The associations between serum antioxidant concentrations and bone mineral density in women aged 50 and over: an analysis of NHANES 2005–6
Bibliographic record
Abstract
It has been shown that diets high in antioxidants are associated with high bone mineral density in postmenopausal women. Conversely, some studies suggest that dietary supplements containing antioxidants may have detrimental effects on bone. Serum concentrations of antioxidants can reflect their intakes from both food and supplements. Thus, the objective of our study was to examine the associations between serum concentrations of several antioxidants and femoral neck BMD (FN_BMD) in postmenopausal women aged ≥50 years. We used cross‐sectional data from the NHANES 2005–6. Multiple regression models with adjustments for relevant confounders were used to examine the associations between serum concentrations of vitamins C and E (alpha‐ and gamma‐tocopherol) and alpha and beta‐carotene, and FN_BMD. The study sample included 168 women, with a mean age of 67.5±0.5 years, who were free from diseases and medications that affect bone metabolism, and were fasting >;9 hours prior to examination. In adjusted models, serum concentration of vitamin C had a positive association with FN_BMD (p=0.018), while serum concentration of beta‐carotene had a negative association with FN_BMD (p=0.008). The association between higher serum vitamin C and higher BMD in our study is consistent with findings of other studies. The negative association between serum beta‐carotene and femoral neck BMD needs further investigation. Grant Funding Source : None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".