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Author Topic: Die Causa Christoph Kleinschnitz: Dubiose Studien hetzen gegen LongCovid-Kranke  (Read 688 times)

Vultratelly

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Zuerst das Beweisstück.

https://www.aerzteblatt.de/nachrichten/149273/Vorgeschlagene-Biomarker-bei-Menschen-mit-Long-COVID-nicht-aussagekraeftig?utm_source=dlvr.it&utm_medium=twitter

[*quote*]
Deutsches Ärzteblatt

Medizin
Vorgeschlagene Biomarker bei Menschen mit Long COVID nicht aussagekräftig
Mittwoch, 14. Februar 2024

Essen – Vielversprechende Biomarkerkandidaten, wie Cortisol und bestimmte Zytokine sind nach Ergebnissen einer aktuellen Studie keine Stütze bei der Diagnose von Long COVID. Alternativ könnte der Fokus stärker auf nicht organische Ursachen gerichtet sein, da viele Long-COVID-Betroffene zum Beispiel gut von einer Psycho­therapie profitieren können
(Therapeutic Advances in Neurological Disorders 2024; DOI: 10.1177/17562864241229567).

„Leider konnten wir nicht bestätigen, dass Cortisol und einige der wichtigsten Entzündungsbotenstoffe all­tagstaugliche Biomarker bei Menschen mit Long COVID sind“, kommentierte Christoph Kleinschnitz, Direktor der Klinik für Neurologie an der Universitätsmedizin in Essen und federführender Autor der Studie.

Das Team um Kleinschnitz nahm vielversprechende Biomarkerkandidaten bei Long COVID genauer unter die Lupe. Dafür wurden die Blutwerte von Cortisol und der Zytokine TNF-alpha, Interleukin-1-beta sowie Inter­leukin-6 in 4 verschiedenen Gruppen an 130 Teilnehmenden bestimmt.

Die untersuchte Kohorte setzten sich aus Menschen zusammen, die nie (n = 13), oder eine SARS-CoV-2-In­fektion durchgemacht hatten, aber kein Long COVID entwickelten (n = 34) sowie erkrankten Menschen mit Long COVID, die vollständig davon genesen waren (n = 40) und jene mit anhaltendem Long-COVID-Beschwer­den (n = 91).

Etwa 0,5 % aller Infizierten entwickeln Long COVID, dass mit bis zu 200 unterschiedlichen Symptomen ein­her­gehen kann, darunter Fatigue, Konzentrationsstörungen, starke Schmerzen oder Angststörungen. Aufgrund der breit gefächerten Symptomatik ist eine Diagnose mitunter schwierig. Daher wären alltagstaugliche Bio­marker vorteilhaft, um die Diagnose Long COVID rasch und sicher zu stellen.

zum Thema

    Abstract der Studie in Therapeutic Advances in Neurological Disorders
    Pressemitteilung des UK Essen

aerzteblatt.de

    Post COVID: Vielversprechender Ansatz mit kognitiver Verhaltenstherapie
    Long COVID: Onlinereha verbessert Lebensqualität in Studie leicht
    Long COVID: Proteomanalyse weist auf Störungen des Komplementsystems hin

Neuere wissenschaftliche Studien schlagen zum Beispiel das Stresshormonlevel von Cortisol und Zytokinlevel von TNF-alpha, Interleukin-1-beta sowie Interleukin-6 als mögliche Biomarker bei Long COVID vor. Laut deren Ergebnissen sei die Konzentration von Cortisol im Blut bei Betroffenen mit Long COVID deutlich niedriger als bei Gesunden, die Menge an entzündungsfördernden Zytokinen dagegen erhöht.

Mit dieser Arbeit stellte sich allerdings heraus, dass alle gemessenen Werte im Normbereich lagen und sich auch nicht zwischen den untersuchten Gruppen unterschieden. Mehrere Gründe, wie zum Beispiel Begleiter­krankungen (v.a. Asthma bronchiale) könnten für die Diskrepanz in den Ergebnissen verantwortlich sein und die zuvor berichteten höheren Zytokinspiegel teilweise erklären, nennen die Studienautoren.

Dennoch sei es sinnvoll, bei Long COVID auch zukünftig nach Faktoren zu suchen, die die Erkrankung be­günstigen, betonen sie. Dabei sollte der Fokus auf die Untersuchung der Pathophysiologie von Long COVID gerichtet sein, die auch nichtorganische Ursachen stärker thematisieren.

„Hier werden wir uns insbesondere auf den psychischen Bereich konzentrieren, da erste Therapiestudien nahelegen, dass viele Long-COVID-Betroffene gut von einer Psychotherapie profitieren“, berichtete Klein­schnitz.
© cw/aerzteblatt.de
[*/quote*]


Des Pudels Kern:

"„Hier werden wir uns insbesondere auf den psychischen Bereich konzentrieren, da erste Therapiestudien nahelegen, dass viele Long-COVID-Betroffene gut von einer Psychotherapie profitieren“, berichtete Klein­schnitz."

Kleinschnitz fällt bei Twitter immer wieder durch seine Hetze gegen LongCovid-Erkrankte auf. Er unterstellt denen etwas mit der Psyche. Kritiker blockt er einfach weg.

Kleinschnitz bei Twitter:  Christoph Kleinschnitz @nervensystemck

https://twitter.com/nervensystemck

Wer von ihm geblockt wurde, sieht höchstens noch das Banner:



https://pbs.twimg.com/profile_banners/1530197046371696640/1693643701/1080x360

Ein Neurologe, sogar ein Professor, befindet es für nötig, ein Bild von Charles Bronson zu seinem Schmuck und seiner Selbstdarstellung zu benötigen? Was kommt als nächstes?

Kleinschnitz sollte sich nicht sicher fühlen. Er kann bei Twitter Surfer aussperren. Aber nicht alle. Bei Nitter kann er gar nichts! Da kann man ihm auf die Finger gucken:

https://nitter.net/nervensystemck/with_replies

Ein Schnappschuß:



https://nitter.net/nervensystemck/status/1755935557127970995#m
[*quote*]
Christoph Kleinschnitz @nervensystemck
Feb 9

Wie zu erwarten, wirken Reha und Psychotherapie eben doch bei #LongCovid. Sogar online. Für klinisch tätige Ärzte ein no brainer, aber wenn man das Offensichtliche auch in einer randomisierten Studie bestätig haben möchte-bitte sehr. Die Rehas zu verteufeln war/ist der Tiefpunkt.

Feb 9, 2024 · 12:43 PM UTC
[*/quote*]

Reha ist bei LongCovid absolut kontraindiziert! Den Kranken geht es hinter deutlich schlechter, und sie erholen sich von dieser Verschlechterung nicht mehr.

Psychotherapie bei LongCovid? Das ist unverantwortlich. Ein Skandal ist das!


https://nitter.net/nervensystemck/status/1755966715035271596#m
[*quote*]
Christoph Kleinschnitz @nervensystemck
Feb 9

Wie zu erwarten, wirken Reha und Psychotherapie eben doch bei #LongCovid. Sogar online. Für klinisch tätige Ärzte ein no brainer, aber wenn man das Offensichtliche auch in einer randomisierten Studie bestätig haben möchte-bitte sehr. Die Rehas zu verteufeln war/ist der Tiefpunkt.

---------------------

Martin Rücker @martinruecker
Feb 9

Diese Studie ist aber wirklich ein nobrainer, schon wegen der Ein- bzw. Ausschlusskritierien 👇 Mir ist rätselhaft, wie man ein Studiendesign zu solcher Frage aufsetzt, ohne detaillierter Symptome inkl. PEM auszuwerten. #longcovid
[...]
[*/quote*]


[Titel geändert, respererso]
« Last Edit: September 03, 2024, 06:19:53 AM by Respererso »
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"Wir sind die Schulsoldaten. Wir sind die letzte Generation."
http://www.allaxys.com/~kanzlerzwo/index.php?topic=11591.msg37835#msg37835

Vultratelly

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Re: Dubiose Studien hetzen gegen LongCovid-Kranke
« Reply #1 on: February 15, 2024, 09:10:32 PM »

Der ORF berichtet über Studien zu Biomarkern:

https://science.orf.at/stories/3223630/

[*quote*]
Studie
ME/CFS-Zahlen steigen wegen Coronavirus


Bis zu 80.000 Menschen in Österreich leiden am Chronischen Fatigue-Syndrom, auch Myalgische Enzephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) genannt. Wegen der CoV-Pandemie werde die Zahl der Betroffenen stark steigen, sich womöglich sogar verdoppeln, noch seien aber weder Ursachen noch ursächliche Behandlungsansätze bekannt, so die MedUni Wien.
Online seit gestern, [15.2.2024] 13.04 Uhr

Dort haben Forschende jetzt mögliche Biomarker identifiziert. Denn bisher fehlten messbare Parameter für die Krankheit. Da die Diagnose deswegen schwierig sei, könne die Zahl der Erkrankten nicht genau beziffert werden, berichtete die MedUni am Donnerstag.

Zwischen 26.000 und 80.000 Menschen in Österreich leiden laut den Angaben an chronischer Fatigue: „Aufgrund von Covid-19 könnte sich diese Zahl in den nächsten Jahren verdoppeln. Die Zusammenhänge zwischen einer Infektion mit SARS-CoV-2 und ME/CFS sind ebenfalls Gegenstand intensiver Forschungen.“

Die Studie des Teams um Eva Untersmayr-Elsenhuber vom Zentrum für Pathophysiologie, Infektiologie und Immunologie ist im „Journal of Clinical Medicine“ erschienen.
Wichtige Hinweise durch Biomarker

Betroffene können laut der neuen Studie anhand der Funktion ihres Immunsystems in Untergruppen unterteilt werden. Dabei seien unterschiedliche Biomarker nachgewiesen worden, die auf Störungen im Immunsystem bzw. auf eine reduzierte Darmbarrierefunktion hindeuten. Es wurden für die klinische Versorgung relevante Unterschiede identifiziert, die ohne die immunologische Unterteilung der ME/CFS-Patientengruppe unentdeckt geblieben wären.

Die immunologische Abklärung sei von entscheidender Bedeutung: „Betroffene, die an Immundefizienzen leiden, sind durch ihre veränderte Immunfunktion charakterisiert. Bei ME/CFS-Patientinnen und -Patienten mit intaktem Immunsystem war die Darmbarrierefunktion herabgesetzt“, so Studienleiterin Untersmayr-Elsenhuber. Die Besonderheiten, die sich anhand von messbaren Markern im Blut nachweisen lassen, erlaubten Rückschlüsse sowohl auf unterschiedliche Krankheitsmechanismen als auch auf Behandlungsoptionen.

ORF SoundLogo von oe1 15.2.2024, 12.45 Uhr
Biomarker für Chronisches Fatigue-Syndrom entdeckt

„Schwere multisystemische Erkrankung“

Die Ergebnisse sollen in einem größeren Rahmen überprüft werden. An der MedUni Wien wird mit Unterstützung der WE&ME-Foundation die erste „ME/CFS-Biobank Austria“ mit biologischen Proben von Betroffenen aufgebaut. „Damit die ME/CFS-Forschung in Zukunft rasch und länderübergreifend stattfinden kann, haben wir uns dabei von Anfang an mit Forschungsgruppen in Großbritannien, den Niederlanden und Deutschland abgestimmt“, so Untersmayr-Elsenhuber.

ME/CFS ist laut MedUni eine schwere multisystemische Erkrankung, die oft zu einem hohen Grad an Einschränkungen führt. 60 Prozent der Patientinnen und Patienten seien nicht in der Lage, einer Vollzeitbeschäftigung nachzugehen, 25 Prozent seien bettlägerig. Die genauen Ursachen der Erkrankung sind noch ungeklärt.

red, science.ORF.at/Agenturen
Mehr zum Thema

    Forscher fanden Hinweise auf Long Covid im Blut
    Erste Hinweise auf Biomarker für Long Covid
    ME/CFS-Erkrankte warnen vor Versorgungsnotstand
[*/quote*]
Logged
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"Wir sind die Schulsoldaten. Wir sind die letzte Generation."
http://www.allaxys.com/~kanzlerzwo/index.php?topic=11591.msg37835#msg37835

Vultratelly

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Natalie Grams hat LongCovid
« Reply #2 on: February 15, 2024, 09:17:31 PM »

Natalie Grams, hier hinlänglich bekannt, hat LongCovid. Die Meldung in dem Blog ist vom 15.2.2024.

Jetzt wird es interessant. Wird Natalie Grams gegen Kleinschnitz vorgehen?

Popcorn!


Das Beweisstück:

https://detektor.fm/wissen/grams-sprechstunde-podcast-long-covid-staffelpause

[*quote*]
detektor.fm » Wissen » Grams’ Sprechstunde | Podcast

Grams’ Sprechstunde | Podcast
Long COVID: Staffelpause auf unbestimmte Zeit
15.02.2024

Natalie Grams ist an Long Covid erkrankt — der Podcast pausiert auf unbestimmte Zeit. In einer persönlichen Nachricht an die Hörerinnen und Hörer erklärt sie, wie es ihr geht und wie die Perspektive ist.

Podcast pausiert auf unbestimmte Zeit

Weil sie selbst an Long Covid erkrankt ist, ist Ärztin und Podcast-Host Natalie Grams auf unbestimmte Zeit krankgeschrieben. Deshalb wird es bis auf Weiteres keine neuen Folgen von Grams’ Sprechstunde geben. Es werde sicher noch eine ganze Weile dauern, bis sie wieder auf dem Damm ist, schätzt Natalie Grams die Situation ein.

„Ich mache im Moment ganz viel Erfahrungen mit nicht so doller Medizin und vielen Versorgungslücken, vielleicht sogar mit Ignoranz im Gesundheitssystem. Insofern kann ich ganz viele Themen für die neue Staffel sammeln.“

„Viel Unverständnis, krasse Hilflosigkeit“

Natalie Grams erklärt, dass oft auch eher jüngere Menschen erkranken, die voll im Berufsleben stehen und in der Familie und im gesellschaftlichen Leben gebraucht werden. Menschen, die an Long Covid erkranken, treffen demnach auf viel Unverständnis und erleben eine Hilflosigkeit.

    Bitte nehmt das Thema in eurem Umfeld ernst. Ich weiß, dass keiner mehr Bock hat, sich mit Corona zu beschäftigen. Aber manche Menschen reißt das Virus immer noch komplett aus dem Leben.

Natalie Grams, Ärztin und Host von Grams‘ Sprechstunde

Woran erkenne ich, dass ich Long Covid habe? Wer kann mir sagen, welche aktuellen Erkenntnisse es zu der Krankheit gibt? Und wer kann mir sagen, wo ich Hilfe finde, wenn ich Long Covid habe? Antworten, Erkenntnisse und Hilfe für Betroffene und ihre Angehörigen bietet eine spezielle Initiative des Bundesministeriums für Gesundheit. In der Wissenschaft gibt es Ansätze, die Prozesse hinter Long Covid zu verstehen und den Ursachen auf den Grund zu gehen.

Diagnose Long COVID

Aktuell geht man davon aus, dass Long Covid kein einheitliches Krankheitsbild ist, sondern dass es verschiedene gesundheitliche Langzeitfolgen nach einer Ansteckung mit dem Coronavirus umfasst. Viele Betroffene berichten über starke, anhaltende Schwäche und schnelle Erschöpfung. Aber auch weitere körperliche und psychische Beschwerden sind möglich. Dazu zählen zum Beispiel Kurzatmigkeit, anhaltender Husten, Muskelschwäche oder Muskelschmerzen sowie Konzentrations- und Gedächtnisprobleme („brain fog“). Auch Schlafstörungen und psychische Probleme wie depressive Symptome und Ängstlichkeit können auftreten.



Der Podcast für echt gute Medizin

Der Podcast ist im Januar 2020 gestartet, damals als monatliches Format gemeinsam mit dem Juristen Christian Nobmann und dem Untertitel „Der Podcast für recht gute Medizin“. In der ersten Folge ging es um die Impfpflicht bei Masern. Seit 2021 sind alle zwei Wochen neue Folgen erschienen, das Konzept des Podcasts hat sich verändert und der Untertitel lautet seitdem: „Der Podcast für echt gute Medizin“. Hier findet ihr den Überblick zu allen Folgen. „Grams’ Sprechstunde“ ist eine Kooperation vom Podcast-Radio detektor.fm und Spektrum der Wissenschaft.

Redaktion: Ina Lebedjew
Grams’ Sprechstunde – Der Podcast für echt gute Medizin

Personalmangel, Zeitdruck, Bürokratie und fehlende Wissenschaftlichkeit – es ist oft schwer, wirklich gute Medizin zu finden. Und dann bekommt man den nächsten Termin sowieso erst in drei Monaten. In „Grams’ Sprechstunde“ geht die Ärztin Natalie Grams mit unterschiedlichsten Menschen aus Alltag, Praxis und Forschung der Frage nach, wie wir alle gemeinsam für ,echt gute Medizin’ sorgen können. Ihr Ziel ist, dass wir uns darin nicht als Nummern, sondern wieder als Menschen gesehen fühlen können – als Behandelte, aber auch als Behandelnde. Ihr Wunsch ist aber auch, dass wir uns dabei nicht von populären Mythen und falschen Heilsversprechen beirren lassen.
[*/quote*]
Logged
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"Wir sind die Schulsoldaten. Wir sind die letzte Generation."
http://www.allaxys.com/~kanzlerzwo/index.php?topic=11591.msg37835#msg37835

Pangwall

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Take that, Kleinschnitz!
« Reply #3 on: February 16, 2024, 08:33:58 PM »

Take that, Kleinschnitz!

https://www.sciencealert.com/long-covid-seems-to-be-a-brain-injury-scientists-discover

[*quote*]
Long COVID Seems to Be a Brain Injury, Scientists Discover

16 February 2024
By David Nield
Series of brain scan images (Tek Image/Science Photo Library/Getty Images)

Some form of brain injury could be behind the symptoms reported by those with long COVID, according to a new study, and adapting tests and treatments to match could aid progress in tackling the condition.

Analyzing 203 patients hospitalized with COVID-19 or its associated symptoms, and comparing the results with 60 people without the infection, researchers noticed elevated levels of four brain injury biomarkers – key signs of biological change – in those infected with COVID-19.

What's more, two of those signs of brain injury persisted into the recovery phase, suggesting that they continue even after the COVID-19 infection has gone. Levels of these two biomarkers were even higher for people who also experienced neurological complications with COVID-19.

"Our study shows that markers of brain injury are present in the blood months after COVID-19, and particularly in those who have had a COVID-19-induced brain complication," says neuroscientist Benedict Michael from the University of Liverpool in the UK.

"This suggests the possibility of ongoing inflammation and injury inside the brain itself which may not be detected by blood tests for inflammation."

These brain complications associated with COVID-19 have ranged from mild (headaches) to potentially life-threatening (seizures, stroke, and encephalitis). As previous research has shown, the consequences can be long-lasting.

Michael and team think that abnormal responses by the body's immune system could be causing the signs of injury they're seeing. If we can find out more about these responses and how they're triggered, new treatments could be developed.

It's now clear that COVID-19 plays some role in impacting the nervous system, and in some cases this impact can continue for an extended period. This new study shows that the effects can be similar to brain injuries.

"The clinical characteristics of our participant cohorts, and the elevation in brain injury markers, provide evidence of both acute and ongoing neurological injury," write the researchers in their published paper.

The researchers are already hard at work following up on their study, looking at how the damage caused by COVID-19 and the associated inflammation might lead to cognitive problems and mental health issues further down the line.

It's thought that tens of millions of people are now living with long COVID in some form, and yet it's still not a condition that we know all that much about. Studies continue to try to spot patterns in its prevalence, which should eventually provide more clues as to how to combat it.

"This work may help set the stage for elucidating the possible underlying mechanisms of these complications," says immunologist Leonie Taams, from King's College London in the UK.

The research has been published in Nature Communications.
https://www.nature.com/articles/s41467-023-42320-4
[*/quote*]



https://www.nature.com/articles/s41467-023-42320-4

[*quote*]
Nature Communications

    Open access
    Published: 22 December 2023

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

    Benedict D. Michael, Cordelia Dunai, Edward J. Needham, Kukatharmini Tharmaratnam, Robyn Williams, Yun Huang, Sarah A. Boardman, Jordan J. Clark, Parul Sharma, Krishanthi Subramaniam, Greta K. Wood, Ceryce Collie, Richard Digby, Alexander Ren, Emma Norton, Maya Leibowitz, Soraya Ebrahimi, Andrew Fower, Hannah Fox, Esteban Tato, Mark A. Ellul, Geraint Sunderland, Marie Held, Claire Hetherington, ISARIC4C Investigators, COVID-CNS Consortium, …David K. Menon

Nature Communications volume 14, Article number: 8487 (2023) Cite this article

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Abstract

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely.

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Introduction

At the beginning of the COVID-19 pandemic, neurological complications occurred in a significant proportion of hospitalised patients1 and even in those with mild COVID-19 infection2. While these neurological ‘complications’ were often mild (headache and myalgia), it became clear that more significant neurological sequelae were observed, including encephalitis/encephalopathies, Guillain Barre Syndrome, seizure, and stroke3,4,5,6.

Although in vitro studies show that SARS-CoV-2 can infect neurons and astrocytes7,8, autopsy studies indicate that direct viral invasion is unlikely to be a cause of neurological dysfunction in vivo9. Post-mortem studies failed to detect viral infection of the brain by immunohistochemistry in the majority of cases, and viral qPCR levels were often low and may simply have reflected viraemia10,11,12. In addition, virus and/or anti-viral antibodies were rarely found in cerebrospinal fluid (CSF)13. Thus, it seems more likely that the virus affects the brain indirectly. This could be through peripherally generated inflammatory mediators, immune cells, autoantibodies and/or blood brain barrier changes associated with endothelial damage14,15. Immune infiltrates have been found in autopsy studies, including neutrophils and T cells, although agonal effects could not be excluded16. On the other hand, elevated IL-6 levels in sera and CSF have been associated with neurological complications, including meningitis, thrombosis, stroke, cognitive and memory deficits, regardless of respiratory disease severity17,18,19,20. One study found that the brain injury markers NfL and GFAP, and inflammatory cytokines were elevated in COVID-19 and scaled with severity21,22,23,24,25; another study showed that baseline CSF NfL levels correlated with neurological outcomes at follow-up26 but overall, the relationships between these immune mediators and markers of brain injury and neuropathology remains to be fully explored. Finally, specific neuronal autoantibodies have been reported in some neurological patients raising the possibility of para- or post-infectious autoimmunity14,27.

To assess the relationship between host immune response and markers of brain injury with neurological injury, we studied two large, multisite cohorts which, in combination, provided acute, early and late convalescent sera from COVID-19-positive (COVID+ve) participants. We measured brain injury markers, a range of cytokines and associated inflammatory mediators, and autoantibodies in these samples, and related them to reduced levels of consciousness (defined as a Glasgow Coma Scale Score [GCS] GCS ≤ 14) in the acute phase, or the history of a neurological complication of COVID-19 in convalescent participants. We tested the hypothesis that immune mediators would correlate with brain injury markers and reveal a signature of neurological complications associated with COVID-19.

Results
COVID-19 results in acute elevation of serum markers of brain injury, more so in participants with abnormal Glasgow coma scale (GCS) score

We used sera from the International Severe Acute Respiratory and emerging Infection Consortium Clinical Characterisation Protocol United Kingdom (ISARIC CCP-UK) study, obtained 1–11 days post admission, that included 111 participants with COVID-19 of varying severity and 60 uninfected healthy controls (labelled Control). Participants were stratified by normal (n = 76) or abnormal (n = 35) Glasgow Coma Scale scores (labelled GCS = 15 or GCS ≤ 14, respectively) to provide a proxy for neurological dysfunction (Fig. 1a). GFAP (glial fibrillary acidic protein, marker of astrocyte injury), UCH-L1 (a marker of neuronal cell body injury), and NfL (neurofilament light) and Tau (both markers of axonal and dendritic injury) were measured. Overall, serum levels of NfL, GFAP, and total-Tau (tTau) were significantly higher in COVID-19 participants compared to the uninfected healthy controls but, as shown in Fig. 1b–e, those participants with abnormal GCS scores had higher levels of NfL and UCH-L1 than those with normal GCS scores. Thus, all four markers of brain injury were raised in COVID-19 participants (both GCS = 15 and GCS ≤ 14) but, in addition, axonal and neuronal body injury biomarkers discriminated between participants with and without reduced GCS.

Fig. 1: Brain injury markers are elevated acutely in COVID-19 participants with an abnormal Glasgow coma scale score (GCS) and in participants who experienced a neurological complication associated with COVID-19.
figure 1

a The acute ISARIC cohort included Day 1–11 hospital admission timepoints. b–e Acute serum brain injury markers were assessed by Simoa: b NfL, c UCH-L1, d GFAP, and e tTau. All four were elevated in COVID-19 cases with normal Glasgow coma scale scores (GCS) relative to controls overall. Dotted lines show lower limit of quantification (LLOQ). f–j The Simoa analyses were performed for the sera from the COVID-CNS COVID and neuro-COVID groups at early and late convalescent timepoints (g, j) showing persistence of NfL, GFAP, and tTau in COVID participants, with NfL higher in neuro-COVID than COVID participants. k Within the combined early and late convalescent COVID-19 neurological cases, the highest levels of NfL were observed in participants who had suffered a cerebrovascular event at the time of SARS-CoV-2 infection. l tTau levels were raised in the cerebrovascular, CNS inflammatory and seizure conditions. m, n Serum NfL remained elevated in both the early (<6 weeks from positive SARS-CoV-2 test) and late convalescent phases (>6 weeks) in neuro-COVID compared to COVID non-neurological cases. o, p GFAP was elevated in neurological cases in the early and late convalescent phase. Box and whisker plots show all data points with median as centre line with 25th and 75th percentiles. Sample sizes shown in (a) and (f). Group comparisons are by Kruskal–Wallis test with Dunn’s post-hoc multiple comparison test, no statistical comparison made for panel (h) as medians were at LLOQ.

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Markers of brain injury remain elevated in the early and late convalescent phases in participants who have had a CNS complication of COVID-19

To ask whether these findings persisted in participants recovering from COVID-19-related neurological complications, ninety-two COVID-19 participants were recruited to the COVID-Clinical Neuroscience Study (COVID-CNS), 56 who had had a new neurological diagnosis that developed as an acute complication of COVID-19 (group labelled “neuro-COVID”), and 36 with no such neurological complication (group labelled “COVID”, Fig. 1f, Table 1, Supplementary Tables 1 and 2). When compared to the same healthy controls (n = 60), across all timepoints, both COVID-19 subgroups (COVID and neuro-COVID) showed increased levels of NfL, GFAP, and tTau (but not UCH-L1 (Fig. 1g–j, Supplementary Table 1)). Furthermore, participants recovering from neuro-COVID had significantly higher levels of NfL, and a trend towards higher levels of tTau, than the COVID participants (Fig. 1g, j). Highest NfL serum levels were present in participants with cerebrovascular conditions, whereas tTau was elevated in participants with cerebrovascular, CNS inflammation and peripheral nerve complications (Fig. 1k, l). NfL remained significantly elevated in a multiple regression model adjusted for age (Supplementary Fig. 1a, b). We then separately compared the two cohorts at early and late convalescent follow-up periods (less than and over six weeks after admission respectively). NfL and GFAP levels remained elevated in all COVID-19 participants in the convalescent period, but only remained elevated beyond 6 weeks in participants who had suffered an acute neurological complication (neuro-COVID, Fig. 1m–p; Supplementary Fig. 1c). The presence of elevated brain injury markers in the acute phase of COVID-19 confirms previous findings14, but the elevated levels of NfL and GFAP in those who are convalescent from acute neurological complications suggest ongoing neuroglial injury.

Table 1 Clinical characteristics of healthy controls and COVID-CNS participants
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Clinical and brain injury markers evidence of neurological insult levels are associated with levels of innate inflammatory mediators in the acute phase of COVID-19

To explore whether the acute and persistent elevation of markers of brain injury observed in participants with COVID-19 was associated with an acute inflammatory response, we measured a panel of 48 inflammatory mediators in serum at the same timepoints. In the ISARIC samples, six mediators were significantly higher in participants with an abnormal GCS than in those with a normal GCS (interleukin [IL]-6, hepatocyte growth factor [HGF], IL-12p40, IL-1RA, CCL2 and macrophage colony stimulating factor [M-CSF]), indicating increased innate inflammation (Fig. 2a, Supplementary Fig. 2a). Pearson’s correlation tests identified correlations between these significant immune mediators in an interrelated pro-inflammatory network (Fig. 2b, c), and unsupervised Euclidean hierarchical cluster analysis revealed clusters of pro-inflammatory mediators elevated together (Fig. 2d). The first cluster incorporated the IL-1 family (including IL-1RA), interferons and M-CSF, and the second cluster included IL-6, CCL2, CXCL9, HGF, and IL-12p40 (boxes in Fig. 2d). Brain injury biomarkers correlated with elevations in these inflammatory mediators: GFAP and UCL-H1 correlated with a number of mediators in the first cluster, whereas tTau and NfL correlated strongly with HGF and IL-12p40 in the second cluster (Supplementary Table 3).

Fig. 2: Immune mediators are elevated acutely and correlate with different brain injury markers at different timepoints.
figure 2

Serum mediators from the ISARIC and COVID-CNS cohorts were assessed by Luminex. a A volcano plot was generated to identify mediators which were elevated in participants with an abnormal GCS (GCS ≤ 14) compared to normal GCS (GCS = 15) and b, c a network analysis identified the highest correlations between the mediators (***significantly different from ISARIC GCS = 15 by Steiger test p < 0.001). d Unbiased Euclidean hierarchical cluster and correlation analyses identified two clusters of up-regulation of several pro-inflammatory mediators in concert. The first group included interleukin (IL)-6, IL-12p40, CCL2, CXCL9 and hepatocyte growth factor (HGF) and the second group included the IL-1 family, interferons, and macrophage colony stimulating factor (M-CSF); to the right is shown the correlations between each cytokine with the four brain injury biomarkers (significance indicated by asterisks). e Network analysis and heatmaps of correlations between mediators did not demonstrate the tight interconnectedness that had been identified in acute samples and there were differences between neuro-COVID (e) and ISARIC GCS = 15 and GCS ≤ 14 by Steiger test (***p < 0.001). f At this later stage several mediators correlated with tTau. Volcano plot used multiple two-tailed Mann–Whitney U tests with a false discovery rate set to 5%. Correlations are Pearson’s coefficients (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

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A more stringent analysis of median-centred cytokine data (which corrected for between participant skewing of mediator levels) confirmed that HGF and IL-12p40 were higher in the abnormal GCS COVID-19 participants, and correlated with cognate NfL levels (Supplementary Table 4). Taken together these data suggest that activation of the innate immune system was related to both clinical and blood marker evidence of CNS insult.
Inflammatory mediators are not elevated across the participant cohort at late timepoints after COVID-19; but late tTau elevations correlate with levels of several inflammatory mediators

In contrast to the acute data, the levels of cytokines and associated mediators were lower when measured during the convalescent periods even in those who had suffered neurological complications of COVID-19 (group labelled “neuro-COVID”. Supplementary Fig. 2b). The correlations between cytokines and associated mediators no longer displayed the same tight clusters (Fig. 2e, f). GFAP remained elevated during the convalescent phase of neurological complications (Fig. 1p) but did not show correlations with the inflammatory mediators. Similarly NfL was higher overall in those with neurological complications (Fig. 1n) but there were no significant correlations with inflammatory mediators (Fig. 2f). However, tTau remained elevated overall in those with neurological complications ((1.7 (1.3, 2.2) pg/mL versus 1.3 (1.1, 1.9) pg/mL)) and levels correlated with eight immune mediators including CCL2, IL-1RA, IL-2Rα and M-CSF along with CCL7, stem cell factor (SCF), IL-16 and IL-18 (Fig. 2f, Supplementary Table 5, Supplementary Fig. 2c). This last association was specific to the late phase of the illness and was not found in acute COVID-19.
Cytokine networks are significantly altered in participants with neurological complications of COVID-19: both acute encephalopathy, and those recovering from a neurological complication

We used graph theoretical approaches to compare these cytokine networks between participants with: acute COVID-19 and normal GCS; acute COVID-19 with altered consciousness (GCS ≤ 14), and convalescent participants recovering from a neurological complication of COVID-19 (neuro-COVID). Participants with both neurological consequences of COVID-19 (GCS ≤ 14) and Neuro-COVID both showed cytokine networks that were different from COVID-19 participants with no neurological problems (Fig. 2b, c, e; p < 0.001, Steiger test), suggesting a specific dysregulated innate immune response that is associated with neurological complications of COVID-19. Further pathway analyses using the KEGG enrichment scores on the significantly different cytokines, revealed many commonalities with other inflammatory syndromes (Supplementary Fig. 3a, b). Interestingly, cytokine profiles of the neurological complications groups from both the ISARIC and COVID-CNS cohort led to JAK-STAT signalling being a significant involved pathway which would be amenable to immunomodulation, for example, by tofacitinib, which has been shown to reduce mortality in COVID-1928.

COVID-19 is associated with an acute adaptive immune response overall, which includes antibodies to viral antigen and CNS autoantigens in those with abnormal GCS scores

Given past reports of autoantibody responses following COVID-1914,27, we sought evidence of similar dysregulated adaptive immune responses in our participant cohorts. We used a bespoke protein microarray of 153 viral and tissue proteins to measure IgM (Fig. 3a–d) and IgG (Fig. 4a–d) reactivity in the acute phase ISARIC sera. The median fluorescence intensities for each putative antigen were normalized for each participant and the Z-scores were compared to healthy control data, to determine positive reactivity to the different antigens (with a threshold for detection set at three standard deviations above controls for each antigen; see Supplementary Table 6, Supplementary Fig. 4a). IgM and IgG responses in COVID-19 participants showed greater reactivity overall (both GCS = 15 and GCS ≤ 14), compared to the controls, with no difference in normalised fluorescence Z scores or the number of participants with IgG ‘hits’ (a Z-score >3) between those with normal or abnormal GCS score (Fig. 3a, b, Fig. 4a, b). However, several IgM and IgG autoantibodies, including those against the CNS antigens UCH-L1, GRIN3B and DRD2, along with the cardiac antigen, myosin light chain (MYL)-7, were present in a greater proportion of participants with an abnormal GCS score, as were antibodies to spike protein (Figs. 3c, 4c). None of the antibodies correlated significantly with levels of brain injury markers (Supplementary Figs. 4b, c, 5b, c), but they did show correlations with each other (Figs. 3d, 4d, h), suggesting a non-specific antibody response in some individuals during the acute phase.

Fig. 3: There is an IgM antibody response in participants with COVID-19 directed at SARS-CoV-2 spike protein and against several self-antigens.
figure 3

a Acute samples were tested for IgM antibodies by protein microarray with normalized fluorescence Z-scores shown. b COVID-19 participants showed considerably more binding ‘hits’ than healthy controls (fluorescence with a Z-score of 3 or above compared to controls), although overall there was no difference in the acute samples between participants with normal (GCS = 15) or abnormal GCS (GCS ≤ 14)). Nevertheless, c COVID-19 participants with abnormal GCS (GCS ≤ 14) more frequently had raised IgM antibodies than COVID-19 participants with a normal GCS (GCS = 15), including those directed at SARS-CoV-2 spike protein (Fisher’s exact tests *p < 0.05). d A chord diagram shows the associations between antibodies, including those against Spike. e IgM antibodies were also analysed in the convalescent participants. f A largr proportion of COVID and Neuro-COVID participants had positive antibody ‘hits’ for IgM (defined by Z-score 3 and above compared to controls). g Of those antibodies against self-antigens identified, they were only two with different frequencies between the groups (Fisher’s exact tests *p < 0.05). At this timepoint there was no significant difference in the proportion of individuals with IgM against SARS-CoV-2 spike or nucleocapsid epitopes. Violin plots show all data points with median at centre line and 25th and 75th quartile lines. Group comparisons are by Kruskal–Wallis test with post-hoc Dunn’s multiple comparison test, pairwise comparisons by two-tailed Mann–Whitney U test, and correlations are Pearson’s coefficients.

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Fig. 4: There is an IgG antibody response in participants with COVID-19 directed at SARS-CoV-2 nucleocapsid and spike proteins and against several self-antigens.
figure 4

a Acute samples were tested for IgG antibodies by protein microarray with normalized fluorescence Z-scores shown. b COVID-19 participants showed considerably more binding ‘hits’ than healthy controls (fluorescence with a Z-score of 3 or above compared to controls) but, overall, there was no difference in the acute samples between participants with normal (GCS = 15) or abnormal GCS (GCS ≤ 14). c COVID-19 participants with abnormal GCS more frequently had several raised IgG antibodies than COVID-19 participants with a normal GCS, including those directed at SARS-CoV-2 spike protein and several CNS proteins ((DRD2, GRIN3B, and UCH-L1) Fisher’s exact tests * p < 0.05)). d A chord diagram shows the association of antibodies with differences in frequency, including those against Spike. e IgG antibodies in early and late convalescent sera were also analysed. f A larger proportion of Neuro-COVID participants had positive antibody ‘hits’ for IgG (defined by Z-score 3 and above compared to controls). g Of those antibodies against self-antigens identified, only five showed a difference in frequency between groups (Fisher’s exact tests *p < 0.05). At this timepoint there was no significant difference in the proportion of individuals with IgG against SARS-CoV-2 spike or nucleocapsid epitopes. h A chord diagram shows the association of antibodies with differences in frequency plus anti-Spike antibody. i Representative images of rat brains incubated with participants’ sera and screened for IHC binding of anti-human IgG to detect CNS reactivity. Scale bars = 1 mm. j Percentage of participant serum IgG reactivity to rat brainstems, detected by Fisher’s exact test with Benjamini and Hochberg correction. Violin plots show all data points with median at centre line and 25th and 75th quartile lines. Group comparisons are by Kruskal–Wallis test with post-hoc Dunn’s multiple comparison test, pairwise comparisons by two-tailed Mann–Whitney U test, and correlations are Pearson’s coefficients.

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Normalized fluorescence Z scores of serum IgM and IgG autoantibodies in the early and late convalescent samples were similar to those in the acute samples (Figs. 3e, 4e), and the IgM and IgG ‘hits’ were more frequent than in controls (highest in the neuro-COVID group, Figs. 3f, 4f, Supplementary Fig. 5a). However, specific autoantibody responses to MYL7, gonadotrophin releasing hormone receptor (GNRHR) and several HLA antigens were common in the neuro-COVID participants (Figs. 3g, 4g, Supplementary Fig. 5a). When the IgM and IgG hits were stratified by condition, cerebrovascular and inflammatory conditions showed the highest number (Supplementary Fig. 5d, e). As in the acute phase, autoantibody responses did not show significant associations with brain injury markers, but did tend to correlate with each other (Fig. 4h, Supplementary Fig. 5b, c).

Finally, to explore binding to native neuronal antigens, sera from acute COVID-19 participants with CNS antigen reactivity were incubated with sections of rat brain, neurons and antigen-expressing cells. Binding to rat brain sections identified 42/185 (23%) of participants with strongly positive immunohistochemical staining (e.g. Fig. 4i) and overall, sera from the COVID+ve ISARIC participants showed more frequent binding to brainstem regions than control sera, but this did not relate to the GCS or neurological disease of the participants (Fig. 4j, Supplementary Fig. 6). In addition, from 34 selected samples tested via cell-based assays to examine for the presence of specific autoantibodies (LGI1, CASPR2, NMDAR, GABAB receptor), only one bound to the extracellular domain of the GABAB receptor (from the ISARIC cohort, Supplementary Fig. 7a, b), as expected of a pathogenic autoantibody.

Discussion

We used several approaches to study neurological complications of COVID-19 infection. These included assessment of immune mediators and markers of brain injury in participants with and without neurological complications, both in the acute and convalescent phases after COVID-19 infection. We demonstrated increased levels of brain injury markers following COVID-19, which showed specific patterns with disease phase (acute or convalescent), and varied with the presence or absence of neurological injury or dysfunction. In the acute phase, all four brain injury markers (GFAP, NfL, tTau and UCH-L1) were elevated in participants when compared to controls, and specific markers of dendritic and axonal injury (tTau and NfL) were significantly higher in participants who showed a reduced level of consciousness (GCS ≤ 14). In the early convalescent phase (<6 weeks post-infection), GFAP, NfL, and tTau were elevated in participants recovering from COVID-19, with no differences between those who had or had not sustained a neurological complication of disease. However, at late timepoints (>6 weeks) elevations of NfL and GFAP were only seen in participants who had sustained a neurological complication of COVID-19 in the acute phase of their illness. These data suggest that clinical neurological dysfunction in COVID-19 is reflected by increases in markers of neuroglial injury, both in the acute phase and at follow-up, which are related to a dysregulated immune response, more robustly in the acute phase of illness.

In the acute phase, when compared to controls, we also observed increases in a range of inflammatory mediators (IL-6, HGF, IL-12p40, IL-1RA, CCL2, and M-CSF) in the overall cohort of COVID-19 participants, with HGF and IL-12p40 showing robust differentiation between participants with and without alterations in consciousness. By contrast, participants at the late phase after COVID-19 showed no group level elevation of inflammatory mediators. However, late elevations in tTau correlated with levels of CCL2, CCL7, IL-1RA, IL-2Rα, M-CSF, SCF, IL-16, and IL-18, suggesting that these markers of the late innate host response were associated with persisting markers of dendritic/axonal injury markers. A network analysis showed that the repertoire of cytokine responses was different in participants both with acute reductions in GCS, or those recovering from a neurological complication of COVID-19 when compared to the GCS = 15 group.

Participants with acute COVID-19 also developed IgG autoantibody responses to a larger number of both neural and non-neural antigens, than seen in controls. These increased IgG responses persisted into the late phase but to different antigens. While the diversity of autoantibody response did not differ between participants with and without neurological dysfunction, autoantibody responses to specific antigens, including the neural antigens UCH-L1, GRIN3B, and DRD2, were more common in participants with abnormal GCS at presentation. In the late phase, participants who had or had not experienced a neurological complication of COVID-19 were distinguished by the presence of autoantibodies to HLA antigens rather than neural antigens.

These data from clinical disease provide important insights regarding the pathophysiology and pathogenesis of neurological injury, dysfunction, and disease in COVID-19. The clinical characteristics of our participant cohorts, and the elevation in brain injury markers, provide evidence of both acute and ongoing neurological injury29. Furthermore, the literature data on the rarity of direct CNS infection by the virus, suggest that the innate and adaptive host responses that we document should be explored as pathogenic mechanisms. The incidence of neurological cases has decreased since the first wave of the pandemic, possibly due to the use of immunosuppressants, such as dexamethasone, although this may also reflect vaccines attenuating disease and changes in the prevalence of different strains of SARS-CoV-230.

The inflammatory mediators that we found to be elevated in the acute phase are broadly concordant with many other publications that have examined innate immune responses in COVID-1921,22 but there are limited data addressing associations between such responses and the development of neurological complications. It is possible that some of the risk of developing such complications is simply related to the severity of systemic infection and the host response, and it would be surprising if these were not strong contributors. However, our data suggest that acute neurological dysfunction in COVID-19 is also associated with a different repertoire of cytokine responses, with HGF and IL-12p40 showing the statistically most robust discrimination between participants with and without an abnormal GCS. HGF has important roles in brain development and synaptic biology31 and its elevation may represent a protective/reparative response in participants with neurological injury. IL-12p40 has a core role in orchestrating Th1 responses, and has been reported to be central in the development of central and peripheral neuroinflammation, with p40 monomer subunits perhaps acting as inhibitors of the process32,33,34. Interestingly, the cytokine network that was activated in the late convalescent phase was different, potentially indicating differential drivers of neurological injury throughout the disease course. Though group level comparisons with controls showed some commonalities in inflammatory mediator increase, most notably in IL-1RA, CCL2, and M-CSF, there were many differences. The late tTau elevation that we demonstrated was significantly associated with elevations in these three mediators, but also CCL7, IL-2Rα, SCF, IL-16, and IL-18. These are all important pro-inflammatory mediators, and their association with tTau levels may reflect the persistence of a systemic inflammatory response that can enhance neuroinflammation32,34,35.

We found a general increase in antibody production following COVID-19 infection and only a few autoantibody frequencies were different when compared by GCS or COVID versus neuro-COVID cases. Of note, absolute levels of autoantibodies were low in comparison to anti-viral antibodies that developed over the course of the acute illness, with the exception of SFTPA1. Antibodies to SFTPA1, a lung surfactant protein, have been found to correlate with COVID-19 severity14, but these antibodies were present in only a few acute cases. HLA antibodies, on the other hand, were more frequent in Neuro-COVID than COVID participants and this requires further investigation. The autoantibodies detected in COVID-19, as in other infections, could be through molecular mimicry or bystander effects36,37,38,39, but the lack of association of autoantibody levels with markers of brain injury is evidence against a causal role for these adaptive immune responses. Further analysis by screening the antibodies against brain antigens ex vivo revealed sporadic reactivity in both cases and controls with only the brainstem showing increased reactivity in acute COVID+ve participants; the frequencies were lower in COVID and neuro-COVID cases with no difference between them.

Our studies have several limitations including: limited clinical information on the acute participants and lack of longitudinal blood samples; in addition, the low GCS could indicate sedation for intubation, rather than CNS disease, in the acute cohort. Although we did not have COVID-19 severity scores, we did know whether participants had required oxygen or not; when data were analysed within the cohorts comparing participants who had or had not required oxygen, 5 out of 6 cytokines remained significantly elevated in the abnormal GCS group. In the COVID-CNS study where we did have in-depth clinical information, we were limited by not having acute blood samples. Nevertheless, several cytokines showed significant positive correlations with the brain injury marker tTau, and interestingly, three of them were cytokines that were significantly associated with abnormal GCS in the acute cohort (IL-1RA, CCL2, and M-CSF) highlighting a network of co-upregulated immune mediators associated with neurological complications. The commonalities in innate immune response in participants who suffered neurological dysfunction/complications, both in the acute phase and at convalescence, is underlined by the results of network analysis. Pro-inflammatory cytokines are expected to be increased in the anti-viral response, but we found that they not only correlate with COVID-19 severity, but with GCS, as well. Strengths of our study include the large cohort of participants studied with well-characterized neurological syndromes and a known range of timings since COVID-19 infection. We studied aspects of the innate and adaptive immune response as well as brain injury markers in order to discover useful markers of neurological complications over time.

Several hypotheses for how SARS-CoV-2 causes neuropathology have been tested. A prospective study of hospitalised patients showing IL-6 and D-dimer as risk factors for neurological complications implicates the innate immune response and coagulation pathways19. The complement pathway and microthrombosis have been associated with brain endothelial damage from the infection, and this phenotype persists months after COVID-1940,41. Animal models have provided key insights into COVID-19 neuropathology that warrant discussion. There have been at least two reports of viral encephalitis and neuron degeneration and apoptosis observed in non-human primates42,43. It is important to note that in these studies the virus was present at low amounts in the brain and predominantly in the vasculature as visualized by co-localization with Von Willebrand Factor43. Similar to the clinical scenario, there was no correlation of neuropathology with respiratory disease severity43. A recent mouse study is particularly relevant to our work and involved assessment of a mouse model that lacked direct viral neural invasion by infecting mice that were intratracheally transfected with human ACE2. This study reported increased CXCL11 (eotaxin) in mouse serum and CSF that correlated with demyelination and was recapitulated by giving CXCL11 intraperitoneally44; this was linked to clinical studies that showed elevated CXCL11 in patients with brain fog44. A combined analysis of hamster and clinical studies showed that COVID-19 led to IL-1β and IL-6 expression within the hippocampus and medulla oblongata and decreased neurogenesis in the hippocampal dentate gyrus which may relate to learning and memory deficits45. This was also borne out during in vitro studies that showed that serum from COVID patients with delirium lead to decreased proliferation and increased apoptosis of a human hippocampal progenitor cell line mediated by elevated IL-646.

In conclusion, we show evidence of quantifiable neuroglial injury markers in blood from COVID-19 infection, which is more prominent in patients with neurological dysfunction in the acute phase of the illness, and persists in the convalescent phase in patients who suffered defined acute neurological complications. These brain injury markers are associated with dysregulated systemic innate and adaptive immune responses in the acute phase of the disease, and suggest that these may represent targets for therapy.

Methods
Human participant studies/healthy controls and ethics information

The ISARIC WHO Clinical Characterization Protocol for Severe Emerging Infections in the UK (CCP-UK) was a prospective cohort study of hospitalised patients with COVID-19, which recruited across England, Wales, and Scotland (National Institute for Health Research Clinical Research Network Central Portfolio Management System ID: 14152). Participants were recruited prospectively during their hospitalisation with COVID-19 between February 2020 and May 2021. The protocol, revision history, case report form, patient information leaflets, consent forms and details of the Independent Data and Material Access Committee are available online47. Ethical approval for CCP-UK was given by the South Central - Oxford C Research Ethics Committee in England (Ref 13/SC/0149) and the Scotland A Research Ethics Committee (Ref 20/SS/0028). We examined 111 participants with anonymized clinical data including Glasgow coma scale score and consented serum sample. ISARIC samples were collected during the acute phase (1–11 days from hospital admission). Healthy control participants between the ages of 20–79 years old were recruited through the Cambridge Biomedical Research Centre (prior to the COVID-19 pandemic) and were non-hospitalised, without SARS-CoV-2 infection, and had no neurological diagnoses. All participants provided written consent. Sex was not considered in the study design and the sex of participants was self-reported.

Participants were recruited into the COVID-Clinical Neuroscience Study (COVID-CNS) between October 2020 and October 2022 and either the participant or their next of kin consented in accordance with the ethically-approved NIHR Bioresource (East of England—Cambridge Central Research Ethics Committee (Ref 17/EE/0025; 22/EE/0230). The purpose of the study was to investigate patients who had been hospitalised with COVID-19 with or without neurological complications. These were defined by the following criteria: neurological disease onset within 6 weeks of acute SARS-CoV-2 infection and no evidence of other commonly associated causes, and diagnostic criteria previously described48. Participants were recruited both as in-patients and retrospectively after discharge. The diagnosis was reviewed and finalized by a multi-disciplinary Clinical Case Evaluation panel. In this study, there were COVID patients without neurological complications (COVID-controls) and COVID patients with neurological complications (Neuro-COVID cases) and these cases were stratified by diagnostic definitions of each type of neurological complication, very few had overlapping syndromes in this relatively small cohort and the Evaluation Panel were able to provide a primary diagnosis for all”4. Co-morbidities and known treatments are shown in Supplementary Table 7. Serum samples were collected at either the early (<6 weeks from COVID-19 positive test) or late convalescent (>6 weeks) phases. The samples were aliquoted, labelled with anonymised identifiers, and frozen immediately at −70 °C.

Human brain injury markers measurements

Brain injury markers were measured in thawed sera using a Quanterix Simoa kit run on an automated HD-X Analyser according to the manufacturer’s protocol (Quanterix, Billerica, MA, USA, Neurology 4-Plex B Advantage Kit, cat#103345). We assessed neurofilament light chain (NfL), Ubiquitin C-Terminal Hydrolase L1 (UCH-L1), total-Tau (tTau), and glial fibrillary acidic protein (GFAP) in sera diluted 1:4 and used the manufacturer’s calibrators to calculate concentrations.

Human serum cytokine measurements

Analytes in thawed sera were quantified using the BioRad human cytokine screening 48-plex kit (Cat# 12007283) following manufacturer’s instructions on a Bioplex 200 using Manager software 6.2. This involved incubation of 1:4 diluted sera with antibody-coated magnetic beads, automated magnetic plate washing, incubating the beads with secondary detection antibodies, and adding streptavidin-PE. Standard curves of known protein concentrations were used to quantify analytes. Samples that were under the limit of detection were valued at the lowest detectable value adjusted for 1:4 dilution factor.

Median-centred normalization of human serum cytokine measurements

To minimise any potential impact of any possible variation in sample storage and transport, concentrations were median-centred and normalised for each participant, using established methodology49,50,51. The pg/mL of cytokines were log-transformed and the median per participant across all cytokines was calculated. The log-transformed median was subtracted from each log-transformed value to generate a normalized set.

Protein microarray autoantibody profiling

Autoantibodies were measured from thawed sera as previously described in Needham et al.14. Briefly, a protein array of antigens (based on the HuProt™ (version 4.0) platform) was used to measure bound IgM and IgG from sera, using secondary antibodies with different fluorescent labels detected by a Tecan LS400 scanner and GenePix Pro v4 software. As developed in previous studies14,52, antibody positivity was determined by measuring the median fluorescence intensity (MFI) of the four quadruplicate spots of each antigen. The MFI was then normalized to the MFI of all antigens for that patient’s sample by dividing each value by the median MFI. Z-scores were obtained from these normalized values based on the distribution derived for each antigen from the healthy control cohort. A positive autoantibody ‘hit’ was defined as an antigen where Z ≥ 3.

Detection of antibodies by immunohistochemistry

Immunohistochemistry was performed on sagittal sections of female Wistar rat brains. Brains were removed, fixed in 4% paraformaldehyde (PFA) at 4 °C for 1 h, cryoprotected in 40% sucrose for 48 h, embedded in freezing medium and snap-frozen in isopentane chilled on dry ice. 10-µm-thick sections were cut and mounted on slides in a cryostat. A standard avidin-biotin peroxidase method was used, as reported previously53,54, where thawed sera were diluted 1:200 in 5% normal goat serum and incubated at 4 °C overnight, and secondary biotinylated goat anti-human IgG Fc was diluted (1:500) and incubated at room temperature for 1 h. Finally, slides were counter-stained using cresyl violet.

Detection of autoantibodies with cell-based assays

HEK293T cells were seeded on 96 well plates in DMEM + 10% FCS at 37 °C and 5% CO2, transiently transfected with polyethylenimine with the relevant antigen-encoding plasmids GABAB-R1 and GABAB-R2 of the GABAB receptor, membrane tethered LGI1, CASPR2 and the NR1 subunit of the NMDA receptor, as described previously55,56,57. Thawed serum samples were incubated at 1:100 dilution for CASPR2 and GABAB receptor assays, and at 1:20 for LGI1 and NMDAR. After washing, cells were fixed with 4% PFA, washed again and incubated with unconjugated goat anti-human IgG Fc antibody, and donkey anti-goat IgG heavy and light chain Alexa Fluor 568 antibody. Cells were co-stained with DAPI.

Statistical analyses

Prism software (version 9.4.1, GraphPad Software Inc.) was used for graph generation and statistical analysis. The Shapiro-Wilk normality test used to check the normality of the distribution. Individual data points, median lines, and first and third quartiles are shown on box and whisker plots and violin plots with minimum and maximum points as error bars. Heatmaps, volcano plots and Chord diagrams were made using R studio (version 4.1.1 RStudio, PBC). The 2D cytokine network analyses were created using the qgraph package in R software and matrices differences were assessed by Steiger test58. Univariate analyses were conducted to test for differences between two groups. Differences between two normally distributed groups were tested using the paired or unpaired Student’s t test as appropriate. The difference between two non-normally distributed groups was tested using Mann–Whitney U test. Volcano plots used multiple Mann–Whitney U tests with a false discovery rate set to 5%, and heatmaps show Pearson’s correlations adjusted for a false discovery rate of 5%. Group comparisons were by Kruskal–Wallis test. Frequency differences of antibodies were measured by Fisher’s exact tests. Proteins which were statistically significantly different in the COVID-positive controls (GCS = 15 or COVID groups, respectively) versus the GCS less than or equal to 14 or neurological cases by Mann-Whitney test (p ≤ 0.05) were analysed with the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. Pathway classifications from the KEGG map search results were ranked by highest number of mapped candidates and exported in the KGML format using R package clusterProfiler. p ≤ 0.05 was considered statistically significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The individual-level data from these studies is not publicly available to main confidentiality. Data generated by the ISARIC4C consortium is available for collaborative analysis projects through an independent data and materials access committee at isaric4c.net/sample_access. Data and samples from the COVID-Clinical Neuroscience Study are available through collaborative research by application through the NIHR bioresource at https://bioresource.nihr.ac.uk/using-our-bioresource/apply-for-bioresource-data-access/. Brain injury marker and immune mediator data are present in the paper and in the source data file. Source data are provided with this paper.

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Acknowledgements

We thank the patients and their loved ones who volunteered to contribute to these studies at one of the most difficult times in their lives, and the research staff in every hospital who recruited patients at personal risk under challenging conditions. This research was funded by the National Institute for Health and Care Research (NIHR) (CO-CIN-01) and jointly by NIHR and UK Research and Innovation (CV220-169, MC_PC_19059). B.D.M. is supported by the UKRI/MRC (MR/V03605X/1), the MRC/UKRI (MR/V007181/1), MRC (MR/T028750/1) and Wellcome (ISSF201902/3). C.D. is supported by MRC (MC_PC_19044). We would like to thank the University of Liverpool GCP laboratory facility team for Luminex assistance and the Liverpool University Biobank team for all their help, especially Dr. Victoria Shaw, Lara Lavelle-Langham, and Sue Holden. We would like to acknowledge the Liverpool Experimental Cancer Medicine Centre for providing infrastructure support for this research (Grant Reference: C18616/A25153). We acknowledge the Liverpool Centre for Cell Imaging (CCI) for provision of imaging equipment (Dragonfly confocal microscope) and excellent technical assistance (BBSRC grant number BB/R01390X/1). Tom Solomon is supported by The Pandemic Institute and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool. D.K.M. and E.N. are supported by the NIHR Cambridge Biomedical Centre and by NIHR funding to the NIHR BioResource (RG94028 and RG85445), and by funding from Brain Research UK 201819-20. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. We thank the National Institute for Health and Care Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. The authors would like to acknowledge the eDRIS team (Public Health Scotland) for their support in obtaining approvals, the provisioning and linking of data and facilitating access to the National Safe Haven. The views expressed are those of the author(s) and not necessarily those of the UKRI, NHS, the NIHR or the Department of Health and Social Care.

Author information
Author notes

    These authors contributed equally: Benedict D. Michael, Cordelia Dunai.

    These authors jointly supervised this work: Leonie S. Taams, David K. Menon.

Authors and Affiliations

    Clinical Infection, Microbiology, and Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, L69 7BE, UK

    Benedict D. Michael, Cordelia Dunai, Yun Huang, Sarah A. Boardman, Greta K. Wood, Ceryce Collie, Mark A. Ellul, Geraint Sunderland, Claire Hetherington, Franklyn N. Egbe, Michael Griffiths, Tom Solomon & Malcolm G. Semple

    NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, Liverpool, L69 7BE, UK

    Benedict D. Michael, Cordelia Dunai, Tom Solomon & Malcolm G. Semple

    The Walton Centre NHS Foundation Trust, Liverpool, L9 7BB, UK

    Benedict D. Michael, Mark A. Ellul & Tom Solomon

    Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK

    Edward J. Needham, Alasdair J. Coles & Patrick F. Chinnery

    Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK

    Edward J. Needham, Richard Digby, Alexander Ren, Emma Norton, Maya Leibowitz, Soraya Ebrahimi & David K. Menon

    Health Data Science, Institute of Population Health, University of Liverpool, Liverpool, L69 3GF, UK

    Kukatharmini Tharmaratnam

    Oxford Autoimmune Neurology Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK

    Robyn Williams, Andrew Fower, Hannah Fox & Sarosh R. Irani

    Departments of Neurology and Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA

    Robyn Williams & Sarosh R. Irani

    University of Liverpool, Liverpool, L69 7BE, UK

    Jordan J. Clark

    Department of Microbiology, Icahn School of Medicine, Mount Sinai, NY, 10029, USA

    Jordan J. Clark

    Center for Vaccine Research and Pandemic Preparedness (C-VARPP), Icahn School of Medicine, Mount Sinai, NY, 10029, USA

    Jordan J. Clark

    Infection Biology & Microbiomes, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, L3 5RF, UK

    Parul Sharma, Krishanthi Subramaniam & James P. Stewart

    Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK

    Esteban Tato, Alish Palmos & Gerome Breen

    NIHR Maudsley Biomedical Research Centre, King’s College London, London, SE5 8AF, UK

    Esteban Tato, Alish Palmos & Gerome Breen

    Centre for Cell Imaging, Liverpool Shared Research Facilities, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, L69 7ZB, UK

    Marie Held

    NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge, CB2 0QQ, UK

    Kathy Stirrups, Nathalie Kingston, John R. Bradley & Patrick F. Chinnery

    Department of Haematology, University of Cambridge, Cambridge, CB2 0QQ, UK

    Kathy Stirrups

    Clinical Neurosciences, Clinical and Experimental Science, Faculty of Medicine, University of Southampton, Southampton, SO17 1BF, UK

    Alexander Grundmann

    Department of Neurology, Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK

    Alexander Grundmann

    Département de médecine interne des spécialités (DEMED), University of Geneva, Geneva, CH-1211, Switzerland

    Anne-Cecile Chiollaz & Jean-Charles Sanchez

    The Pandemic Institute, Liverpool, L7 3FA, UK

    Tom Solomon

    University of Cambridge, Cambridge, CB2 0QQ, UK

    Nathalie Kingston

    Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK

    John R. Bradley

    Centre for Immunology, School of Infection & Immunity, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G12 8TA, UK

    Jonathan Cavanagh

    Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK

    Angela Vincent

    Roslin Institute, University of Edinburgh, Edinburgh, EH25 9RG, UK

    J. Kenneth Baillie

    Intensive Care Unit, Royal Infirmary of Edinburgh, Edinburgh, EH10 5HF, UK

    J. Kenneth Baillie

    National Heart and Lung Institute, Imperial College London, London, SW7 2BX, UK

    Peter J. Openshaw

    Imperial College Healthcare NHS Trust, London, W2 1NY, UK

    Peter J. Openshaw

    Respiratory Unit, Alder Hey Children’s Hospital NHS Foundation Trust, Liverpool, L14 5AB, UK

    Malcolm G. Semple

    Centre for Inflammation Biology and Cancer Immunology, King’s College London, London, SE1 9RT, UK

    Leonie S. Taams

    University of Edinburgh, Edinburgh, UK

    J. Kenneth Baillie, Beatrice Alex, Benjamin Bach, Debby Bogaert, Annemarie B. Docherty, Ewen M. Harrison, Andrew Law, Alison M. Meynert, Andrew Rambaut, Clark D. Russell, Olivia V. Swann, Thomas M. Drake, Cameron J. Fairfield, Stephen R. Knight, Kenneth A. Mclean, Derek Murphy, Lisa Norman, Riinu Pius, Catherine A. Shaw, Sara Clohisey, Ross Hendry, Lucy Norris, James Scott-Brown, Murray Wham, Wilna Oosthuyzen, Andrew M. McIntosh, David P. Breen, Peter M. Fernandes & Scott Semple

    Imperial College London, London, UK

    Peter J. Openshaw, Petros Andrikopoulos, Wendy S. Barclay, Kanta Chechi, Graham S. Cooke, Gonçalo dos Santos, Marc-Emmanuel Dumas, Julian L. Griffin, Matthew R. Lewis, Sonia Liggi, Lynn Maslen, Michael Olanipekun, Anthonia Osagie, Vanessa Sancho-Shimizu, Caroline J. Sands, Shiranee Sriskandan, Zoltan Takats, Panteleimon Takis, Ryan S. Thwaites, Adam Hampshire & Nicholas Davies

    University of Liverpool, Liverpool, UK

    Malcolm G. Semple, William Greenhalf, Karl Holden, Saye Khoo, Shona C. Moore, Carlo Palmieri, William A. Paxton, Georgios Pollakis, Tom Solomon, Lance C. Turtle, Marie Connor, Jo Dalton, Carrol Gamble, Michelle Girvan, Sophie Halpin, Janet Harrison, Clare Jackson, Laura Marsh, Stephanie Roberts, Egle Saviciute, Victoria Shaw, Cara Donegan, Rebecca G. Spencer, Chloe Donohue, Hayley Hardwick, Alastair Darby, Arina Tamborska, Ava Easton, Benedict D. Michael, Bethany Facer, Bhagteshwar Singh, Brendan Sargent, Ceryce Collie, Charles Leek, Cordelia Dunai, Eva M. Hodel, Greta K. Wood, C. Hannah, Julian Hiscox, Merna Samuel, Michael Griffiths, Shahd H. Hamid, Simon Keller, Sophie Pendered & Victoria Grimbly

    Public Health England, London, UK

    Meera Chand, Jake Dunning, Samreen Ijaz, Richard S. Tedder & Maria Zambon

    MRC-University of Glasgow Centre for Virus Research, Glasgow, UK

    Ana da Silva, Antonia Y. Wai, Massimo Palmarini, David L. Robertson, Janet T. Scott, Emma C. Thomson & Sarah E. McDonald

    University of Sheffield, Sheffield, UK

    Thushan de Silva, A. A. R. Thompson, Annalena Venneri, Pamela J. Shaw & Thomas M. Jenkins

    Liverpool School of Tropical Medicine, Liverpool, UK

    Tom Fletcher & Sylviane Defres

    University of Birmingham, Birmingham, UK

    Christoper A. Green, Matthew R. Broome & Tonny Veenith

    University College London, London, UK

    Rishi K. Gupta, Mahdad Noursadeghi, Anthony S. David, Judith Breuer, Laura Benjamin, Michael P. Lunn, Michael S. Zandi & Nicholas Wood

    University of Oxford, Oxford, UK

    Peter W. Horby, Paul Klenerman, Laura Merson, Louise Sigfrid, David Stuart, James Lee, Daniel Plotkin, Gail Carson, Angela Vincent, Eugene Duff, Karla Miller, Masud Husain, Paul J. Harrison, Peter Jezzard, Sarosh Irani & Stephen Smith

    Nottingham University Hospitals NHS Trust, Nottingham, UK

    Wei S. Lim

    John Radcliffe Hospital, Oxford, UK

    Alexander J. Mentzer

    King’s College London, London, UK

    Nicholas Price, Marlies Ostermann, Akshay Nair, Alex Dregan, Alish Palmos, Ammar Al-Chalabi, Daniel J. van, Dina Monssen, Fernando Zelaya, Gerome Breen, Henry C. Rogers, Jonathan R. Coleman, Kiran Glen, Leonie Taams, Lily George, Matthew Butler, Matthew Hotopf, Naomi Martin, Rahul Batra, Silvia Rota, Steven Williams, Sui H. Wong, Thomas Pollak & Timothy Nicholson

    University of Cambridge, Cambridge, UK

    Charlotte Summers, Alaisdair Coles, Angela Roberts, David K. Menon, David M. Christmas, Edward Bullmore, Edward Needham, Ewan Harrison, Guy B. Williams, John R. Bradley, Richard Bethlehem, Sharon Peacock, Stephen J. Sawcer & Virginia Newcombe

    Public Health Scotland, Edinburgh, UK

    Susan Knight & Sarah Tait

    ISARIC4C Investigators, Liverpool, UK

    Eva Lahnsteiner, Richard Clark, Audrey Coutts, Lorna Donnelly, Angie Fawkes, Tammy Gilchrist, Katarzyna Hafezi, Louise MacGillivray, Alan Maclean, Sarah McCafferty, Kirstie Morrice, Lee Murphy, Nicola Wrobel, Kayode Adeniji, Daniel Agranoff, Ken Agwuh, Dhiraj Ail, Erin L. Aldera, Ana Alegria, Sam Allen, Brian Angus, Abdul Ashish, Dougal Atkinson, Shahedal Bari, Gavin Barlow, Stella Barnass, Nicholas Barrett, Christopher Bassford, Sneha Basude, David Baxter, Michael Beadsworth, Jolanta Bernatoniene, John Berridge, Colin Berry, Nicola Best, Pieter Bothma, Robin Brittain-Long, Naomi Bulteel, Tom Burden, Andrew Burtenshaw, Vikki Caruth, David Chadwick, Duncan Chambler, Nigel Chee, Jenny Child, Srikanth Chukkambotla, Tom Clark, Paul Collini, Catherine Cosgrove, Jason Cupitt, Maria-Teresa Cutino-Moguel, Paul Dark, Chris Dawson, Samir Dervisevic, Phil Donnison, Sam Douthwaite, Andrew Drummond, Ingrid DuRand, Ahilanadan Dushianthan, Tristan Dyer, Cariad Evans, Chi Eziefula, Chrisopher Fegan, Adam Finn, Duncan Fullerton, Sanjeev Garg, Atul Garg, Effrossyni Gkrania-Klotsas, Jo Godden, Arthur Goldsmith, Clive Graham, Elaine Hardy, Stuart Hartshorn, Daniel Harvey, Peter Havalda, Daniel B. Hawcutt, Maria Hobrok, Luke Hodgson, Anil Hormis, Joanne Howard, Michael Jacobs, Susan Jain, Paul Jennings, Agilan Kaliappan, Vidya Kasipandian, Stephen Kegg, Michael Kelsey, Jason Kendall, Caroline Kerrison, Ian Kerslake, Oliver Koch, Gouri Koduri, George Koshy, Shondipon Laha, Steven Laird, Susan Larkin, Tamas Leiner, Patrick Lillie, James Limb, Vanessa Linnett, Jeff Little, Mark Lyttle, Michael MacMahon, Emily MacNaughton, Ravish Mankregod, Huw Masson, Elijah Matovu, Katherine McCullough, Ruth McEwen, Manjula Meda, Gary Mills, Jane Minton, Kavya Mohandas, Quen Mok, James Moon, Elinoor Moore, Patrick Morgan, Craig Morris, Katherine Mortimore, Samuel Moses, Mbiye Mpenge, Rohinton Mulla, Michael Murphy, Thapas Nagarajan, Megan Nagel, Mark Nelson, Lillian Norris, Matthew K. O’Shea, Igor Otahal, Mark Pais, Selva Panchatsharam, Danai Papakonstantinou, Padmasayee Papineni, Hassan Paraiso, Brij Patel, Natalie Pattison, Justin Pepperell, Mark Peters, Mandeep Phull, Stefania Pintus, Tim Planche, Frank Post, David Price, Rachel Prout, Nikolas Rae, Henrik Reschreiter, Tim Reynolds, Neil Richardson, Mark Roberts, Devender Roberts, Alistair Rose, Guy Rousseau, Bobby Ruge, Brendan Ryan, Taranprit Saluja, Matthias L. Schmid, Aarti Shah, Manu Shankar-Hari, Prad Shanmuga, Anil Sharma, Anna Shawcross, Jagtur S. Pooni, Jeremy Sizer, Richard Smith, Catherine Snelson, Nick Spittle, Nikki Staines, Tom Stambach, Richard Stewart, Pradeep Subudhi, Tamas Szakmany, Kate Tatham, Jo Thomas, Chris Thompson, Robert Thompson, Ascanio Tridente, Darell Tupper-Carey, Mary Twagira, Nick Vallotton, Rama Vancheeswaran, Rachel Vincent, Lisa Vincent-Smith, Shico Visuvanathan, Alan Vuylsteke, Sam Waddy, Rachel Wake, Andrew Walden, Ingeborg Welters, Tony Whitehouse, Paul Whittaker, Ashley Whittington, Meme Wijesinghe, Martin Williams, Lawrence Wilson, Stephen Winchester, Martin Wiselka, Adam Wolverson, Daniel G. Wootton, Andrew Workman, Bryan Yates, Peter Young, Katie A. Ahmed, Jane A. Armstrong, Milton Ashworth, Innocent G. Asiimwe, Siddharth Bakshi, Samantha L. Barlow, Laura Booth, Benjamin Brennan, Katie Bullock, Nicola Carlucci, Emily Cass, Benjamin W. Catterall, Jordan J. Clark, Emily A. Clarke, Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis, Oslem Dincarslan, Alejandra D. Carracedo, Chris Dunn, Philip Dyer, Angela Elliott, Anthony Evans, Lorna Finch, Lewis W. Fisher, Lisa Flaherty, Terry Foster, Isabel Garcia-Dorival, Philip Gunning, Catherine Hartley, Anthony Holmes, Rebecca L. Jensen, Christopher B. Jones, Trevor R. Jones, Shadia Khandaker, Katharine King, Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant, Diane Latawiec, Lara Lavelle-Langham, Daniella Lefteri, Lauren Lett, Lucia A. Livoti, Maria Mancini, Hannah Massey, Nicole Maziere, Sarah McDonald, Laurence McEvoy, John McLauchlan, Soeren Metelmann, Nahida S. Miah, Joanna Middleton, Joyce Mitchell, Ellen G. Murphy, Rebekah Penrice-Randal, Jack Pilgrim, Tessa Prince, Will Reynolds, P. M. Ridley, Debby Sales, Victoria E. Shaw, Rebecca K. Shears, Benjamin Small, Krishanthi S. Subramaniam, Agnieska Szemiel, Aislynn Taggart, Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu, Libby v. Tonder, Eve Wilcock & J. E. Zhang

    University of Manchester, Manchester, UK

    Gary Leeming, Craig J. Smith & Katherine C. Dodd

    King’s College Hospital, London, UK

    Tassos Grammatikopoulos

    Royal Infirmary Edinburgh, Edinburgh, UK

    Seán Keating

    Roslin Institute, Edinburgh, UK

    Fiona Griffiths

    COVID-CNS Consortium, York, UK

    Adam Sieradzki

    Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK

    Adam W. Seed

    University of Nottingham, Nottingham, UK

    Afagh Garjani, Christopher M. Allen & Nikos Evangelou

    Edinburgh University, Edinburgh, UK

    Alan Carson

    University of Southampton, Southampton, UK

    Alexander Grundmann, Jay Amin & Marc Hardwick

    Nottingham University Hospital, Nottingham, UK

    Angela E. Holland

    University of Glasgow, Glasgow, UK

    Arvind Patel, Emma Thomson, Jonathan Cavanagh & Neil Basu

    Salford Royal Foundation Trust, Manchester, UK

    Bethan Blackledge & Jade D. Harris

    Queens University Belfast, Belfast, UK

    Cherie Armour, Ciaran Mulholland, Claire L. MacIver, Emily McGlinchey & Ryan McIlwaine

    Newcastle University, Newcastle, UK

    Christopher M. Morris, David Cousins, John-Paul Taylor, Mark R. Baker & Stella-Maria Paddick

    Cardiff University, Cardiff, UK

    John P. Aggleton, Jonathan Underwood, Neil A. Harrison & Neil Harrison

    Sheffield Institute for Translational Neuroscience, Sheffield, UK

    Daniel Madarshahian

    Dundee University, Dundee, UK

    David Christmas

    South London and Maudsley NHS Foundation Trust, London, UK

    Gabriella Lewis

    Belfast Health and Social Care Trust, Belfast, UK

    Gavin McDonnell & Stella Hughes

    COVID-CNS Consortium, Edinburgh, UK

    Jacqueline Smith

    The University of Manchester, Manchester, UK

    James B. Lilleker

    Kings College London, London, UK

    Monika Hartmann

    Royal Infirmary of Edinburgh, Edinburgh, UK

    Nadine Cossette

    Aintree University Hospital, Liverpool, UK

    Nathalie Nicholas

    National Institute for Health Research (NIHR) Bioresource, London, UK

    Parisa Mansoori

    Birmingham University, Birmingham, UK

    Rachel Upthegrove & Thomas Jackson

    Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK

    Rebecca Gregory

    Translational and Clinical Research, Newcastle, UK

    Rhys H. Thomas

    The Stroke Association, London, UK

    Richard Francis

    The University of Sheffield, Sheffield, UK

    Ronan O’Malley

    The University of Edinburgh, Edinburgh, UK

    Rustam A. Salman

    Institute of Mental health, Nottingham, UK

    Sandar Kyaw

    Royal Stoke University Hospital, Stoke on Trent, UK

    Savini Gunatilake

    Northern Health and Social Care Trust, Belfast, UK

    Suzanne Barrett

Consortia
ISARIC4C Investigators
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« Reply #4 on: February 16, 2024, 08:38:02 PM »


ISARIC4C Investigators

    J. Kenneth Baillie, Peter J. Openshaw, Malcolm G. Semple, Beatrice Alex, Petros Andrikopoulos, Benjamin Bach, Wendy S. Barclay, Debby Bogaert, Meera Chand, Kanta Chechi, Graham S. Cooke, Ana da Silva, Thushan de Silva, Annemarie B. Docherty, Gonçalo dos Santos, Marc-Emmanuel Dumas, Jake Dunning, Tom Fletcher, Christoper A. Green, William Greenhalf, Julian L. Griffin, Rishi K. Gupta, Ewen M. Harrison, Antonia Y. Wai, Karl Holden, Peter W. Horby, Samreen Ijaz, Saye Khoo, Paul Klenerman, Andrew Law, Matthew R. Lewis, Sonia Liggi, Wei S. Lim, Lynn Maslen, Alexander J. Mentzer, Laura Merson, Alison M. Meynert, Shona C. Moore, Mahdad Noursadeghi, Michael Olanipekun, Anthonia Osagie, Massimo Palmarini, Carlo Palmieri, William A. Paxton, Georgios Pollakis, Nicholas Price, Andrew Rambaut, David L. Robertson, Clark D. Russell, Vanessa Sancho-Shimizu, Caroline J. Sands, Janet T. Scott, Louise Sigfrid, Tom Solomon, Shiranee Sriskandan, David Stuart, Charlotte Summers, Olivia V. Swann, Zoltan Takats, Panteleimon Takis, Richard S. Tedder, A. A. R. Thompson, Emma C. Thomson, Ryan S. Thwaites, Lance C. Turtle, Maria Zambon, Thomas M. Drake, Cameron J. Fairfield, Stephen R. Knight, Kenneth A. Mclean, Derek Murphy, Lisa Norman, Riinu Pius, Catherine A. Shaw, Marie Connor, Jo Dalton, Carrol Gamble, Michelle Girvan, Sophie Halpin, Janet Harrison, Clare Jackson, James Lee, Laura Marsh, Daniel Plotkin, Stephanie Roberts, Egle Saviciute, Sara Clohisey, Ross Hendry, Susan Knight, Eva Lahnsteiner, Gary Leeming, Lucy Norris, James Scott-Brown, Sarah Tait, Murray Wham, Richard Clark, Audrey Coutts, Lorna Donnelly, Angie Fawkes, Tammy Gilchrist, Katarzyna Hafezi, Louise MacGillivray, Alan Maclean, Sarah McCafferty, Kirstie Morrice, Lee Murphy, Nicola Wrobel, Gail Carson, Kayode Adeniji, Daniel Agranoff, Ken Agwuh, Dhiraj Ail, Erin L. Aldera, Ana Alegria, Sam Allen, Brian Angus, Abdul Ashish, Dougal Atkinson, Shahedal Bari, Gavin Barlow, Stella Barnass, Nicholas Barrett, Christopher Bassford, Sneha Basude, David Baxter, Michael Beadsworth, Jolanta Bernatoniene, John Berridge, Colin Berry, Nicola Best, Pieter Bothma, Robin Brittain-Long, Naomi Bulteel, Tom Burden, Andrew Burtenshaw, Vikki Caruth, David Chadwick, Duncan Chambler, Nigel Chee, Jenny Child, Srikanth Chukkambotla, Tom Clark, Paul Collini, Catherine Cosgrove, Jason Cupitt, Maria-Teresa Cutino-Moguel, Paul Dark, Chris Dawson, Samir Dervisevic, Phil Donnison, Sam Douthwaite, Andrew Drummond, Ingrid DuRand, Ahilanadan Dushianthan, Tristan Dyer, Cariad Evans, Chi Eziefula, Chrisopher Fegan, Adam Finn, Duncan Fullerton, Sanjeev Garg, Atul Garg, Effrossyni Gkrania-Klotsas, Jo Godden, Arthur Goldsmith, Clive Graham, Tassos Grammatikopoulos, Elaine Hardy, Stuart Hartshorn, Daniel Harvey, Peter Havalda, Daniel B. Hawcutt, Maria Hobrok, Luke Hodgson, Anil Hormis, Joanne Howard, Michael Jacobs, Susan Jain, Paul Jennings, Agilan Kaliappan, Vidya Kasipandian, Stephen Kegg, Michael Kelsey, Jason Kendall, Caroline Kerrison, Ian Kerslake, Oliver Koch, Gouri Koduri, George Koshy, Shondipon Laha, Steven Laird, Susan Larkin, Tamas Leiner, Patrick Lillie, James Limb, Vanessa Linnett, Jeff Little, Mark Lyttle, Michael MacMahon, Emily MacNaughton, Ravish Mankregod, Huw Masson, Elijah Matovu, Katherine McCullough, Ruth McEwen, Manjula Meda, Gary Mills, Jane Minton, Kavya Mohandas, Quen Mok, James Moon, Elinoor Moore, Patrick Morgan, Craig Morris, Katherine Mortimore, Samuel Moses, Mbiye Mpenge, Rohinton Mulla, Michael Murphy, Thapas Nagarajan, Megan Nagel, Mark Nelson, Lillian Norris, Matthew K. O’Shea, Marlies Ostermann, Igor Otahal, Mark Pais, Selva Panchatsharam, Danai Papakonstantinou, Padmasayee Papineni, Hassan Paraiso, Brij Patel, Natalie Pattison, Justin Pepperell, Mark Peters, Mandeep Phull, Stefania Pintus, Tim Planche, Frank Post, David Price, Rachel Prout, Nikolas Rae, Henrik Reschreiter, Tim Reynolds, Neil Richardson, Mark Roberts, Devender Roberts, Alistair Rose, Guy Rousseau, Bobby Ruge, Brendan Ryan, Taranprit Saluja, Matthias L. Schmid, Aarti Shah, Manu Shankar-Hari, Prad Shanmuga, Anil Sharma, Anna Shawcross, Jagtur S. Pooni, Jeremy Sizer, Richard Smith, Catherine Snelson, Nick Spittle, Nikki Staines, Tom Stambach, Richard Stewart, Pradeep Subudhi, Tamas Szakmany, Kate Tatham, Jo Thomas, Chris Thompson, Robert Thompson, Ascanio Tridente, Darell Tupper-Carey, Mary Twagira, Nick Vallotton, Rama Vancheeswaran, Rachel Vincent, Lisa Vincent-Smith, Shico Visuvanathan, Alan Vuylsteke, Sam Waddy, Rachel Wake, Andrew Walden, Ingeborg Welters, Tony Whitehouse, Paul Whittaker, Ashley Whittington, Meme Wijesinghe, Martin Williams, Lawrence Wilson, Stephen Winchester, Martin Wiselka, Adam Wolverson, Daniel G. Wootton, Andrew Workman, Bryan Yates, Peter Young, Sarah E. McDonald, Victoria Shaw, Katie A. Ahmed, Jane A. Armstrong, Milton Ashworth, Innocent G. Asiimwe, Siddharth Bakshi, Samantha L. Barlow, Laura Booth, Benjamin Brennan, Katie Bullock, Nicola Carlucci, Emily Cass, Benjamin W. Catterall, Jordan J. Clark, Emily A. Clarke, Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis, Oslem Dincarslan, Alejandra D. Carracedo, Chris Dunn, Philip Dyer, Angela Elliott, Anthony Evans, Lorna Finch, Lewis W. Fisher, Lisa Flaherty, Terry Foster, Isabel Garcia-Dorival, Philip Gunning, Catherine Hartley, Anthony Holmes, Rebecca L. Jensen, Christopher B. Jones, Trevor R. Jones, Shadia Khandaker, Katharine King, Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant, Diane Latawiec, Lara Lavelle-Langham, Daniella Lefteri, Lauren Lett, Lucia A. Livoti, Maria Mancini, Hannah Massey, Nicole Maziere, Sarah McDonald, Laurence McEvoy, John McLauchlan, Soeren Metelmann, Nahida S. Miah, Joanna Middleton, Joyce Mitchell, Ellen G. Murphy, Rebekah Penrice-Randal, Jack Pilgrim, Tessa Prince, Will Reynolds, P. M. Ridley, Debby Sales, Victoria E. Shaw, Rebecca K. Shears, Benjamin Small, Krishanthi S. Subramaniam, Agnieska Szemiel, Aislynn Taggart, Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu, Libby v. Tonder, Eve Wilcock, J. E. Zhang, Seán Keating, Cara Donegan, Rebecca G. Spencer, Chloe Donohue, Fiona Griffiths, Hayley Hardwick & Wilna Oosthuyzen

COVID-CNS Consortium

    Adam Hampshire, Adam Sieradzki, Adam W. Seed, Afagh Garjani, Akshay Nair, Alaisdair Coles, Alan Carson, Alastair Darby, Alex Dregan, Alexander Grundmann, Alish Palmos, Ammar Al-Chalabi, Andrew M. McIntosh, Angela E. Holland, Angela Roberts, Angela Vincent, Annalena Venneri, Anthony S. David, Arina Tamborska, Arvind Patel, Ava Easton, Benedict D. Michael, Bethan Blackledge, Bethany Facer, Bhagteshwar Singh, Brendan Sargent, Ceryce Collie, Charles Leek, Cherie Armour, Christopher M. Morris, Christopher M. Allen, Ciaran Mulholland, Claire L. MacIver, Cordelia Dunai, Craig J. Smith, Daniel J. van, Daniel Madarshahian, David Christmas, David Cousins, David K. Menon, David M. Christmas, David P. Breen, Dina Monssen, Edward Bullmore, Edward Needham, Emily McGlinchey, Emma Thomson, Eugene Duff, Eva M. Hodel, Ewan Harrison, Fernando Zelaya, Gabriella Lewis, Gavin McDonnell, Gerome Breen, Greta K. Wood, Guy B. Williams, C. Hannah, Henry C. Rogers, Jacqueline Smith, Jade D. Harris, James B. Lilleker, Jay Amin, John P. Aggleton, John R. Bradley, John-Paul Taylor, Jonathan Cavanagh, Jonathan R. Coleman, Jonathan Underwood, Judith Breuer, Julian Hiscox, Karla Miller, Katherine C. Dodd, Kiran Glen, Laura Benjamin, Leonie Taams, Lily George, Marc Hardwick, Mark R. Baker, Marlies Ostermann, Masud Husain, Matthew Butler, Matthew Hotopf, Matthew R. Broome, Merna Samuel, Michael Griffiths, Michael P. Lunn, Michael S. Zandi, Monika Hartmann, Nadine Cossette, Naomi Martin, Nathalie Nicholas, Neil A. Harrison, Neil Basu, Neil Harrison, Nicholas Davies, Nicholas Wood, Nikos Evangelou, Pamela J. Shaw, Parisa Mansoori, Paul J. Harrison, Peter Jezzard, Peter M. Fernandes, Rachel Upthegrove, Rahul Batra, Rebecca Gregory, Rhys H. Thomas, Richard Bethlehem, Richard Francis, Ronan O’Malley, Rustam A. Salman, Ryan McIlwaine, Sandar Kyaw, Sarosh Irani, Savini Gunatilake, Scott Semple, Shahd H. Hamid, Sharon Peacock, Silvia Rota, Simon Keller, Sophie Pendered, Suzanne Barrett, Stella Hughes, Stella-Maria Paddick, Stephen J. Sawcer, Stephen Smith, Steven Williams, Sui H. Wong, Sylviane Defres, Thomas Jackson, Thomas M. Jenkins, Thomas Pollak, Timothy Nicholson, Tom Solomon, Tonny Veenith, Victoria Grimbly & Virginia Newcombe

Contributions

B.D.M., C.D., E.J.N., K.T., R.W., Y.H., G.K.W., C.C., J. Cavanagh, S.R.I., A.V., L.S.T. and D.K.M. designed, analysed data, interpreted experiments, and wrote the manuscript. T.S., J.P.S., G.B., M.G., J.-C.S., A.J.C., M.A.E. and A.P. provided scientific orientation and critically reviewed the manuscript. K. Stirrups, N.K., J.R.B., and P.F.C. provided oversight of the COVID-Clinical Neuroscience Study. J.K.B., P.J.M.O., and M.G.S. led the ISARIC4C study and provided oversight of the manuscript. G.S., A.G. and A.-C.C. analysed data. S.A.B., J. Clark, P.S., K. Subramaniam, M.H., C.H. and F.N.E. performed preliminary feasibility experiments. C.D., R.D., A.R., E.N., M.L., S.E., A.F. and H.F. performed experiments and analysed the data. E.T. managed the collection of the patients’ samples. ISARIC4C and COVID-CNS consortia recruited the patients for the study.
Corresponding author

Correspondence to Benedict D. Michael.
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Competing interests

T.S. is the Director of The Pandemic Institute which has received funding from Innova and CSL Seqirus and Aviva and DAM Health. T.S. was an advisor to the GSK Ebola Vaccine programme and the Siemens Diagnostic Programme. T.S. Chaired the Siemens Healthineers Clinical Advisory Board. T.S. Co-Chaired the WHO Neuro-COVID task force and sat on the UK Government Advisory Committee on Dangerous Pathogens, and the Medicines and Healthcare Products Regulatory Agency (MHRA) Expert Working Group on Covid-19 vaccines. T.S. Advised to the UK COVID-19 Therapeutics Advisory Panel (UK-TAP). T.S. was a Member of COVID-19 Vaccines Benefit Risk Expert Working Group for the Commission on Human Medicines (CHM) committee of the Medicines and Healthcare products Regulatory Agency (MHRA). T.S. has been a member of the Encephalitis Society since 1998 and President of the Encephalitis Society since 2019.
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Michael, B.D., Dunai, C., Needham, E.J. et al. Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses. Nat Commun 14, 8487 (2023). https://doi.org/10.1038/s41467-023-42320-4

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    Received04 May 2023

    Accepted06 October 2023

    Published22 December 2023

    DOIhttps://doi.org/10.1038/s41467-023-42320-4

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Jemand hat den Kleinschnitz-Account bei Twitter beobachtet. In der Zeit um den Böhmermann-Knaller hat Kleinschnitz Tweets gelöscht. Wurde ihm der Boden zu heiß?



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ME/CFS-#Forschung: Studie offenbart deutliche biologische Unterschiede im Blut
« Reply #7 on: September 04, 2024, 09:25:56 PM »

"Diese Ergebnisse widerlegen die Annahme, dass #MECFS durch Inaktivität verursacht wird."

Damit ist die neue Fake-Studie der Carstens-Stiftung von Anfang an widerlegt. Man sollte die Carstens-Stiftung auflösen, verbieten, sämliche Mittel enteignen, und die Verantwortlichen juristisch zur Verantwortung ziehen.


https://x.com/MMissingGermany/status/1831329892253757539

[*quote*]
#MillionsMissing Deutschland @MMissingGermany

🩸 ME/CFS-#Forschung: Studie offenbart deutliche biologische Unterschiede im Blut


https://pbs.twimg.com/media/GWow1AcX0AATvml?format=jpg&name=900x900

Eine Studie der Universität Edinburgh zeigt erstmals klare biologische Indikatoren für Myalgische Enzephalomyelitis/Chronisches Fatigue Syndrom (ME/CFS) im Blut. Diese Ergebnisse widerlegen die Annahme, dass #MECFS durch Inaktivität verursacht wird.

Die Untersuchung, die auf Daten der UK Biobank basiert, verglich die Blutwerte von 1.450 Personen mit ME/CFS und über 130.000 gesunden Probanden.

Dabei wurden bis zu 300 Molekular- und Zell-Biomarker analysiert, einschließlich fast 3.000 Proteine. Diese umfassende Datenbasis ermöglichte es den Forschern, feinste Unterschiede zu erkennen, die in kleineren Studien möglicherweise nicht sichtbar wären.

Die Analyse offenbarte signifikante Unterschiede in den Blutwerten, selbst nachdem Unterschiede aufgrund von Alter, Geschlecht und Aktivitätsniveau berücksichtigt wurden.

"Die zellulären und molekularen Unterschiede im Blut deuten auf chronische Entzündungen, Insulinresistenz und Lebererkrankungen hin. Das Forschungsteam sagte, dass diese Kombination von Blutunterschieden in keiner anderen ihnen bekannten Krankheit zu sehen ist."

Diese Unterschiede bestehen sowohl bei Männern als auch bei Frauen, was auf einen gemeinsamen Krankheitsmechanismus hinweist.

Trotz der Vielzahl an Markern konnte kein einzelner, spezifischer Biomarker zuverlässig zwischen ME/CFS-Patienten und gesunden Personen unterscheiden. Die Studie zeigt jedoch durch die Vielzahl an subtilen Unterschieden, dass es sich um eine reale biologische Veränderung handelt.

Die Studie hat einige Einschränkungen, wie die Möglichkeit, dass nicht gemessene Faktoren wie Medikamente, Nahrungsergänzungsmittel oder unterschiedliche Ernährungsformen die Ergebnisse beeinflussen könnten. Zudem könnten stark erkrankte Personen, die häufig nicht an einer Biobank-Studie teilnehmen können, in dieser Untersuchung unterrepräsentiert sein.

Insgesamt belegen diese neuen Erkenntnisse, dass ME/CFS klare biologische Signale im Blut hinterlässt und nicht auf Inaktivität zurückzuführen ist. Die Ergebnisse eröffnen neue Perspektiven für die Diagnose und Behandlung von ME/CFS.

Quelle:  @MEAssociation
 | @sjmnotes
 
https://meassociation.org.uk/2024/09/research-myalgic-encephalomyelitis-is-clear-to-see-in-the-blood/
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https://meassociation.org.uk/2024/09/research-myalgic-encephalomyelitis-is-clear-to-see-in-the-blood/

[*quote*]
ME Association

IMAGE DESCRIPTION: A person in a laboratory looking at a tube of blood and a circular image with blood and red blood cells. Title:Myalgic Encephalomyelitis is clear to see in the blood. The ME Association Logo (bottom right).

Research: Myalgic Encephalomyelitis is clear to see in the blood
September 4, 2024

‘These new results are remarkable, showing a biological signal of ME – a clear sign of abnormal biology in the blood that has only been revealed now that patients have been looked at as a very large group, and that is not due to inactivity.'
By Simon McGrath

In a huge study, researchers at Edinburgh University analysing blood biomarker data have found many differences between people with ME and healthy controls. The team also showed that these differences do not result from inactivity, which blows another hole in the deconditioning theory of ME/CFS.

    The paper is a preprint, which means it hasn’t yet been reviewed by other researchers or published. The authors plan to submit their work for review and publication in a journal.

A big data study

The study by mathematicians and scientists, including DecodeME’s Professor Chris Ponting, used data from UK Biobank (UKB) which enrolled around half a million people when they were aged 40 to 65.

    The UKB is different from the UK ME/CFS Biobank, which is much smaller but has more carefully diagnosed patients.

The study compared up to 1,450 people who reported they had a diagnosis of either chronic fatigue syndrome or ME with just over 130,000 healthy controls. No biomedical study to date has had as many patients and controls. The team looked at 300 blood molecular and cell biomarkers. They also looked at nearly 3,000 proteins in the blood, though in a much smaller group of people with ME.

This is big data, so it was important to have mathematicians taking the lead. They used sophisticated data models that took account of blood biomarker differences between individuals due to age, sex and – critically – activity levels (UKB has three measures of activity, including the average daily time spent walking).

The models showed that hundreds of these blood-based biomarkers were different between people with ME and healthy controls, even after allowing for the small impact of inactivity. 115 of these were significantly different for both men and women. This suggests that women and men share a common basis for the illness.

The cellular and molecular differences found in the blood point to chronic inflammation, insulin resistance and liver disease. The research team said this combination of blood differences is not seen in any other disease they know.

They also said that the differences in blood biomarkers for a chronic illness such as ME are likely to be the consequence of the disease rather than its cause.
No diagnostic biomarker

Despite hundreds of positive findings in the study, no single molecular or cell difference could reliably split those with ME from controls. This isn't surprising as many studies have looked at single biomarkers and found nothing convincing.

So how did this study find so many biomarker differences? Big data: the 1,400+ patients and 130,000+ controls gave this research statistical power to find differences that wouldn't show up in smaller studies.

These differences are modest and/or only affect a subset of people with ME. For example, C-reactive protein (CRP) was significantly higher in patients than controls. Yet only 4.5% of those with ME had CRP levels that would be considered high in normal medical tests, compared with 2.2% than for healthy controls.

Instead, it is the large number of modest differences between people with ME and controls that reveal that something is going on.
Limitations

Like every study, this one has limits. The blood differences might be due to something that hasn't been measured, such as people with ME being on medications or supplements, or eating a different diet.

Further, severely ill (housebound or bedbound) people are unlikely to participate in the biobank, so those with ME/CFS in this study will be more mildly affected than in most studies and might not be typical patients.

These new results are remarkable, showing a biological signal of ME – a clear sign of abnormal biology in the blood that has only been revealed now that patients have been looked at as a very large group, and that is not due to inactivity.

Preprint: Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explained by inactivity
https://meassociation.org.uk/2024/08/research-replicated-blood-based-biomarkers-for-me-cfs-not-explained-by-inactivity/
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Dellbrock

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https://meassociation.org.uk/2024/08/research-replicated-blood-based-biomarkers-for-me-cfs-not-explained-by-inactivity/

[*/quote*]
ME Association
ME/CFS & Long Covid
Home2024August
Research: Replicated blood-based biomarkers for ME/CFS not explained by inactivity
August 29, 2024

ME Association comment

“These are some interesting results in the search for a diagnostic biomarker for ME/CFS. They come from Professor Chris Ponting's research group in Edinburgh – who have used information about people with ME/CFS and healthy controls from the UK Biobank.

“Of particular importance is the fact that they identified differences in blood molecules or cells between people with ME/CFS and healthy controls and these differences were not related to inactivity.

“Please note that these findings have been published in what is called a pre-print paper. This means that they have not yet been subjected to peer review scrutiny by other researchers. We will contact the team and ask if they can produce a lay summary of this rather complicated research.

“The MEA Ramsay Research Fund is currently funding diagnostic biomarker restart the University of Surrey and the University of Oxford.”

Dr Charles Shepherd, Trustee and Hon. Medical Adviser to the ME Association.
You can now read a lay summary of this research by Simon McGrath


Preprint: Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explained by inactivity
Sjoerd V Beentjes, Julia Kaczmarczyk, Amanda Cassar, Gemma Louise Samms, Nima S Hejazi, Ava Khamseh, Chris P Ponting.

Abstract
Myalgic Encephalomyelitis (ME; sometimes referred to as chronic fatigue syndrome) is a relatively common and female-biased disease of unknown pathogenesis that profoundly decreases patients’ health-related quality-of-life.

ME diagnosis is hindered by the absence of robustly-defined and specific biomarkers that are easily measured from available sources such as blood, and unaffected by ME patients’ low level of physical activity.

Previous studies of blood biomarkers have not yielded replicated results, perhaps due to low study sample sizes (n < 100). Here, we use UK Biobank (UKB)
data for up to 1,455 ME cases and 131,303 population controls to discover hundreds of molecular and cellular blood traits that differ significantly between cases and controls.

Importantly, 116 of these traits are replicated, as they are significant for both female and male cohorts. Our analysis used semi-parametric efficient estimators, an initial Super Learner fit followed by a one-step correction, three types of mediators, and natural direct and indirect estimands, to decompose the average effect of ME status on molecular and cellular traits.

Strikingly, these trait differences cannot be explained by ME cases’ restricted activity. Of 3,237 traits considered, ME status had a significant effect on only one, via the “Duration of walk” (UKB field 874) mediator. By contrast, ME status had a significant direct effect on 290 traits (9%). As expected, these effects became more significant with increased stringency of case and control definition.

Significant female and male traits were indicative of chronic inflammation, insulin resistance and liver disease. Individually, significant effects on blood traits, however, were not sufficient to cleanly distinguish cases from controls. Nevertheless, their large number, lack of sex-bias, and strong significance, despite the ‘healthy volunteer’ selection bias of UKB participants, keep alive the future ambition of a blood-based biomarker panel for accurate ME diagnosis.

medRxiv: visit the website to read the full paper

[...]
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Dellbrock

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https://www.medrxiv.org/content/10.1101/2024.08.26.24312606v1.full-text

[*quote*]
Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explicable by inactivity

View ORCID ProfileSjoerd Viktor Beentjes, Julia Kaczmarczyk, Amanda Cassar, Gemma Louise Samms, View ORCID ProfileNima S. Hejazi, View ORCID ProfileAva Khamseh, View ORCID ProfileChris P. Ponting
doi: https://doi.org/10.1101/2024.08.26.24312606

This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.
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Abstract

Myalgic Encephalomyelitis (ME; sometimes referred to as chronic fatigue syndrome) is a relatively common and female-biased disease of unknown pathogenesis that profoundly decreases patients’ health-related quality-of-life. ME diagnosis is hindered by the absence of robustly-defined and specific biomarkers that are easily measured from available sources such as blood, and unaffected by ME patients’ low level of physical activity. Previous studies of blood biomarkers have not yielded replicated results, perhaps due to low study sample sizes (n < 100). Here, we use UK Biobank (UKB) data for up to 1,455 ME cases and 131,303 population controls to discover hundreds of molecular and cellular blood traits that differ significantly between cases and controls. Importantly, 116 of these traits are replicated, as they are significant for both female and male cohorts. Our analysis used semi-parametric efficient estimators, an initial Super Learner fit followed by a one-step correction, three types of mediators, and natural direct and indirect estimands, to decompose the average effect of ME status on molecular and cellular traits. Strikingly, these trait differences cannot be explained by ME cases’ restricted activity. Of 3,237 traits considered, ME status had a significant effect on only one, via the “Duration of walk” (UKB field 874) mediator. By contrast, ME status had a significant direct effect on 290 traits (9%). As expected, these effects became more significant with increased stringency of case and control definition. Significant female and male traits were indicative of chronic inflammation, insulin resistance and liver disease. Individually, significant effects on blood traits, however, were not sufficient to cleanly distinguish cases from controls. Nevertheless, their large number, lack of sex-bias, and strong significance, despite the ‘healthy volunteer’ selection bias of UKB participants, keep alive the future ambition of a blood-based biomarker panel for accurate ME diagnosis.
1. Introduction

Physical inactivity accelerates the loss of cardiovascular and strength fitness, shortens healthspan and increases all-cause mortality risk [1, 2, 3]. It lowers insulin sensitivity and elevates the synthesis of triglyceride, ceramide and sphingomyelin in muscle [4]. According to UK National Health Service guidance, exercise is “the miracle cure we’ve all been waiting for” [5]. Nevertheless, exercise is not a universal panacea: it is contraindicated among those with cardiovascular disease, anaemia and hyperthyroidism, for example [6]. A patient might also only accept exercise as treatment if they believe its benefit outweighs its cost [7]. Myalgic encephalomyelitis (ME; also known as chronic fatigue syndrome, CFS) is a disease of unknown pathogenesis defined by post-exertional malaise (PEM), the dramatic worsening of symptoms after even minor mental or physical exertion [8]], which usually lasts at least 24 hours, in contrast to other fatiguing illnesses [9]. ME has no cure and no widely effective therapy [10]. About 10% of people experiencing viral (such as with Epstein-Barr, Ross River virus or SARS-CoV-2 virus) or bacterial (such as with Coxiella burnetii) infection subsequently present ME or ME-like symptoms [11, 12]. In addition, over one-third of people with ME report not experiencing an infectious episode preceding their initial symptoms [13, 14]. Full recovery from ME is rare, at about 5% [15]. It is a female-dominant disease, with females outnumbering males by up to five-to-one; females also report more severe symptoms [13, 14]. In common with many female-biased diseases it has a high burden (e.g., in disability-adjusted life years) and low overall research funding [16]. ME is not rare, as it affects 0.19% − 0.86% of people in western countries [17, 18]. Individuals with ME commonly report PEM, pain, fatigue, sensitivities to noise, and cognitive and autonomic deficits [13] and a health-related quality of life worse than 20 other conditions [19].

There are no clinical biomarkers for ME. A high priority for people with ME is an accurate and reliable diagnostic test [20]. Findings from dozens of biomarker studies have shown limited reproducibility, perhaps due to their typically low sample sizes, their frequent use of inappropriate statistical tests [21] and the known heterogeneity of ME’s symptoms and potentially aetiology [22]. Whilst cardiopulmonary exercise testing does not initially differentiate between people with ME and control individuals, it does so in a follow-up test one day later [23, 24]. This test, however, is not in common use because it risks triggering PEM.

Any clinical biomarker would need to account for individuals’ inactivity relative to the general population. This is because many people with ME do not exercise and often restrict their activity [25] to reduce the risk of subsequent PEM. Some have proposed that it is this avoidance of activity that inhibits recovery by perpetuating ME symptoms following an acute illness [26, 27, 28]. However, therapies based on physical activity or exercise are not effective as a cure [29], implying that ME is instead an ongoing organic illness [30, 31]. It has also been claimed that any physiological abnormalities seen in people with ME might be caused by their inactivity [32].

In this study, we undertake 3 groups of analyses using UK Biobank (UKB) data [33] on (i) 31 blood cell and 30 blood biochemistry phenotypes; (ii) 251 NMR-measured metabolites; and, (iii) 2,923 proteins. Specifically, we quantify which blood traits, Nuclear Magnetic Resonance (NMR) metabolomics, and proteomics features are significantly different between ME cases versus controls, for males or females, or all combined, controlling for age (and sex for male and female combined analyses). The large UK Biobank data sets for ME cases and controls provided substantial statistical power to evaluate hypotheses, also allowing comparison between male-only and female-only analyses, something that had not been previously achievable. We take advantage of three mediators of sedentary lifestyle to determine whether any molecular or cellular trait associated with ME cases is explicable by physical inactivity.
2. Results
2.1. Study population: cases and controls

We first defined 1,455 ME cases and 131,303 nonME population control individuals from the UKB ([33]; see Materials and Methods). For each group of analyses, cases and controls were restricted to those with measurements of 31 blood count and 30 blood biochemistry markers, or 251 NMR metabolites, or 2,923 protein levels, respectively.

Collection of these biological samples was contemporaneous with self-reporting of CFS at the first visit to a UKB Assessment Centre (2006-2010). Numbers of samples in each category are shown in Table 1. ME sample sizes for measured outcome in blood traits, NMR metabolites and proteins are shown in Supplementary Fig. S1.

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Table 1. Numbers of UKB ME cases or non-ME controls per category.
2.2. Molecular and cellular traits significantly associated with ME

We simultaneously quantified two effects of ME case status on molecular and cellular traits, the natural direct effect (NDE) and natural indirect effect (NIE) (Fig. 1A). We needed to control for age and sex because levels of some molecules are known to be age(e.g. HRG protein) and/or sex-dependent (e.g. ALT). NDE and NIE are mediational estimands that decompose the average effect of ME case status on molecular or cellular trait into (a) direct paths – those not involving the mediator (Fig. 1A, green) – and (b) indirect paths – those acting through the mediator (blue) – with level of activity as the mediator variable [34, 35] (see Materials and Methods).
Figure 1.

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Figure 1.

(A) Directed Acyclic Graph for ME, taking age and sex as confounders and sedentary lifestyle (physical activity) as a mediator for ME’s effect on molecular and cellular traits. The causes of ME are an unknown variable (red). Therefore, all effect estimators are quantifying an association between ME and molecular or cellular traits and no causal statements are made. The “Age” variable (UKB field 21022) represents age at recruitment to UKB, rather than age of onset or diagnosis of ME. This variable affects the probability of having a ME diagnosis: recovery is minimal (≈ 5%, [15]), and as they age people are increasingly more likely to be diagnosed with ME. As it also affects the molecular and cellular traits, age is treated as a confounder. (B) Venn diagrams displaying the number of significant findings in the males, females, combined and their intersection for NDE, mediator 874. Proteomics data have the smallest sample size (see Table 1) and least power, implying fewer significant results in male and females separately as compared to the combined analysis.

As a mediator variable, we first used “Duration of walk” (UKB field 874). As expected, ME cases reported a lower duration of walk (mean: 44.0 mins/day) than controls (55.3 mins/day). At a false discovery rate [36] (FDR) < 0.05, significant direct effects were found for 36 (of 61 + 2 composite) blood traits, 189 (of 251) NMR metabolites and 65 (of 2,923) proteins (Fig. 1A). All estimates restrict to complete cases, removing individuals with missing trait data in that estimate. For all three analyses, the number of significant NDE results and their intersection in each of the male, female and combined categories, are presented in Fig. 1B.

Significant NDEs on molecular and cellular blood traits for females or males or combined are shown in Fig. 2A. NDEs are strongly correlated between females and males (Fig. 2B). Twenty traits are separately significant in the two sexes (Fig. 2A, B) and thus their associations to ME status are independently replicated. A single trait (erythrocyte distribution width, sometimes a sign of anaemia) was also significant with positive NDE for males and negative NDE for females (Fig. 2A).
Figure 2.

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Figure 2.

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Figure 2. Associational natural direct effects (NDE) of ME/CFS on molecular and cellular blood traits.

(A) The sex-stratified analyses are presented in orange (female) and blue (male). For the combined analysis (grey), sex is additionally taken as a confounder. All traits that are significant for the UKB 874 mediator are shown (see Supplementary Table 7 for the UKB 884 and 894 mediators). Effect sizes (left) are plotted for the UKB 874 mediator (“Duration of walks”), for significant estimates (FDR < 0.05). Error bars indicate 95% confidence intervals. Note that the scale and unit of measurement for each trait (x-axis) are different. The analysis was repeated for the UKB 884 mediator (“Number of days/week of moderate physical activity”) and for the UKB 894 mediator (“Duration of moderate activity”), with the significant results (FDR < 0.05) in each category indicated by ‘+’ symbols for positive effects and ‘−’ for negative effects. Where there is no symbol, the effect was not significant. Notably, there were no discordant results across the three mediators. All blood trait names are as they appear in the UKB showcase, aside from TyG and TG-to-HDL-C ratio (indicated by *), which are composite measures of other blood traits. (B) Blood trait NDE z-scores, males (x-axis), females (y-axis). The Pearson correlation is 0.67 and significant. The red dots represent 14 blood traits that are significant in both males and females (FDR < 0.05). The yellow and blue dots represent blood traits that are significant in females only and males only, respectively (FDR < 0.05). The grey dots are significant in neither group while controlling FDR < 0.05. (C) Raw data empirical cumulative distribution functions (ECDFs) for TyG (top) and TG-to-HDL-C ratio (bottom), comparing controls (black) and cases (female on the left, male on the right).

Among the 20 significantly associated traits for females and for males were traits indicative of chronic inflammation (elevated C-reactive protein [CRP] and cystatin C levels, and leukocyte and neutrophil counts), insulin resistance (elevated triglycerides-to-HDL cholesterol [TG-to-HDL-C] ratio, alanine aminotransferase [ALT], alkaline phosphatase [ALP] and gamma glutamyltransferase [GGT]), and liver disease (elevated ALT, ALP and GGT, and low urea levels) (Fig. 2A). Fig. 2C illustrates the shifts in two measures of insulin resistance, the TyG index [37, 38] (top) and TG-toHDL-C ratio [39] (bottom), between ME cases and controls. These are the UKB raw data, rather than results from mediation analysis.

Strikingly, for the UKB 874 mediator, significant effects on ME case status were abundant for direct effects (i.e., NDE; Fig. 2A; Fig. S2), but occurred only once (mean corpuscular haemoglobin; adjusted p = 0.043) for indirect effects (i.e., NIE; Fig. 3). For every other one of the 61 + 2 composite blood traits, for females or males or both sexes combined, none was significant when controlling the FDR at ≤ 0.05 (Fig. 3). Results from applying two other mediators (Fig. 2A and Fig. 3) are presented later.
Figure 3.

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Figure 3. Associational natural indirect effects (NIE) of ME/CFS on molecular and cellular blood traits.

The sex-stratified analyses are presented in orange (female) and blue (male). For the combined analysis (grey), sex is additionally taken as a confounder. All traits that are significant for UKB mediator 884 are shown. This is the mediator with the most number of significant indirect effects. UKB mediator 874 has a single significant NIE (mean corpuscular haemoglobin for females) after FDR. UKB mediator 894 has no significant NIEs after FDR. Effect sizes are plotted for UKB mediator 884 “Number of days/week of moderate physical activity”, for significant estimates (FDR < 0.05). Error bars indicate 95% confidence intervals. Two other mediators, 874 “Duration of walks”, and 894 “Duration of moderate activity” do not yield significant NIEs (FDR < 0.05). Note that the scale and unit of measurement for each trait (x-axis) are different. Significant results (FDR < 0.05) for mediator 884 are indicated by ‘+’ for positive effects and ‘−’ for negative effects. Where there is no symbol, the effect was not significant. All blood trait names are as they appear in the UKB showcase, aside from TyG and TG-to-HDL-C ratio (indicated with *) which are composite measures of other blood traits.
2.3. Metabolite traits significantly associated with ME

Of 251 NMR metabolite traits, 189 (75%) were significantly associated with ME status in an NDE analysis with females only (68 traits) or males only (10 traits) or in both the females only and males only analyses (96 traits) (UKB 874 mediator; Fig. 1B, Supplementary Table 4). Significant traits were mostly lipid levels, involving lipoproteins, cholesterol, and triglycerides. Results were highly concordant between females only and males only analyses (Fig. 4A and B) indicating that ME-specific blood metabolite differences are, again, generally not sex-biased. Previous ME/CFS metabolomic biomarker studies used one and three orders-of-magnitude fewer cases and controls, respectively [21]. The largest among these identified lowered phosphatidylcholines and cholines in blood from ME cases ([40], see also [41]), results that we replicated here (Fig. 4A). Higher triglycerides and lower HDL cholesterol in ME cases, observed using UKB enzymatic assays (Fig. 2A), were also observed as significant in the NMR metabolomics assays (Fig. 4A). Of 9 amino acids measured, only alanine was significantly elevated, and then only in female ME cases. Blood pyruvate and lactate, previously predicted to be ME biomarkers [42, 43], were also not significantly different between cases and controls.
Figure 4.

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Figure 4. Associational natural direct effects (NDE) of ME/CFS on NMR metabolites.

(A) The sex-stratified analyses are presented in orange (female) and blue (male). For the combined analysis (grey), sex is additionally taken as a confounder. Eighteen of 184 traits are shown; results for all traits are provided in Supplementary Table 4. Effect sizes are plotted for mediator 874 “Duration of walks” for significant estimates (FDR < 0.05). Error bars indicate 95% confidence intervals. Note that the scale and unit of measurement (X-axis) are different for each metabolite. Asterisks (right) indicate effects that are significant (FDR < 0.05). Where there is no asterisk, the effect was not significant. There were no discordant results across the three analyses. All NMR metabolite names are as they appear in the UKB showcase. (B) NMR NDE values are strongly concordant between the two sexes. Shown are per-metabolite z-scores for males (x-axis) and females (y-axis). The Pearson correlation is 0.8 and significant. Red dots indicate metabolites that are significant in both males and females (FDR < 0.05). Yellow and blue dots represent metabolites that are significant in females only and males only, respectively (FDR < 0.05). Grey dots are significant in neither.

None of the 251 metabolite traits was significant when controlling the FDR at ≤ 0.05 for indirect effects using the “Duration of walk” (UKB 874) mediator, for females or for males or for both combined (Fig. S2B).
2.4. Proteomic traits significantly associated with ME

Repeating this NDE analysis using the UKB 874 mediator on levels of 2,923 proteins, measured using antibody-based assays, yielded only a single protein, extracellular superoxide dismutase or SOD3, whose abundance was significantly altered (FDR < 0.05) between cases and controls in both females and males. Relative to preceding analyses, this proteomic analysis is under-powered owing to there being fewer cases for whom data was available (Table 1) and its larger multiple testing burden. Implications of this association to SOD3 are unclear, although superoxide, SOD3’s substrate, is known to modulate the hyperalgesic response [44].

Maleor female-specific effects for the same protein are again correlated (Fig. 5; Supplementary Table 5). Considering all cases combined, 54 proteins are significant (FDR < 0.05; Figure 1B). Among these are 7 complement proteins (C1RL, C2, CFB, CFH, CFI, CFP and CR2) of the innate immune system, whose levels are all elevated in ME cases, including CR2 (complement C3d receptor 2), the receptor for Epstein-Barr virus (EBV) binding on B and T lymphocytes. Two of the up-regulated proteins (CDHR2 and CDHR5) together form the extracellular portion of the intermicrovillar adhesion complex, whose disruption leads to intestinal dysfunction and inflammatory bowel disease [45, 46]. ME cases also show increase in levels of leptin (LEP), which has a role in energy homeostasis [47]. Again, not a single protein among the 2, 923 yielded a significant NIE estimate for this mediator (Fig. S2B).
Figure 5.

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Figure 5. Protein NDE z-scores, males (x-axis), females (y-axis). The Pearson correlation is 0.26 and significant.

The red dot represents the single protein (SOD3) that is significant in both males and females (FDR < 0.05). Yellow and blue dots indicate proteins that are significant in females only and in males only, respectively (FDR < 0.05). Grey dots show proteins that are significant in neither (i.e., FDR ≥ 0.05).
Figure 6.

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Figure 6.

(A) Blood trait total effect z-scores, males (x-axis), females (y-axis). The Pearson correlation is 0.86 and significant. The red dots represent 20 blood traits that are significant in both males and females (FDR < 0.05). The yellow and blue dots represent blood traits that are significant in females only and males only respectively (FDR < 0.05). The grey dots are significant in neither for FDR < 0.05. The x = y line indicates the line of equal z-scores for males and females. In general, in absolute value, the z-scores are higher for females than males. This is to be expected as the sample size is larger for females. (B) Metabolite total effect values are strongly concordant between the two sexes. Shown are per-metabolite z-scores for males (x-axis) and females (y-axis). The Pearson correlation is 0.91 and significant. Red dots indicate metabolites that are significant in both males and females (FDR < 0.05). Yellow and blue dots represent metabolites that are significant in females only and males only, respectively (FDR < 0.05). Grey dots are significant in neither. (C) Proteins total effect z-scores, males (x-axis), females (y-axis). The Pearson correlation is 0.33 and significant. Red dot represents the proteins (LEP, CDHR5, ADH4, RTN4R) that are significant in both males and females (FDR < 0.05). Yellow and blue dots represent proteins that are significant in females only and males only respectively (FDR < 0.05). The grey dots are significant in neither for FDR < 0.05.
2.5. Total effects

We have shown above that direct effects dominate, so that indirect effects contribute little-or-nothing to molecular and cellular effects. In real-world settings, the quantity of most interest to clinicians will be the total effect (TE), accounting for age and sex, rather than the direct effect. Estimating the total effect for 63 blood traits finds 39 to be significant (FDR < 0.05) predictors of ME case status for females and males combined (Supplementary Fig. S2A). The traits that are robustly predictive of ME are those shown in Fig. 2A (with 4 exceptions: erythrocyte_distribution_width, apoliprotein_b, creatinine and ldl_direct). For one or more of femaleor male-specific or combined TE analyses, a total of 251 proteins and 216 metabolites were additionally significant (FDR < 0.05; Supplementary Fig. S2A).

Significantly enriched Gene Ontology (GO) terms for TE-significant proteins highlighted tumour necrosis factor (TNF) and interleukin-4 (IL4) production, and natural killer (NK) cell mediated cytotoxicity (Fig. S3). Nevertheless, TNF and IL4 proteins themselves were not significantly altered in abundance. Impaired NK cell cytotoxicity in ME/CFS, however, is one of the few cellular or molecular biomarkers that has often been replicated [48].
2.6. Sensitivity analyses for blood traits

Next, we investigated whether blood trait results replicate for 2 further mediators: “Number of days/week of moderate physical activity 10+ mins” (UKB field 884) and “Duration of moderate activity” (UKB field 894) questionnaire responses. As expected, ME cases reported less activity than controls: mean 2.77 vs 3.51 days/week, and 53.9 vs 60.0 mins/day for mediators 884 and 894, respectively. As before, significant effects on ME status were observed for direct effects, never indirect effects for the “Duration of moderate activity” mediator (UKB field 894) (Fig. 2A, Fig. 4). By contrast, for the “Number of days/week of moderate physical activity 10+ mins” (UKB field 884) mediator, 22 significant NIEs were identified: 14 (0.4%) and 8 (0.2%) traits at FDR ≤ 0.05 for combined female and male, and female-only data, respectively. This is an order-of-magnitude lower number of indirect effect findings, compared with the 290 (9% of all traits investigated) identified for direct effects using the UKB 874 (“Duration of walks”) mediator. Importantly, even when significant NIEs are found, they almost always contribute less to the total effect than NDEs (Fig. 7).
Figure 7.

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Figure 7. Associational NDE (blue) and NIE (red) as a fraction of the total effect for the effect of ME/CFS on molecular and cellular blood traits.

The results are presented for male and female combined, for mediator 884 “Number of days/week of moderate physical activity”, the only mediator that exhibit indirect effects. Across all 61 blood traits, and the two composite metrics TyG and TG-to-HLD-C ratio, only 1 feature, Urate, has a larger NIE than NDE, for this mediator only.

We additionally investigated the dependence of results on the choice of fitting algorithm(s) for blood traits. Specifically, the result in Fig. 2A are obtained using a cross-validating library of algorithms (Super Learner (SL), see Material and Methods). Results obtained with no SL – reducing the library to the baseline GLMnet – with mediator 874, for TE, NDE and NIE are provided in Supplementary Table 9. Although we recommend its use, leaving out the SL has only minor effect: 36 of 39 significant TE blood traits using UKB field 874 as mediator with the SL were also significant without its use, Supplementary Table 9. Full NDE and NIE values for all mediators with SL are provided in Supplementary Table 7.

For TEs, 41 blood traits (as well as TyG and TG-to-HDL-C ratio) differ significantly between female or male ME cases and controls (Supplementary Table 3). To test whether extreme values affect these results, we winsorized the blood trait data at 0.5% and 1%. The results on the combined dataset are presented in Supplementary Fig. S4 and Supplementary Table 8, and remain robust. To obtain a high confidence set, we further restricted these traits to those significant for NDE for females and for males (mediators 874 and 884) and for females (mediator 894), resulting in 18 traits listed in Supplementary Table 2.

Lastly, we found that TEs and NDEs increase as the stringency of case and control definitions increases (Fig. 8; see Supplementary Table 10 for full results). Specifically, we compared NDEs for molecular and cellular blood traits calculated from cases and controls as defined in Materials and Methods, but with or without overall health rating (UKB field 2178) of ‘Poor’ or ‘Fair’ at baseline for cases, and/or ‘Good’ or ‘Excellent’ for controls.
Figure 8.

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Figure 8. Total effects and NDEs for blood traits become more significant as the stringency of case and control definitions increases.

(A) Total effect z-scores for ‘Poor/Fair’ for cases and ‘All’ (without restricting by health rating (UKB field 2178)) for controls versus z-scores for ‘All’ for cases and ‘All’ for controls (without restricting by health rating for cases or controls). The null hypothesis – that significance does not change for increasing stringency of case or control definition – is represented by the diagonal line. (B) Total effect z-scores for ‘Poor/Fair’ for cases and ‘Good/Excellent’ for controls, versus ‘Poor/Fair’ for cases vs ‘All’ for controls. (C) and (D) As in (A) or (B) but for NDE.
3. Discussion

Our results reveal 511 blood-based biomarkers whose levels differ significantly between people with ME and those without ME (Fig. S2A). Our approach decomposed the total effect of ME on blood traits into two components: (1) the indirect effect of ME on these traits via activity, and (2) the direct effect through all other paths, not mediated via activity. We do not claim causality for our estimates, because the assumptions of no unmeasured confounding may be violated. Nevertheless, any “causal gap”, the difference between our estimates and any underlying causal estimand, cannot be due to age and sex, as we account for these factors. Our findings constitute differences of population estimates of blood biomarkers between case and control populations and do not provide individual-level predictions of caseness based on biomarker values. However, our results can be used for variable selection in training a prediction model, as long as an independent data set is used. If the same data is used twice, i.e., both for variable selection and for training a prediction model, the resulting predictions will suffer from selective inference [49], with overly optimistic (invalid) prediction scores, and thus will not generalise to new cases.

The large number of discoveries relative to previous studies likely reflects our study’s substantially higher numbers of cases and controls (Table 1). These large numbers allow many small average effects of ME status on molecular and cellular traits to be detected. Importantly, and unlike most previous studies, we independently replicated 166 biomarkers in both females and males (TEs; Fig. S2A). This indicates that our discoveries are both robust and not sex-biased. It thus provides strong evidence for ME disease pathophysiology being equivalent in both sexes. This is despite sex-bias of ME with respect to prevalence and onset, comorbidities, symptoms and other features [13, 50].

Importantly, these biomarker differences are not explicable by dissimilarities in physical activity: among 3,237 NIE estimates we obtained, ME status was significantly associated with only one trait (Fig. S2B). Blood traits thus distinguish ME cases from population controls, but not because of ME cases’ reduced physical activity levels.

What then cause these molecular and cellular changes in blood if not physical activity? Our findings provide strong and replicated evidence for chronic low-level inflammation (elevated CRP and cystatin-C levels, and platelet, leukocyte and neutrophil counts), insulin resistance (elevated triglycerides-to-HDL-C ratio, ALT, ALP, GGT and HbA1c) and/or liver disease (elevated ALT, ALP, and GGT, and low urea levels) in ME (Fig. 2A). ME is thus portrayed by insulin resistance and systemic inflammation, with liver inflammation and dysfunction likely affecting lipid metabolism and the balance between HDL and LDL cholesterol. To our knowledge, the overall combination of blood marker changes we observed does not present in any other disease. For example, although primary biliary cholangitis is accompanied by elevated ALP and GGT levels (and post-exertional malaise [51]) it is also marked by high circulating levels of bilirubin rather than the lower levels we observe for ME (Fig. 2A). Nevertheless, because ME likely arises from multiple pathomechanisms and we did not further stratify cases, we cannot conclude that our results exclude other diseases from sharing a common aetiology with some ME cases.

In general, shifts in trait values were modest. Among all 116 significant femaleand malereplicated traits, 91% had small-to-medium shifts (Cohen’s d between 0.2 and 0.5 [52]; Supplementary Table 11). No trait yielded clear separation in estimated effects between ME cases and controls, rather trait values overlapped extensively. For example, despite CRP level being significantly elevated in ME cases (TE analysis: adjusted p = 2.8 × 10−9; both sexes), only 4.8% of female and 2.5% of male ME cases (versus 2.2% and 1.8% controls, respectively) had CRP levels over 10mg/L, a moderate elevation that can indicate systemic inflammation in autoimmune disease. Consequently, no single blood trait we analysed will be an effective biomarker for ME.

The major strength of the study is its large and deeply phenotyped cohort who were recruited, and their blood traits measured, using a single protocol. The study also controlled for potential confounders such as age, sex and physical inactivity. Additional mediators beyond physical activity were not considered as they were not directly relevant to this study’s principal hypothesis. The study was limited by the UK Biobank’s known healthy volunteer bias [53], possibly resulting in few, if any, people with severe ME symptoms at baseline participating. Future studies could test for the effect of symptom severity on the levels of biomarkers found to be significant in this study. UK Biobank recruited 40-69 year old participants [53], an age range when individuals are less likely to have a clinical diagnosis of ME [54]. We note that the list of cellular and molecular measurements in the UK Biobank is not exhaustive. For example, others have investigated potential biomarkers for oxidative stress [55] as well as gut metagenomics, immune-profiling and cytokines [56], which are absent from UKB.

Evidence that there is a large number of replicated and diverse blood biomarkers that differentiate between ME cases and controls should now dispel any lingering perception that ME is psychosomatic [57]. These findings should also accelerate research into the minimum panel of blood traits required to accurately diagnose ME in real-world populations. Such a panel would be invaluable for diagnosis, for measuring response to future treatment or drug trials, and potentially for determining the worsening or progression of ME. Such a panel might also help to determine the distinctions or overlap between ME and symptomalogically similar diseases such as Long Covid and fibromyalgia.

To assist the search for an effective biomarker panel for ME we provide the full results of this study in Supplementary Tables 3-5.
4. Materials and Methods
4.1. UK Biobank ME/CFS data processing

We defined 1,455 ME cases and 131,303 non-ME control individuals from UKB [33] as follows. Cases self-reported a diagnosis of ‘Chronic Fatigue Syndrome’ (CFS) in verbal interview at their first visit to a UKB Assessment Centre (UKB field 20002); also, either they answered “Yes” to the question “Have you ever been told by a doctor that you have Myalgic Encephalomyelitis/Chronic Fatigue Syndrome?” in the ‘Experience of Pain Questionnaire’ (PQ) (2019-2020) (UKB field 120010), or they did not complete the PQ. They further reported an overall health rating (UKB field 2178) of ‘Poor’ or ‘Fair’ at baseline, and were of known genetic sex. Population controls did not self-report a CFS diagnosis in any of the 4 visits, answered “No” to the PQ question about a ME/CFS diagnosis, and were not linked to a Primary Care record (CTV3 or Read v2 code, Supplementary Table 1) of ME/CFS or to the ICD10:G93.3 code (‘Postviral fatigue syndrome‘) in Hospital Inpatient Data. They further reported an overall health rating (UKB field 2178) of ‘Good’ or ‘Excellent’ at baseline. UKB participants are older and report healthier lifestyles, higher levels of education and better health relative to the general UK population [58, 59]. UKB assessment at baseline was demanding in time (2-3h) and energy, including travel to the nearest of 22 centres. These requirements will have diminished the recruitment of people with severe or moderate, or even mild, ME symptoms. UKB blood samples were acquired and analysed as described previously [60, 61, 62].

For blood traits, we included two composite markers of insulin resistance: the triglyceride glucose (TyG) index [63, 64], and TG-to-HDL-C ratio [65]. Note that TyG is normally calculated using fasting levels of tryglycerides and plasma glucose [66], but these are not available from the UK Biobank. The ratio of triglycerides to HDL-cholesterol correlates inversely with the plasma level of small, dense LDL particles.

For NMR metabolomics, we removed individuals whose NMR metabolite measurement has a QC flag indicating irregularities in the measurement, as per UKB category 221.

For each estimator of type TE, NDE and NIE (below), we only considered individuals with the relevant variables measured. Specifically, for TE, we restricted to individuals with measured age, sex and outcome variable. For NDE and NIE, we additionally restricted to individuals with measured mediators of activity. Furthermore, for NDE and NIE, we removed individuals who answered ‘do not know’ or ‘prefer not to answer’ to the activity question (UKB datafield 874, 884, or 894).
4.2. Mediation estimators

Causal mediation analysis, concerned with the quantification of the portion of a causal effect of an exposure on an outcome through a particular pathway, has been extensively discussed in the literature [67, 68]. The methodologies utilised in this work build upon natural (or pure) mediation estimands [34, 35]. Strategies for the construction of efficient estimators of non-parametrically defined causal mediation estimands, capable of incorporating machine learning, have been used in a variety of applications. Recent examples include understanding the biological mechanisms by which vaccines causally alter infection risk [69, 70, 71, 72], quantifying the effect of novel pharmacological therapies on substance abuse disorder relapse [73, 74] and the effects of housing vouchers on adolescent development [75], and modeling the effects of health disparities on quality of life [76]. Here we use state-of-the-art semi-parametric estimation techniques for non-parametric causal mediation analysis [77], implemented in the R package medoutcon [78, 79].

The NDE and NIE are mediational estimands that decompose the average effect (or average treatment effect, ATE) of ME status on molecular and cellular traits, Eq. 1.

NDEs involve a comparison of two counterfactual trait outcomes, specifically:

    (I) the level of the trait in a hypothetical scenario where every individual has ME, but rather than allowing ME to determine the level of activity, we fix their level of activity to the values they would naturally assume if they were not to have ME; and,

    (II) the level of the trait in a hypothetical scenario where every individual is in the control group and their levels of activity are allowed to naturally respond to being in the control group.

Comparison of these two trait levels yields a “direct” causal effect that quantifies the effect of ME on the trait through all paths other than the one mediated by activity.

NIEs involve a comparison of two counterfactual trait outcomes, specifically:

    (III) the level of the trait when every individual has ME and their levels of activity are allowed to naturally respond to ME; and,

    (IV) the level of the trait in a hypothetical scenario where every individual has ME, but rather than allowing ME to determine the level of activity, we fix their activity level to the value they would naturally assume if they were not to have ME.

Comparison of these two trait levels yields a causal “indirect” effect that quantifies the impact of ME on trait through activity (NIE). Crucially, the counterfactual trait outcomes (I) and (IV) are exactly the same quantity, and this insight gives rise to the “mediation formula” as follows: Embedded Image where Y (1) and Y (0) are potential outcomes in which an individual does or does not have ME, respectively. Similarly, Y (1, M (0)) is the potential outcome of an individual who has ME and whose mediator takes on the value it would have had if the individual did not have ME (given in words as (I) and (IV) above). Note also that Y (1) = Y (1, M (1)) and Y (0) = Y (0, M (0)). The left hand side of Eq. 1 defines the average treatment effect (ATE) of ME on blood trait Y, which we refer to as the total effect (TE). The right hand side of this equation is the sum of the NDE and NIE.

Causal identification is the process of turning a causal quantity we wish to estimate (causal estimand – a functional of unobservable counterfactual data) into a statistical quantity we can estimate from observed data (statistical estimand – a functional of observed data). Causal identification does not require access to any data and is entirely distinct from statistical inference. There are 5 assumptions required for causal identifiability of Eq. 1:

    (i) the Stable Unit Treatment Values Assumption (SUTVA) which includes consistency and no interference between units [80, 81];

    (ii) exchangeability (unconfoundedness), which is analogous to the randomization assumption applied to a joint intervention on both the treatment variable (here ME) and the mediator (here activity);

    (iii) treatment positivity, which states that it must be possible to observe any given treatment value (here ME) across all strata of baseline covariates (age and sex);

    (iv) mediator positivity, which states that it must be possible to observe any given mediator value across all strata defined by both treatment (ME) and baseline covariates (age and sex); and,

    (v) Cross-world counterfactual independence Y (T = t, M = m) ⊥⊥ M (T = t′) conditional on covariates, which is not empirically verifiable [82].

In our case, we do not claim causal identifiability because the assumptions of unconfoundedness (ii) may be violated, as made explicit in Fig. 1A (in red). Nevertheless, we can estimate the NDE and NIE as statistical quantities knowing that any causal gap will not be due to age or sex, as both of these variables have been taken into account as confounders.
4.3. Super Learner and one-step estimation

We have used semi-parametric efficient estimators to estimate the TE, as well as the mediation effects NDE and NIE [79], on multiomic measurements. This estimation procedure consists of an initial Super Learner (SL) [83] fit to estimate relevant nuisance functions in as flexible a manner as allowed by the available data. This ensures that any model mis-specification bias is minimised. We then construct estimates of the NDE and NIE using a one-step bias-correction procedure, which appropriately handles the use of SL for nuisance parameter estimation while also allowing for uncertainty quantification, facilitating the construction of valid Wald-style confidence intervals based on the asymptotic properties of the one-step bias-corrected estimator [84]. The precise specification of these estimators is as follows.

For the total effect, we have used the R package npcausal [85]. This package relies on the SuperLearner R package to specify models for fitting nuisance functions. We used:

    (1) SL.earth, an implementation of multivariate adaptive regression splines [86];

    (2) SL.glmnet, penalised regression with a generalised linear model and hyperparameter α = 1, i.e., L1-penalised or Least Absolute Shrinkage and Selection Operator (LASSO) regression, with default 10-fold cross-validation;

    (3) SL.glm.interaction, generalised linear model with main terms and 2-way interactions;

    (4) SL.xgboost, extreme gradient boosting (XGB) used with default parameters [87].

For the mediation effects NDE and NIE, we used the R package medoutcon [78]. This package instead relies on the sl3 R package [88], an implementation of the ensemble machine learning algorithm of [83], to specify models for fitting nuisance functions. We used:

    (1) Lrnr earth, an implementation of multivariate adaptive regression splines [86];

    (2) Lrnr glmnet, penalised linear regression with a generalised linear model and hyperparameters α = 1, i.e., L1-penalised or Least Absolute Shrinkage and Selection Operator (LASSO) regression, and default 3-fold cross-validation;

    (3) Lrnr glm fast, a fast implementation of a generalised linear model used with main terms and 2-way interactions; and,

    (4) Lrnr lightgbm, a fast and memory-efficient implementation of extreme gradient boosting (XGB) models from the lightgbm R package [89], used with default parameters.

The estimation of NDE and NIE relies on the fitting of further nuisance functions for which we have used algorithms, such as the Highly Adaptive Lasso (HAL) [90, 91, 92], and parameter specifications recommended by medoutcon.
4.4. GO enrichment analysis

We performed Gene Ontology analysis [93, 94, 95] on the set of significant TE estimates (positive only, negative only, or all combined) obtained from the male, female or combined populations. For the background protein set, we used all 2,923 proteins measured in UKB.

We obtained significant results only for the set of proteins with a significant positive total effect in the female subset of the population at FDR < 0.05. The results are presented in Fig. S3. We used Rrvgo [95] to reduce redundancy of GO terms.
Data Availability

No data has been produced. All data is from UK Biobank.
5. Competing interests

No competing interests declared.
Figure S1.

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Figure S1. ME sample sizes for males and females, restricting to complete cases (individuals for whom a measurement is available). The minimum number of cases is indicated on each plot.

(A) Blood traits, (B) NMR metabolites, (C) Proteomics. Neither of the two proteins with case sample size below 30 is significant after FDR correction. Full sample size data is provided as Supplementary Table 6.
Figure S2.

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Figure S2.

Venn diagrams displaying the number of significant findings in the males, females, combined and their intersection, mediator 874, for (A) total effect, and (B) NIE.
Figure S3.

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Figure S3.

GO pathway enrichment [93] for proteins with a significant positive total effect for ME/CFS vs control, restricted to females only. This is the subset with maximal power for GO analysis. All effects are TE, i.e., there are no significant NIE for proteins. We performed a similar pathway GO enrichment analysis for proteins with a significant positive total effect for ME/CFS vs control on the population of males and the combined dataset, as well as all significant negative total effects and all significant total effects on the female, male and combined populations. These resulted in no significant GO term enrichments at FDR< 0.05. All measured UKB proteins were used as background for the GO analyses.
Figure S4.

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Figure S4. Significant blood traits are robust to winsorisation.

The points represent total effect zscores for blood traits in the combined female and male analysis. The three shades of grey represent different degrees of winsorisation of the original data, with cases and controls combined prior to winsorisation. Nucleated red blood cell count and percent are only estimable at 0% winsorisation because for 0.5% winsorisation the number of cases is ≤ 5. Fib4 and eGFR composite measures were not estimated for 0% winsorisation due to extreme values in control samples (e.g., individuals with platelet counts close to 0).
Acknowledgements

This work was supported by a grant for PhD-level research to GLS from ME Research UK (SCIO charity number SCO36942). This research has been conducted using the UK Biobank Resource under Application Number 76173. Access to this data was funded by the National Institute for Health and Care Research (NIHR) and Medical Research Council (MRC) under grant number MC PC 20005. AK was supported by a Langmuir Talent Development Fellowship from the Institute of Genetics and Cancer, and a philanthropic donation from Hugh and Josseline Langmuir. SB, AK and CP are thankful to M. E. Khamseh for helpful discussions, and to Simon McGrath and Julia Oakley for commenting on the draft manuscript.
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Krokant

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Hier geht es weiter:

Die Causa Christoph Kleinschnitz TEIL 1     4170 Views innerhalb weniger Tage! Das ist unser bisher schnellster Start eines Threads.
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Die Causa Christoph Kleinschnitz TEIL 2
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Die Causa Christoph Kleinschnitz TEIL 3
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Die Causa Christoph Kleinschnitz: Dubiose Studien hetzen gegen LongCovid-Kranke
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Rhokia

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https://x.com/wastelanderbsky/status/1836150350572560516

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𝐖𝐀(𝐢)𝐒𝐓𝐄𝐋𝐀𝐍𝐃𝐄𝐑 ᴱⁿᵈᶻᵉⁱᵗ⁻ᴼʳᵃᵏᵉˡ @wastelanderbsky

Neurologe verloren. Raum Essen. Wer kann helfen?
Merkmale: Alter, ca. 50 Jahre. Große Nase, noch größeres Ego. Wenig Haare. Will immer Recht haben. Reagiert allergisch auf Kritik. #Kleinschnitz
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VanLaraklios

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https://x.com/wastelanderbsky/status/1842602588447076366

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𝐖𝐀(𝐢)𝐒𝐓𝐄𝐋𝐀𝐍𝐃𝐄𝐑 ᴱⁿᵈᶻᵉⁱᵗ⁻ᴼʳᵃᵏᵉˡ  @wastelanderbsky

4. Oktober 2024: #Kleinscnitz hat noch nicht fertig. Und wieder stellt er sich gegen allgemein bekannte Fakten, die durch zahlreiche Studien belegt sind.🤦‍♂️#longcovid #postcovid #MECFS
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