Evaluating haemodialysis patient's nutritional status: Body mass index or body cell mass index?

Phase angle is a useful predicting indicator for protein-energy wasting and cardiovascular risk among maintenance hemodialysis patients

Protein-energy wasting (PEW) is a major contributor to the high mortality among maintenance hemodialysis (MHD) patients. Cardiovascular disease (CVD) is the leading cause of death in dialysis patients, and PEW can significantly increase cardiovascular mortality in MHD patients. Previous studies have confirmed that PA may be a good objective indicator for determining the nutritional status and prognosis of MHD patients. Our study aimed to determine the predictive value of phase angle (PA) as detected by bioelectrical impedance analysis (BIA) on PEW and cardiovascular (CV) risk among MHD patients. Our retrospective observational study involved 161 adult patients with HD. The Cardiovascular risk score is a risk model based on the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS). We established LASSO logistic regression analysis model to identify key parameters related to body composition that can predict PEW in MHD patients. The area under the curve (AUC) values for PA, appendicular skeletal muscle mass index (ASMI), body cell mass (BCM), and mid-arm circumference (MAC) in predicting PEW in male MHD patients were relatively large, with 0.708, 0.674, 0.663, and 0.735, respectively. The predicted PEW values of these parameters were slightly lower in female patients than in men. We incorporated PA, ASMI, BCM, and MAC into a model that predicted the incidence of PEW in maintenance hemodialysis patients using LASSO technology. We discovered that the model predicted a greater AUC of PEW occurrence than any single factor, 0.877 for men and 0.76 for women. The results of the univariate logistic regression analysis showed that the low PA tertile array group had a greater incidence of PEW than the high PA group (P < 0.001). Additionally, we also found that lower PA was associated with higher CV risk scores. The PA detected by bioelectrical impedance analysis could predict the risk of PEW and cardiovascular events among patients with MHD. When used in conjunction, PA, ASMI, BCM, and MAC have a high diagnostic efficacy for PEW in patients on maintenance hemodialysis.

Study on the correlation between bioelectrical impedance analysis index and protein energy consumption in maintenance dialysis patients

BACKGROUND

Protein-energy wasting (PEW) has been reported to be pretty common in maintenance dialysis patients. However, the existing PEW diagnostic standard is limited in clinical use due to the complexity of it. Bioelectrical impedance analysis (BIA), as a non-invasive nutritional assessment method, can objectively and quantitatively analyze the changes of body tissue components under different nutritional states. We aim to explore the association between PEW and BIA and establish a reliable diagnostic model of PEW.

METHODS

We collected cross-sectional data of 609 maintenance dialysis patients at the First Affiliated Hospital, College of Medicine, Zhejiang University. PEW was diagnosed according to International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Among them, 448 consecutive patients were included in the training set for the establishment of a diagnostic nomogram. 161 consecutive patients were included for internal validation. 52 patients from Zhejiang Hospital were included for external validation of the diagnostic model. Correlation analysis of BIA indexes with other nutritional indicators was performed. Logistic regression was used to examine the association of BIA indexes with PEW. 12 diagnostic models of PEW in maintenance dialysis patients were developed and the performance of them in terms of discrimination and calibration was evaluated using C statistics and Hosmer-Lemeshow-type χ2 statistics. After comparing to existing diagnostic models, and performing both internal and external validation, we finally established a simple but reliable PEW diagnostic model which may have great value of clinical application.

RESULTS

A total of 609 individuals from First Affiliated Hospital, College of Medicine, Zhejiang University and 52 individuals from Zhejiang Hospital were included. After full adjustment, age, peritoneal dialysis (compared to hemodialysis), subjective global assessment (SGA, compared to non-SGA) and water ratio were independent risk factors, while triglyceride, urea nitrogen, calcium, ferritin, BCM, VFA and phase angle were independent protective factors of PEW. The model incorporated water ratio, VFA, BCM, phase angle and cholesterol revealed best performance. A nomogram was developed according to the results of model performance. The model achieved high C-indexes of 0.843 in the training set, 0.841 and 0.829 in the internal and external validation sets, respectively, and had a well-fitted calibration curve. The net reclassification improvement (NRI) showed 8%, 13%, 2%, 38%, 36% improvement of diagnostic accuracy of our model compared with "PEW score model", "modified PEW score model", "3-index model", "SGA model" and "BIA decision tree model", respectively.

CONCLUSIONS

BIA can be used as an auxiliary tool to evaluate PEW risk and may have certain clinical application value.

Application of Bioelectrical Impedance Analysis in Nutritional Management of Patients with Chronic Kidney Disease

Body composition measurement plays an important role in the nutritional diagnosis and treatment of diseases. In the past 30 years, the detection of body composition based on bioelectrical impedance analysis (BIA) has been widely used and explored in a variety of diseases. With the development of technology, bioelectrical impedance analysis has gradually developed from single-frequency BIA (SF-BIA) to multi-frequency BIA (multi-frequency BIA, MF-BIA) and over a range of frequencies (bioimpedance spectroscopy, BIS). As the clinical significance of nutrition management in chronic kidney disease has gradually become prominent, body composition measurement by BIA has been favored by nephrologists and nutritionists. In the past 20 years, there have been many studies on the application of BIA in patients with CKD. This review describes and summarizes the latest research results of BIA in nutritional management of patients with CKD including pre-dialysis, hemodialysis, peritoneal dialysis and kidney transplantation, in order to provide reference for the application and research of BIA in nutritional management of chronic kidney disease in the future.

Status of Nutrition In Hemodialysis Patients Survey (SNIPS): Malnutrition risk by diabetes status

BACKGROUND

Increased malnutrition risk has been observed in more than 40% people on haemodialysis in Israel. It is not clear that this risk is homogeneously distributed among people with versus without diabetes.

OBJECTIVES

To examine the influence of diabetes on malnutrition risk among people on haemodialysis.

METHODS

This cross-sectional study included a representative sample of 375 individuals on haemodialysis treated in hospital dialysis centres throughout Israel. Of these, 126 had diabetes. Dietary intake, biochemistry, anthropometric and hemodynamic measures were recorded. Malnutrition risk categories were defined: "minimal": body mass index (BMI) ≥23 kg/m2 and serum albumin ≥38 mmol/L; "mild": BMI <23 kg/m2 and albumin ≥38 mmol/L; "moderate": BMI ≥23 kg/m2 and albumin <38 mmol/L; "severe": BMI<23 k/m2 and serum albumin <38 mmol/L. These categories were dichotomized to "minimal" versus elevated malnutrition risk.

RESULTS

Despite greater BMI, elevated malnutrition risk was identified in 58.8% of individuals with versus 39.3% without diabetes. Adherence to International Society for Renal Nutrition and Metabolism nutrition guidelines was poor regardless of diabetes status. In multivariable logistic regression analysis, diabetes: OR 2.15; C-reactive protein (nmol/L): OR 1.02; delivered dialysis dose (Kt/V): OR 6.07; and haemoglobin (g/L): OR 0.79, predicted elevated malnutrition risk, even after controlling for age, sex and years on haemodialysis.

DISCUSSION

Individuals on haemodialysis who have diabetes have elevated malnutrition risk compared to those without diabetes despite greater BMI.

The cut-off values of handgrip strength and lean mass index for sarcopenia among patients on peritoneal dialysis

Background

Sarcopenia is common and contributes to a high risk of mortality among general population. There is no consensus regarding the cut-off values for sarcopenia in terms of mortality among chronic kidney disease patients. This study aimed to explore and validate cut-off points of handgrip strength (HGS) and lean mass index (LMI) for estimating the risk of mortality in peritoneal dialysis (PD) patients.

Methods

This single-center prospective cohort study enrolled 1089 incident PD patients between October 2002 and July 2019. All patients were followed until death, transfer to hemodialysis, receiving renal transplantation or the end date of study (December 2019). All participants were randomly sampled to development cohort (70% participants) and validation cohort (30% participants), matched by gender and diabetes. Lean body mass was calculated by using the equation published by our center. Cubic spline regression analysis was used to examine the relationship between HGS or LMI values and mortality, and explore the cut-off points after adjusting for age, diabetes, cardiovascular disease and serum albumin in the development cohort. The derived cut-off values were verified by the agreement rate for predicting mortality and then compared with cut-off values from various clinical guidelines in the validation cohort.

Results

All 1089 patients were followed up with the median of 36.0 (18.0, 71.0) months. In the development cohort, cut-off points for predicting the higher mortality were derived as 24.5 kg and 14 kg of HGS for males and females, 16.7 kg/m² and 13.8 kg/m² of LMI for males and females respectively. In the validation cohort, these cut-off values significantly predicted worse outcomes, with HR 1.96 (1.35, 2.84) of HGS and HR 1.76 (1.26, 2.47) of LMI for all-cause mortality after multivariate adjustment. The newly derived cut-off points of HGS have numerically higher prognostic values in all-cause mortality compared with those from current clinical guidelines, and agreement rates of HGS were 65.2 versus 62.5–64.6 respectively.

Conclusions

The derived cut-off values of HGS and LMI have sufficient and better prognostic value in predicting all-cause mortality in PD patients compared with the cut-off values in the existing guidelines. These cut-off values are only validated in a single population, thus limiting the generalizability.

Comparative Performance of Body Composition Parameters in Prediction of Death in Hospitalized Patients on Maintenance Hemodialysis: A Cohort Study

We compared the prognostic value of nutritional or volumetric parameters measured by body composition in hospitalized patients on maintenance hemodialysis. We conducted a cohort study to assess the association of different parameters of body composition with all-cause mortality in inpatients admitted to our nephrology department from January 2014 to December 2016. Of the 704 study patients, 160 (22.7%) died during a median follow-up of 33 months. In multivariate adjusted Cox models, higher ratio of extracellular water to body cell mass (ECW/BCM) (adjusted HR per 1-SD, 1.49; 95% CI, 1.19 to 1.85), lower lean tissue index (LTI) (adjusted HR per 1-SD, 0.70; 95% CI, 0.57 to 0.86) and lower body cell mass index (BCMI) (adjusted HR per 1-SD, 0.70; 95% CI, 0.58 to 0.85) were associated with a significantly greater risk of death. When these parameters were added to the fully adjusted model, BCMI performed best in improving the predictability for all-cause mortality (integrated discrimination improvement = 0.02, P = 0.04; net reclassification index = 0.11, P = 0.04). Among body composition indexes, ECW/BCM was the most relevant fluid volume indices to mortality and BCMI and LTI were the most relevant nutritional status indices to mortality in maintenance hemodialysis patients.