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Record W3087559768 · doi:10.1371/journal.pmed.1003316

Age at diagnosis, glycemic trajectories, and responses to oral glucose-lowering drugs in type 2 diabetes in Hong Kong: A population-based observational study

2020· article· en· W3087559768 on OpenAlexafffund
Calvin Ke, Thérèse A. Stukel, Baiju R. Shah, Eric S. H. Lau, Ronald C.W., Wing‐Yee So, Alice P.S. Kong, Elaine Chow, Juliana C.N. Chan, Andrea O. Y. Luk

Bibliographic record

VenuePLoS Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanadian Society of Endocrinology and MetabolismHospital AuthorityRoyal College of Physicians and Surgeons of Canada
KeywordsMedicineGlycemicType 2 diabetesPopulationCohortDiabetes mellitusInternal medicineConfidence intervalAge of onsetCohort studyPediatricsEndocrinologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Lifetime glycemic exposure and its relationship with age at diagnosis in type 2 diabetes (T2D) are unknown. Pharmacologic glycemic management strategies for young-onset T2D (age at diagnosis <40 years) are poorly defined. We studied how age at diagnosis affects glycemic exposure, glycemic deterioration, and responses to oral glucose-lowering drugs (OGLDs). METHODS AND FINDINGS: In a population-based cohort (n = 328,199; 47.2% women; mean age 34.6 and 59.3 years, respectively, for young-onset and usual-onset [age at diagnosis ≥40 years] T2D; 2002-2016), we used linear mixed-effects models to estimate the association between age at diagnosis and A1C slope (glycemic deterioration) and tested for an interaction between age at diagnosis and responses to various combinations of OGLDs during the first decade after diagnosis. In a register-based cohort (n = 21,016; 47.1% women; mean age 43.8 and 58.9 years, respectively, for young- and usual-onset T2D; 2000-2015), we estimated the glycemic exposure from diagnosis until age 75 years. People with young-onset T2D had a higher mean A1C (8.0% [standard deviation 0.15%]) versus usual-onset T2D (7.6% [0.03%]) throughout the life span (p < 0.001). The cumulative glycemic exposure was >3 times higher for young-onset versus usual-onset T2D (41.0 [95% confidence interval 39.1-42.8] versus 12.1 [11.8-12.3] A1C-years [1 A1C-year = 1 year with 8% average A1C]). Younger age at diagnosis was associated with faster glycemic deterioration (A1C slope over time +0.08% [0.078-0.084%] per year for age at diagnosis 20 years versus +0.02% [0.016-0.018%] per year for age at diagnosis 50 years; p-value for interaction <0.001). Age at diagnosis ≥60 years was associated with glycemic improvement (-0.004% [-0.005 to -0.004%] and -0.02% [-0.027 to -0.0244%] per year for ages 60 and 70 years at diagnosis, respectively; p-value for interaction <0.001). Responses to OGLDs differed by age at diagnosis (p-value for interaction <0.001). Those with young-onset T2D had smaller A1C decrements for metformin-based combinations versus usual-onset T2D (metformin alone: young-onset -0.15% [-0.105 to -0.080%], usual-onset -0.17% [-0.179 to -0.169%]; metformin, sulfonylurea, and dipeptidyl peptidase-4 inhibitor: young-onset -0.44% [-0.476 to -0.405%], usual-onset -0.48% [-0.498 to -0.459%]; metformin and α-glucosidase inhibitor: young-onset -0.40% [-0.660 to -0.144%], usual-onset -0.25% [-0.420 to -0.077%]) but greater responses to other combinations containing sulfonylureas (sulfonylurea alone: young-onset -0.08% [-0.099 to -0.065%], usual-onset +0.06% [+0.059 to +0.072%]; sulfonylurea and α-glucosidase inhibitor: young-onset -0.10% [-0.266 to 0.064%], usual-onset: 0.25% [+0.196% to +0.312%]). Limitations include possible residual confounding and unknown generalizability outside Hong Kong. CONCLUSIONS: In this study, we observed excess glycemic exposure and rapid glycemic deterioration in young-onset T2D, indicating that improved treatment strategies are needed in this setting. The differential responses to OGLDs between young- and usual-onset T2D suggest that better disease classification could guide personalized therapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.299
Teacher spread0.224 · 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 teacher head, 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

Citations63
Published2020
Admission routes2
Has abstractyes

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