Relationship between sarcopenia and cachexia with prognostic markers of middle-aged and older inpatients with COVID-19: a case-control study

doi:10.21203/rs.3.rs-2635637/v1

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Relationship between sarcopenia and cachexia with prognostic markers of middle-aged and older inpatients with COVID-19: a case-control study

https://doi.org/10.21203/rs.3.rs-2635637/v1

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Purpose:SARS-CoV-2 infection can lead to various manifestations beyond an inflammatory response, such as anorexia, hyposmia, and other symptoms that may increase the risk of nutritional disorders. Sarcopenia and cachexia (nutritional disorders) are conditions that appear to influence COVID-19 evolution. Thus, this study aimed to evaluate sarcopenia and cachexia in hospitalized patients with COVID-19, verifying their clinical impacts and relationship with prognostic markers.

Methods: This is a case-control study involving inpatients with and without a COVID-19 diagnosis. The occurrence of sarcopenia was evaluated according to European Working Group on Sarcopenia 2 criteria, and cachexia was also evaluated. Inflammatory markers and the 4C Mortality Score were evaluated.

Results: The groups were similar regarding age (p = 0.656), sex (p = 0.777), presence of comorbidities (type 2 diabetes p = 0.659; systemic arterial hypertension p = 0.427), and Body Mass Index (p = 0.657). The frequency of sarcopenia and cachexia in patients with COVID-19 was 21.9% and 28.1%, respectively. Sarcopenic patients with COVID-19 had a higher risk of death (4C Mortality Score) (p = 0.038). The occurrence of sarcopenia or cachexia within the COVID-19 group was not associated with inflammatory biomarkers or a higher number of COVID-19 symptoms (p > 0.05).

Conclusion: The presence of sarcopenia among COVID-19 patients increased the risk of mortality.

COVID-19

Sarcopenia

Cachexia

Body composition

Mortality

Aging

Aim

To identify the influence of nutritional disorders (sarcopenia and cachexia) in prognostic markers in COVID-19 inpatients.

Findings

Sarcopenia is presented as a risk factor for mortality in COVID-19 inpatients.

Message

The accurate evaluation of nutritional disorders in hospitalized patients diagnosed with COVID-19 may help to reduce the risk for adverse outcomes.

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Coronavirus disease-2019 (COVID-19) has caused thousands of deaths since late 2019 [1]. The SARS-CoV-2 virus can cause an unbalanced immune response, leading to an inflammatory reaction of great magnitude, known as a cytokine storm [1, 2].

The most common symptoms of these conditions are fever, fatigue, anorexia, headache, rash, diarrhea, arthralgia, myalgia and neuropsychiatric disturbances [3, 4], which may be a consequence of the damage caused by the cytokines to tissues, or physiological changes caused by the acute phase or may derive from responses mediated by immune cells [3]. Furthermore, infectious respiratory diseases may lead to body composition abnormalities, which is a path to malnutrition. Malnutrition is related to poor prognosis of affected individuals [5].

Current evidence has demonstrated a high prevalence of malnutrition in hospitalized patients with COVID-19 [1, 5, 6]. Although still poorly understood, these abnormalities lead to the hypothesis that COVID-19 may increase the risk of sarcopenia and cachexia development.

Recognized as a muscle disease, sarcopenia is defined as a decrease in skeletal muscle mass and low muscle strength [7]. Sarcopenia is commonly related to the aging process, however, acute sarcopenia can develop in hospitalized patients, especially as a consequence of infectious catabolism [8]. Sarcopenia is associated with a higher risk of adverse outcomes, such as hospital complications, mortality, rehabilitation needs, and a longer hospitalization time [9, 10].

Cachexia, on the other hand, is recognized as the worsening of malnutrition. It is a complex metabolic syndrome defined by the 5% weight loss (free of edema) in the last 12 months or a Body Mass Index (BMI) < 20 kg/m2 in patients with chronic diseases associated with at least three of the following clinical or laboratory criteria: decreased muscle strength, fatigue, anorexia, low lean mass index and abnormal biochemistry [11].

The impact of coronavirus on health is still not fully understood, including the effect of the presence of sarcopenia and cachexia on the evolution of SARS-CoV-2 infection. However, evidence suggests that the protein-muscle status can influence an individual's risk for the progression of COVID-19. Early identification and correction of these conditions may potentially improve patients’ outcomes. In this context, this study aimed to evaluate sarcopenia and cachexia in middle-aged and elderly patients hospitalized with COVID-19 compared to a control group and to investigate the relationship of these conditions with prognostic markers.

Study design and sample size

The study is a case-control design that includes middle-aged and older inpatients from two university hospitals in Northeast Brazil. Data collection occurred between August 2021 and August 2022.

The COVID-19 group consists of patients aged ≥ 40 years, of both sexes, who were hospitalized with a confirmed diagnosis of COVID-19 by RT-PCR molecular test or SARS-CoV-2 Rapid Antigen Test, using swab of naso-oropharyngeal secretion. The control group comprises patients aged ≥ 40 years, of both sexes, who were hospitalized for reasons other than COVID-19 and tested negative for COVID-19.

Patients with pre-existing consumptive diseases (e.g., cancer, HIV infection, chronic heart failure, chronic obstructive pulmonary disease, chronic kidney disease on renal replacement therapy), or physical limitations were excluded from both groups.

The study used a pairing of 1 case to 2 controls (1:2). Each case was paired based on age (± 3 years), sex, nutritional status according to BMI, and the presence of comorbidities. Recruitment was done by selecting two controls for each patient diagnosed with COVID-19 who was entered into the study.

The sample size was calculated using Epi-Info software® version 6.04, considering the following parameters: the lowest prevalence outcome (sarcopenia), a significance level of 95%, a test power (1 – β) of 80%, a ratio of two controls for each case, and a cross-product ratio (Odds Ratio – OR) of 3.8. The estimated prevalence of exposure to sarcopenia among cases was 22% [13] and 7% among controls [14]. The minimum required sample was 32 cases and 64 controls.

Sarcopenia And Cachexia Evaluation

Sarcopenia was determined by the coexistence of low muscle strength and low muscle mass [8]. Muscle strength was obtained from the Hand Grip Strength (HGS), measured by a JAMAR® digital dynamometer, following established techniques (American Society of Hand Therapists). A cutoff of < 27 kg/F for men and < 16 kg/F for women was adopted to define low HGS [7].

Appendicular skeletal muscle mass (MMAE) was assessed to verify low muscle mass and was determined by the equation: \(MMAE \left(kg\right)=\left(\text{0,227} \times RI\right)+\left(\text{0,0064}\times {X}_{c}\right)+\left(\text{0,0095}\times weight\right)+\left(\text{1,384}\times sex\right)-\text{3,964}\), in which RI represents the resistive index (height²(cm)/resistance (OHMS)); X_c represents reactance in OHMS and for sex is applied values of 1 to men, and 0 to women [7, 15].

The measurements of resistance and reactance were obtained from Bioelectrical Impedance (BIA), using portable equipment from the brand Biodynamics®, model 450, which applies a current of 800 µA, with a simple frequency of 50 kHz according to evidenced techniques [15]. From Sergi's equation results, the Appendicular Skeletal Muscle Index (ASMI) was calculated [7]. Values of ≤ 7.7 kg/m² in men and ≤ 5.62 kg/m2 defined low muscle mass [16].

The diagnosis of cachexia was established using the criteria proposed by the Cachexia Consensus: 5% weight loss (free of edema) in the last 12 months or BMI < 20 Kg/m2 and at least three of the following associated clinical or laboratory criteria: decrease muscle strength, fatigue, anorexia, low muscle mass, abnormal biochemistry, characterized by increased C-reactive protein (CRP), hemoglobin < 12g/dL or albumin < 3.2 g/dL [11].

Weight loss was documented considering the usual weight in the last 12 months and the weight measured at the time of hospitalization. The percentage of weight loss (%WL) was calculated. Fatigue was assessed using the Dutch Fatigue Scale (DUFS). Anorexia was assessed using the Simplified Nutritional Appetite Questionnaire (QNSA). Scores > 14.5 on DUFS are indicative of the presence of fatigue, while scores < 14 on QNSA indicate anorexia [17, 18].

Prognostic Markers

4C (Coronavirus Clinical Characterization Consortium) Mortality Score, number of vaccine doses, Total Lymphocyte Count (TLC), Neutrophil-Lymphocyte Ratio (NLR), Monocyte-Lymphocyte Ratio (MLR) and Phase Angle (PA) were used as prognostic markers.

The 4C Mortality Score uses patient demographics data such as age in years and biological sex, clinical observations such as number of comorbidities, oxygen saturation, respiratory rate, Glasgow scale, and blood parameters such as serum urea, which are usually available at the time of hospital admission. Four risk groups were defined with corresponding mortality rates determined: low risk (score 0–3, mortality rate 1.2%), intermediate risk (score 4–8, mortality rate 9.9%), high risk (score 9–14, mortality rate 31.4%) and very high risk (≥ 15 points, mortality rate 61.5%) [19].

The total lymphocyte count (TLC) was defined using the equation: % lymphocytes x leukocytes/100. Values below 1500 mm³/dl defined a low TLC. NLR > 4.27 [20] and MLR > 0.23 [21] were used as indicators of worse patient outcomes with COVID-19. PA values (< 5º) were used as indicators of poor nutritional status and worse prognosis [22].

Demographical And Clinical Data

Sex, age, presence of comorbidities such as systemic arterial hypertension (SAH) and diabetes (DM), chronic kidney disease, thyroid and psychiatric disorders, cognitive dysfunction, and physical dependence were considered.

Self-reported signs and symptoms related to COVID-19 were documented, including hyposmia, hypogeusia, fever, dry cough, productive cough, headache, sore throat, dyspnea, asthenia, myalgia, dysphagia, anorexia, and diarrhea. Respiratory rate (RR) and oxygen saturation (SpO2) were also measured. Patients were asked about their COVID-19 vaccination status and the number of doses received. Biochemical data were obtained from medical records within 72 hours of the patient's admission to the hospital. Blood count parameters and TLC were also considered.

Data analysis

Regarding the analysis, the data were described using relative and absolute frequencies, mean and standard deviation or median and interquartile range, depending on the normality of the distribution, which was assessed by the Kolmogorov-Smirnov test. The Statistical Package for Social Sciences (SPSS®) software version 28 was used for the analysis.

To compare differences and distribution between proportions, the chi-square test was used. To verify differences between means or medians, Student's t test or Mann Whitney U test were used, respectively. In all analyses, a significance level of p < 0.05 was considered.

A total of 96 patients were included in the study, with 32 in the COVID-19 group and 64 in the control group. There were no significant differences in mean age between the groups (COVID-19: 63.3 ± 11.8 years, control group: 64.3 ± 5.5 years) (p = 0.656), as well as in demographic, clinical, and nutritional status data (p > 0.05). However, the TLC was significantly higher in the COVID-19 group compared to the control group (p < 0.001), as shown in Table 1.

Table 1

Demographic, clinical and nutritional characteristics of hospitalized patients diagnosed with COVID-19 compared to a control group in two hospitals in Northeast Brazil/2022 (n = 96).
Variables	Control group N = 64 (%)	COVID-19 group N = 32 (%)	p-value*
Sex (%)
Men	38 (59,4)	18 (56,3)	0,777
Women	26 (40,6)	14 (43,8)
Age (%)			0,656
40–49 years 50–59 years 60–69 years	0 (0,0) 6 (9,4) 47 (73,4)	5 (15,6) 5 (15,6) 14 (43,8)
70–79 years	10 (15,6)	6 (18,8)
≥ 80 years	1 (1,6)	2 (6,2)
Comorbidities (%)
SAH (%)	56 (69,1)	13 (68,4)	0,952
DM (%)	32 (39,5)	5 (26,3)	0,211
Nutritional status and biochemical data
BMI (kg/m²)	26,3 ± 5,2	26,8 ± 5,6	0,657
BMI classification (%)			0,958
Malnutrition	3 (4,7)	2 (6,3)
Eutrophy	27 (42,2)	9 (28,1)
Excessive weight	34 (53,1)	21 (65,6)
Hemoglobin (mg/dl)	12,0 ± 2,2	11,30 ± 2,2	0,199
Leukocytes (mm³)	8200 (6543–10195)	7010 (4840–9513)	0,157
TLC (mm³)	1320,3 (840–3750)	1502,5 (819–3144)	< 0,0001
HS (kg/f)	24,1 ± 10,8	25,4 ± 12,8	0,584
ASMI (kg/m²)	6,6 (5,8 − 7,3)	7,0 (6,5–7,9)	0,120
Sarcopenia (%)	19 (29,7)	7 (21,9)	0,522
Cachexia (%)	17 (26,6)	9 (28,1)	0,528

BMI: Body Mass Index; TLC: Total Lymphocyte Count; HS: Handgrip strength; ASMI: Appendicular Skeletal Muscle Mass Index; *Pearson chi-square or Fisher Exact Test to frequency comparison. Student T Test to mean comparison and Mann Whitney U Test to compare median.

Anthropometric data showed that almost 66% of the COVID-19 group had excessive weight according to their BMI. The frequency of excessive weight in the control group was 53.1%. No significant differences were observed between the two groups (p = 0.958).

The frequency of sarcopenia and cachexia was similar between groups (p > 0.05). In the COVID-19 group, the frequency of sarcopenia was 21.9%, while cachexia was 28.1%. In the control group, sarcopenia was present in 29.7% of the sample, while cachexia was present in 26.6%. The frequency of sarcopenia and cachexia did not differ with sex, comorbidities, or number of symptoms of disease within the COVID-19 group, and these frequencies are described in Table 2.

Table 2

Demographic and clinical variables according to the presence of sarcopenia and cachexia in hospitalized patients diagnosed with COVID-19 in two hospitals in Northeast Brazil/2022 (n = 32).
Variables	Sarcopenia (N = 7)	Without sarcopenia (N = 25)	p-value^*	Cachexia (N = 9)	Without caquexia (N = 23)	p-value^*
Sex			0,318			0,125
Men	5 (71,4)	13 (52,0)		7 (77,8)	11 (47,8)
Women	2 (28,6)	12 (48,0)		2 (22,2)	12 (52,2)
SAH			0,456			0,115
Yes	4 (57,1)	17 (68,0)		4 (44,4)	17 (73,9)
No	3 (42,9)	8 (32,0)		5 (65,6)	8 (26,1)
DM			0,535			0,761
Yes	3 (42,9)	9 (36,0)		3 (33,3)	9 (39,1)
No	4 (57,1)	16 (64,0)		6 (66,7)	14 (60,9)
Symptoms^A			0,326			0,398
< 3	5 (83,3)	15 (62,5)		5 (55,6)	15 (71,4)
≥ 3	1 (16,7)	9 (37,5)		4 (44,4)	6 (28,6)
Comorbidities			0,424			0,863
< 2	3 (42,9)	14 (56,0)		5 (55,6)	12 (52,2)
≥ 2	4 (57,1)	11 (44,0)		4 (44,4)	11 (47,8)

*Pearson chi-square or Fisher Exact Test; ^A: Fever; Dry Cough; Productive Cough; Headache; Sore throat; Dyspnea; Asthenia; Myalgia; Hyposmia, Hypogeusia; Dysphagia, Anorexia and Diarrhea.

Patients with COVID-19 who had sarcopenia had a higher mean age (72.6 ± 10.2 vs 60.7 ± 11.0; p = 0.016), lower mean BMI (20.9 ± 3.1 vs 28.5 ± 5.0; p = 0.001), lower mean phase angle (PA) (3.6 ± 2.0 vs 6.2 ± 3.7; p = 0.025), and higher scores on the 4C Mortality Score (9.0 (9.0–11.0) vs 7.0 (6.0-9.5); p = 0.038). The number of vaccine doses, TLC, NLR, and MLR were similar in patients with or without sarcopenia (p > 0.05), as shown in Table 3.

Table 3

Comparison of nutritional, clinical and prognostic variables regarding the presence or absence of sarcopenia in hospitalized patients diagnosed with COVID-19 (n = 32).
Variables	Sarcopenia		Without sarcopenia		p-value^A
Variables	Mean	SD	Mean	SD	p-value^A
Age (years)	72,6	10,2	60,7	11,0	0,016
BMI (kg/m²)	20,9	3,1	28,5	5,0	0,001
Leukocytes (mm³)	6594	3693	8157	3827	0,352
TLC (mm³)	1466	1036	1499	735	0,940
PA (º)	3,6	2,0	6,2	3,7	0,025
Respiratory Rate	21,2	7,0	21,3	4,7	0,944
SpO₂	97,0	1,1	96,6	1,3	0,516
Variables	Sarcopenia		Without sarcopenia		p-value^B
Variables	Median	QI	Median	QI	p-value^B
%WL	9,4	0,0–21,7	0,0	0,0–5,2	0,092
NLR	4,0	1,3–6,4	2,5	1,6 − 5,3	0,859
MLR	0,4	0,2 − 0,9	0,6	0,2 − 0,9	0,412
4C Mortality score	9,0	9,0–11,0	7,0	6,0–9,5	0,038
Number of vaccine doses	2,0	1,0–2,0	2,0	2,0–3,0	0,104

BMI: Body Mass Index; TLC: Total Lymphocyte Count; NLR: Neutrophil to Lymphocytes Ratio; MLR: Monocytes to Lymphocytes Ratio; SpO2: Oxygen Saturation; PA: Phase Angle; %WL: % Weight Loss; ^A Student T Test; ^B Mann Whitney U Test; SD: standard deviation; QI: Quartile Interval.

Cachexia data are presented in Table 4. COVID-19 patients with cachexia had a lower mean BMI (22.6 ± 4.7 vs. 28.5 ± 5.0, p = 0.005) and a higher percentage of weight loss (10.2 (7.7–24.0) vs. 0.0 (0.0-1.3), p < 0.001). However, the prognostic markers were similar between patients with and without cachexia (p > 0.05).

Table 4

Comparison of nutritional, clinical and prognostic variables regarding the presence or absence of cachexia in hospitalized patients diagnosed with COVID-19 (n = 32).
Variables	Cachexia		Without cachexia		p-value^A
Variables	Mean	SD	Mean	SD	p-value^A
Age (years)	64,0	10,6	63,0	12,4	0,833
BMI (kg/m²)	22,6	4,7	28,5	5,0	0,005
Leukocytes (mm³)	8298	5214	7626	3206	0,726
TLC (mm³)	1261	1034	1582	680	0,409
Respiratory Rate	19,4	4,5	22,1	5,2	0,198
SpO2	97,1	1,2	96,5	1,2	0,240
Variables	Cachexia		Without cachexia		p-value^B
Variables	Median	QI	Median	QI	p-value^B
%WL	10,2	7,7–24,0	0,0	0,0–1,3	< 0,001
NLR	5,3	1,7–19,4	2,1	1,6 − 5,1	0,180
MLR	0,7	0,2 − 1,5	0,5	0,2 − 0,8	0,801
4C Mortality score	9,0	6,5–10,0	7,0	6,0–10,0	0,592
Number of vaccine doses	2,0	1,3 − 2,0	2,0	2,0–2,8	0,320
BMI: Body Mass Index; TLC: Total Lymphocyte Count; NLR: Neutrophil to Lymphocytes Ratio; MLR: Monocytes to Lymphocytes Ratio; SatO2: Oxygen Saturation; PA: Phase Angle; %WL: % Weight Loss; ^A Student T Test; ^B Mann Whitney U Test; SD: standard deviation; QI: Quartile Interval.

The interest in verifying the occurrence of sarcopenia and cachexia in the population with COVID-19 is not only due to a better understanding of the nutritional impairment promoted by the disease, but also to verify the prognostic impact of these previous nutritional conditions on the evolution of the disease. It is already well established that the occurrence of sarcopenia and cachexia is associated with higher morbidity and adverse outcomes [9, 10].

To the best of our knowledge, this is the first case-control study to investigate the association between the occurrence of sarcopenia and cachexia with prognostic markers in hospitalized patients with COVID-19. The prevalence of sarcopenia among COVID-19 inpatients found in our study was similar to that reported in a retrospective cohort study by Kim et al. (2021) [23] (prevalence of 23.97%) and a study by Moctezuma-Velazquez et al. (2021) [13] (prevalence of 22.16%).

There is growing evidence suggesting that SARS-CoV-2 infection can result in gastrointestinal abnormalities, which can lead to inadequate nutritional intake. In addition, the infection can also cause body composition abnormalities by recruiting inflammatory cytokines, such as IFN-γ, IL-1β, IL-6, IL-17, and TNF-α, which can cause pathological changes in skeletal muscle tissue. The virus can directly bind to muscle tissue through the angiotensin 2 receptor (AT2 receptor), leading to muscle fiber proteolysis and inhibition of protein synthesis, which can lead to the development of sarcopenia and cachexia [24, 25].

Underweight was present in 6.5% of the COVID-19 patients, indicating that sarcopenia occurred independently of changes in weight. It is important to highlight that the occurrence of sarcopenia is associated with a higher risk of adverse outcomes, such as susceptibility to infections, rehabilitation needs, longer hospitalization, and mortality. Therefore, the occurrence of sarcopenia should be evaluated in hospitalized patients, especially in acute conditions such as COVID-19 [8, 9, 10].

Regarding cachexia, few studies have evaluated this condition in patients with COVID-19. So far, only two studies have investigated the diagnosis of cachexia during the hospitalization of these patients, and both focused on patients admitted to the Intensive Care Unit (ICU) or semi-intensive care unit. Allard et al. (2020) [26] reported a prevalence of 37.04%, while Pironi et al. (2021) [27] described a prevalence of 24.25%.

Our findings indicated a cachexia prevalence of 28.1% in the COVID-19 group. This is concerning data, as cachexia is a complex metabolic condition that is not only associated with underlying disease but can also result from worsening malnutrition and/or sarcopenia [28]. Cachexia further increases the risk of disability and mortality, and individuals who survive the cachexia condition often require longer rehabilitation [29, 30].

The lack of differences in the prevalence of sarcopenia and cachexia between COVID-19 patients and controls in our study does not necessarily imply that COVID-19 does not increase the risk for these conditions. It is important to note that our study protocol involved an evaluation for sarcopenia and cachexia within 72 hours of hospital admission, and thus, we were unable to evaluate the effects of the progression of SARS-CoV-2 infection on nutritional parameters.

In addition, it is important to highlight the changes in the epidemiological scenario of COVID-19 worldwide after expanding vaccine coverage. At the time the study was developed, there was already vaccination coverage with up to 2 doses, which may have contributed to less muscle stress caused by the viral infection. In accordance, some studies have already evaluated the nutritional status of patients with COVID-19, based on serum markers, such as Retinol Binding Protein (RBP) in the period before and after vaccination. These studies saw that individuals after vaccination maintained RBP at higher levels, reflecting that vaccination would be able to reduce comorbidities and protein-muscle impairment [31].

In our study, TLC was higher in the COVID-19 group compared to the control group. This can be attributed to the inflammatory-infectious condition observed in COVID-19 patients, and thus, this result was expected. While TLC is often used as a nutritional parameter, its interpretation should be approached with caution in the presence of infection and inflammation.

The higher mean age in individuals with sarcopenia is an expected finding, supported by evidence showing an association between older age and lower BMI in this population [14]. Aging itself increases the risk of sarcopenia due to the decline in muscle mass and strength caused by changes in muscle cellular metabolism. These changes promote alterations in the structure of muscle fibers, neurodegeneration, a decrease in the number of motor muscle cells, impairment of protein anabolism, hormonal resistance, and when coupled with physical inactivity and inadequate nutritional intake, ultimately lead to sarcopenia [32]. The higher mean age in individuals with sarcopenia is an expected finding, supported by evidence showing an association between older age and lower BMI in this population [14]. Aging itself increases the risk of sarcopenia due to the decline in muscle mass and strength caused by changes in muscle cellular metabolism. These changes promote alterations in the structure of muscle fibers, neurodegeneration, a decrease in the number of motor muscle cells, impairment of protein anabolism, hormonal resistance, and when coupled with physical inactivity and inadequate nutritional intake, ultimately lead to sarcopenia [32].

The lowest PA values observed in patients with sarcopenia in our study are consistent with previous findings that show lower PA levels in sarcopenic individuals and a higher prevalence of sarcopenia in those with lower PA [33, 34]. PA has been demonstrated to be a useful risk marker for sarcopenia [35, 36], as well as having prognostic value for adverse outcomes [37, 38].

Similar results were observed in patients with COVID-19. PA values were found to be independently and inversely correlated with adverse outcomes in COVID-19, including the risk of ICU admission, complications, length of hospital stay, and mortality [39, 40, 41]. Some evidence suggests that PA is associated with sarcopenia, as it may reflect water distribution, body muscle cellularity, and muscle strength, which are components used in the definition of sarcopenia [34].

The higher 4C Mortality Score observed in sarcopenic patients in the COVID-19 group suggests a greater risk among these patients. This finding supports the hypothesis that muscle-protein depletion may lead to worse outcomes in individuals with COVID-19. In a recent meta-analysis, individuals hospitalized with COVID-19 who had prior nutritional impairment had a 10-fold higher risk of mortality compared to those without impairment [42]. However, it is important to consider the higher mean age of sarcopenic individuals in this study, as it may be a confounding variable in the analysis of this association.

The lowest BMI and highest percentage of weight loss observed in patients with cachexia are expected findings, as these two aspects are included in the diagnostic criteria for this condition and are consistent and well-predicted in scientific evidence [11, 43, 44].

Since the onset of the current pandemic, several scientific studies have aimed to identify clinical and laboratory predictors of poor prognosis that can assist healthcare teams in monitoring strategies and making therapeutic decisions.

All patients included in our study had received at least one dose of the COVID-19 vaccine at the time of data collection. However, nearly 80% of the sample did not have a complete vaccination schedule, which may pose an additional risk for severe COVID-19 progression. The lack of a complete vaccination schedule may be associated with a greater inflammatory profile and, consequently, a higher risk of sarcopenia (muscle-protein depletion). To the best of our knowledge, this is the first study to investigate the association between COVID-19 vaccine doses and the occurrence of sarcopenia and cachexia.

NLR is an inflammatory biomarker that can be used as an indicator of systemic inflammation [45]. It has been extensively investigated as a possible prognostic factor in the progression of COVID-19. NLR at the time of hospital admission can predict mortality and ICU admission in COVID-19 patients [21]. The association of NLR with cachexia may suggest that cachexia is linked to a worse inflammatory profile and a higher risk of developing negative outcomes. However, our study did not find significant values for this association.

Our study had some limitations. The sample size was relatively small, which may limit the generalizability of the results. Additionally, the evaluation of sarcopenia and other nutritional measurements was conducted up to 72 hours after hospitalization, which may restrict the observation of the effects of COVID-19 evolution on nutritional status. Nevertheless, the inclusion of a control group for comparative purposes was a strong point of our investigation.

The prevalence of sarcopenia and cachexia was elevated in middle-aged and older inpatients with COVID-19, but was not higher when compared to control group. In our study, sarcopenia was related to higher mortality risk (4C mortality score) and lower PA. According to our findings, we can suggest that the proper evaluation of previous nutritional impairments, such as sarcopenia may help the development of multimodal strategies to treat and to prevent these conditions, decreasing the mortality risk.

Further research with a prospective design is needed to investigate the bidirectional relationship between COVID-19 and body composition. This would help to elucidate the effects of the disease on body composition and the impact of impaired body composition on the evolution of COVID-19. Moreover, it is necessary to investigate the long-term consequences of COVID-19 on body composition, particularly in patients who have recovered from the acute phase of the disease. Such studies would provide valuable information for the development of prevention and treatment strategies for COVID-19-related body composition impairments.

Acknowledgements

The authors acknowledge the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco for providing support to this project. We also want to thank all the patients included in our research.

Statement of authorship

All authors made substantial contributions to the conception and design of the study, acquisition, analysis and interpretation of data. They also contributed to the manuscript drafting and critical review of the intellectual content.

All authors critically revised the manuscript, agree to be fully accountable for ensuring the integrity and accuracy of the work, and read and approved the final manuscript.

Authors’ contribution

José Reginaldo Alves de Queiroz Júnior: Conception and design of the research, writing original manuscript, acquisition, visualization, software and validation of the data. Jarson Pedro da Costa Pereira: Conception and design of the research, writing original manuscript, acquisition and analyzes of the data. Raquel de Arruda Campos Benjamim: acquisition of the data and writing original. Nahara Oliveira Lima da Silva: acquisition of the data. Maria Eduarda de Paiva Silva: acquisition of the data. Cláudia Porto Sabino Pinho Ramiro: Visualization, Software, Validation of data, supervision and review.

Funding

There is no funding source.

Availability of data and materials

Upon request.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Data collection was performed after consideration and approval by the Research Ethics Committee. This study was approved by the ethics committee of the respective institutions, under number of approvals: 4.690.900.

Informed consent

All participants provided informed consent to participate in the study and to have their data published.

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Relationship between sarcopenia and cachexia with prognostic markers of middle-aged and older inpatients with COVID-19: a case-control study

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Abstract

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Introduction

Methods

Study design and sample size

Sarcopenia And Cachexia Evaluation

Prognostic Markers

Demographical And Clinical Data

Data analysis

Results

Discussion

Conclusion

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References

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