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