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The relationship between glycemic variability and diabetic peripheral neuropathy in type 2 diabetes with well-controlled HbA1c
BACKGROUND: Diabetic peripheral neuropathy (DPN) is one of the most common microvascular complications of diabetes. Glycemic variability could be an independent risk factor for diabetes complications in addition to average glucose. Type 2 diabetes with well-controlled glycosylated hemoglobin A1c (Hb...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272789/ https://www.ncbi.nlm.nih.gov/pubmed/25530811 http://dx.doi.org/10.1186/1758-5996-6-139 |
Sumario: | BACKGROUND: Diabetic peripheral neuropathy (DPN) is one of the most common microvascular complications of diabetes. Glycemic variability could be an independent risk factor for diabetes complications in addition to average glucose. Type 2 diabetes with well-controlled glycosylated hemoglobin A1c (HbA1c) may have different terms of glycemic variability and vascular complication consequences. The aim of the study is to investigate the relationship between glycemic variability and DPN in type 2 diabetes with well-controlled HbA1c (HbA1c < 7.0%). METHODS: 45 type 2 diabetes with well-controlled HbA1c(HbA1c < 7.0%) and with DPN (DM/DPN group) were recruited in the study, and 45 type 2 diabetes with well-controlled HbA1c and without DPN (DM/–DPN group) were set as controls. The two groups were also matched for age and diabetic duration. Blood pressure, body mass index(BMI), insulin sensitivity index (Matsuda index, ISI), total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDLC), and low density lipoprotein cholesterol (LDLC) were tested in the two groups. And all patients were monitored using the continuous glucose monitoring (CGM) system for consecutive 72 hours. The multiple parameters of glycemic variability included the standard deviation of blood glucose (SDBG), mean of daily differences (MODD) and mean amplitude of glycemic excursions (MAGE). RESULTS: The DM/DPN group had a greater SDBG, MODD and MAGE, when compared to the DM/–DPN group (p < 0.05). BMI, TC, and LDLC of DM/DPN group were lower than those of DM/–DPN group (p < 0.05). The patients with hypoglycemia were comparable between the two groups (p > 0.05). Univariate analysis showed DPN was closely associated with BMI (OR 0.82, CI 0.72–0.94, p = 0.005), TC (OR 0.63, CI 0.42–0.93, p = 0.02), LDLC (OR 0.4, CI 0.20–0.80, p = 0.009), SDBG (OR 2.95, CI 1.55–5.61, p = 0.001), MODD (OR 4.38, CI 1.48–12.93, p = 0.008), MAGE (OR 2.18, CI 1.47–3.24, p < 0.001). Multivariate logistic regression analysis showed that MAGE (OR 2.05, CI 1.36–3.09, p = 0.001) and BMI (OR 0.85, CI 0.73–0.99, p = 0.033) were significantly correlating with DPN. Glycemic variability, evaluated by MAGE, was the most significantly independent risk factor for DPN. CONCLUSIONS: There was a close relationship between glycemic variability evaluated by MAGE and DPN in type 2 diabetes with well-controlled HbA1c. |
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