Cytokine Genes <i>TNF</i>, <i>IL1A</i>, <i>IL1B</i>, <i>IL6</i>, <i>IL1RN</i> and <i>IL10</i>, and Childhood-Onset Mood Disorders
Bibliographic record
Abstract
BACKGROUND/AIMS: Inflammatory cytokines induce a behavioral syndrome, known as sickness behavior, that strongly resembles symptoms typically seen in depression. This resemblance has led to the theory that an imbalance of inflammatory cytokine activity may be a contributing factor in depressive disorders. Support for this is found in multiple lines of evidence, such as the effects of cytokines on the activities of the hypothalamic-pituitary-adrenal axis, serotonin and brain-derived neurotrophic factor, and hippocampal function, all of which are implicated in the etiology of depression. In addition, associations between inflammatory activity and depressive symptomology have been documented in a number of studies, and the depressogenic effects of cytokine therapy are well known. Accordingly, given that depression has a substantial genetic basis, genes involved in the regulation of inflammatory cytokine activity are strong candidates for involvement in genetic susceptibility to depressive disorders. Here, we have tested 6 key genes of this type, TNF, IL1A, IL1B, IL6, IL1RN and IL10, as candidates for involvement in childhood-onset mood disorders. METHODS: In this study of 384 families, each ascertained through a child with depression diagnosed before the age of 15 years, 11 polymorphisms of known or likely functional significance (coding and regulatory variants) were analyzed. RESULTS: Testing for biased transmission of alleles from parents to their affected offspring, we found no evidence for an association between childhood-onset mood disorders and any of the polymorphisms, either individually or as haplotypes. CONCLUSION: The present study does not support the involvement of the TNF, IL1A, IL1B, IL6, IL1RN and IL10 variants as major genetic risk factors contributing to early-onset mood disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.017 | 0.018 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.017 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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; both teacher heads agree on what is shown here.
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".