Impact of Dehydroepiandrosterone Sulfate on Newborn Leukocyte Telomere Length
Bibliographic record
Abstract
Abstract The newborn setting of leukocyte telomere length (LTL) likely has important implications for telomere dynamics over the lifespan. However, its determinants are poorly understood. Hormones play an important role during pregnancy and delivery. We hypothesized that exposure to hormones may impact the fetal telomere biology system. To test this hypothesis, cortisol, estradiol, dehydroepiandrosterone sulfate (DHEAS) and reactive oxygen species (ROS) were measured in cord blood of 821 newborns from a prospective study. After accounting for the effects of potential determinants of newborn LTL, a 10-fold increase in DHEAS concentration was associated with a 0.021 increase in T/S ratio of newborn LTL (95% confidence interval: 0.009–0.034, P = 0.0008). For newborns who fell in the lowest quartile of DHEAS level, the mean newborn LTL was estimated to be approximately 2.0% shorter than the newborns in the highest DHEAS concentration quartile (P = 0.0014). However, no association was found between newborn LTL and cortisol or estradiol. As expected, newborns with higher ROS level (ROS > 260 mol/L) had lower LTL compared to that with lower ROS level (ROS ≤ 260 mol/L) (P = 0.007). There was also an inverse relationship between DHEAS and ROS (P < 1×10−4). Our findings suggest that exposure to DHEAS may exert a “programming” effect on the newborn telomere biology system.
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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 | 0.002 |
| 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 | 0.000 |
| 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".