Approaches to predict future type 2 diabetes mellitus and chronic kidney disease: A scoping review

Demographic change and changing lifestyles are leading to a steady increase in so-called population diseases such as type 2 diabetes mellitus and chronic kidney disease. Both conditions are often preceded by a latency period during which lifestyle changes and/or medications have the potential to delay or even prevent disease onset. Thus, detection of those at an increased risk of these diseases is of great importance. A scoping review was conducted to collate different prediction approaches for type 2 diabetes mellitus and chronic kidney disease.

Literature searches were performed in PubMed, Embase, Web of Science, and Google Scholar. A stepwise approach was used, consisting of searches for systematic reviews and primary literature, and additional Google searches for novel approaches. Included was literature that (1) presented an approach for risk prediction of incident type 2 diabetes mellitus or chronic kidney disease, (2) contained information on the risk factors considered and application, (3) targeted the general population, (4) was written in English or German language, and (5) for which an abstract and full-text was available. Literature screening was carried out by two persons independently.

Studies extracted literature from 1940 to 2023. Prediction approaches were included from 25 literature reviews, eight primary studies and nine studies found in additional searches. Several different approaches were identified, including methods based on clinical parameters, biological parameters (blood, urine, microbiome, genetics), the combinations of those, sequential approaches, and exposure and lifestyle factors. Most of the identified approaches were risk surveys that usually ask for simple and readily available parameters. Novel approaches cover transdermal optical imaging, prediction based on facial blood flow and using deoxyribonucleic acid methylation data.

This scoping review provides an overview of different tools for the risk prediction of type 2 diabetes mellitus and chronic kidney disease. In addition to established tools, which are primarily risk surveys, innovative approaches have been developed and evaluated in recent years in which the potential of machine learning is utilized. As cardio-renal-metabolic diseases share predicting factors and given the social and economic importance of these diseases, approaches that address multiple relevant diseases such as type 2 diabetes mellitus, chronic kidney disease and cardiovascular disease can be of great interest, especially in time- and resource-constrained healthcare settings.