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Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia
INTRODUCTION: Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926775/ https://www.ncbi.nlm.nih.gov/pubmed/36788507 http://dx.doi.org/10.1186/s12884-023-05440-9 |
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author | Yuan, Yingdi Zhu, Qingyi Yao, Xiaodie Shi, Zhonghua Wen, Juan |
author_facet | Yuan, Yingdi Zhu, Qingyi Yao, Xiaodie Shi, Zhonghua Wen, Juan |
author_sort | Yuan, Yingdi |
collection | PubMed |
description | INTRODUCTION: Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M. METHODS: Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model. RESULTS: We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR < 0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P (Hosmer-Lemeshow test) = 0.897). CONCLUSION: The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05440-9. |
format | Online Article Text |
id | pubmed-9926775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99267752023-02-15 Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia Yuan, Yingdi Zhu, Qingyi Yao, Xiaodie Shi, Zhonghua Wen, Juan BMC Pregnancy Childbirth Research INTRODUCTION: Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M. METHODS: Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model. RESULTS: We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR < 0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P (Hosmer-Lemeshow test) = 0.897). CONCLUSION: The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05440-9. BioMed Central 2023-02-14 /pmc/articles/PMC9926775/ /pubmed/36788507 http://dx.doi.org/10.1186/s12884-023-05440-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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. |
spellingShingle | Research Yuan, Yingdi Zhu, Qingyi Yao, Xiaodie Shi, Zhonghua Wen, Juan Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia |
title | Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia |
title_full | Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia |
title_fullStr | Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia |
title_full_unstemmed | Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia |
title_short | Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia |
title_sort | maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926775/ https://www.ncbi.nlm.nih.gov/pubmed/36788507 http://dx.doi.org/10.1186/s12884-023-05440-9 |
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