Sex Differences in Kidney Failure in Adults Enrolled in CureGN
Bibliographic record
Abstract
Background: Prior studies of sex differences in CKD are from earlier treatment eras or among older, post-menopausal cohorts. We sought to assess sex differences in a longitudinal contemporary cohort of primary glomerular disease (GN) to understand sex-related risk factors and treatment patterns in females vs males. Methods: All adults enrolled in CureGN were included. The primary outcome was kidney failure (KF), defined as reaching dialysis, transplant, or an eGFR < 15ml/min/1.73m2 on two occasions. Kaplan-Meier curves and Cox proportional hazard models adjusted for diagnosis, race and ethnicity, age, eGFR, UPCR, and hypertension at enrollment, assessed risk for KF in females vs males. Longitudinal logistic regression assessed odds of renin-angiotensin aldosterone system inhibitor (RAASi) prescription at each study visit by sex and age. Subgroups by age were categorized based on the mean age of menopause in women (18-50 yrs vs > 50 yrs). Results: Table 1 describes the cohort at enrollment. In adjusted models, females had lower risk of KF than males (aHR 0.76, 95% CI 0.58-0.98). In disease-stratified models, females with MN had lower risk of KF but there was no sex difference in IgA nephropathy or FSGS (Figure 1). Over follow-up, females 18-50 yrs had lower odds of receiving RAASi than all other subgroups (vs males 18-50 yrs: OR 0.25, p<0.001) excluding those with pregnancy after enrollment. Females >50 yrs had similar odds to both male subgroups. Conclusion: Females were at lower risk of KF after a median follow-up of 5.6 years, after adjustment for enrollment characteristics. Given that younger females were also less likely to receive supportive RAASi therapy, other factors likely play a protective role. Funding: NIDDK SupportCharacteristics at enrollment - Total N Mean age (years) MN (n) IgA (n) FSGS (n) MCD (n) Mean eGFR (ml/min/1.73m2) Mean UPCR (g/g) % HTN % on RAASi Males 1030 46 342 310 256 122 69 3.4 26 70 Females 792 45 215 229 213 135 73 3.1 21 65
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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) | 0.000 | 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) | 0.002 | 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".