Antidiuretic Hormone and Serum Osmolarity Physiology and Related Outcomes: What Is Old, What Is New, and What Is Unknown?
Bibliographic record
Abstract
CONTEXT: Although the physiology of sodium, water, and arginine vasopressin (AVP), also known as antidiuretic hormone, has long been known, accumulating data suggest that this system operates as a more complex network than previously thought. EVIDENCE ACQUISITION: English-language basic science and clinical studies of AVP and osmolarity on the development of kidney and cardiovascular disease and overall outcomes. EVIDENCE SYNTHESIS: Apart from osmoreceptors and hypovolemia, AVP secretion is modified by novel factors such as tongue acid-sensing taste receptor cells and brain median preoptic nucleus neurons. Moreover, pharyngeal, esophageal, and/or gastric sensors and gut microbiota modulate AVP secretion. Evidence is accumulating that increased osmolarity, AVP, copeptin, and dehydration are all associated with worse outcomes in chronic disease states such as chronic kidney disease (CKD), diabetes, and heart failure. On the basis of these pathophysiological relationships, an AVP receptor 2 blocker is now licensed for CKD related to polycystic kidney disease. CONCLUSION: From a therapeutic perspective, fluid intake may be associated with increased AVP secretion if it is driven by loss of urine concentration capacity or with suppressed AVP if it is driven by voluntary fluid intake. In the current review, we summarize the literature on the relationship between elevated osmolarity, AVP, copeptin, and dehydration with renal and cardiovascular outcomes and underlying classical and novel pathophysiologic pathways. We also review recent unexpected and contrasting findings regarding AVP physiology in an attempt to explain and understand some of these relationships.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".