Hypomagnesemia and the Metabolic Syndrome among Apparently Healthy Kuwaiti Adults: A Cross-Sectional Study
Bibliographic record
Abstract
Magnesium plays a key role in metabolic disorder development, and hypomagnesemia may be implicated in the pathogenesis of metabolic syndrome (MetS) and its components. In this cross-sectional study, we investigated the associations between hypomagnesemia, MetS, and MetS components among 231 adults (193 women and 38 men) living in Kuwait who were apparently healthy without chronic diseases. We used the International Diabetes Federation (IDF) and the United States National Cholesterol Education Program Adult Treatment Panel III (ATP III) criteria to define participants with MetS. The Ministry of Health cutoff for hypomagnesemia (<0.74 mmol/L) was employed. IDF- and ATP III-defined MetS prevalence was 22.1% and 15.2%, respectively. Hypomagnesemia occurred in 33.3% of all participants and 53.2% of participants with MetS (p < 0.001). Magnesemia correlated negatively with body mass index, waist circumference, systolic blood pressure [SBP], diastolic blood pressure (DBP), fasting blood glucose (FBG), low-density lipoprotein cholesterol level, and triglyceride level; magnesemia correlated positively with high-density lipoprotein cholesterol (HDL-C) levels (p < 0.001). Multivariate logistic regression, adjusting for BMI, age, and sex, showed that hypomagnesemia was associated with a 12- and 5-fold greater odds of getting IDF-defined (adjusted odds ratio [aOR] 11.70; 95% confidence interval [CI] 4.87−28.14) and ATP-defined (aOR 5.44; 95% CI 2.10−14.10) MetS, respectively, in the study population. Hypomagnesemia was significantly associated with a 3.62, 9.29, 7.01, 2.88, 3.64, and 3.27 higher odds of an increased waist circumference (95% CI 1.48−8.85), elevated serum triglyceride level (95% CI 3.97−21.73), elevated FBG (95% CI 3.25−15.11), elevated SBP (95% CI 1.16−7.11), elevated DBP (95% CI: 1.22−10.89), and lowered HDL-C level (95% CI 1.69−6.32), respectively. Hypomagnesemia could be a consequence of the pathophysiology of MetS and its individual components among adults in Kuwait.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".