The Antibody Mess: Contradictions and Confusion

Illustration by Peter Hamlin.;

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

https://www.medicinenet.com/seroconversion/definition.htm

Antibodies are supposed to be a sign that one has fought off an infection. These tiny unseen theoretical proteins are supposed to attach to the “virus” and clear it out of the body while leaving behind an army of copies patrolling within to fight off any future infections of the same “virus.” What does it say about the antibody theory if people who recover from an infection have no antibodies whatsoever? What if asymptomatic people have few antibodies while the symptomatic have many? What if that scenario is reversed? What if the supposedly specific antibodies cross-react with other “viruses?” What does it mean if the previously established correlation of protection turns out not to correlate with protection at all?

The “it’s all about the timing” excuse.

On April 20th, 2021 a study was published which attempted to look at the antibody response in regards to influenza infection. They were trying to decipher why some people mount a strong antibody response to the influenza “virus” while others do not. Unfortunately for the researchers, instead of uncovering why this lack of seroconversion occurs, their results showed that airway monocytes, which are typically associated with severe disease, were somehow linked to a protective effect. Obviously wanting to have their cake and eat it too, the researchers concluded that sometimes immune factors play a protective role and other times they play a pathogenic role:

Immune markers offer clues to antibody production in response to flu

“Antibodies provide potent protection against the flu and other viruses. Vaccines are designed to stimulate antibody production. Researchers measure antibodies to gauge the strength of the immune response to the infection.

But questions remain about immune factors that drive the magnitude of the antibody response, including why flu infections do not always trigger a rise in blood antibodies. Better understanding of the multistep process could improve flu diagnosis, vaccine design and strategies to track viral spread.”

“The immune factors that explain why some people mount a strong antibody response and others don’t has not been well understood,” said Richard Webby, PhD, of the Infectious Diseases Department. “This study identifies some of those factors, providing information needed to design more effective vaccines for flu and other viruses.”

“Airway monocytes often show up as correlates of severe disease, so it was interesting here to see them also associated with protective immune responses,” said Paul Thomas, PhD, of the Immunology Department. “It points to the fact that many immune factors play protective and pathogenic roles at the same time.”

https://blogs.stjude.org/progress/flu-clues-antibody-production-immune-markers/

Ah, so antibody measures are more accurate for people without the “virus”….wait, what?!?!

While their conclusion regarding the simultaneous opposing nature of immune system factors is absolutely absurd, this study had some very insightful admittances regarding the question of why antibodies form for some but not others. It also cited studies which provide some answers to the questions asked above. Below are highlights from the 2021 study followed by a few quick highlights from some of the relavant research linked within:

Activated CD4+ T cells and CD14hiCD16+ monocytes correlate with antibody response following influenza virus infection in humans

Summary

“The failure to mount an antibody response following viral infection or seroconversion failure is a largely underappreciated and poorly understood phenomenon. Here, we identified immunologic markers associated with robust antibody responses after influenza virus infection in two independent human cohorts, SHIVERS and FLU09, based in Auckland, New Zealand and Memphis, Tennessee, USA, respectively. In the SHIVERS cohort, seroconversion significantly associates with (1) hospitalization, (2) greater numbers of proliferating, activated CD4+ T cells, but not CD8+ T cells, in the periphery during the acute phase of illness, and (3) fewer inflammatory monocytes (CD14hiCD16+) by convalescence. In the FLU09 cohort, fewer CD14hiCD16+ monocytes during early illness in the nasal mucosa were also associated with the generation of influenza-specific mucosal immunoglobulin A (IgA) and IgG antibodies. Our study demonstrates that seroconversion failure after infection is a definable immunological phenomenon, associated with quantifiable cellular markers that can be used to improve diagnostics, vaccine efficacy, and epidemiologic efforts.

Introduction

An increase in antigen-specific antibody titer in the serum, known as seroconversion, has long been accepted to be a serological hallmark of a recent infection or antigen exposure. However, the advent of molecular diagnosis has led to the observation that some infections do not always result in the subsequent production of detectable antibodies, particularly those with neutralizing and protective activity. This has been documented in infections with influenza virus,12345 human coronaviruses,6 the Middle East respiratory syndrome (MERS) coronavirus, 7 and the recently emerged severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)8,9 infections. The immunological mechanisms underlying seroconversion failure are not well understood, but a better understanding is important, particularly for vaccine design.

The production of high-affinity, durable antibody and B cell memory responses requires the initiation of a germinal center (GC) response in secondary lymphoid organs, which is a multi-step process involving multiple innate and adaptive immune cells and cytokine signals.101112 In humans, the majority of our understanding of what is required for the generation of a robust antibody response have been derived from vaccination studies. Such studies have the advantage of having temporally defined pre- and post-antigen exposures that facilitate sample and data acquisition and have used targeted or systems-wide approaches to identify correlates of robust antibody production. These studies have generally found that the activation of B cell maturation pathways and engagement of innate immunity are critical for robust antibody production after vaccination.1314151617 The early proliferation of antigen-specific plasmablasts was another characteristic that was found to precede the development of vaccine-induced serum antibody responses and in limited studies of influenza and dengue virus infections.181920 However, given the differences in antigenic composition and exposure route, post-vaccination responses may not necessarily reflect the post-infection immune response, particularly for respiratory viral infections.

For influenza viruses, antibodies that target the major surface viral glycoproteins, hemagglutinin (HA) and neuraminidase (NA) are induced after infection. In terms of seroresponses to influenza virus infection, HA antibodies measured in a hemagglutination-inhibition (HAI) assay are considered to be the gold standard. Antibodies detected by HAI assay primarily bind to the HA globular head, which contains the receptor-binding domain and the major antigenic sites. These antibodies confer high antigenic specificity and can provide potent protection from infection in humans and animal studies.21,22 The HAI assay does not strictly measure virus neutralizing activity, but its relative ease of use and established correlation with protection212223 have justified its use as a major serological endpoint in influenza vaccine and infection studies. Serum HAI-antibody titers ≥40 have been shown to be associated with protection from seasonal influenza virus infections21 and have been adopted as the minimal immunogenicity requirement for the licensure of seasonal influenza vaccines.24 Compared to HA, NA antibodies are less well studied, although they have also been recently identified as a potential additional correlate of protection from severe influenza disease.4,25,26

The failure to seroconvert by the standard HAI assay after laboratory-confirmed influenza virus infection has been reported in seroepidemiological,1,2 vaccination,27 and even challenge studies.345,28 A review of human challenge studies showed that between 50% and 90% of individuals with PCR confirmation of influenza virus infection failed to seroconvert by HAI assay.345,28 The underlying immunologic factors driving the magnitude of the antibody response following influenza virus infection is still, however, poorly defined.”

Results

Description of seroconverters (SCs) and non-seroconverters (non-SCs) in the SHIVERS cohort

“In the SHIVERS cohort, participants were enrolled according to the World Health Organization (WHO) case definition for influenza-like illness (ILI) or severe acute respiratory illness (SARI) from general practices or hospitals in Auckland between August and October 2013, which coincided with the peak influenza season in New Zealand.29 Of those with PCR-confirmed infection (N = 66), 21 (32%) participants met the definition of HAI seroconversion (at least a 4-fold increase in antibody titer in the paired sera). No significant associations of seroconversion with age (Figure 1A), gender, ethnicity, or virus subtypes were detected. Hospitalization was, however, associated with more robust responses, with SARI participants more likely to seroconvert than ILI participants (86% versus 14%, respectively, p = 0.0012; Table S1). Since the time of sampling and baseline titers are important factors associated with seroconversion, we also examined these variables in our cohort. Although the time from symptom onset to first sample was shorter (median days, interquartile range [IQR]: 9–13.5 versus 12–13.5) (Figure 1B) and the average first sera HAI titer was lower (geometric mean titer [GMT], 95% confidence interval [CI]: 36.9, 18.3–74.4 versus 39.4, 24.6–63.4) for SCs than for non-SCs (Figure 1C), these differences were not statistically significant. The large variance within these two datasets suggests that the failure to seroconvert is likely due to multiple factors.”

CD14hiCD16+ monocytes were also important in the mucosal antibody response

“Having determined the correlations between CD16+ monocytes and serum antibody responses, we next asked whether similar patterns were seen in the mucosal compartment. To address this, we measured the influenza-specific IgA and IgG titers in the nasal washes of influenza-confirmed individuals from a cohort in which we had previously characterized their monocyte subpopulations.31 As there is no equivalent seroconversion standard for mucosal antibody titers, we identified 10 individuals with influenza-specific IgA titer >40 in their nasal wash samples within 2 weeks of symptom onset as SCs (GMT, 95% CI: 186.6, 98.4–354.1) (Figure 4A) and another 9 individuals, with no detectable IgA titers during the course of study, as non-SCs (Table S2). To ensure the validity of our classification of SCs and non-SCs, we also evaluated the influenza-specific IgG responses. Nine of the 10 SCs had influenza-specific IgG titers >40 (GMT, 95% CI: 28.9, 2.8–292), while none of the non-SCs had detectable titers at any of the time points tested (Figure 4B). While classical monocytes were predominant in the peripheral blood, it was CD14hiCD16+ monocytes that were mostly recovered in the nasal washes (Figure 4C). Within 1 week of enrollment, greater numbers of CD14hiCD16 and fewer numbers of CD14hiCD16+ monocytes were found in SCs compared to the non-SCs (Figure 4C). Thus, the early dynamics of the monocyte subpopulations in the nasal mucosa were also important in regulating downstream mucosal antibody response.

Discussion

Seroconversion in influenza is typically defined by measuring the strain-specific HAI antibody response and is generally thought to follow infection. Somewhat unexpectedly, we found that only 32% of individuals who were PCR confirmed to be infected with influenza virus met this standard definition of seroconversion in SHIVERS. While technical reasons (e.g., late sampling of sera, mismatched antigen) may have accounted for some of this seroconversion failure, we were able to identify cellular signatures associated with the ability, or lack thereof, to seroconvert in a proportion of infected individuals. When we more closely examined those SCs who had low HAI antibody titers in their acute samples, the magnitude of their increase in HA-specific IgG and NAI antibody titers were only modest compared to HAI antibody responses, suggesting that the distinct cellular immune profile that we observed here is important for the induction of conformationally specific HAI antibodies and not necessarily the magnitude of antibody response overall. On average, the CD8+ T cell responses were not significantly different between SCs and non-SCs, suggesting that the CD4+ and CD8+ T cell responses are regulated independently, which is consistent with findings previously observed in mouse studies.32

“The higher incidence of seroconversion in hospitalized individuals supports a correlation between the severity of infection and antibody responsiveness in the periphery and nasal mucosa5,41 Although our data cannot preclude the possibility of antibody-dependent enhancement (ADE) of influenza disease, we think it is unlikely within the context of our study design (antibody increases were detected post-hospitalization) and other available evidence. ADE of influenza disease has mostly been described during the 2009 H1N1 pandemic42,43 and within critically ill or fatal cases.44,45 In our cohort, although they were hospitalized, most of the patients were on the “milder” end of the severity spectrum.36 Furthermore, in the present and previous SHIVERS studies on the immunological correlates of disease severity, we found no significant differences in the acute-phase antibody titers between mild (outpatient) and severe (hospitalized) patients. Thus, it appears that, paradoxically, a strong proinflammatory response that is associated with symptomatic infections31,36 could also help induce a good antibody response, while a weak signal may not provide adequate stimulation.46 This is a particularly important consideration for the ongoing coronavirus disease 2019 (COVID-19) pandemic, in which a proportion of mild or asymptomatic COVID-19 cases fail to mount detectable antibody responses despite evidence of active infection, while severely ill patients had comparably stronger antibody responses.8,9,47 Incidentally, the lack of association between age and seroconversion indicates that disease severity may be a stronger factor driving post-infection antibody response than age, suggesting a strong proinflammatory response may compensate for the decline in immune function in the elderly. However, it should be noted that the association between antibody response and severity of infection should not be broadly interpreted, as it had also been shown that fatal H7N9 and COVID-19 patients failed to mount robust antibody responses in some cases.47,48

In summary, our data provide immunological evidence that respiratory viral infections do not always lead to successful seroconversions. These data have implications for our understanding of post-infection immunity and on studies that rely on such metrics. Identification of key cellular elements associated with robust antibody response will also help in our understanding of vaccine failure and contribute to the development of more efficacious vaccines.”

“Finally, it is unknown whether the present failure to mount an antibody response would have any impact on the memory B cell responses and their capacity to mount a recall response. “

https://www.sciencedirect.com/science/article/pii/S2666379121000537

A. “Covid” is fake. B. Antibodies are fake. C. Obviously, they are both fake.

From the above study on influenza antibody response, it is very clear that there is much they do not understand about the lack of seroconversions in these patients. The researchers admit that this is not an unknown phenomenon as it has been documented with many “viruses,” however, it remains understudied. Their findings throw quite a bit of shade at the accepted correlation of protection of an HAI >40 and demonstrates that antibodies tend to be linked to greater inflammation. The researchers attempt to claim that this may be why seroconversions don’t happen for asymptomatic/mild cases yet then caution that interpretation as there are examples of non-seroconversion in severe cases of disease as well. Overall, it is a jumbled mess of contradictory findings that are attempted to be rationalized by the researchers by way of the accepted antibody theory framework.

As promised, here is a quick (and not so quick in some cases) roundup of highlights from a few of the relevant linked studies showing evidence of cross-reactions, non-specificity, lack of antibodies, lack of accurate correlates of protection, differing seroconversion outcomes, and inconsistent antibody results:

1. Serological response in RT-PCR confirmed H1N1-2009 influenza a by hemagglutination inhibition and virus neutralization assays: an observational study

Conclusions and significance: Appropriately timed paired serology detects 80-90% RT-PCR confirmed H1N1-2009; Antibodies from infection with H1N1-2009 cross-reacted with seasonal influenza viruses.

https://pubmed.ncbi.nlm.nih.gov/20814575/

2. 2009 influenza A(H1N1) seroconversion rates and risk factors among distinct adult cohorts in Singapore

“Conclusion: Following the June-September 2009 wave of 2009 influenza A (H1N1), 13% of the community participants seroconverted, and most of the adult population likely remained susceptible.”

https://pubmed.ncbi.nlm.nih.gov/20388894/

3. Virus-Specific Antibody Secreting Cell, Memory B-cell, and Sero-Antibody Responses in the Human Influenza Challenge Model 

“Although no correlation between the influenza virus–specific B-cell response and serological HI antibody level was found, these data clearly supported that acute infections could quickly induce the recalling response of virus-specific B cells, even where the preexisting antibody level is undetectable.”

https://academic.oup.com/jid/article/209/9/1354/886344

4. Evaluation of Antihemagglutinin and Antineuraminidase Antibodies as Correlates of Protection in an Influenza A/H1N1 Virus Healthy Human Challenge Model

“Despite long-term investment, influenza continues to be a significant worldwide problem. The cornerstone of protection remains vaccination, and approved vaccines seek to elicit a hemagglutination inhibition (HAI) titer of ≥1:40 as the primary correlate of protection. However, recent poor vaccine performance raises questions regarding the protection afforded and whether other correlates of protection should be targeted.”

“This study represents the first time the current gold standard for evaluating influenza vaccines as set by the U.S. Food and Drug Administration and the European Medicines Agency Committee for Medicinal Products for Human Use, a “protective” hemagglutination inhibition (HAI) titer of ≥1:40, has been evaluated in a well-controlled healthy volunteer challenge study since the cutoff was established. We used our established wild-type influenza A healthy volunteer human challenge model to evaluate how well this antibody titer predicts a reduction in influenza virus-induced disease. We demonstrate that although higher HAI titer is predictive of some protection, there is stronger evidence to suggest that neuraminidase inhibition (NAI) titer is more predictive of protection and reduced disease.”

“The U.S. Food and Drug Administration and the European Medicines Agency Committee for Medicinal Products for Human Use both define “protective” titers as a hemagglutination inhibition (HAI) titer of ≥1:40 (7).

The evidence for this cutoff comes from a seminal live influenza virus challenge trial conducted in 1972 by Hobson et al., which established that a prechallenge serum HAI titer of 18 to 36 was associated with 50% protection from infection (8), and a similar study demonstrating a 29% infection rate in those with HAI titers of 1:40 to 60 (9). Other studies in the setting of live and attenuated influenza virus challenge have been less conclusive (10,12). A more recent study, conducted using live attenuated viruses, demonstrated 50% protection from intranasal infection in those with titers of ≥1:40 (13), but a recent epidemiological study of vaccine performance has brought this into question, with only 22% protection of children with postvaccination titers of ≥1:40 (14). Even more concerning are data from the recent influenza seasons suggesting that current seasonal vaccines held to these standards are greatly underperforming, as overall seasonal vaccine effectiveness over the past 10 years has ranged from 10 to 56% with a mean of 40% (15). This is especially concerning for at risk populations who most require protection and in whom even worse performance has been observed (16).

“The variability of results in past studies and variations in recent influenza vaccine efficacy are likely multifactorial and suggest that more work is needed to better understand the correlates of protection in influenza infection. Differences in outcome measures, well described interlaboratory variations in HAI and microneutralization assay results (1718), evolving viral phenotypes, host factors, and differences in how protection is defined could all further confound interpretation of results and contribute to the variation seen in these and other studies of influenza immunity.”

“Antibody responses were measured postchallenge demonstrating no significant increase in the HAI GMT in the high-HAI-titer group after challenge (week 8), but a clear increase was noted in the low-HAI-titer group (Fig. 7A). The response in the low-titer group was variable, with approximately 50% of those participants showing a very significant >4-fold rise in titer, while the other 50% of participants showed no significant increase in HAI titer (Fig. 7B).”

“If these results are consistent with outcomes following natural influenza infection, the protection afforded by an HAI titer of ≥1:40 would not necessarily include the prevention of a clinical influenza-like illness, even if it does predict some reduction in the duration of illness and viral shedding, and thus spread of disease.”

“Although the incidence of influenza-like illness symptoms was not reduced in those with high HAI titers, there was a trend toward reduction of all disease severity measures, with the most significant being in duration of both viral shedding and symptoms. Overall though, many participants with high HAI baseline titers still suffered from influenza symptoms, making clear that the protection predicted by baseline HAI titers is not complete.”

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959521/#!po=0.684932

5. Evaluation of influenza A/Hong Kong/123/77 (H1N1) ts-1A2 and cold-adapted recombinant viruses in seronegative adult volunteers

No evidence for transmission of the ca or ts-1A2 recombinant virus to controls was observed. A serum hemagglutination inhibition response was detected in less than 50% of the infected vaccinees.” 

https://pubmed.ncbi.nlm.nih.gov/7216417/

6. Serologic responses of 42 MERS-coronavirus-infected patients according to the disease severity

“We evaluated serologic response of 42 Middle East respiratory syndrome coronavirus (MERS-CoV)-infected patients according to 4 severity groups: asymptomatic infection (Group 0), symptomatic infection without pneumonia (Group 1), pneumonia without respiratory failure (Group 2), and pneumonia progressing to respiratory failure (Group 3). None of the Group 0 patients showed seroconversion, while the seroconversion rate gradually increased with increasing disease severity (0.0%, 60.0%, 93.8%, and 100% in Group 0, 1, 2, 3, respectively; P = 0.001). Group 3 patients showed delayed increment of antibody titers during the fourth week, while Group 2 patients showed robust increment of antibody titer during the third week. Among patients having pneumonia, 75% of deceased patients did not show seroconversion by the third week, while 100% of the survived patients were seroconverted (P = 0.003).”

https://pubmed.ncbi.nlm.nih.gov/28821364/

7. Dynamics of anti-SARS-Cov-2 IgM and IgG antibodies among COVID-19 patients

“Of the six patients in the symptomatic group, all had positive anti-SARS-CoV-2 IgG and four had positive anti-SARS-CoV-2 IgM responses (Fig. 1 A). The duration of positive rRT-PCR results ranged from 12 to 46 days. Patients with positive anti-SARS-CoV-2 IgM results seemed to have a short duration of viral shedding (Fig. 1A). For the eight patients in the asymptomatic/mild symptom group, none had positive anti-SARS-CoV-2 IgM results and three (cases 11-13) had negative anti-SARS-CoV-2 IgG results (Fig. 1B).”

“Several limitations were found in our study. First, the sample size is small, as we included only 14 COVID-19 patients. Second, a further western blot assay for detecting the anti-SARS-CoV-2-specific protein IgG or IgM antibodies was not performed to validate the performance of the ALLTEST 2019-nCoV IgG/IgM Rapid Test and understand the inconsistency in the presence of anti-SARS-CoV-2 IgM and IgG. Third, cross reaction of serum specimens from the acute phase of different viral infections (e.g., influenza, respiratory syncytial virus, and rhinovirus.) in the IgM portion of this SARS-CoV-2 assay was not performed. Finally, different SARS-CoV-2 strains or genotypes may also interfere with disease outcome and antibody response; however, viral cultures for all patients in our study were not performed and genetic characteristics of SARS-CoV-2 strains were not investigated.8

In summary, we performed an anti-SARS-CoV-2 IgG/IgM test on 14 confirmed COVID-19 patients and 28 negative controls. Antibody response varied with different clinical manifestations and disease severity. Patients with symptoms and development of anti-SARS-CoV-2 IgM antibodies had a shorter duration of positive rRT-PCR result and no worsening clinical conditions compared to those without the presence of anti-SARS-CoV-2 IgM antibodies. The serological data can provide more information about epidemiological linkage and disease prognosis for the new emerging COVID-19 pandemic.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177139/#!po=32.8571

8. Longitudinal Change of Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies in Patients with Coronavirus Disease 2019 

“One hundred twelve patients were recruited with symptoms of fever, cough, fatigue, myalgia, and diarrhea. All patients underwent antibody tests. Fifty-eight (51.79%) were positive for both immunoglobulin M (IgM) and immunoglobulin G (IgG), 7 (6.25%) were negative for both antibodies, 1 (0.89%) was positive for only IgM, and 46 (41.07%) were positive for only IgG. IgM antibody appeared within a week post–disease onset, lasted for 1 month, and gradually decreased, whereas IgG antibody was produced 10 days after infection and lasted for a longer time. However, no significant difference in levels of IgM and IgG antibodies between positive and negative patients of nucleic acid test after treatment was found.”

https://academic.oup.com/jid/article/222/2/183/5828055

“But say the test is 95% “specific,” meaning that it returns false positives 5% of the time. Then among the 960 people who are truly negative, 48 people would get a false positive. In this scenario, more people would get a false positive result than a true positive.” https://www.google.com/amp/s/www.propublica.org/article/what-antibody-studies-can-tell-you-and-more-importantly-what-they-cant/amp

In Summary:

  • Antibodies are said to provide potent protection against the flu and other “viruses”
  • Vaccines are designed to stimulate antibody production
  • Researchers measure antibodies to gauge the strength of the immune response to the infection
  • However, questions remain about immune factors that drive the magnitude of the antibody response, including why flu infections do not always trigger a rise in blood antibodies
  • The immune factors that explain why some people mount a strong antibody response and others don’t has not been well understood
  • Airway monocytes often show up as correlates of severe disease, yet they were associated with protective immune responses in a recent study on influenza
  • This was said to point to the “fact” that many immune factors play protective and pathogenic roles at the same time
  • The failure to mount an antibody response following “viral” infection or seroconversion failure is a largely underappreciated and poorly understood phenomenon
  • The researchers claim their study demonstrates that seroconversion failure after infection is a definable immunological phenomenon
  • An increase in antigen-specific antibody titer in the serum, known as seroconversion, has long been accepted to be a serological hallmark of a recent infection or antigen exposure
  • However, the advent of molecular diagnosis has led to the observation that some infections do not always result in the subsequent production of detectable antibodies, particularly those with neutralizing and protective activity
  • This lack of antibodies has been documented in infections with:
    1. Influenza 
    2. Human “coronaviruses”
    3. Middle East Respiratory Syndrome (MERS) “coronavirus”
    4. “SARS-CoV-2”
  • The immunological mechanisms underlying seroconversion failure are not well understood
  • In humans, the majority of our understanding of what is required for the generation of a robust antibody response have been derived from vaccination studies
  • Given the differences in antigenic composition and exposure route, post-vaccination responses may not necessarily reflect the post-infection immune response, particularly for respiratory “viral” infections
  • HA antibodies measured in a hemagglutination-inhibition (HAI) assay are considered to be the gold standard to detect seroconversion in influenza
  • The HAI assay does not strictly measure “virus” neutralizing activity, but its relative ease of use and established “correlation with protection” have justified its use as a major serological endpoint in influenza vaccine and infection studies
  • Serum HAI-antibody titers ≥40 have been shown to be associated with protection from seasonal influenza “virus” and have been adopted as the minimal immunogenicity requirement for the licensure of seasonal influenza vaccines
  • The failure to seroconvert by the standard HAI assay after laboratory-confirmed influenza “virus” infection has been reported in seroepidemiological, vaccination, and even challenge studies 
  • A review of human challenge studies showed that between 50% and 90% of individuals with PCR confirmation of influenza virus infection failed to seroconvert by HAI assay
  • The underlying immunologic factors driving the magnitude of the antibody response following influenza “virus” infection is still poorly defined

Quick Detour:

Reasons for the inability to show seroconversion and the unreliability of the HAI correlation of protection were addressed in a 2018 study. It is interesting that in the defining 1972 study for the >40 HAI correlate of protection, those without any HAI activity were better “protected” than those with low levels of HAI activity. Two brief snippets:

The establishment of surrogates and correlates of protection: Useful tools for the licensure of effective influenza vaccines?

“The situation with inactivated influenza vaccines is a good example of this phenomenon. The most frequently used tests to define vaccine-induced immunity are all serologic assays: hemagglutination inhibition (HI), single radial hemolysis (SRH) and microneutralization (MN). The first two, and particularly the HI assay, have achieved reference status and criteria have been established in many jurisdictions for their use in licensing new vaccines and to compare the performance of different vaccines. However, all of these assays are based on biological reagents that are notoriously difficult to standardize and can vary substantially by geography, by chance (i.e. developing reagents in eggs that may not antigenitically match wild-type viruses) and by intention (ie: choosing reagents that yield the most favorable results). This review describes attempts to standardize these assays to improve their performance as surrogates, the dangers of over-reliance on ‘reference’ serologic assays, the ways that manufacturers can exploit the existing regulatory framework to make their products ‘look good’ and the implications of this long-established system for the introduction of novel influenza vaccines.”

“Some of the difficulties currently confronting the influenza vaccine community were foreshadowed to some extent by Hobson’s iconic 1972 article in which an HI-based correlate of protection was proposed but which also noted that subjects without any detectable HI response were better protected than those with low HI titres.33 Given what we have learned about influenza and host-virus interactions over the last half-century, it is somewhat disappointing but not particularly surprising that vaccines optimized for an egg-biased HI response do not perform very well against a family of viruses as varied and as mutationally ‘slippery’ as influenza. It now seems clear that forcing all candidate vaccines to leap through the same serologic ‘hoop’ is probably a mistake since a single surrogate or correlate of protection is highly unlikely to apply to vaccines that work by distinct mechanisms.”

https://www.tandfonline.com/doi/full/10.1080/21645515.2017.1413518

We have biological reagents notoriously difficult to standardize which vary widely due to geographical location, by chance due to development using the wrong eggs, and by intention due to attempting to obtain favorable results. We also have the conflicting results obtained in the seminal study establishing the correlate of protection. Sounds like a rather “trustworthy” indirect method of detection, doesn’t it?

End Detour

  • Of those with PCR-confirmed infection (N = 66), 21 (32%) participants met the definition of HAI seroconversion (at least a 4-fold increase in antibody titer in the paired sera)
  • Hospitalization was associated with more robust antibody responses, with SARI participants more likely to seroconvert than ILI participants (86% versus 14%, respectively)
  • The large variance within these two datasets suggests that the failure to seroconvert is likely due to multiple factors (and also seems to suggest antibodies are a measure of illness, not protection)
  • As there is no equivalent seroconversion standard for mucosal antibody titers, they identified 10 individuals with influenza-specific IgA titer >40 in their nasal wash samples within 2 weeks of symptom onset as SCs and another 9 individuals, with no detectable IgA titers during the course of study, as non-SCs
  • Nine of the 10 SCs had influenza-specific IgG titers >40 (GMT, 95% CI: 28.9, 2.8–292), while none of the non-SCs had detectable titers at any of the time points tested
  • Seroconversion in influenza is typically defined by measuring the strain-specific HAI antibody response and is generally thought to follow infection
  • Somewhat unexpectedly, the researchers found that only 32% of individuals who were PCR confirmed to be infected with influenza “virus” met this standard definition of seroconversion in SHIVERS
  • The higher incidence of seroconversion in hospitalized individuals supports a correlation between the severity of infection and antibody responsiveness in the periphery and nasal mucosa
  • The researchers state it appears that, paradoxically, a strong proinflammatory response that is associated with symptomatic infections could also help induce a good antibody response, while a weak signal may not provide adequate stimulation (in other words, more inflammation = more antibodies)
  • They believe this is a particularly important consideration for the ongoing “COVID-19” pandemic, in which a proportion of mild or asymptomatic “COVID-19” cases fail to mount detectable antibody responses despite evidence of active infection, while severely ill patients had comparably stronger antibody responses
  • However, this goes both ways as there are asymptomatic with antibodies and symptomatic without: a Pennsylvania study found neither the presence of symptoms, severity of illness, race, ethnicity or gender predicted those who were less likely to develop antibodies https://www.pinejournal.com/newsmd/coronavirus/7183938-Study-A-third-of-those-who-got-COVID-19-did-not-generate-antibodies
  • The researchers admit it should be noted that the association between antibody response and severity of infection should not be broadly interpreted, as it had also been shown that fatal H7N9 and “COVID-19” patients failed to mount robust antibody responses in some cases
  • They conclude that respiratory “viral” infections do not always lead to successful seroconversions
  • It is unknown whether the present failure to mount an antibody response would have any impact on the memory B cell responses and their capacity to mount a recall response
  • A 2010 study found that antibodies from infection with H1N1-2009 cross-reacted with seasonal influenza “viruses”
  • Another study from 2010 found that following the June-September 2009 wave of 2009 influenza A (H1N1), only 13% of the community participants seroconverted
  • A 2014 study found no correlation between the influenza “virus–specific” B-cell response and serological HI antibody level
  • A 2016 study stated that recent poor vaccine performance raised questions regarding the protection afforded and whether other correlates of protection should be targeted
  • This study represented the first time the current gold standard for evaluating influenza vaccines, a “protective” (their quotations, not mine) hemagglutination inhibition (HAI) titer of ≥1:40, had been evaluated in a well-controlled healthy volunteer challenge study since the cutoff was established in 1972
  • The evidence for this cutoff comes from a study in 1972 by Hobson et al., which established that a prechallenge serum HAI titer of 18 to 36 was associated with 50% protection from infection
  • Other studies in the setting of live and attenuated influenza “virus” challenge have been less conclusive
  • A more recent study from 2010, conducted using live attenuated “viruses,” demonstrated 50% protection from intranasal infection in those with titers of ≥1:40 but an epidemiological study of vaccine performance from 2011 has brought this into question, with only 22% protection of children with postvaccination titers of ≥1:40
  • Even more concerning are data from the recent influenza seasons suggesting that current seasonal vaccines held to these standards are greatly underperforming, as overall seasonal vaccine effectiveness over the past 10 years has ranged from 10 to 56% with a mean of 40%
  • Reasons (i.e. excuses) for the high variability and a lack of correlation of protection which could further confound interpretation of results and contribute to the variation seen in these and other studies of influenza immunity include:
    1. Differences in outcome measures
    2. Well described interlaboratory variations in HAI and microneutralization assay results
    3. Evolving “viral” phenotypes
    4. Host factors
    5. Differences in how protection is defined
  • Antibody responses were measured postchallenge demonstrating no significant increase in the HAI GMT in the high-HAI-titer group after challenge
  • The response in the low-titer group was variable, with approximately 50% of those participants showing a very significant >4-fold rise in titer, while the other 50% of participants showed no significant increase in HAI titer
  • The protection afforded by an HAI titer of ≥1:40 would not necessarily include the prevention of a clinical influenza-like illness
  • The incidence of influenza-like illness symptoms was not reduced in those with high HAI titers
  • Many participants with high HAI baseline titers still suffered from influenza symptoms, making clear that the protection predicted by baseline HAI titers is not complete
  • A study in 1980 found no evidence for transmission of the ca or ts-1A2 recombinant “virus” to controls and a serum hemagglutination inhibition response was detected in less than 50% of the infected vaccinees
  • A 2017 study on MERS presented plenty of contradictory antibody seroconversions in different groups:
    1. None of the Group 0 patients showed seroconversion
    2. Seroconversion rate gradually increased with increasing disease severity (0.0%, 60.0%, 93.8%, and 100%) in Group 0, 1, 2, 3
    3. Group 3 patients showed delayed increment of antibody titers during the fourth week
    4. Group 2 patients showed robust increment of antibody titer during the third week
    5. Among patients having pneumonia, 75% of deceased patients did not show seroconversion by the third week, while 100% of the survived patients were seroconverted
  • In 2020, a study noted that six patients in a symptomatic group all had positive “anti-SARS-CoV-2” IgG and four had positive “anti-SARS-CoV-2” IgM responses while none of the eight patients in the asymptomatic/mild symptom group had positive “anti-SARS-CoV-2” IgM results and three had negative “anti-SARS-CoV-2” IgG results
  • They found inconsistency in the presence of “anti-SARS-CoV-2 IgM and IgG”
  • They try to explain the inconsistency by stating different “SARS-CoV-2” strains or genotypes may interfere with disease outcome and antibody response
  • Antibody response varied with different clinical manifestations and disease severity
  • Patients with symptoms and development of “anti-SARS-CoV-2” IgM antibodies had a shorter duration of positive rRT-PCR result and no worsening clinical conditions compared to those without the presence of “anti-SARS-CoV-2 IgM” antibodies
  • Another 2020 study looking at antibody responses in “Covid” patients found:
    1. Fifty-eight (51.79%) were positive for both immunoglobulin M (IgM) and immunoglobulin G (IgG)
    2. Seven (6.25%) were negative for both antibodies
    3. One (0.89%) was positive for only IgM
    4. Forty-six (41.07%) were positive for only IgG
  • No significant difference in levels of IgM and IgG antibodies between positive and negative patients of nucleic acid test after treatment was found
An accurate depiction of viroLIEgists searching for their fictional creations.

Taken all together, these studies provide indirect evidence that the unseen hypothetical/theoretical particles known as antibodies can be found in some cases of disease. These studies also provide evidence that these unseen hypothetical/theoretical particles are not found in many cases of disease. Antibodies may be associated with more severe disease but are also not present in cases of severe disease. They may be present in asymptomatic/mild cases of disease but are also not present in many asymptomatic/mild cases of disease. The HAI gold standard of antibody titers > 40 has been correlated with “protection” in a few studies but this level has also been shown to be completely ineffective at providing “protection” in many studies post vaccination.

The problem for immunology and “antibodies” is that, just as in the case with virology and “viruses,” they rely primarily on correlation equaling causation. However, these correlations are inconsistent at best and many times non-existent and/or contradicted in future studies. Instead of admitting that they may be wrong by throwing out the old theories and starting over, the researchers try to cram new, contradictory findings/theories into the existing theory thus creating a nearly incomprehensible and overly confusing mosaic made up of pieces from completely unrelated puzzles.

This is why they can claim antibodies are “protective” even though they are not found in many cases of disease. This is how they can state that antibodies are a sign the immune system is working in regards to vaccinations yet they are also a sign one has a weakened immune system with HIV. This is why the presence of antibodies mean one has successfully fought off a disease and will be “protected” from future infections yet in the case of Dengue Fever, antibodies will make a reinfection worse and potentially fatal. This is how we end up with multiple immune systems with factors that can be simultaneously protective and pathogenic. It is a long chain of confusing, confounding, and contradictory indirect evidence all rolled up into evolving incomprehensible theories. In other words, it’s a mess.

8 comments

  1. Excellent analysis. Keep up the great work. I see the day when they will be forced to rethink the science of the past, in favor of a true scientific study of what really makes us ill.

    Liked by 1 person

    1. Thanks so much! I appreciate the kind words and support. 🙂 I do believe that day is coming. The lies have been stacked on top of each other so much it is one big house of cards just waiting to tumble.

      Like

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