Magnesium intake, insulin resistance and markers of endothelial function among women
Bibliographic record
Abstract
OBJECTIVE: We investigated the association of dietary Mg intake with insulin resistance and markers of endothelial function among Iranian women. DESIGN: A cross-sectional study. SETTING: Usual dietary intakes were assessed using a validated FFQ. Dietary Mg intake was calculated by summing up the amount of Mg in all foods. A fasting blood sample was taken to measure serum concentrations of glycemic indices (fasting plasma glucose and insulin) and endothelial function markers (E-selectin, soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1). Insulin resistance and sensitivity were estimated using the Homeostasis Model Assessment-Insulin Resistance (HOMA-IR), Homeostasis Model Assessment β-cell function (HOMA-β) and quantitative insulin sensitivity check index (QUICKI). PARTICIPANTS: Iranian female nurses (n 345) selected by a multistage cluster random sampling method. RESULTS: The Mg intake across energy-adjusted quartiles was 205 (se 7), 221·4 (se 8), 254·3 (se 7) and 355·2 (se 9) mg/d, respectively. After adjustments for potential confounders, QUICKI level was significantly different across quartiles of Mg intake (Q1: 0·34 (se 0·02), Q2: 0·36 (se 0·01), Q3: 0·40 (se 0·01), and Q4: 0·39 (se 0·02), P = 0·02); however, this association disappeared after considering markers of endothelial function, indicating that this relation might be mediated through endothelial dysfunction. After controlling for all potential confounders, Mg intake was inversely, but not significantly, associated with serum concentrations of sICAM (Q1: 239 (se 17), Q2: 214 (se 12), Q3: 196 (se 12), and Q4: 195 (se 17), P = 0·29). There was no other significant association between dietary Mg intake and other indicators of glucose homoeostasis or endothelial markers. CONCLUSIONS: Higher dietary Mg intake was associated with better insulin sensitivity in Iranian females. This linkage was mediated through reduced endothelial dysfunction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".