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BDNF Levels and Cognitive Function in Patients with Type 2 Diabetes Treated with SGLT2 Inhibitors

2025· article· en· W4406847741 on OpenAlexaboutno aff
Betül Sümbül Şekerci, Şeymanur Demirhan, Abdüsselam Şekerci

Bibliographic record

VenueBezmialem Science · 2025
Typearticle
Languageen
FieldMedicine
TopicApelin-related biomedical research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineType 2 diabetesCognitionDiabetes mellitusEndocrinologyPsychiatry

Abstract

fetched live from OpenAlex

Objective: As cognitive impairment becoming more widely recognized as a complication of type 2 diabetes mellitus (T2DM), it is discussed in the literature that antihyperglycemic treatment may also improve cognitive functions.Clinical research on this topic is particularly limited regarding sodium-glucose-cotransporter-2 (SGLT2) inhibitors.Brain-derived neurotrophic factor (BDNF) is a protein essential for cognitive functions and glucose metabolism.The aim of our research was to examine cognitive performance and BDNF levels in users of SGLT2 inhibitors.Methods: This cross-sectional study was conducted with 86 patients with T2DM, including 41 patients using metformin and 45 patients using SGLT2 inhibitors.Patients' cognitive performance was assessed with the Montreal cognitive assessment (MoCA) test, and their serum BDNF levels were measured using the ELISA method.Results: There were no significant differences between SGLT2 inhibitor users and metformin users in MoCA total scores, as well as in the Visuospatial/Executive, Naming, Attention, Language, Abstraction, Memory, and Orientation subdomains.Although BDNF levels were relatively higher in the SGLT2 inhibitor group, the difference was not statistically significant.Significant correlations were observed between BDNF levels and the levels of microalbumin, microalbumin/creatinine, estimated glomerular ÖZ Amaç: Kognitif bozukluk tip 2 diyabetes mellitusun (T2DM) bir komplikasyonu olarak giderek daha fazla kabul görmekte ve literatürde antihiperglisemik tedavinin bilişsel işlevleri de iyileştirebileceği tartışılmaktadır.Bu konudaki özellikle sodyumglukoz-kotransporter-2 (SGLT2) inhibitörleri ile ilgili klinik araştırmalar sınırlıdır.Beyin-kaynaklı nörotrofik faktör (BDNF), kognitif işlevler ve glukoz metabolizması için önemli bir proteindir.Araştırmamızın amacı SGLT2 inhibitörü kullananlarda kognitif performans ve BDNF düzeylerini incelemektir.Yöntemler: Bu kesitsel çalışma, metformin kullanan 41 hasta ve SGLT2 inhibitörü kullanan 45 hasta olmak üzere toplam 86 T2DM hastası ile gerçekleştirilmiştir. Hastaların kognitif performansı Montreal bilişsel değerlendirme (MoCA) testi ile serum BDNF düzeyleri ELISA yöntemi kullanılarak ölçüldü.Bulgular: SGLT2 inhibitörü kullananlar ile metformin kullananlar arasında MoCA toplam skorlarında ve Vizuospasyal/Yürütücü, İsimlendirme, Dikkat, Dil, Soyutlama, Hafıza ve Oryantasyon alt alanlarında anlamlı bir fark bulunmadı.SGLT2 inhibitörü grubunda BDNF seviyeleri nispeten daha yüksek olmasına rağmen, fark istatistiksel olarak anlamlı değildi.BDNF düzeyleri ile mikroalbümin, mikroalbümin/kreatinin, glomerüler filtrasyon hızı (eGFR), lenfosit düzeyleri arasında anlamlı korelasyonlar gözlenmiştir.Lineer regresyon analizine göre, eGFR düzeylerinin BDNF düzeylerini öngörmedeki etkisi anlamlıdır.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.287
Teacher spread0.274 · 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".

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Citations1
Published2025
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