Biomarkers in bipolar disorder: A positional paper from the International Society for Bipolar Disorders Biomarkers Task Force
Bibliographic record
Abstract
Although the etiology of bipolar disorder remains uncertain, multiple studies examining neuroimaging, peripheral markers and genetics have provided important insights into the pathophysiologic processes underlying bipolar disorder. Neuroimaging studies have consistently demonstrated loss of gray matter, as well as altered activation of subcortical, anterior temporal and ventral prefrontal regions in response to emotional stimuli in bipolar disorder. Genetics studies have identified several potential candidate genes associated with increased risk for developing bipolar disorder that involve circadian rhythm, neuronal development and calcium metabolism. Notably, several groups have found decreased levels of neurotrophic factors and increased pro-inflammatory cytokines and oxidative stress markers. Together these findings provide the background for the identification of potential biomarkers for vulnerability, disease expression and to help understand the course of illness and treatment response. In other areas of medicine, validated biomarkers now inform clinical decision-making. Although the findings reviewed herein hold promise, further research involving large collaborative studies is needed to validate these potential biomarkers prior to employing them for clinical purposes. Therefore, in this positional paper from the ISBD-BIONET (biomarkers network from the International Society for Bipolar Disorders), we will discuss our view of biomarkers for these three areas: neuroimaging, peripheral measurements and genetics; and conclude the paper with our position for the next steps in the search for biomarkers for bipolar disorder.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".