Associations of placental lncRNA expression with maternal pre-pregnancy BMI and infant birthweight in two birth cohorts
Bibliographic record
Abstract
Abstract Pre-pregnancy obesity (ppOB) is linked to pregnancy complications and abnormal fetal growth through placental mechanisms, and long non-coding RNAs (lncRNAs) may play an epigenetic role in these processes. We investigated overall and sex-specific associations of pre-pregnancy body mass index (ppBMI), ppOB, and birthweight with placental lncRNA transcripts in two birth cohorts. Study participants were mother-child dyads recruited to the CANDLE (Memphis, TN)(n = 725) and GAPPS (Seattle and Yakima, WA)(n = 159) cohorts. Maternal ppBMI was assessed at enrollment using interviewer-administered questionnaires. LncRNAs (1,077 and 1,033 for CANDLE and GAPPS, respectively) were sequenced from placental samples collected at birth. Placental lncRNA was regressed on ppBMI, ppOB (ppBMI ≥30kg/m2), or continuous birthweight in cohort-specific weighted linear models controlling for a priori-specified confounders and experimental variables. Potential effect modification by infant-sex was examined in sex-stratified analyses and models including BMI-infant-sex interaction terms. No lncRNA transcripts were significantly associated with ppBMI, ppOB, or birthweight in primary models. Among male infants in CANDLE, expression of three lncRNA transcripts (ERVH48-1, AC139099.1, CEBPA-DT) was associated with ppBMI and one transcript (AC104083.1) with birthweight. In GAPPS, ppBMI was associated with two lncRNA transcripts (AP000879.1 and AL365203.2) among males, and birthweight was associated with 17 lncRNA transcripts (including LINC02709, KANSL1-AS1, DANCR, EPB41L4A-AS1, and GABPB1-AS1) among females. No BMI-infant-sex interactions were observed. Though many of these potential associations are for uncharacterized transcripts, several identified lncRNAs (e.g., ERVH48-1 and CEBPA-DT) have been linked to pathways controlling cancer or placental growth, trophoblast differentiation, and gene expression. These associations warrant validation in future studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".