Inward Bound - The T'ai Chi Corner

Inward Bound - The T'ai Chi Corner
thelivyjr
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Re: Inward Bound - The T'ai Chi Corner

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NCBI

US National Library of Medicine

National Institutes of Health

Nature Public Health Emergency Collection

PMC7097533

Nat Immunol. 2007; 8(11): 1188–1193.

Published online 2007 Oct 19. doi: 10.1038/ni1530

PMCID: PMC7097533

PMID: 17952044

Protective immunity and susceptibility to infectious diseases: lessons from the 1918 influenza pandemic

Rafi Ahmed, corresponding author, Michael B A Oldstone, and Peter Palese

Abstract

The influenza pandemic of 1918 killed nearly 50 million people worldwide and was characterized by an atypical W-shaped mortality curve, where adults between the ages of 30–60 years fared better than younger adults aged 18–30 years.

In this review, we will discuss why this influenza virus strain was so virulent and how immunological memory to the 1918 virus may have shaped the W mortality curve.


We will end on the topic of the 'honeymoon' period of infectious diseases — the clinically documented period between the ages of 4–13 years during which children demonstrate less morbidity and/or mortality to infectious diseases, in general, compared with young adults.

Main

The very young and the very old are the most susceptible to infectious diseases.

One of the main reasons for this is that the young are often immunologically naive and the old are undergoing immune senescence.


This pattern of susceptibility is characteristic of most infections (Fig. 1a).

These data show the death rate from influenza virus during the years 1911–1915 and illustrate the typical U-shaped curve of mortality as a function of age.

These historical data from 1911–1915 highlight the markedly different mortality curve that was observed during the influenza pandemic of 1918 that killed over 50 million people worldwide, making it one of the deadliest plagues ever experienced by mankind (Fig. 1b) 1.

The most notable difference between the mortality curves of 1918 compared with those of 1911–1915 is that the 1918 pandemic was particularly deadly for young adults between the ages of 18–30, whereas, quite surprisingly, adults in the 30–60-year-old age group fared better.

As expected, the very young (<2 years) and the elderly (>70 years) had a high mortality rate.


This pattern of susceptibility resulted in the unique W mortality curve of the 1918 influenza pandemic.

There has been much debate about the reasons for this W-shaped curve and why the young adults were more susceptible than the >30-year-old adults.

Because many of these deaths were among young men fighting in World War I, it has been suggested that battle conditions (stress, fatigue, chemical exposure, etc.) may have weakened the soldiers' immune systems, thereby increasing their vulnerability to disease.

However, similar mortality rates were seen in young men and women not involved in the war.

Thus, one must consider the possibility that the >30 year olds may have had some degree of protective immunity against the 1918 influenza virus pandemic strain and that this immunity was lacking in the younger adults (18–30 year olds) (Fig. 1c).

In this review, we will address this issue and consider how immunological memory may have shaped the W mortality curve of the 1918 influenza pandemic.

We will also discuss why this 1918 pandemic flu strain was so virulent.


Finally, we will end on one of the great mysteries of infectious diseases: why did children (ages 4–12) fare much better than young adults did during the 1918 influenza pandemic?

Virulence of the influenza pandemic strain

Influenza viruses belong to the orthomyxovirus family and come in three types: A, B and C.

Only influenza A and B viruses are important for causing disease in humans.

These viruses have a negative-sense, segmented RNA genome and can code for up to 11 proteins 2.

By virtue of possessing a segmented genome, influenza viruses can easily reassort (exchange RNA segments between human and animal viruses), and thereby acquire new antigenic properties (antigenic shift).

The fact that influenza viruses have an error-prone RNA-dependent RNA polymerase explains the fact that mutations occur frequently and that, through selection, new antigenic variants emerge (antigenic drift).


The 1918 virus was responsible for one of the most devastating pandemics in recorded history, and a question of great interest has been why this particular influenza virus strain was so virulent.

A major breakthrough toward addressing this question was made when available pathology materials from patients who had died during the 1918 pandemic were used to obtain the entire sequence of the 1918 virus 3 and to subsequently reconstruct the extinct strain in the laboratory using reverse genetics 4,5.

The virus turned out to be highly virulent in intranasally inoculated mice, with a lethal dose 50 (LD50) that was more than 1,000-fold lower than that of other human (non–mouse adapted) influenza virus strains.

In embryonated eggs, the 1918 virus was 106-fold more virulent than the human control strain, as measured by the dose required to kill an 8-d-old embryo, and the 1918 influenza strain grew to titers that were at least one log unit higher than those of control influenza viruses in tissue culture of human bronchial epithelial cells 4.

Further studies in mice showed excessive immune-cell infiltration in the lung following infection with viruses containing genes from the 1918 strain and higher lung virus titers than in the controls.

Specifically, an increased influx of neutrophils and alveolar macrophages and an increase in the production of cytokines and chemokines were observed in lung tissues with a virus expressing only two proteins, hemagglutinin (HA) and neuraminidase (NA), from the 1918 strain 6.

In mice infected with a virus expressing all eight genes from the 1918 virus, a marked activation of pro-inflammatory and cell death pathways was observed, which was less pronounced in reassortant viruses that only contained a subset of genes from the 1918 virus 7.

A question of substantial interest is whether the enhanced production of inflammatory cytokines and associated pathology that was seen after infection with the 1918 pandemic influenza virus strain is due to some specific interactions of the viral genes of the 1918 virus with the immune system or if this is primarily a reflection of the rapid growth and spread of this virus.

The two possibilities are not mutually exclusive, and it is conceivable that both contribute to the complex pathogenesis that is seen in vivo.

Pathogenicity of the pandemic strain was also studied in the cynomolgus macaque (Macaca fascicularis) model.

Macaques infected with the 1918 virus became symptomatic within 24 h of infection and had to be euthanized by day 8 as a result of the severity of the symptoms.

The animals showed severe respiratory signs, with an increase in respiration rate and a decrease in lung function, as measured by a decrease in blood oxygen saturation.

Also, interleukin-6 (IL-6), IL-8 and the chemokines CCL2 (monocyte chemotactic protein 1) and CCL5 (RANTES) were elevated in infected animals.

Notably, compared with a control non-1918 influenza virus, the 1918 virus demonstrated reduced activation of the RNA helicase sensor proteins RIG-I and MDA-5 in infected macaques.

These data suggest that the NS1 protein of the 1918 virus, which is an interferon antagonist, has an important immunomodulatory role.

By effectively downregulating the innate immune response of the host, the NS1 protein may very well have contributed to the extraordinary virulence of the 1918 virus in humans.

Although one can measure the contribution to virulence of individual genes of the 1918 virus (as, for example, in the case of the 1918 virus NS1 gene and the 1918 HA and NA genes), it appears that the interplay — or combination of the natural biological functions — of all eight 1918 genes results in a virus with the highest virulence.

Thus, the 1918 virus is a unique influenza virus strain by virtue of its 'matching' genes or because of genes that express viral proteins that affect hundreds of cellular proteins during replication.

By the same token, any reassortment of genes in the 1918 virus with RNAs from other influenza viruses has, in most cases, led to a decrease in virulence, highlighting the extraordinary gene constellation of the 1918 virus 4,5,6,7.


Genetic variation in influenza virus and immune memory

Prior to addressing the important issue of immunological memory and the 1918 pandemic influenza virus strain, it is essential to first consider the degree of genetic variation in influenza viruses and the epidemiology of the various influenza virus strains that have been in circulation among the human population.

A hallmark of influenza viruses is their ability to undergo genetic shift and drift.

Specifically, reassortment can lead to influenza viruses acquiring RNA segments, most likely from avian influenza viruses, that can lead to new pandemic (globally epidemic) strains.

The pandemic 1957 strain sported a new HA (subtype 2) and a new NA (subtype 2) and caused worldwide morbidity and mortality.

In 1968, a new pandemic strain had only the HA (subtype 3) exchanged, and in 1977 an H1N1 virus appeared that had circulated around 1950 in the human population (Fig. 2).

In 1977, it was mostly young people born after the end of the H1 period (1957 and later) that came down with the disease when infected with this new (recycled) virus.

Individuals older than 20–25 years of age had ostensibly been exposed to similar H1 strains and were thus partially protected.


It is likely that an antigenic shift also occurred in 1918, when an H1N1 virus caused the major pandemic of the 20th century (Fig. 2) 4.

As for the subtype strain circulating before 1918, only indirect evidence from serologic patient data are available that suggest an H3-like virus circulated in humans starting in 1889 (ref. 8).

Viruses circulating before 1889 were postulated to be of the H1 subtype 9,10,11,12.

In each case in 1889, 1918, 1957, 1968 and 1977, a large segment of the population lacked protective antibodies against these previously unknown (reassortant) viruses, and it is thought that this single antigenic shift is the single most important factor responsible for the outbreaks of pandemics.

However, influenza viruses also undergo antigenic drift and can change their surface glycoproteins by accumulation of nucleotide mutations in the glycoprotein gene.

Such drift variants can re-infect and cause disease in individuals who were infected just 2–4 years earlier with a virus belonging to the same subtype.

Why influenza viruses undergo antigenic drift remains unclear.

Measles and mumps viruses are also negative-sense RNA viruses and their RNA-dependent RNA polymerases are probably as error-prone as that of influenza virus.

However, these viruses stay more or less the same antigenically, as evidenced by our present day use of measles and mumps vaccine strains that were first introduced in humans in the 1960s.


Although we have no satisfactory explanation for the molecular basis of the continuing antigenic change in influenza viruses, we nevertheless recognize this by changing the vaccine formulation of the three influenza virus components on an annual or biannual schedule.

Thus, the trivalent influenza virus vaccine for the 2007–2008 season contains A/Wisconsin/67/2005(H3N2), A/Solomon Islands/3/2006(H1N1) and B/Malaysia/2506/2006) components.

As a direct demonstration of the consequences of antigenic drift in influenza, the A/Solomon Islands/3/2006 (H1N1) virus isolated in 2006 replaced the A/New Caledonia/20/1999 (H1N1) virus in the vaccine preparations from the previous seasons; the latter virus was first isolated in 1999, and thus does not adequately protect against the new antigenic drift variants circulating in the human population at the present time.

Immunological memory to the 1918 influenza virus

Why were adults in the 30–60-year-old group more resistant to the 1918 pandemic influenza virus strain than the 18–30-year-old young adults?

Did people older than 30 years in 1918 have some level of protective immunity to the influenza virus pandemic strain, and could this immune memory explain the W-shaped 1918 mortality curve?

If, as postulated (Fig. 2), an H3 influenza strain was in circulation from 1889–1918 and H1-type viruses were present before 1889, then people born in or after 1889 would have been immunologically naive to the 1918 H1 pandemic strain (that is, at least to the HA of the 1918 H1 strain).

In contrast, individuals born before 1889 (>30 year olds in 1918) would have had prior exposure to H1-type influenza viruses.


How would this encounter have resulted in protective immunity 30 years later?

The viral proteins that are immunologically relevant for protective antibody responses are HA and, to a much lesser extent, NA13,14,15, both of which are viral surface glycoproteins, and are thus targets for protective antibodies.

Pre-existing antibody is the first level of defense against pathogens, and if there were individuals in 1918 with circulating HA-specific antibody that was reactive against the H1 pandemic strain, then those individuals would have fared better during the pandemic.

It is now well-established that circulating antibody can be detected in the serum for decades after acute viral infections and even after some subunit protein vaccines, such as tetanus and diphtheria 16,17,18.

Thus, it is plausible that some of the individuals in the 30–60-year-old group still had some circulating antibody against the pandemic strain.

Several studies have now shown that one of the major mechanisms for maintaining antibody levels in the serum for extended periods of time is the long-lived plasma cell that resides in the bone marrow 16,17,19,20,21.

Plasma cells are end-stage differentiated cells that constitutively produce antibody in the absence of antigen.


Antigen is, of course, needed for the differentiation of naive or memory B cells into antibody-secreting cells, but it is not required for maintaining antibody production by fully differentiated plasma cells.

Not all plasma cells are long-lived, but a proportion of these cells can live for extended periods of time in the bone marrow and constitute the major source of long-term antibody production after infection or vaccination.

These long-lived plasma cells are not only the major source of antibody in the serum, but can also contribute to antibody in the mucosa by the process of transudation.


In addition to plasma cells, memory B cells can also be involved in protective immunity by making rapid recall responses and producing high-affinity antibody 17,22.

Memory B cells cannot prevent infection, but can control the spread of virus infection by rapidly differentiating into antibody-secreting cells and producing antibody that neutralizes the virus.

A notable feature of the memory B cell response is its longevity.

Several studies have demonstrated that memory B cells induced by our commonly used childhood vaccines (tetanus, measles, polio, etc.) persist for years in humans 16,17,18,22.


In one of the most striking examples, it was shown that memory B cells generated after smallpox vaccination were still detectable 40–50 years after immunization 23,24.

This is particularly noteworthy, as smallpox was eradicated in the 1970s and smallpox–specific memory B cells were maintained for >30 years in the absence of re-exposure to the pathogen.

In light of these extensive studies demonstrating the longevity of human memory B cells, it is very likely that individuals who were exposed to the H1 virus in 1889 or earlier would have still have had some memory B cells that were specific for H1 influenza virus in 1918, and it is possible that these memory B cells also contributed toward protective immunity against the pandemic flu strain.

Immune memory and protective immunity against infectious diseases consist of three key components: pre-existing antibodies in the blood and at mucosal sites, memory B cells and memory T cells.

Both CD4+ and CD8+ memory T cells provide a critical second line of defense against pathogens as a result of their higher numbers (compared with their naive counterparts), faster responses (can elaborate effector functions much faster than naive T cells) and better location (present in both lymphoid and nonlymphoid tissues) 22,25.


Could memory T cells have had any role in protection against the 1918 pandemic strain?

Infection with influenza virus generates a broad range of CD4+ and CD8+ T cells that are reactive against most of the viral proteins 13, and many of these T cell epitopes are conserved across the various influenza virus strains.

Substantial progress has been made in understanding the mechanisms by which influenza virus–specific T cells control infection in the lung (reviewed in refs. 13,26).

Also, several studies in animal models have shown that memory T cells do contribute to protective immunity against influenza virus, and there are also human clinical data that are consistent with this notion 13,27,28.

But given the extreme virulence of the pandemic flu strain and the rapid appearance of clinical disease after infection with this virus, it is unlikely that memory T cells alone would have been of much benefit during the pandemic.

However, in individuals that still had residual humoral immunity against the H1 virus, memory T cells could have acted in concert with the H1-specific plasma cells and memory B cells to confer some degree of protection against the pandemic flu strain.

Infectious diseases and the honeymoon period

The influenza epidemic reached Alaska by the end of 1918 and took a terrible toll on the local population.

Because of the geographic isolation of Alaska, it is likely that most of the natives had not been exposed to the 1889 H1 influenza virus and, consequently, a large percentage of the local population was immunologically naive.

As a result of this, the Alaskan natives showed almost no resistance to the H1N1 pandemic strain, and there were many instances where villages lost their entire adult population (the W mortality curve was not observed among these isolated populations).


Notably, the only survivors were the children in some of these villages.

A photograph of the 'Flu Orphans' is shown in Figure 3.

Why did the children survive and the parents die during this epidemic?

This pattern of susceptibility, so dramatically illustrated among the immunologically naive population of Alaska, was also seen in other parts of the world.

The general trend was that children between the ages of 4 and 12 showed a substantially decreased mortality rate during the 1918 pandemic (Fig. 1 and Table 1).

It should be emphasized that these children were not protected from infection, but, for reasons that are as mysterious today as they were in 1918, they were able to cope with the disease much better than their adult counterparts.

This pattern of disease susceptibility, where children fare better than adults, is not unique to influenza virus and is also seen in other infections.

A classic example is that of tuberculosis, where it is well documented that children between the ages of 5 and 14 have a lower clinical case rate compared with any other segment of the population (Fig. 4) 10,29.

In fact, in the older German literature, this age period (5–14 years of age) is referred to as the 'favorable school age period'.

Similarly, morbidity and mortality to several viruses, such as mumps, measles, Varicella-Zoster virus (chicken pox, VZV), poliomyelitis, Epstein Barr virus (EBV) and hepatitis E virus (HEV), are much more pronounced if the infection is acquired for the first time as an adult (or during adolescence) than they are if the infection is acquired as a child 29,30.

The severe manifestations of EBV infection (for example, infectious mononucleosis) are rarely, if ever, seen in children.

Also, chicken pox is a relatively mild disease, but it can be disfiguring and even life threatening if the infection is first acquired as an adult.

It is also worth noting that in the 2003 severe acute respiratory syndrome (SARS) epidemic, the death rate was much lower in children than in adults 31,32.

Why do children cope with these various infectious agents better than adults?

What are the reasons for this honeymoon period with infectious diseases?

Disease is usually the result of direct damage to the host by the pathogen.

However, the immune response generated against the pathogen can end up causing immunopathological damage and exacerbating the disease 33,34.

There is a delicate balance between the protective and pathogenic aspects of an immune response, and it is possible that the regulation of this critical balance is different between children and adults such that beneficial responses are favored over harmful ones in children.


Given the complex nature of these immune interactions and the fine balance between protective and pathogenic responses, it is possible that even subtle differences in regulation could have profound effects on the clinical outcome.

It would be interesting to see whether there are differences in the generation of regulatory T cells, in the expression of inhibitory receptors such as PD-1, or in the production of cytokines such as IL-10 or IL-17 that modulate immune responses to pathogens in children versus adults following infection 35,36,37,38,39,40,41,42.

It is also possible that children fare better against infectious diseases because they have a greater regenerative capacity for the immune system — their thymuses and bone marrow more actively produce immune cells — and also for other tissues, thereby resulting in faster repair of damaged organs.

It is important to note that this change in disease susceptibility occurs around the time of puberty and it is possible that sex-associated hormones are involved in this transition 43.

The outcome of viral infections is greatly influenced by early innate responses: in particular, the production of type 1 interferons that not only provide a critical early check on viral growth, but also activate natural killer cells and enhance the development of specific immune responses 44,45.

It is conceivable that the type 1 interferon response after viral infection is more efficient in children than in adults.


From this perspective, it would be interesting to examine Toll-like receptor or MDA-5/RIG-I expression on dendritic cells from children versus adults and to look at the numbers and function of plasmacytoid dendritic cells, the major interferon-producing cells 46,47.

Also, it would be useful to quantitate antigen-specific T and B cell responses and to determine if the quality of these specific responses is different between adults and children.

In fact, a recent study analyzing the immune response to the human papilloma virus vaccine has shown that pre-adolescent girls (9–12 years old) made higher antibody responses than 18–23-year-old young women 48,49 (Fig. 5).

Although the phenomenon of the honeymoon period has been recognized for nearly a hundred years, there have been few, if any, studies directly addressing this issue.

It is important to try and understand the underlying mechanisms of this pattern of disease susceptibility.

It should be possible to address some of the questions directly in human studies, but it will also be necessary to start developing small animal models to carry out more mechanistic studies.

Also, valuable information and insight will come from studies in nonhuman primates using the same pathogens that have shown a difference in their ability to cause disease in children versus adults.

The knowledge gained from these studies will provide a better understanding of host-pathogen interactions and better prepare us for dealing with future epidemics and emerging infections.

Acknowledgements

We thank S. Sarkar for discussions and for his help in preparing the manuscript.

We also thank D. Lewis for helpful discussions about disease susceptibility in children.

We are grateful to E. Barr and M. Esser of Merck Research Laboratories for providing the immunogenicity data on the HPV vaccine.

References

1. Johnson NP, Mueller J. Updating the accounts: global mortality of the 1918–1920 “Spanish” influenza pandemic. Bull. Hist. Med. 2002;76:105–115. doi: 10.1353/bhm.2002.0022.

2. Palese P, Shaw ML. Fields' Virology. 2007. Orthomyxoviridae: the viruses and their replication.

3. Taubenberger JK, et al. Characterization of the 1918 influenza virus polymerase genes. Nature. 2005;437:889–893. doi: 10.1038/nature04230.

4. Tumpey TM, et al. Characterization of the reconstructed 1918 Spanish influenza pandemic virus. Science. 2005;310:77–80. doi: 10.1126/science.1119392.

5. Kobasa D, et al. Aberrant innate immune response in lethal infection of macaques with the 1918 influenza virus. Nature. 2007;445:319–323. doi: 10.1038/nature05495.

6. Tumpey TM, et al. Pathogenicity of influenza viruses with genes from the 1918 pandemic virus: functional roles of alveolar macrophages and neutrophils in limiting virus replication and mortality in mice. J. Virol. 2005;79:14933–14944. doi: 10.1128/JVI.79.23.14933-14944.2005.

7. Kash JC, et al. Genomic analysis of increased host immune and cell death responses induced by 1918 influenza virus. Nature. 2006;443:578–581. doi: 10.1038/nature05181.

8. Dowdle WR. Influenza A virus recycling revisited. Bull. World Health Organ. 1999;77:820–828.

9. Crosby AW. America's Forgotten Pandemic: The Influenza of 1918. 1989.

10. Langford C. The age pattern of mortality in the 1918–1919 influenza pandemic: an attempted explanation based on data for England and Wales. Med. Hist. 2002;46:1–20.

11. Palese P. Influenza: old and new threats. Nat. Med. 2004;10:S82–S87. doi: 10.1038/nm1141.

12. Palese P, Tumpey TM, Garcia-Sastre A. What can we learn from reconstructing the extinct 1918 pandemic influenza virus? Immunity. 2006;24:121–124. doi: 10.1016/j.immuni.2006.01.007.

13. Doherty PC, Turner SJ, Webby RG, Thomas PG. Influenza and the challenge for immunology. Nat. Immunol. 2006;7:449–455. doi: 10.1038/ni1343.

14. Horimoto T, Kawaoka Y. Influenza: lessons from past pandemics, warnings from current incidents. Nat. Rev. Microbiol. 2005;3:591–600. doi: 10.1038/nrmicro1208.

15. Sandbulte MR, et al. Cross-reactive neuraminidase antibodies afford partial protection against H5N1 in mice and are present in unexposed humans. PLoS Med. 2007;4:e59. doi: 10.1371/journal.pmed.0040059.

16. Amanna IJ, Slifka MK, Crotty S. Immunity and immunological memory following smallpox vaccination. Immunol. Rev. 2006;211:320–337. doi: 10.1111/j.0105-2896.2006.00392.x.

17. Bernasconi NL, Traggiai E, Lanzavecchia A. Maintenance of serological memory by polyclonal activation of human memory B cells. Science. 2002;298:2199–2202. doi: 10.1126/science.1076071.

18. Plotkin SA, Orenstein WA. Vaccine. 2004.

19. Crotty S, Kersh EN, Cannons J, Schwartzberg PL, Ahmed R. SAP is required for generating long-term humoral immunity. Nature. 2003;421:282–287. doi: 10.1038/nature01318.

20. Manz RA, Thiel A, Radbruch A. Lifetime of plasma cells in the bone marrow. Nature. 1997;388:133–134. doi: 10.1038/40540.

21. Slifka MK, Antia R, Whitmire JK, Ahmed R. Humoral immunity due to long-lived plasma cells. Immunity. 1998;8:363–372. doi: 10.1016/S1074-7613(00)80541-5.

22. Kalia V, Sarkar S, Gourley TS, Rouse BT, Ahmed R. Differentiation of memory B and T cells. Curr. Opin. Immunol. 2006;18:255–264. doi: 10.1016/j.coi.2006.03.020.

23. Crotty S, et al. Cutting edge: long-term B cell memory in humans after smallpox vaccination. J. Immunol. 2003;171:4969–4973. doi: 10.4049/jimmunol.171.10.4969.

24. Hammarlund E, et al. Duration of antiviral immunity after smallpox vaccination. Nat. Med. 2003;9:1131–1137. doi: 10.1038/nm917.

25. Ahmed R, Rouse BT. Immunological memory. Immunol. Rev. 2006;211:5–7. doi: 10.1111/j.0105-2896.2006.00424.x.

26. Hikono H, et al. T-cell memory and recall responses to respiratory virus infections. Immunol. Rev. 2006;211:119–132. doi: 10.1111/j.0105-2896.2006.00385.x.

27. McMichael AJ, Gotch FM, Noble GR, Beare PA. Cytotoxic T-cell immunity to influenza. N. Engl. J. Med. 1983;309:13–17. doi: 10.1056/NEJM198307073090103.

28. Swain SL, et al. CD4+ T-cell memory: generation and multi-faceted roles for CD4+ T cells in protective immunity to influenza. Immunol. Rev. 2006;211:8–22. doi: 10.1111/j.0105-2896.2006.00388.x.

29. Feigin RD, Cherry J, Demmler GJ, Kaplan S. Textbook of Pediatric Infectious Diseases. 2004.

30. Svitil, K.A. Discover dialogue: virologist David Baltimore. The danger of getting sick from this disease in the United States is trivial. Discover, 1 August 2003 (http://discovermagazine.com/2003/aug/featdialogue).

31. Stadler K, et al. SARS—beginning to understand a new virus. Nat. Rev. Microbiol. 2003;1:209–218. doi: 10.1038/nrmicro775.

32. Finlay BB, See RH, Brunham RC. Rapid response research to emerging infectious diseases: lessons from SARS. Nat. Rev. Microbiol. 2004;2:602–607. doi: 10.1038/nrmicro930.

33. Zinkernagel RM, Hengartner H. Protective 'immunity' by pre-existent neutralizing antibody titers and preactivated T cells but not by so-called 'immunological memory'. Immunol. Rev. 2006;211:310–319. doi: 10.1111/j.0105-2896.2006.00402.x.

34. Oldstone MB. The role of cytotoxic T lymphocytes in infectious disease: history, criteria and state of the art. Curr. Top. Microbiol. Immunol. 1994;189:1–8.

35. Brooks DG, et al. Interleukin-10 determines viral clearance or persistence in vivo. Nat. Med. 2006;12:1301–1309. doi: 10.1038/nm1492.

36. Ejrnaes M, et al. Resolution of a chronic viral infection after interleukin-10 receptor blockade. J. Exp. Med. 2006;203:2461–2472. doi: 10.1084/jem.20061462.

37. Liston A, Rudensky AY. Thymic development and peripheral homeostasis of regulatory T cells. Curr. Opin. Immunol. 2007;19:176–185. doi: 10.1016/j.coi.2007.02.005.

38. Shevach EM, et al. The lifestyle of naturally occurring CD4+ CD25+ Foxp3+ regulatory T cells. Immunol. Rev. 2006;212:60–73. doi: 10.1111/j.0105-2896.2006.00415.x.

39. Miyara M, Sakaguchi S. Natural regulatory T cells: mechanisms of suppression. Trends Mol. Med. 2007;13:108–116. doi: 10.1016/j.molmed.2007.01.003.

40. Bettelli E, Oukka M, Kuchroo VKT. (H)-17 cells in the circle of immunity and autoimmunity. Nat. Immunol. 2007;8:345–350. doi: 10.1038/ni0407-345.

41. Barber DL, et al. Restoring function in exhausted CD8 T cells during chronic viral infection. Nature. 2006;439:682–687. doi: 10.1038/nature04444.

42. Sharpe AH, Wherry EJ, Ahmed R, Freeman GJ. The function of programmed cell death 1 and its ligands in regulating autoimmunity and infection. Nat. Immunol. 2007;8:239–245. doi: 10.1038/ni1443.

43. Roberts CW, Walker W, Alexander J. Sex-associated hormones and immunity to protozoan parasites. Clin. Microbiol. Rev. 2001;14:476–488. doi: 10.1128/CMR.14.3.476-488.2001.

44. Garcia-Sastre A, Biron CA. Type 1 interferons and the virus-host relationship: a lesson in detente. Science. 2006;312:879–882. doi: 10.1126/science.1125676.

45. Kolumam GA, Thomas S, Thompson LJ, Sprent J, Murali-Krishna K. Type I interferons act directly on CD8 T cells to allow clonal expansion and memory formation in response to viral infection. J. Exp. Med. 2005;202:637–650. doi: 10.1084/jem.20050821.

46. van Duin D, Medzhitov R, Shaw AC. Triggering TLR signaling in vaccination. Trends Immunol. 2006;27:49–55. doi: 10.1016/j.it.2005.11.005.

47. Pulendran B, Ahmed R. Translating innate immunity into immunological memory: implications for vaccine development. Cell. 2006;124:849–863. doi: 10.1016/j.cell.2006.02.019.

48. Reisinger KS, et al. Safety and persistent immunogenicity of a quadrivalent human papillomavirus types 6, 11, 16, 18 L1 virus–like particle vaccine in preadolescents and adolescents: a randomized controlled trial. Pediatr. Infect. Dis. J. 2007;26:201–209. doi: 10.1097/01.inf.0000253970.29190.5a.

49. Giuliarno AR, et al. Impact of baseline covariates on the immunogenicity of a quadrivariant (Type 6, 11, 16, 18) Human Papilloma Virus–like particle vaccine. J. Infect. Dis. 2007;196:1153–1162. doi: 10.1086/521679.

50. Linder, F.E. & Grove, R.D. Vital statistics rates in the United States: 1900–1940. (US Government Printing Office, Washington, D.C., 1947) http://www.cdc.gov/nchs/data/vsus/vsrates1900_40.pdf.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7097533/

thelivyjr
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Re: Inward Bound - The T'ai Chi Corner

Post by thelivyjr » Mon Jun 01, 2020 1:40 p

WILEY

Exercise effects on mucosal immunity


Maree Gleeson, David B Pyne

First published: 01 October 2000

https://doi.org/10.1111/j.1440-1711.2000.t01-8-.x

Dr Maree Gleeson, Hunter Immunology Unit, Royal Newcastle Hospital, PO Box 664J, Newcastle, NSW 2300, Australia.

Email: mgleeson@mail.newcastle.edu.au

Abstract

The present review examines the effects of exercise on mucosal immunity in recreational and elite athletes and the role of mucosal immunity in respiratory illness.

Habitual exercise at an intense level can cause suppression of mucosal immune parameters, while moderate exercise may have positive effects.


Saliva is the most commonly used secretion for measurement of secretory antibodies in the assessment of mucosal immune status.

Salivary IgA and IgM concentrations decline immediately after a bout of intense exercise, but usually recover within 24 h.

Training at an intense level over many years can result in a chronic suppression of salivary immunoglobulin levels.

The degree of immune suppression and the recovery rates after exercise are associated with the intensity of exercise and the duration or volume of the training.

Low levels of salivary IgM and IgA, particularly the IgA1 subclass, are associated with an increased risk of respiratory illness in athletes.

Monitoring mucosal immune parameters during critical periods of training provides an assessment of the upper respiratory tract illness risk status of an individual athlete.

The mechanisms underlying the mucosal immune suppression are unknown.

https://onlinelibrary.wiley.com/doi/ful ... 0.t01-8-.x

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Post by thelivyjr » Mon Jun 01, 2020 1:40 p

NEJM

Asymptomatic Transmission, the Achilles’ Heel of Current Strategies to Control Covid-19


Monica Gandhi, M.D., M.P.H., Deborah S. Yokoe, M.D., M.P.H., and Diane V. Havlir, M.D.

May 28, 2020

N Engl J Med 2020; 382:2158-2160

DOI: 10.1056/NEJMe2009758

Traditional infection-control and public health strategies rely heavily on early detection of disease to contain spread.

When Covid-19 burst onto the global scene, public health officials initially deployed interventions that were used to control severe acute respiratory syndrome (SARS) in 2003, including symptom-based case detection and subsequent testing to guide isolation and quarantine.

This initial approach was justified by the many similarities between SARS-CoV-1 and SARS-CoV-2, including high genetic relatedness, transmission primarily through respiratory droplets, and the frequency of lower respiratory symptoms (fever, cough, and shortness of breath) with both infections developing a median of 5 days after exposure.

However, despite the deployment of similar control interventions, the trajectories of the two epidemics have veered in dramatically different directions.


Within 8 months, SARS was controlled after SARS-CoV-1 had infected approximately 8100 persons in limited geographic areas.

Within 5 months, SARS-CoV-2 has infected more than 2.6 million people and continues to spread rapidly around the world.

What explains these differences in transmission and spread?

A key factor in the transmissibility of Covid-19 is the high level of SARS-CoV-2 shedding in the upper respiratory tract,1 even among presymptomatic patients, which distinguishes it from SARS-CoV-1, where replication occurs mainly in the lower respiratory tract.2


Viral loads with SARS-CoV-1, which are associated with symptom onset, peak a median of 5 days later than viral loads with SARS-CoV-2, which makes symptom-based detection of infection more effective in the case of SARS CoV-1.3

With influenza, persons with asymptomatic disease generally have lower quantitative viral loads in secretions from the upper respiratory tract than from the lower respiratory tract and a shorter duration of viral shedding than persons with symptoms, 4 which decreases the risk of transmission from paucisymptomatic persons (i.e., those with few symptoms).

Arons et al. now report in the Journal an outbreak of Covid-19 in a skilled nursing facility in Washington State where a health care provider who was working while symptomatic tested positive for infection with SARS-CoV-2 on March 1, 2020. 5

Residents of the facility were then offered two facility-wide point-prevalence screenings for SARS-CoV-2 by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) of nasopharyngeal swabs on March 13 and March 19–20, along with collection of information on symptoms the residents recalled having had over the preceding 14 days.

Symptoms were classified into typical (fever, cough, and shortness of breath), atypical, and none.

Among 76 residents in the point-prevalence surveys, 48 (63%) had positive rRT-PCR results, with 27 (56%) essentially asymptomatic, although symptoms subsequently developed in 24 of these residents (within a median of 4 days) and they were reclassified as presymptomatic.

Quantitative SARS-CoV-2 viral loads were similarly high in the four symptom groups (residents with typical symptoms, those with atypical symptoms, those who were presymptomatic, and those who remained asymptomatic).

It is notable that 17 of 24 specimens (71%) from presymptomatic persons had viable virus by culture 1 to 6 days before the development of symptoms.

Finally, the mortality from Covid-19 in this facility was high; of 57 residents who tested positive, 15 (26%) died.

An important finding of this report is that more than half the residents of this skilled nursing facility (27 of 48) who had positive tests were asymptomatic at testing.

Moreover, live coronavirus clearly sheds at high concentrations from the nasal cavity even before symptom development.


Although the investigators were not able to retrospectively elucidate specific person-to-person transmission events and although symptom ascertainment may be unreliable in a group in which more than half the residents had cognitive impairment, these results indicate that asymptomatic persons are playing a major role in the transmission of SARS-CoV-2.

Symptom-based screening alone failed to detect a high proportion of infectious cases and was not enough to control transmission in this setting.

The high mortality (>25%) argues that we need to change our current approach for skilled nursing facilities in order to protect vulnerable, enclosed populations until other preventive measures, such as a vaccine or chemoprophylaxis, are available.

A new approach that expands Covid-19 testing to include asymptomatic persons residing or working in skilled nursing facilities needs to be implemented now.

Despite “lockdowns” in these facilities, coronavirus outbreaks continue to spread, with 1 in 10 nursing homes in the United States (>1300 skilled nursing facilities) now reporting cases, with the likelihood of thousands of deaths. 6

Mass testing of the residents in skilled nursing facilities will allow appropriate isolation of infected residents so that they can be cared for and quarantine of exposed residents to minimize the risk of spread.

Mass testing in these facilities could also allow cohorting 7 and some resumption of group activities in a nonoutbreak setting.

Routine rRT-PCR testing in addition to symptomatic screening of new residents before entry, conservative guidelines for discontinuation of isolation, 7 and periodic retesting of long-term residents, as well as both periodic rRT-PCR screening and surgical masking of all staff, are important concomitant measures.

There are approximately 1.3 million Americans currently residing in nursing homes. 8

Although this recommendation for mass testing in skilled nursing facilities could be initially rolled out in geographic areas with high rates of community Covid-19 transmission, an argument can be made to extend this recommendation to all U.S.-based skilled nursing facilities now because case ascertainment is uneven and incomplete and because of the devastating consequences of outbreaks.

Immediately enforceable alternatives to mass testing in skilled nursing facilities are few.

The public health director of Los Angeles has recommended that families remove their loved ones from nursing homes, 9 a measure that is not feasible for many families.

Asymptomatic transmission of SARS-CoV-2 is the Achilles’ heel of Covid-19 pandemic control through the public health strategies we have currently deployed.

Symptom-based screening has utility, but epidemiologic evaluations of Covid-19 outbreaks within skilled nursing facilities such as the one described by Arons et al. strongly demonstrate that our current approaches are inadequate.

This recommendation for SARS-CoV-2 testing of asymptomatic persons in skilled nursing facilities should most likely be expanded to other congregate living situations, such as prisons and jails (where outbreaks in the United States, whose incarceration rate is much higher than rates in other countries, are increasing), enclosed mental health facilities, and homeless shelters, and to hospitalized inpatients.

Current U.S. testing capability must increase immediately for this strategy to be implemented.

Ultimately, the rapid spread of Covid-19 across the United States and the globe, the clear evidence of SARS-CoV-2 transmission from asymptomatic persons, 5 and the eventual need to relax current social distancing practices argue for broadened SARS-CoV-2 testing to include asymptomatic persons in prioritized settings.

These factors also support the case for the general public to use face masks 10 when in crowded outdoor or indoor spaces.

This unprecedented pandemic calls for unprecedented measures to achieve its ultimate defeat.

This editorial was published on April 24, 2020, at NEJM.org.

Author Affiliations

From the Department of Medicine, University of California, San Francisco.

References

1. Wölfel R, Corman VM, Guggemos W, et al. Virological assessment of hospitalized patients with COVID-2019. Nature 2020 April 1 (Epub ahead of print).

2. Cheng PK, Wong DA, Tong LK, et al. Viral shedding patterns of coronavirus in patients with probable severe acute respiratory syndrome. Lancet 2004;363:1699-1700.

3. To KK-W, Tsang OT-Y, Leung W-S, et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect Dis 2020 March 23 (Epub ahead of print).

4. Ip DKM, Lau LLH, Leung NHL, et al. Viral shedding and transmission potential of asymptomatic and paucisymptomatic influenza virus infections in the community. Clin Infect Dis 2017;64:736-742.

5. Arons MM, Hatfield KM, Reddy SC, et al. Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. N Engl J Med 2020;382:2081-2090.

6. Cenziper D, Jacobs J, Mulcahy S. Nearly 1 in 10 nursing homes nationwide report coronavirus cases. Washington Post. April 20, 2020 (https://www.washingtonpost.com/business ... outbreaks/. opens in new tab).

7. Centers for Disease Control and Prevention. Key strategies to prepare for COVID-19 in long-term care facilities (LTCFs): updated interim guidance. April 15, 2020 (https://www.cdc.gov/coronavirus/2019-nc ... -care.html. opens in new tab).

8. Centers for Disease Control and Prevention. Nursing home care. March 11, 2016 (https://www.cdc.gov/nchs/fastats/nursing-home-care.htm. opens in new tab).

9. Dolan J, Hamilton M. Consider pulling residents from nursing homes over coronavirus, says county health director. Los Angeles Times. April 7, 2020 (https://www.latimes.com/california/stor ... -la-county. opens in new tab).

10. Centers for Disease Control and Prevention. Use of cloth face coverings to help slow the spread of COVID-19. April 3, 2020 (https://www.cdc.gov/coronavirus/2019-nc ... cover.html. opens in new tab).

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Re: Inward Bound - The T'ai Chi Corner

Post by thelivyjr » Mon Jun 01, 2020 1:40 p

WEB MD

What is viral shedding?


ANSWER

Viral shedding occurs when a virus replicates inside your body and is released into the environment.

At that point, it may be contagious.


For the coronavirus that causes COVID-19, it's not known exactly when this occurs after someone is infected.

Evidence suggests that the novel coronavirus is most contagious when symptoms are worse and viral shedding is high.

However, it appears that someone is contagious prior to developing symptoms, suggesting that viral shedding is occurring even early in the infection.

Medically Reviewed on 05/28/2020

https://www.webmd.com/lung/qa/what-is-viral-shedding

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Post by thelivyjr » Mon Jun 01, 2020 1:40 p

STAT

People ‘shed’ high levels of coronavirus, study finds, but most are likely not infectious after recovery begins


By Helen Branswell @HelenBranswell

March 9, 2020

People who contract the novel coronavirus emit high amounts of virus very early on in their infection, according to a new study from Germany that helps to explain the rapid and efficient way in which the virus has spread around the world.

At the same time, the study suggests that while people with mild infections can still test positive by throat swabs for days and even weeks after their illness, those who are only mildly sick are likely not still infectious by about 10 days after they start to experience symptoms.


The study, by scientists in Berlin and Munich, is one of the first outside China to look at clinical data from patients who have been diagnosed with Covid-19, the disease caused by the coronavirus, and one of the first to try to map when people infected with the virus can infect others.

It was published Monday on a preprint server, meaning it has not yet been peer-reviewed, but it could still provide key information that the public health response has been lacking.

“This is a very important contribution to understanding both the natural history of Covid-19 clinical disease as well as the public health implications of viral shedding,” said Michael Osterholm, director of the University of Minnesota’s Center for Infectious Diseases Research and Policy.

The researchers monitored the viral shedding of nine people infected with the virus.

In addition to tests looking for fragments of the virus’s RNA, they also tried to grow viruses from sputum, blood, urine, and stool samples taken from the patients.

The latter type of testing — trying to grow viruses — is critical in the quest to determine how people infect one another and how long an infected person poses a risk to others.

Importantly, the scientists could not grow viruses from throat swabs or sputum specimens after day 8 of illness from people who had mild infections.

“Based on the present findings, early discharge with ensuing home isolation could be chosen for patients who are beyond day 10 of symptoms with less than 100,000 viral RNA copies per ml of sputum,” the authors said, suggesting that at that point “there is little residual risk of infectivity, based on cell culture.”


Public health officials and hospitals have been trying to make sense of patients who seem to have recovered from Covid-19 but who still test positive for the virus based in throat swabs and sputum samples.

In some cases, people test positive for weeks after recovery, the World Health Organization has noted.

Those tests are conducted using PCR — polymerase chain reaction — which looks for tiny sections of the RNA of the virus.

That type of test can indicate whether a patient is still shedding viral debris, but cannot indicate whether the person is still infectious.

The researchers found very high levels of virus emitted from the throat of patients from the earliest point in their illness —when people are generally still going about their daily routines.

Viral shedding dropped after day 5 in all but two of the patients, who had more serious illness.

The two, who developed early signs of pneumonia, continued to shed high levels of virus from the throat until about day 10 or 11.

This pattern of virus shedding is a marked departure from what was seen with the SARS coronavirus, which ignited an outbreak in 2002-2003.

With that disease, peak shedding of virus occurred later, when the virus had moved into the deep lungs.

Shedding from the upper airways early in infection makes for a virus that is much harder to contain.

The scientists said at peak shedding, people with Covid-19 are emitting more than 1,000 times more virus than was emitted during peak shedding of SARS infection, a fact that likely explains the rapid spread of the virus.


The SARS outbreak was contained after about 8,000 cases; the global count of confirmed Covid-19 cases has already topped 110,000.

Osterholm said the data in the paper confirm what the spread of the disease has been signaling — “early and potentially highly efficient transmission of the virus occurs before clinical symptoms or in conjunction with the very first mild symptoms.”

The study also looked at whether people who have been infected shed infectious virus in their stool.

The report of last month’s international mission to China — co-led by the WHO and China — said that in several case studies in China, “viable virus” had been recovered from stool but that isn’t likely driving transmission of the virus.

The German researchers found high levels of viral fragments in 13 stool samples from four patients in their study, but they were unable to grow virus from any of them.

The paper noted, though, that all the patients had mild illness, and the fact that they could not find virus in their stool doesn’t rule out that it could happen in other cases.

“Further studies should therefore address whether SARS-CoV-2 shed in stool is rendered non-infectious though contact with the gut environment,” they wrote, adding that their findings suggest measures to try to stop spread of the virus should focus on respiratory tract transmission — protecting others from the coughs and sneezes of people infected with the virus.

Virus could not be grown from blood or urine samples taken from the patients, the authors reported.

The study also noted that people who are infected begin to develop antibodies to the virus quickly, typically within six to 12 days.

The rapid rise of antibodies may explain why about 80% of people infected with the virus do not develop severe disease.


About the Author

Helen Branswell, Senior Writer, Infectious Disease

Helen covers issues broadly related to infectious diseases, including outbreaks, preparedness, research, and vaccine development.

@HelenBranswell

https://www.statnews.com/2020/03/09/peo ... ry-begins/

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Post by thelivyjr » Tue Jun 02, 2020 1:40 p

Infection fatality rate of SARS-CoV-2 infection in a German
community with a super-spreading event


Hendrik Streeck1, Bianca Schulte1, Beate M. Kümmerer1, Enrico Richter1, Tobias Höller5, Christine Fuhrmann5, Eva Bartok4, Ramona Dolscheid4, Moritz Berger3, Lukas Wessendorf1, Monika Eschbach-Bludau1, Angelika Kellings5, Astrid Schwaiger6, Martin Coenen5, Per Hoffmann7, Birgit Stoffel-Wagner4, Markus M. Nöthen7, Anna-Maria Eis-Hübinger1, Martin Exner2, Ricarda Maria Schmithausen2, Matthias Schmid3 and Gunther Hartmann4

1 Institute of Virology, University Hospital, University of Bonn, Germany, and German Center for Infection Research (DZIF), partner site Bonn-Cologne

2 Institute for Hygiene and Public Health, University Hospital, University of Bonn, Germany

3 Institute for Medical Biometry, Informatics and Epidemiology, University Hospital, University of Bonn, Germany

4 Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital, University of Bonn, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne

5 Clinical Study Core Unit, Study Center Bonn (SZB), Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital, University of Bonn, Germany

6 Biobank Core Unit, University Hospital, University of Bonn, Germany

7 Institute of Human Genetics, University Hospital, University of Bonn, Germany

Co-corresponding authors:

Hendrik Streeck, Institute of Virology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany

E-Mail: hendrik.streeck@ukbonn.de

Gunther Hartmann, Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany

E-Mail: gunther.hartmann@ukbonn.de

Abstract

The world faces an unprecedented SARS-CoV2 pandemic where many critical factors still remain unknown.

The case fatality rates (CFR) reported in the context of the SARS-CoV-2 pandemic substantially differ between countries.

For SARS-CoV-2 infection with its broad clinical spectrum from asymptomatic to severe disease courses, the infection fatality rate (IFR) is the more reliable parameter to predict the consequences of the pandemic.

Here we combined virus RT-PCR testing and assessment for SARS-CoV2 antibodies to determine the total number of individuals with SARS-CoV-2 infections in a given population.

Methods:

A sero-epidemiological GCP- and GEP-compliant study was performed in a small German town which was exposed to a super-spreading event (carnival festivities) followed by strict social distancing measures causing a transient wave of infections.

Questionnaire-based information and biomaterials were collected from a random, household-based study population within a seven-day period, six weeks after the outbreak.

The number of present and past infections was determined by integrating results from anti-SARS-CoV-2 IgG analyses in blood, PCR testing for viral RNA in pharyngeal swabs and reported previous positive PCR tests.

Results:

Of the 919 individuals with evaluable infection status (out of 1,007; 405 households) 15.5%(95% CI: [12.3%; 19.0%]) were infected.

This is 5-fold higher than the number of officially reported cases for this community (3.1%).

Infection was associated with characteristic symptoms such as loss of smell and taste.

22.2% of all infected individuals were asymptomatic.

With the seven SARS-CoV-2-associated reported deaths the estimated IFR was 0.36%[0.29%; 0.45%].

Age and sex were not found to be associated with the infection rate.

Participation in carnival festivities increased both the infection rate (21.3% vs. 9.5%, p<0.001) and the number of symptoms in the infected (estimated relative mean increase 1.6, p=0.007).

The risk of a person being infected was not found to be associated with the number of study participants in the household this person lived in.

The secondary infection risk for study participants living in the same household increased from 15.5% to 43.6%, to 35.5% and to 18.3% for households with two, three or four people respectively (p<0.001).

Conclusions:

While the number of infections in this high prevalence community is not representative for other parts of the world, the IFR calculated on the basis of the infection rate in this community can be utilized to estimate the percentage of infected based on the number of reported fatalities in other places with similar population characteristics.

Whether the specific circumstances of a super-spreading event not only have an impact on the infection rate and number of symptoms but also on the IFR requires further investigation.

The unexpectedly low secondary infection risk among persons living in the same household has important implications for measures installed to contain the SARS-CoV-2 virus pandemic.

Introduction

The novel SARS-CoV-2 coronavirus causing a respiratory disease termed COVID-191,2 is affecting almost every country worldwide3.

One of the reasons for its rapid spread is its ability to transmit already during the asymptomatic phase of infection, reported to be responsible for approximately 40% of SARS-CoV-2 transmission events4,5.

As the COVID-19 pandemic continues to grow in extent, severity, and socio-economic consequences, its fatality rate remains unclear.

SARS-CoV-2 infection presents with a broad spectrum of clinical courses from asymptomatic to fatal, complicating the definition of a 'case'.

About 80-91% of the infections have been reported to show only mild to moderate symptoms including sore throat, dry cough and fever6.


These are currently often left undiagnosed.

Together with different PCR test capacities and different regulations for testing, the ratio of SARS-CoV-2-associated deaths to overall reported cases (case fatality rate, CFR) inherently differs between countries 7.

The current estimate of the CFR in Germany by the World Health Organization (WHO) is between 2.2% and 3.4%3.

The data basis, however, for calculating the CFR is weak, with the consequence that epidemiological modeling is currently associated with a large degree of uncertainty.


However, epidemiological modeling is urgently needed to design the most appropriate prevention and control strategies to counter the pandemic and to minimize collateral damage to societies.

Unlike the CFR, the infection fatality rate (IFR) includes the whole spectrum of infected individuals, from asymptomatic to severe.

The IFR is recommended as a more reliable parameter than the CFR for evidence-based assessment of the SARS-CoV-2 pandemic (Center for Evidence-Based Medicine, CEBM in Oxford).

The IFR includes infections based on both PCR testing and virus-specific antibodies.

Mild and moderate disease courses are also included, which tend to not be captured and documented by PCR testing alone.

Active infections before seroconversion are included into IFR-calculation by PCR testing.

In this testing scheme, only those individuals may be missed who already became negative in the PCR test but have not yet reached antibody levels above the threshold of the antibody detection assay 8.

Recently commercial assays became available with a specificity of up to 99% to allow for a reliable serological analysis of SARS-CoV-2-specific antibodies 9.

Of note, lower specificities of ELISA tests reported in the literature are partially due to the use of beta versions of the ELISA and to different calculation algorithms (>0.3 log(Ig ratio)) defining positive values 10.

Furthermore, even an assay with a validated specificity of 99% has limitations in its accuracy to reliably identify infected individuals in populations with low seroprevalence (e.g. <1 %).

We chose the community of Gangelt, where due to a super-spreading event, the officially reported cases were 3% (time of study period).

In this community, carnival festivities around February 15th were followed by a massive outbreak of SARS-CoV-2 infections.

Strict measures including a suggested curfew were immediately taken to slow down further spreading of the infection.

Given its relatively closed community with little tourism and travel, this community was identified as an ideal model to better understand SARS-CoV2 spreading, prevalence of symptoms, as well as the infection fatality rate.

The results presented here were obtained in the context of the larger study program termed COVID-19-Case-Cluster Study.

The parts of a larger study program which are presented here were specifically designed to determine the total number of infected and the IFR.

In addition, the spectrum of symptoms, as well as the associations with age, sex, household size, co-morbidities and participation in carnival festivities, were examined.

Materials and methods

Study design


This study was conducted between March 31st, 2020 and April 6th, 2020 in Gangelt, a community with 12,597 inhabitants (as of Jan 1st, 2020) located in the German county of Heinsberg in North Rhine-Westphalia.

For this cross-sectional epidemiological study, all inhabitants of Gangelt were eligible.

Enrollment was based on a sample of 600 persons contained in the Heinsberg civil register ("Melderegister"), which is the public authority that collects all names and addresses of the inhabitants of Gangelt.

All study participants provided written and informed consent before enrolment.

For children under 18 years, written and informed consent was provided by the persons with care and custody of the children following aged-adapted participant information.

In addition to the data provided by the study participants, aggregated data on mortality and socio-demographic characteristics were collected.

The latter data were provided by the district administration of Heinsberg and the Statistics & IT Service of the German federal state of North Rhine-Westphalia.

The study was approved by the Ethics Committee of the Medical Faculty of the University of Bonn (approval number 085/20) and has been registered at the German Clinical Trials Register (https://www.drks.de, identification number DRKS00021306).

The study was conducted in accordance with good clinical (GCP) and epidemiological practice (GEP) standards and the Declaration of Helsinki.

Sampling and procedures

Based on the sample size recommendations of the World Health Organisation (WHO) (see below), the aim was to collect data from at least 300 households in Gangelt.

To reach this target, a sample of 600 persons aged older than 18 years was drawn from the civil register.

Sampling was done randomly under the side condition that all 600 persons had different surnames, as it was assumed that different surnames were likely to indicate different households.

After sampling, the 600 selected persons were contacted by mail and were invited to the study acquisition center, which was established at the site of a public school in Gangelt.

The letters sent to the 600 selected persons also included invitations for all persons living in the respective households to participate in the study.

Persons aged older than 80 years or immobile were offered the opportunity to be visited at home.

After having provided written and informed consent, study participants completed a questionnaire querying information including demographics, symptoms, underlying diseases, medication and participation in carnival festivities (main carnival session "Kappensitzung" and others).

Furthermore, study participants were asked to provide blood specimens and pharyngeal swabs.

Blood was centrifuged and EDTA-plasma was stored until analysis (-80°C).

Analyses were performed in batches at the central laboratory of the University Hospital Bonn (UKB), which is accredited according to DIN EN ISO 15189:2014.

Anti-SARS-CoV-2 IgA and Anti-SARS-CoV-2 IgG were determined with enzyme-linked immunosorbent assays (ELISA) on the EUROIMMUN Analyzer I platform (most recent CE version for IgG ELISA as of April 2020, specificity 99.1%, sensitivity 90.9%, data sheet as of April 7, 2020, validated in cooperation with the Institute of Virology of the Charité in Berlin, and the Erasmus MC in Rotterdam, Euroimmun, Lübeck, Germany).

The data sheet (April 7, 2020) reports cross-reactivities with anti-SARS-CoV-1-IgG-antibodies, but not with MERS-CoV-, HCoV-229E-, HCoV-NL63-, HCoV-HKU1- or HCoV-OC43-IgG antibodies.

In our study, infected included positives (ratio of 1.1 or higher, 91% positive in neutralization assay) and equivocal positives (ratio 0.8 to 1.1, 56% positive in neutralization assays).

Assays were performed in line with the guidelines of the German Medical Association (RiliBÄK) with stipulated internal and external quality controls.

Pharyngeal swabs were stored in UTM Viral 5 Stabilization Media at 4 °C at the study acquisition center for up to four hours.

The cold chain remained uninterrupted during transport.

At the Institute of Virology of the UKB swab samples were homogenized by short vortexing, and 300 μl of the media containing sample were transferred to a sterile 1.5 ml microcentrifuge tube and stored at 4 °C.

Viral RNA was extracted on the chemagic™ Prime™ instrument platform (Perkin Elmer) using the chemagic Viral 300 assay according to manufacturer’s instructions.

The RNA was used as template for three real time RT-PCR reactions (SuperScript™III One-Step RT-PCR System with Platinum™ TaqDNA Polymerase, Thermo Fisher) to amplify sequences of the SARS-CoV-2 E gene (primers E_Sarbeco_F1 and R, and probe E_Sarbeco_P111), the RdRP gene (primers RdRP_SARSr_F, and R, and probe RdRP_SARSr-P211), and an internal control for RNA extraction, reverse transcription, and amplification (innuDETECT Internal Control RNA Assay, Analytik Jena #845-ID-0007100).

Samples were considered positive for SARS-CoV-2 if amplification occurred in both virus-specific reactions.

All PCR protocols and materials were used according to clinical diagnostics standards and guidelines of the Virology Diagnostics Department of the UKB.

Neutralization assays were performed using a SARS-CoV-2 strain isolated in Bonn from a throat swab of a patient from Heinsberg.

Plasma samples from study participants were inactivated at 56°C for 30 min.

In a first round, neutralizing activity was analyzed by a microneutralization test using 100 TCID50 similar as described12.

Serial 2-fold dilutions (starting dilution 1:2, 50 μl per well) of plasma were performed and mixed with equal volumes of virus solution.

All dilutions were made in DMEM (Gibco) supplemented with 3% fetal bovine serum (FBS, Gibco) and each plasma dilution was run in triplicate.

After incubation for 1 h at 37°C, 2x104 Vero E6 cells were added to each well and the plates were incubated at 37°C for 2 days in 5% CO2 before evaluating the cytopathic effect (CPE) via microscopy.

In each experiment, plasma from a SARS-CoV-2 IgG negative person was included and back titration of the virus dilution was performed.

Titers were calculated according to the Spearman-Kaerber formula13 and are presented as the reciprocals of the highest plasma dilution protecting 50% of the wells.

To further assess the neutralizing activity of plasma samples exhibiting neutralizing antibody titers below 2.8 in the microneutralization test, a plaque reduction neutralization test was performed.

To this end, heat inactivated plasma samples were serially two-fold diluted starting with 1: 2 up to 1:1,024.

120 μl of each plasma dilution was mixed with 100 plaque forming units (PFU) of SARS-CoV-2 in 120 μl OptiPROTMSFM (Gibco) cell culture medium.

After incubation of 1 h at 37°C, 200 μl of each mixture were added to wells of a 24 well plate seated the day before with 1.5x105 Vero E6 cells/well.

After incubation for 1 h at 37°C, the inoculum was removed and cells were overlayed with a 1:1 mixture of 1.5% carboxymethylcellulose (Sigma) in 2xMEM (Biochrom) with 4% FBS (Gibco).

After incubation at 37°C for three days in 5% CO2, the overlay was removed and the 24 well plates were fixed using a 6% formaldehyde solution and stained with 1% crystal violet in 20% ethanol.

Data management and quality control

Planning and conduct of the study were supported by the Clinical Study Core Unit
(Studienzentrale) of the Study Centre Bonn (SZB).

Support included protocol and informed consent development following specifications of the World Health Organization with regards to pandemic events14, data management, submission to the ethics committee, clinical trial monitoring and quality control.

Study data were collected and managed using REDCap electronic data capture tools hosted at Institute for Medical Biometry, Informatics and Epidemiology13, 14.

REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and 6 export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.

Questionnaire data were recorded on site using paper case report forms and were entered into the electronic study database using double data entry by trained study personnel.

Comparisons between entries were made by the data management unit of the SZB; nonmatches were corrected, and duplicated entries were deleted, after assessing the original paper case report forms.

Additionally, plausibility checks of demographic data were performed.

Study personnel were trained with respect to informed consent and study procedures prior to inclusion of first study participant.

The study team was supported on site in Gangelt by a quality control manager who refined workflow processes and monitored critical processes such as obtaining informed consent.

Furthermore, regulatory advice could be given whenever asked for or needed.

Data entry personnel was trained for double data entry prior to data entry and only then granted database access authorization.

Contact with the responsible data managers could be established when needed.

Diagnostic data were imported into the trial database automatically via validated interfaces.

Following the completion of the study, critical data was monitored by an experienced clinical trial monitor which included (but was not limited to) a check of availability of source data (completed questionnaires), random source data verification of diagnostic data and a check of signatures of all informed consent forms obtained.

Statistical analysis

In the absence of any pilot data on SARS-CoV-2 infection rates in Gangelt, sample size calculations were based on the WHO population-based age-stratified seroepidemiological investigation protocol for COVID-19 virus infection14.

According to the recommendations stated in the protocol, a size of 200 samples is sufficient to estimate SARS-CoV-2-prevalence rates <10% with an expected margin of error (defined by the expected width of the 95% confidence interval associated with the seroprevalence point estimate obtained using binomial likelihood) smaller than 10%.

In order to rule out larger margins of error due to dependencies of persons living in the same household and to be able to analyze seroprevalence (i.e., infection rates) also in subgroups defined by participant age, it was planned to recruit 1,000 participants living in at least 300 households.

Statistical analysis was carried out by two independently working statisticians (MS, MB) using version 3.6.1 of the R Language for Statistical Computing (R Core Team 2019: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria) and version 9.4 of the SAS System for Windows (copyright © 2002-2012 by SAS Institute Inc., Cary, NC, USA).

Participants with a missing anti-SARS-CoV-2 IgG/A or PCR test result were excluded from analysis, as they were not evaluable for infection status (Fig. 1B).

Participants that did not report a previous positive PCR test result were documented as PCRrep negative.

Missing and unknown values in the comorbidity and symptom variables were not imputed, as listwise deletion reduced sample sizes by less than 5%.

Age groups were formed according to the classification system of the Robert Koch Institute (RKI), which is the German federal government agency and research institute responsible for infectious disease control and prevention.

Descriptive analyses included the calculation of means (plus standard deviations, sds) and medians (plus minimum and maximum values) for continuous variables, and numbers (n, with percentages) for categorical variables.

Associations between continuous variables were analyzed using the Pearson correlation coefficient (r).

Generalized estimation equations (GEE)15 with exchangeable correlation structure within household clusters were used to adjust point estimates and confidence intervals (CIs) for possible dependencies between participants living in the same household.

By definition, GEE models employ quasi-likelihood methods to obtain point estimates and CIs.

Adjustments for 7 possible sex and age effects were made by including these variables as additional covariables in the GEE models.

One person of diverse sex (Table 1) was excluded from the models including sex as covariable.

For binary outcomes (e.g. infection status), GEE models with a logistic link function were applied.

Results of logistic GEE models are presented in terms of either back-transformed mean estimates (GEE models with a single covariable) or odds ratios (ORs, GEE models with ≥ 1 covariables).

For count data (e.g. number of symptoms), Poisson GEE models with a logarithmic link function were used.

Results of Poisson GEE models are presented in terms of either back-transformed mean estimates (GEE models with a single covariable) or estimated relative mean increases/decreases (GEE models with ≥ 1 covariables).

For all GEE models, the estimated correlation between participants living in the same household cluster (rho) is reported.

On a household-level basis (with households assumed to be independent sampling units), quasi-Poisson models with offset values defined by the logarithmized household cluster size were applied.

Wald tests were used to test covariables for statistical significance.

All CIs presented in this work were computed using the 95% level.

CIs are Wald CIs and were not adjusted for multiple comparisons unless otherwise stated.

All statistical hypothesis tests were two-sided; p-values < 0.05 were considered significant.

The Bonferroni-Holm procedure was applied to adjust p-values for multiple comparisons as indicated.

Infection rates obtained from IgG and IgA measurements were additionally corrected for possible misclassification bias using the matrix method16, with sensitivity and specificity values obtained from the ELISA manufacturer’s (Euroimmun, Lübeck, Germany) validation data sheet (version: April 7, 2020).

No adjustments were made for age and sex, as these variables were not found to be associated with infection status (Fig. 6A).

To account for possible clustering effects due to participants living in the same household, confidence intervals for the corrected infection rate estimates were computed using a cluster bootstrap procedure with 10,000 bootstrap samples17.

With this procedure, household clusters were sampled with replacement.

Within sampled clusters, no additional resampling of household members was carried out.

The distributions of the bootstrapped corrected infection rate estimates were symmetrical and close to normality (as indicated by normal quantile-quantile plots), and the percentile method was applied to calculate CI limits.

Note: Throughout the paper, the term rate refers to the number of persons experiencing an event divided by the number of the reference population, in line with the definition of the IFR18.

We adopted this definition due to its widespread use in the context of COVID-19 research, keeping in mind that “rates” are usually defined in terms of person-time (e.g. Rothman et al19).

Results

Study design and study population


The major objective of this study was to determine the total number of individuals infected by SARS-CoV-2 in the given defined population.

This number together with the reported SARSCoV-2-associated fatalities in that same population allows the calculation of the infection fatality rate (IFR, according to Centre for Evidence-Based Medicine, CEBM, Oxford University, to be distinguished from case fatality rate, CFR).

In the German community Gangelt (12,597 inhabitants), a super-spreading event (carnival festivities incl. "Kappensitzung" on February 15, 2020), was followed by numerous measures starting February 28 (shut-down) to limit the further spread of infections (Fig. 1A).

This local infection hotspot was closely monitored by health authorities, and a high PCR test rate revealed an increase in officially reported cases, 8 with a maximum around March 13 when 85 individuals tested PCR positive for SARS-CoV-2 in a 4-day period.

Numbers declined afterwards down to 48 PCR positive cases officially reported during the 7-day period of the present study (March 30th - April 6th), not counting the 33 new PCR positives detected in this study.

The total number of officially reported PCR positives on April 6th was 388, also excluding the 33 PCR positives of this study.

By the end of the 7-day study period, a total of 7 SARS-CoV-2-positive individuals had died in the community of Gangelt since the super-spreading event (average age 80.8 years, sd ± 3.5 years).

In January, February and March 2020, a total of 48 people died in Gangelt, which was 3 people more than in the same period the year before.

At the start date of data and material acquisition of the study, 340 PCR positives were reported in the community which is 2.7% of the population.

Our study design addressed the recommendations for COVID-19 studies by the WHO14.

For the study, 600 adult persons with different surnames in Gangelt were randomly selected, and all household members were asked to participate in the study.

Data and materials were collected over a 7-day period (March 30 to April 6) six weeks after super-spreading event.

Of the 1,007 individuals participating in the study, 987 individuals were seen in the local study acquisition center in a community school, and 20 individuals were visited in their homes due to age or limited mobility.

Complete information from both pharyngeal swabs and blood samples was available for 919 study participants living in 405 households (Fig. 1B).

The demographic characteristics of the study participants, including age, sex and the number of participants living in the same household, are summarized in Table 1.

The comparison of age groups in the study population to the community Gangelt, to the state North Rhine-Westphalia, (NRW) and to Germany is illustrated in supplementary figure 1.

Characteristics of the 88 study participants who were not evaluable for infection status, mainly children due to lack of biomaterials, are provided in the supplementary table.

Number of SARS-CoV-2-infected and infection fatality rate (IFR)

The analysis of IgA and IgG levels measured in plasma samples of all study participants by ELISA (Euroimmun) showed a positive correlation (r=0.778, CI 95%: [0.751-0.802]: Fig. 2A).

While 18.50% of all study participants were found to be IgA positive, 13.60% were IgG positive (Fig. 2B).

Correction for sensitivity and specificity of the ELISA (specificity 99.1%, sensitivity 90.9%) revealed a much lower corrected value of 10.63% [7.48%; 13.88%] for IgA and a slightly higher value of 14.11% [11.15%; 17.27%] for IgG (Fig. 2B).

The higher specificity of the IgG ELISA (99.1%, validation reported April 7, 2020 by company based on 1,656 samples) compared to IgA ELISA (91.2%) was confirmed by our own independent analysis of control samples (specificity 98.3%: 1 positive in 68 samples of healthy control individuals, 1 positive in 32 samples of patients with cardiovascular disease, 0 positive in 9 samples of 7 patients with PCR-confirmed infection with endemic coronaviruses).

To illustrate the difference between a specificity of 99% and 98%, the correction for specificity of 98% is added in light gray (Fig. 2B,C).

Based on these data, a "seropositive" study participant was defined as being positive for IgG (mean of values corrected for sensitivity and specificity of all study participants; Fig. 2B).

The neutralization activity of IgG-positive plasma samples was analyzed using a microneutralization assay combined with a plaque reduction neutralization test.

Results are shown in Suppl. Fig. 2.

To determine the total number of infected individuals, all study participants were tested for the presence of virus in pharyngeal swabs by SARS-CoV-2 PCR in addition to serology.

Of the 919 participants of the study, 33 tested positive (PCRnew: 3.59%).

Furthermore, based on the information collected from the questionnaire, 22 study participants reported that they had had 9 a SARS-CoV-2 positive PCR test in the past (PCRrep: 2.39%).

The combination of serology (non-corrected IgG values) and past and present PCR testing yielded a total number of 138 study participants (15.02%) that had been previously or were at that time point infected by SARS-CoV-2 as illustrated in Fig. 2C.

The inclusion of IgG values corrected for sensitivity and specificity in the calculation resulted in an estimated 15.53% [12.31%; 18.96%] cumulative SARS-CoV-2-infected of all study participants.

To determine the infection fatality rate (IFR), the estimated infection rate of 15.53% in the study population was applied to the total population in the community (12,597) yielding an estimated number of 1,956 [1,551; 2,389] infected people.

With 7 SARS-CoV-2-associated deaths, as reported to the authors by the local administration, the estimated IFR was 7/1,956 = 0.00358 [0.00293; 0.00451] (0.358% [0.293%; 0.451%]) (Fig. 3A) at the end of the acquisition period.

While the percentage of previously reported cases as collected from the questionnaire in the study population was 2.39% (PCRrep+), the percentage of officially reported cases in the community of Gangelt at the end of the study period (April 6) was 3.08% (388/12,597).

This indicates that previously SARS-CoV-2 diagnosed individuals were somewhat underrepresented in our study, possibly due to previously diagnosed people not opting to participate in the study given their known infection status, or for other reasons, such as quarantine, not feeling well or hospitalization.

Thus, applying the corresponding correction factor (3.08% / 2.39% = 1.29) to the infection rate of 15.53% of our study population, the resulting corrected infection rate was 19.98% [15.84%; 24.40%] (Fig. 3B).

Accordingly, the corrected higher infection rate reduced the IFR to an estimated 0.278% [0.228%; 0.351%] (Fig. 3C).

Infection rate, symptoms and intensity of disease

A number of symptoms have been reported to be associated with SARS-CoV-2 infection1.

In the questionnaire, study participants were asked to indicate whether they experienced any of the described symptoms since the beginning of the pandemic February 15th.

Noting that symptoms may vary in both frequency (Table 2) and intensity, and that causal relationships cannot be established by a cross-sectional study, the following symptoms were found to be significantly associated with SARS-CoV-2 infection (based on IgG+, PCRrep+, PCRnew+, ranked by odds ratios with 95% CIs, adjusted for sex and age, Bonferroni-Holm corrected p-values indicated): loss of smell (OR: 19.06 [8.72; 41.68]; p<0.001), loss of taste (OR: 17.01 [8.49; 34.10]; p<0.001), fever (OR: 4.94 [2.87; 8.50]; p<0.001), sweats and chills (OR: 3.74 [2.31; 6.07]; p<0.001), fatigue (OR: 2.99 [1.97; 4.56]; p<0.001), cough (OR: 2.81 [1.92; 4.11]; p<0.001), muscle and joint ache (OR: 2.42 [1.46; 4.00]; p=0.005), chest tightness (OR: 2.32 [1.31; 4.11]; p=0.019), head ache (OR: 2.28 [1.46; 3.56]; p=0.003), sore throat (OR: 1.92 [1.25; 2.96]; p=0.017), and nasal congestion (OR: 1.91 [1.28; 2.85]; p=0.010).

Not significant were shortness of breath, other respiratory symptoms, stomach pain, nausea and vomiting (Table 2).

The number of symptoms reported by an individual participant served as an indicator for the intensity of the disease and was 2.18-fold higher (adjusted for sex and age, 95% CI: [1.78; 2.66]) in SARS-CoV-2-infected (IgG+, PCRrep+, PCRnew+) compared to participants without infection (Fig. 4A, p<0.001). 22.22% of infected (IgG+, PCRrep+, PCRnew+) reported no symptoms at all (Fig. 4B); for the other infected participants symptom numbers varied between 0 and 11 (Fig. 4B).

IgG levels of infected study participants were not significantly associated with the number of symptoms (Fig. 4C).

Association between household size and rate of infection

SARS-CoV-2 is thought to be highly contagious.

As a consequence, people living in the same household are expected to be at a much higher risk of infection.

The average number of people 10 in household clusters examined in this study was 2.27 (sd = 1.11, range 1-6) compared to Gangelt (2.44 as of 2011), the state NRW (2.02, as of Dec 2018) and Germany (1.99, as of Dec 2018).

Household clusters with 5 or more people were excluded from the analysis below because of insufficient numbers (15 clusters).

First, we analyzed whether the fact that an individual person was part of a one-, two-, three- or four-person household cluster changed the probability of this person being infected.

We found that the infection risk was not associated with the number of people in a household cluster (Fig. 5A).

Second, we analyzed the infection risk of a person in a household in which at least one other person was infected (Fig. 5B).

Under the theoretical assumption that there was no increased infection risk for a second, a third or a fourth person in a household cluster in which one person was infected, the average risk in this household cluster was calculated to be 0.578 (two-person household cluster; (1 + 0.1553) / 2), 0.4369 (three-person household cluster; (1 + 2 x 0.1553) / 3) or 0.3665 (four-person household cluster; (1 + 3 x 0.1553) / 4) (Fig. 5B, lower gray curve).

The estimated infection risk as calculated from the data was significantly above the theoretical risk without enhanced transmission (Fig. 5B, black curve, dotted lines indicate CI 95%).

A significant association between household cluster size and the per-person infection risk was found (Fig. 5B, p<0.001).

In a two-person household cluster, the estimated risk for the second infection increased from 15.53% to 43.59% [25.26%; 64.60%]; in a three-person household cluster the estimated risk for the second and third persons increased from 15.53% to 35.71% [19.57%; 55.60%] each, and in a four-person household cluster the estimated risk for the second, third and fourth persons increased from 15.53% to 18.33% [9.67%; 28.74%] each.

For household clusters with at least one infected child (< 18 years), the estimated per-person risk for the other person to be infected in three-person household clusters increased from 15.53% to 66.67% [21.83%, 100.00%] and in four-person household clusters from 15.53% to 33.33% [9.02%; 71.60%].

Associations between sex, age, co-morbidities and super-spreading event with the rate of infection, the number of symptoms and IgA/IgG

Sex and age were not associated with the rate of infection (Fig. 6A).

Neither IgA nor IgG of infected study participants showed significant associations with age or sex (suppl. Fig. 3).

It is well-established that severe disease courses and fatal outcomes of SARS-CoV-2 infection are associated with the extent of underlying diseases, especially lung diseases with reduced respiratory reserves and cardiovascular diseases.

We therefore analyzed the associations between co-morbidities on both the infection rate and the number of symptoms.

In the questionnaire, study participants were asked to report whether they had pre- existing diseases or disease states, including lung diseases, cardiovascular diseases, neurological diseases and stroke, cancer and diabetes.

Neither increased rate of infection (Fig. 6B) nor a higher number of symptoms were found in infected individuals (suppl. Fig. 4B).

For infected study participants the self-reported use of medications queried in the questionnaire (not in figure) (ibuprofen, ACE inhibitors or AT1 agonists) all had no significant associations with the infection rate or number of symptoms.

Underlying morbidities of infected study participants were not associated with Ig levels (suppl. Fig. 5).

Associations between celebrating carnival and rate of infection and number of symptoms

The impact of super-spreading events on the dynamics of the SARS-CoV-2 pandemic is well established20,21.

The carnival festivities in Gangelt are mostly visited by local people staying in the area after the event, therefore providing a unique setting to study the mechanisms of superspreading more closely than in events where people are traveling and thus disappearing from a local study population.

We analyzed whether celebrating carnival (main carnival session "Kappensitzung" or other carnival festivities) was associated with the rate of infection and the 11 intensity of the infection, based on the number of symptoms.

Study participants were asked to indicate whether they had participated in carnival events.

There was a significant positive association between celebrating carnival and infection (OR = 2.56 [1.67; 3.93], p < 0.001, Fig. 6C).

Furthermore, there was a significant positive association between celebrating carnival and the number of symptoms in infected study participants (estimated relative mean increase: 1.63 [1.15; 2.33], p=0.007, Fig. 6D).

While the percentage of asymptomatic infected participants was 36% without celebrating carnival, only 16% who had celebrated carnival were asymptomatic (Fig. 6E).

Discussion

One key parameter to assessing the potential impact that SARS-CoV-2 infection poses on societies is the fatality rate.

However, the fatality rate of 'cases' (case fatality rate, CFR) widely varies between countries.

'Cases' do not cover the whole spectrum of SARS-CoV-2 infections reaching from asymptomatic to lethal.

Therefore, we set out to determine the infection fatality rate (IFR) based on the total number of SARS-CoV-2-infected individuals.

We chose the German community Gangelt which had been exposed to a super-spreading event.

A random population sample revealed that an estimated 15.53% of the population in this community is or was infected with the virus, which is 5-fold higher than the officially reported number of PCR positives.

Based on the estimated percentage of infected people in this population, the IFR was estimated to be 0.36% [0.29%; 0.45%].

Infection was highly associated with known characteristic symptoms of SARS-CoV-2 infection such as loss of smell and taste.

The risk of being infected was not found to be associated with the number of participants living in the same household, and the estimated risk to be infected in a household cluster with one person already infected (secondary infection risk) was distinctly below 100%.

The frequency of infection did not significantly differ between age groups from children to the elderly and was not found to be associated with sex.

Co-morbidities such as underlying lung disease or cardiovascular disease did not show associations with the risk of infection.

Notably, this does not contradict the well-established fact that co-morbidities such as lung disease predispose for severe disease outcomes22,23.

The use of ACE-inhibiting drugs or ibuprofen did not show an association, as previously speculated24.

In our study, infection is defined as either PCR positive, anti-SARS-CoV2+ IgG seropositive or both, thus including present and past infections.

Since SARS-CoV-2 only arrived in February, seropositives are expected to cover all infections except the very recent.

This may become different as the pandemic continues, since a decrease in antibody titers over time needs to be considered in the calculation of the IFR.

Furthermore, in our study, the number of reported PCR positives (2.39%) was lower than in the overall population (3.08%) of this high-prevalence community.

This indicates that infected individuals may be underrepresented in our study population.

Although this is plausible (no response to study request due to illness, hospital, ICU, already known infection status, etc.) and would lead to a correction by factor 1.29, we chose to use the uncorrected lower percentage to conservatively estimate the total number of infected and the resulting IFR in the population.

To determine the IFR, the collection of materials and information including the reported cases and deaths was closed at the end of the study acquisition period (April 6th), and the IFR was calculated based on those data.

However, some of the individuals still may have been acutely infected at the end of the study acquisition period (April 6th) and thus may have succumbed to the infection later on.

In fact, in the 2-week follow-up period (until April 20th) one additional 12 COVID-19 associated death was registered.

The inclusion of this additional death would bring up the IFR from 0.36% to an estimated 0.41% [0.33%; 0.52%].

Although the IFR is much less variable than the infection rate in different parts of the country, the IFR may still be affected by certain circumstances.

The community in which this study was performed experienced a super-spreading event.

The IFR was unlikely affected by an overwhelmed health care system because sufficient numbers of ICU beds and ventilators were available at all times.

However, it is possible that the super-spreading event itself caused more severe cases.

In our study, we found a highly significant increase in both infection rate and number of symptoms when people attended carnival festivities, as compared to people who did not celebrate carnival.

This association with carnival was at the same level when adjusted for the age of the participants.

At this point, the reason for the association with celebrating carnival remains speculative.

Thus far, we could not identify confounding factors that would explain the observed difference.

However, it is well established that the rate of particle emission and superemission during human speech increases with voice loudness 25.

Because of loud voices and singing in close proximity are common in carnival events, it is reasonable to speculate that a higher viral load at the time of infection caused the higher intensity of symptoms and thus more severe clinical courses of the infection.

Notably, results from experimental human influenza infection studies have demonstrated that the symptom score depends on the viral dose administered26,27.

Similar observations have been made for MERS28 and SARS29.

Little is known about the infection dynamics of SARS-CoV-2.

Future studies designed to specifically analyze the infection chains after super-spreading events may provide further insight.

If substantiated, the IFR under strict hygiene measures might be lower than the IFR in the context of a super-spreading event in this study, with important consequences for the strategy against the pandemic.

In this context, it is interesting to note that in our study, 22% of infected individuals were asymptomatic, confirming previous reports of about 20% asymptomatic carriers that contribute to the spread of infection30-32.

Notably, asymptomatic infected individuals in our study present with substantial antibody titers.

Furthermore, since the mean symptom number of non-infected in our study was 1.6 (of 15 symptoms), it would be also appropriate to count infected study participants reporting up to 1 symptom as individuals with no symptoms above the baseline level of uninfected study participants, thereby increasing the percentage of asymptomatic infected individuals to 30.1%.


Given the high contagiousness of SARS-CoV-2, one would expect high rates of transmission.

However, in our study we found a relatively moderate increase of the secondary infection risk which depended on the household cluster size (increase from 15.5% baseline risk by 28% for two people, 20% for three people, 3% for four people).

This finding is consistent with recent observations of secondary infection risk of 16.3% in Chinese33 and 7.56% in South Korea34.

The reason for the comparably low secondary infection risk despite the high rate of transmission is currently unknown, but it is seen with other respiratory infections such as influenza (H1N1) 14.5%35 or SARS 14.9%36.

Secondary household members may have acquired a level of immunity (e.g. T cell immunity) that is not detected as positive by our ELISA, but still could protect those household members from a manifest infection26,37.

To date, knowledge about SARS-CoV-2 immunity is rather scarce.

Whether the Ig levels detected in infected individuals in our study are protective and how long such a protection lasts is not currently known.


Virus neutralization control assays as performed in our study add information, but do not provide evidence for the presence of an effective immunity.

As other tests, virus neutralization assays in general can be false positive, as cross-reactivity between betacoronaviruses is well-known38,39.

Likewise a lack of virus neutralization does also not exclude a past infection as there is ample evidence that not all antibody responses neutralize but still may provide some degree of protective immunity40,41.

Therefore, at this point our study 13 uses IgG values as indicator whether an individual was infected but not as evidence for existing immunity.

However, one may assume that a certain degree of protection might exist even if the IgG levels are below the threshold of the ELISA.

Such individuals are not counted as infected in our study, yet this hidden number of infected could possibly represent an important component towards immunity in a population.

The analysis of anti-SARS-CoV-2 IgM might help to further close this window in the future.

Since i) a high degree of PCR testing was performed in this community by the health authorities during the outbreak of SARS-CoV-2 infection, and ii) the outbreak was largely over, this community was chosen as an ideal site to estimate the real number of infected individuals.

It is important to note that the infection rate in Gangelt is not representative for other regions in Germany or other countries.

However, with the limitations discussed above, the IFR calculated here remains a useful metric for other regions with higher or lower infection rates.

If in a theoretical model the here calculated IFR is applied to Germany with currently approximately 6,575 SARS-CoV-2 associated deaths (May 2nd, 2020, RKI), the estimated number of infected in Germany would be higher than 1.8 Mio (i.e. 2.2% of the German population).

It will be very important to determine the true average IFR for Germany.

However, because of the currently low infection rate of approximately 2% (estimated based on IFR), an ELISA with 99% specificity will not provide reliable data.

Therefore, under the current non-superspreading conditions, it is more reasonable to determine the IFR in high prevalence hotspots such as Heinsberg county.

The data of the study reported here will serve as baseline for follow up studies on the delta of infections and deaths to identify the corresponding IFR under those changed conditions.

References

1. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497-506.

2. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507-13.

3. Coronavirus (COVID-19) Dashboard. 2020. (Accessed at https://covid19.who.int.)

4. Shujuan Ma, Jiayue Zhang, Minyan Zeng, et al. Epidemiological parameters of coronavirus disease 2019: a pooled analysis of publicly reported individual data of 1155 cases from seven countries. medRxiv 2020032120040329; doi:
https://doiorg/101101/2020032120040329 2020.

5. Lavezzo E, Franchin E, Ciavarella C, et al. Suppression of COVID-19 outbreak in the municipality of Vo, Italy. 2020.

6. Bi Q, Wu Y, Mei S, et al. Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts. 2020.

7. Statistics and Research Coronavirus Disease (COVID-19). 2020. (Accessed at
(https://ourworldindata.org/coronavirus# ... m-covid-19). .)

8. Yongchen Z, Shen H, Wang X, et al. Different longitudinal patterns of nucleic acid and serology testing results based on disease severity of COVID-19 patients. Emerg Microbes Infect 2020:1-14.

9. Wolfel R, Corman VM, Guggemos W, et al. Virological assessment of hospitalized patients with COVID-2019. Nature 2020.

10. Okba NMA, Muller MA, Li W, et al. SARS-CoV-2 specific antibody responses in COVID-19 patients. 2020.

11. Corman VM, Landt O, Kaiser M, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill 2020;25.

12. Ge XY, Li JL, Yang XL, et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 2013;503:535-8.

13. Ramakrishnan MA. Determination of 50% endpoint titer using a simple formula. World J Virol 2016;5:85-6.

14. Population-based age-stratified seroepidemiological investigation protocol for COVID- 9 virus infection. 2020. (Accessed at https://www.who.int/publications-detail ... -infection.)

15. Hanley JA, Negassa A, Edwardes MD, Forrester JE. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol 2003;157:364-75.

16. Rogan WJ, Gladen B. Estimating prevalence from the results of a screening test. Am J Epidemiol 1978;107:71-6.

17. C. DA, V. HD. Bootstrap methods and their application.: Cambridge University Press; 1978.

18. Global Covid-19 Case Fatality Rates. 2020. (Accessed at https://www.cebm.net/covid-19/global-co ... lity-rates.)

19. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3 ed: Wolters Kluwer; 2008.

20. Liu Y, Eggo RM, Kucharski AJ. Secondary attack rate and superspreading events for SARS-CoV-2. Lancet 2020;395:e47.

21. Al-Tawfiq JA, Rodriguez-Morales AJ. Super-spreading events and contribution to transmission of MERS, SARS, and COVID-19. J Hosp Infect 2020.

22. Feng Y, Ling Y, Bai T, et al. COVID-19 with Different Severity: A Multi-center Study of Clinical Features. Am J Respir Crit Care Med 2020.

23. Wang T, Du Z, Zhu F, et al. Comorbidities and multi-organ injuries in the treatment of COVID-19. Lancet 2020;395:e52.16

24. Fang L, Karakiulakis G, Roth M. Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? Lancet Respir Med 2020;8:e21.

25. Asadi S, Wexler AS, Cappa CD, Barreda S, Bouvier NM, Ristenpart WD. Aerosol emission and superemission during human speech increase with voice loudness. Sci Rep 2019;9:2348.

26. Wilkinson TM, Li CK, Chui CS, et al. Preexisting influenza-specific CD4+ T cells correlate with disease protection against influenza challenge in humans. Nat Med
2012;18:274-80.

27. Memoli MJ, Czajkowski L, Reed S, et al. Validation of the wild-type influenza A human challenge model H1N1pdMIST: an A(H1N1)pdm09 dose-finding investigational new drug study. Clin Infect Dis 2015;60:693-702.

28. Oh MD, Park WB, Choe PG, et al. Viral Load Kinetics of MERS Coronavirus Infection. N Engl J Med 2016;375:1303-5.

29. Hung IF, Cheng VC, Wu AK, et al. Viral loads in clinical specimens and SARS manifestations. Emerg Infect Dis 2004;10:1550-7.

30. Lai CC, Liu YH, Wang CY, et al. Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARSCoV-2): Facts and myths. J Microbiol Immunol Infect 2020.

31. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill 2020;25.

32. Bai Y, Yao L, Wei T, et al. Presumed Asymptomatic Carrier Transmission of COVID-19. JAMA 2020.

33. Li W, Zhang B, Lu J, et al. The characteristics of household transmission of COVID-19. Clin Infect Dis 2020.

34. Covid-19 National Emergency Response Center E, Case Management Team KCfDC, Prevention. Coronavirus Disease-19: Summary of 2,370 Contact Investigations of the First 30 Cases in the Republic of Korea. Osong Public Health Res Perspect 2020;11:81-4.

35. Carcione D, Giele CM, Goggin LS, et al. Secondary attack rate of pandemic influenza A(H1N1) 2009 in Western Australian households, 29 May-7 August 2009. Euro Surveill 2011;16.

36. Lau JT, Lau M, Kim JH, Tsui HY, Tsang T, Wong TW. Probable secondary infections in households of SARS patients in Hong Kong. Emerg Infect Dis 2004;10:235-43.

37. Braun J, Loyal L, Frentsch M, et al. 2020.

38. Meyer B, Drosten C, Muller MA. Serological assays for emerging coronaviruses: challenges and pitfalls. Virus Res 2014;194:175-83.

39. Chan JF, Lau SK, To KK, Cheng VC, Woo PC, Yuen KY. Middle East respiratory syndrome coronavirus: another zoonotic betacoronavirus causing SARS-like disease. Clin Microbiol Rev 2015;28:465-522.

40. Ilinykh PA, Huang K, Santos RI, et al. Non-neutralizing Antibodies from a Marburg Infection Survivor Mediate Protection by Fc-Effector Functions and by Enhancing Efficacy of Other Antibodies. Cell Host Microbe 2020.

41. Corey L, Gilbert PB, Tomaras GD, Haynes BF, Pantaleo G, Fauci AS. Immune correlates of vaccine protection against HIV-1 acquisition. Sci Transl Med 2015;7:310rv7.17

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Re: Inward Bound - The T'ai Chi Corner

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MedicineNet

Medical Definition of Seroconversion

Seroconversion: The development of detectable antibodies in the blood that are directed against an infectious agent.

Antibodies do not usually develop until some time after the initial exposure to the agent.

Following seroconversion, a person tests positive for the antibody when given tests that are based on the presence of antibodies, such as ELISA.


https://www.medicinenet.com/script/main ... lekey=9388
Nature Medicine

Antibody responses to SARS-CoV-2 in patients with COVID-19


Brief Communication

Published: 29 April 2020

Quan-Xin Long, Bai-Zhong Liu, Ai-Long Huang

Abstract

We report acute antibody responses to SARS-CoV-2 in 285 patients with COVID-19.

Within 19 days after symptom onset, 100% of patients tested positive for antiviral immunoglobulin-G (IgG).


Seroconversion for IgG and IgM occurred simultaneously or sequentially.

Both IgG and IgM titers plateaued within 6 days after seroconversion.

Serological testing may be helpful for the diagnosis of suspected patients with negative RT–PCR results and for the identification of asymptomatic infections.

Main

The continued spread of coronavirus disease 2019 (COVID-19) has prompted widespread concern around the world, and the World Health Organization (WHO), on 11 March 2020, declared COVID-19 a pandemic.

Studies on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) showed that virus-specific antibodies were detectable in 80–100% of patients at 2 weeks after symptom onset1,2,3,4,5,6.

Currently, the antibody responses against SARS-CoV-2 remain poorly understood and the clinical utility of serological testing is unclear7.

A total of 285 patients with COVID-19 were enrolled in this study from three designated hospitals; of these patients, 70 had sequential samples available.

The characteristics of these patients are summarized in Supplementary Tables 1 and 2.

We validated and used a magnetic chemiluminescence enzyme immunoassay (MCLIA) for virus-specific antibody detection (Extended Data Fig. 1a–d and Supplementary Table 3).

Serum samples from patients with COVID-19 showed no cross-binding to the S1 subunit of the SARS-CoV spike antigen.

However, we did observe some cross-reactivity of serum samples from patients with COVID-19 to nucleocapsid antigens of SARS-CoV (Extended Data Fig. 1e).

The proportion of patients with positive virus-specific IgG reached 100% approximately 17–19 days after symptom onset, while the proportion of patients with positive virus-specific IgM reached a peak of 94.1% approximately 20–22 days after symptom onset (Fig. 1a and Methods).

During the first 3 weeks after symptom onset, there were increases in virus-specific IgG and IgM antibody titers (Fig. 1b).

However, IgM showed a slight decrease in the >3-week group compared to the ≤3-week group (Fig. 1b).

IgG and IgM titers in the severe group were higher than those in the non-severe group, although a significant difference was only observed in IgG titer in the 2-week post-symptom onset group (Fig. 1c, P = 0.001).

Sixty-three patients with confirmed COVID-19 were followed up until discharge.

Serum samples were collected at 3-day intervals.

Among these, the overall seroconversion rate was 96.8% (61/63) over the follow-up period.

Two patients, a mother and daughter, maintained IgG- and IgM-negative status during hospitalization.

Serological courses could be followed for 26 patients who were initially seronegative and then underwent seroconversion during the observation period.

All these patients achieved seroconversion of IgG or IgM within 20 days after symptom onset.

The median day of seroconversion for both IgG and IgM was 13 days post symptom onset.

Three types of seroconversion were observed: synchronous seroconversion of IgG and IgM (nine patients), IgM seroconversion earlier than that of IgG (seven patients) and IgM seroconversion later than that of IgG (ten patients) (Fig. 2a).

Longitudinal antibody changes in six representative patients of the three types of seroconversion are shown in Fig. 2b–d and Extended Data Fig. 2a–c.

We found no association between plateau IgG levels and the clinical characteristics of the patients (Extended Data Fig. 5a–d).

We next analyzed whether the criteria for confirmation of MERS-CoV infection recommended by WHO, including (1) seroconversion or (2) a fourfold increase in IgG-specific antibody titers, are suitable for the diagnosis of COVID-19 (using paired samples from 41 patients).

The initial sample was collected in the first week of illness and the second was collected 2–3 weeks later.

Of the patients whose IgG was initially seronegative in the first week of illness, 51.2% (21/41) underwent seroconversion.

A total of 18 patients were initially seropositive in the first week of illness; of these, eight patients had a fourfold increase in virus-specific IgG titers (Extended Data Fig. 6).

Overall, 70.7% (29/41) of the patients with COVID-19 met the criteria of IgG seroconversion and/or fourfold increase or greater in the IgG titers.

To investigate whether serology testing could help identify patients with COVID-19, we screened 52 suspected cases in patients who displayed symptoms of COVID-19 or abnormal radiological findings and for whom testing for viral RNA was negative in at least two sequential samples.

Of the 52 suspected cases, four had virus-specific IgG or IgM in the initial samples (Extended Data Fig. 7).

Patient 3 had a greater than fourfold increase in IgG titer 3 days after the initial serology testing.

Interestingly, patient 3 also tested positive for viral infection by polymerase chain reaction with reverse transcription (RT–PCR) between the two antibody measurements.

IgM titer increased over three sequential samples from patient 1 (<4-fold).

Patient 4 had 100-fold higher IgG and tenfold higher IgM titers than the cutoff values in two sequential samples.

Patient 2 tested positive for both virus-specific IgG and IgM.

An increase of IgG and/or IgM in sequential samples or a positive result in a single sample collected 2 weeks after symptoms suggest that these three patients were infected with SARS-CoV-2.

We further demonstrated the application of serology testing in surveillance in a cluster of 164 close contacts of patients with known COVID-19.

Sixteen individuals were confirmed to be infected with SARS-CoV-2 by RT–PCR, with three cases reporting no symptoms.

The other 148 individuals had negative RT–PCR results and no symptoms (Extended Data Fig. 8).

Serum samples were collected from these 164 individuals for antibody tests ~ 30 days after exposure.

The 16 RT–PCR-confirmed cases were all positive for virus-specific IgG and/or IgM.

Moreover, 7 of the 148 individuals with negative RT–PCR results had positive virus-specific IgG and/or IgM, indicating that 4.3% (7/164) of the close contacts were missed by the nucleic acid test.

Ten of the 164 close contacts who had positive virus-specific IgG and/or IgM were asymptomatic.

Our study showed that the criteria for the confirmation of MERS-CoV infection are suitable for most patients with COVID-19.

However, a collection of the first serum sample as early as possible is required for some patients to meet these criteria, because 12.2% (5/41) of the patients had already plateaued in IgG titer within 7 days of symptom onset (Extended Data Fig. 6).

For those patients who were not sampled during the ideal window, repeated serological tests would be needed to confirm an antibody response to SARS-CoV-2 infection.

Our study has some limitations.

First, we did not test samples for virus neutralization and therefore the neutralizing activities of the detected IgG antibodies are unknown.

Second, due to the small sample size of patients in severe and critical condition, it is difficult to determine the association between antibody response and clinical course.

RT–PCR-based viral RNA detection is sensitive and can effectively confirm early SARS-CoV2 infection8.

Our data indicate that virus-specific antibody detection for COVID-19 could be important (1) as a complement to nucleic acid testing for the diagnosis of suspected cases with negative RT–PCR results and (2) in surveying for asymptomatic infection in close contacts.

Confirming suspected COVID-19 cases as early as possible with the help of serological testing could reduce exposure risk during repeated sampling and save valuable RT–PCR tests.

In our small-scale survey, seven cases with negative nucleic acid results and no symptoms showed positive IgG and/or IgM.

This highlights the importance of serological testing to achieve more accurate estimates of the extent of the COVID-19 pandemic.

Methods

Study design


A total of 285 patients with COVID-19 were enrolled in this cross-sectional study from three designated hospitals in Chongqing, a province-level municipality adjacent to Hubei Province, which was the starting point and epicenter of the COVID-19 epidemic.

These three hospitals — Chongqing Three Gorges Central Hospital (TGH), Yongchuan Hospital Affiliated to Chongqing Medical University (CQMU) (YCH) and Chongqing Public Health Medical Center (CQPHMC) — were assigned by the Chongqing municipal people’s government to admit patients from the three designated areas.

All enrolled patients were confirmed to be infected with SARS-CoV-2 by RT–PCR assays on nasal and pharyngeal swab specimens.

The median age of these enrolled patients was 47 years (IQR, 34–56 years) and 55.4% were males.

Among them, 250 patients had an epidemiological history, while 262 patients had a clear record of symptom onset and 70 patients had multiple serum samples.

A total of 363 serum samples from patients with a clear symptom onset history were included in the analysis.

Of the 285 patients, 39 were classified as in a severe or critical condition according to the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7), released by the National Health Commission & State Administration of Traditional Chinese Medicine.

For the follow-up cohort, serum samples from 63 patients at YCH were taken at 3-day intervals from 8 February 2020 until hospital discharge.

To analyze whether the serological criteria for MERS-CoV confirmation recommended by WHO were suitable for the diagnosis of COVID-19, two inclusion criteria were set to screen patients: (1) first serum sample collected within the first week of illness onset or (2) first serum sample collected within at least 7 days of illness onset but with negative IgG.

Thirty-four patients met criterion 1 and seven patients met criterion 2.

To evaluate the potential of the serological test in COVID-19 diagnosis, we enrolled 52 patients with suspected COVID-19 admitted to Wanzhou People’s Hospital (Chongqing, China) who had respiratory symptoms or abnormal pulmonary imaging, but negative RT–PCR results in at least two sequential tests.

Serum samples were collected at the time indicated in Extended Data Fig. 7 and antibodies against SARS-CoV-2 were tested.

A serological survey was performed in a cluster of close contacts composed of 164 individuals, identified by the local center for disease control and prevention (Wanzhou, Chongqing).

A couple who had traveled back from Wuhan city, and who were confirmed to be SARS-CoV-2 infected on 4 February 2020, were deemed the first-generation patients in this contact network.

All other cases in this cohort had close contact (either directly or indirectly) with this couple in the period from 20 January to 6 February 2020.

On 1 March, serum samples were collected from these 164 cases for antibody tests.

Definitions

Patients with epidemiologic history were defined as follows: Wuhan residents; recently been to Wuhan (30 days preceding symptom onset); local resident who had contact with confirmed cases.

Seroconversion was defined as a transition of the test results for IgG or IgM against SARS-CoV-2 from negative to positive results in sequential samples.

Antibody levels were presented as the measured chemiluminescence values divided by the cutoff (absorbance/cutoff, S/CO): S/CO > 1 was defined as positive and S/CO ≤ 1 as negative.

Detection of IgG and IgM against SARS-CoV-2

To measure the level of IgG and IgM against SARS-CoV-2, serum samples were collected from the patients.

All serum samples were inactivated at 56 °C for 30 min and stored at −20 °C before testing.

IgG and IgM against SARS-CoV-2 in plasma samples were tested using MCLIA kits supplied by Bioscience Co. (approved by the China National Medical Products Administration; approval numbers 20203400183(IgG) and 20203400182(IgM)), according to the manufacturer’s instructions.

MCLIA for IgG or IgM detection was developed based on a double-antibody sandwich immunoassay.

The recombinant antigens containing the nucleoprotein and a peptide from the spike protein of SARS-CoV-2 were conjugated with FITC and immobilized on anti-FITC antibody-conjugated magnetic particles.

Alkaline phosphatase conjugated anti-human IgG/IgM antibody was used as the detection antibody.

The tests were conducted on an automated magnetic chemiluminescence analyzer (Axceed 260, Bioscience) according to the manufacturer’s instructions.

All tests were performed under strict biosafety conditions.

The antibody titer was tested once per serum sample.

Antibody levels are presented as the measured chemiluminescence values divided by the cutoff (S/CO).

The cutoff value of this test was defined by receiver operating characteristic curves.

Antibody levels in the figures were calculated as log2(S/CO + 1).

Performance evaluation of the SARS-CoV-2-specific IgG/IgM detection assay

The precision and reproducibility of the MCLIA kits were first evaluated by the National Institutes for Food and Drug Control.

Moreover, 30 serum samples from patients with COVID-19 showing different titers of IgG (range 0.43–187.82) and IgM (range 0.26–24.02) were tested.

Each individual sample was tested in three independent experiments, and the coefficient of variation (CV) was used to evaluate the precision of the assay.

Finally, 46 serum samples from patients with COVID-19 were assessed using different batches of the diagnostic kit for SARS-CoV-2-specific IgG or IgM antibody; reproducibility was calculated based on the results from two batch experiments.

Cross-reactivity of antigens from SARS-CoV and SARS-CoV-2

Two recombinant SARS-CoV nucleocapsid (N) proteins from two different sources (Sino Biological, cat. no. 40143-V08B; Biorbyt, cat. no. orb82478), the recombinant S1 subunit of the SARS-CoV spike (Sino Biological, cat. no. 40150-V08B1) and the homemade recombinant N protein of SARS-CoV-2 were used in a chemiluminescence enzyme immunoassay (CLEIA), respectively.

The concentration of antigens used for plate coating was 0.5 μg ml−1.

The dilution of alkaline phosphatase conjugated goat anti-human IgG antibody was 1:2,500.

Five serum samples from patients with COVID-19 and five serum samples from healthy controls were diluted (1:50) and tested using CLEIA assays.

The binding ability of antibody to antigen in a sample was measured in relative luminescence units.

Statistical analyses

Continuous variables are expressed as the median (IQR) and were compared with the Mann–Whitney U-test.

Categorical variables are expressed as numbers (%) and were compared by Fisher’s exact test.

A P value of <0.05 was considered statistically significant.

Statistical analyses were performed using R software, version 3.6.0.

References

1. Corman, V. M. et al. Viral shedding and antibody response in 37 patients with Middle East respiratory syndrome coronavirus infection. Clin. Infect. Dis. 62, 477–483 (2016).

2. Li, G., Chen, X. & Xu, A. Profile of specific antibodies to the SARS-associated coronavirus. N. Engl. J. Med. 349, 508–509 (2003).

3. Hsueh, P. R., Huang, L. M., Chen, P. J., Kao, C. L. & Yang, P. C. Chronological evolution of IgM, IgA, IgG and neutralisation antibodies after infection with SARS-associated coronavirus. Clin. Microbiol. Infect. 10, 1062–1066 (2004).

4. Park, W. B. et al. Kinetics of serologic responses to MERS coronavirus infection in humans, South Korea. Emerg. Infect. Dis. 21, 2186–2189 (2015).

5. Drosten, C. et al. Transmission of MERS-coronavirus in household contacts. N. Engl. J. Med. 371, 828–835 (2014).

6. Meyer, B., Drosten, C. & Muller, M. A. Serological assays for emerging coronaviruses: challenges and pitfalls. Virus Res. 194, 175–183 (2014).

7. Tang, Y. W., Schmitz, J. E., Persing, D. H. & Stratton, C. W. The laboratory diagnosis of COVID-19 infection: current issues and challenges. J. Clin. Microbiol. https://doi.org/10.1128/JCM.00512-20 (2020).

8. Zou, L. et al. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N. Engl. J. Med. 382, 1177–1179 (2020).

https://www.nature.com/articles/s41591-020-0897-1

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Re: Inward Bound - The T'ai Chi Corner

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The NEW ENGLAND JOURNAL of MEDICINE

Editor’s Note:
This letter was published on February 19, 2020, at NEJM.org.

Correspondence

SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients

March 19, 2020

N Engl J Med 2020; 382:1177-1179

DOI: 10.1056/NEJMc2001737

To the Editor:

The 2019 novel coronavirus (SARS-CoV-2) epidemic, which was first reported in December 2019 in Wuhan, China, and has been declared a public health emergency of international concern by the World Health Organization, may progress to a pandemic associated with substantial morbidity and mortality.

SARS-CoV-2 is genetically related to SARS-CoV, which caused a global epidemic with 8096 confirmed cases in more than 25 countries in 2002–2003.1

The epidemic of SARS-CoV was successfully contained through public health interventions, including case detection and isolation.


Transmission of SARS-CoV occurred mainly after days of illness2 and was associated with modest viral loads in the respiratory tract early in the illness, with viral loads peaking approximately 10 days after symptom onset.3

We monitored SARS-CoV-2 viral loads in upper respiratory specimens obtained from 18 patients (9 men and 9 women; median age, 59 years; range, 26 to 76) in Zhuhai, Guangdong, China, including 4 patients with secondary infections (1 of whom never had symptoms) within two family clusters.

The patient who never had symptoms was a close contact of a patient with a known case and was therefore monitored.

A total of 72 nasal swabs (sampled from the mid-turbinate and nasopharynx) (Figure 1A) and 72 throat swabs (Figure 1B) were analyzed, with 1 to 9 sequential samples obtained from each patient.

Polyester flock swabs were used for all the patients.

From January 7 through January 26, 2020, a total of 14 patients who had recently returned from Wuhan and had fever (≥37.3°C) received a diagnosis of Covid-19 (the illness caused by SARS-CoV-2) by means of reverse-transcriptase–polymerase-chain-reaction assay with primers and probes targeting the N and Orf1b genes of SARS-CoV-2; the assay was developed by the Chinese Center for Disease Control and Prevention.

Samples were tested at the Guangdong Provincial Center for Disease Control and Prevention.

Thirteen of 14 patients with imported cases had evidence of pneumonia on computed tomography (CT).

None of them had visited the Huanan Seafood Wholesale Market in Wuhan within 14 days before symptom onset.

Patients E, I, and P required admission to intensive care units, whereas the others had mild-to-moderate illness.

Secondary infections were detected in close contacts of Patients E, I, and P.

Patient E worked in Wuhan and visited his wife (Patient L), mother (Patient D), and a friend (Patient Z) in Zhuhai on January 17.

Symptoms developed in Patients L and D on January 20 and January 22, respectively, with viral RNA detected in their nasal and throat swabs soon after symptom onset.

Patient Z reported no clinical symptoms, but his nasal swabs (cycle threshold [Ct] values, 22 to 28) and throat swabs (Ct values, 30 to 32) tested positive on days 7, 10, and 11 after contact.

A CT scan of Patient Z that was obtained on February 6 was unremarkable.

Patients I and P lived in Wuhan and visited their daughter (Patient H) in Zhuhai on January 11 when their symptoms first developed.

Fever developed in Patient H on January 17, with viral RNA detected in nasal and throat swabs on day 1 after symptom onset.

We analyzed the viral load in nasal and throat swabs obtained from the 17 symptomatic patients in relation to day of onset of any symptoms (Figure 1C).

Higher viral loads (inversely related to Ct value) were detected soon after symptom onset, with higher viral loads detected in the nose than in the throat.

Our analysis suggests that the viral nucleic acid shedding pattern of patients infected with SARS-CoV-2 resembles that of patients with influenza4 and appears different from that seen in patients infected with SARS-CoV.3

The viral load that was detected in the asymptomatic patient was similar to that in the symptomatic patients, which suggests the transmission potential of asymptomatic or minimally symptomatic patients.


These findings are in concordance with reports that transmission may occur early in the course of infection5 and suggest that case detection and isolation may require strategies different from those required for the control of SARS-CoV.

How SARS-CoV-2 viral load correlates with culturable virus needs to be determined.

Identification of patients with few or no symptoms and with modest levels of detectable viral RNA in the oropharynx for at least 5 days suggests that we need better data to determine transmission dynamics and inform our screening practices.

Lirong Zou, M.Sc.
Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China

Feng Ruan, M.Med.
Zhuhai Center for Disease Control and Prevention, Zhuhai, China

Mingxing Huang, Ph.D.
Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China

Lijun Liang, Ph.D.
Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China

Huitao Huang, B.Sc.
Zhuhai Center for Disease Control and Prevention, Zhuhai, China

Zhongsi Hong, M.D.
Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China

Jianxiang Yu, B.Sc.
Min Kang, M.Sc.
Yingchao Song, B.Sc.
Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China

Jinyu Xia, M.D.
Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China

Qianfang Guo, M.Sc.
Tie Song, M.Sc.
Jianfeng He, B.Sc.
Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China

Hui-Ling Yen, Ph.D.
Malik Peiris, Ph.D.
University of Hong Kong, Hong Kong, China

Jie Wu, Ph.D.
Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
771276998@qq.com

This letter was published on February 19, 2020, and updated on February 20, 2020, at NEJM.org.

Ms. Zou, Mr. Ruan, and Dr. Huang contributed equally to this letter.

References

1. Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July 2003. Geneva: World Health Organization, 2004 (https://www.who.int/csr/sars/country/ta ... _04_21/en/. opens in new tab).

2. Lipsitch M, Cohen T, Cooper B, et al. Transmission dynamics and control of severe acute respiratory syndrome. Science 2003;300:1966-1970.

3. Peiris JSM, Chu CM, Cheng VCC, et al. Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study. Lancet 2003;361:1767-1772.

4. Tsang TK, Cowling BJ, Fang VJ, et al. Influenza A virus shedding and infectivity in households. J Infect Dis 2015;212:1420-1428.

5. Rothe C, Schunk M, Sothmann P, et al. Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N Engl J Med 2020;382:970-971.

https://www.nejm.org/doi/10.1056/NEJMc2001737

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Re: Inward Bound - The T'ai Chi Corner

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ScienceMag.org

Good News on the Human Immune Response to the Coronavirus


By Derek Lowe

15 May, 2020

One of the big (and so far unanswered) questions about the coronavirus epidemic is what kind of immunity people have after becoming infected.

This is important for the idea of “re-infection” (is it even possible?) and of course for vaccine development.

We’re getting more and more information in this area, though, and this new paper is a good example.

A team from the La Jolla Institute for Immunology, UNC, UCSD, and Mt. Sinai (NY) reports details about the T cells of people who have recovered from the virus.


To get into this, a quick explainer seems appropriate, so the next bit will be on the background of T cells and adaptive immunity – then we’ll get into these latest results.

So everyone’s heard of the broad category of white blood cells.

One group of those are the lymphocytes (literally “lymph cells”, where they’re most easily found), and the lymphocytes include T cells, B cells, and NK cells.

You’re looking at three big branches of the immune system right there.

The NK (“natural killer”) cells are part of the innate immunity, the nonspecific kind, and they’re in the cell-mediated cytotoxic wing of that.

The other side of the immune system is adaptive immunity.

The B cells feature in my antibody background posts, because as part of the adaptive system they’re the ones that produce more of some specific antibody once one of the zillions of them present in the body turns out to fit onto a new antigen.

The T cells are in the adaptive side as well, but they’re in the cell-mediated part of that army.

T cells come from the thymus (thus the “T”), so if you’ve been wondering what your thymus has done for you lately, that’s one good answer.

They all have a particular surface protein, the T cell receptor.

Similar to the way that the immune system generates a huge number of antibodies by shuffling and mixing protein expression, there are a huge number of different T cell receptors waiting to recognize what antigens may come along.

The precursors of T cells come from the bone marrow and migrate to the thymus, where they branch out into different lines (and that branching out continues even once they leave the thymus and begin circulating in the lymph and in the blood).

The most direct of those are the cytotoxic T cells, also known as CD8+ T cells and by several other names.

CD8 is another particular cell-surface protein that distinguishes this type.

These cells aren’t going after viral particles; they’re going after the body’s own virus-infected cells and killing them off before they can break open and spread more viral particles.

They’ll kill off bacterial cells in the same way.

These are also the ones that the CAR-T therapies are trying to mobilize so that they’ll recognize cancer cells and do the same thing to them.


How do they accomplish the deed?

They’re thorough; there are several deadly mechanisms that kick in.

One general one is to secrete cytokines, especially TNF-alpha and interferon-gamma, that alert other cellular systems to the fact that they’ve detected targets to attack.

(The monoclonal antibody drugs for arthritis are actually aimed to shut down that TNF-alpha pathway, because in RA the T cells are – very inappropriately – attacking the body’s own joint tissue).

A second CD8+ action is to release “cytotoxic granules”.

These are payloads of destruction aimed at the target cell once the T cell is closely connected to it (the “immune synapse”).

You need that proximity because cytotoxic granules are bad news – they contain proteins that open up pores in the target cell, and blunderbuss serine protease enzymes that slide in through them, whereupon they start vigorously cleaving intracellular proteins and causing general chaos (and eventually cell death).

And the third killing mode is via another cell-surface protein the CD8+ cells have called FasL – it binds to a common protein on the target cells called Fas, and that sets off a signaling cascade inside the target cells that also leads to cell death.

(Interestingly, the CD8+ cells use this system after an infection has subsided to kill each other off and get their levels back down to normal!)

And then there’s another crowd, the CD4+ T cells, also known as T-helper cells and by other names.

They work with another class of immune cells, the antigen-presenting cells, which go around taking in all sorts of foreign proteins and presenting them on their cell surfaces.

A CD4+ cell, when it encounters one of those, goes through a two-stage activation process kicks in (the second stage is sort of a verification check to make sure that it’s really a foreign antigen and not something already present in the body).

If that’s successful, they start to proliferate.

And you’re going to hate me for saying this, but that’s where things get complicated.

Immunology!

The helper T cells have a list of immune functions as long as your leg, interacting with many other cell types.

Among other things, they help set off proliferation of the CD8+ cells just detailed, they activate B cells to start producing specific antibodies, and they’re involved with secretion of more cytokine signaling molecules than I can even stand to list here.

These are in fact the cells targeted by HIV, and it’s the loss of such crucial players in the immune response that makes that disease so devastating.

OK, there’s some background for this new paper.

What it’s looking at in detail are the virus-specific CD8+ and CD4+ cells that have been raised up in response to the infection in recovering patients.

As you’ve seen, both of these subtypes are adaptive; they’re recognizing particular antigens and responding to those – so how robust was this response, and what coronavirus antigens set things off?

You can see how important these details are – depending on what happens, you could have an infection that doesn’t set off enough of a response to leave behind B and T cells that will remember what happened, leaving people vulnerable to re-infection.

Or you could set off too huge a response – all those cytokines in the “cytokine storm” that you hear about?

CD4+ cells are right in the middle of that, and I’ve already mentioned the TNF-alpha problems that are a sign of misaligned CD8+ response.

The current coronavirus is pretty good at evading the innate immune system, unfortunately, so the adaptive immune system is under more pressure to deliver.

And one reason (among many) that the disease is more severe in elderly patients is that the number of those antigen-presenting cells decline with age, so one of the key early steps of that response gets muted.

That can lead to a too-late too-heavy T cell response when things finally do get going, which is your cytokine storm, etc.


In between the extremes is what you want: a robust response that clears the virus, remembers what happened for later, and doesn’t go on to attack the body’s own tissues in the process.

Comparing infected patients with those who have not been exposed to the coronavirus, this team went through the list of 25 viral proteins that it produces.

In the CD4+ cells, the Spike protein, the M protein, and the N protein stood out: 100% of the exposed patients had CD4+ cells that responded to all three of these.

There were also significant CD4+ responses to other viral proteins: nsp3, nsp4, ORF3s, ORF7a, nsp12 and ORF8.

The conclusion is that a vaccine that uses Spike protein epitopes should be sufficient for a good immune response, but that there are other possibilities as well – specifically, adding in M and N protein epitopes might do an even more thorough job of making a vaccine mimic a real coronavirus infection to train the immune system.


As for the CD8+ cells, the situation looked a bit different.

The M protein and the Spike protein were both strong, with the N protein and two others (nsp6 and ORF3a) behind it.

Those last three, though, were still about 50% of the response, when put together, so there was no one single dominant protein response.

So if you’re looking for a good CD8+ response, adding in epitopes from one or more of those other proteins to the Spike epitope looks like a good plan – otherwise the response might be a bit narrow.

And here’s something to think about: in the unexposed patients, 40 to 60% had CD4+ cells that already respond to the new coronavirus.

This doesn’t mean that people have already been exposed to it per se, of course – immune crossreactivity is very much a thing, and it would appear that many people have already raised a response to other antigens that could be partially protective against this new virus.

What antigens those are, how protective this response is, and whether it helps to account for the different severity of the disease in various patients (and populations) are important questions that a lot of effort will be spent answering.


As the paper notes, such cross-reactivity seems to have been a big factor in making the H1N1 flu epidemic less severe than had been initially feared – the population already had more of an immunological head start than thought.

So overall, this paper makes the prospects for a vaccine look good: there is indeed a robust response by the adaptive immune system, to several coronavirus proteins.

And vaccine developers will want to think about adding in some of the other antigens mentioned in this paper, in addition to the Spike antigens that have been the focus thus far.

It seems fair to say, though, that the first wave of vaccines will likely be Spike-o-centric, and later vaccines might have these other antigens included in the mix.

But it also seems that Spike-protein-targeted vaccines should be pretty effective, so that’s good.

The other good news is that this team looked for the signs of an antibody-dependent-enhancement response, which would be bad news, and did not find evidence of it in the recovering patients (I didn’t go into these details, but wanted to mention that finding, which is quite reassuring).

And it also looks like the prospects for (reasonably) lasting immunity after infection (or after vaccination) are good.

This, from what I can see, is just the sort of response that you’d want to see for that to be the case.


Clinical data will be the real decider on that, but there’s no reason so far to think that a person won’t have such immunity if they fit this profile.

Onward from here, then – there will be more studies like this coming, but this is a good, solid look into the human immunology of this outbreak.

And so far, so good.

https://blogs.sciencemag.org/pipeline/a ... oronavirus

thelivyjr
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Re: Inward Bound - The T'ai Chi Corner

Post by thelivyjr » Thu Jun 04, 2020 1:40 p

NCBI

US National Library of Medicine

National Institutes of Health

Clin Mol Allergy

v.15; 2017

PMC5731094

Clin Mol Allergy. 2017; 15: 21.

Published online 2017 Dec 14. doi: 10.1186/s12948-017-0077-0

PMCID: PMC5731094

PMID: 29259496

Immunosenescence in aging: between immune cells depletion and cytokines up-regulation

Maria Teresa Ventura, Marco Casciaro, corresponding author Sebastiano Gangemi, and Rosalba Buquicchio

Abstract

Background


The immunosenescence is a relatively recent chapter, correlated with the linear extension of the average life began in the nineteenth century and still in progress.

The most important feature of immunosenescence is the accumulation in the “immunological space” of memory and effector cells as a result of the stimulation caused by repeated clinical and subclinical infections and by continuous exposure to antigens (inhalant allergens, food, etc.).

This state of chronic inflammation that characterizes senescence has a significant impact on survival and fragility.


In fact, the condition of frail elderly occurs less frequently in situations characterized by poor contact with viral infections and parasitic diseases.

Furthermore the immunosenescence is characterized by a particular “remodelling” of the immune system, induced by oxidative stress.

Apoptosis plays a central role in old age, a period in which the ability of apoptosis can change.

The remodelling of apoptosis, together with the Inflammaging and the up-regulation of the immune response with the consequent secretion of pro-inflammatory lymphokines represents the major determinant of the rate of aging and longevity, as well as of the most common diseases related with age and with tumors.


Other changes occur in the innate immunity, the first line of defence providing rapid, but unspecific and incomplete protection, consisting mostly of monocytes, natural killer cells and dendritic cells, acting up to the establishment of a adaptive immune response, which is slower, but highly specific, which cellular substrate consists of T and B lymphocytes.

The markers of “Inflammaging” in adaptive immunity in centenarians are characterized by a decrease in T cells “naive.”

The reduction of CD8 virgins may be related to the risk of morbidity and death, as well as the combination of the increase of CD8+ cells and reduction of CD4+ T cells and the reduction of CD19+ B cells.

The immune function of the elderly is weakened to due to the exhaustion of T cell-virgin (CD95−), which are replaced with the clonal expansion of CD28-T cells.

Conclusions

The increase of pro-inflammatory cytokines is associated with dementia, Parkinson’s disease, atherosclerosis, diabetes type 2, sarcopenia and a high risk of morbidity and mortality.

A correct modulation of immune responses and apoptotic phenomena can be useful to reduce age-related degenerative diseases, as well as inflammatory and neoplastic diseases.

Background

Recent researches stigmatize that the steady increase in life expectancy in Europe, USA, Canada, Japan and England will allow many of the children born in 2000 to reach 100 years of life [1].

In this perspective, the main objective of the geriatrician is to analyze risk factors for diseases and conditions that can lead to functional limitations for the elderly, in order to avoid people to reach a disability state.

In summary, the increase in life expectancy must coincide with an expectation of health, good health and self-sufficiency for the last part of life [2].

Old age is a situation in which a number of factors (molecular, cellular, physiological, immunological and psycho-social events) help to set up a scenario of “exhaustion of reserves”; this consist of a inability to functional adaptations and an accumulation of deficits of many organs [3].

This situation undergoes a dynamic process that oscillates between a “successful” and pathological aging, which establishes a situation of vulnerability that is identified with the fragility state.

Undoubtedly, to the fragility state contribute the same factors that have contributed to the increased life expectancy, which is attested at the moment at 80 years of age [4].

Many factors have allowed this “stretching”, i.e. the decrease of infant mortality, antibiotic therapy and prevention of cardiovascular and metabolic diseases, but also, especially in industrialized countries, the improvement of hygienic and nutrition conditions [5].

However, if aging is not accompanied by a healthy condition, the costs related to disabilities or frailty age-related could lead to the overgrowth of the public health expense with a negative impact on social welfare.


Immunosenescence

“The aging phenotype”, including the immunosenescence is the result of an imbalance between inflammatory and anti-inflammatory mechanisms with the consequence of a state defined by some authors as “inflammaging” [6–8].

The “Inflammaging” is due to chronic antigen stimulation that occurs in the course of life and to the oxidative stress that involves the production of oxygen free radicals and toxic products.

Both these factors are able to modify the potential of apoptotic lymphocytes.


In fact, the phenomenon of “remodelling” and the “up-regulation” of pro-inflammatory cytokines (including IL-6) are the components most heavily implicated in the processes of longevity and diseases related to senescence [9–12].

As pro-inflammatory and anti-inflammatory chemokines and other signalling molecules, might propagate from already activated cells to adjacent ones and systemically by circulating products and microvesicles, recent studies claimed the possibility of understanding molecular basis of inflammaging by novel omic approaches [13].

In this sense, the state of good health in the elderly is the result not only of low pro inflammatory mechanisms, but also of an efficient network capable of neutralizing anti inflammatory antigenic insults received in the course of life.

For this reason, the inflammaging would not only be important for the mechanisms of immunosenescence but also for the problem of longevity.

It is reported that fragility is the result of an inflammatory state associated with the overproduction of certain lymphokines, including IL-6, called cytokine of geriatricians [14].

This factor, together with hormonal changes, nutritional deficiencies and physical inactivity would lead to one of the most important component of fragility that is sarcopenia [15, 16], as well as the reduction of bone mass.


In this context, immunity appears to play an important role, both in the regulation of the mechanisms of aging as well as in the onset of the diseases typical of aging (i.e. infectious diseases, autoimmunity, cancer, metabolic diseases and neurodegenerative diseases).

Oxidative damage

Another factor influencing the Immune System, in addition to the antigenic stimulus, is the intervention of metabolites of oxygen (ROS), a consequence of the activation of the respiratory burst; as they can cause significant damage, is plausible to assume that aging is also due to an accumulation of free radicals [17].

The increase in oxygen metabolites and their accumulation causes damage to important cellular components (lipid membranes, the structural and enzymatic proteins and nucleic acids), which are contrasted by enzymatic and non-enzymatic defence systems, reparative enzymes, DNA damage and apoptotic processes damage-induced [18, 19].

These protective mechanisms become less effective as a result of continued exposure to oxidative stress and the accumulation of senescent and mutated cells, leading to an increased risk of cancer.

The p53 protein counteracts the development of a neoplasm flaunting how cells respond to injury (DNA repair, or, if it fails, apoptosis).

p53 plays an important role in senescence: if it increases, the incidence of neoplasia is reduced, but on the other hand it increases the speed of aging [20].

The result is a delicate balance between reduced p53 that leads to death by cancer and increased p53 leading to death for acceleration of senescence.

Furthermore, an over expression of p53 generates reactive oxygen intermediates [21].

An important source of oxygen intermediates are the mitochondria.

Although the main mitochondrial function is the production of energy, the isolated mitochondria generate oxygen radicals during oxidative phosphorylation.

The mitochondrial electron transport chain is imperfect because it generates a superoxide radical by a process of reduction of O2.

The enzymatic dismutation of the superoxide radical produces H2O2, another important biological oxidant.

Another factor contributing to the senescence is apoptosis, especially the one induced by acid metabolites arising from the paths of lipoxygenase and cyclooxygenase; however it is not clear whether these products induce the production of reactive intermediates or if they act independently as oxidants to induce apoptosis.

Another intermediate product of oxygen metabolism is nitric oxide, a free radical known as an important regulator of mitochondrial function, capable of increasing the apoptotic phenomena, but also, at physiological levels, of preventing apoptosis and of interfering with the cascade of capsaicin [22].

In conclusion, high levels of oxidants can change the potential of oxido-reduction, by reducing the levels of ATP and increasing the porosity of the membranes leading to a progressive aging phenomenon of the cells, included the Immune System ones [23].

The “Remodelling” of the immune system

As a result of ROS accumulation, cells become resistant to apoptosis -induced damage and the number of senescent cells increases, while chronic antigenic stimulation induces increase of activated immune cells and overproduction of pro-inflammatory lymphokines that contribute to the remodelling of the immune system and of the ‘inflammaging’.

Senescence is a highly dynamic phenomenon characterised by continuous body adaptation to deteriorative changes [24].

ROS are closely linked to senescence and age-related diseases, in fact, genomic instability, caused by oxidative damage is the primary cause of aging.

A caloric restriction can increase the average life-attenuating oxidative stress caused by normal metabolism [25].

As result of both inflammaging and of ROS increase, the modulation of apoptosis mechanisms becomes particularly delicate during senescence.

The reduced sensitivity to the damage-induced apoptosis, typical of senescent cells, contributes to the accumulation of dysfunctional cells, clones of CD8+ and memory cells with a reduction of immunological space and an increased risk of infections and neoplastic diseases or degenerative disorders.

The increase of the activation-induced apoptosis in response to inflammatory cytokines contributes to: the depletion of cells “naive”, the reduction of the capacity of clonal expansion, the reduction of T cell responses with decreased ability to mount strong immune responses to antigenic stimuli and reduction of the immune repertoire.

The shortening of the telomeric DNA is age specific, and, regardless of the genetic influence, it is the result of the immunological history of each individual, with a close association between telomere length and mortality of individuals older than 65 years [26].


The input of virgin T cells gradually decreases, and, recently, the marker of the lymphocytes of the new generation are the T REC which represent markers of replication of T cells, with a progressive reduction of each subsequent division.

Of course, the T REC decay dramatically with age in peripheral T lymphocytes.

Apoptosis

Apoptosis, a complex mechanism of programmed cell death, allows the maintenance of a physiological homeostasis mechanisms between survival and removal of damaged cells, allowing also the prevention of many diseases including neoplastic ones.

Apoptosis is a strategic mechanism for the manifestation of the clonotypic diversity during lymphocyte selection, permitting to control the clonal expansion after antigenic stimulation.

Apoptosis can be induced after a cellular damage (damage-induced cell death), or can be “activated” by a series of signals and anchor ligands to programmed-death receptors (activation-induced cell death).

Apoptosis is part of many changes typical of the immunosenescence, such as thymic involution, the alteration of the “repertoire” of T cells and the accumulation of effector memory cells, all events at the basis of autoimmunity.


Studies about apoptosis in aging are controversial.

In fact, during senescence both the two apoptotic process can be modulated differently, resulting in a variable impact in the process of senescence.

A proper modulation of this important function can extend the lifespan and reduce the degenerative processes and inflammatory and neoplastic diseases that are very common during senescence [6, 27].

Hematopoietic bone and thymus

The immune system cells are constantly renewed from hematopoietic stem cells (HSC), but this ability declines during senescence and the total amount of hematopoietic tissue decreases.

This event also seems to correlate with telomere shortening [28].

The changes affect also the myeloid and erythroid progenitors as B cells, with the consequence of a reduction in mature B cells.

The precursors of T cells seem to suffer less; the changes that occur with age to the thymus gland, also lead to changes in the T cell compartment.

The thymus undergoes a process of physiological involution, with volume reduction and replacement with adipose tissue in the functional part of both cortex and medulla, contraction of soluble factors and hormonal cytokines production.

This process begins early in life and is almost complete at the age of 40–50 years [29].

Moreover, the immune system has the important function to protect the body from any form of damaging agent (chemical, traumatic or infectious).

There are two kind of immunity working together in a cooperative manner: the natural immunity (innate) immunity and adaptive (acquired).


The natural immunity is the first line of defence because it provides a fast protection, but unspecific and incomplete, consisting mostly of monocytes, natural killer cells and dendritic cells, which acts until the adaptive immune response is established; this immune response is slower, but highly specific and permanent, with a cellular substrate consisting of T and B lymphocytes.

T cells

The T cells are generated through a Thymic selection and they can be distinguished in CD4+ and CD8+, by their co-receptor molecules.

These two cellular subtypes show during the aging process of the organism some changes in their percentages: the CD8+ cells increase their number during senescence.

The CD4+ and the CD8+ cells express mutually exclusive the phenotype CD45RA and CD45RO.

The first phenotype makes let to recognize naïve T cells, instead the second one to individuate memory/activated T cells [30].

The reduction of naïve lymphocytes may be a consequence of both thymic involution and chronic antigenic stimulation [31].

This event helps to explain the reduced ability of the elderly to resist to new infections [32].


Furthermore in the elderly naïve T-cells show multiple alterations, including the shortening of telomeres, the reduced production of IL-2 and the diminished ability to differentiate themselves into effector-cells.

The loss in the number and function of the naïve T-cells is compensated in about 30% of the elderly, with the expansion of T CD8+ , CD45RO+, CD25+ clones, capable of producing IL-2, and with a protective humoral capacity towards vaccinations with the expansion of effector “memory” cells [33].

In particular, in the elderly the vaccinations induce the accumulation of CD8+ effector cells with phenotypic changes, such as the loss of costimulatory CD8 molecules [32].


CD28-cells are responsible for the production of proinflammatory cytokines and are resistant to apoptosis.

The origin of the CD28-cells has not yet been fully elucidated, but it is assumed that they represent cells undergoing a replicative senescence, due to the shortening of telomeres and to a reduction of the proliferative capacity [34].

The inversion of the CD4+/CD8+ cell number ratio, the increased number of the memory-effector cells and the seropositivity for the Citomegalvirus (CMV), identify an immune risk phenotype (IRP) in elderly patients [35].

At the same time in elderly patients it has been shown an increased production of IL-1, IL-4, IL-6 and IFN-gamma.

These cytokines control B Cells differentiation through the isotype switch and the Ig production.

Further alterations concern a compromised response to the oxidative stress, that causes an increased susceptibility to damage-induced cell death [36], and calcium flow kinetics [37].

Recently it has been associated with the senescence a reduction of Mir 181 (MicroRNA precursor), that in T cell causes an impairment in the antigen recognition [38].

Regulatory T cells (Tregs) are a subset characterized by a high expression of CD25 and FOXP3, a transcriptional factor for the function and differentiation of Treg cells.

The number of CD4+ FOXP3+ lymphocytes increases in the senile age.

The accumulation of these cells in the elderly plays an important role in reactivating chronic infections and the change in the T17/Treg ratio can cause alterations in immune response with the appearance of inflammatory or autoimmune diseases [39].

B cells

Also the reservoir of B cells is influenced by age.

In fact, humoral immunity undergoes both quantitative and qualitative alterations [40].

The reduced function of B cells was thought being due to a lack of helper T function in T-dependent responses.

On the other hand, there are functions of B cells which are T-independent; one example is the response to the polysaccharide, which is crucial for antibacterial protection and that seems to be inefficient too [41].

In addition, some data suggest that B cells are important antigen-presenting cells themselves and that can be regulatory with key function for the development of T cells.

Therefore, it is conceivable that some of the lack in the T cell functions may be due to an insufficient help from B cells.

At the same time, changes are described in the number of B cells.

In the elderly, there are also reported reduced levels of IgM and IgD (M and D type immunoglobulins) certainly connected to the transition from naive cells to memory B cells area [42].

On the contrary, during the senescence it occurs an increase of the IgG (G type immunoglobulins) level, especially of IgG1, IgG2 and IgG3; the level of IgA is also increased [43].

In particular, IgAs undergo significant changes, with a marked increase of monomeric IgA1, both in serum and saliva and a reduction of polymeric IgA2, especially in the sputum [44].

This imbalance could be charged to the reduction of the Peyer’s patches at the level of the gastrointestinal mucosa as regards as IgA2, while the increase of IgA1 may be secondary to a deficiency of the activity of T “suppressor” subset and consequent hyperfunction of B lymphocytes [45].

The deficits taking place in this area are largely due to infectious events in the elderly, particularly in the gastrointestinal and respiratory system.

The reduced number of plasma cells in the elderly bone marrow [46] causes a lack of antibody production, a reduced ability to respond to viruses and bacteria [47] and an altered response to vaccines against B hepatitis virus [48].

The innate immunity system

Alterations in innate immunity have a crucial role and the amount of related studies have identified a trend in the chapter of immunogerontology starting with the reduction of barriers in the epithelial layer of the skin and gastrointestinal and respiratory mucosa [49] with a consequent changes in local immunoglobulin ratio.

Moreover, even some physiological events, such as the reduction of the thymic mass, seems to support the hypothesis that the immune system plays an important role in the phenomenon of aging, thus justifying a theory to explain some of the immunological diseases typical of this age such as autoimmune diseases, malignancies and infections.


The high incidence of infectious events in old age can, however, be secondary to alterations in the phagocyte system [50].

With regard to the skin, immunosenescence is characterized by an impairment of all the structures with loss of the “barrier” function, reduction of the number and the volume of hairs, reduction of the number of sebaceous glands, loss of skin elasticity, impairment of the immunological defence of the skin [51].

Dendritic cells, responsible for the very first recognition of pathogens in the skin, show mitochondrial dysfunctions that interfere with their protective role [52].


In particular there is an impairment of the antigen uptake and of the apoptotic function [53].

Comparing the elderly plasmocytoid dendritic cell (PCD) capacity of antigen uptake with the one of the PCD of the young it is possible to observe a reduced ability of the elderly PCD to induce proliferation and stimulate secretion of INF gamma in CD4+ and CD8+ cells [54].

Macrophages

Macrophages, able to produce pro-inflammatory cytokines (TNF-alpha, IL-1, IL-6 e IL-8), have the function of processing and presenting antigens to T cells.

During senescence, a decrease of macrophages precursors has been described, instead the number of monocytes appears unchanged [55].

The shortening of telomeres occurring in the senile age results in a reduction in the production of GS-CSF but also of Cytokines such as TNF-alpha and IL-6 [56].


In older animals it occurs a reduction of the production of superoxide anion after incubation with INF-gamma [57].

The phagocytic function appears to be reduced, while, chemotaxis seems to be conserved, especially in the presence of certain factors such as stimulants of the complement fragment C5a.

The production of lymphocyte derived chemotactic factors (LDCF) is reduced, as well as chemotaxis in the presence of this stimulator factor.

In this case the inhibitory mechanism appears to be related to prostaglandins that are produced in high quantities, during senescence, and which exert an inhibitory action [58].

The reduced production of LDCF could be related to a low percentage of lymphocytes involved in the synthesis of the cytokine.

Neutrophils

Their number is preserved in the elderly, while the expression of CD16 Fc gamma receptor is reduced, with the consequence that, both the generation of superoxide mediated by the Fc receptor and the phagocytosis are impaired in the elderly; this suggests that the decline of the effector response of Fc receptors is particularly important for neutrophil dysfunction of the elderly [59].

In elderly people, the reduced response of these cells to Streptococcus Aureus is of fundamental clinical importance, because this event increases susceptibility to lung infections.

At the same time in the aged mice the migration of neutrophils into the lungs is reduced and this increases the risk of pulmonary infections and recurrences [60].

In addition, very recently, an alteration of the pathogen-mediated destruction of neutrophil extracellular Traps (NETs) has been described, confirming the reasons of the increase of infections in the elderly [61].

NK cells

The high incidence of immunoproliferative diseases in the elderly suggests that in this age a deficiency of an important mechanism of immune surveillance such as NK activity can be occur.

In 1986, through the use of a “slow” target such as a cell line derived from an hepatocellular carcinoma, allowing an optimal evaluation of NK function, it was conceivable to demonstrate that in the elderly it was a significant reduction of the spontaneous cytotoxic capacity [62].

Recent studies pointed out that a high NK cytotoxicity is associated with longevity and good health, while a low NK function is associated with an increase in morbidity and mortality, and consequently, infections, mechanisms of atherosclerotic and neurodegenerative diseases.

Furthermore, NK cells by producing cell lysis could cause the release of perforin and granzymes, which, in turn, activate caspases and provoke apoptosis of target cells.

During senescence it occurs the reduction of an important lymphokine for the lymphocyte activation processes like the IL-2 and also for the killing of the NK-resistant cell lines in response to IL-2.

This contributes to the deficit of the function, even in the presence of a normal number of NK cells [63].


Particularly during senescence there is a redistribution of NK cells with decreasing CD56 cells, characterized by a high density of surface CD56 antigen.

In contrast, there is an increase in CD56–CD16 Nk cells [64].

This results in a reduction in IFN secretion for the elderly compared with the secreted quantity in young subjects [65].

In addition, during the senescence there is a decrease in the expression of the receptor activation expression, especially linked to the receptor NKp30 and NKp46 [66].

Of course, it’s easy to imagine the consequences that may follow an alteration to the function of this population during senescence; in fact, NK cells intervene both in the elimination of tumor or viral-infected cells and also in the innate and adaptive immunological regulation, through the production of cytokines and chemokines [67].

Phenotype of immunological RISK (IRP) during senescence

According to recent studies, the IRP is predictive of the development of cognitive deficits and, as outlined by some authors, [35] is a prelude for a mortality rate over the next 4 years in 58% of cases.

The IRP is defined, in the studies of this group of Swedish researchers carried out on elderly octogenarians and nonagenarians, by identifying some characteristic “markers” of this phenotype, including the inversion of the CD4/CD8 ratio, the increase in CD8 * CD28-memory/effector cells, the increase of proinflammatory cytokines such as IL-6, the reduction of B lymphocytes and a marked seropositivity for cytomegalovirus [68].

The pro-inflammatory profile resulting from an interaction between the genotype and environmental factors, becomes strategic through the years; the increase of cytokine secretion also correlated with the impact of cytomegalovirus infection is responsible for an unsuccessful aging [69].

CMV-specific cells, both CD4+ and CD8+ cells have short telomeres and this leads to chromosomal instability and DNA damage repair processes in growth arrest and/or apoptosis.

The consequence is that not all T memory cells differ in the same way and that can happen an expansion of this cell pool, whose clinical consequences consist in an increase in infectious diseases and neoplasms [70].

Conclusion

As shown above, immunosenescence is an unavoidable process typical of life being.

Many immune system cells undergoes this process; however, the senescence process differ from one subject to the other.

The development of a pro-inflammatory cytokines phenotype together with the counterbalance of an anti-inflammatory profile could let people reach an old age without disability.

A correct modulation of immune responses and of apoptotic phenomena, in fact, can be useful to reduce age-related degenerative diseases, as well as inflammatory and neoplastic diseases in order to reach a successful aging.

Contributor Information

Maria Teresa Ventura, Email: ti.abinu@arutnev.aseretairam.

Marco Casciaro, Phone: +39 090 2212049, Email: ti.eminu@oraicsacm.

Sebastiano Gangemi, Email: ti.eminu@simegnag.

Rosalba Buquicchio, Email: ti.abinu@oihcciuqub.ablasor.

References

1. Manton KG, Vaupel JW. Survival after the age of 80 in the United States, Sweden, France, England, and Japan. N Engl J Med. 1995;333:1232–1235. doi: 10.1056/NEJM199511023331824.

2. Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. Lancet. 2009;374:1196–1208. doi: 10.1016/S0140-6736(09)61460-4.

3. Kirkwood TB. Understanding the odd science of aging. Cell. 2005;120:437–447. doi: 10.1016/j.cell.2005.01.027.

4. Troen BR. The biology of aging. Mt Sinai J Med. 2003;70:3–22.

5. Jeune B, Brønnum-Hansen H. Trends in health expectancy at age 65 for various health indicators, 1987–2005, Denmark. Eur J Ageing. 2008;5:279. doi: 10.1007/s10433-008-0100-x.

6. Franceschi C, Capri M, Monti D, Giunta S, Olivieri F, Sevini F, Panourgia MP, Invidia L, Celani L, Scurti M, Cevenini E, Castellani GC, Salvioli S. Inflammaging and anti-inflammaging: a systemic perspective on aging and longevity emerged from studies in humans. Mech Ageing Dev. 2007;128:92–105. doi: 10.1016/j.mad.2006.11.016.

7. Cevenini E, Monti D, Franceschi C. Inflamm-ageing. Curr Opin Clin Nutr Metab Care. 2013;16:14–20. doi: 10.1097/MCO.0b013e32835ada13.

8. Minciullo PL, Catalano A, Mandraffino G, Casciaro M, Crucitti A, Maltese G, Morabito N, Lasco A, Gangemi S, Basile G. Inflammaging and anti-inflammaging: the role of cytokines in extreme longevity. Arch Immunol Ther Exp. 2016;64:111–126. doi: 10.1007/s00005-015-0377-3.

9. Ershler WB, Keller ET. Age-associated increased interleukin-6 gene expression, late-life diseases, and frailty. Annu Rev Med. 2000;51:245–270. doi: 10.1146/annurev.med.51.1.245.

10. Gangemi S, Basile G, Monti D, Merendino RA, Di Pasquale G, Bisignano U, Nicita-Mauro V, Franceschi C. Age-related modifications in circulating IL-15 levels in humans. Mediat Inflamm. 2005;2005:245–247. doi: 10.1155/MI.2005.245.

11. Gangemi S, Parisi P, Ricciardi L, Saitta S, Minciullo PL, Cristani MT, Nicita-Mauro V, Saija A, Basile G. Is interleukin-22 a possible indicator of chronic heart failure’s progression? Arch Gerontol Geriatr. 2010;50:311–314. doi: 10.1016/j.archger.2009.05.003.

12. Basile G, Paffumi I, D’Angelo AG, Figliomeni P, Cucinotta MD, Pace E, Ferraro M, Saitta S, Lasco A, Gangemi S. Healthy centenarians show high levels of circulating interleukin-22 (IL-22) Arch Gerontol Geriatr. 2012;54:459–461. doi: 10.1016/j.archger.2011.05.004.

13. Monti D, Ostan R, Borelli V, Castellani G, Franceschi C. Inflammaging and human longevity in the omics era. Mech Ageing Dev. 2017;165(Part B):129–138. doi: 10.1016/j.mad.2016.12.008.

14. Di Bona D, Vasto S, Capurso C, Christiansen L, Deiana L, Franceschi C, Hurme M, Mocchegiani E, Rea M, Lio D, Candore G, Caruso C. Effect of interleukin-6 polymorphisms on human longevity: a systematic review and meta-analysis. Ageing Res Rev. 2009;8:36–42. doi: 10.1016/j.arr.2008.09.001.

15. Pel-Littel RE, Schuurmans MJ, Emmelot-Vonk MH, Verhaar HJ. Frailty: defining and measuring of a concept. J Nutr Health Aging. 2009;13:390–394. doi: 10.1007/s12603-009-0051-8.

16. Evans WJ, Paolisso G, Abbatecola AM, Corsonello A, Bustacchini S, Strollo F, Lattanzio F. Frailty and muscle metabolism dysregulation in the elderly. Biogerontology. 2010;11:527–536. doi: 10.1007/s10522-010-9297-0.

17. Liochev SI. Reactive oxygen species and the free radical theory of aging. Free Radic Biol Med. 2013;60:1–4. doi: 10.1016/j.freeradbiomed.2013.02.011.

18. Ginaldi L, De Martinis M, Monti D, Franceschi C. The immune system in the elderly: activation-induced and damage-induced apoptosis. Immunol Res. 2004;30:81–94. doi: 10.1385/IR:30:1:081.

19. De Martinis M, Franceschi C, Monti D, Ginaldi L. Apoptosis remodeling in immunosenescence: implications for strategies to delay ageing. Curr Med Chem. 2007;14:1389–1397. doi: 10.2174/092986707780831122.

20. Minciullo PL, Inferrera A, Navarra M, Calapai G, Magno C, Gangemi S. Oxidative stress in benign prostatic hyperplasia: a systematic review. Urol Int. 2015;94:249–254. doi: 10.1159/000366210.

21. Jackson JG, Pant V, Li Q, Chang LL, Quintas-Cardama A, Garza D, Tavana O, Yang P, Manshouri T, Li Y, El-Naggar AK, Lozano G. p53-mediated senescence impairs the apoptotic response to chemotherapy and clinical outcome in breast cancer. Cancer Cell. 2012;21:793–806. doi: 10.1016/j.ccr.2012.04.027.

22. Cristani M, Speciale A, Saija A, Gangemi S, Minciullo PL, Cimino F. Circulating advanced oxidation protein products as oxidative stress biomarkers and progression mediators in pathological conditions related to inflammation and immune dysregulation. Curr Med Chem. 2016;23:3862–3882. doi: 10.2174/0929867323666160902154748.

23. Dorn GW., 2nd Molecular mechanisms that differentiate apoptosis from programmed necrosis. Toxicol Pathol. 2013;41:227–234. doi: 10.1177/0192623312466961.

24. Ostan R, Bucci L, Capri M, Salvioli S, Scurti M, Pini E, Monti D, Franceschi C. Immunosenescence and immunogenetics of human longevity. Neuroimmunomodulation. 2008;15:224–240. doi: 10.1159/000156466.

25. Ford DW, Jensen GL, Hartman TJ, Wray L, Smiciklas-Wright H. Association between dietary quality and mortality in older adults: a review of the epidemiological evidence. J Nutr Gerontol Geriatr. 2013;32:85–105. doi: 10.1080/21551197.2013.779622.

26. Buchner N, Ale-Agha N, Jakob S, Sydlik U, Kunze K, Unfried K, Altschmied J, Haendeler J. Unhealthy diet and ultrafine carbon black particles induce senescence and disease associated phenotypic changes. Exp Gerontol. 2013;48:8–16. doi: 10.1016/j.exger.2012.03.017.

27. Franceschi C, Bonafe M, Valensin S, Olivieri F, De Luca M, Ottaviani E, De Benedictis G. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann N Y Acad Sci. 2000;908:244–254. doi: 10.1111/j.1749-6632.2000.tb06651.x.

28. Maicher A, Kastner L, Dees M, Luke B. Deregulated telomere transcription causes replication-dependent telomere shortening and promotes cellular senescence. Nucleic Acids Res. 2012;40:6649–6659. doi: 10.1093/nar/gks358.

29. George AJ, Ritter MA. Thymic involution with ageing: obsolescence or good housekeeping? Immunol Today. 1996;17:267–272. doi: 10.1016/0167-5699(96)80543-3.

30. Cossarizza A, Ortolani C, Paganelli R, Barbieri D, Monti D, Sansoni P, Fagiolo U, Castellani G, Bersani F, Londei M, Franceschi C. CD45 isoforms expression on CD4+ and CD8+ T cells throughout life, from newborns to centenarians: implications for T cell memory. Mech Ageing Dev. 1996;86:173–195. doi: 10.1016/0047-6374(95)01691-0.

31. Haynes L, Eaton SM, Burns EM, Randall TD, Swain SL. CD4 T cell memory derived from young naive cells functions well into old age, but memory generated from aged naive cells functions poorly. Proc Natl Acad Sci USA. 2003;100:15053–15058. doi: 10.1073/pnas.2433717100.

32. Vallejo AN. CD28 extinction in human T cells: altered functions and the program of T-cell senescence. Immunol Rev. 2005;205:158–169. doi: 10.1111/j.0105-2896.2005.00256.x.

33. Schwaiger S, Wolf AM, Robatscher P, Jenewein B, Grubeck-Loebenstein B. IL-4-producing CD8+ T cells with a CD62L ++(bright) phenotype accumulate in a subgroup of older adults and are associated with the maintenance of intact humoral immunity in old age. J Immunol. 2003;170:613–619. doi: 10.4049/jimmunol.170.1.613.

34. Sansoni P, Vescovini R, Fagnoni F, Biasini C, Zanni F, Zanlari L, Telera A, Lucchini G, Passeri G, Monti D, Franceschi C, Passeri M. The immune system in extreme longevity. Exp Gerontol. 2008;43:61–65. doi: 10.1016/j.exger.2007.06.008.

35. Wikby A, Ferguson F, Forsey R, Thompson J, Strindhall J, Lofgren S, Nilsson BO, Ernerudh J, Pawelec G, Johansson B. An immune risk phenotype, cognitive impairment, and survival in very late life: impact of allostatic load in Swedish octogenarian and nonagenarian humans. J Gerontol A Biol Sci Med Sci. 2005;60:556–565. doi: 10.1093/gerona/60.5.556.

36. Sikora E. Activation-induced and damage-induced cell death in aging human T cells. Mech Ageing Dev. 2015;151:85–92. doi: 10.1016/j.mad.2015.03.011.

37. Kollar S, Berta L, Vasarhelyi ZE, Balog A, Vasarhelyi B, Rigo J, Jr, Toldi G. Impact of aging on calcium influx and potassium channel characteristics of T lymphocytes. Oncotarget. 2015;6:13750–13756. doi: 10.18632/oncotarget.3808.

38. Li G, Yu M, Lee W-W, Tsang M, Krishnan E, Weyand CM, Goronzy JJ. Decline in miR-181a expression with age impairs T cell receptor sensitivity by increasing DUSP6 activity. Nat Med. 2012;18:1518–1524. doi: 10.1038/nm.2963.

39. Lages CS, Suffia I, Velilla PA, Huang B, Warshaw G, Hildeman DA, Belkaid Y, Chougnet C. Functional regulatory T cells accumulate in aged hosts and promote chronic infectious disease reactivation. J Immunol. 2008;181:1835–1848. doi: 10.4049/jimmunol.181.3.1835.

40. Colonna-Romano G, Bulati M, Aquino A, Vitello S, Lio D, Candore G, Caruso C. B cell immunosenescence in the elderly and in centenarians. Rejuvenation Res. 2008;11:433–439. doi: 10.1089/rej.2008.0664.

41. Antonaci S, Jirillo E, Ventura MT, Garofalo AR, Bonomo L. Lipoprotein-induced inhibition of plaque-forming cell generation and natural killer cell frequency in aged donors. Ann Immunol. 1984;135:241–249.

42. Weksler ME, Szabo P. The effect of age on the B-cell repertoire. J Clin Immunol. 2000;20:240–249. doi: 10.1023/A:1006659401385.

43. Paganelli R, Quinti I, Fagiolo U, Cossarizza A, Ortolani C, Guerra E, Sansoni P, Pucillo LP, Scala E, Cozzi E, et al. Changes in circulating B cells and immunoglobulin classes and subclasses in a healthy aged population. Clin Exp Immunol. 1992;90:351–354. doi: 10.1111/j.1365-2249.1992.tb07954.x.

44. Ventura M. Determination of total IgA, IgA1 and IgA2 in the serum and saliva of an aged population. Recenti Prog Med. 1985;76:576–577.

45. Ventura MT. Evaluation of IgA1-IgA2 levels in serum and saliva of young and elderly people. Allergol Immunopathol. 1991;19:183–185.

46. Pritz T, Lair J, Ban M, Keller M, Weinberger B, Krismer M, Grubeck-Loebenstein B. Plasma cell numbers decrease in bone marrow of old patients. Eur J Immunol. 2015;45:738–746. doi: 10.1002/eji.201444878.

47. Buffa S, Pellicano M, Bulati M, Martorana A, Goldeck D, Caruso C, Pawelec G, Colonna-Romano G. A novel B cell population revealed by a CD38/CD24 gating strategy: CD38(−) CD24 (−) B cells in centenarian offspring and elderly people. Age. 2013;35:2009–2024. doi: 10.1007/s11357-012-9488-5.

48. Rosenberg C, Bovin NV, Bram LV, Flyvbjerg E, Erlandsen M, Vorup-Jensen T, Petersen E. Age is an important determinant in humoral and T cell responses to immunization with hepatitis B surface antigen. Hum Vaccin Immunother. 2013;9:1466–1476. doi: 10.4161/hv.24480.

49. Nomellini V, Gomez CR, Kovacs EJ. Aging and impairment of innate immunity. Contrib Microbiol. 2008;15:188–205. doi: 10.1159/000136358.

50. Antonaci S, Jirillo E, Ventura MT, Garofalo AR, Bonomo L. Non-specific immunity in aging: deficiency of monocyte and polymorphonuclear cell-mediated functions. Mech Ageing Dev. 1984;24:367–375. doi: 10.1016/0047-6374(84)90121-0.

51. De Martinis M, Sirufo MM, Ginaldi L. Allergy and Aging: an old/new emerging health issue. Aging Dis. 2017;8:162–175. doi: 10.14336/AD.2016.0831.

52. Simon AK, Hollander GA, McMichael A. Evolution of the immune system in humans from infancy to old age. Proc R Soc B Biol Sci. 2015;282:20143085. doi: 10.1098/rspb.2014.3085.

53. Gupta S. Role of dendritic cells in innate and adaptive immune response in human aging. Exp Gerontol. 2014;54:47–52. doi: 10.1016/j.exger.2013.12.009.

54. Prakash S, Agrawal S, Cao JN, Gupta S, Agrawal A. Impaired secretion of interferons by dendritic cells from aged subjects to influenza: role of histone modifications. Age. 2013;35:1785–1797. doi: 10.1007/s11357-012-9477-8.

55. Della Bella S, Bierti L, Presicce P, Arienti R, Valenti M, Saresella M, Vergani C, Villa ML. Peripheral blood dendritic cells and monocytes are differently regulated in the elderly. Clin Immunol. 2007;122:220–228. doi: 10.1016/j.clim.2006.09.012.

56. Davalos AR, Coppe JP, Campisi J, Desprez PY. Senescent cells as a source of inflammatory factors for tumor progression. Cancer Metastasis Rev. 2010;29:273–283. doi: 10.1007/s10555-010-9220-9.

57. Sebastian C, Herrero C, Serra M, Lloberas J, Blasco MA, Celada A. Telomere shortening and oxidative stress in aged macrophages results in impaired STAT5a phosphorylation. J Immunol. 2009;183:2356–2364. doi: 10.4049/jimmunol.0901131.

58. Ventura MT, Serlenga E, Tortorella C, Antonaci S. In vitro vitamin E and selenium supplementation improves neutrophil-mediated functions and monocyte chemoattractant protein-1 production in the elderly. Cytobios. 1994;77:225–232.

59. Butcher SK, Chahal H, Nayak L, Sinclair A, Henriquez NV, Sapey E, O’Mahony D, Lord JM. Senescence in innate immune responses: reduced neutrophil phagocytic capacity and CD16 expression in elderly humans. J Leukoc Biol. 2001;70:881–886.

60. Chen MM, Palmer JL, Plackett TP, Deburghgraeve CR, Kovacs EJ. Age-related differences in the neutrophil response to pulmonary pseudomonas infection. Exp Gerontol. 2014;54:42–46. doi: 10.1016/j.exger.2013.12.010.

61. Brinkmann V, Zychlinsky A. Beneficial suicide: why neutrophils die to make NETs. Nat Rev Microbiol. 2007;5:577–582. doi: 10.1038/nrmicro1710.

62. Ventura MT, Crollo R, Lasaracina E. In vitro zinc correction of natural killer (NK) activity in the elderly. Clin Exp Immunol. 1986;64:223–224.

63. Borrego F, Alonso MC, Galiani MD, Carracedo J, Ramirez R, Ostos B, Pena J, Solana R. NK phenotypic markers and IL2 response in NK cells from elderly people. Exp Gerontol. 1999;34:253–265. doi: 10.1016/S0531-5565(98)00076-X.

64. Solana R, Campos C, Pera A, Tarazona R. Shaping of NK cell subsets by aging. Curr Opin Immunol. 2014;29:56–61. doi: 10.1016/j.coi.2014.04.002.

65. Krishnaraj R. Senescence and cytokines modulate the NK cell expression. Mech Ageing Dev. 1997;96:89–101. doi: 10.1016/S0047-6374(97)00045-6.

66. Almeida-Oliveira A, Smith-Carvalho M, Porto LC, Cardoso-Oliveira J, Ribeiro Ados S, Falcao RR, Abdelhay E, Bouzas LF, Thuler LC, Ornellas MH, Diamond HR. Age-related changes in natural killer cell receptors from childhood through old age. Hum Immunol. 2011;72:319–329. doi: 10.1016/j.humimm.2011.01.009.

67. Camous X, Pera A, Solana R, Larbi A. NK cells in healthy aging and age-associated diseases. J Biomed Biotechnol. 2012;2012:195956. doi: 10.1155/2012/195956.

68. Wikby A, Nilsson BO, Forsey R, Thompson J, Strindhall J, Lofgren S, Ernerudh J, Pawelec G, Ferguson F, Johansson B. The immune risk phenotype is associated with IL-6 in the terminal decline stage: findings from the Swedish NONA immune longitudinal study of very late life functioning. Mech Ageing Dev. 2006;127:695–704. doi: 10.1016/j.mad.2006.04.003.

69. Vasto S, Colonna-Romano G, Larbi A, Wikby A, Caruso C, Pawelec G. Role of persistent CMV infection in configuring T cell immunity in the elderly. Immun Ageing. 2007;4:2. doi: 10.1186/1742-4933-4-2.

70. Weiskopf D, Weinberger B, Grubeck-Loebenstein B. The aging of the immune system. Transpl Int. 2009;22:1041–1050. doi: 10.1111/j.1432-2277.2009.00927.x.

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