Is Serum Ferritin an Additional Cardiovascular Risk Factor for All Postmenopausal Women?
Bibliographic record
Abstract
BACKGROUND: Most of the studies on cardiovascular disease (CVD) risk factors in menopause have focused on serum lipid(lipoprotein) abnormalities and were conducted in populations which were not well controlled for several important influential factors. METHODS: Two homogenous groups of 30 apparently healthy Caucasian premenopausal women and 3-5 years postmenopausal women who were nonobese, nonsmoking and not using estrogen were compared in a well-controlled cross-sectional design. Fasting serum ferritin and plasma total homocysteine (tHcy) were evaluated concomitantly to classical serum lipid(lipoprotein) risk factors. Relationships between risk factors and the influence of other contributing variables such as diet and body weight were also examined. RESULTS: Serum total cholesterol (p < 0.01), low-density lipoproteins (LDL; p < 0.05) and triglycerides (p < 0.05) of postmenopausal women were greater than that of their menstruating counterparts, even though they ate a CVD-preventive diet, had similar body weight and body fat distribution. Their serum ferritin was almost 3-fold greater (p < 0.0001) but was still within normal limits, except for the 38.5% of postmenopausal women who exhibited values above the 80 mug/l limit that has been associated with sharp increases in the rate of heart disease in either gender. Serum ferritin was low in one third of the postmenopausal group (as low as in the premenopausal control group, whose dietary iron intake was slightly below the nutritional recommendation). The mean plasma tHcy of the postmenopausal group was almost twice as elevated (p < 0.0001). Both ferritin and tHcy were found to be linked to serum cholesterol. The correlation between tHcy and triglycerides was also significant. CONCLUSION: Early menopause is not associated with blood iron overload and CVD risk factor in an important proportion of women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".