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Purpose: To investigate the result of body impedance analysis (BIA) in type 1 diabetes mellitus patients with diabetic peripheral neuropathy (DPN). Methods: A total of 125 T1DM patients were selected and enrolled into the DPN group (N=30) or the control group (N=95). BIA parameters and clinical characteristics were compared to find possible risk factors. After that, they were screened by binary logistic regression. The receiver operating characteristic (ROC) curve of meaningful index was portrayed, and then, the best cutoff was founded. Results: The DNP group has elder age, longer diabetes duration, higher blood urea nitrogen (BUN) concentration, and lower estimated glomerular filtration rate (eGFR) level. The DNP group has larger odds of other diabetic complications including diabetic peripheral vascular disease (DPVD) and diabetic nephropathy (DN). In BIA parameters, the DPN group had higher ratio of extracellular water (ECW) and total body water (TBW) than the control group (0.3969±0.0097 vs 0.3886±0.0086, p<0.001). Binary logistic regression indicated that increase of ECW/TBW (ECW/TBW≥0.395 vs ECW/TBW<0.395, OR=4.589, 95%CI 1.609~13.091, p=0.004) may be one of risk factors of DPN. In the ROC curve, area under curve (AUC) was 0.752 (95%CI 0.646~0.857) and the best cutoff of ECW/TBW to diagnose DPN was 0.3965. Conclusion: The increase of ECW/TBW may be a new risk factor of DPN and could help doctors predict the risk of DPN in T1DM patients. [ABSTRACT FROM AUTHOR]

Background: Little is known about the association between abnormal metabolic obesity states and the outcomes of chronic myeloid leukemia (CML), especially in patients with obesity with different metabolic status. Here, we used the Nationwide Readmissions Database to assess the effects of metabolically defined obesity on adverse outcomes of CML. Methods: Of the 35 460 557 (weighted) patients, we included 7931 adults with discharge diagnoses of CML from January 1, 2018 to June 30, 2018. The study population was observed until December 31, 2018 and divided into four groups based on body mass index and metabolic status. The primary outcome was the adverse outcomes of CML, including nonremission (NR)/relapse and severe mortality risk. Multivariate logistic regression analysis was performed to analyze data. Results: Metabolically unhealthy normal weight and metabolically unhealthy obesity were all risk factors for adverse outcomes of CML compared with metabolically healthy normal weight (all p < 0.01), and a significant difference was not found in the metabolically healthy obese. Female patients with metabolically unhealthy normal weight and metabolically unhealthy obesity had 1.23‐fold and 1.40‐fold increased NR/relapse risk, while male patients did not have this risk. Moreover, patients with a higher number of metabolic risk factors or with dyslipidemia were at higher risk of adverse outcomes, regardless of obesity status. Conclusions: Metabolic abnormalities were associated with adverse outcomes in patients with CML, irrespective of obesity status. Future treatment of patients with CML should consider the effects of obesity on their adverse outcomes under different metabolic status, especially in female patients. [ABSTRACT FROM AUTHOR]

Objectives. Peripheral artery disease (PAD) in diabetic populations is a vital chronic disease all over the world due to its high morbidity and mortality. It is important to find early simple screening biomarkers and find residual risk factors that may provide a new target for prevention and treatment of PAD in diabetic patients besides traditional cardiometabolic risk factors. Methods. We performed a cross-sectional retrospective study, and a total of 1671 T2DM participants were recruited. Receiver operating characteristic analysis, stepwise logistic regression analysis, points score system, and decision curve analysis were performed to assess the risk factors for PAD. Results. The prevalence of PAD in the study was 7.18% (n = 120). Compared to the participants with the lowest quartile of cystatin C (CysC), the risk of developing PAD in participants with the highest quartile of CysC increased 6.339-fold. The CysC was the superior indicators to distinguish participants with PAD from those without PAD, with an AUC of 0.716. Stepwise logistic regression analysis showed that CysC was independent risk factor for PAD besides traditional risk factors. Combined exposure to these traditional risk factors and CysC was associated with a stepwise increase in the risk of developing PAD and even increased 11.976-fold in participants with the highest quintiles of combined exposure score (CES) based on traditional risk factors and CysC compared to the participants with the lowest quintiles of CES. Conclusions. CysC was associated with PAD independent of potential risk factors in diabetic populations. The CysC was a reliable marker for the early screening of PAD in diabetic patients besides traditional cardiometabolic risk factors. [ABSTRACT FROM AUTHOR]