Development of Risk Prediction Nomogram for Sarcopenia in Patients Receiving Maintenance Hemodialysis

doi:10.21203/rs.3.rs-1299010/v1

Background

Sarcopenia is a progressive and generalized skeletal muscle disorder characterized by accelerated loss of muscle mass and function that is more commonly observed in patients receiving maintenance hemodialysis compared to the general population. This study aimed to explore a simple nomogram to evaluate the risk of developing sarcopenia.

Methods

From March to June 2021, 615 patients on maintenance hemodialysis were identified at the First Affiliated Hospital College of Medicine Zhejiang University and randomly divided into development cohorts (n=369) and validation cohorts (n=246) in a 6:4 ratio. Multi-factor logistic regression analysis was used to screen out statistically significant variables to construct risk prediction models. The line plots were drawn to evaluate the effectiveness of the predictive models from three aspects: differentiation, calibration, and clinical net benefit, and were further tested by Bootstrap method.

Results

Our study indicated that 16.6% patients enrolled in our study were diagnosed with sarcopenia. Serum creatinine, serum albumin, C-reactive protein, serum phosphorus, body mass index, and upper arm muscle circumference were identified as independent risk factors for the development of sarcopenia in patients on maintenance hemodialysis. The area under the ROC curve of the line chart model was 0.88 with 90% sensitivity and 75% specificity. The Yoden index was 0.64, and the internal verification C-statistic was 0.864.

Conclusions

Our study not only proved that sarcopenia was commonly observed in patients on maintenance hemodialysis but also established a prediction nomogram to evaluate the risk for developing sarcopenia in such patients.

Sarcopenia

Hemodialysis

Nomogram

Maintenance hemodialysis (MHD) is one of the most common treatments for patients with end-stage renal disease (ESRD) [1]. Epidemiology study[2] suggest that approximately 84% of all ESRD patients eventually receive hemodialysis treatment. Previous studies indicate that patients with MHD are predisposed to sarcopenia due to chronic inflammatory status, metabolic acidosis, malnutrition, and decreased physical activity[3]. Sarcopenia[4, 5] is characterized by a progressive and systemic loss of muscle mass and strength/function that is often associated with a variety of adverse outcomes. It has been estimated that 20-50% of all MHD patients can develop sarcopenia, which is much higher than the general population[6–9]. Previous studies indicated that MHD patients who were complicated with sarcopenia were associated with an increased risk of falling, fracture and cardiovascular events as well as re-hospitalization rate and mortality [10–12]. Fortunately, studies have shown that sarcopenia is not difficult to treat and even reversible if identified early[13, 14]. Therefore, it is important to establish a practical method to identify patients with increasing risks so prompt treatment can be arranged.

The diagnosis of sarcopenia[4] is mainly based on reduced skeletal muscle mass, decreased skeletal muscle strength, and skeletal muscle dysfunction that can be measured by magnetic resonance imaging (MRI), computed tomography (CT), dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA). However, the use of DXA, MRI, and CT is impeded in clinical practice by disadvantages such as high cost, complex procedures, and radiation exposure[15]. On the other hand, even though the BIA is convenient to perform, it is well-known that not all MHD patients can achieve the ideal volume status which subsequently jeopardizes the authority of the result [16]. In addition, muscle strength and physical function tests are often restricted under comorbid conditions such as joint problems and cognitive impairment[15]. To address the difficulties above, we developed and verified a simple risk prediction nomogram for sarcopenia in patients receiving maintenance hemodialysis

Study population

A total of 615 adult patients on maintenance hemodialysis were identified at the First Affiliated Hospital College of Medicine Zhejiang University from March to June 2021, and randomly divided into development cohorts (n=369) and validation cohorts (n=246) in a 6:4 ratio. Patients with the following conditions were excluded from the study: receiving maintenance hemodialysis for less than 3 months; implanted pacemaker or amputation surgeries. This study was reviewed and approved by the hospital ethics committee and all patients signed informed consent.

Measurements

After a systemic literature review, a total of 27 predictors for sarcopenia were identified. The following data were collected from each patient: ① demographic characteristics: age, gender, primary disease, dialysis age, etc.; ② self-rating anxiety scale (SAS), self-rating depression scale (SDS) psychological status assessment indicators; ③ anthropometric indicators: height, post-dialysis weight, skeletal muscle mass index (SMI), grip strength, pacing speed, upper arm circumference, and triceps skinfold thickness, and body mass index (BMI) and upper arm muscle circumference (MAMC) which was calculated as brachial circumference − 3.14 × triceps skinfold thickness[16]. ④ laboratory examination parameters that were measured at the first hemodialysis in each week after the enrolment: serum creatinine, blood uric acid, blood urea nitrogen, hemoglobin, serum albumin, prealbumin, C-reactive protein, serum phosphorus, serum calcium, blood lipid, serum potassium, parathyroid hormone and urea clearance index. Grip strength test was measured before dialysis with electronic grip strength meter. The patient was required to stand with naturally positioned feet and arms. Three measurements with an interval of 5 seconds were taken and the maximum value was taken. The walking speed test was also evaluated before dialysis and the average value of two consecutive measurements was documented. Triceps skinfold thickness and upper arm circumference were measured at the end of dialysis. In addition, BIA was performed 15-20 minutes after the end of dialysis treatment to measure limb skeletal muscle mass (ASM), which was later used to calculate the SMI = ASM/height² [13].

Diagnosis

In this study, the diagnosis of sarcopenia was based on the 2019 Asian Sarcopenia Working Group guideline[17]. The diagnosis of sarcopenia was made in patients with decreased skeletal muscle content of four extremities: BIA < 7.0 kg/m² in males and < 5.7 kg/m² in females who had either of the following two conditions: decreased muscle strength: grip strength < 28 kg in males and < 18 kg in females or decreased physical function: 6-m walking speed < 1.0 m/s.

Statistical analysis

SPSS 26.0 and STATA 15 were used to analyze the collected data. Among the 27 predictor variables, prealbumin, urea clearance index, parathyroid hormone, upper arm circumference, and skinfold thickness were identified with missing data of less than 5% which were replaced by the sequential mean of the continuous variables before the model was developed. To ensure a more convenient clinical use, continuous variables such as age, serum phosphorus, serum calcium, parathyroid hormone, triglycerides, cholesterol, high-density lipoprotein, low-density lipoprotein, and very-low-density lipoprotein were converted into categorical variables according to clinical references. Other continuous variables were transformed into dichotomous variables according to the receiver operating characteristic curve using the optimal cut-off points of the analyzed variables with the Youden index criterion. Frequency or constituent ratio was used for statistical description, and chi-square test was used for comparison between groups. Through univariate logistic regression analysis, variables with univariate analysis results of p < 0.10 were included in multivariate logistic regression analysis. The backward stepwise regression method was then applied to select statistically significant factors that were used to construct the clinical risk prediction nomogram. The prediction nomogram was assessed in three aspects: discrimination, calibration, and clinical net benefit. The area under the receiver operating characteristic curve (ROC) was used to assess the discriminatory ability. The calibration curve and Hosmer-Lemeshow goodness of fit test were used to assess the calibration ability. The decision curve analysis (DCA) was used to assess the clinical effectiveness. The model was internally validated using the Bootstrap method. A p-value < 0.05 was considered statistically significant.

Demographic parameters

A total of 615 MHD patients were eventually included in this study and were randomly divided into the development cohort (n = 369) and the validation cohort (n = 246) in a 6:4 ratio, as shown in Fig. 1(Figure 1). Among all enrolled patients, 381 (62%) were male and 234 (38%) were female. The most common reason for dialysis was chronic glomerulonephritis (430 patients), followed by diabetic nephropathy (101 patients), and other causes (hypertensive nephropathy, polycystic kidney disease, lupus nephritis, etc.). There were 102 patients (16.60%) diagnosed with sarcopenia and the demographic parameters of patients with or without sarcopenia were summarized in (Table 1).

Table 1

Baseline characteristics of the Development and Validation cohorts.(n,%)
Characteristics	All patients(N=615)	Development cohor(N=369)	Validation cohort(N=246)	Pvalue
	615(100.00)	369(60.00)	246(40.00)
Gender
Male	381(62.00)	231(62.60)	150(61.00)	0.684
Female	234(38.00)	138(37.40)	96(39.00)
sarcopenia
no	513(83.40)	310(84.00)	203(82.50)	0.626
yes	102(16.60)	59(16.00)	43(17.50)
Cause of ESKD
Chronic glomerulonephrit	430(69.90)	259(70.20)	171(69.50)	0.11
Diabetic nephropathy	101(16.40)	67(18.20)	34(13.80)
Other	84(13.70)	43(11.70)	41(16.70)
Age
≤65years	380(61.80)	235(63.70)	145(58.90)	0.236
>65years	235(38.20)	134(36.30)	101(41.10)
Duration of dialysis
≤60months	308(50.10)	190(51.50)	74(30.10)	0.392
>60months	307(49.90)	179(48.50)	172(69.90)
Hemoglobin
≤106g/L	198(32.8)	124(33.6)	103(41.9)	0.36
>106g/L	417(67.80)	245(66.4)	143(58.1)
Serum Creatinine
≤804µmol/L	196(31.90)	124(33.60)	72(29.30)	0.258
>804µmol/L	419(68.10)	245(66.40)	174(70.70)
Uric acid
≤353µmol/L	109(17.70)	69(18.70)	40(16.30)	0.438
>353µmol/L	506(82.30)	300(81.30)	206(83.70)
Urea nitrogen
≤21.52mmol/L	240(39.00)	146(39.60)	94(38.20)	0.736
>21.52mmol/L	375(61.00)	223(60.40)	152(61.80)
Serum Albumin
≤40g/L	336(54.60)	194(52.60)	142(57.70)	0.209
>40g/L	279(45.40)	175(47.40)	104(42.30)
CRP
≤7.935mg/L	494(80.30)	292(79.10)	202(82.10)	0.362
>7.935mg/L	121(19.70)	77(20.90)	44(17.90)
Serum Phosphorus
<1.13mmol/L	22(3.60)	14(3.80)	8(3.30)	0.696
1.13-1.78mmol/L	238(38.70)	147(39.80)	91(37.00)
>1.78mmol/L	355(57.70)	208(56.40)	147(59.70)
Serum Calcium
<2.1mmol/L	133(21.60)	68(18.40)	65(26.40)	0.048
2.1-2.5mmol/L	376(61.20)	238(64.50)	138(56.10)
>2.5mmol/L	106(17.20)	63(17.10)	43(17.50)
alkaline phosphatase
≤70.5 U/L	284(46.20)	175(47.40)	109(44.30)	0.448
>70.5 U/L	331(53.80)	194(52.60)	137(55.70)
BMI
<18.5kg/m2	117(19.00)	71(19.20)	46(18.70)	0.972
18.5-24.9kg/m2	354(57.60)	211(57.20)	143(58.10)
>25kg/m2	144(23.40)	87(23.60)	57(23.20)
Serum Prealbumin
≤28.4mg/dL	182(29.60)	112(30.40)	70(28.50)	0.614
>28.4mg/dL	433(70.40)	257(69.60)	176(71.50)
Kt/V
≤1.4	199(32.40)	121(32.80)	78(31.70)	0.778
>1.4	416(67.60)	248(67.20)	168(68.30)
MAC
≤27.8cm	303(49.30)	176(47.70)	127(51.60)	0.34
>27.8cm	312(50.70)	193(52.30)	119(48.40)
MAMC
≤22.64cm	263(42.80)	151(40.90)	112(45.50)	0.258
>22.64cm	352(57.20)	218(59.10)	134(54.50)
TSF
≤1.36cm	358(58.20)	218(59.10)	140(56.90)	0.593
>1.36cm	257(41.80)	151(40.90)	106(43.10)
SAS
≤40	579(94.10)	346(93.80)	233(94.70)	0.624
>40	36(5.90)	23(6.20)	13(5.30)
SDS
≤41	541(88.00)	328(88.90)	213(86.60)	0.39
>41	74(12.00)	41(11.10)	33(13.40)
Serum Potassium
≤5.2mmol/L	377(61.30)	219(59.30)	158(64.20)	0.224
>5.2mmol/L	238(38.70)	150(40.70)	88(35.80)
PTH
<150ng/L	254(41.30)	157(42.50)	97(39.40)	0.18
150-300ng/L	222(36.10)	138(37.40)	84(34.10)
>300ng/L	139(22.60)	74(20.10)	65(26.40)
Triglycerides
≤1.7mmol/L	321(52.20)	194(52.60)	127(51.60)	0.818
>1.7mmol/L	294(47.80)	175(47.40)	119(48.40)
Cholesterol
≤5.2mmol/L	578(94.00)	348(94.30)	230(93.50)	0.678
>5.2mmol/L	37(6.00)	21(5.70)	16(6.50)
HDL
≤1.03mmol/L	381(62.00)	228(61.80)	153(62.20)	0.919
>1.03mmol/L	234(38.00)	141(38.20)	93(37.80)
LDL
≤3.37mmol/L	601(97.70)	362(98.10)	239(97.20)	0.44
>3.37mmol/L	14(2.30)	7(1.90)	7(2.80)
VLDL
≤0.78mmol/L	329(53.50)	198(53.70)	131(53.30)	0.921
>0.78mmol/L	286(46.50)	171(46.30)	115(46.70)
Kt/V:Urea clearance index;MAC:Mid-upper arm circumference;MAMC:Mid-upper arm muscle circumference;TSF:triceps skin fold;SAS:self rating anxiety scale;SDS:self rating depression scale;PTH:Parathyroid hormone ;HDL:High-density lipoprotein;LDL:Low-density lipoprotein;VLDL:Very low-density lipoprotein.

Construction of prediction nomogram

Univariate logistic regression analysis was performed with the included 27 independent variables and the results suggested that age, serum creatinine, blood uric acid, blood urea nitrogen, hemoglobin, albumin, C-reactive protein, serum phosphorus, alkaline phosphatase, BMI, prealbumin, urea clearance index, upper arm circumference, upper arm muscle circumference, triglycerides, and high-density lipoprotein levels were significantly different between two groups (P < 0.1), as shown in (Table 2). Next, significant independent variables obtained from the above univariate logistic regression analysis were included in multivariate logistic regression analysis following a backward stepwise regression method. The results showed that serum creatinine, albumin, C-reactive protein, serum phosphorus, BMI, and upper arm muscle circumference were independent risk factors for sarcopenia in MHD patients, as shown in Table 2.

Table 2

Univariate and multivariate analysis of Sarcopenia in the Development cohort(n=369)
Characteristics	Univariate analysis			Multivariate analysis
Characteristics	β	Pvalue	OR(95%CI)	β	Pvalue	OR(95%CI)
Age
≤60year			Reference
>60year	-1.134	<0.001	0.322(0.182~0.570)
Serum Creatinine
≤804µmol/L			Reference			Reference
>804µmol/L	1.829	<0.001	6.230(3.390~11.448)	-0.883	0.019	0.414(0.198~0.863)
Uric acid
≤353µmol/L			Reference
>353µmol/L	1.202	<0.001	3.327(1.804~6.137)
Urea nitrogen
≤21.5mmol/L			Reference
>21.5mmol/L	0.794	0.006	2.213(1.259~3.888)
Serum Albumin
≤40g/L			Reference			Reference
>40g/L	0.943	0.002	2.568(1.401~4.707)	-0.67	0.086	0.512(0.238~1.1)
CRP
≤7.935mg/L			Reference			Reference
>7.935mg/L	-1.479	<0.001	0.228(0.126~0.413)	1.368	<0.001	3.926(1.863~8.272)
alkaline phosphatase
≤71U/L			Reference
>71U/L	-0.58	0.049	0.560(0.314~0.997)
Serum Prealbumin
≤28.4mg/dL			Reference
>28.4mg/dL	0.888	0.002	2.430(1.376~4.293)
Kt/V
≤1.4			Reference
>1.4	-1.297	0.001	0.273(0.125~0.597)
MAMC
≤22.64cm			Reference			Reference
>22.64cm	1.933	<0.001	6.908(3.574~13.354)	-1.17	0.008	0.31(0.13~0.741)
Triglycerides
≤1.7mmol/L			Reference
>1.7mmol/L	0.668	0.025	1.950(1.008~3.494)
HDL
≤1.03mmol/L			Reference
>1.03mmol/L	-0.536	0.061	0.585(0.334~1.025)
BMI
<18.5kg/m²			Reference			Reference
18.5-24.9kg/m²	2.662	<0.001	14.327(4.725~43.445)	-1.289	0.001	0.276(0.126~0.603)
>25kg/m²	1.07	0.053	2.916(0.986~8.622)	-2.217	0.003	0.109(0.025~0.48)
Serum Phosphorus
<1.13mmol/L			Reference			Reference
1.13-1.78mmol/L	2.828	<0.001	16.920(5.166~55.419)	-2.403	0.001	0.09(0.022~0.368)
>1.78mmol/L	0.88	0.005	2.410(1.308~4.441)	-3.322	<0.001	0.036(0.008~0.156)
Hemoglobin
≤106g/L			Reference
>106g/L	0.7	0.015	2.013(1.145~3.542)
Kt/V:Urea clearance index;MAMC:Mid-upper arm muscle circumference;CRP:C-reactive protein;HDL:High-density lipoprotein;BMI:Body mass index .

A risk prediction model for sarcopenia in MHD patients was then established based on the formula: P = 1/(1 + e^Y) where e was the base of natural logarithm, Y = 2.839 – 0.883 × predialysis creatinine > 804 µmol/L – 0.670 × albumin > 40 g/L + 1.368 × C-reactive protein > 7.935 mg/L – 2.403 × serum phosphorus 1.13 – 1.78 mmol/L – 3.322 × serum phosphorus > 1.78 mmol/L – 1.289 × BMI18.5 – 24.9 kg/m² – 2.217 × BMI ≥ 25 kg/m² – 1.170 × upper arm muscle circumference > 22.64 cm. A nomogram of the risk prediction model for sarcopenia in MHD patients was also plotted, as shown in (Figure 2). The corresponding score for each variable was obtained by crossing to the gram, and the total score was used to predict the risk of developing sarcopenia in MHD patients.

Validation of prediction model

The validation of this prediction model was performed by plotting the ROC curve and calculating the AUC. Our data indicated that the AUC for this model was 0.8806 (95% CI (0.83913 to 0.92205)(Figure 3), with a 90% sensitivity and 75% specificity as well as a Youden index of 0.643 in the development cohort. Similarly, the AUC of the validation cohort was 0.8613 (95% CI (0.80085 to 0.92180)(Figure 4), with a sensitivity of 88%, a specificity of 74%, and a Youden index of 0.628. In addition, the Hosmer-Leme show goodness-of-fit test showed promising fit (development cohort X² = 4.67, P = 0.9119; validation cohort X² = 12.66, P = 0.2435). The above data demonstrated that the predicted probability of this model was well documented with good calibration and alignment between development (Figure 5)and validation cohorts(Figure 6). The model was then internally validated using the Bootstrap method with an internal validation C-statistic of 0.864.

Clinical validity

The clinical validity of this predictive model was assessed using a decision curve (DCA). The analyses of DCA for the development and validation cohorts were shown in (Figure 7) and (Figure 8), which suggested that patients can benefit from this novel prediction model when the threshold was set as 10 – 90% and 10 – 70% for the development and validation cohorts, respectively.

In the present study, we developed and validated a simple nomogram model to predict the risk of developing sarcopenia in MHD patients with a total of six clinical relevant variables including serum creatinine, albumin, C-reactive protein, serum phosphorus, BMI and upper arm muscle circumference. The AUC, internal validation C-statistic, calibration curve, and DCA curve were constructed and verified the reliability as well as the accuracy of this model. The discovery of this model would not only help to identify MHD patients with increased risk of developing sarcopenia, but may also predict the disease course and provide reference for prognosis estimation.

Nomogram can predict the probability of disease by analyzing and integrating identified disease risk factors, therefore providing valuable information for making better clinical decisions. It has been widely used in oncology and chronic diseases worldwide[18]. For instance, Cheng et al. [19]developed a nomogram to predict the risk of initiating renal replacement therapy within 3 years in patients with diabetic nephropathy and Jing et al [20]developed a nomogram including multiple echocardiographic measures to assess 3-year all-cause mortality in hemodialysis patients which both showed good accuracy and reliability. In addition, Ouyang et al. [21]developed and validated an easy-to-use nomogram that can accurately predict 1-year, 5-year, and 10-year survival in hemodialysis patients. However, to the best of our knowledge, no study has been conducted to develop a nomogram that can predict sarcopenia in MHD patients.

In this study, the multivariate logistic regression analysis indicated that serum creatinine was an independent risk factor for sarcopenia in MHD patients, which was consistent with the findings of Lin et al.[22] Creatinine is a metabolite of human skeletal muscle and is mainly excreted through glomerular filtration. In MHD patients with limited renal function, serum creatinine is not only a marker of renal failure, but also a predictor of nutritional status and decreased skeletal muscle mass [23]. Hyperphosphatemia is a common complication in MHD patients which is closely related to increased risk of vascular calcification and cardiovascular mortality [24]. Interestingly, our study indicated that lower serum phosphorus level was associated with the development of sarcopenia in MHD patients, which was consistent with the findings of Ren et al[8]. We hypothesized that a high-protein diet is the main source of phosphorus for uremic patients who are often accompanied by loss of appetite or even anorexia. A decrease in food intake will inevitably lead to a decrease in serum phosphorus, malnutrition, and protein energy expenditure in patients which ultimately leads to the occurrence of sarcopenia [25]. In our clinical practice, renal function and electrolytes are measured every 1-3 months in MHD patients to assess changes in the condition. In this study, when the measured serum creatinine and serum phosphorus levels were significantly decreased, a detailed assessment of the presence of malnutrition and sarcopenia was conducted in MHD patients and nutritional support with dietary guidance and health education was provided. This intervention might help to reduce the occurrence of sarcopenia.

BMI and MAMC are conventional nutritional assessments for MHD patients and previous studies[26–28] indicated that both were independent predictors of survival. The study by Su et al [29]showed that in MHD patients, the decrease in MAC was associated with increased all-cause mortality and cardiac events, especially those with low BMI. Unsurprisingly, data in our study showed that MHD patients with decreased BMI and MAMC were more likely to develop sarcopenia, which was consistent with previous studies[22, 30]. Therefore, more attention should be paid to MHD patients with low BMI and/or low MAC and sufficient nutritional intervention should be applied in a timely manner to reduce the occurrence of sarcopenia.

Consistent with previous studies[12, 26], this study also found out that the level of C-reactive protein was increased while the level of serum albumin was decreased in MHD patients who developed sarcopenia compared to non-sarcopenia patients. C-reactive protein is one of the most commonly used biochemical indicators to examine inflammation. It has been well established in the field that hemodialysis patients are often under microinflammatory state due to multiple reasons[31]. The close association between inflammation and sarcopenia has been well studied. Inflammatory factors can activate many signaling pathways involved in the pathogenesis of sarcopenia, resulting in decreased anabolism and increased catabolism of proteins [32]. Serum albumin is often used to estimate nutrition levels in MHD patients and is closely related to patient prognosis [33]. Low serum albumin may lead to increased protein catabolism and decreased muscle strength[34]. However, the study by Alves et al. [35]showed that the nutritional status of patients with low serum albumin group was not significantly different from that of patients with normal serum albumin level under non-inflammatory conditions but significantly associated with higher mortality under systemic inflammation. Therefore, more attention should be paid to patients’ inflammatory status, especially for those with decreased serum albumin level.

At present, the commonly used scales for sarcopenia are SARC-F score which measures strength, assistance walking, rising from a chair, climbing stairs and falling, and modified SARC-assisted Cal F score which also measures the situation of the calf. A meta-analysis [36]showed that the sensitivity of SARC-F was low to moderate (28.9% – 55.3%) as well as the specificity (68.9% – 88.9%). Although SARC-CalF is associated with a higher specificity (87.7% – 91.3%), its sensitivity was not satisfying (45.9% – 57.2%). The relatively low sensitivity of these two scales renders a higher risk for miss diagnosis. On the contrary, our novel nomogram provided an alternative method with increased clinical efficacy. The AUC of our constructed nomogram model was 0.8806 in the development cohort and 0.8613 in the validation cohort with an internal validation C-statistic of 0.864. The validity of this novel model was further verified by calibration curves and DCA curves. More importantly, all six variables included in this model are laboratory tests and anthropometric measures that are routinely measured in clinical practice and do not require additional examinations as well as costs.

However, this study has some limitations. First, even though the number of enrollments was relatively large, it was conducted at a single center that might not be representable for the general population. In addition, the constructed nomogram model was not validated with external data. Furthermore, this study excluded cases with incomplete data, which may lead to a selection bias. Therefore, this model should be validated through prospective, multicenter clinical studies in the future.

In this study, we not only proved that sarcopenia was commonly observed in patients on maintenance hemodialysis but also established a prediction nomogram model based on conventional serology and noninvasive anthropometric measurements that was able to accurately predict the risk of developing sarcopenia in MHD patients.

MHD

maintenance hemodialysis

ESRD

end-stage renal disease

MRI

magnetic resonance imaging

CT

computed tomography

DXA

dual energy X-ray absorptiometry

BIA

bioelectrical impedance analysis

SAS

self-rating anxiety scale

SDS

self-rating depression scale

BMI

body mass index

ASM

appendicular skeletal muscle mass

ROC

receiver operating characteristic

AUC

the area under the receiver operrating characteristic curve

DCA

decision curve analysis

Kt/V

Urea clearance index

MAC

Mid-upper arm circumference

MAMC

Mid-upper arm muscle circumference

TSF

triceps skin fold

SAS

self rating anxiety scale

SDS

self rating depression scale

PTH

Parathyroid hormone

HDL

High-density lipoprotein

LDL

Low-density lipoprotein

VLDL

Very low-density lipoprotein

SARC-F

strength, assistance walking, rise from a chair, climb stairs and falls

SARC-CalF

strength, assistance walking, rise from a chair, climb stairs, falls and calf

Ethics approval and consent to participate

The research protocol was approved by the Institutional Review Board of the First Affiliated Hospital, Zhejiang University School of Medicine, and conducted according to the principles of the Declaration of Helsinki. Due to the retrospective nature of the study, written informed consent for participation was waived by the Institutional Review Board of the First Affiliated Hospital, Zhejiang University School of Medicine.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to limitations of ethical approval involving the patient data and anonymity but are available from the corresponding author on reasonable request.

Competing interest

The authors declare that they have no conflict of interest.

Funding

This work was supported by the Zhejiang Medical and Health Science and Technolgy Plan Project,Grant number is 2022KY142.

Authors’ contributions

Genlian Cai participated in study design, data collection, data analysis, and manuscript draft.Xiabing Lang, Mengyan Pan are contributed to the performance of research and modifcation of the draft.Weiping Yu,Qinqin Zhang participated in data collection. Jinping Ying contributed to the study design, implementation, manuscript draft, and critical revision of the article. All authors read and approved the final manuscript.

Acknowledgements

We would like to acknowledge all the participants in this study.

Liyanage T, Ninomiya T, Jha V, Neal B, Patrice HM, Okpechi I, Zhao M-h, Lv J, Garg AX, Knight J et al: Worldwide access to treatment for end-stage kidney disease: a systematic review. The Lancet 2015, 385(9981):1975–1982.
Kramer A, Pippias M, Noordzij M, Stel VS, Andrusev AM, Aparicio-Madre MI, Arribas Monzon FE, Asberg A, Barbullushi M, Beltran P et al: The European Renal Association - European Dialysis and Transplant Association (ERA-EDTA) Registry Annual Report 2016: a summary. Clin Kidney J 2019, 12(5):702-720.
Fahal IH: Uraemic sarcopenia: aetiology and implications. Nephrol Dial Transplant 2014, 29(9):1655–1665.
Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM et al: Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010, 39(4):412–423.
Cruz-Jentoft AJ, Sayer AA: Sarcopenia. The Lancet 2019, 393(10191):2636–2646.
Kim JK, Choi SR, Choi MJ, Kim SG, Lee YK, Noh JW, Kim HJ, Song YR: Prevalence of and factors associated with sarcopenia in elderly patients with end-stage renal disease. Clin Nutr 2014, 33(1):64–68.
Mori K, Nishide K, Okuno S, Shoji T, Emoto M, Tsuda A, Nakatani S, Imanishi Y, Ishimura E, Yamakawa T et al: Impact of diabetes on sarcopenia and mortality in patients undergoing hemodialysis. BMC Nephrol 2019, 20(1):105.
Ren H, Gong D, Jia F, Xu B, Liu Z: Sarcopenia in patients undergoing maintenance hemodialysis: incidence rate, risk factors and its effect on survival risk. Ren Fail 2016, 38(3):364–371.
Shafiee G, Keshtkar A, Soltani A, Ahadi Z, Larijani B, Heshmat R: Prevalence of sarcopenia in the world: a systematic review and meta- analysis of general population studies. J Diabetes Metab Disord 2017, 16:21.
Kim JK, Kim SG, Oh JE, Lee YK, Noh JW, Kim HJ, Song YR: Impact of sarcopenia on long-term mortality and cardiovascular events in patients undergoing hemodialysis. Korean J Intern Med 2019, 34(3):599–607.
Lai S, Muscaritoli M, Andreozzi P, Sgreccia A, De Leo S, Mazzaferro S, Mitterhofer AP, Pasquali M, Protopapa P, Spagnoli A et al: Sarcopenia and cardiovascular risk indices in patients with chronic kidney disease on conservative and replacement therapy. Nutrition 2019, 62:108–114.
Wilkinson TJ, Miksza J, Yates T, Lightfoot CJ, Baker LA, Watson EL, Zaccardi F, Smith AC: Association of sarcopenia with mortality and end-stage renal disease in those with chronic kidney disease: a UK Biobank study. J Cachexia Sarcopenia Muscle 2021, 12(3):586–598.
Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA et al: Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019, 48(1):16–31.
Yoshimura Y, Wakabayashi H, Yamada M, Kim H, Harada A, Arai H: Interventions for Treating Sarcopenia: A Systematic Review and Meta-Analysis of Randomized Controlled Studies. J Am Med Dir Assoc 2017, 18(6):553 e551-553 e516.
Buckinx F, Landi F, Cesari M, Fielding RA, Visser M, Engelke K, Maggi S, Dennison E, Al-Daghri NM, Allepaerts S et al: Pitfalls in the measurement of muscle mass: a need for a reference standard. J Cachexia Sarcopenia Muscle 2018, 9(2):269–278.
Slee A, McKeaveney C, Adamson G, Davenport A, Farrington K, Fouque D, Kalantar-Zadeh K, Mallett J, Maxwell AP, Mullan R et al: Estimating the Prevalence of Muscle Wasting, Weakness, and Sarcopenia in Hemodialysis Patients. J Ren Nutr 2020, 30(4):313–321.
Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S et al: Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 2020, 21(3):300-307 e302.
Balachandran VP, Gonen M, Smith JJ, DeMatteo RP: Nomograms in oncology: more than meets the eye. The Lancet Oncology 2015, 16(4):e173-e180.
Cheng Y, Shang J, Liu D, Xiao J, Zhao Z: Development and validation of a predictive model for the progression of diabetic kidney disease to kidney failure. Ren Fail 2020, 42(1):550–559.
Zhu J, Tang C, Ouyang H, Shen H, You T, Hu J: Prediction of All-Cause Mortality Using an Echocardiography-Based Risk Score in Hemodialysis Patients. Cardiorenal Med 2021, 11(1):33–43.
Ouyang H, Shi Q, Zhu J, Shen H, Jiang S, Song K: Nomogram for predicting 1-, 5-, and 10-year survival in hemodialysis (HD) patients: a single center retrospective study. Ren Fail 2021, 43(1):1508–1519.
Lin YL, Liou HH, Wang CH, Lai YH, Kuo CH, Chen SY, Hsu BG: Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study. J Formos Med Assoc 2020, 119(7):1219–1229.
Vodicar J, Pajek J, Hadzic V, Bucar Pajek M: Relation of Lean Body Mass and Muscle Performance to Serum Creatinine Concentration in Hemodialysis Patients. Biomed Res Int 2018, 2018:4816536.
Palmer SC, Hayen A, Macaskill P, Pellegrini F, Craig JC, Elder GJ, Strippoli GF: Serum levels of phosphorus, parathyroid hormone, and calcium and risks of death and cardiovascular disease in individuals with chronic kidney disease: a systematic review and meta-analysis. JAMA 2011, 305(11):1119–1127.
Fouque D, Horne R, Cozzolino M, Kalantar-Zadeh K: Balancing nutrition and serum phosphorus in maintenance dialysis. Am J Kidney Dis 2014, 64(1):143–150.
Lin TY, Lim PS, Hung SC: Impact of Misclassification of Obesity by Body Mass Index on Mortality in Patients With CKD. Kidney Int Rep 2018, 3(2):447–455.
Noori N, Kopple JD, Kovesdy CP, Feroze U, Sim JJ, Murali SB, Luna A, Gomez M, Luna C, Bross R et al: Mid-arm muscle circumference and quality of life and survival in maintenance hemodialysis patients. Clin J Am Soc Nephrol 2010, 5(12):2258–2268.
Stosovic M, Stanojevic M, Simic-Ogrizovic S, Jovanovic D, Djukanovic L: The predictive value of anthropometric parameters on mortality in haemodialysis patients. Nephrol Dial Transplant 2011, 26(4):1367–1374.
Su CT, Yabes J, Pike F, Weiner DE, Beddhu S, Burrowes JD, Rocco MV, Unruh ML: Changes in anthropometry and mortality in maintenance hemodialysis patients in the HEMO Study. Am J Kidney Dis 2013, 62(6):1141–1150.
Hortegal EVF, Alves J, Santos EJF, Nunes LCR, Galvao JC, Nunes RF, Lula DA, Carvalho SCR, Franca A, Santos EMD et al: Sarcopenia and inflammation in patients undergoing hemodialysis. Nutr Hosp 2020, 37(4):855–862.
Carrero JJ, Johansen KL, Lindholm B, Stenvinkel P, Cuppari L, Avesani CM: Screening for muscle wasting and dysfunction in patients with chronic kidney disease. Kidney Int 2016, 90(1):53–66.
Crossland H, Skirrow S, Puthucheary ZA, Constantin-Teodosiu D, Greenhaff PL: The impact of immobilisation and inflammation on the regulation of muscle mass and insulin resistance: different routes to similar end-points. J Physiol 2019, 597(5):1259–1270.
Dalrymple LS, Johansen KL, Chertow GM, Grimes B, Anand S, McCulloch CE, Kaysen GA: Longitudinal measures of serum albumin and prealbumin concentrations in incident dialysis patients: the comprehensive dialysis study. J Ren Nutr 2013, 23(2):91–97.
Uemura K, Doi T, Lee S, Shimada H: Sarcopenia and Low Serum Albumin Level Synergistically Increase the Risk of Incident Disability in Older Adults. J Am Med Dir Assoc 2019, 20(1):90–93.
Alves FC, Sun J, Qureshi AR, Dai L, Snaedal S, Barany P, Heimburger O, Lindholm B, Stenvinkel P: The higher mortality associated with low serum albumin is dependent on systemic inflammation in end-stage kidney disease. PLoS One 2018, 13(1):e0190410.
Voelker SN, Michalopoulos N, Maier AB, Reijnierse EM: Reliability and Concurrent Validity of the SARC-F and Its Modified Versions: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc 2021, 22(9):1864-1876 e1816.

No competing interests reported.

Development of Risk Prediction Nomogram for Sarcopenia in Patients Receiving Maintenance Hemodialysis

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Version 1