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. 2021 Jan 15;16(1):e0245625. doi: 10.1371/journal.pone.0245625

Association of the erythropoiesis-stimulating agent resistance index and the geriatric nutritional risk index with cardiovascular mortality in maintenance hemodialysis patients

Takahiro Yajima 1,*, Kumiko Yajima 2, Hiroshi Takahashi 3
Editor: Bhagwan Dass4
PMCID: PMC7810304  PMID: 33449974

Abstract

Objective

Hyporesponsiveness to erythropoiesis-stimulating agent (ESA) may be associated with protein-energy wasting. We investigated the relationship of the ESA resistance index (ERI) and the geriatric nutritional risk index (GNRI) for cardiovascular mortality in hemodialysis (HD) patients.

Methods

A total of 180 maintenance HD patients were enrolled. The patients were stratified by the GNRI of 91.2, a previously reported cut-off value, and the ERI of 13.7 (IU/week/kg/g/dL), a cut-off value for predicting cardiovascular-specific mortality, and they were classified into four groups (group 1[G1]: higher GNRI and lower ERI, G2: higher GNRI and higher ERI, G3: lower GNRI and lower ERI, G4: lower GNRI and higher ERI).

Results

The ERI was independently associated with the GNRI (β = −0.271, p = 0.0005). During a median follow-up of 4.6 years, higher ERI and lower GNRI were independently associated with cardiovascular mortality, respectively (adjusted hazard ratio [aHR], 3.10; 95% confidence interval [CI], 1.31–7.34, and aHR, 6.64; 95%CI, 2.60–16.93, respectively). The 7-year survival rates were 96.1%, 70.3%, 77.3%, and 50.1% in G1, G2, G3, and G4, respectively. The aHR values for G4 versus G1 were 12.63 (95%CI, 3.58–44.59). With regards to model discrimination, adding the GNRI alone, the ERI alone, and both to the traditional risk model significantly improved the net reclassification improvement by 0.421, 0.662, and 0.671, respectively. Similar results were obtained for all-cause mortality.

Conclusion

The ERI was independently associated with the GNRI, and could predict cardiovascular mortality in HD patients. Moreover, the combination of GNRI and ERI could improve the predictability for cardiovascular mortality.

Introduction

Renal anemia, which is caused by decreased erythropoietin production due to kidney injury, is common among patients undergoing hemodialysis (HD), and is treated with erythropoiesis-stimulating agents (ESAs). It has been shown that HD patients who receive a high dose of ESAs relative to the hemoglobin (Hb) response experience poor outcomes, including increased risk of cardiovascular events or mortality[1–3]. It is not yet known whether these risks are caused by ESAs themselves, or underlying processes leading to increased ESA requirements. ESA hyporesponsiveness, or resistance, is generally defined as the requirement of higher than average doses of ESA to achieve an increase in Hb concentration[3–5]. The ESA resistance index (ERI) has been proposed as an indicator for ESA hyporesponsiveness, and some previous studies have shown that the ERI can predict all-cause mortality and/or cardiovascular events[6–8]. However, the associations between the ERI and cardiovascular mortality remain unclear.

The mechanisms of ESA hyporesponsiveness are not fully understood, but are likely to be multifactorial, relating to iron deficiency, inflammation, and malnutrition[9]. Recently, some studies have speculated that ESA hyporesponsiveness may be related to protein-energy wasting (PEW), a form of malnutrition characterized by loss of body protein and fuel reserves due to catabolic inflammation[10,11]. Okazaki et al. recently reported that high ERI and low geriatric nutritional risk index (GNRI) were associated with an increased risk of all-cause mortality in HD patients[12]. The GNRI can be used to classify patients according to a risk of complications in relation to conditions associated with PEW[13,14], and is also known to be an effective tool to identify those with malnutrition-related risks of all-cause and cardiovascular mortality in this population[15–17].

We investigated the associations of the ERI and the GNRI with cardiovascular and all-cause mortality in maintenance HD patients. In addition, we evaluated the combined predictability of the ERI and the GNRI for mortality in this population.

Materials and methods

Study participants

We conducted a retrospective cohort study of patients who had undergone maintenance hemodialysis therapy for at least 6 months. Patients who were treated with epoetin beta or darbepoetin alfa, but not with epoetin beta pegol for renal anemia were included. The study was performed using the medical records of the outpatient clinic of Matsunami General Hospital (Kasamatsu, Japan) between January 2008 and March 2020. Patients’ data were fully anonymized prior to access, and as such, the requirement for informed consent was waived. This study adhered to the principles of the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of Matsunami General Hospital (No. 459).

Data collection

The following patient data were collected from medical records: Age; sex; underlying kidney disease; duration of hemodialysis; history of alcohol, smoking, diabetes, hypertension, and cardiovascular disease (CVD); dry weight; and height. CVD was defined as heart failure, angina pectoris, myocardial infarction, stroke, and peripheral artery disease. Diabetes was defined as a history or presence of diabetes, or prescription of glucose-lowering agents. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg before hemodialysis, and/or prescription of anti-hypertensive drugs. Blood samples were collected in the supine position before hemodialysis sessions, which were conducted on either a Monday or a Tuesday. For the assessment of ESA responsiveness, the ERI was calculated by dividing the weekly weight-adjusted ESA dose (IU/week/kg) by the Hb concentration (g/dL)[6]. The darbepoetin alfa dose was harmonized with erythropoietin data by multiplying by 200[18,19]. The GNRI was calculated as follows: GNRI = (14.89 × albumin g/dL) + [41.7× (dry weight/ideal body weight)][13]. When the dry weight exceeded the ideal body weight, the “(dry weight/ideal body weight)” element was set to 1.

Follow-up study

The primary endpoint was CVD mortality, and the secondary endpoint was all-cause mortality. Patients were divided by each cut-off point of ERI and GNRI; thereafter, patients were divided into four groups based on the combinations of each cut-off point of ERI and GNRI: Group 1 (G1), higher GNRI and lower ERI; G2, higher GNRI and higher ERI; G3, lower GNRI and lower ERI; and G4, lower GNRI and higher ERI. The patients were followed up until March 2020.

Statistical analysis

Normally distributed variables are expressed as means ± standard deviations, and non-normally distributed variables are expressed as medians and interquartile ranges. The differences among the four subgroups divided by the GNRI and the ERI were compared by one-way analysis of variance or the Kruskal-Wallis test for continuous variables, or by the chi-squared test for categorical variables. Univariate regression analysis was performed to determine factors correlated with the ERI. Multivariate regression analysis was performed with the factors that were significantly associated with the ERI in the univariate analysis.

A cut-off value of GNRI 91.2 was used; this value was defined from a previous study[13]. Receiver operating characteristic (ROC) analysis was used to determine a cut-off value of the ERI to maximize the predictive value for cardiovascular-specific mortality. The Kaplan-Meier method was used to estimate the survival rate, and the difference was analyzed using the log-rank test. Hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular and all-cause mortality were calculated by Cox proportional hazard regression analysis. The multiple regression model included all covariates that were significant at p < 0.05 in the univariate analysis.

The C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated in order to assess whether the accuracy of predicting mortality improved after adding the GNRI and/or the ERI to the baseline model. The C-index was defined as the area under the receiver operating characteristic curve between individual predictive probabilities for mortality and the incidence of mortality. The C-index was compared between the baseline model, with all established risk factors, and the enriched model, including the GNRI and/or the ERI[20]. The NRI was used as a relative indicator of the number of patients for whom the predicted mortality risk improved, and the IDI was used to show the average improvement in predicted mortality risk after adding the new variables to the baseline model[21]. All statistical analyses were performed using IBM SPSS Version 21 (IBM Corp., Armonk, NY, USA). A p-value < 0.05 was considered statistically significant.

Results

Baseline characteristics

The baseline characteristics of the included patients are shown in Table 1. A total of 180 HD patients were included (age, 63.4 ± 13.9 years; male, 68.3%; HD duration, 0.6 [0.5–4.5] years; history of CVD, 67.8%). Hemoglobin, ferritin, transferrin saturation (TSAT), ESA dose, ERI, and GNRI levels were 10.7 ± 1.3 g/dL, 110 (49–201) ng/mL, 26.1 ± 12.7%, 4500 (4000–9000) IU/week, 8.7 (5.2–14.9) IU/week/kg/g/dL, and 94.5 ± 6.9, respectively. Univariate regression analysis showed that the ERI was significantly correlated with age (β = 0.254, p = 0.0006), creatinine (β = -0.174, p = 0.020), TSAT (β = -0.326, p <0.0001), and the GNRI (β = -0.349, p <0.0001). Multivariate regression analysis, following adjustment for all significant confounders in univariate analysis, revealed that the ERI was independently correlated with TSAT (β = -0.289, p <0.0001) and the GNRI (β = -0.271, p = 0.0005) (Table 2).

Table 1. Baseline characteristics of the study patients.

All patients (n = 180) G1 (n = 93) G2 (n = 20) G3 (n = 32) G4 (n = 35) p-value
Age (years) 63.4 ± 13.9 59.0 ± 13.6 67.2 ± 10.3 64.7 ± 16.3 71.5 ± 9.3 < 0.0001
Male (%) 68.3 68.8 75.0 68.8 62.9 0.82
Underlying kidney disease 0.92
Diabetic kidney disease (%) 45.0 48.4 45.0 37.5 42.9
Chronic glomerulonephritis (%) 32.2 32.3 25.0 37.5 31.4
Nephrosclerosis (%) 17.8 16.1 25.0 15.6 20.0
Others (%) 5.0 3.2 5.0 9.4 5.7
HD duration (years) 0.6 (0.5–4.5) 1.0 (0.5–4.7) 0.6 (0.5–3.8) 0.5 (0.5–4.8) 0.5 (0.5–3.9) 0.55
Alcohol (%) 26.7 28.0 25.0 25.0 25.7 0.98
Smoking (%) 30.0 32.2 30.0 28.1 25.7 0.90
Hypertension (%) 95.6 96.8 100 90.6 94.3 0.30
Diabetes (%) 46.7 48.4 55.0 40.6 42.9 0.72
History of CVD (%) 67.8 66.7 60.0 65.6 77.1 0.54
Dw (kg) 57.7 ± 11.9 62.8 ± 11.9 52.2 ± 8.4 54.7 ± 8.2 50.2 ± 10.3 < 0.0001
BMI (kg/m2) 22.2 ± 3.6 23.8 ± 3.5 19.4 ± 2.9 22.0 ± 2.7 19.9 ± 2.9 < 0.0001
BUN (mg/dL) 61.4 ± 16.7 64.9 ± 16.4 59.2 ± 13.2 58.0 ± 20.6 56.3 ± 14.0 0.028
Creatinine (mg/dL) 9.2 ± 3.2 9.8 ± 3.6 8.4 ± 2.2 9.0 ± 2.7 8.3 ± 2.1 0.050
Single-pool Kt/V for urea 1.3 ± 0.3 1.3 ± 0.3 1.4 ± 0.3 1.4 ± 0.3 1.4 ± 0.4 0.19
Albumin (g/dL) 3.7 ± 0.4 3.9 ± 0.3 3.4 ± 0.4 3.8 ± 0.2 3.3 ± 0.4 < 0.0001
Hemoglobin (g/dL) 10.7 ± 1.3 11.0 ± 1.1 10.8 ± 1.2 10.1 ± 1.3 10.4 ± 1.5 0.0016
T-Cho (mg/dL) 152 ± 34 155 ± 35 146 ± 28 155 ± 34 142 ± 33 0.19
Uric acid (mg/dL) 7.1 ± 1.3 7.2 ± 1.7 7.4 ± 1.7 6.7 ± 2.3 6.8 ± 1.6 0.43
Ca (mg/dL) 8.8 ± 0.9 8.9 ± 0.9 8.5 ± 0.9 9.0 ± 0.8 8.7 ± 0.9 0.10
P (mg/dL) 5.2 ± 1.3 5.4 ± 1.3 4.7 ± 0.9 5.1 ± 1.5 4.9 ± 1.3 0.067
iPTH (pg/mL) 126 (45–215) 142 (52–231) 69 (23–130) 115 (48–266) 127 (25–210) 0.064
Glucose (mg/dL) 138 ± 59 144 ± 66 141 ± 64 123 ± 39 133 ± 53 0.35
CRP (mg/dL) 0.15 (0.06–0.39) 0.11 (0.06–0.28) 0.24 (0.07–0.85) 0.18 (0.06–0.33) 0.29 (0.03–1.13) 0.048
Ferritin (ng/mL) 110 (49–201) 116 (60–204) 144 (83–199) 86 (27–167) 99 (40–221) 0.25
TSAT (%) 26.1 ± 12.7 27.3 ± 11.2 33.1 ± 20.6 23.1 ± 11.8 21.6 ± 9.1 0.0041
ESA dose (IU/week) 4500 (4000–9000) 4500 (2250–4500) 4000 (2250–4500) 9000 (9000–9000) 9000 (8000–9000) < 0.0001
ERI (IU/week/kg/g/dL) 8.7 (5.2–14.9) 6.1 (3.7–8.6) 6.5 (4.8–7.5) 16.8 (14.8–19.4) 15.2 (11.8–19.9) < 0.0001
GNRI 94.5 ± 6.9 98.8 ± 4.2 87.0 ± 3.8 96.1 ± 3.0 86.0 ± 5.0 < 0.0001
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Abbreviations: HD: Hemodialysis, BMI: Body mass index, BUN: Blood urea nitrogen, T-Cho: Total cholesterol, CRP: C-reactive protein, CVD: Cardiovascular disease, Dw: Dry weight, ESA: Erythropoiesis-stimulating agent, ERI: Erythropoiesis-stimulating agent resistance index, GNRI: Geriatric nutritional risk index, TSAT: Transferrin saturation

G1: Higher GNRI and lower ERI, G2: Higher GNRI and higher ERI, G3: Lower GNRI and lower ERI, G4: Lower GNRI and higher ERI.

Table 2. Univariate and multivariate regression analysis of the associations between the erythropoiesis-stimulating agent resistance index and baseline variables.

Univariate Multivariate
Variables β p-value β p-value
Age 0.254 0.0006 0.054 0.53
Creatinine -0.174 0.020 -0.069 0.38
TSAT -0.326 < 0.0001 -0.289 < 0.0001
GNRI -0.349 < 0.0001 -0.271 0.0005
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Abbreviations: TSAT: Transferrin saturation, GNRI: Geriatric nutritional risk index.

Associations of the GNRI and ERI with CVD mortality

A total of 63 patients died during the follow-up period (4.6 [2.5–8.2] years), including 28 (44.4%) due to CVD-specific causes (14 heart failures, 7 sudden cardiac deaths or fatal arrhythmias, 4 strokes, and 3 myocardial infarctions), and 35 due to non-CVD-specific causes (25 infections, 7 malignancies, and 3 others).

In the multivariate Cox proportional hazards analysis adjusted by age, history of cardiovascular disease, creatinine, and C-reactive protein (CRP), which were significant in the univariate analysis, the GNRI and ERI were significant predictors for CVD mortality (HR, 0.87; 95%CI, 0.82–0.92, and HR, 1.09; 95%CI, 1.05–1.13, respectively) (Table 3). ROC analysis was performed to obtain the optimal cut-off values of the ERI for predicting the risk of CVD mortality. The cut-off value of the ERI was 13.7 IU/week/kg/g/dL (AUC = 0.655, p = 0.025). First, patients were divided by the GNRI of 91.2 into low and high groups, in which the 7-year CVD survival rates were 60.1% and 91.6%, respectively (p < 0.0001) (Fig 1A). Second, patients were then divided by the ERI of 13.7 IU/week/kg/g/dL into low and high groups, in which the 7-year CVD survival rates were 91.8% and 65.2%, respectively (p < 0.0001) (Fig 1B). Third, the patients were divided by each cut-off point of the GNRI and ERI into G1, G2, G3, and G4 groups, in which the 7-year CVD survival rates were 96.1%, 70.3%, 77.3%, and 50.1% (Fig 1C). Multivariate Cox proportional hazards analysis was performed after adjusting for age, history of CVD, creatinine, and CRP, which were significant in the univariate analysis. The adjusted HR (aHR) values for CVD mortality were 3.10 (95%CI, 1.31–7.34, p = 0.0099) for lower GNRI, and 6.64 (95%CI, 2.60–16.93, p < 0.0001) for higher ERI. Moreover, the aHR values were 6.70 (95%CI, 1.60–28.16, p = 0.0094) for G2 vs G1, 9.58 (95%CI, 2.83–32.45, p = 0.0003) for G3 vs G1, and 12.63 (95%CI, 3.58–44.59, p < 0.0001) for G4 vs G1 (Table 3). Similar results were obtained for all-cause mortality (Table 3, Fig 1D–1F).

Table 3. Cox proportional hazards analysis of the erythropoiesis-stimulating agent resistance index and the geriatric nutritional risk index for mortality.

Variables Non-adjusted Adjusted*
HR (95%CI) p-value HR (95%CI) p-value
Cardiovascular mortality
GNRI (continuous) 0.87 (0.82–0.92) < 0.0001 0.90 (0.84–0.96) 0.0020
ERI (continuous) 1.09 (1.05–1.13) < 0.0001 1.07 (1.02–1.11) 0.0050
Lower GNRI 4.52 (2.10–9.73) 0.0001 3.10 (1.31–7.34) 0.0099
Higher ERI 8.19 (3.58–18.74) < 0.0001 6.64 (2.60–16.93) < 0.0001
Cross-classified (vs. G1) < 0.0001 < 0.0001
G2 6.77 (1.85–24.76) 0.0039 6.70 (1.60–28.16) 0.0094
G3 8.62 (2.94–25.30) < 0.0001 9.58 (2.83–32.45) 0.0003
G4 13.75 (4.74–39.88) < 0.0001 12.63 (3.58–44.59) < 0.0001
All-cause mortality
GNRI (continuous) 0.88 (0.85–0.91) < 0.0001 0.92 (0.88–0.96) 0.00012
ERI (continuous) 1.08 (1.05–1.10) < 0.0001 1.06 (1.03–1.08) 0.00019
Lower GNRI 5.14 (3.09–8.56) < 0.0001 3.36 (1.92–5.87) < 0.0001
Higher ERI 3.38 (2.01–5.70) < 0.0001 2.49 (1.42–4.37) 0.0015
Cross-classified (vs. G1) < 0.0001 < 0.0001
G2 6.05 (2.85–12.86) < 0.0001 4.33 (1.93–9.72) 0.0004
G3 3.55 (1.70–7.44) 0.0008 2.91 (1.34–6.32) 0.0071
G4 9.18 (4.73–17.82) < 0.0001 5.87 (2.81–12.24) < 0.0001
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Abbreviations: ERI: Erythropoiesis-stimulating agent resistance index, GNRI: Geriatric nutritional risk index.

* Adjusted for age, history of cardiovascular disease, creatinine, and C-reactive protein, which were significant in the univariate analysis.

Fig 1. Kaplan-Meier survival curves for cardiovascular and all-cause mortality.

Fig 1

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Cardiovascular mortality for lower GNRI vs. higher GNRI (a), lower ERI vs. higher ERI (b), and among the four groups divided by the GNRI and the ERI (c). All-cause mortality for lower GNRI vs. higher GNRI (d), lower ERI vs. higher ERI (e), and among the four groups divided by the GNRI and the ERI (f). ERI: Erythropoiesis-stimulating agent resistance index, GNRI: Geriatric nutritional risk index. G1: Higher GNRI and lower ERI, G2: Higher GNRI and higher ERI, G3: Lower GNRI and lower ERI, G4: Lower GNRI and higher ERI.

With regards to the model discrimination, the C-index for CVD mortality was greater in the model adding the GNRI alone (0.708, p = 0.83), the ERI alone (0.747, p = 0.28), and both variables (0.753, p = 0.26) compared to the traditional risk model (0.708), but did not reach statistical significance. However, The NRI and IDI values for CVD mortality improved by adding the GNRI alone (0.421 [p = 0.020] and 0.011 [p = 0.093], respectively), the ERI alone (0.662 [p = 0.00065] and 0.041 [p = 0.025], respectively), and both variables (0.671 [p = 0.00055] and 0.041 [p = 0.0097], respectively) to the traditional risk model (Table 4). Similar results were obtained for all-cause mortality (Table 4).

Table 4. Predictive accuracy of the erythropoiesis-stimulating agent resistance index and the geriatric nutritional risk index for mortality.

Variables C-index p-value NRI p-value IDI p-value
Cardiovascular mortality
Traditional risk factors* 0.704 (0.589–0.820) Ref. Ref.
+ GNRI 0.708 (0.589–0.827) 0.83 0.421 0.020 0.011 0.093
+ ERI 0.747 (0.650–0.844) 0.28 0.662 0.00065 0.041 0.025
+ GNRI and ERI 0.753 (0.659–0.846) 0.26 0.671 0.00055 0.041 0.0097
All-cause mortality
Traditional risk factors* 0.722 (0.645–0.799) Ref. Ref.
+ GNRI 0.744 (0.667–0.820) 0.37 0.574 0.00012 0.051 0.0016
+ ERI 0.729 (0.653–0.805) 0.70 0.374 0.0084 0.021 0.043
+ GNRI and ERI 0.767 (0.693–0.841) 0.11 0.713 <0.00001 0.072 0.0001
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Abbreviations: ERI: Erythropoiesis-stimulating agent resistance index, GNRI: Geriatric nutritional risk index.

* Traditional risk factors include age, history of cardiovascular disease, creatinine, and C-reactive protein.

Discussion

The results of the present study showed that the ERI was negatively and independently associated with the GNRI, and could predict CVD and all-cause mortality in patients undergoing maintenance HD. Moreover, the combination of the ERI and the GNRI could not only stratify the risk, but also improve the predictability for mortality. Therefore, both the ERI and the GNRI should be evaluated to more accurately predict CVD mortality in this population.

The most common causes of ESA hyporesponsiveness are iron deficiency, either absolute or functional, inflammation, and malnutrition[9]. Absolute iron deficiency may be due to external blood losses through the extracorporeal blood circuit or dialyzers, and/or exhaustion of iron stores due to an increase in erythropoiesis caused by ESA treatment. In this situation, iron administration to maintain adequate iron stores is required for reducing the ESA dose and for enhancing ESA efficacy. However, some clinical trials have shown that iron administration to ESRD patients is associated with increased risks of infection, CVD, hospitalization, and mortality[22–24]. Functional iron deficiency is a condition in which iron utilization is defective in the bone marrow due to chronic inflammation despite sufficient iron stores. Malnutrition is closely related to inflammation and atherosclerosis[25], and through common mediators such as IL-6 or TNF-α, it may play a relevant role in ESA hyporesponsiveness[26].

On the other hand, PEW is a state of malnutrition, which is frequently complicated with chronic kidney disease, and is associated with an increased risk of mortality[27–29]. Some previous studies have reported that a loss of muscle mass and fat mass in the presence of inflammation leads to an increased risk of CVD mortality by promoting vascular endothelial damage[28–32]. As an indicator of PEW, the GNRI, a simple and objective method for evaluating nutritional status, is well-known in HD patients. Bouillanne et al. firstly reported that the GNRI was a prognostic indicator of morbidity and mortality in elderly hospitalized patients at nutritional risk[33]. Yamada et al. reported that the GNRI was the most reliable screening tool for predicting malnutrition compared with other simple nutritional screening tools in maintenance hemodialysis patients[13]. They also determined the cutoff value of 91.2 for GNRI with the use of MIS as the standard reference, in this population[13]. Thereafter, many studies showed that the GNRI is a useful tool for stratifying malnutritional risks[14] and identifying nutrition-related risks of CVD events and all-cause or CVD mortality in HD patients[15,16,34]. A meta-analysis conducted by Xiong et al. also concluded that the GNRI is a significant indicator for predicting both all-cause and CVD mortality in patients undergoing HD[17]. Furthermore, we have recently reported that the ratio of extracellular fluid to intracellular fluid measured by bio-impedance analysis, a new marker of PEW, predicted not only all-cause, but also CVD mortality in patients undergoing HD[35]. Moreover, we have also revealed that the combining the ratio of extracellular fluid to intracellular fluid with the GNRI could improve predictability for mortality[35].

Several recent studies have reported the possible association between ESA hyporesponsiveness and PEW. Rattanasompattikul et al. have reported that the ERI was independently correlated with malnutritional-inflammation score, a comprehensive scoring system of nutrition in maintenance HD patients, and that the score was worse in the 4th quartile of ERI compared to the 1st quartile[10]. Furthermore, González-Ortiz et al. recently reported that HD patients with PEW, which was classified by malnutritional-inflammation score, have increased risks for the poorer response to ESA therapy than those without PEW[11]. In this study, the ERI was negatively, independently associated with GNRI, a marker of PEW; therefore, our findings supported that the ERI may be a plausible indicator of PEW.

In this study, higher ERI and lower GNRI were independently associated with CVD and all-cause mortality, respectively. Many observational studies have shown that the GNRI predicts all-cause and CVD mortality[15–17]. Although the associations between the ERI and all-cause mortality and CVD events has been already reported[6–8], the relationship between the ERI and CVD mortality remains unknown. Therefore, for the first time, we show that the ERI was a significant predictor for CVD mortality in HD patients. In the present study, the proportion of patients with a history of previous CVD was relatively high, and the study follow-up period was relatively long; thus, it was possible to determine the association between the ERI and CVD mortality. More interestingly, combining the ERI with the GNRI could stratify the risk of CVD mortality and improve the predictability. Therefore, both the ERI and the GNRI should be simultaneously evaluated in HD patients.

There were several limitations to this study. First, the present study was a single-center retrospective study with a relatively small number of participants. Second, patients with renal anemia who were treated with epoetin beta or darbepoetin alfa, but not with epoetin beta pegol were included. Since darbepoetin alfa but not epoetin beta pegol can be converted to the ESA dose of epoetin beta, our results might not be applicable to all patients in whom renal anemia is treated with ESAs. Third, the use of only baseline ERI and GNRI for data analysis was not allowed to consider any changes of these indicators during the follow-up periods. In addition, the changes of dialysis dose, nutritional status, and iron status markers might help to clarify the potential causes of a linked change of ERI, therefore future study may be needed to reveal these associations. Fourth, this study only included Japanese patients, and as such, our findings might not be representative of maintenance HD patients in other countries. Therefore, a further large-scale multicenter study may be needed to validate our results.

In conclusion, the ERI was independently associated with the GNRI and could predict CVD, as well as all-cause mortality in patients undergoing HD. Moreover, combining the ERI and the GNRI could not only stratify the risk of CVD and all-cause mortality, but could also improve the predictability for mortality. Therefore, both the ERI and the GNRI should be evaluated to more accurately predict CVD and all-cause mortality in this population.

Supporting information

S1 Data

(XLSX)

Click here for additional data file. (56.4KB, xlsx)

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Bhagwan Dass

18 Dec 2020

PONE-D-20-33311

Association of the Erythropoiesis-Stimulating Agent Resistance Index and the Geriatric Nutritional Risk Index with Cardiovascular Mortality in Maintenance Hemodialysis Patients

PLOS ONE

Dear Dr. Takahiro Yajima,

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PLOS ONE

Additional Editor Comments:

Since you have mentioned that ERI was independently associated with the GNRI, and could predict cardiovascular mortality in HD patients. There are several existing studies showing relationship between the mechanisms of ESA hypo responsiveness and malnutrition. As we all know Malnutrition is closely related to inflammation and through mediators such as IL-6 or TNF- a, causing ESA hypo responsiveness, Both ESA hyporesposiveness and malnutrition based on current literature are closely related. The Geriatric Nutritional Risk Index (GNRI) is a simple screening tool to predict the risk of nutrition-related morbidity and mortality in mostly used in elderly patients. The population in your study group was age, 63.4 ± 13.9 years; this GNRI tool may not be uniformly applicable. The possible use of this tool GNRI.in HD patients may need some more studies to prove it as a more robust indicator of nutritional status in HD.patients.,Please add some more in the discussion section. .

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Reviewer #2: Yes

**********

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**********

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Reviewer #1: Over all well done study, single center retrospective , evaluating ERI association with the GNRI to predict CVD, all-cause mortality in patients on hemodialysis. This study adds to our knowledge of risk stratification of patients on HD with ERI and GNRI, Few questions as below:

1. Please can you elaborate the concept that ERI can predict all-cause mortality and/or cardiovascular events but link with cardiovascular mortality remain unclear.

2. Table 1- HD duration is not clearly visible in different groups, can you please correct or its not showing correctly on the pdf. Is there a correlation with dialysis vintage to your finding?

Reviewer #2: Yajima T and coworkers have explored the predictive value of erythropoiesis-stimulating agent index (ERI) or geriatric nutritional risk index (GNRI) used alone versus used in combination (ERI plus GNRI) on all-cause and cardiovascular mortality in hemodialysis patients. For this purpose, they performed a retrospective cross-sectional study enrolling 180 prevalent maintenance HD patients. Patients were stratified according to the GNRI (threshold 91.2) and the ERI (threshold 13.7 IU/week/kg/g/dL). Four groups were then defined: group 1: higher GNRI and lower ERI, G2: higher GNRI and higher ERI, G3: lower GNRI and lower ERI, G4: lower GNRI and higher ERI. ERI was independently correlated with GNRI. Higher ERI and lower GNRI are independently associated with cardiovascular mortality. Survival rates are also inversely correlated with these predefined groups. It is also shown that ERI is independently associated with GNRI with a high predictive value for cardiovascular mortality. Furthermore, the combination of GNRI and ERI tend to improve cardiovascular mortality risk estimate.

This is an interesting study, well conducted and clearly written. However, this study is confirmatory by nature in showing that ERI is a sensitive and reliable marker of outcome in HD patients. It is originality relies in the additional use of GNRI either alone or in combination with ERI to assess mortality risk in HD patients.

My concerns are the following:

1. Looking at the composition of the GNRI equation two questions came in mind. Does serum albumin concentration alone would not have the same predictive value in assessing mortality risk in this population? Does ideal body weight relative to dry weight would not have the same predictive value in estimating mortality risk in this population? Therefore, what is the added value of using GNRI equation rather than albumin or body mass index as combined markers with ERI?

2. What is the role of inflammation marker (CRP) in this predictive estimate since it is already known as a strong marker and/or actor in this poor outcome complex? Interestingly, CRP seems higher in the group 4 but very low compared to European data.

3. What would be the additive value of simplified creatinine index as a more specific surrogate of protein energy wasting in the predictive value of ERI on dialysis patient outcomes?

4. What are the dynamic changes and predictive value of ERI and/or GNRI associated with these changes over time? That could be explored in this HD population since they were followed up to 7 years. It would be interesting to assess the fact that positive or negative trend changes in ERI or GNRI are associated with different outcomes.

5. Data summarizing dialysis efficiency would be of interest, as well as time trend changes of dialysis dose, nutritional, anemia and iron status markers to identify potential causes of high ERI or low GNRI.

**********

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Reviewer #2: No

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PLoS One. 2021 Jan 15;16(1):e0245625. doi: 10.1371/journal.pone.0245625.r002

Author response to Decision Letter 0

31 Dec 2020

Response to Reviewers

Response to editor

Thank you very much for your constructive comments.

Additional Editor Comments:

Since you have mentioned that ERI was independently associated with the GNRI, and could predict cardiovascular mortality in HD patients. There are several existing studies showing relationship between the mechanisms of ESA hypo responsiveness and malnutrition. As we all know Malnutrition is closely related to inflammation and through mediators such as IL-6 or TNF- a, causing ESA hypo responsiveness, Both ESA hyporesposiveness and malnutrition based on current literature are closely related. The Geriatric Nutritional Risk Index (GNRI) is a simple screening tool to predict the risk of nutrition-related morbidity and mortality in mostly used in elderly patients. The population in your study group was age, 63.4 ± 13.9 years; this GNRI tool may not be uniformly applicable. The possible use of this tool GNRI.in HD patients may need some more studies to prove it as a more robust indicator of nutritional status in HD patients. Please add some more in the discussion section. .

Thank you very much for the comments. According to your advice, we added more detailed explanations of GNRI in the third paragraph of discussion section as following: As an indicator of PEW, the GNRI, a simple and objective method for evaluating nutritional status, is well-known in HD patients. Bouillanne et al. firstly reported that the GNRI was a prognostic indicator of morbidity and mortality in elderly hospitalized patients at nutritional risk33. Yamada et al. reported that the GNRI was the most reliable screening tool for predicting malnutrition compared with other simple nutritional screening tools in maintenance hemodialysis patients13. They also determined the cutoff value of 91.2 for GNRI with the use of MIS as the standard reference, in this population13. Thereafter, many studies showed that the GNRI is a useful tool for stratifying malnutritional risks14 and identifying nutrition-related risks of CVD events and all-cause or CVD mortality in HD patients15,16,34. A meta-analysis conducted by Xiong et al. also concluded that the GNRI is a significant indicator for predicting both all-cause and CVD mortality in patients undergoing HD17.

Response to Reviewer #1

Thank you very much for your constructive comments.

Reviewer #1: Over all well done study, single center retrospective , evaluating ERI association with the GNRI to predict CVD, all-cause mortality in patients on hemodialysis. This study adds to our knowledge of risk stratification of patients on HD with ERI and GNRI, Few questions as below:

1. Please can you elaborate the concept that ERI can predict all-cause mortality and/or cardiovascular events but link with cardiovascular mortality remain unclear.

Thank you very much for the comment.

As mentioned in the Introduction section, some previous studies showed that the ERI was a predictor for all-cause mortality and cardiovascular events. If the rates of cardiovascular events increase, those of cardiovascular mortality might increase. However, there is no study which investigated the associations between the ERI and cardiovascular mortality. Thus, we investigated these associations in the present study.

2. Table 1- HD duration is not clearly visible in different groups, can you please correct or its not showing correctly on the pdf. Is there a correlation with dialysis vintage to your finding?

Thank you very much for the comments.

We showed that HD duration is clearly visible in the divided four groups on the PDF. HD vintage was not a significant predictor in the univariate analysis (HR 1.029, 95%CI 0.955-1.091). Moreover, even after the addition of HD vintage into multivariate model, higher ERI was still independently associated with an increased risk of CVD mortality (HR 6.017, 95%CI 2.381-15.21).

Response to Reviewer #2

Thank you very much for your constructive comments.

Reviewer #2: Yajima T and coworkers have explored the predictive value of erythropoiesis-stimulating agent index (ERI) or geriatric nutritional risk index (GNRI) used alone versus used in combination (ERI plus GNRI) on all-cause and cardiovascular mortality in hemodialysis patients. For this purpose, they performed a retrospective cross-sectional study enrolling 180 prevalent maintenance HD patients. Patients were stratified according to the GNRI (threshold 91.2) and the ERI (threshold 13.7 IU/week/kg/g/dL). Four groups were then defined: group 1: higher GNRI and lower ERI, G2: higher GNRI and higher ERI, G3: lower GNRI and lower ERI, G4: lower GNRI and higher ERI. ERI was independently correlated with GNRI. Higher ERI and lower GNRI are independently associated with cardiovascular mortality. Survival rates are also inversely correlated with these predefined groups. It is also shown that ERI is independently associated with GNRI with a high predictive value for cardiovascular mortality. Furthermore, the combination of GNRI and ERI tend to improve cardiovascular mortality risk estimate.

This is an interesting study, well conducted and clearly written. However, this study is confirmatory by nature in showing that ERI is a sensitive and reliable marker of outcome in HD patients. It is originality relies in the additional use of GNRI either alone or in combination with ERI to assess mortality risk in HD patients.

My concerns are the following:

1. Looking at the composition of the GNRI equation two questions came in mind. Does serum albumin concentration alone would not have the same predictive value in assessing mortality risk in this population? Does ideal body weight relative to dry weight would not have the same predictive value in estimating mortality risk in this population? Therefore, what is the added value of using GNRI equation rather than albumin or body mass index as combined markers with ERI?

Thank you very much for the comments.

As you mentioned above, the GNRI is considered to be a marker composed with albumin (Alb) and body mass index (BMI). In the present study, the increased of Alb (HR 0.115, 95%CI 0.047-0.295, p <0.0001) and BMI (HR 0.886, 95%CI 0.781-0.993, p = 0.037) were also associated with decreased risks of cardiovascular mortality, respectively as well as the GNRI. However, Takahashi et al. (Journal of Cardiology 2014;64:32–36) have already reported that the predictability of all-cause and cardiovascular mortality improved when GNRI was added into baseline model compared to when Alb or BMI was added. Therefore, we think the GNRI rather than Alb or BMI may be suitable as a combined marker with ERI. We thanks for your kind understanding.

2. What is the role of inflammation marker (CRP) in this predictive estimate since it is already known as a strong marker and/or actor in this poor outcome complex?

Thank you very much for the comments.

As reviewer mentioned, CRP was a significant predictor for CVD mortality (HR 1.486, 95%CI 1.152-1.807, p=0.0054) by univariate analysis, therefore we included it in multivariate Cox proportional hazards analysis, and the results were shown in table 3.

Interestingly, CRP seems higher in the group 4 but very low compared to European data.

Thank you very much for the comment.

CRP concentrations are five times lower in Japan than in Europe (Lancet. 2016; 388: 294–306). Thus, we think our results were not surprising.

3. What would be the additive value of simplified creatinine index as a more specific surrogate of protein energy wasting in the predictive value of ERI on dialysis patient outcomes?

Thank you very much for the comment.

As you mentioned above, simplified creatinine index has been recently developed as a surrogate marker of lean body mass and is calculated using age, sex, pre-dialysis serum creatinine level, and single-pool Kt/V for urea. In the present study, simplified creatinine index was 20.4 ± 3.1 mg/kg/d. In univariate Cox proportional hazards analysis, simplified creatinine index was a significant predictor of cardiovascular mortality (HR 0.741, 95%CI 0.643-0.846, p <0.0001). After adjusting for age, previous history of CVD, and CRP, simplified creatinine index was an independent predictor of cardiovascular mortality (HR 0.825, 95%CI 0.692-0.984, p = 0.033).

However, the modified creatinine index is affected by day-to-day dietary protein intake and dialysis dose, and it may be affected by residual kidney function (J Ren Nutr. 2020 Sep 17:S1051-2276(20)30211-9, Am J Kidney Dis. 2020 Feb;75(2):195-203.). Because the median HD vintage of the present study participants was 0.6 years, therefore the influence of residual kidney function to modified creatinine index might not be ignored. Moreover, Yamada et al. reported that GNRI and modified creatinine index equally predicted the risks of mortality in patients undergoing HD (scientific reports 2020;10:5756). Thus, we think it may be appropriate to use GNRI instead of modified creatinine index for evaluating the predictability of the ERI in the present study.

4. What are the dynamic changes and predictive value of ERI and/or GNRI associated with these changes over time? That could be explored in this HD population since they were followed up to 7 years. It would be interesting to assess the fact that positive or negative trend changes in ERI or GNRI are associated with different outcomes.

Thank you very much for the comments. We fully agree to your comments.

Unfortunately, we have only baseline ERI and GNRI data, which was approved by the Ethics Committee, in the present study. However, we think that this is a very important issue, therefore we state it in the limitation.

5. Data summarizing dialysis efficiency would be of interest, as well as time trend changes of dialysis dose, nutritional, anemia and iron status markers to identify potential causes of high ERI or low GNRI.

Thank you very much for the comments. We deeply agree to your comments.

We added single-pool Kt/V for urea as a baseline dialysis dose in the table 1. The baseline ERI was not associated with baseline single-pool Kt/V for urea (β = 0.099, p = 0.18). However, as we mentioned above, we have no follow-up data, therefore we cannot investigate the relationships between ERI or GNRI and the changes of dialysis dose, nutritional, and anemia and iron status markers. We think that this is a limitation, therefore we also state it in our manuscript.

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Decision Letter 1

Bhagwan Dass

5 Jan 2021

Association of the Erythropoiesis-Stimulating Agent Resistance Index and the Geriatric Nutritional Risk Index with Cardiovascular Mortality in Maintenance Hemodialysis Patients

PONE-D-20-33311R1

Dear Dr. Yajima,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Bhagwan Dass, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Bhagwan Dass

7 Jan 2021

PONE-D-20-33311R1

Association of the Erythropoiesis-Stimulating Agent Resistance Index and the Geriatric Nutritional Risk Index with Cardiovascular Mortality in Maintenance Hemodialysis Patients

Dear Dr. Yajima:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Bhagwan Dass

Academic Editor

PLOS ONE

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