Novel type 2 diabetes prediction score based on traditional risk factors and circulating metabolites: model derivation and validation in two large cohort studies

Abstract

Background: We aimed to evaluate the incremental predictive value of metabolomic biomarkers for assessing the 10-year risk of type 2 diabetes when added to the clinical Cambridge Diabetes Risk Score (CDRS).

Methods: We utilized 86,232 UK Biobank (UKB) participants (recruited between 13 March 2006 and 1 October 2010) for model derivation and internal validation. Additionally, we included 4383 participants from the German ESTHER cohort (recruited between 1 July 2000 and 30 June 2002 for external validation). Participants were followed up for 10 years to assess the incidence of type 2 diabetes. A total of 249 NMR-derived metabolites were quantified using nuclear magnetic resonance (NMR) spectroscopy. Metabolites were selected with LASSO regression and model performance was evaluated with Harrell's C-index.

Findings: 11 metabolomic biomarkers, including glycolysis related metabolites, ketone bodies, amino acids, and lipids, were selected. In internal validation within the UKB, adding these metabolites significantly increased the C-index (95% confidence interval (95% CI)) of the clinical CDRS from 0.815 (0.800, 0.829) to 0.834 (0.820, 0.847) and the continuous net reclassification index (NRI) with 95% CI was 39.8% (34.6%, 45.0%). External validation in the ESTHER cohort showed a comparable statistically significant C-index increase from 0.770 (0.750, 0.791) to 0.798 (0.779, 0.817) and a continuous NRI of 33.8% (26.4%, 41.2%). A concise model with 4 instead of 11 metabolites yielded similar results.

Interpretation: Adding 11 metabolites to the clinical CDRS led to a novel type 2 diabetes prediction model, we called UK Biobank Diabetes Risk Score (UKB-DRS), substantially outperformed the clinical CDRS. The concise version with 4 metabolites performed comparably. As only very few clinical information and a blood sample are needed for the UKB-DRS, and as high-throughput NMR metabolomics are becoming increasingly available at low costs, these models have considerable potential for routine clinical application in diabetes risk assessment.

Funding: The ESTHER study was funded by grants from the Baden-Württemberg state Ministry of Science, Research and Arts (Stuttgart, Germany), the Federal Ministry of Education and Research (Berlin, Germany), the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany), and the Saarland State Ministry of Health, Social Affairs, Women and the Family (Saarbrücken, Germany). The UK Biobank project was established through collaboration between various entities including the Wellcome Trust, the Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. Additional funding was provided by the Welsh Assembly Government, British Heart Foundation, Cancer Research UK, and Diabetes UK, with support from the National Health Service (NHS). The German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Culture and Science of the state North Rhine-Westphalia (Düsseldorf, Germany) and receives additional funding from the German Federal Ministry of Education and Research (BMBF) through the German Center for Diabetes Research (DZD e.V.).

Keywords: Metabolite; Metabolomics; Prediction model; Risk score; Type 2 diabetes.

Fig. 1

Flow-charts for participant inclusion and exclusion.

Fig. 2

Correlation matrix of Pearson correlation coefficients for the 11 selected metabolites. Abbreviations: IDL-CE-pct, cholesteryl esters to total lipids in IDL percentage; LA-pct, linoleic acid to total fatty acids percentage; M-LDL-TG-pct, triglycerides to total lipids in medium LDL percentage.

Fig. 3

Associations between selected metabolites and incident type 2 diabetes in the test set (30% of UK Biobank, N = 25,870) and external validation set (ESTHER, N = 5904). Notes: Hazard ratios are expressed per 1 standard deviation of the respective metabolite concentration and are adjusted for the variables of the clinical CDRS. The exact HRs with 95% CIs and P-values per SD increments and the SDs of the metabolites are shown in Supplemental Table S3. Abbreviations: CI, confidence interval; IDL-CE-pct, cholesteryl esters to total lipids in IDL percentage; LA-pct, linoleic acid to total fatty acids percentage; M-LDL-TG-pct, triglycerides to total lipids in medium LDL percentage; SD, standard deviation.

Fig. 4

ROC curves with 95% confidence interval bands for the UKB-DRS, concise UKB-DRS, and clinical CDRS in internal validation (30% UK Biobank, N = 25,870) and external validation (ESTHER, N = 4383). Abbreviations: ROC, receiver operating characteristic; CDRS, Cambridge Diabetes Risk Score; UKB-DRS, UK Biobank Diabetes Risk Score.

Fig. 5

Calibration plots for the UKB-DRS, Concise UKB-DRS, and Clinical CDRS in internal validation (30% UK Biobank, N = 25,870) and external validation (ESTHER, N = 4383). Notes: Confidence intervals are represented by shaded regions for each model. Abbreviations: CDRS, Cambridge Diabetes Risk Score; UKB-DRS, UK Biobank Diabetes Risk Score.

Fig. 6

Subgroup analyses comparing the C-index of the UKB-DRS and the clinical CDRS in internal validation (30% UK Biobank, N = 25,870) and external validation (ESTHER, N = 4383). Notes: Middle-aged is defined as <65 years and older age as ≥65 years. Obesity is defined as a BMI ≥ 30 kg/m². Abbreviations: CDRS, Cambridge Diabetes Risk Score; UKB-DRS, UK Biobank Diabetes Risk Score; CI, confidence interval.

Fig. 7

The incremental discrimination of each metabolite for the model after the selected metabolites were added to the clinical Cambridge Diabetes Risk Score in the test set (30% of UK Biobank, N = 25,870) and external validation set (ESTHER, N = 4383). Abbreviations: CI, confidence interval; IDL-CE-pct, cholesteryl esters to total lipids in IDL percentage; LA-pct, linoleic acid to total fatty acids percentage; M-LDL-TG-pct, triglycerides to total lipids in medium LDL percentage.