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      Obesity in patients with COVID-19: a systematic review and meta-analysis

      research-article
      a , b , c , 1 , a , b , c , 1 , d , c , a , b , c , e , f , a , b , c , *
      Metabolism
      Elsevier Inc.
      COVID-19, Coronavirus Disease 2019, SARS-CoV-2, severe acute respiratory syndrome coronavirus 2, ICU, intensive care unit, BMI, body mass index, CNKI, Chinese National Knowledge Infrastructure, MeSH, Medical Subject Headings, VAT, Visceral adipose tissue, IMV, invasive mechanical ventilation, OR, odds ratio, 95%CI, 95% confidence interval, SMD, standardized mean difference, NOS, Newcastle-Ottawa Scale, CT, computed tomography, IAV, Influenza A virus, ACE2, angiotensin-converting enzyme 2, SAT, subcutaneous adipose tissue, DKD, diabetic kidney disease, Obesity, Coronavirus disease 2019, Visceral adipose tissue, Intensive care, Invasive mechanical ventilation, Mortality

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          Abstract

          Background

          Obesity is common in patients with coronavirus disease 2019 (COVID-19). The effects of obesity on clinical outcomes of COVID-19 warrant systematical investigation.

          Objective

          This study explores the effects of obesity with the risk of severe disease among patients with COVID-19.

          Methods

          Body mass index (BMI) and degree of visceral adipose tissue (VAT) accumulation were used as indicators for obesity status. Publication databases including preprints were searched up to August 10, 2020. Clinical outcomes of severe COVID-19 included hospitalization, a requirement for treatment in an intensive care unit (ICU), invasive mechanical ventilation (IMV), and mortality. Risks for severe COVID-19 outcomes are presented as odds ratios (OR) and 95% confidence interval (95%CI) for cohort studies with BMI-defined obesity, and standardized mean difference (SMD) and 95%CI for controlled studies with VAT-defined excessive adiposity.

          Results

          A total of 45, 650 participants from 30 studies with BMI-defined obesity and 3 controlled studies with VAT-defined adiposity were included for assessing the risk of severe COVID-19. Univariate analyses showed significantly higher ORs of severe COVID-19 with higher BMI: 1.76 (95%: 1.21, 2.56, P = 0.003) for hospitalization, 1.67 (95%CI: 1.26, 2.21, P<0.001) for ICU admission, 2.19 (95%CI: 1.56, 3.07, P<0.001) for IMV requirement, and 1.37 (95%CI: 1.06, 1.75, P = 0.014) for death, giving an overall OR for severe COVID-19 of 1.67 (95%CI: 1.43, 1.96; P<0.001). Multivariate analyses revealed increased ORs of severe COVID-19 associated with higher BMI: 2.36 (95%CI: 1.37, 4.07, P = 0.002) for hospitalization, 2.32 (95%CI: 1.38, 3.90, P = 0.001) for requiring ICU admission, 2.63 (95%CI: 1.32, 5.25, P = 0.006) for IMV support, and 1.49 (95%CI: 1.20, 1.85, P<0.001) for mortality, giving an overall OR for severe COVID-19 of 2.09 (95%CI: 1.67, 2.62; P<0.001). Compared to non-severe COVID-19 patients, severe COVID-19 cases showed significantly higher VAT accumulation with a SMD of 0.49 for hospitalization (95% CI: 0.11, 0.87; P = 0.011), 0.57 (95% CI: 0.33, 0.81; P<0.001) for requiring ICU admission and 0.37 (95% CI: 0.03, 0.71; P = 0.035) for IMV support. The overall SMD for severe COVID-19 was 0.50 (95% CI: 0.33, 0.68; P<0.001).

          Conclusions

          Obesity increases risk for hospitalization, ICU admission, IMV requirement and death among patients with COVID-19. Further, excessive visceral adiposity appears to be associated with severe COVID-19 outcomes. These findings emphasize the need for effective actions by individuals, the public and governments to increase awareness of the risks resulting from obesity and how these are heightened in the current global pandemic.

          Highlights

          • Obesity increases risk for hospitalization among patients with COVID-19

          • Obesity increases risk for needing ICU admission among patients with COVID-19

          • Obesity increases risk for requiring IMV support among patients with COVID-19

          • Obesity increases risk for death among patients with COVID-19

          • Excessive visceral adiposity appears to be associated with severe COVID-19 outcomes

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          Most cited references67

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          Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China

          China and the rest of the world are experiencing an outbreak of a novel betacoronavirus known as severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). 1 By Feb 12, 2020, the rapid spread of the virus had caused 42 747 cases and 1017 deaths in China and cases have been reported in 25 countries, including the USA, Japan, and Spain. WHO has declared 2019 novel coronavirus disease (COVID-19), caused by SARS-CoV-2, a public health emergency of international concern. In contrast to severe acute respiratory system coronavirus and Middle East respiratory syndrome coronavirus, more deaths from COVID-19 have been caused by multiple organ dysfunction syndrome rather than respiratory failure, 2 which might be attributable to the widespread distribution of angiotensin converting enzyme 2—the functional receptor for SARS-CoV-2—in multiple organs.3, 4 Patients with cancer are more susceptible to infection than individuals without cancer because of their systemic immunosuppressive state caused by the malignancy and anticancer treatments, such as chemotherapy or surgery.5, 6, 7, 8 Therefore, these patients might be at increased risk of COVID-19 and have a poorer prognosis. On behalf of the National Clinical Research Center for Respiratory Disease, we worked together with the National Health Commission of the People's Republic of China to establish a prospective cohort to monitor COVID-19 cases throughout China. As of the data cutoff on Jan 31, 2020, we have collected and analysed 2007 cases from 575 hospitals (appendix pp 4–9 for a full list) in 31 provincial administrative regions. All cases were diagnosed with laboratory-confirmed COVID-19 acute respiratory disease and were admitted to hospital. We excluded 417 cases because of insufficient records of previous disease history. 18 (1%; 95% CI 0·61–1·65) of 1590 COVID-19 cases had a history of cancer, which seems to be higher than the incidence of cancer in the overall Chinese population (285·83 [0·29%] per 100 000 people, according to 2015 cancer epidemiology statistics 9 ). Detailed information about the 18 patients with cancer with COVID-19 is summarised in the appendix (p 1). Lung cancer was the most frequent type (five [28%] of 18 patients). Four (25%) of 16 patients (two of the 18 patients had unknown treatment status) with cancer with COVID-19 had received chemotherapy or surgery within the past month, and the other 12 (25%) patients were cancer survivors in routine follow-up after primary resection. Compared with patients without cancer, patients with cancer were older (mean age 63·1 years [SD 12·1] vs 48·7 years [16·2]), more likely to have a history of smoking (four [22%] of 18 patients vs 107 [7%] of 1572 patients), had more polypnea (eight [47%] of 17 patients vs 323 [23%] of 1377 patients; some data were missing on polypnea), and more severe baseline CT manifestation (17 [94%] of 18 patients vs 1113 [71%] of 1572 patients), but had no significant differences in sex, other baseline symptoms, other comorbidities, or baseline severity of x-ray (appendix p 2). Most importantly, patients with cancer were observed to have a higher risk of severe events (a composite endpoint defined as the percentage of patients being admitted to the intensive care unit requiring invasive ventilation, or death) compared with patients without cancer (seven [39%] of 18 patients vs 124 [8%] of 1572 patients; Fisher's exact p=0·0003). We observed similar results when the severe events were defined both by the above objective events and physician evaluation (nine [50%] of 18 patients vs 245 [16%] of 1572 patients; Fisher's exact p=0·0008). Moreover, patients who underwent chemotherapy or surgery in the past month had a numerically higher risk (three [75%] of four patients) of clinically severe events than did those not receiving chemotherapy or surgery (six [43%] of 14 patients; figure ). These odds were further confirmed by logistic regression (odds ratio [OR] 5·34, 95% CI 1·80–16·18; p=0·0026) after adjusting for other risk factors, including age, smoking history, and other comorbidities. Cancer history represented the highest risk for severe events (appendix p 3). Among patients with cancer, older age was the only risk factor for severe events (OR 1·43, 95% CI 0·97–2·12; p=0·072). Patients with lung cancer did not have a higher probability of severe events compared with patients with other cancer types (one [20%] of five patients with lung cancer vs eight [62%] of 13 patients with other types of cancer; p=0·294). Additionally, we used a Cox regression model to evaluate the time-dependent hazards of developing severe events, and found that patients with cancer deteriorated more rapidly than those without cancer (median time to severe events 13 days [IQR 6–15] vs 43 days [20–not reached]; p<0·0001; hazard ratio 3·56, 95% CI 1·65–7·69, after adjusting for age; figure). Figure Severe events in patients without cancer, cancer survivors, and patients with cancer (A) and risks of developing severe events for patients with cancer and patients without cancer (B) ICU=intensive care unit. In this study, we analysed the risk for severe COVID-19 in patients with cancer for the first time, to our knowledge; only by nationwide analysis can we follow up patients with rare but important comorbidities, such as cancer. We found that patients with cancer might have a higher risk of COVID-19 than individuals without cancer. Additionally, we showed that patients with cancer had poorer outcomes from COVID-19, providing a timely reminder to physicians that more intensive attention should be paid to patients with cancer, in case of rapid deterioration. Therefore, we propose three major strategies for patients with cancer in this COVID-19 crisis, and in future attacks of severe infectious diseases. First, an intentional postponing of adjuvant chemotherapy or elective surgery for stable cancer should be considered in endemic areas. Second, stronger personal protection provisions should be made for patients with cancer or cancer survivors. Third, more intensive surveillance or treatment should be considered when patients with cancer are infected with SARS-CoV-2, especially in older patients or those with other comorbidities.
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            Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study

            Abstract Objective To describe outcomes of people admitted to hospital with coronavirus disease 2019 (covid-19) in the United States, and the clinical and laboratory characteristics associated with severity of illness. Design Prospective cohort study. Setting Single academic medical center in New York City and Long Island. Participants 5279 patients with laboratory confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between 1 March 2020 and 8 April 2020. The final date of follow up was 5 May 2020. Main outcome measures Outcomes were admission to hospital, critical illness (intensive care, mechanical ventilation, discharge to hospice care, or death), and discharge to hospice care or death. Predictors included patient characteristics, medical history, vital signs, and laboratory results. Multivariable logistic regression was conducted to identify risk factors for adverse outcomes, and competing risk survival analysis for mortality. Results Of 11 544 people tested for SARS-Cov-2, 5566 (48.2%) were positive. After exclusions, 5279 were included. 2741 of these 5279 (51.9%) were admitted to hospital, of whom 1904 (69.5%) were discharged alive without hospice care and 665 (24.3%) were discharged to hospice care or died. Of 647 (23.6%) patients requiring mechanical ventilation, 391 (60.4%) died and 170 (26.2%) were extubated or discharged. The strongest risk for hospital admission was associated with age, with an odds ratio of >2 for all age groups older than 44 years and 37.9 (95% confidence interval 26.1 to 56.0) for ages 75 years and older. Other risks were heart failure (4.4, 2.6 to 8.0), male sex (2.8, 2.4 to 3.2), chronic kidney disease (2.6, 1.9 to 3.6), and any increase in body mass index (BMI) (eg, for BMI >40: 2.5, 1.8 to 3.4). The strongest risks for critical illness besides age were associated with heart failure (1.9, 1.4 to 2.5), BMI >40 (1.5, 1.0 to 2.2), and male sex (1.5, 1.3 to 1.8). Admission oxygen saturation of 1 (4.8, 2.1 to 10.9), C reactive protein level >200 (5.1, 2.8 to 9.2), and D-dimer level >2500 (3.9, 2.6 to 6.0) were, however, more strongly associated with critical illness than age or comorbidities. Risk of critical illness decreased significantly over the study period. Similar associations were found for mortality alone. Conclusions Age and comorbidities were found to be strong predictors of hospital admission and to a lesser extent of critical illness and mortality in people with covid-19; however, impairment of oxygen on admission and markers of inflammation were most strongly associated with critical illness and mortality. Outcomes seem to be improving over time, potentially suggesting improvements in care.
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              Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study

              Summary Background Over 40 000 patients with COVID-19 have been hospitalised in New York City (NY, USA) as of April 28, 2020. Data on the epidemiology, clinical course, and outcomes of critically ill patients with COVID-19 in this setting are needed. Methods This prospective observational cohort study took place at two NewYork-Presbyterian hospitals affiliated with Columbia University Irving Medical Center in northern Manhattan. We prospectively identified adult patients (aged ≥18 years) admitted to both hospitals from March 2 to April 1, 2020, who were diagnosed with laboratory-confirmed COVID-19 and were critically ill with acute hypoxaemic respiratory failure, and collected clinical, biomarker, and treatment data. The primary outcome was the rate of in-hospital death. Secondary outcomes included frequency and duration of invasive mechanical ventilation, frequency of vasopressor use and renal replacement therapy, and time to in-hospital clinical deterioration following admission. The relation between clinical risk factors, biomarkers, and in-hospital mortality was modelled using Cox proportional hazards regression. Follow-up time was right-censored on April 28, 2020 so that each patient had at least 28 days of observation. Findings Between March 2 and April 1, 2020, 1150 adults were admitted to both hospitals with laboratory-confirmed COVID-19, of which 257 (22%) were critically ill. The median age of patients was 62 years (IQR 51–72), 171 (67%) were men. 212 (82%) patients had at least one chronic illness, the most common of which were hypertension (162 [63%]) and diabetes (92 [36%]). 119 (46%) patients had obesity. As of April 28, 2020, 101 (39%) patients had died and 94 (37%) remained hospitalised. 203 (79%) patients received invasive mechanical ventilation for a median of 18 days (IQR 9–28), 170 (66%) of 257 patients received vasopressors and 79 (31%) received renal replacement therapy. The median time to in-hospital deterioration was 3 days (IQR 1–6). In the multivariable Cox model, older age (adjusted hazard ratio [aHR] 1·31 [1·09–1·57] per 10-year increase), chronic cardiac disease (aHR 1·76 [1·08–2·86]), chronic pulmonary disease (aHR 2·94 [1·48–5·84]), higher concentrations of interleukin-6 (aHR 1·11 [95%CI 1·02–1·20] per decile increase), and higher concentrations of D-dimer (aHR 1·10 [1·01–1·19] per decile increase) were independently associated with in-hospital mortality. Interpretation Critical illness among patients hospitalised with COVID-19 in New York City is common and associated with a high frequency of invasive mechanical ventilation, extrapulmonary organ dysfunction, and substantial in-hospital mortality. Funding National Institute of Allergy and Infectious Diseases and the National Center for Advancing Translational Sciences, National Institutes of Health, and the Columbia University Irving Institute for Clinical and Translational Research.
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                Author and article information

                Journal
                Metabolism
                Metab. Clin. Exp
                Metabolism
                Elsevier Inc.
                0026-0495
                1532-8600
                28 September 2020
                28 September 2020
                : 154378
                Affiliations
                [a ]Center for Diabetic Systems Medicine, Guangxi Key Laboratory of Excellence, Guilin Medical University, Guilin 541100, China
                [b ]Department of Immunology, Guangxi Area of Excellence, Guilin Medical University, Guilin 541100, China
                [c ]Institute of Basic Medical Sciences, Guilin Medical University, Guilin 541100, China
                [d ]Department of Geriatrics, Zhongshan Hospital, Fudan University, Shanghai 200032, China
                [e ]Department of Endocrinology, Xiangya Hospital, Central South University, Changsha 410008, China
                [f ]Department of Clinical Nutrition, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
                Author notes
                [* ]Corresponding author at: Chair Professor in Immunology, Guangxi Area of Excellence, Guilin Medical University, Zhiyuan Road 1, Lingui District, Guilin 541100, China.
                [1]

                Yi Huang and Yao Lu contributed equally to this work.

                Article
                S0026-0495(20)30242-0 154378
                10.1016/j.metabol.2020.154378
                7521361
                33002478
                41b42a76-006e-479d-9df7-3c302b2c412c
                © 2020 Elsevier Inc. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 5 August 2020
                : 15 September 2020
                : 19 September 2020
                Categories
                Article

                Molecular biology
                covid-19, coronavirus disease 2019,sars-cov-2, severe acute respiratory syndrome coronavirus 2,icu, intensive care unit,bmi, body mass index,cnki, chinese national knowledge infrastructure,mesh, medical subject headings,vat, visceral adipose tissue,imv, invasive mechanical ventilation,or, odds ratio,95%ci, 95% confidence interval,smd, standardized mean difference,nos, newcastle-ottawa scale,ct, computed tomography,iav, influenza a virus,ace2, angiotensin-converting enzyme 2,sat, subcutaneous adipose tissue,dkd, diabetic kidney disease,obesity,coronavirus disease 2019,visceral adipose tissue,intensive care,invasive mechanical ventilation,mortality

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