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Prognostic value of the geriatric nutritional risk index in patients with non-metastatic clear cell renal cell carcinoma: a propensity score matching analysis

Nutrition Journal volume 23, Article number: 114 (2024) Cite this article

Abstract

Background

This study aimed to investigate the prognostic value of the geriatric nutritional risk index (GNRI) in patients with non-metastatic clear cell renal cell carcinoma (ccRCC) who underwent nephrectomy.

Methods

Patients with non-metastatic ccRCC who underwent nephrectomy between 2013 and 2021 were analyzed retrospectively. The GNRI was calculated within one week before surgery. The optimal cut-off value of GNRI was determined using X-tile software, and the patients were divided into a low GNRI group and a high GNRI group. The Kaplan-Meier method was used to compare the overall survival (OS), cancer-specific survival (CSS) and recurrence-free survival (RFS) between the two groups. Univariate and multivariate Cox proportional hazard models were used to determine prognostic factors. In addition, propensity score matching (PSM) was performed with a matching ratio of 1:3 to minimize the influence of confounding factors. Variables entered into the PSM model were as follows: sex, age, history of hypertension, history of diabetes, smoking history, BMI, tumor sidedness, pT stage, Fuhrman grade, surgical method, surgical approach, and tumor size.

Results

A total of 645 patients were included in the final analysis, with a median follow-up period of 37 months (range: 1-112 months). The optimal cut-off value of GNRI was 98, based on which patients were divided into two groups: a low GNRI group (≤ 98) and a high GNRI group (> 98). Kaplan-Meier analysis showed that OS (P < 0.001), CSS (P < 0.001) and RFS (P < 0.001) in the low GNRI group were significantly worse than those in the high GNRI group. Univariate and multivariate Cox analysis showed that GNRI was an independent prognostic factor of OS, CSS and RFS. Even after PSM, OS (P < 0.05), CSS (P < 0.05) and RFS (P < 0.05) in the low GNRI group were still worse than those in the high GNRI group. In addition, we observed that a low GNRI was associated with poor clinical outcomes in elderly subgroup (> 65) and young subgroup (≤ 65), as well as in patients with early (pT1-T2) and low-grade (Fuhrman I-II) ccRCC.

Conclusion

As a simple and practical tool for nutrition screening, the preoperative GNRI can be used as an independent prognostic indicator for postoperative patients with non-metastatic ccRCC. However, larger prospective studies are necessary to validate these findings.

Peer Review reports

Introduction

Renal cell cancer (RCC) is one of the most common malignant tumors of the urinary system. According to statistics, there are about 403,000 new cases and 175,000 deaths every year worldwide [1]. Clear cell RCC (ccRCC) is the most common histological subtype of RCC, and it was characterized by a worse prognosis compared to papillary renal cell carcinoma and chromophobe cell carcinoma [2]. Surgery is the primary treatment for localized RCC. However, approximately 30% of patients will experience postoperative relapse or metastasis [3]. According to statistics, the 5-year overall survival rate of RCC patients is 74%, as low as 8% in patients with metastatic RCC [4]. Currently, although targeted therapy and immunotherapy have extended the survival time of patients with advanced RCC, their efficacy is limited by the low objective remission rate and drug resistance [5]. Therefore, with the continuous advances in systemic treatments, prognostic risk assessment plays an increasing role in risk stratification, clinical decision-making, and survival in patients with RCC. At present, common indicators used to evaluate the prognosis of RCC include tumor stage, nuclear grade, histologic subtype, etc. However, these indicators can only be obtained through surgical procedures, and patients with the same tumor stage exhibit significant differences in prognosis [6]. Therefore, developing a simple and effective clinical index that can accurately predict clinical outcomes is of utmost importance in formulating individualized treatment plans for RCC patients.

In general, nutritional status is related to not only the occurrence of complications in tumor patients but also their long-term survival [7]. Nutritional therapy has been proven to improve the nutritional status, treatment compliance, and even long-term prognosis in patients [8]. Therefore, it is particularly important to find simple and efficient indicators for nutrition assessment. Nutritional indicators such as serum albumin, body mass index (BMI) and serum cholesterol have been confirmed to be associated with prognosis in tumor patients [9,10,11]. These indicators have several advantages, such as speed, accuracy, practicality and cost, and they have great potential in clinical application. Geriatric nutritional risk index (GNRI), based on body weight, height and serum albumin levels, is a novel nutritional status screening tool, and it is initially used to assess the risk of nutrition-related complications and mortality in hospitalized elderly patients over 65 years [12]. The GNRI has also been widely used to assess the nutritional status and clinical outcomes in surgical patients. Recent studies have confirmed that GNRI is associated with the prognosis of malignant tumors, such as head and neck cancer [13], gastrointestinal cancer [14], pancreatic cancer [15], etc. The aim of this retrospective study was to evaluate the prognostic value of the GNRI in non-metastatic ccRCC patients undergoing nephrectomy. In order to reduce the influence of selection bias and confounding factors, we further performed propensity score matching (PSM) analysis to assess the real impact of the GNRI on the prognosis of patients with ccRCC.

Materials and methods

Study population and research design

The study included 989 patients who underwent nephrectomy at the First Hospital of Shanxi Medical University between January 2013 and December 2021. The inclusion criteria were as follows: 1) patients aged ≥ 18 years old; 2) patients who underwent radical nephrectomy or partial nephrectomy; 3) postoperative pathological confirmation of ccRCC (pT1-4N0M0). The exclusion criteria were as follows: 1) lack of clinicopathological and follow-up data; 2) presence of other diseases that may affect the nutritional status of the body, such as gastrointestinal diseases, abnormal liver function, etc.; 3) patients with other malignant tumors; 4) patients receiving radiotherapy, chemotherapy, or other anti-tumor immunotherapy. This study complied with the Declaration of Helsinki. This study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (Ethical code: [2021] K048) . The written informed consent was waived by the First Hospital of Shanxi Medical University of the Institutional Review Board. Finally, 645 patients were included in the study. The details of the inclusion process are shown in Fig. 1.

Fig. 1
figure 1

Flow chart of patient inclusion

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Data extraction

Clinicopathological data, including sex, age, height, body weight, smoking history, history of hypertension, history of diabetes, tumor sidedness, surgical method, surgical approach, pT stage, Fuhrman grade, and tumor size, were collected through the electronic medical record system of our hospital. Hematological indexes, including albumin and total cholesterol, were collected within one week before surgery. The GNRI was calculated as follows: GNRI= (1.489 × albumin [g/l]) + 41.7 × (present/ideal body weight [kg]) [12]. The ideal body weight was calculated by Lorentz formula [12]: ideal body weight (males) = height [cm]-100-((height [cm]-150)/4) and ideal body weight (females) = height (cm)-100-(height (cm)-150)/2.5). When the present body weight exceeded the ideal body weight, the ratio of present/ideal body weight was set to 1 [12]. Tumor stage was determined according to the guidelines provided by the American Joint Commission on Cancer, and tumor grading was performed using the Fuhrman grading system [16, 17].

Follow-up

Regular follow-up was conducted for all patients after surgery. The follow-up period was every 3 months for the initial 3 years, every 6 months for the following 2 years, and annually thereafter. The methods of follow-up included regular outpatient visits and telephone consultations, and the follow-up items included medical history inquiry, physical examination, laboratory tests, and imaging. Clinical outcomes included overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). OS indicates the duration from the date of surgery to death due to any cause or the last follow-up. CSS refers to the period from the date of surgery to death due to RCC or the last follow-up, with the cause of death determined by the therapist and the death certificate. RFS denotes the time from the date of surgery to the postoperative recurrence, metastasis or the end point of follow-up.

Statistic analysis

Statistical analysis was performed using SPSS. Continuous variables were reported as mean ± standard deviation, and group comparisons were conducted using the Student’s t-test. Categorical variables were expressed as frequencies and percentages, and group comparisons were performed using either the Chi-squared test or Fisher’s exact test. The X-tile software was utilized to determine the optimal cut-off value of GNRI. The X-tile software can perform automatic segmentation of survival data, analyze the survival probability through Kaplan-Meier curve, and calculate the p value and risk ratio of each possible segmentation point, among which the optimal cut-off value is usually the segmentation point that makes the most significant difference in the survival curve [18]. The Kaplan-Meier survival curve and the log-rank test were carried out to compare the differences in survival between two groups. Univariate Cox proportional hazard models were used to identify the prognostic factors. Significant variables determined in the univariate analysis were further analyzed using the forward LR strategy in the multivariate Cox analysis (P < 0.05) .

Furthermore, to mitigate the impact of potential confounding factors on survival analysis, PSM was performed using the MatchIt package in R. The propensity score was calculated by a logistic regression model using the following variables, including sex, age, history of hypertension, history of diabetes, smoking history, BMI, tumor sidedness, pT stage, Fuhrman grade, surgical method, surgical approach, and tumor size. To each participant with a low GNRI, up to 3 participants with a high GNRI were matched, and the caliper value was set to 0.02. Group comparisons were conducted using the conditional logistic regression in propensity score matched samples. The Kaplan-Meier survival curve and the stratified log-rank test were carried out to compare the differences in survival between two groups after PSM. Robust Cox proportional hazard models were used to identify the prognostic factors after PSM. Two-sided P < 0.05 was considered statistically significant for all tests.

Results

Clinicopathological characteristics of patients and their association with GNRI

A total of 645 patients were included in this study. The optimal cut-off value of GNRI was determined to be 98. Based on this value, patients were divided into two groups: 87 patients in the low GNRI group (≤ 98) and 558 patients in the high GNRI group (> 98). The patients included 413 males (64.0%) and 232 females (36.0%), with 485 young patients (≤ 65) (75.2%) and 160 elderly patients (> 65) (24.8%). There were 627 early (pT1-T2) ccRCC patients (97.2%) and 18 late (pT3-T4) patients(2.8%). According to the Fuhrman system, there were 494 low-grade (Fuhrman I-II) ccRCC patients (76.6%), and 151 high-grade (Fuhrman III-IV) patients (23.4%). The comparison of clinicopathological characteristics of patients between the two groups is presented in Table 1. The GNRI was significantly associated with age (χ2 = 14.808, P < 0.001), Fuhrman grade (χ2 = 10.027, P = 0.002), albumin (t = 20.650, 95% CI: 8.053-9.745, P < 0.001), and total cholesterol (t = 7.193, 95% CI: 22.372-39.173, P < 0.001). There were no significant differences in sex, history of hypertension, history of diabetes, smoking history, BMI, tumor sidedness, pT stage, surgical method, surgical approach, and tumor size between the two groups. After PSM, 268 patients were included, with 78 patients in the low GNRI group and 190 patients in the high GNRI group. There were no significant differences in the clinicopathological characteristics between the two groups (Table 1).

Table 1 Changes in clinicopathological characteristics of patients with non-metastatic RCC before and after propensity score matching
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Prognostic value of GNRI in OS

As for OS, the median follow-up time was 37 months (range: 1-112 months). At the last follow-up, a total of 45 patients had died. Kaplan-Meier analysis showed that OS was significantly worse in the low GNRI group than in the high GNRI group (5-year OS: 83.9% VS 94.8%, P < 0.001) (Fig. 2a). Univariate analysis indicated that sex, age, history of diabetes, smoking history, pT stage, Fuhrman grade, surgical method, surgical approach, tumor size and GNRI significantly influenced OS. Multivariate analysis showed that age (HR: 0.427, 95% CI: 0.232-0.784, P = 0.006), diabetes history (HR: 0.326, 95% CI: 0.158-0.670, P = 0.002), smoking history (HR: 0.273, 95% CI: 0.150-0.498, P < 0.001), tumor size (HR: 1.494, 95% CI: 1.351-1.652, P < 0.001) and GNRI (HR: 2.686, 95% CI: 1.403-5.145, P = 0.003) were independent prognostic factors for OS (Table 2).

Fig. 2
figure 2

Kaplan-Meier survival curves in patients with non-metastatic ccRCC before and after propensity score matching. a, d overal survival (OS), b, e cancer-specific survival (CSS), and c, f recurrence-free survival (RFS). GNRI, geriatric nutritional risk index

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Table 2 Univariate and multivariate analysis of prognostic factors for OS in patients with non-metastatic ccRCC before and after propensity score matching
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After PSM, Kaplan-Meier analysis showed that the low GNRI group still had worse OS compared with the high GNRI group (5-year OS: 85.9% VS 93.7%, P < 0.05) (Fig. 2d). Univariate analysis indicated that smoking history, pT stage, Fuhrman grade, tumor size and GNRI were significantly correlated with OS. Multivariate analysis showed that smoking history (HR: 0.386; 95% CI: 0.175-0.855, P = 0.019), tumor size (HR: 1.569; 95% CI: 1.263-1.948, P < 0.001) and GNRI (HR: 3.234; 95% CI: 1.360-7.692, P = 0.008) were independent prognostic factors for OS (Table 2).

Prognostic value of GNRI in CSS

As for CSS, the median follow-up time was 37 months (range: 1-112 months). At the last follow-up, 33 patients had died from RCC. Kaplan-Meier analysis showed that the CSS was significantly worse in the low GNRI group than in the high GNRI group (5-year CSS: 87.4% VS 96.2% P < 0.001) (Fig. 2b). Univariate analysis indicated that history of diabetes, smoking history, pT stage, Fuhrman grade, surgical method, surgical approach, tumor size and GNRI significantly influenced CSS. Multivariate analysis showed that history of diabetes (HR: 0.241; 95% CI: 0.109-0.533, P < 0.001), smoking history (HR: 0.420; 95% CI: 0.210-0.838, P = 0.014), tumor size (HR: 1.449; 95% CI: 1.287-1.630, P < 0.001) and GNRI (HR: 2.987; 95% CI: 1.411-6.324, P = 0.004) were independent prognostic factors for CSS (Table 3).

Table 3 Univariate and multivariate analysis of prognostic factors for CSS in patients with non-metastatic ccRCC before and after propensity score matching
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After PSM, Kaplan-Meier analysis showed that the low GNRI group still had worse CSS compared with the high GNRI group (5-year CSS: 87.2% VS 95.8%, P < 0.05) (Fig. 2e). Univariate analysis indicated that pT stage, Fuhrman grade, tumor size and GNRI were significantly correlated with CSS. Multivariate analysis showed that tumor size (HR: 1.348; 95% CI: 1.128-1.610, P = 0.001) and GNRI (HR: 4.440; 95% CI: 1.638-12.033, P = 0.003) were independent prognostic factors for CSS (Table 3).

Prognostic value of GNRI in RFS

As for RFS, the median follow-up time was 35 months (range: 1-112 months). At the last follow-up, 51 patients had recurrence or metastasis. Kaplan-Meier analysis showed that the RFS was significantly worse in the low GNRI group than in the high GNRI group (5-year RFS: 83.9 VS 93.7% P < 0.001) (Fig. 2c). Univariate analysis indicated that smoking history, pT stage, Fuhrman grade, surgical method, surgical approach, tumor size and GNRI significantly influenced RFS. Multivariate analysis showed that smoking history (HR: 0.473; 95% CI: 0.270-0.830, P = 0.009), tumor size (HR: 1.373; 95% CI: 1.255-1.503, P < 0.001) and GNRI (HR: 2.731; 95% CI: 1.455-5.129, P = 0.002) were independent prognostic factors for RFS (Table 4).

Table 4 Univariate and multivariate analysis of prognostic factors for RFS in patients with non-metastatic ccRCC before and after propensity score matching
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After PSM, Kaplan-Meier analysis showed that the low GNRI group still had worse RFS compared with the high GNRI group (5-year RFS: 85.9% VS 93.2% P < 0.05) (Fig. 2f). Univariate analysis indicated that pT stage, Fuhrman grade, tumor size and GNRI were significantly correlated with RFS. Multivariate analysis showed that tumor size (HR: 1.376; 95% CI: 1.171-1.618, P < 0.001) and GNRI (HR: 3.433; 95% CI: 1.453-8.113, P = 0.005) were independent prognostic factors for RFS (Table 4).

Subgroup analysis

To verify the robustness and consistency of our results, we performed subgroup analysis based on age (≤ 65 / > 65), early (pT1-T2) and low-grade (Fuhrman I-II) ccRCC. The subgroup analysis based on age showed that among young patients (≤ 65), OS (P = 0.001), CSS (P = 0.013) and RFS (P = 0.002) in the high GNRI group were significantly better than those in the low GNRI group (Fig. 3a-c). Similarly, among elderly patients (> 65), OS (P = 0.016), CSS (P = 0.006) and RFS (P = 0.048) in the high GNRI group were significantly better than those in the low GNRI group (Fig. 3d-f). In the early (pT1-T2) ccRCC subgroup, patients in the high GNRI group had higher OS (P < 0.001), CSS (P < 0.001) and RFS (P < 0.001) compared to those in the low GNRI group (Fig. 4a-c). In the low-grade (Fuhrman I-II) ccRCC subgroup, patients with high GNRI also had better OS (P < 0.001), CSS (P < 0.001) and RFS (P = 0.044) compared with patients with low GNRI (Fig. 5a-c).

Fig. 3
figure 3

Kaplan-Meier survival curves in patients with non-metastatic ccRCC in the young subgroup; Kaplan-Meier curves for OS(d) , CSS(e) and RFS(f) of patients with non-metastatic ccRCC in the elderly subgroup. a, d overal survival (OS), b, e cancer-specific survival (CSS), and c, f recurrence-free survival (RFS). GNRI, geriatric nutritional risk index

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Fig. 4
figure 4

Kaplan-Meier survival curves in patients with pT1-T2 non-metastatic ccRCC. a overal survival (OS), b cancer-specific survival (CSS), and c recurrence-free survival (RFS). GNRI, geriatric nutritional risk index

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Fig. 5
figure 5

Kaplan-Meier survival curves in patients with Fuhrman grades I-II non-metastatic ccRCC. a overal survival (OS), b cancer-specific survival (CSS), and c recurrence-free survival (RFS). GNRI, geriatric nutritional risk index

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Discussion

In this study, we investigated the prognostic value of preoperative GNRI in the long-term survival of patients with non-metastatic ccRCC. Our findings revealed that OS, CSS and RFS of patients were worse in the low GNRI group than in the high GNRI group. Multivariate analysis further showed that GNRI was an independent prognostic factor for OS, CSS and RFS in patients with non-metastatic ccRCC. In addition, after PSM, OS, CSS and RFS were still worse in the low GNRI group than in the high GNRI group, and GNRI was still an independent prognostic factor for OS, CSS and RFS. Therefore, we believe that GNRI, a simple and readily available nutritional assessment index, has a good prognostic evaluation value for ccRCC patients undergoing nephrectomy.

It is reported that 12.6% of the elderly population suffers from malnutrition in China [19]. Malnutrition is a complex condition characterized by reduced protein reserves, calorie depletion and compromised immune function, which promotes tumor development through inflammatory responses and metabolic changes [20]. It is estimated that approximately one-third of tumor patients experience malnutrition and weight loss [21], which can be attributed to reduced food intake, abnormal nutritional metabolism and cancer treatment [22]. Malnutrition in tumor patients can lead to muscular dystrophy and cancer cachexia, which significantly affect prognosis [23]. Unlike traditional prognostic factors, malnutrition is a modifiable risk factor. Early assessment of the nutritional status of ccRCC patients, together with early nutritional intervention, can significantly improve the prognosis [24]. Some indicators, such as albumin, total cholesterol and BMI, may not be sufficient to accurately identify patients at risk of malnutrition. At present, several scoring systems have been proposed for standardized assessment of nutritional status, including nutritional risk screening [25], subjective global assessment [26], and malnutrition universal screening tool [27]. However, their clinical application is limited due to reliance on subjective factors and relatively inaccurate results. GNRI is an improvement of the Nutritional Risk Index, which requires the data of usual body weight. By contrast, GNRI uses ideal weight instead of usual weight, which solves the problem that usual weight is difficult to obtain accurately in patients, and reduces the interference of hydration status on nutritional status evaluation [12]. By comprehensively assessing serum albumin and ideal body weight, GNRI provides a more accurate reflection of the nutritional and immune status of tumor patients.

The specific mechanism by which GNRI affects the survival of RCC patients has not been fully elucidated, but serum albumin plays a significant role. Serum albumin is also a common indicator for evaluating malnutrition. Studies indicate that hypoalbuminemia may be associated with impaired immune response, which leads to tumor recurrence and metastasis through inhibition of specific immunity [28]. The decreased albumin levels are also related to the production of pro-inflammatory cytokines, such as interleukin-6, interleukin-1, etc., which lead to tumor invasion and metastasis by regulating tumor cell apoptosis, proliferation and angiogenesis [29,30,31]. Since albumin levels can be significantly affected by inflammation and liver function, some researchers question its reliability as a means of nutritional assessment [32]. The contradictory results of the prognosis value of BMI in RCC patients have been reported [11, 33]. In our study, BMI did not show its significant prognostic value. The combination of serum albumin levels with body weight and height reduces the potential confounding effect caused by changes in serum albumin levels due to hydration or acute inflammation [12]. Therefore, the prognostic prediction capability of GNRI is better than that of BMI or albumin levels alone.

A meta-analysis of 15 studies comprising 8,046 patients with hematological malignant tumors, esophageal cancers, renal cell carcinoma and so on, showed that low GNRI is significantly associated with worse OS, CSS, and disease-free survival and progression-free survival [34]. Miyake et al. retrospectively analyzed 432 non-metastatic RCC patients who underwent surgery in Japan and found that GNRI was an independent prognostic factor for CSS, but not for RFS [35], which is slightly different from our results. This discrepancy may be attributed to their relatively small sample size, variations in histological subtypes, and differences in the study populations. It is generally believed that cohorts composed of patients with homogeneous histological subtypes can provide more reliable results due to the complexity of tumor biology and variation in histological subtypes. Kang et al. reported that low preoperative GNRI is significantly associated with worse RFS and CSS in patients with surgically treated ccRCC. In addition, preoperative GNRI is an independent prognostic factor for RFS and CSS, both as a continuous variable and as a categorical variable [36]. However, this study did not focus on OS. There is currently no uniform standard regarding the optimal cut-off value of GNRI, but mostly ranging from 92 to 99.2. High-quality prospective and multicenter studies are needed to better determine the optimal cut-off value for patients with different tumors [12, 34,35,36].

To our knowledge, this is the first study to employ PSM to evaluate the effects of GNRI on the prognosis of patients with non-metastatic ccRCC undergoing surgery. After PSM, there were no significant differences in clinicopathological characteristics between the low and high GNRI groups, suggesting that PSM reduced the interference of confounding factors on survival outcomes to some extent. However, survival analysis after PSM still revealed some differences in survival outcomes between the two groups, indicating the effectiveness and reliability of GNRI as a prognostic factor. Additionally, although GNRI is initially developed for the clinical assessment of elderly patients, recent studies have suggested its suitability for nutritional and prognostic assessment in patients across different age groups [37, 38]. Therefore, we conducted an age subgroup analysis (≤ 65 and > 65) and found that low GNRI was associated with poor clinical outcomes not only in elderly patients but also in young patients. TNM stage and Fuhrman grade have been widely used to predict the prognosis of RCC patients in clinical practice. However, predicting the prognosis of patients with early and low-grade RCC remains challenging. In this study, we found that GNRI maintained good prognostic value in early (pT1-T2) and low-grade (Fuhrman I-II) ccRCC patients. Furthermore, in addition to GNRI, tumor size has always been identified as an independent prognostic factor for ccRCC, which was consistent with previous studies [39].

Our research has some limitations. First, this is a retrospective study, so selection bias and confounding bias are inevitable. We performed PSM to minimize the impact of confounding bias. Second, we did not collect the data on postoperative GNRI, which prevents us from monitoring and analyzing the clinical importance of dynamic changes in GNRI. Finally, this study is a single-center study with a moderate sample size, and further large-scale multicenter prospective studies are needed to verify the clinical significance of GNRI.

Conclusion

In conclusion, this study showed that the preoperative GNRI is an independent prognostic factor for OS, CSS and RFS in patients with ccRCC. As an objective, non-invasive and easily available nutrition screening tool, GNRI may play a crucial role in identifying patients with high nutritional risk. With the GNRI, healthcare professionals could implement appropriate nutritional intervention measures during the perioperative period to improve the prognosis of these patients.

Availability of data and material

The data that support the findings of the research are available from the corresponding author upon rational request.

Abbreviations

GNRI:

Geriatric Nutritional Risk Index

ccRCC:

clear cell Renal Cell Carcinoma

OS:

Overall Survival

CSS:

Cancer-Specific Survival

RFS:

Recurrence-Free Survival

PSM:

Propensity Score Matching

RCC:

Renal Cell Cancer

BMI:

Body Mass Index

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.

    Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7–30.

    Article  PubMed  Google Scholar 

  3. Morgan TM, Tang D, Stratton KL, Barocas DA, Anderson CB, Gregg JR, et al. Preoperative nutritional status is an important predictor of survival in patients undergoing surgery for renal cell carcinoma. Eur Urol. 2011;59:923–8.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Palumbo C, Pecoraro A, Knipper S, Rosiello G, Luzzago S, Deuker M, et al. Contemporary age-adjusted incidence and mortality rates of renal cell carcinoma: analysis according to gender, race, stage, grade, and histology. Eur Urol Focus. 2021;7:644–52.

    Article  PubMed  Google Scholar 

  5. Sekino Y, Teishima J, Liang G, Hinata N. Molecular mechanisms of resistance to tyrosine kinase inhibitor in clear cell renal cell carcinoma. Int J Urol. 2022;29:1419–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Escudier B, Porta C, Schmidinger M, RiouxLeclercq N, Bex A, Khoo V, et al. Renal cell carcinoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2019;30:706–20.

    Article  CAS  PubMed  Google Scholar 

  7. Ramos R, Nadal E, Peiró I, Masuet-Aumatell C, Macia I, Rivas F, et al. Preoperative nutritional status assessment predicts postoperative outcomes in patients with surgically resected non-small cell lung cancer. Eur J Surg Oncol. 2018;44:1419–24.

    Article  PubMed  Google Scholar 

  8. Sharma K, Mogensen KM, Robinson MK. Pathophysiology of critical illness and role of nutrition. Nutr Clin Pract. 2019;34:12–22.

    Article  PubMed  Google Scholar 

  9. Zhou X, Fu G, Zu X, Xu Z, Li HT, D’souza A, et al. Albumin levels predict prognosis in advanced renal cell carcinoma treated with tyrosine kinase inhibitors: a systematic review and meta-analysis. Urol Oncol. 2022;40:12–3.

    Article  Google Scholar 

  10. Li B, Huang D, Zheng H, Cai Q, Guo Z, Wang S. Preoperative serum total cholesterol is a predictor of prognosis in patients with renal cell carcinoma: a meta-analysis of observational studies. Int Braz J Urol. 2020;46:158–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Parker AS, Lohse CM, Cheville JC, Thiel DD, Leibovich BC, Blute ML. Greater body mass index is associated with better pathologic features and improved outcome among patients treated surgically for clear cell renal cell carcinoma. Urology. 2006;68:741–6.

    Article  PubMed  Google Scholar 

  12. Bouillanne O, Morineau G, Dupont C, Coulombel I, Vincent JP, Nicolis I, et al. Geriatric nutritional risk index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82:777–83.

    Article  CAS  PubMed  Google Scholar 

  13. Nakayama M, Gosho M, Adachi M, Ii R, Matsumoto S, Miyamoto H, et al. The geriatric nutritional risk index as a prognostic factor in patients with advanced head and neck cancer. Laryngoscope. 2021;131:E151–6.

    Article  CAS  PubMed  Google Scholar 

  14. Xie H, Tang S, Wei L, Gan J. Geriatric nutritional risk index as a predictor of complications and long-term outcomes in patients with gastrointestinal malignancy: a systematic review and meta-analysis. Cancer Cell Int. 2020;20:530.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hu SP, Chen L, Lin CY, Lin WH, Fang FQ, Tu MY. The prognostic value of preoperative geriatric nutritional risk index in patients with pancreatic ductal adenocarcinoma. Cancer Manag Res. 2020;12:385–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Novara G, Ficarra V, Antonelli A, Artibani W, Bertini R, Carini M, et al. Validation of the 2009 TNM version in a large multi-institutional cohort of patients treated for renal cell carcinoma: are further improvements needed? Eur Urol. 2010;58:588–95.

    Article  PubMed  Google Scholar 

  17. Fuhrman SA, Lasky LC, Limas C. Prognostic significance of morphologic parameters in renal cell carcinoma. Am J Surg Pathol. 1982;6:655–63.

    Article  CAS  PubMed  Google Scholar 

  18. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10:7252–9.

    Article  CAS  PubMed  Google Scholar 

  19. Wei JM, Li S, Claytor L, Partridge J, Goates S. Prevalence and predictors of malnutrition in elderly Chinese adults: results from the China health and retirement longitudinal study. Public Health Nutr. 2018;21:3129–34.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Xia L, Oyang L, Lin J, Tan S, Han Y, Wu N, et al. The cancer metabolic reprogramming and immune response. Mol Cancer. 2021;20:28.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Gangadharan A, Choi SE, Hassan A, Ayoub NM, Durante G, Balwani S, et al. Protein calorie malnutrition, nutritional intervention and personalized cancer care. Oncotarget. 2017;8:24009–30.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Baracos VE. Cancer-associated malnutrition. Eur J Clin Nutr. 2018;72:1255–9.

    Article  PubMed  Google Scholar 

  23. Pamoukdjian F, Bouillet T, Lévy V, Soussan M, Zelek L, Paillaud E. Prevalence and predictive value of pre-therapeutic sarcopenia in cancer patients: a systematic review. Clin Nutr. 2018;37:1101–13.

    Article  PubMed  Google Scholar 

  24. Laviano A, Di Lazzaro L, Koverech A. Nutrition support and clinical outcome in advanced cancer patients. Proc Nutr Soc. 2018;77:388–93.

    Article  PubMed  Google Scholar 

  25. Kondrup J, Rasmussen HH, Hamberg O, Stanga Z, Ad Hoc ESPEN Working Group. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. 2003;22:321–36.

    Article  PubMed  Google Scholar 

  26. Detsky AS, McLaughlin JR, Baker JP, Johnston N, Whittaker S, Mendelson RA, et al. What is subjective global assessment of nutritional status? JPEN J Parenter Enteral Nutr. 1987;11:8–13.

    Article  CAS  PubMed  Google Scholar 

  27. Stratton RJ, Hackston A, Longmore D, Dixon R, Price S, Stroud M, et al. Malnutrition in hospital outpatients and inpatients: prevalence, concurrent validity and ease of use of the ‘malnutrition universal screening tool’ (‘MUST’) for adults. Br J Nutr. 2004;92:799–808.

    Article  CAS  PubMed  Google Scholar 

  28. Roxburgh CS, Mcmillan DC. Role of systemic inflammatory response in predicting survival in patients with primary operable cancer. Future Oncol. 2010;6:149–63.

    Article  CAS  PubMed  Google Scholar 

  29. Fu XL, Duan W, Su CY, Mao FY, Lv YP, Teng YS, et al. Interleukin 6 induces M2 macrophage differentiation by STAT3 activation that correlates with gastric cancer progression. Cancer Immunol Immunother. 2017;66:1597–608.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kumari N, Dwarakanath BS, Das A, Bhatt AN. Role of interleukin-6 in cancer progression and therapeutic resistance. Tumour Biol. 2016;37:11553–72.

    Article  CAS  PubMed  Google Scholar 

  31. McMillan DC. Systemic inflammation, nutritional status and survival in patients with cancer. Curr Opin Clin Nutr Metab Care. 2009;12:223–6.

    Article  PubMed  Google Scholar 

  32. Evans DC, Corkins MR, Malone A, Miller S, Mogensen KM, Guenter P, et al. The use of visceral proteins as nutrition markers: an ASPEN position paper. Nutr Clin Pract. 2021;36:22–8.

    Article  CAS  PubMed  Google Scholar 

  33. Schips L, Lipsky K, Zigeuner R, Gidaro S, Salfellner M, Rehak P, et al. Does overweight impact on the prognosis of patients with renal cell carcinoma? A single center experience of 683 patients. J Surg Oncol. 2004;88:57–61.

    Article  PubMed  Google Scholar 

  34. Lv GY, An L, Sun DW. Geriatric nutritional risk index predicts adverse outcomes in human malignancy: a meta-analysis. Dis Markers. 2019;2019:4796598.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Miyake H, Tei H, Fujisawa M. geriatric nutrition risk index is an important predictor of cancer-specific survival, but not recurrence-free survival, in patients undergoing surgical resection for non-metastatic renal cell carcinoma. Curr Urol. 2017;10:26–31.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kang HW, Seo SP, Kim WT, Yun SJ, Lee SC, Kim WJ, et al. A low geriatric nutritional risk index is associated with aggressive pathologic characteristics and poor survival after nephrectomy in clear renal cell carcinoma: a multicenter retrospective study. Nutr Cancer. 2020;72:88–97.

    Article  CAS  PubMed  Google Scholar 

  37. Hirahara N, Matsubara T, Fujii Y, Kaji S, Hyakudomi R, Yamamoto T, et al. Geriatric nutritional risk index as a prognostic marker of pTNM-stage I and II esophageal squamous cell carcinoma after curative resection. Oncotarget. 2020;11:2834–46.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hino H, Saito T, Matsui H, Taniguchi Y, Murakawa T. Utility of geriatric nutritional risk index in patients with lung cancer undergoing surgery. Eur J Cardiothorac Surg. 2020;58:775–82.

    Article  PubMed  Google Scholar 

  39. Chen L, Ma X, Li H, Gu L, Li X, Gao Y, et al. Influence of tumor size on oncological outcomes of pathological T3aN0M0 renal cell carcinoma treated by radical nephrectomy. PLoS One. 2017;12:e173953.

    Google Scholar 

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Authors and Affiliations

  1. Department of Urology, The First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China

    Huiyu Zhou, Dingyang Lv, Fan Cui,  Qian Gong, Jinshuai Li, Jie Wen, Mohan Jia, Yinbo Kang, Yi Rong, Wenlong Zhang & Weibing Shuang

  2. First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China

    Huiyu Zhou, Dingyang Lv, Fan Cui,  Qian Gong, Jinshuai Li, Jie Wen, Mohan Jia, Yinbo Kang, Yi Rong, Wenlong Zhang & Weibing Shuang

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  1. Huiyu Zhou
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Contributions

The authors’ responsibilities were as follows—HZ, DL and MJ : designed the research and participated in data collection; FC, QG and YK: conducted the statistical analyses; HZ, JL and JW: drafted the manuscript; WS, YR and WZ: revised the manuscript, and read and approved the final manuscript. All authors have read and approved the final version of the manuscript.

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Correspondence to Weibing Shuang.

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Ethics approval and consent to participate

This study complied with the Declaration of Helsinki. This study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (Ethical code: [2021] K048). The written informed consent was waived by the First Hospital of Shanxi Medical University of the Institutional Review Board.

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Zhou, H., Lv, D., Cui, F. et al. Prognostic value of the geriatric nutritional risk index in patients with non-metastatic clear cell renal cell carcinoma: a propensity score matching analysis. Nutr J 23, 114 (2024). https://doi.org/10.1186/s12937-024-01010-7

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Keywords

Nutrition Journal

ISSN: 1475-2891

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