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Record W4297516490 · doi:10.1101/2022.09.28.509841

A mathematical model of potassium homeostasis: Effect of feedforward and feedback controls

2022· preprint· en· W4297516490 on OpenAlexafffund
Melissa M. Stadt, Jessica Leete, Sophia Devinyak, Anita T. Layton

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAldosteroneHomeostasisInternal medicineEndocrinologyExtracellularExcretionChemistryKidneyExtracellular fluidIntracellularSecretionBiologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Abstract Maintaining normal potassium (K + ) concentrations in the extra- and intracellular fluid is critical for cell function. K + homeostasis is achieved by ensuring proper distribution between extra- and intracellular fluid compartments and by matching K + excretion with intake. The Na + -K + -ATPase pump facilitates K + uptake into the skeletal muscle, where most K + is stored. Na + -K + -ATPase activity is stimulated by insulin and aldosterone. The kidneys regulate long term K + regulation by controlling the amount of K + excreted through urine. Renal handling of K + is mediated by a number of regulatory mechanisms, including an aldosterone-mediated feedback control, in which high extracellular K + concentration stimulates aldosterone secretion which enhances urine K + excretion, and a gastrointestinal feedforward control mechanism, in which dietary K + intake increases K + excretion. Recently, a muscle-kidney cross talk signal has been hypothesized, where the K + concentration in skeletal muscle cells directly affects urine K + excretion without changes in extracellular K + concentration. To understand how these mechanisms coordinate under different K + challenges, we have developed a compartmental model of whole-body K + regulation. The model represents the intra- and extracellular fluid compartments in a human (male) as well as a detailed kidney compartment. We included (i) the gastrointestinal feedforward control mechanism, (ii) the effect of insulin and (iii) aldosterone on Na + -K + -ATPase K + uptake, and (iv) aldosterone stimulation of renal K + secretion. We used this model to investigate the impact of regulatory mechanisms on K + homeostasis. Model predictions showed how the regulatory mechanisms synthesize to ensure that the extra- and intracelluller fluid K + concentrations remain in normal range in times of K + loading and fasting. Additionally, we predict that without the hypothesized muscle-kidney cross talk signal, the model was unable to predict a return to normal extracellular K + concentration after a period of high K + loading or depletion. Author summary Potassium (K + ) homeostasis is crucial for normal cell function. Dysregulation of K + can have dangerous consequences and is a common side effect of pathologies, medications, or changes in hormone levels. Due to its complexities, how the body maintains extra- and intracellular K + concentrations each day is not fully understood. Of particular interest is capturing how regulatory mechanisms synthesize to be able to keep extracellullar K + concentration within a tight range of 3.5-5.0 mEq/L. There are a multitude of physiological processes involved in K + balance, making its study well suited for investigation using mathematical modeling. In this study, we developed a compartment model of extra- and intracellular K + regulation including the various regulatory mechanisms and a detailed kidney model. The significance of our research is to quantify the effect of individual regulatory mechanisms on K + regulation as well as predict the potential impact of a hypothesized signal: muscle-kidney cross talk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.231
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

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