Modeling of red blood cell life-spans in hematologically normal populations

Abstract

Despite the impact of red blood cell (RBC) Life-spans in some disease areas such as diabetes or anemia of chronic kidney disease, there is no consensus on how to quantitatively best describe the process. Several models have been proposed to explain the elimination process of RBCs: random destruction process, homogeneous life-span model, or a series of 4-transit compartment model. The aim of this work was to explore the different models that have been proposed in literature, and modifications to those. The impact of choosing the right model on future outcomes prediction--in the above mentioned areas--was also investigated. Both data from indirect (clinical data) and direct life-span measurement (biotin-labeled data) methods were analyzed using non-linear mixed effects models. Analysis showed that: (1) predictions from non-steady state data will depend on the RBC model chosen; (2) the transit compartment model, which considers variation in life-span in the RBC population, better describes RBC survival data than the random destruction or homogenous life-span models; and (3) the additional incorporation of random destruction patterns, although improving the description of the RBC survival data, does not appear to provide a marked improvement when describing clinical data.

Models for the red blood cell lifespan

“…Before discussing these issues we describe the two approaches, direct and indirect, commonly used to study RBC survival and the types of RBC samples used in such studies [45]. We then provide a brief literature review.…”

“…Direct models fall primarily into two categories: 1) empirical models for simple curve fitting [9, 13, 32, 42, 44, 46, 53, 65], and 2) “phenomenological” models accounting for macroscopic phenomena such as random destruction of the cells, lifespan-based elimination of the cells, neocytolysis (selective hemolysis of young RBCs under conditions of RBC excess when acutely exposed to increased tissue oxygen content), etc., and method-specific phenomena such as radioactive decay of the label, elution, vesiculation, etc. in the case of labeling by 51 Cr [8, 9, 16, 17, 24, 35, 36, 45, 61]. …”

“…Instead of the direct measurement of RBC survival, the indirect approach [45] relies on native Hb, glycosylated Hb, or other pharmacokinetic/pharmacodynamic (PK/PD) information over time to make inferences about the RBC lifespan [22, 41, 45, 59, 62, 64]. Indirect models are lifespan-based compartmental PK/PD models (e.g., lifespan-based indirect response or LIDR models [38]), of which transit compartment (TC) models are a special case [37].…”

Longitudinal Modeling of the Relationship Between Mean Plasma Glucose and HbA1c Following Antidiabetic Treatments

“…5 A comparison of the models (based on both clinical data and direct measures of biotin-labeled RBCs) revealed that an extended version of the transit-compartment model with 12 compartments was superior for description of RBC survival. 7 Although the choice of RBC model was shown to impact predictions of HbA1c in a clinical trial, our results suggest that accurate predictions can still be obtained in a population with type 2 diabetes subjects without inclusion of difference in RBC life-span.…”

“…To improve the utilization of data from these shorter trials, where HbA1c has not always reached a stable level, models of glucose and HbA1c homeostasis have been suggested. 5,6,7,8 …”