Brain‐derived neurotrophic factor, impaired glucose metabolism, and bipolar disorder course
Bibliographic record
Abstract
OBJECTIVES: The neurotrophin brain-derived neurotrophic factor (BDNF) has been proposed as a potential biomarker in bipolar disorder (BD). However, current evidence is limited and results have been highly heterogeneous. This study aimed to assess the moderating effect of impaired glucose metabolism (IGM) on plasma levels of BDNF in individuals with BD, and on the relationship between BDNF and variables of illness course. METHODS: We measured and compared the plasma levels of BDNF in individuals with BD (n=57) and healthy controls (n=26). IGM was operationalized as pre-diabetes or type 2 diabetes mellitus. Information related to current and past psychiatric/medical history, as well as prescription of pharmacological treatments was also captured. RESULTS: Individuals with BD had lower levels of BDNF, relative to healthy controls, after adjustment for age, gender, current medications, smoking, alcohol use, and IGM (P=.046). There was no effect of IGM (P=.860) and no interaction between BD diagnosis and IGM (P=.893). Peripheral BDNF levels were positively correlated with lifetime depressive episodes (P<.001), psychiatric hospitalizations (P=.001) and suicide attempts (P=.021). IGM moderated the association between BDNF and the number of previous mood episodes (P<.001), wherein there was a positive correlation in euglycemic participants and a negative correlation in individuals with IGM. CONCLUSIONS: BD is independently associated with lower levels of BDNF; IGM may modify the relationship between BDNF and BD course, suggesting an interactive effect of BDNF with metabolic status on illness progression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".