An updated meta-analysis on the relationship between obesity and COVID-19 mortality
Authors: Yadong Wang, Jie Xu, Ying Wang, Hongjie Hou, Huifen Feng, Haiyan Yang
Journal: Metabolism (2021)

Recently, Huang et al. published an article titled "Obesity in patients with COVID-19: a systematic review and meta-analysis" in the journal of Metabolism [[1]Huang Y. Lu Y. Huang Y.M. Wang M. Ling W. Sui Y. et al.Obesity in patients with COVID-19: a systematic review and meta-analysis.Metab Clin Exp. 2020; 113: 154378Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar]. The authors reported that coronavirus disease 2019 (COVID-19) patients with obesity were at high risk for death based on seven studies with multivariate analyses (odds ratio = 1.49, 95% confidence interval (CI): 1.20–1.85) [[1]Huang Y. Lu Y. Huang Y.M. Wang M. Ling W. Sui Y. et al.Obesity in patients with COVID-19: a systematic review and meta-analysis.Metab Clin Exp. 2020; 113: 154378Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar]. This study was greatly interesting, but had limited sample sizes. In addition, several eligible studies [2Deiana G. Azara A. Dettori M. Delogu F. Vargiu G. Gessa I. et al.Deaths in SARS-Cov-2 positive patients in Italy: the influence of underlying health conditions on lethality.Int J Environ Res Public Health. 2020; 17: 4450Crossref Scopus (31) Google Scholar, 3Hashemi N. Viveiros K. Redd W.D. Zhou J.C. McCarty T.R. Bazarbashi A.N. et al.Impact of chronic liver disease on outcomes of hospitalized patients with COVID-19: a multicentre United States experience.Liver Int. 2020; 40: 2515-2521Crossref PubMed Scopus (103) Google Scholar, 4Shah P. Owens J. Franklin J. Mehta A. Heymann W. Sewell W. et al.Demographics, comorbidities and outcomes in hospitalized Covid-19 patients in rural Southwest Georgia.Ann Med. 2020; 52: 354-360Crossref PubMed Scopus (71) Google Scholar, 5Arshad S. Kilgore P. Chaudhry Z.S. Jacobsen G. Wang D.D. Huitsing K. et al.Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19.Int J Infect Dis. 2020; 97: 396-403Abstract Full Text Full Text PDF PubMed Scopus (398) Google Scholar] published before August 10, 2020 were not included. To our knowledge, a considerable number of emerging studies on this topic have been reported since Huang et al.'s study was published online. Therefore, the association between obesity and COVID-19 mortality is needed to be clarified by a meta-analysis based on updated data. This meta-analysis was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement [[6]Moher D. Liberati A. Tetzlaff J. Altman D.G. Group P Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.Ann Intern Med. 2009; 151 ([W64]): 264-269Crossref PubMed Scopus (19874) Google Scholar]. We performed a comprehensive literature search in PubMed, Web of Science and EMBASE to identify all potential studies published between January 1, 2020 and June 7, 2021. The keywords were used: "COVID-19" or "SARS-CoV-2" or "coronavirus disease 2019" and "obesity" or "obese" or "body mass index" and "mortality" or "death" or "deceased". We included studies investigating the association between obesity and COVID-19 mortality by multivariable analyses. Preprints, reviews, duplicates, errata, comments, and studies with crude effect sizes were excluded. The statistical analyses were done using R software (Version 3.6.3) [[7]Balduzzi S. Rucker G. Schwarzer G. How to perform a meta-analysis with R: a practical tutorial.Evid Based Ment Health. 2019; 22: 153-160Crossref PubMed Scopus (2166) Google Scholar]. The pooled effect size and 95% CI were estimated by a random-effects model [[8]DerSimonian R. Laird N. Meta-analysis in clinical trials revisited.Contemp Clin Trials. 2015; 45: 139-145Abstract Full Text Full Text PDF PubMed Scopus (1663) Google Scholar,[9]Wang Y, Feng R, Xu J, Hou H, Feng H, Yang H. An updated meta-analysis on the association between tuberculosis and COVID-19 severity and mortality. J Med Virol. 2021;DOI: https://doi.org/10.1002/jmv.27119.Google Scholar]. I2 statistic and Cochran's Q test were applied to evaluate statistical heterogeneity across studies [10Higgins J.P. Thompson S.G. Deeks J.J. Altman D.G. Measuring inconsistency in meta-analyses.Bmj. 2003; 327: 557-560Crossref PubMed Scopus (44447) Google Scholar, 11Yang H, Xu J, Shi L, Duan G, Wang Y. Correspondence on 'Prevalence and clinical outcomes of COVID-19 in patients with autoimmune diseases: a systematic review and meta-analysis'. Ann Rheum Dis. 2021;DOI: https://doi.org/10.1136/annrheumdis-2020-219821.Google Scholar, 12Xu J, Xiao W, Shi L, Wang Y, Yang H. Is cancer an independent risk factor for fatal outcomes of coronavirus disease 2019 patients? Arch Med Res. 2021;DOI: https://doi.org/10.1016/j.arcmed.2021.05.003:2670.Google Scholar]. Begg's test was used to assess publication bias [[13]Begg C.B. Mazumdar M. Operating characteristics of a rank correlation test for publication bias.Biometrics. 1994; 50: 1088-1101Crossref PubMed Scopus (13016) Google Scholar]. Leave-one-out sensitivity analysis was performed to assess the stability of the results [[14]Kang X, Dong L, Yang T, Wang Z, Huang G, Chen X. Clinical and radiographic outcomes of upper thoracic versus lower thoracic upper instrumented vertebrae for adult scoliosis: a meta-analysis. Braz J Med Biol Res. 2018;51:e6651.Google Scholar,[15]Hou H, Li Y, Zhang P, Wu J, Shi L, Xu J, et al. Smoking is independently associated with an increased risk for COVID-19 mortality: a systematic review and meta-analysis based on adjusted effect estimates. Nicotine Tob Res. 2021;DOI: https://doi.org/10.1093/ntr/ntab112.Google Scholar]. P < 0.05 was considered statistically significant. The main characteristics of the included studies are summarized in Table 1. A total of 138 studies with 3,863,516 cases were included. Our results demonstrated that COVID-19 patients with obesity had a significantly higher risk for mortality compared to those without obesity (pooled effect size = 1.29, 95% CI: 1.24–1.35; Fig. 1A ). Sensitivity analysis revealed that our results were stable and robust (Fig. 1B). Consistent findings were observed in the subgroup analyses by sample size, age, male percentage and setting. Begg's test indicated that there was no potential publication bias (P = 0.331).Table 1General information of the included studies.AuthorCasesObesity (%)Age (years)Male (%)Study typeCountry/regionDefinition of obesityEffect size (95% CI)Klang E*34061231 (36.1)66 ± 12.275.6Retrospective studyUSABMI ≥ 301.63 (1.0–2.65)Rottoli M482104 (21.6)66.2 ± 16.862.7Retrospective studyItalyBMI ≥ 302.35 (1.17–4.75)Antwi-Amoabeng D17289 (51.7)53 (33.5–68)55.8Retrospective studyUSABMI ≥ 3010.55 (1.07–104.45)Deiana G1223NANA40.8Retrospective studyItalyBMI ≥ 301.1 (0.4–2.9)Hashemi N363NA63.34 ± 16.555.4Retrospective studyUSABMI ≥ 301.03 (0.51–2.09)Pettit NN238146 (61.3)58.5 ± 1747.5Retrospective studyUSABMI ≥ 301.7 (1.1–2.8)Shah P*522347 (66.5)63 (50–72)41.8Retrospective studyUSABMI ≥ 301.79 (1.12–2.88)Aeshad S25411250 (52.3)63.7 ± 16.551.1Retrospective studyUSABMI ≥ 300.775 (0.624–0.962)Gupta S*2215NA60.5 ± 14.564.8Multicenter cohort studyUSABMI ≥ 301.2 (0.92–1.56)Nakeshbandi M504215 (43)68 ± 1552Retrospective studyUSABMI ≥ 301.3 (1–1.7)Hernandez-Galdamez DR211,00341,344 (19.59)45.7 ± 16.354.71Cross-sectional studyMexicoBMI ≥ 301.42 (1.37–1.47)Berenguer J4035497 (13.8)70 (56–80)61Retrospective studySpainBMI ≥ 301.21 (1.01–1.44)Almazeedi S109644 (4)47 ± 31.1181Retrospective studyKuwaitBMI ≥ 300.223 (0.033–1.513)Posso M83455 (6.6)78.2 ± 9.846.5Retrospective studySpainBMI ≥ 301.21 (0.6–2.45)Tartof SY*69163171 (45.9)49.1 ± 16.645Retrospective studyUSABMI ≥ 301.95 (1.09–3.50)Parra-Bracamonte GM142,69028,432 (20)45 (34.0–57.0)56DatasetMexicoBMI ≥ 301.264 (1.207–1.323)Yehia BR71392044 (28.6)68 (56–79)51.3Retrospective studyUSAObesity0.97 (0.81–1.16)Ng JH*10,482NA65.38 ± 15.259.5Retrospective studyUSABMI ≥ 301.05 (0.91–1.22)Czernichow S*57951264 (21.8)59.765.4Prospective studyFranceBMI ≥ 302.3 (1.78–2.98)Nimkar A327113 (34.6)71 (59–82)55.7Retrospective studyUSABMI ≥ 301.3 (0.1–14.9)Biran N764276 (36.1)65.29 ± 1465.7Retrospective studyUSABMI ≥ 301.06 (0.85–1.32)Giorgi Rossi P265365 (2.7)63.250.1Prospective studyItalyObesity1.3 (0.6–2.9)Seiglie J450191 (42.4)63.357.6NAUSAObesity1.1 (0.5–2.45)Fried MW11,7211891 (16.1)6253.4Retrospective studyUSAObesity1.07 (0.93–1.24)Mukherjee V137104 (77.6)59.0 (51.0–70.0)72.3Retrospective studyUSABMI ≥ 300.7 (0.5–1.2)Carrillo-Vega MF99462053 (20.82)48.15 ± 14.3557.84DatasetMexicoObesity1.74 (1.35–2.26)Morgenthau AS73371993 (27.2)61.5 ± 18.8555.2Retrospective studyUSAObesity1.5 (1.3–1.7)Lauriola M37730 (8.0)71.8 ± 13.465.8Retrospective studyItalyObesity1.329 (0.779–2.268)Miller J36331758 (51.8)58.4 ± 18.146.2Retrospective studyUSAObesity0.94 (0.71–1.24)Ioannou GN10,13178 (0.8)63.6 ± 16.291Longitudinal cohort studyUSAObesity1.66 (0.99–2.77)Peters SAE*NANANANAProspective studyUKObesity1.95 (1.58–2.41)Nachega JB76639 (5.1)46 (34–58)65.6Retrospective studyCongoObesity2.3 (1.24–4.27)Gutierrez JP*654,858122,917 (18.77)46.07 (45.84–46.30)52.21Public dataMexicoObesity2.11 (1.74–2.56)Mallow PJ21,6763029 (14.0)64.9 ± 17.252.8Retrospective studyUSAObesity1.3 (1.15–1.47)Ionescu F*34801767 (50.8)64.5 ± 17.048.5Retrospective studyUSABMI ≥ 300.91 (0.67–1.23)Smati S*1965805 (41.0)70.1 ± 12.564.5Retrospective studyFranceBMI ≥ 301.37 (0.76–2.46)Lunski MJ47602482 (48.2)NA39.1Retrospective studyUSAObesity1.3 (1.03–1.63)de Souza CD980713 (1.1)70.21 ± 8.3747.5Cross-sectional studyBrazilObesity1.77 (0.84–3.74)Kim TS*10,8614090 (37.7)65 (54–77)59.6Retrospective studyUSAObesity1.25 (0.95–1.65)Hilbrands LB1073247 (23)6560.6ERACODA database26 countriesObesity1.87 (1.18–2.95)Nunez-Gil IJ1021NA68 (52.0–79.0)59.5Retrospective study4 countries(Ecuador, Germany, Italy and Spain)Obesity1.52 (0.83–2.76)Poterucha TJ887309 (35)64.158Retrospective studyUSAObesity1.16 (0.83–1.62)Parikh R16083 (51.9)60.3565.6Retrospective studyUSAObesity1.2 (0.6–2.6)Polverino F3179218 (6.9)69.0 (57–78)68.3Retrospective studyItalyObesity2.03 (1.3–3.17)Saand AR495241 (48.7)68.00 (58.00–77.00)58.4Retrospective studyUSABMI ≥ 300.788 (0.544–1.14)Filardo TD261109 (41.8)58 (50–67)67.4Retrospective studyUSABMI ≥ 301.37 (1.07–1.74)Canevelli M41515 (3.6)84.3 ± 8.152.8Retrospective studyItalyObesity0.48 (0.25–0.92)D'Alto M9431 (33.0)6474.5Prospective studyItalyObesity0.626 (0.171–2.295)FAI2R*675123 (22.7)55.933.4Retrospective studyFranceBMI ≥ 302.27 (1.14–4.49)Nyabera A*29089 (30.7)77.6 ± 8.351.7Retrospective studyUSABMI ≥ 300.67 (0.32–1.4)Kaeuffer C1045351 (33.6)66.3 ± 16.058.6Prospective studyFranceBMI ≥ 301.4 (0.7–2.5)Alguwaihes AM439178 (42.2)55 (19–101)68.3Retrospective studySaudi ArabiaObesity1 (0.6–1.6)Pantea Stoian A43256 (12.96)66.97 ± 13.0765NARomaniaObesity1.305 (0.843–2.019)Stefan G3711 (30)64 (55–71)51Retrospective studyRomaniaObesity1.38 (0.25–7.58)Murillo-Zamora E66,123NANA60.7Retrospective studyMexicoObesity1.08 (1.05–1.11)Ling SF*98420 (4.5)74 (63–83)54.3Retrospective studyUKObesity0.43 (0.09–2.05)Izurieta HS27,961NA75 (70–85)48.8Retrospective studyUSAObesity1.1 (1.05–1.16)Lundon DJ8928631 (7.1)58.0 ± 18.846.2Cross-sectional studyUSAObesity1.39 (1.11–1.73)Kim SY40571159 (28.57)4042.5NAKoreaObesity1.71 (1.1–2.66)Eastment MC*25,92512,672 (48.9)60.4 ± 17.089.8Retrospective studyUSAObesity1.05 (0.81–1.37)Lanini S37924 (6.3)61.67 ± 15.6072.03Longitudinal cohort studyItalyObesity5.13 (1.81–14.5)Schwartz KL56,606722 (1.3)3148.4Cross-sectional studyCanadaObesity1.66 (1.19–2.3)Li Y20292 (45.5)58 (49–69)54Retrospective studyUSABMI ≥ 301.45 (0.5–4.2)Mejía F369157 (42.55)59 (49–68)65.31Retrospective studyPeruObesity0.99 (0.72–1.35)Pena JE*323,67158,517 (18.1)40.1252.2Retrospective studyMexicoObesity1.52 (1.06–2.18)Guerra Veloz MF44729 (6.5)55.06 ± 22.5542.5Retrospective studySpainObesity1.3 (0.41–4.12)Kim SW2254426 (28.5)58.0 (42.0–70.0)35.8Retrospective studyKoreaObesity1.92 (0.97–3.77)Martos-Benítez FD38,3248014 (20.9)46.9 ± 15.758.3Retrospective studyMexicoObesity1.53 (1.38–1.71)Hobbs ALV*502257 (51.6)62 (49–71)55.2Retrospective studyUSABMI ≥ 301.28 (0.68–2.46)Ahlstrom B1981123 (6.2)61 (52–69)74Retrospective studySwedenObesity0.94 (0.56–1.56)Eskandar EN4711NA63.453.3Retrospective studyUSABMI ≥ 301.09 (1–1.2)Lohia P1871879 (47.0)66 (54–75)51.6Retrospective studyUSABMI ≥ 301.23 (0.98–1.54)Apea VJ1996384 (19.2)63.460.6Prospective studyUKBMI ≥ 301.42 (1.09–1.85)Meizlish ML2785NANA50.1Retrospective studyUSAObesity1.356 (1.101–1.67)Mayer MA23,8445181 (21.7)49.93 ± 19.442.3Retrospective studySpainObesity1.08 (0.91–1.27)Lopez Zuniga MA31848 (15.2)64.9 ± 14.158.5Prospective studySpainObesity1.238 (0.393–3.9)Marjot T932248 (27)59 (48–68)67Retrospective studyThree multinational registriesObesity1.07 (0.69–1.65)Balfanz P12544 (35)6670Retrospective studyGermanyObesity1.3 (0.29–5.74)Olivas-Martínez A800357 (44.8)51.9 ± 13.961Prospective studyUSAObesity1.62 (1.14–2.32)Yoshida Y776409 (53.1)60.5 ± 16.147.3Retrospective studyUSAObesity1.33 (0.87–2.03)Geriatric Medicine Research Collaborative57111092 (19.1)74 (54–83)55.2Cohort study12 countriesBMI ≥ 301.03 (0.86–1.24)Crouse AB604371 (61.4)53.0245Retrospective studyUSAObesity1.21 (0.66–2.21)Timberlake DT275102 (37.1)57.977.1Retrospective studyUSAObesity1.07 (0.53–2.14)Cedano J13259 (45)63 (53–71)59Retrospective studyUSABMI ≥ 302.92 (1.07–8.01)Girardin JL42101660 (39.4)61.958.1Retrospective studyUSAObesity1.19 (1.04–1.37)Le Borgne P1023258 (34.1)69.0 (58.0–79.0)58.9Retrospective studyFranceObesity1.366 (0.74–2.52)Rossi AP9534 (35.8)62.46 ± 11.8182.1NAItalyObesity5.3 (1.26–22.34)Gavioli EM43769 (16)67 (56–79)48Retrospective studyUSAObesity2.08 (1.14–3.78)Dai CL*54,64519,763 (41.9)47.8 ± 19.247.4Retrospective studyUSAObesity1.22 (0.96–1.53)Gupta YS*18068 (40)68 (59–80)54Retrospective studyUSABMI > 303.36 (1.53–7.34)Navaratnam AV91,5417920 (8.7)71.5255.4Retrospective studyUKObesity1.476 (1.383–1.575)Merzon E11244 (39.3)62.89 ± 14.6755.4Retrospective studyIsraelObesity0.75 (0.04–12.49)Zamoner W10122 (21.7)57.89 ± 15.854.4Prospective studyBrazilObesity1.28 (1.04–11.52)Aoun M23152 (22.5)61.46 ± 13.9955.4Retrospective studyLebanonObesity0.88 (0.41–1.88)Porta-Etessam J5399NA64.27 ± 16.9359.2NASpainObesity1.12 (0.91–1.39)Li WX1249353 (28.3)36 (27–50)61.9Retrospective studyChinaBMI ≥ 301.69 (1.12–3.57)Suresh S19891031 (52)63.82 ± 16.5550Retrospective studyUSAObesity1.1 (0.83–1.44)Sonmez A9213870 (9.4)6143.3Retrospective studyTurkeyObesity2.36 (1.18–4.74)Ibarra-Nava I416,54679,635 (19.1)46.153.1Retrospective studyMexicoObesity1.39 (1.35–1.42)Bloom CI*65,6536007 (9.1)75.756.3Prospective studyUKObesity1.46 (0.88–2.42)Giacomelli A52092 (17.7)61 (50–72)76Prospective studyItalyObesity2.17 (1.1–4.31)Argoty-Pantoja AD*412,01777,566 (18.8)45.353.2Longitudinal analysisMexicoObesity1.53 (0.83–2.81)Satman I18,6581024 (5.5)5344Retrospective studyTurkeyObesity2.83 (1.45–5.53)Grivas P49661704 (34)66 (56–76)49Retrospective studyUSAObesity1.09 (0.88–1.35)Wu X1091285 (26.1)59 (49–67)46.7Retrospective studyChinaObesity1.74 (0.73–4.21)Muñoz-Rodríguez JR12,1262100 (18.8)66.453.3Prospective studySpainObesity1.3 (1.1–1.5)Mehta HB*137,11937,318 (27.2)7634Retrospective studyUSABMI > 300.92 (0.88–0.97)Schavemaker R1099324 (29.5)64.77 ± 10.9173Cohort studyUKObesity1 (0.72–1.38)Bonifazi M26351 (19.4)45.3 (40.4–48.4)62.4Retrospective studyItalyBMI ≥ 300.79 (0.27–2.27)Mulhem E32191642 (51.0)65.2 (52.6–77.2)49Retrospective studyUSAObesity1.25 (1.01–1.56)Kurtz P4188NA63 (49–76)64Prospective studyBrazilObesity1.11 (0.99–1.24)Sallis R*48,44024,831 (51.3)47.5 ± 16.9738.1Retrospective studyUSABMI ≥ 301.29 (0.62–2.72)Mendizabal M2211383 (17.3)54.3 ± 17.360.6Prospective study11 Latin American countriesObesity1.7 (1.3–2.3)Alwafi H70688 (12.5)48.0 ± 15.668.5Retrospective studySaudi ArabiaBMI ≥ 300.25 (0.06–1.01)Baggio JAO59,659138 (0.2)4144.6Retrospective studyBrazilObesity3.22 (1.87–5.54)Vera-Zertuche JM15,5293215 (20.7)46.6 ± 15.557.8Retrospective studyMexicoObesity2.37 (1.96–2.86)Nikniaz Z31776 (24.0)65.09 ± 13.2951.4Prospective studyIranObesity2.72 (1.13–7.44)Ayala Gutierrez MDM13,9402711 (19.4)67.357.1Retrospective studySpainObesity1.33 (1.17–1.51)Cereda E*22268 (30.6)58.6 ± 11.277.9Prospective studyItalyBMI ≥ 302.06 (1.17–3.63)Cummins L1781481 (27.1)51.7455.2Retrospective studyUKObesity1.15 (0.86–1.55)Castro MC176,559NANANARetrospective studyBrazilObesity1.07 (1.04–1.1)Guerson-Gil A*34991472 (42.1)65 (55–76)55.27Retrospective studyUSABMI ≥ 301.45 (1.09–1.91)Gray WK*117,43810,426 (8.9)70.554.6Retrospective studyUKObesity1.18 (0.81–1.72)Song J56211260 (22.4)50.2141.2Retrospective studyKoreaObesity0.883 (0.751–1.054)Dres M*1199NA74 (72–78)73Prospective studyFranceBMI ≥ 300.9 (0.69–1.16)Bravata DM*13,5105940 (44.0)67.5890.8Observational cohort studyUSABMI ≥ 300.88 (0.64–1.21)Verna EC1070184 (17.2)6052.5Retrospective studyUSAObesity0.9 (0.76–1.06)Xu W1131320 (28.3)36 (26–50)61Retrospective studyChinaObesity1.75 (1.21–4.32)Celejewska-Wojcik N11643 (37.1)61 (51–70)78.4Prospective studyPolandObesity1.14 (0.65–2.01)Goncalves DA182,7006470 (3.5)NA56.6Retrospective studyBrazilObesity1.411 (1.309–1.521)Heldman MR1051365 (34.7)57.462.2Multicenter cohort studyUSAObesity1.8 (1.2–2.5)Aminian A*28391357 (47.8)52.7 ± 20.146.4Retrospective studyUSABMI ≥ 300.94 (0.45–1.97)Robles-Perez E70,5319906 (14.0)NA43.2Retrospective studyMexicoObesity2.05 (1.67–2.6)Henein MY213122 (57.3)49.6 ± 12NARetrospective studyEgyptObesity3.403 (1.902–4.694)Marciniak SJ*85,006NANANAProspective studyUKObesity1.05 (0.96–1.15)Wander PL*35,87915,147 (52)60.3 ± 17.089Retrospective studyUSABMI ≥ 301.01 (0.76–1.35)Tramunt B*2380929 (39.0)70 (61–79)63.5Retrospective/Prospective studyFranceBMI ≥ 300.85 (0.57–1.27)Nogues X67868 (8.1)62.159.1Prospective studySpainObesity3.71 (1.45–9.5)Note: The age (years) was expressed as mean ± standard deviation (SD) and median (interquartile range, IQR). BMI, body mass index; CI, confidence interval; NA, not available; UK, United Kingdom; USA, the United States of America. * indicates the combined effect size and 95% CI were used. Open table in a new tab Note: The age (years) was expressed as mean ± standard deviation (SD) and median (interquartile range, IQR). BMI, body mass index; CI, confidence interval; NA, not available; UK, United Kingdom; USA, the United States of America. * indicates the combined effect size and 95% CI were used. Several limitations existed in this meta-analysis. First, most of the included studies were from Americas and Europe, thus the findings should be explained with caution in other regions (such as Asia and Africa). Second, although the pooled effect size was estimated on the basis of adjusted effect sizes, the adjusted factors are not fully consistent among the included studies. Third, most of the enrolled studies are retrospective studies, thus further meta-analysis with more prospective studies should be performed to verify our results. In conclusion, this updated meta-analysis demonstrated that obesity was significantly associated with an increased risk for COVID-19 mortality. We hope that the updated findings will contribute to more accurate elaboration and substantiation of the data reported by Huang et al. [[1]Huang Y. Lu Y. Huang Y.M. Wang M. Ling W. Sui Y. et al.Obesity in patients with COVID-19: a systematic review and meta-analysis.Metab Clin Exp. 2020; 113: 154378Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar]. This study was supported by grants from the National Natural Science Foundation of China (No. 81973105), Key Scientific Research Project of Henan Institution of Higher Education (No. 21A330008), and Joint Construction Project of Henan Medical Science and Technology Research Plan (No. LHGJ20190679). The funders have no role in the data collection, data analysis, preparation of manuscript and decision to submission.