Biological indicators of illness risk in offspring of bipolar parents: targeting the hypothalamic‐pituitary‐adrenal axis and immune system
Bibliographic record
Abstract
AIM: The study aims to provide a selective review of the literature pertaining to the hypothalamic-pituitary-adrenal (HPA) axis and immune abnormalities as informative biological indicators of vulnerability in bipolar disorder (BD). METHOD: We summarize key findings relating to HPA axis and immunological abnormalities in bipolar patients and their high-risk offspring. Findings derive from a review of selected original papers published in the literature, and supplemented by papers identified through bibliography review. Neurobiological findings are discussed in the context of emergent BD in those at genetic risk and synthesized into a neurodevelopmental model of illness onset and progression. RESULTS: BD is associated with a number of genetic and possibly epigenetic abnormalities associated with neurotransmitter, hormonal and immunologically mediated neurobiological pathways. Data from clinical and high-risk studies implicate HPA axis and immune system abnormalities, which may represent inherited vulnerabilities important for the transition to illness onset. Post-mortem and clinical studies implicate intracellular signal transduction processes and disturbance in energy metabolism associated with established BD. Specifically, long-standing maladaptive alterations such as changes in neuronal systems may be mediated through changes in intracellular signalling pathways, oxidative stress, cellular energy metabolism and apoptosis associated with substantial burden of illness. CONCLUSIONS: Prospective longitudinal studies of endophenotypes and biomarkers such as HPA axis and immune abnormalities in high-risk offspring will be helpful to understand genetically mediated biological pathways associated with illness onset and progression. A clinical staging model describing emergent illness in those at genetic risk should facilitate this line of investigation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".