4.1. Integrating multiple effects of single drugs to tailor type 2 diabetes

Part of this high residual risk is due to the fact that not all patients beneficially respond to the drugs that target the abovementioned cardiovascular risk factors. Indeed, previous studies from our group have shown that some patients benefit from a given treatment, but many others do not. In addition to this between individual variability in drug response in a single risk factor, we have shown that a single drug affects many more risk markers than the one intended. For example, the antihypertensive angiotensin receptor blocker (ARB) losartan decreases uric acid, hemoglobin, albuminuria and increases serum potassium. Some of these effects may be beneficial for renal and cardiovascular outcomes, such as a reduction in blood pressure, albuminuria, or uric acid. Yet, other effects, such as an increase in potassium may increase renal and cardiovascular risk. We have shown that the multiple effects of a drug on multiple renal and cardiovascular risk factors vary within individuals indicating that some patients show a reduction in blood pressure in response to ARB treatment but no change in albuminuria or vice versa.

Given the large variation in drug response in multiple cardiovascular risk factors one should combine the short term effects of a single drug in each individual to obtain a more accurate estimate of the ultimate drug effect per patient. We therefore developed an algorithm, a so-called multiple risk Parameter Response Efficacy (PRE) score, to predict the potential long renal effect of a drug based on the composite of short term drug effects in individual patients. In previous work we showed that integrating the short-term changes in all measured cardiovascular risk markers following ARB treatment gave a much better prediction who would benefit from the ARB losartan compared to when based on blood pressure alone (the on-target risk factor).

Clinical practice currently lacks an holistic and patient-centered treatment approach, which can be attributed to several reasons:

1. Current treatment guideline recommendations focus mostly on the treatment of single risk factors and give little support on how to provide personalized care.
2. There is insufficient guidance to make an integrated assessment of the renal and cardiovascular risk changes of patients after start of an intervention.
3. Translation of risk factors to individual risk scores and subsequent treatment changes is difficult without (computer-based) decision support systems.
4. Involving patients and making patient-centered decisions is difficult without decision support aids.

Thus, a novel tool that is both accurate in predicting long-term outcomes and feasible to use in clinical practice to optimize pharmacotherapy remains urgently needed in order to maximize renal and cardiovascular prognosis of patients with type 2 diabetes.

The aim of this project is to design and test a decision support system based on a personalized multiple parameter efficacy (PRE) score that translates the short term response in multiple renal and cardiovascular risk markers into a predicted long term drug effect in type 2 diabetes care. Ultimately this should lead to a more personalized treatment approach and improved outcomes.

In this project a prospective study will be conducted to determine if the PRE score website (web app) can improve risk marker management for individual patients with type 2 diabetes. The presentation of the PRE score will be tailored to be informative for both primary care physicians as well as patients, and will include the following:

1. Personalized risk information by integrating drug effects on all relevant risk markers and translating this to long-term risk changes of renal and cardiovascular outcomes.
2. Tailor-made treatment guidance for individual patients indicating which guideline recommended targets (e.g. cholesterol) are not achieved, and recommendations for additional therapy, including possible risk reductions attained by optimal treatment.

Firstly, we will develop a version of the PRE score that is suitable for use in primary care, with the ability to predict long-term renal and cardiovascular outcomes for a diverse type 2 diabetes population. The PRE score will be tested and validated in independent datasets including the GIANTT study. Secondly, we will determine the feasibility of using the score in practice by performing qualitative and semi-quantitative studies. Thirdly, the PRE score will be pilot tested in primary care practice in a pragmatic trial design (comparing PRE score decision support guided therapy vs. standard of care).

Ad 1: The study will be performed by using the already prepared GIANTT database that consists of data from over 25.000 patients with type 2 diabetes that are managed in primary care. The database contains individualized patient data on demographics, drug treatment, risk marker responses and clinical outcomes during long-term follow-up. Data from the following risk markers will be included in the PRE score: systolic blood pressure, albuminuria, potassium, cholesterol, body weight, HbA1c. From the databases we will use all patients that initiated either treatment with ACEi/ARBs, metformin, or statins starting from 2007 onwards. We will measure change in the markers and assess if we are more accurate in predicting outcomes than single marker changes.
Ad 2: In this study we will determine the acceptability and usefulness of the validated PRE score in combination with treatment decision support for primary care. This will be done by performing a mixed method approach of semi-qualitative and quantitative methods in small groups of primary care physicians and practice assistants.
Ad 3: A pragmatic controlled pilot study will be performed comparing the PRE score with care as usual. The population consists of primary care practices in the northern part of the Netherlands, with possible extension to other parts of the Netherlands. Outcomes are tailoring of treatment and risk factor control.

1. Introduction and review on decision support tools and personalized medicine
2. GIANTT retrospective analysis on PRE score prediction in clinical practice
3. Focus report on applicability and usefulness of PRE score in practice
4. Results of pragmatic controlled study on PRE score
5. TBD
6. Conclusions and future perspective

This project addresses Precision Medicine in its purest form. It deals with the variable drug effects in individual patients, aims to optimize treatment based on the individual patient response and integrates a patient centered approach taking involving the patient’s voice in decision making.

The project thus addresses the 4-Ps (Prevention, Prediction, Personalized and Patient) of precision medicine:

1. Prevention: the project focuses on prevention of cardiovascular disease
2. Prediction: the project aims to validate a novel prediction algorithm in clinical practice
3. Personalized: The algorithm takes into account the individual drug response
4. Patient: The decision support tool is patient centered

This project links to the research program intended by Prof. P Denig focusing on how to involve patient and physician in implementing personalized therapy approaches. In addition, it also links to the project of Prof. Hillege and Mol which is focused on how to interpret drug efficacy for regulatory approaches based on multiple effects of single drugs.