Insulin-like growth factor 1 (IGF-1) expression is up-regulated in lymphoblastoid cell lines of lithium responsive bipolar disorder patients
Bibliographic record
Abstract
Bipolar disorder (BD) is a debilitating psychiatric disease characterized by alternating episodes of mania and depression. Among mood stabilizers, lithium is the mainstay for the treatment of BD, with approximately one-third of patients showing remission from episode recurrence. While there is evidence suggesting genetic load for lithium response in BD, its molecular underpinnings are still not completely understood. To identify genes potentially involved in (or correlated with) lithium response, we carried out a genome-wide expression analysis on lymphoblastoid cell lines (LCLs) from 10 BD patients responders (R) and 10 non-responders (NR) to lithium. We compared expression levels of the two groups and tested whether in vitro lithium treatment had different effects in LCLs of R compared to NR. At basal, 2060 genes were differentially expressed between R and NR while no genes were differentially regulated by lithium in the two groups. After pathway analysis based on the 2060 genes, 9 genes were selected for validation with qRT-PCR. Eight genes were validated in the same sample of LCLs while only insulin-like growth factor 1 (IGF-1) was significantly over-expressed in R compared to NR in the same sample as well as in an independent sample comprised of 6 R and 6 NR (sample 1, fold change=1.94; p=0.005; sample 2, fold change=2.21; p=0.005). IGF-1 was also significantly over-expressed in R but not in NR when compared to a sample of non-psychiatric controls. Our findings suggest that IGF-1 may be involved in lithium response, supporting further investigation on its potential as a biomarker.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".