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 HenningerGuenter 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 < .001) and pancreatic R2* values (32.5 s -1 vs. 24.9 s -1 , p = .011) were significantly higher compared with 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 = .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 a useful diagnostic method to estimate the need for iron supplementation and to guide therapy.
Keyphrases
- iron deficiency
- artificial intelligence
- oxidative stress
- end stage renal disease
- chronic kidney disease
- newly diagnosed
- ejection fraction
- peritoneal dialysis
- machine learning
- big data
- deep learning
- magnetic resonance imaging
- prognostic factors
- heart failure
- systematic review
- high resolution
- computed tomography
- magnetic resonance
- atrial fibrillation
- stem cells
- mesenchymal stem cells
- diffusion weighted imaging