Effects of magnesium supplementation on carotid intima–media thickness and metabolic profiles in diabetic haemodialysis patients: a randomised, double-blind, placebo-controlled trial
Post-publication record
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Bibliographic record
Abstract
This study evaluated the effects of Mg administration on carotid intima-media thickness (CIMT), glycaemic control and markers of cardio-metabolic risk in diabetic haemodialysis (HD) patients. This randomised, double-blind, placebo-controlled clinical trial was conducted in fifty-four diabetic HD patients. Participants were randomly divided into two groups to take either 250 mg/d Mg as magnesium oxide (n 27) or placebo (n 27) for 24 weeks. Mg supplementation resulted in a significant reduction in mean (P<0·001) and maximum levels of left CIMT (P=0·02) and mean levels of right CIMT (P=0·004) compared with the placebo. In addition, taking Mg supplements significantly reduced serum insulin levels (β=-9·42 pmol/l; 95% CI -14·94, -3·90; P=0·001), homoeostasis model of assessment-insulin resistance (β=-0·56; 95 % CI -0·89, -0·24; P=0·001) and HbA1c (β=-0·74 %; 95 % CI -1·10, -0·39; P<0·001) and significantly increased the quantitative insulin sensitivity check index (β=0·008; 95 % CI 0·002, 0·01; P=0·002) compared with the placebo. In addition, Mg administration led to a significant reduction in serum total cholesterol (β=-0·30 mmol/l; 95% CI -0·56, -0·04; P=0·02), LDL-cholesterol (β=-0·29 mmol/l; 95% CI -0·52, -0·05; P=0·01), high-sensitivity C-reactive protein (hs-CRP) (P<0·001) and plasma malondialdehyde (MDA) (P=0·04) and a significant rise in plasma total antioxidant capacity (TAC) levels (P<0·001) compared with the placebo. Overall, we found that taking Mg for 24 weeks by diabetic HD patients significantly improved mean and maximum levels of left and mean levels of right CIMT, insulin metabolism, HbA1c, total cholesterol and LDL-cholesterol, hs-CRP, TAC and MDA levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".