Abstract
Context
Metabolic syndrome (MetS) is associated with increased risk of severe COVID-19. MetS inflammatory biomarkers share similarities with those of COVID-19, yet this association is poorly explored.
Objective
Biomarkers of COVID-19 patients with and without MetS, the combination of diabetes, hypertension, obesity, and/or dyslipidemia, were analyzed to identify biological predictors of COVID-19 severity.
Methods
In this prospective observational study, at a large academic emergency department in Boston, Massachusetts, clinical and proteomics data were analyzed from March 24 to April 30, 2020. Patients age ≥18 with a clinical concern for COVID-19 upon arrival and acute respiratory distress were included. The main outcome was severe COVID-19 as defined using World Health Organization COVID-19 outcomes scores ≤4, which describes patients who died, required invasive mechanical ventilation, or required supplemental oxygen.
Results
Among 155 COVID-19 patients, 90 (58.1%) met the definition of MetS and 65 (41.9%) were identified as Control. The MetS cohort was more likely to have severe COVID-19 compared with the Control cohort (OR 2.67 [CI 1.09-6.55]). Biomarkers, including CXCL10 (OR 1.94 [CI 1.38-2.73]), CXCL9 (OR 1.79 [CI 1.09-2.93]), HGF (OR 3.30 [CI 1.65-6.58]), and IL6 (OR 2.09 [CI 1.49-2.94]) were associated with severe COVID-19. However, when stratified by MetS, only CXCL10 (OR 2.39 [CI 1.38-4.14]) and IL6 (OR 3.14 [CI 1.53-6.45]) were significantly associated with severe COVID-19.
Conclusions
MetS-associated severe COVID-19 is characterized by an immune signature of elevated levels of CXCL10 and IL6. Clinical trials targeting CXCL10 or IL6 antagonism in this population may be warranted.
Keywords: metabolic syndrome, proteomics, COVID-19
Since its initial outbreak, coronavirus disease 2019 (COVID-19) has continued to be a leading cause of death [1], causing more than 6.7 million deaths as of January 2023 [2]. While mortality has improved over time due to active immunization and innate immunity, those who required invasive mechanical ventilation (IMV) had significantly higher mortality than those who did not [3], suffering high mortality rates up to 40% to 72% [4]. Cytokine profiles in patients with COVID-19 have demonstrated a hyperinflammatory innate immune response to infection that is associated with lung injury, disease severity, and mortality [5, 6]. Rather than the virus itself, the host immune response is believed to be the major cause of lung inflammation and edema, resulting in acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) [7].
Accumulated evidence demonstrates that certain comorbidities such as obesity [8], diabetes mellitus (DM) [9, 10], and hypertension (HTN) [11] increase the risk of severe COVID-19. Moreover, the clustering of obesity, prediabetes/DM, HTN, and dyslipidemia as metabolic syndrome (MetS) increases the odds of COVID-19 mortality relative to these comorbidities individually [12, 13]. The reasons for this association are unclear, but the well-recognized, chronic low-grade inflammation (meta-inflammation) associated with MetS may be a contributing factor [14, 15]. It is known that adipose tissues recruit proinflammatory M1 macrophages, which lead to both local and systemic release of proinflammatory cytokines [16]. Thus, MetS, via meta-inflammation, may create a conducive environment for an exaggerated innate immune response and therefore predispose patients with MetS to COVID-19-associated ARDS [15].
Evidence suggests that the inflammatory biomarker profile associated with COVID-19 is similar to that of MetS and characterized by elevated levels of chemokines and cytokines including C-X-C motif chemokine ligand 10 (CXCL10), interleukin 6 (IL6), and IL10 among others [17-20]. We hypothesized that certain biomarkers may be elevated in patients with MetS with severe COVID-19, and therefore, provide justification for a more targeted treatment approach in this population. In this study, we examined the association between MetS-associated biomarkers and COVID-19 severity in patients with and without the combination of diabetes, hypertension, obesity, and/or dyslipidemia, as a proxy for MetS.
Methods
We performed an ancillary study of clinical and proteomics data that were obtained as part of a prospective observational study of acutely ill patients with COVID-19 at a large academic hospital's emergency department in Boston, Massachusetts from March 24, 2020, until April 30, 2020. The study was approved by the Massachusetts General Hospital (MGH) Institutional Review Board.
The population included patients age ≥18 with a clinical concern for COVID-19 upon arrival and acute respiratory distress. COVID-19 positive patients had blood sampled and clinical data collected on days 0, 3, and 7, as available. Proteomics was performed using the Olink Explore protein biomarker discovery platform (Olink Explore 1536). We excluded days 3 and 7 blood samples due to high proportions of missing data, presumably from hospital discharges. Olink quantified plasma proteins using Normalized Protein eXpression (NPX) to minimize assay variation; NPX is in a log2 scale, meaning 1NPX difference equals a doubling of protein concentration [21]. Demographics, past medical history, and clinical data were retrospectively collected by MGH COVID-19 study investigators and included COVID-19 status, age category, body mass index (BMI) category, preexisting diseases, maximum ordinal scale score modified from the World Health Organization (WHO) outcomes scores [22] (1 = death, 2 = IMV, 3 = noninvasive ventilation [NIV] or high-flow nasal cannula [HFNC], 4 = hospitalized and supplementary oxygen required, 5 = hospitalized and no supplementary oxygen required, 6 = not hospitalized) within 28 days, and protein measurements. Race and sex were not available. Patients with a preexisting history of DM were identified if DM was listed in the electronic medical record as part of the past medical history or note documentation, or if the patient was taking DM medications. History of HTN and dyslipidemia were identified if listed in the electronic medical record past medical history or note documentation. Obesity was identified by a calculated BMI ≥30 kg/m2.
Patients were divided into 2 cohorts, MetS and Control. MetS was defined similarly to our previously published criteria [13] as having 3 or 4 of the following comorbidities: obesity, DM, HTN, and/or dyslipidemia. Control was defined as having none of the 4 MetS comorbidities. We assessed the association of MetS with a primary clinical outcome of severe COVID-19 as defined by WHO outcome scores ≤4, which describe patients who died, required IMV, or required supplemental oxygen [23].
We conducted a literature review to define a set of biomarkers associated with COVID-19 and MetS: C-C motif chemokine ligand 2 (CCL2) [24, 25], CCL5 [26, 27], colony stimulating factor 3 (CSF3) [18, 28], C-X-C motif chemokine ligand 10 (CXCL10) [26, 29], CXCL9 [17, 30], hepatocyte growth factor (HGF) [18, 31], interferon-gamma (IFNγ) [18, 19], interleukin 10 (IL10) [19, 26], IL18 [32, 33] and IL6 [20, 24]. Using this list, we compared levels of each respective biomarker for day 0 between the MetS and Control cohorts.
Student t test was used for comparison of numerical variables in different groups, with a two-tailed P value ≤.05 for significance. Pearson χ2 test was used to compare categorical variables. Fisher exact test was used for categorical variable comparison if patient number in one category was too small. To adjust to age, sex, race, and ethnicity, multivariate logistic regression was used for comparison of primary clinical outcomes between MetS and Control. Two-way ANOVA was used for comparison of biomarker levels. Univariate and multivariate analyses was used to calculate crude odds ratios, and stratified analysis (by MetS status) was used to determine possible confounding and effect modification. Statistical analyses were performed using SAS Enterprise Guide version 6.1, SAS version 9.4. Figures were created using Prism version 9.0.1.
The dataset analyzed during the current study was provided by the MGH Emergency Department COVID-19 Cohort with Olink Proteonomics [34] and available in the MGH COVID-19 study repository, https://www.olink.com/mgh-covid-study/.
Results
In this study of 155 COVID-19 patients (Table 1), the most common age category was 50 to 64 years (31.0%) and rates of HTN (52.9%), DM (48.4%), obesity (40.7%), and dyslipidemia (52.9%) were higher than other preexisting comorbidities. 116 (74.8%) patients had WHO outcome scores classifying as severe COVID-19, of whom 65 (41.9%) required supplemental oxygen, 33 (21.3%) required IMV, and 18 (11.6%) died. No patient received a score of 3 (noninvasive ventilation or high-flow nasal cannula) due to recommendations at the time to minimize aerosol transmission.
Table 1.
Patient characteristics
| Characteristics | Total (n = 155) | Severe (n = 116) | Non-Severe (n = 39) |
|---|---|---|---|
| Age, n (%) | |||
| 20-34 | 16 (10.3) | 6 (5.2) | 10 (25.6) |
| 36-49 | 31 (20.0) | 20 (17.2) | 11 (28.2) |
| 50-64 | 48 (31.0) | 38 (32.8) | 10 (25.6) |
| 65-79 | 37 (23.9) | 32 (27.6) | 5 (12.8) |
| 80+ | 23 (14.8) | 20 (17.2) | 3 (7.7) |
| Female | 81 (52.3) | 64 (55.2) | 17 (43.6) |
| White | 84 (54.2) | 66 (56.9) | 18 (46.2) |
| Comorbidities, n (%) | |||
| CVDa | 31 (20.0) | 25 (21.6) | 6 (15.4) |
| CPDb | 34 (21.9) | 25 (21.6) | 9 (23.1) |
| CKDc | 22(14.2) | 20 (17.2) | 2 (5.1) |
| HTN | 82 (52.9) | 70 (60.3) | 12 (30.8) |
| DM | 75 (48.4) | 64 (55.2) | 11 (28.2) |
| Immunocompromisedd | 13 (8.4) | 10 (8.6) | 3 (7.8) |
| Obesitye | 63 (40.7) | 55 (47.4) | 8 (20.5) |
| Dyslipidemia | 82 (52.9) | 70 (60.3) | 12 (30.8) |
| WHO outcome scores, n (%) | |||
| 1: Death | 18 (11.6) | 18 (15.5) | 0 (0) |
| 2: Invasive mechanical ventilation | 33 (21.3) | 33 (28.5) | 0 (0) |
| 3: Noninvasive ventilation or high-flow nasal cannula | 0 (0) | 0 (0) | 0 (0) |
| 4: Hospitalized, supplementary oxygen | 65 (41.9) | 65 (56.0) | 0 (0) |
| 5: Hospitalized, no supplementary oxygen | 22 (14.2) | 0 (0) | 22 (56.4) |
| 6: Not hospitalized | 17 (11.0) | 0 (0) | 17 (43.6) |
Patient demographics, comorbidities, and World Health Organization (WHO) outcome scores presented in total and divided into severe or non-severe cases. Severe COVID-19 was defined using WHO COVID-19 outcome scores ≤4, which describes patients who died, required invasive mechanical ventilation, or required supplemental oxygen.
Cardiovascular disease (CVD) included coronary artery disease, congestive heart failure, and valvular disease.
Chronic pulmonary disease (CPD) included asthma, chronic obstructive pulmonary disease, home oxygen, and chronic lung conditions.
Chronic kidney disease (CKD) included chronic kidney disease, baseline creatinine level >1.5 and end stage renal disease.
Immunocompromised diseases included with cancer, chemotherapy, transplantation, immunosuppressants, and asplenia.
Obesity was defined as BMI ≥30 kg/m2. Patients with unknown BMI excluded.
Ninety (58.1%) patients had 3 or 4 MetS comorbidities and were included in the MetS cohort, while 65 (41.9%) patients had none of the 4 MetS comborbidities and were included in the Control cohort (Table 2). The MetS cohort was older (P < .0001) with more preexisting cardiovascular disease (P < .0001) and chronic kidney disease (P = .0008) compared with the Control cohort. Controlling for age, sex, race, and ethnicity, the MetS cohort was more likely to have severe COVID-19 compared to the Control cohort (OR 2.67 [CI 1.09-6.55], P = .0325), with higher frequencies of death (16.7% vs 4.6%), IMV (24.4% vs 16.9%), and supplemental oxygen use (45.6% vs 36.9%).
Table 2.
Control and MetS criteria cohort characteristics
| Characteristics | Control (n = 65) | MetS (n = 90) |
|---|---|---|
| Age, n (%) | ||
| 20-34 | 16 (24.6) | 0 (0) |
| 36-49 | 19 (29.2) | 12 (13.3) |
| 50-64 | 16 (24.6) | 32 (35.6) |
| 65-79 | 10 (15.4) | 27 (30.0) |
| 80+ | 4 (6.2) | 19 (21.1) |
| Female | 32 (46.2) | 49 (54.4) |
| White | 30 (46.2) | 54 (60.0) |
| Comorbidities, n (%) | ||
| CVDa | 2 (3.1) | 29 (32.2) |
| CPDb | 11 (16.9) | 23 (25.6) |
| CKDc | 2 (3.1) | 20 (22.2) |
| HTN | 0 (0) | 82 (91.1) |
| DM | 0 (0) | 75 (83.3) |
| Immunocompromisedd | 5 (7.7) | 8 (8.9) |
| Obesitye | 0 (0) | 63 (70.0) |
| Dyslipidemia | 0 (0) | 82 (91.1) |
| Severe COVID-19, n (%) | 38 (58.5) | 78 (86.7) |
| WHO outcome scores, n (%) | ||
| 1: Death | 3 (4.6) | 15 (16.7) |
| 2: Invasive mechanical ventilation | 11 (16.9) | 33 (24.4) |
| 3: Noninvasive ventilation or high-flow nasal cannula | 0 (0) | 0 (0) |
| 4: Hospitalized, supplementary oxygen |
24 (36.9) | 41 (45.6) |
| 5: Hospitalized, no supplementary oxygen | 13 (20.0) | 9 (10.0) |
| 6: Not hospitalized | 14 (21.5) | 3 (3.3) |
Severe COVID-19 was defined using WHO COVID-19 outcome scores ≤4, which describes patients who died, required invasive mechanical ventilation, or required supplemental oxygen.
Cardiovascular disease (CVD) included coronary artery disease, congestive heart failure, and valvular disease.
Chronic pulmonary disease (CPD) included asthma, chronic obstructive pulmonary disease, home oxygen, and chronic lung conditions.
Chronic kidney disease (CKD) included chronic kidney disease, baseline creatinine level >1.5 and end stage renal disease.
Immunocompromised diseases included with cancer, chemotherapy, transplantation, immunosuppressants, and asplenia.
Obesity defined as BMI ≥30 kg/m2. Patients with unknown BMI excluded.
Following the clinical characterization of these cohorts, we used blood proteomics data to identify biomarkers shared by both MetS and severe COVID-19 patients and define predictors of severe COVID-19. Among COVID-19 patients (n = 155), after adjusting for age, sex, race, and ethnicity, several biomarkers were associated with severe disease: CXCL10 (OR 1.94 [CI 1.38-2.73]), CXCL9 (OR 1.79 [CI 1.09-2.93]), HGF (OR 3.30 [CI 1.65-6.58]) and IL6 (OR 2.09 [CI 1.49-2.94]) (Table 3). Mean biomarker levels between MetS and Control patients are presented in Table 4. However, in analyses stratifying biomarkers by severity and MetS status, CXCL10 (OR 2.39 [CI 1.38-4.14]) and IL6 (OR 3.14 [CI 1.53-6.45]) were significantly associated with increased odds of having severe COVID-19 in patients with MetS. Furthermore, CXCL10 (mean NPX values 7.98 vs 7.35, P = .0164) and IL6 (mean NPX values 6.04 vs 5.18, P = .0065) levels were also significantly higher in severe COVID-19 patients with MetS compared to severe Control patients (Fig. 1). While no statistically significant differences were observed among other predetermined biomarker levels in MetS and Control patients with severe COVID-19, CXCL9 (P = .0171) and HGF (P = .0028) were elevated in non-severe COVID-19 MetS patients compared to non-severe Controls.
Table 3.
MetS as an effect modifier and biomarkers associated with severe COVID-19
| Biomarkers | Stratified MetS (95% CI) (n = 90) |
Stratified Control OR (95% CI) (n = 65) |
Adjusted OR (95% CI) (n = 155) |
|---|---|---|---|
| CCL2 | 2.08 (0.85, 5.09) | 2.43 (1.46, 4.04) | 1.71 (0.99, 2.93) |
| CCL5 | 0.56 (0.33, 0.95) | 1.00 (0.77, 1.31) | 0.71 (0.51, 0.98) |
| CSF3 | 2.05 (0.77, 5.46) | 1.49 (0.89, 2.49) | 1.58 (0.90, 2.76) |
| CXCL10 | 2.39 (1.38, 4.14) | 1.82 (1.33, 2.48) | 1.94 (1.38, 2.73) |
| CXCL9 | 1.89 (0.85, 4.18) | 1.57 (1.06, 2.32) | 1.79 (1.09, 2.93) |
| HGF | 2.26 (0.84, 6.12) | 5.42 (2.81, 10.47) | 3.30 (1.65, 6.58) |
| IFNγ | 0.85 (0.56, 1.29) | 1.07 (0.90, 1.27) | 1.12 (0.91, 1.38) |
| IL10 | 1.43 (0.85, 2.40) | 1.06 (0.78, 1.44) | 1.34 (0.94, 1.90) |
| IL18 | 1.44 (0.47, 4.46) | 0.97 (0.61, 1.54) | 1.12 (0.60, 2.08) |
| IL6 | 3.14 (1.53, 6.45) | 2.41 (1.76, 3.31) | 2.09 (1.49, 2.94) |
Odds ratios (OR) for severe vs non-severe COVID-19 stratified by MetS status, adjusted for age, sex, race, and ethnicity. The OR is for a 1-unit increase in Olink's Normalized Protein eXpression (NPX) which means 1NPX difference equals a doubling of protein concentration. Severe COVID-19 was defined using a World Health Organization COVID-19 outcome score ≤4, which describes patients who died, required invasive mechanical ventilation, or required supplemental oxygen.
Abbreviations: CCL, C-C motif chemokine ligand; CSF, colony stimulating factor; CXCL, C-X-C motif chemokine ligand; HGF, hepatocyte growth factor; IFNγ, interferon-γ; IL, interleukin.
Table 4.
Mean values of biomarkers between MetS and Control patients
| Biomarkers | MetS Mean ± SD (NPX) (n = 90) | Control Mean ± SD (NPX) (n = 65) | P |
|---|---|---|---|
| CCL2 | 6.248 ± 0.910 | 5.931 ± 0.870 | .0308 |
| CCL5 | 3.557 ± 1.318 | 3.635 ± 1.216 | .7048 |
| CSF3 | 0.827 ± 0.933 | 0.744 ± 0.750 | .5564 |
| CXCL10 | 7.792 ± 1.302 | 6.881 ± 1.543 | .0001 |
| CXCL9 | 4.489 ± 1.132 | 3.763 ± 1.144 | .0001 |
| HGF | 7.708 ± 0.770 | 7.207 ± 0.957 | .0007 |
| IFNγ | 3.476 ± 1.560 | 3.285 ± 2.268 | .5603 |
| IL10 | 0.401 ± 1.609 | 0.028 ± 1.174 | .0965 |
| IL18 | 5.604 ± 0.583 | 5.311 ± 0.766 | .0108 |
| IL6 | 5.796 ± 1.636 | 4.557 ± 1.679 | <.0001 |
Olink quantified plasma proteins using Normalized Protein eXpression (NPX) to minimize assay variation; NPX is in a log2 scale, meaning 1NPX difference equals a doubling of protein concentration.
Abbreviations: CCL, C-C motif chemokine ligand; CSF, colony stimulating factor; CXCL, C-X-C motif chemokine ligand; HGF, hepatocyte growth factor; IFNγ, interferon-γ; IL, interleukin.
Figure 1.
Box plots of MetS biomarker levels among severe and non-severe COVID-19 patients. Severe COVID-19 was defined using WHO COVID-19 outcome scores ≤4, which describes patients who died, required invasive mechanical ventilation, or required supplemental oxygen. Mean levels were compared between 1) severe MetS and severe Control and 2) non-severe MetS and non-severe Control using two-way ANOVA. Olink quantified plasma proteins using Normalized Protein eXpression (NPX) to minimize assay variation; NPX is in a log2 scale, meaning 1NPX difference equals a doubling of protein concentration. CXCL10 and IL6 had significantly higher means in severe MetS compared to Control. CXCL9 and HGF had significantly higher means in non-severe MetS compared to Control. Abbreviations: CCL, C-C motif chemokine ligand; CSF, colony stimulating factor; CXCL, C-X-C motif chemokine ligand; HGF, hepatocyte growth factor; IFNγ, interferon-γ; IL, interleukin; NPX, Normalized Protein eXpression. *P < .05. **P < .01.
Discussion
In this observational study of clinical and circulating biomarkers from 155 COVID-19 patients, we found that a MetS-related phenotype, characterized by having a combination of obesity, HTN, DM, and dyslipidemia, was more than twice as likely to have severe COVID-19, including the need for higher levels of oxygen supplementation and death, as compared to the Control cohort. These results are consistent with other studies [8, 9], as well as our own from different populations [12, 13] that indicate poor metabolic health and MetS are significant risk factors for severe COVID-19 and ARDS. However, the primary aim for this study was not to explore clinical outcomes, as we have done previously [13] but rather to identify biomarkers that may be used to predict COVID-19 severity in patients with a MetS phenotype. We hypothesize that the metabolic inflammation associated with MetS and its comorbidities may lie along the causal pathway between COVID-19 infection and severe disease as the result of a maladaptive host immune response and subsequent lung inflammation. Using this theory, we would expect to see differential results between groups stratified by MetS or Control and be able to identify certain biomarkers that can be targeted in future trials for patients with poor metabolic health.
In the overall population of this study, several biomarkers were associated with severe COVID-19, including CXCL10, CXCL9, HGF, and IL6. However, in stratified analyses considering MetS as an effect modifier, only 2 biomarkers, the inflammatory chemokine CXCL10 (OR 2.39 [CI 1.38-4.14]) and the proinflammatory cytokine IL6 (OR 3.14 [CI 1.53-6.45]), were associated with increased odds of severe COVID-19 in patients with MetS. Furthermore, both CXCL10 and IL6 levels were also significantly higher in severe COVID-19 patients with MetS compared to severe Control patients (Fig. 1). Altogether, these findings suggest that CXCL10 and IL6 may be considered as an immune signature of potential evolution toward severe COVID-19 in patients with MetS.
The inflammatory chemokine CXCL10 is involved in chemoattraction, cell growth, and angiogenesis [35]. CXCL10 is believed to play a central role in SARS-CoV-2-induced cytokine storm [36]. In adipose tissues, CXCL10 is expressed and secreted in response to leptin. Furthermore, as hyperleptinemia is a classical feature of obesity, leptin-induced upregulation of CXCL10 may contribute to severe COVID-19 outcomes. Consistent with this possibility, leptin correlates with monocyte activation in severe COVID-19 patients [37]. In the lungs, acute injury increases CXCL10 that activates oxidative burst and chemotaxis, leading to fulminant pulmonary inflammation and ARDS [38]. Similar to patients with COVID-19 [26], patients who developed ARDS from severe acute respiratory syndrome coronavirus [39] and H1N1 influenza [40] exhibited high levels of CXCL10. Previous research in mouse models of lung injury showed that mice lacking CXCL10 exhibited decreased severity and improved survival from ARDS, suggesting CXCL10 may be a therapeutic avenue for research for COVID-19-induced ALI [38]. MDX-1100, an anti-CXCL10 human monoclonal antibody that demonstrated efficacy and safety in patients with chronic inflammatory diseases [41, 42] also showed improvement of H1N1-induced ALI/ARDS in mice [43], and may be a promising approach. Our study further supports the consideration of CXCL10 as a therapeutic target for severe COVID-19.
IL6 is a well-known proinflammatory cytokine with a pleiotropic effect on inflammation, immunity, and hematopoiesis. IL6 is elevated in DM, particularly in those with MetS [44], and has been proposed to impair glucose homeostasis and metabolism [45]. Identified early as a marker of COVID-19 severity, increased IL6 levels have been associated with critical illness and mortality [5, 6]. In ongoing trials, the IL6 receptor antagonists tocilizumab and sarilumab have improved organ support-free days and mortality in critically ill COVID-19 patients [46, 47]. However, another trial found tocilizumab was not effective for preventing IMV or death in moderately ill patients [48]. These mixed results are not surprising, as recent studies found that IL6 levels were not significantly increased in most COVID-19 ARDS when compared to non-COVID-19 ARDS [49, 50]. However, our data support the premise that IL6 may be an appropriate therapeutic avenue for research for a precision-based approach to treat COVID-19 patients with a metabolic phenotype.
This study has several limitations. As an observational study, the conclusions do not imply causation and the sample size was small, limiting our ability to account for potential confounders due to inadequate power. Our modified MetS definition excludes other less severe MetS criteria, such as prediabetes, as well as waist circumference, and may have misclassified patients with MetS as Control.
In conclusion, metabolic syndrome, the combination of DM, HTN, obesity, and dyslipidemia, is a significant risk factor for severe COVID-19 outcomes that can be differentiated by an immune signature of elevated CXCL10 or IL6.
Abbreviations
- ALI
acute lung injury
- ARDS
acute respiratory distress syndrome
- BMI
body mass index
- CCL2
C-C motif chemokine ligand 2
- CXCL10
C-X-C motif chemokine ligand 10
- DM
diabetes mellitus
- HGF
hepatocyte growth factor
- HTN
hypertension
- IFNγ
interferon-gamma
- IL6
interleukin 6
- IL10
interleukin 10
- IMV
invasive mechanical ventilation
- MetS
metabolic syndrome
- MGH
Massachusetts General Hospital
- NPX
Normalized Protein eXpression
- OR
odds ratio
- WHO
World Health Organization
Contributor Information
Thaidan T Pham, Email: thp005@health.ucsd.edu, Department of Internal Medicine, UC San Diego Health, San Diego, CA 92103, USA.
Yuanhao Zu, Department of Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.
Farhad Ghamsari, Section of Pulmonary Diseases, Critical Care and Environmental Medicine, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA.
Janice Oh, Department of Internal Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Franck Mauvais-Jarvis, Section of Endocrinology, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA; Department of Endocrinology, Southeast Louisiana VA Medical Center, New Orleans, LA 70112, USA.
Hui Zheng, Massachusetts General Hospital Biostatistics Center, Massachusetts General Hospital, Boston, MA 02114, USA.
Michael Filbin, Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
Joshua L Denson, Email: jdenson@tulane.edu, Section of Pulmonary Diseases, Critical Care and Environmental Medicine, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA.
Funding
Funding was provided in part by the American Diabetes Association COVID-19 Research Award 7-20-COVID-053 (J.L.D.) and National Institutes of Health (NIH) Award U54 GM104940 which funds the Louisiana Clinical and Translational Science Center Roadmap Scholars Award (J.L.D.). F.M.J. was supported by NIH awards (DK107444 and DK074970) and a U.S. Department of Veterans Affairs Merit Award (BX003725).
Author Contributions
T.P. and J.L.D. contributed to the study conception and design. Y.Z., H.Z., M.F., F.G., and J.O. performed data analyses. T.P. wrote the first draft of the manuscript. F.M.J. provided critical review and edited the manuscript. All authors reviewed/edited the manuscript.
Disclosure
The authors have nothing to disclose.
Data Availability
The dataset analyzed during the current study was provided by the MGH Emergency Department COVID-19 Cohort with Olink Proteonomics and available in the MGH COVID-19 study repository, https://www.olink.com/mgh-covid-study/.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset analyzed during the current study was provided by the MGH Emergency Department COVID-19 Cohort with Olink Proteonomics and available in the MGH COVID-19 study repository, https://www.olink.com/mgh-covid-study/.