Revolutionizing type 2 diabetes treatment: personalized approach shows promise in matching patients with optimal glucose-lowering therapies

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In a recent study posted to the medRxiv* preprint server, researchers developed a personalized therapy selection algorithm for two diabetes type 2 (T2D) treatment drug classes, i.e., sodium-glucose cotransporter 2 (SGLT2)-inhibitors (SGLT2i, reference class) and glucagon-like peptide-1 (GLP1)-receptor agonist (GLP1-RA) medications.

Study: Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes. Image Credit: AnastasiyaArtcomma/Shutterstock.comStudy: Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes. Image Credit: AnastasiyaArtcomma/Shutterstock.com

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Background

T2D patients prone to cardiorenal diseases are prescribed GLP1-RA and SGLT2i therapies. However, limited evidence exists on the advantages of these therapies for individual patients, and data on their efficacy in broader populations are limited.

Further research is required on the two classes of drugs to increase their generalizability and widen the therapeutic landscape of T2D.

About the study

In the present study, researchers developed and validated an estimation model to provide individualized estimates of differences in one-year glycemic outcomes for GLP1-receptor agonists and SGLT2-inhibitors.

The algorithm was designed to predict differences in one-year glycemic outcomes [based on glycated hemoglobin (HbA1c)] between the two therapies, using routine clinical features from 46,394 people with type 2 diabetes in England (27,319 for developing the model and 19,075 for validation, respectively), with additional external validation from 2,252 T2D patients in Scotland.

The model was built using the Bayesian Causal Forest (BCF) framework, which was meant to identify and estimate conditional average treatment effects (CATEs), which indicate the differential impacts of drug types on glycated hemoglobin outcomes based on the patient's clinical characteristics.

Specific cohorts were developed for secondary outcomes to maximize the number of patients included in each study. The researchers evaluated the impact of glycemic response-based targeted treatment on secondary outcomes like tolerability, weight change, long-term risks of adverse renal events, and incident microvascular and macrovascular problems.

Each decile calibration was based on comparing mean projected CATE estimations to mean HbA1c differences in people taking SGLT2i versus GLP1-RA. The model's performance was also examined in a separate sample of 2,252 Scottish people, 1,837, and 415 started SGLT2i and GLP1-RA, respectively.

Individuals with measured HbA1c outcomes were randomly divided into a 60:40 ratio between the development (31,346 individuals) and validation (20,865 individuals) groups to build the one-year glycemic response therapy selection model.

The team evaluated calibration using estimated CATE quintiles. It focused on individual-level randomized clinical trial (RCT) data of GLP1-RA from the HARMONY program [Liraglutide (389 individuals) and Albiglutide (1,682 individuals)], the PRIBA study [Liraglutide (397 individuals), exenatide (223 individuals), and Tayside & Fife (415 individuals).

Results

The model detected 112,274 T2D patients who did not receive insulin therapy and started GLP1-receptor agonists (28,081 individuals) or SGLT2 inhibitors (84,193 individuals) in the United Kingdom (UK) from January 2013 to October 2020. The mean participant age was 58, 59% were male, and 79% were White.

The mean uncorrected one-year glycemic responses for GLP1-RA and SGLT2i were -11.7 and -12 mmol/mol, respectively. The BCF framework model revealed many clinical characteristics that predict glycemic responses with SGLT2i (prognostic factors) and multiple factors that predict differentiated glycemic responses with GLP1-receptor agonist versus SGLT2 inhibitor therapy (differential factors).

The model included 87% (n=27,319) of individuals with adequate clinical factor information. The estimated CATE was of a 0.10-mmol/mol advantage with the glucagon-like-peptide-1 receptor agonists over SGLT2 inhibitors, indicating that both therapies had comparable average effectiveness.

However, there was significant variation in estimated CATE among people, with the BCF model indicating a mean glycemic advantage on SGLT2 inhibitor treatment for 48% (n=13,110) of participants and on the glucagaon-like-peptide-1 receptor agonist treatment for 52% (n=14,209) of individuals.

A 7.40 mmol/mol advantage for SGLT2i was reported among 4.0% (n=81) of patients with a model-estimated glycemic advantage higher than 5.0 mmol/mol for SGLT2 inhibitors over GLP1-receptor agonists.

In contrast, a 5.60 mmol/mol advantage on GLP1-RA was reported among 6.7% (n=150) of persons with model-estimated glycemic benefits of higher than 5.0 mmol per mol for glucagon-lile-peptide-1 receptor agonists over SGLT2 inhibitors.

Using CATE values to divide the combined study cohorts with estimator information (46,394 individuals) into sub-cohorts revealed that those with a greater estimated glycemic advantage with glucagon-lile-peptide-1 receptor agonists over SGLT2 inhibitors were predominantly older and female, with lower initial HbA1c, body mass index (BMI), and estimated glomerular filtration rate (eGFR).

For 32.0% of individuals with initial HbA1c values of 5.0 mmol/mol, SGLT2i was expected to have a larger glycemic advantage versus GLP1-RA. SGLT2i receivers exhibited a 23 mmol/mol drop in HbA1c, and GLP1-RA recipients showed an 18 mmol/mol decrease in HbA1c of 6,856 individuals (8.0%), with an estimated HbA1c advantage on SGLT2 inhibitors of 5.0 mmol/mol.

In comparison, 7,293 individuals (8.0%) with an estimated HbA1c advantage on GLP1-RA showed a 16 mmol/mol drop in HbA1c, whereas SGLT2i recipients had a nine mmol/mol decrease in HbA1c. Across subgroups, the weight change was consistently larger for SGLT2i recipients than for GLP1-RA recipients.

Short-term drug termination was lower among drug recipients predicted by the model to demonstrate the largest HbA1c improvement, owing mostly to variations in SGLT2 inhibitor treatment termination across anticipated differences in glycemic responses.

The relative risks of incident microvascular events showed subgroup variations, with SGLT2i being associated with a decreased risk than GLP1-RA among those expected to gain glycemic benefits with SGLT2 inhibitors.

Conclusion

Based on the study findings, precision medicine approaches to type 2 diabetes can help with successful tailored therapy selection, and the utilization of regularly obtained clinical data might help with cost-efficient implementation in many nations.

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Pooja Toshniwal Paharia

Written by

Pooja Toshniwal Paharia

Pooja Toshniwal Paharia is an oral and maxillofacial physician and radiologist based in Pune, India. Her academic background is in Oral Medicine and Radiology. She has extensive experience in research and evidence-based clinical-radiological diagnosis and management of oral lesions and conditions and associated maxillofacial disorders.

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