Hyperferritinemia screening to aid identification and differentiation of patients with hyperinflammatory disorders

Abstract

High ferritin is an important and sensitive biomarker for hemophagocytic lymphohistiocytosis (HLH), a diverse and deadly group of cytokine storm syndromes. Early action to prevent immunopathology in HLH often includes empiric immunomodulation, which can complicate etiologic work-up and prevent collection of early/pre-treatment research samples. To address this, we instituted an alert system where serum ferritin > 1000ng/mL triggered real-time chart review, assessment of whether the value reflected "inflammatory hyperferritnemia (IHF)", and biobanking of remnant samples from consenting IHF patients. We extracted relevant clinical data; periodically measured serum total IL-18, IL-18 binding protein (IL-18BP), and CXCL9; retrospectively classified patients by etiology into infectious, rheumatic, or immune dysregulation; and subjected a subgroup of samples to a 96-analyte biomarker screen. 180 patients were identified, 30.5% of which had IHF. Maximum ferritin levels were significantly higher in patients with IHF than with either hemoglobinopathy or transplant, and highly elevated total IL-18 levels were distinctive to patients with Stills Disease and/or Macrophage Activation Syndrome (MAS). Multi-analyte analysis showed elevation in proteins associated with cytotoxic lymphocytes in all IHF samples when compared to healthy controls and depression of proteins such as ANGPT1 and VEGFR2 in samples from hyperferritinemic sepsis patients relative to non-sepsis controls. This single-center, real-time IFH screen proved feasible and efficient, validated prior observations about the specificity of IL-18, enabled early sample collection from a complex population, suggested a unique vascular biomarker signature in hyperferritinemic sepsis, and expanded our understanding of IHF heterogeneity.

Keywords: Hemophagocytic Lymphohistiocytosis (HLH); Hyperferritinemia; Hyperinflammatory disorders; Interleukin-18; Macrophage Activation Syndrome (MAS).

Figure 1

Steps in the hyperferritinemia screening protocol.

Figure 2

Distribution of total hyperferritinemia alerts and distinct patients triggering alerts by disease category (A). Bar graphs display the number of alerts per subgroup of inflammatory hyperferritinemia (B) and the number of distinct patients represented in each subgroup (C).

Figure 3

Distribution of ferritin values and maximum ferritin value per distinct patient by disease group for all alerts (A-B). Of enrolled patients, all and maximum ferritins by subgroup of inflammatory hyperferritinemia (C-D), and maximum values for other HLH-related laboratory criteria by subgroup of inflammatory hyperferritinemia (E-I). *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 shown.

Figure 4

IL-18, IL-18BP and CXCL9 levels by subgroup of inflammatory hyperferritinemia (A). Patients with rheumatic disease had significantly higher IL-18 levels (P<0.0001 for both) and significantly lower IL-18BP levels (P<0.05, P<0.0001, respectively) compared to those with infection or immune dysregulation. Panel B shows the distribution of IL-18/CXCL9 ratios amongst the subgroups with rheumatologic patients having significantly higher ratios than those in the other two subgroups (P<0.0001). *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 shown.

Figure 5

Biomarker screen using Olink. A. Left: Analysis of IL-18 NPX values does not show levels uniquely elevated in SJIA/MAS samples compared to other hyperferritenemic samples. Right: Correlation between IL-18 NPX values and IL-18 measured via Luminex as in Weiss et al. [9]. Dashed lines indicate IL-18 NPX values of the low-grade CRS samples for which Luminex IL-18 values were not available. B. 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 (full list of PC loadings in Supplemental Table 3). C. Both CXCL9 and Granzyme B contribute to PC1 and separate hyperferritinemic samples from healthy controls (all graphs contributing to PC1 in Supplemental Figure 3). D. ANGPT1, VEGFR2, and IL15 contribute to PC2. ANGPT1 and VEGFR2 are downregulated and IL15 is upregulated in hyperferritinemic septic samples compared to other hyperferritinemic samples and healthy controls (all graphs contributing to PC2 in Supplemental Figure 4). Abbreviations: PDGF= PDGF subunit B.