Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer

doi:10.21203/rs.3.rs-4313120/v1

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Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer

https://doi.org/10.21203/rs.3.rs-4313120/v1

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published 29 Sep, 2024

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Objective

This study aimed to compare three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer (GC).

Method

Objective nutritional screening tools including geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were evaluated in patients with GC at our institution. Malnutrition was diagnosed according to the GLIM criteria. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM criteria.

Results

A total of 316 patients were enrolled in this study, and malnutrition was diagnosed in 151 patients (47.8%) based on the GLIM criteria. The GNRI demonstrated good diagnostic accuracy (AUC = 0.805, 95% CI: 0.758–0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641–0.757) and 0.665 (95% CI: 0.605–0.725) respectively. Among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k = 0.452) with GLIM-defined malnutrition.

Conclusions

Compared to PNI and COUNT scores, GNRI demonstrated superior performance as an objective nutritional screening tool for identifying GLIM-defined malnutrition in GC patients.

Health sciences/Diseases/Nutrition disorders/Malnutrition

Health sciences/Biomarkers

Health sciences/Diseases/Nutrition disorders/Malnutrition

Health sciences/Biomarkers

geriatric nutritional risk index

objective nutritional screening tool

GLIM-defined malnutrition

gastric cancer

Gastric cancer (GC) is a prevalent malignant tumor, ranking fifth in terms of incidence and fourth in terms of mortality globally¹. Due to the impact of the disease itself and the anti-tumor treatment process, GC patients are at a higher risk of experiencing malnutrition. It has been reported that the prevalence of malnutrition in GC patients can reach as high as 65%-85%². Numerous studies have demonstrated that malnutrition is not only significantly associated with a higher risk of complications and mortality but also has an adverse effect on patients' treatment outcomes and quality of life^3–6. Therefore, early screening and identification of malnourished patients followed by effective intervention measures play a pivotal role in multimodal cancer treatment and are currently gaining increasing attention.

The Global Leadership Initiative on Malnutrition (GLIM) presents a novel diagnostic framework aimed at identifying malnutrition through a two-step approach⁷. Firstly, validated screening tools are used to identify patients who are at risk of malnutrition. Secondly, the diagnosis of malnutrition is established based on meeting at least one phenotypic criterion (involuntary weight loss, low body mass index or reduced muscle mass) and one etiological criterion (reduced food intake or assimilation, disease burden or inflammation). At present, the subjective tools used for nutritional risk screening include Nutrition Risk Screening-2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), and Malnutrition Screening Tool (MST)^{2, 7}. Although these tools have been widely used in clinical practice, there are still some limitations in practical application. For example, the assessment of weight loss, previous dietary intake, and disease history relies on patient recall and subjective descriptions, which may introduce potential biases. In addition, to ensure accuracy and reliability, it is imperative that these assessment tools are utilized by qualified medical professionals, which may pose challenges in areas with insufficient medical and healthcare resources.

Currently, various objective nutritional tools have been utilized for screening and evaluating malnutrition, as well as predicting clinical outcomes. The geriatric nutritional risk index (GNRI) is a simple nutritional screening tool that evaluates the risk of malnutrition by combining serum albumin levels with ideal body weight⁸. It has been demonstrated to be associated with adverse outcomes in various malignancies and can be applicable for both young and elderly patients⁹. The prognostic nutritional index (PNI), which is calculated based on total lymphocyte counts and serum albumin levels, has been used to assess nutritional status and serve as a prognostic indicator for different types of malignancies¹⁰. The controlling nutritional status (CONUT) score, a conveniently calculated tool using three blood parameters (albumin level, total cholesterol level, and total lymphocyte count), has also been utilized as a valuable tool for evaluating nutritional status and predicting outcomes in patients with diverse cancer types¹¹. Based on laboratory examinations and anthropometric measurements, these objective nutritional tools can be easily performed in the clinical setting and used for dynamic surveillance.

However, there is a paucity of studies evaluating the efficacy of these objective nutritional screening tools in identifying malnutrition based on the GLIM criteria. Furthermore, it remains unclear which objective nutritional screening tool is most effective for detecting GLIM-defined malnutrition in patients with GC. Therefore, this study aims to compare three commonly used objective nutritional screening tools (GNRI, PNI, and CONUT score) to determine the optimal tool for identifying GLIM-defined malnutrition in GC patients.

2.1 Study Patients

This cross-sectional study enrolled consecutive patients diagnosed with GC in our Department from October 2021 to March 2023. Inclusion criteria were: (1) age between 18 and 80 years; (2) confirmed pathological diagnosis of gastric adenocarcinoma through gastroscopic biopsy; and (3) no prior neoadjuvant therapy. Exclusion criteria were: (1) absence of abdominal CT scans from our institution; (2) presence of severe comorbidities including heart failure, chronic kidney disease, or liver cirrhosis; and (3) concurrent presence of other malignancies or a history of other malignancies within the past 5 years. All patients provided written informed consent for data collection and analysis. This study followed the Declaration of Helsinki and obtained approval from the Ethics Committee of The Affiliated People's Hospital of Jiangsu University (No. K-20220028-Y).

2.2 Data collection

Demographic and clinical data were collected, including age, sex, body mass index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, Charlson Comorbidity Index (CCI) score, tumor-node-metastasis (TNM) stage according to the eighth edition of the American Joint Committee on Cancer (AJCC). Additionally, laboratory data were also obtained, including albumin level, hemoglobin level, C-reactive protein (CRP) with the cut-off value set at 5.0 mg/L, neutrophil and lymphocyte counts and neutrophil/lymphocyte ratio (NLR).

2.3 Objective nutritional screening tools

The GNRI is calculated as 14.87 × serum albumin concentration (g/L) + 41.7 × weight/ideal weight (kg) ⁸. The ideal weight was determined using the formula: for males, it was calculated as 0.75×height (cm) – 62.5; and for females, it was calculated as 0.60×height (cm) – 40⁸. The PNI is calculated as 10 × serum albumin (g/dL) + 5 × lymphocyte count (10⁹/L)¹². The CONUT score was calculated based on the concentrations of albumin, lymphocyte count, and total cholesterol, with each parameter being assigned scores as previously described¹³. The cumulative sum of these scores constituted the CONUT score.

2.4 Body composition analysis

Body composition analysis was performed on CT images at the L3 level using Slice-O-Matic software V 5.0 (Tomovision, Magog, QC, Canada). Tissue-specific Hounsfield unit (HU) thresholds were applied to identify the cross-sectional areas of skeletal muscle (-29 to + 150 HU), subcutaneous fat (-190 to -30 HU), and visceral fat (-150 to -50 HU). These cross-sectional areas (cm²) of L3 were then normalized for height squared (m²) to calculate skeletal muscle index (L3-SMI, cm²/m²), subcutaneous fat index (L3-SFI, cm²/m²), and visceral fat index (L3-VFI, cm²/m²).

2.5 Diagnosis of malnutrition

Malnutrition was diagnosed according to the GLIM criteria⁷, and the diagnostic procedure followed the description provided in our previous study¹⁴. Briefly, patients with GC were considered to fulfil the etiological criterion of inflammation or disease burden, and the diagnosis of malnutrition was based on meeting at least one of the phenotypic criteria: (1) involuntary weight loss defined as > 5% within the past 6 months or > 10% beyond 6 months; (2) low BMI defined as < 18.5 kg/m² for patients under 70 years old and < 20 kg/m² for those over 70 years; (3) reduced muscle mass defined as ≤ 40.8 cm²/m² in males and ≤ 34.9 cm²/m² in females using sex-specific cut-off values of L3-SMI¹⁴.

2.6 Statistical analysis

Continuous variables that follow a normal distribution (assessed using the Shapiro-Wilk test) are reported as mean ± standard deviation (SD) and analyzed using an independent t-test. Non-normally distributed continuous variables are presented as median (interquartile range) and analyzed using the Mann-Whitney U-test. Categorical variables were expressed as numbers (percentages) and analyzed using the chi-squared test. Univariate or multivariate logistic regression analyses were further employed to evaluate the association between these nutritional indices and GLIM-defined malnutrition. The adjusted model included age, sex, BMI, ECOG performance status, CCI, and TNM stage. The presence of collinearity among independent variables was assessed by calculating the Variance Inflation Factor (VIF) and correlation coefficients. If the VIF is ≥ 10 or the correlation coefficients are > 0.7, it indicates the existence of collinearity between independent variables, which are subsequently excluded from analysis¹⁵. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The optimal cut-off value was established based on the maximal Youden's index formula: sensitivity + specificity − 1. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM malnutrition criteria. The statistical analyses were performed using SPSS version 25.0 (IBM Corp, Armonk, NY, USA), and statistical significance was defined as p < 0.05.

3.1 Patient characteristics

As shown in Fig. 1, a total of 316 patients diagnosed with GC were analyzed in this study. Their demographic and clinical characteristics are presented in Table 1. Among them, there were 224 (70.9%) males and 92 (29.1%) females, with a median age of 68 (62-72.75) years and a mean BMI of 23.44 ± 3.33 kg/m². A total of 151 patients (47.8%) were diagnosed with malnutrition based on GLIM criteria. Compared to patients without GLIM-defined malnutrition, those with GLIM-defined malnutrition exhibited a higher proportion of advanced TNM stage, elevated CRP levels (≥ 5mg/L), and poor ECOG performance status (p < 0.05, Table 1). Additionally, the group with GLIM malnutrition demonstrated significantly lower levels of BMI, albumin, hemoglobin, lymphocyte count, L3-SMI, L3-VFI, L3-SFI, GNRI, and PNI; while showing higher levels of age and COUNT score (p < 0.05, Table 1).

Table 1

Clinical characteristics of study patients based on GLIM-defined malnutrition.
	Total (n = 316)	With malnutrition (n = 151)	Without malnutrition (n = 165)	P-value
Age, years	68(62-72.75)	69(64–73)	67(59.5–71.5)	0.008
Sex, n (%)				0.627
Male	224(70.9)	109(72.2)	115(69.7)
Female	92(29.1)	42(27.8)	50(30.3)
BMI, kg/m²	23.44 ± 3.33	21.66 ± 2.75	25.06 ± 2.98	< 0.001
CCI score, n (%)				0.187
0	240(75.9)	111(73.5)	129(78.2)
1	53(16.8)	31(20.5)	22(13.3)
2	23(7.3)	9(6.0)	14(8.5)
ECOG performance status, n (%)				< 0.001
0	190(60.1)	61(40.4)	129(78.2)
1	84(26.6)	52(34.4)	32(19.4)
2	34(10.8)	30(19.9)	4(2.4)
3	8(2.5)	8(5.3)	0(0)
TNM stage, n (%)				< 0.001
Ⅰ	78(24.7)	17(11.3)	61(37.0)
Ⅱ	62(19.6)	35(23.2)	27(16.4)
Ⅲ	134(42.4)	73(48.3)	61(37.0)
Ⅳ	42(13.3)	26(17.2)	16(9.7)
NRS-2002 score	3(2–4)	4(3–5)	2(2–3)	< 0.001
Albumin, g/L	37.09 ± 4.34	35.82 ± 4.12	38.24 ± 4.22	< 0.001
Hemoglobin, g/L	119.5(103–135)	112(90–123)	127(114.5–137)	< 0.001
Neutrophil, 10⁹/L	3.7(2.8–4.58)	3.6(2.6–4.6)	3.8(2.95–4.5)	0.080
Lymphocyte, 10⁹/L	1.45(1.13–1.8)	1.4(1.1–1.7)	1.6(1.2-2.0)	< 0.001
NLR	2.37(1.80–3.33)	2.38(1.77–3.69)	2.36(1.83–3.20)	0.479
CRP ≥ 5mg/L, n (%)	73(23.1)	43(28.5)	30(18.2)	0.030
L3-SMI, cm²/m²	44.91 ± 7.57	40.99 ± 6.41	48.51 ± 6.73	< 0.001
L3-VFI, cm²/m²	41.39(25.01–61.93)	28.24(12.09–46.20)	52.40(37.37–71.65)	< 0.001
L3-SFI, cm²/m²	38.78(25.86–55.45)	30.16(18.32–45.09)	46.35(34.99–60.41)	< 0.001
GNRI	99.03 ± 9.49	93.83 ± 8.00	103.79 ± 8.17	< 0.001
PNI	44.82 ± 5.30	42.98 ± 5.11	46.51 ± 4.91	< 0.001
COUNT score	2 (1–4)	3 (2–4)	2 (1–3)	< 0.001
BMI, body mass index; CCI, Charlson Comorbidity Index; CONUT, controlling nutritional status; CRP, C-reactive protein; ECOG, Eastern cooperative oncology group; GLIM, Global Leadership Initiative on Malnutrition; GNRI, geriatric nutritional risk index; L3, the third lumbar vertebra; NLR, neutrophil-to-lymphocyte ratio; NRS-2002, Nutritional Risk Screening 2002; PNI, prognostic nutritional index. SFI, subcutaneous fat index. SMI, skeletal muscle index. TNM, tumor–node–metastasis; VFI, visceral fat index.

3.2 ROC curves for predicting GLIM malnutrition

As shown in Fig. 2, the diagnostic accuracy of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed using ROC curves. The GNRI demonstrated good diagnostic accuracy (AUC = 0.805, 95% CI: 0.758–0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641–0.757) and 0.665 (95% CI: 0.605–0.725) respectively. The optimal cut-off values for GNRI, PNI, and COUNT score to identify GLIM-defined malnutrition were determined as 97, 45.4, and 3, correspondingly. Based on these defined thresholds, the prevalence rates of low GNRI (< 97), low PNI (< 45.4), and high CONUT score (≥ 3) were found to be 41.5%, 53.5%, and 49.4%, respectively.

3.3 Association between objective nutritional indicators and GLIM-defined malnutrition

The results of crude and adjusted logistic regression analyses evaluating the association between objective nutritional indicators and GLIM-defined malnutrition are presented in Table 2. After adjusting for age, sex, BMI, CCI, ECOG performance status, and TNM stage, a one-unit decrease in PNI was found to be significantly associated with GLIM-defined malnutrition (OR = 1.16, 95% CI: 1.12–1.21, p = 0.010). Furthermore, there was also a significant association between a one-unit increase in COUNT score and GLIM-defined malnutrition (OR = 1.22, 95% CI: 1.02–1.44, p = 0.025). Due to the high correlation between GNRI and BMI (r > 0.7), BMI was excluded from the multiple analysis model for GNRI; nevertheless, a one-unit decrease in GNRI remained significantly associated with GLIM-defined malnutrition after multivariable adjustments (OR = 1.13, 95% CI: 1.09–1.18, p < 0.001). Additionally, it was observed that patients with low GNRI (OR = 4.69, 95% CI: 2.46–8.33, p < 0.001) exhibited a significantly higher risk of GLIM-defined malnutrition compared to those with low PNI (OR = 2.10, 95% CI: 1.12–3.93, p = 0.021). However, there was no significant association between high CONUT score and the risk of GLIM-defined malnutrition (OR = 1.59, 95% CI: 0.86–2.93, p = 0.140).

Table 2

Crude and adjusted logistic regression analysis assessing the association between objective nutritional indicators and GLIM malnutrition.
	Units of increase or decrease	Crude OR (95% CI)	P-value	Adjusted OR (95% CI)	P-value
GNRI ^a	1 unit ↓	1.16 (1.12–1.21)	< 0.001	1.13 (1.09–1.18)	< 0.001
	< 97	7.40 (4.45–12.28)	< 0.001	4.69 (2.64–8.33)	< 0.001
PNI ^b	1 unit ↓	1.15 (1.10–1.21)	< 0.001	1.09 (1.02–1.16)	0.010
	< 45.4	4.04 (2.52–6.48)	< 0.001	2.10 (1.12–3.93)	0.021
CONUT score ^b	1 unit ↑	1.37 (1.21–1.56)	< 0.001	1.22 (1.02–1.44)	0.025
	≥ 3	3.06 (1.19–4.83)	< 0.001	1.59 (0.86–2.93)	0.140
a Adjusting for age, sex, CCI, ECOG performance status, and TNM stage.
b Adjusting for age, sex, BMI, CCI, ECOG performance status, and TNM stage.
CONUT, controlling nutritional status; GNRI, geriatric nutritional risk index; PNI, prognostic nutritional index.

3.4 Comparative analysis of three objective nutritional screening tools

Table 3 presents statistical evaluations for comparative analysis of three objective nutritional screening tools. The prevalence of GLIM-defined malnutrition was 74.8% (98/131), 63.3% (107/169), and 61.5% (96/156) in patients with low GNRI, low PNI, and high CONUT score, respectively. Based on the GLIM criterion for diagnosing malnutrition, GNRI accurately identified 72.8% (230/316) of the patients, demonstrating a sensitivity of 64.9% and a specificity of 80%. PNI correctly identified 66.5% (230/316) of the patients with a sensitivity of 70.9% and a specificity of 62.4%. Furthermore, the CONUT score accurately identified 63.6% (201/316) of the patients, exhibiting both sensitivity and specificity at 63.6%. The kappa statistics analysis revealed a moderate agreement (k = 0.452) between low GNRI and GLIM-defined malnutrition, while low PNI (k = 0.331) and high CONUT score (k = 0.272) demonstrated fair agreement with GLIM-defined malnutrition. Therefore, among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity, accuracy, PPV, NPV, and consistency with GLIM-defined malnutrition.

Table 3

statistical evaluations for comparative analysis of three objective nutritional screening tools.
	GLIM malnutrition		Sensitivity (%)	Specificity (%)	Accuracy (%)	PPV (%)	NPV (%)	Kappa	P-value
	Yes	NO	Sensitivity (%)	Specificity (%)	Accuracy (%)	PPV (%)	NPV (%)	Kappa	P-value
GNRI
< 97	98	33	64.9	80	72.8	74.8	71.4	0.452	<0.001
≥ 97	53	132	64.9	80	72.8	74.8	71.4	0.452	<0.001
PNI
< 45.4	107	62	70.9	62.4	66.5	63.3	70.1	0.331	<0.001
≥ 45.4	44	103	70.9	62.4	66.5	63.3	70.1	0.331	<0.001
COUNT score
≥ 3	96	60	63.6	63.6	63.6	61.5	65.6	0.272	<0.001
< 3	55	105
CONUT, controlling nutritional status; GLIM, Global Leadership Initiative on Malnutrition; GNRI, geriatric nutritional risk index; NPV, negative predictive value; PNI, prognostic nutritional index; PPV, positive predictive value.

This is one of the few studies that compare different objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with GC. The findings of this study indicate that GNRI, PNI, and COUNT scores were independently associated with the risk of malnutrition based on the GLIM criteria, thereby potentially serving as predictive indicators for GLIM-defined malnutrition. Moreover, patients with low GNRI exhibited a significantly higher risk of GLIM-defined malnutrition compared to those with low PNI and high CONUT score. Amony these tools, GNRI demonstrated the highest diagnostic accuracy for detecting GLIM-defined malnutrition (AUC = 0.805, 95% CI: 0.758–0.852). Furthermore, the GNRI-based malnutrition risk assessment showed the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k = 0.452) with GLIM-defined malnutrition. Thus, GNRI is considered the most effective objective screening tool for identifying GLIM-defined malnutrition in this study.

According to a recent systematic review and meta-analysis, the prevalence of GLIM-defined malnutrition varied widely from 11.9–87.9% in patients with cancer ¹⁶. Xu et al. revealed that the prevalence of malnutrition according to the GLIM criteria was 38.3% (343 out of 895) in patients with GC⁵, which is lower than the observed prevalence of 47.8% reported in this study. This difference may be attributed to their inclusion of patients who underwent radical gastrectomy while excluding those with stage IV cancer. As the global consensus on diagnostic criteria for malnutrition, GLIM-defined malnutrition has been demonstrated to be significantly associated with an increased risk of postoperative complications and poor survival in various cancer ^16–18, including gastrointestinal cancer ¹⁹. The application of GLIM criteria for assessing malnutrition in cancer patients can provide valuable insights for guiding nutrition management and intervention strategies. Therefore, timely identification of cancer patients with GLIM-defined malnutrition is crucial for the effective implementation of targeted nutritional interventions.

The GLIM proposes a two-step strategy for diagnosing malnutrition: initial screening using any validated tool to identify patients at risk, followed by evaluating five phenotypic/etiologic criteria to establish a diagnosis of malnutrition⁷. However, the GLIM does not specify a particular screening tool for the first step. Zhang and colleagues conducted a comparative study evaluating the efficacy of three commonly used malnutrition risk screening tools in identifying GLIM-defined malnutrition among patients diagnosed with gastrointestinal cancer. Their findings suggest that NRS-2002 is the optimal tool for detecting malnutrition in gastrointestinal cancer patients under the age of 65, while MNA-SF is most effective for those over 65 years old²⁰. However, it is crucial to acknowledge that both NRS-2002 and MNA-SF involve subjective assessments, which may be subject to bias and heavily rely on the expertise of the examiner. Therefore, there is a requirement of objective screening tools that are user-friendly, time-efficient, and operator-independent for identify patients at risk of malnutrition. Further studies are required to validate these objective tools in screening patients with GLIM-defined malnutrition.

In the present study, we conducted a comparative analysis of three commonly used objective nutritional screening tools (GNRI, PNI, and CONUT score) to determine the most effective tool for identifying GLIM-defined malnutrition in patients with GC. The results of our study demonstrated that GNRI exhibited superior performance as an objective nutritional screening tool compared to PNI and CONUT scores in identifying GLIM-defined malnutrition. Recently, Chen et al. investigated GNRI, PNI, and advanced lung cancer inflammation index (ALI) for detecting malnutrition based on GLIM criteria among rectal cancer patients. Consistent with our study, their findings also revealed that GNRI exhibited optimal performance among the three nutritional tools ²¹. Furthermore, Cohen-Cesla et al. conducted a study aiming to assess the concurrent validity of four nutritional scores - malnutrition-inflammation score (MIS), objective score of nutrition on dialysis (OSND), GNRI, and nutritional risk index (NRI) - in relation to the GLIM criteria for diagnosing malnutrition in maintenance hemodialysis patients. Importantly, their results also indicated that GNRI exhibited superior sensitivity and had the largest AUC for predicting malnutrition based on the GLIM criteria ²². Hence, the GNRI can serve as an optimal objective nutritional screening tool for identifying GLIM-defined malnutrition.

The initial aim of GNRI was to evaluate the morbidity and mortality risk in elderly patients during hospitalization, with cut-off values of 98 established for identifying nutrition-related risks⁸. Subsequent studies have demonstrated that GNRI can effectively serve as a prognostic tool in patients with different types of cancers, including gastrointestinal cancer²³, rectal cancer²⁴, head and neck cancer²⁵, and lung cancer²⁶. However, the association between GNRI and GLIM-defined malnutrition remains unclear. Our study is the first to demonstrate that the GNRI exhibits good predictive power in identifying GLIM-defined malnutrition in patients with GC. Furthermore, the GNRI cut-off value of 97 for identifying GLIM-defined malnutrition in this study closely approximated the original classification value of 98. Based on this cut-off value, GNRI exhibited a sensitivity of 64.9%, specificity of 80%, PPV of 72.8% and NPV of 71.4% for identifying GLIM-defined malnutrition. Therefore, the GNRI-based assessment may facilitate the identification of GLIM-defined malnutrition and enable early implementation of nutritional interventions to improve outcomes in patients with GC.

There are also several limitations in the present study. Firstly, due to the single-center design of our study, it is imperative to validate our findings through prospective multi-center studies. Additionally, the nutritional screening tools were evaluated solely upon admission. It is crucial to further explore the clinical significance of these tools in patients' treatment and follow-up outcomes. Lastly, muscle mass assessment in this study was based on CT measurements; however, there is a lack of generally accepted standardized cut-off values for defining low muscle mass ²⁷. The cut-off values used in this study were defined based on a large cohort of GC patients in China ²⁸. However, it should be noted that these cut-off values may not be applicable to other tumor types or ethnic groups.

In conclusion, our study showed that compare to PNI and COUNT scores, GNRI was the best objective nutritional screening tool for identifying GLIM-defined malnutrition in patients with GC. The use of GNRI is recommended due to its simplicity and cost-effectiveness, as it can be easily calculated using the parameters commonly employed in daily clinical practice. The results of our study provide further evidence supporting the selection of GNRI as an effective screening tool for identifying GLIM-defined malnutrition in cancer patients. Further studies with larger sample sizes are warranted to establish its validity and reliability.

Competing Interests

The authors declare no competing interests.

Author Contribution

Xuefeng Bu and Junbo Zuo designed the study. Zhenhua Huang, JingXin Zhang, Wenji Hou, Chen Wang, and Xiuhua Wang collected the data. Junbo Zuo, Yan Huang, and Xuefeng Bu analyzed and interpreted the data. Junbo Zuo and Yan Huang wrote the manuscript. All the authors critically reviewed and approved the final manuscript.

Acknowledgments

We would like to thank all the physicians and nurses in general surgery and patients for their great cooperation.

Data Availability

Applications for data access for non-commercial use can be submitted to author Junbo Zuo, [email protected].

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Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer

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Journal Publication

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Abstract

Objective

Method

Results

Conclusions

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1. Introduction

2. Materials and methods

2.1 Study Patients

2.2 Data collection

2.3 Objective nutritional screening tools

2.4 Body composition analysis

2.5 Diagnosis of malnutrition

2.6 Statistical analysis

3. Result

3.1 Patient characteristics

3.2 ROC curves for predicting GLIM malnutrition

3.3 Association between objective nutritional indicators and GLIM-defined malnutrition

3.4 Comparative analysis of three objective nutritional screening tools

4. Discussion

Declarations

Competing Interests

Author Contribution

Acknowledgments

Data Availability

References

Additional Declarations

Status:

Journal Publication

Version 1