Epidemiological and Clinical Factors Modulating COVID-19: History
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Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Laure-Alix Clerbaux
,
Maria Cristina Albertini
,
Núria Amigó
,
Anna Beronius
,
Gillina F. G. Bezemer
,
Sandra Coecke
,
Evangelos P. Daskalopoulos
,
Giusy del Giudice
,
Dario Greco
,
Lucia Grenga
,
Alberto Mantovani
,
Amalia Muñoz
,
Elma Omeragic
,
Nikolaos Parissis
,
Mauro Petrillo
,
Laura A. Saarimäki
,
Helena Soares
,
Kristie Sullivan
,
Brigitte Landesmann

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. While there is a multitude of factors modulating COVID-19. These modulating factors (MFs) were selected based on epidemiological and/or clinical studies to be representative of different categories: intrinsic (age, sex and genetic factors), co-morbidities (history of dyslipidemia, obesity, pre-existing heart failure and gut dysbiosis), lifestyle-related (vitamin D deficiency and diet) and environmental (air pollution and exposure to chemicals). 

  • Epidemiological
  • COVID-19
  • Clinical

1. Introduction

In March 2020, the World Health Organization declared the first pandemic caused by a coronavirus [1]. Researchers worldwide have intensively investigated the biological mechanisms underlying the disease and the factors rendering some populations more vulnerable, resulting in a flood of publications. In the summer of 2020, the CIAO project (Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway Framework) was initiated to provide a unique overview of the available knowledge on COVID-19 pathogenesis using the Adverse Outcome Pathway (AOP) framework [2][3]. The AOP concept provides a pragmatic means for organizing fast-evolving scientific knowledge based on existing data and literature from different fields [2][4]. An AOP describes a sequence of biological events starting from an initial interaction at the molecular level, called Molecular Initiating Event (MIE), through key biological events up to an adverse outcome (AO) [5][6][7]. AOPs are not intended to describe all details, but rather capture essential events, called Key Events (KEs), which drive downstream KEs and ultimately the AO. Importantly, KEs must be measurable in laboratory or clinical settings. A significant distinguishing aspect of the AOP approach is the structured evaluation and representation of the weight of evidence supporting the causality of the relationships between two KEs, called Key Event Relationships (KERs) [8]. Available qualitative and quantitative information is considered in the KER weight of evidence evaluation, including the magnitude, direction, and time concordance of a perturbation in the upstream KE needed to elicit a detectable change in the downstream KE. Thus, a single AOP proposes one biological pathway leading to an AO and a set of methods to measure KEs and predict the AO. The Organisation for Economic Co-operation and Development (OECD) maintains an online platform called AOP-Wiki [9], where information captured in AOPs is openly accessible to interested researchers, regulators, and clinicians. It is important to mention that there is a rigorous approach to collect evidence at the core of AOP development; however, this approach does not rely integrally on systematic principles.
AOPs are widely acknowledged as valuable tools in chemical safety assessments and regulatory toxicology analyses to provide methods and predictive models [10] while minimizing animal testing. The utility of AOPs for regulatory application is defined by the confidence with which AOPs facilitate extrapolation of data measured at low levels of biological organization (molecular and cellular-based methods) to relevant outcomes in organs, individuals, or populations. AOPs can also be used for assessing the mechanistic plausibility of epidemiological observations, such as the relationships between exposure to chemicals or nanomaterials and risk for disease [11]. While currently mainly exploited in toxicology, AOPs could have great value for other fields, such as biomedical research, thus profiting from growing AOP networks that describe the effects of chemical and non-chemical stressors. Human diseases are generally classified based on clinical phenotypes. Transitioning to a mechanistically-based understanding of the diseases could facilitate the design of hypothesis-based research strategies, improve predictive modeling and contribute to drug discovery [8][12].
The CIAO project explores this broader application of AOPs by expanding it to a viral stressor, namely SARS-CoV-2 [2][3][13]. Within the project, many linear AOPs have been developed describing key steps leading to a specific AO in COVID-19 [14]. The AOP framework also considers modulating factors (MFs) that intervene at KERs, affecting the quantitative response-response relationship between two Kes [15]. Addressing MFs is crucial since abundant clinical evidence shows that COVID-19 outcomes are markedly heterogeneous. Patients range from being asymptomatic or having a mild upper respiratory illness to having severe lung injury that requires hospitalization and may progress to hyperinflammation, acute respiratory distress syndrome, multi-organ failure, and death [16][17]. The variation in clinical outcomes suggests that socio-demographic and biological factors, characteristic of individuals or populations, modulate the course of the disease, corresponding to the concept of the epidemiological triad in infectious diseases. A better understanding of these host-virus interactions could allow for the refinement of preventive measures and improvement of treatment options. This requires identifying MFs contributing to outcome heterogeneity and a mechanistic understanding of their influence on COVID-19.

2. Biological (Intrinsic)

Age. Age is by far the strongest risk factor for life-threatening COVID-19. Age distribution of COVID-19 deaths has been supported by multivariate analyses across continents [18][19]. The infection fatality ratio has been estimated to be lowest among 5- to 9-year-old children, with a log-linear increase in individuals older than 30 years [20]. Most deaths involved people older than 80 years, in particular in care facilities [21][22][23][24], while people over 65 years without co-morbidities have a very low COVID-19 mortality rate [25]. Surprisingly, infants and young toddlers seem protected from severe disease [26].
Sex. Epidemiological data and observational reports have shown that COVID-19 has different outcomes for men and women. Despite similar infection rates in men and women, men are at higher risk for hospitalisation, admission to an intensive care unit (ICU), and death [27][28]. COVID-19 outcomes are also influenced by gender differences, the different social roles and behavioural factors, but these aspects are not discussed here [29].
Genetic factors. Undoubtedly, host genetic factors also play a role in SARS-CoV-2 pathophysiology, influencing an individual or a population susceptibility [30][31]. Here, researchers aim to investigate the impact on COVID-19 of polymorphisms in ACE2 and TMPRSS2, serving as main SARS-CoV-2 cell entry gateways, as well as in Toll-like receptors (TLRs), acting as crucial actors of the immune system alert during infections. Epidemiological studies showed that around twenty natural ACE2 variants might partially account for the differences of COVID-19 prevalence and mortality rates observed between Europe and East Asia [32][33]. Regarding TLRs, it has been shown that certain patients with severe COVID-19 were associated with a rare putative loss-of-function variants of X-chromosomal TLR7, that causes poor defense against coronavirus [34][35]. Gene analysis also revealed elevated expression of TLR7 and TLR9, leading to exaggerated immune response, in certain ethnic groups with higher COVID-19 mortality [36][37]. Clinical evidence showing associations between TLR gain-of-function polymorphisms and higher incidence of ICU acquired infection have been reported for other immune-mediated diseases and infections [38][39][40]. In addition, patients with blood group A were reported to have an increased risk of severe COVID-19 while the protective effect of the O allele is small, with an odds ratio of ~0.90.

3. Pre-Existing Co-Morbidities

Atherogenic dyslipidemia and obesity. Beyond age, sex, and genetic factors, co-morbidities are the main clinical determinants of COVID-19 severity [17][41][42][43]. Particularly, COVID-19 patients with metabolic syndrome had significantly higher hospitalization and mortality rates [44][45]. Metabolic syndrome is the common denominator of hypertension, diabetes, obesity and dyslipidemia. Here, researchers will particularly investigate how a history of dyslipidemia and obesity impact COVID-19. Several studies reported associations between low HDL-cholesterol and elevated triglycerides (TG) in serum and severe COVID-19 [43][46][47][48][49][50]. This lipid signature is known as atherogenic dyslipidemia. In addition, many studies support that excess body weight is an important risk factor for severe COVID-19 [51][52]. One study found a linear increase in the risk of severe COVID-19, admission to the hospital and death, starting already at a Body Mass Index (BMI) > 23 kg/m2, which is not attributable to excess risks of related diseases. This risk due to elevated BMI was particularly notable in people younger than 40 years of age [53]. However, a study in Costa Rica showed that the host co-morbidities are not specific of a particular clinical profile of patients during pre-vaccination time [54].
Pre-existing heart failure (HF). Early in the pandemic, reports exhibited that patients with pre-existing cardiovascular disease had increased mortality from COVID-19 [55]. A study in Italy showed that death was more than double in patients with cardiac disease, including HF, atrial fibrillation and coronary artery disease, compared to patients without cardiac pathologies [56]. HF is a chronic and progressive clinical syndrome in which the myocardium is unable to maintain an adequate cardiac output to meet the body needs for blood and oxygen as a consequence of structural and/or functional cardiac abnormalities [57]. Pre-existing HF was identified as a major factor leading to poorer prognosis in COVID-19 patients [56] and as a determinant for non-cardiovascular organ failure [58]. Approximately half of HF patients hospitalized for COVID-19 died (from all causes) [59], while 10% of the patients hospitalized for COVID-19 had a history of HF with higher levels of troponin (i.e., marker of myocardial injury) [60]. A recent meta-analysis confirmed that pre-existing HF is associated with higher mortality, worse prognosis and higher risk for hospitalization [61]. All this led people to investigate the mechanisms by which pre-existing HF modulate COVID-19.
Gut dysbiosis. Many studies show a link between COVID-19 and gut dysbiosis, defined as a reduction in gut microbial diversity and depletion of beneficial bacteria with enrichment of pathogenic microbes. Infection with SARS-CoV-2 directly alters the gut microbiota in mice, hamsters, and non-human primates [62][63][64]. Gut dysbiosis can thus be considered an AO caused by SARS-CoV-2. Studies in humans showed that COVID-19 patients exhibited fecal microbiome alterations at all times of hospitalization compared to controls [65][66][67][68]. In addition to being a consequence, a correlation between dysbiosis and severe COVID-19 has been shown in at least two human studies [65]. Compared to patients with mild symptoms, the gut microbiota of patients with severe COVID-19 showed lower Firmicutes/Bacteroidetes ratio, enrichment of Proteobacteria, and lowered abundance of beneficial butyrate-producing bacteria [69]. In addition, alteration of the gut microbiota was associated with several risk factors for severe COVID-19 [70]. This prompts people to explore how gut dysbiosis acts as a detrimental pre-condition in COVID-19 patients [64].

4. Lifestyle Factors

Diet. Dietary patterns, such as the “Mediterranean” or the “Western” diet, as well as certain foods, are proposed to impact COVID-19 prognosis and account—at least partially—for regional differences in death rates [71][72][73]. Epidemiological analyses highlighted a role for diet in disease prognosis in some cases. Severity was negatively associated with habitual intake of legumes, grains, breads, and cereals [73]. Two studies have associated a “Mediterranean” diet with lower risk of infection and severity [74][75]. Another study found an association between higher frequency of consumption of plant-based foods and lower risk of COVID-19, after accounting for social determinants of health, pre-existing conditions, and measures to reduce viral transmission [76]. However, another similar study with fewer participants found no association [76]. A growing awareness of the impact of diet on health requires an investigation of the effects of dietary intake—both nutrients and types of foods—on COVID-19 infection and prognosis. Here, researchers will explore the potential direct impacts of foods eaten regularly prior to infection but will not discuss the treatment of COVID-19 patients with food-derived compounds, or the effects of diet-related diseases on COVID-19.
Vitamin D deficiency. Vitamin D deficiency affects around half of the population worldwide [77][78]. Vitamin D deficiency prior to infection was shown to increase the risk of COVID-19 severity and mortality in most studies [79][80]. However, some data do not support a correlation between vitamin D deficiency and severity [81]. Conflicting results might be related to the complex interactions of vitamin D with the status of vitamins A and K, calcium, potassium and phosphorus [82]. Furthermore, some studies also suggest a correlation between supplementation of high levels of vitamin D and reduced hospital mortality in COVID-19 patients [83] or with reduced severity, but not mortality [84]. However, a recent Cochrane research found that strength of evidence about the beneficial effect of vitamin D supplementation in COVID-19 patients is low [85]. More research is needed to fully capture the impact of vitamin D on COVID-19. As low vitamin D status prior to infection appears as an impactful MF [79], it prompted people to investigate how a vitamin D deficiency interferes with COVID-19 underlying mechanisms and locate the current knowledge gaps and inconsistencies.

5. Environmental Factors

Air pollution. Air pollution covers a range of substances released into the atmosphere due to human activities, with some of the most prevalent and researched compounds including nitrogen oxides (NOx), ozone (O3), and fine particulate matter (PM2.5). The aggravating effects of these compounds on lung diseases are well-established [86], and the number of papers speculating on a connection between air pollution and COVID-19 spread and mortality is growing [87][88][89][90]. At the same time, the direct effects of air pollution on the transmission of the virus are debated [91]. This association is often confounded with high population density and other factors known to affect the levels of transmission. While the positive relationship between air pollution and the transmission of the virus is still largely speculative, evidence points to air pollution as a MF increasing COVID-19 severity and mortality [88][90][92][93][94]. The AOP approach is a relevant and effective framework for investigating the mechanistic aspects of the correlations discussed above.
Chemical exposure, exemplified by PFAS. Humans are continuously exposed to a mixture of chemicals from different sources, such as from food, drinking water, and consumer products. A number of chemicals are known to cause adverse health effects, including impaired immunity, at current exposure levels [95]. Hence, it is reasonable to believe that exposure to certain chemicals may affect the response to COVID-19 [96]. Per- and polyfluoroalkyl substances (PFAS), a group of several thousand widespread synthetic biopersistent and bioaccumulative chemicals sharing similar molecular structure (including Perfluorooctane sulfonic acid (PFOS) and Perfluorooctanoic acid (PFOA)) have been shown to cause negative effects on metabolism, thyroid function and the immune system [97], particularly when exposure occurs prenatally and/or during childhood [97].
Few studies have investigated correlations between PFAS exposure and COVID-19 thus far. A population living in a PFAS-contaminated region in Italy exhibited a higher mortality risk for COVID-19 [98]. In two regions heavily polluted by PFAS in China, a significant correlation between the urinary concentration of 12 PFAS and COVID-19 infection risk was reported [99]. Increased plasma-PFAS concentrations were associated with severe COVID-19 in Denmark, and this tendency remained after adjustment [100]. Finally, a study from the highly contaminated area in Sweden estimated that the incidence ratio of COVID-19 in the adult population was 1.19 compared to a reference population, suggesting a potential link between high PFAS exposure and susceptibility to COVID-19 [101]. More epidemiological studies are needed to assess the strength of the association between PFAS exposure and COVID-19 severity. However, PFAS exposure seems a plausible MF due to their immunotoxic properties and will be explored here as an example of how chemical exposure could modulate COVID-19 [102].

6. Therapeutic Intervention against COVID-19

The fast-spreading and disruptive nature of COVID-19 pandemic highlighted the need for rapid and reliable identification of therapeutic options. In the first phases of the health emergency, several hundreds of medications were trialed, creating a huge amount of data of divergent quality. However, only a small percentage of the proposed drugs showed to be effective against the virus [103]. Understanding the mechanisms underlying the disease is essential to propose adequate therapeutic interventions against COVID-19. Furthermore, drug repositioning rapidly emerged, being a quick and cost-effective solution to screen large libraries of candidate compounds [104]. Especially when the information on the molecular pathogenesis is limited, repurposing existing drugs has also the advantage of already proven safety profiles and known targets [105][106].

This entry is adapted from the peer-reviewed paper 10.3390/jcm11154464

References

  1. Cucinotta, D.; Vanelli, M. WHO Declares COVID-19 a Pandemic. Acta Biomed 2020, 91, 157–160.
  2. Nymark, P.; Sachana, M.; Leite, S.B.; Sund, J.; Krebs, C.E.; Sullivan, K.; Edwards, S.; Viviani, L.; Willett, C.; Landesmann, B.; et al. Systematic Organization of COVID-19 Data Supported by the Adverse Outcome Pathway Framework. Front. Public Health 2021, 9, 638605.
  3. Wittwehr, C.; Amorim, M.J.; Clerbaux, L.A.; Krebs, C.; Landesmann, B.; Macmillan, D.S.; Nymark, P.; Ram, R.; Garcia-Reyero, N.; Sachana, M.; et al. Understanding COVID-19 through adverse outcome pathways—2nd CIAO AOP Design Workshop. ALTEX 2021, 38, 351–357.
  4. Vinken, M. COVID-19 and the liver: An adverse outcome pathway perspective. Toxicology 2021, 455, 152765.
  5. Ankley, G.T.; Bennett, R.S.; Erickson, R.J.; Hoff, D.J.; Hornung, M.W.; Johnson, R.D.; Mount, D.R.; Nichols, J.W.; Russom, C.L.; Schmieder, P.K.; et al. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 2010, 29, 730–741.
  6. Villeneuve, D.L.; Crump, D.; Garcia-Reyero, N.; Hecker, M.; Hutchinson, T.H.; LaLone, C.A.; Landesmann, B.; Lettieri, T.; Munn, S.; Nepelska, M.; et al. Adverse outcome pathway (AOP) development I: Strategies and principles. Toxicol. Sci. 2014, 142, 312–320.
  7. Leist, M.; Ghallab, A.; Graepel, R.; Marchan, R.; Hassan, R.; Bennekou, S.H.; Limonciel, A.; Vinken, M.; Schildknecht, S.; Waldmann, T.; et al. Adverse outcome pathways: Opportunities, limitations and open questions. Arch. Toxicol. 2017, 91, 3477–3505.
  8. Marshall, L.J.; Austin, C.P.; Casey, W.; Fitzpatrick, S.C.; Willett, C. Recommendations toward a human pathway-based approach to disease research. Drug Discov. Today 2018, 23, 1824–1832.
  9. AOP-Wiki. Available online: https://aopwiki.org/ (accessed on 16 March 2022).
  10. Kleinstreuer, N.C.; Sullivan, K.; Allen, D.; Edwards, S.; Mendrick, D.L.; Embry, M.; Matheson, J.; Rowlands, J.C.; Munn, S.; Maull, E.; et al. Adverse outcome pathways: From research to regulation scientific workshop report. Regul. Toxicol. Pharmacol. 2016, 76, 39–50.
  11. Terron, A.; Bal-Price, A.; Paini, A.; Monnet-Tschudi, F.; Bennekou, S.H.; Members, E.W.E.; Leist, M.; Schildknecht, S. An adverse outcome pathway for parkinsonian motor deficits associated with mitochondrial complex I inhibition. Arch. Toxicol. 2018, 92, 41–82.
  12. Korn, D.; Thieme, A.J.; Alves, V.M.; Yeakey, M.; Borba, J.; Capuzzi, S.J.; Fecho, K.; Bizon, C.; Edwards, S.W.; Chirkova, R.; et al. Defining clinical outcome pathways. Drug Discov. Today 2022, 27, 1671–1678.
  13. Kim, Y.; Park, C.G.; Lim, S.R.; Jun, I.; Lee, Y.O. Advanced Adverse Outcome Pathways Potentially Bridging the Pathogenesis of COVID-19. Preprints 2021.
  14. Modelling the Pathogenesis of COVID-19 Using the Adverse Outcome Pathway Framework. Available online: www.ciao-covid.net (accessed on 16 March 2022).
  15. OECD. Users’ Handbook Supplement to the Guidance Document for Developing and Assessing Adverse Outcome Pathways. 2018. Available online: https://www.oecd-ilibrary.org/docserver/5jlv1m9d1g32-en.pdf?expires=1659523666&id=id&accname=guest&checksum=A77C7633D48B3B36547F25CC76CAE409 (accessed on 16 March 2022).
  16. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506.
  17. Richardson, S.; Hirsch, J.S.; Narasimhan, M.; Crawford, J.M.; McGinn, T.; Davidson, K.W.; the Northwell COVID-19 Research Consortium. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA 2020, 323, 2052–2059.
  18. Jordan, R.E.; Adab, P.; Cheng, K.K. Covid-19: Risk factors for severe disease and death. BMJ 2020, 368, m1198.
  19. Farshbafnadi, M.; Kamali Zonouzi, S.; Sabahi, M.; Dolatshahi, M.; Aarabi, M.H. Aging & COVID-19 susceptibility, disease severity, and clinical outcomes: The role of entangled risk factors. Exp. Gerontol. 2021, 154, 111507.
  20. O’Driscoll, M.; Ribeiro Dos Santos, G.; Wang, L.; Cummings, D.A.T.; Azman, A.S.; Paireau, J.; Fontanet, A.; Cauchemez, S.; Salje, H. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature 2021, 590, 140–145.
  21. Cohen, J.F.; Korevaar, D.A.; Matczak, S.; Chalumeau, M.; Allali, S.; Toubiana, J. COVID-19-Related Fatalities and Intensive-Care-Unit Admissions by Age Groups in Europe: A Meta-Analysis. Front. Med. 2020, 7, 560685.
  22. Sepulveda, E.R.; Stall, N.M.; Sinha, S.K. A Comparison of COVID-19 Mortality Rates Among Long-Term Care Residents in 12 OECD Countries. J. Am. Med. Dir. Assoc. 2020, 21, 1572–1574.e1573.
  23. ECDC Public Health Emergency Team; Danis, K.; Fonteneau, L.; Georges, S.; Daniau, C.; Bernard-Stoecklin, S.; Domegan, L.; O‘Donnell, J.; Hauge, S.H.; Dequeker, S.; et al. High impact of COVID-19 in long-term care facilities, suggestion for monitoring in the EU/EEA, May 2020. Eurosurveillance 2020, 25, 2000956.
  24. Onder, G.; Rezza, G.; Brusaferro, S. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA 2020, 323, 1775–1776.
  25. Ioannidis, J.P.A.; Axfors, C.; Contopoulos-Ioannidis, D.G. Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters. Environ. Res. 2020, 188, 109890.
  26. Williamson, E.J.; Walker, A.J.; Bhaskaran, K.; Bacon, S.; Bates, C.; Morton, C.E.; Curtis, H.J.; Mehrkar, A.; Evans, D.; Inglesby, P.; et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020, 584, 430–436.
  27. Peckham, H.; de Gruijter, N.M.; Raine, C.; Radziszewska, A.; Ciurtin, C.; Wedderburn, L.R.; Rosser, E.C.; Webb, K.; Deakin, C.T. Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission. Nat. Commun. 2020, 11, 6317.
  28. The COVID-19 Sex-Disaggregated Data Tracker. Available online: https://globalhealth5050.org/the-sex-gender-and-covid-19-project/the-data-tracker/ (accessed on 16 March 2022).
  29. Takahashi, T.; Iwasaki, A. Sex differences in immune responses. Science 2021, 371, 347–348.
  30. Wang, F.; Huang, S.; Gao, R.; Zhou, Y.; Lai, C.; Li, Z.; Xian, W.; Qian, X.; Li, Z.; Huang, Y.; et al. Initial whole-genome sequencing and analysis of the host genetic contribution to COVID-19 severity and susceptibility. Cell Discov. 2020, 6, 83.
  31. Pathak, G.A.; Singh, K.; Miller-Fleming, T.W.; Wendt, F.R.; Ehsan, N.; Hou, K.; Johnson, R.; Lu, Z.; Gopalan, S.; Yengo, L.; et al. Integrative analyses identify susceptibility genes underlying COVID-19 hospitalization. medRxiv 2020.
  32. Chen, F.; Zhang, Y.; Li, X.; Li, W.; Liu, X.; Xue, X. The Impact of ACE2 Polymorphisms on COVID-19 Disease: Susceptibility, Severity, and Therapy. Front. Cell Infect. Microbiol. 2021, 11, 753721.
  33. Suryamohan, K.; Diwanji, D.; Stawiski, E.W.; Gupta, R.; Miersch, S.; Liu, J.; Chen, C.; Jiang, Y.P.; Fellouse, F.A.; Sathirapongsasuti, J.F.; et al. Human ACE2 receptor polymorphisms and altered susceptibility to SARS-CoV-2. Commun. Biol. 2021, 4, 475.
  34. Solanich, X.; Vargas-Parra, G.; van der Made, C.I.; Simons, A.; Schuurs-Hoeijmakers, J.; Antoli, A.; Del Valle, J.; Rocamora-Blanch, G.; Setien, F.; Esteller, M.; et al. Genetic Screening for TLR7 Variants in Young and Previously Healthy Men With Severe COVID-19. Front. Immunol. 2021, 12, 719115.
  35. Asano, T.; Boisson, B.; Onodi, F.; Matuozzo, D.; Moncada-Velez, M.; Maglorius Renkilaraj, M.R.L.; Zhang, P.; Meertens, L.; Bolze, A.; Materna, M.; et al. X-linked recessive TLR7 deficiency in ~1% of men under 60 years old with life-threatening COVID-19. Sci. Immunol. 2021, 6, eabl4348.
  36. Jacob, C.O. On the genetics and immunopathogenesis of COVID-19. Clin. Immunol. 2020, 220, 108591.
  37. Tal, Y.; Adini, A.; Eran, A.; Adini, I. Racial disparity in Covid-19 mortality rates—A plausible explanation. Clin. Immunol. 2020, 217, 108481.
  38. Bezemer, G.F.G.; Garssen, J. TLR9 and COVID-19: A Multidisciplinary Theory of a Multifaceted Therapeutic Target. Front. Pharmacol. 2020, 11, 601685.
  39. Chatzi, M.; Papanikolaou, J.; Makris, D.; Papathanasiou, I.; Tsezou, A.; Karvouniaris, M.; Zakynthinos, E. Toll-like receptor 2, 4 and 9 polymorphisms and their association with ICU-acquired infections in Central Greece. J. Crit. Care 2018, 47, 1–8.
  40. Zhao, P.; Lu, G.; Cai, L. Polymorphisms in the toll-like receptor 9 gene associated with sepsis and multiple organ dysfunction after major blunt trauma (Br J Surg 2011; 98: 1252–1259). Br. J. Surg. 2012, 99, 145, author reply 145.
  41. Grasselli, G.; Zangrillo, A.; Zanella, A.; Antonelli, M.; Cabrini, L.; Castelli, A.; Cereda, D.; Coluccello, A.; Foti, G.; Fumagalli, R.; et al. Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA 2020, 323, 1574–1581.
  42. Ji, W.; Huh, K.; Kang, M.; Hong, J.; Bae, G.H.; Lee, R.; Na, Y.; Choi, H.; Gong, S.Y.; Choi, Y.H.; et al. Effect of Underlying Comorbidities on the Infection and Severity of COVID-19 in Korea: A Nationwide Case-Control Study. J. Korean Med. Sci. 2020, 35, e237.
  43. Choi, G.J.; Kim, H.M.; Kang, H. The Potential Role of Dyslipidemia in COVID-19 Severity: An Umbrella Review of Systematic Reviews. J. Lipid Atheroscler. 2020, 9, 435–448.
  44. Wu, S.; Zhou, K.; Misra-Hebert, A.; Bena, J.; Kashyap, S.R. Impact of Metabolic Syndrome on Severity of COVID-19 Illness. Metab. Syndr. Relat. Disord. 2022, 20, 191–198.
  45. Ghoneim, S.; Butt, M.U.; Hamid, O.; Shah, A.; Asaad, I. The incidence of COVID-19 in patients with metabolic syndrome and non-alcoholic steatohepatitis: A population-based study. Metabol. Open 2020, 8, 100057.
  46. Hu, X.; Chen, D.; Wu, L.; He, G.; Ye, W. Declined serum high density lipoprotein cholesterol is associated with the severity of COVID-19 infection. Clin. Chim. Acta 2020, 510, 105–110.
  47. Hu, X.; Chen, D.; Wu, L.; He, G.; Ye, W. Low Serum Cholesterol Level Among Patients with COVID-19 Infection in Wenzhou, China. SSRN Electron. J. 2020.
  48. Masana, L.; Correig, E.; Ibarretxe, D.; Anoro, E.; Arroyo, J.A.; Jerico, C.; Guerrero, C.; Miret, M.; Naf, S.; Pardo, A.; et al. Low HDL and high triglycerides predict COVID-19 severity. Sci. Rep. 2021, 11, 7217.
  49. Wang, G.; Zhang, Q.; Zhao, X.; Dong, H.; Wu, C.; Wu, F.; Yu, B.; Lv, J.; Zhang, S.; Wu, G.; et al. Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: An observational study. Lipids Health Dis. 2020, 19, 204.
  50. Hariyanto, T.I.; Kurniawan, A. Dyslipidemia is associated with severe coronavirus disease 2019 (COVID-19) infection. Diabetes Metab. Syndr. 2020, 14, 1463–1465.
  51. Ho, J.S.Y.; Fernando, D.I.; Chan, M.Y.; Sia, C.H. Obesity in COVID-19: A Systematic Review and Meta-analysis. Ann. Acad. Med. Singap. 2020, 49, 996–1008.
  52. Demeulemeester, F.; de Punder, K.; van Heijningen, M.; van Doesburg, F. Obesity as a Risk Factor for Severe COVID-19 and Complications: A Review. Cells 2021, 10, 933.
  53. Gao, M.; Piernas, C.; Astbury, N.M.; Hippisley-Cox, J.; O’Rahilly, S.; Aveyard, P.; Jebb, S.A. Associations between body-mass index and COVID-19 severity in 6·9 million people in England: A prospective, community-based, cohort study. Lancet Diabetes Endocrinol. 2021, 9, 350–359.
  54. Molina-Mora, J.A.; Gonzalez, A.; Jimenez-Morgan, S.; Cordero-Laurent, E.; Brenes, H.; Soto-Garita, C.; Sequeira-Soto, J.; Duarte-Martinez, F. Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach. Phenomics 2022, 1–11.
  55. Verity, R.; Okell, L.C.; Dorigatti, I.; Winskill, P.; Whittaker, C.; Imai, N.; Cuomo-Dannenburg, G.; Thompson, H.; Walker, P.G.T.; Fu, H.; et al. Estimates of the severity of coronavirus disease 2019: A model-based analysis. Lancet Infect. Dis. 2020, 20, 669–677.
  56. Inciardi, R.M.; Adamo, M.; Lupi, L.; Cani, D.S.; Di Pasquale, M.; Tomasoni, D.; Italia, L.; Zaccone, G.; Tedino, C.; Fabbricatore, D.; et al. Characteristics and outcomes of patients hospitalized for COVID-19 and cardiac disease in Northern Italy. Eur. Heart J. 2020, 41, 1821–1829.
  57. McDonagh, T.A.; Metra, M.; Adamo, M.; Gardner, R.S.; Baumbach, A.; Bohm, M.; Burri, H.; Butler, J.; Celutkiene, J.; Chioncel, O.; et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 2021, 42, 3599–3726.
  58. Tomasoni, D.; Inciardi, R.M.; Lombardi, C.M.; Tedino, C.; Agostoni, P.; Ameri, P.; Barbieri, L.; Bellasi, A.; Camporotondo, R.; Canale, C.; et al. Impact of heart failure on the clinical course and outcomes of patients hospitalized for COVID-19. Results of the Cardio-COVID-Italy multicentre study. Eur. J. Heart Fail. 2020, 22, 2238–2247.
  59. Belarte-Tornero, L.C.; Valdivielso-More, S.; Vicente Elcano, M.; Sole-Gonzalez, E.; Ruiz-Bustillo, S.; Calvo-Fernandez, A.; Subinara, I.; Cabero, P.; Soler, C.; Cubero-Gallego, H.; et al. Prognostic Implications of Chronic Heart Failure and Utility of NT-proBNP Levels in Heart Failure Patients with SARS-CoV-2 Infection. J. Clin. Med. 2021, 10, 323.
  60. Lala, A.; Johnson, K.W.; Januzzi, J.L.; Russak, A.J.; Paranjpe, I.; Richter, F.; Zhao, S.; Somani, S.; Van Vleck, T.; Vaid, A.; et al. Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection. J. Am. Coll. Cardiol. 2020, 76, 533–546.
  61. Yonas, E.; Alwi, I.; Pranata, R.; Huang, I.; Lim, M.A.; Gutierrez, E.J.; Yamin, M.; Siswanto, B.B.; Virani, S.S. Effect of heart failure on the outcome of COVID-19—A meta analysis and systematic review. Am. J. Emerg. Med. 2021, 46, 204–211.
  62. Sokol, H.; Contreras, V.; Maisonnasse, P.; Desmons, A.; Delache, B.; Sencio, V.; Machelart, A.; Brisebarre, A.; Humbert, L.; Deryuter, L.; et al. SARS-CoV-2 infection in nonhuman primates alters the composition and functional activity of the gut microbiota. Gut Microbes 2021, 13, 1893113.
  63. Sencio, V.; Machelart, A.; Robil, C.; Benech, N.; Hoffmann, E.; Galbert, C.; Deryuter, L.; Heumel, S.; Hantute-Ghesquier, A.; Flourens, A.; et al. Alteration of the gut microbiota following SARS-CoV-2 infection correlates with disease severity in hamsters. Gut Microbes 2022, 14, 2018900.
  64. Venzon, M.; Bernard-Raichon, L.; Klein, J.; Axelrad, J.; Hussey, G.; Sullivan, A.; Casanovas-Massana, A.; Noval, M.; Valero-Jimenez, A.; Gago, J.; et al. Gut microbiome dysbiosis during COVID-19 is associated with increased risk for bacteremia and microbial translocation. Res. Sq. 2021.
  65. Yeoh, Y.K.; Zuo, T.; Lui, G.C.; Zhang, F.; Liu, Q.; Li, A.Y.; Chung, A.C.; Cheung, C.P.; Tso, E.Y.; Fung, K.S.; et al. Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. Gut 2021, 70, 698–706.
  66. Zuo, T.; Liu, Q.; Zhang, F.; Lui, G.C.; Tso, E.Y.; Yeoh, Y.K.; Chen, Z.; Boon, S.S.; Chan, F.K.; Chan, P.K.; et al. Depicting SARS-CoV-2 faecal viral activity in association with gut microbiota composition in patients with COVID-19. Gut 2021, 70, 276–284.
  67. Zuo, T.; Zhang, F.; Lui, G.C.Y.; Yeoh, Y.K.; Li, A.Y.L.; Zhan, H.; Wan, Y.; Chung, A.C.K.; Cheung, C.P.; Chen, N.; et al. Alterations in Gut Microbiota of Patients with COVID-19 During Time of Hospitalization. Gastroenterology 2020, 159, 944–955.e8.
  68. Zhang, F.; Wan, Y.; Zuo, T.; Yeoh, Y.K.; Liu, Q.; Zhang, L.; Zhan, H.; Lu, W.; Xu, W.; Lui, G.C.Y.; et al. Prolonged Impairment of Short-Chain Fatty Acid and L-Isoleucine Biosynthesis in Gut Microbiome in Patients With COVID-19. Gastroenterology 2022, 162, 548–561.e544.
  69. Moreira-Rosario, A.; Marques, C.; Pinheiro, H.; Araujo, J.R.; Ribeiro, P.; Rocha, R.; Mota, I.; Pestana, D.; Ribeiro, R.; Pereira, A.; et al. Gut Microbiota Diversity and C-Reactive Protein Are Predictors of Disease Severity in COVID-19 Patients. Front. Microbiol. 2021, 12, 705020.
  70. Sarkar, A.; Harty, S.; Moeller, A.H.; Klein, S.L.; Erdman, S.E.; Friston, K.J.; Carmody, R.N. The gut microbiome as a biomarker of differential susceptibility to SARS-CoV-2. Trends Mol. Med. 2021, 27, 1115–1134.
  71. Bousquet, J.; Anto, J.M.; Czarlewski, W.; Haahtela, T.; Fonseca, S.C.; Iaccarino, G.; Blain, H.; Vidal, A.; Sheikh, A.; Akdis, C.A.; et al. Cabbage and fermented vegetables: From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19. Allergy 2021, 76, 735–750.
  72. Losso, J.N.; Losso, M.N.; Toc, M.; Inungu, J.N.; Finley, J.W. The Young Age and Plant-Based Diet Hypothesis for Low SARS-CoV-2 Infection and COVID-19 Pandemic in Sub-Saharan Africa. Plant Foods Hum. Nutr. 2021, 76, 270–280.
  73. Bousquet, J.; Anto, J.M.; Iaccarino, G.; Czarlewski, W.; Haahtela, T.; Anto, A.; Akdis, C.A.; Blain, H.; Canonica, G.W.; Cardona, V.; et al. Is diet partly responsible for differences in COVID-19 death rates between and within countries? Clin. Transl. Allergy 2020, 10, 16.
  74. Ponzo, V.; Pellegrini, M.; D’Eusebio, C.; Bioletto, F.; Goitre, I.; Buscemi, S.; Frea, S.; Ghigo, E.; Bo, S. Mediterranean Diet and SARS-COV-2 Infection: Is There Any Association? A Proof-of-Concept Study. Nutrients 2021, 13, 1721.
  75. Greene, M.W.; Roberts, A.P.; Fruge, A.D. Negative Association Between Mediterranean Diet Adherence and COVID-19 Cases and Related Deaths in Spain and 23 OECD Countries: An Ecological Study. Front. Nutr. 2021, 8, 591964.
  76. Merino, J.; Joshi, A.D.; Nguyen, L.H.; Leeming, E.R.; Mazidi, M.; Drew, D.A.; Gibson, R.; Graham, M.S.; Lo, C.H.; Capdevila, J.; et al. Diet quality and risk and severity of COVID-19: A prospective cohort study. Gut 2021, 70, 2096–2104.
  77. Holick, M.F. The vitamin D deficiency pandemic: Approaches for diagnosis, treatment and prevention. Rev. Endocr. Metab. Disord. 2017, 18, 153–165.
  78. Tangpricha, V.; Pearce, E.N.; Chen, T.C.; Holick, M.F. Vitamin D insufficiency among free-living healthy young adults. Am. J. Med. 2002, 112, 659–662.
  79. Akbar, M.R.; Wibowo, A.; Pranata, R.; Setiabudiawan, B. Low Serum 25-hydroxyvitamin D (Vitamin D) Level Is Associated With Susceptibility to COVID-19, Severity, and Mortality: A Systematic Review and Meta-Analysis. Front. Nutr. 2021, 8, 660420.
  80. Vanegas-Cedillo, P.E.; Bello-Chavolla, O.Y.; Ramirez-Pedraza, N.; Rodriguez Encinas, B.; Perez Carrion, C.I.; Jasso-Avila, M.I.; Valladares-Garcia, J.C.; Hernandez-Juarez, D.; Vargas-Vazquez, A.; Antonio-Villa, N.E.; et al. Serum Vitamin D Levels Are Associated With Increased COVID-19 Severity and Mortality Independent of Whole-Body and Visceral Adiposity. Front. Nutr. 2022, 9, 813485.
  81. Ghelani, D.; Alesi, S.; Mousa, A. Vitamin D and COVID-19: An Overview of Recent Evidence. Int. J. Mol. Sci. 2021, 22, 10559.
  82. EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA). Dietary reference values for vitamin D. EFSA J. 2016, 14, e04547.
  83. Szarpak, L.; Filipiak, K.J.; Gasecka, A.; Gawel, W.; Koziel, D.; Jaguszewski, M.J.; Chmielewski, J.; Gozhenko, A.; Bielski, K.; Wroblewski, P.; et al. Vitamin D supplementation to treat SARS-CoV-2 positive patients. Evidence from meta-analysis. Cardiol. J. 2021, 29, 188–196.
  84. Tentolouris, N.; Samakidou, G.; Eleftheriadou, I.; Tentolouris, A.; Jude, E.B. The effect of vitamin D supplementation on mortality and intensive care unit admission of COVID-19 patients. A systematic review, meta-analysis and meta-regression. Diabetes Metab. Res. Rev. 2021, 38, e3517.
  85. Cara, K.C.; Beauchesne, A.R.; Li, R.; Chung, M. Cochrane Review Summary on “Vitamin D Supplementation for the Treatment of COVID-19: A Living Systematic Review”. J. Diet. Suppl. 2022, 19, 143–145.
  86. Brook, R.D.; Rajagopalan, S.; Pope, C.A., 3rd; Brook, J.R.; Bhatnagar, A.; Diez-Roux, A.V.; Holguin, F.; Hong, Y.; Luepker, R.V.; Mittleman, M.A.; et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 2010, 121, 2331–2378.
  87. Paez-Osuna, F.; Valencia-Castaneda, G.; Rebolledo, U.A. The link between COVID-19 mortality and PM2.5 emissions in rural and medium-size municipalities considering population density, dust events, and wind speed. Chemosphere 2022, 286, 131634.
  88. Ismail, I.M.I.; Rashid, M.I.; Ali, N.; Altaf, B.A.S.; Munir, M. Temperature, humidity and outdoor air quality indicators influence COVID-19 spread rate and mortality in major cities of Saudi Arabia. Environ. Res. 2022, 204, 112071.
  89. Fattorini, D.; Regoli, F. Role of the chronic air pollution levels in the COVID-19 outbreak risk in Italy. Environ. Pollut. 2020, 264, 114732.
  90. Wu, X.; Nethery, R.C.; Sabath, M.B.; Braun, D.; Dominici, F. Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Sci. Adv. 2020, 6, eabd4049.
  91. Ishmatov, A. “SARS-CoV-2 is transmitted by particulate air pollution”: Misinterpretations of statistical data, skewed citation practices, and misuse of specific terminology spreading the misconception. Environ. Res. 2022, 204, 112116.
  92. Conticini, E.; Frediani, B.; Caro, D. Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy? Environ. Pollut. 2020, 261, 114465.
  93. Tsai, D.H.; Riediker, M.; Berchet, A.; Paccaud, F.; Waeber, G.; Vollenweider, P.; Bochud, M. Effects of short- and long-term exposures to particulate matter on inflammatory marker levels in the general population. Environ. Sci. Pollut. Res. Int. 2019, 26, 19697–19704.
  94. Chen, Z.; Huang, B.Z.; Sidell, M.A.; Chow, T.; Eckel, S.P.; Pavlovic, N.; Martinez, M.P.; Lurmann, F.; Thomas, D.C.; Gilliland, F.D.; et al. Near-roadway air pollution associated with COVID-19 severity and mortality—Multiethnic cohort study in Southern California. Environ. Int. 2021, 157, 106862.
  95. The Consequences of Inaction. OECD Environmental Outlook to 2050. 2012. Available online: https://www.oecd.org/g20/topics/energy-environment-green-growth/oecdenvironmentaloutlookto2050theconsequencesofinaction.htm (accessed on 11 May 2022).
  96. Quinete, N.; Hauser-Davis, R.A. Drinking water pollutants may affect the immune system: Concerns regarding COVID-19 health effects. Environ. Sci. Pollut. Res. Int. 2021, 28, 1235–1246.
  97. Chain, E.; Schrenk, D.; Bignami, M.; Bodin, L.; Chipman, J.K.; Del Mazo, J.; Grasl-Kraupp, B.; Hogstrand, C.; Hoogenboom, L.R.; Leblanc, J.C.; et al. Risk to human health related to the presence of perfluoroalkyl substances in food. EFSA J. 2020, 18, e06223.
  98. Catelan, D.; Biggeri, A.; Russo, F.; Gregori, D.; Pitter, G.; Da Re, F.; Fletcher, T.; Canova, C. Exposure to Perfluoroalkyl Substances and Mortality for COVID-19: A Spatial Ecological Analysis in the Veneto Region (Italy). Int. J. Environ. Res. Public Health 2021, 18, 2734.
  99. Ji, J.; Song, L.; Wang, J.; Yang, Z.; Yan, H.; Li, T.; Yu, L.; Jian, L.; Jiang, F.; Li, J.; et al. Association between urinary per- and poly-fluoroalkyl substances and COVID-19 susceptibility. Environ. Int. 2021, 153, 106524.
  100. Grandjean, P.; Timmermann, C.A.G.; Kruse, M.; Nielsen, F.; Vinholt, P.J.; Boding, L.; Heilmann, C.; Molbak, K. Severity of COVID-19 at elevated exposure to perfluorinated alkylates. PLoS ONE 2020, 15, e0244815.
  101. Nielsen, C.; Joud, A. Susceptibility to COVID-19 after High Exposure to Perfluoroalkyl Substances from Contaminated Drinking Water: An Ecological Study from Ronneby, Sweden. Int. J. Environ. Res. Public Health 2021, 18, 10702.
  102. Neagu, M.; Constantin, C.; Bardi, G.; Duraes, L. Adverse outcome pathway in immunotoxicity of perfluoroalkyls. Curr. Opin. Toxicol. 2021, 25, 23–29.
  103. WHO Solidarity Trial Consortium; Pan, H.; Peto, R.; Henao-Restrepo, A.M.; Preziosi, M.P.; Sathiyamoorthy, V.; Abdool Karim, Q.; Alejandria, M.M.; Hernandez Garcia, C.; Kieny, M.P.; et al. Repurposed Antiviral Drugs for Covid-19—Interim WHO Solidarity Trial Results. N. Engl. J. Med. 2021, 384, 497–511.
  104. Serra, A.; Fratello, M.; Federico, A.; Ojha, R.; Provenzani, R.; Tasnadi, E.; Cattelani, L.; Del Giudice, G.; Kinaret, P.A.S.; Saarimaki, L.A.; et al. Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation. Brief Bioinform. 2022, 23, bbab507.
  105. Morselli Gysi, D.; do Valle, I.; Zitnik, M.; Ameli, A.; Gan, X.; Varol, O.; Ghiassian, S.D.; Patten, J.J.; Davey, R.A.; Loscalzo, J.; et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc. Natl. Acad. Sci. USA 2021, 118, e2025581118.
  106. Pavel, A.; Del Giudice, G.; Federico, A.; Di Lieto, A.; Kinaret, P.A.S.; Serra, A.; Greco, D. Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment. Brief Bioinform. 2021, 22, 1430–1441.
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