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Record W4404852102 · doi:10.1101/2024.11.27.24318119

Effect of Change Potassium Intake on Systolic Blood Pressure: A Dose-Response Meta-Analysis of Randomized Clinical Trials (2000-2024)

2024· preprint· en· W4404852102 on OpenAlexaff
Maelys Granal, Victoria Sourd, Michel Burnier, Jean‐Pierre Fauvel, A. Gougeon

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsHypertension Canada
Fundersnot available
KeywordsBlood pressurePotassiumMedicineInternal medicineChemistry

Abstract

fetched live from OpenAlex

Abstract Background The global prevalence of hypertension in adults has doubled in 30 years, rising from 650 million to 1.3 billion between 1990 and 2019. Although reducing sodium intake is one of the first core of non-pharmacological hypertension management guidelines, recent guidelines strongly emphasize the need for increased potassium intake due to accumulating evidence of its cardiovascular benefits. Methods We conducted a systematic review to identify randomized controlled trials evaluating the effect of potassium supplementation (measured through 24-hour urinary potassium excretion) on systolic blood pressure. A dose-response meta-analysis was performed using three regression models: linear, quadratic and one-stage cubic spline. Subgroup analyses were performed according to hypertensive or normotensive status. Results The meta-analysis included 10 randomized clinical trials comprising 4 studies in normotensive individuals and 6 in hypertensive patients. The dose-response relationship differed based on participants’ blood pressure status. Subgroup analyses revealed a weak negative linear effect of potassium on systolic blood pressure in normotensive individuals, while a stronger negative linear relationship was observed in hypertensive patients. The dose-response meta-analysis predicted that for a 50 mmol increase in urinary potassium excretion over 24 hours, systolic blood pressure decreased by 0.5 mmHg in the normotensive group and by 5.3 mmHg in the hypertensive group. Conclusion These meta-analysis results confirm a dose-response relationship between potassium supplementation and systolic blood pressure reduction, particularly in individuals with hypertension. Predictions should be viewed with caution due to the limited number of studies included in the study. Novelty and relevance What is new? The dose-response relationship between change in potassium intake and change in systolic blood pressure was described using three statistical models (linear, quadratic and cubic spline) to avoid a priori assumptions. The dose-response meta-analysis revealed a weak linear relationship between increased potassium intake and decreased systolic blood pressure in normotensive individuals. The effect was more pronounced in hypertensive patients. What is relevant? The meta-analysis specifically addresses the impact of change in potassium intake on change in systolic blood pressure selecting only recent (since the 2000s), high-quality randomized clinical trials in witch potassium intake was accurately estimated through 24-hour urinary potassium excretion. The included studies underwent rigorous assessment of bias. These analyses stratified by normotensive or hypertensive status align with current therapeutic and nutritional management practices, as well as recent diagnostic methods (measuring devices, thresholds). These findings aim to facilitate decision-making for future national and international nutritional recommendations. Clinical/pathophysiological implications? The results of this dose-response meta-analysis support current nutritional recommendations to increase dietary potassium intake to lower blood pressure, particularly in hypertensive patients. Improved potassium intake management could significantly improve blood pressure control and reduce cardiovascular risk, particularly in high-risk hypertensive patients.

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)0.138
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (broad), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1380.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0210.017
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.249
GPT teacher head0.460
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

Study designMeta-analysis
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

Citations0
Published2024
Admission routes1
Has abstractyes

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