key: cord-1002314-c3cgj16i
authors: Chowdhury, Akibul Islam; Alam, Mohammad Rahanur; Rabbi, Md. Fazley; Rahman, Tanjina; Reza, Sompa
title: Does higher Body Mass Index increase COVID-19 severity?: a systematic review and meta-analysis
date: 2021-04-15
journal: Obes Med
DOI: 10.1016/j.obmed.2021.100340
sha: d978116075f84fb3baaf514a76f2364048f1e949
doc_id: 1002314
cord_uid: c3cgj16i

INTRODUCTION: Obesity and higher BMI is one of the leading comorbidities to increase the risk of COVID-19 severity. This paper presents a systematic review and meta-analysis estimating the effects of overweight and obesity on COVID-19 disease severity. METHOD: ology: Two electronic databases (Medline and Cochrane library) and one grey literature database (Grey Literature Report) were searched. The risks of bias of the selected studies were assessed by using the Navigation Guide method for human data. Both random and fixed effect meta-analyses were determined using Review Manager (RevMan) software version 5.4. RESULTS: After initial screening, 12 studies were fulfilled the eligibility criteria, comprising a total of 405359 patients, and included in the systematic review. The pooled risk of COVID-19 severity was 1.31 times higher based on both fixed and random effect model among those overweight patients, I(2) 0% and 2.09 and 2.41 times higher based on fixed and random effect respectively among obese patients, I(2) 42% compared to healthy individuals. CONCLUSION: Overweight and obesity are found to be risk factors for disease severity of COVID-19 patients. However, further assessment of metabolic parameters is required to estimate the risk factors of COVID-19 patients and understanding the mechanism between COVID-19 and body mass index.

Coronavirus disease 2019 (COVID-2019)-caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus-was declared a pandemic by the World Health Organization on March 11, 2020 [1] . As of February 11, 2021, COVID-19 has infected almost 107 million people worldwide, with a death toll of over 2.3 million [2] . Previously, two highly pathogenic Coronaviruses resulted in outbreaks of a severe acute respiratory syndrome (SARS) in 2003 in Guangdong province, China, and the Middle East respiratory syndrome (MERS) in Middle Eastern countries in 2012 [3] [4] [5] [6] . Multiple risk factors are associated with incidence and mortality in COVID-19 patients [7] . An increasing body of data suggests that individuals with diabetes mellitus [8] , hypertension, and severe obesity (BMI ≥ 40 kg/m 2 ) are more likely to be infected and are at a higher risk for complications and death from COVID-19 [9] [10] [11] [12] [13] [14] [15] . Many countries mentioned body mass index (BMI) as a clinical risk factor of COVID-19, such as China [17] , Italy [18] , United States [10], as the immunity system plays a vital role in obesity-induced adipose tissue inflammation [12] .

Emerging literature suggested that adults with obesity under the age of 60 are more likely to be hospitalized [19] . The prevalence of obesity among adults is increasing day by day due to insufficient physical activities. A previous study showed a strong correlation between obesity and complications of viral infections (influenza virus, SARS, and MERS) [20] . Many studies found that excessive weight gain ≥ 18 kg may increase the risk of developing communityacquired pneumonia [8, 21] . Severe obesity might increase the duration of hospital stay and the case fatality rate [15, 22] . However, two earlier reports suggested no difference in body mass index(BMI) between severe and non-severe groups [19, 23] . Although several studies addressed the impact of the body mass index (BMI) on COVID-19, a definite conclusion has not been drawn yet. Hence, this meta-analysis was conducted to elucidate the relationship between obesity and COVID-19 by searching existing literature.

We searched two electronic databases: MEDLINE (on October 20, 2020), Cochrane library (on October 21, 2020), and one grey literature database: Grey Literature Report (on October 21, 2020). Searches were carried out following an appropriate search strategy in English. We searched the literature using the following keywords: overweight, obesity, body mass index, respiratory disease, coronavirus, COVID-19. Manual searching was also performed to identify potentially eligible studies.

All articles found in the searches were downloaded, and duplicate articles were identified and excluded. Two independent authors screened the titles and abstracts for finding duplicates and then screened the full texts to select the eligible articles. If there were any disagreements between the review authors, a third author's option was considered to reach a decision.

Following the PRISMA guideline, the study selection process is presented in a flow chart ( Figure 1 ).

PECO definitions are described below: All other comparators were excluded.

 Outcomes: Severity of disease was used as an outcome in this systematic review.

Here, the term "Severity" is defined as the impact of COVID-19 on fatality, utilization of health care resources such as increase of hospital stay, ventilation, other services, and comorbidities [24] .

We included studies that measured the effect of overweight and obesity on COVID-19 disease severity. Eligible studies were randomized control trials, cohort studies (both prospective and retrospective), case-control studies. Records published only in the English language were considered. We have included both published and unpublished studies. Studies conducted using unethical practices were excluded.

We included measures of the relative effect of overweight and obesity on the severity of disease (prevalence and incidence), compared with the patient with optimum BMI. We included relative effect measures such as RRs, ORs, and Hazard ratios. Some of our studies were retrospective case-control studies for which RR could not be calculated. Hence, we had to recalculate the RR and HR of other studies into OR (Supplementary Table 1 ). If a study presented estimates for effect from two or more alternative models that had been adjusted for different variables, then we systematically prioritized the estimate from the model that provided information on the relevant confounders or mediators, at least the core variables:

age, sex, and socioeconomic position. We prioritized estimates from models adjusted for more potential confounders over those from models adjusted for fewer. For example, if a J o u r n a l P r e -p r o o f study presents estimates from a crude, unadjusted model (Model A), a model adjusted for one potential confounder (e.g., age; Model B) and a model adjusted for two potential confounders (e.g., age and sex; Model C), then we prioritized the estimate from Model C.

Two independent reviewers extracted the data on study characters (study authors, study country, population size, study year, exposure, and outcome), study design, and risk of bias (including source population representation, blinding, exposure assessment, outcome assessment, confounding, incomplete outcome data, selective outcome reporting, conflict of interest and other sources of bias).

There is no standard method of assessing the risk of bias of selected studies for the systematic review. The risk of bias of this review was assessed by nine risk factors of bias included in the Navigation Guide method for human data. These were: (i) source population representation; (ii) blinding; (iii) exposure assessment; (iv) outcome assessment; (v) confounding; (vi) incomplete outcome data; (vii) selective outcome reporting; (viii) conflict of interest; and other sources of bias. The ratings for all domains were: "low,"; "unclear," and "high." Two independent reviewers assessed the risk of bias of selected studies. When there is a disagreement between the two reviewers, a third reviewer's option was considered.

Funnel plots were generated to assess the potential concerns on publication bias (Supplementary figure 1-4) .

We assessed heterogeneity by reporting the I 2 (% residual variation due to heterogeneity) and tau 2 (method of moments estimates of between-study variance) of the pooled estimate. As to account for cross-study heterogeneity and check for robustness and potential outliers, both random effect and fixed-effect models were used to measure the relationship between obesity and COVID-19 disease severity. The 95% confidence interval has been reported in a pooled analysis. All analysis was done by using Review Manager (RevMan) software version 5.4.

A total of 193 individual studies were identified in our searches. Twelve studies fulfilled our eligibility criteria and were included in the systematic review ( Figure 1 ). Of the 12 included studies [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] , nine studies were included in the meta-analysis [25, 27-31, 33, 35, 36] . The initial excluded 121 articles had little to no relevance to our present study. Some of the excluded articles were review articles, meta-analysis, and in some cases, complete literature was unavailable.

Most of the studies were cohort studies (7 studies), followed by case-control studies (4 studies) and one cross-sectional analysis. The total population of the included studies was 405359. The most commonly studied countries were the United States (6 studies) and China (4 studies). The comparator of most studied was BMI ≤ 25kg/m 2 (Table 1) .

Nine studies reported the relation between COVID-19 disease severity with overweight and obesity ( Table 3) .

The risks of bias rating for each domain for all 12 studies for this outcome are presented in Table 2 . Our symmetrical funnel plots depict that our study is not prone to publication or reporting bias (Supplementary figure 1-4) .

The effect of overweight on disease severity of COVID-19 patients was measured comparing with normal body weight. The meta-analysis of selected nine studies showed that the pooled risk of disease severity was 1.31 times higher based on both fixed and random effect model among those patients who were overweight, I 2 0% (Figure 2 and 3) .

The pooled risk of disease severity was 2.09 and 2.41 higher based on fixed and random effect respectively among obese patients compared with regular bodyweight patients, (Figure 4 and 5 ).

Of the 41 studies examined, some studies failed to find any association between BMI and COVID-19 [26, 28] , and some studies did not measure BMI as a risk factor of COVID-19. So finally, we included 12 studies that covered all the selection criteria. The present study has accumulated all the findings related to COVID-19 and BMI. Interestingly, it was found that BMI is a risk factor for COVID-19 patients. Another meta-analysis showed that obesity increased the severity of COVID-19 patients, and J o u r n a l P r e -p r o o f obesity is considered a significant risk factor [38] . Various studies previously documented for different viral pathogens, including influenza, that obesity was a substantial risk factor for disease severity [43] [44] [45] . During the 2009 H1N1 pandemic, it was found that the rates of hospitalization and deaths were higher among overweight and obese patients [8] .

Several parameters with overweight and obesity play a role in the disease severity of COVID-19. However, there is no exact mechanism that explains the contribution of overweight and obesity to severe COVID-19 outcomes. Nevertheless, obesity has adverse effects on lung function, diminishing forced expiratory volume and forced vital capacity [46] .

It is also reported that respiratory physiology is changed by obesity with the decreased functional ability of the respiratory system [47] . Another study found that obesity impaired immune system surveillance and response [48] . Obesity was also found to weaken the respiratory function, gas exchange, lung volume, increase comorbidities (CVD, T2D, kidney disease), and metabolic risk (hypertension, insulin resistance, and dyslipidemia), which contributed to the disease severity of COVID-19 patients [49] . Some studies explained why obese people presented a worse clinical outcome than a typical patient. These studies concluded that overweight and obese people have a different innate and adaptive immune response and have higher leptin and lower adiponectin concentrations, which leads to dysregulation of immune response and contributes to worsening pathogenesis conditions [50] [51] [52] . Another study found that obesity reduced the activity of macrophages when an antigen is presented [53] . Obesity was also directly associated with basal inflammatory status characterized by higher circulating Interleukin 6 and C-reactive protein levels [46] . Obesity also impaired the adaptive immune system responses to the influenza virus [54] . It is crucial to understand and determine the relationship between obesity and COVID-19 to reduce the risk of developing severe COVID-19 illness. The lifestyle of people should be improved to lessen risk both in the current and subsequent waves of COVID-19.

The present study has some limitations. We only used articles that were published in the English language and had full-text availability. In few instances, we could not find full articles that were excluded from our study. Some of our studies were retrospective casecontrol studies; therefore, we could not calculate the RR for those studies. We had to recalculate the RR and HR of other studies into OR, which has a likelihood of overestimation. Since our study population is predominantly from China, the USA, UK, and France, it limits the opportunity to assess the universal scenario.

The study found that overweight and obesity to be potential risk factors for increased disease severity of COVID-19 patients. Nevertheless, further assessment of metabolic biomarkers is required to estimate the risk factors of COVID-19 patients and understanding the mechanism between COVID-19 and body mass index. Therefore, we recommend that additional attention be given to obese patients and other patients during this epidemic.

Mohammad Rahanur Alam and Akibul Islam Chowdhury have conceptualized the study, analyzed and interpreted the data, and wrote the first draft of the manuscript. Md. Fazley Rabbi, Tanjina Rahman, and Sompa Reza helped in the data acquisition and study selection process and revised the draft critically for important intellectual content. All authors read and approved the final manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethics approval was not required for this study.

The datasets generated during this study are available from the corresponding author on a reasonable request.

The authors have declared no conflict of interest. J o u r n a l P r e -p r o o f 

World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)

WHO Coronavirus disease (COVID-19) dashboard [WWW document

Hospital outbreak of Middle East respiratory syndrome coronavirus

Identification of a novel coronavirus in patients with severe acute respiratory syndrome

Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia

Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People's Republic of China

Comorbidities might be a risk factor for the incidence 10

Covid-19 in critically ill patients in the Seattle region-case series

Clinical characteristics of coronavirus disease 2019 in China

Risk of COVID-19 for patients with obesity

Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy

Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention

Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia

Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region

Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China

COVID-19 pandemic, coronaviruses, and diabetes mellitus

A novel risk factor for a novel virus: obesity and 2009 pandemic influenza A (H1N1)

Clinical determinants for fatality of 44,672 patients with COVID-19

Metformin and COVID-19: a novel deal of an old drug

Encyclopedia of behavioral medicine

Obesity and COVID-19 severity in a designated hospital in Shenzhen

Suspected myocardial injury in patients with COVID-19: evidence from front-line clinical observation in Wuhan, China

Lifestyle risk factors

Obesity and the risk and outcome of infection

Obesity and impaired metabolic health in patients with COVID-19

Impact of obesity and metabolic syndrome on immunity

Individuals with obesity and type 2 diabetes have additional immune dysfunction compared with obese individuals who are metabolically healthy

Adipokines in inflammation and metabolic disease

The effect of lipopolysaccharide-induced obesity and its chronic inflammation on influenza virus-related pathology

Obesity impairs the adaptive immune response to influenza virus

We would like to express our gratitude to the authors of the studies included in our study.J o u r n a l P r e -p r o o f