MétaCan
Menu
Back to cohort
Record W4281658340 · doi:10.2337/db22-1102-p

1102-P: Identifying Blood Biomarkers for Type 2 Diabetes Subtyping: A Report from the ORIGIN Trial

2022· article· en· W4281658340 on OpenAlexaffabout
Marie Pigeyre, Hertzel C. Gerstein, Leif Groop, Sibylle Hess, Guillaume Paré

Bibliographic record

VenueDiabetes · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsMedicineInternal medicineSubtypingLogistic regressionDiabetes mellitusType 2 diabetesPopulationBiomarkerOncologyEndocrinologyBiologyGenetics

Abstract

fetched live from OpenAlex

Diabetes (DM) can be classified into 5 subtypes characterized by distinct progression in dysglycaemia and complications. Using 5 clinical variables, we categorized 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into 1/auto-immune DM (n=241) , 2/insulin-deficient DM (n=1594) , 3/insulin-resistant DM (n=914) , 4/obesity-related DM (n=1595) , 5/age-related DM (n=2673) .Yet, whether blood biomarkers are associated with these subtypes is unknown. Forward-selection logistic regression models were used to identify biomarkers that were each independent determinant of one cluster versus the others, among 233 selected cardiometabolic proteins measured at baseline. Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration. A total of 13, 2, 7 and biomarkers were independent determinants of DM subtypes 2 to 5 respectively (all P<4.3x10-5) . A combination of 5 biomarkers that were distinctively associated with clusters (fig) , showed a diagnosis performance, through AUC-ROC curves, of 0.71, 0.86, 0.88, 0.82 to respectively distinguish cluster 2 to 5 from the others. No biomarkers other than GAD antibodies were determinants of cluster 1. We identified 5 serum biomarkers, as independent determinants of DM subtypes, that could be used as a diagnosis test for DM subtyping. Although this requires further validation in an independent population. Disclosure M.Pigeyre: n/a. H.C.Gerstein: Advisory Panel; Abbott, Eli Lilly and Company, Hanmi Pharm. Co., Ltd., Novo Nordisk, Pfizer Inc., Sanofi, Viatris Inc., Consultant; Kowa Company, Ltd., Other Relationship; DKSH, Eli Lilly and Company, Sanofi, Zuellig Pharma Holdings Pte. Ltd., Research Support; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Sanofi. L.Groop: None. S.Hess: Employee; Sanofi. G.Pare: Advisory Panel; Amgen Inc., Bayer AG, Sanofi, Research Support; Bayer AG. Funding NCT00069784 Canadian Institute of Health Research Sanofi

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.054
GPT teacher head0.308
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueDiabetesSame topicDiabetes Treatment and ManagementFrench-language works237,207