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.

Background: The aim of our study was to evaluate the effectiveness of Global Leadership Initiative on Malnutrition (GLIM) criteria in assessing malnutrition within the peritoneal dialysis (PD) population.Methods: We conducted a retrospective analysis involving 1057 PD patients across multiple institutions, characterized by an age of 56.1 ± 14.4 years, 464 (43.9%) female, and a median follow-up of 45 (25, 68) months. Malnutrition was diagnosed according to GLIM criteria. The endpoint event was overall mortality. The survival rate and hazard ratio (HR) of death between malnutrition and well-nourished were analyzed in all patients and various subgroups. Receiver operator characteristic curve and integrated discrimination improvement (IDI) were used to distinguish the efficacy of the nutritional tools prediction model.Results: According to the GLIM criteria, the prevalence of malnutrition among the study population was 34.9%. The adjusted HR of overall mortality was 2.91 (2.39 - 3.54, p < 0.001) for malnutrition versus well-nourished. In sensitivity analyses, the HR remained robust except the cardiovascular disease subgroup. The area under the curve of GLIM predicting 5-year mortality was 0.65 (0.62-0.68, p < 0.001). As a complex model for forecast the long-term mortality, the performance of adjusted factors combined with GLIM was poorer than combined malnutrition inflammation score (MIS) (IDI >0, p < 0.001), but fitter than combined geriatric nutritional risk index (GNRI) (IDI <0, p < 0.001).Conclusions: The GLIM criteria provide a viable tool for nutritional assessment in patients with PD, and malnutrition defined according to the GLIM can predict prognosis with an acceptable performance.

Malnutrition is an alarming and ongoing healthcare problem globally. Malnutrition has a negative impact on the individual patient, leading to poorer clinical outcomes and increased mortality, but also poses an economic burden on society. Proper identification and diagnostics are prerequisites for initiation of treatment. In 2019, the Global Leadership Initiative on Malnutrition, a consensus-based global framework to uniformly diagnose malnutrition across populations, healthcare settings, and countries was published. Identifying and treating malnutrition is an interdisciplinary team effort. Nonetheless, the nutrition and dietetics profession is specifically trained for diagnosing and treating nutrition(-related) conditions, and therefore has a key role in the interdisciplinary team in implementing the GLIM framework in clinical practice. For the nutrition and dietetics profession, GLIM offers a great opportunity for moving both the scientific and clinical knowledge of malnutrition management forward. While the GLIM framework has been extensively studied since its launch, various knowledge gaps still remain. For the nutrition and dietetics profession, these knowledge gaps mainly relate to the GLIM implementation process, to the role of GLIM in relation to the nutrition care process, and to treatment strategies for various nutrition-related conditions. In this opinion paper, we aimed to describe the rationale for implementing the GLIM framework in clinical dietetic practice, and propose a research agenda based on knowledge gaps regarding GLIM in relation to nutrition care from a dietetic point of view.

During the last two decades, the definition, diagnosis, and management of malnutrition have significantly evolved. Malnutrition is generally defined as deficiencies, excesses, or imbalances in a person\'s intake of energy and/or nutrients. While malnutrition is associated with a significantly increased risk of morbidity, mortality, and healthcare cost, it is often underdiagnosed both in healthcare and community settings. One contributing factor is the lack of a consensus on its definition and appropriate diagnostic indicators. In the current article, we review the evolution of frameworks for the diagnosis of malnutrition. Recently published consensuses by prominent clinical nutrition societies have established a trajectory for the uniform global diagnosis of malnutrition. Limiting the use of body mass index (BMI) as a diagnostic criterion while emphasizing the use of muscle mass enables a more consistent and accurate diagnosis of malnutrition in the clinical setting. Guidance for the unified methodology and terminology for diagnosing malnutrition, such as the one proposed in the current article will enable policy makers to systematically address the two faces of malnutrition, starvation- and disease-related malnutrition applicable to both pediatric and adult populations. Policies and programs that could address issues of food insecurity and scarcity as well as early diagnosis and management of disease-related malnutrition will empower better care of community nutrition.

UNASSIGNED: There are no standardized assessment criteria for selecting nutritional risk screening tools or indicators to assess reduced muscle mass (RMM) in the Global Leadership Initiative on Malnutrition (GLIM) criteria. We aimed to compare the consistency of different GLIM criteria with Subjective Global Assessment (SGA) and protein-energy wasting (PEW).
UNASSIGNED: In this study, nutritional risk screening 2002 first four questions (NRS-2002-4Q), Nutritional Risk Screening 2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), and Mini-Nutritional Assessment Short-Form (MNA-SF) tools were used as the first step of nutritional risk screening for the GLIM. The RMM is expressed using different metrics. The SGA and PEW were used to diagnose patients and classify them as malnourished and non-malnourished. Kappa (κ) tests were used to compare the concordance between the SGA, PEW, and GLIM of each combination of screening tools.
UNASSIGNED: A total of 157 patients were included. Patients with Chronic kidney disease (CKD) stage 1-3 accounted for a large proportion (79.0%). The prevalence rates of malnutrition diagnosed using the SGA and PEW were 18.5% and 19.7%, respectively. The prevalence of GLIM-diagnosed malnutrition ranges from 5.1% to 37.6%, depending on the different screening methods for nutritional risk and the different indicators denoting RMM. The SGA was moderately consistent with the PEW (κ = 0.423, p < 0.001). The consistency among the GLIM, SGA, and PEW was generally low. Using the NRS-2002-4Q to screen for nutritional risk, GLIM had the best agreement with SGA and PEW when skeletal muscle index (SMI), fat-free mass index (FFMI), and hand grip strength (HGS) indicated a reduction in muscle mass (SGA: κ = 0.464, 95% CI 0.28-0.65; PEW: κ = 0.306, 95% CI 0.12-0.49).
UNASSIGNED: The concordance between the GLIM criteria and the SGA and PEW depended on the screening tool used in the GLIM process. The inclusion of RMM in the GLIM framework is important. The addition of HGS could further improve the performance of the GLIM standard compared to the use of body composition measurements.