BACKGROUND: The newly released Asian Working Group for Cachexia (AWGC) criteria share similar diagnostic items with the Global Leadership Initiative on Malnutrition (GLIM) criteria. This study aims to compare the AWGC cachexia and GLIM malnutrition in patients with gastric cancer and investigate whether one diagnosis continues to be a prognostic factor in individuals diagnosed with the other condition.
METHODS: Data of patients who underwent radical gastrectomy for gastric adenocarcinoma were prospectively collected from 2013 to 2019. The AWGC and GLIM criteria were applied to diagnosis cachexia and malnutrition, respectively. Univariate and multivariate logistic and Cox regression were used to verify the effect of relevant factors on postoperative complications and overall survival.
RESULTS: A total of 1420 patients were included, among whom 174 (12.3 %) were diagnosed with AWGC-cachexia alone, 85 (6.0 %) were diagnosed with GLIM-malnutrition alone, and 324 (22.8 %) had both AWGC-cachexia and GLIM-malnutrition. Both AWGC-cachexia and GLIM-malnutrition were independent risk factors for complications and overall survival. When they coexisted, the odds ratios (OR) and hazard ratios (HR) tended to be higher. In the AWGC-cachexia subset, GLIM-malnutrition remained an independent risk factor (HR = 1.544, 95 % CI = 1.098-2.171, P = 0.012) for overall survival after the adjustment of confounding factors. Similarly, in the GLIM-malnutrition subset, AWGC-cachexia remained an independent risk factor for overall survival (HR = 1.697, 95 % CI = 1.087-2.650, P = 0.020). Patients with both cachexia and malnutrition had the worst overall survival.
CONCLUSIONS: AWGC-cachexia and GLIM-malnutrition criteria were two non-redundancy tools in reflecting mortality risk in preoperative nutritional assessment.

UNASSIGNED: To validate the role of the albumin-derived neutrophil-to-lymphocyte (ALB-dNLR) score in diagnosing malnutrition in medical inpatients over 70 years old.
UNASSIGNED: This is a retrospective cross-sectional study involving 7 departments from 14 Chinese hospitals. The ALB-dNLR score was calculated, and outcomes between groups with positive and negative ALB-dNLR scores were compared after propensity score matching (PSM). Afterwards, the outcomes were compared between the groups receiving nutrition support and those not receiving support among malnourished patients diagnosed using the Global Leadership Initiative Malnutrition (GLIM) criteria after PSM.
UNASSIGNED: Out of 10,184 cases, 6165 were eligible. 2200 cases were in the positive ALB-dNLR score group. After PSM, 1458 pairs were analyzed, showing lower in-hospital mortality (0.8 % vs. 2.1 %, p = 0.005) and a lower nosocomial infection rate (5.9 % vs. 11.0 %, p < 0.001) in the negative ALB-dNLR score group. In malnourished patients, 259 pairs were analyzed after PSM. It showed better outcomes in mortality (0.8 % vs. 3.5 %, p = 0.033), nosocomial infection rate (5.4 % vs. 15.4 %, p < 0.001), length of stay (LOS) (13.8 ± 10.3 vs. 18.4 ± 14.1, p < 0.001), and total hospital cost (3315.3 ± 2946.4 vs. 4795.3 ± 4198.2, p < 0.001) in the support group. In malnourished patients with ALB-dNLR score as the sole etiological criterion, 94 pairs were calculated. It showed better outcomes in mortality (0.0 % vs. 6.4 %, p = 0.029), nosocomial infection rate (7.4 % vs. 18.1 %, p = 0.029), LOS (13.7 ± 8.3 vs. 19.8 ± 15.2, p = 0.001), and total hospital cost (3379.3 ± 2955.6 vs. 4471.2 ± 4782.4, p = 0.029) in the support group.
UNASSIGNED: The ALB-dNLR score was validated to predict in-hospital mortality in medical inpatients over 70 years old. Malnutrition patients diagnosed by the GLIM criteria and using the ALB-dNLR score might benefit from nutrition support.

OBJECTIVE: Malnutrition is prevalent among elderly patients with COPD, who also experience a high rate of readmission. Therefore, it is imperative to investigate the nutrition status of these patients, identify risk factors for readmission, and offer insights for clinical management. To achieve this, a cross-sectional study was conducted to investigate factors influencing nutrition status using GLIM criteria and explore the 180-day readmission factors among hospitalized elderly COPD patients.
UNASSIGNED: The data were collected from a hospital in Southwest China, encompassing a cohort of 319 eligible patients. Among elderly hospitalized COPD patients, the prevalence of malnutrition was 49.53% (158/319). Multivariate logistic regression revealed malnutrition (OR = 3.184), very severe airway obstruction (OR = 3.735), and Number of comorbidities ≥ 3 (OR = 5.754) as significant risk factors for 180-day readmission.
CONCLUSIONS: These findings suggest that malnutrition is a prevalent issue among elderly hospitalized patients with COPD and constitutes one of the risk factors contributing to the 180-day readmission rate. Therefore, timely identification and treatment for malnourished patients are crucial.

BACKGROUND: Global Leadership Initiative on Malnutrition (GLIM) and Patient-Generated Subjective Global Assessment (PG-SGA) are commonly used nutrition assessment tools, whose performance does not reach a consensus due to different and imperfect reference standards.
OBJECTIVE: This study aimed to evaluate and compare the diagnostic accuracy of GLIM and PG-SGA, using a hierarchical Bayesian latent class model, in the absence of a gold standard.
METHODS: A systematic search was undertaken in PubMed, Embase, and Web of Science from inception to October 2022. Diagnostic test studies comparing (1) the GLIM and/or (2) PG-SGA with \"semi-gold\" standard assessment tools for malnutrition were included.
METHODS: Two authors independently extracted data on sensitivity, specificity, and other key characteristics. The methodological quality of each included study was appraised according to the criteria in the Quality Assessment of Diagnostic Accuracy Studies-2.
METHODS: A total of 45 studies, comprising 20 876 individuals evaluated for GLIM and 11 575 for PG-SGA, were included. The pooled sensitivity was 0.833 (95% CI 0.744 to 0.896) for GLIM and 0.874 (0.797 to 0.925) for PG-SGA, while the pooled specificity was 0.837 (0.780 to 0.882) for GLIM and 0.778 (0.707 to 0.836) for PG-SGA. GLIM showed slightly better performance than PG-SGA, with a higher diagnostic odds ratio (25.791 vs 24.396). The diagnostic performance of GLIM was most effective in non-cancer patients with an average body mass index (BMI) of <24 kg/m2, followed by non-cancer patients with an average age of ≥60 years. PG-SGA was most powerful in cancer patients with an average age of <60 years, followed by cancer patients with an average BMI of <24 kg/m2.
CONCLUSIONS: Both GLIM and PG-SGA had moderately high diagnostic capabilities. GLIM was most effective in non-cancer patients with a low BMI, while PG-SGA was more applicable in cancer patients.
BACKGROUND: PROSPERO registration No. CRD42022380409.

Neurocritically ill patients frequently exhibit coma, gastroparesis, and intense catabolism, leading to an increased risk of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria for the diagnosis of malnutrition was created to achieve a consistent malnutrition diagnosis across diverse populations. This study aimed to validate the concurrent and predictive validity of GLIM criteria in patients with neurocritical illnesses. A total of 135 participants were followed from admission to the neurocritical unit (NCU) until discharge. Comparing GLIM criteria to the Subjective Global Assessment (SGA), sensitivity was 0.95 and specificity was 0.69. Predictive validity of GLIM criteria was assessed using a composite adverse clinical outcome, comprising mortality and various major complications. Adjusted hazard ratios for moderate and severe malnutrition were 2.86 (95% CI 1.45-5.67) and 3.88 (95% CI 1.51-9.94), respectively. Changes in indicators of nutritional status, including skeletal muscle mass and abdominal fat mass, within 7 days of admission were obtained for 61 participants to validate the predictive capability of the GLIM criteria for the patients\' response of standardized nutritional support. The GLIM criteria have a statistically significant predictive validity on changes in rectus femoris muscle thickness and midarm muscle circumference. In conclusion, the GLIM criteria demonstrate high sensitivity for diagnosing malnutrition in neurocritically ill patients and exhibit good predictive validity.

BACKGROUND: Reduced muscle mass is a criterion for diagnosing malnutrition using the Global Leadership Initiative on Malnutrition (GLIM) criteria; however, the choice of muscle-mass indicators within the GLIM criteria remains contentious. This study aimed to establish muscle-measurement-based GLIM criteria using data from bio-electrical impedance analysis (BIA) and anthropometric evaluations and evaluate their ability to predict overall survival (OS), short-term outcomes, and healthcare burden in patients with cancer.
METHODS: This was a multicenter, prospective study that commenced in 2013 and enrolled participants from various clinical centers across China. We constructed GLIM criteria based on various muscle measurements, including fat-free mass index (FFMI), skeletal muscle index (SMI), calf circumference (CC), midarm circumference (MAC), midarm muscle circumference (MAMC), and midarm muscle area (MAMA). Survival was estimated using the Kaplan-Meier method and survival curves were compared using the log-rank test. Cox proportional hazards regression was used to assess the independent association between the GLIM criteria and OS. The discriminatory performance of different muscle-measurement-based GLIM criteria for mortality was evaluated using Harrell\'s concordance index (C-index). Logistic regression was used to evaluate the association of the GLIM criteria with short-term outcomes and healthcare burden.
RESULTS: A total of 4769 patients were included in the analysis, of whom 1659 (34.8%) died during the study period. The Kaplan-Meier curves demonstrated that all muscle-measurement-based GLIM criteria significantly predicted survival in patients with cancer (all p < 0.001). The survival rate of malnourished patients was approximately 10% lower than that of non-malnourished patients. Cox proportional hazards regression showed that all the muscle-measurement-based GLIM could independently predict the OS of patients (all p < 0.001). The prognostic discrimination was as follows: MAMC (Chi-square: 79.61) > MAMA (Chi-square: 79.10) > MAC (Chi-square: 64.09) > FFMI (Chi-square: 62.33) > CC (Chi-square: 58.62) > ASMI (Chi-square: 57.29). In comparison to the FFMI-based GLIM criteria, the ASMI-based criteria (-0.002, 95% CI: -0.006 to 0.002, p = 0.334) and CC-based criteria (-0.003, 95% CI: -0.007 to 0.002, p = 0.227) did not exhibit a significant advantage. However, the MAC-based criteria (0.001, 95% CI: -0.003 to 0.004, p = 0.776), MAMA-based criteria (0.004, 95% CI: 0.000-0.007, p = 0.035), and MAMC-based criteria (0.005, 95% CI: 0.000-0.007, p = 0.030) outperformed the FFMI-based GLIM criteria. Logistic regression showed that muscle measurement-based GLIM criteria predicted short-term outcomes and length of hospital stay in patients with cancer.
CONCLUSIONS: All muscle measurement-based GLIM criteria can effectively predict OS, short-term outcomes, and healthcare burden in patients with cancer. Anthropometric measurement-based GLIM criteria have potential for clinical application as an alternative to BIA-based measurement.

BACKGROUND: Older patients with cancer have a higher risk for malnutrition and impaired quality of life (QoL). The present study aimed to investigate the relationship between malnutrition diagnosed according to the Global Leadership Initiative Malnutrition (GLIM) criteria and QoL across various tumor types, and to evaluate the combined prognostic value of malnutrition and QoL in predicting survival among older patients with cancer.
METHODS: This multicenter, observational cohort study included 5310 older patients with cancer and 2184 with malnutrition (moderate stage, n = 1023; severe stage, n = 1161). An empirical cumulative distribution curve was performed to illustrate the correlation between malnutrition and QoL. The primary objective was to investigate the association between malnutrition and QoL using logistic regression analysis. Survival analyses were performed to assess the combined prognostic value of malnutrition and QoL.
RESULTS: The median age of the patients (66.9% male, 33.1% female) was 70 years (interquartile range [IQR] 67-74 years) years. The median QoL score was highest in patients without malnutrition (91.88 [IQR 84.44-97.44]), followed by those with moderate (86.15 [IQR 76.18-93.85) and severe (82.31 [IQR 69.87-91.11]) malnutrition. Logistics regression revealed that the risk for developing impaired QoL increased 1.98 (95% confidence interval [CI] 1.64-2.38; P < 0.001) and 2.33 (95% CI 1.93-2.81; P < 0.001) times in patients with moderate and severe malnutrition, respectively. Kaplan-Meier curves showed that QoL in combination with GLIM criteria demonstrated a significant discriminative performance for survival and served as an independent prognostic factor among older patients with cancer, especially for lung and gastric cancers.
CONCLUSIONS: Malnutrition diagnosed according to the GLIM criteria was a predictor of impaired QoL. Additionally, the combination of QoL and malnutrition demonstrated utility for predicting survival outcomes in older patients with cancer.

UNASSIGNED: Patients with inflammatory bowel disease (IBD) often have the condition of malnutrition, which can be presented as sarcopenia, micronutrient deficiencies, etc. Trace elements (magnesium, calcium, iron, copper, zinc, plumbum and manganese) belonging to micronutrients, are greatly vital for the assessment of nutritional status in humans. Trace element deficiencies are also the main manifestation of malnutrition. Calcium (Ca) has been proved to play an important part in maintaining body homeostasis and regulating cellular function. However, there are still a lack of studies on the association between malnutrition and Ca deficiency in IBD. This research aimed to investigate the role of Ca for malnutrition in IBD patients.
UNASSIGNED: We prospectively collected blood samples from 149 patients and utilized inductively coupled plasma mass spectrometry to examine their venous serum trace element concentrations. Logistic regression analyses were used to investigate the association between Ca and malnutrition. Receiver operating characteristic (ROC) curves were generated to calculate the cutoffs for determination of Ca deficiency.
UNASSIGNED: Except Ca, the concentrations of the other six trace elements presented no statistical significance between non-malnutrition and malnutrition group. In comparison with the non-malnutrition group, the serum concentration of Ca decreased in the malnutrition group (89.36 vs 87.03 mg/L, p = 0.023). With regard to ROC curve, Ca < 87.21 mg/L showed the best discriminative capability with an area of 0.624 (95% CI: 0.520, 0.727, p = 0.023). Multivariate analyses demonstrated that Ca < 87.21 mg/L (OR = 3.393, 95% CI: 1.524, 7.554, p = 0.003) and age (OR = 0.958, 95% CI: 0.926, 0.990, p = 0.011) were associated with malnutrition risk. Serum Ca levels were significantly lower in the malnutrition group than those in the non-malnutrition group among UC patients, those with severe disease state or the female group.
UNASSIGNED: In patients with IBD, Ca deficiency is an independent factor for high malnutrition risk.

OBJECTIVE: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients\' historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information.
METHODS: This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance.
RESULTS: NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/).
CONCLUSIONS: Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.

(Protein-energy) malnutrition in individuals living with obesity presents complex diagnostic challenges due to the distinctive physiological characteristics of obesity. This narrative review critically examines the identification of malnutrition within the population with obesity, distinguishing malnutrition in obesity from related conditions such as sarcopenic obesity. While noting some shared features, the review highlights key differences between these conditions. The review also highlights the limitations of current malnutrition screening tools, which are not designed for individuals living with obesity. These tools primarily rely on anthropometric measurements, neglecting (among others) nutrient intake assessment, which hinders accurate malnutrition detection. Additionally, this review discusses limitations in existing diagnostic criteria, including the Global Leadership Initiative on Malnutrition (GLIM) criteria, when applied to individuals living with obesity. Challenges include the identification of appropriate cut-off values for phenotypic criteria (unintentional weight loss, low body mass index and muscle mass) and aetiological criteria such as reduced food intake and inflammation for the population with obesity. Overall, this review emphasises the need for modified screening tools and diagnostic criteria to recognise and assess malnutrition in obesity, leading to improved clinical outcomes and overall wellbeing.