Bone mineral density, urinary sodium, and urinary calcium in healthy young Asian and Caucasian women
Bibliographic record
Abstract
Sodium (Na) intake has been linked to bone density through effects on urinary calcium (Ca) excretion, but few data are available in healthy young women. We assessed 24‐hr urinary Na excretion and its relationship with urinary Ca and areal bone mineral density (aBMD) at the total body, lumbar spine and total hip in a cross‐sectional study. 110 healthy non‐obese Asian and Caucasian women completed timed 24‐hr urine collections which were analyzed for Na and Ca. Dietary intakes were estimated using the Diet History Questionnaire, a validated food frequency questionnaire (FFQ), and aBMD was measured using dual energy x‐ray absorptiometry. Urinary Na was 2968 ± 1090 mg/d, significantly higher than intake of 2670 ± 1088 mg/d assessed by FFQ. Neither urinary Na nor Na intake differed between Asians and Caucasians. Dietary intakes correlated with 24‐hr urinary losses for both Na (r = 0.23, p = 0.017) and Ca (r = 0.21, p = 0.025). Controlling for calcium intake, urinary Na correlated positively with urinary Ca (r = 0.26, p = 0.007) and negatively with hip aBMD (r = −0.23, p = 0.015). Associations with total body (r = −0.16) and spine (r = −0.10) aBMD were also negative, but not significant. In conclusion, 24‐hr urinary Na (a proxy for intake) is associated with higher urinary Ca loss in young women, and may affect aBMD. Supported by Canadian Institutes of Health Research 79563.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".