High ferritin is an important and sensitive biomarker for the diverse and deadly group of cytokine storm syndromes grouped together under the term hemophagocytic lymphohistiocytosis. Early identification of the syndrome and its contributors are critical to guiding targeted treatments and preventing immunopathology, morbidity, and mortality. Unfortunately, we lack specific diagnostic biomarkers, which complicates etiologic workup and delays targeted intervention. Through implementing a hyperferritinemia alert system, we hoped to identify what diagnoses are associated with hyperferritinemic, collect the earliest samples for research purposes, and test these samples for relevant biomarkers. We instituted an alert system at UPMC Children’s from June 1st, 2017, to June 30th, 2019, wherein serum ferritin >1000 ng/mL triggered via real-time chart review and biobanking of remnant samples from willing patients deemed to have “inflammatory hyperferritinemia (IHF)” (Figure 1). From consenting patients, we extracted relevant clinical data; retrospectively classified patients by etiology into infectious, rheumatic, or immune dysregulation; measured certain serum biomarkers (total IL-18, IL-18-binding protein, and CXCL9); and subjected a subgroup of samples to a 96-analyte O-link biomarker screen. The alert system identified 181 patients with hyperferritinemia, 30.5% of which had IHF (Figure 2A). Of the IHF patients, the majority had infection, followed by immune dysregulation, with the least common cause being rheumatic—all Still’s (Figure 2A). Highly elevated total IL-18 levels were distinctive to Still’s with or without MAS compared with other IHF, whereas CXCL9 did not differentiate IHF subcategories (Figure 2C). Other lab values—triglycerides, AST, platelets, and fibrinogen—did not differentiate different causes of IHF. Principal component analysis of a 96-analyte biomarker screen showed distributed elevation of proteins associated with T cell activation and IFNγ activity (e.g. granzyme B and CXCL9) in all IHF samples compared with healthy controls. Samples from patients with hyperferritinemic sepsis were distinctively lower in proteins involved in vessel homeostasis (e.g. ANGPT-1 and VEGFR-2) compared with other IHF subgroups and healthy controls (Figure 3). This IHF study proved a variety of diagnoses are associated with hyperferritinemia, enabled early sample collection, validated prior observations about the specificity of IL-18, expanded our understanding of IHF heterogeneity, and suggested a unique hyperferritinemic sepsis signature.
Steps in the hyperferritinemia screening protocol.
Clinical diagnosis and laboratory characteristics of patients with positive ferritin screens. A. Distribution of distinct patients triggering alerts by disease category and bar graphs display the number of distinct patients representing inflammatory hyperferritinemia subgroup. B. Distribution of ferritin values and maximum ferritin value per distinct patient by subgroup of inflammatory hyperferritinemia. C. IL-18, IL-18BP, and CXCL9 levels by subgroup of inflammatory hyperferritinemia. Patients with rheumatic disease have significantly higher IL-18 levels (P < 0.001 for both) and significantly lower IL-18BP levels (P < 0.05, P < 0.0001, respectively) compared with those with infection or immune dysregulation. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 Kruskal-Wallis with Dunn’s post-test; only comparisons with p < 0.05.
Clinical diagnosis and laboratory characteristics of patients with positive ferritin screens. A. Distribution of distinct patients triggering alerts by disease category and bar graphs display the number of distinct patients representing inflammatory hyperferritinemia subgroup. B. Distribution of ferritin values and maximum ferritin value per distinct patient by subgroup of inflammatory hyperferritinemia. C. IL-18, IL-18BP, and CXCL9 levels by subgroup of inflammatory hyperferritinemia. Patients with rheumatic disease have significantly higher IL-18 levels (P < 0.001 for both) and significantly lower IL-18BP levels (P < 0.05, P < 0.0001, respectively) compared with those with infection or immune dysregulation. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 Kruskal-Wallis with Dunn’s post-test; only comparisons with p < 0.05.
Biomarker screen using Olink. Principal component analysis shows unsupervised clustering of analyte NPX values with PC1 accounting for 27% of the variability and PC2 accounting for 18%. The analytes with the highest absolute value contribution to PC loading are listed on their respective axes. PC1 separate hyperferritinemic samples from healthy controls. PC2 separate hyperferritinemic sepsis samples from all other hyperferritinemic samples and healthy controls. Abbreviations: PDGF = PDGF subunit B.
Biomarker screen using Olink. Principal component analysis shows unsupervised clustering of analyte NPX values with PC1 accounting for 27% of the variability and PC2 accounting for 18%. The analytes with the highest absolute value contribution to PC loading are listed on their respective axes. PC1 separate hyperferritinemic samples from healthy controls. PC2 separate hyperferritinemic sepsis samples from all other hyperferritinemic samples and healthy controls. Abbreviations: PDGF = PDGF subunit B.
