Introduction: Type 1 diabetes (T1D) increases risk for cardiovascular disease (CVD) at least 2-4 fold, with only half the excess risk explained by known risk factors. We sought to identify novel proteomic, lipidomic, and metabolomic biomarkers of CVD in T1D.

Methods: We first built a predictive model of coronary artery calcium progression (CACp) using LASSO regression of clinical variables measured at baseline in 365 participants with T1D in the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study. The selected model was refit to a subgroup of 102 participants with complete clinical information and measured omics markers (targeted and untargeted metabolomics, global proteomics, glycated proteomics, and lipidomics) . Omics biomarkers were identified for inclusion in the final model by retaining the top markers that predicted CACp by moderated t-tests and sPLS-DA models.

Results: The table shows variables retained in the clinical model (AUC=0.91) and in the model with biomarkers added (AUC=0.94) . Higher age, urine albumin:creatinine, and insulin dose predicted CACp in both models. Longer duration of T1D predicted CACp in the clinical model. In the model with D-Glucosamine/D-galactosamine and glycated fibrinogen peptides, D-Glucosamine/D-Galactosamine predicted CACp.

Conclusion: Novel metabolites improved prediction of CACp in people with T1D.

Disclosure

L.Pyle: None. T.B.Vigers: None. R.K.Johnson: None. Q.Zhang: None. J.K.Snell-bergeon: Stock/Shareholder; GlaxoSmithKline plc.

Funding

American Heart Association (17CSA33570025) , NIH (P30-DK116073)

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at http://www.diabetesjournals.org/content/license.