Precision prognostics for the development of complications in diabetes

Abstract

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Keywords: Cardiovascular diseases; Complications in diabetes; Macrovascular complications; Microvascular complications; Personalised medicine; Precision medicine; Precision prognostics; Review; Risk prediction; Risk scores.

Fig. 1

Precision prognostics. Precision prognostics refers to the prognosis of diabetes complications by probabilistic models using information on individual demographic and biological factors (pre-existing complications, routine clinical information, pathological findings, genetics, non-routine [omics-] biomarkers), lifestyle, environment or context. This process allows calculation of an individual’s absolute complication risk, with severity indicated by colour (red, high risk; yellow, medium risk; green, low risk). This figure is available as part of a downloadable slideset

Fig. 2

Illustrative example of the distribution of absolute 10 year CVD risk estimated by the Pooled Cohort Equation (PCE) [22] in individuals without and with type 2 diabetes from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study (n = 25,993) [85]. The distribution of absolute risk of CVD is on average higher in individuals with diabetes compared with individuals without diabetes. While the prognostic model performs well in the full general population, performance within the subgroup of individuals with diabetes may be substantially lower. This figure is available as part of a downloadable slideset

Fig. 3

Novel biomarkers for prediction of nephropathy in diabetes. Evaluation of non-conventional blood or urinary biomarkers, either hypothesis-based candidates or from large-scale omics-based technologies, has resulted in several predictive biomarkers for nephropathy. Importantly, such biomarkers need to provide predictive information beyond classical risk factors (demographic and lifestyle factors, routine clinical parameters). BMP7, bone morphogenetic protein 7; KIM-1, kidney injury molecule-1. This figure is available as part of a downloadable slideset