Telomere length and reproductive aging
Bibliographic record
Abstract
BACKGROUND: Rate of reproductive aging may be related to rate of biological aging. Thus, indicators of aging, such as short telomere length, may be more frequent in women with a history suggestive of premature reproductive senescence. METHODS: Telomere-specific quantitative PCR was used to assess telomere length in two groups of women with evidence of reproductive aging: (i) patients with idiopathic premature ovarian failure (POF, N = 34) and (ii) women with a history of recurrent miscarriage (RM, N = 95); and two control groups: (1) women from the general population (C1, N = 108) and (2) women who had a healthy pregnancy after 37 years of age (C2, N = 46). RESULTS: The RM group had shorter age-adjusted mean telomere length than controls (8.46 versus 8.92 kb in C1 and 9.11 kb in C2, P = 0.0004 and P = 0.02 for C1 and C2, respectively), although short telomeres were not confined to subsets of this group known to have experienced single or multiple trisomic pregnancies. Although sample size is limited, mean telomere length in the POF group was significantly longer than that in C1 (9.58 versus 8.92 kb, P = 0.01). CONCLUSIONS: Women experiencing RM may have shorter telomeres as a consequence of a more rapid rate of aging, or as a reflection of an increased level of cellular stress. Longer telomere length in the POF group may be explained by abnormal hormone exposure, slow cell division rates or autoimmunity in these women. Despite small sample sizes, these results suggest that different manifestations of reproductive aging are likely influenced by distinct physiological factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".