Tissue iron distribution in patients with anemia of inflammation: results of a pilot study.
Lukas LanserMichaela PlaiknerAndrea SchrollFrancesco Robert BurkertStefanie SeiwaldJosia FauserVerena PetzerRosa Bellmann-WeilerGernot FritscheIvan TancevskiChristina DuftnerAndreas PircherAndreas SeeberHeinz ZollerChristian KremserBenjamin HenningerGünter WeissPublished in: American journal of hematology (2023)
Anemia of inflammation (AI) is frequently present in subjects with inflammatory disorders primarily caused by inflammation-driven iron retention in macrophages. So far, only limited data on qualitative and quantitative estimates of tissue iron retention in AI patients exist. We performed a prospective cohort study analyzing splenic, hepatic, pancreatic and cardiac iron content with MRI-based R2*-relaxometry in AI patients including subjects with concomitant true iron deficiency (AI+IDA) hospitalized between 05/2020-01/2022. Control-groups were individuals without inflammation. Spleen R2*-values in AI patients with ferritin ≤200 μg/L (AI+IDA) were comparable with those found in controls. In AI patients with ferritin >200 μg/L, spleen (47.6 s -1 vs. 19.3 s -1 , p < 0.001) and pancreatic R2*-values (32.5 s -1 vs. 24.9 s -1 , p = 0.011) were significantly higher compared to controls, while liver and heart R2*-values did not differ. Higher spleen R2*-values were associated with higher ferritin, hepcidin, CRP and IL-6 concentrations. Spleen R2*-values normalized in AI patients after recovery (23.6 s -1 vs. 47.6 s -1 , p = 0.008), while no changes were found in patients with baseline AI+IDA. This is the first study investigating tissue iron distribution in patients with inflammatory anemia and AI with concomitant true iron deficiency. The results support the findings in animal models demonstrating iron retention in macrophages which are primarily accumulating in the spleen under inflammatory conditions. MRI related iron measurement may help to better characterize actual iron needs and to define better biomarker thresholds in the diagnosis of true ID in patients with AI. It may qualify as useful diagnostic method to estimate the need for iron supplementation and to guide therapy. This article is protected by copyright. All rights reserved.
Keyphrases
- iron deficiency
- artificial intelligence
- end stage renal disease
- oxidative stress
- chronic kidney disease
- ejection fraction
- newly diagnosed
- heart failure
- machine learning
- big data
- prognostic factors
- magnetic resonance imaging
- systematic review
- stem cells
- magnetic resonance
- deep learning
- left ventricular
- patient reported outcomes
- bone marrow
- high resolution
- mass spectrometry
- smoking cessation