Transcapillary escape rate of ¹²⁵I-albumin in relation to timing of blood sampling: the need for standardization

Abstract

Background

Increased vascular permeability is an early sign of vascular damage and can be measured with the transcapillary escape rate of albumin (TER_alb). Although TER_alb has a multi-exponential kinetic model, most published TER_alb data are based on mono-exponential kinetic models with variation in blood sampling schemes. Aim of this posthoc study was to evaluate the influence of variation in blood sampling schemes and the impact of mono- or bi-exponential analyses on the calculation of TER_alb. Study participants were part of a cross-over intervention study protocol, investigating effects of sodium loading on blood pressure, endothelial surface layer and microcirculation. Multiple blood samples were drawn between 3 and 60 min after injection of radioactive iodide labeled human serum albumin (rHSA).

Results

In total 27 male participants with 54 measurements were included. For all participants the maximum serum radioactivity was reached within 20 min, while 85% of the participants had their maximum serum activity within 10 min. The TER_alb calculated with the subsequently chosen T_{20–60 min} reference scheme (6.19 ± 0.49%/h) was significantly lower compared to the TER_alb of the T_{3–60 min}, T_{5–60 min}, and T_{max – 60 min} schemes. There was no significant difference between the T_{20–60 min} reference scheme and the T_{10–60 min} and T_{15–60 min} schemes. Bi-exponential kinetic modeling did not result in significant different observations compared to the mono-exponential kinetic analysis.

Conclusions

As there is variation in the timing of the maximum serum radioactivity of rHSA, blood sampling schemes starting before 10 min after administration of rHSA will result in a significant overestimation of TER_alb. In addition, variation in kinetic modeling did not result in significant changes in TER_alb. Therefore, we emphasize the need to standardize TER_alb and for practical and logistical reasons advocate the use of a mono-exponential model with blood sampling starting 20 min after rHSA administration.

Background

Diabetes mellitus and hypertension are characterized by an increased risk of vascular complications. An early sign of vascular damage is increased vascular permeability, which can be determined by the transcapillary escape rate of albumin (TER_alb) (Broekhuizen et al., 2010).

TER_alb is the rate in which intravenous albumin escapes from the intravascular to the extravascular volume in the first hour after injection of radioactive iodide labeled human serum albumin (rHSA) (Parving, 1975). The pharmacokinetics of rHSA could be described as the sum of three exponential components, with respective half-life’s of 6.8 h, 1.29 days and 19.4 days (Berson et al., 1953; Bauman et al., 1955; CIS bio International, 2010). The disappearance of rHSA, in the first hour after injection, could be described as a bi-exponential decay curve with a inflection point after approximately 10 min (Margarson & Soni, 2002).

Despite the fact that rHSA has a multi-compartment kinetic model, all published TER_alb data analyses are based on a mono-exponential kinetic model. This mono-exponential TER_alb model has four assumptions: the rHSA behaves like endogenous albumin; the albumin metabolism is in steady state during the TER_alb test; rHSA has a mono-exponential blood pool elimination during the first hour after injection, with a rate constant equal to that of time zero; the initial blood pool elimination reflects extravasation and is not influenced by the rHSA metabolism rate (Parving & Gyntelberg, 1973).

The original protocol of Parving et al. describes that a small amount of I-125 or I-131 labeled rHSA is injected in an arm vein, and eight venous blood samples were drawn from the contralateral arm at 10, 15, 20, 30, 40, 50, 55, and 60 min after the injection. The radioactivity of the rHSA in each blood sample was measured in duplicate. The TER_alb was calculated and expressed as the percentage decline of radioactivity during the first hour (%/h) (Parving & Gyntelberg, 1973).

However most studies using TER_alb values show variation in sampling schemes ranging from 3 to 13 blood samples (Margarson & Soni, 2002; Jensen et al., 1992). Some of the schemes started already 1 min after the injection of rHSA, while others started blood sampling 20 min after the administration of rHSA (Margarson & Soni, 2002; Norberg et al., 2015).

This variation in sampling schemes does impact the calculated TER_alb. Sampling schemes that started 5 min after administration found TER_alb in the range of 6.9–9.1%/h (Dell'omo et al., 2006; Dell'Omo et al., 2000; Haskell et al., 1997; Pedrinelli et al., 2000; Pedrinelli et al., 1999; van Eijk et al., 2005; Rorije et al., 2018). While studies which started sampling 10 min after injection found a lower TER_alb of approximately 5.5%/h (Parving, 1975; Jensen et al., 1992; Nannipieri et al., 1997; Jensen, 1995; Nannipieri et al., 1995; Staberg et al., 1982; Zietse et al., 1995). These differences in TER_alb were not related to differences in patient population, but are in line with the multi-exponential kinetics of rHSA.

As the use of TER_alb for clinical research seems to gain in popularity, standardization of the technique is essential: i.e. reducing variation in performing the test and thereby reducing variation in the test result. As we observed large variations between different publications in TER_alb sampling schemes and most likely thereby variation in TER_alb results, we therefore aimed to study the influence of different sampling schemes and the use of a mono- or bi-exponential analysis on the calculation of TER_alb.

Methods

Study population and study design

Selected participants of this post hoc study were part of a cross-over intervention study protocol investigating whether an acute intravenous sodium load, as compared to a chronic dietary sodium load, differs in its effects on blood pressure, the endothelial surface layer and microcirculation (Rorije et al., 2018). Participants included healthy men, and both male type 1 diabetes mellitus and hereditary multiple exostosis patients (i.e., patients with, respectively, acquired and genetically determined glycocalyx changes) (Mooij et al., 2014). Exclusion criteria were hypertension (≥ 140/90 mmHg), obesity (body mass index (BMI) ≥ 30 kg/m²), history of primary hyperlipoproteinemia, coagulation disorders, and renal or cardiovascular diseases. All participants were randomized to a low sodium diet (LSD, < 50 mmol Na⁺ daily) or to a high sodium diet (HSD, > 200 mmol Na⁺ daily) for 8 days, separated by a crossover period of at least 1 week. The study was performed at the Amsterdam UMC, location AMC, Amsterdam, The Netherlands. All participants provided written informed consent and approval was obtained from the local ethics committee. The trial is registered in the Netherlands Trial Register (NTR4095 and NTR4788).

Transcapillary escape rate of rHSA

An intravenous (IV) bolus of saline solution with rHSA labeled with 100 kBq I-125 was administered in a cubital vein. Blood samples were drawn from the contralateral arm at baseline and between 3 and 60 min after injection of rHSA. Radioactivity in plasma was measured in duplicate with a Wizard2 2480 automatic gamma counter (PerkinElmer, Waltham, Massachusetts, USA) with a coefficient of variation of < 3%. The routine quality controls of the gamma counter were performed according to the standard GLP features of PerkinElmer, including detector energy resolution, background, absolute - and relative detector efficiency, detector stability probability and calibration.

The TER_alb was calculated with PKSolver, a free Microsoft Excel add-in for pharmacokinetic (PK) and pharmacodynamic (PD) data analysis (Zhang et al., 2010). PKSolver has been validated and has been used in different PK/PD studies (Zhang et al., 2010; Kulo et al., 2017; de Velde et al., 2016; Nezic et al., 2014; Balakumar et al., 2013; Wenstedt et al., 2020).

TER_alb was expressed as percentage decline in plasma radioactivity per hour (%/h). The TER_alb calculation with PKSolver was performed for an IV bolus administration. The formula used for the calculation of TER_alb was:

$$ {\mathrm{TER}}_{\mathrm{alb}}=\left({\mathrm{A}}_{0\ \min }-{\mathrm{A}}_{60\ \min}\right)/{\mathrm{A}}_{0\ \min } $$

The predicted activity of rHSA at T_{0 min} (A _{0 min}) and at T_{60 min} (A _{60 min}) were calculated by PKSolver (Microsoft Excel 2016) based on a mono- and bi-exponential kinetic model. This program also calculated the correlation coefficient (R) between the observed and predicted data.

Sampling schemes

After acquiring the PK curves of rHSA, we calculated the TER_alb according the following simulated blood sampling schemes:

• T_{3–60 min}: 3, 4, 5, 10, 15, 20, 30, 45, and 60 min

• T_{5–60 min}: 5, 10, 15, 20, 30, 45, and 60 min

• T_{10–60 in}: 10, 15, 20, 30, 45, and 60 min

• T_{15–60 min}: 15, 20, 30, 45, and 60 min

• T_{20–60 min}: 20, 30, 45, and 60 min

• T_{max – 60 min}: from individual A _max till 60 min

All blood samples before A _max of the PK curves were excluded for the calculation of TER_alb, irrespective of the sampling scheme.

Statistics

The effect of different blood sampling schemes on the TER_alb values were analyzed by fitting a mixed model as implemented in IBM SPSS Statistics (version 26, IBM, USA). This mixed model uses a compound symmetry covariance matrix and is fitted using maximum likelihood. In the absence of missing values, this method results in the same p values as multiple comparisons tests (e.g. repeated measures ANOVA) that are less able to deal with missing values. Therefore, in the presence of missing values, the results can be interpreted like repeated measures ANOVA (Harrison et al., 2018). We used Bonferroni correction as post hoc test and p values < 0.05 were considered statistically significant. Results were reported as mean ± standard error of the mean (SEM). Bland-Altman plots were used to evaluate the level of agreement between two different blood sample schemes.

Results

Patient demographics

In total 27 men were included resulting in 54 PK curves (27 linked to the LSD and 27 linked to the HSD), based on 486 (54*9 samples) blood sample analyses. The study population consisted of 12 healthy volunteers, 8 diabetes mellitus type I patients, and 7 patients with hereditary multiple exostoses. All volunteers were between 18 and 38 years old with a median age of 24 (range 18–38 years). Other characteristics of study participants are displayed in Table 1.

Table 1 Characteristics of study participants

The blood serum disappearance of rHSA of the study participants is shown in Fig. 1. The pharmacokinetic graphic shows a bi-exponential slope of decay curve with inflexion point at 15–20 min.

Fig. 1

Relative plasma activity in the first hour after administration of rHSA (Tmax is 100% activity for each individual participant)

T_max after rHSA administration

The T_max after rHSA administration showed a large inter-individual variability (Fig. 2). The mean T_max was 6.9 ± 0.6 min. In 85% of the participants A_max of rHSA was reached within 10 min, while T_max was reached at 20 min after administration for all participants without an effect of subject category (HME, DM type 1 or healthy volunteer) or the diet followed (LSD vs HSD). Therefore T_{20–60 min} was used as the reference scheme. The mean TER_alb values of the other time schemes were compared to the reference scheme T_{20–60 min} based on mono-exponential kinetic analysis.

Fig. 2

Percentage of participants reaching serum maximum rHSA activity after administration of rHSA (T_max)

TER_alb based on mono-exponential kinetic analysis

The reference T_{20–60 min} scheme included 54 of the 54 PK curves. The mean TER_alb of the T_{max – 60 min} scheme resulted in the highest calculated TER_alb: 8.30 ± 0.49%/h (Fig. 3). The TER_alb calculated with the T_{20–60 min} reference scheme (6.19 ± 0.49%/h) was significantly lower compared to the TER_alb of the T_{3–60 min} (mean difference = − 1.63%/h, CI = − 2.65 – − 0.61%/h, p < 0.001), T_{5–60 min} (mean difference = − 1.32%/h, CI = − 2.27 – − 0.36%/h, p = 0.001), and T_{max – 60 min} (mean difference = − 2.11%/h, CI = − 3.04 – − 0.48%/h, p < 0.001) schemes. There were no significant difference between the mean TER_alb of the T_{20–60 min} reference scheme and the T_{10–60 min} and T_{15–60 min} scheme.

Fig. 3

TER_alb for the different blood sampling time schemes based on a mono-exponential kinetic analysis and compared with the T_{20–60 min} reference scheme

TER_alb based on bi-exponential kinetic analysis

Using a bi-exponential analysis according to the T_{20–60 min} scheme did not result in significant different TER_alb values when compared to the mono-exponential analysis based T_{20–60 min} reference scheme (respectively 6.19 ± 0.46%/h vs. 6.05 ± 0.46%/h, p = 1.000). The mean TER_alb of the reference T_{20–60 min} scheme was significantly lower compared to the mean TER_alb of the bi-exponential kinetic analysis of the T_{3–60 min} (mean difference = − 1.49%/h, CI = − 2.68 – − 0.29%/h, p = 0.004), T_{5–60 min} (mean difference = − 1.09%/h, CI = − 2.18 – − 0.01%/h, p = 0.050), and T_{max – 60 min} (mean difference = − 1.87%/h, CI = − 2.95 – − 0.80%/h, p < 0.001) schemes. There were no significant difference between the mean TER_alb of the T_{20–60 min} reference scheme and the T_{10–60 min} and T_{15–60 min} schemes based on bi-exponential kinetic analysis (Fig. 4).

Fig. 4

TER_alb for the different blood sampling schemes based on a bi-exponential kinetic model, compared with the T_{20–60 min} reference scheme

Figure 5 shows the Bland-Altman plot with agreement between the bi-exponential analysis based on T_{20–60 min} scheme and T_{20–60 min} reference scheme. The TER_alb showed a bias of − 0.1%/h between the different time schemes without a significant trend over the data range (1.7–12.8%/h) and with a consistent variability over the data range.

Fig. 5

Bland-Altman plot with differences between TER_alb values calculated according T_{20–60 min, bi-exp.} scheme and the T_{20–60 min} reference scheme. Solid line: bias (− 0.1%/h) between the two sampling schemes, dotted lines: 95% limits of agreement (− 2.7–2.4%/h)

Discussion

To our knowledge, this study is the first to examine the influence of different blood sampling schemes and the impact of mono- or bi-exponential analyses on the calculation of TER_alb. Our findings emphasize the necessity to standardize TER_alb calculations.

We found that the TER_alb became lower when blood sample collection started later. This phenomenon has been reported previously (Margarson & Soni, 2002). In this context it is remarkable that the majority of published studies used a fixed time sampling scheme with the first blood sampling within 10 min (Dell'omo et al., 2006; Dell'Omo et al., 2000; Haskell et al., 1997; Pedrinelli et al., 2000; Pedrinelli et al., 1999; van Eijk et al., 2005; Rorije et al., 2018). This practice will have caused a overestimation of the reported TER_alb. In addition, this makes the reported findings based on TER_alb difficult to reproduce and troublesome to extrapolate. Especially when TER_alb values of different sampling schemes are compared with each other.

Although the blood serum disappearance of rHSA should be described as a bi-exponential kinetic model, as shown in Fig. 1, the mean TERalb values between mono- and bi-exponential analysis were not significant different. Therefore, we concluded that the mono-exponential kinetic analysis, which is common used for TER_alb analysis, is a robust and easy to use approach to calculate the TER_alb in the daily practice.

Our data showed that biodistribution of rHSA seems to be complete after 15–20 min. Apparently rHSA may need up to 20 min to reach an equilibrium. This inter-individual variation may be explained by the rate of lymphatic return or redistribution into the hepatic and splenic interstitium (Margarson & Soni, 2002; Henriksen & Schlichting, 1981). To minimize the number of blood samples, we advocate the use a mono-exponential model with blood sampling starting 20 min after rHSA administration for the daily practice. For scientific purposes, we suggest to use the T_max scheme to correct for the inter- and intra-individual variability. It should be noted that these TERalb values are significant higher compared to the daily practice scheme.

This study has several limitations that need to be addressed. First, we pooled data, because no differences between healthy participants, type 1 diabetes mellitus and hereditary multiple exostosis patients were detected. One would have expected higher TERalb values in type 1 diabetes mellitus patients. However, our study included young male with uncomplicated type 1 diabetes without albuminuria. Likely, at this stage of the disease, TERalb is still unaffected. Secondly, we did not collect any blood samples after T_{60 min}. Blood sampling for longer time periods after administration, for example up to 24 h after rHSA injection, could have helped in better understanding the kinetics of rHSA blood clearance.

Conclusions

To our knowledge, this study examined for the first time whether different blood sampling schemes impact TER_alb values. We found significant differences between the blood sampling schemes which will cause bias in reporting TER_alb and makes it difficult to reproduce and extrapolate outcomes of TER_alb.

As there is a large variation in the timing of the maximum serum radioactivity of rHSA, blood sampling schemes starting before 10 min after administration of rHSA will result in a significant overestimation of TER_alb. In addition, variation in mono- or bi-exponential kinetic modeling did not result in significant changes in TER_alb. Therefore, we emphasize the need to standardize TER_alb and for practical and logistical reasons advocate the use of a mono-exponential model with blood sampling starting 20 min after rHSA administration.