Prediction models in nutritional epidemiology

Peer-reviewed | Manuscript received: October 30, 2012 | Revision accepted: April 29, 2012

With the German diabetes risk score (GDRS) as an example

Background

The aim of studies in the field of nutritional epidemiology is the identification of risk factors for specific diseases. The European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study is a good example of such a study, with cancer and chronic diseases as the main objectives [1]. The findings of such studies can particularly be transferred to disease prevention. Type 2 diabetes is a good example due to its relation to lifestyle and its increasing rate of newly diagnosed patients (incidence) [2].

The most important risk factors can also be used as the basis for a preferably accurate risk prediction; with the help of such risk predictions, people at particularly high disease risk can be identified. This was the rationale for the development of risk prediction models which became popular mainly in the area of cardiovascular diseases; examples are the Framingham risk score or the PROCAM score [3, 4]. For type 2 diabetes, more than 200 single prediction models were published since 1999 [5, 6]. One example of such a diabetes prediction model is the German diabetes risk score (GDRS) [7]. With the use of (statistical) regression analysis, prediction models allow the calculation of individual risks based on a specific risk profile. In general, these risks are related to a time horizon of 5, 8 or 10 years in the future.

In the following basic methodological requirements and methods will be discussed which are meaningful for the development or derivation of a prediction model as well as for their evaluation. For illustrative purposes, the derivation of the GDRS will be described in more detail and how this prediction model can be evaluated.

Summary

Research in nutritional epidemiology includes the identification of risk factors for specific diseases but also makes it possible to predict individual disease risks based on prediction models. The objectives or outcomes of such prediction models are mainly lifestyle related diseases such as cardiovascular diseases or type 2 diabetes mellitus; these diseases are particularly suitable for the initiation of preventive interventions. In this review article, the aims and methodological aspects of developing such a prediction model will be described as well as the evaluation of a prediction model based on the criteria sensitivity, specificity or area under the receiver-operating-characteristic (ROC) curve. For a better understanding, the development of the German diabetes risk score (GDRS) will be demonstrated with regard to the aforementioned criteria. For the illustration of clinical application of the GDRS, the online tool as well as the simplified paper version will be presented.

Keywords: nutritional epidemiology, risk prediction, prediction model, discrimination, calibration, German diabetes risk score

Full text PDF (free access)