key: cord-0843372-etbf76sk
authors: Rossi, Andrea P.; Gottin, Leonardo; Donadello, Katia; Schweiger, Vittorio; Nocini, Riccardo; Taiana, Matteo; Zamboni, Mauro; Polati, Enrico
title: Obesity as a risk factor for unfavourable outcomes in critically ill patients affected by Covid 19
date: 2020-11-20
journal: Nutr Metab Cardiovasc Dis
DOI: 10.1016/j.numecd.2020.11.012
sha: 73021dc2ccf6fd52de23abd83a53b8cf5a67bca6
doc_id: 843372
cord_uid: etbf76sk

Background and aims Recent studies show that obesity is a risk factor for hospital admission and for critical care need in patients with coronavirus disease 2019 (COVID-19). The aim was to determine whether obesity is a risk factor for unfavourable health outcomes in patients affected by COVID-19 admitted to ICU. Methods and results 95 consecutive patients with COVID-19 (78 males and 18 females) were admitted to ICU and included in the study. Height, weight, BMI, Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, CRP, CPK, ICU and hospital length of stay and comorbidities were evaluated. Participants with obesity had a lower 28 day survival rate from ICU admission than normal weight subjects. Cox proportional hazard model-derived estimates, adjusted for age, gender and comorbidity, confirmed the results of the survival analysis (HR:5.30,95%C.I.1.26-22.34). Obese subjects showed longer hospital and ICU stay as compared with normal weight counterpart.Subjects with obesity showed significantly higher CRP and CPK levels than normal weight subjects. Conclusion In individuals with obesity, careful management and prompt intervention in case of suspected SARS-CoV-2 infection is necessary to prevent the progression of the disease towards severe outcomes and the increase of hospital treatment costs.

INTRODUCTION 1 kg/m2), overweight (BMI between 27 and 29.9 kg/m2) and subjects with obesity (BMI≥30). 1 2

Repeated venous blood samples for C-reactive protein (CRP) and creatin phosphokinase (CPK) 4 levels were obtained after overnight fasting. 5

For the measurement of CRP a kit was used for the quantitative determination of CRP in serum 6 (CRP ROCHE applied on ROCHE/HITACHI COBAS 702, Roche Diagnostics GmbH, Mannheim , 7

Germany). Measurement uncertainty declared 5.8%. The coefficient of variation (CV) of annual 8 CQI to 6 mg/L is 6%. 9

Creatine phosphokinase (CPK) concentration was determined on a fully automated analyser 10 ANALIZER ROCHE/HITACHI COBAS 702 (Roche Diagnostics GmbH, Mannheim , Germany). 11

A multicenter evaluation of the within-run precision of the Advia 2120 system showed CV always 12 lower than 0.7% for CPK . 13 Procalcitonin concentration were determined on the fully automated analyser ANALIZER 14 ROCHE/HITACHI COBAS 801 (Roche Diagnostics GmbH, Mannheim , Germany). A multicenter 15 evaluation of the within-run precision of the ROCHE/HITACHI COBAS 801 system showed CV 16 always lower than 1.8% for PCT. 17 D-dimer concentration were determined on the fully automated analyser ANALIZER ACL TOP 18 700 (Roche Diagnostics GmbH, Mannheim , Germany). A multicenter evaluation of the within-run 19 precision of the Advia 2120 system showed CV of 2% for D-dimer. 20

Further routine variables, such as sodium, potassium, creatinine and white blood cells where also 21 collected at ICU admission and reported. 22

Our primary outcome (goal) was to assess if obesity is associated with mortality, ICU length of stay 25 (LOS) and hospital LOS. Our secondary outcome (goal) was to assess if obesity condition can 1 influence the trend of inflammation as evaluated with CRP and critically ill induced myopathy as 2 evaluated with CPK. 3 4

Results are shown as mean ± SD. Variables not normally distributed were log-transformed before 6 analysis. Discrete variables are expressed as counts (percentage) and continuous variables as means 7 ± standard deviation (SD). For the demographic and clinical characteristics of the patients, 8 differences between groups were assessed using the chi-squared test and Fisher's exact test for 9 categorical variables and the Student's t-test or Mann-Whitney U test for continuous variables. 10 Differences in mortality rates across the three groups of participants were preliminarily evaluated 11

fitting Kaplan-Meier survival curves adjusted for age. Cox proportional hazard models and logistic 12 regression models were used to assess the risk of death. 13

Hazard Ratio (HR) and 95% Confidence intervals (95%C.I.) were estimated to investigate the 14 association of the three study groups with the risk of mortality. Three models were fitted for each 15 outcome: unadjusted, age and gender adjusted and adjusted for all potential confounders. Candidate 16 variables to be included in the third model were selected on the basis of the biological and clinical 17 plausibility as risk factors for mortality (age, sex, smoking status, coronary heart disease, congestive 18 heart failure, hypertension, diabetes, neurological pathology, chronic obstructive pulmonary 19 disease, chronic renal failure, immunodepression and cancer). Additional models such as interaction 20

terms BMI x age and BMI x sex were also included. 21

Students t-test for impaired data was used to compare the trend of different biochemical variables 22 between normal weight subjects, overweight and subjects with obesity. 23

The BMI value associated with higher mortality was evaluated by means of the receiver operating 24 characteristics (ROC) curve, and was obtained by plotting estimates of the true positive rate (i.e. 25 sensitivity) against the false positive rate (1-specificity) for each possible BMI value. Youden's 1 index was then calculated. 2 A p-value of 0.05 or less was considered to be statistically significant. Table 1 shows the main characteristics of the study population (mean ± SD) at baseline, divided in 25 normal weight, overweight and subjects with obesity. 95 subjects, 78 men and 17 women (17.9%) 1 were included in the study. 2

All patients were COVID-19 positive as confirmed by Nasopharyngeal-swab specimens which were 3 performed before or within the first 6 hours of ICU stay. 4

As regards therapy all the patients received hydroxychloroquine. Eighty-seven patients (92%) 5 received lopinavir/ritonavir, while 8 patients (8%) were treated with remdesivir. Tocilizumab was 6 administered to 20 patients (21%). 7

Mean delay between the onset of symptoms and hospital admission was 3.87 ± 2.2 days and 8 between hospital and ICU admission was 2.15 ± 2.37 days. 9

The mean APACHE II score was 23.47 ± 9.96 and the mean SOFA score pre-intubation was 5.94 ± 10 2.38. 91 out of 95 subjects required invasive mechanical ventilation. The most common 11 comorbidities were hypertension (47.4.%), followed by heart disease (38.9%), obesity (35.78%) and 12 diabetes (18.95%). 13 18 patients (18.9%) died during the first 28 days since ICU admission. The mean CPK level 14 adjusted for weight at admission and on day 2 was significantly higher in subjects who died than in 15

survivors. 16 Figure 1 shows that participants with obesity had the shorter survival at 28 After excluding individuals who died in the first 28 days since ICU admission, obese subjects 3 showed longer hospital and intensive care stay as compared with normal weight counterpart. 4

From day 5 to day 8 CRP values were significantly higher in the group of subjects with obesity as 5 compared with the normal weight counterpart (Figure 2A ). 6

Subjects with obesity showed significantly higher CPK values from day 7 and day 8 and overweight 7 higher values at admission, day 4 and day 5 as compared with normal weight subjects, even after 8 adjustment for body weight ( Figure 2B ). The threshold for BMI associated with a higher risk of 9 mortality in ICU was calculated locating the point on the ROC curve that maximizes the sum of 10 sensitivity and specificity (i.e. Youden's index). This cutoff associated with sensitivity of 0.611 and 11 specificity of 0.519 was 29.23 kg/m 2 . 12 DISCUSSION 1

Our study shows that in COVID-19 critically ill subject obesity (BMI≥30) is associated with a 5 2 times higher risk of mortality and longer hospital stay. During the first 7 days in ICU subjects with 3 obesity showed increased CRP and CPK levels as compared with normal weight subjects. When the study population was further divided according to BMI categories (normal weight, 9 overweight and obese), subjects with obesity showed higher hospital and ICU length of stay. 10

Previous reports on hospitalized patients observed that higher BMI is associated with increased 11 morbidity and LOS [23, 24], which is one of the major determinants of hospital costs [25] . 12

We found that subjects with obesity have a longer ICU stay than those with normal weight. Preliminary reports on SARS-CoV-2 spread showed that obese subjects are at greater risk for ICU 16 admission and invasive mechanical ventilation [27], but the potential mechanisms that explain why 17 obesity is associated with increased COVID-19 severity have not been determined. 18

We found that during the first week in ICU, CRP levels were higher in subjects with obesity when 19 compared with normal weight subjects. COVID-19 is characterized by a reduction of T-20

Lymphocytes, involving in particular CD4+ and CD8+ lines with concomitant 4 times increased 21 mortality when CD8+ level is below 75 cells/uL [28] . De Biasi et al [28] recently showed that 22

Covid-19 is characterized by a "cytokine storm" with increase in TNF-alfa, CCL4, CD 27, CD 40, 23 IL-2, IL-4, IL-7, IL 8, IL-10, IL-15, Il-17A, but the greatest increase compared to controls has been 24 observed for IL-6. Obesity is associated with a state of low grade chronic inflammation that contributes to the onset of metabolic disease, type 2 diabetes, and dyslipidemia in particular and can 1 determine a dampened immune response to infectious agents, leading to poorer outcomes post-2 infection [29, 30] . 3

Obesity has been also associated with higher circulating IL-6 and CRP levels [25, 26] . Adipose 4 tissue, in particular visceral fat, has been recognized to be able to secrete great amounts of 5 inflammatory cytokines [31]. 6

Therefore, together, SARS-CoV-2 infection and obesity may lead to a dysregulated immune 7 response and increased viral shedding, which could impact the outcome of COVID-19 patients [32] . 8

In both, human and murine models, it has been shown that obese subjects present increased viral 9 replication process, respiratory epithelium damage, pulmonary oedema, increased inflammatory 10 response, immunopathology and poor wound recovery as compared with lean subjects, resulting in 11 It could be hypothesized that this histopathological feature, associated with significant deficit of the 6 upper and lower limbs can be determined by the exaggerated inflammatory response observed in 7 patients with COVID-19 and that in subjects with obesity this phenomenon can be even more 8 relevant, leading to worse clinical outcomes and higher mortality, as shown in our study population. 9

In fact, obesity is associated with increased muscle vulnerability, characterized by higher levels of 10 myostatin, IL-6 and macrophages, interfering with satellite cell activation and myoblast 11 proliferation and differentiation, which are necessary steps for the muscle repair process after 12

Moreover obesity is characterized by an increase of lipid fat deposition between muscle fibers, the 14 so-called intermuscular adipose tissue. This fat depot is a metabolically active tissue, capable of 15 secreting inflammatory cytokines, increasing ceramides content and contributing to muscle damage 16 in paracrine manner [43, 44] . We can theorize that the close proximity to muscle fibers may impair 17 the local muscle environment, leading to an impairment of muscle regeneration, also in critically ill 18 COVID-19 patients. 19 CPK elevation observed in subjects with obesity could be a sign of muscle injury and partially 20 explains the increased risk for ICU mortality and LOS observed in our population. In fact, patients 21 with high CPK levels are more likely to receive invasive treatment in the ICU, so their muscle mass 22

and strength may decrease with increasing ICU stay [37] and are less likely to be discharged home, 23 because of hospital related independence loss. Further investigation of the true incidence, 24 complication rate and independent risk factors for elevated CPK might be warranted.

Some limitations need to be mentioned. Firstly this is a single center observational prospective 1 study. Secondly a limited number of subjects were enrolled, with few younger patients and very few 2 older (very old) subjects. 3

The study sample size precludes meaningful exploration of the association of wasting with specific 4 disease entities. 5

Thirdly, we used CRP as a nonspecific marker of inflammation. It is known that CRP responds 6 relatively slowly to inflammatory stimuli and can be influenced by liver function, given that it is 7 hepatically synthesized. Although strictly regulated by cytokines, mostly IL-6 and TNF-alfa [45, 8 46] , once daily CRP measurement has limited capacity to define the nature, cause, and scale of 9 global and sustained inflammatory load. Further studies evaluating IL-6, TNF-alfa and adipokines 10 should be conducted in order to better understand the pathogenetic link between obesity and 11 unfavourable ICU health outcomes. 12

Last, BMI is a surrogate of total adiposity and to better understand the role of adipose tissue on 13 COVID-19 severity, studies evaluating body fat distribution and consequent organ fibrosis should 14 be implemented. 15

Our study shows that in COVID-19 ICU subject obesity is a risk factor for unfavourable health 16 outcomes with higher mortality and longer hospital stay. 17

As the SARS-CoV-2 may continue to spread worldwide, clinicians should maintain a high level of 18 attention in patients with obesity. Obese subjects should be carefully monitored and managed with a 19 prompt and aggressive approach in order to prevent serious and life-threatening consequences and 20 the increase of treatment associated costs. Table 1 Baseline demographic, anthropometric and clinical characteristics in different BMI groups (females, n = 78; males, n = 18) Normal weight (n=28) Overweight (n=33) Obese (n=34)

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