The approximate formulas predicting personal somatic telomere length using patient blood test data
Bibliographic record
Abstract
Biological aging underlies lifestyle-related diseases. It can be assessed by measuring personal somatic cell telomere length. However, measuring the telomere length is laborious, and its clinical surrogate parameters have not been developed. This study analyzed the correlation between telomere length in peripheral leukocytes and laboratory data to select test items relating closely to biological aging. We established formulas from these clinical data to predict the personal telomere length. The subjects were patients having visited Kyushu University Beppu Hospital from 2012 to 2015. Two hundred and thirty-two patients were enrolled. The blood data were collected and telomere lengths were measured by Southern blotting method. The patients showed significant correlations between the telomere length and several blood test data with a sex-related difference. Candidate formulas are as follows: Predicted telomere length (kb) in men = 8.59 − 0.037 × Age (years) + 0.024 × Hemoglobin (g/dL); Predicted telomere length (kb) in women = 4.83 − 0.019 × Age (years) + 0.23 × Albumin (g/dL) + 0.0001 × White blood cells (/mm3) + 0.0020 × Red blood cells (× 104/mm3) + 0.0032 × Total cholesterol (mg/dL). Thus, the derived formulas allow for the accurate differential prediction of telomeric length in male and female patients.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".