ABSTRACT Neutropenic fever (NF) is often the first sign of infection in patients with hematologic malignancies, but its cause is frequently unknown, leading to broad-spectrum antibiotic use without confirmed infections. Although research links gut microbiome disruptions to treatment-related infections, it typically examines NF as the outcome, leaving a gap in understanding how the microbiome and metabolic factors distinguish infectious from non-infectious cases. Stool samples from acute myeloid leukemia patients were analyzed to characterize gut microbiome composition and fecal metabolites at baseline and at fever onset. Machine learning models, network analyses, and functional profiling were used to differentiate infectious NF vs non-infectious NF at baseline and at fever onset. The baseline model (area under the receiver operating characteristic AUROC = 0.769) identified higher levels of Akkermansia , Enterobacter , Escherichia – Shigella , and Flavonifractor as predictors of infectious NF, while Collinsella , Lachnospiraceae , Coprococcus , and acetic acid were linked to non-infectious cases. At fever onset, Enterococcus was enriched in infectious NF, whereas Gemella , Butyrate, Lachnospiraceae , Ruminococcaceae , and Eisenbergiella abundances favored non-infectious NF outcomes (AUROC = 0.752). Network analyses also revealed greater functional diversity and microbiome–metabolome connectivity in non-infectious cases at fever onset. This study suggests that gut microbiota and metabolites may serve as biomarkers for distinguishing infectious from non-infectious neutropenic fever, warranting further validation in larger cohorts. IMPORTANCE Our study tackles the challenge of managing neutropenic fever (NF) in immunocompromised patients whose numbers have increased due to various immunodeficiencies and treatments that suppress immune function. Fever is often the only sign of a serious infection in these patients, yet there are neither clear patterns linking risk factors to infection nor biomarkers reliable for ruling out non-infectious causes. As a result, febrile patients are typically empirically treated for major pathogens, even in the absence of confirmed infections, which propagates antimicrobial resistance and gut dysbiosis. Our research utilizes gut microbiome and targeted metabolomic profiling from two cohorts of patients with acute myeloid leukemia undergoing chemotherapy and employs a machine learning framework to distinguish between infectious and non-infectious NFs at baseline and upon fever onset.
Franklin et al. (Tue,) studied this question.