Magnesium (Mg) is the main intracellular divalent cation, and under basal conditions the small intestine absorbs 30-50% of its intake. Normal serum Mg ranges between 1.7-2.3 mg/dl (0.75-0.95 mmol/l), at any age. Even though eighty percent of serum Mg is filtered at the glomerulus, only 3% of it is finally excreted in the urine. Altered magnesium balance can be found in diabetes mellitus, chronic renal failure, nephrolithiasis, osteoporosis, aplastic osteopathy, and heart and vascular disease. Three physiopathologic mechanisms can induce Mg deficiency: reduced intestinal absorption, increased urinary losses, or intracellular shift of this cation. Intravenous or oral Mg repletion is the main treatment, and potassium-sparing diuretics may also induce renal Mg saving. Because the kidney has a very large capacity for Mg excretion, hypermagnesemia usually occurs in the setting of renal insufficiency and excessive Mg intake. Body excretion of Mg can be enhanced by use of saline diuresis, furosemide, or dialysis depending on the clinical situation.
"Mg 2+ also plays an important role in sustaining genomic stability and preventing carcinogenicity . Therefore, it is quite apprehensible that alterations in Mg 2+ metabolism would correlate to many important human diseases , including metabolic syndromes such as type 2 diabetes mellitus and hypertension . In higher plants, Mg 2+ also plays a critical role in regulating the chloroplast mRNA stability . "
"Magnesium, one of the most abundant minerals in the body, is essential for over 300 biochemical processes and plays important roles in activating cellular enzymatic activity, such as those needed to synthesize DNA and RNA, and also in metabolism (Musso, 2009). For athletes it is important because of its involvement in glycolysis, the citric acid cycle and creatine phosphate production. "
[Show abstract][Hide abstract] ABSTRACT: The effects of magnesium supplementation on blood pressure (BP) have been studied for over 25 years and results have been inconsistent. Blood pressure reductions in randomized studies have varied from 12 mmHg reductions to no reduction. The objective of this pilot intervention was to investigate the effect of magnesium supplementation on systolic blood pressure whilst resting and during recovery from aerobic and resistance exercise and on performance. A further objective was to see whether the effect of a high vs low habitual dietary magnesium intake affected these results. Sixteen male volunteers were randomly assigned to either a 300 mg·d-1 magnesium oxide supplementation (MO) or a control group (CG) for 14 days. Resting blood pressure (BP) and heart rate (HR) were measured before subjects performed a maximal 30 minute cycle, immediately followed by three x 5 second isometric bench press, both at baseline and after the intervention. Blood pressure and heart rate were recorded immediately post exercise and after five minutes recovery. A 3 day food diary was recorded for all subjects to measure dietary magnesium intake. At the end of the intervention, the supplemented group, had a reduction in mean resting systolic BP by 8.9 mmHg (115.125 ± 9.46 mmHg, p = 0.01) and post exercise by 13 mmHg (122.625 ± 9.88 mmHg, p = 0.01). Recovery BP was 11.9 mmHg lower in the intervention group compared to control (p = 0.006) and HR decreased by 7 beats per minute in the experimental group (69.0 ± 11.6 bpm, p = 0.02). Performance indicators did not change within and between the groups. Habitual dietary magnesium intake affected both resting and post exercise systolic BP and the subsequent effect of the magnesium supplementation. These results have an implication in a health setting and for health and exercise but not performance.
Journal of sports science & medicine 10/2013; 12(1):144-50. · 1.03 Impact Factor
"We prepared some artificial samples, at different concentration of sodium, potassium, calcium, magnesium, based on the following considerations. In normal physiological conditions, the sodium concentration in plasma is around 140 mEq/l; the concentration of the other cations, which represent the main confounders for conductivity-based sodium measurements, are around 4-5 mEq/l for potassium and calcium, and 1.5 mEq/l for magnesium [11,12]. "
[Show abstract][Hide abstract] ABSTRACT: Sodium measurement during hemodialysis treatment is important to preserve the patient from clinical events related to hypo- or hyper-natremia Usually, sodium measurement is performed through laboratory equipment which is typically expensive, and requires manual intervention. We propose a new method, based on conductivity measurement after treatment of dialysate solution through ion-exchange resin. To test this method, we performed in vitro experiments. We prepared 40 ml sodium chloride (NaCl) samples at 280, 140, 70, 35, 17.5, 8.75, 4.375 mEq/l, and some "mixed samples", i.e., with added potassium chloride (KCl) at different concentrations (4.375-17.5 mEq/l), to simulate the confounding factors in a conductivity-based sodium measurement. We measured the conductivity of all samples. Afterwards, each sample was treated for 1 min with 1 g of Dowex G-26 resin, and conductivity was measured again. On average, the difference in the conductivity between mixed samples and corresponding pure NaCl samples (at the same NaCl concentration) was 20.9%. After treatment with the exchange resin, it was 14.7%, i.e., 42% lower. Similar experiments were performed with calcium chloride and magnesium chloride as confounding factors, with similar results. We also performed some experiments on actual dialysate solution during hemodialysis sessions in 15 patients, and found that the correlation between conductivity measures and sodium concentration improved after resin treatment (R=0.839 before treatment, R=0.924 after treatment, P<0.0001). We conclude that ion-exchange resin treatment coupled with conductivity measures may improve the measurement of sodium compared to conductivity measures alone, and may become a possible simple approach for continuous and automatic sodium measurement during hemodialysis.
PLoS ONE 07/2013; 8(7):e69227. DOI:10.1371/journal.pone.0069227 · 3.23 Impact Factor
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