Circulating let-7g-5p and miR-191-5p Are Independent Predictors of Chronic Kidney Disease in Hypertensive Patients
Bibliographic record
Abstract
BACKGROUND: Hypertension (HTN) is associated with target organ damage such as cardiac, vascular, and kidney injury. Several studies have investigated circulating microRNAs (miRNAs) as biomarkers of cardiovascular disease, but few have examined them as biomarker of target organ damage in HTN. We aimed to identify circulating miRNAs that could serve as biomarkers of HTN-induced target organ damage using an unbiased approach. METHODS AND RESULTS: Fifteen normotensive subjects, 16 patients with HTN, 15 with HTN associated with other features of the metabolic syndrome (MetS), and 16 with HTN or chronic kidney disease (CKD) were studied. Circulating RNA extracted from platelet-poor plasma was used for small RNA sequencing. Differentially expressed (DE) genes were identified with a threshold of false discovery rate <0.1. DE miRNAs were identified uniquely associated with HTN, MetS, or CKD. However, only 2 downregulated DE miRNAs (let-7g-5p and miR-191-5p) could be validated by reverse transcription-quantitative PCR. Let-7g-5p was associated with large vessel stiffening, miR-191-5p with MetS, and both miRNAs with estimated glomerular filtration rate (eGFR) and neutrophil and lymphocyte fraction or number and neutrophil-to-lymphocyte ratio. Using the whole population, stepwise multiple linear regression generated a model showing that let-7g-5p, miR-191-5p, and urinary albumin/creatinine ratio predicted eGFR with an adjusted R2 of 0.46 (P = 8.5e-7). CONCLUSIONS: We identified decreased circulating let-7g-5p and miR-191-5p as independent biomarkers of CKD among patients with HTN, which could have pathophysiological and therapeutic implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".