Anemia is one of the main comorbidities related to Chronic Kidney Disease (CKD). Until the advent of Erythropoiesis Stimulating Agents (ESA), endogenous erythropoietin deficiency has been thought to be the main culprit of anemia in CKD patients. The use of ESA’s has shed new light on the physiology of CKD anemia, where iron homeostasis plays an increasingly important role. Disorders of iron homeostasis occurring in CKD turn the anemia management in those patients into a complex multifactorial therapeutic task, where ESA and Iron dose must be properly balanced to achieve the desired outcome without exposing the patients to the risk of serious adverse events. This review covers diagnostic markers traditionally used for quantifying iron status in CKD patients, such as serum ferritin and transferrin saturation, new ones, such as reticulocyte hemoglobin content and percent hypochromic red cells, as well as experimental ones, such as hepcidin and soluble transferrin receptor. Each marker is presented in terms of their diagnostic performance, followed by biological and analytical variability data. Advantages and disadvantages of each marker are briefly discussed. Although serum ferritin and transferrin saturation are easily available, they exhibit large biological variability and require caution when used for diagnosing iron status in CKD patients. Reticulocyte hemoglobin content and the percentage of hypochromic red cells are more powerful, but their widespread use is hampered by the issue of sample stability in storage. Soluble transferrin receptor and hepcidin show promise, but require further investigation as well as the development of standardized, low-cost assay platforms.

One-size-fits-all protocol-based approaches to anemia management with erythropoiesis-stimulating agents (ESAs) may result in undesired patterns of hemoglobin variability. In this single-center, doubleblind, randomized controlled trial, we tested the hypothesis that individualized dosing of ESA improves hemoglobin variability over a standard population-based approach. We enrolled 62 hemodialysis patients and followed them over a 12-month period. Patients were randomly assigned to receive ESA doses guided by the Smart Anemia Manager algorithm (treatment) or by a standard protocol (control). Dose recommendations, performed on a monthly basis, were validated by an expert physician anemia manager. The primary outcome was the percentage of hemoglobin concentrations between 10 and 12 g/dl over the follow-up period. A total of 258 of 356 (72.5%) hemoglobin concentrations were between 10 and 12 g/dl in the treatment group, compared with 208 of 336 (61.9%) in the control group; 42 (11.8%) hemoglobin concentrations were ,10 g/dl in the treatment group compared with 88 (24.7%) in the control group; and 56 (15.7%) hemoglobin concentrations were .12 g/dl in the treatment group compared with 46 (13.4%) in the control group. The median ESA dosage per patient was 2000 IU/wk in both groups. Five participants received 6 transfusions (21 U) in the treatment group, compared with 8 participants and 13 transfusions (31 U) in the control group. These results suggest that individualized ESA dosing decreases total hemoglobin variability compared with a population protocol-based approach. As hemoglobin levels are declining in hemodialysis patients, decreasing hemoglobin variability may help reduce the risk of transfusions in this population.

Background and objectives: The erythropoietic response in hemodialysis patients depends on several physiologic factors. Most epidemiologic studies include the effect of these factors by representing them as confounders. This study tested the hypothesis that iron stores, inflammation, dialysis adequacy, nutritional status, and hyperparathyroidism act as nonlinear effect modifiers of the erythropoietic response and quantified the magnitude of those effects over clinically relevant ranges.Design, setting, participants, & measurements: The following retrospective data from 209 hemodialysis patients receiving Epoetin alfa (Epo) were collected: monthly: predialysis hemoglobin (Hgb), transferrin saturation, serum albumin, dialysis adequacy (Kt/V); quarterly: predialysis serum ferritin and intact parathyroid hormone over a period of 13 to 69 months. The study analyzed the dynamic relationship between hemoglobin and Epo, considering nonlinear effect modification by ferritin, transferrin saturation, Kt/V, albumin, and parathyroid hormone individually.Results: Maximum Hgb response to Epo was achieved for serum ferritin between 350 and 500 ng/ml, transferrin saturation greater than 30%, Kt/V greater than 1.4, and albumin greater than 3.8 g/dl. Hgb sensitivity to Epo decreases by about 30% as parathyroid hormone increases from 0 through 1000 pg/ml.Conclusions: Serum ferritin, transferrin saturation, Kt/V, serum albumin, and intact parathyroid hormone are markers of nonlinear effect modification of the erythropoietic response in hemodialysis patients.

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Introduction: Nephrotic syndrome (NS) is a characterized by massive proteinuria, edema, hypoalbuminemia, and dyslipidemia. Glucocorticoids (GCs), the primary therapy for >60 years, are ineffective in approximately 50% of adults and approximately 20% of children. Unfortunately, there are no validated biomarkers able to predict steroid-resistant NS (SRNS) or to define the pathways regulating SRNS. Methods: We performed proteomic analyses on paired pediatric NS patient plasma samples obtained both at disease presentation before glucocorticoid initiation and after approximately 7 weeks of GC therapy to identify candidate biomarkers able to either predict steroid resistance before treatment or define critical molecular pathways/targets regulating steroid resistance. Results: Proteomic analyses of 15 paired NS patient samples identified 215 prevalent proteins, including 13 candidate biomarkers that predicted SRNS before GC treatment, and 66 candidate biomarkers that mechanistically differentiated steroid-sensitive NS (SSNS) from SRNS. Ingenuity Pathway Analyses and protein networking pathways approaches further identified proteins and pathways associated with SRNS. Validation using 37 NS patient samples (24 SSNS/13 SRNS) confirmed vitamin D binding protein (VDB) and APOL1 as strong predictive candidate biomarkers for SRNS, and VDB, hemopexin (HPX), adiponectin (ADIPOQ), sex hormone-binding globulin (SHBG), and APOL1 as strong candidate biomarkers to mechanistically distinguish SRNS from SSNS. Logistic regression analysis identified a candidate biomarker panel (VDB, ADIPOQ, and matrix metalloproteinase 2 [MMP-2]) with significant ability to predict SRNS at disease presentation (P ¼ 0.003; area under the receiver operating characteristic curve ¼ 0.78). Conclusion: Plasma proteomic analyses and immunoblotting of serial samples in childhood NS identified a candidate biomarker panel able to predict SRNS at disease presentation, as well as candidate molecular targets/pathways associated with clinical steroid resistance.