BRD4 as a Biomarker in Bipolar Disorder and Multiple Sclerosis
Research indicated that BRD4 may serve as a non-invasive biomarker with moderate-to-good ability to differentiate between bipolar disorder (BD) and multiple sclerosis (MS). The study reported various discriminative performances in different datasets, suggesting BRD4's potential role in identifying these conditions.
- Tags
- #MSResearch
- Most relevant to
- —
- Human efficacy
- Not established from this source
- Mechanism
- Not reported
- Evidence strength
- Not graded
- Outcome
- Not reported
Why it matters
Identifying effective biomarkers could enhance diagnostic processes for BD and MS, leading to better treatment planning and understanding of their relationship.
What this does not prove
The study did not explore how BRD4 functions as a biomarker, leaving its mechanisms unclear.
Next milestone
No next milestone was established from the available source.
Study facts
- Study design
- Not reported
- Participants / samples
- Not reported
- Randomised
- Not reported
- Controlled
- Not reported
- Primary endpoint met
- Not reported
- Relevant MS type
- Not reported
- Publication date
- 2026-10-19
- Evidence reviewed
- Abstract only
- Regulatory approval
- Not reported
- Research areas
- Not classified
- Biological target
- Not reported
- Pathway
- Not reported
- Population
- Not reported
- Size of effect
- Not reported
- Secondary endpoints
- Not reported
- MRI endpoints
- Not reported
- Biomarkers
- Not reported
- Safety
- Not reported
Original sources
- Primary evidenceIdentification of BRD4 as a Shared Diagnostic Biomarker Between Bipolar Disorder and Multiple Sclerosis Using Machine Learning. ↗DOI: 10.3390/cimb48101077
Supporting passages (5)
publication datePublication date: 2026-10-19
findingsBRD4 shows moderate-to-good discriminative performance as a non-invasive peripheral biomarker and may aid further investigation of shared BD-MS mechanisms.
limitationsTitle: Identification of BRD4 as a Shared Diagnostic Biomarker Between Bipolar Disorder and Multiple Sclerosis Using Machine Learning.
peer reviewedPublication types: Journal Article
subjectsDiscriminative performance (AUC, 95% CI) was: BD discovery GSE23848 (20 cases/15 controls) 0.713 (0.54-0.89); MS discovery GSE21942 (12/15) 0.998 (0.99-1.00); BD external GSE39653 (8/24) 0.775 (0.59-0.96); MS external GSE203241 (38/21) 0.828 (0.71-0.93).
AI assessment, not yet reviewed by a person · version 1 · Community votes are separate from evidence review.