key: cord-0758690-iz6nrfpc
authors: Epsi, Nusrat J; Richard, Stephanie A; Laing, Eric D; Fries, Anthony C; Millar, Eugene; Simons, Mark P; English, Caroline; Colombo, Christopher J; Colombo, Rhonda E; Lindholm, David A; Ganesan, Anuradha; Maves, Ryan C; Huprikar, Nikhil; Larson, Derek; Mende, Katrin; Chi, Sharon W; Madar, Cristian; Lalani, Tahaniyat; Broder, Christopher C; Tribble, David; Agan, Brian K; Burgess, Timothy H; Pollett, Simon D
title: Clinical, immunological and virological SARS-CoV-2 phenotypes in obese and non-obese military health system beneficiaries
date: 2021-07-31
journal: J Infect Dis
DOI: 10.1093/infdis/jiab396
sha: 0b3c74be89a190c98b31de4c8e66d2df95743af7
doc_id: 758690
cord_uid: iz6nrfpc

BACKGROUND: The mechanisms underlying the association between obesity and COVID-19 severity remain unclear. After verifying that obesity was a correlate of severe COVID-19 in U.S. Military Health System (MHS) beneficiaries, we compared immunological and virological phenotypes of SARS-CoV-2 infection in both obese and non-obese participants. METHODS: COVID-19-infected MHS beneficiaries were enrolled, and anthropometric, clinical, and demographic data were collected. We compared the SARS-CoV-2 peak IgG humoral response and RT-PCR viral load in obese and non-obese patients, stratified by hospitalization, utilizing logistic regression models. RESULTS: 511 COVID-19 patients were analyzed, among whom 24% were obese and 14% severely-obese. Obesity was independently associated with hospitalization (aOR = 1.91, 95% CI = 1.15–3.18) and need for oxygen therapy (aOR = 3.39, 95% CI = 1.61–7.11). In outpatients, severely-obese had a log10 (1.89) higher N1 genome equivalents (GE)/reaction and log10 (2.62) higher N2 GE/reaction than non-obese (p = 0.03 and p < 0.001, respectively). We noted a correlation between BMI and peak anti-spike protein IgG in inpatients and outpatients (coefficient = 5.48, p < 0.001). CONCLUSIONS: Obesity is a strong correlate of COVID-19 severity in MHS beneficiaries. These findings offer new pathophysiological insights into the relationship between obesity and COVID-19 severity.

Worldwide, nearly 114 million people have been infected by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and more than 2.5 million people have died from coronavirus disease 2019 (COVID-19) [1] . Manifestations of COVID-19 vary widely, from asymptomatic infection to critical illness. Increased disease severity has been associated with age, race, and medical comorbidities such as diabetes, hypertension, asthma, chronic kidney disease, cancer, and neurological disease [2] [3] [4] [5] [6] [7] [8] [9] .

Many studies have reported that obesity is one of the strongest risk factors for severe COVID-19 and mortality. Early in the pandemic, investigators in Wuhan, China reported that each 1unit increase in body mass index (BMI; kg/m 2 ) was associated with a 12% increase in the risk of severe COVID-19, and obesity was associated with a three-fold increase in risk of severe COVID-19, compared with non-obesity [10] . A French study reported that, among 124 patients admitted to the intensive care unit for COVID-19, 69% were obese or severely obese [11] . Obesity is associated with complex interactions between genetic, behavioral, metabolic, hormonal, and environmental influences. Obesity is known to be a risk factor for conditions associated with severe COVID-19 (e.g. diabetes [12, 13] , cardiovascular disease [14, 15] , cancer [16] [17] [18] , and other causes of mortality [19] ).

Determining the independent effect of obesity on COVID-19 severity requires consideration of these comorbidities, which may act as confounders or mediators of this relationship [11, [20] [21] [22] .

The mechanisms of this association between obesity and severe COVID-19 remain unclear and are likely multi-factorial. Obesity-related impairments in cardiovascular, respiratory, metabolic, and thrombotic pathways may decrease a given patient's physiologic reserve and ability to recover from SARS-CoV-2 [23] . Obese individuals may have amplified or dysregulated immune responses that lead to greater viral replication that may potentiate inflammatory immune responses [24] [25] [26] . In addition, obesity has been associated with a higher level of angiotensin-converting enzyme 2 (ACE2) receptor expression [27, 28] , the membrane-bound host cell proteins that mediate SARS-CoV-2 A c c e p t e d M a n u s c r i p t attachment and entry. Some posit that this difference in ACE2 receptor expression may potentially leading to higher SARS-CoV-2 viral loads among obese individuals [25, 26] .

It remains unclear whether the association between obesity and COVID-19 severity is due to an individual's putative increased viremia, an aberrant immune response, related comorbidities, or

other, yet to be identified, risk factors. Therefore, we leveraged a prospective cohort of 511 Military

Health System (MHS) beneficiaries with documented anthropometry and extensively characterized SARS-CoV-2 infection. Clinical and demographic data were collected through prospective methods, and for some participants anthropometry data were collected through routinely collected MHS electronic medical report (EMR). We evaluated whether obesity was independently associated with COVID-19 severity after adjusting for a wide range of covariates, particularly obesity-related comorbidities. Additionally, we compared the SARS-CoV-2 humoral responses in those with and without obesity, adjusted for clinical severity. Finally, we compared sampling-time-adjusted peak SARS-CoV-2 viral load by BMI strata. Collectively, this study clarifies the prognostic associations of obesity and SARS-CoV-2 infection outcomes in MHS beneficiaries, a population characterized by a high prevalence of obesity, and offers new insights into the possible mechanisms underlying the association of obesity and severe COVID-19 [29] [30] [31] . [32] . Briefly, the EPICC study A c c e p t e d M a n u s c r i p t enrolled participants who were eligible for healthcare within the MHS and who met one of the following criteria: a) confirmed SARS-CoV-2 infection; b) COVID-like illness; or c) high risk of developing COVID-19 due to a recent exposure. Demographic and clinical data were collected at enrollment and periodically during follow up. Upper respiratory tract swabs were collected on a weekly basis (including Day 0 and Day 3) for inpatients during hospitalization (and Day 14 if discharged), and on Days 0 and 14 post-enrollment for outpatients. Sera were collected at the same timepoints plus at 28 days and 6 months for inpatients, and 7 days, 28 days and 6 months post enrollment for outpatients. in Additional clinical records pertaining to hospitalization were reviewed in the MHS electronic medical record (EMR). We excluded from the analysis those participants with negative SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) tests (n = 101) and those for whom we did not have height and weight data. In addition, children (age < 18 years) were excluded from the analysis due to the small number (n = 19) enrolled in the study and the qualitatively different categorization of obesity in pediatric populations (Supplemental Figure 1 ). Serological testing was performed by a SARS-CoV-2 spike protein-based multiplex microsphere immunoassay, described in detail elsewhere [37] . For the detection of SARS-CoV-2 spike reactive IgG from serum samples collected 10 -60 days after the onset of symptoms, this immunoassay has a sensitivity of 99% and specificity of 100%. Briefly, prefusion stabilized spike protein ectodomain timers were coupled to magnetic microspheres (Bio-Rad, Hercules, CA). Samples for serology and spike-coupled microspheres were added to 96-well plates. Serum samples were diluted 1:400, added to 96-well plates and tested in technical duplicates. Antibodies in serum samples were detected with biotinylated, cross-absorbed anti-human secondary antibodies.

Antigen-antibody complexes were then incubated with streptavidin-phycoerythrin and quantified with a Bio-Plex 200 HTF multiplexing system (Bio-Rad). IgG levels are reported as median fluorescence intensity (MFI).

The primary outcome of interest in this analysis was SARS-CoV-2-infection-related hospitalization. Hospitalization status was obtained from the case report forms (CRF) (completed at the site using the participant's medical record) and directly using the MHS EMR. Because hospitalization may be a limited proxy of COVID-19 severity (for example, hospital admissions may be for non-medical reasons and thresholds for admission may vary by provider and setting), we also used an alternative severity outcome based on whether subjects required treatment with A c c e p t e d M a n u s c r i p t supplemental oxygen. Anthropometric measures were obtained either through collection of height and weight during a prior medical visit or from the participant via survey. BMI was calculated using weight and height values, and individuals were classified as normal/underweight (<24.9 kg/m 2 ), overweight (25-29.9 kg/m 2 ), obese (30-34.9 kg/m 2 ), and severely obese (≥35 kg/m 2 ). In addition, the case report form assessed whether the subject had a diagnosis of obesity along with a range of comorbidities, many of which were possible confounders or mediators of the relationship between obesity and severe COVID-19 (See Table 1 ). To measure the burden of comorbidities in a subject, we calculated the Charlson Comorbidity Index (CCI) [38] , which is a standardized method of categorizing comorbidities of patients; for this study, we used the updated version age-adjusted CCI [39] . Age, sex, race, and ethnicity were reported by the participant.

The viral load (genome equivalents (GE)/reaction) of each specimen was calculated for each N gene target (N1 and N2) by constructing plate specific standard curves from three dilutions of a known SARS-CoV-2 gene copy number standard (10000, 1000, 100, 10 copies/µL) on each RT-PCR assay run. The viral load calculations were log transformed for normalization such that a rise in log10 quantity GE/reaction equated to a rise in viral load (Supplemental Figure 2 ). We defined peak viral load as the highest log10 GE/reaction measured for the subject (from any time point or any specimen type). We used the SARS-CoV-2 spike protein-based multiplex microsphere immunoassay IgG MFI read-out as a quantitative measurement of anti-S IgG antibody against the SARS-CoV-2 spike protein [32] .

A c c e p t e d M a n u s c r i p t

Descriptive statistics were calculated for the demographic characteristics, comorbidities, and illness severity by BMI category with p-values computed using Fisher's exact test. Univariate logistic regression was performed to evaluate whether COVID-19 severity was significantly associated with other independent variables, and then multivariable regression was performed, adjusting for other putative risk factors for COVID-19 including gender, age group, race, smoking history, and comorbidities (diabetes, hypertension, chronic kidney disease, asthma, chronic pulmonary disease, chronic neurological disorder, peripheral vascular disease, venous thromboembolism, ischemic heart disease, and cancer). Model fit was estimated by the Akaike information criterion (AIC) and Bayesian information criterion (BIC), with the best fitting model used to present the adjusted odds ratio of obesity diagnosis and BMI strata on an outcome of hospitalization. This model was then fit to an alternative outcome of COVID-19 severity, as defined by requirement of supplemental oxygenation.

We further carried out sensitivity analyses to minimize the risk of model misspecification by presenting comparative models to ensure that an estimate of the independent association of obesity and severe COVID-19 were robust.

Univariate and multivariate linear regression models were fit to evaluate whether peak viral load and/or peak anti-S IgG antibody response was associated with obesity and BMI strata (Supplemental Figure 3) . These analyses aimed to determine whether obesity was associated with a difference in virological and humoral immune response phenotypes. These regression analyses were further stratified by disease severity (inpatient versus outpatient). All statistical analyses were conducted using R version 4.0.2 [40] . 

Among 619 COVID-19 positive participants who were enrolled in EPICC from March 20, 2020 through September 15, 2020, 511 (69%) were included in this analysis, as they were classified as COVID-19 cases and had anthropometric data available (Supplemental Figure 1) . Over half of our study sample was male (64.2%), 18-44 years of age (62%), active-duty military (51.5%), and 48.6%

were dependents and retired military; 72.6% were overweight, obese, and/or severely obese (Table   1 ). Overall, 25% of our participants were inpatients, incrementally from 14 to 52% in normal/underweight to severely obese participants, respectively (Table S1 ). When considered as a continuous variable, average BMI values were higher in inpatients when compared with outpatients, and BMI was higher in those who received supplemental oxygen when compared with those who did not ( Figure 1 ). Thirty-five percent of the participants had at least one other comorbidity (range 25 -68% in normal/underweight to severely obese participants, respectively). The most common additional comorbidity in the sample was hypertension (20.1%), followed by diabetes (12%), both of which were more common in increasing categories of obesity (Table S1) . (Table S4) .

In upper respiratory tract specimens, we observed peaks in SARS-CoV-2 viral load at the time of or shortly after the onset of symptoms in the early stages of infection and decreasing in the convalescent stage ( Figure 2 ). Conversely, we observed rising serum IgG soon after the onset of symptoms and plateauing thereafter, with persistence out to three months post symptom onset ( Figure 3 ).

When pooling inpatient and outpatient participants, we observed no association between viral load and BMI category (Table 3) . However, when we stratified by COVID-19 severity (inpatient versus outpatients), outpatients in the severely obese group had a log10 1.89 higher N1 GE/reaction and a log10 2.62 N2 GE/reaction increase in peak viral load compared to those with normal weight, adjusting for sampling time (Table S3 ). We also observed that obese or severely obese participants had a higher IgG antibody response compared to normal weight participants, with an increasing M a n u s c r i p t coefficient by weight strata (Table 4 ). When stratified on inpatients and outpatients, there was an increased IgG response in severely obese subjects compared to normal weight participants that was statistically significant (Table 4) .

The findings of our study corroborate those of previous studies; categories of higher BMI are associated with increased odds for severe COVID-19 (20) (21) (22) (23) (24) . Implicating obesity as a risk factor for severe COVID-19 can be challenging, given the multitude of obesity-associated comorbidities that may confound or mediate this association. To account for these, we conducted several sensitivity analyses to examine a range of obesity-associated comorbidities and to reduce the risk of model misspecification (Table S2 ). Our analysis of BMI strata revealed a 'dose-response' association between increasing BMI categories and odds of severe COVID-19, thereby strengthening causal inference. These findings are of concern to MHS beneficiaries given the known prevalence of obesity in United States military beneficiaries, including active-duty service members who comprised a substantive proportion of our study population [29] [30] [31] . Indeed, among the study participants included in this analysis, we noted that over three-quarters of SARS-CoV-2 infections were in overweight MHS beneficiaries, and over one third of subjects were obese or severely obese (Table   1 ). These findings also have relevance in other populations where obesity is prevalent.

To elucidate potential underlying mechanisms to which the association between obesity and severe COVID-19 might be attributed, we examined both virologic and immunologic characteristics of SARS-CoV-2 infection in the study population. In outpatients, we noted that obesity (BMI ≥ 30) was associated with a substantively higher viral load. Interestingly, this was not seen in inpatients. It is unknown whether this reflects antiviral drug use in inpatients or sampling of inpatient illness A c c e p t e d M a n u s c r i p t relatively later in their course of illness when viral loads may be expected to fall more precipitously.

To date, there has been only limited other data which have reported a positive correlation between obesity and SARS-CoV-2 viral load. Maltezou et al., described a higher viral load in obese versus nonobese patients but did not adjust for sampling time or disease severity and only used a dichotomous categorization of obesity [41] . The association between BMI and viral load has also been demonstrated for influenza [42, 43] .

In inpatients, we noted an increase in anti-S IgG MFI by BMI strata (BMI ≥ 30, coefficient = 4.17, p = 0.02; BMI ≥ 35 coefficient = 3.53, p = 0.04). As higher levels of anti-S IgG have been described during hospitalization in patients with more severe disease [44, 45] , this may reflect increasing severity in obese inpatients. However, a similar magnitude of effect was seen in severely obese outpatients (coefficient = 4.09, p = 0.04, Table 4 ). Further analyses leveraging this study population will examine whether non-neutralizing antibody, T-cell, and innate immune responses are quantitatively or qualitatively different in obese versus non-obese subjects, within and between strata of clinical severity.

Our study had several strengths. EPICC employs a comprehensive collection of demographic, epidemiologic, clinical, and laboratory data across the time course of infection. This enabled adjustment for important confounders and mediators. The prospective, longitudinal designed allowed ascertainment of peak viral load and anti-S IgG magnitude across illness time, as well as careful ascertainment of COVID-19 case status. Our sample was also relatively heterogeneous for age, gender, and race, as it draws from the population of MHS beneficiaries comprised of active duty service members, retirees, and their dependents.

A c c e p t e d M a n u s c r i p t There were several limitations of our study. The paucity of early illness sampling was a limitation across this study, with the first study specimen being collected, on average, 34 (IQR: 20 -54) days after symptom onset. While sampling time was adjusted in regression models this delay likely resulted in many subjects with resolving or resolved viral load. Another possible limitation was use of BMI as one set of criteria for obesity. We determined that our alternative methods of measuring obesity -BMI and a diagnosis of obesity as indicated in the EMR and/or CRF -were highly correlated. 511 participants had both BMI and obesity reported on the EMR, and CRF form respectively, among whom 404 (79%) were found to be obese using both data sources (Cohen's kappa = 0.58; p < 0.001). Nevertheless, elevated BMI in young muscular active duty soldiers may be a result of increased muscle mass, and not an overweight status per se [46] . Future studies in this population could use waist circumference, neck circumference and/or body fat percentage which is an indicator of abdominal obesity and would resolve the possible misclassification [47] . However, it is important to note that such a misclassification of obesity would be expected to underestimate the magnitude of the association between obesity and severe COVID-19.

Although the current study examined humoral IgG responses in obesity and severe COVID-19, further virologic and immunologic exploration are necessary for a comprehensive understanding.

Obesity is associated with chronic low-grade systemic inflammation, including higher levels of IL-6 [48, 49] . Investigation of inflammatory cytokine biomarkers (e.g. IL-6, TNF-α, CRP), adipokines, innate immunity such as natural killer cells, memory T cells, macrophages, and inflammasome signaling would allow us to understand complex pathophysiology of obesity and severe COVID-19 [48, 50] . Further studies are also needed to explore the role of obesity-associated insulin resistance and microbiome derangement in COVID-19 outcomes.

A c c e p t e d M a n u s c r i p t Other putative mechanisms that may explain the association between obesity and disease severity that were observed in this study could include excess soft tissue in the upper respiratory tract that results in obstruction of the airway [22] , or decreased diaphragm contractility and poor pulmonary mechanics that result in hypoxia or contribute to type II respiratory failure which may complicate the outcomes of some severe COVID-19 cases [23] . Such physiological pathways are more challenging to measure but could drive future studies in this [48] and other populations at risk of COVID-19.

In conclusion, we recapitulate the epidemiological association between obesity and severe COVID-19 seen in other studies, including using alternative definitions of severity. We further show an increase in SARS-CoV-2 viral load in outpatient COVID-19 with obesity, and a more robust anti-IgG response in obese SARS-CoV-2 infections, even when stratified to outpatients. These findings prompt further study into the mechanisms of severe COVID-19 in those with obesity, including other examination of other facets of host response to SARS-CoV-2 such as innate and T cell immunity. A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t oxygen supplement no (blue), respectively (1B). Statistically significant differences by non-parametric ttests are noted. Each dot represents as a subject. Boxplots denote median, first quartile (25th percentile) and third quartile (75th percentile); statistical significance was determined by Wilcoxon rank sum test. Figure 2 : Viral load as measured by qPCR N1 GE/reaction (Fig 2A) and N2 GE/reaction (Fig 2B) , log10

transformed and plotted by symptom day and stratified by obesity status. Each dot represents as a subject. Local polynomial regression curves were fit to non-obese (beige), and obese (turquoise) groups;

95% CIs are shaded for non-obese and obese groups, respectively; statistical significance was determined by Wilcoxon rank sum test. A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t 

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We thank Camille Estupigan for her editorial assistance in the preparation of this manuscript. We thank the members of the EPICC COVID-19 Cohort Study Group for their many contributions in conducting the study and ensuring effective protocol operations. The following members were all closely involved with the design, implementation, and oversight of the study.

A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t