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A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations
PURPOSE: To investigate the utility of biomarkers of maturity-onset diabetes of the young (MODY), high-sensitivity C-reactive protein (hsCRP), and 1,5-anhydroglucitol (1,5-AG) in conjunction with other clinical and laboratory features to improve diagnostic accuracy and provide a diagnostic algorithm...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6453873/ https://www.ncbi.nlm.nih.gov/pubmed/30778899 http://dx.doi.org/10.1007/s12020-019-01863-7 |
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author | Szopa, Magdalena Klupa, Tomasz Kapusta, Maria Matejko, Bartlomiej Ucieklak, Damian Glodzik, Wojciech Zapala, Barbara Sani, Cyrus Maurice Hohendorff, Jerzy Malecki, Maciej T. Skupien, Jan |
author_facet | Szopa, Magdalena Klupa, Tomasz Kapusta, Maria Matejko, Bartlomiej Ucieklak, Damian Glodzik, Wojciech Zapala, Barbara Sani, Cyrus Maurice Hohendorff, Jerzy Malecki, Maciej T. Skupien, Jan |
author_sort | Szopa, Magdalena |
collection | PubMed |
description | PURPOSE: To investigate the utility of biomarkers of maturity-onset diabetes of the young (MODY), high-sensitivity C-reactive protein (hsCRP), and 1,5-anhydroglucitol (1,5-AG) in conjunction with other clinical and laboratory features to improve diagnostic accuracy and provide a diagnostic algorithm for HNF1A MODY. METHODS: We examined 77 patients with HNF1A MODY, 88 with GCK MODY mutations, 99 with type 1 diabetes, and 92 with type 2 diabetes. In addition to 1,5-AG and hsCRP, we considered body mass index (BMI), fasting glucose, and fasting serum C-peptide as potential biomarkers. Logistic regression and receiver operating characteristic curves were used in marker evaluation. RESULTS: Concentration of hsCRP was lowest in HNF1A MODY (0.51 mg/l) and highest in type 2 diabetes (1.33 mg/l). The level of 1,5-AG was lowest in type 1 diabetes and HNF1A MODY, 3.8 and 4.7 μg/ml, respectively, and highest (11.2 μg/ml) in GCK MODY. In the diagnostic algorithm, we first excluded patients with type 1 diabetes based on low C-peptide (C-statistic 0.98) before using high BMI and C-peptide to identify type 2 diabetes patients (C-statistic 0.92). Finally, 1,5-AG and hsCRP in conjunction yielded a C-statistic of 0.86 in discriminating HNF1A from GCK MODY. We correctly classified 92.9% of patients with type 1 diabetes, 84.8% with type 2 diabetes, 64.9% HNF1A MODY, and 52.3% GCK MODY patients. CONCLUSIONS: Plasma 1,5-AG and serum hsCRP do not discriminate sufficiently HNF1A MODY from common diabetes types, but could be potentially useful in prioritizing Sanger sequencing of HNF1A gene. |
format | Online Article Text |
id | pubmed-6453873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-64538732019-04-26 A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations Szopa, Magdalena Klupa, Tomasz Kapusta, Maria Matejko, Bartlomiej Ucieklak, Damian Glodzik, Wojciech Zapala, Barbara Sani, Cyrus Maurice Hohendorff, Jerzy Malecki, Maciej T. Skupien, Jan Endocrine Original Article PURPOSE: To investigate the utility of biomarkers of maturity-onset diabetes of the young (MODY), high-sensitivity C-reactive protein (hsCRP), and 1,5-anhydroglucitol (1,5-AG) in conjunction with other clinical and laboratory features to improve diagnostic accuracy and provide a diagnostic algorithm for HNF1A MODY. METHODS: We examined 77 patients with HNF1A MODY, 88 with GCK MODY mutations, 99 with type 1 diabetes, and 92 with type 2 diabetes. In addition to 1,5-AG and hsCRP, we considered body mass index (BMI), fasting glucose, and fasting serum C-peptide as potential biomarkers. Logistic regression and receiver operating characteristic curves were used in marker evaluation. RESULTS: Concentration of hsCRP was lowest in HNF1A MODY (0.51 mg/l) and highest in type 2 diabetes (1.33 mg/l). The level of 1,5-AG was lowest in type 1 diabetes and HNF1A MODY, 3.8 and 4.7 μg/ml, respectively, and highest (11.2 μg/ml) in GCK MODY. In the diagnostic algorithm, we first excluded patients with type 1 diabetes based on low C-peptide (C-statistic 0.98) before using high BMI and C-peptide to identify type 2 diabetes patients (C-statistic 0.92). Finally, 1,5-AG and hsCRP in conjunction yielded a C-statistic of 0.86 in discriminating HNF1A from GCK MODY. We correctly classified 92.9% of patients with type 1 diabetes, 84.8% with type 2 diabetes, 64.9% HNF1A MODY, and 52.3% GCK MODY patients. CONCLUSIONS: Plasma 1,5-AG and serum hsCRP do not discriminate sufficiently HNF1A MODY from common diabetes types, but could be potentially useful in prioritizing Sanger sequencing of HNF1A gene. Springer US 2019-02-18 2019 /pmc/articles/PMC6453873/ /pubmed/30778899 http://dx.doi.org/10.1007/s12020-019-01863-7 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, 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 license, and indicate if changes were made. |
spellingShingle | Original Article Szopa, Magdalena Klupa, Tomasz Kapusta, Maria Matejko, Bartlomiej Ucieklak, Damian Glodzik, Wojciech Zapala, Barbara Sani, Cyrus Maurice Hohendorff, Jerzy Malecki, Maciej T. Skupien, Jan A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations |
title | A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations |
title_full | A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations |
title_fullStr | A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations |
title_full_unstemmed | A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations |
title_short | A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations |
title_sort | decision algorithm to identify patients with high probability of monogenic diabetes due to hnf1a mutations |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6453873/ https://www.ncbi.nlm.nih.gov/pubmed/30778899 http://dx.doi.org/10.1007/s12020-019-01863-7 |
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