434-P: Urinary Proteome Classifiers in Prediabetes Clusters
Bibliographic record
Abstract
Objective: Patients with prediabetes have recently been classified into 6 prediabetes clusters, that stratify the risk of diabetes and complications. Cluster 1, 2 and 4 are low risk-clusters while cluster 3, 5 and 6 are high-risk clusters. Of notice, especially patients of cluster 6 have an elevated risk of nephropathy despite their low diabetes risk. To investigate if differences across clusters can be captured from urine peptidome, we studied differences in pre-specified urinary peptidome classifiers across age and sex matched participants from 6 prediabetes clusters (n=249, median age 46 (IQR 39 - 54) years, BMI 30.4 (26.9 - 39.4) kg/m², HbA1c 5.5 (5.3 - 5.8) %). Methods: Peptides in spot urine samples collected at the University Hospital Tuebingen from 11/2004 to 11/2012 were investigated using capillary electrophoresis coupled mass spectroscopy. The following classifiers based on multiple urine peptides had been developed to detect different conditions and were assessed: CKD273 (chronic kidney disease, CKD), validated in a large clinical cohort), HF1 and HF2 (heart failure), CAD238 (coronary artery disease, CAD), Col_death (collagen type-1 turnover), solid_tumor (solid tumor). Matsuda index and NEFA insulin sensitivity index (ISI) were used for estimation of insulin sensitivity. Results: The urinary peptidome classifiers CKD273, HF2 and CAD238 were significantly different between prediabetes clusters, with elevated values in cluster 6 compared to the healthiest cluster 2. These classifiers also correlated with Matsuda index and NEFA ISI. Classifiers HF1, Col_death and solid_tumor did not associate with prediabetes clusters or insulin sensitivity indices. Conclusions: Beyond corroboration of a previously described increased risk of CKD, urinary peptidome classifiers suggest elevated risk of heart failure and coronary artery disease in persons of the prediabetes cluster 6. This could underlie increased mortality in this group, independent of diabetes development. Disclosure A.Schork: None. A.L.Birkenfeld: None. R.Wagner: Advisory Panel; Daiichi Sankyo, Speaker's Bureau; Novo Nordisk, Sanofi. A.Fritsche: Advisory Panel; Novo Nordisk, Lilly, Sanofi, Boehringer-Ingelheim, Speaker's Bureau; AstraZeneca, SYNLAB Holding Deutschland GmbH. E.D.Schleicher: None. A.Peter: None. M.Heni: Advisory Panel; Boehringer-Ingelheim, Sanofi, Research Support; Boehringer Ingelheim Inc., Speaker's Bureau; Lilly, Bayer Inc., Sanofi, Boehringer-Ingelheim, Novo Nordisk, Amryt Pharma Plc. N.Stefan: Advisory Panel; Pfizer Inc., Research Support; Sanofi, Speaker's Bureau; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Lilly Diabetes, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. R.Jumpertz von schwartzenberg: Other Relationship; Sanofi, Amgen Inc., Lilly, Novo Nordisk. H.Mischak: Stock/Shareholder; Mosaiques Diagnostics. J.Siwy: Employee; Mosaiques-Diagnostics GmbH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".