| Literature DB >> 28664159 |
Christos P Argyropoulos1, Shan Shan Chen1, Yue-Harn Ng1, Maria-Eleni Roumelioti1, Kamran Shaffi1, Pooja P Singh1, Antonios H Tzamaloukas1,2.
Abstract
There is currently an unmet need for better biomarkers across the spectrum of renal diseases. In this paper, we revisit the role of beta-2 microglobulin (β2M) as a biomarker in patients with chronic kidney disease and end-stage renal disease. Prior to reviewing the numerous clinical studies in the area, we describe the basic biology of β2M, focusing in particular on its role in maintaining the serum albumin levels and reclaiming the albumin in tubular fluid through the actions of the neonatal Fc receptor. Disorders of abnormal β2M function arise as a result of altered binding of β2M to its protein cofactors and the clinical manifestations are exemplified by rare human genetic conditions and mice knockouts. We highlight the utility of β2M as a predictor of renal function and clinical outcomes in recent large database studies against predictions made by recently developed whole body population kinetic models. Furthermore, we discuss recent animal data suggesting that contrary to textbook dogma urinary β2M may be a marker for glomerular rather than tubular pathology. We review the existing literature about β2M as a biomarker in patients receiving renal replacement therapy, with particular emphasis on large outcome trials. We note emerging proteomic data suggesting that β2M is a promising marker of chronic allograft nephropathy. Finally, we present data about the role of β2M as a biomarker in a number of non-renal diseases. The goal of this comprehensive review is to direct attention to the multifaceted role of β2M as a biomarker, and its exciting biology in order to propose the next steps required to bring this recently rediscovered biomarker into the twenty-first century.Entities:
Keywords: acute kidney injury; beta-2 microglobulin; biomarkers; chronic kidney disease; glomerular filtration rate; kidney transplantation; multiple myeloma; pediatric nephrology
Year: 2017 PMID: 28664159 PMCID: PMC5471312 DOI: 10.3389/fmed.2017.00073
Source DB: PubMed Journal: Front Med (Lausanne) ISSN: 2296-858X
Figure 1Molecular structure of beta-2 microglobulin (β2M). Depiction of the secondary structure of β2M relative to the center of gravity of the molecule (red cross). X-ray diffraction at resolution of 1.13 Å (30). Image rendered from the Protein Data Bank entry 2YXF.
Figure 2Bicompartmental beta-2 microglobulin (β2M) kinetics. Bicompartmental system describing β2M kinetics consisting of a plasma/perfusing (P) and non-perfusing/non-plasma (NP) with additional material fluxes for patients during hemodialysis sessions (stippled shapes). In each compartment, the symbols V, Φ, and C denote the absolute and fractional volume of each compartment and the concentration of β2M, respectively. Generation (G) takes place in both compartments, in direct proportion to their fractional volumes. K, K, and K are the dialyzer clearance, extrarenal, and residual renal clearances. Adapted from Supplementary Figure S1 of Ref. (117), reused under the Creative Commons CC BY license terms.
Relationship between beta-2 microglobulin (β2M) and glomerular filtration rate (GFR) in adults.
| Study | GFR measure | Correlation (1/β2M) | Correlation (β2M) | Slope linear regression |
|---|---|---|---|---|
| Vincent et al. ( | Inulin clearance | – | – | −0.87 |
| Wibell et al. ( | Inulin clearance | – | −0.94 | −0.89 |
| Swanson et al. ( | Iothalamate clearance | – | – | −0.82 |
| Shea et al. ( | Iothalamate clearance | 0.90 | – | – |
| Inker et al. ( | Iothalamate clearance | – | – | −0.85 |
| Aparicio et al. ( | 51Cr-EDTA | 0.79 | – | −0.75 |
| Grubb et al. ( | 51Cr-EDTA | 0.59 | – | – |
| Yun et al. ( | 24-h creatinine clearance | – | – | −0.79 |
| Jovanović et al. ( | 24-h creatinine clearance | 0.80 | – | – |
| Shea et al. ( | 24-h creatinine clearance | 0.87 | – | – |
| Aksun et al. ( | 99mTc-DTPA GFR | – | −0.48 | – |
| Bianchi et al. ( | 99mTc-DTPA GFR | 0.76 | – | −0.81 |
| Donadio et al. ( | 99mTc-DTPA GFR | 0.73 | – | – |
| Donadio et al. ( | 99mTc-DTPA GFR | – | – | −0.81 |
| Fry et al. ( | Timed urea collections | – | −0.63 | – |
| Vilar et al. ( | Average of urea and creatinine collections | 0.82 | −0.72 | – |
The table reports the slope of the linear regression between log concentration and log clearance.
– Not reported.
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Performance of beta-2 microglobulin (β2M), creatinine (Cr), and/or cystatin C-derived equations.
| Equation | Interquartile range (95% CI) | 1 − | 1 − | Root mean square error (95% CI) |
|---|---|---|---|---|
| Chronic kidney disease (CKD)-EPI β2M | 12.9 (12.2–13.8) | 18.4 (16.2–20.8) | 37.2 (34.6–40.1) | 0.24 (0.231–0.257) |
| CKD-EPI Cr | 11.6 (10.9–12.4) | 16.4 (14.2–18.6) | 34.5 (31.7–37.3) | 0.224 (0.213–0.236) |
| CKD-EPI Cys | 11.4 (10.6–12.4) | 16.9 (14.9–18.6) | 34.8 (32.1–37.6) | 0.228 (0.217–0.239) |
| CKD-EPI Cr-Cys | 9.3 (8.7–10.1) | 11.3 (9.5–13.2) | 25.5 (23.1–28.0) | 0.189 (0.180–0.199) |
Figure 3Simulated serum beta-2 microglobulin (β2M) as a function of the glomerular filtration rate. To generate the figure we simulated 10,000 individuals from the population model for β2M kinetics (117) at different levels of renal function. At each level of renal function, we computed the mean (red line), the median (blue line), and the associated population 95% range (gray band). Finally, we superimposed the Chronic Kidney Disease Epidemiology Collaboration β2M estimating equation (black line).