Low <i>CLOCK</i> and <i>CRY2</i> in 2nd trimester human maternal blood and risk of preterm birth: a nested case-control study†
Bibliographic record
Abstract
Previous studies have observed an association between maternal circadian rhythm disruption and preterm birth (PTB). However, the underlying molecular mechanisms and the potential of circadian clock genes to serve as predictors of PTB remain unexplored. We examined the association of 10 core circadian transcripts in maternal blood with spontaneous PTB (sPTB) vs term births using a nested case-control study design. We used a public gene expression dataset (GSE59491), which was nested within the All Our Babies (AOB) study cohort in Canada. Maternal blood was sampled in Trimesters 2-3 from women with sPTB (n = 51) and term births (n = 106), matched for five demographic variables. In 2nd trimester maternal blood, only CLOCK and CRY2 transcripts were significantly lower in sPTB vs term (P = 0.02-0.03, false discovery rate (FDR) < 0.20). A change of PER3 mRNA from trimesters 2-3 was significantly associated with sPTB (decline in sPTB, P = 0.02, FDR < 0.20). When CLOCK and CRY2 were modeled together in 2nd trimester blood, the odds of being in the low level of both circadian gene transcripts was greater in sPTB vs term (OR = 4.86, 95%CI = (1.75,13.51), P < 0.01). Using GSVA and Pearson correlation, we identified 98 common pathways that were negatively or positively correlated with CLOCK and CRY2 expression (all P < 0.05, FDR < 0.10). The top three identified pathways were amyotrophic lateral sclerosis, degradation of extracellular matrix, and inwardly rectifying potassium channels. These three processes have previously been shown to be involved in neuron death, parturition, and uterine excitability during pregnancy, respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".