Flaxseed enhances the beneficial effect of low-dose estrogen therapy at reducing bone turnover and preserving bone microarchitecture in ovariectomized rats
Bibliographic record
Abstract
Our previous research showed greatest protection to vertebral bone mineral density and strength in ovariectomized (OVX) rats when lignan- and α-linolenic acid-rich flaxseed (FS) is combined with low-dose estrogen therapy (LD) compared with either treatment alone. This study determined the effects of combined FS+LD on serum and tissue markers of bone turnover and microarchitecture to explain our previous findings. Three-month-old OVX rats were randomized to negative control (NEG), FS, LD or FS+LD for 2 or 12 weeks, meaningful time points for determining effects on markers of bone metabolism and bone structure, respectively. Ground FS was added to the AIN-93M diet (100 g/kg diet) and LD (0.42 μg 17β-estradiol/(kg body weight·day)) was delivered by subcutaneous implant. Sham rats were included as positive control. Bone formation (e.g., osteocalcin), bone resorption (e.g., tartrate-resistant acid phosphatase-5β (TRAP-5β)), as well as osteoprotegerin (OPG) and receptor activator of nuclear factor κ-B ligand (RANKL) were analyzed from the 2-week study by commercial assays (serum) and (or) histology (vertebra). Vertebral bone microarchitecture was measured from the 12-week study using microcomputed tomography. In serum, FS+LD and LD induced lower TRAP-5β and osteocalcin, and higher OPG and OPG/RANKL ratio versus NEG and FS (p < 0.05). In vertebrae, FS+LD induced higher OPG and lower osteocalcin versus NEG (p < 0.01) and did not differ from LD and FS. FS+LD improved bone microarchitecture versus NEG, FS, and LD (p < 0.05). In conclusion, FS+LD protects bone tissue because of a reduction in bone turnover. However, elucidating the distinctive action of FS+LD on bone turnover compared with LD requires further investigation.
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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 | 0.000 |
| 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 | 0.001 |
| 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".