An immunological age index in bipolar disorder: A confirmatory factor analysis of putative immunosenescence markers and associations with clinical characteristics
Bibliographic record
Abstract
OBJECTIVES: The study aims to generate an immunological age (IA) trait on the basis of immune cell differentiation parameters, and to test whether the IA is related to age and disease characteristics. METHODS: Forty-four euthymic type I bipolar disorder patients were included in this study. Five immunosenescence-related parameters were assessed: proportions of late-differentiated cells (e.g., CD3+CD8+CD28-CD27- and CD3-CD19+IgD-CD27-), and the expression of CD69, CD71, and CD152 after stimulation. Confirmatory factor analysis was applied to generate an IA trait underling the 5 measures. RESULTS: The best-fit model was constituted by 4 parameters that were each related to an underlying IA trait, with 1 cell population positively correlated (CD3+CD8+CD28-CD27- [λ = 0.544, where λ represents the loading of the parameter onto the IA trait] and 3 markers negatively correlated (CD69 [λ = -0.488], CD71 [λ = -0.833], and CD152 [λ = -0.674]). The IA trait was associated with chronological age (β = 0.360, p = .013) and the number of previous mood episodes (β = 0.426, p = .006). In a mediation model, 84% of the effect between manic episodes, and IA was mediated by body mass index. CONCLUSION: In bipolar disorder type I, premature aging of the immune system could be reliably measured using an index that validated against chronological age, which was related to adverse metabolic effects of the disease course.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".