Differential circulating proteomic responses associated with ancestry during severe COVID-19 infection
Notice bibliographique
Résumé
Abstract Background COVID-19 led to a disruption in nearly all aspects of society, yet these impacts were not the same across populations. During the pandemic, it became apparent that ancestry was associated with COVID-19 severity and morbidity, such that individuals of African descent tended to have worse outcomes than other populations. One factor that may influence COVID-19 outcomes is the circulating proteomic response to infection. This study examines how different ancestries had differential circulating protein levels in response to severe COVID-19 infection. Methods 4,979 circulating proteins from 1,272 samples were measured using the SomaScan platform. We used a linear mixed model to assess the ancestry-specific association between the level of each protein and severe COVID-19 illness, accounting for sex, age, and days since symptom onset. We then compared each ancestry-specific effect size of severe COVID-19 illness on protein level to one another in a pairwise manner to generate Z-scores. These Z-scores were then converted into p-values and corrected for multiple comparisons using a Benjamini-Hochberg false discovery rate of 5%. Results Comparing ancestries, we found that 62% of the tested proteins are associated with severe COVID-19 in European-ancestry individuals, compared to controls. We found that 45% and 22% of the tested proteins were different between COVID-19 infected and control individuals in people of African and East Asian ancestry, respectively. There was a strong correlation in effect size between ancestries. We found that individuals of European and African ancestry had the most similar response with a Pearson correlation of 0.868, 95% CI [0.861, 0.875] while European and East Asian ancestries had a Pearson correlation of 0.645, 95% CI [0.628, 0.661] and, East Asian and African ancestries had a Pearson correlation of 0.709, 95% CI [0.695, 0.722]. However, we found 39 unique proteins that responded differently (FDR < 0.05) between the three ancestries. Conclusions Examining 4,979 protein levels in 1,272 samples, we identified that the majority of measured proteins had similar responses to infection across individuals of European, African and East Asian ancestry. However, there were 39 proteins that may have a differential response to infection, when stratified by ancestry. These proteins could be investigated to assess whether they explain the differences in observed severity of COVID-19 between ancestral populations.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».