Tissue Sodium Storage in Patients With Heart Failure: A New Therapeutic Target?
Bibliographic record
Abstract
Background: Preclinical data suggest sodium deposited (without water) in tissues may lead to aberrant remodeling and systemic inflammation, independently of fluid overload in patients with heart failure (HF). Tissue salt storage can be measured noninvasively and quantitatively with 23 Na-magnetic resonance imaging. We aimed to investigate the possibility that patients with HF complicated by renal dysfunction are subject to higher tissue sodium concentration exposure than patients with chronic kidney disease alone. Methods: We conducted an exploratory study including 18 patients with HF, 34 hemodialysis patients (with no meaningful renal clearance of sodium), and 31 patients with chronic kidney disease, with glomerular filtration rate matched to the patients with HF. Every patient underwent 23 Na-magnetic resonance imaging of the calf, to quantify tissue sodium and allow comparison among the 3 patient groups. Results: There were no differences in age, sex, and body mass index between groups. Median (interquartile range) skin sodium content in HF (31 [23–37] mmol/L) was very high and indistinguishable from skin sodium content in hemodialysis patients (30 [22–35] mmol/L), P =0.6. Patients with HF exhibited significantly higher skin sodium content than matched estimated glomerular filtration rate chronic kidney disease patients (22 [19–26] mmol/L), P =0.005. Median muscle sodium content in patients with HF was significantly higher than in patients with chronic kidney disease, P =0.002. There was no relationship with estimated glomerular filtration rate in patients with HF. We report a significant correlation between skin sodium and urinary sodium ( P =0.04) but no correlation with muscle sodium. Patients who were assessed as being volume depleted (sodium excretion fraction <1%) had lower skin sodium content than patients with sodium excretion fraction >1% ( P =0.03). Conclusions: We have demonstrated that patients with HF characteristically have very high levels of skin sodium storage, comparable to well-characterized extreme levels seen in patients with end-stage kidney disease requiring hemodialysis. 23 Na-magnetic resonance imaging may allow precision medicine in the management of this challenging group of patients with HF. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03004547.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".