Observational studyIntensive care medicine2025
Identification of transcriptomic sepsis endotypes in sub-Saharan Africa: derivation, validation, and global alignment in two Ugandan cohorts.
Observational study in Intensive care medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A clinician's guide to corticosteroid therapy in pneumonia and critical illness: agent selection, dosing, monitoring, and interactions.Pneumonia (Nathan Qld.) · 2026Review
- Approximating Molecular Sepsis Subtypes Using Bedside Data in Resource-Limited Settings: A Multicenter Analysis From Uganda.Critical care explorations · 2026Observational
- Assessment of Bedside-Adaptable Models to Predict Molecular Sepsis Subtypes in a Resource-Limited Setting: A Multicenter Analysis from Uganda.medRxiv : the preprint server for health sciences · 2026Article
- Immune Endotype-Guided Precision Immunotherapy in Sepsis: From Transcriptomics to Bedside Application.Cureus · 2026Review
- Clinical subphenotypes and molecular endotypes in sepsis: toward an integrated and dynamic framework.Annals of intensive care · 2026Review
- Sepsis phenotypes in sub-Saharan Africa: a step toward fixing the growing scientific fracture with resource-limited settings.Intensive care medicine · 2026Article
- Sepsis biomarkers: recent advances and future perspectives.Frontiers in immunology · 2026Review
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Authors and funding
30 authors.
Funding
Abstract
purposeSub-Saharan Africa carries the highest global burden of critical illness, yet transcriptomic sepsis endotypes have not been defined in the region. Their clinical relevance and alignment with endotypes identified in high-income countries (HICs) remain unknown.
methodsWe analyzed data from two prospective observational cohorts of critically ill adults with sepsis in Uganda (discovery cohort [Tororo, rural], N = 243; validation cohort [Entebbe, urban], N = 112). Unsupervised clustering of whole-blood RNAseq data was used to identify endotypes in the discovery cohort. A random forest classifier was used to predict endotype assignment in the validation cohort. Differential gene expression, pathway enrichment, and digital cytometry were used to define endotype pathobiology and determine overlap with HIC-derived endotypes.
resultsTwo endotypes-Uganda Sepsis Endotypes 1 (USE-1) and 2 (USE-2)-were identified in the discovery cohort. USE-2, marked by neutrophil-driven innate immune activation and lymphocyte suppression, was associated with greater physiological severity and higher mortality (41.3% vs. 22.0%; absolute difference 19.3%, 95% CI 7.6-30.9%), irrespective of HIV, tuberculosis, or malaria infection. A 13-gene classifier (misclassification rate 1.43%) replicated two endotypes in the validation cohort with similar biological and clinical profiles. USE-2 showed strong transcriptional overlap with SRS1 and inflammopathic endotypes but only modest concordance in patient-level assignments. Overlap with Mars1 was variable.
conclusionsWe identified two transcriptomic sepsis endotypes in Uganda that reflect inter-individual differences in targetable pathobiology and confer prognostic enrichment across high-burden infections. Divergence from HIC-derived endotypes highlights the need for sepsis classifications that are both globally relevant and locally responsive.
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