Serum Magnesium Concentrations in the Canadian Population and Associations with Diabetes, Glycemic Regulation, and Insulin Resistance
Bibliographic record
Abstract
Total serum magnesium (Mg) concentration (SMC) is commonly used to assess Mg status. This study reports current SMCs of Canadians and their associations with demographic factors, diabetes, and measures of glycemic control and insulin resistance using results from the Canadian Health Measures Survey cycle 3 (2012–2013). Associations were examined in adults aged 20–79 years using linear mixed models. Mean SMCs and percentile distributions for 11 sex-age groups between 3 and 79 years (n = 5561) are reported. SMCs were normally distributed and differences (p < 0.05) among sex and age groups were small. Between 9.5% and 16.6% of adult sex-age groups had a SMC below the lower cut-off of a population-based reference interval (0.75–0.955 mmol·L−1) established in the United States population as part of the NHANES I conducted in 1971–1974. Having diabetes was associated with 0.04 to 0.07 mmol·L−1 lower SMC compared to not having diabetes in the various models. Body mass index, glycated hemoglobin, serum glucose and insulin concentrations, and homeostatic model assessment of insulin resistance were negatively associated with SMC. This is the first study to report SMCs in a nationally representative sample of the Canadian population. A substantial proportion of Canadians are hypomagnesaemic in relation to a population-based reference interval, and SMC was negatively associated with diabetes and indices of glycemic control and insulin resistance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".