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Record W4399684251 · doi:10.2337/db24-919-p

919-P: Identifying Clinical Phenotypes in Patients with Type 2 Diabetes Using Unsupervised Cluster Analysis—Insights from the CANVAS Program and CREDENCE Trial

2024· article· en· W4399684251 on OpenAlexaboutno aff
Amir Razaghizad, Jiayi Ni, Elite Possik, MICHAEL TSOUKAS, Thomas A. Mavrakanas, Thao Huynh, Walter Swardfager, Pedro Vieira‐Marques, Abhinav Sharma

Bibliographic record

VenueDiabetes · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCanagliflozinMedicinePhenotypeInternal medicineType 2 diabetesDiseaseDiabetes mellitusEndocrinologyGeneticsBiology

Abstract

fetched live from OpenAlex

Background: Limited understanding exists regarding phenotypic heterogeneity in type 2 diabetes (T2D) patients and its therapeutic impact. Methods: A pooled latent class analysis of the CANVAS Program and CREDENCE trial was conducted to identify T2D clinical phenotypes. The composite outcome of cardiovascular death (CVD) or hospitalization for heart failure (HHF) was analyzed and proportional hazards models assessed the effect of canagliflozin across phenotypes. Results: Four phenotypes were identified: Phenotype 1 (n=966, 6.6%) with the lowest prevalence of heart failure, kidney dysfunction, and hypertension; Phenotype 2 (n=4169, 28.7%), primarily females with high atherosclerotic cardiovascular disease (ASCVD) prevalence; Phenotype 3 (n=7108, 48.9%), predominately men with high ASCVD prevalence; and Phenotype 4 (n=2300, 15.8%) with the highest prevalence of heart failure and renal dysfunction. Differential risk for CVD or HHF across phenotypes was observed, with the lowest risk in Phenotype 1 and the highest risk in Phenotype 4 (HR 5.21 [95% CI: 3.69-7.36]; Fig 1A and B). Canagliflozin significantly reduced CVD or HHF across phenotypes (P-interaction > 0.05). Conclusion: This study identified four distinct T2D phenotypes with differential CV risk. Canagliflozin significantly reduced the risk of CVD or HHF across phenotypes. Disclosure A. Razaghizad: None. J. Ni: None. E. Possik: None. M. Tsoukas: Speaker's Bureau; Novo Nordisk, Eli Lilly and Company, Boehringer-Ingelheim, Janssen Pharmaceuticals, Inc., AstraZeneca, Sanofi, Bausch Health, Abbott. T.A. Mavrakanas: Research Support; AstraZeneca. Other Relationship; AstraZeneca, Janssen Pharmaceuticals, Inc., Pfizer Inc. Advisory Panel; Bayer Inc. Other Relationship; Bayer Inc. Advisory Panel; Servier Laboratories. Other Relationship; Boehringer-Ingelheim. Advisory Panel; Boehringer-Ingelheim, GlaxoSmithKline plc. T. Huynh: Research Support; Pfizer Inc., Merck & Co., Inc., Bayer Inc. Speaker's Bureau; GlaxoSmithKline plc. W. Swardfager: None. P. Marques: Other Relationship; Janssen Pharmaceuticals, Inc. A. Sharma: Other Relationship; Boehringer-Ingelheim. Research Support; Janssen Pharmaceuticals, Inc. Other Relationship; Novo Nordisk. Advisory Panel; Novartis Canada. Other Relationship; AstraZeneca. Advisory Panel; Servier Laboratories, Takeda Pharmaceutical Company Limited. Research Support; Boston Scientific Corporation, Medtronic, Amgen Inc.

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)0.007
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
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.320
Teacher spread0.281 · 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
Published2024
Admission routes1
Has abstractyes

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