Serum NGAL is an effective biomarker for detecting early-stage renal damage in CKD patients. Serum NGAL was significantly correlated with the severity of renal damage and the progression of renal function deterioration.

Background:High peritoneal transport status was previously thought to be a poor prognostic factor in peritoneal dialysis (PD) patients. However, its effect on diabetic nephropathy PD patients is unclear in consideration of the adverse impact of diabetes itself. The purpose of this study was to investigate the influence of peritoneal transport characteristics on nutritional status and clinical outcome in diabetic nephropathy patients on PD.Methods:One hundred and two diabetic nephropathy patients on PD were enrolled in this observational cohort study. According to the initial peritoneal equilibration test result, patients were divided into two groups: Higher transport group (HT, including high and high average transport) and lower transport group (LT, including low and low-average transport). Demographic characteristics, biochemical data, dialysis adequacy, and nutritional status were evaluated. Clinical outcomes were compared. Risk factors for death-censored technique failure and mortality were analyzed.Results:Compared with LT group (n = 37), serum albumin was significantly lower and the incidence of malnutrition by subjective global assessment was significantly higher in HT group (n = 65) (P < 0.05). Kaplan–Meier analyses showed that death-censored technique failure and mortality were significantly increased in HT group compared with that in LT group. On multivariate Cox analyses, higher peritoneal transport status and lower residual renal function (RRF) were independent predictors of death-censored technique failure when adjusted for serum albumin and total weekly urea clearance (Kt/V). Independent predictors of mortality were advanced age, anemia, hypoalbuminemia, and lower RRF, but not higher peritoneal transport status.Conclusions:Higher peritoneal transport status has an adverse influence on nutrition for diabetic nephropathy patients on PD. Higher peritoneal transport status is a significant independent risk factor for death-censored technique failure, but not for mortality in diabetic nephropathy patients on PD.

IntroductionSarcopenia is associated with significant cardiovascular risk, and death in patients undergoing peritoneal dialysis (PD). Three tools are used for diagnosing sarcopenia. The evaluation of muscle mass requires dual energy X‐ray absorptiometry (DXA) or computed tomography (CT), which is labor‐intensive and relatively expensive. This study aimed to use simple clinical information to develop a machine learning (ML)‐based prediction model of PD sarcopenia.MethodsAccording to the newly revised Asian Working Group for Sarcopenia (AWGS2019), patients were subjected to complete sarcopenia screening, including appendicular skeletal muscle mass, grip strength, and five‐time chair stand time test. Simple clinical information such as general information, dialysis‐related indices, irisin and other laboratory indices, and bioelectrical impedance analysis (BIA) data were collected. All data were randomly split into training (70%) and testing (30%) sets. Difference, correlation, univariate, and multivariate analyses were used to identify core features significantly associated with PD sarcopenia.Result12 core features (C), namely, grip strength, body mass index (BMI), total body water value, irisin, extracellular water/total body water, fat‐free mass index, phase angle, albumin/globulin, blood phosphorus, total cholesterol, triglyceride, and prealbumin were excavated for model construction. Two ML models, the neural network (NN), and support vector machine (SVM) were selected with tenfold cross‐validation to determine the optimal parameter. The C‐SVM model showed a higher area under the curve (AUC) of 0.82 (95% confidence interval [CI]: 0.67–1.00), with a highest specificity of 0.96, sensitivity of 0.91, positive predictive value (PPV) of 0.96, and negative predictive value (NPV) of 0.91.ConclusionThe ML model effectively predicted PD sarcopenia and has clinical potential to be used as a convenient sarcopenia screening tool.