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Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation
Bone histomorphometric analysis is the most accurate method for the evaluation of bone turnover, but non-invasive tools are also required. We studied whether bone biomarkers can predict high bone turnover determined by bone histomorphometry after kidney transplantation. We retrospectively evaluated...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860959/ https://www.ncbi.nlm.nih.gov/pubmed/34668028 http://dx.doi.org/10.1007/s00223-021-00917-1 |
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author | Keronen, Satu M. Martola, Leena A. L. Finne, Patrik Burton, Inari S. Tong, Xiaoyu F. Kröger, Heikki P. Honkanen, Eero O. |
author_facet | Keronen, Satu M. Martola, Leena A. L. Finne, Patrik Burton, Inari S. Tong, Xiaoyu F. Kröger, Heikki P. Honkanen, Eero O. |
author_sort | Keronen, Satu M. |
collection | PubMed |
description | Bone histomorphometric analysis is the most accurate method for the evaluation of bone turnover, but non-invasive tools are also required. We studied whether bone biomarkers can predict high bone turnover determined by bone histomorphometry after kidney transplantation. We retrospectively evaluated the results of bone biopsy specimens obtained from kidney transplant recipients due to the clinical suspicion of high bone turnover between 2000 and 2015. Bone biomarkers were acquired concurrently. Of 813 kidney transplant recipients, 154 (19%) biopsies were taken at a median of 28 (interquartile range, 18–70) months after engraftment. Of 114 patients included in the statistical analysis, 80 (70%) presented with high bone turnover. Normal or low bone turnover was detected in 34 patients (30%). For discriminating high bone turnover from non-high, alkaline phosphatase, parathyroid hormone, and ionized calcium had the areas under the receiver operating characteristic curve (AUCs) of 0.704, 0.661, and 0.619, respectively. The combination of these markers performed better with an AUC of 0.775. The positive predictive value for high turnover at a predicted probability cutoff of 90% was 95% while the negative predictive value was 35%. This study concurs with previous observations that hyperparathyroidism with or without hypercalcemia does not necessarily imply high bone turnover in kidney transplant recipients. The prediction of high bone turnover can be improved by considering alkaline phosphatase levels, as presented in the logistic regression model. If bone biopsy is not readily available, this model may serve as clinically available tool in recognizing high turnover after engraftment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00223-021-00917-1. |
format | Online Article Text |
id | pubmed-8860959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-88609592022-02-23 Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation Keronen, Satu M. Martola, Leena A. L. Finne, Patrik Burton, Inari S. Tong, Xiaoyu F. Kröger, Heikki P. Honkanen, Eero O. Calcif Tissue Int Original Research Bone histomorphometric analysis is the most accurate method for the evaluation of bone turnover, but non-invasive tools are also required. We studied whether bone biomarkers can predict high bone turnover determined by bone histomorphometry after kidney transplantation. We retrospectively evaluated the results of bone biopsy specimens obtained from kidney transplant recipients due to the clinical suspicion of high bone turnover between 2000 and 2015. Bone biomarkers were acquired concurrently. Of 813 kidney transplant recipients, 154 (19%) biopsies were taken at a median of 28 (interquartile range, 18–70) months after engraftment. Of 114 patients included in the statistical analysis, 80 (70%) presented with high bone turnover. Normal or low bone turnover was detected in 34 patients (30%). For discriminating high bone turnover from non-high, alkaline phosphatase, parathyroid hormone, and ionized calcium had the areas under the receiver operating characteristic curve (AUCs) of 0.704, 0.661, and 0.619, respectively. The combination of these markers performed better with an AUC of 0.775. The positive predictive value for high turnover at a predicted probability cutoff of 90% was 95% while the negative predictive value was 35%. This study concurs with previous observations that hyperparathyroidism with or without hypercalcemia does not necessarily imply high bone turnover in kidney transplant recipients. The prediction of high bone turnover can be improved by considering alkaline phosphatase levels, as presented in the logistic regression model. If bone biopsy is not readily available, this model may serve as clinically available tool in recognizing high turnover after engraftment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00223-021-00917-1. Springer US 2021-10-19 2022 /pmc/articles/PMC8860959/ /pubmed/34668028 http://dx.doi.org/10.1007/s00223-021-00917-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Keronen, Satu M. Martola, Leena A. L. Finne, Patrik Burton, Inari S. Tong, Xiaoyu F. Kröger, Heikki P. Honkanen, Eero O. Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation |
title | Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation |
title_full | Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation |
title_fullStr | Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation |
title_full_unstemmed | Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation |
title_short | Clinical Prediction of High-Turnover Bone Disease After Kidney Transplantation |
title_sort | clinical prediction of high-turnover bone disease after kidney transplantation |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860959/ https://www.ncbi.nlm.nih.gov/pubmed/34668028 http://dx.doi.org/10.1007/s00223-021-00917-1 |
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