Lifecourse Adversity and Telomere Length in Older Women from Northeast Brazil
Bibliographic record
Abstract
We examined associations between adverse childhood experiences (ACEs) and shorter telomere length (TL) in 83 older women, including 42 women with less than secondary education and 41 with secondary or more education in a city of Northeast Brazil, a region with substantial socioeconomic inequalities. The low education sample was selected from a representative survey at local neighborhood health centers, while the high education group consisted of a convenience sample recruited by advertising in community centers and centers affiliated with the local university. Relative leukocyte TL was measured by quantitative polymerase chain reaction from blood samples. ACEs were self-reported. Spline linear regression was fitted to assess the strength of the associations between ACEs and TL. Among women with low education, median TL was 1.02 compared with 0.64 in the high education group (p = 0.0001). Natural log-transformed T/S ratio as the dependent variable was used in analysis. Women with low education had been exposed to more ACEs, and among them those experiencing two or more ACEs had longer TL than women exposed to ≤1 ACEs (p = 0.03); among women with high education, this difference was not significant (p = 0.49). In analyses adjusted by age, education, and parental abuse of alcohol, the linear trend of higher TL with increasing ACEs was confirmed (p = 0.02), and the mean difference in TL between groups remained significant (p = 0.002). The unexpected positive relationship between low education and ACEs with TL suggests that older adults who have survived harsh conditions prevailing in Northeast Brazil have the longest TL of their birth cohort.
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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 | 0.001 | 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) | 0.001 | 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".