Differential circulating proteomic responses associated with ancestry during severe COVID-19 infection
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".