluestone, PhD, University of California, San Francisco, San Francisco, California, USA. Efforts are currently underway to developing additional Treg therapies from umbilical cord blood.

Antigen-specific Tregs have been shown to prevent and even reverse diabetes in animal models. Efforts to generate antigen-specific human Tregs to suppress the immune response targeting islet b cells, while leaving intact the immune response to foreign agents, are ongoing, said Dr. Brusko. In addition, his laboratory is developing a "negative vaccine” in an attempt to block the immune response directed at insulin that is present in patients with T1D. The vaccine uses time-released biomaterials loaded with autoantigens to direct T-cell responses to one of immunologic tolerance and nonresponsiveness to specific proteins such as insulin.


Srinath Sanda, MD, University of California, San Francisco, San Francisco, California, USA, reviewed attempts to prevent the development of T1D. The decision to use drugs in prevention is not yet informed by a mechanistic understanding of why the drugs have been successful in the new-onset population, she said. In one study, modulation of costimulatory signals between T cell and antigen-presenting cells using abatacept slowed the decline of b cell function over 2 years in a study of patients with recent-onset T1D (Figure 1) [Orban T et al. Lancet 2011]. The ideal time to apply immunotherapeutic agents to potentially prevent T1D remains unknown.

Figure 1. Decline in Cell Function Over Time According to C-Peptide Levels

Reproduced from Orban T et al. Co-stimulation modulation with abatacept in patients with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled trial. Lancet2011;378(9789):412-419. With permission from Elsevier.

The role of innate inflammation in new-onset T1D is evident by elevations in expression of interleukin (IL) 1B by immune effector cells, leading to hyperglycemia-induced b cell dysfunction. Antagonists of IL-1 (canakinumab and anakinra), however, did not improve b cell function in T1D of recent onset in randomized, double-blind, placebo-controlled trials [Moran A et al. Lancet 2013]. Other data indicate that signaling through receptors that use the MyD88 adaptor protein is critical for development of T1D, and deficiency of MyD88 changes the composition of distal gut microbiota to that interaction between intestinal microbes and the innate immune system is critical to a predisposition to T1D [Wen L et al. Nature 2008]. Factors that influence b cell function before a clinical diagnosis of T1D, the relevant perturbations in the immune system prior to clinical diagnosis, the timing of their occurrence in relation to disease progression, and the mechanisms by which therapies induce a clinical response in the new-onset period require further study, said Dr. Sanda.


Andrew A. Bremer, MD, PhD, Vanderbilt University, Nashville, Tennessee, USA, provided an update of closedloop therapy. The goal of an artificial pancreas is the coupling of a continuous glucose monitor and insulin pump therapy with a control algorithm to deliver insulin in a continuous glucose-responsive manner.

Closed-loop control algorithms respond with insulin infusion that reacts to current glucose, accounts for glucose level over time, and can adjust for the rate of change in glucose. Insulin delivery algorithms based on b cell physiology use an insulin feedback feature that reduces insulin delivery in a manner analogous to the "insulin-onboard” feature of the insulin pump [Palerm CC. Comput Methods Programs Biomed 2011]. Modified algorithms have been able to control hyperglycemia and reduce late postmeal hypoglycemia even with the additional challenge of daytime exercise [Steil GM et al. J Clin Endocrinol Metab 2011].

Model predictive control (MPC) is an algorithm that seeks to optimize blood glucose over time based on models that take into account previous continuous blood glucose measurements and insulin infusion rates, and uses these data to predict future glucose and insulin infusion requirements. The model is then updated based on error in prediction, thereby minimizing the difference between the blood glucose level the model predicts and the target blood glucose.

A bihormonal closed-loop system using insulin and glucagon in separate pumps under individual control algorithms (MPC-based for insulin, proportional-integralderivative-based for glucagon) achieved a mean blood glucose concentration of 140 mg/dL and prevented hypoglycemia while achieving a mean blood glucose concentration of 164 mg/dL in separate experiments in patients with T1D [El-Khatib FH et al. Sci Transl Med 2010].

An inpatient hybrid closed-loop therapy initiated within 7 days of a diagnosis of T1D rapidly reversed glucose toxicity and established near-normal glycemic control, with mean glucose concentration falling from 240 mg/dL on initiation to 146 mg/dL on Day 1, a level of control that was sustained on Days 2 and 3 (Figure 2) [DirecNet Study Group. Diabetes Technol Ther 2013].

Figure 2. Effects of Inpatient Hybrid Closed-Loop Therapy Started Within 7 Days of Type 1 Diabetes Diagnosis

Reproduced from DirecNet Study Group. The Effects of Inpatient Hybrid Closed-Loop Therapy Initiated Within 1 Week of Type 1 Diabetes Diagnosis Diabetes Research in Children Network (DirecNet) and Type 1 Diabetes TrialNet Study Groups*. Diabetes Technol Ther 2013;15(5):401-408. With permission from Mary Ann/Liebert, Inc. Publishers.

Wireless wearable ambulatory closed-loop systems that integrate continuous glucose monitoring, continuous subcutaneous insulin infusion, and a controller algorithm onto a Smartphone device are under investigation. Sandra Puczynski, PhD, Southern Illinois University, Springfield, Illinois, USA, discussed patient hopes for a cure for T1D in the face of the reality of research efforts in this endeavor. At least $1.8 billion annually, diabetes research expenditures are not keeping up with the costs associated with diabetes, she said. The status of research is often misrepresented in the media, with discoveries sometimes portrayed as being further along in clinical development than they actually are, causing premature excitement among patients.



Previously published results from the Action to Control Cardiovascular Risk in Diabetes study [ACCORD; ACCORD Study Group. N Engl J Med 2008] showed an increased risk of all-cause and cardiovascular (CV) mortality in the intensive control group (HbA1C target, <6.0%) compared with the less intensive group (HbA1C target, 7.0% to 7.9%). "This brought a huge puzzle to the diabetes community, to figure out why we were seeing this result,” said Elias S. Siraj, MD, Temple University School of Medicine, Philadelphia, Pennsylvania, USA. Several post hoc analyses have been conducted to date to determine what factors may have influenced this outcome [ACCORD Study Group. N Engl J Med 2008; Bonds DE et al. BMJ 2010; Riddle MC et al. Diabetes Care 2010; Seaquist ER et al. Diabetes Care 2012]. These analyses did not find a conclusive link between the ACCORD results and factors such as hypoglycemia, low HbA1C, the rapid decline in HbA1C during the first year of the study, weight gain, and specific medication use.

To better understand the findings of this study, ACCORD investigators hypothesized that higher doses of exogenous insulin may be associated with the CV mortality results from the ACCORD trial. To investigate this idea, data for insulin exposure and CV mortality from 10,163 patients were analyzed. HRs and 95% CIs were calculated, and multivariable Cox regression performed to choose the most appropriate baseline covariates and models.

The updated average total, basal, and bolus insulin doses were significantly higher in the intensive-control arm (all p<0.0001). In addition, there was a significant linear association between the updated average HbA1C level and updated average insulin dose in both groups (both p<0.0001). Four different Cox proportional hazards models were employed to determine HRs for CV mortality. The first model controlled for the following 14 baseline covariates: age, history of CV disease, heart failure, QTindex, baseline HbA1C value, high-density lipoprotein, amputation, presence of peripheral neuropathy, serum creatinine, urinary ratio of albumin to creatinine, use of angiotensin receptor blockers, educational status, presence of integrated health plan, and presence of certified diabetes educator on staff at randomization. Model 2 added assignment to blood pressure or lipid trial, treatment assignment within these, severe hypoglycemia, and weight change. Model 3 added the updated average HbA1C, and Model 4 added the glycemic treatment arm assignment. Results from all 4 models by total, basal, and bolus insulin are presented in Table 1.

Table 1. HRs for CV Mortality of Insulin Dose (per 1 unit/kg) From Cox Proportional Hazards Model

Based on the unadjusted HRs, a daily insulin dose increase by 1 unit/kg of body weight was associated with a 1.83- (total insulin), 2.29- (basal insulin) and 3.36-fold increase in risk of CV mortality (all p<0.0001). However, results from the four models did not confirm these findings. After adjustment for baseline covariates in Model 1, the HRs became nonsignificant indicating no association of insulin dose with CV mortality. Additionally, no association between insulin dose and CV mortality emerged after adjustments were made for on-treatment factors.

Dr. Siraj concluded that these results do not support the idea that insulin dose is an independent risk factor for CV mortality in the ACCORD population.




Patients with type 1 diabetes (T1D) experienced a gradual decline in certain cognitive functions, but the decline was not comparable to mild cognitive impairment, which is a precursor of dementia. The decline in brain volume loss was associated with higher baseline systolic blood pressure (SBP), while a decline in executive function was associated with baseline elevated HbA1C in patients with T1D, according to Eelco van Duinkerken, PhD, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, who presented the 4-year follow-up data.

The study of cognitive decline compared the brain function of 25 patients with T1D and 25 matched controls. All participants had to be free of diseases affecting the brain and psychiatric comorbidities, and had to be right-handed. To explore the theory that microangiopathy is related to cognitive decline, all diabetic patients had proliferative retinopathy as a marker of angiopathy, and diabetes duration of at least 10 years.

At baseline, the mean age for all was ~45 years (range, 18 to 56 years), and the mean duration of diabetes was 35 years. The SBP was 133.9 mm Hg in the diabetes group, compared with 126.9 mm Hg in the control group (p=0.045). The baseline HbA1C values were 7.9% and 5.4% (p<0.001) in the diabetes and control groups, respectively.

There was no difference in general cognitive ability, information processing speed, or attention at follow-up. The researchers noted a slight increase in performance over time in patients with diabetes, but not in the control group; however, this lacked statistical significance.

However, an accelerated decrease in executive function was found in T1D patients relative to the controls, which was ascertained by asking the subjects to identify the color of ink or state how many animals they could think of in 2 minutes.

The changes in brain volume from baseline to follow-up (mean, 4.28 years T1D group; 4.73 years control group) are measured by MRI. The decrease in brain volume was 1.34% in the patients with T1D compared with 0.68% in the control patients (p=0.036).

The brain volume loss was most notable in the frontal and central areas of the right hemisphere, which are associated with executive function performance (p=0.021). While the baseline SBP was found to correlate with brain volume loss and baseline elevated HbA1C was found to correlate with executive function loss, there was no correlation with severe hypoglycemic events. The change in brain volume was not seen in speed-related domains, as some researchers had theorized.

T1D patients with angiopathy face the possibility of progressive cognitive decline, which is unrelated to mild cognitive impairment.



Patients with type 2 diabetes mellitus (T2DM) appear to have dysfunctional islet β cells due to unknown mechanisms [Leahy JL and Pratley RE. Translational Endo Metab 2011]. Multiple new pharmacologic agents that are under development for the treatment of T2DM specifically target islet cells. Jack L. Leahy, MD, University of Vermont, Burlington, Vermont, USA, presented two signaling pathways in β cells that may be important drug targets.

Dr. Leahy pointed out that there are several therapeutic targets within β cells for the treatment of T2DM [Bailey CJ. Lancet2012]. One target is an unsaturated medium- to long-chain free fatty acid receptor called GPR40 (also called FFAR1) that plays a role in free fatty acid-induced insulin secretion [Mancini AD, Poitout V. Trends Endocrinol Metab 2013]. Studies have demonstrated decreased insulin secretion in GPR40 null mice compared with controls [Steneberg P et al.Cell Metab 2005; Kebede M et al. Diabetes 2008]. Interestingly, GPR40 expression in human pancreas islet cells is decreased in patients with T2DM by up to 80%, when compared with controls [Del Guerra S et al. Nutr Metab Cardiovasc Dis 2010].

Dr. Leahy highlighted data from a Phase 2 trial in which 426 patients with T2DM were treated with the GPR40 agonist TAK-875, glimepiride, or placebo for 12 weeks [Burant CF et al. Lancet 2012]. Patients were counseled on diet and exercise and may or may not have been stable on metformin. A significant improvement in HbA1C levels was observed in patients who received TAK-875 or glimepiride, compared with those who received placebo (Figure 1). Patients treated with TAK-875 or glimepiride did not have improved insulin sensitivity or fasting glucagon levels. Additionally, treatment-related adverse events were similar among patients who received TAK-875 or placebo. However, a greater number of patients who received glimepiride demonstrated hypoglycemic events, as compared with those who received placebo or TAK-875.

Figure 1. Effects of TAK-875, Glimepiride, and Placebo on Glycemia 


Reproduced from Burant CF et al. TAK-875 versus placebo or glimepiride in type 2 diabetes mellitus: a phase 2, randomised, double-blind, placebo-controlled trial. Lancet 21012;379(9824):1403-1411. With permission from Elsevier.

Dr. Leahy gave an example of identifying new therapeutic targets in well-studied signaling pathways in β cells, such as the FoxO1 and PPAR-g pathway in β cells that is important for β cell survival, function, metabolism, and response to incretin. Rats with hyperglycemia demonstrate reduced signaling through the FoxO1/PPAR-g pathway due to the potentially impaired translocation of FoxO1 from the nucleus to the cytoplasm [Gupta D et al. Diabetes 2010]. Treatment of hyperglycemic rats with the DPP-4 inhibitor alogliptin appears to restore FoxO1/PPAR-g signaling [Gupta D et al. ADA 2013 (poster 2275)].

Abd A. Tahrani, MD, PhD, University of Birmingham, Edgbaston, United Kingdom, discussed the mechanisms underlying insulin resistance and potential drug targets. Prof. Tahrani pointed out that the most important factor associated with insulin resistance is obesity; however, he noted that it is difficult to treat obesity. Targets for the treatment of insulin resistance include obesity and the mechanisms of insulin resistance such as inflammation, oxidative stress, lipotoxicity, and glucotoxicity, as well as targeting the insulin receptor and insulin post-receptor signaling.

Prof. Tahrani highlighted several drugs that target insulin resistance. The compounds demethylasterriquinone B1, compound 2, and D-410639 target insulin receptor activation [Zhang B et al. Science 1999; Strowski MZ et al. Endocrinology 2004; Tsai KW et al. J Biomed Sci 2009]. Some agents prolong phosphorylation of the b subunit of the insulin receptor following insulin binding [Cohen P et al. Nat Rev Mol Cell Biol 2006], such as the agent TLK16998 [Manchem VP et al. Diabetes2001]. Other targets include interleukin-6 [Ridker PM et al. Circulation 2012], resveratrol [Ito-Nagahata T et al. Biosci Biotechnol Biochem2013], and inhibitor k-B kinase-b inhibitors [Kamon J et al. Biochem Biophys Res Commun 2004].

Postreceptor insulin signaling may also harbor potential targets for the treatment of insulin resistance. Prof. Tahrani highlighted signaling molecules within the insulin signaling pathway that may be ideal targets, such as inhibition of protein kinase C [Naruse K et al. Diabetes2006], promotion of phosphatidylinositol-3 kinase [Croze ML et al. Biochimie2013], inositol metabolites, inhibition of PTEN [Pal A et al. N Engl J Med 2012], and inhibition of inositol phosphatases.

Prof. Tahrani pointed out that nonpharmacologic interventions, such as weight loss, adequate sleep, and obstructive sleep apnea are also important to consider. For example, patients who were sleep restricted in a laboratory study had a significant decrease in glucose tolerance, acute insulin response to glucose, glucose effectiveness, and insulin sensitivity as compared with a well-rested state [Leproult R, Van Cauter E. Endocr Dev 2010].

Robert R. Henry, MD, University of California, San Diego, La Jolla, California, USA, presented information on treating inflammation in adipose and skeletal tissues. Individuals that tend to accumulate adipose tissue in the visceral region are more likely to have inflammation in their adipose tissue. In addition, patients with T2DM have greater fat deposition in the visceral versus subcutaneous region, as compared with patients without diabetes.

Dr. Henry highlighted that adipose tissue produces chemicals called adipokines, which can have autocrine, paracrine, or endocrine signaling effects [Blüher M. Diabetes Metab J 2012]. As adiposity increases, the adipokines that are secreted become predominantly proinflammatory.

A prevailing theory of a mechanism underlying insulin resistance in obesity is lipotoxicity [Johnson AM, Olefalsy JM. Cell 2013]. Excessive caloric intake can result in cellular stress and tissue inflammation, which can lead to insulin resistance [Odegaard JI, Chawla A. Science 2013]. Ultimately, inflamed adipose tissue with some level of metabolic dysfunction demonstrates an altered milieu as compared with lean adipose tissue with normal metabolic function [Ouchi N et al. Nat Rev Immunol 2011]. Adipocytes that live in an inflamed tissue have reduced insulin action [Odegaard JI, Chawla A et al. Science2013].

A recent study demonstrated that treatment with salsalate, a tumor necrosis factor-a inhibitor, resulted in a significant decrease in white blood cell count, neutrophil count, and lymphocyte count over a 48-week period [Goldfine AB et al. Diabetalogia 2013]. In addition, salsalate treatment caused a reduction in nuclear factor kappalight chain enhancer (NF-κB) activity in visceral adipose tissue, compared with placebo, after 12 weeks of treatment. Importantly, NF-κB is a major player in inflammatory signaling pathways [Reilly SM et al. Nat Med 2013].

Inflammation of skeletal muscle is increased in obese patients compared with lean patients, as measured by macrophage infiltration and, as body mass index increases, so does the macrophage content of skeletal muscle [Varma V et al. Am J Physiol Endocrinol Metab 2009]. However, other studies have suggested that this finding may be due to cross-contamination by adipose tissue that is found within skeletal tissue [Tam CS et al. Obesity 2012].

Bernard Zinman, CM, MD, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Canada, discussed targeting mechanisms of glucose absorption and excretion in the treatment of T2DM. The alpha-glucosidase inhibitors [AGI] are a drug class that modifies glucose absorption by inhibiting the breakdown of carbohydrates in the upper intestine. Interestingly the AGIs also modify the secretion of gastrointestinal (GI) peptides like GLP1 but have modest efficacy. However, the tolerability of AGI is a major problem and their widespread acceptance in the United States has been limited by frequent GI side effects, such as diarrhea, flatulence, and abdominal distention.

A new strategy focuses on targeting the sodium glucose cotransporter-2 (SGLT-2), which is an important mechanism responsible for the filtered glucose to be reabsorbed in the proximal tuble of the kidney. Nonetheless the capacity of the two transporters, SGLT-1 [high-affinity, low-capacity] and SGLT-2 [low-affinity, high-capacity] to reabsorb glucose is limited; therefore, excess blood glucose levels above ~180 mg/dL causes glucose to remain in the urine filtrate [Gerich JE et al. Diabetic Med 2010]. However, in patients with poorly controlled diabetes there is an adaptive response whereby SGLT-2 is upregulated so that reabsorption of glucose is increased. Thus SGLT-2 inhibitors reduce glucose reabsorption in the kidney and with resultant glucosuria [increased urinary glucose]. Several SGLT-2 inhibitors that have shown promising results in clinical trials include empagliflozin [Hach T et al. Diabetes 2013], canagliflozin (Figure 2) [Schernthaner G et al. Diabetes Care 2013], and dapagliflozin [FDA. Dapagliflozin Advisory Committee Meeting. Published July 19, 2011].

Figure 2. Effect of Canagliflozin Versus Sitagliptin on HbA1C Levels



Reproduced from Schernthaner G et al. Canagliflozin Compared With Sitagliptin for Patients With Type 2 Diabetes Who Do Not Have Adequate Glycemic Control With Metformin Plus Sulfonylurea: A 52-week randomized trial. Diabetes Care 2013;36(4):908-913. With permission from the American Diabetes Association.



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