Clinical Significance of Adropin and Salusins Circulating Levels in End‐Stage Kidney Disease Patients With Cardiovascular Events
Bibliographic record
Abstract
INTRODUCTION: Patients with end-stage kidney disease (ESKD) face a significantly increased risk of cardiovascular disease (CVD). Effective management of these patients necessitates the early identification and continuous monitoring of cardiovascular complications. This study aimed to evaluate the clinical utility of circulating adropin, salusin-α (Sal-α), and salusin-β (Sal-β) levels in ESKD patients, with and without co-occurring CVD. METHODS: We enrolled 149 participants, categorized into three groups: healthy controls (n = 50), ESKD patients without CVD (n = 50), and ESKD patients with CVD (n = 49). We measured anthropometric parameters, markers of kidney and cardiac function, and circulating levels of adropin, Sal-α, and Sal-β. RESULTS: Serum adropin and Sal-α levels were significantly lower in ESKD patients with CVD compared to both healthy controls and ESKD patients without CVD. Conversely, ESKD patients with CVD exhibited significantly higher Sal-β levels and a higher Sal-β/Sal-α ratio when compared to controls and ESKD patients without CVD. Furthermore, high circulating adropin levels were associated with a decreased risk of CVD, whereas elevated circulating Sal-β levels and an increased Sal-β/Sal-α ratio were associated with an increased CVD risk. The Sal-β level and Sal-β/Sal-α ratio demonstrated the highest diagnostic efficacy in differentiating ESKD patients with CVD from those without CVD. Combining these parameters further improved diagnostic efficacy. CONCLUSION: Adropin insufficiency and an imbalance in salusin levels (specifically an elevated Sal-β/Sal-α ratio) may play a role in the pathogenesis of CVD in ESKD patients. The circulating levels of adropin and Sal-β, along with the Sal-β/Sal-α ratio, appear to be valuable diagnostic biomarkers for CVD in this high-risk population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".