Identification of peripheral biomarkers through metabolomic analysis in patients with bipolar disorder treated with mood stabilizers: an exploratory study
Bibliographic record
Abstract
IMPORTANCE: Bipolar disorder (BD) is mainly treated with mood stabilizers, among which lithium represents the gold standard. Despite its high clinical efficacy, the molecular players involved in lithium response and nonresponse remain partly unclear. Therefore, the identification of peripheral biomarkers would significantly improve the management of pharmacological interventions in BD. OBJECTIVE AND DESIGN: In this study, we sought to investigate the blood metabolome in patients with BD to identify biosignatures of treatment with different mood stabilizers as well as possible biomarkers of response to lithium. SETTING AND PARTICIPANTS: The blood metabolome was measured in a sample of 89 patients with BD either under prophylactic lithium treatment (n = 47), and characterized as responders or nonresponders, or with other mood stabilizers (MS, n = 42). For each patient the plasma metabolome was measured with hydrogen nuclear magnetic resonance (1H-NMR) and gas chromatography-mass spectrometry (GC-MS). Data were investigated with multivariate analyses accounting for covariates. RESULTS: Patients exposed to lithium or to other MS showed different, specific metabolic signatures, with different levels of metabolites belonging to pathways involved in glucose, pyruvate, and glutamate metabolism, which were previously suggested to be implicated in BD and to be regulated by lithium. On the other hand, we were not able to identify significant differences in the metabolomic profile between responders and nonresponders to lithium. CONCLUSIONS AND RELEVANCE: The findings from this exploratory study suggest that patients treated with lithium show distinctive metabolomic biosignatures, specifically pointing to energy metabolism and mitochondria functioning, thus potentially suggesting possible biosignatures of mood-stabilizing treatments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".