Estimating tissue iron burden: current status and future prospects

Abstract

Iron overload is becoming an increasing problem as haemoglobinopathy patients gain greater access to good medical care and as therapies for myelodysplastic syndromes improve. Therapeutic options for iron chelation therapy have increased and many patients now receive combination therapies. However, optimal utilization of iron chelation therapy requires knowledge not only of the total body iron burden but the relative iron distribution among the different organs. The physiological basis for extrahepatic iron deposition is presented in order to help identify patients at highest risk for cardiac and endocrine complications. This manuscript reviews the current state of the art for monitoring global iron overload status as well as its compartmentalization. Plasma markers, computerized tomography, liver biopsy, magnetic susceptibility devices and magnetic resonance imaging (MRI) techniques are all discussed but MRI has come to dominate clinical practice. The potential impact of recent pancreatic and pituitary MRI studies on clinical practice are discussed as well as other works-in-progress. Clinical protocols are derived from experience in haemoglobinopathies but may provide useful guiding principles for other iron overload disorders, such as myelodysplastic syndromes.

Keywords: imaging; iron; iron overload; sickle cell anaemia; thalassaemia.

Figure 1

(Left) Schematic illustrating regulation of total body iron. Elevated iron flux through the gut and reticuloendothelial system increases transferrin saturation, upregulating hepcidin production in the liver. Hepcidin, by triggering internalization of ferroportin, limits iron export into the blood, serving as the central counter-regulatory mechanism. Erythropoietic drive suppresses hepcidin, thereby increasing iron flux from the gut and reticuloendothelial system. Inflammatory cytokines have the opposite effect. (Right) Schematic illustrating iron balance at the cellular level. When transferrin is not fully saturated, all iron entering parenchymal cells occurs through the transferrin receptor. Iron response proteins “sense” intracellular iron, and modulate gene expression of transferrin receptor, ferritin, and ferroportin to properly balance import, storage and export of labile iron. When transferrin is fully saturated, circulating non-transferrin-bound iron (NTBI) can pass unrestricted through divalent cation channels, overwhelming the cells ability to store and export iron.

Figure 2

Distribution plot illustrating liver iron concentration (LIC) at the onset of primary cardiac iron loading in patients with sickle cell disease (SCD), thalassaemia major (TM) and other rare anaemias. Patients were undergoing routine clinical assessment of cardiac T2* and LIC at one- to two-year intervals. The onset of cardiac iron loading was defined as the time that a patient passed from a T2* > 20 ms to a T2* < 20 ms. Shaded areas depict “classic” liver risk stratification, low (< 7 mg/g), intermediate (7 – 15 mg/g) and high (> 15 mg/g). In SCD patients, cardiac siderosis only developed in patients with extremely high LIC values. In contrast, a majority of thalassaemia major and rare anaemia patients developed cardiac siderosis at LIC values considered “low risk”. Figure reproduced from: Wood, J.C. (2014) Use of magnetic resonance imaging to monitor iron overload. Hematology Oncology Clinics of North America 28, 747–764, vii., with permission.

Figure 3

(Left) Plot of liver R2* versus LIC by biopsy from three validation studies. Both axes are on a logarithmic scale. Data derived from primary sources (Garbowski et al, 2014;Hankins et al, 2009;Wood et al, 2005a). Calibration curves reproduced from published equations (Garbowski et al, 2014;Wood et al, 2005a). (Right) Plot of liver R2 versus LIC by biopsy from two validation studies. Axes are linear. Data derived from primary sources (St Pierre et al, 2005; Wood et al, 2005a). Calibration curve reproduced from published equation (St Pierre et al, 2005).

Figure 4

Prevalence of clinical hypogonadism (left) and abnormal glucose tolerance (right) as a function of pancreas and cardiac iron metrics. Low risk patients are defined as having no detectable cardiac iron (T2* > 20 ms) and a pancreas R2* < 100 Hz. Intermediate risk patients have increased pancreas iron (R2* >100 Hz) but no cardiac siderosis. High risk patients have cardiac siderosis (all of them also have pancreas R2* > 100 Hz). Both hypogonadism and diabetes are extremely common in patients with cardiac siderosis, indicating that cardiac iron is a late manifestation of poor non-transferrin-bound iron control. Pancreatic iron deposition, in the absence of cardiac iron overload, also conveys an intermediate risk of both pituitary and pancreatic dysfunction. Figures derived from data presented previously(Noetzli et al, 2012a;Noetzli et al, 2012b). IFG, impaired fasting glucose; IGT, impaired glucose tolerance; DM, diabetes mellitus

Figure 5

Algorithm depicting our routine iron surveillance practices. Based upon our initial complete iron assessment, patients are assigned to one of three tracks based upon the perceived risk of developing cardiac iron. Risk stratification is the same as shown in Figure 4. Graph redrawn from prior work (Wood 2014). LIC, liver iron concentration; LV, left ventricular Figure adapted from: Wood, J.C. (2014) Use of magnetic resonance imaging to monitor iron overload. Hematology Oncology Clinics of North America 28, 747–764, vii., with permission.

Figure 6

(Left) Schematic depiction of the global iron homeostasis, illustrating factors that modulate transferrin saturation and the risk of non-transferrin-bound iron (NTBI) and extrahepatic iron deposition. Plus and minus signs depict the impact of a particular process on NTBI, not on hepcidin secretion. For example, effective erythropoiesis increases bone marrow uptake of transferrin, regenerating apotransferrin and thereby lowering NTBI. (Right) Approximate ranking of cardiac risk across disease states; CDA is an abbreviation for congenital dyserythropoetic anemia. In general, cardiac risk is reciprocally related to absolute effective erythropoetic rate.