IGF‐I and testosterone levels as predictors of bone mineral density in healthy, community‐dwelling men
Bibliographic record
Abstract
OBJECTIVE: Age-related decline in IGF-I and gonadal hormones have been postulated to play an important role in the pathogenesis of age-related bone loss in men. In this cross-sectional study, the relation between serum IGF-I and gonadal hormones with bone mineral density (BMD) was examined in community-dwelling men. DESIGN AND SUBJECTS: Serum IGF-I, testosterone and BMD were examined in 61 community-dwelling men over the age of 27, who were randomly selected from the Calgary cohort of 1000 subjects in the Canadian Multicentre Osteoporosis Study. In the present study, IGF-I, serum testosterone, SHBG, free androgen index (FAI), parathyroid hormone (PTH), 25-hydroxy-vitamin D [25(OH)D] and other markers of bone turnover were measured. BMD was measured at the spine and hip (HOLOGIC 4500). Simple linear regression was used to assess the linear relation between IGF-I, testosterone, BMD and other biochemical markers of bone metabolism and potential confounding variables and subsequent multivariate regression models were constructed separately for each BMD measurement to assess the importance of IGF-I and testosterone in the presence of potential confounding variables. RESULTS: Serum IGF-I, FAI and SHBG significantly decreased as a function of age, whereas serum levels of PTH increased. Only 25(OH)D, total testosterone and FAI were positively associated with serum IGF-I after adjusting for age and BMI. Multiple linear regression models revealed that IGF-I was a significant predictor of BMD at the total hip, femoral neck and femoral trochanter neck (P < or = 0.001). In contrast, the FAI was a significant predictor of BMD at the lumbar spine and wards area (P < or = 0.011), and SHBG was a significant predictor at the total hip and femoral trochanter (P < or = 0.045). CONCLUSION: These data support the hypothesis that the age-related decline in bone mass in men is associated with declining levels of IGF-I and testosterone.
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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 | 0.000 | 0.000 |
| 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".