Variable number of tandem repeats polymorphism in parathyroid hormone-related protein as predictor of peak bone mass in young healthy Finnish males
Bibliographic record
Abstract
OBJECTIVE: Mice with osteoblast-specific deletion of parathyroid hormone-related protein (PTHrP) exhibit impaired recruitment and increased apoptosis of osteogenic cells resulting in decreased bone formation and premature osteoporosis. The PTHrP levels within the bone microenvironment are therefore critical in influencing bone mass acquisition. Whether this is applicable in humans has not been established. Here, we studied the association of a variable number of tandem repeats (VNTR) polymorphism in PTHrP with peak bone mass. METHODS: Enrolled in the study were 234 young Finnish males, with median age of 19.6 years (range 18.3-20.6 years). Lifestyle factors, serum bone markers, osteodensitometric measurements (lumbar spine and hip) and calcaneal quantitative ultrasound readings were obtained. The PTHrP VNTR length was determined by the PCR amplification of genomic DNA extracted from peripheral blood and correlated to bone parameters by the multiple regression models. RESULTS: The presence of at least one 252 bp allele was associated with increased lumbar spine bone mineral density (BMD; P<0.0034), broadband ultrasound attenuation (BUA; P<0.0012) and speed-of-sound (SOS; P<0.0023) measurements. The correlation with increased lumbar spine BMD (P=0.0008), BUA (P=0.005) and SOS (P=0.001) was further strengthened by the pairing of the 252 bp allele with a 460 bp allele in comparison with those without any 252 bp allele. Electrophoretic mobility shift assays were used to illustrate the potential transcriptional functionality of the VNTR sequence. CONCLUSION: The results indicate that the PTHrP VNTR sequence likely modulates local PTHrP expression within the skeletal microenvironment and could serve as a diagnostic predictor of peak bone mass acquisition.
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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 | 0.000 |
| 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) | 0.002 | 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".