MétaCan
Menu
Back to cohort
Record W2948844053 · doi:10.2337/db19-1630-p

1630-P: Glycemic Burden and Treatment Effectiveness in Young-Onset Type 2 Diabetes

2019· article· en· W2948844053 on OpenAlexaboutno aff
Calvin Ke, Andrea O. Y. Luk, Baiju R. Shah, Thérèse A. Stukel, Eric S. H. Lau, Ronald C.W., Alice P.S. Kong, Elaine Chow, Juliana C.N. Chan

Bibliographic record

VenueDiabetes · 2019
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlycemicType 2 diabetesInsulinDiabetes mellitusInternal medicinePopulationCohortAge of onsetCohort studyPediatricsEndocrinologyDisease

Abstract

fetched live from OpenAlex

Objective: To examine how T2D onset age affects A1C trajectory and A1C lowering with insulin and other drugs. Methods: We conducted a cohort study (2000-16) in adults aged 18-75 years (y) with young-onset T2D (YOD; onset age <40 y) and usual-onset T2D (UOD; onset age ≥40 y). In the Hong Kong Diabetes Registry (n=21,016), we estimated the mean glycemic burden (area under curve of A1C over time) across the lifespan, stratifying by onset age. In the population-based Hong Kong Diabetes Surveillance Database (n=328,199), we examined the effect of onset age on A1C trajectory for incident T2D using linear mixed effects models, accounting for drugs using time-varying covariates. We followed for 10 y, censoring at death or insulin start. For those started on insulin (n=20,877), we used similar methods to examine the effect of time to insulin start on the post-insulin A1C trajectory. Results: Glycemic burden was tripled in YOD versus UOD (38.8 versus 11.8 A1C-y). After accounting for drugs, YOD was associated with a higher baseline A1C (7.6%) and slope (0.05%/y) versus UOD (7.4%, 0.00%/y). Non-insulin drugs lowered A1C similarly across onset ages. The post-insulin A1C remained elevated for YOD, regardless of time to insulin start, but improved significantly for UOD. Conclusions: YOD is associated with an aggressive A1C rise and excess glycemic burden despite insulin and other drugs. More effective strategies are needed to lower A1C in people with YOD. Disclosure C. Ke: None. A. Luk: None. B.R. Shah: None. T. Stukel: None. E.S. Lau: None. R.C. Ma: Advisory Panel; Self; Boehringer Ingelheim International GmbH. Research Support; Self; AstraZeneca, Bayer AG, Pfizer Inc. Stock/Shareholder; Self; GemVCare. Other Relationship; Self; AstraZeneca. A.P. Kong: Advisory Panel; Self; Lilly Diabetes. Research Support; Self; AstraZeneca, Lilly Diabetes. Speaker's Bureau; Self; Abbott. Other Relationship; Self; AstraZeneca, Novartis Pharmaceuticals Corporation, Sanofi. E. Chow: Research Support; Self; Powder Pharmaceuticals Inc., Sanofi-Aventis. J.C. Chan: Board Member; Self; Asia Diabetes Foundation. Consultant; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Medtronic, Merck Sharp & Dohme Corp., Sanofi-Aventis. Research Support; Self; Amgen Inc., AstraZeneca, Lee Powder, Lilly Diabetes, Pfizer Inc., Sanofi-Aventis. Speaker's Bureau; Self; Ascensia Diabetes Care. Stock/Shareholder; Self; GemVCare. Funding University of Toronto; Royal College of Physicians and Surgeons of Canada; Canadian Society of Endocrinology and Metabolism; Chinese University of Hong Kong; Asia Diabetes Foundation

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)0.003
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueDiabetesSame topicDiet and metabolism studiesFrench-language works237,207