Predicational ability of phase angle on protein energy wasting in kidney disease patients with renal replacement therapy: A cross‐sectional study

Abstract Objective To investigate the ability of phase angle (PA) and body composition for predicting protein energy wasting (PEW) in renal replacement therapy (RRT) patients. Methods Renal replacement therapy (RRT) patients were enrolled in this study. Body composition was measured by direct segmental multi‐frequency biolectrical impedance analysis method (DSM‐BIA); phase angle (PA), fat‐free mass (FFM), fat mass (FM), mid‐arm circumference (MAC), WC (waist circumference), and ECW/TBW (extracellular water/total body water) were obtained. Biochemicals (serum albumin, triglyceride, and cholesterol) were tested. PEW patients were classified according to ISRNM (The International Society of Renal Nutrition and Metabolism) criteria. Cutoff value of PA and related variables was calculated by ROC analysis. The ability of body composition variables as indicators to predict PEW was evaluated. Results Sixty‐four patients were enrolled in this study. Thirty‐three patients (52.6%) were males, and forty (62.5%) patients were diagnosed with PEW. The ROC curve showed that the optimal cutoff values of PA, FFMI (fat‐free mass index), MAC, WC, and BMI for PEW risk were 4.45°, 16.71, 29.7 cm, 86.4 cm, and 21.1 kg/m2, respectively. These indicators showed significant association with PEW; meanwhile, the PA and MAC can be used as the predictors for PEW with OR 6.333 (95% CI, 1.956–20.505) and 3.267 (95% CI, 1.136–9.394), respectively. Both groups have a lower BUN/Cr ratio (<20). Conclusion In the RRT patients, over than 60% patients were diagnosed with PEW. PA, MAC, and other body composition can be used as the independent indicators for predicting PEW in renal replacement therapy kidney disease patients.


| INTRODUC TI ON
Chronic kidney disease (CKD) is a common public health disease globally. In South Asian region, the incidence of CKD ranges from 10.2% to 21.2%, which is similar to the global prevalence (13.4%; Hasan et al., 2018;Hill et al., 2016). CKD burden existed in both developing and developed countries. Studies have revealed that nearly 120 million adults were with kidney disease in China (2012), and the prevalence of CKD in USA was 13% in 2007 (Herrera Valdés et al., 2020;Lv & Zhang, 2019). In the past 30 years, the mortality of CKD rised from 20th (1990) to 16th in the leading causes of death (China, 2017;Zhou et al., 2019). The rising number of patients required RRT for it could reduce the complications and kidney burden. CKD also could be affected by factors like diet, physical activity, and metabolic diseases (obesity, diabetes, and hypertension) except for the treatment of RRT (Herrera Valdés et al., 2020;Kelly et al., 2019). In the CKD adverse outcomes, PEW is a denominated problem, and this malnutrition would reduce the patients' quality of life, would need more healthcare resource and higher medical costs, and would contribute to the mortality risk (Bonanni et al., 2011;Chao et al., 2017). A meta-analysis showed that the PEW prevalence among 30 countries was 28%-54% (Carrero et al., 2018). In As'habi's cross-sectional study, the prevalence of PEW in peritoneal dialysis patients was 29% (As'Habi et al., 2019). In Lydia Namuyimbwa's study, PEW prevalence was 47.3% in the CKD subjects and significantly higher than those without CKD (21.3%; Vermeulen, Lopes, Grilo, et al., 2019). Since the dominate prevalence of PEW has great impact on the quality of life and treatments of CKD, the early diagnosis and intervention would benefit the health situation and reduce the medical cost and burden (Chao et al., 2017).
Actually, some biomarkers and indicators could be used for the diagnosis of PEW. ISRNM recommends that serum chemistry, body mass, muscle mass, and dietary intake can be regarded as indicators for PEW diagnosis (Carrero et al., 2013). The criterion is that, if three characteristics are present (low serum levels of albumin (must), reduced body mass and reduced muscle mass), the PEW could be diagnosed. Other biomarkers like C-reactive protein (hsCRP), log IL-6, soluble intercellular adhesion molecule-1 (sICAM-1), gelsolin, adipokines, serum leptin levels, serum creatinine, and TNF-alpha were also used to diagnose for PEW (Chiu et al., 2015;Choi et al., 2010).
Besides the test indicators, some noninvasive diagnosis also could be used for the screening of PEW. In Arias-Guillen's study, bioimpedance spectroscopy was used to detect PEW, and results showed that it could be used as a practical instrument to assess nutritional status in patients using body composition (Arias-Guillén et al., 2018). Srinivasan Beddhu and Castellano-Gasch also recommended body composition was valid in the diagnosis of PEW (Beddhu et al., 2017;Castellano-Gasch et al., 2014).
PA was calculated by arctangent (reactance (Xc)/resistance (R)) × (180/π) and could be obtained by bioelectrical impedance analysis in 50 kHz. PA could be used in the evaluation of nutritional assessment, muscle function, and type 2 diabetes (Chen & Zhou, (Yoshida et al., 2017).
Besides PA, MAC and body composition also could be used as the indicators for PEW diagnosis in RRT patients (Krishnamoorthy et al., 2015;Leal Escobar et al., 2019;Powrózek et al., 2019;Shin et al., 2017). PEW is a malnourished problem and difficult to be assessed, while many studies demonstrated that PA is a practical indicator for this assessment (Player et al., 2019;Tan et al., 2019).
Studies on the ability of PA, MAC, and related body compositions in noninvasive diagnosis of PEW are very valuable. This present study aimed to investigate the predicting ability of PA and body composition in the prediction of PEW on RRT patients.

| Data collection
Direct segmental multi-frequency biolectrical impedance technology was used to analyze body composition (InBody ® model 770). All the patients who finished the hemodiafiltration treatment would be turned to body composition analysis with four couple of electrode holders placed on the ankles and forefingers of the hands, and at least 8 hr or overnight fasting with light clothes on the body. Weight, MAC, WC, fat mass (%), FFM, ECW, and TBW were obtained by this instrument. PA was obtained at a frequency of 50 Hz. Height was measured by height meter (InBody ® model BSM 170), and FFMI and BMI were calculated.

| Biochemical data
Serum cholesterol, triglyceride, and albumin levels were obtained by medical examination (serum biomarkers must be tested during the hemodiafiltration process), and we conduct a further analysis based on these data.

| Statistical analyses
Normal distribution data were presented as mean ± SD, and nonnormal numeric variables were presented by median and interquartile distance. Student's t test or Fisher's exact test was used to analyze the difference between groups; Spearman's rank correlation was used between body composition and biochemical variables with MAC and PA. Using a receiver operating characteristic curve (ROC) and area under the curve (AUC), cutoff values were calculated. With the ROC optimum cutoff values, chi-square analysis was conducted, and odds ratio and 95% confidence interval (95% CI) were calculated. p value <.05 was considered to be statistically significant.

| RE SULTS
Sixty-four patients were enrolled in this study, thirty-three (52.6%) were male, and the average age was sixty years old. Forty (62.5%) patients were diagnosed as PEW, and over than 60% patients have edema symptoms. Patient's demographical and body composition parameters are shown in Table 1.
According to the diagnosis expressed above, patients were divided into PEW and non-PEW groups. Patients with non-PEW has a lower weight (p = .012). Compared with PEW group, MAC (p = .008), cholesterol (p < .01), albumin concentration, (p = .004), and PA (p < .01) were significantly higher in non-PEW group. Creatinine in serum, BUN in serum, and ECW/TBW showed no statistically significant difference between groups (p < .05; Table 2).
Correlations of body composition and biochemical variables with mid-arm circumference and phase angle were analyzed (Table 3) (Table 4).

| D ISCUSS I ON
Bioelectrical impedance analysis is a simple, noninvasive, and reliable technique for estimation of body composition and has been used in the diagnosis of diseases such as Type 2 diabetes, muscle dysfunction, hydration, and nutritional assessment (Dittmar et al., 2015;Norman et al., 2012;Vermeulen, Lopes, Grilo et al., 2019). In CKD patients, malnutrition has been widely recognized and the manifested is PEW. Detecting and managing nutrition status would be beneficial to the treatment of patients and decrease the mortality (Bataille et al., 2019;Bolasco et al., 2019). BUN and creatinine are related with the function of renal and conditionally nutrition status.
In our study, BUN/Cr was below 20, and there was no difference between PEW and non-PEW groups. Since creatinine is affected by dietary protein intakes, this result may be inaccurate (Singh et al., 2015). Preview studies have reported that aging was associ-   (Windahl et al., 2018). The correlation between body composition and biomarkers was conducted.

| CON CLUS ION
In the aging RRT patients, more than 60% patients were diagnosed with PEW. PA and MAC can be used as the independent and reliable indicators for the noninvasive prediction of PEW and evaluation of nutritional status in aging CKD patients on RRT.

ACK N OWLED G M ENTS
The authors thank the Jingjiang People's Hospital for supporting this work of data collection. We also thank professor Sun Guiju, who has provided me with valuable guidance in the writing of this manuscript; thank to Pan Da and Yao Wenlong for their constructive data collection and analysis; and also to Yang Chao and Liu Hechun for their help in the field.

CO N FLI C T O F I NTE R E S T
The authors declared that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

E TH I C A L A PPROVA L
This study does not involve any human or animal testing.

I N FO R M E D CO N S E NT
Written informed consent was obtained from all study participants.