Differential Circulating Proteomic Responses Associated with Ancestry during Severe COVID-19 Infection
Bibliographic record
Abstract
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. This study examines the differential circulating protein levels between three continental ancestries in response to severe COVID-19 infection. 4979 circulating proteins from 1272 samples were measured using the SomaScan platform. We used a linear mixed model to assess the ancestry-specific association between protein levels and severe COVID-19 illness. Comparing ancestries, we found that 62% of the tested proteins are associated with severe COVID-19 infectionin European-ancestry individuals, compared to 45% and 22% of the tested proteins between COVID-19-infected and control individuals in people of African and East Asian ancestry, respectively, likely reflecting differences in sample sizes. We found that all ancestries had strong correlations between each other with individuals of European and African ancestry having the most similar response and European and East Asian ancestries having the least similar. However, we did find 39 unique proteins that responded differently (FDR <0.05) between the three ancestries. 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".