Estradiol and mortality in women with end-stage kidney disease
Bibliographic record
Abstract
BACKGROUND: Young women with end-stage kidney disease (ESKD) have early menopause compared with women in the general population and the highest mortality among the dialysis population. We hypothesized that low estrogen status was associated with death in women with ESKD. METHODS: We measured estradiol and sex hormone levels in female ESKD patients initiating hemodialysis from 2005 to 2012 in four Canadian centers. We divided women into quintiles based on estradiol levels and tested for associations between the estradiol level and cardiovascular (CV), non-CV and all-cause mortality. Participants were further dichotomized by age. RESULTS: A total of 482 women (60 ± 15 years of age, 53% diabetic, estradiol 116 ± 161 pmol/L) were followed for a mean of 2.9 years, with 237 deaths (31% CV). Estradiol levels were as follows (mean ± standard deviation): Quintile 1: 19.3 ± 0.92 pmol/L; Quintile 2: 34.6 ± 6.6 pmol/L; Quintile 3: 63.8 ± 10.6 pmol/L; Quintile 4: 108.9 ± 19.3; Quintile 5: 355 ± 233 pmol/L. Compared with Quintile 1, women in Quintiles 4 and 5 had significantly higher adjusted all-cause mortality {hazard ratio [HR] 2.12 [95% confidence interval (CI) 1.38-3.25] and 1.92 [1.19-3.10], respectively}. Similarly, compared with Quintile 1, women in Quintile 5 had higher non-CV mortality [HR 2.16 (95% CI 1.18-3.96)]. No associations were observed between estradiol levels and CV mortality. When stratified by age, higher quintiles were associated with greater all-cause mortality (P for trend <0.001) and non-CV mortality (P for trend = 0.02), but not CV mortality in older women. CONCLUSIONS: In women with ESKD treated with hemodialysis, higher estradiol levels were associated with greater all-cause and non-CV mortality. Further studies are required to determine the mechanism for the observed increased risk.
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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 | 0.000 | 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".