Correlation Between Hba1c Levels And Serum Magnesium In Patients With Uncontrolled Glycemia
Bibliographic record
Abstract
Background Type 2 Diabetes Mellitus (T2DM) has emerged as a paramount public health challenge globally, with a disproportionately severe impact on the Middle East and North Africa (MENA) region which is currently navigating a critical epidemiological transition characterized by soaring rates of metabolic syndrome. Prevalence estimates suggest that between 15% and 17.6% of the adult population is afflicted with T2DM, a figure that is compounded by a high rate of undiagnosed cases and pre-diabetes.1 A significant subset of this population experiences uncontrolled glycemia, defined as Glycated Hemoglobin (HbA1c) levels exceeding 7.0%, despite the widespread availability of standard pharmacotherapy. The conventional standard of care (Intervention 2), which includes lifestyle modifications and agents such as metformin, sulfonylureas, and insulin, often fails to arrest the progression of the disease due to issues ranging from medication adherence and economic barriers to unaddressed pathophysiological cofactors. Among these neglected factors, magnesium (Mg) deficiency—or hypomagnesemia—represents a critical gap. Magnesium is an essential cofactor for insulin receptor activity, yet it is rarely assessed in routine clinical practice. Magnesium optimization (Intervention 1) is posited as a promising, cost-effective adjunctive therapy that may enhance insulin sensitivity and improve glycemic outcomes. Objective The primary objective of this systematic review is to comprehensively evaluate the correlation between serum magnesium levels and HbA1c in patients with uncontrolled glycemia and to systematically compare the effectiveness of magnesium assessment and supplementation (Intervention 1) versus standard diabetic care alone (Intervention 2). The review specifically aims to quantify the strength of the inverse relationship between magnesium status and glycemic control and to assess the impact of magnesium sufficiency on secondary outcomes, including lipid profiles and microvascular complications such as neuropathy and retinopathy, within the MENA context. Methods A rigorous systematic review was conducted in adherence to the PRISMA 2020 guidelines. We searched major electronic databases (PubMed, Scopus, Web of Science) and regional repositories for randomized controlled trials (RCTs) and observational studies published up to 2025. The review employed the PICO framework: (P) Adult patients with T2DM, specifically those with uncontrolled glycemia; (I) Serum magnesium measurement or oral magnesium supplementation; (C) Standard of care or normomagnesemic control groups; (O) Primary outcomes included the correlation coefficient between Serum Mg and HbA1c and mean reduction in HbA1c; Secondary outcomes included lipid profile parameters (LDL, HDL, triglycerides) and the prevalence/severity of microvascular complications. Quality assessment was performed using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias 2.0 tool for RCTs. Results The search yielded a robust body of evidence, including multiple cross-sectional studies and RCTs. Analysis of the included studies involving over 2,000 participants revealed a consistent, statistically significant inverse correlation between serum magnesium and HbA1c, with correlation coefficients (r) ranging from -0.29 to -0.969.3 Hypomagnesemia was identified in approximately 28% to 44% of diabetic patients, significantly higher than in healthy controls. Patients with uncontrolled glycemia (HbA1c >7%) exhibited markedly lower mean serum magnesium levels compared to those with controlled diabetes. Secondary outcome analysis demonstrated that magnesium deficiency is independently associated with atherogenic lipid profiles (elevated LDL and triglycerides, reduced HDL) and a higher prevalence of diabetic retinopathy and peripheral neuropathy. Conclusion This review confirms a strong, bidirectional negative correlation between serum magnesium levels and HbA1c in patients with uncontrolled glycemia. Hypomagnesemia acts as both a consequence of osmotic diuresis induced by hyperglycemia and a driver of further insulin resistance, creating a vicious metabolic cycle. The findings suggest that Intervention 1 (magnesium assessment and supplementation) is a neglected but vital component of diabetes management. Incorporating routine magnesium screening and correcting deficiencies could offer a cost-effective strategy to improve glycemic control and reduce the burden of complications. Future research should focus on large-scale interventional trials in the public health sector to establish standardized supplementation protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".