key: cord-1009887-otsgiiln authors: Yang, Jun; Tian, Congmin; Chen, Ying; Zhu, Chunyan; Chi, Hongyu; Li, Jiahao title: Obesity aggravates COVID‐19: An updated systematic review and meta‐analysis date: 2020-12-01 journal: J Med Virol DOI: 10.1002/jmv.26677 sha: f78777800e9ec99d42f730f9caa22cd0c075ba46 doc_id: 1009887 cord_uid: otsgiiln This review aimed to evaluate the impact of obesity on the onset, exacerbation, and mortality of coronavirus disease 2019 (COVID‐19); and compare the effects of different degrees of obesity. PubMed, EMBASE, and Web of Science were searched to find articles published between December 1, 2019, and July 27, 2020. Only observational studies with specific obesity definition were included. Literature screening and data extraction were conducted simultaneously by two researchers. A random‐effects model was used to merge the effect quantity. Sensitivity analysis, subgroup analysis, and meta‐regression analysis were used to deal with the heterogeneity among studies. Forty‐one studies with 219,543 subjects and 115,635 COVID‐19 patients were included. Subjects with obesity were more likely to have positive SARS‐CoV‐2 test results (OR = 1.50; 95% CI: 1.37–1.63, I (2) = 69.2%); COVID‐19 patients with obesity had a higher incidence of hospitalization (OR = 1.54, 95% CI: 1.33–1.78, I (2) = 60.9%); hospitalized COVID‐19 patients with obesity had a higher incidence of intensive care unit admission (OR = 1.48, 95% CI: 1.24–1.77, I (2) = 67.5%), invasive mechanical ventilation (OR = 1.47, 95% CI: 1.31–1.65, I (2) = 18.8%), and in‐hospital mortality (OR = 1.14, 95% CI: 1.04–1.26, I (2) = 74.4%). A higher degree of obesity also indicated a higher risk of almost all of the above events. The region may be one of the causes of heterogeneity. Obesity could promote the occurrence of the whole course of COVID‐19. A higher degree of obesity may predict a higher risk. Further basic and clinical therapeutic research needs to be strengthened. Some studies have systematically evaluated the impact of obesity on the whole process of COVID-19. 5, 6 However, whether the influences of various degrees of obesity are different, especially for the positive diagnosis of COVID-19 and hospitalization, have not been involved in current systematic reviews. The aim of this systematic review and meta-analysis of observational studies is to evaluate the impact of obesity on the positive SARS-CoV-2 test result of subjects, hospitalization of COVID-19 patients, and intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and in-hospital mortality of hospitalized COVID-19 patients, and to compare the effects of different body mass index (BMI) ranges. We reported this systematic review and meta-analysis based on the PRISMA statement and the MOOSE checklist. 7, 8 Our research protocol has been registered in PROSPERO-the international prospective register of systematic reviews (CRD42020203399). We carried out the project according to the research protocol. After importing the downloaded titles and abstracts into EndnoteX9 software, the duplicate documents were removed using the software's de-duplication function and manual reading of the authors, The final included studies should be observational ones with targeted outcome indicators, including positive SARS-CoV-2 test results, hospitalization, ICU admission, invasive mechanical ventilation, and in-hospital mortality. These papers should also include the odds ratio (OR) and 95% confidence intervals (CIs) of outcome indicators of obesity compared to those without. For in-hospital mortality, the value could be OR, risk ratio (RR), or hazard ratio (HR). Those studies with the following characteristics would be excluded: cases overlap with other larger ones, have less than 20 cases, or without a clear definition of obesity (for non-Asian: BMI ≥ 30 kg/m 2 ; for Asian: BMI ≥ 28 kg/m 2 ) or overweight (for non-Asian: 30 kg/m 2 >BMI ≥ 25 kg/m 2 ; for Asian: 28 kg/m 2 >BMI ≥ 24 kg/m 2 ). In this process, if two studies overlap in both source hospital and collection time, we consider the overlap of cases. In this case, we would include the larger one. We have contacted 11 authors by E-mail who had not mentioned the exact definition of obesity in their papers. Six of them have responded with the detailed diagnostic criteria for obesity. One has provided data that were not published in the original article. The other five studies were not included in the final analysis. We established an information extraction table. Two researchers with medical postgraduate education background (Jun Yang and Congmin Tian) independently extracted the literature information. If there were any differences, it would be solved through negotiation and discussion. Information extracted from each of the included studies included: (1) basic information of the article (the first author and title); (2) characteristics of the survey (country, the period of participation, the cut-off point of BMI, outcome indicators, study type); (3) characteristics of subjects (source of subjects, caseload, number of males, age); (4) summary measures: OR, RR, or HR as mentioned above. We would prioritize the adjusted values provided in the original text rather than the unadjusted ones calculated based on binary variables. Stata16.0 software was used to merge effect indicators and calculate other related values. I 2 was used to calculate the heterogeneity among studies. It would be considered low, medium, high, and very high in the range of ≤25%, 25%-50%, 50%-75%, and ≥75%. 9 The advantage of this approach relies on its independence of the number of studies included. When the heterogeneity was high or very high, we would find possible sources by sensitivity analysis, subgroup analysis, and meta-regression analysis. These potential sources include region, caseload, age, study type, and type of value. When the heterogeneity was very high, we would not carry out a meta-analysis but just conduct a systematic review. As the heterogeneity among studies could not be entirely measured by I 2 , we used the randomeffects model to merge the effect indicators. This method would take into account both intra-study and inter-study variation. To assess the risk of bias in the included literature, two researchers (Jun Yang and Congmin Tian) independently used the Newcastle-Ottawa Quality Assessment Scale (NOS) to score the quality of each research. 10 Most of the included studies scored 7 or above, indicating that the overall quality was high. The inconsistency was solved through negotiation and discussion. We drew a funnel plot and made a preliminary judgment from the visual symmetry to assess the risk of bias among the included articles. Also, Egger's test was conducted, and p < .05 indicated that the existence of publication bias could not be rejected. Two pairs of studies with overlapping cases were included in the meta-analysis but belonged to different outcome indicators. 3, 13, 11, 12 The included studies were mainly conducted in the USA and Europe, including 23, 5, 3, 2, 2, 2, 1, 1, 1, and 1 from the USA, Italy, Table 1 . The research quality scores based on the NOS are shown in Table S1 . This section included three studies, which were from the USA, 11 Mexico, 14 Pooled analysis showed that subjects with obesity had a higher incidence of positive test results than those without (OR = 1.50, 95% CI: 1.37-1.63, I 2 = 69.2%, Figure 2A ). The trend of the pooled results did not change after each study was removed ( Figure S1 ). Due to the small number of included studies, no subsequent subgroup analysis, meta-regression, or funnel plot were conducted. Figure S2 ). A total of 11 studies were included in this section, including eight from the USA 11,13,16-21 and the remaining three from Brazil, 22 Mexico, 14 and Spain. 23 Of the 70795 confirmed patients included, 25 ,403 were hospitalized. The hospitalization rate ranged from 10.8% to 85.0% among included studies. All the research studies were case-control studies. Pooled analysis showed that COVID-19 patients with obesity had a higher incidence of hospitalization than those without (OR = 1.54, 95% CI: 1.33-1.78, I 2 = 60.9%, Figure 2B ). The trend of this result did not change after each study was excluded ( Figure S3 ). We conducted subgroup analysis and meta-regression analysis on all included studies. We found no confounding factors causing heterogeneity among studies (Tables S2 and S5) . We further compared the possibility of hospitalization among COVID-19 patients with different BMI ranges. The results showed that a higher BMI would predict higher possibility of hospitalization ( Figure S4 ). A total of 15 studies were included, 10 from the USA, 3, 13, 16, 19, [24] [25] [26] [27] [28] [29] two from Italy, 30, 31 and the remaining three from China, 32 Mexico, 14 and Spain, 33 Pooled analysis showed that hospitalized COVID-19 patients with obesity had a higher incidence of ICU admission than those without (OR = 1.48, 95% CI: 1.24-1.77, I 2 = 67.5%, Figure 2C ). The trend of this result did not change after each study was excluded ( Figure S5 ). We conducted subgroup analysis and meta-regression analysis on all included studies and found region to be the possible confounding factor causing heterogeneity among studies (Tables S3 and S6) . We further compared the possibility of ICU admission among hospitalized patients with different BMI ranges. The results showed that patients with a higher BMI may have a higher trend of ICU admission, though they were not significant ( Figure S6 ). A total of 14 studies were included, including nine from the USA, 19 Figure 2D ). We further compared the possibility of IMV among hospita- Figure S7 ). A total of 23 studies were included in this section, including 11 from the USA, 11, 12, 25, 26, 28, 29, 35, 37, [39] [40] [41] five from Italy, 30, 31, [42] [43] [44] and the remaining seven from Brazil, 22 China, 45 France, 46 Greece, 47 Mexico, 14 UK, 48 Pooled analysis showed that hospitalized COVID-19 patients with obesity had a higher incidence of in-hospital mortality than those without (OR = 1.14, 95% CI: 1.04-1.26, I 2 = 74.4%, Figure 2E ). The direction of this result did not change after each study was excluded ( Figure S8 ). We conducted subgroup analysis and metaregression analysis on all included studies. We found no confounding factor causing heterogeneity among the included studies (Tables S4 and S7) . We further compared the possibility of in-hospital mortality among 1.07-2.26, I 2 = 0.0%; Figure S9 ). (Table S8) . This systematic review and meta-analysis found that subjects with obesity were more likely to show positive results in the detection of SARS-CoV-2. Obese COVID-19 patients were more likely to be hospitalized than those without. Hospitalized COVID-19 patients with obesity were more likely to receive ICU admission, invasive mechanical ventilation, and die than those without. A higher degree of obesity also indicates a higher risk of occurrence for the above events. Ten systematic reviews have assessed the relationship between obesity and COVID-19. Tamara et al. 50 almost the same time. 51 We have found that the BMI of COVID-19 patients with severe conditions was significantly higher than those with mild conditions. The risk of developing severe conditions in COVID-19 patients with obesity was significantly higher than those without. Zhou et al., 52 Sales-Peres et al., 53 and Malik et al. 6 have come to almost the same conclusion as us. Pranata et al. 54 looked at the association between higher BMI and the risk of composite adverse endpoints, death, and critical illness. They found that a higher BMI was associated with increased risk of these events. However, most studies' BMI ranges were not consistent, which may lead to a decline in the reliability and extrapolation of the results. Hussain et al have found a significant correlation between BMI > 25 kg/m 2 and increased mortality, needs for respiratory support, and critical illness in COVID-19 patients. 55 Földi et al. 56 found that obesity was a risk factor for ICU admission and IMV therapy. They have also compared the risk of receiving IMV in different BMI ranges of COVID-19 patients and found that a higher BMI indicates a higher risk of receiving IMV. Malik et al. 57 Obesity is a risk factor for many diseases, and eating healthily and exercising regularly should be a program to be adhered to by people worldwide. Also, the pathophysiological characteristics of obese F I G U R E 3 Funnel plots of the literature concerning effect indicators. A, Hospitalization. B, ICU admission. C, Invasive mechanical ventilation. D, In-hospital mortality COVID-19 patients need further study. When writing this paper, a newly published multicenter study further suggested the relationship between obesity and mortality in COVID-19 patients, which further confirmed our conclusion. 62 This also indicates the importance of high-quality observational studies and basic research. Obesity could promote the occurrence of positive SARS-Cov-2 test results, hospitalization of COVID-19 patients, ICU admission, invasive mechanical ventilation therapy, and in-hospital mortality of hospitalized COVID-19 patients. Subjects with a higher degree of obesity may have a greater risk of developing the adverse outcomes mentioned above. Further basic and clinical therapeutic research concerning this aggravation needs to be strengthened. The authors declare that there are no conflict of interests. Jun Yang and Chunyan Zhu conceived the plan for this study. The data that support the findings of this study are available on request from the corresponding author. 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