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Association of SUDOSCAN Values with Vibration Perception Threshold in Chinese Patients with Type 2 Diabetes Mellitus.

ABSTRACT: SUDOSCAN has been proved to be an efficient method in detecting diabetic microvascular complications. In this study, we determine to detect the possible relationship between vibration perception threshold (VPT) and cardiac autonomic neuropathy (CAN) values produced by SUDOSCAN.A total of 920 Chinese patients with T2DM were enrolled in the study. Spearman correlation analysis and multivariate regression analysis were performed to determine the relation between CAN and VPT values. Mean VPT values across the CAN value tertiles were analyzed stratified by HbA1c status.In the study, we discovered a relatively high correlation between CAN value and both VPT values from dorsal feet and toes. Multivariate regression analyses also showed a significant relation between VPT and CAN values after adjusting all covariates. The mean value of VPT decreased across the SUDOSCAN-CAN value quartiles in both groups, and it was higher in patients with HbA1C > 9% than in patients with HbA1C < 9% across all quartiles of the SUDOSCAN-CAN except for the VPT mean in the low quartile of the SUDOSCAN-CAN value.All these results suggested that SUDOSCAN-CAN result was associated with VPT value which indicated a probable link between VPT value and cardiovascular autonomic dysfunction.

SUBMITTER: Zhu X

PROVIDER: S-EPMC5541789 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BackgroundType 2 diabetes mellitus (T2DM), one of the most common public diseases threatening human health, is always accompanied by infection. Though there are still a variety of flaws in the treatment of some infectious diseases, metabolomics provides a fresh perspective to explore the relationship between T2DM and infection. Our research aimed to investigate the association between plasma free amino acids (PFAAs) and T2DM complicated with infection in Chinese patients.MethodsA cross-sectional study was conducted from May 2015 to August 2016. We retrieved the medical records of 1032 inpatients with T2DM from Liaoning Medical University First Affiliated Hospital and we used mass spectrometry to quantify 23 PFAAs. Infections contained 15 individual categories that could be retrieved from the database. Principal component analysis was used to extract factors of PFAAs. Multi-variable binary logistic regression was used to obtain odds ratios (OR) and their 95% confidence intervals (CI).ResultsAmong 1032 inpatients,109 (10.6%) had infectious diseases. Six factors, accounting for 68.6% of the total variance, were extracted. Factor 4 consisted of Glu, Asp and Orn. Factor 5 consisted of Hcy and Pip. After adjusting for potential confounders, factor 4 was positively correlated with T2DM complicated with infection in Chinese T2DM patients (OR: 1.27, 95%CI: 1.06-1.52). Individual Hcy in factor 5 was positively associated with T2DM complicated with infection (OR: 1.33, 95%CI: 1.08-1.64). Furthermore, factor 4 (OR: 1.44, 95%CI: 1.11-1.87), Orn (OR: 1.01, 95%CI: 1.00-1.02) and Hcy (OR: 1.56, 95%CI: 1.14-3.14) were positively associated with bacterial infection in Chinese T2DM patients, while factor 5 (OR: 0.71, 95%CI: 0.50-1.00) was negatively associated with bacterial infection.ConclusionsUrea cycle-related metabolites (Orn, Asp, Glu) and Hcy were positively associated with T2DM complicated with infection in China. Orn and Hcy were positively associated with bacterial infection in T2DM patients in China.

Project description:BackgroundAcylcarnitines play a role in type 2 diabetes mellitus (T2DM), but the relationship between acylcarnitine and diabetic nephropathy was unclear. We aimed to explore the association of acylcarnitine metabolites with diabetic nephropathy and estimate the predictive value of acylcarnitine for diabetic nephropathy.MethodsA total of 1032 (mean age: 57.24 ± 13.82) T2DM participants were derived from Liaoning Medical University First Affiliated Hospital. Mass Spectrometry was utilized to measure levels of 25 acylcarnitine metabolites in fasting plasma. Diabetic nephropathy was ascertained based on the medical records. Factor analysis was used to reduce the dimensions and extract factors of the 25 acylcarnitine metabolites. Logistic regression was used to estimate the relationship between factors extracted from the 25 acylcarnitine metabolites and diabetic nephropathy. Receiver operating characteristic curves were used to test the predictive values of acylcarnitine factors for diabetic nephropathy.ResultsAmong all T2DM participants, 138 (13.37%) patients had diabetic nephropathy. Six factors were extracted from 25 acylcarnitines, which account for 69.42% of the total variance. In multi-adjusted logistic regression models, the odds ratio (OR, 95% confidence interval [CI]) of diabetic nephropathy on factor 1 (including butyrylcarnitine/glutaryl-carnitine/hexanoylcarnitine/octanoylcarnitine/decanoylcarnitine/lauroylcarnitine/tetradecenoylcarnitine), factor 2 (including propionylcarnitine/palmitoylcarnitine/hydroxypalmitoleyl-carnitine/octadecanoylcarnitine/arachidiccarnitine), and factor 3 (including tetradecanoyldiacylcarnitine/behenic carnitine/tetracosanoic carnitine/hexacosanoic carnitine) were 1.33 (95%CI 1.12-1.58), 0.76 (95%CI 0.62-0.93), and 1.24 (95%CI 1.05-1.47), respectively. The area under the curve for diabetic nephropathy prediction was significantly increased after the complement of factors 1, 2, and 3 in traditional factors model (P < 0.01).ConclusionsSome plasma acylcarnitine metabolites extracted in factors 1 and 3 were higher in diabetic nephropathy, while factor 2 was lower in diabetic nephropathy among T2DM patients. The addition of acylcarnitine to traditional factors model improved the predictive value for diabetic nephropathy.

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Association of SUDOSCAN Values with Vibration Perception Threshold in Chinese Patients with Type 2 Diabetes Mellitus.

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