Alignment of Global Leadership Initiative on Malnutrition (GLIM) and Mini Nutritional Assessment (MNA) in the evaluation of nutritional status and its association with clinical outcomes of hospitalized COVID-19 patients

doi:10.21203/rs.3.rs-3084276/v1

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Research Article

Alignment of Global Leadership Initiative on Malnutrition (GLIM) and Mini Nutritional Assessment (MNA) in the evaluation of nutritional status and its association with clinical outcomes of hospitalized COVID-19 patients

https://doi.org/10.21203/rs.3.rs-3084276/v1

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Background and aims:

Considering that nutritional status is one of the influential factors in the process of infection and recovery of patients with covid-19, this study was conducted to determine the nutritional status of covid-19 patients and its relationship with clinical outcomes.

Methods

This cross-sectional study included 155 patients diagnosed with covid-19 disease (aged 18 to 80 years) with PCR tests and Chest CT scans. The nutritional status of participants was assessed using the Mini Nutritional Assessment (MNA) questionnaire, Global Leadership Initiative on Malnutrition (GLIM) criteria, and the NUTRIC score tool.

Results

The analysis performed on the collected data from 81 men (52.3%) and 74 women (47.7%) showed that, based on the results of the MNA tool, 30 participants (19.4%) and based on the GLIM criteria, 42 participants (27.1%) were suffering from malnutrition. Also, systolic and diastolic blood pressure (GLIM SBP, DBP p-value = 0.038, 0.008 and MNA: SBP, DBP p-value = 0.04, 0.01) and blood oxygen saturation (MNA p-value = 0.01 and GLIM p-value = 0.012) was significantly associated with nutritional status of the participants. In addition, the results from the MNA and GLIM questionnaires showed a relative agreement with a value of 0.35 and p-value < 0.0001.

Conclusion

This study revealed that nutritional status is a critical factor that affects the clinical condition of covid-19 patients, which can affect the recovery process. In addition, the GLIM criterion is a practical and reliable tool to measure the nutritional status for diagnosis and prognosis of patients with Covid-19.

Covid-19

GLIM

MNA

Nutrition assessment

Clinical outcomes

SARS-CoV-2 is one of the seven types of viruses in the Coronaviridae family. It is a new type of coronavirus capable of infecting humans identified in December 2019 in Wuhan, Hubei province, China^1,2. Throughout the spread of the coronavirus, different types of the virus have emerged due to various mutations in its structure³.

World Health Organization statistics indicate that over 659 million people worldwide and more than 7.5 million people in Iran have been infected with the coronavirus. Unfortunately, more than 144,000 people have lost their lives⁴. So far, various approaches have been proposed to treat and prevent this disease, and using multiple vaccines has significantly reduced the rate of infection, complications, and mortality caused by it⁵. Based on studies on the effect of vaccination on Covid-19, although the rate of complications and mortality caused by the disease has decreased, it has not yet reached zero. We are still witnessing complications and mortality caused by Covid-19 due to various reasons, including underlying diseases, old age, allergic reactions to vaccination, and drug interactions^6,7. In addition, malnutrition can lead to lower immune responses to the infection and the production of fewer antigens with lower response rates to vaccines⁸.

The COVID-19 disease affects body systems, including the respiratory, cardiovascular, and nervous systems, and has long-term complications, especially respiratory and cardiovascular complications, after infection^9–11. Patients with COVID-19 show symptoms such as fever, cough, fatigue, and muscle pain, and in severe cases, they are hospitalized with respiratory failure, septic shock, and other organ dysfunction¹².

Many factors are associated with the risk of COVID-19 infection, among which nutritional status is of paramount importance¹³. Nutrition and food are the most critical exogenous factors affecting the immune defense response. Adequate nutrition can reduce the risk of chronic diseases caused by the immune system or minimize their complications^14–16 Therefore, individuals with malnutrition are more susceptible to infectious diseases, including coronavirus¹³. On the other hand, studies have shown that patients infected with COVID-19 may be at risk of malnutrition due to decreased food consumption, catabolism related to inflammation, and decreased mobility due to long-term hospitalization¹⁷, which creates a vicious cycle for hospitalized COVID-19 patients. Malnutrition increases sensitivity to infection, while infection exacerbates the need for nutrients by reducing appetite¹⁸. Deficiencies in nutrients such as selenium, zinc, copper, and vitamins D, C, A, and E can adversely affect the immune system and its function⁸.

There is no widely accepted tool to measure nutritional status, and various tools have been proposed to determine the risk of malnutrition. Among these tools, the Mini Nutritional Assessment tool (MNA) has high sensitivity, specificity, and diagnostic accuracy in different clinical environments¹⁹. The Global Leadership Initiative on Malnutrition index (GLIM) is another tool for assessing nutritional status, the efficacy of which has been demonstrated in various studies in COVID-19 patients^20,21.

The MNA, GLIM, and Nutrition Risk in Critically Ill score (NUTRIC score) scoring tools are very useful for determining the nutritional status during various diseases, including COVID-19, and establishing an agreement between the results provided by these tools can be beneficial for decision-making in using them to determine nutritional status. Therefore, this study aims to define the correlation between nutritional status and clinical outcomes in COVID-19 patients using nutritional status assessment tools, including MNA, GLIM, and NUTRIC-score, and to evaluate the agreement between them.

Study design and population

The present study is an analytical cross-sectional study conducted on 155 COVID-19 patients aged 18 to 80 years to investigate the nutritional condition of these patients admitted to the COVID ward and Intensive Care Unit (ICU). The sampling in the study was carried out non-randomly and simply from the hospitals of Shahid Beheshti and Shahid Jalil in Yasuj County for about three months. The inclusion criteria for the study included patients willing to participate, a definite diagnosis of COVID based on a positive PCR and chest CT scan, and an age between 18 and 80 years. The exclusion criteria also included unwillingness to participate in the study, pregnant and breastfeeding women, and fewer than 24 hours of admission time. The study protocol was confirmed by the ethics committee of Yasuj University of Medical Sciences (IR.YUMS.REC.1400.146).

Evaluation of Nutritional Status

MNA:

The MNA questionnaire is a nutritional status evaluation tool for elderly patients in all healthcare fields recommended by ESPEN (European Society for Clinical Nutrition and Metabolism). We used the short form in this study. The questionnaire includes six questions on reducing weight loss, food intake, mobility, psychological pressure or acute illness, psychological or neurological problems, and Body Mass Index (BMI). Its score ranges from 0 to 14. A score of 12–14 represents a normal state of nutrition, while a score of 8–11 shows malnutrition, and a score of 0–7 indicates the severe status of malnutrition¹⁹.

GLIM:

The GLIM criteria were developed in 2016 to evaluate the nutritional status using phenotypic and etiologic criteria. Phenotypic criteria include unwanted weight loss (more than 5% in the past six months or more than 10% in the past, over six months), low BMI (less than 18.5 kg/m2 in young individuals and less than 20 kg/m2 in individuals older than 70 years), and reduced muscle mass, determined using the fat-free mass index (FFMI), which was not used in this study. Etiologic criteria also include two criteria: inadequate food intake (more than 50% for more than one week or any chronic gastrointestinal problem that impairs digestion and absorption) and inflammation or disease burden that may result from acute diseases or chronic injuries. Malnutrition is diagnosed if at least one phenotypic and one etiologic criterion are present²².

mNUTRIC score:

The NUTRIC score tool was developed by Holland and colleagues in 2011 to assess the nutritional status of patients admitted to the ICU. This tool consists of two sections: APACHE II and SOFA, and the final score is the sum of age, APACHE II, SOFA, number of comorbidities, number of days hospitalized from the hospital to the ICU, and interleukin-6 (IL-6). In the newer version of this tool, called the modified NUTRIC score (mNUTRIC score), the IL-6 variable has been removed²³.

Demographic information questionnaire:

The demographic information questionnaire includes age, gender, weight, height, BMI, alcohol and smoking status, medical information and history, method of nutrition, various diseases, medications used, allergies to medications, and signs and symptoms of the disease. Additionally, information on the severity of the illness, complications, and length of hospitalization was obtained from patients.

Statistical analysis

First, the Shapiro-Wilk test was used to test the data normality. Then, qualitative data were reported as numbers (percentages), and quantitative data were reported as mean ± standard deviation (if normal) and median (interquartile range) (if non-normal). Qualitative variables were compared with different levels of nutritional status using the Chi-square test or Fisher's exact test, and quantitative variables were compared with different levels of nutritional status using a one-sided analysis of variance (if normal) and the Kruskal-Wallis test (if non-normal). Cohen's kappa test was used to evaluate the agreement of the results obtained from different nutritional status assessment tools. After collecting data, they were analyzed and interpreted using SPSS version 25 software. Also, a significance level of p-value < 0.05 was considered in all tests.

Table 1 presents the general characteristics of patients. In this study, 155 patients met eligibility criteria of the study including patients with a mean age of 48.60 ± 15.38 and a mean BMI of 27.85 ± 13.09. The mean of symptom onset to admission was 7.34 ± 4.70 days, while the mean length of hospitalization was 6.5 ± 4.98 days. According to the MNA score and GLIM criteria, malnutrition was found in 30 (19.4%) and 42 (27.1%) patients, and 12 patients were hospitalized in ICU (Table 1). During the sampling process, 38.7% of patients recovered completely, and 3.2% of patients died.

Based on the results from the mNUTRIC score, all of the 12 patients hospitalized in the ICU had low risk in terms of nutritional status (score: 0 to 4).

Table 1

Baseline demographic and clinical data
Variable	Mean ± SD
Age (years)	48.60 ± 15.38
Height (cm)	168.38 ± 9.58
Male n(%)	81 (52.3%)
Weight (kg)	78.89 ± 13.09
BMI (cm/kg²)	27.85 ± 4.62
Admission duration (days)	7.34 ± 4.70
Duration of hospitalization (days)	6.5 ± 4.98
MNA score
Normal (12–14)	125 (80.6%)
Malnutrition (0–11)	30 (19.4%)
GLIM criteria (22)
Normal	113 (72.9)
Malnutrition	42 (27.1%)
NUTRIC score
Low risk (0–4)	12 (100%)
At risk (5–9)	0 (0%)
Data are reported as Mean ± SD

The information of the patients based on the nutritional status according to MNA and GLIM questionnaires is reported in Table 2. In normal patients compared to malnutrition patients, some variables, including Systolic and Diastolic Blood Pressure (SBP and DBP), and O2 saturation, were significantly different in both MNA and GLIM tools. Other variables in both questionnaires were not significantly different in normal and malnourished patients. Due to the small number of patients hospitalized in the ICU, the NUTRIC-score questionnaire was not compared with the other two tools.

Table 2

NUTRITIONAL STATUS BY MNA AND GLIM
	MNA		P	GLIM criteria		P
Variables	Normal (Mean ± SD)	Malnutrition (Mean ± SD)		Normal (Mean ± SD)	Malnutrition (Mean ± SD)
Duration of hospitalization (days)	6.42 ± 5.10	6.83 ± 4.51	0.68	6.46 ± 5.30	6.60 ± 4.10	0.88
Disease Severity			0.07			0.16
Mild	56 (36.12%)	10 (6.45%)		53 (34.16%)	13 (8.38%)
Moderate	58 (37.40%)	20 (12.90%)		51 (32.90%)	27 (17.41%)
Severe	11 (7.09%)	0 (0.00%)		9 (5.80%)	2 (1.29%)
Covid-19 complications
Shortness of breath	73 (47.09%)	15 (9.67%)	0.08	62 (40%)	26 (16.77%)	0.62
Wheezing	55 (35.48%)	18 (11.61%)	0.31	49 (31.61%)	24 (15.48%)	0.21
Cough	96 (61.93%)	19 (12.25%)	0.38	84 (54.19%)	31 (20%)	0.88
Fever	18 (11.61%)	10 (6.45%)	0.06	15 (9.67%)	13 (8.38%)	0.27
Chilling	22 (14.19%)	7 (4.51%)	0.76	17 (10.96%)	12 (7.74%)	0.24
Headache	39 (25.16%)	13 (8.38%)	0.43	35 (22.58%)	17 (10.96%)	0.61
Diarrhea	9 (5.80%)	3 (1.93%)	0.72	8 (5.16%)	4 (2.58%)	0.80
Constipation	16 (10.32%)	2 (1.29%)	0.53	16 (10.32%)	2 (1.29%)	0.15
Nausea	19 (12.25%)	6 (3.87%)	0.55	19 (12.25%)	6 (3.87%)	0.77
Vomiting	11 (7.09%)	1 (0.64%)	0.80	8 (5.16%)	4 (2.58%)	0.73
Comorbidities
Heart diseases	24 (15.48%)	8 (5.16%)	0.45	24(15.5%)	8(5.2%)	0.82
Kidney diseases	18 (11.61%)	3 (1.93%)	0.58	18(11.61%)	3(1.93%)	0.19
Hypertension	26 (16.77%)	8 (5.16%)	0.62	28(18.10%)	6(3.90%)	0.19
Anemia	12 (7.74%)	0 (0%)	0.13	12 (7.74%)	0 (0%)	0.03
Diabetes	22 (14.19%)	3 (1.93%)	0.41	20 (12.90%)	5 (3.22%)	0.46
brain ischemia	1 (0.6%)	0 (0%)	1	0(0%)	1(0.6%)	0.27
Pulmonary diseases	11 (7.09%)	6 (3.87%)	0.10	11 (7.10%)	6 (3.87%)	0.56
anorexia disease	1 (0.6%)	0 (0%)	1	1 (0.6%)	0 (0%)	1.00
cancer disease	5 (3.2%)	1 (0.6%)	1	5 (3.2%)	1 (0.6%)	1.00
liver disease	5 (3.2%)	1 (0.6%)	1	6 (3.9%)	0 (0%)	0.12
Thyroid diseases	15 (9.67%)	6 (3.87%)	0.37	16 (10.32%)	5 (3.22%)	0.79
mental disease	3 (1.9%)	2 (1.3%)	0.25	4 (2.6%)	1 (0.6%)	1.00
Other diseases	34 (21.93%)	13 (8.38%)		35 (22.58%)	12 (7.74%)
Vital signs
SBP (mmHg)	116.67 ± 16.68	108.37 ± 13.92	0.04^*	116.73 ± 16.74	110.57 ± 15.01	0.038^*
DBP (mmHg)	72.88 ± 10.50	68.37 ± 8.37	0.01^*	73.33 ± 10.43	68.45 ± 8.93	0.008^*
PR	78.60 ± 14.28	78.73 ± 10.13	0.53	79.04 ± 13.97	77.48 ± 12.42	0.526
RR	20.61 ± 4.53	20.13 ± 1.43	0.80	20.57 ± 4.71	20.38 ± 1.68	0.799
BT (degrees Celsius)	36.58 ± 2.43	36.31 ± 0.18		36.61 ± 2.56	36.31 ± 0.23	0.676
SO2	92.59 ± 3.84	89.77 ± 5.76	0.01^*	92.70 ± 3.75	90.31 ± 5.47	0.012^*
Drugs
Corticosteroid	116 (74.83%)	28 (18.06%)	1	105 (67.74%)	39 (25.16%)	1.00
Anticoagulant	117 (75.43%)	29 (18.70%)	0.69	107 (69.0%)	39 (25.2%)	0.70
Antiviral	108 (69.67%)	23 (14.83%)	0.26	94 (60.64%)	37 (23.87%)	0.47
Respiratory	98 (63.22%)	24 (15.48%)	1	89 (57.41%)	33 (21.29%)	1.00
Painkiller	52 (33.54%)	13 (8.38%)	1	46 (29.67%)	19 (12.25%)	0.71
Antibacterial	29 (18.70)	5 (3.22%)	0.48	27 (17.41%)	7 (4.51%)	0.38
Gastrointestinal	72 (46.45%)	21 (13.54%)	0.30	64 (41.29%)	29 (18.70%)	0.19
Anti-diabetes	28 (18.06%)	7 (4.51%)	1	26 (16.77%)	9 (5.80%)	1.00
Supplement	39 (25.16%)	10 (6.45%)	0.83	38 (24.51%)	11 (7.09%)	0.44
Laboratory data
WBC	7.57 ± 4.10	7.60 ± 4.38	0.72	7.00 ± 3.00	7.00 ± 4.00	0.72
RBC (10^6)	5.00 ± 1.17	4.91 ± 0.79	0.79	4.00 ± 1.00	4.00 ± 0.00	0.80
Hb (g/dl)	14.03 ± 2.20	13.31 ± 1.86	0.91	13.00 ± 2.00	13.00 ± 2.00	0.90
Hct (%)	38.74 ± 5.31	38.01 ± 4.80	0.55	38.00 ± 5.00	38.00 ± 5.00	0.55
MCV (FL)	79.53 ± 9.11	78.33 ± 10.22	0.2	79.00 ± 9.00	77.00 ± 8.00	0.20
MCH (pg)	28.78 ± 3.70	27.55 ± 4.48	0.49	28.00 ± 3.00	28.00 ± 4.04	0.50
MCHC (g/dl)	36.16 ± 2.05	36.06 ± 5.12	0.05	35.00 ± 2.00	36.00 ± 4.00	0.04^*
Plt (U/L)	196.30 ± 81.98	201.90 ± 85.02	0.36	193.00 ± 74.00	2.7.00 ± 100.00	0.36
Neutrophil (%)	76.98 ± 10.19	78.77 ± 10.30	0.83	77.00 ± 9.00	77.00 ± 10.00	0.82
Lymphocyte (%)	18.56 ± 10.39	16.11 ± 9.14	0.56	18.00 ± 10.00	17.00 ± 9.00	0.56
Monocyte (%)	4.36 ± 3.00	3.72 ± 2.38	0.36	4.00 ± 3.00	3.00 ± 2.00	0.35
BS (mg/dl)	142.03 ± 75.22	153.83 ± 102.56	0.58	146.00 ± 83.00	138.00 ± 74.00	0.58
BUN (mg/dl)	17.93 ± 8.68	18.75 ± 9.56	0.93	18.00 ± 9.00	17.00 ± 8.07	0.93
Cr (mg/dl)	1.06 ± 0.35	1.04 ± 0.26	0.24	1.07 ± 0.00	1.00 ± 0.00	0.23
Alb (mg/dl)	4.21 ± 0.54	4.05 ± 1.02	0.92	4.00 ± 0.00	4.00 ± 0.00	0.92
Abbreviations, WBC: White Blood Cell, RBC: Red Blood Cell, Hb: Hemoglobin, Hct: Hematocrit, MCV: Mean Corpuscular Volume, MCH: Mean Corpuscular Hemoglobin, MCHC: Mean Corpuscular Hemoglobin Concentration, Plt: Platelet, BS: Blood Sugar, BUN: Blood Urea Nitrogen, Cr: Creatinine, Alb: Albumin
Data are reported as Mean ± SD
*Significant at P < 0:05

As shown in Table 3, there was no significant relationship between GLIM variables in ICU and non-ICU patients. While in the MNA questionnaire, a significant relationship was observed between food intake in the last three months (p-value = 0.01) and mobility (p-value = 0.003) among these patients (Table 4).

Table 3

GLIM; ICU and non-ICU
variables		ICU	non - ICU	P – value
Phenotypic criteria (weight loss)	Yes	2	37	0.73
Phenotypic criteria (weight loss)	No	10	106	0.73
Phenotypic criteria (BMI)	Yes	0	6	1.00
Phenotypic criteria (BMI)	No	12	137	1.00
Etiologic criteria (intake)	Yes	4	90	0.063
Etiologic criteria (intake)	No	8	53	0.063

Table 4

MNA; ICU and non-ICU
variables	Scores	ICU	non - ICU	P – value
MNA 1: Has food intake declined over the past 3 months due to loss of appetite, digestive problems, chewing or swallowing difficulties?	0: severe decrease in food intake	0	19	0.01^*
	1: moderate decrease in food intake	5	97
	2: no decrease in food intake	7	27
MNA 2: Weight loss during the last 3 months	0: weight loss greater than 3kg	2	31	0.09
	1: does not know	5	18
	2: weight loss between 1 and 3kg	1	23
	3: no weight loss	4	71
MNA 3: Mobility	0: bed or chair bound	4	8	0.003^*
	1: able to get out of bed/chair but does not go out	6	71
	2: goes out	2	64
MNA 4: Has suffered psychological stress or acute disease in the past 3 months?	0: yes	2	68	0.06
	2: no	10	75	0.06
MNA 5: Neuropsychological problems	0: severe dementia or depression	1	20	0.24
	1: mild dementia	0	24
	2: no psychological problems	11	99
MNA 6: Body Mass Index (BMI)	0: BMI less than 19	0	2	0.77
	1: BMI 19 to less than 21	0	5
	2: BMI 21 to less than 23	0	14
	3: BMI 23 or greater	12	122
*Significant at P < 0:05

The results of Cohen's kappa analysis showed the relative compatibility of these two tools with a value of 0.35 and p < 0.0001.

The present study was conducted to examine the nutritional status of COVID-19 patients and its relationship with clinical outcomes of the disease, including disease complications, severity, and length of hospitalization. The MNA and GLIM tools were used to determine the nutritional status of patients, and their concordance was examined.

The data analysis in this study shows a significant relationship between the reduction of systolic (SBP) and diastolic blood pressure (DBP) with the nutritional status of COVID-19 patients (p-value = 0.04, 0.01 MNA: SBP, DBP, and GLIM: SBP, DBP p-value = 0.038, 0.008), where systolic blood pressure decreased by 6–8 units and diastolic blood pressure decreased by approximately five units. There are various mechanisms for this phenomenon; on t one hand, it can be said that systemic oxidative stress plays a fundamental role in causing high blood pressure, which is prevented by calorie intake limitation, and as a result of the endothelium-dependent vasomotor function, blood pressure decreases²⁴. The study conducted by Kunduraci et al. on patients with metabolic syndrome showed that calorie intake limitation could reduce blood pressure²⁵. Additionally, Alam et al. examined the effect of limited calorie intake on cardiovascular factors such as blood pressure and found that calorie intake reduction can lead to a decrease in systolic and diastolic blood pressure²⁶.

The oxygen saturation level (SaO2) of patients is another factor that was observed to have a significant difference between normal and malnourished states in this study through the performed analyses (MNA: p-value = 0.01 and GLIM: p-value = 0.012). According to the obtained results, consuming non-normal food causes a decrease of approximately 2.5% in the oxygen saturation level in malnourished individuals compared to normal individuals. Insufficient intake of iron-containing foods can lead to a decrease in the available iron for hemoglobin production, which can limit the blood oxygen saturation level²⁷. Another factor involved in hemoglobin biosynthesis is the active form of vitamin B6 (pyridoxal 5-phosphate or PLP), and also the amino acid glycine. PLP is the co-factor of the amino levulinic acid synthase (ALA) enzyme, which is the first enzyme in hemoglobin biosynthesis and its role is to combine glycine and succinyl-Coenzyme A (succinyl-CoA) as the initial step in hemoglobin production ²⁸. Therefore, it can be concluded that one of the consequences of malnutrition is insufficient intake of vitamin B6 and glycine, which can disrupt the process of hemoglobin production and lead to a decrease in oxygen saturation level and hypoxia.

Based on this information and its analysis, it can be said that nutritional status can be considered important for managing the clinical consequences of COVID-19 and reducing its complications. Various studies have been conducted to determine this importance; for example, Bedock and colleagues examined the nutritional status and its relationship with the severity of the disease in 114 patients with COVID-19 in their study. The importance of initial nutritional screening in patients with COVID-19 was emphasized in this study²². Additionally, Rouget and colleagues calculated a high rate of malnutrition (37.5%) in patients with COVID-19 in their study, and based on this data, it was determined that nutritional support is essential in COVID-19 care²⁹. Mohammadi and colleagues found that nutritional status is of particular importance in hospitalized patients with COVID-19, and malnutrition can lead to longer hospitalization periods and even increased mortality in these patients³⁰.

In this study, the GLIM criteria were compared with the MNA tool to determine their validity and reliability in assessing nutritional status. The analyses showed that the MNA tool and GLIM index had a relative agreement based on Cohen's analysis with a value of 0.35 and a p-value < 0.0001. Furthermore, the GLIM criteria were found to be more practical and accessible, making it a suitable tool for assessing the nutritional status of individuals, especially those suffering from malnutrition. As Shahbazi et al. also demonstrated, the GLIM criteria provide a fast diagnostic power with adequate accuracy and reliability and can be a good diagnostic tool for assessing nutritional status due to the reduced time for patient interaction²¹. However, other studies have found that the GLIM criteria are not a precise and reliable measure for assessing the severity and prevalence of malnutrition, contrary to our study's findings. For example, Rouget's study used the GLIM criteria to determine the level of malnutrition in patients with COVID-19 and ultimately reported that GLIM may not accurately determine the prevalence of malnutrition²⁹.

In this study, we analyzed each item of MNA and GLIM separately in hospitalized and non-hospitalized ICU patients. The results showed significant differences between hospitalized and non-hospitalized ICU patients in item 1 of MNA (p-value = 0.01), which is related to a reduction in food consumption in the three months before the disease, and item 3 (p-value = 0.003), which is related to the level of patient mobility during hospitalization. Approximately 67.8% of non-hospitalized ICU patients had a moderate reduction in food intake in item 1 of MNA, while 41.6% of hospitalized ICU patients had a moderate decrease in food intake in the three months before COVID-19. On the other hand, 18.8% of non-hospitalized ICU patients did not have a decrease in food intake, and 58.3% of hospitalized ICU patients did not have a decrease in food intake. It can be interpreted that hospitalized ICU patients consume more medication, including corticosteroids. One of the side effects of these medications is an increase in patients' appetite³¹, as some hospitalized ICU patients reported an increase in their appetite and food intake during hospitalization.

In the analysis conducted on item 3 of the MNA tool, which relates to patients' mobility, approximately 5.6% of non-ICU hospitalized patients and 33.3% of ICU hospitalized patients were unable to get out of bed; 49.6% of non-ICU hospitalized patients and 50% of ICU hospitalized patients could get out of bed but were unable to walk, and 44.7% of non-ICU hospitalized patients could leave the ward and walk, while only 16.7% of ICU hospitalized patients had this ability. The reason for this is related to the clinical condition of patients and their hospital ward, where COVID-19 patients hospitalized in the ICU due to various reasons, including low oxygen saturation and shortness of breath, have less mobility compared to non-ICU hospitalized patients.

In this study, there were also some limitations. In addition to the MNA tool and GLIM criteria, we also used the NUTRIC-score questionnaire, but meaningful data could not be obtained due to the low number of analyzed patients. Furthermore, due to limitations in facing patients to measure their nutritional status using the GLIM criteria, their muscle mass was not determined from the phenotypic criteria, which based on previous studies, does not affect the results¹⁷. Additionally, since all patients were diagnosed with COVID-19, inflammatory conditions were considered for all of them.

Nutritional status is one of the factors that significantly affect the clinical status of COVID-19 patients. It can be said that nutritional status is an important factor that can be considered for managing these patients and mitigating the related complications. The GLIM criteria is one of the indicators used to assess nutritional status, and based on this study, the results of using this criterion are reliable, and it can be used to determine the prevalence, severity, and status of malnutrition in different individuals, including COVID-19 patients. GLIM criteria has good accuracy in determining nutritional status compared to other tools like MNA, and due to its ease of use, quickness, and accessibility, it can be a good option for use in various settings, including clinical environments such as hospitals.

Ethics approval and consent to participate

Ethics approval and consent to participate Ethic committee of Yasuj University of Medical Sciences (Ethic code: IR.YUMS.REC.1400.146). All methods were carried out following relevant guidelines or the Declaration of Helsinki, and written informed consent was received from all subjects or their care givers before the study began.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

Authors declared no personal or financial competing interests.

Funding

The study was funded by the Yasuj University of Medical Sciences, Yasuj, Iran.

Authors' contributions

NS, MB, MVB and MN contributed to data collection. NR contributed to data analysis. NS, MB and MRJ drafted the manuscript. NS, BP and ZH contributed to manuscript revising and editing. ZS and JMM obtained funding. AP supervised the study. All authors read and approved final manuscript.

Acknowledgements

We would like to thank the School of Health and Nutrition of Yasuj University of Medical sciences and all patients for their involvement in the study.

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No competing interests reported.

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Alignment of Global Leadership Initiative on Malnutrition (GLIM) and Mini Nutritional Assessment (MNA) in the evaluation of nutritional status and its association with clinical outcomes of hospitalized COVID-19 patients

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Abstract

Background and aims:

Methods

Results

Conclusion

Background

Methods

Study design and population

Evaluation of Nutritional Status

MNA:

GLIM:

mNUTRIC score:

Demographic information questionnaire:

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

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