Global mean potassium intake: a systematic review and Bayesian meta-analysis
Bibliographic record
Abstract
PURPOSE: Increasing potassium intake, especially in populations with low potassium intake and high sodium intake, has emerged as an important population-level intervention to reduce cardiovascular events. Current guideline recommendations, such as those made by the World Health Organisation, recommend a potassium intake of > 3.5 g/day. We sought to determine summary estimates for mean potassium intake and sodium/potassium (Na/K) ratio in different regions of the world. METHODS: We performed a systematic review and meta-analysis. We identified 104 studies, that included 98 nationally representative surveys and 6 multi-national studies. To account for missingness and incomparability of data, a Bayesian hierarchical imputation model was applied to estimating summary estimates of mean dietary potassium intake (primary outcome) and sodium/potassium ratio. RESULTS: Overall, 104 studies from 52 countries were included (n = 1,640,664). Mean global potassium intake was 2.25 g/day (57 mmol/day) (95% credible interval (CI) 2.05-2.44 g/day), with highest intakes in Eastern and Western Europe (mean intake 3.53g/day, 95% CI 3.05-4.01 g/day and 3.29 g/day, 95% CI 3.13-3.47 g/day, respectively) and lowest intakes in East Asia (mean intake 1.89 g/day; 95% CI 1.55-2.25 g/day). Approximately 31% (95% CI, 30-41%) of global population included have an estimated potassium intake > 2.5 g/day, with 14% (95% CI 11-17%) above 3.5 g/day. CONCLUSION: Global mean potassium intake (2.25 g/day) falls below current guideline recommended intake level of > 3.5 g/day, with only 14% (95% CI 11-17%) of the global population achieving guideline-target mean intake. There was considerable regional variation, with lowest mean potassium intake reported in Asia, and highest intake in Eastern and Western Europe.
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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 | 0.030 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".