Big Data Study: Big Data Confirm Type 2 Diabetes Treatment Approach

Sep 3, 2018 | Big Data, Diabetes, Diabetes Type 2

As reported in Medscape and Miriam E. Tucker,  when added to metformin, dipeptidyl peptidase 4 (DPP-4) inhibitors and sulfonylureas reduce hemoglobin A1c to a similar degree, but other differences may favor the former, new research suggests.  See the study results here:

The findings, derived from a novel approach that analyzed real-world data from more than 246 million patients, were published online August 24 in JAMA Open, by Rohit Vashisht, PhD, from the Center for Biomedical Informatics Research, Stanford University School of Medicine, California, and colleagues with the Observational Health Data Sciences and Informatics in New York City.

None of the drugs raised the risk for kidney disorders, according to the analysis, which examined the effects of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones added to metformin.

However, sulfonylureas were associated with a small increased risk for myocardial infarction and eye disorders compared with DPP-4 inhibitors.

“Large-scale characterization of the effectiveness of type 2 diabetes therapy via an open collaborative research network suggests DPP-4 inhibitors over sulfonylureas in patients with diabetes for whom metformin was the first-line treatment,” principal investigator Nigam H. Shah, MBBS, PhD, also from the Center for Biomedical Informatics Research, Stanford University School of Medicine, told Medscape Medical News.

Asked to comment, M. Sue Kirkman, MD, professor of medicine and medical director of the Diabetes Care Center Clinical Trials Unit at the University of North Carolina School of Medicine in Chapel Hill, noted, “For clinicians, this study supports prior thinking that most oral agents lower HbA1c about the same amount on average. The concerns about sulfonylureas being associated with cardiovascular disease are again raised.”

Kirkman added, “For now, clinicians need to continue to individualize therapy beyond metformin, taking into account outcomes of importance to patients, such as cost, side effects, hypoglycemia, and weight gain, and incorporating what we know from cardiovascular outcome trials for patients with known cardiovascular disease.”

Novel Approach Allows for Examination of Heterogeneous Data

For the study, the authors used patient data from eight healthcare systems in three countries. To allow incorporation into a single dataset and subsequent analysis, they standardized the data with regard to terminology and structure using the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM).

Shah said that the platform allows for large datasets to inform clinical decision-making. “This study is an example of a large multinational open collaborative research network, which can produce evidence at scale and is made feasible via the adoption of a common data model and open-source analytical tools.”

The platform is being used in many areas of medicine, including assessment of treatments for hypertension, fracture prevention, and thyroid conditions.

Kirkman commented, “Since we cannot do randomized controlled trials to answer every question regarding medical therapy of type 2 diabetes, this type of observational big data analysis is very important. However, concerns about unmeasured confounders are always present.”


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