Personalizing Second-Line Type 2 Diabetes Treatment Selection: Combining Network Meta-analysis, Individualized Risk, and Patient Preferences for Unified Decision Support

Abstract

Background-Personalizing medical treatment often requires practitioners to compare multiple treatment options, assess a patient's unique risk and benefit from each option, and elicit a patient's preferences around treatment. We integrated these three considerations into a decision modeling framework for the selection of second-line glycemic therapy for type 2 diabetes. Methods-Based on Multi-Criteria Decision Analysis, we developed a unified treatment decision support tool accounting for three factors; patient preferences, disease outcomes, and medication efficacy and safety profiles. By standardizing and multiplying these three factors, we calculated the ranking score for each medication. This approach was applied to determining second-line glycemic therapy by integrating: (i) treatment efficacy and side-effect data from network metaanalysis of 301 randomized trials (N=219,277); (ii) validated risk equations for type 2 diabetes complications; and (iii) patient preferences around treatment (e.g., to avoid daily glucose testing).

Empagliflozin treatment effects across categories of baseline HbA1c, body weight and blood pressure as an add‐on to metformin in patients with type 2 diabetes

“…Although previous reports from the EMPA‐REG OUTCOME trial have reported consistent CV and HF benefits, regardless of the magnitude of reduction in HbA1c with empagliflozin, 24 a better understanding of the clinical efficacy of glucose‐lowering drugs across the spectrum of cardio‐metabolic characteristics may help to better individualize therapy 12,13 . These are therefore important data to consider when choosing add‐on therapy to metformin for patients with specific glycaemic or weight considerations.…”

“…A better understanding of the clinical efficacy of specific glucose‐lowering drugs across phenotypical characteristics of key cardio‐metabolic factors will help clinicians to better tailor therapy, while potentially helping patients achieve their treatment goals. This is particularly important when discussing the efficacy of different glucose‐lowering drugs as second‐line therapy to metformin, also in the context of the need to take patient preferences into account when selecting therapy 12,13 and the treatment inertia that often occurs during T2D management 14 …”

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

“…Some of the drugs we identified may be suitable for treating acute IR, such as occurring during infection ( Ceriello et al, 2020 ; Donath, 2021 ), and encouragingly several positive scoring drugs appear tolerable in longer-term preclinical models of metabolic or neurogenerative disease ( Li et al, 2018b ; Wang et al, 2012 ). The present approach could be extended to include a stratified medicine component, where evaluation of positively acting compounds is first trialled in T2DM patients with extreme IR ( Choi et al, 2019 ). A number of positively acting IR-DR compounds, including selected mTOR inhibitors ( Appendix 1—figure 3 ), are able to mimic a longevity-related RNA signature ( Timmons et al, 2019 ) and thus may be potential geroprotectors ( Fuentealba et al, 2019 ).…”

Combining Multiple Treatment Comparisons with Personalized Patient Preferences: A Randomized Trial of an Interactive Platform for Statin Treatment Selection

“…30 For each of these conditions, it would be possible to develop tools that combine information on the comparative effects of treatment options with user preferences. 31 This may be particularly important when considering treatment options with more varied benefit or side effect profiles.…”