Zhinan Li1Yueying Lin1†Yanmei Shi2†Ting Yang1Liya An1Yuxing Qi1Pengcheng Zhang1Xingzong Huang1Xianming Su1Yinlong Deng1Jian Hu1Guobin Liu2*
Dali Sun1*- 1Department of Gastrointestinal Surgery, Second Affiliated Hospital of Kunming Medical University/Second Faculty of Clinical Medicine, Kunming Medical University, Kunming, China
- 2Department of Gastroenterology, Second Affiliated Hospital of Kunming Medical University/Second Faculty of Clinical Medicine, Kunming Medical University, Kunming, China
Background: Malnutrition can lead to adverse clinical outcomes in hospitalized patients, timely and accurate diagnosis of malnutrition is crucial for initiating early nutritional support programs. To assess the correlation between malnutrition diagnosed by different malnutrition diagnostic tools and patients’ clinical outcomes.
Methods: Meta-analyses of the associations between malnutrition and patients’ clinical outcomes were screened and included by searching databases. For each association, this study used fixed and random effects models, calculated 95% CI (confidence intervals) and 95% PI (prediction intervals), and assessed heterogeneity, evidence of small-study effects, and excess significance bias.
Results: A total of 138 meta-analyses were included in this study, and 407 associations were evaluated. For oncology patients, malnutrition diagnosed by eight tools was associated with oncological survival, with three evidence scores of PNI (prognostic nutritional index), GNRI (geriatric nutritional risk index), and CONUT (controlling nutritional status) being highly recommended (Class II). For nontumor patients, malnutrition diagnosed by nine tools was associated with poor clinical outcomes, with four tools with high evidence scores (Class II) of PNI, BMI (body mass index) < 18.5 kg/m2, GNRI, and CONUT being highly recommended.
Conclusion: This study demonstrated a significant correlation (Class II) between malnutrition diagnosed by four tools, the PNI, BMI < 18.5 kg/m2, GNRI, and CONUT, and clinical outcomes, and the other tools need to be validated in future high-quality studies despite their correlation.
Systematic review registration: PROSPERO CRD42024586175.
1 Introduction
Malnutrition is prevalent among hospitalized patients and those with chronic conditions. By 2050, the proportion of the global population aged 65 and over is projected to rise to 22%, aging may contribute to malnutrition, leading to a range of adverse clinical outcomes (1). Prompt and accurate diagnosis of malnutrition is crucial for clinical staff to identify malnourished patients and for clinicians to formulate appropriate nutritional support plans. At the same time, different malnutrition diagnosis and assessment tools not only yield varying malnutrition prevalence rates, but also cause significant confusion for clinicians and nursing staff due to differences in diagnostic accuracy (2).
The mini-nutritional assessment short-form (MNA-SF), subjective global assessment (SGA), global leadership initiative on malnutrition (GLIM), nutritional risk screening 2002 (NRS 2002), naples prognostic score (NPS), modified glasgow prognostic score (mGPS), glasgow prognostic score (GPS), mini-nutritional assessment (MNA), CONUT, GNRI, PNI, phase angle (PA), and BMI are commonly used tools for nutritional screening and assessment. Numerous meta-analyses have focused on the correlation between malnourished patients and clinical outcomes (3–79); however, since different studies have focused on different tools, the results obtained vary widely, which made it difficult for clinical specialists to obtain a comprehensive understanding of the advantages and disadvantages of different malnutrition diagnostic tools.
Therefore, by comprehensively reviewing the latest data from published systematic reviews and meta-analyses to better understand the potential bias in the correlation between different malnutrition diagnostic tools and clinical outcomes, this study provides references for clinicians and nursing staff to screen for suitable malnutrition diagnostic tools.
2 Methods
2.1 Data resources and search strategy
Systematic searches of the PubMed, Cochrane Library, Embase, ScienceDirect, Web of Science, Wanfang, CNKI, and VIP databases were conducted independently by two authors (Zhinan Li and Yueying Lin) up to October 2024. The results of the systematic searches are summarized in the following table. Disagreements were discussed with the involvement of a third researcher (Dali-Sun).
2.2 Study selection and extraction
Two researchers (Zhinan Li and Yueying Lin) independently screened the article titles and abstracts in October 2024, excluded irrelevant literature based on the following inclusion and exclusion criteria, according to the PICOS (Population, Intervention, Comparison, Outcome, Study design) criteria (Table 1), and independently read the full texts of eligible studies, with any inconsistencies resolved through discussion involving a third researcher (Dali-Sun). The literature inclusion criteria were as follows: (1) hospitalized adult patients; (2) patients whose nutritional status was evaluated during hospitalization via nutritional screening and assessment tools; (3) reporting correlations between nutritional risk or malnutrition and adverse clinical outcomes (e.g., survival time, mortality rate, complications, etc.); and (4) meta-analyses of observational or interventional studies. Exclusion criteria were as follows: (1) non meta-analysis; (2) not having the desired clinical outcomes (e.g., survival time, mortality rate, complications, etc.); (3) systematic reviews or meta-analyses that did not provide study-specific data; (4) meta-analyses that did not include data from studies on nutritional risk or correlation of malnutrition with poor clinical outcomes; and (5) unavailability of the original article.
Table 1. PICOS criteria for inclusion of studies.
2.3 Data extraction
Data extraction was performed independently by two authors (Zhinan Li and Yueying Lin) in November 2024, with disagreements, if any, resolved through discussions involving a third researcher (Dali-Sun). We extracted the following data from each meta-analysis (Supplementary material 2): the first author, year of publication, type of study, number of studies, number of cases, study population, the age range of patients, and quality assessment tools.
2.4 Assessment of summary effects and heterogeneity
We analyzed the data using Stata version 17.0. For each meta-analysis, we estimated the pooled effect sizes and their 95% CI via fixed-effects and random-effects models (80, 81). After accounting for uncertainty in the pooled effects estimated in the random effects model and heterogeneity among studies, we calculated 95% PI to predict the range of expected effect sizes in the original study (82). For the largest dataset in each meta-analysis, we calculated standard error (SE) of the effect size and determined whether SE was less than 0.10. Heterogeneity between studies was assessed using the I2 statistic, and heterogeneity was considered to be significant or considerable when the I2 exceeded 50% or 75%, respectively (83).
2.5 Assessment of methodological quality
The methodological quality assessment of each study was carried out independently by two authors (Yanmei Shi and Guobin Liu) using the quality assessment tool “A MeaSurement Tool to Assess Systematic Reviews-2” (AMSTAR-2) (84). Disagreements were discussed with the involvement of a third researcher (Dali-Sun).
2.6 Assessment of small-study effects
We used the Egger’s test to determine the small-study effect, which was determined by (1) the p value of the Egger’s test being less than 0.10 and (2) the effect size of the largest study being smaller than the combined effect size (85).
2.7 Evidence of excess significance bias
P-curve and statistical standards (PSST) and effect size and significance (ESS) were introduced to calculate the expected statistically significant findings in the absence of selective reporting or publication bias, based on the mean and variance of the true distribution of effects estimated from the SE and meta-analysis of each study (86). An oversignificance test was considered positive when the p value was less than 0.05.
2.8 Sensitivity analysis
When studies with a high risk of bias and very low to low quality of evidence were excluded, the strength of evidence increased to moderate for 1 association, which was the overall survival of cholangiocarcinoma patients with malnutrition diagnosed by the CONUT score, and to high for 3 associations, which were the overall survival of patients with malnutrition and colorectal cancer diagnosed by the PNI, all-cause mortality of patients with heart failure, and hepatocellular cancer patients’ overall survival.
2.9 Reviewing the existing evidence
Statistically significant (p < 0.05) correlations between nutritional screening and assessment tools and clinical outcomes were categorized into five levels based on specific criteria (strong recommendation, high recommendation, recommendation, weak evidence, and irrelevant).
Strong recommendations included p < 10−6, number of cases > 1,000, p < 0.05 for the largest study in the meta-analysis, heterogeneity I2 < 50, 95% PI to exclude nulls, no small-study effect (Egger’s test p > 0.1), and no excessive significance bias (PSST and ESS = 0).
High recommendations included p < 10−6, number of cases >1,000, p < 0.05 for the largest study in the meta-analysis.
Recommendations included p < 10−3, number of cases >1,000.
For weak evidence, the only criterion was p < 0.05 (87).
When the p value is greater than 0.05, there is no association.
3 Results
3.1 Literature review and characteristics of the included articles
After a systematic search, we obtained 21,345 records from five electronic databases. After removing duplicates, 17,797 records were excluded by browsing titles and abstracts, and 125 records were excluded after the full texts were assessed. Ultimately, 138 studies met our inclusion criteria and were included in the final analysis (Figure 1). The characteristics of the included studies are shown in Supplementary material 2.
Figure 1. Flow diagram of the study selection process.
Methodological quality was assessed for 138 studies using AMSTAR-2. The descriptive characteristics of the included meta-analyses by type of nutritional screening and assessment tools are summarized in Table 2. The studies included 407 associations between different nutritional screening and assessment tools and clinical outcomes, and all the articles were published in 2012–2024, with sample sizes ranging from 187 to 308,430 cases.
Table 2. Descriptive statistics for the overall meta-analysis and the types of nutritional screening and assessment tools included in the umbrella review that graded nutritional screening and assessment tools against evidence of clinical outcomes.
3.2 Summary effect size
A meta-analysis of 407 associations was conducted using random and fixed effects models. Among these, 161 (39.6%) meta-analyses had statistically significant pooled random and fixed effects estimates at p ≤ 0.05. Ninety-four (23.1%) meta-analyses showed significant results when the more stringent p ≤ 10−3 was used as the significance threshold, whereas 85 (20.9%) meta-analyses remained significant at a threshold of 10−6 (Table 3). Approximately half (42.5%) of the meta-analyses were highly heterogeneity, with the largest proportion of highly heterogeneity meta-analyses of MNA with clinical outcomes (66.7%). SGA and GPS did not have highly heterogeneity meta-analyses with clinical outcomes, followed by BMI (61.5%), NPS (60%), GLIM (44.8%), PNI (43.8%), CONUT (41.3%), and GNRI (41.3%) (Table 3). The proportion of meta-analyses with little evidence of heterogeneity (I2 ≤ 25%) was 39.6%. There were 396 (97.3%) associations with 95% PI excluding the null (Table 3).
Table 3. Number and percentage of meta-analyses that met the individual and overall criteria for individual and overall and nutritional screening and assessment tool types used for nutritional screening and assessment tools and clinical outcomes.
3.3 Small-study effects
According to the Egger’s test (p < 0. 10), 199 (48.9%) meta-analyses showed evidence of low study effect, with the highest bias for the MNA-SF score and patient clinical outcomes (75%; only 1 meta-analyses available), followed by PA (66.7%), BMI (61.5%), GNRI (63.8%), CONUT (56.3%) and GPS (50%) (Table 3).
3.4 Excess significance
The proportion of meta-analyses showing evidence of excess significance bias was 39.3%, ranging from 0% for the MNA-SF, SGA, and PA to 66.7% for the MNA (Table 3), and the highest correlations were between the MNA and overall survival (33.3%) in older cancer patients and all-cause mortality (33.3%) in patients with heart failure (Supplementary material 4 and Table 3).
3.5 Grading of the evidence
Thirty-three meta-analyses (8.1%) provided high-level evidence (Figures 1, 2 and Supplementary materials 3, 4), mainly concerning the PNI (n = 20), followed by the GNRI (n = 8), CONUT score (n = 4) and BMI (n = 1). PNI-diagnosed malnutrition was strongly associated with overall survival in patients with non-small cell lung, colorectal, hepatocellular, esophageal, renal cell, prostate and gynecologic malignancies; mortality and major adverse cardiovascular events (MACEs) in patients with coronary artery disease; and the risk of postoperative acute kidney injury (PO-AKI). Malnutrition diagnosed by the GNRI was strongly associated with overall survival in patients with gastric cancer, hematologic malignancies, and undifferentiated malignancies and mortality in patients with heart failure, patients after transcatheter aortic valve implantation (TAVI), and those receiving hemodialysis. The CONUT score was strongly associated with the risk of complications in patients with gastric cancer, overall survival in patients with upper urinary tract uroepithelial or renal cell carcinoma and in patients with undifferentiated cancer, and all-cause mortality in patients with heart failure. Underweight (BMI < 18.5 kg/m2) was associated with long-term mortality after myocardial infarction. Sixty-one (15.0%) meta-analyses provided suggestive evidence that malnutrition diagnosed by the MNA-SF was strongly associated with mortality in patients after hip fracture surgery. Malnutrition diagnosed by the GLIM criteria was strongly associated with the overall survival of cancer patients, complications in patients with hepatopancreatobiliary and gastric cancers, and patients with undifferentiated cancers. Malnutrition, as determined by the NRS 2002, was associated with the length of hospitalization in patients undergoing abdominal surgery and overall survival in cancer patients. An NPS-based diagnosis of malnutrition is associated with overall survival in patients with gastrointestinal and lung cancers. The presence of malnutrition according to the mGPS was strongly associated with overall survival in patients with pancreatitis and cholangiocarcinoma. A diagnosis of malnutrition via the MNA was associated with overall survival in elderly cancer patients. A CONUT score indicating malnutrition was associated with overall survival in patients with pancreatic cancer, cholangiocarcinoma, lymphoma, and undifferentiated malignancies; complications in patients with gastric, hepatopancreatobiliary, and undifferentiated malignancies; mortality in patients with decompensated heart failure and after TAVI; and MACEs in patients with coronary artery disease. GNRI-diagnosed malnutrition was associated with survival in patients with diffuse large B-cell lymphoma, urologic cancers, pancreatic cancer, and undifferentiated hematologic malignancies; mortality in patients hospitalized for postoperative hip fracture and decompensated heart failure; and major cardiovascular events in elderly patients with heart failure (RR, 2.00; 95% CI, 1.24–3.22). PNI-diagnosed malnutrition was associated with survival in patients with lung, oral cavity, esophageal, gastric, colorectal, renal, breast, ovarian malignancies, undifferentiated gynecologic malignancies, glioma and patients treated with immune checkpoint inhibitor; mortality in patients with heart failure and acute coronary syndromes; and the risk of intensive care unit admission for PO-AKI (MD, 0.98; 95% CI, 0.15–1.81). In addition, underweight (BMI < 18.5 kg/m2) was associated with overall survival in patients after colorectal cancer surgery. A total of 67 meta-analyses (16.5%) were supported by weak evidence, and the remaining 246 (60.4%) meta-analyses had no statistically significant results.
Figure 2. Scatterplot showing the results of an umbrella review grading nutritional screening and assessment tools against clinical outcomes. The y-axis shows the strength of the evidence. The x-axis corresponds to different nutritional screening and assessment tools. CAD, coronary artery disease; CRC, colorectal cancer; EC, esophageal cancer; GI, gynecological cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; HF, heart failure; HMs, hematologic malignancies; MHP: maintenance hemodialysis patients; MI, myocardial infarction; NSCLC, non-small cell lung cancer; OC, ovarian cancer; PC, pancreatic cancer; PCa, prostate cancer; PO-AKI, postoperative acute kidney injury risk; RCC, renal cell carcinoma; UTUC, upper tract urothelial carcinoma.
4 Discussion
In order to provide a comprehensive understanding of the strength of the association between malnutrition diagnosed by different nutritional screening and assessment tools and adverse clinical outcomes in patients, we systematically evaluated 138 meta-analyses and 13 different nutritional screening and assessment tools. Thirty-three associations were supported by high-level recommendations, including that PNI-diagnosed malnutrition is associated with survival in oncology patients with non-small cell lung cancer, colorectal cancer, hepatocellular carcinoma, esophageal cancer, renal cell carcinoma, prostate cancer, and gynecologic malignancies; mortality and MACEs in patients with coronary artery disease; and patients’ risk of PO-AKI, which are closely related to adverse clinical outcomes (88–100); underweight (BMI < 18.5 kg/m2) and long-term mortality after myocardial infarction in patients (101); GNRI-diagnosed malnutrition is strongly associated with survival in patients with gastric cancer, hematologic malignancies, and undifferentiated malignancies; and mortality in patients with heart failure, patients after TAVI, and hemodialysis patients (20, 91, 102–106). CONUT score-diagnosed malnutrition is strongly associated with the risk of complications in patients with gastric cancer, survival in patients with upper urinary tract uroepithelial or renal cell carcinoma and in patients with undifferentiated cancer, and all-cause mortality in patients with heart failure (90, 107). Sixty-one meta-analyses provided suggestive evidence that MNA-SF-diagnosed malnutrition is strongly associated with mortality in patients after hip fracture surgery (108). GLIM-diagnosed malnutrition is strongly associated with overall survival of cancer patients, complications in patients with esophageal, gastric cancers and patients with undifferentiated cancers (108–110). Malnutrition diagnosed by the NRS 2002 is strongly associated with length of hospital stay in patients who underwent abdominal surgery and cancer patients’ overall survival (38, 111). NPS-diagnosed malnutrition is associated with survival in patients with gastrointestinal and lung cancers (112, 113). MGPS-diagnosed malnutrition is strongly associated with survival in patients with pancreatic and bile duct cancers (114, 115). MNA-diagnosed malnutrition is associated with survival in elderly cancer patients (116). CONUT-diagnosed malnutrition is associated with survival in patients with pancreatic cancer, cholangiocarcinoma, lymphoma, and undifferentiated malignancies, complications in patients with gastric cancer, hepatopancreaticobiliary, and undifferentiated malignancies, mortality in patients after TAVI, and MACEs in patients with coronary heart disease (105, 117–124). GNRI-diagnosed malnutrition is associated with survival in patients with diffuse large B-cell lymphoma, urologic cancers, pancreatic cancer, and undifferentiated hematologic malignancies, mortality in patients hospitalized for postoperative hip fracture and decompensated heart failure, and major cardiovascular events in elderly patients with heart failure (106, 108, 121, 125–128). PNI-diagnosed malnutrition is associated with survival in patients with lung, oral cavity, esophageal, gastric, colorectal, renal, breast, ovarian, undifferentiated gynecologic malignancies, glioma, and patients treated with immune checkpoint inhibitors, mortality in patients with heart failure and acute coronary syndromes, and the risk of intensive care unit admission for patients with PO-AKI (46, 75, 88, 94, 96, 97, 100, 129–141). Underweight (BMI < 18.5 kg/m2) is associated with colorectal cancer overall survival in postoperative patients (142).
Malnutrition diagnosed by eight tools is associated with tumor survival, with three evidence scores of PNI, GNRI, and CONUT being highly recommended, followed by five evidence scores of GLIM, NRS 2002, NPS, mGPS, and MNA being recommended, which were analyzed for the following reasons: (1) the tools used assessment indicators with different focuses, and the five tools, PNI, CONUT, GNRI, NPS, mGPS, used serum albumin levels, which have previously been regarded as indicators of nutritional status (143, 144); however, it is now considered that serum albumin levels reflect the ability of the body’s liver to synthesize albumin. In patients with malignant tumors, the relatively insufficient intake of substrates for protein synthesis by the liver is caused by the high consumption of tumors, leading to decreased gastrointestinal intake ability due to the side effects of radiotherapy; on the other hand, it may be caused by the poor synthesis function of the liver due to liver tumors, drug-induced liver injuries, and other factors. Therefore, the albumin level can reflect the survival of patients with tumors to a certain extent (144). The four tools, the PNI, GNRI, GLIM and mGPS, add immune indices as indicators of malnutrition; the PNI and GNRI use lymphocyte counts, and the GLIM and mGPS use C-reactive protein (CRP). Malignant tumors are often considered to be caused by immune deficiency, and lymphocyte counts represent, to a certain degree, the body’s immune status. CRP is part of the nonspecific immune mechanism of the body and is often used to represent the level of inflammation in the body. GLIM, NRS 2002, and MNA include changes in eating status and weight/BMI, and the eating status is related to whether the tumor site is involved in the digestive system. At the same time, the gastrointestinal side effects of radiation and chemotherapy and inflammation caused by tumors can also lead to a decrease in appetite and inadequate food intake. A decrease in weight/BMI is caused by malignant tumor consumption in the body and insufficient food intake in the body, which are related to the rate of tumor progression, and often, a significant short-term decrease in weight/BMI predicts a poor clinical outcome. (2) Tools for different populations, such as the GNRI and MNA, are malnutrition diagnostic tools for elderly individuals. In addition to age as an important factor, the MNA takes into account the mental and psychological status and the activities and physical function of the nutritional status of elderly individuals, but in elderly patients with malignant tumors, other nontumor diseases, such as cardio-cerebrovascular disease and diabetes, has a greater impact on survival. (3) Differences in the sensitivity of tools to diagnose malnutrition in different tumors, for example, digestive malignancies and changes in eating status, and tools covering eating status, such as the MNA, NRS 2002 and GLIM, are more likely to identify malnourished patients, whereas eating status can clearly affect the survival of patients with tumors.
Malnutrition diagnosed with four tools, PNI, BMI < 18.5 kg/m2, GNRI, and CONUT, is associated with nononcologic disease adverse clinical outcomes, and the tools are highly recommended. The five tools, PNI, GNRI, CONUT, MNA-SF, and NRS 2002-diagnosed malnutrition associated with nononcologic disease adverse clinical outcomes are recommended, and the reasons for this are as follows: (1) The impact of the disease site on tool evaluation and adverse clinical outcomes of nononcologic diseases mainly include mortality from coronary heart disease, mortality from myocardial infarction, mortality from heart failure, mortality from patients after TAVI, adverse cardiovascular events, mortality from patients on hemodialysis, mortality from patients after hip fracture surgery, PO-AKI, postoperative length of stay and complications. For example, tools that include activity and physical function (e.g., MNA-SF) are more likely to identify malnourished patients with hip fracture, whereas in most patients with cardiac-related diseases, changes in cardiac function leading to fluid retention and increased vascular permeability can lead to a decrease in plasma albumin, which is more pronounced if there is also a lack of hepatic synthesis, so plasma albumin is important as an indicator to evaluate the nutritional status (e.g., PNI, GNRI, CONUT, etc.). (2) The relationships between the indicators of adverse clinical outcomes and nutritional status varies; for nononcologic diseases, malnutrition status tends to be closely related to the indicators of infectious complications and length of hospitalization, whereas there is no significant relationship with noninfectious complications (145). In the present study, malnutrition in the NRS 2002 diagnosis and treatment and length of hospitalization of patients undergoing abdominal surgery were closely related. However, the adverse clinical outcomes of nononcologic diseases were mainly mortality from cardiovascular diseases, which are closely related to adverse cardiovascular events. Although the application of these tools (PNI, BMI < 18.5 kg/m2, GNRI, CONUT) can predict adverse clinical outcomes in patients with cardiovascular diseases, it should not be assumed that these adverse clinical outcomes are closely related to malnutrition.
According to the principles of the WHO’s disease screening, the main criterion for validating any screening tool or diagnostic method is that the available treatment will improve the clinical outcome in a group of patients who test positive for that screening (146). As a malnutrition diagnostic tool, we can consider a malnutrition diagnostic tool valid if it can diagnose the malnutrition status of patients in a timely manner, advocate for adequate nutritional support, and improve the clinical outcome of patients.
This study involves 13 diagnostic tools for malnutrition, the results of which include clinical manifestations, signs, body fluids and blood. Some clinical manifestations, such as digestive symptoms, eating status, activity and physical functioning, the disease itself, and changes in weight/BMI, are often the reasons for a patient’s visit to the clinic, and administering the diagnostic tool at the time of the patient’s hospital admission offers the early opportunity for clinicians to detect the beginnings of malnutrition. Malnutrition diagnostic tools (GLIM, NRS 2002, MNA, SGA, MNA-SF, and BMI) that use clinical signs as indicators may offer a better chance of detecting a patient’s malnutrition status. Fluid and blood indicators are used in other malnutrition diagnostic tools, such as NPS, mGPS, GPS, CONUT, and PNI. Changes in fluid and blood indicators tend to lag behind changes in clinical indicators, and thus, these diagnostic tools tend to lag behind in identifying malnutrition status, although they may be more accurate. With the delay, the prognosis of improving clinical outcomes is poor, even with adequate nutritional support. The clinical outcome of such patients is also poor. Moreover, changes in body fluid and blood indices are strongly influenced by the state of the body; for example, when acute infection occurs, the serum albumin and lymphocyte counts are significantly reduced, and the CRP level is significantly increased. When the infection is controlled, these indices return to normal in a short period of time, and evaluating nutritional status in such a state is inappropriate. Therefore, when selecting body fluid and blood indicators as diagnostic markers for malnutrition, it is necessary to exclude disease states such as acute infection and septic shock, while also requiring repeated monitoring. Instead, evaluation tools that combine clinical manifestations and signs with blood indicators (e.g., GLIM) are recommended due to their ability to balance early detection and diagnostic sensitivity. In this study, only evidence for a recommendation of GLIM was found in relation to the overall survival of cancer patients, and considering that GLIM was released in 2019, most recently among the 13 tools, there is a lack of adequate research for further validation.
The strengths of this study are as follows: (1) This is the first umbrella review to comprehensively explore the correlation between malnutrition diagnostic tools and adverse clinical outcomes. (2) Thirteen malnutrition diagnostic tools currently in use internationally were analyzed, and malnutrition diagnostic tools with a high level of evidence were recommended for use by clinical experts. The limitations of this study include the following: (1) The early or late development of tools has a great impact on the amount of evidence, resulting in the lack of sufficient high-quality evidence for some malnutrition diagnostic tools of recent years, such as the GLIM and the NPS, which are newer tools released in 2019 and 2017, respectively. The lack of sufficient evidence for these tools does not mean the tools themselves are ineffective. (2) Owing to the ethical considerations in clinical research, most of the clinical studies in recent years were unable to use blank control; therefore, the results from the meta-analysis included in this study could not completely exclude the effect of nutritional support on clinical outcomes. (3) Some of the tools were designed for different target populations, such as the MNA and GNRI, which were designed specifically for elderly individuals, and the NPS was designed for oncology patients, which resulted in the validation of the tools only in the target populations and could not be involved in the comparison of validity in other population.
Recommendations for diagnostic tools for malnutrition (Figures 3, 4): (1) In oncology and surgical patients, malnutrition diagnosed by the PNI, GNRI, and CONUT is strongly associated with survival (Class II) and is highly recommended (20, 88, 89, 92–98, 102, 103, 106, 107); malnutrition diagnosed by the GLIM, NRS 2002, NPS, mGPS, and MNA is associated with survival (Class III) and recommended (109, 111–116). (2) In nontumor patients, malnutrition diagnosed by the PNI, BMI < 18.5 kg/m2, GNRI, and CONUT is strongly associated with poor clinical outcomes in nontumor disease (Class II) and highly recommended (90, 91, 99, 101, 104, 105); malnutrition diagnosed by the PNI, GNRI, CONUT, MNA-SF, and NRS-2002 is associated with poor clinical outcomes in nontumor disease (Class III) and recommended (38, 100, 105, 108, 121, 122, 124, 127, 133, 147). (3) Under resource-constrained conditions, BMI/GNRI may be considered for assessment.
Figure 3. The forest plot shows the results of highly suggestive evidence from an umbrella review of nutritional screening and assessment tools and clinical outcomes. Data are expressed as hazard/risk/odds ratios and 95% confidence intervals. CAD, coronary artery disease; CRC, colorectal cancer; CSS, cancer-specific survival; DFS, disease free survival; EC, esophageal cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; HF, heart failure; HMs, hematologic malignancies; MACEs, major adverse cardiovascular events; NSCLC, non-small cell lung cancer; OC, ovarian cancer; OS, overall survival; PCa, prostate cancer; PFS, progression-free survival; PO-AKI, postoperative acute kidney injury; RCC, renal cell carcinoma; TAVI, patients who underwent transcatheter aortic valve implantation; UTUC, upper tract urothelial carcinoma.
Figure 4. The forest plot shows the results of the suggestive evidence obtained from a comprehensive evaluation of nutritional screening assessment tools and clinical outcomes. Data are expressed as hazard/risk/odds ratios and 95% confidence intervals. BC, breast cancer; BTC, biliary tract carcinoma; ACS, acute coronary syndrome; CAD, coronary artery disease; CRC, colorectal cancer; CSS, cancer-specific survival; DFS, disease free survival; DLBCL, diffuse large B-cell lymphoma; EC, esophageal cancer; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; GI, gastrointestinal cancers; HF, heart failure; HMs, hematologic malignancies; HPB, hepatopancreatobiliary; ICIs, patients treated with immune checkpoint inhibitors; ICUS, intensive care unit stay; LC, lung cancer; MACEs, major adverse cardiovascular events; NSCLC, non-small cell lung cancer; OC, ovarian cancer; OS, overall survival; PC, pancreatic cancer; PFS, progression-free survival; PO-AKI, postoperative acute kidney injury; POCs, postoperative complications; RCC, renal cell carcinoma; RFS, relapse-free survival; SCLC, small cell lung cancer; TAVI, patients who underwent transcatheter aortic valve implantation; UCs, urological cancers.
5 Conclusion
For oncology patients, malnutrition diagnosed by eight tools was associated with oncological survival, with three evidence scores of the PNI, GNRI, and CONUT being highly recommended (Class II), followed by five evidence scores of the GLIM, NRS 2002, NPS, mGPS, and MNA being recommended (Class III). For nontumor patients, malnutrition diagnosed by nine tools was associated with adverse clinical outcomes, with four tools (PNI, BMI < 18.5 kg/m2, GNRI, and CONUT) providing evidence of highly recommended (Class II), and five tools (PNI, GNRI, CONUT, MNA-SF, and NRS 2002) providing evidence of recommended (Class III). These tools are developed based on clinical presentation and signs of fluid and blood indicators, and screening for nutritional risk or risk of malnutrition, followed by malnutrition diagnosis, needs to be considered before selecting the tool, considering the predictability and accuracy of the tool, such as the GLIM. How to select a reasonable malnutrition diagnostic tool still requires relevant and high-quality evidence in a broader disease population to provide clinical experts with the ability to select a malnutrition diagnostic tool to provide favorable evidence.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
Author contributions
ZL: Writing – review & editing, Data curation, Writing – original draft. YL: Writing – review & editing, Funding acquisition. YS: Supervision, Writing – original draft, Funding acquisition, Data curation. TY: Writing – review & editing, Visualization. LA: Visualization, Writing – review & editing. YQ: Writing – review & editing, Software. PZ: Writing – review & editing. XH: Writing – review & editing. XS: Writing – review & editing. YD: Writing – review & editing. JH: Writing – review & editing. GL: Writing – review & editing, Writing – original draft, Data curation. DS: Data curation, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the National Natural Science Foundation of China (NSFC) (No. 82460115) to DS, the Yunnan Province Joint Special Project of Science & Technology Department of Yunnan Province and Kunming Medical University (Nos. 202401AY070001-076, 202301AY070001-274, and 202301AY070001-297) to YL, TY, DS, the Scientific Research Fund Project of Yunnan Provincial Department of Education (No. 2024J0347) to YS and the Yunnan young academic and technical leaders reserve talent project (No. 202105AC160049) to DS.
Acknowledgments
Thank you to Ji Gang’s team from the Department of Gastroenterology at the Air Force Medical University Affiliated Hospital for their assistance in the research design and paper writing process.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2025.1676201/full#supplementary-material
References
1. Isautier, JMJ, Bosnić, M, Yeung, SSY, Trappenburg, MC, Meskers, CGM, Whittaker, AC, et al. Validity of nutritional screening tools for community-dwelling older adults: a systematic review and meta-analysis. J Am Med Dir Assoc. (2019) 20:1351–e13-.e25. doi: 10.1016/j.jamda.2019.06.024
2. Maia, I, Peleteiro, B, Xará, S, and Amaral, TF. Undernutrition risk and undernutrition in pulmonology department inpatients: a systematic review and meta-analysis. J Am Coll Nutr. (2017) 36:137–47. doi: 10.1080/07315724.2016.1209728
3. Xu, J, Jie, Y, Sun, Y, Gong, D, and Fan, Y. Association of global leadership initiative on malnutrition with survival outcomes in patients with cancer: a systematic review and meta-analysis. Clin Nutr. (2022) 41:1874–80. doi: 10.1016/j.clnu.2022.07.007
4. Hu, X, Wang, Y, Yang, WX, Dou, WC, Shao, YX, and Li, X. Modified Glasgow prognostic score as a prognostic factor for renal cell carcinomas: a systematic review and meta-analysis. Cancer Manag Res. (2019) 11:6163–73. doi: 10.2147/CMAR.S208839
5. Niu, X, Zhu, Z, and Bao, J. Prognostic significance of pretreatment controlling nutritional status score in urological cancers: a systematic review and meta-analysis. Cancer Cell Int. (2021) 21:126. doi: 10.1186/s12935-021-01813-2
6. Liu, Z, Zhou, H, Zhou, Y, Yu, M, Cheng, Y, and 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. doi: 10.3389/fonc.2023.1240008
7. Chen, J, Cao, D, Peng, Z, Song, P, Liu, Z, Yang, L, et al. The prognostic value of the controlling nutritional status score on patients undergoing nephrectomy for upper tract urothelial carcinoma or renal cell carcinoma: a systematic review and meta-analysis. Br J Nutr. (2022) 128:217–24. doi: 10.1017/S0007114521002889
8. Peng, L, Meng, C, Li, J, You, C, Du, Y, Xiong, W, et al. The prognostic significance of controlling nutritional status (CONUT) score for surgically treated renal cell cancer and upper urinary tract urothelial cancer: a systematic review and meta-analysis. Eur J Clin Nutr. (2022) 76:801–10. doi: 10.1038/s41430-021-01014-0
9. Zhang, C, Li, XK, Cong, ZZ, Zheng, C, Luo, C, Xie, K, et al. Controlling nutritional status is a prognostic factor for patients with lung cancer: a systematic review and meta-analysis. Ann Palliat Med. (2021) 10:3896–905. doi: 10.21037/apm-20-2328
10. Lv, J, Chen, P, Wu, J, and Hu, C. Prognostic value of pretreatment controlling nutritional status score in esophageal cancer: a meta-analysis. Pathol Oncol Res. (2023) 29:1611221. doi: 10.3389/pore.2023.1611221
11. Niu, Z, and Yan, B. Prognostic and clinicopathological impacts of controlling nutritional status (CONUT) score on patients with gynecological cancer: a meta-analysis. Nutr J. (2023) 22:33. doi: 10.1186/s12937-023-00863-8
12. Lu, C, Chen, Q, Fei, L, Wang, J, Wang, C, and Yu, L. Prognostic impact of the controlling nutritional status score in patients with hematologic malignancies: a systematic review and meta-analysis. Front Immunol. (2022) 13:952802. doi: 10.3389/fimmu.2022.952802
13. Yiu, CY, Liu, CC, Wu, JY, Tsai, WW, Liu, PH, Cheng, WJ, et al. Efficacy of the geriatric nutritional risk index for predicting overall survival in patients with head and neck cancer: a meta-analysis. Nutrients. (2023) 15:4348. doi: 10.3390/nu15204348
14. Mao, Y, and Lan, J. Prognostic value of the geriatric nutritional index in colorectal cancer patients undergoing surgical intervention: a systematic review and meta-analysis. Front Oncol. (2022) 12:1066417. doi: 10.3389/fonc.2022.1066417
15. Yuan, F, Yuan, Q, Hu, J, and An, J. Prognostic role of pretreatment geriatric nutritional risk index in colorectal cancer patients: a meta-analysis. Nutr Cancer. (2023) 75:276–85. doi: 10.1080/01635581.2022.2109692
16. Xu, J, Sun, Y, Gong, D, and Fan, Y. Predictive value of geriatric nutritional risk index in patients with colorectal cancer: a meta-analysis. Nutr Cancer. (2023) 75:24–32. doi: 10.1080/01635581.2022.2115521
17. Zhao, H, Xu, L, Tang, P, and Guo, R. Geriatric nutritional risk index and survival of patients with colorectal cancer: a meta-analysis. Front Oncol. (2022) 12:906711. doi: 10.3389/fonc.2022.906711
18. Fan, Y, He, L, Zhou, Y, and Man, C. Predictive value of geriatric nutritional risk index in patients with coronary artery disease: a meta-analysis. Front Nutr. (2021) 8:736884. doi: 10.3389/fnut.2021.736884
19. Yu, J, Zhang, W, Wang, C, and Hu, Y. The prognostic value of pretreatment geriatric nutritional risk index in esophageal cancer: a meta-analysis. Nutr Cancer. (2022) 74:3202–10. doi: 10.1080/01635581.2022.2069273
20. Lu, W, Shen, J, Zou, D, Li, P, Liu, X, and Jian, Y. Predictive role of preoperative geriatric nutritional risk index for clinical outcomes in surgical gastric cancer patients: a meta-analysis. Front Surg. (2022) 9:1020482. doi: 10.3389/fsurg.2022.1020482
21. Jiang, AM, Zhao, R, Liu, N, Ma, YY, Ren, MD, Tian, T, et al. The prognostic value of pretreatment prognostic nutritional index in patients with small cell lung cancer and it’s influencing factors: a meta-analysis of observational studies. J Thorac Dis. (2020) 12:5718–28. doi: 10.21037/jtd-20-1739
22. Hu, Y, Shen, J, Liu, R, Feng, Z, Zhang, C, Ling, L, et al. Prognostic value of pretreatment prognostic nutritional index in non-small cell lung cancer: a systematic review and meta-analysis. Int J Biol Markers. (2018) 33:372–8. doi: 10.1177/1724600818799876
23. Chen, MY, Wen, JX, Lu, MT, Jian, XY, Wan, XL, Xu, ZW, et al. Association between prognostic nutritional index and prognosis in patients with heart failure: a meta-analysis. Front Cardiovasc Med. (2022) 9:918566. doi: 10.3389/fcvm.2022.918566
24. Luan, CW, Tsai, YT, Yang, HY, Chen, KY, Chen, PH, and Chou, HH. Pretreatment prognostic nutritional index as a prognostic marker in head and neck cancer: a systematic review and meta-analysis. Sci Rep. (2021) 11:17117. doi: 10.1038/s41598-021-96598-9
25. Liao, G, Zhao, Z, Yang, H, Chen, M, and Li, X. Can prognostic nutritional index be a prediction factor in esophageal cancer?: a meta-analysis. Nutr Cancer. (2020) 72:187–93. doi: 10.1080/01635581.2019.1631859
26. Mao, C, Xu, W, Ma, W, Wang, C, Guo, Z, and Yan, J. Prognostic value of pretreatment prognostic nutritional index in patients with renal cell carcinoma: a meta-analysis. Front Oncol. (2021) 11:719941. doi: 10.3389/fonc.2021.719941
27. Tang, M, Jia, Z, and Zhang, J. The prognostic role of prognostic nutritional index in nasopharyngeal carcinoma: a systematic review and meta-analysis. Int J Clin Oncol. (2021) 26:66–77. doi: 10.1007/s10147-020-01791-x
28. Gao, QL, Shi, JG, and Huang, YD. Prognostic significance of pretreatment prognostic nutritional index (PNI) in patients with nasopharyngeal carcinoma: a meta-analysis. Nutr Cancer. (2021) 73:1657–67. doi: 10.1080/01635581.2020.1810715
29. Sun, K, Chen, S, Xu, J, Li, G, and He, Y. The prognostic significance of the prognostic nutritional index in cancer: a systematic review and meta-analysis. J Cancer Res Clin Oncol. (2014) 140:1537–49. doi: 10.1007/s00432-014-1714-3
30. Bullock, AF, Greenley, SL, McKenzie, GAG, Paton, LW, and Johnson, MJ. Relationship between markers of malnutrition and clinical outcomes in older adults with cancer: systematic review, narrative synthesis and meta-analysis. Eur J Clin Nutr. (2020) 74:1519–35. doi: 10.1038/s41430-020-0629-0
31. Zhao, P, Wu, Z, Wang, Z, Wu, C, Huang, X, and Tian, B. Prognostic role of the prognostic nutritional index in patients with pancreatic cancer who underwent curative resection without preoperative neoadjuvant treatment: a systematic review and meta-analysis. Front Surg. (2022) 9:992641. doi: 10.3389/fsurg.2022.992641
32. Ni, L, Huang, J, Ding, J, Kou, J, Shao, T, Li, J, et al. Prognostic nutritional index predicts response and prognosis in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis. Front Nutr. (2022) 9:823087. doi: 10.3389/fnut.2022.823087
33. Yan, X, Wang, J, Mao, J, Wang, Y, Wang, X, Yang, M, et al. Identification of prognostic nutritional index as a reliable prognostic indicator for advanced lung cancer patients receiving immune checkpoint inhibitors. Front Nutr. (2023) 10:1213255. doi: 10.3389/fnut.2023.1213255
34. Fernández-Jiménez, R, Martín-Masot, R, Cornejo-Pareja, I, Vegas-Aguilar, IM, Herrador-López, M, Tinahones, FJ, et al. Phase angle as a marker of outcome in hospitalized pediatric patients. A systematic review of the evidence (GRADE) with meta-analysis. Rev Endocr Metab Disord. (2023) 24:751–65. doi: 10.1007/s11154-023-09817-1
35. Cao, C, Wang, R, Wang, J, Bunjhoo, H, Xu, Y, and Xiong, W. Body mass index and mortality in chronic obstructive pulmonary disease: a meta-analysis. PLoS One. (2012) 7:e43892. doi: 10.1371/journal.pone.0043892
36. He, X, Ji, J, Liu, C, Luo, Z, Tang, J, Yan, H, et al. Body mass index and weight loss as risk factors for poor outcomes in patients with idiopathic pulmonary fibrosis: a systematic review and meta-analysis. Ann Med. (2024) 56:2311845. doi: 10.1080/07853890.2024.2311845
37. Yin, L, Chong, F, Huo, Z, Li, N, Liu, J, and Xu, H. GLIM-defined malnutrition and overall survival in cancer patients: a meta-analysis. JPEN J Parenter Enteral Nutr. (2023) 47:207–19. doi: 10.1002/jpen.2463
38. Sun, Z, Kong, XJ, Jing, X, Deng, RJ, and Tian, ZB. Nutritional risk screening 2002 as a predictor of postoperative outcomes in patients undergoing abdominal surgery: a systematic review and meta-analysis of prospective cohort studies. PLoS One. (2015) 10:e0132857. doi: 10.1371/journal.pone.0132857
39. Nie, D, Zhang, L, Wang, C, Guo, Q, and Mao, X. A high Glasgow prognostic score (GPS) or modified Glasgow prognostic score (mGPS) predicts poor prognosis in gynecologic cancers: a systematic review and meta-analysis. Arch Gynecol Obstet. (2020) 301:1543–51. doi: 10.1007/s00404-020-05581-8
40. Takagi, K, Domagala, P, Polak, WG, Buettner, S, Wijnhoven, BPL, and Ijzermans, JNM. Prognostic significance of the controlling nutritional status (CONUT) score in patients undergoing gastrectomy for gastric cancer: a systematic review and meta-analysis. BMC Surg. (2019) 19:129. doi: 10.1186/s12893-019-0593-6
41. Shao, J, Li, J, Zhang, XL, and Wang, G. Prognostic significance of the preoperative controlled nutritional status score in lung cancer patients undergoing surgical resection. Nutr Cancer. (2021) 73:2211–8. doi: 10.1080/01635581.2020.1850814
42. Takagi, K, Buettner, S, and 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. (2020) 78:91–6. doi: 10.1016/j.ijsu.2020.04.046
43. Takagi, K, Buettner, S, Ijzermans, JNM, and Wijnhoven, BPL. Systematic review on the controlling nutritional status (CONUT) score in patients undergoing esophagectomy for esophageal cancer. Anticancer Res. (2020) 40:5343–9. doi: 10.21873/anticanres.14541
44. Peng, J, Hao, Y, Rao, B, and Cao, Y. Prognostic impact of the pre-treatment controlling nutritional status score in patients with non-small cell lung cancer: a meta-analysis. Medicine (Baltimore). (2021) 100:e26488. doi: 10.1097/MD.0000000000026488
45. Li, H, Zhou, P, Zhao, Y, Ni, H, Luo, X, and Li, J. Prediction of all-cause mortality with malnutrition assessed by controlling nutritional status score in patients with heart failure: a systematic review and meta-analysis. Public Health Nutr. (2022) 25:1799–806. doi: 10.1017/S1368980021002470
46. Peng, J, Li, X, Huang, M, Ma, M, Huang, Q, Huang, N, et al. Prognostic value of prognostic nutritional index score and controlling nutritional status score in patients with glioblastoma: a comprehensive meta-analysis. Front Oncol. (2023) 13:1117764. doi: 10.3389/fonc.2023.1117764
47. Zhang, Y, and Zhang, X. Controlling nutritional status score, a promising prognostic marker in patients with gastrointestinal cancers after surgery: a systematic review and meta-analysis. Int J Surg. (2018) 55:39–45. doi: 10.1016/j.ijsu.2018.05.018
48. Jiao, H, Wang, L, Zhou, X, Wu, J, and Li, T. Prognostic ability of nutritional indices for outcomes of bladder cancer: a systematic review and meta-analysis. Urol Int. (2023) 107:886–94. doi: 10.1159/000531884
49. Cao, D, and Zhang, Z. Prognostic and clinicopathological role of geriatric nutritional risk index in patients with diffuse large B-cell lymphoma: a meta-analysis. Front Oncol. (2023) 13:1169749. doi: 10.3389/fonc.2023.1169749
50. Shen, F, Ma, Y, Guo, W, and Li, F. Prognostic value of geriatric nutritional risk index for patients with non-small cell lung cancer: a systematic review and meta-analysis. Lung. (2022) 200:661–9. doi: 10.1007/s00408-022-00567-6
51. Yang, M, Liu, Z, Li, G, Li, B, Li, C, Xiao, L, et al. Geriatric nutritional risk index as a prognostic factor of patients with non-small cell lung cancer: a meta-analysis. Horm Metab Res. (2022) 54:604–12. doi: 10.1055/a-1903-1943
52. Zhang, Q, Zhang, L, Jin, Q, He, Y, Wu, M, Peng, H, et al. The prognostic value of the GNRI in patients with stomach cancer undergoing surgery. J Pers Med. (2023) 13:155. doi: 10.3390/jpm13010155
53. Wang, H, Li, C, Yang, R, Jin, J, Liu, D, and Li, W. Prognostic value of the geriatric nutritional risk index in non-small cell lung cancer patients: a systematic review and meta-analysis. Front Oncol. (2021) 11:794862. doi: 10.3389/fonc.2021.794862
54. Wang, Y, Luo, L, Li, J, Wang, Y, Che, G, and Xie, X. Prognostic value of pretreatment geriatric nutrition risk index in lung cancer patients: a meta-analysis. Nutr Cancer. (2022) 74:3164–71. doi: 10.1080/01635581.2022.2059093
55. Xie, H, Tang, S, Wei, L, and 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. doi: 10.1186/s12935-020-01628-7
56. Liu, G, Zou, C, Jie, Y, Wang, P, Wang, X, and Fan, Y. Predictive value of geriatric nutritional risk index in patients with lower extremity peripheral artery disease: a meta-analysis. Front Nutr. (2022) 9:903293. doi: 10.3389/fnut.2022.903293
57. Zhou, J, Fang, P, Li, X, Luan, S, Xiao, X, Gu, Y, et al. Prognostic value of geriatric nutritional risk index in esophageal carcinoma: a systematic review and meta-analysis. Front Nutr. (2022) 9:831283. doi: 10.3389/fnut.2022.831283
58. Yang, Q, Shen, A, Chen, X, Guo, L, Peng, H, and Gao, M. Clinical significance of nutrition and inflammation in esophageal cancer patients with surgery: a meta-analysis. Nutr Cancer. (2022) 74:3128–39. doi: 10.1080/01635581.2022.2056620
59. Fan, H, Ma, W, Fu, Y, Yi, T, and Tian, J. Association of geriatric nutritional risk index with survival outcomes in patients with esophageal squamous cell carcinoma: a meta-analysis. Nutr Cancer. (2022) 74:2796–802. doi: 10.1080/01635581.2022.2028865
60. Luan, C, Wang, F, Wei, N, and Chen, B. Prognostic nutritional index and the prognosis of diffuse large b-cell lymphoma: a meta-analysis. Cancer Cell Int. (2020) 20:455. doi: 10.1186/s12935-020-01535-x
61. Ren, W, Wang, H, Xiang, T, and Liu, G. Prognostic role of preoperative Onodera’s prognostic nutritional index (OPNI) in gastrointestinal stromal tumors: a systematic review and meta-analysis. J Gastrointest Cancer. (2023) 54:731–8. doi: 10.1007/s12029-022-00878-0
62. Kang, N, Gu, H, Ni, Y, Wei, X, and Zheng, S. Prognostic and clinicopathological significance of the prognostic nutritional index in patients with gastrointestinal stromal tumours undergoing surgery: a meta-analysis. BMJ Open. (2022) 12:e064577. doi: 10.1136/bmjopen-2022-064577
63. Li, Z, Zhang, D, Mo, C, Zhu, P, Fan, X, and Tang, T. The prognostic significance of prognostic nutritional index in gastrointestinal stromal tumors: a systematic review and meta-analysis. Medicine (Baltimore). (2022) 101:e32067. doi: 10.1097/MD.0000000000032067
64. Hung, KC, Ko, CC, Wang, LK, Liu, PH, Chen, IW, Huang, YT, et al. Association of prognostic nutritional index with severity and mortality of hospitalized patients with COVID-19: a systematic review and meta-analysis. Diagnostics (Basel). (2022) 12:1515. doi: 10.3390/diagnostics12071515
65. Yang, Y, Gao, P, Song, Y, Sun, J, Chen, X, Zhao, J, et al. The prognostic nutritional index is a predictive indicator of prognosis and postoperative complications in gastric cancer: a meta-analysis. Eur J Surg Oncol. (2016) 42:1176–82. doi: 10.1016/j.ejso.2016.05.029
66. Lv, X, Zhang, Z, and Yuan, W. Pretreatment prognostic nutritional index (PNI) as a prognostic factor in patients with biliary tract cancer: a meta-analysis. Nutr Cancer. (2021) 73:1872–81. doi: 10.1080/01635581.2020.1817955
67. Qi, F, Zhou, X, Wang, Y, Wang, Y, Wang, Y, Zhang, Q, et al. Pre-treatment prognostic nutritional index may serve as a potential biomarker in urinary cancers: a systematic review and meta-analysis. Cancer Cell Int. (2018) 18:207. doi: 10.1186/s12935-018-0708-7
68. Li, S, Tian, G, Chen, Z, Zhuang, Y, and Li, G. Prognostic role of the prognostic nutritional index in pancreatic cancer: a meta-analysis. Nutr Cancer. (2019) 71:207–13. doi: 10.1080/01635581.2018.1559930
69. Shi, Y, Zhang, Y, Niu, Y, Chen, Y, and Kou, C. Prognostic role of the prognostic nutritional index (PNI) in patients with head and neck neoplasms undergoing radiotherapy: a meta-analysis. PLoS One. (2021) 16:e0257425. doi: 10.1371/journal.pone.0257425
70. Meng, C, Gan, L, Li, K, Yi, F, Peng, L, Li, J, et al. Prognostic nutritional index before surgical treatment may serve as a prognostic biomarker for patients with upper tract urothelial carcinoma: a systematic review and meta-analysis. Front Nutr. (2022) 9:972034. doi: 10.3389/fnut.2022.972034
71. Fan, X, Chen, G, Li, Y, Shi, Z, He, L, Zhou, D, et al. The preoperative prognostic nutritional index in hepatocellular carcinoma after curative hepatectomy: a retrospective cohort study and meta-analysis. J Investig Surg. (2021) 34:826–33. doi: 10.1080/08941939.2019.1698679
72. Kim, SI, Kim, SJ, Kim, SJ, and Cho, DS. Prognostic nutritional index and prognosis in renal cell carcinoma: a systematic review and meta-analysis. Urol Oncol. (2021) 39:623–30. doi: 10.1016/j.urolonc.2021.05.028
73. Tu, X, Ren, J, and Zhao, Y. Prognostic value of prognostic nutritional index in nasopharyngeal carcinoma: a meta-analysis containing 4511 patients. Oral Oncol. (2020) 110:104991. doi: 10.1016/j.oraloncology.2020.104991
74. Guo, H, Yang, L, Liu, J, Yu, X, Chen, L, and Huang, Y. Prognostic nutritional index and the risk of postoperative complications after spine surgery: a meta-analysis. World Neurosurg. (2024) 185:e572–81. doi: 10.1016/j.wneu.2024.02.077
75. Li, P, Wang, X, Lai, Y, Zhou, K, Tang, Y, and Che, G. The prognostic value of pre-treatment prognostic nutritional index in esophageal squamous cell carcinoma: a meta-analysis. Medicine (Baltimore). (2019) 98:e15280. doi: 10.1097/MD.0000000000015280
76. Zhang, L, Ma, W, Qiu, Z, Kuang, T, Wang, K, Hu, B, et al. Prognostic nutritional index as a prognostic biomarker for gastrointestinal cancer patients treated with immune checkpoint inhibitors. Front Immunol. (2023) 14:1219929. doi: 10.3389/fimmu.2023.1219929
77. Zheng, WH, Zhao, YH, Yao, Y, and Huang, HB. Prognostic role of bioelectrical impedance phase angle for critically ill patients: a systemic review and meta-analysis. Front Med (Lausanne). (2022) 9:1059747. doi: 10.3389/fmed.2022.1059747
78. Arab, A, Karimi, E, Vingrys, K, and Shirani, F. Is phase angle a valuable prognostic tool in cancer patients’ survival? A systematic review and meta-analysis of available literature. Clin Nutr. (2021) 40:3182–90. doi: 10.1016/j.clnu.2021.01.027
79. Ahmadi, SF, Zahmatkesh, G, Streja, E, Mehrotra, R, Rhee, CM, Kovesdy, CP, et al. Association of body mass index with mortality in peritoneal dialysis patients: a systematic review and meta-analysis. Perit Dial Int. (2016) 36:315–25. doi: 10.3747/pdi.2015.00052
80. Chinn, S. A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat Med. (2000) 19:3127–31. doi: 10.1002/1097-0258(20001130)19:22<3127::AID-SIM784>3.0.CO;2-M
81. Lau, J, Ioannidis, JP, and Schmid, CH. Quantitative synthesis in systematic reviews. Ann Intern Med. (1997) 127:820–6. doi: 10.7326/0003-4819-127-9-199711010-00008
82. Riley, RD, Higgins, JP, and Deeks, JJ. Interpretation of random effects meta-analyses. BMJ. (2011) 342:d549. doi: 10.1136/bmj.d549
83. Higgins, JP, and Thompson, SG. Quantifying heterogeneity in a meta-analysis. Stat Med. (2002) 21:1539–58. doi: 10.1002/sim.1186
84. Fangyuan, Z, Aomei, S, Xiantao, Z, Wanmin, Q, and Yinghui, J. An introduction to AMSTAR 2: a critical appraisal tool for systematic reviews. Chin J Evid Based Cardiovasc Med. (2018) 10:14–8. doi: 10.3969/j.issn.1674-4055.2018.01.03
85. Sterne, JA, Sutton, AJ, Ioannidis, JP, Terrin, N, Jones, DR, Lau, J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. (2011) 343:d4002. doi: 10.1136/bmj.d4002
86. Stanley, TD, Doucouliagos, H, Ioannidis, JPA, and Carter, EC. Detecting publication selection bias through excess statistical significance. Res Synth Methods. (2021) 12:776–95. doi: 10.1002/jrsm.1512
87. Sun, H, Gong, TT, Xia, Y, Wen, ZY, Zhao, LG, Zhao, YH, et al. Diet and ovarian cancer risk: an umbrella review of systematic reviews and meta-analyses of cohort studies. Clin Nutr. (2021) 40:1682–90. doi: 10.1016/j.clnu.2020.11.032
88. Wang, Z, Wang, Y, Zhang, X, and Zhang, T. Pretreatment prognostic nutritional index as a prognostic factor in lung cancer: review and meta-analysis. Clin Chim Acta. (2018) 486:303–10. doi: 10.1016/j.cca.2018.08.030
89. Sun, G, Li, Y, Peng, Y, Lu, D, Zhang, F, Cui, X, et al. Impact of the preoperative prognostic nutritional index on postoperative and survival outcomes in colorectal cancer patients who underwent primary tumor resection: a systematic review and meta-analysis. Int J Color Dis. (2019) 34:681–9. doi: 10.1007/s00384-019-03241-1
90. Hu, Y, Yang, H, Zhou, Y, Liu, X, Zou, C, Ji, S, et al. Prediction of all-cause mortality with malnutrition assessed by nutritional screening and assessment tools in patients with heart failure: a systematic review. Nutr Metab Cardiovasc Dis. (2022) 32:1361–74. doi: 10.1016/j.numecd.2022.03.009
91. Ni, J, Fang, Y, Zhang, J, and Chen, X. Predicting prognosis of heart failure using common malnutrition assessment tools: a systematic review and meta-analysis. Scott Med J. (2022) 67:157–70. doi: 10.1177/00369330221122300
92. Tan, X, and Chen, H. The prognostic value of prognostic nutritional index in patients with ovarian cancer: a systematic review and meta-analysis. Nutr Cancer. (2023) 75:73–81. doi: 10.1080/01635581.2022.2104879
93. Man, Z, Pang, Q, Zhou, L, Wang, Y, Hu, X, Yang, S, et al. Prognostic significance of preoperative prognostic nutritional index in hepatocellular carcinoma: a meta-analysis. HPB (Oxford). (2018) 20:888–95. doi: 10.1016/j.hpb.2018.03.019
94. Xue, Y, Zhou, X, Xue, L, Zhou, R, and Luo, J. The role of pretreatment prognostic nutritional index in esophageal cancer: a meta-analysis. J Cell Physiol. (2019) 234:19655–62. doi: 10.1002/jcp.28565
95. Xiong, SC, Hu, X, Lia, T, Wang, YH, and Li, X. Prognostic significance of prognostic nutritional index in patients with renal cell carcinoma: a meta-analysis. Nutr Cancer. (2022) 74:860–8. doi: 10.1080/01635581.2021.1931702
96. Xue, S, Zhao, H, Zhang, K, Zhang, H, and Wang, W. Prognostic and clinicopathological correlations of pretreatment prognostic nutritional index in renal cell carcinoma: a meta-analysis. Urol Int. (2022) 106:567–80. doi: 10.1159/000521353
97. Wang, X, and Wang, Y. The prognostic nutritional index is prognostic factor of gynecological cancer: a systematic review and meta-analysis. Int J Surg. (2019) 67:79–86. doi: 10.1016/j.ijsu.2019.05.018
98. Zheng, Y, Wang, K, Ou, Y, Hu, X, Wang, Z, Wang, D, et al. Prognostic value of a baseline prognostic nutritional index for patients with prostate cancer: a systematic review and meta-analysis. Prostate Cancer Prostatic Dis. (2023) 27:604–13. doi: 10.1038/s41391-023-00689-9
99. Zhang, S, Wang, H, Chen, S, Cai, S, Zhou, S, Wang, C, et al. Prognostic nutritional index and prognosis of patients with coronary artery disease: a systematic review and meta-analysis. Front Nutr. (2023) 10:1114053. doi: 10.3389/fnut.2023.1114053
100. Liu, CC, Liu, PH, Chen, HT, Chen, JY, Lee, CW, Cheng, WJ, et al. Association of preoperative prognostic nutritional index with risk of postoperative acute kidney injury: a meta-analysis of observational studies. Nutrients. (2023) 15:2929. doi: 10.3390/nu15132929
101. De Paola, L, Mehta, A, Pana, TA, Carter, B, Soiza, RL, Kafri, MW, et al. Body mass index and mortality, recurrence and readmission after myocardial infarction: systematic review and meta-analysis. J Clin Med. (2022) 11:2581. doi: 10.3390/jcm11092581
102. He, L, Li, Y, Qu, LL, and Zhang, F. Prognostic and clinicopathological value of the geriatric nutritional risk index in gastric cancer: a meta-analysis of 5,834 patients. Front Surg. (2023) 9:1087298. doi: 10.3389/fsurg.2022.1087298
103. Lv, GY, An, L, and Sun, DW. Geriatric nutritional risk index predicts adverse outcomes in human malignancy: a meta-analysis. Dis Markers. (2019) 2019:4796598. doi: 10.1155/2019/4796598
104. Yuan Na, LF, and Liu, H-B. GNRI can predict the outcomes of maintenance hemodialysis patients—a meta-analysis. Chin J Blood Purif. (2021) 20:516–20+35. doi: 10.3969/j.issn.1671-4091.2021.08.004
105. Kazemian, S, Tavolinejad, H, Rashedi, S, Yarahmadi, P, Farrokhpour, H, and Kolte, D. Meta-analysis on the association between nutritional status and outcomes after transcatheter aortic valve implantation. Am J Cardiol. (2023) 186:109–16. doi: 10.1016/j.amjcard.2022.10.016
106. Yu, Q, Tian, M, Pi, G, Jia, Y, and Jin, X. Geriatric nutritional risk index as a predictor of prognosis in hematologic malignancies: a systematic review and meta-analysis. Front Nutr. (2023) 10:1274592. doi: 10.3389/fnut.2023.1274592
107. Xue, W, Hu, X, and Zhang, Y. The Association of Controlling Nutritional Status (CONUT) score with survival in patients with surgically treated renal cell carcinoma and upper tract urothelial carcinoma: a systematic review and meta-analysis. Nutr Cancer. (2022) 74:1907–16. doi: 10.1080/01635581.2021.1974894
108. Liu, N, Lv, L, Jiao, J, Zhang, Y, and Zuo, XL. Association between nutritional indices and mortality after hip fracture: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. (2023) 27:2297–304. doi: 10.26355/eurrev_202303_31763
109. Peng, DD, Zong, KZ, Yang, H, Huang, ZT, Mou, T, Jiang, P, et al. Malnutrition diagnosed by the global leadership initiative on malnutrition criteria predicting survival and clinical outcomes of patients with cancer: a systematic review and meta-analysis. Front Nutr. (2022) 9:1053165. doi: 10.3389/fnut.2022.1053165
110. Matsui, R, Rifu, K, Watanabe, J, Inaki, N, and Fukunaga, T. Impact of malnutrition as defined by the GLIM criteria on treatment outcomes in patients with cancer: a systematic review and meta-analysis. Clin Nutr. (2023) 42:615–24. doi: 10.1016/j.clnu.2023.02.019
111. Zang, Y, Xu, W, Qiu, Y, Gong, D, and Fan, Y. Association between risk of malnutrition defined by the nutritional risk screening 2002 and postoperative complications and overall survival in patients with cancer: a meta-analysis. Nutr Cancer. (2023) 75:1600–9. doi: 10.1080/01635581.2023.2227402
112. Chen, FM, Xie, C, Ren, K, and Xu, XM. Prognostic value of the naples prognostic score in patients with gastrointestinal cancers: a meta-analysis. Nutr Cancer. (2023) 75:1520–30. doi: 10.1080/01635581.2023.2212426
113. Wang, YS, Niu, L, Shi, WX, Li, XY, and Shen, L. Naples prognostic score as a predictor of outcomes in lung cancer: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. (2023) 27:8144–53. doi: 10.26355/eurrev_202309_33574
114. Wu, D, Wang, X, Shi, G, Sun, H, and Ge, G. Prognostic and clinical significance of modified Glasgow prognostic score in pancreatic cancer: a meta-analysis of 4,629 patients. Aging (Albany NY). (2021) 13:1410–21. doi: 10.18632/aging.202357
115. Zhou, Y, Liu, Z, Cheng, Y, Li, J, and Fu, W. Prognostic value of the modified Glasgow prognostic score in biliary tract cancer patients: a systematic review and meta-analysis. J Gastrointest Surg. (2024) 28:559–65. doi: 10.1016/j.gassur.2024.01.023
116. Zhang, X, Tang, T, Pang, L, Sharma, SV, Li, R, Nyitray, AG, et al. Malnutrition and overall survival in older adults with cancer: a systematic review and meta-analysis. J Geriatr Oncol. (2019) 10:874–83. doi: 10.1016/j.jgo.2019.03.002
117. Ma, XF, Zou, WH, and Sun, Y. Prognostic value of pretreatment controlling nutritional status score for patients with pancreatic cancer: a meta-analysis. Front Oncol. (2022) 11:770894. doi: 10.3389/fonc.2021.770894
118. Liu, H, Yang, XC, Liu, DC, Tong, C, Wen, W, and Chen, RH. Clinical significance of the controlling nutritional status (CONUT) score in gastric cancer patients: a meta-analysis of 9,764 participants. Front Nutr. (2023) 10:1156006. doi: 10.3389/fnut.2023.1156006
119. Jiang, H, and Wang, Z. Prognostic role of the controlling nutritional status (CONUT) score in patients with biliary tract cancer: a meta-analysis. Ann Med. (2023) 55:2261461. doi: 10.1080/07853890.2023.2261461
120. Chen, J, Song, P, Peng, Z, Liu, Z, Yang, L, Wang, L, et al. The controlling nutritional status (CONUT) score and prognosis in malignant tumors: a systematic review and meta-analysis. Nutr Cancer. (2022) 74:3146–63. doi: 10.1080/01635581.2022.2059091
121. Osório, AF, Ribeiro, ÉCT, Parahiba, SM, Forte, GC, Clausell, NO, and Souza, GC. Prognostic value of nutritional screening tools in hospitalized patients with decompensated heart failure: a systematic review and meta-analysis. Nutr Res. (2023) 120:1–19. doi: 10.1016/j.nutres.2023.09.009
122. Takagi, K, Domagala, P, Polak, WG, Buettner, S, and Ijzermans, JNM. The controlling nutritional status score and postoperative complication risk in gastrointestinal and hepatopancreatobiliary surgical oncology: a systematic review and meta-analysis. Ann Nutr Metab. (2019) 74:303–12. doi: 10.1159/000500233
123. Li, L, and Shou, L. Prognostic and clinicopathological significance of the controlling nutritional status (CONUT) score in patients with lymphoma: a meta-analysis. BMJ Open. (2024) 14:e078320. doi: 10.1136/bmjopen-2023-078320
124. Huang, LJ, He, RL, Sun, XJ, Lv, J, and Chen, SX. Association of controlling nutritional status score with adverse outcomes in patients with coronary artery disease: a systematic review and meta-analysis. Angiology. (2023) 74:149–58. doi: 10.1177/00033197221094409
125. Yan, CK, Xie, YY, Hua, YQ, Li, SH, Fu, HX, Cheng, ZQ, et al. Prognostic value of geriatric nutritional risk index in patients with diffuse large B-cell lymphoma: a meta-analysis. Clin Transl Oncol. (2023) 26:515–23. doi: 10.1007/s12094-023-03271-w
126. Wu, Q, and Ye, FG. Prognostic impact of geriatric nutritional risk index on patients with urological cancers: a meta-analysis. Front Oncol. (2023) 12:1077792. doi: 10.3389/fonc.2022.1077792
127. Li, H, Cen, K, Sun, W, and Feng, B. Prognostic value of geriatric nutritional risk index in elderly patients with heart failure: a meta-analysis. Aging Clin Exp Res. (2021) 33:1477–86. doi: 10.1007/s40520-020-01656-3
128. Li, L, and He, J. Prognostic role of geriatric nutritional risk index in patients with pancreatic cancer: a meta-analysis. Nutr Cancer. (2023) 75:1531–40. doi: 10.1080/01635581.2023.2209345
129. Zhang, Q, Bao, J, Zhu, ZY, and Jin, MX. Prognostic nutritional index as a prognostic factor in lung cancer patients receiving chemotherapy: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. (2021) 25:5636–52. doi: 10.26355/eurrev_202109_26783
130. Liu, M, and Wang, L. Prognostic significance of preoperative serum albumin, albumin-to-globulin ratio, and prognostic nutritional index for patients with glioma: a meta-analysis. Medicine (Baltimore). (2020) 99:e20927. doi: 10.1097/MD.0000000000020927
131. Li, J, Xu, R, Hu, DM, Zhang, Y, Gong, TP, and Wu, XL. Prognostic nutritional index predicts outcomes of patients after gastrectomy for cancer: a systematic review and meta-analysis of nonrandomized studies. Nutr Cancer. (2019) 71:557–68. doi: 10.1080/01635581.2019.1577986
132. Yang, Y, Gao, P, Chen, X, Song, Y, Shi, J, Zhao, J, et al. Prognostic significance of preoperative prognostic nutritional index in colorectal cancer: results from a retrospective cohort study and a meta-analysis. Oncotarget. (2016) 7:58543–52. doi: 10.18632/oncotarget.10148
133. Zhang, X, and Su, Y. Low prognostic nutritional index predicts adverse outcomes in patients with heart failure: a systematic review and meta-analysis. Angiology. (2024) 75:305–13. doi: 10.1177/00033197231159680
134. Chang, WT, Sun, CK, Wu, JY, Huang, PY, Liu, TH, Chang, YJ, et al. Association of prognostic nutritional index with risk of contrast induced nephropathy: a meta-analysis. Front Nutr. (2023) 10:1154409. doi: 10.3389/fnut.2023.1154409
135. Peng, P, Chen, L, Shen, Q, Xu, Z, and 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) 39:1535–41. doi: 10.12669/pjms.39.5.7781
136. Hu, G, Ding, Q, Zhong, K, Wang, S, Wang, S, and Huang, L. Low pretreatment prognostic nutritional index predicts poor survival in breast cancer patients: a meta-analysis. PLoS One. (2023) 18:e0280669. doi: 10.1371/journal.pone.0280669
137. Dai, Y, Liu, M, Lei, L, and Lu, S. Prognostic significance of preoperative prognostic nutritional index in ovarian cancer: a systematic review and meta-analysis. Medicine (Baltimore). (2020) 99:e21840. doi: 10.1097/MD.0000000000021840
138. Hao, J, Chen, C, Wan, F, Zhu, Y, Jin, H, Zhou, J, et al. Prognostic value of pre-treatment prognostic nutritional index in esophageal cancer: a systematic review and meta-analysis. Front Oncol. (2020) 10:797. doi: 10.3389/fonc.2020.00797
139. Peng, Q, Liu, L, Li, T, Lei, C, and Wan, H. Prognostic impact of prognostic nutritional index on renal cell carcinoma: a meta-analysis of 7,629 patients. PLoS One. (2022) 17:e0265119. doi: 10.1371/journal.pone.0265119
140. Dai, M, and Sun, Q. Prognostic and clinicopathological significance of prognostic nutritional index (PNI) in patients with oral cancer: a meta-analysis. Aging (Albany NY). (2023) 15:1615–27. doi: 10.18632/aging.204576
141. Xu, XT, Qian, Y, Tian, MX, Ding, CC, Guo, H, Tang, J, et al. Predictive impact of prognostic nutritional index in patients with cancer treated with immune checkpoint inhibitors: a systematic review and meta-analysis. Nutr Cancer. (2023) 75:1413–26. doi: 10.1080/01635581.2023.2203355
142. Simillis, C, Taylor, B, Ahmad, A, Lal, N, Afxentiou, T, Powar, MP, et al. A systematic review and meta-analysis assessing the impact of body mass index on long-term survival outcomes after surgery for colorectal cancer. Eur J Cancer. (2022) 172:237–51. doi: 10.1016/j.ejca.2022.05.020
143. Raposeiras Roubín, S, Abu Assi, E, Cespón Fernandez, M, Barreiro Pardal, C, Lizancos Castro, A, Parada, JA, et al. Prevalence and prognostic significance of malnutrition in patients with acute coronary syndrome. J Am Coll Cardiol. (2020) 76:828–40. doi: 10.1016/j.jacc.2020.06.058
144. Sze, S, Pellicori, P, Kazmi, S, Rigby, A, Cleland, JGF, Wong, K, et al. Prevalence and prognostic significance of malnutrition using 3 scoring systems among outpatients with heart failure: a comparison with body mass index. JACC Heart Fail. (2018) 6:476–86. doi: 10.1016/j.jchf.2018.02.018
145. Kondrup, J, Rasmussen, HH, Hamberg, O, and Stanga, Z. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. (2003) 22:321–36. doi: 10.1016/s0261-5614(02)00214-5
146. Wilson, JM, and Jungner, YG. Principles and practice of mass screening for disease. Bol Oficina Sanit Panam. (1968) 65:281–393.
147. Chang, WT, Sun, CK, Wu, JY, Yu, CH, Chang, YJ, Lin, MC, et al. Association of prognostic nutritional index with long-term mortality in patients receiving percutaneous coronary intervention for acute coronary syndrome: a meta-analysis. Sci Rep. (2023) 13:13102. doi: 10.1038/s41598-023-40312-4
Glossary
AMSTAR-2 - a measurement tool to assess systematic reviews version 2
BMI - body mass index
CI - confidence intervals
CONUT - controlling nutritional status
CRP - C-reactive protein
ESS - effect size and significance
GLIM - global leadership initiative on malnutrition
GNRI - geriatric nutritional risk index
GPS - Glasgow Prognostic Score
GRADE - grading of recommendations assessment development and evaluation
MACEs - major adverse cardiovascular events
mGPS - modified Glasgow Prognostic Score
MNA - mini-nutritional assessment
MNA-SF - mini-nutritional assessment short-form
NOS - Newcastle–Ottawa Scale
NPS - Naples prognostic score
NRS 2002 - nutritional risk screening 2002
PA - phase angle
PI - prediction intervals
PNI - prognostic nutritional index
PO-AKI - postoperative acute kidney injury
PSST - p-curve and statistical standards
ROBINS-I - risk of bias in non-randomized studies-of interventions
SE - standard error
SGA - subjective global assessment
SIGN - Scottish Intercollegiate Guidelines Network
TAVI - transcatheter aortic valve implantation
Keywords: clinical outcomes, malnutrition, nutritional screening, nutritional screening and assessment tools, umbrella review
Citation: Li Z, Lin Y, Shi Y, Yang T, An L, Qi Y, Zhang P, Huang X, Su X, Deng Y, Hu J, Liu G and Sun D (2025) Association between malnutrition diagnosed by different screening and assessment tools and clinical outcomes: an umbrella review. Front. Nutr. 12:1676201. doi: 10.3389/fnut.2025.1676201
Edited by:
Abdullahi Aborode, Florida International University, United StatesReviewed by:
Michał Czapla, Wroclaw Medical University, PolandFatimah Fatimah, Indiana University Purdue University, United States
Copyright © 2025 Li, Lin, Shi, Yang, An, Qi, Zhang, Huang, Su, Deng, Hu, Liu and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Guobin Liu, MTU2OTkwMTE3QHFxLmNvbQ==; Dali Sun, c3VuZGFsaTIwMThAMTI2LmNvbQ==
†These authors have contributed equally to this work