254-OR: Novel Biomarkers Predicting Renal Dysfunction in People with Dysglycemia in the ORIGIN Trial
Bibliographic record
Abstract
Diabetes is the most common cause of renal failure accounting for more than 50% of cases. Whereas HbA1c, age and albuminuria are independent risk factors for renal decline, adding novel serum biomarkers to routine clinical risk factors may help identify patients at high risk for renal decline. One ml of stored serum from 7,482 ORIGIN participants with a recorded eGFR at baseline and either the 2 year and/or final visit was assayed for 237 biomarkers using Myriad RBM’s customized human discovery multi-analyte profile 250+ panel, and hs troponin and anti-GAD biomarkers were measured locally. During a median follow-up of 6.2 years, 1,646 (22%) developed the renal outcome comprising renal failure, albuminuria regression or doubling of some creatinine. A linear mixed model using a forward selection approach was used to identify the subset of biomarkers that independently predicted a decline in eGFR after accounting for 9 clinical risk factors for renal decline including age, sex, smoking, body mass index, mean arterial pressure, log (albumin:creatinine), cholesterol, baseline eGFR and HbA1c. A Bonferroni-corrected P value for inclusion in the model of less than 0.00021 (i.e., 0.05/239) identified 13 significant and independent biomarkers that included: alpha 1 microglobulin (beta = -0.157); NT-pro BNP (beta = -0.081); IGF binding protein 4 (beta = -0.110); growth differentiation factor 15 (beta = -0.091); RAGE (beta = -0.052); myoglobin (beta = -0.073); growth regulated alpha protein (beta = 0.058); fibulin-1C (beta = -0.048); apolipoprotein A-4 (beta = -0.057); apolipoprotein A-2 (beta = 0.065); fas ligand (beta = -0.099); eotaxin E (beta = -0.052); and beta amyloid 1-40 (beta = -0.057). The addition of these selected biomarkers to the clinical variables improved the variance of the eGFR from 0.053 to 0.155 (difference in log likelihood = 849, P < 0.001). These novel biomarkers are potential therapeutic targets. They can also help identify dysglycemic people at risk for renal decline. (NCT00069784) Disclosure H. Gerstein: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Research Support; Self; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Sanofi. G. Pare: Consultant; Self; Amgen Inc., Bristol-Myers Squibb Company, Lexicomp, Sanofi. Research Support; Self; Canada Research Chair in Genetic and Molecular Epidemiology, CISCO Professorship in Integrated Health Biosystems, Sanofi. M.J. McQueen: None. S. Lee: None. A. Kannt: Employee; Self; Sanofi. S. Hess: None. Funding Sanofi; Innovative Medicines Initiative (115974); European Union; European Federation of Pharmaceutical Industries; JDRF
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".