Abstract

Introduction

Chronic kidney disease (CKD) has attracted global attention in recent years as an epidemiological health problem¹. Currently, an estimated 850 million people worldwide are living with CKD. About 1.2 million people die from CKD annually, making it the sixth fastest-growing cause mortality². Renal replacement treatment is required when chronic kidney disease (CKD) reaches the end stage, which is known as end-stage renal disease (ESRD). Over the past 20 years, hemodialysis (HD) has been the most common kidney replacement therapy and life-sustaining treatment for patients with ESRD³. As CKD progresses, patients may develop combined disorders of muscle catabolism and nutrition, leading to protein-energy wasting (PEW)^4,5.

A number of prior studies have identified PEW as a multifactorial, poorly adapted metabolic state primarily characterized by loss of protein mass and energy reserves, which is a major contributor to the high mortality among MHD patients^4,6–8. According to the recent reviews, the prevalence of PEW in dialysis patients may range from 15 to 75%⁹. The proportion of Chinese patients among the global hemodialysis population has reached 7.4%, and the annual mortality rate has reached approximately 10%, which is related to the high level of PEW¹⁰. It has been found that in hemodialysis patients, the PEW condition can be caused by the loss of amino acids and albumin in dialysis, which in turn can trigger an acute inflammatory response^10,11. In addition, hemodialysis patients usually require adequate nutritional control, so the patient should limit the intake of micronutrients, all of which may contribute to the occurrence of PEW^10,12.

Cardiovascular disease (CVD) is the leading cause of death in dialysis patients, and PEW can significantly increase cardiovascular mortality in the CKD population, particularly in hemodialysis patients¹³. Therefore, early detection of PEW and cardiovascular risk is particularly important for the survival of hemodialysis patients.

Bioelectrical impedance analysis (BIA) is the measurement of the body’s impedance (Z) to determine body composition, which is the so-called resistance (R) and reactance (Xc) of the human body to alternating current (AC), and it can be expressed as Z²=R²+Xc²¹⁴. By plugging the participant’s height and the obtained impedance values into the regression equations for each study population, the BIA allowed estimates of measures such as fat mass (FM), skeletal muscle mass (SMM), and body water content^14–16. Phase angle (PA) is a BIA parameter, which is calculated from the relationship between resistance and reactance values¹¹, and the measurement is expressed in degrees (°)¹⁷. When alternating current flows through the body, healthy cell membranes act like capacitors to store electrical energy, which can cause a delay in the flow of alternating current. This hysteresis of the current penetrating the interface between cell membranes and tissues creates a phase difference between current and voltage known as PA¹⁴. In recent years, studies suggest that PA can indicate the body cell mass and cell membrane function, such as cell membrane integrity¹⁴. Previous studies have confirmed the value of PA as an objective indicator for determining the nutritional status and prognosis of HD patients^18,19. PA can also be used to assess muscle function and diabetes²⁰.

Our study focused on analyzing the various independent influencing factors that can contribute to both PEW and the risk of cardiovascular disease among patients undergoing maintenance hemodialysis. We aimed to develop a novel predictive model for PEW and enhance the objectivity and simplicity of its diagnosis, thereby improving the quality of life of MHD patients and reducing mortality.

Methods

Results

The clinical characteristics of hemodialysis patients by PA tertile.

Table ​Table11 presents the clinical and biochemical baseline characteristics of the 161 subjects, categorized according to the quantiles of phase angle (PA) levels: PA T1 group (<5), PA T2 group (≥5~<6.2), and PA T3 group (≥6.2). Notably, more male patients were identified in the third tertile compared to those in the other tertiles. Additionally, patients in the low PA group exhibited a higher prevalence of diabetes and hypertension, along with elevated cardiovascular risk scores. Different covariates (Age, TC, TG, FPG, BMI, ALB, SUA, VFA, and BCM) exhibited some differences among groups with different PA levels (P<0.05). There was no significant difference in FMI among different PA levels. However, patients with higher PA also tended to have higher ASMI.

Table 1

Demographics and clinical characteristics of 162 hemodialysis patients under PA tertiles.

Variables	First tertile group (PA<5) n=55	Second tertile group (5≤PA<6.2) n=59	Third tertile group (PA≥6.2) n=47	P value among groups
Female/male	22/33	23/36	6/41	0.004
Age, years	64.04±12.17	57.32±13.30	46.49±11.48	<0.001
BMI, kg/m2	22.24±3.25	22.87±3.77	24.01±3.36	0.037
Diabetes mellitus, n%	36(65.5%)	23(39.7%)	6(12.8%)	<0.001
Hypertension,n%	52(94.5%)	46(78.0%)	39(83.0%)	0.041
CV diseases,n%	21(38.2%)	16(27.1%)	9(19.1%)	0.101
TC,mmol/l	3.32±1.06	3.77±1.01	3.70±0.89	0.042
TG,mmol/l	1.34±0.67	1.61±0.76	2.00±1.69	0.011
FPG,mmol/l	8.16±4.01	6.53±3.17	5.41±1.97	<0.001
ALB, g/l	42.41±4.05	43.6±3.62	44.86±3.53	0.005
SUA, μmol/l	384.25±97.74	402.29±92.69	445.98±108.44	0.007
WBC, 109/l	5.81±1.99	6.27±1.74	6.33±1.52	0.251
HB, g/l	105.04±19.43	103.83±19.90	111.11±16.74	0.121
Kt/V	1.38±0.31	1.34±0.34	1.20±0.51	0.059
hs-CRP, mg/l	5.00(1.75~5.84)	5.00(3.23~5.00)	5.00(2.61~5.00)	0.281
25-OH-VD, ng/ml	14.21±8.73	16.03±9.67	19.84±17.84	0.086
VFA, cm2	104.60±49.67	85.73±40.13	71.69±40.37	<0.001
HbA1c, %	8.34±2.48	7.58±2.04	5.70±0.80	0.166
CV Score	6.0(4.0~7.5)	3.0(2.0~5.0)	1.0(1.0~3.0)	<0.001
nPCR	0.70±0.26	0.72±0.22	0.79±0.24	0.545
Cr/BSA	440.58±100.28	535.55±132.39	626.67±154.56	0.567
PBF, %	30.80±9.66	31.35±8.58	29.71±9.26	0.661
FM, kg	19.89±7.79	19.01±7.94	18.38±7.49	0.662
FFM, kg	41.65±6.51	44.22±8.05	48.81±8.27	0.757
ECW, l	12.98±2.51	12.74±2.92	12.63±2.24	0.436
TBW, l	33.73±6.41	32.82±8.05	32.87±6.16	0.377
ECW/TBW, %	0.40±0.01	0.38±0.01	0.37±0.01	0.551
BCM, kg	26.45±4.11	28.64±5.19	32.24±5.46	0.019
MAC, cm	27.71±2.90	28.56±3.57	30.34±3.31	0.328
MAMC, cm	21.96±2.02	22.91±2.56	24.16±2.23	0.252
FMI, kg/m2	12.03±4.78	11.44±4.65	10.95±4.34	0.472
ASMI, kg/m2	6.14±0.87	6.64±1.02	7.28±1.04	<0.001

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PA phase angle, BMI body mass index, CV cardiovascular risk, TC total cholesterol, TG triglyceride, FPG fasting plasma glucose, ALB albumin, SUA serum uric acid, WBC white blood cell count, HB hemoglobin, hs-CRP high-sensitivity C-reactive protein, 25-OH-VD 25-hydroxyvitamin D, VFA abdominal visceral fat area, HbA1c hemoglobin, CV cardiovascular, nPCR normalized protein catabolic rate, Cr creatinine, BSA body surface area, PBF percent body fat, FM fat mass, FFM fat-free mass, ECW extracellular water, TBW total body water, BCM body cell mass, MAC mid-arm circumference, MAMC mid-arm muscle circumference, FMI fat mass index, ASMI appendicular skeletal muscle mass index.

The predictive value of single index and LASSO model of BIA parameters in PEW evaluation

The AUC of BIA parameters including PA, BCM, ECW, FFMF, ECW/TBW, FM, MAC, MAMC and ASMI for predicting PEW in male maintenance hemodialysis patients were found to be 0.708, 0.663, 0.551, 0.648, 0.654, 0.665, 0.735, 0.692 and 0.674, respectively (Table ​(Table2).2). The predicted PEW values of these parameters were slightly lower in female patients than in men. Through LASSO regression, four BIA feature parameters PA, ASMI, BCM and MAC were identified as statistically significant and subsequently included in the model (Table ​(Table2).2). The AUC of LASSO model for predicting PEW in male and female patients were 0.877 and 0.76, respectively (Table ​(Table2).2). The effectiveness of the ROC curve model in predicting the risk of PEW occurrence based on LASSO regression was analyzed, resulting in an AUC of 0.843 (Fig. 1).

Table 2

The AUC of BIA parameters potentially associated with PEW among hemodialysis patients.

Variables	Female				Male
	AUC	SE	p-value	95%CI	AUC	SE	p-value	95%CI
PA, °	0.579	0.082	0.337	0.418~0.740	0.708	0.052	0.001	0.605~0.810
BCM, kg	0.623	0.079	0.136	0.468~0.778	0.663	0.061	0.01	0.544~0.782
ECW, l	0.483	0.081	0.837	0.324~0.642	0.551	0.061	0.434	0.430~0.670
FFM, kg	0.607	0.079	0.195	0.452~0.762	0.648	0.061	0.02	0.528~0.768
ECW/TBW, %	0.446	0.084	0.514	0.282~0.611	0.654	0.054	0.015	0.548~0.760
FM, kg	0.544	0.080	0.595	0.387~0.700	0.665	0.060	0.009	0.548~0.782
MAC, cm	0.614	0.077	0.168	0.462~0.765	0.735	0.053	<0.001	0.631~0.839
MAMC, cm	0.681	0.075	0.028	0.535~0.828	0.692	0.055	0.002	0.584~0.800
ASMI, kg/m2	0.461	0.080	0.634	0.304~0.618	0.674	0.059	0.006	0.559~0.790
Model*	0.760	0.066	0.002	0.632~0.889	0.877	0.032	<0.001	0.813~0.940

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The model incorporated PA, ASMI, BCM, and MAC. To refine the variables, we employed the LASSO technique*

PA phase angle, BCM body cell mass, ECW extracellular water, FFM fat-free mass, TBW total body water, FM fat mass, MAC mid-arm circumference, MAMC mid-arm muscle circumference, ASMI appendicular skeletal muscle mass index.

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Fig. 1

The ROC curve analysis of the LASSO regression model to examine protein-energy wasting in hemodialysis patients. The LASSO regression model incorporated PA, ASMI, BCM, and MAC. ROC receiver operating characteristics, AUC area under the curve, PA phase angle, ASMI appendicular skeletal muscle mass index, BCM body cell mass, MAC mid-arm circumference.

The relationship between phase angle and protein-energy wasting

The multivariate linear regression model was used for assessing relationship between phase angle and PEW (Table ​(Table3).3). In tertiles of phase angle, a significant decrease in the risk of PEW was observed (P<0.001). The risk of PEW remained significantly decreased even after adjusting for sex and age (P<0.001). Moreover, after further adjustment for CV score, FPG, SUA, HB, and hs-CRP, the potential association between lower phase angle and high risk of PEW persisted (P<0.001). Interestingly, the risk of PEW in the subgroup with the lowest quantile of phase angle was found to be 6.49 times higher than that in the subgroup with the highest quantile.

Table 3

Hazard ratio (95% C.I) of the PA levels for protein-energy wasting (PEW).

	Non-PEW	PEW	Model1		Model2		Model3
			OR	95%C.I	OR	95%C.I	OR	95%C.I
PA_T3	45(95.7%)	2(4.3%)	1	1	1	1	1	1
PA_T2	56(94.9%)	3(5.1%)	0.22	0.136~6.254	0.895	0.131~6.095	0.848	0.124~5.783
PA_T1	36(65.5%)	19(34.5%)	7.989	1.474~43.308	7.273	1.302~40.624	6.49	1.156~36.434
P for trend	<0.001		<0.001		<0.001		<0.001

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Model1: adjusted sex, age.

Model2: adjusted sex, age, CV score.

Model3: adjusted sex, age, CV score, FPG, SUA, HB, hs-CRP.

The LASSO regression model incorporated PA, BCM, and MAC.

C.I confidence interval, PA phase angle, OR odds ratio, CV cardiovascular risk, FPG fasting plasma glucose, SUA serum uric acid, HB hemoglobin, hs-CRP high-sensitivity C-reactive protein, BCM body cell mass, MAC mid-arm circumference.

Cardiovascular (CV) risk score at different phase angle levels

Figure 2 depicts the levels of CV risk score by phase angle classification. The results of one-way analysis of variance revealed significant differences in CV risk scores among different phase angle groups. The three groups were compared in pairwise sequence, and the differences were observed to be statistically significant among all the groups (P<0.05).

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Fig. 2

Cardiovascular (CV) risk score levels under different phase angle (PA) classifications in hemodialysis patients.

Discussion

PEW is a prevalent complication in maintenance hemodialysis patients, characterized by various metabolic and nutritional disorders, including protein and fat loss²⁶. PEW is caused by inflammation, catabolic diseases, and reduced nutrient intake²⁷. PEW not only diminishes the quality of life for HD patients but also escalates the risk of hospitalization, morbidity, and mortality²⁸. The concept of PEW was first proposed in 2008 by the International Society of Renal Nutrition and Metabolism (ISRNM) defining it as a pathological state characterized by a combination of malnutrition and hypercatabolic conditions²⁹. Due to the complexity of ISRNM’s proposed comprehensive diagnostic criteria for PEW and the relative lack of nutritionists in China, the routine application of ISRNM guidelines in assessing PEW among maintenance hemodialysis patients in Chinese hospitals is significantly hindered. As a result, our study aimed to identify relatively simple, easy-to-implement methods to evaluate the PEW of hemodialysis patients in China. As a technology for measuring body composition, BIA enables the calculation of various body composition indicators, such as fat, muscle, cell mass, volume load status, etc.²⁸. BIA-derived phase angle has emerged as an important marker to reflect the health and nutritional status of individual cells^30–32. A growing body of researches indicate that low PA values are associated with malnutrition and an increased risk of nutritional deficiencies, particularly among individuals affected by various diseases^32,33. Therefore, this study aimed to explore whether PA could serve as a simple and alternative tool to facilitate the early detection of PEW in MHD patients.

Cells consist of conductive intracellular fluid surrounded by a cell membrane that allows the selective permeation of certain ions. PA is an index that can reflect the relationship between resistance and capacitance, because the electrical properties of extracellular and intracellular fluids are very close to resistance, and the cell membrane can be equivalent to capacitance^34,35. The baseline data analysis revealed that PA was higher in men compared to women. These sex differences in PA are consistent with previous observations in both healthy subjects³⁶ and diabetic patients³⁷. The relatively higher PA observed in men may be attributed to a larger fat-free mass, resulting in lower resistance^38,39. Previous studies of lung cancer patients found that men may have a higher percentage of muscle and a higher percentage of water (both intracellular and extracellular water) than women, and they suggested that PA could be more sensitive to changes in body composition of men³⁴. Moreover, our study corroborates that PA decreases with age, which is consistent with previous findings in healthy subjects⁴⁰. The biological significance of PA remains unclear, but it is hypothesized to serve as an indicator of cell health, with higher PA values indicating stronger cellular function^39,41,42. Interestingly, previous studies have suggested that reductions in PA noted in older individuals reflect declines in overall health and physical function associated with aging⁴¹. This may be related to reduced cell integrity and loss of tissue mass, which are commonly observed during the normal aging process^41,43,44. In the group with a reduced phase angle, there were significantly more patients with DM, which was consistent with Jun et al. ‘s study ⁴⁵. They found that the onset of diabetes itself lowered PA values compared with participants without diabetes and that PA values became lower with longer duration of diabetes⁴⁵. They suggested that PA reduction may reflect tissue damage caused by hyperglycemia or glucose fluctuations. In addition, it is also possible that higher resistance is due to the moderate osmotic effect of hyperglycemia associated with diabetes^45,46. The proportion of hypertension was also relatively higher in the lower PA group, which was consistent with previous studies of type 2 diabetes in which people with lower PA were characterized by older age, longer duration of diabetes, and higher systolic blood pressure⁴⁷. BMI is generally utilized as a measure to assess nutrition status and obesity. Interestingly, our study revealed a positive association between BMI and PA. This finding suggests that higher BMI values are correlated with higher PA values. One plausible explanation for this association is that an increase in BMI may correspond to an increase in the number of fat or muscle cells within the body. This increase in cellular mass could influence reactance and result in higher PA values⁴⁵.

Our study revealed a significant relationship between phase angle and the risk of developing PEW among maintenance hemodialysis patients. Importantly, this association was found to be independent of traditional risk factors such as sex, age, CV score, FPG, SUA, HB and hs-CRP. However, since previous studies have already established a positive association between PA and indicators of nutrient metabolism, such as albumin and BMI^45,48, similar to our results, we hypothesized that the detrimental effects associated with low levels of PA may manifest at the early stages of PEW development.

Body cell mass (BCM), which can be measured using BIA, represents a component of lean tissue devoid of bone mass and extracellular water, making it a valuable indicator of metabolically active tissues^49,50. The loss of BCM can be masked by an increase in extracellular water volume, thus keeping lean tissue stable⁴⁹. Therefore, BCM can act as a sensitive indicator of lean tissue loss. In addition, study conducted by Ruperto et al. confirmed that PEW patients exhibit lower body cell mass compared to well-nourished patients⁵¹. Our study also validated that BCM was independently associated with the risk of protein-energy wasting among male patients. Additionally, the mid-arm muscle circumference (MAMC) has been suggested as a marker reflecting muscle mass adequacy, caloric intake, and protein status, serving as an early indicator of malnutrition and nutrient depletion⁵². A study performed among 133 hemodialysis patients in Bangladesh reported a prevalence of PEW of 18%, with these patients exhibiting significantly lower MAMC values compared to those without PEW [(19.4±2.4)cm vs. (22.2±3.8)cm]²⁶. Interestingly, our study found that MAMC had some predictive values for the occurrence of PEW in both males and females. Although PEW is a wasting of muscle and fat loss, the baseline data showed no statistical difference between different levels of PA and FMI, so the LASSO model did not include FMI . Based on these findings, four BIA feature parameters, (including PA, ASMI, BCM and MAC) were selected using LASSO analysis to assess whether their combination could improve the predictive power for PEW. Multivariate logistic regression analysis revealed that PA, ASMI, BCM and MAC were closely associated with PEW among male patients. Furthermore, compared to any single BIA parameter, the LASSO model demonstrated superior predictive ability for the occurrence of PEW, with a much higher area under the curve for males than for females (0.877 vs 0.76).

Patients with end-stage renal disease (ESRD) face a heightened risk of cardiovascular disease, which is the leading cause of mortality in this population⁵³. Hypotension, myocardial stunning and electrolyte imbalance during hemodialysis treatment can cause significant damage to the myocardium^53,54. Eventually, these factors lead to subendocardial ischemia, increased left ventricular wall mass, diastolic dysfunction, and the development of severe arrhythmias through complex pathophysiological mechanisms^53,55. These cardiovascular complications are specific to hemodialysis patients and significantly elevate the risk of acute coronary syndrome and sudden cardiac death^56,57. In this study, we employed the cardiovascular risk score model developed by the Japanese Dialysis Outcomes and Practice Patterns Study (J-DOPPS) to investigate the relationship between PA and the risk of cardiovascular events. Our results revealed that lower PA values were associated with a higher risk of cardiovascular events. Interestingly, prior research conducted among Chinese populations has also demonstrated that low PA can independently predict heart failure in patients undergoing HD and those with diabetes⁵⁸.

Our study has several limitations that should be acknowledged. Firstly, the sample size was relatively small, which may introduce potential biases and limit the generalizability of our findings. Therefore, future studies should aim to recruit a larger and more diverse sample to enhance the reliability of the results. Secondly, we did not conduct follow-up assessments on the subjects, which prevented us from examining the longitudinal relationship between PA, other BIA parameters, and the prognosis of PEW in maintenance hemodialysis patients.

Thus, moving forward, it would be valuable to conduct longitudinal studies with larger sample sizes to further explore the association between PA, BIA-related parameters, and the prognosis of PEW in maintenance hemodialysis patients. This would provide more comprehensive insights into the role of BIA parameters, including PA, in predicting and managing PEW in this patient population.

Conclusions

Patients with low phase angles were more likely to develop PEW in comparison to patients with higher phase angles. The PA detected by bioelectrical impedance analysis can be potentially used as a valuable predictive tool to predict PEW and its associated risk of cardiovascular events among maintenance hemodialysis patients. This study demonstrated that PA, ASMI, BCM and MAC can serve as significant risk factors for PEW in HD patients. A combination of the above indicators can yield a high diagnostic efficiency for identifying PEW in maintenance hemodialysis patients. Thus. employing a multi-index combined diagnosis approach may offer an objective, simple, and reliable method for evaluating PEW in this patient population.

Acknowledgements

Author contributions

Y W, Y C and LQ Z drafted this manuscript. L Z, QQ Y, QJ W, ZW T, BR F, SM S and LH C collated and analyzed the data. H Z, Y X and TN X were responsible for the integrity of the work as a whole. All authors have read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (82201713).

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Declarations

Footnotes

Contributor Information

Tongneng Xue, Email: moc.361@481421ntx.

Yong Xu, Email: moc.361@yxyeah.

Hui Zhou, Email: moc.361@309_iuhuohz.

References