BDNF Levels and Cognitive Function in Patients with Type 2 Diabetes Treated with SGLT2 Inhibitors
Bibliographic record
Abstract
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 Z Ama: Kognitif bozukluk tip 2 diyabetes mellitusun (T2DM) bir komplikasyonu olarak giderek daha fazla kabul grmekte ve literatrde antihiperglisemik tedavinin bilisel ilevleri de iyiletirebilecei tartlmaktadr.Bu konudaki zellikle sodyumglukoz-kotransporter-2 (SGLT2) inhibitrleri ile ilgili klinik aratrmalar snrldr.Beyin-kaynakl nrotrofik faktr (BDNF), kognitif ilevler ve glukoz metabolizmas iin nemli bir proteindir.Aratrmamzn amac SGLT2 inhibitr kullananlarda kognitif performans ve BDNF dzeylerini incelemektir.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".