Testosterone and frailty in ageing men
Bibliographic record
Abstract
Introduction: Frailty, a common cause of disability and dependency in the elderly is a multifactorial condition; ageing-associated endocrine dyregulations may play an important role. It is not known if testosterone (T) deficiency contributes to frailty, through its effects on muscle (sarcopenia) and physical function. Objective: To investigate the relationship between T and frailty in elderly men in a cross-sectional observational study in community-dwelling men. Methods: One thousand six hundred and sixty one men, median age 72 yr, (range 65–95) were screened for frailty using Fried’s criteria1 (weight loss of 10 lbs or more in the past year, self-reported exhaustion, decreased grip strength, slow walking speed and reduced physical activity). Frail (F) men were characterized by the presence of ≥3 criteria, prefrail (PF) men by 1-2 criteria and non-frail (NF) men by the absence of any criteria. Total testosterone (TT), sex hormone binding globulin (SHBG), follicle stimulating hormone (FSH), and luteinizing hormone (LH) were measured. The local research ethics committee approved the study. Results: The prevalence of F and PF was 7% and 45% respectively. Mean (95%CI) TT was lower in F and PF; 11.9 (10.9,12.9) and 13.6 (13.5,14) nmol/L respectively vs. 14.3 (13.9,14.7) nmol/L in NF (P<0.001). Logistic regression analysis including TT, LH, age, body mass index (BMI), number of co-morbidities and prescribed medication as predictor variables revealed that TT ≤12 nmol/L was associated with an odds ratio (OR) (95% CI) of 1.86 (1.07, 3.23) (P<0.05) for being F compared to NF. Among PF, unadjusted logistic regression analysis revealed that a TT ≤10 nmol/L was associated with an OR (95% CI) of 1.39 (1.08,1.79) (P<0.01), when compared with NF. However, hormone measures were not predictive of PF when age and other covariates were included. Conclusions: Although a causal relationship cannot be attributed to testosterone in the aetiology of frailty, low T levels are significantly associated with frailty.
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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 | 0.002 |
| 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) | 0.002 | 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".