Very Low and High Levels of Vitamin D Are Associated with Shorter Leukocyte Telomere Length in 148,321 UK Biobank Participants
Bibliographic record
Abstract
Background: Shorter leukocyte telomere length (LTL) is observed in multiple age-related diseases, which are also associated with vitamin D deficiency (i.e., osteosarcopenia, neurocognitive disorders, cancer, osteoarthritis, etc.), suggesting a close association between vitamin D and LTL. In this study, we examined the relationship between vitamin D levels and LTL in older participants of the UK Biobank. Methods: Data were collected from the UK Biobank. Participants aged 60 and older (n = 148,321) were included. Baseline LTL was measured using a multiplex qPCR technique and expressed as the ratio of the telomere amplification product (T) to that of a single-copy gene (S) (T/S ratio). Serum 25-hydroxyvitamin D (25OHD) was stratified by z score and linked to LTL in a linear regression model adjusting for covariates. Results: Compared to the medium level, a low (in the range of 16.6 nmol/L, 29.7 nmol/L) or extremely low (≤16.6 nmol/L) level of serum 25OHD was associated with shorter LTL: 0.018 SD (standardized β = −0.018, 95% CI −0.033 to −0.003, p = 0.022) and 0.048 SD (standardized β = −0.048, 95% CI −0.083 to −0.014, p = 0.006), respectively. Additionally, the high serum 25OHD groups (>95.9 nmol/L) had 0.038 SD (standardized β = −0.038, 95% CI −0.072 to −0.004, p = 0.030) shorter mean LTL than the group with medium 25OHD levels. The associations above were adjusted for multiple variables. Conclusions: In this population-based study, we identified an inverted U-shape relationship between LTL and vitamin D status. Our findings could be affected by unmeasured confounders. Whether high or low vitamin D-associated shorter LTL is mechanistically related to age-related conditions remains to be elucidated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".