Influence of magnesium on biochemical parameters of iron and oxidative stress in patients with type 2 diabetes

Introduction: Studies have shown that oxidative stress, found in patients with type 2 diabetes, may be due to changes in the metabolism of minerals, such as magnesium and iron. Data related to compartmentalization of these minerals in diabetes are scarce and controversial. Objective: This study assessed the influence of magnesium on biochemical parameters of iron and oxidative stress in patients with type 2 diabetes. Methods: A case-control study in male and female subjects aged 27-59 years, divided into two groups: type 2 diabetes (n=40) and control (n=48). Intake of magnesium and iron was assessed by three-day food record. Plasma, erythrocyte and urinary levels of magnesium, serum iron, ferritin, total iron binding capacity, fasting glucose, glycated hemoglobin, insulin, creatinine clearance and plasma thiobarbituric acid reactive substances (TBARS) were analyzed. Results and Discussion: Magnesium intake and plasma magnesium were lower in diabetic subjects. There was low urinary magnesium excretion, with no difference between groups. Although normal, the diabetic group had lower serum iron and ferritin concentrations compared to control subjects. Plasma TBARS in diabetic patients was higher than control while creatinine clearance was lower. An inverse correlation between erythrocyte magnesium and serum iron and ferritin was observed in the diabetes group. Conclusions: Diabetes induced hypomagnesemia and this, associated with chronic hyperglycemia, may have enhanced oxidative stress. Erythrocyte magnesium may have contributed to prevent iron overload and worsening of oxidative stress and hyperglycemic status.

Comparison of the traditional pharmaceutical validation method versus an assisted pharmaceutical validation in hospitalized patients

Objective: To analyze pharmaceutical interventions that have been carried out with the support of an automated system for validation of treatments vs. the traditional method without computer support. Method: The automated program, ALTOMEDICAMENTOS® version 0, has 925 052 data with information regarding approximately 20 000 medicines, analyzing doses, administration routes, number of days with such a treatment, dosing in renal and liver failure, interactions control, similar drugs, and enteral medicines. During eight days, in four different hospitals (high complexity with over 1 000 beds, 400-bed intermediate, geriatric and monographic), the same patients and treatments were analyzed using both systems. Results: 3,490 patients were analyzed, with 42 155 different treatments. 238 interventions were performed using the traditional system (interventions 0.56% / possible interventions) vs. 580 (1.38%) with the automated one. Very significant pharmaceutical interventions were 0.14% vs. 0.46%; significant was 0.38% vs. 0.90%; non-significant was 0.05% vs. 0.01%, respectively. If both systems are simultaneously used, interventions are performed in 1.85% vs. 0.56% with just the traditional system. Using only the traditional model, 30.5% of the possible interventions are detected, whereas without manual review and only the automated one, 84% of the possible interventions are detected. Conclusions: The automated system increases pharmaceutical interventions between 2.43 to 3.64 times. According to the results of this study the traditional validation system needs to be revised relying on automated systems. The automated program works correctly in different hospitals.