Short telomere subtelomeric hypomethylation is associated with telomere attrition in elderly diabetic patients
Bibliographic record
Abstract
Telomere shortening is well known to be associated with the aging process and aging-associated diseases, including diabetes. The telomere length and subtelomeric methylation status in peripheral leucocytes (LTL) were compared in elderly type 2 diabetes (T2D) patients and diabetes-free controls (C). The methylation status was analyzed between MspI-TRF lengths and HpaII-TRF lengths by using methylation-sensitive and -insensitive restriction enzyme isoschizomers, MspI and HpaII, respectively. The mean telomere lengths, MspI-TRF or HpaII-TRF, were not significantly different between C and T2D patients. The percentage of fractionated densitometry showed that long and middle telomeres (>9.4 kb, 4.4-9.4 kb) were unaltered but short telomeres (<4.4 kb) in T2D patients were increased compared with C group. The methylation status revealed subtelomeric hypomethylation in short telomeres of T2D patients. When some patients with T2D were treated with 3-hydroxy-3-methylglutaril coenzyme A (HMG-CoA) reductase inhibitors (statin), results seen in short telomere of T2D patients were not observed and were not different from C. This suggested that this altered subtelomeric hypomethylation may be associated with the accelerated telomere shortening in elderly diabetic patients. These results also mean that the subtelomeric hypomethylation can also be influenced by statin treatment in T2D.
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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 | 0.001 |
| 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) | 0.001 | 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".