Additional file 1 of Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study
Creators: name: Bragg, Fiona
name: Trichia, Eirini
name: Aguilar-Ramirez, Diego
name: Bešević, Jelena
name: Lewington, Sarah
name: Emberson, Jonathan
Publisher: name: figshare
Year: 2022
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Physiology
subject: FOS: Biological sciences
subject: Pharmacology
subject: Biotechnology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Cancer
subject: 60506 Virology
subject: Computational Biology

description: Additional file 1: Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study. Table S1. Distribution of metabolic biomarkers and their associations with incident type 2 diabetes among 50,519 participants in the association analyses population. Table S2. Diagnosis and medication codes for assessment of type 2 diabetes status in primary and secondary healthcare and death registry records and UK Biobank verbal interview. Table S3. Baseline characteristics of participants with and without NMR-metabolomics profiling. Table S4. Baseline characteristics of 50,519 participants in the association analyses population by incident type 2 diabetes status. Table S5. Associations of the first 11 metabolic biomarker principal components with risk of incident type 2 diabetes among 50,519 participants in the association analyses population. Table S6. Regression models for risk of incident type 2 diabetes among 65,684 participants in the risk prediction population. Table S7. Performance of risk prediction models for incident type 2 diabetes among 13,695 participants taking lipid-lowering medication at recruitment. Table S8. Performance of risk prediction models including waist-to-hip ratio for incident type 2 diabetes among 65,684 participants in the risk prediction population. Table S9. Performance of risk prediction models for incident type 2 diabetes incorporating co-variates, where relevant, as continuous variables among 65,684 participants in the risk prediction population. Fig. S1. Participant exclusions to derive risk prediction and association analyses populations. Fig. S2. Cross-correlations of metabolic biomarkers. Fig. S3. Associations of metabolic biomarkers with risk of incident type 2 diabetes among 50,519 participants in the association analyses population. Fig. S4. Associations of metabolic biomarkers with risk of incident type 2 diabetes by sex among 50,519 participants in the association analyses population. Fig. S5. Associations of metabolic biomarkers with risk of incident type 2 diabetes by age among 50,519 participants in the association analyses population. Fig. S6. Associations of metabolic biomarkers with risk of incident type 2 diabetes excluding the first three years of follow−up in the association analyses population. Fig. S7. Importance of the first 20 metabolic biomarker principal components. Fig. S8. Characterisation of the first 11 metabolic biomarker principal components among 65,684 participants in the risk prediction population. Fig. S9. Comparison of biomarkers measured by NMR and routine clinical chemistry assays among 65,684 participants in the risk prediction population.