Integrated multi-omics characterization across clinically relevant subgroups of long COVID
Bibliographic record
Abstract
When SARS-CoV-2 became regional epidemics, a substantial number of patients suffered from post-acute sequelae of COVID-19 (PASC, aka long COVID). Exploring the pathogenesis and especially the heterogenicity features of long COVID subgroups is of paramount importance for understanding its etiology. In this study, through integrative multi-omics analyses encompassing transcriptomics, proteomics, and metabolomics, long COVID patients exhibited overall elevated MAPK pathway activation, while patients who have recovered from long COVID showed down-regulation of this response. Long COVID heterogenicity is described by multi-omics distinct signatures for each subgroup. The Multisystemic (MULTI) symptom subgroup is characterized by enhanced glycerophospholipid and ether lipid metabolism, Neurological (NEU) by augmented glycoprotein synthesis metabolism, Cardio cerebral (CACRB) by increased pyruvate metabolism and suppressed macrophage polarization, Musculoskeletal + Systemic (MSK + SYST) by elevated glycerophospholipid metabolism, and Cardiopulmonary (CAPM) by inhibited NF-κB signaling pathways. ABHD17A, CSNK1D, PSME4 and SYVN1 were general long COVID combination biomarkers, while CRH (MULTI), FPGT (NEU), CBX6 (CACRB) and RBBP4 (CAPM) were selected as serum-specific subgroup proteins. Our study provides a commonly shared and distinct pathophysiological explanation underpinning PASC, paving the way for future diagnosis and therapeutic interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".