Abstract
Background
Obesity significantly increases the risk of severe COVID-19 and mortality. Concerns have emerged regarding the efficacy of COVID-19 vaccines in obese populations due to potential alterations in immune responses.
Methods
Between March 2021 and March 2022, 5,071 participants with comorbidities received CoronaVac administered in two doses 28 days (± 7 days) apart and a single subsequent booster dose of ChAdOx1 with an interval of 180 days (± 30 days). Body composition was analyzed using a tetrapolar bioimpedance measurement scale. Anti-RBD IgG was dosed at baseline (D0) and on days 28, 90, 180, 270, and 360.
Results
1,181 participants with obesity (body fat percentage > P50), non-immune at baseline, showed significantly lower antibody titers (IgG anti-RBD) until D180 than 576 non-obese individuals. However, there was no difference in the frequency of COVID-19, COVID-19-related hospitalization or death. After the booster, no significant changes were seen. Patients with very excessive body fat (body fat percentage > P90) showed even slower seroconversion, with increased COVID-19 after D180. Similar results were observed using the routine body mass index (BMI); however, waning immunity (serology) was observed in individuals classified as having very excessive body fat only when bioimpedance was used.
Conclusion
Participants with obesity demonstrated weaker and slower seroconversion, which worsened in higher body fat mass. Bioimpedance revealed quicker waning immunity, emphasizing its importance over BMI in assessing vaccine response.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-025-12295-2.
Keywords: Obesity, COVID-19, Vaccine efficacy, CoronaVac, ChAdOx1-S, Bioimpedance, Body composition, Immunogenicity
Background
Obesity, defined as a body mass index (BMI) higher than 30, has emerged as a critical global health concern characterized by excessive body fat accumulation, posing significant challenges to health systems worldwide [1]. The prevalence of obesity has steadily risen over the past few decades, contributing to a range of comorbidities, including cardiovascular and metabolic diseases and respiratory disorders [2, 3]. Multiple anthropometric indicators are commonly used to evaluate obesity and its related health risks, including body mass index (BMI), waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio [4]. Although BMI offers a broad measure of overall adiposity, indices based on abdominal measurements more accurately capture visceral fat accumulation [5].
As the COVID-19 pandemic caused by SARS-CoV-2 evolved over the years, increasing evidence showed that obesity was a significant risk factor for severe outcomes and higher mortality rates [6–8]. The relationship between COVID-19 severity and obesity highlights the disproportionate effect on this susceptible group [9, 10], which is closely associated with impaired lung function, dysregulated immune responses, and chronic inflammation, all of which increase the risk of respiratory infections and worsen clinical outcomes [11].
Epidemiological studies have consistently demonstrated a dose-response relationship between increasing BMI and the risk of severe COVID-19 outcomes, including the need for hospitalization, mechanical ventilation, and mortality [12, 13]. Moreover, obese individuals often present with underlying conditions such as hypertension, type 2 diabetes, and compromised immune function, further compounding the risks associated with COVID-19 infection [3, 14].
Vaccination emerged as a pivotal tool in controlling the spread of COVID-19 and mitigating its impact on public health [2, 15]. While increased immune boosting is noted in normal-weight patients [16], concerns have been raised regarding the efficacy of COVID-19 vaccines in obese populations, given the potential for altered immune responses and vaccine effectiveness. Although evidence suggests that humoral responses to COVID-19 vaccines may be reduced in individuals with overweight or obesity, conclusive data on vaccine safety in this population are lacking [17]. Understanding the nuances of seroconversion in the context of obesity is paramount for optimizing vaccine strategies and ensuring equitable access to effective COVID-19 immunization programs. Hence, this study evaluated the immune response of participants with obesity who received two doses of the CoronaVac vaccine with a booster dose of the AstraZeneca vaccine in Manaus, Brazil.
Methods
Study design
This observational study was part of the CovacManaus I and II, assessed the effectiveness of the adsorbed inactivated COVID-19 vaccine CoronaVac (D0 and D28) with a booster dose of the AstraZeneca vaccine (D180) in Manaus, Brazil (NCT04789356/NCT05289206). In the study context, participants with a higher risk of infection and severity (defined by the national plan but not yet the priority group) anticipated the vaccine, which was only available to older groups during the rollout campaign in Brazil due to limited supply. No randomization was performed, and participants were allocated to intervention using a risk-based allocation. Details of the CovacManaus trial will be published elsewhere.
Population
Participants were screened virtually, with a scheduled in-person appointment to confirm eligibility criteria at two sites in Manaus between March 2021 and March 2022. For this analysis, inclusion criteria were (a) age between 18 and 49 years; (b) presence of at least one comorbidity listed in the Brazilian National Immunization Plan; and (c) willingness to be monitored during the follow-up period defined in the study through visits, telephone contacts, or other means of digital communication. Exclusion criteria were (a) confirmed diagnosis of COVID-19 in the previous 28 days (antigen test or RT-PCR), in this case, vaccination could be delayed until 30 days later; (b) report of fever in the 72 h before vaccination (inclusion could be delayed until the participant had been fever-free for 72 h and COVID-19 has been ruled out); (c) have received a live attenuated vaccine in the last 28 days or an inactivated vaccine in the previous 14 days before inclusion in the study; (d) Participants were excluded if they had any clinically significant condition that, in the opinion of the investigator, could increase the risk associated with study participation or interfere with the interpretation of safety data—such as unstable chronic illnesses, significant immunosuppression, poorly controlled metabolic or autoimmune diseases, a history of serious vaccine-related adverse reactions, or any medical, psychiatric, or social factor likely to compromise safety or follow-up; (e) pregnancy or lactation and (f) failure to meet any inclusion criteria.
Procedures and interventions
After a review of eligibility, demographic information, pre-existing medical conditions, and contact information were collected. Blood samples were collected before each vaccine dose and during follow-up visits (D0, D28, D90, D180, D270, and D360).
BMI was calculated on D0, and tetrapolar bioimpedance analysis (BIA) was performed on D28 (for logistical reasons, but it was used as a proxy for the exam performed before inclusion). BIA was carried out using an InBody 270 (Biospace, California, USA), and body fat (BF) levels were classified following a normative table for BF percentages validated in Brazilian adults per gender [18] (Supplementary Table 1). Subjects were classified as above normal body fat (BF)/obese (>P50) and normal BF/non-obese (< P50), and as very excessive BF (>P90) and non-very excessive BF (< P90), through BIA, and following percentiles used for the Brazilian population.
Nasopharyngeal swabs were collected for COVID-19 confirmation through RT-PCR during scheduled visits and through passive surveillance whenever the participant reported any symptoms to the study team.
The CoronaVac (Sinovac Biotech, China) vaccine was administered in two doses, 28 days (± 7 days) apart, via intramuscular injection using 25 mm needles. A single booster dose of ChAdOx1 vaccine (Oxford-AztraZeneca) was administered intramuscularly in the deltoid, using 30 mm needles, with an interval of 180 days (± 30 days) apart from the second dose of the CoronaVac. Total antibodies against the viral nucleocapsid and IgG anti-RBD (anti-receptor-binding domain) antibodies against the Spike protein were measured using the commercial kits (Elecsys Anti-SARS-CoV-2 Total and Elecsys Anti-SARS-CoV-2). The results are expressed in U/mL, and samples are categorized as reactive (> 0.8 U/mL) or highly reactive (≥ 250 U/mL) for SARS-CoV-2 RBD-specific antibodies.
Outcomes
Outcomes were the titration of antibodies against SARS-CoV-2 (IgG Anti-RBD) on D28, D90, D180, D270, and D360 post-vaccination in participants without previous immunity (cut point < 0.8 U/mL at D0). Other outcomes included the development of COVID-19, the need for hospitalization attributed to COVID-19, and death attributed to COVID-19 at any follow-up time point.
Statistical analysis
The proportions between the groups were compared using the chi-square test and Fisher’s exact test for categorical variables. Mean and standard deviation, median, and interquartile ranges were calculated for continuous variables and analyzed using ANOVA and Wilcoxon (two groups). All analyses were carried out using Stata (v.17).
Results
Between March 2021 and March 2022, 5,071 participants were included, and 1,757 had non-reactive IgG levels at baseline. Most were females (57.4%), with a median age of 40 years (IQR 35.0–45.0), and of admixed race (62.9%). Table 1 summarizes demographic data. Overall, the non-obese group had a higher proportion of reactive IgG at D28 and highly reactive IgG on D180 than the participants with obesity. No difference in disease, hospitalization, and death rates was found at any follow-up timepoint (Table 2).
Table 1.
Baseline demographic data
| Total | Normal BF | Above normal BF | |
|---|---|---|---|
| N = 1757 | n = 576 | n = 1181 | |
| Gender Women, % | 1009/1757 (57.43) | 423/576 (73.44) | 586/1181 (49.62) |
| Age, Median (IQR) | 40.0 (35.0–45.0) | 42.0 (37.0–46.0) | 39.0 (34.0–44.0) |
| Race, % | |||
| White | 460/1757 (26.18) | 131/576 (22.74) | 329/1181 (27.86) |
| Black | 144/1757 (8.20) | 47/576 (8.16) | 97/1181 (8.21) |
| Admixed | 1105/1757 (62.89) | 378/576 (65.63) | 727/1181 (61.56) |
| Asian | 31/1757 (1.76) | 13/576 (2.26) | 18/1181 (1.52) |
| Indigenous | 12/1757 (0.68) | 5/576 (0.87) | 7/1181 (0.59) |
| BMI, Median (IQR) | 32.30 (29.60–36.10) | 28.00 (25.00-30.10) | 34.30 (32.10–38.20) |
| Weight in Kg, Median (IQR) | 88.1 (76.9-100.2) | 71.9 (64.6–78.0) | 95.2 (87.0-106.6) |
| Height in in centimeters, Median (IQR) | 164.0 (158.0-172.0) | 160.0 (155.0-167.0) | 166.0 (159.0-173.0) |
| Diabetes, % | 170/1757 (9.68) | 70/576 (12.15) | 100/1181 (8.47) |
| Severe chronic lung disease, % | 110/1757 (6.26) | 73/576 (12.67) | 37/1181 (3.13) |
| Systemic arterial hypertension, % | 379/1757 (21.57) | 187/576 (32.47) | 192/1181 (16.26) |
| Cardiovascular diseases, % | 32/1757 (1.82) | 25/576 (4.34) | 7/1181 (0.59) |
| Cerebrovascular disease, % | 3/1757 (0.17) | 2/576 (0.35) | 1/1181 (0.08) |
| Chronic kidney disease, % | 6/1757 (0.34) | 5/576 (0.87) | 1/1181 (0.08) |
| Immunosuppressed, % | 98/1757 (5.58) | 79/576 (13.72) | 19/1181 (1.61) |
| Sickle cell anemia, % | 1/1757 (0.06) | 1/576 (0.17) | 0/1181 (0.00) |
BF, body fat. D, day. IQR, interquartile range
Table 2.
Seroconversion (IgG anti-RBD) in participants without previous immunity between D0 and D360 in participants with normal and above normal body fat
| Total | Normal BF | Above normal BF | p | |
|---|---|---|---|---|
| N = 1757 | n = 576 | n = 1181 | ||
| D0 to D28 | ||||
| COVID-19, % | 42/1757 (2.39) | 11/576 (1.91) | 31/1181 (2.62) | 0.36 |
| Hospitalization by COVID-19, % | 1/1757 (0.06) | 0/576 (0.00) | 1/1181 (0.08) | 1.00 |
| Reactive serology in D28 (> 0.8 U/mL), % | 1345/1741 (77.25) | 464/571 (81.26) | 881/1170 (75.30) | 0.005 |
| Reactive serology in D28 (≥ 250 U/mL), % | 9/1741 (0.52) | 3/571 (0.53) | 6/1170 (0.51) | 0.97 |
| D0 to D90 | ||||
| COVID-19, % | 117/1757 (6.66) | 44/576 (7.64) | 73/1181 (6.18) | 0.25 |
| Hospitalization by COVID-19, % | 2/1757 (0.11) | 0/576 (0.00) | 2/1181 (0.17) | 1.00 |
| Reactive serology in D90 (> 0.8 U/mL), % | 1626/1637 (99.33) | 533/537 (99.26) | 1093/1100 (99.36) | 0.80 |
| Reactive serology in D90 (≥ 250 U/mL), % | 230/1637 (14.05) | 85/537 (15.83) | 145/1100 (13.18) | 0.15 |
| D0 to D180 | ||||
| COVID-19, % | 122/1757 (6.94) | 41/576 (7.12) | 81/1181 (6.86) | 0.84 |
| Hospitalization by COVID-19, % | 5/1757 (0.28) | 0/576 (0.00) | 5/1181 (0.42) | 0.18 |
| Reactive serology in D180 (> 0.8 U/mL), % | 1603/1615 (99.26) | 537/541 (99.26) | 1066/1074 (99.26) | 0.99 |
| Reactive serology in D180 (≥ 250 U/mL), % | 211/1615 (13.07) | 88/541 (16.27) | 123/1074 (11.45) | 0.007 |
| D0 to D270 | ||||
| COVID-19, % | 713/1757 (40.58) | 238/576 (41.32) | 475/1181 (40.22) | 0.66 |
| Hospitalization by COVID-19, % | 6/1757 (0.34) | 0/576 (0.00) | 6/1181 (0.51) | 0.19 |
| Reactive serology in D180 (> 0.8 U/mL), % | 1358/1359 (99.93) | 466/467 (99.79) | 892/892 (100.00) | 0.17 |
| Reactive serology in D180 (≥ 250 U/mL), % | 1319/1359 (97.06) | 450/467 (96.36) | 869/892 (97.42) | 0.27 |
| D0 to D360 | ||||
| COVID-19, % | 736/1757 (41.89) | 248/576 (43.06) | 488/1181 (41.32) | 0.49 |
| Hospitalization by COVID-19, % | 6/1757 (0.34) | 0/576 (0.00) | 6/1181 (0.51) | 0.19 |
| Reactive serology in D360 (> 0.8 U/mL), % | 1016/1016 (100.00) | 368/368 (100.00) | 648/648 (100.00) | -- |
| Reactive serology in D360 (≥ 250 U/mL), % | 1007/1016 (99.11) | 364/368 (98.91) | 643/648 (99.23) | 0.61 |
BF, body fat. D, day
When further subclassifying participants, less reactive antibodies were seen in very excessive BF as compared to non-very excessive BF on D28, and less highly reactive were seen in D90 and D360. When following participants until D270 or D360, very excessive BF presented with more clinical COVID-19, however, without any impact on hospitalization and death (Table 3). When BMI was used, similar results were seen, except when subgrouping participants with severe obesity (BMI < 40 vs. ≥ 40), in which no difference in disease and seroconversion was observed after D180 (supplementary Tables 2 and 3). No differences were observed when grouping patients per visceral fat level (supplementary Table 4).
Table 3.
Seroconversion (IgG anti-RBD) in participants without previous immunity between D0 and D360 in very excessive vs. non-very excessive BF
| Total | Non-very excessive BF | Very excessive BF |
p | |
|---|---|---|---|---|
| N = 1757 | n = 1409 | n = 348 | ||
| D0 to D28 | ||||
| COVID-19, % | 42/1757 (2.39) | 33/1409 (2.34) | 9/348 (2.59) | 0.79 |
| Hospitalization by COVID-19, % | 1/1757 (0.06) | 1/1409 (0.07) | 0/348 (0.00) | 1.00 |
| Reactive serology in D28 (> 0.8 U/mL), % | 1345/1741 (77.25) | 1113/1398 (79.61) | 232/343 (67.64) | < 0.001 |
| Reactive serology in D28 (≥ 250 U/mL), % | 9/1741 (0.52) | 8/1398 (0.57) | 1/343 (0.29) | 0.52 |
| D0 to D90 | ||||
| COVID-19, % | 117/1757 (6.66) | 91/1409 (6.46) | 26/348 (7.47) | 0.50 |
| Hospitalization by COVID-19, % | 2/1757 (0.11) | 2/1409 (0.14) | 0/348 (0.00) | 1.00 |
| Reactive serology in D90 (> 0.8 U/mL), % | 1626/1637 (99.33) | 1309/1316 (99.47) | 317/321 (98.75) | 0.16 |
| Reactive serology in D90 (≥ 250 U/mL), % | 230/1637 (14.05) | 200/1316 (15.20) | 30/321 (9.35) | 0.007 |
| D0 to D180 | ||||
| COVID-19, % | 122/1757 (6.94) | 94/1409 (6.67) | 28/348 (8.05) | 0.37 |
| Hospitalization by COVID-19, % | 5/1757 (0.28) | 3/1409 (0.21) | 2/348 (0.57) | 0.26 |
| Reactive serology in D180 (> 0.8 U/mL), % | 1603/1615 (99.26) | 1285/1293 (99.38) | 318/322 (98.76) | 0.24 |
| Reactive serology in D180 (≥ 250 U/mL), % | 211/1615 (13.07) | 175/1293 (13.53) | 36/322 (11.18) | 0.26 |
| D0 to D270 | ||||
| COVID-19, % | 713/1757 (40.58) | 554/1409 (39.32) | 159/348 (45.69) | 0.030 |
| Hospitalization by COVID-19, % | 6/1757 (0.34) | 3/1409 (0.21) | 3/348 (0.86) | 0.096 |
| Reactive serology in D180 (> 0.8 U/mL), % | 1358/1359 (99.93) | 1098/1099 (99.91) | 260/260 (100.00) | 0.63 |
| Reactive serology in D180 (≥ 250 U/mL), % | 1319/1359 (97.06) | 1066/1099 (97.00) | 253/260 (97.31) | 0.79 |
| D0 to D360 | ||||
| COVID-19, % | 736/1757 (41.89) | 574/1409 (40.74) | 162/348 (46.55) | 0.049 |
| Hospitalization by COVID-19, % | 6/1757 (0.34) | 3/1409 (0.21) | 3/348 (0.86) | 0.096 |
| Reactive serology in D360 (> 0.8 U/mL), % | 1016/1016 (100.00) | 831/831 (100.00) | 185/185 (100.00) | -- |
| Reactive serology in D360 (≥ 250 U/mL), % | 1007/1016 (99.11) | 826/831 (99.40) | 181/185 (97.84) | 0.041 |
BF, body fat. D, day
Discussion
In this study, participants with obesity exhibited slower and weaker early immune responses than their comparators. Additionally, those with severe obesity experienced differences in the durability of their long-term immune response, with an accelerated waning of humoral response and clinical disease, which was only evidenced using BIA.
COVID-19 patients typically produce antibodies within the first weeks after symptom onset and remain elevated for several months [19, 20]. Although COVID-19 vaccination is highly effective in eliciting a protective humoral response, antibody titers may be lower in individuals with obesity compared to the general population [21, 22], and the decline in antibody titers over time may also be associated with increased morbidity and mortality [21]. The influence of other conditions, such as chronic kidney disease, diabetes, and hypertension, usually associated with obesity, may also explain the heterogeneity of COVID-19 vaccine response [23–26]. Reduced immunogenicity in overweight patients has also been observed in vaccines for other diseases [27, 28].
Vaccination may also play a protective role against the development and severity of long COVID, primarily through the reduction of viral burden and modulation of the inflammatory signaling pathways implicated in post-acute sequelae [29, 30]. Also, while conventional vaccine platforms have demonstrated substantial success in COVID-19, novel technologies such as circular RNA vaccines may be alternatives with potential advantages in stability and immunogenicity [31].
The length of the needle used for vaccination may affect vaccine delivery in individuals with obesity [32, 33]. The thicker subcutaneous fat layer hinders proper delivery into muscle tissue, potentially compromising vaccine absorption and efficacy. After a preliminary analysis, and with the possibility that smaller needles could have impacted the adequate immunization of the obese population because of the fat layer in front of the muscle, a larger needle was used for the booster dose administration.
The group with very high body fat levels exhibited waning immunity over time, even after receiving a booster dose—an effect not observed with similar BMI classifications. While BMI has been widely used to categorize nutritional status, it only approximates the degree of adiposity and fails to account for body fat distribution, particularly visceral fat. Consequently, individuals with high muscle mass may be inaccurately classified as overweight, whereas those with low lean mass and increased adiposity may be misclassified as average weight. In contrast, body composition analysis through bioimpedance measurement offers a more accurate assessment of lean body mass and body fat in clinical evaluations [34]. Research indicates visceral adiposity is a more sensitive predictor of adverse COVID-19 outcomes than BMI alone [35]. While negative correlations between BMI and serum levels of SARS-CoV-2 Spike-specific IgG antibodies have also been documented [36, 37], and bioimpedance may not always be readily available in clinical settings, it highlights the importance of employing more precise metrics in understanding the immune response in obese individuals that would otherwise not be seen using BMI.
This study had limitations. Due to its design, it may be unclear whether the vaccine or possible infections during follow-up elicited immunity in participants with obesity. Although comorbidities were recorded, we could not assess their effects from the outcomes assessed. Residual confounding related to underlying conditions is therefore possible, particularly because some comorbidities may influence immune response, clinical evolution, or treatment effectiveness. An additional limitation is the potential influence of SARS-CoV-2 lineage evolution over the study period [38]. The emergence and circulation of distinct viral variants may have affected infection rates, hospitalization, and mortality independently of vaccine or host-related factors. The cellular response was not assessed in this study. Further dilutions in antibody titrations could not be performed when levels were ≥ 250 U/mL.
Additionally, this study also has several strengths. It included a large cohort with prospective follow-up and standardized vaccination schedules using two vaccine platforms. The use of serial serological measurements across multiple time points allowed for the assessment of antibody kinetics over time. Additionally, body composition was evaluated through tetrapolar bioimpedance, providing a more accurate estimate of adiposity than traditional BMI alone.
In conclusion, our study demonstrated that participants with obesity with no previous immunity exhibited slower and weaker seroconversion rates following COVID-19 vaccination. Importantly, we found that waning immunity was accelerated in individuals classified as having severe obesity based on bioimpedance measures, a pattern not observed with traditional BMI classifications. Given that both short-term and long-term seroconversion may be compromised in this population, there is a critical need for targeted vaccine prioritization policies to ensure adequate protection for obese individuals.
Supplementary information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank all the participants for their paramount importance to the study. We also thank the whole team for their valuable contribution to conducting the study.
Abbreviations
- ANOVA
Analysis of variance
- BF
Body fat
- BIA
Bioimpedance analysis
- BMI
Body mass index
- COVID-19
Coronavirus disease 2019
- IgG
Immunoglobulin G
- IQR
Interquartile range
- RBD
Receptor-binding domain of SARS-CoV-2 spike protein
- RT-PCR
Reverse transcription polymerase chain reaction
- SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
- U/mL
Units per milliliter
Author contributions
Study design: MPM, ML, MVC, Data curation: VSS, MGAR, Investigation: MVC, AM, JN, FC, MGAR, TB, SL, EH, EG, RLAN, SS, AVSN, AS, BMS, GSM, JSMC, DCBS, JDBS, KPP, WM, FN, VSS, FAV, GC, MPM, ML, Supervision: MPM, ML, Statistical analysis: VSS, MGAR, Draft preparation: MVC, JDBS and DCBS, All authors read and approved the final manuscript.
Funding
This study was funded by Fundação de Amparo à Pesquisa do Amazonas (FAPEAM) (PRÓ-ESTADO-Project 01.02.016301.00649/2021 - Nº 002/2008, Nº 007/2018 and Nº 005/2019) and XP Investimentos (57th work plan of the Unidos Contra a COVID-19 program). DCBS is supported with a National Visiting Research Fellowship II from the FAPEAM. ML, GCM, MLN, WMM, and VS are CNPq productivity fellows. JDBS was supported by the Amazonas State University and Fiocruz. Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM) also supported this work through Edital Nº 038/2022 - PDPG/CAPES/FAPEAM - Coordenador/Auxílio Financeiro.
Data availability
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Brazilian National Research Ethics Commission (CONEP) under the identifiers 44076721.5.0000.0005 and 51701821.6.0000.0005. Informed consent was obtained from all subjects involved in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.WHO(world health organization). Obesity and overweight. 2023. Available: https://www.who.int/es/news-room/fact-sheets/detail/obesity-and-overweight.
- 2.Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396:1223–49. 10.1016/S0140-6736(20)30752-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Popkin BM, Du S, Green WD, Beck MA, Algaith T, Herbst CH, et al. Individuals with obesity and COVID-19: a global perspective on the epidemiology and biological relationships. Obes Rev. 2020;21(11):e13128. 10.1111/obr.13128. [DOI] [PMC free article] [PubMed]
- 4.Czernichow S, Kengne AP, Stamatakis E, Hamer M, Batty GD. Body mass index, waist circumference and waist–hip ratio: which is the better discriminator of cardiovascular disease mortality risk? evidence from an individual-participant meta-analysis of 82 864 participants from nine cohort studies. Obes Rev. 2011;12:680–7. 10.1111/J.1467-789X.2011.00879.X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular riska review of the literature. Eur J Clin Nutr. 2010;64:16–22. 10.1038/EJCN.2009.68;KWRD. [DOI] [PubMed] [Google Scholar]
- 6.Kass DA, Duggal P, Cingolani O. Obesity could shift severe COVID-19 disease to younger ages. Lancet. 2020;395:1544–5. 10.1016/S0140-6736(20)31024-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A, et al. High prevalence of obesity in severe acute respiratory syndrome Coronavirus-2 (SARS‐CoV‐2) requiring invasive mechanical ventilation. Obesity. 2020;28:1195–9. 10.1002/oby.22831. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Petrilli CM, Jones SA, Yang J, Rajagopalan H, O’Donnell L, Chernyak Y et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ. 2020; m1966. 10.1136/bmj.m1966. [DOI] [PMC free article] [PubMed]
- 9.Sattar N, McInnes IB, McMurray JJV. Obesity is a risk factor for severe COVID-19 infection: multiple potential mechanisms. Circulation. 2020;142:4–6. 10.1161/CIRCULATIONAHA.120.047659. [DOI] [PubMed] [Google Scholar]
- 10.Singh R, Rathore SS, Khan H, Karale S, Chawla Y, Iqbal K, et al. Association of obesity with COVID-19 severity and mortality: an updated systemic review, meta-analysis, and meta-regression. Front Endocrinol (Lausanne). 2022;13. 10.3389/fendo.2022.780872. [DOI] [PMC free article] [PubMed]
- 11.Tartof SY, Qian L, Hong V, Wei R, Nadjafi RF, Fischer H, et al. Obesity and mortality among patients diagnosed with COVID-19: results from an integrated health care organization. Ann Intern Med. 2020;173:773–81. 10.7326/M20-3742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Petrakis D, Margină D, Tsarouhas K, Tekos F, Stan M, Nikitovic D, et al. Obesity - a risk factor for increased COVID-19 prevalence, severity and lethality (Review). Mol Med Rep. 2020;22:9–19. 10.3892/mmr.2020.11127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Tadayon Najafabadi B, Rayner DG, Shokraee K, Shokraie K, Panahi P, Rastgou P et al. Obesity as an independent risk factor for COVID-19 severity and mortality. Cochrane Database of Syst Rev. 2023;2023. 10.1002/14651858.CD015201. [DOI] [PMC free article] [PubMed]
- 14.Westheim AJF, Bitorina AV, Theys J, Shiri-Sverdlov R. COVID‐19 infection, progression, and vaccination: focus on obesity and related metabolic disturbances. Obes Rev. 2021;22. 10.1111/obr.13313. [DOI] [PMC free article] [PubMed]
- 15.Watson OJ, Barnsley G, Toor J, Hogan AB, Winskill P, Ghani AC. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis. 2022;22:1293–302. 10.1016/S1473-3099(22)00320-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Onyango TB, Zhou F, Bredholt G, Brokstad KA, Lartey S, Mohn KG-I, et al. SARS-CoV-2 specific immune responses in overweight and obese COVID-19 patients. Front Immunol. 2023;14. 10.3389/fimmu.2023.1287388. [DOI] [PMC free article] [PubMed]
- 17.Fu C, Lin N, Zhu J, Ye Q. Association between Overweight/Obesity and the safety and efficacy of COVID-19 vaccination: a systematic review. Vaccines (Basel). 2023;11:996. 10.3390/VACCINES11050996/S1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Branco BHM, Bernuci MP, Marques DC, Carvalho IZ, Barrero CAL, de Oliveira FM, et al. Proposal of a normative table for body fat percentages of Brazilian young adults through bioimpedanciometry. J Exerc Rehabil. 2018;14:974–9. 10.12965/jer.1836400.200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wajnberg A, Amanat F, Firpo A, Altman DR, Bailey MJ, Mansour M, et al. Robust neutralizing antibodies to SARS-CoV-2 infection persist for months. Sci (1979). 2020;370:1227–30. 10.1126/science.abd7728. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Hou H, Wang T, Zhang B, Luo Y, Mao L, Wang F, et al. Detection of IgM and IgG antibodies in patients with coronavirus disease 2019. Clin Transl Immunol. 2020;9. 10.1002/CTI2.1136. [DOI] [PMC free article] [PubMed]
- 21.van der Klaauw AA, Horner EC, Pereyra-Gerber P, Agrawal U, Foster WS, Spencer S, et al. Accelerated waning of the humoral response to COVID-19 vaccines in obesity. Nat Med 2023. 2023;29:5. 10.1038/s41591-023-02343-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Faizo AA, Qashqari FS, El-Kafrawy SA, Barasheed O, Almashjary MN, Alfelali M, et al. A potential association between obesity and reduced effectiveness of COVID-19 vaccine-induced neutralizing humoral immunity. J Med Virol. 2023;95. 10.1002/JMV.28130. [DOI] [PMC free article] [PubMed]
- 23.Soegiarto G, Wulandari L, Purnomosari D, Dhia Fahmita K, Ikhwan Gautama H, Tri Hadmoko S, et al. Hypertension is associated with antibody response and breakthrough infection in health care workers following vaccination with inactivated SARS-CoV-2. Vaccine. 2022;40:4046–56. 10.1016/J.VACCINE.2022.05.059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Babel N, Hugo C, Westhoff TH. Vaccination in patients with kidney failure: lessons from COVID-19. Nat Reviews Nephrol 2022. 2022;18:11. 10.1038/s41581-022-00617-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Li H, Cai D, Jiang D, Li X, Liao X, Liu D, et al. Risk of waning humoral responses after inactivated or subunit Recombinant SARS-CoV-2 vaccination in patients with chronic diseases: findings from a prospective observational study in China. J Med Virol. 2023;95:e28434. 10.1002. /JMV.28434;CTYPE:STRING:JOURNAL. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Pal R, Sachdeva N, Mukherjee S, Suri V, Zohmangaihi D, Ram S, et al. Impaired anti-SARS-CoV-2 antibody response in non-severe COVID-19 patients with diabetes mellitus: A preliminary report. Diabetes Metabolic Syndrome: Clin Res Reviews. 2021;15:193–6. 10.1016/J.DSX.2020.12.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Honce R, Schultz-Cherry S. Impact of obesity on influenza a virus pathogenesis, immune response, and evolution. Front Immunol. 2019;10. 10.3389/fimmu.2019.01071. [DOI] [PMC free article] [PubMed]
- 28.Frasca D, Ferracci F, Diaz A, Romero M, Lechner S, Blomberg BB. Obesity decreases < scp > B cell responses in young and elderly individuals. Obesity. 2016;24:615–25. 10.1002/oby.21383. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Guo M, Shang S, Li M, Cai G, Li P, Chen X et al. Understanding autoimmune response after SARS-CoV-2 infection and the pathogenesis/mechanisms of long COVID-19. Medical review (2021). 2024;4: 367–383. 10.1515/MR-2024-0013. [DOI] [PMC free article] [PubMed]
- 30.Ewing AG, Salamon S, Pretorius E, Joffe D, Fox G, Bilodeau S et al. Review of organ damage from COVID-19 and long COVID-19: a disease with a spectrum of pathology. Medical review (2021). 2024;5: 66–75. 10.1515/MR-2024-0030. [DOI] [PMC free article] [PubMed]
- 31.Xie J, Ye F, Deng X, Tang Y, Liang JY, Huang X, et al. Circular RNA: a promising new star of vaccine. J Transl Int Med. 2023;11:372–81. 10.2478/JTIM-2023-0122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Chhabria S, Stanford FC. A long shot: the importance of needle length in vaccinating patients with obesity against COVID-19. Vaccine. 2022;40:9–10. 10.1016/J.VACCINE.2021.11.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Doppen M, Kearns C, Weatherall M, Kearns N, McIntyre P, Hills T, et al. When should a longer needle be used for intramuscular injection in obese patients? a combined analysis of new Zealand data. Vaccine. 2023;41:2690–5. 10.1016/J.VACCINE.2023.03.018. [DOI] [PubMed] [Google Scholar]
- 34.Golec M, Zembala-John J, Fronczek M, Konka A, Bochenek A, Wystyrk K, et al. Relationship between anthropometric and body composition parameters and anti-SARS-CoV-2 specific IgG titers in females vaccinated against COVID-19 according to the heterologous vaccination course: a cohort study. Ito E, editor. PLoS ONE. 2023;18:e0287128. 10.1371/journal.pone.0287128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Graziano E, Peghin M, De Martino M, De Carlo C, Da Porto A, Bulfone L, et al. The impact of body composition on mortality of COVID-19 hospitalized patients: a prospective study on abdominal fat, obesity paradox and sarcopenia. Clin Nutr ESPEN. 2022;51:437–44. 10.1016/j.clnesp.2022.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Frasca D, Reidy L, Cray C, Diaz A, Romero M, Kahl K et al. Influence of obesity on serum levels of SARS-CoV-2-specific antibodies in COVID-19 patients. Ansari AA, editor. PLoS ONE. 2021;16: e0245424. 10.1371/journal.pone.0245424. [DOI] [PMC free article] [PubMed]
- 37.Fu C, Lin N, Zhu J, Ye Q. Association between Overweight/Obesity and the safety and efficacy of COVID-19 vaccination: A systematic review. Vaccines (Basel). 2023;11:996. 10.3390/vaccines11050996. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Choi JH, Jun MS, Jeon JY, Kim HS, Kim YK, Jeon CH, et al. Global lineage evolution pattern of SARS-CoV-2 in Africa, America, Europe, and Asia: a comparative analysis of variant clusters and their relevance across continents. J Transl Int Med. 2023;11:410. 10.2478/JTIM-2023-0118. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.