Gene expression changes associated with trajectories of psychopathology in a longitudinal cohort of children and adolescents
Bibliographic record
Abstract
We aimed to identify blood gene expression patterns associated to psychopathological trajectories retrieved from a large community, focusing on the emergence and remission of general psychiatric symptoms. Hundred and three individuals from the Brazilian High-Risk Cohort Study (BHRCS) for mental disorders were classified in four groups according to Child Behavior Checklist (CBCL) total score at the baseline (w0) and after 3 years (w1): low-high (L-H) (N = 27), high-low (H-L) (N = 12), high-high (H-H) (N = 34) and low-low (L-L) groups (N = 30). Blood gene expression profile was measured using Illumina HT-12 Beadchips, and paired analyses comparing w0 and w1 were performed for each group. Results: 98 transcripts were differentially expressed comparing w0 and w1 in the L-H, 33 in the H-L, 177 in the H-H and 273 in the L-L. Of these, 66 transcripts were differentially expressed exclusively in the L-H; and 6 only in the H-L. Cross-Lagged Panel Models analyses revealed that RPRD2 gene expression at w1 might be influenced by the CBCL score at w0. Moreover, COX5B, SEC62, and NDUFA2 were validated with another technique and were also differentially regulated in postmortem brain of subjects with mental disorders, indicating that they might be important not only to specific disorders, but also to general psychopathology and symptoms trajectories. Whereas genes related to metabolic pathways seem to be associated with the emergence of psychiatric symptoms, mitochondrial inner membrane genes might be important over the course of normal development. These results suggest that changes in gene expression can be detected in blood in different psychopathological trajectories.
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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 | 0.001 |
| 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) | 0.001 | 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".