Childhood Growth, IQ and Education as Predictors of White Blood Cell Telomere Length at Age 49–51 Years: The Newcastle Thousand Families Study
Bibliographic record
Abstract
BACKGROUND: Telomere length is emerging as a potential factor in the pathogenesis of cardiovascular disease. We investigated whether birth weight, infant growth, childhood cognition and adult height, as well as a range of lifestyle, socio-economic and educational factors, were associated with white blood cell telomere length at age 49-51 years. METHODS: The study included 318 members of the Newcastle Thousand Families Study, a prospectively followed birth cohort which includes all individuals born in Newcastle, England in May and June 1947, who attended for clinical examination at age 49-51 years, and had telomere length successfully measured using real-time PCR analyses of DNA extracted from peripheral blood mononuclear cells. RESULTS: No association was found between birth weight and later telomere length. However, associations were seen with other factors from early life. Education level was the only predictor in males, while telomere length in females was associated with gestational age at birth, childhood growth and childhood IQ. CONCLUSIONS: While these findings may be due to chance, in particular where differing associations were seen between males and females, they do provide evidence of early life associations with telomere length much later in life. Our findings of sex differences in the education association may reflect the sex differences in achieved education levels in this generation where few women went to university regardless of their intelligence. Our findings do not support the concept of telomere length being on the pathway between very early growth and later disease risk.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".