Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

Wenqian Lu; Mingjuan Luo; Xiangnan Fang; Rong Zhang; Shanshan Li; Mengyang Tang; Xiangtian Yu; Cheng Hu

doi:10.1186/s12986-021-00606-8

Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

Nutr Metab (Lond). 2021 Aug 21;18(1):79. doi: 10.1186/s12986-021-00606-8.

Authors

Wenqian Lu^#^{1

2}, Mingjuan Luo^#^{1

2

3}, Xiangnan Fang^#^{1

2

4}, Rong Zhang⁵, Shanshan Li⁵, Mengyang Tang^{1

2}, Xiangtian Yu⁶, Cheng Hu^{7

8

9}

Affiliations

¹ The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China.
² Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China.
³ Department of Endocrinology, University of Hong Kong Shenzhen Hospital, Shenzhen, China.
⁴ Department of Endocrinology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
⁵ Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
⁶ Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China. graceyu1985@163.com.
⁷ The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China. alfredhc@sjtu.edu.cn.
⁸ Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai, China. alfredhc@sjtu.edu.cn.
⁹ Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China. alfredhc@sjtu.edu.cn.

^# Contributed equally.

Abstract

Background: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.

Methods: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.

Results: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.

Conclusions: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Keywords: Gestational diabetes mellitus; Metabolic biomarkers; Pathogenesis; Pregnancy.

Abstract

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