Skip to main content
Download PDF
Download ePub

Suitability of α1-microglobulin reduction rate as a biomarker of removal efficiency of online hemodiafiltration: a retrospective cohort study

Renal Replacement Therapy volume 7, Article number: 10 (2021) Cite this article

Abstract

Background

Online hemodiafiltration (OL-HDF), whether in pre-dilution OL-HDF (pre-HDF) or post-dilution OL-HDF (post-HDF), is conducted to efficiently remove low molecular weight proteins from the blood of patients requiring dialysis. β2-microglobulin (β2-MG) and α1-microglobulin (α1-MG) are used as biomarkers to evaluate removal efficiency of OL-HDF.

We aimed to evaluate the relationship between β2-MG and α1-MG reduction rates and the amount of albumin leakage. Furthermore, we statistically analyzed the relationship between the α1-MG reduction rate and α1-MG removal amount, and its suitability as a biomarker for evaluating the removal efficiency of OL-HDF.

Methods

We collected the results of regularly conducted routine evaluations to assess the efficiency of OL-HDF from cases of patients undergoing maintenance dialysis at our clinic from 2018 to 2019. Data on was collected on both pre-HDF and post-HDF sessions. β2-MG and α1-MG reduction rates were analyzed. Regression analysis on reduction rates showed a significant correlation between the α1-MG reduction rate and the α1-MG removal amount.

Results

We conducted 435 tests on OL-HDF efficiency in 87 cases undergoing maintenance dialysis at our clinic in 2018 and 2019. There were 80.7 ± 4.5% for the β2-MG reduction rate, 33.8 ± 9.4% for the α1-MG reduction rate, and 3.9 ± 1.8 g/s for the amount of albumin leakage. There was no correlation between the β2-MG reduction rate and the α1-MG reduction rate, or between the amount of albumin leakage and β2-MG reduction rate.

Conclusion

α1-MG reduction rate was found to correlate with its removal amount, demonstrating its suitability as a biomarker for evaluating the removal efficiency of OL-HDF.

Trial registration

Retrospectively registered.

Background

Online hemodiafiltration (OL-HDF), whether in pre-dilution OL-HDF (pre-HDF) or post-dilution OL-HDF (post-HDF), is conducted to efficiently remove low molecular weight proteins (LMWP) (molecular weight [MW]: 10,000–55,000), particularly larger LMWPs (MW > 30,000), from blood of dialysis patients. This is accomplished by increasing the convection volume from high-volume replacement fluid. Biomarkers are important for evaluating the efficiency of OL-HDF. The small molecular size of β2-microglobulin (β2-MG) (MW: 11,800, Stokes radius: 16A) allows for removal dependent on both diffusion and convection. Therefore, β2-MG cannot be used as a biomarker to accurately assess OL-HDF removal efficiency.

α1-Microglobulin (α1-MG) (MW: 33,000, Stokes radius: 28.4A) is mainly removed by convection in dialysis therapy with a more suitable molecular size; hence, it has often been used as a biomarker to assess the efficiency of removal of middle- and large-MW solutes in Japan for over 30 years. Similarly, we reported a strong correlation between changes in the clinical symptoms of dialysis patients and changes in the α1-MG reduction rate [1, 2]. We also reported that OL-HDF should aim for a β2-MG reduction rate of 80% and an α1-MG reduction rate of over 35% when performed to treat complications in long-term dialysis patients [3, 4].

There is a twofold difference in molecular weight between albumin (alb) (MW: 66,000, Stokes radius: 35.5A) and α1-MG, with a difference in Stokes radius of 20% [5]. Thus, it is impossible to separate α1-MG and alb based on differences in molecular size alone and remove them using current dialysis membranes. Some degree of alb leakage is inevitable when removing α1-MG at high efficiency. There is no report examining the appropriateness of the reduction rate of the α1-MG reduction rate as a biomarker using statistical analysis. In this study, we evaluated the relationship between β2-MG and α1-MG reduction rates and the amount of alb leakage, as well as the relationship between the reduction rates of β2-MG and α1-MG based on the results of OL-HDF removal efficiency tests regularly conducted as routine examinations in 2018 and 2019. Furthermore, we statistically analyzed the relationship between the α1-MG reduction rate and the α1-MG removal amount, and we investigated whether this was a suitable biomarker for evaluating the removal efficiency of OL-HDF.

Methods

Four hundred thirty-five tests for evaluating OL-HDF efficiency in 87 patients undergoing maintenance dialysis at our clinic between January 2018 and December 2019 (male/female: 60/27, age: 60.5 ± 12.0 years, dry weight: 60.5 ± 13.0 kg, dialysis vintage: 146.0 ± 130.9 months). Pre- and post-HDF comprised 304 and 131 sessions, respectively. Blood flow rate (Qb) was 261.5 ± 28.5 mL/min, total dialysate flow rate (Qd total) was 400 mL/min for 22 cases and 500 mL/min for 413 cases, and replacement fluid volume was 48.7 ± 8.2 L/session(s) for pre-HDF and 14.2 ± 3.1 L/s for post-HDF. Treatment time was 4.1 ± 0.2 h/s (Table 1). Different types of hemodiafilters commercially available in Japan were used. The obtained test values were used to investigate the relationship between β2-MG and α1-MG reduction rates and the amount of alb leakage, as well as the relationship between the β2-MG reduction rate and α1-MG reduction rate.

Table 1 Patients background and treatment mode
Full size table

Thereafter, the relationship between the α1-MG reduction rate and its removal amount was analyzed, and whether the reduction rate was suitable for evaluating the removal efficiency was investigated. This was done by setting a 95% prediction interval of the regression curve created from the α1-MG removal amount (horizontal axis, mg) and reduction rate (vertical axis, %), and extracting test values that were outliers from this interval from the regression analysis. Values with reduction rates that were lower than the lower limit or higher than the upper limit of the calculated 95% prediction interval were defined as outlier test values. A histogram and Q-Q plot was used to assess if a set of data have a normal distribution. Continuous variables were expressed as the mean ± standard deviation (SD) and categorical variables as frequencies. A P value of < 0.05 was considered statistically significant, and all P values were two-sided. All statistical analyses were performed using SPSS statistics ver. 23.0 (IBM Japan, Ltd., Tokyo, Japan) software.

Post-dialysis values used to calculate the β2-MG and α1-MG reduction rates were values that were corrected by the hematocrit level to exclude concentration effects. The waste dialysate was partially stored throughout the dialysis session at a rate of 2 L/h, after which the total amount stored was well-mixed, and part of this was used to measure the β2-MG and α1-MG amounts removed and the amount of alb leakage.

This study has been approved by our institutional committee on human and/or animal research. All patients provided informed written consent.

Results

We conducted 435 tests on OL-HDF efficiency in 87 cases undergoing maintenance dialysis at our clinic in 2018 and 2019 (male/female: 60/27, age: 60.5 ± 12.0 years, dry weight: 60.5 ± 13.0 kg, dialysis vintage: 146.0 ± 130.9 months). Pre-HDF comprised 304 sessions, and post-HDF comprised 131 sessions. Qb was 261.5 ± 28.5 mL/min, Qd total was 400 mL/min for 22 cases and 500 mL/min for 413 cases, and replacement fluid volume was 48.7 ± 8.2 L/session(s) for pre-HDF and 14.2 ± 3.1 L/s for post-HDF. Treatment time was 4.1 ± 0.2 h/s (Table 1). Ten types of hemodiafilters commercially available in Japan were used (Table 2). The membrane surface area was 2.1 ± 0.1 m2.

Table 2 Hemodiafilter Specs from NIKKISO CO., LTD., Tokyo, Japan; Nipro CO., Osaka Japan; Toray Medical Co., Ltd., Tokyo Japan; ASAHI KASEI Medical Co., Ltd., Tokyo Japan; Gambro Dialysatoren GmbH, Hechingen, Germany; Fresenius Medical Care Japan K.K., Fukuoka, Japan
Full size table

The results of all 435 tests (mean ± SD) were 80.7 ± 4.5% for the β2-MG reduction rate, 33.8 ± 9.4% for the α1-MG reduction rate, and 3.9 ± 1.8 g/s for the amount of alb leakage. There were six cases in ten sessions where the α1-MG reduction rate was > 50%, and the maximum α1-MG reduction rate value was 60.0%. No significant correlation was observed between the amount of alb leakage and β2-MG reduction rate (Fig. 1). There was a slightly strong significant correlation between the amount of alb leakage and α1-MG reduction rate. The removal dynamics of alb leakage and α1-MG removal were almost identical (Fig. 1). There was no correlation between the β2-MG reduction rate and the α1-MG reduction rate (Fig. 2).

Fig. 1
figure 1

Relationship between reduction rate and amount of albumin leakage. No significant correlation is found between the amount of alb leakage and β2-MG reduction rate. Since β2-MG is primarily removed by diffusion as well as convection, this indicates that high-efficiency removal is possible with little to no alb leakage. α1-MG cannot be removed with high efficiency without some degree of alb leakage. Furthermore, as 30–50% of α1-MG is bound to dimeric IgA in renal failure, the maximum reduction rate is around 60%

Full size image
Fig. 2
figure 2

Relationship between reduction rate of β2-MG and α1-MG. Since there is no correlation between β2-MG and α1-MG reduction rates, the advantages of OL-HDF cannot be evaluated with β2-MG reduction rate alone. As efficient removal of larger LMWPs is an objective of OL-HDF, efficiency tests that use α1-MG as a biomarker are necessary

Full size image

Results of the regression analysis on reduction rates showed a significant correlation between the α1-MG reduction rate and the α1-MG removal amount (Table 3). Additionally, 17 out of the 435 total sessions (3.9%) were outlier values from the 95% prediction interval of the regression curve (Fig. 3). It was shown from these analyses that it was possible to appropriately evaluate the removal efficiency of OL-HDF by investigating the reduction rate without determining the removal amount.

Table 3 Results of regression analysis on reduction rates. To evaluate the association between removal amount and reduction rate, and to identify cases that fell outside of the association, we performed a regression analysis with reduction rate as the dependent variable and removal amount as the independent variable. The removal amount was log-transformed
Full size table
Fig. 3
figure 3

Relationship between Reduction rate and removal amount of α1-MG. From the results of the regression analysis, a logarithmic approximation curve including the 95% prediction interval is created with the removal amount as the horizontal axis and the reduction rate as the vertical axis. Values with reduction rates that fell outside the 95% prediction interval are defined as outlier cases. There are 17 outliers (3.9%) in this study

Full size image

Discussion

In this study, we evaluated the relationship between β2-MG, α1-MG reduction rates, and amount of alb leakage, as well as the relationship between the reduction rates of β2-MG and α1-MG. There was no correlation between the β2-MG reduction rate and the α1-MG reduction rate, nor between the amount of alb leakage and β2-MG reduction rate. We found that alb leakage was inevitable when removing α1-MG at high efficiency. Further, we revealed that there was a significant correlation between the α1-MG reduction rate and the α1-MG removal amount. Our study showed that α1-MG reduction rate was a suitable biomarker for evaluating the efficiency of removal of OL-HDF.

In Japan, α1-MG is widely used as a biomarker for evaluating the performance of removal efficiency of OL-HDF for several reasons: (1) optimal molecular size, (2) low likelihood of measurement errors due to its non-negligible blood concentration, (3) low likelihood of concentration changes under physiological conditions, (4) stable synthesis rate, (5) accumulation in renal failure, and (6) convection-based removal. α1-MG is primarily synthesized in the liver. Under physiological conditions, 50% of it is present in the blood in free form, while 50% is bound with dimeric immunoglobulin A (IgA) [6]. It is estimated that free α1-MG increases to around 70% because its excretion from the kidneys is reduced during renal dysfunction, but there are no detailed reports on these dynamics. α1-MG has strong antioxidant activity and has been reported as a protective molecule by scavenging free radicals, binding to heme, and undergoing reduction reactions when exposed to oxidative stresses [7,8,9,10,11]. The antioxidant activity of α1-MG in dialysis patients may be a topic of future studies.

The MW of IgA is 160,000; hence, the combined MW of the resulting α1-MG-IgA complex is 350,000, which is impossible to remove using dialysis. Since free α1-MG is the target in dialysis therapy and total α1-MG is usually measured, the reduction rate is affected by the binding affinity of α1-MG and IgA. Consequently, the α1-MG reduction rate calculated with the pre- and post-dialysis values do not appropriately indicate the removal efficiency of OL-HDF.

Therefore, we investigated whether the α1-MG reduction rate and the α1-MG removal amount obtained from the 435 OL-HDF sessions were significantly correlated. Results showed that there was a significant correlation between the two measures. However, there were 17 test values outside the 95% prediction interval of the regression curve. Specifically, outliers were observed below the lower limit in seven patients across seven sessions, whereas outliers were observed over the upper limit in five patients across ten sessions. Test conditions when outlier values were obtained may be a topic of future studies.

Measuring the amount of a given solute removed during dialysis is complicated due to the need for the installation of a device that is capable of continuously collecting waste fluid from a drainage line of the patient monitoring system. However, the reduction rate can be calculated simply by collecting blood before and after dialysis and calculating the reduction rate using the following equation:

$$ \mathrm{RR}=\left(1-\frac{\left(1-{\mathrm{Ht}}_{\mathrm{post}}\right){\mathrm{Ht}}_{\mathrm{pre}}{C}_{\mathrm{post}}}{\left(1-{\mathrm{Ht}}_{\mathrm{pre}}\right){\mathrm{Ht}}_{\mathrm{post}}{C}_{\mathrm{pre}}}\right)\times 100\ \left(\%\right) $$

Where Ht is hematocrit and C is concentration. This enables the evaluation of removal efficiency of middle- and large-MW solutes.

It is essential in the evaluation of high-efficiency OL-HDF to investigate the removal efficiency of solutes with a MW of 30,000–50,000 using α1-MG as a biomarker. Our study supported the use of α1-MG reduction rate to be an appropriate evaluation method indicating that past accumulated data can be applied to future studies.

Several reports investigated the effects of OL-HDF on a patient’s survival [12,13,14]. Kikuchi et al. [15] reported that pre-HDF with high-replacement fluid volume had a more favorable effect on patient’s survival than hemodialysis and pre-HDF with low-replacement fluid volume. However, both reports only investigated the impacts of replacement fluid volume on a patient’s survival, and neither mentioned specific numerical values for removal efficiency. In Europe, α1-MG has been used for over five years as a biomarker for removal performance when evaluating OL-HDF or high-performance dialyzers, and the evaluation of dialysis efficiency using α1-MG reduction rate has become increasingly common [16, 17]. The groundbreaking JAMREDS study, which was started in Japan this spring, examined the effect of not only replacement fluid volume but also α1-MG reduction rate on patient survival of OL-HDF. Hence, the usefulness of removing middle- and large-MW toxins is anticipated to become even more apparent in the future.

The limitation of this study was that it was performed in a single facility, which can lead to selection bias.

Conclusions

We found that α1 reduction rate may be used as a valid biomarker to evaluate the removal efficiency of OL-HDF. Furthermore, some alb leakage is inevitable when removing α1-MG at high efficiency.

Availability of data and materials

The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

alb:

Albumin

HD:

Hemodialysis

IgA:

Immunoglobulin A

LMWP:

Low molecular weight protein

MW:

Molecular weight

OL-HDF:

Online hemodiafiltration

pre-HDF:

Pre-dilution online hemodiafiltration

post-HDF:

Post-dilution online hemodiafiltration

SD:

Standard deviation

Qb:

Blood flow rate

Qd total:

Total dialysate flow rate

α1-MG:

α1-microglobulin

β2-MG:

β2-microglobulin

References

  1. Sakurai K. Biomarkers for evaluation of clinical outcomes of hemodiafiltration. Blood Purif. 2013;35(suppl 1):64–8.

    Article  CAS  Google Scholar 

  2. Sakurai K, Saito T, Hosoya H, et al. Therapeutic effect of high-effciency online hemodiafiltration for recurrent restless legs syndrome in dialysis patients. J Artif Organs. 2020;23:296–301.

    Article  CAS  Google Scholar 

  3. Yamashita CA, Sakurai K. Clinical effect of pre-dilution hemodiafiltration based on the permeation of the hemodiafilter. Contrib Nephrol. 2015;185:1–7.

    Article  Google Scholar 

  4. Masakane I, Sakurai K. Current approaches to middle molecule removal: room for innovation. Nephrol Dial Transplant. 2018;33(suppl_3):iii12–21.

    Article  CAS  Google Scholar 

  5. Sakurai K, Saito T, Yamauchi F, et al. Alpha-1-microglbulin is an important marker and some albumin leakage increases therapeutic effects in hemodiafiltration therapy. Nephrol Dial Transplant. 2018;33(suppl_1):i185 https://doi.org/10.1093/ndt/gfy104.

    Article  Google Scholar 

  6. Berggård T, Thelin N, Falkenberg C, et al. Prothrombin, albumin and immunoglobulin A form covalent complexes with alpha1-microglobulin in human plasma. Eur J Biochem. 1997;245(3):676–83.

    Article  Google Scholar 

  7. Allhorn M, Berggard T, et al. Processing of the lipocalin α1-microglobulin by hemoglobin induces hemo-binding and heme-degradation properties. Blood. 2002;99(6):1894–901.

  8. Meining W, Berggard T, et al. The crystal structure of human α1-microglobulin reveals a potential haem-binding site. Biochem J. 2012;445:175–82.

    Article  CAS  Google Scholar 

  9. Olsson MG, Gram M, et al. Pathological conditions involving extracellular hemoglobin: molecular mechanisms, clinical significance, and novel therapetic opportunities for α1-microglobulin. Antioxid Redox Signal. 2012;17:813–45.

    Article  CAS  Google Scholar 

  10. Åkerström B, Gram M, et al. A1M, an extravascular tissue cleaning and housekeeping protein. Free Radic Biol Med. 2014;74:274–82.

    Article  Google Scholar 

  11. Kristiansson A, Bergwick J, et al. Human radical scavenger α1-microglobulin protects against hemolysis in vitro and α1-microglobulin knockout mice exhibit a macrocytic anemia phenotype. Free Radic Biol Med. 2020; In press.

  12. Canaud B, Bragg-Gresham JL, Marshall MR, et al. Mortality risk for patients receiving hemodiafiltration versus hemodiafiltration: European results from DOPPS. Kidney Int. 2006;69:2087–93.

    Article  CAS  Google Scholar 

  13. Grooteman MP, van den Dorpel MA, Bots ML, et al. Effect of online hemodiafiltration on all-cause mortality and cardiovascular outcome. J Am Soc Nephrol. 2012;23:1087–96.

    Article  CAS  Google Scholar 

  14. Maduell F, Moreso F, Pons M, et al. ESOHOL Study Group: High-efficiency postdilution online hemodiafiltration reduces all-cause motality in hemodialysis patients. J Am Soc Nephrol. 2013;24:487–97.

    Article  Google Scholar 

  15. Kikuchi K, Hamano T, Wada A, et al. Predilution online hemodiafiltration is associated with improved survival compared with hemodialysis. Kidney Int. 2019;95:929–38.

    Article  Google Scholar 

  16. Maduell F, Broseta JJ, et al. Comparison of solute removal properties between high-efficient dialysis modalities in low blood flow rate. Apher Dial. 2020;24(4):387–92.

    Article  CAS  Google Scholar 

  17. Maduell F, Rodas L, et al. Evaluation of the influence of the surface membrane and blood flow in medium «cut-off» (MCO) dialyzers. Nefrologia. 2019;36(6):623–8.

    Article  Google Scholar 

Download references

Acknowledgements

This study received no external funding and was self-funded by Kenji Sakurai.

Funding

None.

Author information

Authors and Affiliations

  1. Hashimoto Clinic, 3-21-5 Hashimoto, Midori-ku, Sagamihara-shi, Kanagawa, 252-0143, Japan

    Kenji Sakurai, Hiromi Hosoya, Yoshitaka Kurihara & Takeshi Saito

Authors
  1. Kenji Sakurai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiromi Hosoya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yoshitaka Kurihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Takeshi Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KS designed the study and drafted the manuscript. HH and YK acquired and analyzed the data. TS designed the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kenji Sakurai.

Ethics declarations

Ethics approval and consent to participate

This study has been approved by our institutional committee on human and/or animal research. All patients provided informed written consent.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interest

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sakurai, K., Hosoya, H., Kurihara, Y. et al. Suitability of α1-microglobulin reduction rate as a biomarker of removal efficiency of online hemodiafiltration: a retrospective cohort study. Ren Replace Ther 7, 10 (2021). https://doi.org/10.1186/s41100-021-00326-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s41100-021-00326-y

Keywords

Renal Replacement Therapy

ISSN: 2059-1381

Contact us