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Prognostic value of the controlling nutritional status (CONUT) score in patients with diffuse large B-cell lymphoma: a meta-analysis

World Journal of Surgical Oncology volume 23, Article number: 28 (2025) Cite this article

Abstract

Background

The significance of the controlling nutritional status (CONUT) score in predicting the prognostic outcomes of diffuse large B-cell lymphoma (DLBCL) has been widely explored, with conflicting results. Therefore, the present meta-analysis aimed to identify the prognostic significance of the CONUT in DLBCL by aggregating current evidence.

Methods

The Web of Science, PubMed, Embase, CNKI and Cochrane Library databases were searched for articles from inception to October 15, 2024. The prognostic value of CONUT for DLBCL was analyzed by determining the pooled hazard ratios (HRs) with 95% confidence intervals (CIs). The Newcastle–Ottawa Scale (NOS) was used to analyze study quality.

Results

Eight studies including 2687 cases were included in this work. The NOS scores of these studies were 7–9 (median, 8), demonstrating high quality. Our analyses revealed that an elevated CONUT score significantly predicted poor overall survival (OS) (HR = 1.63, 95%CI = 1.29–2.05, p < 0.001) and inferior progression-free survival (PFS) (HR=1.22, 95%CI = 1.12–1.33, p < 0.001) in patients with DLBCL. Further, the elevated CONUT score showed a significant correlation with the following clinicopathological factors in DLBCL: Ann Arbor stage III-IV, Eastern Cooperative Oncology Group Performance Status (ECOG PS) of 2–4, presence of extranodal disease, ≥high intermediate National Comprehensive Cancer Network International Prognostic Index (NCCN IPI), presence of B symptoms, elevated lactose dehydrogenase (LDH) levels, and presence of bone marrow infiltration.

Conclusions

An increased CONUT score was dramatically associated with poor OS and PFS in patients with DLBCL, as well as with clinicopathological characteristics representing DLBCL tumor development.

Introduction

Diffuse large B-cell lymphoma (DLBCL) is a form of non-Hodgkin lymphoma (NHL) with high aggressiveness and heterogeneity [1]. In 2022, there were 85,200 new cases and 41,600 deaths caused by lymphoma in China [2]. DLBCL accounts for approximately 30% of NHL cases diagnosed annually and over 1/4 of lymphomas across the USA [3]. The most common symptoms of DLBCL are rapidly progressive lymphadenopathy, B-symptoms, and higher lactate dehydrogenase (LDH) levels [4]. Approximately 60% of DLBCL patients are managed with the rituximab, cyclophosphamide, adriamycin, vincristine, and prednisone (R-CHOP) regimen [5]. Most patients with DLBCL respond well to first-line treatment; however, 30–40% fail to achieve remission or relapse [6, 7]. Therefore, effective prognostic marker for DLBCL must be urgently identified to enhance survival outcomes.

Prior studies have shown that inflammation and nutritional status can influence the prognosis of patients with cancer [8]. Previous studies have further indicated that a series of nutrition-related indexes, including the albumin-to-globulin ratio [9], prognostic nutritional index (PNI) [10], geriatric nutritional risk index (GNRI) [11], fibrinogen-to-albumin ratio [12], and skeletal muscle index (SMI) [13] are significant prognostic biomarkers of different tumors [14,15,16,17,18]. The controlling nutritional status (CONUT) score is a new nutritional parameter which was first proposed in 2005 [19]. CONUT assesses serum albumin, total lymphocyte quantity, and total cholesterol, to yield a total score of 0–12 (Table 1). Higher CONUT scores have been widely suggested to be associated with poor tumor prognosis in many malignancies, including non-small cell lung cancer [20], multiple myeloma [21], prostate cancer [22], cervical cancer [23], and renal cell carcinoma [24]. The value of CONUT in predicting DLBCL prognosis has been previously assess; however, the results remain inconsistent [25,26,27,28,29,30,31,32]. In certain studies, a high CONUT score was identified as a significant prognostic factor of DLBCL [25, 28, 32]; however, others failed to identify any such correlation [26]. Consequently, we performed a meta-analysis to analyze the utility of the CONUT score in predicting DLBCL patient prognosis.

Table 1 The CONUT scoring system
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Materials and methods

Study guideline

This work was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [33]. The PRISMA checklist is provided in Supplementary file 1. This meta-analysis was registered in INPLASY under the number INPLASY2024120090 (The DOI number is https://doiorg.publicaciones.saludcastillayleon.es/10.37766/inplasy2024.12.0090).

Search strategy

The PubMed, Web of Science, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases were searched from inception to October 15, 2024 using the following search strategy: (controlling nutritional status score OR controlling nutritional status OR CONUT) AND (lymphoma large B-cell OR diffuse large B-cell lymphoma OR lymphoma OR DLBCL). This search was conducted by combining keywords with free words. No limitation was placed on the language of publication. The detailed search strategies for each database are provided in Supplementary file 2. To identify other relevant studies, the reference lists of studies selected by electronic searches were also searched manually.

Eligibility standards

The eligibility criteria for studies were as follows: (1) pathological diagnosis of DLBCL; (2) studies investigating the relationship between the CONUT score and DLBCL prognosis; (3) studies with hazard ratios (HRs) with 95% confidence intervals (CIs) could be obtained or Kaplan‒Meier survival curves; (4) a CONUT cut-off value was used to stratify patients with low/high CONUT.

The exclusion criteria were as follows: (1) reviews, meeting abstracts, comments, and case reports; (2) those having duplicate cases; and (3) animal studies.

Information acquisition and quality analysis

Two reviewers (JZ and YW) reviewed and obtained data from the qualified articles. Any dispute was settled through negotiation until a consensus was reached. Data on author, year, age, sex, sample size, study period, study design, study center, Ann Arbor stage, treatment, threshold, threshold determination, follow-up, survival analysis types, survival outcomes, and HRs with 95%CIs were acquired. Overall survival (OS) and progression-free survival (PFS) were the primary and secondary outcomes, respectively. OS was defined as the time from diagnosis to death from any causes or the last follow-up. PFS was defined as the time from the date of diagnosis until the last follow-up, documented progression, relapse, or death from any cause. HRs for OS and PFS were derived from the HR for high vs. low CONUT in the included studies. Higher lactose dehydrogenase (LDH) levels were defined as ≥ 2U/L [27]. The definitions of each stage on the “Ann Arbor”, “National Comprehensive Cancer Network International Prognostic Index (NCCN IPI)”, and “Eastern Cooperative Oncology Group Performance Status (ECOG PS)” criteria are standard concepts which can be found at: https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1480. The Newcastle–Ottawa Scale (NOS) was applied to analyze the study quality [34]; this scale evaluates the study design in three ways: comparability, population selection, and outcome. NOS scores range from 0 to 9, with a score ≥ 6 points indicating high-quality.

Statistical analysis

Pooled HRs with 95%CIs were analyzed to assess the prognostic value of CONUT for DLBCL. The between-study heterogeneity was assessed using the I2 statistics as well as Cochran’s Q-test. I2 > 50% or a p-value < 0.10 (Q-test) indicates obvious heterogeneity, in which case a random-effects model was utilized; otherwise, the fixed-effects model was selected. Subgroup analyses were performed to investigate the prognostic value of CONUT in different DLBCL population groups. The relation of CONUT with patient clinicopathological factors was assessed through pooled odds ratios (ORs) with 95%CIs. Sensitivity analysis was performed by eliminating one article and calculating new HRs to assess their robustness and stability. We further adopted Funnel plot, Begg’s and Egger’s tests to evaluate publication bias in the enrolled articles. Further, we employed Stata version 12.0 software (Stata Corp, College Station, Texas, USA) in statistical analysis. Statistical significance was set at P < 0.05.

Results

Literature search process

The primary literature search identified 163 studies, of which 118 were retained after duplicate removal (Fig. 1). An additional 106 articles were excluded after title- and abstract-screening due to irrelevance or animal studies. Subsequently, the full-texts of 12 articles were analyzed, among which four were discarded due to lacking survival data (n = 2), irrelevant to DLBCL (n = 1), or not investigating CONUT (n = 1). Eventually, eight studies comprising 2687 cases [25,26,27,28,29,30,31,32] were included in the present study (Fig. 1).

Fig. 1
figure 1

Study selection flow diagram according to PRISMA guideline

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Included study characteristics

Table 2 presents the baseline study characteristics of the enrolled participants. All studies were published in 2020–2024, including three performed in Japan [25, 26, 31], two in China [29, 32], two in Turkey [27, 28], and one in Korea [30]. There were six English-language [25,26,27,28, 30, 31] and two Chinese-language [29, 32] articles, with sample sizes of 81–654 (median, 285.5). The enrolled articles had a retrospective design. There were six single center articles [26,27,28,29,30,31] and two multicenter studies [25, 32]. Seven studies included DLBCL cases of Ann Arbor stage I-IV [25,26,27,28,29,30,31], while one enrolled only stage III-IV cases [32]. Six articles managed patients using R-CHOP regimen [25, 27,28,29,30, 32], and two studies used the R-CHOP/ rituximab, cyclophosphamide, tetrahydropyranyl-adriamycin, vincristine, and prednisone (R-THP-COP) protocol [26, 31]. Three studies used a cut-off value of ≥ 5 [25, 28, 30], and one each applied ≥ 2 [27], ≥ 3 [31], ≥ 4 [26], ≥ 6 [29], and ≥ 7 [32], respectively. Six articles utilized receiver operating characteristic (ROC) curve to determine cut-off values [25,26,27,28, 31, 32], while two referred to the literature [29, 30]. Seven [25,26,27,28, 30,31,32] and four [26, 27, 29, 30] studies reported the relationship between CONUT and OS and PFS in patients with DLBCL, respectively. Six articles obtained HRs with 95%CIs through multivariate regression [26,27,28,29,30,31], while two studies used univariate analysis [25, 32]. The NOS scores of enrolled studies were 7–9 (median, 8), demonstrating high quality (Table 2).

Table 2 Basic characteristics of included studies in this meta-analysis
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CONUT and OS

Seven studies involving 2600 patients [25,26,27,28, 30,31,32] reported the utility of CONUT in predicting the OS of DLBCL. We further used the random-effects model giving obvious heterogeneity (I2 = 87.6%, p < 0.001). Based on the combined results, an elevated CONUT score was found to markedly predict poorer OS of DLBCL (HR = 1.63, 95%CI = 1.29–2.05, p < 0.001; Fig. 2; Table 3). As demonstrated in the subgroup analyses, the significant prognostic function of CONUT for OS was not influenced by study center, Ann Arbor stage, threshold, threshold measurement, or survival analysis (Table 3). Moreover, subgroup analyses also indicated that CONUT apparently forecast the OS in subgroups of Chinese and Korean studies, sample size ≥ 300, and R-CHOP treatment (all p < 0.05; Table 3).

Fig. 2
figure 2

Meta-analyses of association between CONUT score and OS in DLBCL

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Table 3 Subgroup analysis of prognostic value of CONUT score for OS in patients with DLBCL
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CONUT and PFS

Four studies comprising 1134 patients [26, 27, 29, 30] showed an association between CONUT and PFS in DLBCL. Due to a lack of any obvious heterogeneity (I2 = 0, p = 0.548), a fixed-effects model was applied (Fig. 3). Based on the pooled results, a higher CONUT was found to significantly predict poor PFS (HR = 1.22, 95%CI = 1.12–1.33, p < 0.001) in DLBCL (Fig. 3; Table 4). Subgroup analyses further showed that an elevated CONUT still significantly predicted inferior PFS, despite differences in sample size, threshold, or threshold determination (Table 4). Furthermore, a high CONUT was still markedly correlated with poor PFS in patients with DLBCL receiving R-CHOP therapy (p < 0.05; Table 4).

Fig. 3
figure 3

Meta-analyses of association between CONUT score and PFS in DLBCL

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Table 4 Subgroup analysis of prognostic value of CONUT score for PFS in patients with DLBCL
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Relation between CONUT and clinicopathological features

Five articles including 1749 cases [25,26,27, 29, 30] reported on the association of CONUT with patient clinicopathological characteristics. From the combined data, the greater CONUT score was evidently correlated with the following clinicopathological factors in DLBCL: Ann Arbor stage III-IV (OR = 4.07, 95%CI = 3.21–5.15, p < 0.001), ECOG PS of 2–4 (OR = 3.67, 95%CI = 2.31–5.84, p < 0.001), presence of extranodal disease (OR = 2.99, 95%CI = 2.15–4.17, p < 0.001), ≥high intermediate National Comprehensive Cancer Network International Prognostic Index (NCCN IPI) (OR = 7.36, 95%CI = 5.66–9.57, p < 0.001), B symptoms (OR = 4.65, 95%CI = 3.35–6.45, p < 0.001), higher lactose dehydrogenase (LDH) levels (OR = 3.73, 95%CI = 2.80–4.98, p < 0.001), and bone marrow infiltration (OR = 3.84, 95%CI = 1.91–7.75, p < 0.001) (Figs. 4 and 5, and Table 5). However, the CONUT score did not show any marked correlation with sex (OR = 1.10, 95%CI = 0.76–1.59, p = 0.623; Fig. 4; Table 5).

Fig. 4
figure 4

The correlation between CONUT score and clinicopathological features of DLBCL. (A) Gender (male vs. female); (B) Ann Arbor stage (III-IV vs. I-II); (C) ECOG PS (2–4 vs. 0–1); and (D) Extranodal disease (yes vs. no)

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Fig. 5
figure 5

The correlation between CONUT score and clinicopathological features of DLBCL. (A) NCCN IPI (≥ high intermediate vs. < high intermediate); (B) Presence of B symptoms (yes vs. no); (C) LDH (elevated vs. normal); and (D) Bone marrow infiltration (yes vs. no)

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Table 5 The association between CONUT score and clinicopathological features in patients with DLBCL
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Sensitivity analysis

We later conducted sensitivity analysis by sequentially removing individual studies to assess whether our combined results were stable. Neither the OS nor PFS results changed significantly after excluding any individual article, verifying that our meta-analysis findings were robust and reliable (Fig. 6).

Fig. 6
figure 6

Sensitivity analysis. (A) OS and (B) PFS

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Publication bias

We utilized Funnel plots, Begg’s, and Egger’s tests to analyze possible publication bias. Symmetrical Funnel plots with p values > 0.05 indicated the absence of publication bias. Overall, neither OS (p = 0.230, p = 0.118 by Begg’s and Egger’s tests) or PFS (p = 1.000, p = 0.227 by Begg’s and Egger’s tests) showed any obvious publication bias (Fig. 7).

Fig. 7
figure 7

Publication bias test. (A) Begg’s test for OS, p = 0.230; (B) Egger’s test for OS, p = 0.118; (C) Begg’s test for PFS, p = 1.000; and (D) Egger’s test for PFS, p = 0.227

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Discussion

Gene expression analysis has identified two separate molecular DLBCL subtypes: the germinal center B-cell–like (GCB) and the activated B-cell–like (ABC) subtypes, with 10 to 15% of cases remaining unclassifiable [1]. The ABC subtype of DLBCL involves continuous B-cell receptor signaling and nuclear factor κB activation, whereas the GCB subtype is characterized by the expression of genes typical of germinal center B cells, such as BCL6 and EZH2 [35, 36]. This phenotypic difference is important, as targeted treatments may be more effective in one subtype.

The role of CONUT in predicting DLBCL prognosis has been previously analyzed; however, conclusions remain inconsistent. In this meta-analysis, we aggregated the data of 8 articles with 2687 patients [25,26,27,28,29,30,31,32], finding that a higher CONUT score evidently forecast poor OS and inferior PFS in patients with DLBCL. Additionally, an elevated CONUT score was remarkable correlated with advanced stage, extranodal disease, high NCCN IPI, presence of B symptoms, high LDH levels and bone marrow infiltration in DLBCL. Collectively, the CONUT score clearly predicted poor long- and short-term survival outcomes of DLBCL. This meta-analysis provides the first investigation of the prognostic significance of the CONUT score in patients with DLBCL.

CONUT assesses three factors: albumin, cholesterol, and lymphocyte count [19], with higher CONUT scores resulting from low contents of the three components. The exact mechanisms related to the prognostic value of CONUT for DLBCL remain to be further explored, but can be interpreted as follows. First, in the plasma, albumin is the most abundant protein and represents both the nutritional status of the human body and the systemic inflammation [37]. Cancer patients with hypoalbuminemia may suffer from immune deficiency, resulting in a reduced therapeutic effect and a consequent increased mortality [38]. Studies in the literature have indicated that patient malnutrition correlates with tumor progression and invasion, indicating that nutrition-related factors may affect malignancy prognosis [39]. Second, lymphocytes show anticancer activity and stimulate anticancer responses by diffusing in tumor-infiltrating lymphocytes [40]. Lymphocytes are the most important antigen-responding immune cells, initiating specific immune responses following stimulation, thereby inhibiting tumor growth and improving tumor prognosis [41]. Intra-tumoral CD8 + T lymphocytes are associated with better OS, which can independently predict prognosis [42]. Third, cholesterol is predominantly synthesized in the liver, but also circulates in blood as low-density lipoprotein (LDL). LDL receptors (LDLRs) are distributed in either normal cell or tumor cell surface. The function of cell membranes, which aid in the transmission of signals, is dependent upon cholesterol [43]. A decrease in cholesterol levels can influence the antitumor activity of immune-competent cells. Further, previous studies have indicated an association between high LDL levels and low tumor survival [44]. Consequently, CONUT is considered a reliable prognostic marker of cancer prognosis.

The CONUT score has been demonstrated to have a prognostic value in various cancers through prior meta-analyses [45,46,47,48,49]. As shown by Peng et al., a higher CONUT score is associated with dismal OS in breast cancer in one meta-analysis involving 9 studies [45]. In one meta-analysis with 3783 patients, Lv et al. further reported that a high pretreatment CONUT score predicted poor PFS and cancer-specific survival (CSS) in esophageal cancer [46]. In another meta-analysis of 1409 cases, a high CONUT score showed a strong correlation with worse OS and recurrence-free survival (RFS) in biliary tract cancer [47]. According to Niu et al., one meta-analysis enrolling 3562 patients found that higher CONUT scores are associated with dismal survival in urological cancer patients [48]. Further, Takagi et al., showed that a higher CONUT score predicted dismal OS, CSS, and RFS in colorectal cancer surgical patients in a meta-analysis comprising 9 studies [49].

This work has some limitations. Firstly, most enrolled articles were from Asian countries. Consequently, the findings are more applicable to Asian DLBCL cases. Secondly, all of the included studies had a retrospective design. Consequently, inherent heterogeneity may exist. Thirdly, the thresholds of a high CONUT score remain non-uniform among eligible articles. Fourth, only one of the studies accounts for 84% of the weight in the PFS analysis, meaning that the combined results largely mirror the outcomes of this single study (Fig. 3). Therefore, large-scale multi-regional prospective studies are warranted to validate our meta-analysis findings. Moreover, the accurate mechanisms for the prognostic value of CONUT in DLBCL should be further investigated in future studies.

Conclusions

In summary, this meta-analysis revealed a significant correlation between an elevated CONUT score and poor OS and in of patients with DLBCL. Moreover, a high CONUT score was significantly correlated with the clinicopathological features representing DLBCL development.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

CONUT:

Controlling nutritional status

DLBCL:

Diffuse large B-cell lymphoma

HR:

Hazard ratio

CI:

Confidence interval

OS:

Overall survival

PFS:

Progression-free survival

LDH:

Lactose dehydrogenase

R-CHOP:

Rituximab, cyclophosphamide, adriamycin, vincristine, and prednisone

PNI:

Prognostic nutritional index

GNRI:

Geriatric nutritional risk index

SMI:

Skeletal muscle index

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

NOS:

Newcastle–Ottawa Scale

OR:

Odds ratio

ROC:

Receiver operating characteristic

CSS:

Cancer-specific survival

ECOG PS:

Eastern Cooperative Oncology Group Performance Status

NCCN IPI:

National Comprehensive Cancer Network International Prognostic Index

References

  1. Sehn LH, Salles G. Diffuse large B-Cell lymphoma. N Engl J Med. 2021;384(9):842–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4(1):47–53.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lopez-Santillan M, Lopez-Lopez E, Alvarez-Gonzalez P, Martinez G, Arzuaga-Mendez J, Ruiz-Diaz I, Guerra-Merino I, Gutierrez-Camino A, Martin-Guerrero I. Prognostic and therapeutic value of somatic mutations in diffuse large B-cell lymphoma: a systematic review. Crit Rev Oncol/Hematol. 2021;165:103430.

    Article  PubMed  Google Scholar 

  4. Moore DC, Peery MR, Tobon KA, Raheem F, Hwang GS, Alhennawi L, Hughes ME. New and emerging therapies for the treatment of relapsed/refractory diffuse large B-cell lymphoma. J Oncol Pharm Practice: Official Publication Int Soc Oncol Pharm Practitioners. 2022;28(8):1848–58.

    Article  CAS  Google Scholar 

  5. Feugier P, Van Hoof A, Sebban C, Solal-Celigny P, Bouabdallah R, Fermé C, Christian B, Lepage E, Tilly H, Morschhauser F, et al. Long-term results of the R-CHOP study in the treatment of elderly patients with diffuse large B-cell lymphoma: a study by the Groupe d’Etude Des Lymphomes De l’Adulte. J Clin Oncology: Official J Am Soc Clin Oncol. 2005;23(18):4117–26.

    Article  CAS  Google Scholar 

  6. Harris LJ, Patel K, Martin M. Novel therapies for relapsed or refractory diffuse large B-Cell lymphoma. Int J Mol Sci 2020, 21(22).

  7. Melchardt T, Egle A, Greil R. How I treat diffuse large B-cell lymphoma. ESMO open. 2023;8(1):100750.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Argilés JM. Cancer-associated malnutrition. Eur J Oncol Nursing: Official J Eur Oncol Nurs Soc. 2005;9(Suppl 2):S39–50.

    Article  Google Scholar 

  9. Taguchi S, Kawai T, Nakagawa T, Nakamura Y, Kamei J, Obinata D, Yamaguchi K, Kaneko T, Kakutani S, Tokunaga M et al. Prognostic significance of the albumin-to-globulin ratio for advanced urothelial carcinoma treated with pembrolizumab: a multicenter retrospective study. Scientific Reports 2021, 11(1).

  10. Abe A, Hayashi H, Ishihama T, Furuta H. Prognostic impact of the prognostic nutritional index in cases of resected oral squamous cell carcinoma: a retrospective study. BMC Oral Health 2021, 21(1).

  11. An S, Han GY, Eo W, Kim DH, Lee S. Comparison of the geriatric nutritional risk index and the prognostic nutritional index in determining survival outcome in patients with non-small cell lung cancer undergoing surgical resection: a cohort study. Med (Baltim). 2022;101(45):e31591.

    Article  CAS  Google Scholar 

  12. Sun H, Ma J, Lu J, Yao ZH, Ran HL, Zhou H, Yuan ZQ, Huang YC, Xiao YY. Fibrinogen-to-albumin ratio predicts overall survival of hepatocellular carcinoma. World J Gastrointest Oncol. 2023;15(9):1662–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yang L, Liao X, Xie Z, Li H. Prognostic value of pretreatment skeletal muscle index in pancreatic carcinoma patients: a meta-analysis. Med (Baltim). 2023;102(19):e33663.

    Article  Google Scholar 

  14. Xu X, Tian M, Ding CC, Xu H, Wang H, Jin X. Skeletal muscle index-based Cachexia Index as a predictor of prognosis in patients with Cancer: a Meta-analysis and systematic review. Nutr Rev 2024;nuae094.

  15. Liu W, Li M, Lian S, Hou X, Ling Y. Geriatric nutritional risk index as a predictor for postoperative complications in patients with solid cancers: a meta-analysis. Front Oncol. 2024;14:1266291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xu XT, Qian Y, Tian MX, Ding CC, Guo H, Tang J, Pi GL, Wu Y, Dai Z, Jin X. Predictive impact of Prognostic Nutritional Index in patients with Cancer treated with Immune Checkpoint inhibitors: a systematic review and Meta-analysis. Nutr Cancer. 2023;75(6):1413–26.

    Article  CAS  PubMed  Google Scholar 

  17. Roberts WS, Delladio W, Price S, Murawski A, Nguyen H. The efficacy of albumin-globulin ratio to predict prognosis in cancer patients. Int J Clin Oncol. 2023;28(9):1101–11.

    Article  CAS  PubMed  Google Scholar 

  18. Li B, Deng H, Lei B, Chen L, Zhang X, Sha D. The prognostic value of fibrinogen to albumin ratio in malignant tumor patients: a meta-analysis. Front Oncol. 2022;12:985377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ignacio de Ulíbarri J, González-Madroño A, de Villar NG, González P, González B, Mancha A, Rodríguez F. Fernández G: CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp. 2005;20(1):38–45.

    PubMed  Google Scholar 

  20. Miyazaki R, Tamura M, Sakai T, Furukawa N, Yamamoto M, Okada H. Using the combined C-reactive protein and controlling nutritional status index for elderly non-small cell lung cancer. J Thorac Dis. 2024;16(7):4400–8.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Li Y, Kong LZ. Prognostic value of the Controlling Nutritional Status score in patients with newly diagnosed multiple myeloma. Am J Transl Res. 2024;16(9):4788–95.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Li SY, Wan LL, Liu YF, Li YW, Huang X, Liu RJ. Prognostic value of three clinical nutrition scoring system (NRI, PNI, and CONUT) in elderly patients with prostate cancer. Front Nutr. 2024;11:1436063.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Fu J, Xu X, Tian M, Wang H, Jin X. The controlling nutritional status score as a new prognostic predictor in patients with cervical cancer receiving radiotherapy: a propensity score matching analysis. BMC Cancer. 2024;24(1):1093.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Boltri M, Traunero F, Ongaro L, Migliozzi F, Vianello F, Lenardon O, Visalli F, Buttazzi L, Maruzzi D, Trombetta C et al. The Added Value of Controlling Nutritional Status (Conut) Score for Preoperative Counselling on Significant Early Loss of Renal Function After Radical Nephrectomy for Renal Cell Carcinoma. Cancers (Basel) 2024, 16(20).

  25. Matsukawa T, Suto K, Kanaya M, Izumiyama K, Minauchi K, Yoshida S, Oda H, Miyagishima T, Mori A, Ota S, et al. Validation and comparison of prognostic values of GNRI, PNI, and CONUT in newly diagnosed diffuse large B cell lymphoma. Ann Hematol. 2020;99(12):2859–68.

    Article  PubMed  Google Scholar 

  26. Nagata A, Kanemasa Y, Sasaki Y, Nakamura S, Okuya T, Funasaka C, Kageyama A, Shimoyama T, Omuro Y. Clinical impact of controlling nutritional status score on the prognosis of patients with diffuse large B-cell lymphoma. Hematol Oncol. 2020;38(3):309–17.

    Article  CAS  PubMed  Google Scholar 

  27. Akgün Çağlıyan G, Hacıoğlu S, Ünver Koluman B, İlkkılıç K, Nar R, Başer MN, Bozdemir A, Şenol H, Şen Türk N, Erol V, et al. Is CONUT score a prognostic index in patients with diffuse large cell lymphoma? Turk J Med Sci. 2021;51(4):2112–9.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Baysal M, Bas V, Demirci U, Gulsaran SK, Umit E, Kirkizlar HO, Demir AM. The utility of CONUT score in diffuse large B cell lymphoma patients. Niger J Clin Pract. 2021;24(8):1194–9.

    Article  CAS  PubMed  Google Scholar 

  29. Yao J, Zhou S, Gao H, Chen R. The value of CONUT score in predicting the prognosis of patients with diffuse large B-cell lymphoma undergoing chemotherapy. Chin J Clin. 2021;49(12):1442–4.

    Google Scholar 

  30. Go SI, Choi BH, Park MJ, Park S, Kang MH, Kim HG, Kang JH, Jeong EJ, Lee GW. Prognostic impact of pretreatment skeletal muscle index and CONUT score in diffuse large B-cell lymphoma. BMC Cancer. 2023;23(1):1071.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kaneda Y, Kanemura N, Nakamura N, Ikoma Y, Yamaguchi K, Takada E, Shibata Y, Lee S, Fujita K, Morishita T, et al. The pretreatment Controlling Nutritional Status score is an independent prognostic factor in elderly patients with diffuse large B-cell lymphoma. Leuk Lymphoma. 2024;65(3):339–45.

    Article  CAS  PubMed  Google Scholar 

  32. Shan H, Chen X, Zhang H, Miao Y, Wang F, Shi Y, Wang L, Ye J, Shen Z, Sang W, et al. Predictive value of controlling nutritional status score in the prognosis of patients with advanced diffuse large B-cell lymphoma. J Leuk Lymphoma. 2024;33(2):104–9.

    Google Scholar 

  33. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.

    Article  PubMed  Google Scholar 

  35. Lenz G, Wright G, Dave SS, Xiao W, Powell J, Zhao H, Xu W, Tan B, Goldschmidt N, Iqbal J, et al. Stromal gene signatures in large-B-cell lymphomas. N Engl J Med. 2008;359(22):2313–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Scott DW, Mottok A, Ennishi D, Wright GW, Farinha P, Ben-Neriah S, Kridel R, Barry GS, Hother C, Abrisqueta P, et al. Prognostic significance of diffuse large B-Cell lymphoma cell of Origin determined by Digital Gene Expression in Formalin-fixed paraffin-embedded tissue biopsies. J Clin Oncology: Official J Am Soc Clin Oncol. 2015;33(26):2848–56.

    Article  CAS  Google Scholar 

  37. Artigas A, Wernerman J, Arroyo V, Vincent JL, Levy M. Role of albumin in diseases associated with severe systemic inflammation: Pathophysiologic and clinical evidence in sepsis and in decompensated cirrhosis. J Crit Care. 2016;33:62–70.

    Article  CAS  PubMed  Google Scholar 

  38. Jansson H, Cornillet M, Björkström NK, Sturesson C, Sparrelid E. Prognostic value of preoperative inflammatory markers in resectable biliary tract cancer - validation and comparison of the Glasgow Prognostic Score and Modified Glasgow Prognostic Score in a western cohort. Eur J Surg Oncology: J Eur Soc Surg Oncol Br Association Surg Oncol. 2020;46(5):804–10.

    Article  Google Scholar 

  39. Yin L, Liu J, Lin X, Li N, Shi M, Zhang H, Guo J, Chen X, Wang C, Wang X, et al. Development and validation of a rapid-decision pathway to diagnose malnutrition in patients with lung cancer. Nutr (Burbank Los Angeles Cty Calif). 2021;84:111102.

    Article  Google Scholar 

  40. Engblom C, Pfirschke C, Pittet MJ. The role of myeloid cells in cancer therapies. Nat Rev Cancer. 2016;16(7):447–62.

    Article  CAS  PubMed  Google Scholar 

  41. Liakou CI, Narayanan S, Ng Tang D, Logothetis CJ, Sharma P. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human bladder cancer. Cancer Immun. 2007;7:10.

    PubMed  PubMed Central  Google Scholar 

  42. Sato E, Olson SH, Ahn J, Bundy B, Nishikawa H, Qian F, Jungbluth AA, Frosina D, Gnjatic S, Ambrosone C, et al. Intraepithelial CD8 + tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc Natl Acad Sci U S A. 2005;102(51):18538–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chimento A, Casaburi I, Avena P, Trotta F, De Luca A, Rago V, Pezzi V, Sirianni R. Cholesterol and its metabolites in Tumor Growth: therapeutic potential of statins in Cancer Treatment. Front Endocrinol (Lausanne). 2018;9:807.

    Article  PubMed  Google Scholar 

  44. Qiu Q, Song JR, Zheng XQ, Zheng H, Yi H. The impact of diabetes, hypercholesterolemia, and low-density lipoprotein (LDL) on the survival of cervical cancer patients. Discov Oncol. 2024;15(1):349.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Peng P, Chen L, Shen Q, Xu Z, Ding X. Prognostic Nutritional Index (PNI) and Controlling Nutritional Status (CONUT) score for predicting outcomes of breast cancer: a systematic review and meta-analysis. Pak J Med Sci. 2023;39(5):1535–41.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Lv J, Chen P, Wu J, Hu C. Prognostic value of pretreatment Controlling Nutritional Status score in esophageal cancer: a meta-analysis. Pathol Oncol Research: POR. 2023;29:1611221.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Liu Z, Zhou H, Zhou Y, Yu M, Cheng Y, Li J. Prognostic impact of the Controlling Nutritional Status score in patients with biliary tract cancer: a systematic review and meta-analysis. Front Oncol. 2023;13:1240008.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Niu X, Zhu Z, Bao J. Prognostic significance of pretreatment controlling nutritional status score in urological cancers: a systematic review and meta-analysis. Cancer Cell Int. 2021;21(1):126.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Takagi K, Buettner S, Ijzermans JNM. Prognostic significance of the controlling nutritional status (CONUT) score in patients with colorectal cancer: a systematic review and meta-analysis. Int J Surg (London England). 2020;78:91–6.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Editage (www.editage.com) for the English language editing. The language editing certificate from Editage (www.editage.com) is provided as supplementary file 3.

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This research received no external funding.

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Authors and Affiliations

  1. Clinical Laboratory, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, 313000, China

    Jinqiang Zhao

  2. Department of Hematology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, 313000, China

    Ying Wu

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  1. Jinqiang Zhao
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Contributions

JZ and YW contributed to conception and design of the study, and reviewed and revised the article. JZ and YW performed the literature search and data extraction. JZ performed statistical analysis and prepared Figures and Tables. YW drafted the article. All authors have approved the final version of the manuscript.

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Correspondence to Ying Wu.

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Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1: The CONUT scoring system

Supplementary Material 2: The detailed search strategies for each database

12957_2025_3663_MOESM3_ESM.pdf

Supplementary Material 3: The language editing certificate from Editage (www.editage.com)

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Zhao, J., Wu, Y. Prognostic value of the controlling nutritional status (CONUT) score in patients with diffuse large B-cell lymphoma: a meta-analysis. World J Surg Onc 23, 28 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12957-025-03663-y

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Keywords

World Journal of Surgical Oncology

ISSN: 1477-7819

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