LOWER CIRCULATING MIR-191-5P AND LET-7G-5P ARE INDEPENDENT BIOMARKERS OF RENAL INJURY
Bibliographic record
Abstract
Objective: Hypertension is associated with subclinical target organ damage including cardiac, vascular and kidney injury. Circulating microRNAs have been investigated as biomarkers of cardiovascular disease, but few studies have examined them as target organ damage biomarkers in hypertension. We aimed to identify circulating microRNAs that could serve as biomarkers of hypertension-induced target organ damage using an unbiased approach. Design and method: Normotensive subject, hypertensive patients (HTN) and patients with HTN associated with at least 2 other features of the metabolic syndrome (MetS) or with chronic kidney disease (CKD) grades 3–4 were studied (n = 15–16). Blood pressure was determined by automated office measurement (AOBP). Blood and urine were collected for determination of blood cell count, blood biochemistry and urinalysis. Platelet-free plasma (6 mL) was isolated from blood collected on EDTA, and RNA extracted for small RNA deep sequencing using Illumina HiSeq-2500. Differentially expressed (DE) genes were identified with a threshold of false discovery rate < 0.1. The most abundant DE miRNAs were confirmed by reverse transcription-quantitative PCR (RT-qPCR). Right common carotid artery remodeling and stiffness were assessed by ultrasound and aorta stiffness by carotid to femoral pulse wave velocity. Results: We found 4 up-regulated and 4 down-regulated miRNAs uniquely associated with the HTN group, 1 up-regulated uniquely associated with the MetS group, 1 up-regulated and 12 down-regulated miRNAs uniquely associated with the CKD group and 8 were found similarly DE in different groups (P < 0.01 and q < 0.1). Two down-regulated miRNAs, let-7g-5p and miR-191–5p of 11, the most abundant DE miRNAs, were validated by RT-qPCR. Correlation analysis revealed that let-7g-5p was associated with large vessel stiffening, miR-191–5p with diabetes, and both microRNAs with estimated glomerular filtration rate (eGFR) and inflammatory markers. Using let-7g-5p and miR-191–5p and parameters that correlated with eGFR as candidate variables, 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 0.40 (P = 1.1e-5). Conclusions: Circulating let-7g-5p and miR-191–5p were identified as independent biomarkers of chronic kidney disease among patients with hypertension, which could have pathophysiological and therapeutic implications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".