Phosphate in Cardiovascular Disease: From New Insights Into Molecular Mechanisms to Clinical Implications
Bibliographic record
Abstract
Hyperphosphatemia is a common feature in patients with impaired kidney function and is associated with increased risk of cardiovascular disease. This phenomenon extends to the general population, whereby elevations of serum phosphate within the normal range increase risk; however, the mechanism by which this occurs is multifaceted, and many aspects are poorly understood. Less than 1% of total body phosphate is found in the circulation and extracellular space, and its regulation involves multiple organ cross talk and hormones to coordinate absorption from the small intestine and excretion by the kidneys. For phosphate to be regulated, it must be sensed. While mostly enigmatic, various phosphate sensors have been elucidated in recent years. Phosphate in the circulation can be buffered, either through regulated exchange between extracellular and cellular spaces or through chelation by circulating proteins (ie, fetuin-A) to form calciprotein particles, which in themselves serve a function for bulk mineral transport and signaling. Either through direct signaling or through mediators like hormones, calciprotein particles, or calcifying extracellular vesicles, phosphate can induce various cardiovascular disease pathologies: most notably, ectopic cardiovascular calcification but also left ventricular hypertrophy, as well as bone and kidney diseases, which then propagate phosphate dysregulation further. Therapies targeting phosphate have mostly focused on intestinal binding, of which appreciation and understanding of paracellular transport has greatly advanced the field. However, pharmacotherapies that target cardiovascular consequences of phosphate directly, such as vascular calcification, are still an area of great unmet medical need.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".