Gas chromatography-mass spectrometry-based metabolomics to identify molecular signatures of Type 1 Diabetes (T1D) in urine. We utilize three cohorts in different stages post-diagnosis: (1) new onset, (2) within one year of diagnosis and (3) after 6 years of diagnosis.
There were 91 metabolites identified in all three datasets with complete data represented in each cohort dataset.Cohort 1 (CNMC):
Cohort 2 (BDCD):
Cohort 3 (IUSOM):
Cohort | Group | Time Post-Dx | Age (std dev) | % Female |
---|---|---|---|---|
CNMC | Control (Sibling) | - | 11.56 (3.54) | 53.12 |
CNMC | T1D (Sibling) | 6 yrs | 11.94 (3.13) | 46.88 |
BDCD | Control (Sibling) | - | 11.44 (2.74) | 37.04 |
BDCD | T1D (Sibling) | 1 yrs | 10.37 (2.49) | 25.93 |
IUSOM | Control (Unrelated) | - | 12.1 (3.74) | 40.00 |
IUSOM | T1D (Unrelated) | 48 hrs | 10.75 (3.19) | 25.00 |
Owners: The Diabetes Autoimmunity Study in the Young
(DAISY), Barbara Davis Center for Diabetes (BDCD), Children’s National
Medical Center (CNMC), Indiana University School of Medicine
(IUSOM)
Objective: Predict new onset T1D based on available
metabolomic biomarkers, and evaluate model generalizability through
measuring cross-cohort predictive performance.
Model: Random Forest
Preprocessing: A near-zero variance filter was applied to the data prior to model fitting. No predictors were ultimately filtered. Metabolite ratios were additionally computed based on all unique pairings of the 91 measured metabolites for each cohort (4095 ratios per cohort).
Tuning: Grid-search
Final Models:
Data are accessible on DataHub. Code for data processing, model tuning, and final model fitting/evaluation is available on GitHub.
Note: Metabolites (or metabolite ratios) highlighted in red are those that indicated statistically significant differences at the 0.05 level for at least one cohort.