Plasma proteomics reveals markers of metabolic stress in HIV infected children with severe acute malnutrition
Bibliographic record
Abstract
HIV infection affects up to 30% of children presenting with severe acute malnutrition (SAM) in Africa and is associated with increased mortality. Children with SAM are treated similarly regardless of HIV status, although mechanisms of nutritional recovery in HIV and/or SAM are not well understood. We performed a secondary analysis of a clinical trial and plasma proteomics data among children with complicated SAM in Kenya and Malawi. Compared to children with SAM without HIV (n = 113), HIV-infected children (n = 54) had evidence (false discovery rate (FDR) corrected p < 0.05) of metabolic stress, including enriched pathways related to inflammation and lipid metabolism. Moreover, we observed reduced plasma levels of zinc-α-2-glycoprotein, butyrylcholinesterase, and increased levels of complement C2 resembling findings in metabolic syndrome, diabetes and other non-communicable diseases. HIV was also associated (FDR corrected p < 0.05) with higher plasma levels of inflammatory chemokines. Considering evidence of biomarkers of metabolic stress, it is of potential concern that our current treatment strategy for SAM regardless of HIV status involves a high-fat therapeutic diet. The results of this study suggest a need for clinical trials of therapeutic foods that meet the specific metabolic needs of children with HIV and SAM.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".