Effects of lithium on blood glucose and insulin in individuals with bipolar disorder: A systematic review
Bibliographic record
Abstract
BACKGROUND: Lithium is the gold-standard pharmacotherapy for both acute episodes and long-term management of bipolar disorder (BD). Lithium treatment is associated with metabolic changes including weight gain; however, the exact mechanisms mediating weight increase are poorly understood. This systematic review assesses the effects of lithium on glucose, glucose-tolerance and insulin in persons with BD. METHODS: A systematic search was conducted on PubMed, MEDLINE, Embase, CENTRAL, APA PsychINFO, Scopus and Web of Science. Primary studies assessing validated metrics of serum glucose or insulin in bipolar patients receiving lithium treatment were included. RESULTS: 16 studies were included in this review. Fasting plasma glucose (FPG) was the most studied metabolic biomarker. Most studies did not report an association between lithium and changes in FPG. Lithium treatment did increase glucose tolerance in the short-term, however most short-term effects appear to be transient and do not persist. LIMITATIONS: There is a lack of a consistent criteria for BD across the reviewed studies. Many included studies lack controls and do not stratify for relevent metabolic factors such as body mass index (BMI) and age. CONCLUSIONS: Lithium does not appear to consistently affect FPG in persons with BD. Current evidence does not suggest that lithium directly and consistently affects FPG; instead changes to FPG in persons receiving lithium may be indirectly affected by BMI, age or mood status.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".