A modeling analysis of whole-body potassium regulation on a high potassium diet: Proximal tubule and tubuloglomerular feedback effects
Bibliographic record
Abstract
Abstract Potassium (K + ) is an essential electrolyte that plays a key role in many physiological processes, including mineralcorticoid action, systemic blood-pressure regulation, as well as hormone secretion and action. Indeed, maintaining K + balance is critical for normal cell function, as too high or too low K + levels can have serious and potentially deadly health consequences. K + homeostasis is achieved by an intricate balance between the intracellular and extracellular fluid as well as balance between K + intake and excretion. This is achieved via the coordinated actions of regulatory mechanisms such as the gastrointestinal feedforward effect, insulin and aldosterone upregulation of Na + -K + -ATPase uptake, and hormone and electrolyte impacts on renal K + handling. We recently developed a mathematical model of whole-body K + regulation to unravel the individual impacts of regulatory mechanisms. In this study, we extend our mathematical model to incorporate recent experimental findings that showed decreased fractional proximal tubule reabsorption under a high K + diet. We conducted model simulations and sensitivity analyses to unravel how these renal alterations impact whole-body K + regulation. Our results suggest that the reduced proximal tubule K + reabsorption under a high K + diet could achieve K + balance in isolation, but the resulting tubuloglomerular feedback reduces filtration rate and thus K + excretion. Model predictions quantify the sensitivity of K + regulation to various levels of proximal tubule K + reabsorption adaptation and tubuloglomerular feedback. Additionally, we predict that without the hypothesized muscle-kidney cross talk signal, intracellular K + stores can exceed normal range under a high K + diet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".