While various studies have linked strength to a lower risk of type 2 diabetes, a new study published in the journal Mayo Clinic Proceedings indicates that even ‘moderate amounts of muscle strength’ were associated with a 32% reduced risk of developing T2DM.
Type 2 diabetes mellitus (T2DM) is associated with multiple comorbidities, including cardiovascular disease (CVD), hypertension, obesity, dyslipidemia and kidney disease, which increase its morbidity and mortality and complicate treatment decisions.1 Death from CV disease (CVD) is 70% higher in adults with diabetes compared to those without diabetes, and patients with diabetes have a decreased life expectancy, mostly due to premature CV death. The recent and continuously emerging results from cardiovascular outcomes trials (CVOTs) with the newer antihyperglycemic drugs have shown not only cardiovascular safety, but also CV benefits in addition to non-CV (renal, blood pressure, weight loss) beneficial effects, and are poised to change the clinical management of T2DM and its comorbidities.2 As such, guidelines to individualize T2DM therapy based on patients’ comorbidities have already according to the results from these landmark randomized clinical trials (RCTs).2
However, while RCTs are the gold-standard for evaluating the safety and efficacy of new pharmacotherapies, including for T2DM, the exclusion of many patient populations due to strict criteria in RCTs can sometimes mean that the population studied does not fully represent the patients seen in everyday practice.3 Real-world studies, which use evidence and information from large electronic health and insurance claims databases, may help bridge the gap between randomized trials and actual clinical practice by providing real-world insights to patient care.3 Real-world studies can be either retrospective or prospective, as well as observational or interventional (called prospective pragmatic studies).3 Retrospective real-world studies can include data from several sources (such as those from electronic health records—EHRs, patient registries and claims databases) and can inform or complement the results of RCTs in the real-world setting, as well as a retrospective external control arms for RCTs.3 In addition, large prospective trials with a randomized design that evaluate the efficacy and safety of a therapy in a diverse and heterogenous patient population that is closer to what is seen in clinical practice, are an increasing source or real-world evidence.3
One of the main differences between RCTs and real-world studies is that RCTs are usually conducted in very select patient populations, dictated by the strict inclusion and exclusion criteria determined by the investigators.3 However, due to these stricter criteria, RCTs may lack generalizability, and real-world evidence has the potential to more efficiently provide additional answers that inform outcomes, quality, efficacy and patient care and to fill in the gaps that remain unanswered from RCTs.3 In this setting, real-world studies can provide valuable information on how drugs perform within specific subgroups and patient populations that are often excluded from RCTs and to assess long-term efficacy and safety.3
In fact, several real-world studies have provided key insights that have informed T2D treatment decisions and contributed to the development of clinical practice guidelines.3 A prime example is the UK Diabetes Study which confirmed the importance of glycemic control in the prevention of microvascular and macrovascular complications of T2DM in a real-world population .4 More recently, real-world studies have corroborated some of the evidence from CVOTs. The CVD-REAL and OBSERVE-4D real-world studies have shown a significant reduction in heart failure hospitalizations with SGLT-2 inhibitors, similar to those observed in RCTs.5,6 In addition, real-world studies with SGLT-2 inhibitors have helped to address some of the safety concerns that have emerged from RCTs, particularly the observed increased risk of lower extremity amputations. For example, the OBSERVE-4D study showed no increased risk of below-knee extremity (BLKE) amputations with SGLT-2 inhibitors in patients with T2DM compared to other antihyperglycemic agents (including GLP-1 RAs, DPP-4 inhibitors, thiazolidinediones, sulfonylureas, and insulin).6 However, it is important to remember that real-world studies can have several limitations, including being subject to additional bias and confounding factors, which can reduce their internal validity.3 Being up-to-date on landmark RCT results is important, but clinicians should also pay attention to well-designed real-world studies which might help bridge the gap between current literature and everyday patient care.
- Baptist, Gallwitz. “The Cardiovascular Benefits Associated with the Use of Sodium-Glucose Cotransporter 2 Inhibitors–Real-World Data.” European Endocrinology1 (2018): 17.
- Davies, Melanie J., et al. “Management of hyperglycaemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD).” Diabetologia12 (2018): 2461-2498.
- Blonde, Lawrence, et al. “Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician.” Advances in therapy(2018): 1-12.
- Stratton, Irene M., et al. “Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study.” Bmj7258 (2000): 405-412.
- Kosiborod, Mikhail, et al. “Lower Risk of Heart Failure and Death in Patients Initiated on Sodium-Glucose Cotransporter-2 Inhibitors Versus Other Glucose-Lowering DrugsClinical Perspective: The CVD-REAL Study (Comparative Effectiveness of Cardiovascular Outcomes in New Users of Sodium-Glucose Cotransporter-2 Inhibitors).” Circulation3 (2017): 249-259.
- Ryan, Patrick B., et al. “Comparative effectiveness of canagliflozin, SGLT2 inhibitors and non‐SGLT2 inhibitors on the risk of hospitalization for heart failure and amputation in patients with type 2 diabetes mellitus: A real‐world meta‐analysis of 4 observational databases (OBSERVE‐4D).” Diabetes, Obesity and Metabolism11 (2018): 2585-2597.