The effect of sodium glucose Co-transport 2 inhibitors on cognitive impairment and depression in type 2 diabetes mellitus patients
Bibliographic record
Abstract
Background and aim Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder often associated with thyroid dysfunction. Sodium-glucose co-transport 2 (SGLT2) inhibitors, a novel class of medications for T2DM, have shown potential effects on various physiological systems, including the central nervous system (CNS). This study aimed to investigate the impact of SGLT2 inhibitors on cognition and depression in T2DM patients and its potential association with thyroid dysfunction. Methods A case-control observational study was conducted involving 138 participants, including T2DM cases and healthy controls. Data on demographics, anthropometric measures, metabolic parameters, thyroid function, depression (Physical Health questionnaire- 9), and cognition (Montreal Cognitive Assessment questionnaire) were collected. Subgroup analysis compared different SGLT2 inhibitors. Results T2DM patients taking SGLT2 inhibitors exhibited significantly higher levels of thyroid-stimulating hormone (TSH) compared to controls (p < 0.001). Additionally, T2DM patients on SGLT2 inhibitors showed a higher prevalence of mild to moderate depression (p < 0.001, odds ratio = 1.74) and cognitive impairment (p = 0.039, odds ratio = 1.32) compared to controls. Subgroup analysis revealed varying effects among different SGLT2 inhibitors on depression and cognitive function. Conclusion SGLT2 inhibitors in T2DM patients may have unexpected consequences on thyroid function, depression, and cognition. These findings highlight the need for comprehensive patient care, including mental health assessment, when prescribing SGLT2 inhibitors. Further research is warranted to elucidate the underlying mechanisms and optimize the management of T2DM patients using these medications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".