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Record W3112956508 · doi:10.1002/alz.041312

Relationships between oral hypoglycemic drugs and memory decline in people with type 2 diabetes: A stratified longitudinal observational study

2020· article· en· W3112956508 on OpenAlexaff
Che‐Yuan Wu, Michael Ouk, Yuen Yan Wong, Natasha Z. Anita, Jodi D. Edwards, Pearl Yang, Baiju R. Shah, Moira K. Kapral, Nathan Herrmann, Krista L. Lanctôt, Bradley J. MacIntosh, Jennifer S. Rabin, Sandra E. Black, Walter Swardfager

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSunnybrook HospitalHealth Sciences CentreHeart and Stroke FoundationToronto Rehabilitation InstituteSunnybrook Health Science CentreUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsMedicineConfoundingType 2 diabetesDementiaDiabetes mellitusInternal medicinePoisson regressionMetforminThiazolidinedioneCognitive declineDemographyDiseaseEndocrinologyPopulation

Abstract

fetched live from OpenAlex

Abstract Background There is increased recognition that diabetes can elevate Alzheimer’s disease (AD) risk and exacerbate memory decline. However, few studies have compared memory decline among patients with diabetes using different oral hypoglycemic drug classes. Method Participants using any hypoglycemic medications from 2005 to 2019 were identified from the National Alzheimer's Coordinating Center database. Analyses were conducted separately in elderly with normal cognition (NC), amnestic mild cognitive impairment (aMCI), and dementia due to AD. Drug classes of interest included metformin, sulfonylureas, thiazolidinedione and dipeptidyl peptidase‐4 inhibitors (gliptins). Immediate and delayed recall outcomes were assessed using the Wechsler Memory Scale–Logical Memory test. Linear mixed‐effects models (in NC and aMCI) and mixed‐effects zero‐inflated Poisson or quasi‐Poisson regressions (in AD) were used to examine the associations between drug classes (time‐varying exposures) and memory performance over time. Confounding by indications were corrected by inverse probability treatment weighting. Models were adjusted for covariates including demographics, baseline MMSE, comorbidities and concurrent medications. In post‐hoc analyses, we examined whether ApoE genotype moderated the associations between drug class and memory. Result In NC (n=1,201, follow‐up years=3.43±3.27), metformin use was associated with better immediate (β=0.069 [0.011, 0.12]) and delayed (β=0.089 [0.032, 0.15]) memory over time, relative to no metformin use. No oral hypoglycemic drug class exhibited significant associations with memory in aMCI (n=671, follow‐up years=1.50±2.13). In AD (n=807, follow‐up years=1.90±2.21), DPP4 inhibitor use was associated with slower rate of decline in delayed memory (RR=1.22 [1.06, 1.40]), and thiazolidinedione use was associated with a faster rate of decline in immediate memory (RR=0.89 [0.82, 0.97]). In NC, DPP4 inhibitor use was associated with better delayed memory over time specifically among ApoE ε4 carriers (interaction: β=0.038 [0.0039, 0.072]). In AD, ApoE ε4 did not significantly interact with DPP4 inhibitor use, and thiazolidinedione use was associated with a faster rate of decline in immediate memory among non‐ε4 carriers (interaction: RR=1.26 [1.07, 1.47]). Conclusion Associations between memory and particular oral hypoglycemic medications may depend on the presence of AD symptoms. ApoE ε4 may further modify these relationships. Confirmatory analyses in other cohorts, particularly those with more complete clinical data, are required.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.124
GPT teacher head0.303
Teacher spread0.179 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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