Telomere length across the spectrum of metabolic health – an analysis from the LIPIDOGEN2015 study
Bibliographic record
Abstract
Introduction Background: Telomere length is a cellular aging marker and correlates with various cardiovascular disease (CVD) risk factors. The current study assessed the association between obesity, metabolic syndrome (MetS), and telomere length. Material and methods Material and methods: The LIPIDOGRAM&LIPIDOGEN2015 study was conducted in primary care in 2015-2016. Recruited patients to the LIPIDOGEN2015 cohort (n=1788) were a random subset of patients of the LIPIDOGRAM2015 (n=13,724) study. For the aims of this analysis, the recruited patients were divided into four groups based on the presence of MetS: healthy slim (HS), metabolically healthy obese (MHO), non-obese with MetS (NOMS), and metabolically unhealthy obese (MUO). Relative telomere length (RTL) was measured using quantitative polymerase chain reaction (qPCR). Results Results: 1516 patients (85%; females - 59.7%, mean age- 50.3 years) were included for final analyses. An increase in body mass index (BMI), waist circumference, prevalence of diabetes mellitus, hypertension, dyslipidemia, and history of myocardial infarction moving from HS to MUO were observed. MUO group exhibited the highest triglycerides and lowest high-density lipoprotein (HDL-C) levels. Univariable regression analyses indicated that NOMS (p=0.038) and MUO (p=0.003) were associated with significantly decreased RTL. After adjustment for age, gender, education, smoking, place of residence, and myocardial infarction, the association was no longer statistically significant. Conclusions Conclusions: Despite the lack of statistical significance in the multivariate analysis, the univariate results suggest that both MUO and NOMS phenotypes contribute to the shortening of telomere length. These results may also indicate that MetS, irrespectively on obesity occurrence, is responsible for the shortened lifespan.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".