All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease

Abstract

Aims The rate of all-cause mortality is twofold higher in type 2 diabetes than in the general population. Being able to identify patients with the highest risk from the very beginning of the disease would help tackle this burden. Methods We tested whether ENFORCE, an established prediction model of all-cause mortality in type 2 diabetes, performs well also in two independent samples of patients with early-stage disease prospectively followed up. Results ENFORCE’s survival C-statistic was 0.81 (95%CI: 0.72–0.89) and 0.78 (95%CI: 0.68–0.87) in both samples. Calibration was also good. Very similar results were obtained with RECODe, an alternative prediction model of all-cause mortality in type 2 diabetes. Conclusions In conclusion, our data show that two well-established prediction models of all-cause mortality in type 2 diabetes can also be successfully applied in the early stage of the disease, thus becoming powerful tools for educated and timely prevention strategies for high-risk patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00592-021-01746-2.

Validation in type 2 diabetes of a metabolomic signature of all‐cause mortality

“…8 The 5year all-cause mortality prediction accuracy of the 14-MS was measured by the survival C-statistic. 20 To examine whether a reweighted (using weights estimated in our study by Cox regression model), parsimonious 12-MS increases the prediction accuracy of all-cause mortality in type 2 diabetes, two different, well-established models (RECODe) 12,13 and ENFORCE [9][10][11] were used. Predictors included in the two models are reported in the supplementary Table S1.…”

“…We aimed at investigating whether the association with and the ability to predict all-cause mortality in the general population of a 14-metabolite score (14-MS), 8 also known as MetaboHealth, is confirmed in type 2 diabetes. We also investigated whether these metabolites can be used to improve well-established and wellperforming mortality prediction models of all-cause mortality in type 2 diabetes: Estimation of Mortality Risk in Type 2 Diabetic Patients (ENFORCE), an user-friendly and freely available risk 9variable algorithm, which has been validated in several different context, 7,[9][10][11] and Risk Equations for Complications of Type 2 Diabetes (RECODe), a 14-variable algorithm also validated in many distinct cohorts derived from both trial and population-based studies. 12,13 To this aim, the Nightingale Health's metabolomics technology, which is widely exploited in people with type 2 diabetes, [14][15][16] was used.…”