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]. 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]. This study was greatly interesting, but had limited sample sizes. In addition, several eligible studies [[2], [3], [4], [5]] 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]. 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]. The pooled effect size and 95% CI were estimated by a random-effects model [8,9]. I2 statistic and Cochran's Q test were applied to evaluate statistical heterogeneity across studies [[10], [11], [12]]. Begg's test was used to assess publication bias [13]. Leave-one-out sensitivity analysis was performed to assess the stability of the results [14,15]. 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 1.
General information of the included studies.
| Author | Cases | Obesity (%) | Age (years) | Male (%) | Study type | Country/region | Definition of obesity | Effect size (95% CI) |
|---|---|---|---|---|---|---|---|---|
| Klang E* | 3406 | 1231 (36.1) | 66 ± 12.2 | 75.6 | Retrospective study | USA | BMI ≥ 30 | 1.63 (1.0–2.65) |
| Rottoli M | 482 | 104 (21.6) | 66.2 ± 16.8 | 62.7 | Retrospective study | Italy | BMI ≥ 30 | 2.35 (1.17–4.75) |
| Antwi-Amoabeng D | 172 | 89 (51.7) | 53 (33.5–68) | 55.8 | Retrospective study | USA | BMI ≥ 30 | 10.55 (1.07–104.45) |
| Deiana G | 1223 | NA | NA | 40.8 | Retrospective study | Italy | BMI ≥ 30 | 1.1 (0.4–2.9) |
| Hashemi N | 363 | NA | 63.34 ± 16.5 | 55.4 | Retrospective study | USA | BMI ≥ 30 | 1.03 (0.51–2.09) |
| Pettit NN | 238 | 146 (61.3) | 58.5 ± 17 | 47.5 | Retrospective study | USA | BMI ≥ 30 | 1.7 (1.1–2.8) |
| Shah P* | 522 | 347 (66.5) | 63 (50–72) | 41.8 | Retrospective study | USA | BMI ≥ 30 | 1.79 (1.12–2.88) |
| Aeshad S | 2541 | 1250 (52.3) | 63.7 ± 16.5 | 51.1 | Retrospective study | USA | BMI ≥ 30 | 0.775 (0.624–0.962) |
| Gupta S* | 2215 | NA | 60.5 ± 14.5 | 64.8 | Multicenter cohort study | USA | BMI ≥ 30 | 1.2 (0.92–1.56) |
| Nakeshbandi M | 504 | 215 (43) | 68 ± 15 | 52 | Retrospective study | USA | BMI ≥ 30 | 1.3 (1–1.7) |
| Hernandez-Galdamez DR | 211,003 | 41,344 (19.59) | 45.7 ± 16.3 | 54.71 | Cross-sectional study | Mexico | BMI ≥ 30 | 1.42 (1.37–1.47) |
| Berenguer J | 4035 | 497 (13.8) | 70 (56–80) | 61 | Retrospective study | Spain | BMI ≥ 30 | 1.21 (1.01–1.44) |
| Almazeedi S | 1096 | 44 (4) | 47 ± 31.11 | 81 | Retrospective study | Kuwait | BMI ≥ 30 | 0.223 (0.033–1.513) |
| Posso M | 834 | 55 (6.6) | 78.2 ± 9.8 | 46.5 | Retrospective study | Spain | BMI ≥ 30 | 1.21 (0.6–2.45) |
| Tartof SY* | 6916 | 3171 (45.9) | 49.1 ± 16.6 | 45 | Retrospective study | USA | BMI ≥ 30 | 1.95 (1.09–3.50) |
| Parra-Bracamonte GM | 142,690 | 28,432 (20) | 45 (34.0–57.0) | 56 | Dataset | Mexico | BMI ≥ 30 | 1.264 (1.207–1.323) |
| Yehia BR | 7139 | 2044 (28.6) | 68 (56–79) | 51.3 | Retrospective study | USA | Obesity | 0.97 (0.81–1.16) |
| Ng JH* | 10,482 | NA | 65.38 ± 15.2 | 59.5 | Retrospective study | USA | BMI ≥ 30 | 1.05 (0.91–1.22) |
| Czernichow S* | 5795 | 1264 (21.8) | 59.7 | 65.4 | Prospective study | France | BMI ≥ 30 | 2.3 (1.78–2.98) |
| Nimkar A | 327 | 113 (34.6) | 71 (59–82) | 55.7 | Retrospective study | USA | BMI ≥ 30 | 1.3 (0.1–14.9) |
| Biran N | 764 | 276 (36.1) | 65.29 ± 14 | 65.7 | Retrospective study | USA | BMI ≥ 30 | 1.06 (0.85–1.32) |
| Giorgi Rossi P | 2653 | 65 (2.7) | 63.2 | 50.1 | Prospective study | Italy | Obesity | 1.3 (0.6–2.9) |
| Seiglie J | 450 | 191 (42.4) | 63.3 | 57.6 | NA | USA | Obesity | 1.1 (0.5–2.45) |
| Fried MW | 11,721 | 1891 (16.1) | 62 | 53.4 | Retrospective study | USA | Obesity | 1.07 (0.93–1.24) |
| Mukherjee V | 137 | 104 (77.6) | 59.0 (51.0–70.0) | 72.3 | Retrospective study | USA | BMI ≥ 30 | 0.7 (0.5–1.2) |
| Carrillo-Vega MF | 9946 | 2053 (20.82) | 48.15 ± 14.35 | 57.84 | Dataset | Mexico | Obesity | 1.74 (1.35–2.26) |
| Morgenthau AS | 7337 | 1993 (27.2) | 61.5 ± 18.85 | 55.2 | Retrospective study | USA | Obesity | 1.5 (1.3–1.7) |
| Lauriola M | 377 | 30 (8.0) | 71.8 ± 13.4 | 65.8 | Retrospective study | Italy | Obesity | 1.329 (0.779–2.268) |
| Miller J | 3633 | 1758 (51.8) | 58.4 ± 18.1 | 46.2 | Retrospective study | USA | Obesity | 0.94 (0.71–1.24) |
| Ioannou GN | 10,131 | 78 (0.8) | 63.6 ± 16.2 | 91 | Longitudinal cohort study | USA | Obesity | 1.66 (0.99–2.77) |
| Peters SAE* | NA | NA | NA | NA | Prospective study | UK | Obesity | 1.95 (1.58–2.41) |
| Nachega JB | 766 | 39 (5.1) | 46 (34–58) | 65.6 | Retrospective study | Congo | Obesity | 2.3 (1.24–4.27) |
| Gutierrez JP* | 654,858 | 122,917 (18.77) | 46.07 (45.84–46.30) | 52.21 | Public data | Mexico | Obesity | 2.11 (1.74–2.56) |
| Mallow PJ | 21,676 | 3029 (14.0) | 64.9 ± 17.2 | 52.8 | Retrospective study | USA | Obesity | 1.3 (1.15–1.47) |
| Ionescu F* | 3480 | 1767 (50.8) | 64.5 ± 17.0 | 48.5 | Retrospective study | USA | BMI ≥ 30 | 0.91 (0.67–1.23) |
| Smati S* | 1965 | 805 (41.0) | 70.1 ± 12.5 | 64.5 | Retrospective study | France | BMI ≥ 30 | 1.37 (0.76–2.46) |
| Lunski MJ | 4760 | 2482 (48.2) | NA | 39.1 | Retrospective study | USA | Obesity | 1.3 (1.03–1.63) |
| de Souza CD | 9807 | 13 (1.1) | 70.21 ± 8.37 | 47.5 | Cross-sectional study | Brazil | Obesity | 1.77 (0.84–3.74) |
| Kim TS* | 10,861 | 4090 (37.7) | 65 (54–77) | 59.6 | Retrospective study | USA | Obesity | 1.25 (0.95–1.65) |
| Hilbrands LB | 1073 | 247 (23) | 65 | 60.6 | ERACODA database | 26 countries | Obesity | 1.87 (1.18–2.95) |
| Nunez-Gil IJ | 1021 | NA | 68 (52.0–79.0) | 59.5 | Retrospective study | 4 countries (Ecuador, Germany, Italy and Spain) |
Obesity | 1.52 (0.83–2.76) |
| Poterucha TJ | 887 | 309 (35) | 64.1 | 58 | Retrospective study | USA | Obesity | 1.16 (0.83–1.62) |
| Parikh R | 160 | 83 (51.9) | 60.35 | 65.6 | Retrospective study | USA | Obesity | 1.2 (0.6–2.6) |
| Polverino F | 3179 | 218 (6.9) | 69.0 (57–78) | 68.3 | Retrospective study | Italy | Obesity | 2.03 (1.3–3.17) |
| Saand AR | 495 | 241 (48.7) | 68.00 (58.00–77.00) | 58.4 | Retrospective study | USA | BMI ≥ 30 | 0.788 (0.544–1.14) |
| Filardo TD | 261 | 109 (41.8) | 58 (50–67) | 67.4 | Retrospective study | USA | BMI ≥ 30 | 1.37 (1.07–1.74) |
| Canevelli M | 415 | 15 (3.6) | 84.3 ± 8.1 | 52.8 | Retrospective study | Italy | Obesity | 0.48 (0.25–0.92) |
| D'Alto M | 94 | 31 (33.0) | 64 | 74.5 | Prospective study | Italy | Obesity | 0.626 (0.171–2.295) |
| FAI2R* | 675 | 123 (22.7) | 55.9 | 33.4 | Retrospective study | France | BMI ≥ 30 | 2.27 (1.14–4.49) |
| Nyabera A* | 290 | 89 (30.7) | 77.6 ± 8.3 | 51.7 | Retrospective study | USA | BMI ≥ 30 | 0.67 (0.32–1.4) |
| Kaeuffer C | 1045 | 351 (33.6) | 66.3 ± 16.0 | 58.6 | Prospective study | France | BMI ≥ 30 | 1.4 (0.7–2.5) |
| Alguwaihes AM | 439 | 178 (42.2) | 55 (19–101) | 68.3 | Retrospective study | Saudi Arabia | Obesity | 1 (0.6–1.6) |
| Pantea Stoian A | 432 | 56 (12.96) | 66.97 ± 13.07 | 65 | NA | Romania | Obesity | 1.305 (0.843–2.019) |
| Stefan G | 37 | 11 (30) | 64 (55–71) | 51 | Retrospective study | Romania | Obesity | 1.38 (0.25–7.58) |
| Murillo-Zamora E | 66,123 | NA | NA | 60.7 | Retrospective study | Mexico | Obesity | 1.08 (1.05–1.11) |
| Ling SF* | 984 | 20 (4.5) | 74 (63–83) | 54.3 | Retrospective study | UK | Obesity | 0.43 (0.09–2.05) |
| Izurieta HS | 27,961 | NA | 75 (70–85) | 48.8 | Retrospective study | USA | Obesity | 1.1 (1.05–1.16) |
| Lundon DJ | 8928 | 631 (7.1) | 58.0 ± 18.8 | 46.2 | Cross-sectional study | USA | Obesity | 1.39 (1.11–1.73) |
| Kim SY | 4057 | 1159 (28.57) | 40 | 42.5 | NA | Korea | Obesity | 1.71 (1.1–2.66) |
| Eastment MC* | 25,925 | 12,672 (48.9) | 60.4 ± 17.0 | 89.8 | Retrospective study | USA | Obesity | 1.05 (0.81–1.37) |
| Lanini S | 379 | 24 (6.3) | 61.67 ± 15.60 | 72.03 | Longitudinal cohort study | Italy | Obesity | 5.13 (1.81–14.5) |
| Schwartz KL | 56,606 | 722 (1.3) | 31 | 48.4 | Cross-sectional study | Canada | Obesity | 1.66 (1.19–2.3) |
| Li Y | 202 | 92 (45.5) | 58 (49–69) | 54 | Retrospective study | USA | BMI ≥ 30 | 1.45 (0.5–4.2) |
| Mejía F | 369 | 157 (42.55) | 59 (49–68) | 65.31 | Retrospective study | Peru | Obesity | 0.99 (0.72–1.35) |
| Pena JE* | 323,671 | 58,517 (18.1) | 40.12 | 52.2 | Retrospective study | Mexico | Obesity | 1.52 (1.06–2.18) |
| Guerra Veloz MF | 447 | 29 (6.5) | 55.06 ± 22.55 | 42.5 | Retrospective study | Spain | Obesity | 1.3 (0.41–4.12) |
| Kim SW | 2254 | 426 (28.5) | 58.0 (42.0–70.0) | 35.8 | Retrospective study | Korea | Obesity | 1.92 (0.97–3.77) |
| Martos-Benítez FD | 38,324 | 8014 (20.9) | 46.9 ± 15.7 | 58.3 | Retrospective study | Mexico | Obesity | 1.53 (1.38–1.71) |
| Hobbs ALV* | 502 | 257 (51.6) | 62 (49–71) | 55.2 | Retrospective study | USA | BMI ≥ 30 | 1.28 (0.68–2.46) |
| Ahlstrom B | 1981 | 123 (6.2) | 61 (52–69) | 74 | Retrospective study | Sweden | Obesity | 0.94 (0.56–1.56) |
| Eskandar EN | 4711 | NA | 63.4 | 53.3 | Retrospective study | USA | BMI ≥ 30 | 1.09 (1–1.2) |
| Lohia P | 1871 | 879 (47.0) | 66 (54–75) | 51.6 | Retrospective study | USA | BMI ≥ 30 | 1.23 (0.98–1.54) |
| Apea VJ | 1996 | 384 (19.2) | 63.4 | 60.6 | Prospective study | UK | BMI ≥ 30 | 1.42 (1.09–1.85) |
| Meizlish ML | 2785 | NA | NA | 50.1 | Retrospective study | USA | Obesity | 1.356 (1.101–1.67) |
| Mayer MA | 23,844 | 5181 (21.7) | 49.93 ± 19.4 | 42.3 | Retrospective study | Spain | Obesity | 1.08 (0.91–1.27) |
| Lopez Zuniga MA | 318 | 48 (15.2) | 64.9 ± 14.1 | 58.5 | Prospective study | Spain | Obesity | 1.238 (0.393–3.9) |
| Marjot T | 932 | 248 (27) | 59 (48–68) | 67 | Retrospective study | Three multinational registries | Obesity | 1.07 (0.69–1.65) |
| Balfanz P | 125 | 44 (35) | 66 | 70 | Retrospective study | Germany | Obesity | 1.3 (0.29–5.74) |
| Olivas-Martínez A | 800 | 357 (44.8) | 51.9 ± 13.9 | 61 | Prospective study | USA | Obesity | 1.62 (1.14–2.32) |
| Yoshida Y | 776 | 409 (53.1) | 60.5 ± 16.1 | 47.3 | Retrospective study | USA | Obesity | 1.33 (0.87–2.03) |
| Geriatric Medicine Research Collaborative | 5711 | 1092 (19.1) | 74 (54–83) | 55.2 | Cohort study | 12 countries | BMI ≥ 30 | 1.03 (0.86–1.24) |
| Crouse AB | 604 | 371 (61.4) | 53.02 | 45 | Retrospective study | USA | Obesity | 1.21 (0.66–2.21) |
| Timberlake DT | 275 | 102 (37.1) | 57.9 | 77.1 | Retrospective study | USA | Obesity | 1.07 (0.53–2.14) |
| Cedano J | 132 | 59 (45) | 63 (53–71) | 59 | Retrospective study | USA | BMI ≥ 30 | 2.92 (1.07–8.01) |
| Girardin JL | 4210 | 1660 (39.4) | 61.9 | 58.1 | Retrospective study | USA | Obesity | 1.19 (1.04–1.37) |
| Le Borgne P | 1023 | 258 (34.1) | 69.0 (58.0–79.0) | 58.9 | Retrospective study | France | Obesity | 1.366 (0.74–2.52) |
| Rossi AP | 95 | 34 (35.8) | 62.46 ± 11.81 | 82.1 | NA | Italy | Obesity | 5.3 (1.26–22.34) |
| Gavioli EM | 437 | 69 (16) | 67 (56–79) | 48 | Retrospective study | USA | Obesity | 2.08 (1.14–3.78) |
| Dai CL* | 54,645 | 19,763 (41.9) | 47.8 ± 19.2 | 47.4 | Retrospective study | USA | Obesity | 1.22 (0.96–1.53) |
| Gupta YS* | 180 | 68 (40) | 68 (59–80) | 54 | Retrospective study | USA | BMI > 30 | 3.36 (1.53–7.34) |
| Navaratnam AV | 91,541 | 7920 (8.7) | 71.52 | 55.4 | Retrospective study | UK | Obesity | 1.476 (1.383–1.575) |
| Merzon E | 112 | 44 (39.3) | 62.89 ± 14.67 | 55.4 | Retrospective study | Israel | Obesity | 0.75 (0.04–12.49) |
| Zamoner W | 101 | 22 (21.7) | 57.89 ± 15.8 | 54.4 | Prospective study | Brazil | Obesity | 1.28 (1.04–11.52) |
| Aoun M | 231 | 52 (22.5) | 61.46 ± 13.99 | 55.4 | Retrospective study | Lebanon | Obesity | 0.88 (0.41–1.88) |
| Porta-Etessam J | 5399 | NA | 64.27 ± 16.93 | 59.2 | NA | Spain | Obesity | 1.12 (0.91–1.39) |
| Li WX | 1249 | 353 (28.3) | 36 (27–50) | 61.9 | Retrospective study | China | BMI ≥ 30 | 1.69 (1.12–3.57) |
| Suresh S | 1989 | 1031 (52) | 63.82 ± 16.55 | 50 | Retrospective study | USA | Obesity | 1.1 (0.83–1.44) |
| Sonmez A | 9213 | 870 (9.4) | 61 | 43.3 | Retrospective study | Turkey | Obesity | 2.36 (1.18–4.74) |
| Ibarra-Nava I | 416,546 | 79,635 (19.1) | 46.1 | 53.1 | Retrospective study | Mexico | Obesity | 1.39 (1.35–1.42) |
| Bloom CI* | 65,653 | 6007 (9.1) | 75.7 | 56.3 | Prospective study | UK | Obesity | 1.46 (0.88–2.42) |
| Giacomelli A | 520 | 92 (17.7) | 61 (50–72) | 76 | Prospective study | Italy | Obesity | 2.17 (1.1–4.31) |
| Argoty-Pantoja AD* | 412,017 | 77,566 (18.8) | 45.3 | 53.2 | Longitudinal analysis | Mexico | Obesity | 1.53 (0.83–2.81) |
| Satman I | 18,658 | 1024 (5.5) | 53 | 44 | Retrospective study | Turkey | Obesity | 2.83 (1.45–5.53) |
| Grivas P | 4966 | 1704 (34) | 66 (56–76) | 49 | Retrospective study | USA | Obesity | 1.09 (0.88–1.35) |
| Wu X | 1091 | 285 (26.1) | 59 (49–67) | 46.7 | Retrospective study | China | Obesity | 1.74 (0.73–4.21) |
| Muñoz-Rodríguez JR | 12,126 | 2100 (18.8) | 66.4 | 53.3 | Prospective study | Spain | Obesity | 1.3 (1.1–1.5) |
| Mehta HB* | 137,119 | 37,318 (27.2) | 76 | 34 | Retrospective study | USA | BMI > 30 | 0.92 (0.88–0.97) |
| Schavemaker R | 1099 | 324 (29.5) | 64.77 ± 10.91 | 73 | Cohort study | UK | Obesity | 1 (0.72–1.38) |
| Bonifazi M | 263 | 51 (19.4) | 45.3 (40.4–48.4) | 62.4 | Retrospective study | Italy | BMI ≥ 30 | 0.79 (0.27–2.27) |
| Mulhem E | 3219 | 1642 (51.0) | 65.2 (52.6–77.2) | 49 | Retrospective study | USA | Obesity | 1.25 (1.01–1.56) |
| Kurtz P | 4188 | NA | 63 (49–76) | 64 | Prospective study | Brazil | Obesity | 1.11 (0.99–1.24) |
| Sallis R* | 48,440 | 24,831 (51.3) | 47.5 ± 16.97 | 38.1 | Retrospective study | USA | BMI ≥ 30 | 1.29 (0.62–2.72) |
| Mendizabal M | 2211 | 383 (17.3) | 54.3 ± 17.3 | 60.6 | Prospective study | 11 Latin American countries | Obesity | 1.7 (1.3–2.3) |
| Alwafi H | 706 | 88 (12.5) | 48.0 ± 15.6 | 68.5 | Retrospective study | Saudi Arabia | BMI ≥ 30 | 0.25 (0.06–1.01) |
| Baggio JAO | 59,659 | 138 (0.2) | 41 | 44.6 | Retrospective study | Brazil | Obesity | 3.22 (1.87–5.54) |
| Vera-Zertuche JM | 15,529 | 3215 (20.7) | 46.6 ± 15.5 | 57.8 | Retrospective study | Mexico | Obesity | 2.37 (1.96–2.86) |
| Nikniaz Z | 317 | 76 (24.0) | 65.09 ± 13.29 | 51.4 | Prospective study | Iran | Obesity | 2.72 (1.13–7.44) |
| Ayala Gutierrez MDM | 13,940 | 2711 (19.4) | 67.3 | 57.1 | Retrospective study | Spain | Obesity | 1.33 (1.17–1.51) |
| Cereda E* | 222 | 68 (30.6) | 58.6 ± 11.2 | 77.9 | Prospective study | Italy | BMI ≥ 30 | 2.06 (1.17–3.63) |
| Cummins L | 1781 | 481 (27.1) | 51.74 | 55.2 | Retrospective study | UK | Obesity | 1.15 (0.86–1.55) |
| Castro MC | 176,559 | NA | NA | NA | Retrospective study | Brazil | Obesity | 1.07 (1.04–1.1) |
| Guerson-Gil A* | 3499 | 1472 (42.1) | 65 (55–76) | 55.27 | Retrospective study | USA | BMI ≥ 30 | 1.45 (1.09–1.91) |
| Gray WK* | 117,438 | 10,426 (8.9) | 70.5 | 54.6 | Retrospective study | UK | Obesity | 1.18 (0.81–1.72) |
| Song J | 5621 | 1260 (22.4) | 50.21 | 41.2 | Retrospective study | Korea | Obesity | 0.883 (0.751–1.054) |
| Dres M* | 1199 | NA | 74 (72–78) | 73 | Prospective study | France | BMI ≥ 30 | 0.9 (0.69–1.16) |
| Bravata DM* | 13,510 | 5940 (44.0) | 67.58 | 90.8 | Observational cohort study | USA | BMI ≥ 30 | 0.88 (0.64–1.21) |
| Verna EC | 1070 | 184 (17.2) | 60 | 52.5 | Retrospective study | USA | Obesity | 0.9 (0.76–1.06) |
| Xu W | 1131 | 320 (28.3) | 36 (26–50) | 61 | Retrospective study | China | Obesity | 1.75 (1.21–4.32) |
| Celejewska-Wojcik N | 116 | 43 (37.1) | 61 (51–70) | 78.4 | Prospective study | Poland | Obesity | 1.14 (0.65–2.01) |
| Goncalves DA | 182,700 | 6470 (3.5) | NA | 56.6 | Retrospective study | Brazil | Obesity | 1.411 (1.309–1.521) |
| Heldman MR | 1051 | 365 (34.7) | 57.4 | 62.2 | Multicenter cohort study | USA | Obesity | 1.8 (1.2–2.5) |
| Aminian A* | 2839 | 1357 (47.8) | 52.7 ± 20.1 | 46.4 | Retrospective study | USA | BMI ≥ 30 | 0.94 (0.45–1.97) |
| Robles-Perez E | 70,531 | 9906 (14.0) | NA | 43.2 | Retrospective study | Mexico | Obesity | 2.05 (1.67–2.6) |
| Henein MY | 213 | 122 (57.3) | 49.6 ± 12 | NA | Retrospective study | Egypt | Obesity | 3.403 (1.902–4.694) |
| Marciniak SJ* | 85,006 | NA | NA | NA | Prospective study | UK | Obesity | 1.05 (0.96–1.15) |
| Wander PL* | 35,879 | 15,147 (52) | 60.3 ± 17.0 | 89 | Retrospective study | USA | BMI ≥ 30 | 1.01 (0.76–1.35) |
| Tramunt B* | 2380 | 929 (39.0) | 70 (61–79) | 63.5 | Retrospective/Prospective study | France | BMI ≥ 30 | 0.85 (0.57–1.27) |
| Nogues X | 678 | 68 (8.1) | 62.1 | 59.1 | Prospective study | Spain | Obesity | 3.71 (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.
Fig. 1.
(A) The forest plot demonstrated the significant relationship between obesity and the increased risk for mortality among patients with coronavirus disease 2019 (COVID-19) on the basis of 138 eligible studies with a total of 3,863,516 cases reporting adjusted effect estimates and (B) Leave-one-out sensitivity analysis indicated that our results were stable and robust. * 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].
Funding
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.
CRediT authorship contribution statement
Yadong Wang, Haiyan Yang and Huifen Feng conceptualized the study. Hongjie Hou, Jie Xu and Yadong Wang performed literature search and data extraction. Jie Xu, Ying Wang, Huifen Feng and Haiyan Yang analyzed the data. Yadong Wang wrote the manuscript. All the authors approved the final manuscript.
Declaration of competing interest
All authors report that they have no potential conflicts of interest.
Acknowledgements
We would like to thank Li Shi, Wenwei Xiao, Xuan Liang, Jian Wu, Peihua Zhang and Yang Li (All are from Department of Epidemiology, School of Public Health, Zhengzhou University) for their kind help in searching articles and collecting data, and valuable suggestions for analyzing data.
References
- 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;113:154378. doi: 10.1016/j.metabol.2020.154378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Deiana 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;17:4450. doi: 10.3390/ijerph17124450. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Hashemi 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;40:2515–2521. doi: 10.1111/liv.14583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Shah 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;52:354–360. doi: 10.1080/07853890.2020.1791356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Arshad 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;97:396–403. doi: 10.1016/j.ijid.2020.06.099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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;151:264–269. doi: 10.7326/0003-4819-151-4-200908180-00135. [W64] [DOI] [PubMed] [Google Scholar]
- 7.Balduzzi S., Rucker G., Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health. 2019;22:153–160. doi: 10.1136/ebmental-2019-300117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.DerSimonian R., Laird N. Meta-analysis in clinical trials revisited. Contemp Clin Trials. 2015;45:139–145. doi: 10.1016/j.cct.2015.09.002. [DOI] [PMC free article] [PubMed] [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: 10.1002/jmv.27119. [DOI] [PMC free article] [PubMed]
- 10.Higgins J.P., Thompson S.G., Deeks J.J., Altman D.G. Measuring inconsistency in meta-analyses. Bmj. 2003;327:557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Yang 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: 10.1136/annrheumdis-2020-219821. [DOI] [PubMed]
- 12.Xu 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: 10.1016/j.arcmed.2021.05.003:2670. [DOI] [PMC free article] [PubMed]
- 13.Begg C.B., Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–1101. [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed]
- 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: 10.1093/ntr/ntab112. [DOI] [PMC free article] [PubMed]