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[Preprint]. 2020 Dec 11:2020.11.11.20229724. Originally published 2020 Nov 13. [Version 2] doi: 10.1101/2020.11.11.20229724

Epidemiological and immunological features of obesity and SARS-CoV-2

Eric J Nilles 1,2,3,4,#, Sameed M Siddiqui 5,6, Stephanie Fischinger 7, Yannic C Bartsch 7, Michael de Saint Aubin 3, Guohai Zhou 1,2, Matthew J Gluck 8,14, Samuel Berger 8, Justin Rhee 8, Eric Petersen 8, Benjamin Mormann 1,8, Michael Loesche 1,8, Zhilin Chen 7, Jingyou Yu 7,9, Makda Gebre 7,9, Caroline Atyeo 7, Matthew J Gorman 7, Alex Lee Zhu 7, John Burke 7, Matthew Slein 7, Mohammad A Hasdianda 1,2, Guruprasad Jambaulikar 1,2, Edward Boyer 1,2, Pardis Sabeti 4,6,9,10, Dan H Barouch 6,11, Boris D Julg 6, Adam J Kucharski 13, Elon R Musk 8, Douglas A Lauffenburger 12, Galit Alter 4,6, Anil S Menon 8
PMCID: PMC7668749  PMID: 33200139

Abstract

Obesity is a key correlate of severe SARS-CoV-2 outcomes while the role of obesity on risk of SARS-CoV-2 infection, symptom phenotype, and immune response are poorly defined. We examined data from a prospective SARS-CoV-2 cohort study to address these questions. Serostatus, body mass index, demographics, comorbidities, and prior COVID-19 compatible symptoms were assessed at baseline and serostatus and symptoms monthly thereafter. SARS-CoV-2 immunoassays included an IgG ELISA targeting the spike RBD, multiarray Luminex targeting 20 viral antigens, pseudovirus neutralization, and T cell ELISPOT assays. Our results from a large prospective SARS-CoV-2 cohort study indicate symptom phenotype is strongly influenced by obesity among younger but not older age groups; we did not identify evidence to suggest obese individuals are at higher risk of SARS-CoV-2 infection; and, remarkably homogenous immune activity across BMI categories suggests natural- and vaccine-induced protection may be similar across these groups.

Keywords: SARS-CoV-2, COVID-19, obesity, epidemiology, immunity

Background

Obesity is a key risk factor for severe disease and death from novel coronavirus disease 2019 (COVID-19) [1,2], the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With over 1.9 billion people overweight or obese globally [3], implications for SARS-CoV-2 morbidity and mortality are substantial. After adjusting for age and obesity-related comorbidities such as diabetes, hypertension and coronary heart disease, obesity remains a strong independent predictor of excess morbidity and mortality [1,4,5]. These findings are not entirely unexpected [6]. Obesity and poor clinical outcomes have been described with other viral pathogens, most notably influenza A (H1N1) during the 2009 pandemic, when obesity was associated with increased hospitalizations, need for intensive care support, and deaths [4,7,8]. In addition to the relationship between obesity and clinical outcomes, emerging evidence suggests a link between higher body mass index (BMI) and higher incidence rates of COVID-19 or SARS-CoV-2 infection [4,9,10] suggesting increased BMI may enhance susceptibility to infection, with important implications for individual-level risks and population-level transmission dynamics [4]. The role of obesity on the immune response to SARS-CoV-2 has also been the focus of intense recent attention [4,11]. Obesity has been linked to less robust and/or effective immune response after natural influenza infection [12] or vaccination [13], raising concerns about diminished protective immunity following natural SARS-CoV-2 infection or vaccination [4,14]. However, no directly relevant data addresses this question.

Thus, given substantial uncertainly about multiple features of obesity and SARS-CoV-2, we investigated if BMI is associated with differential (i) risks of testing positive for anti-SARS-CoV-2 IgG antibodies, (ii) symptom phenotype, and (iii) adaptive immune features.

Methods

Ethical Disclosures

The study protocol was approved by the Western Institutional Review Board. The use of de-identified data and biological samples was approved by the Mass General Brigham Healthcare Institutional Review Board. All participants provided written informed consent.

Study design, setting and study population

This study examines data from a prospective observational cohort study using serial serological assessment to characterize the immunoepidemiology of SARS-CoV-2 infection among industry employees. Serostatus was unknown at the time of subject enrollment. The study population was comprised of Space Exploration Technologies Corporation employees, all of whom were invited to participate. Study enrollment commenced 20 April and employees were invited to participate on a rolling basis through 28 July 2020; 4469 volunteered and were enrolled from ~8400 total employees from seven work locations in four US states. Serial blood sampling and interim symptom reporting were performed monthly.

Covariates

Standardized data measures included demographic and medical history variables (listed in Table 1) and COVID-19 compatible symptoms between March 1, 2020, and study enrollment. Symptoms were classified as primary (fever, chills or feverish, cough, anosmia, ageusia) and other compatible symptoms (body or muscle aches, sore throat, nausea or vomiting, diarrhea, congestion, and increased fatigue/generalized weakness). Blood was sampled and interim symptoms were monitored monthly.

Table 1.

Characteristics and serostatus of study participants (n=4469)

Characteristic1 All participants (n=4469) Seropositive participants (n=322) OR (95% CI) P-Value5
N N %
Age group
18–29 y 1668 133 8.0% ref
30–39 y 1761 104 5.9% 0.72 (0.56 to 0.94) 0.0174*
40–49 y 584 50 8.6% 1.08 (0.77 to 1.52) 0.6545
50–59 y 315 26 8.3% 1.04 (0.67 to 1.61) 0.8666
60+ y 85 2 2.4% 0.28 (0.07 to 1.14) 0.076
BMI
<18.5 34 3 8.8% 1.44 (0.43 to 4.80) 0.5500
18.5–<25 1686 106 6.3% ref
25–<30 1523 101 6.6% 1.06 (0.80 to 1.40) 0.6916
30–<35 676 61 9.0% 1.48 (1.06 to 2.05) 0.0196*
35–<40 246 23 9.3% 1.54 (0.96 to 2.47) 0.0742
≥40 105 5 4.8% 0.75 (0.30 to 1.87) 0.5308
Ethnicity
Not Hispanic/Not Latinx 2492 113 4.5% ref
Hispanic/Latinx 1274 155 12.2% 2.91 (2.26 to 3.75) <0.0001****
Race
White 2862 185 6.5% ref
American Indian/Alaska Native 32 3 9.4% 1.50 (0.45 to 4.96) 0.5092
Asian 442 18 4.1% 0.61 (0.37 to 1.01) 0.0535
Black 72 2 2.8% 0.41 (0.10 to 1.70) 0.2207
Native Hawaiian/Pacific Islander 29 2 6.9% 1.07 (0.25 to 4.54) 0.9249
More than one race 292 13 4.5% 0.67 (0.38 to 1.20) 0.1796
Sex 2
Female 600 40 6.7% ref
Male 3730 267 7.2% 1.08 (0.77 to 1.52) 0.6634
Children ≤ 18 y in household
No 3014 204 6.8% ref
Yes 1342 106 7.9% 1.18 (0.93 to 1.51) 0.1808
No. in household
1 640 41 6.4% ref
2–4 3027 214 7.1% 1.11 (0.79 to 1.57) 0.5490
>4 659 51 7.7% 1.23 (0.80 to 1.88) 0.3499
Primary work location
Cape Canaveral, Florida 268 17 6.3% ref
Hawthorne, California 2859 111 3.9% 0.60 (0.35 to 1.01) 0.0544
McGregor, Texas 257 21 8.2% 1.31 (0.68 to 2.55) 0.4202
Seattle, Washington 253 5 2.0% 0.30 (0.11 to 0.82) 0.0190*
South Texas, Texas 712 160 22.5% 4.28 (2.54 to 7.21) <0.0001****
Other 69 1 1.4% 0.23 (0.03 to 1.79) 0.1623
Comorbidities3,4
Asthma 368 20 5.4% 0.72 (0.45 to 1.15) 0.1721
Hypertension 356 26 7.3% 1.02 (0.67 to 1.54) 0.9405
Diabetes mellitus 101 11 10.9% 1.59 (0.84 to 3.01) 0.1509
Coronary heart disease 17 1 5.9% 0.80 (0.11 to 6.08) 0.8329
Stroke 9 2 22.2% 3.70 (0.76 to 17.87) 0.1039
Emphysema/COPD 9 1 11.1% 1.61 (0.20 to 12.93) 0.6532
Cancer-not receiving treatment 39 2 5.1% 0.69 (0.17 to 2.89) 0.6163
Other lung disease 26 2 7.7% 1.07 (0.25 to 4.56) 0.9233
Other immunocompromised 61 4 6.6% 0.92 (0.33 to 2.55) 0.8710
Other chronic medical condition 176 9 5.1% 0.72 (0.36 to 1.43) 0.3471
Smoking history
Never 3769 263 7.0% ref
Prior 367 24 6.5% 0.93 (0.61 to 1.44) 0.7514
Current 229 23 10.0% 1.49 (0.95 to 2.33) 0.0826
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1

Not reported data: age group (n=56), BMI (199), ethnicity (703), race (740), sex (139), children in HH (113), No. in HH (143), primary location (51), comorbidities (105)

2

Four (4) reported “other sex”, none were seropositive

3

For comorbidities reference value for OR is no. COPD chronic obstuctive pulmonary disease

4

Other comorbidities with no seropositive participants: chronic kidney disease (10), Heart failure (4), Cancer receiving treatment (3), Other heart disease (22).

5

P-values unadjusted for multiple hypothesis testing:

*

<0.05.

**

<0.01,

***

<0.001,

****

<0.0001

Laboratory analyses

Serological analyses were performed using the Ragon/MGH enzyme-linked immunosorbent assay, which detects IgG against the receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein using a previously described method [15] (Supplemental 1). Assay performance has been externally validated in a blinded fashion at 99·6% specific and benchmarked against commercial EUA approved assays [16]. Immune profiling methods are detailed in Supplement 1. Briefly, specific antibody subclasses and isotypes and FcγR binding against SARS-CoV-2 RBD, nucleocapsid and full spike proteins were assessed using a custom Luminex multiplexed assay (Luminex Corp, TX). Viral neutralization was assessed on a SARS-CoV-2 pseudovirus assay, as described previously [17] with neutralization titer defined as the sample dilution associated with a 50% reduction in luminescent units. The presence of neutralizing activity was defined as a titer >20. T cell activity was assessed on an enzyme-linked interferon-gamma immunospot assay with ≥ 25 spot forming cells per 106 peripheral blood mononuclear cells considered positive.

Data classification and analyses

Seropositivity was determined by the detection of SARS-CoV-2 specific IgG. BMI was calculated by dividing weight in kilograms by height in meters squared and categorized by underweight (<18·5 kg/m2), normal weight (18·5 to 24 kg/m2; reference), overweight (25 to 29 kg/m2), obesity class 1 (30 to 34 kg/m2), obesity class 2 (35 to 39 kg/m2), and obesity class 3 or severe obesity (≥40 kg/m2) according to the World Health Organization.

We performed discrete analyses to address the three aims of the study. For assessment of risk of seropositivity by BMI, the primary exposure of interest was BMI and the outcome variable of interest was seropositivity at any time point. We assessed the unadjusted association between a range of demographic (n=7) and medical history (n=17) covariates using χ2 to compare proportions and ANOVA or Kruskal-Wallis tests to compare means. For adjusted analyses, we constructed a multivariable logistic regression model that included, in addition to BMI and serostatus, age, sex, ethnicity, race, comorbidities, primary work location, number of individuals in the household, and children in the household.

To understand if obesity status is associated with differential reporting of symptoms, we computed the proportion of seropositive individuals reporting COVID-19 compatible symptoms stratified by obesity status. Symptoms were analyzed from the period preceding the first seropositive result. For example, if an individual was seronegative at baseline and seropositive at the subsequent time point, the symptoms reported between those timepoints were analyzed. The primary exposure of interest was obesity, and symptoms the outcome variables of interest. Given data suggesting the adverse impact of obesity on COVID-19 mortality may decline with age [9], we assessed if similar age-dependent obesity risk may be observed for symptom reporting by conducting subgroup analysis stratified by < or ≥ 40 years, with categorization selected due to sparsity of older participants.

Lastly, given an accumulation of evidence that obesity impairs the immune response to a range of pathogens [6,13,18–20] we stratified 20 discrete immune features by obesity status to identify univariate differences. We also performed uniform manifold approximation and projection (UMAP) [21] a mathematical approach for exploratory analyses that constructs a visualizable summary of multiple subjects’ characteristics, with each point representing an individual and clusters representing underlying uniformities in subject characteristics.

Binomial exact 95% confidence intervals were calculated and p-values <0·05 were considered statistically significant· For adjusted analyses, variables with a p-value <0·10 were assessed by backward elimination and excluded if the p-value was >0·10 and did not meaningfully alter the point estimates of the remaining variables. Luminex UMAP and Mann-Whitney U Tests were conducted using scikit-learn, a machine learning toolkit for the Python programming language. Analyses were performed using the R software package (Version 4·0, www.R-project.org/) or the Python programming language (Version 3·7, python.org). All available relevant data was included.

Results

A total of 4469 Space Exploration Technologies Corporation employees out of a total of ~8400 employees (53%) were enrolled. Baseline characteristics are included in Table 1. Mean BMI was 27·1 kg/m2 (SD 5·4) with a median of 25·8 kg/m2 (range 15·6–60·9). Most subjects were normal weight (18·5–24 kg/m2) (1686, 39·5%) or overweight (25–29 kg/m2) (1523, 35·7%), and 24·1% and 0·80% met criteria for obese (≥30 kg/m2) and underweight (≤18·5 kg/m2) respectively.

322 of 4469 (7·21%) study participants were seropositive of which five (1·6%) were hospitalized and none required critical care support or died. Unadjusted rates are detailed in Table 1 and were higher in South Texas (Odd Ratio 4·28 [95% CI, 2·54 to 7·21), p<0·0001]) and among Hispanics (2·91 [95% CI 2·26–3·75], p<0·0001); and were lower in Seattle, Washington (0·30 [95% CI, 0·11 to 0·82], p=0·02]), and in the 30–39 year age group (0·72 [95% CI, 0·56 to 0·94], p=0·02). Adjusted associations for all covariates are listed in Table 1; only primary work location remained significantly associated with serostatus with increased OR in South Texas (OR 4·28 [2·54–7·21], p<0·0001) and lower in Seattle (OR 0·30 [0·11–0·82], p=0·02) largely reflecting local transmission rates. The strong univariate association between Hispanic ethnicity and serostatus was not retained after adjusting for work location (OR 1·27 [0·94–1·73], p=0·12).

BMI and serostatus

4270 of 4469 participants (95·5%) provided weight and height data and are included in BMI analyses. Unadjusted risks of seropositivity stratified by BMI are listed in the Table 1; only BMI 30 to 34 kg/m2 (versus normal/healthy weight, 18·5–24 kg/m2) was associated with differential serostatus (OR 1·48 [1·06 to 2·05], p<0·02). However, after adjusting for all candidate variables (Table 1), no association was detected. Rather, higher BMI and in particular severe obesity (BMI ≥40 kg/m2) trended non-significantly to lower seroprevalence (Figure 1A). Subgroup analysis from a single high prevalence location where, given the high force of infection as evidenced by high seroprevalence (22·5% versus 4·2% for all other sites combined), we predict risks for infection – including any effect of BMI – would be more clearly delineated (Supplementary 2). Findings were similar to the primary analysis with no evidence of increased seroprevalence with increasing BMI. Rather, point prevalence measures consistently trended lower than normal/healthy weight (Figure 1B).

Figure 1. Forest plots of adjusted odds ratio for seropositivity by BMI as a categorical variable with normal BMI (18·5-<25) as reference.

Figure 1.

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(A) Includes participants with BMI measures and demonstrates a non-significant trend to declining seroprevalence with BMI ≥40 kg/m2 when compared to normal/healthy weight (BMI 18·5–24 kg/m2) (n=4270). (B) Includes only participants from a single high seroprevalence (22·5%) location in South Texas where the high force of infection may more clearly delineate infection risks (n=629).

BMI and COVID-19 compatible symptoms

Of 262 seropositive participants with complete symptom data, three (1·1%) were underweight (<18·5 kg/m2), 89 (34·0%) normal weight (18·5–24 kg/m2), 89 (34·0%) overweight (25–29 kg/m2), and 81 (30·9%) obese (≥30 kg/m2). A total of 106/262 (40·5%) reported one or more of 11 COVID-19-compatible symptoms and 68/262 (26·0%) reported one or more of five primary COVID-19 symptom. When comparing symptoms between normal weight and overweight (but not obese) individuals, there were no meaningful differences or trends (Supplementary 3) and therefore subsequent analyses were stratified by obese versus non-obese.

Obesity was associated with increased reporting of multiple symptoms including fever, chills or feverish but no measured fever, myalgias, and ≥6 symptoms (Figure 2). With the exception of congestion (0·87 [0·43–1·70]), a similar and consistent but non-significant trend was observed for all symptoms. Overall, obese individuals registered more symptoms and more primary symptoms. Age appears to play an important role when assessing obesity and symptom phenotype and fever was more commonly reported among obese vs non-obese individuals under 40 years of age (OR 4·99 [1·97–13·35]) but not over 40 years (OR 1·32 [0·30–5·57]). Similarly, reporting ≥6 symptoms was more common among obese vs non–obese under 40 years (OR 3·0 [1·32–6·85]) but not for those greater than 40 years (OR 0·94 [0·18–4·26]). A strikingly similar trend was observed for most other symptoms and aggregate symptom measures (Figure 3). No similar age-dependent effect on obesity and symptomatology was observed when comparing age groups < 40 years of age (Supplementary 4).

Figure 2.

Figure 2.

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Forest plot of odds ratios of reported COVID-19 compatible symptoms among obese (n=85) versus non-obese (n=179) SARS-CoV-2 seropositive individuals

Figure 3.

Figure 3.

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Forest plot of odds ratios of COVID-19 compatible symptoms among obese versus non-obese SARS-CoV-2 seropositive individuals stratified by (A) <40 years (n=195) and (B) ≥ 40 years (n=67)

Obesity and functional immune response

Among the same 262 seropositive individuals, peak SARS-CoV-2 RBD IgG titers were 0·92 ug/ml (SD 2·47) among obese (n=81) and 1·12 ug/ml (SD 3·21) among non-obese (n=181) participants (p=0·601). Deep immune profiling was performed among a subset of 77 participants including 25 obese and 52 non-obese individuals. Mean ELISA NC IgG titers were 0·35 (SD 0·48) among obese versus 0·30 (0·34) among non-obese individuals (p=0·57). Viral neutralization activity was detected in 3/25 (12·0%) and 6/52 (11·5%) of obese and non-obese individuals respectively (p=0·95). When assessing 20 immune features measured by Luminex, no univariate differences were observed between obesity categories, with sparse levels across both obese and non-obese individuals tightly linked to antibody titers (Figure 4A, Supplementary 5). Similarly, no clustering or trends between BMI and immunological features were identifiable either by UMAP (Figure 4B) or Spearman’s correlation (Figure 4C).

Figure 4. Limited influence of BMI on SARS-CoV-2 antibody profiles (n=77).

Figure 4.

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(A) The dot plots show similar mean fluorescent intensity levels of IgG1, IgM, IgG3, and IgA levels across individuals classified as normal weight (n=29), overweight (n=23), and obese (n=25). (B) The uniform manifold approximation and projection (UMAP) shows the relationship between antibody profiles and BMI (dot size, color intensity), highlighting the limited influence of BMI on shaping SARS-CoV-2 antibody responses. (C) Correlation plot of shows limited correlation between BMI and 20 immunological features.

Lastly, given evidence that T cells may be key mediators of adaptive immunity in SARS-CoV-2, we examined responses to nucleocapsids protein or spike protein overlapping peptide pools quantified by IFN-g ELISpot among 12 obese and 28 non-obese individuals. There was no difference in the proportion with SARS-CoV-2 T cell activity (≥25 SFC/106 PBMCs) against nucleocapsid peptides (3/12 [25%] versus 7/28 [25·0%]) or spike peptides (3/12 [25%] versus 7/28 [25·0%]). In fact, the only difference observed was higher SFC against nucleocapsid (mean 124 SFC/106 PBMCs [77] versus 47 SFC/106 PBMCs [14], p=0·02) but not spike (44 SFC/106 PBMCs [4·0] versus 44 SFC/106 PBMCs [20·0], p=1·00) among obese versus non-obese individuals with T cell activity.

Discussion

We present data from a multi-site prospective cohort of non-hospitalized individuals unbiased to serostatus at study entry to investigate the association between BMI, SARS-CoV-2 serostatus, symptom phenotype, and functional and non-functional immune measures. Given the prevalence of overweight/obese among adults is close to 70% in most high income countries and ≥50%, in many lower and middle income countries, the scientific and public health implications for the current pandemic are substantial [4]. By combining traditional epidemiological approaches with deep immune profiling, these data provide key insights into the epidemiology and immune characteristics of obesity in SARS-CoV-2 infections.

Studies that report an increased risk of COVID-19 or SARS-CoV-2 infection with higher BMI are intriguing and raise essential questions about factors driving transmission. Given the global burden of obesity, delineating risks for infection is a public health priority. Interestingly, our findings diverge from recent published reports that examine the risk for COVID-19 by BMI including a nationwide case-control study from South Korea [9] and a cross-sectional study from a primary care surveillance network in the United Kingdom [22] that identified an increased risk of COVID-19 with increasing BMI. A recent meta-analysis of 20 studies reported a pooled increased risk of 46·0% (OR = 1·46; 95% CI, 1·30–1·65; p < 0·0001) with 18 of 20 studies demonstrating higher COVID-19 risk among obese individuals.[4] Our study did not identify an increase in adjusted seroprevalence with increasing BMI and conversely identified a trend to lower infection risk with higher obesity classes. This trend was consistent when both considering all data and when performing subgroup analysis on a high transmission site where the increased force of infection may more precisely delineate heterogeneity in infection risks. Reasons for the difference between our and prior study outcomes are likely multifactorial with differences in study design, obesity classification, and population behaviors likely influencing findings. However, a key difference is we examined SARS-CoV-2 infection risk using serological methods unbiased to exposure risks or presence or absence of symptoms at study entry versus prior studies examined risks for clinically apparent infection (i.e. COVID-19). As such, our primary outcome measure was SARS-CoV-2 infection rather than clinically apparent disease, a key difference that likely contributes to differences in study findings and conclusions. Given obese individuals exhibit more pronounced symptomatology including fever, as our data indicates, this population is more likely to meet testing criteria and therefore likely to be over-represented in studies that identify study populations through routine surveillance approaches [23,24]. Our finding that obesity is associated with increased COVID-19 compatible symptoms among SARS-CoV-2 seropositive individuals provides benchmark data for understanding symptom heterogeneity in mild infections by BMI. We demonstrate that not only are well established measures of severe disease such as hospitalization, intensive care requirements and death more common among obese individuals [5,25,26], but obesity is also an important driver of symptomatology in non-severe infections. While our data does not provide insights into the mechanism driving these findings, it informs our understanding of symptom phenotype and obesity, guides our interpretation of epidemiological data, and highlights potential implications of using passively collected symptom-driven surveillance data to characterize the epidemiology of infectious pathogens. We also identify an intriguing influence of age on obesity symptom reporting, with a compelling association below 40 years of age but near complete absence of effect in older adults. These findings are notable given they imply the established interaction between obesity and age on COVID-19 morbidity and mortality, with obesity disproportionately driving increased disease severity among younger age groups [5,25], extend throughout the spectrum of disease and are not restricted to severe disease.

Given the fundamental role of the adaptive immune response in both the resolution of infection and the severity of disease [11], we also probed multiple binding and functional immune markers to assess differential immune responses by obesity status. While, previous studies noted poor seroconversion and inadequate seroprotection across vaccine trials targeting other pathogens [27], we did not detect meaningful differences in binding or neutralizing antibodies, T cell activity, or other functional humoral measures by BMI among SARS-CoV-2 infections. These findings, while notable, should be considered in the context of this cohort in which >98% of infections were asymptomatic or mild and therefore may not capture the full range of disease burden associated with SARS-CoV-2. Yet, the overlapping and indistinguishable antibody and T-cell helper profiles point to unaltered adaptive immunity with BMI, raising the possibility that BMI-driven immunological changes during SARS-CoV-2 infection may manifest largely within the innate immune response. Significant alterations in chronic inflammation, particularly driven by persistent innate cytokine responses from adipocytes, have been noted in the setting of obesity [28]. Dissecting the influence of adipocyte inflammatory responses, associated cytokine storm, and enhanced symptomatology, particularly among individuals with a high BMI, may point to mechanistic differences in viral sensing across populations. These data point to remaining knowledge gaps on the relative importance and interplay of the humoral, cellular and innate immunity in SARS-CoV-2 infection and disease [29].

Limitations

Although this study is unique in combining a large prospective, multisite serology-based SARS-CoV-2 cohort with deep immune profiling, there are limitations. The study population are industry employees with higher representation of Hispanic ethnicity, white race, male sex, and younger individuals with less comorbidities than the US population, therefore findings may not be generalizable. Other potential study limitations should be noted but their impact would be expected to be evenly distributed across cohort participants and therefore not introduce a systematic bias and impact study findings. These include (i) limited recall of COVID-19 compatible symptoms, (ii) delayed seroconversion relative to reported symptoms, so depending on timing of infection and blood sampling, some registered symptoms may not be due to SARS-CoV-2 infection, and (ii) false positive serological results. Lastly, behavioral factors, which can be critical drivers of transmission, and may be influenced by BMI, were not assessed in this study.

Conclusion

We demonstrate that obesity influences symptom phenotype in mild COVID-19 infections, suggesting obesity impacts the pathophysiology of COVID-19 throughout the spectrum of disease severity. Our findings do not, however, suggest that obesity increases susceptibility to SARS-CoV-2 infection. Nor did we identify immunological features differentiating obese from non-obese individuals across mild and asymptomatic infection, a hopeful signal that both natural infection- and vaccine-induced protective immunity may be similar across these populations.

Supplementary Material

Supplement 1
media-1.pdf (925.2KB, pdf)

Acknowledgements

We would like to thank Eric Fischer for S protein production efforts and Jared Feldman, Blake Marie Hauser, Tim Caradonna and Aaron Schmidt for generating receptor binding domain antigen. We would also like to thank the many SpaceX employees that volunteered to participate in the study.

Funding

This work was supported by Space Exploration Technologies Corporation; the Brigham and Women’s Department of Emergency Medicine; the Ragon Institute of MGH, MIT and Harvard; the Massachusetts Consortium on Pathogen Readiness; the Gates foundation Global Health Vaccine Accelerator Platform funding; Nancy Zimmerman; Mark and Lisa Schwartz; Terry and Susan Ragon; SAMANA Kay MGH Research Scholars award; US Food and Drug Administration [HHSF223201810172C] to SMS; and National Institute for Allergy and Infectious Disease [U19 AI135995] to DAL.

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