Association of antidiabetic medications with psychiatric disorders in patients with type 2 diabetes: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Antihyperglycemic medications can affect more than just glucose control; they may also influence mental health outcomes. This study aimed to investigate the relationships between various antidiabetic medications and psychiatric disorders, including depression, anxiety, and sleep disturbances, in individuals with type 2 diabetes (T2DM). METHODS: This cross-sectional study analyzed data from patients with T2DM, assessing psychiatric outcomes among these patients using various antidiabetic therapies. Sleep quality, anxiety, and depression were measured using validated scales, including the Pittsburgh Sleep Quality Index (PSQI), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9), respectively. RESULTS: , with an interquartile range (IQR) of 24.55-30.10, and the median duration of diabetes was 11.00 years (IQR: 6.00-16.00). The use of sodium glucose cotransporter-2 (SGLT-2) inhibitors was significantly associated with poorer sleep quality, as indicated by a higher odds ratio (odds ratio [OR] = 2.076, 95% confidence interval [CI]: 1.016-4.242, P = 0.045). Insulin use was linked to increased anxiety, with an OR of 1.985 (95% CI: 1.007-3.913, P = 0.048). In contrast, sulfonylureas and glinides were associated with lower odds of depression, with an OR of 0.374 (95% CI: 0.182-0.768, P = 0.007). No significant associations were found between thiazolidinediones, metformin, or dipeptidyl peptidase-4 (DPP-4) inhibitors and any psychiatric outcomes. CONCLUSION: The use of SGLT2 inhibitors may negatively impact sleep quality, whereas insulin therapy is associated with increased anxiety symptoms. Conversely, sulfonylureas and glinides appear to have a protective effect against depression. These findings underscore the importance of considering psychiatric outcomes when prescribing antidiabetic 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".