Health Effects of Calcium: Evidence From Mendelian Randomization Studies
Bibliographic record
Abstract
ABSTRACT Calcium is widely used in conjunction with vitamin D to prevent osteoporosis. The use of calcium supplementation is also promoted for its potential benefits in lowering the risk for metabolic syndromes and cancers. However, the causal link between calcium and various health outcomes remains unclear. This review focuses on the evidence from 24 Mendelian randomization (MR) studies that were designed to minimize bias from confounding and reverse causation. These MR studies evaluated the effect of lifelong genetically higher serum calcium levels on various health outcomes. Overall, available MR studies found no conclusive effects of serum calcium levels on bone mineral density and fracture, ischemic stroke and heart failure, cancers, type 2 diabetes, Parkinson disease, or offspring birth weight. However, a higher serum calcium concentration was reported to have estimated causal effects on increased risks for coronary artery disease (especially myocardial infarction), migraine, renal colic, allergy/adverse effect of penicillin, and reduced risks for osteoarthrosis and osteoarthritis. In conclusion, supplementation of calcium in individuals from the general population is not predicted to influence the risk of most investigated diseases to date. Moreover, long‐term high serum calcium concentrations may result in adverse health outcomes. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".