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Optimizing Diabetes Care: A More Personalized Patient-Centered Approach

May 25, 2019
 
Editor: David L. Joffe, BSPharm, CDE, FACA

Author: Adam Chalela, B.S. Doctor of Pharmacy Candidate USF College of Pharmacy

New research on five diabetes subgroups: which baseline clinical characteristics of patients with newly diagnosed diabetes lead us to providing more personalized care?

Previous studies have suggested that the diagnosis of diabetes be expanded into five major types based on multiple physiological and genetic characteristics: one of them being related to type 1 diabetes (severe autoimmune diabetes) while the other four can be considered sub groups of the historical type 2 diabetes mellitus diagnosis (severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes). It is important to find the best route possible to take for initial treatment approaches for such a growing chronic condition as the results of ongoing studies can lead us to the direction of a more personalized medication therapy plan.

Newer research suggests that certain clinical features, instead of the aforementioned five diabetes subgroups, determine the level of responsive to different classes of medications. A recent study out of the United Kingdom by Dennis and colleagues analyzed the data sets from two ongoing studies. One of the studies enrolled 4,351 newly diagnosed, drug-naïve patients over two years to determine the relatively short-term level of glycemic control in patients randomly assigned to metformin, a sulfonylurea, or thiazolidinedione over six years. The second study enrolled slightly older patients with an established chronic disease to receive the same drug classes as the previous study as an add-on second line therapy; the study period was also over six years.

In the study by Dennis and colleagues out of the United Kingdom, endpoints such as glycemic response to the randomized medication (metformin, sulfonylurea, or thiazolidinedione)  or incidence of chronic kidney disease were obtained. Glycemic response from a patient would be met if there was a cumulative HbA1c decrease over a period of three years as measured by total area-under-curve. All the patients enrolled into this study exhibited normal renal function, as determined by a serum creatinine value within normal limits. Incidence of chronic kidney disease in the study population was determined if the estimated glomular filtration rate progressed from baseline of >60 mL/min to a value <60 mL/min, indicating stage three chronic kidney disease.

After analyzing the data sets from the two previous studies, the researchers out of the United Kingdom found that sub-group approach for categorizing patients with diabetes was reproducible, showing the same results that were previously recorded. What they also found, however, was that clinical features such as age at diagnosis or baseline renal function as measured by estimated glomular filtration rate (eGFR) were accurate predictors of disease progression, just as the subgroup approach was. Age at diagnosis was found to be a highly specific predictor for level of glycemic control a patient was able to exhibit while eGFR was found to be a highly specific predictor for renal functions years down the line in patients with diabetes. Also, HbA1c values were also improved to a greater extent in patients that underwent randomizations with the clinical feature approach compared to those that underwent randomization with the subgroup approach.

The authors concluded that the five diabetes subgroups, severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes, were accurate predictors of disease progression and medication response for patients with diabetes. Through attempts at reproducibility, Dennis and colleagues have determined that patients with severe insulin-resistant diabetes respond best to thiazolidinediones over time, while patients with mild age-related diabetes respond best to sulfonylureas. It is important to remember that both strategies, a subgroup-driven approach and a clinical feature-driven approach, be utilized to cater to each individual patient in order to personalize the best therapy plan for diabetes management and disease progression.

Practice Pearls:

  • Subgroups are a great approach to determining initial treatment choices for patients with newly-diagnosed diabetes, as results have been shown and reproduced throughout multiple studies.
  • The level of glycemic control and progression as well as renal function exhibited by patients enrolled in these studies were best predicted by age at diagnosis and estimated glomular filtration rate at baseline.
  • Patients with severe insulin-resistant diabetes respond best to thiazolidinediones and patients with mild age-related diabetes respond best to sulfonylureas.

Reference: Dennis JM, Shields BM, Henley WE, et al. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol 2019; 7:442-51. DOI: 10.1016/s2213-858(19)30087-7

Adam Chalela, B.S. Doctor of Pharmacy Candidate USF College of Pharmacy