Kidney Transcriptomics of Blood Pressure (BP) in Minimal Change Disease (MCD) and Focal Segmental Glomerulosclerosis (FSGS)
Bibliographic record
Abstract
Background: Individuals with MCD and FSGS are at high risk for hypertension and cardiovascular disease, however molecular markers of BP in this population are unknown. The objective was to investigate kidney tissue differential gene expression associated with BP in MCD/FSGS. Methods: Participants with biopsy-proven MCD or FSGS from the Nephrotic Syndrome Study Network (NEPTUNE) with previously sequenced genome-wide mRNA expression profiling of kidney tissue were included. Glomerular and tubulointerstitial transcriptomics were assessed for differentially expressed (DE) genes in adjusted linear models for enrollment BP. Systolic and diastolic BP were indexed (SBPi/DBPi) to the 95th%ile for children <13 years and to 130 mmHg for those ≥13 years. Results: Participants included 192 children (age 11 IQR 5-14 yr, 57.3% male) and 370 adults (age 45 IQR 32.8-58.3 yr, 60.8% male), with 28.7% FSGS. Median SBPi was 0.8 IQR 0.70-0.9 and DBPi was 0.87 IQR 0.78-1, with 47.3% on RAAS blockade. Adjusting for sex only, there were 865 genes at 5% and 1622 at 10% FDR associated with SBPi, but none for DBPi. Adjusting for sex, age, and glomerular filtration rate (eGFR) revealed no significant genes for either SBPi or DBPi at 10% FDR (Figure 1). By p-value, the top genes in the SBPi and DBPI models were PNMA8C (p=3.2e-5) and PHTF1 (p=5.6e-5), respectively. There were no DE genes from tubulointerstitial tissue associated with BP.Figure 1.: Glomerular differential gene expression of blood pressure adjusted for age, sex and eGFRConclusions: Though hypertension is an important risk factor for progression of kidney disease, BP was not associated with differential gene expression in kidney tissue after adjusting for common confounders in patients with MCD/FSGS enrolled in NEPTUNE. Funding: NIDDK Support
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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 | 0.000 | 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".