Body composition predictors of outcome in patients with COVID-19

Association of Calf Circumference with Clinical and Biochemical Markers in Older Adults with COVID-19 Admitted at Intensive Care Unit: A Retrospective Cross-Sectional Study

BACKGROUND

COVID-19 is an infectious disease characterized by a severe catabolic and inflammatory state, leading to loss of muscle mass. The assessment of muscle mass can be useful to identify nutritional risk and assist in early management, especially in older adults who have high nutritional risks. The aim of this study was to evaluate the association of calf circumference (CC) with clinical and biochemical markers and mortality in older adults with COVID-19 admitted to the intensive care unit (ICU).

METHODS

A retrospective cross-sectional study was conducted in a public hospital. CC was adjusted for body mass index (BMI), reducing 3, 7, or 12 cm for a BMI of 25-29.9, 30-39.9, and ≥40 kg/m2, respectively, and classified as reduced when <33 cm for women and <34 cm for men. Pearson's correlation between BMI and CC was performed to assess the association between variables. Regression analysis was adjusted for sex, age, and BMI variables. Cox regression was used to assess survival related to CC.

RESULTS

A total of 208 older adults diagnosed with COVID-19 admitted to ICU were included, of which 84% (n = 176) were classified as having reduced CC. These patients were older, with lower BMI, higher nutritional risk, malnourished, and higher concentration of urea and urea-creatinine ratio (UCR) compared with the group with normal CC. There was an association between edematous patients at nutritional risk and malnourished with reduced CC in the Cox regression, either adjusted or not for confounding.

CONCLUSIONS

CC was not associated with severity, biochemical markers, or mortality in older adults with COVID-19 admitted to the ICU, but it was associated with moderately malnourished patients assessed by subjective global assessment (SGA).

GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition

PURPOSE

Body composition analysis (BCA) of the body torso plays a vital role in the study of physical health and pathology and provides biomarkers that facilitate the diagnosis and treatment of many diseases, such as type 2 diabetes mellitus, cardiovascular disease, obstructive sleep apnea, and osteoarthritis. In this work, we propose a body composition tissue segmentation method that can automatically delineate those key tissues, including subcutaneous adipose tissue, skeleton, skeletal muscle tissue, and visceral adipose tissue, on positron emission tomography/computed tomography scans of the body torso.

METHODS

To provide appropriate and precise semantic and spatial information that is strongly related to body composition tissues for the deep neural network, first we introduce a new concept of the body area and integrate it into our proposed segmentation network called Geographical Attention Network (GA-Net). The body areas are defined following anatomical principles such that the whole body torso region is partitioned into three non-overlapping body areas. Each body composition tissue of interest is fully contained in exactly one specific minimal body area. Secondly, the proposed GA-Net has a novel dual-decoder schema that is composed of a tissue decoder and an area decoder. The tissue decoder segments the body composition tissues, while the area decoder segments the body areas as an auxiliary task. The features of body areas and body composition tissues are fused through a soft attention mechanism to gain geographical attention relevant to the body tissues. Thirdly, we propose a body composition tissue annotation approach that takes the body area labels as the region of interest, which significantly improves the reproducibility, precision, and efficiency of delineating body composition tissues.

RESULTS

Our evaluations on 50 low-dose unenhanced CT images indicate that GA-Net outperforms other architectures statistically significantly based on the Dice metric. GA-Net also shows improvements for the 95% Hausdorff Distance metric in most comparisons. Notably, GA-Net exhibits more sensitivity to subtle boundary information and produces more reliable and robust predictions for such structures, which are the most challenging parts to manually mend in practice, with potentially significant time-savings in the post hoc correction of these subtle boundary placement errors. Due to the prior knowledge provided from body areas, GA-Net achieves competitive performance with less training data. Our extension of the dual-decoder schema to TransUNet and 3D U-Net demonstrates that the new schema significantly improves the performance of these classical neural networks as well. Heatmaps obtained from attention gate layers further illustrate the geographical guidance function of body areas for identifying body tissues.

CONCLUSIONS

(i) Prior anatomic knowledge supplied in the form of appropriately designed anatomic container objects significantly improves the segmentation of bodily tissues. (ii) Of particular note are the improvements achieved in the delineation of subtle boundary features which otherwise would take much effort for manual correction. (iii) The method can be easily extended to existing networks to improve their accuracy for this application.

The impact of muscle mass and myosteatosis on mortality in critically ill patients with Sars-Cov2-related pneumonia

BACKGROUND & AIMS

Sars-Cov-2 pneumonia can lead to severe complications, requiring invasive mechanical ventilation and admission to the intensive care unit (ICU). Low muscle quantity and quality (the latter evaluated by the amount of ectopic fat infiltration in the muscle [myosteatosis]) at ICU admission are associated with worse outcomes in critically ill patients. The purpose of the present study is to assess muscle mass and myosteatosis of paravertebral skeletal muscle, in critically ill patients with Sars-Cov2 pneumonia and its association with mortality.

METHODS

We conducted a retrospective observational study in 110 critically ill patients with severe Sars-Cov-2 pneumonia that had a high - resolution chest Computerized Tomography (HR-CT) at ICU admission. We acquired CT images at the level of the thoracic 12 (T12) vertebral body and measured skeletal muscle area (SMA), intermuscular adipose tissue (IMAT), and low attenuation muscle area (LAMA). Patients were followed until ICU mortality or discharge.

RESULTS

Patients were 59.8 ± 8.1 years old, 77% were male. Seventy-nine percent of patients were considered at nutritional risk, and 22% were obese. Average Acute Physiology and Chronic Health Evaluation II (APACHE II) score was 17 ± 5.4, and the overall ICU mortality was 48,2% (53/110). At ICU admission, both parameters of myosteatosis were associated with higher mortality (IMAT [per 10% increase] HR: 2.01 (95% Confidence Interval [CI] 1.27 to 3.17), P = 0.003; LAMA HR [per 10% increase]: 1.53 (95% CI 1.10 to 2.13), P = 0.012).

CONCLUSION

Myosteatosis as assessed by CT scans plays a relevant role as a prognostic marker in critically ill patients with Sars-Cov2 severe pneumonia.

Assessment of body composition in adults hospitalized with acute COVID-19: a scoping review

Introduction

Body composition (BC) assessment can supply accurate information for in-hospital nutritional evaluation. The aim of this study was to explore in the literature how the studies assessed BC, for what purpose, and investigate the role of BC findings in COVID-19 hospitalized patients' outcomes.

Methods

A scoping review was conducted according to the methodology available on the Joanna Briggs Institute website. We used the PCC acronym for the systematic search (population: adults with COVID-19, concept: assessment of BC, context: hospital setting) and performed it on PubMed, Scopus, and the Web of Science on 16 September 2022. Eligibility criteria consisted of the utilization of BC assessment tools in COVID-19 patients. Studies in which BC was solely measured with anthropometry (perimeters and skinfolds) were excluded. No language restriction was applied.

Results

Fifty-five studies were eligible for the review. Out of the 55 studies, 36 used computed tomography (CT), 13 used bioelectrical impedance (BIA), and 6 used ultrasound (US). No studies with D3-creatinine, 24  h urine excretion, dual-energy X-ray absorptiometry, or magnetic resonance were retrieved. BC was mainly assessed to test associations with adverse outcomes such as disease severity and mortality.

Discussion

Studies assessing BC in hospitalized patients with COVID-19 used mainly CT and BIA and associated the parameters with severity and mortality. There is little evidence of BC being assessed by other methods, as well as studies on BC changes during hospitalization.

Bayesian networks and imaging-derived phenotypes highlight the role of fat deposition in COVID-19 hospitalisation risk

Objective: Obesity is a significant risk factor for adverse outcomes following coronavirus infection (COVID-19). However, BMI fails to capture differences in the body fat distribution, the critical driver of metabolic health. Conventional statistical methodologies lack functionality to investigate the causality between fat distribution and disease outcomes. Methods: We applied Bayesian network (BN) modelling to explore the mechanistic link between body fat deposition and hospitalisation risk in 459 participants with COVID-19 (395 non-hospitalised and 64 hospitalised). MRI-derived measures of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat were included. Conditional probability queries were performed to estimate the probability of hospitalisation after fixing the value of specific network variables. Results: The probability of hospitalisation was 18% higher in people living with obesity than those with normal weight, with elevated VAT being the primary determinant of obesity-related risk. Across all BMI categories, elevated VAT and liver fat (>10%) were associated with a 39% mean increase in the probability of hospitalisation. Among those with normal weight, reducing liver fat content from >10% to <5% reduced hospitalisation risk by 29%. Conclusion: Body fat distribution is a critical determinant of COVID-19 hospitalisation risk. BN modelling and probabilistic inferences assist our understanding of the mechanistic associations between imaging-derived phenotypes and COVID-19 hospitalisation risk.

Anthropometric Measurements and Admission Parameters as Predictors of Acute Respiratory Distress Syndrome in Hospitalized COVID-19 Patients

Aim: We aimed to single out admission predictors of acute respiratory distress syndrome (ARDS) in hospitalized COVID-19 patients and investigate the role of bioelectrical impedance (BIA) measurements in ARDS development. Method: An observational, prospective cohort study was conducted on 407 consecutive COVID-19 patients hospitalized at the University Clinical Center Kragujevac between September 2021 and March 2022. Patients were followed during the hospitalization, and ARDS was observed as a primary endpoint. Body composition was assessed using the BMI, body fat percentage (BF%), and visceral fat (VF) via BIA. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis. Results: Patients with BMI above 30 kg/m², very high BF%, and/or very high VF levels were at a significantly higher risk of developing ARDS compared to nonobese patients (OR: 4.568, 8.892, and 2.448, respectively). In addition, after performing multiple regression analysis, six admission predictors of ARDS were singled out: (1) very high BF (aOR 8.059), (2) SaO₂ < 87.5 (aOR 5.120), (3) IL-6 > 59.75 (aOR 4.089), (4) low lymphocyte count (aOR 2.880), (5) female sex (aOR 2.290), and (6) age < 68.5 (aOR 1.976). Conclusion: Obesity is an important risk factor for the clinical deterioration of hospitalized COVID-19 patients. BF%, assessed through BIA measuring, was the strongest independent predictor of ARDS in hospitalized COVID-19 patients.

Abdominal imaging associates body composition with COVID-19 severity

The main drivers of COVID-19 disease severity and the impact of COVID-19 on long-term health after recovery are yet to be fully understood. Medical imaging studies investigating COVID-19 to date have mostly been limited to small datasets and post-hoc analyses of severe cases. The UK Biobank recruited recovered SARS-CoV-2 positive individuals (n = 967) and matched controls (n = 913) who were extensively imaged prior to the pandemic and underwent follow-up scanning. In this study, we investigated longitudinal changes in body composition, as well as the associations of pre-pandemic image-derived phenotypes with COVID-19 severity. Our longitudinal analysis, in a population of mostly mild cases, associated a decrease in lung volume with SARS-CoV-2 positivity. We also observed that increased visceral adipose tissue and liver fat, and reduced muscle volume, prior to COVID-19, were associated with COVID-19 disease severity. Finally, we trained a machine classifier with demographic, anthropometric and imaging traits, and showed that visceral fat, liver fat and muscle volume have prognostic value for COVID-19 disease severity beyond the standard demographic and anthropometric measurements. This combination of image-derived phenotypes from abdominal MRI scans and ensemble learning to predict risk may have future clinical utility in identifying populations at-risk for a severe COVID-19 outcome.

Prevalence and clinical implications of abnormal body composition phenotypes in patients with COVID-19: a systematic review

BACKGROUND

The impact of body composition (BC) abnormalities on coronavirus disease 2019 (COVID-19) outcomes remains to be determined.

OBJECTIVE

We summarized the evidence on BC abnormalities and their relationship with adverse clinical outcomes in patients with COVID-19.

METHODS

A systematic search was conducted up until September 26, 2022 for observational studies using BC techniques to quantify skeletal muscle mass (or related compartments), muscle radiodensity or echo intensity, adipose tissue (AT, or related compartments), and phase angle (PhA) in adults with COVID-19. Methodological quality of studies was assessed using the Newcastle-Ottawa Scale. A synthesis without meta-analysis was conducted to summarize the prevalence of BC abnormalities and their significant associations with clinical outcomes.

RESULTS

We included 62 studies (69.4% low risk of bias) with 12 to 1,138 participants, except three with up to 490,301 participants. Using computed tomography and different cut-offs, prevalence ranged approximately from 22-90% for low muscle mass, 12-85% for low muscle radiodensity, 16-70% for high visceral AT. Using bioelectrical impedance analysis (BIA), prevalence of high fat mass was 51% and low PhA was 22-88%. Mortality was inversely related to PhA (3/4 studies) and positively related to intramuscular AT (4/5 studies), muscle echo intensity (2/2 studies), and BIA-estimated fat mass (2/2 studies). Intensive care unit admission was positively related to visceral AT (6/7 studies) and total AT (2/3 studies). Disease severity and hospitalization outcomes were positively related to intramuscular AT (2/2 studies). Inconsistent associations were found for the rest of BC measures and hospitalization outcomes.

CONCLUSIONS

Abnormalities in BC were prevalent in patients with COVID-19. Although conflicting associations were observed among certain BC abnormalities and clinical outcomes, higher muscle echo intensity (reflective of myosteatosis) and lower PhA were more consistently associated with greater mortality risk. Likewise, high IMAT and VAT were associated with mortality and ICU admission, respectively.

Phase Angle and Handgrip Strength as Predictors of Clinical Outcomes in Hospitalized COVID-19 Patients

Phase angle (PhA) and muscle strength are predictors of clinical outcomes in critically ill patients. Malnutrition may affect body composition measurements. The aim of this prospective study was to investigate the association between PhA and handgrip strength (HGS), and clinical outcomes in hospitalized COVID-19 patients. The study included a total of 102 patients. Both PhA and HGS were measured twice, within 48 h of hospital admission and on the 7th day of hospitalization. The primary outcome was the clinical status on the 28th day of hospitalization. Secondary outcomes included the hospital length of stay (LOS), the concentrations of ferritin, C-reactive protein and albumin, oxygen requirements and the severity of pneumonia. A one-way analysis of variance (ANOVA) test and Spearman rS correlation coefficient were used for statistical analysis. No differences were found for PhA [on day 1 (p = 0.769) and day 7 (p = 0.807)] and the primary outcome. A difference was found between HGS on day 1 and the primary outcome (p = 0.008), while no difference was found for HGS on day 7 (p = 0.476). Body mass index was found to be associated with the oxygen requirement on day 7 (p = 0.005). LOS was correlated neither with PhA (rs = −0.081, p = 0.422) nor with HGS (rs = 0.137, p = 0.177) on the first day. HGS could be a useful indicator of clinical outcomes in COVID-19 patients, while PhA does not seem to have a clinical impact. However, further research is needed to validate the results of our study.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

Visceral to subcutaneous fat ratio predicts short-term mortality in patients with Covid 19. A multicenter study

OBJECTIVE

To evaluate the association of body composition parameters with outcomes in Covid-19.

METHODS

173 patients hospitalized for Covid-19 infection in 6 European centers were included in this retrospective study. Measurements were performed at L3-level and comprised skeletal muscle index (SMI), muscle density (MD), and adipose tissue measurements [visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral-to-subcutaneous-adipose-tissue-area-ratio (VSR)]. The association with mortality, the need for intubation (MV), and the need for admission to ICU within 30 days were evaluated.

RESULTS

Higher SAT density was associated with a greater risk of MV (OR = 1.071, 95%CI=(1.034;1.110), p < 0.001). Higher VAT density was associated with admission to ICU (OR = 1.068, 95%CI=(1.029;1.109), p < 0.001). Higher MD was a protective factor for MV and ICU admission (OR = 0.914, 95%CI=(0.870;0.960), p < 0.001; OR = 0.882, 95%CI=(0.832;0.934), p = 0.028). Higher VSR was associated with mortality (OR = 2.147, 95%CI=(1.022;4.512), p = 0.044). Male sex showed the strongest influence on the risk of ICU admission and MV. SMI was not associated with either parameter.

CONCLUSION

In patients hospitalized for Covid-19 infection, higher VSR seems to be a strong prognostic factor of short-term mortality. Weak associations with clinical course were found for MD and adipose tissue measurements. Male sex was the strongest prognostic factor of adverse clinical course.

ADVANCES IN KNOWLEDGE

VSR is a prognostic biomarker for 30-day mortality in patients hospitalized for Covid-19 disease.

Differential Bias for Creatinine- and Cystatin C- Derived Estimated Glomerular Filtration Rate in Critical COVID-19

COVID-19 is a systemic disease, frequently affecting kidney function. Dexamethasone is standard treatment in severe COVID-19 cases, and is considered to increase plasma levels of cystatin C. However, this has not been studied in COVID-19. Glomerular filtration rate (GFR) is a clinically important indicator of renal function, but often estimated using equations (eGFR) based on filtered metabolites. This study focuses on sources of bias for eGFRs (mL/min) using a creatinine-based equation (eGFRLMR) and a cystatin C-based equation (eGFRCAPA) in intensive-care-treated patients with COVID-19. This study was performed on 351 patients aged 18 years old or above with severe COVID-19 infections, admitted to the intensive care unit (ICU) in Uppsala University Hospital, a tertiary care hospital in Uppsala, Sweden, between 14 March 2020 and 10 March 2021. Dexamethasone treatment (6 mg for up to 10 days) was introduced 22 June 2020 (n = 232). Values are presented as medians (IQR). eGFRCAPA in dexamethasone-treated patients was 69 (37), and 74 (46) in patients not given dexamethasone (p = 0.01). eGFRLMR was not affected by dexamethasone. eGFRLMR in females was 94 (20), and 75 (38) in males (p = 0.00001). Age and maximal CRP correlated negatively to eGFRCAPA and eGFRLMR, whereas both eGFR equations correlated positively to BMI. In ICU patients with COVID-19, dexamethasone treatment was associated with reduced eGFRCAPA. This finding may be explained by corticosteroid-induced increases in plasma cystatin C. This observation is important from a clinical perspective since adequate interpretation of laboratory results is crucial.

Nutritional risk, not obesity, is associated with mortality in critically ill COVID-19 patients

Background

Despite the identification of obesity as a risk factor for higher rates of hospital and Intensive Care Unit (ICU) admissions and complications due to COVID-19, the association between obesity and mortality in critically ill COVID-19 patients remains controversial, and the nutritional risk is little considered. Hence, our study sought to evaluate the association between obesity, nutritional risk, and mortality in critically ill patients diagnosed with COVID-19.

Methods

Retrospective study were condutcted including adult critically ill COVID-19 patients admitted to an ICU between April 2020 and March 2021. Clinical and laboratory data were collected from electronic medical records. Obesity was classified by body mass index ≥ 30 kg/m^2. A mNUTRIC score of ≥ 5 indicated high nutritional risk. Multiple Cox Regression was used to estimate the association between mNUTRIC, obesity, and mortality.

Results

From 71 patients aged 59 (± 15) years, 71.8 % were male. The frequencies of obesity (58.7 %) and death (49.3 %) were high, but obesity was not associated with mortality. Based on mNUTRIC, 85.9 % of patients were at high nutritional risk, presenting a higher frequency of mortality than patients at low nutritional risk (50.8 % vs 40.0 %; p = 0.014). Multiple Cox Regression showed that for each unit increase in mNUTRIC score the probability of death almost doubled, regardless of the presence of obesity (HR = 1.74; p < 0.001).

Conclusions

A higher nutritional risk was positively associated with mortality in critically ill COVID-19 patients, regardless of obesity, showing the importance of early identification of nutritional risk for appropriate nutritional interventions in this population.

The Role of Bioelectrical Impedance Analysis in Predicting COVID-19 Outcome

Background

Published data regarding the impact of obesity on COVID-19 outcomes are inconsistent. However, in most studies, body composition was assessed using body mass index (BMI) alone, thus neglecting the presence and distribution of adipose tissue. Therefore, we aimed to investigate the impact of body and visceral fat on COVID-19 outcomes.

Methods

Observational, prospective cohort study included 216 consecutive COVID-19 patients hospitalized at University Clinical Center Kragujevac (Serbia) from October to December 2021. Body composition was assessed using the BMI, body fat percentage (%BF), and visceral fat (VF) via bioelectrical impedance analysis (BIA). In addition to anthropometric measurements, variables in the research were socio-demographic and medical history data, as well as admission inflammatory biomarkers. Primary end-points were fatal outcomes and intensive care unit (ICU) admission.

Results

The overall prevalence of obesity was 39.3% according to BMI and 50.9% according to % BF, while 38.4% of patients had very high VF levels. After adjusting odds ratio values for cofounding variables and obesity-related conditions, all three anthropometric parameters were significant predictors of primary end-points. However, we note that % BF and VF, compared to BMI, were stronger predictors of both mortality (aOR 3.353, aOR 3.05, and aOR 2.387, respectively) and ICU admission [adjusted odds ratio (aOR) 7.141, aOR 3.424, and aOR 3.133, respectively].

Conclusion

Obesity is linked with COVID-19 mortality and ICU admission, with BIA measurements being stronger predictors of outcome compared to BMI use alone.

Poor Nutritional Status and Dynapenia Are Highly Prevalent in Post-Acute COVID-19

Poor nutritional status is common (estimated prevalence 5–69%) in acute coronavirus disease-2019 (COVID-19), and has been associated with hospitalization, the need for intensive care, and mortality. Body composition (BC) and muscle function have also been related in such patients to poor disease outcomes.

As the evidence in the literature is limited, a cross-sectional study was carried out to determine the frequency of malnutrition in a cohort of post-acute COVID-19 patients referred to a rehabilitation center after hospital discharge. BC and muscle strength were assessed and the differences between bedridden and not bedridden patients were specifically evaluated.

The study sample was composed of 144 post-acute COVID-19 patients (mean age 64.8 years; males = 95), 37% of whom were bedridden (males = 60%). Nutritional status was evaluated with Mini-Nutritional Assessment (MNA) and Controlling Nutritional status (CONUT). Fat-free mass (FFM) and skeletal muscle mass (SM) were estimated using bioelectrical impedance analysis (BIA). Raw BIA variables (phase angle = PhA and impedance ratios = IRs) were also determined and handgrip strength (HGS) was measured. Dynapenia was identified according to the 2019 EWGSOP criteria.

According to MNA, 18% (n. 26) of patients were malnourished and 62% (n. 89) were at risk of malnutrition. As for CONUT, 21% (n. 31) of cases had moderate–severe malnutrition and 58% (n. 83) had light malnutrition. Abnormalities of raw BIA variables (low PhA and high IRs) and low HGS were more common in bedridden patients, in those who were malnourished, or had low FFM or SM. Dynapenic patients were 65% men and 47% women.

In conclusion, malnutrition, BC alterations, and low HGS occur in post-acute COVID-19 patients and are more common in bedridden patients. Further studies are needed to identify reliable algorithms for assessing nutritional status in post-acute COVID-19 patients undergoing rehabilitation.

Function Over Mass: A Meta-Analysis on the Importance of Skeletal Muscle Quality in COVID-19 Patients

COVID-19 caused by SARS-CoV-2 infection is a highly contagious disease affecting both the higher and lower portions of the respiratory tract. This disease reached over 265 million people and has been responsible for over 5.25 million deaths worldwide. Skeletal muscle quality and total mass seem to be predictive of COVID-19 outcome. This systematic review aimed at providing a critical analysis of the studies published so far reporting on skeletal muscle mass in patients with COVID-19, with the intent of examining the eventual association between muscle status and disease severity. A meta-analysis was performed to evaluate whether skeletal muscle quantity, quality and function were related to disease severity. Systematic reviews and meta-analyses were conducted according to the guidelines of the Cochrane Handbook for Systematic Reviews of Interventions and reported according to the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guide. From a total of 1,056 references found, 480 were selected after removing duplicates. Finally, only 7 met the specified inclusion criteria. The results of this meta-analysis showed that skeletal muscle quality, rather than quantity, was associated with COVID-19 severity, as confirmed by lower skeletal muscle density and lower handgrip strength in patients with severe disease. Muscle function assessment can thus be a valuable tool with prognostic value in COVID-19.

Prediction of abdominal CT body composition parameters by thoracic measurements as a new approach to detect sarcopenia in a COVID-19 cohort

As most COVID-19 patients only receive thoracic CT scans, but body composition, which is relevant to detect sarcopenia, is determined in abdominal scans, this study aimed to investigate the relationship between thoracic and abdominal CT body composition parameters in a cohort of COVID-19 patients. This retrospective study included n = 46 SARS-CoV-2-positive patients who received CT scans of the thorax and abdomen due to severe disease progression. The subcutaneous fat area (SF), the skeletal muscle area (SMA), and the muscle radiodensity attenuation (MRA) were measured at the level of the twelfth thoracic (T12) and the third lumbar (L3) vertebra. Necessity of invasive mechanical ventilation (IMV), length of stay, or time to death (TTD) were noted. For statistics correlation, multivariable linear, logistic, and Cox regression analyses were employed. Correlation was excellent for the SF (r = 0.96) between T12 and L3, and good for the respective SMA (r = 0.80) and MRA (r = 0.82) values. With adjustment (adj.) for sex, age, and body-mass-index the variability of SF (adj. r² = 0.93; adj. mean difference = 1.24 [95% confidence interval (95% CI) 1.02–1.45]), of the SMA (adj. r² = 0.76; 2.59 [95% CI 1.92–3.26]), and of the MRA (adj. r² = 0.67; 0.67 [95% CI 0.45–0.88]) at L3 was well explained by the respective values at T12. There was no relevant influence of the SF, MRA, or SMA on the clinical outcome. If only thoracic CT scans are available, CT body composition values at T12 can be used to predict abdominal fat and muscle parameters, by which sarcopenia and obesity can be assessed.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Obesity and Leptin Resistance in the Regulation of the Type I Interferon Early Response and the Increased Risk for Severe COVID-19

Obesity, and obesity-associated conditions such as hypertension, chronic kidney disease, type 2 diabetes, and cardiovascular disease, are important risk factors for severe Coronavirus disease-2019 (COVID-19). The common denominator is metaflammation, a portmanteau of metabolism and inflammation, which is characterized by chronically elevated levels of leptin and pro-inflammatory cytokines. These induce the “Suppressor Of Cytokine Signaling 1 and 3” (SOCS1/3), which deactivates the leptin receptor and also other SOCS1/3 sensitive cytokine receptors in immune cells, impairing the type I and III interferon early responses. By also upregulating SOCS1/3, Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 adds a significant boost to this. The ensuing consequence is a delayed but over-reactive immune response, characterized by high-grade inflammation (e.g., cytokine storm), endothelial damage, and hypercoagulation, thus leading to severe COVID-19. Superimposing an acute disturbance, such as a SARS-CoV-2 infection, on metaflammation severely tests resilience. In the long run, metaflammation causes the “typical western” conditions associated with metabolic syndrome. Severe COVID-19 and other serious infectious diseases can be added to the list of its short-term consequences. Therefore, preventive measures should include not only vaccination and the well-established actions intended to avoid infection, but also dietary and lifestyle interventions aimed at improving body composition and preventing or reversing metaflammation.

Cross-sectional area of erector spinae muscles is associated with activities of daily living at discharge in middle- to older-aged patients with coronavirus disease 2019

Objectives

Measurement of skeletal muscle wasting using computed tomography (CT) is widely known to be useful in predicting prognosis. Although some reports have been found in patients with coronavirus disease 2019 (COVID-19), few reports have focused on the ability to perform activities of daily living (ADLs). This study retrospectively investigated the relationship between the erector spinae muscle area measured from CT images and ADL at the time of hospital discharge in patients with COVID-19.

Methods

Among patients aged 40 years or older, 271 patients (median age, 65 years; 180/271 male patients) who had CT cross-sectional images of the 12th thoracic vertebral level on admission were included. The Katz index was used to assess ADLs, and patients who were not completely independent were defined as dependent. Multivariable logistic and Poisson regression analyses were applied to examine the relationship between the cross-sectional area of the erector spinae muscles and the onset of ADL dependence at discharge.

Results

A total of 75 (27.7%) patients became dependent on ADL at the time of hospital discharge. Decreased erector spinae muscle area was significantly related to dependent ADL at discharge (adjusted odds ratio: 0.886, 95% confidence interval: 0.805–0.975). In addition, the erector spinae muscle area was significantly related to the number of ADL items for which independence was not achieved (adjusted incidence rate ratio: 0.959, P < 0.001).

Conclusions

The cross-sectional area of the erector spinae muscles from the thoracic CT image was associated with the ability to perform basic ADL at hospital discharge.

Whole-Body MRI-Derived Adipose Tissue Characterization and Relationship to Pulmonary Function Impairment

Background: Specification of adipose tissues by whole-body magnetic resonance imaging (MRI) was performed and related to pulmonary function parameters in a population-based cohort. Methods: 203 study participants underwent whole-body MRI and pulmonary function tests as part of the KORA (Cooperative Health Research in the Augsburg Region) MRI study. Both visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were derived from the T1-Dixon sequence, and hepatic adipose tissue from the proton density fat fraction (PDFFhepatic). Associations between adipose tissue parameters and spirometric indices such as forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and Tiffeneau-index (FEV1/FVC) were examined using multivariate linear regression analysis excluding cofounding effects of other clinical parameters. Results: VAT (β = −0.13, p = 0.03) and SAT (β = −0.26, p < 0.001), but not PDFFhepatic were inversely associated with FEV1, while VAT (β = −0.27, p < 0.001), SAT (β = −0.41, p < 0.001), and PDFFhepatic (β = −0.17, p = 0.002) were inversely associated with FVC. PDFFhepatic was directly associated with the Tiffeneau index (β = 2.46, p < 0.001). Conclusions: In the adjusted linear regression model, VAT was inversely associated with all measured spirometric parameters, while PDFFhepatic revealed the strongest association with the Tiffeneau index. Non-invasive adipose tissue quantification measurements might serve as novel biomarkers for respiratory impairment.

Association of non-alcoholic fatty liver disease with COVID-19 severity and pulmonary thrombosis: CovidFAT, a prospective, observational cohort study

Background

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease associated with systemic changes in immune response, which might be associated with coronavirus disease 2019 (COVID-19) severity. The aim of this study was to investigate the impact of NAFLD on COVID-19 severity and outcomes.

Methods

A prospective observational study included consecutively hospitalized adult patients, hospitalized between March and June 2021, with severe COVID-19. Patients were screened for fatty liver by ultrasound and subsequently diagnosed with NAFLD. Patients were daily followed until discharge, and demographic, clinical, and laboratory data were collected and correlated to clinical outcomes.

Results

Of the 216 patients included, 120 (55.5%) had NAFLD. The NAFLD group had higher C-reactive protein (interquartile range [IQR]) (84.7 [38.6–129.8] mg/L vs 66.9 [32.2–97.3] mg/L; P = .0340), interleukin-6 (49.19 [22.66–92.04] ng/L vs 13.22 [5.29–39.75] ng/L; P < .0001), aspartate aminotransferase (58 [40–81] IU/L vs 46 [29–82] IU/L; P = .0123), alanine aminotransferase (51 [32–73] IU/L vs 40 [23–69] IU/L; P = .0345), and lactate dehydrogenase (391 [285–483] IU/L vs 324 [247–411] IU/L; P = .0027). The patients with NAFLD had higher disease severity assessed by 7-category ordinal scale, more frequently required high-flow nasal cannula or noninvasive ventilation (26, 21.66%, vs 10, 10.42%; P = .0289), had longer duration of hospitalization (IQR) (10 [8–15] days vs 9 [6–12] days; P = .0018), and more frequently had pulmonary thromboembolism (26.66% vs 13.54%; P = .0191). On multivariable analyses, NAFLD was negatively associated with time to recovery (hazard ratio, 0.64; 95% CI, 0.48 to 0.86) and was identified as a risk factor for pulmonary thrombosis (odds ratio, 2.15; 95% CI, 1.04 to 4.46).

Conclusions

NAFLD is associated with higher COVID-19 severity, more adverse outcomes, and more frequent pulmonary thrombosis.

Sleep Deprivation, Immune Suppression and SARS-CoV-2 Infection

Sleep health and its adaptation to individual and environmental factors are crucial to promote physical and mental well-being across animal species. In recent years, increasing evidence has been reported regarding the relationship between sleep and the immune system and how sleep disturbances may perturb the delicate balance with severe repercussions on health outcomes. For instance, experimental sleep deprivation studies in vivo have reported several major detrimental effects on immune health, including induced failure of host defense in rats and increased risk for metabolic syndrome (MetS) and immune suppression in humans. In addition, two novel risk factors for dysregulated metabolic physiology have recently been identified: sleep disruption and circadian misalignment. In light of these recent findings about the interplay between sleep and the immune system, in this review, we focus on the relationship between sleep deprivation and immunity against viruses, with a special interest in SARS-CoV-2 infection.

COVID-19 infection and body weight: A deleterious liaison in a J-curve relationship

During the course of the COVID-19 pandemic, obesity has been shown to be an independent risk factor for high morbidity and mortality. Obesity confers poor outcomes in younger (<60 years) patients, an age-group considered low-risk for complications, a privilege that is negated by obesity. Findings are consistent, the higher the body mass index (BMI) the worse the outcomes. Ectopic (visceral) obesity also promotes proinflammatory, prothrombotic, and vasoconstrictive states, thus enhancing the deleterious effects of COVID-19 disease. Less, albeit robust, evidence also exists for a higher risk of COVID-19 infection incurred with underweight. Thus, the relationship of COVID-19 and BMI has a J-curve pattern, where patients with both overweight/obesity and underweight are more susceptible to the ailments of COVID-19. The pathophysiology underlying this link is multifactorial, mostly relating to the inflammatory state characterizing obesity, the impaired immune response to infectious agents coupled with increased viral load, the overexpression in adipose tissue of the receptors and proteases for viral entry, an increased sympathetic activity, limited cardiorespiratory reserve, a prothrombotic milieu, and the associated comorbidities. All these issues are herein reviewed, the results of large studies and meta-analyses are tabulated and the pathogenetic mechanisms and the BMI relationship with COVID-19 are pictorially illustrated.