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Wu H, Wang Q, Chen Y, Chen D. The association between circulating phenylalanine and the temporal risk of impaired insulin markers in gestational diabetes mellitus. Mol Genet Metab Rep 2024; 40:101090. [PMID: 38974841 PMCID: PMC11227027 DOI: 10.1016/j.ymgmr.2024.101090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 07/09/2024] Open
Abstract
Background We aimed to contrast plasma amino acid concentrations in pregnant women with Gestational Diabetes Mellitus (GDM) to those without, to analyze the link between plasma amino acid concentrations, GDM, insulin resistance, and insulin secretion at 24-28 weeks of gestation. Methods The research employed a retrospective case-control study design at a single center. Basic demographic and laboratory data were procured from the hospital's case system. The study encompassed seventy women without gestational diabetes mellitus (GDM) and thirty-five women with GDM matched in a 1-to-2 ratio for age and pre-pregnancy BMI. Utilizing high-performance liquid chromatography-mass spectrometry (HPLC-MS), peripheral fasting plasma amino acid concentrations in these women, during mid-pregnancy, were duly measured. We carefully evaluated the significant differences in the quantitative data between the two groups and developed linear regression models to assess the independent risk factors affecting insulin resistance and insulin secretion. Results Significant variations in insulin secretion and resistance levels distinguished GDM Group from the non-GDM group at three distinct time points, alongside relatively elevated serum Glycosylated Hemoglobin (HbA1c) levels. Triglycerides (TG) were also significantly increased in those with GDM during adipocytokine observations. Apart from glutamic acid and glutamine, the concentrations of the remaining 16 amino acids were notably increased in GDM patients, including all branched chain amino acids(BCAAs) and aromatic amino acids(AAAs). Ultimately, it was ascertained that fasting serum phenylalanine levels were independent risk factors affecting insulin resistance index and insulin secretion at various phases. Conclusions Various fasting serum amino acid levels are markedly increased in patients with GDM, specifically phenylalanine, which may play role in insulin resistance and secretion.
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Affiliation(s)
- Hao Wu
- Department of Obstetrics Central Laboratory, Women's Hospital School of Medicine Zhejiang University, China
| | - Qiong Wang
- Department of Maternity Inpatient, Women's Hospital School of Medicine Zhejiang University, China
| | - Yanmin Chen
- Department of Maternity Inpatient, Women's Hospital School of Medicine Zhejiang University, China
| | - Danqing Chen
- Department of Maternity Inpatient, Women's Hospital School of Medicine Zhejiang University, China
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Li X, Liu Y, Qi Y, Wu Y, Wang M, Gao J, Su Q, Ma J, Qin L. Maternal Serum Polyols and Its Link to Gestational Diabetes Mellitus: A Population-Based Nested Case-Control Study. J Clin Endocrinol Metab 2024; 109:1858-1865. [PMID: 38189482 PMCID: PMC11180503 DOI: 10.1210/clinem/dgae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/28/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
CONTEXT Sugar alcohols (also called polyols) are regarded as a "healthy" sugar substitute. One of the possible reasons for their safe use in pregnant women is their natural origin and the presence of polyols in maternal and fetal samples during normal human gestation. But little is known about the association between circulating sugar alcohols levels and maternal metabolic disorders during pregnancy. OBJECTIVE We aimed to detect the concentration of the polyols in participants with and without gestational diabetes mellitus (GDM), and to investigate the association between maternal serum levels of polyols and GDM, as well as newborn outcomes. METHODS A nested population-based case-control study was conducted in 109 women with and without GDM. Maternal concentrations of serum erythritol, sorbitol, and xylitol in the fasting state were quantified using a time of flight mass spectrometry system. RESULT In women with GDM, serum concentrations of erythritol and sorbitol were higher, but serum concentrations of xylitol were lower than those in women without GDM. Per 1-SD increment of Box-Cox-transformed concentrations of erythritol and sorbitol were associated with the increased odds of GDM by 43% and 155% (95% CI 1.07-1.92 and 95% CI 1.77-3.69), while decreased odds were found for xylitol by 25% (95% CI 0.57-1.00). Additionally, per 1-SD increase of Box-Cox-transformed concentrations of serum sorbitol was associated with a 52% increased odds of large for gestational age newborns controlling for possible confounders (95% CI 1.00-2.30). CONCLUSION Maternal circulating sugar alcohols levels during pregnancy were significantly associated with GDM. These findings provide the potential roles of polyols on maternal metabolic health during pregnancy.
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Affiliation(s)
- Xiaoyong Li
- Department of Endocrinology and Metabolism, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Yu Liu
- Department of Endocrinology and Metabolism, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yicheng Qi
- Department of Endocrinology and Metabolism, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yiming Wu
- Department of Endocrinology, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Chongming Branch of Xinhua Hospital), Shanghai 202150, China
| | - Meng Wang
- Department of Endocrinology and Metabolism, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Jing Gao
- Department of Traditional Chinese Medicine, Pujiang Community Health Service Center, Minhang District, Shanghai 201112, China
| | - Qing Su
- Department of Endocrinology and Metabolism, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Jing Ma
- Department of Endocrinology and Metabolism, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Li Qin
- Department of Endocrinology and Metabolism, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Department of Endocrinology, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Chongming Branch of Xinhua Hospital), Shanghai 202150, China
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Alvernaz SA, Wenzel ES, Nagelli U, Pezley LB, LaBomascus B, Gilbert JA, Maki PM, Tussing-Humphreys L, Peñalver Bernabé B. Inflammatory Dietary Potential Is Associated with Vitamin Depletion and Gut Microbial Dysbiosis in Early Pregnancy. Nutrients 2024; 16:935. [PMID: 38612969 PMCID: PMC11013194 DOI: 10.3390/nu16070935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 04/14/2024] Open
Abstract
Pregnancy alters many physiological systems, including the maternal gut microbiota. Diet is a key regulator of this system and can alter the host immune system to promote inflammation. Multiple perinatal disorders have been associated with inflammation, maternal metabolic alterations, and gut microbial dysbiosis, including gestational diabetes mellitus, pre-eclampsia, preterm birth, and mood disorders. However, the effects of high-inflammatory diets on the gut microbiota during pregnancy have yet to be fully explored. We aimed to address this gap using a system-based approach to characterize associations among dietary inflammatory potential, a measure of diet quality, and the gut microbiome during pregnancy. Forty-seven pregnant persons were recruited prior to 16 weeks of gestation. Participants completed a food frequency questionnaire (FFQ) and provided fecal samples. Dietary inflammatory potential was assessed using the Dietary Inflammatory Index (DII) from the FFQ data. Fecal samples were analyzed using 16S rRNA amplicon sequencing. Differential taxon abundances with respect to the DII score were identified, and the microbial metabolic potential was predicted using PICRUSt2. Inflammatory diets were associated with decreased vitamin and mineral intake and a dysbiotic gut microbiota structure and predicted metabolism. Gut microbial compositional differences revealed a decrease in short-chain fatty acid producers such as Faecalibacterium, and an increase in predicted vitamin B12 synthesis, methylglyoxal detoxification, galactose metabolism, and multidrug efflux systems in pregnant individuals with increased DII scores. Dietary inflammatory potential was associated with a reduction in the consumption of vitamins and minerals and predicted gut microbiota metabolic dysregulation.
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Affiliation(s)
- Suzanne A. Alvernaz
- Department of Biomedical Engineering, University of Illinois, Chicago, IL 60607, USA; (S.A.A.); (U.N.)
| | - Elizabeth S. Wenzel
- Department of Psychology, University of Illinois, Chicago, IL 60607, USA; (E.S.W.); (P.M.M.)
| | - Unnathi Nagelli
- Department of Biomedical Engineering, University of Illinois, Chicago, IL 60607, USA; (S.A.A.); (U.N.)
| | - Lacey B. Pezley
- Department of Kinesiology and Nutrition, University of Illinois, Chicago, IL 60612, USA; (L.B.P.); (B.L.); (L.T.-H.)
| | - Bazil LaBomascus
- Department of Kinesiology and Nutrition, University of Illinois, Chicago, IL 60612, USA; (L.B.P.); (B.L.); (L.T.-H.)
| | - Jack A. Gilbert
- Department of Pediatrics, University of California, San Diego, CA 92093, USA;
- Scripps Oceanographic Institute, University of California, San Diego, CA 92037, USA
| | - Pauline M. Maki
- Department of Psychology, University of Illinois, Chicago, IL 60607, USA; (E.S.W.); (P.M.M.)
- Department of Psychiatry, University of Illinois, Chicago, IL 60612, USA
- Department of Obstetrics and Gynecology, University of Illinois, Chicago, IL 60612, USA
| | - Lisa Tussing-Humphreys
- Department of Kinesiology and Nutrition, University of Illinois, Chicago, IL 60612, USA; (L.B.P.); (B.L.); (L.T.-H.)
| | - Beatriz Peñalver Bernabé
- Department of Biomedical Engineering, University of Illinois, Chicago, IL 60607, USA; (S.A.A.); (U.N.)
- Center for Bioinformatics and Quantitative Biology, University of Illinois, Chicago, IL 60612, USA
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Yu J, Ren J, Ren Y, Wu Y, Zeng Y, Zhang Q, Xiao X. Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes. EBioMedicine 2024; 101:105008. [PMID: 38368766 PMCID: PMC10882130 DOI: 10.1016/j.ebiom.2024.105008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/20/2024] Open
Abstract
Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.
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Affiliation(s)
- Jie Yu
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jing Ren
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yaolin Ren
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yifan Wu
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yuan Zeng
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Qian Zhang
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Xinhua Xiao
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
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Jiang Z, Ye X, Cao D, Xiang Y, Li Z. Association of Placental Tissue Metabolite Levels with Gestational Diabetes Mellitus: a Metabolomics Study. Reprod Sci 2024; 31:569-578. [PMID: 37794198 DOI: 10.1007/s43032-023-01353-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/09/2023] [Indexed: 10/06/2023]
Abstract
The purpose of the study is to investigate the metabolic characteristics of placental tissue in patients diagnosed with gestational diabetes mellitus (GDM). Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) was employed to qualitatively and quantitatively analyze the metabolites in placental tissues obtained from 25 healthy pregnant women and 25 pregnant women diagnosed with GDM. Multilevel statistical methods are applied to process intricate metabolomics data. Meanwhile, we applied machine learning techniques to identify biomarkers that could potentially predict the risk of long-term complications in patients with GDM as well as their offspring. We identified 1902 annotated metabolites, out of which 212 metabolites exhibited significant differences in GDM placentas. In addition, the study identifies a set of risk biomarkers that effectively predict the likelihood of long-term complications in both pregnant women with GDM and their offspring. The accuracy of this panel was measured by the area under the receiver operating characteristic curve (ROC), which was found to be 0.992 and 0.960 in the training and validation sets, respectively. This study enhances our understanding of GDM pathogenesis through metabolomics. Furthermore, the panel of risk markers identified could prove to be a valuable tool in predicting potential long-term complications for both GDM patients and their offspring.
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Affiliation(s)
- Zhifa Jiang
- Department of Obstetrics and Gynecology, Huizhou First Maternal and Child Health Care Hospital, Guangdong, Huizhou, China
- Guangdong Medical University, Guangdong, Zhanjiang, China
| | - Xiangyun Ye
- Guangdong Medical University, Guangdong, Zhanjiang, China
| | - Dandan Cao
- Guangdong Medical University, Guangdong, Zhanjiang, China
| | - Yuting Xiang
- Department of Obstetrics and Gynecology, Affiliated Dongguan Hospital, Southern Medical University of Major Diseases in Obstetrics and Gynecology, Dongguan, China
| | - Zhongjun Li
- Guangdong Medical University, Guangdong, Zhanjiang, China.
- Department of Obstetrics and Gynecology, Affiliated Dongguan Hospital, Southern Medical University of Major Diseases in Obstetrics and Gynecology, Dongguan, China.
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Ma L, Liu J, Deng M, Zhou L, Zhang Q, Xiao X. Metabolomics analysis of serum and urine in type 1 diabetes patients with different time in range derived from continuous glucose monitoring. Diabetol Metab Syndr 2024; 16:21. [PMID: 38238828 PMCID: PMC10797982 DOI: 10.1186/s13098-024-01257-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Time in range (TIR), as an important glycemic variability (GV) index, is clearly associated with disease complications in type 1 diabetes (T1D). Metabolic dysregulation is also involved in the risks of T1D complications. However, the relationship between metabolites and TIR remains poorly understood. We used metabolomics to investigate metabolic profile changes in T1D patients with different TIR. METHODS This study included 85 T1D patients and 81 healthy controls. GV indices, including TIR, were collected from continuous glucose monitoring system. The patients were compared within two subgroups: TIR-L (TIR < 50%, n = 21) and TIR-H (TIR > 70%, n = 14). To screen for differentially abundant metabolites and metabolic pathways, serum and urine samples were obtained for untargeted metabolomics by ultra-performance liquid chromatography‒mass spectrometry. Correlation analysis was conducted with GV metrics and screened biomarkers. RESULTS Metabolites were significantly altered in T1D and subgroups. Compared with healthy controls, T1D patients had higher serum levels of 5-hydroxy-L-tryptophan, 5-methoxyindoleacetate, 4-(2-aminophenyl)-2,4-dioxobutanoate, and 4-pyridoxic acid and higher urine levels of thromboxane B3 but lower urine levels of hypoxanthine. Compared with TIR-H group, The TIR-L subgroup had lower serum levels of 5-hydroxy-L-tryptophan and mevalonolactone and lower urine levels of thromboxane B3 and phenylbutyrylglutamine. Dysregulation of pathways, such as tryptophan, vitamin B6 and purine metabolism, may be involved in the mechanism of diabetic complications related to glycemic homeostasis. Mevalonolactone, hypoxanthine and phenylbutyrylglutamine showed close correlation with TIR. CONCLUSIONS We identified altered metabolic profiles in T1D individuals with different TIR. These findings provide new insights and merit further exploration of the underlying molecular pathways relating to diabetic complications.
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Affiliation(s)
- Liyuan Ma
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Jieying Liu
- Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Mingqun Deng
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Liyuan Zhou
- Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China
| | - Qian Zhang
- Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Xinhua Xiao
- Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
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7
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Alvernaz SA, Wenzel ES, Nagelli U, Pezley LB, LaBomascus B, Gilbert JA, Maki PM, Tussing-Humphreys L, Peñalver Bernabé B. Inflammatory dietary potential is associated with vitamin depletion and gut microbial dysbiosis in early pregnancy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.02.23299325. [PMID: 38076865 PMCID: PMC10705629 DOI: 10.1101/2023.12.02.23299325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Background Pregnancy alters many physiological systems, including the maternal gut microbiota. Diet is a key regulator of this system and can alter the host immune system to promote inflammation. Multiple perinatal disorders have been associated with inflammation, maternal metabolic alterations, and gut microbial dysbiosis, including gestational diabetes mellitus, preeclampsia, preterm birth, and mood disorders. However, the effects of high inflammatory diets on the gut microbiota during pregnancy have yet to be fully explored. Objective To use a systems-based approach to characterize associations among dietary inflammatory potential, a measure of diet quality, and the gut microbiome during pregnancy. Methods Forty-nine pregnant persons were recruited prior to 16 weeks of gestation. Participants completed a food frequency questionnaire (FFQ) and provided fecal samples. Dietary inflammatory potential was assessed using the Dietary Inflammatory Index (DII) from FFQ data. Fecal samples were analyzed using 16S rRNA amplicon sequencing. Differential taxon abundance with respect to DII score were identified, and microbial metabolic potential was predicted using PICRUSt2. Results Inflammatory diets were associated with decreased vitamin and mineral intake and dysbiotic gut microbiota structure and predicted metabolism. Gut microbial compositional differences revealed a decrease in short chain fatty acid producers such as Faecalibacterium, and an increase in predicted vitamin B12 synthesis, methylglyoxal detoxification, galactose metabolism and multi drug efflux systems in pregnant individuals with increased DII scores. Conclusions Dietary inflammatory potential was associated with a reduction in the consumption of vitamins & minerals and predicted gut microbiota metabolic dysregulation.
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Affiliation(s)
- Suzanne A. Alvernaz
- Department of Biomedical Engineering, University of Illinois, Chicago, IL, USA
| | | | - Unnathi Nagelli
- Department of Biomedical Engineering, University of Illinois, Chicago, IL, USA
| | - Lacey B. Pezley
- Department of Kinesiology and Nutrition, University of Illinois, Chicago, IL, USA
| | - Bazil LaBomascus
- Department of Kinesiology and Nutrition, University of Illinois, Chicago, IL, USA
| | - Jack A. Gilbert
- Department of Pediatrics, University of California, San Diego, CA, USA
- Scripps Oceanographic Institute, University of California, San Diego, CA, USA
| | - Pauline M. Maki
- Department of Psychology, University of Illinois, Chicago, IL, USA
- Department of Psychiatry, University of Illinois, Chicago, IL, USA
- Department of Obstetrics and Gynecology, University of Illinois, Chicago, IL, USA
| | | | - Beatriz Peñalver Bernabé
- Department of Biomedical Engineering, University of Illinois, Chicago, IL, USA
- Center for Bioinformatics and Quantitative Biology, University of Illinois, Chicago, IL, USA
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Zhang Z, Zhou Z, Li H. The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis. J Diabetes Investig 2024; 15:15-25. [PMID: 38095269 PMCID: PMC10759727 DOI: 10.1111/jdi.14119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024] Open
Abstract
Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta-cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non-alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery-based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post-delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.
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Affiliation(s)
- Ziyi Zhang
- Department of Endocrinology, Sir Run Run Shaw HospitalZhejiang University, School of MedicineHangzhouChina
| | - Zheng Zhou
- Zhejiang University, School of MedicineHangzhouChina
| | - Hong Li
- Department of Endocrinology, Sir Run Run Shaw HospitalZhejiang University, School of MedicineHangzhouChina
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9
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Merrill AK, Sobolewski M, Susiarjo M. Exposure to endocrine disrupting chemicals impacts immunological and metabolic status of women during pregnancy. Mol Cell Endocrinol 2023; 577:112031. [PMID: 37506868 PMCID: PMC10592265 DOI: 10.1016/j.mce.2023.112031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/12/2023] [Accepted: 07/24/2023] [Indexed: 07/30/2023]
Affiliation(s)
- Alyssa K Merrill
- Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, NY, USA
| | - Marissa Sobolewski
- Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, NY, USA
| | - Martha Susiarjo
- Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, NY, USA.
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10
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Gao J, Yang T, Song B, Ma X, Ma Y, Lin X, Wang H. Abnormal tryptophan catabolism in diabetes mellitus and its complications: Opportunities and challenges. Biomed Pharmacother 2023; 166:115395. [PMID: 37657259 DOI: 10.1016/j.biopha.2023.115395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/20/2023] [Accepted: 08/26/2023] [Indexed: 09/03/2023] Open
Abstract
In recent years, the incidence rate of diabetes mellitus (DM), including type 1 diabetes mellitus(T1DM), type 2 diabetes mellitus(T2DM), and gestational diabetes mellitus (GDM), has increased year by year and has become a major global health problem. DM can lead to serious complications of macrovascular and microvascular. Tryptophan (Trp) is an essential amino acid for the human body. Trp is metabolized in the body through the indole pathway, kynurenine (Kyn) pathway and serotonin (5-HT) pathway, and is regulated by intestinal microorganisms to varying degrees. These three metabolic pathways have extensive regulatory effects on the immune, endocrine, neural, and energy metabolism systems of the body, and are related to the physiological and pathological processes of various diseases. The key enzymes and metabolites in the Trp metabolic pathway are also deeply involved in the pathogenesis of DM, playing an important role in pancreatic function, insulin resistance (IR), intestinal barrier, and angiogenesis. In DM and its complications, there is a disruption of Trp metabolic balance. Several therapy approaches for DM and complications have been proven to modify tryptophan metabolism. The metabolism of Trp is becoming a new area of focus for DM prevention and care. This paper reviews the impact of the three metabolic pathways of Trp on the pathogenesis of DM and the alterations in Trp metabolism in these diseases, expecting to provide entry points for the treatment of DM and its complications.
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Affiliation(s)
- Jialiang Gao
- Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Ting Yang
- Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Bohan Song
- Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xiaojie Ma
- Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yichen Ma
- Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xiaowei Lin
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
| | - Hongwu Wang
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
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Thomas G, Syngelaki A, Hamed K, Perez-Montaño A, Panigassi A, Tuytten R, Nicolaides KH. Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models. Am J Obstet Gynecol MFM 2023; 5:101110. [PMID: 37752025 DOI: 10.1016/j.ajogmf.2023.101110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 09/28/2023]
Abstract
BACKGROUND Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia. OBJECTIVE This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index. STUDY DESIGN This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate. RESULTS First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes. CONCLUSION Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.
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Affiliation(s)
- Grégoire Thomas
- SQU4RE, Lokeren, Belgium (Dr Thomas); Metabolomic Diagnostics, Cork, Ireland (Drs Thomas, Panigassi, and Tuytten)
| | - Argyro Syngelaki
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
| | - Karam Hamed
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
| | - Anais Perez-Montaño
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
| | - Ana Panigassi
- Metabolomic Diagnostics, Cork, Ireland (Drs Thomas, Panigassi, and Tuytten)
| | - Robin Tuytten
- Metabolomic Diagnostics, Cork, Ireland (Drs Thomas, Panigassi, and Tuytten).
| | - Kypros H Nicolaides
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
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12
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Kang BS, Lee SU, Hong S, Choi SK, Shin JE, Wie JH, Jo YS, Kim YH, Kil K, Chung YH, Jung K, Hong H, Park IY, Ko HS. Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms. Sci Rep 2023; 13:13356. [PMID: 37587201 PMCID: PMC10432552 DOI: 10.1038/s41598-023-39680-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/28/2023] [Indexed: 08/18/2023] Open
Abstract
This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks' gestation), E1 (11-13 weeks' gestation) and M1 (14-24 weeks' gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.
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Affiliation(s)
- Byung Soo Kang
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seon Ui Lee
- Department of Obstetrics and Gynecology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Subeen Hong
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sae Kyung Choi
- Department of Obstetrics and Gynecology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jae Eun Shin
- Department of Obstetrics and Gynecology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jeong Ha Wie
- Department of Obstetrics and Gynecology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yun Sung Jo
- Department of Obstetrics and Gynecology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yeon Hee Kim
- Department of Obstetrics and Gynecology, Uijeongbu St. Mary's Hospital,, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kicheol Kil
- Department of Obstetrics and Gynecology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yoo Hyun Chung
- Department of Obstetrics and Gynecology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | | | - In Yang Park
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun Sun Ko
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
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Roverso M, Dogra R, Visentin S, Pettenuzzo S, Cappellin L, Pastore P, Bogialli S. Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers. MASS SPECTROMETRY REVIEWS 2023; 42:1424-1461. [PMID: 35474466 DOI: 10.1002/mas.21777] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/15/2021] [Accepted: 04/04/2022] [Indexed: 06/07/2023]
Abstract
Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.
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Affiliation(s)
- Marco Roverso
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Raghav Dogra
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Silvia Visentin
- Department of Women's and Children's Health, University of Padova, Padova, Italy
| | - Silvia Pettenuzzo
- Department of Chemical Sciences, University of Padova, Padova, Italy
- Center Agriculture Food Environment (C3A), University of Trento, San Michele all'Adige, Italy
| | - Luca Cappellin
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Paolo Pastore
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Sara Bogialli
- Department of Chemical Sciences, University of Padova, Padova, Italy
- Institute of Condensed Matter Chemistry and Technologies for Energy (ICMATE), National Research Council-CNR, Padova, Italy
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14
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Razo-Azamar M, Nambo-Venegas R, Meraz-Cruz N, Guevara-Cruz M, Ibarra-González I, Vela-Amieva M, Delgadillo-Velázquez J, Santiago XC, Escobar RF, Vadillo-Ortega F, Palacios-González B. An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers. Diabetol Metab Syndr 2023; 15:116. [PMID: 37264408 DOI: 10.1186/s13098-023-01098-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM. METHODS A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM. RESULTS Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873-0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015-0.25 μmol/L and of C5:1 with a range of 0.015-0.19 μmol/L. CONCLUSION Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.
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Affiliation(s)
- Melissa Razo-Azamar
- Unidad de Vinculación Científica, Facultad de Medicina UNAM en Instituto Nacional de Medicina Genómica (INMEGEN), Periférico Sur 4809, Tlalpan, Arenal Tepepan, 14610, Mexico City, México
- Laboratorio de Envejecimiento Saludable del INMEGEN en el Centro de Investigación sobre Envejecimiento (CIE-CINVESTAV Sede Sur), 14330, Mexico City, México
| | - Rafael Nambo-Venegas
- Laboratorio de Bioquímica de Enfermedades Crónicas Instituto Nacional de Medicina Genómica (INMEGEN), 14610, Mexico City, Mexico
| | - Noemí Meraz-Cruz
- Unidad de Vinculación Científica, Facultad de Medicina UNAM en Instituto Nacional de Medicina Genómica (INMEGEN), Periférico Sur 4809, Tlalpan, Arenal Tepepan, 14610, Mexico City, México
| | - Martha Guevara-Cruz
- Departamento de Fisiología de la Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", 14080, Mexico City, Mexico
| | | | - Marcela Vela-Amieva
- Laboratorio de Errores Innatos del Metabolismo, Instituto Nacional de Pediatría (INP), 04530, Mexico City, México
| | - Jaime Delgadillo-Velázquez
- Unidad de Vinculación Científica, Facultad de Medicina UNAM en Instituto Nacional de Medicina Genómica (INMEGEN), Periférico Sur 4809, Tlalpan, Arenal Tepepan, 14610, Mexico City, México
| | - Xanic Caraza Santiago
- Centro de Salud T-III Dr. Gabriel Garzón Cossa, Jurisdicción Sanitaria Gustavo A. Madero, SSA de la Ciudad de México, Mexico City, México
| | - Rafael Figueroa Escobar
- Centro de Salud T-III Dr. Gabriel Garzón Cossa, Jurisdicción Sanitaria Gustavo A. Madero, SSA de la Ciudad de México, Mexico City, México
| | - Felipe Vadillo-Ortega
- Unidad de Vinculación Científica, Facultad de Medicina UNAM en Instituto Nacional de Medicina Genómica (INMEGEN), Periférico Sur 4809, Tlalpan, Arenal Tepepan, 14610, Mexico City, México
| | - Berenice Palacios-González
- Unidad de Vinculación Científica, Facultad de Medicina UNAM en Instituto Nacional de Medicina Genómica (INMEGEN), Periférico Sur 4809, Tlalpan, Arenal Tepepan, 14610, Mexico City, México.
- Laboratorio de Envejecimiento Saludable del INMEGEN en el Centro de Investigación sobre Envejecimiento (CIE-CINVESTAV Sede Sur), 14330, Mexico City, México.
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15
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Heath H, Rosario R, McMichael LE, Fanter R, Alarcon N, Quintana-Diaz A, Pilolla K, Schaffner A, Jelalian E, Wing RR, Brito A, Phelan S, La Frano MR. Gestational Diabetes Is Characterized by Decreased Medium-Chain Acylcarnitines and Elevated Purine Degradation Metabolites across Pregnancy: A Case-Control Time-Course Analysis. J Proteome Res 2023. [PMID: 37129248 DOI: 10.1021/acs.jproteome.2c00430] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Gestational Diabetes Mellitus (GDM) results in complications affecting both mothers and their offspring. Metabolomic analysis across pregnancy provides an opportunity to better understand GDM pathophysiology. The objective was to conduct a metabolomics analysis of first and third trimester plasma samples to identify metabolic differences associated with GDM development. Forty pregnant women with overweight/obesity from a multisite clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 20; GDM group) were matched with those who did not develop GDM (n = 20; Non-GDM group). Plasma samples collected at the first (10-16 weeks) and third (28-35 weeks) trimesters were analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Cardiometabolic risk markers, dietary recalls, and physical activity metrics were also assessed. Four medium-chain acylcarnitines, lauroyl-, octanoyl-, decanoyl-, and decenoylcarnitine, significantly differed over the course of pregnancy in the GDM vs Non-GDM group in a group-by-time interaction (p < 0.05). Hypoxanthine and inosine monophosphate were elevated in the GDM group (p < 0.04). In both groups over time, bile acids and sorbitol increased while numerous acylcarnitines and α-hydroxybutyrate decreased (p < 0.05). Metabolites involved in fatty acid oxidation and purine degradation were altered across the first and third trimesters of GDM-affected pregnancies, providing insight into metabolites and metabolic pathways altered with GDM development.
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Affiliation(s)
- Hannah Heath
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Rodrigo Rosario
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Lauren E McMichael
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Rob Fanter
- College of Agriculture, Food and Environmental Sciences, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Cal Poly Metabolomics Service Center, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Noemi Alarcon
- Department of Kinesiology and Public Health, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Center for Health Research, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Adilene Quintana-Diaz
- Department of Kinesiology and Public Health, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Center for Health Research, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Kari Pilolla
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Center for Health Research, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Andrew Schaffner
- Center for Health Research, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Department of Statistics, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Elissa Jelalian
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, Rhode Island 02903, United States
| | - Rena R Wing
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, Rhode Island 02903, United States
| | - Alex Brito
- Laboratory of Pharmacokinetics and Metabolomic Analysis. Institute of Translational Medicine and Biotechnology. I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Suzanne Phelan
- Department of Kinesiology and Public Health, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Center for Health Research, California Polytechnic State University, San Luis Obispo, California 93407, United States
| | - Michael R La Frano
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Cal Poly Metabolomics Service Center, California Polytechnic State University, San Luis Obispo, California 93407, United States
- Center for Health Research, California Polytechnic State University, San Luis Obispo, California 93407, United States
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16
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Yang J, Wu J, Tekola-Ayele F, Li LJ, Bremer AA, Lu R, Rahman ML, Weir NL, Pang WW, Chen Z, Tsai MY, Zhang C. Plasma Amino Acids in Early Pregnancy and Midpregnancy and Their Interplay With Phospholipid Fatty Acids in Association With the Risk of Gestational Diabetes Mellitus: Results From a Longitudinal Prospective Cohort. Diabetes Care 2023; 46:722-732. [PMID: 36701229 PMCID: PMC10090921 DOI: 10.2337/dc22-1892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/29/2022] [Indexed: 01/27/2023]
Abstract
OBJECTIVE We prospectively evaluated plasma amino acids (AAs) in early pregnancy and midpregnancy and their interplay with phospholipid fatty acids (FAs) in association with gestational diabetes mellitus (GDM) risk. RESEARCH DESIGN AND METHODS From a longitudinal pregnancy cohort of 2,802 individuals, concentrations of 24 plasma AAs at 10-14 and 15-26 gestational weeks (GW) were assessed among 107 GDM case subjects and 214 non-GDM control subjects. We estimated adjusted odds ratios (OR) and 95% CI for the associations of plasma AAs and the joint associations of plasma AAs and phospholipid FAs with GDM risk, adjusting for risk factors including age, prepregnancy BMI, and family history of diabetes. RESULTS Glycine at 10-14 GW was inversely associated with GDM (adjusted OR [95% CI] per SD increment: 0.55 [0.39-0.79]). Alanine, aspartic acid, and glutamic acid at 10-14 GW were positively associated with GDM (1.43 [1.08-1.88], 1.41 [1.11-1.80], and 1.39 [0.98-1.98]). At 15-26 GW, findings for glycine, alanine, aspartic acid, and the glutamine-to-glutamic acid ratio were consistent with the directions observed at 10-14 GW. Isoleucine, phenylalanine, and tyrosine were positively associated with GDM (1.64 [1.19-2.27], 1.15 [0.87-1.53], and 1.56 [1.16-2.09]). All P values for linear trend were <0.05. Several AAs and phospholipid FAs were significantly and jointly associated with GDM. For instance, the lowest risk was observed among women with higher glycine and lower even-chain saturated FAs at 10-14 GW (adjusted OR [95% CI] 0.15 [0.06, 0.37]). CONCLUSIONS Plasma AAs may be implicated in GDM development starting in early pregnancy. Associations of AAs with GDM may be enhanced in the copresence of phospholipid FA profile.
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Affiliation(s)
- Jiaxi Yang
- Global Centre for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jing Wu
- Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD
| | - Fasil Tekola-Ayele
- Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD
| | - Ling-Jun Li
- Global Centre for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Andrew A. Bremer
- Division of Extramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD
| | - Ruijin Lu
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO
| | - Mohammad L. Rahman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD
| | - Natalie L. Weir
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | - Wei Wei Pang
- Global Centre for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zhen Chen
- Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD
| | - Michael Y. Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | - Cuilin Zhang
- Global Centre for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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17
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Heath H, Degreef K, Rosario R, Smith M, Mitchell I, Pilolla K, Phelan S, Brito A, La Frano MR. Identification of potential biomarkers and metabolic insights for gestational diabetes prevention: A review of evidence contrasting gestational diabetes versus weight loss studies that may direct future nutritional metabolomics studies. Nutrition 2023; 107:111898. [PMID: 36525799 DOI: 10.1016/j.nut.2022.111898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/22/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
Gestational diabetes mellitus (GDM) significantly increases maternal health risks and adverse effects for the offspring. Observational studies suggest that weight loss before pregnancy may be a promising GDM prevention method. Still, biochemical pathways linking preconception weight changes with subsequent development of GDM among women who are overweight or obese remain unclear. Metabolomic assessment is a powerful approach for understanding the global biochemical pathways linking preconception weight changes and subsequent GDM. We hypothesize that many of the alterations of metabolite levels associated with GDM will change in one direction in GDM studies but will change in the opposite direction in studies focusing on lifestyle interventions for weight loss. The present review summarizes available evidence from 21 studies comparing women with GDM with healthy participants and 12 intervention studies that investigated metabolite changes that occurred during weight loss using caloric restriction and behavioral interventions. We discuss 15 metabolites, including amino acids, lipids, amines, carbohydrates, and carbohydrate derivatives. Of particular note are the altered levels of branched-chain amino acids, alanine, palmitoleic acid, lysophosphatidylcholine 18:1, and hypoxanthine because of their mechanistic links to insulin resistance and weight change. Mechanisms that may explain how these metabolite modifications contribute to GDM development in those who are overweight or obese are proposed, including insulin resistance pathways. Future nutritional metabolomics preconception intervention studies in overweight or obese are necessary to investigate whether weight loss through lifestyle intervention can reduce GDM occurrence in association with these metabolite alterations and to test the value of these metabolites as potential diagnostic biomarkers of GDM development.
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Affiliation(s)
- Hannah Heath
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California
| | - Kelsey Degreef
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California
| | - Rodrigo Rosario
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California
| | - MaryKate Smith
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California
| | - Isabel Mitchell
- Department of Biological Sciences, California Polytechnic State University, San Luis Obispo, California
| | - Kari Pilolla
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California; Center for Health Research, California Polytechnic State University, San Luis Obispo, California
| | - Suzanne Phelan
- Center for Health Research, California Polytechnic State University, San Luis Obispo, California; Department of Kinesiology and Public Health, California Polytechnic State University, San Luis Obispo, California
| | - Alex Brito
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow State Medical University, Moscow, Russia; World-Class Research Center "Digital Biodesign and Personalized Health Care," I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Michael R La Frano
- Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, California; Center for Health Research, California Polytechnic State University, San Luis Obispo, California; Cal Poly Metabolomics Service Center, California Polytechnic State University, San Luis Obispo, California
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18
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Abstract
PURPOSE OF REVIEW Epidemiological and mechanistic studies have reported relationships between blood lipids, mostly measured by traditional method in clinical settings, and gestational diabetes mellitus (GDM). Recent advances of high-throughput lipidomics techniques have made available more comprehensive lipid profiling in biological samples. This review aims to summarize evidence from prospective studies in assessing relations between blood lipids and GDM, and discuss potential underlying mechanisms. RECENT FINDINGS Mass spectrometry and nuclear magnetic resonance spectroscopy-based analytical platforms are extensively used in lipidomics research. Epidemiological studies have identified multiple novel lipidomic biomarkers that are associated with risk of GDM, such as certain types of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, cholesterol, and lipoproteins. However, the findings are inconclusive mainly due to the heterogeneities in study populations, sample sizes, and analytical platforms. Mechanistic evidence indicates that abnormal lipid metabolism may be involved in the pathogenesis of GDM by impairing pancreatic β-cells and inducing insulin resistance through several etiologic pathways, such as inflammation and oxidative stress. SUMMARY Lipidomics is a powerful tool to study pathogenesis and biomarkers for GDM. Lipidomic biomarkers and pathways could help to identify women at high risk for GDM and could be potential targets for early prevention and intervention of GDM.
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Affiliation(s)
- Yi Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Xiong-Fei Pan
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University
- Shuangliu Institute of Women's and Children's Health, Shuangliu Maternal and Child Health Hospital, Chengdu, China
| | - An Pan
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
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19
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Role of Impaired Glycolysis in Perturbations of Amino Acid Metabolism in Diabetes Mellitus. Int J Mol Sci 2023; 24:ijms24021724. [PMID: 36675238 PMCID: PMC9863464 DOI: 10.3390/ijms24021724] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
The most frequent alterations in plasma amino acid concentrations in type 1 and type 2 diabetes are decreased L-serine and increased branched-chain amino acid (BCAA; valine, leucine, and isoleucine) levels. The likely cause of L-serine deficiency is decreased synthesis of 3-phosphoglycerate, the main endogenous precursor of L-serine, due to impaired glycolysis. The BCAA levels increase due to decreased supply of pyruvate and oxaloacetate from glycolysis, enhanced supply of NADH + H+ from beta-oxidation, and subsequent decrease in the flux through the citric acid cycle in muscles. These alterations decrease the supply of α-ketoglutarate for BCAA transamination and the activity of branched-chain keto acid dehydrogenase, the rate-limiting enzyme in BCAA catabolism. L-serine deficiency contributes to decreased synthesis of phospholipids and increased synthesis of deoxysphinganines, which play a role in diabetic neuropathy, impaired homocysteine disposal, and glycine deficiency. Enhanced BCAA levels contribute to increased levels of aromatic amino acids (phenylalanine, tyrosine, and tryptophan), insulin resistance, and accumulation of various metabolites, whose influence on diabetes progression is not clear. It is concluded that amino acid concentrations should be monitored in patients with diabetes, and systematic investigation is needed to examine the effects of L-serine and glycine supplementation on diabetes progression when these amino acids are decreased.
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20
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Xie J, Li L, Xing H. Metabolomics in gestational diabetes mellitus: A review. Clin Chim Acta 2023; 539:134-143. [PMID: 36529269 DOI: 10.1016/j.cca.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022]
Abstract
Gestational diabetes mellitus (GDM), a common complication of pregnancy, is a type of diabetes that is first detected and diagnosed during pregnancy. The incidence of GDM is increasing annually and is associated with many adverse pregnancy outcomes. Early prediction of the risk of GDM and intervention are thus important to reduce adverse pregnancy outcomes. Studies have revealed a correlation between the levels of amino acids, fatty acids, triglycerides, and other metabolites in early pregnancy and the occurrence of GDM. The development of high-throughput technologies used in metabolomics has enabled the detection of changes in the levels of small-molecule metabolites during early pregnancy, which can help reflect the overall physiological and pathological status of the body and explore the underlying mechanisms of the development of GDM. This review sought to summarize current research in this field and provide data for the development of strategies to manage GDM.
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Affiliation(s)
- Jiewen Xie
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China
| | - Ling Li
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China
| | - Haoyue Xing
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China.
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21
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Ye Z, Wang S, Huang X, Chen P, Deng L, Li S, Lin S, Wang Z, Liu B. Plasma Exosomal miRNAs Associated With Metabolism as Early Predictor of Gestational Diabetes Mellitus. Diabetes 2022; 71:2272-2283. [PMID: 35926094 PMCID: PMC9630082 DOI: 10.2337/db21-0909] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 08/02/2022] [Indexed: 01/25/2023]
Abstract
To date, the miRNA expression profile of plasma exosomes in women whose pregnancy is complicated by gestational diabetes mellitus (GDM) has not been fully clarified. In this study, differentially expressed miRNAs in plasma exosomes were identified by high-throughput small-RNA sequencing in 12 pregnant women with GDM and 12 with normal glucose tolerance (NGT) and validated in 102 pregnant women with GDM and 101 with NGT. A total of 22 exosomal miRNAs were found, five of which were verified by real-time qPCR. Exosomal miR-423-5p was upregulated, whereas miR-122-5p, miR-148a-3p, miR-192-5p, and miR-99a-5p were downregulated in women whose pregnancy was complicated by GDM. IGF1R and GYS1 as target genes of miR-423-5p, and G6PC3 and FDFT1 as target genes of miR-122-5p were associated with insulin and AMPK signaling pathways and may participate in the regulation of metabolism in GDM. The five exosomal miRNAs had an area under the curve of 0.82 (95%CI, 0.73, ∼0.91) in early prediction of GDM. Our study demonstrates that dysregulated exosomal miRNAs in plasma from pregnant women with GDM might influence the insulin and AMPK signaling pathways and could contribute to the early prediction of GDM.
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Affiliation(s)
- Zhixin Ye
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Songzi Wang
- Department of Laboratory Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Xiaoqing Huang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Peisong Chen
- Department of Laboratory Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Langhui Deng
- Department of Laboratory Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Shiqi Li
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Suiwen Lin
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Zilian Wang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Bin Liu
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
- Corresponding author: Bin Liu,
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22
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Fuller H, Iles M, Moore JB, Zulyniak MA. Unique Metabolic Profiles Associate with Gestational Diabetes and Ethnicity in Low- and High-Risk Women Living in the UK. J Nutr 2022; 152:2186-2197. [PMID: 35883228 PMCID: PMC9535440 DOI: 10.1093/jn/nxac163] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/28/2022] [Accepted: 07/20/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is the most common global pregnancy complication; however, prevalence varies substantially between ethnicities, with South Asians (SAs) experiencing up to 3 times the risk of the disease compared with white Europeans (WEs). Factors driving this discrepancy are unclear, although the metabolome is of great interest as GDM is known to be characterized by metabolic dysregulation. OBJECTIVES The primary aim was to characterize and compare the metabolic profiles of GDM in SA and WE women (at <28 wk of gestation) from the Born in Bradford (BIB) prospective birth cohort in the United Kingdom. METHODS In total, 146 fasting serum metabolites, from 2,668 pregnant WE and 2,671 pregnant SA women (average BMI 26.2 kg/m2, average age 27.3 y) were analyzed using partial least squares discriminatory analyses to characterize GDM status. Linear associations between metabolite values and post-oral glucose tolerance test measures of dysglycemia (fasting glucose and 2 h postglucose) were also examined. RESULTS Seven metabolites associated with GDM status in both ethnicities (variable importance in projection ≥1), whereas 6 additional metabolites associated with GDM only in WE women. Unique metabolic profiles were observed in healthy-weight women who later developed GDM, with distinct metabolite patterns identified by ethnicity and BMI status. Of the metabolite values analyzed in relation to dysglycemia, lactate, histidine, apolipoprotein A1, HDL cholesterol, and HDL2 cholesterol associated with decreased glucose concentration, whereas DHA and the diameter of very low-density lipoprotein particles (nm) associated with increased glucose concertation in WE women, and in SAs, albumin alone associated with decreased glucose concentration. CONCLUSIONS This study shows that the metabolic risk profile for GDM differs between WE and SA women enrolled in BiB in the United Kingdom. This suggests that etiology of the disease differs between ethnic groups and that ethnic-appropriate prevention strategies may be beneficial.
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Affiliation(s)
- Harriett Fuller
- Nutritional Epidemiology Group, School of Food Science and Nutrition, University of Leeds, Leeds, UK
| | - Mark Iles
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - J Bernadette Moore
- Nutritional Epidemiology Group, School of Food Science and Nutrition, University of Leeds, Leeds, UK
| | - Michael A Zulyniak
- Nutritional Epidemiology Group, School of Food Science and Nutrition, University of Leeds, Leeds, UK
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23
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Quotah OF, Poston L, Flynn AC, White SL. Metabolic Profiling of Pregnant Women with Obesity: An Exploratory Study in Women at Greater Risk of Gestational Diabetes. Metabolites 2022; 12:metabo12100922. [PMID: 36295825 PMCID: PMC9612230 DOI: 10.3390/metabo12100922] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is one of the most prevalent obstetric conditions, particularly among women with obesity. Pathways to hyperglycaemia remain obscure and a better understanding of the pathophysiology would facilitate early detection and targeted intervention. Among obese women from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we aimed to compare metabolic profiles early and mid-pregnancy in women identified as high-risk of developing GDM, stratified by GDM diagnosis. Using a GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c, 231 women were identified as being at higher-risk, of whom 119 women developed GDM. Analyte data (nuclear magnetic resonance and conventional) were compared between higher-risk women who developed GDM and those who did not at timepoint 1 (15+0−18+6 weeks) and at timepoint 2 (23+2−30+0 weeks). The adjusted regression analyses revealed some differences in the early second trimester between those who developed GDM and those who did not, including lower adiponectin and glutamine concentrations, and higher C-peptide concentrations (FDR-adjusted p < 0.005, < 0.05, < 0.05 respectively). More differences were evident at the time of GDM diagnosis (timepoint 2) including greater impairment in β-cell function (as assessed by HOMA2-%B), an increase in the glycolysis-intermediate pyruvate (FDR-adjusted p < 0.001, < 0.05 respectively) and differing lipid profiles. The liver function marker γ-glutamyl transferase was higher at both timepoints (FDR-adjusted p < 0.05). This exploratory study underlines the difficulty in early prediction of GDM development in high-risk women but adds to the evidence that among pregnant women with obesity, insulin secretory dysfunction may be an important discriminator for those who develop GDM.
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Affiliation(s)
- Ola F. Quotah
- Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London, 10th Floor North Wing, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
- Department of Clinical Nutrition, Faculty of Applied Medical Science, King Abdulaziz University, Jeddah 999088, Saudi Arabia
| | - Lucilla Poston
- Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London, 10th Floor North Wing, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
| | - Angela C. Flynn
- Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London, 10th Floor North Wing, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
- Department of Nutritional Sciences, School of Life Course and Population Sciences, King’s College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Sara L. White
- Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London, 10th Floor North Wing, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
- Correspondence:
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24
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Lu W, Hu C. Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes. Chin Med J (Engl) 2022; 135:1940-1951. [PMID: 36148588 PMCID: PMC9746787 DOI: 10.1097/cm9.0000000000002160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Indexed: 11/25/2022] Open
Abstract
ABSTRACT Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.
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Affiliation(s)
- Wenqian Lu
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510630, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai 201400, China
| | - Cheng Hu
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510630, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai 201400, China
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25
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Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review. Artif Intell Med 2022; 132:102378. [DOI: 10.1016/j.artmed.2022.102378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/21/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022]
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26
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Holeček M. Serine Metabolism in Health and Disease and as a Conditionally Essential Amino Acid. Nutrients 2022; 14:nu14091987. [PMID: 35565953 PMCID: PMC9105362 DOI: 10.3390/nu14091987] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 02/04/2023] Open
Abstract
L-serine plays an essential role in a broad range of cellular functions including protein synthesis, neurotransmission, and folate and methionine cycles and synthesis of sphingolipids, phospholipids, and sulphur containing amino acids. A hydroxyl side-chain of L-serine contributes to polarity of proteins, and serves as a primary site for binding a phosphate group to regulate protein function. D-serine, its D-isoform, has a unique role. Recent studies indicate increased requirements for L-serine and its potential therapeutic use in some diseases. L-serine deficiency is associated with impaired function of the nervous system, primarily due to abnormal metabolism of phospholipids and sphingolipids, particularly increased synthesis of deoxysphingolipids. Therapeutic benefits of L-serine have been reported in primary disorders of serine metabolism, diabetic neuropathy, hyperhomocysteinemia, and amyotrophic lateral sclerosis. Use of L-serine and its metabolic products, specifically D-serine and phosphatidylserine, has been investigated for the therapy of renal diseases, central nervous system injury, and in a wide range of neurological and psychiatric disorders. It is concluded that there are disorders in which humans cannot synthesize L-serine in sufficient quantities, that L-serine is effective in therapy of disorders associated with its deficiency, and that L-serine should be classified as a “conditionally essential” amino acid.
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Affiliation(s)
- Milan Holeček
- Department of Physiology, Faculty of Medicine in Hradec Králové, Charles University, Šimkova 870, 500 03 Hradec Králové, Czech Republic
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27
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Maternal and Fetal Metabolites in Gestational Diabetes Mellitus: A Narrative Review. Metabolites 2022; 12:metabo12050383. [PMID: 35629887 PMCID: PMC9143359 DOI: 10.3390/metabo12050383] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 02/05/2023] Open
Abstract
Gestational diabetes mellitus (GDM) is a major public health issue of our century due to its increasing prevalence, affecting 5% to 20% of all pregnancies. The pathogenesis of GDM has not been completely elucidated to date. Increasing evidence suggests the association of environmental factors with genetic and epigenetic factors in the development of GDM. So far, several metabolomics studies have investigated metabolic disruptions associated with GDM. The aim of this review is to highlight the usefulness of maternal metabolites as diagnosis markers of GDM as well as the importance of both maternal and fetal metabolites as prognosis biomarkers for GDM and GDM’s transition to type 2 diabetes mellitus T2DM.
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28
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Raczkowska BA, Mojsak P, Rojo D, Telejko B, Paczkowska-Abdulsalam M, Hryniewicka J, Zielinska-Maciulewska A, Szelachowska M, Gorska M, Barbas C, Kretowski A, Ciborowski M. Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus. Front Pharmacol 2021; 12:770240. [PMID: 34867398 PMCID: PMC8640240 DOI: 10.3389/fphar.2021.770240] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/28/2021] [Indexed: 12/14/2022] Open
Abstract
Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography–mass spectrometry (GC–MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α–hydroxybutyric acid (α–HB), β–hydroxybutyric acid (β–HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC–MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8–14 gwk) who were NGT at this time, but in the second trimester (24–28 gwk) they were diagnosed with GDM. It was found that α–HB, β–HB, and several fatty acids were associated with aGT-GDM. A combination of α–HB, β–HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.
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Affiliation(s)
- Beata A Raczkowska
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Patrycja Mojsak
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - David Rojo
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Madrid, Spain
| | - Beata Telejko
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | | | - Justyna Hryniewicka
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Anna Zielinska-Maciulewska
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Malgorzata Szelachowska
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Maria Gorska
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Madrid, Spain
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland.,Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
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29
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Zhang H, Zhao Y, Zhao D, Chen X, Khan NU, Liu X, Zheng Q, Liang Y, Zhu Y, Iqbal J, Lin J, Shen L. Potential biomarkers identified in plasma of patients with gestational diabetes mellitus. Metabolomics 2021; 17:99. [PMID: 34739593 DOI: 10.1007/s11306-021-01851-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 10/29/2021] [Indexed: 12/26/2022]
Abstract
Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.
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Affiliation(s)
- Huajie Zhang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Yuxi Zhao
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Danqing Zhao
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People's Republic of China
| | - Xinqian Chen
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Naseer Ullah Khan
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xukun Liu
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Qihong Zheng
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Yi Liang
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People's Republic of China
| | - Yuhua Zhu
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People's Republic of China
| | - Javed Iqbal
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Jing Lin
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, Shenzhen, 518071, People's Republic of China
| | - Liming Shen
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China.
- Brain Disease and Big Data Research Institute, Shenzhen University, Shenzhen, 518071, People's Republic of China.
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Zhao Y, Zhao Y, Fan K, Jin L. Serum uric acid in early pregnancy and risk of gestational diabetes mellitus: a cohort study of 85,609 pregnant women. DIABETES & METABOLISM 2021; 48:101293. [PMID: 34666165 DOI: 10.1016/j.diabet.2021.101293] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 11/16/2022]
Abstract
AIMS . - Higher serum uric acid (UA) has been associated with increased risk of type 2 diabetes mellitus. This cohort study examined whether there are any associations between serum UA in early pregnancy and the subsequent risk of gestational diabetes mellitus (GDM). METHODS . - This cohort study was conducted in Shanghai, China, and included 85,609 pregnant women. Generalised additive models were used to estimate the associations of serum UA with risk of GDM. RESULTS . - The prevalence of GDM was 14.0% (11,960/85,609). Non-linear associations between serum UA and GDM risk were observed and these associations varied by gestational ages. Only elevated serum UA levels at 13-18 weeks gestation was associated with substantially increased risk of GDM. Analysis by UA quintiles at 13-18 weeks gestation showed the odds ratios for GDM were 1.11 (95%CI, 1.03-1.20) for the second, 1.27 (95%CI, 1.17-1.37) for the third, 1.37 (95%CI, 1.27-1.48) for the fourth and 1.70 (95%CI, 1.58-1.84) for the fifth quintile of serum UA in comparison with the first quintile. Stratified analysis showed the associations of serum UA with GDM were stronger among pregnant women aged 35 years or older. CONCLUSION . - We found higher serum UA at 13-18 gestational weeks was a risk factor for GDM. Our findings provide new evidence for the role of serum UA in the prevention and early intervention of GDM, and highlighted the need for monitoring serum UA at 13-18 gestational weeks.
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Affiliation(s)
- Yan Zhao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China
| | - Yongbo Zhao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China
| | - Kechen Fan
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China
| | - Liping Jin
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
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Omazić J, Viljetić B, Ivić V, Kadivnik M, Zibar L, Müller A, Wagner J. Early markers of gestational diabetes mellitus: what we know and which way forward? Biochem Med (Zagreb) 2021; 31:030502. [PMID: 34658643 PMCID: PMC8495622 DOI: 10.11613/bm.2021.030502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 08/28/2021] [Indexed: 12/11/2022] Open
Abstract
Women’s metabolism during pregnancy undergoes numerous changes that can lead to gestational diabetes mellitus (GDM). The cause and pathogenesis of GDM, a heterogeneous disease, are not completely clear, but GDM is increasing in prevalence and is associated with the modern lifestyle. Most diagnoses of GDM are made via the guidelines from the International Association of Diabetes and Pregnancy Study Groups (IADSPG), which involve an oral glucose tolerance test (OGTT) between 24 and 28 weeks of pregnancy. Diagnosis in this stage of pregnancy can lead to short- and long-term implications for the mother and child. Therefore, there is an urgent need for earlier GDM markers in order to enable prevention and earlier treatment. Routine GDM biomarkers (plasma glucose, insulin, C-peptide, homeostatic model assessment of insulin resistance, and sex hormone-binding globulin) can differentiate between healthy pregnant women and those with GDM but are not suitable for early GDM diagnosis. In this article, we present an overview of the potential early biomarkers for GDM that have been investigated recently. We also present our view of future developments in the laboratory diagnosis of GDM.
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Affiliation(s)
- Jelena Omazić
- Department of Laboratory and Transfusion Medicine, National Memorial Hospital Vukovar, Vukovar, Croatia.,Department of Medical Chemistry, Biochemistry and Clinical Chemistry, Faculty of Medicine, J.J. Strossmayer University, Osijek, Croatia
| | - Barbara Viljetić
- Department of Medical Chemistry, Biochemistry and Clinical Chemistry, Faculty of Medicine, J.J. Strossmayer University, Osijek, Croatia
| | - Vedrana Ivić
- Department of Medical Biology and Genetics, Faculty of Medicine, J.J. Strossmayer University, Osijek, Croatia
| | - Mirta Kadivnik
- Clinic of Obstetrics and Gynecology, University Hospital Center Osijek, Osijek, Croatia.,Department of Obstetrics and Gynecology, Faculty of Medicine, J.J. Strossmayer University, Osijek, Croatia
| | - Lada Zibar
- Department of Pathophysiology, Faculty of Medicine, J.J. Strossmayer University, Osijek, Croatia.,Department of Nephrology, Clinical Hospital Merkur, Zagreb, Croatia
| | - Andrijana Müller
- Clinic of Obstetrics and Gynecology, University Hospital Center Osijek, Osijek, Croatia.,Department of Obstetrics and Gynecology, Faculty of Medicine, J.J. Strossmayer University, Osijek, Croatia
| | - Jasenka Wagner
- Department of Medical Biology and Genetics, Faculty of Medicine, J.J. Strossmayer University, Osijek, Croatia
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32
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Wang QY, You LH, Xiang LL, Zhu YT, Zeng Y. Current progress in metabolomics of gestational diabetes mellitus. World J Diabetes 2021; 12:1164-1186. [PMID: 34512885 PMCID: PMC8394228 DOI: 10.4239/wjd.v12.i8.1164] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/20/2021] [Accepted: 07/07/2021] [Indexed: 02/06/2023] Open
Abstract
Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.
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Affiliation(s)
- Qian-Yi Wang
- School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 21000, Jiangsu Province, China
| | - Liang-Hui You
- Nanjing Maternity and Child Health Care Institute, Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing 21000, Jiangsu Province, China
| | - Lan-Lan Xiang
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing 21000, Jiangsu Province, China
| | - Yi-Tian Zhu
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing 21000, Jiangsu Province, China
| | - Yu Zeng
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing 21000, Jiangsu Province, China
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Hosseinkhani S, Dehghanbanadaki H, Aazami H, Pasalar P, Asadi M, Razi F. Association of circulating omega 3, 6 and 9 fatty acids with gestational diabetes mellitus: a systematic review. BMC Endocr Disord 2021; 21:120. [PMID: 34130655 PMCID: PMC8207652 DOI: 10.1186/s12902-021-00783-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 06/07/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is associated with increased risks of disease for mother and child during pregnancy and after that. Early diagnosis of GDM would promote both maternal and fetal health. Metabolomics can simplify and develop our understanding of the etiology, manifestation, or pathophysiology of the disease. This systematic review investigates the association of circulating omega 3, 6, and 9 fatty acids with GDM. METHODS We conducted a systematic search of PubMed, Scopus, Web of Science, and EMBASE databases up to May 8, 2020, using the key term combinations of all types of omega fatty acids with gestational diabetes mellitus. Additional articles were identified through searching the reference lists of included studies. RESULTS This systematic review included 15 articles. Five were cohort studies, four included nested case-control studies and four were case-control studies. The results of this study demonstrate an increasing trend in the amount of oleic acid and palmitoleic acid in the second trimester and an increase in decosahexanoic acid in the third trimester of GDM mothers. The changes in other fatty acids of interest are either not significant or if significant, their results are inconsistent with the other existing articles. CONCLUSIONS Omega fatty acids, as potential biomarkers, are considered to be associated with GDM risk and thus provide useful information regarding the prevention and early diagnosis of GDM. Moreover, existing metabolomic studies on GDM are shown to provide conflicting results about metabolite profile characteristics. This systematic review was registered at PROSPERO ( www.crd.york.ac.uk/PROSPERO ) as CRD42020196122.
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Affiliation(s)
- Shaghayegh Hosseinkhani
- Department of Clinical Biochemistry, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hojat Dehghanbanadaki
- Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Aazami
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Parvin Pasalar
- Department of Clinical Biochemistry, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mojgan Asadi
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Farideh Razi
- Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
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Meng X, Zhu B, Liu Y, Fang L, Yin B, Sun Y, Ma M, Huang Y, Zhu Y, Zhang Y. Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling. J Diabetes Res 2021; 2021:6689414. [PMID: 34212051 PMCID: PMC8211500 DOI: 10.1155/2021/6689414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/18/2021] [Accepted: 05/15/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is a type of glucose intolerance disorder that first occurs during women's pregnancy. The main diagnostic method for GDM is based on the midpregnancy oral glucose tolerance test. The rise of metabolomics has expanded the opportunity to better identify early diagnostic biomarkers and explore possible pathogenesis. METHODS We collected blood serum from 34 GDM patients and 34 normal controls for a LC-MS-based metabolomics study. RESULTS 184 metabolites were increased and 86 metabolites were decreased in the positive ion mode, and 65 metabolites were increased and 71 were decreased in the negative ion mode. Also, it was found that the unsaturated fatty acid metabolism was disordered in GDM. Ten metabolites with the most significant differences were selected for follow-up studies. Since the diagnostic specificity and sensitivity of a single differential metabolite are not definitive, we combined these metabolites to prepare a ROC curve. We found a set of metabolite combination with the highest sensitivity and specificity, which included eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, arachidonic acid, citric acid, α-ketoglutaric acid, and genistein. The area under the curves (AUC) value of those metabolites was 0.984 between the GDM and control group. CONCLUSIONS Our results provide a direction for the mechanism of GDM research and demonstrate the feasibility of developing a diagnostic test that can distinguish between GDM and normal controls clearly. Our findings were helpful to develop novel biomarkers for precision or personalized diagnosis for GDM. In addition, we provide a critical insight into the pathological and biological mechanisms for GDM.
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Affiliation(s)
- Xingjun Meng
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China
| | - Bo Zhu
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China
| | - Yan Liu
- School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Lei Fang
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China
| | - Binbin Yin
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China
| | - Yanni Sun
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China
| | - Mengni Ma
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China
| | - Yuli Huang
- Department of Cardiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528300, China
| | - Yuning Zhu
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China
| | - Yunlong Zhang
- Key Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 511436, China
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Alesi S, Ghelani D, Rassie K, Mousa A. Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence. Int J Mol Sci 2021; 22:ijms22115512. [PMID: 34073737 PMCID: PMC8197243 DOI: 10.3390/ijms22115512] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 05/20/2021] [Accepted: 05/20/2021] [Indexed: 12/14/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is the fastest growing type of diabetes, affecting between 2 to 38% of pregnancies worldwide, varying considerably depending on diagnostic criteria used and sample population studied. Adverse obstetric outcomes include an increased risk of macrosomia, and higher rates of stillbirth, instrumental delivery, and birth trauma. Metabolomics, which is a platform used to analyse and characterise a large number of metabolites, is increasingly used to explore the pathophysiology of cardiometabolic conditions such as GDM. This review aims to summarise metabolomics studies in GDM (from inception to January 2021) in order to highlight prospective biomarkers for diagnosis, and to better understand the dysfunctional metabolic pathways underlying the condition. We found that the most commonly deranged pathways in GDM include amino acids (glutathione, alanine, valine, and serine), carbohydrates (2-hydroxybutyrate and 1,5-anhydroglucitol), and lipids (phosphatidylcholines and lysophosphatidylcholines). We also highlight the possibility of using certain metabolites as predictive markers for developing GDM, with the use of highly stratified modelling techniques. Limitations for metabolomic research are evaluated, and future directions for the field are suggested to aid in the integration of these findings into clinical practice.
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Affiliation(s)
- Simon Alesi
- Monash Centre for Health Research and Implementation (MCHRI), School of Public Health and Preventive Medicine, Monash University, Melbourne 3168, Australia; (S.A.); (D.G.); (K.R.)
| | - Drishti Ghelani
- Monash Centre for Health Research and Implementation (MCHRI), School of Public Health and Preventive Medicine, Monash University, Melbourne 3168, Australia; (S.A.); (D.G.); (K.R.)
| | - Kate Rassie
- Monash Centre for Health Research and Implementation (MCHRI), School of Public Health and Preventive Medicine, Monash University, Melbourne 3168, Australia; (S.A.); (D.G.); (K.R.)
- Department of Diabetes, Monash Health, Melbourne 3168, Australia
| | - Aya Mousa
- Monash Centre for Health Research and Implementation (MCHRI), School of Public Health and Preventive Medicine, Monash University, Melbourne 3168, Australia; (S.A.); (D.G.); (K.R.)
- Correspondence:
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Circular RNA expression profiles in umbilical cord blood exosomes from normal and gestational diabetes mellitus patients. Biosci Rep 2021; 40:226898. [PMID: 33146699 PMCID: PMC7670577 DOI: 10.1042/bsr20201946] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/26/2020] [Accepted: 11/02/2020] [Indexed: 02/06/2023] Open
Abstract
Circular RNA (circRNA) is a novel member of endogenous noncoding RNAs with widespread distribution and diverse cellular functions. Recently, circRNAs have been identified for their enrichment and stability in exosomes. However, the roles of circRNAs from umbilical cord blood exosomes in gestational diabetes mellitus (GDM) occurrence and fetus growth remains poorly understood. In the present study, we used microarray technology to construct a comparative circRNA profiling of umbilical cord blood exosomes between GDM patients and controls. We found the exosome particle size was larger, and the exosome concentration was higher in the GDM patients. A total of 88,371 circRNAs in umbilical cord blood exosomes from two groups were evaluated. Of these, 229 circRNAs were significantly up-regulated and 278 circRNAs were significantly down-regulated in the GDM patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway analyses demonstrated that circRNA parental genes involved in the regulation of metabolic process, growth and development were significantly enriched, which are important in GDM development and fetus growth. Further circRNA/miRNA interactions analysis showed that most of the exosomal circRNAs harbored miRNA binding sites, and some miRNAs were associated with GDM. Collectively, these results lay a foundation for extensive studies on the role of exosomal circRNAs in GDM development and fetus growth.
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Zhan Y, Wang J, He X, Huang M, Yang X, He L, Qiu Y, Lou Y. Plasma metabolites, especially lipid metabolites, are altered in pregnant women with gestational diabetes mellitus. Clin Chim Acta 2021; 517:139-148. [PMID: 33711327 DOI: 10.1016/j.cca.2021.02.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 02/23/2021] [Accepted: 02/24/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND AND AIMS Gestational diabetes mellitus (GDM) is a pathological condition of glucose intolerance associated with adverse pregnancy outcomes and increased risk of developing maternal type 2 diabetes later in life. Metabolomics is finding increasing use in the study of GDM. To date, GDM-specific metabolomic changes have not been completely elucidated. MATERIALS AND METHODS In this pilot study, metabolomics fingerprinting data, obtained by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS), of 54 healthy pregnant women and 49 patients with GDM at the second and third gestational trimesters were analyzed. Multilevel statistical methods were used to process complex metabolomic data from the retrospective cohorts. RESULTS Using univariate analysis (p < 0.05), 41 metabolites were identified as having the most significant differences between these two groups. Lipid metabolites, particularly glycerophospholipids, were the most prevalent class of altered compounds. In addition, metabolites with previously unknown connection to GDM - such as monoacylglycerol, dihydrobiopterin, and 13S-hydroxyoctadecadienoic acid - were identified with strong discriminative power. The main metabolic pathways affected by GDM included glycerophospholipid metabolism, linoleic acid metabolism, and D-arginine and D-ornithine metabolism. CONCLUSION Our data provide a comprehensive overview of metabolite changes at different stages of pregnancy, which offers further insights into the pathogenesis of GDM.
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Affiliation(s)
- Yaqiong Zhan
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China
| | - Jiali Wang
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China
| | - Xiaoying He
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China
| | - Mingzhu Huang
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China
| | - Xi Yang
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China
| | - Lingjuan He
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China.
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, People's Republic of China.
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Early pregnancy metabolites predict gestational diabetes mellitus: implications for fetal programming. Am J Obstet Gynecol 2021; 224:215.e1-215.e7. [PMID: 32739399 DOI: 10.1016/j.ajog.2020.07.050] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/20/2020] [Accepted: 07/29/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Aberrant fetal programming in gestational diabetes mellitus seems to increase the risk of obesity, type 2 diabetes, and cardiovascular disease. The inability to accurately identify gestational diabetes mellitus in the first trimester of pregnancy has thwarted ascertaining whether early therapeutic interventions reduce the predisposition to these prevalent medical disorders. OBJECTIVE A metabolomics study was conducted to determine whether advanced analytical methods could identify accurate predictors of gestational diabetes mellitus in early pregnancy. STUDY DESIGN This nested observational case-control study was composed of 92 gravidas (46 in the gestational diabetes mellitus group and 46 in the control group) in early pregnancy, who were matched by maternal age, body mass index, and gestational age at urine collection. Gestational diabetes mellitus was diagnosed according to community standards. A comprehensive metabolomics platform measured 626 endogenous metabolites in randomly collected urine. Consensus multivariate criteria or the most important by 1 method identified low-molecular weight metabolites independently associated with gestational diabetes mellitus, and a classification tree selected a subset most predictive of gestational diabetes mellitus. RESULTS Urine for both groups was collected at a mean gestational age of 12 weeks (range, 6-19 weeks' gestation). Consensus multivariate analysis identified 11 metabolites independently linked to gestational diabetes mellitus. Classification tree analysis selected a 7-metabolite subset that predicted gestational diabetes mellitus with an accuracy of 96.7%, independent of maternal age, body mass index, and time of urine collection. CONCLUSION Validation of this high-accuracy model by a larger study is now needed to support future studies to determine whether therapeutic interventions in the first trimester of pregnancy for gestational diabetes mellitus reduce short- and long-term morbidity.
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Tian M, Ma S, You Y, Long S, Zhang J, Guo C, Wang X, Tan H. Serum Metabolites as an Indicator of Developing Gestational Diabetes Mellitus Later in the Pregnancy: A Prospective Cohort of a Chinese Population. J Diabetes Res 2021; 2021:8885954. [PMID: 33628838 PMCID: PMC7884125 DOI: 10.1155/2021/8885954] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/23/2020] [Accepted: 01/20/2021] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE Gestational diabetes mellitus (GDM) is a common metabolic disorder with onset during pregnancy. However, the etiology and pathogenesis of GDM have not been fully elucidated. In this study, we used a metabolomics approach to investigate the relationship between maternal serum metabolites and GDM in early pregnancy. METHODS A nested case-control study was performed. To establish an early pregnancy cohort, pregnant women in early pregnancy (10-13+6 weeks) were recruited. In total, 51 patients with GDM and 51 healthy controls were included. Serum samples were analyzed using an untargeted high-performance liquid chromatography mass spectrometry metabolomics approach. The relationships between metabolites and GDM were analyzed by an orthogonal partial least-squares discriminant analysis. Differential metabolites were evaluated using a KEGG pathway analysis. RESULTS A total of 44 differential metabolites were identified between GDM cases and healthy controls during early pregnancy. Of these, 26 significant metabolites were obtained in early pregnancy after false discovery rate (FDR < 0.1) correction. In the GDM group, the levels of L-pyroglutamic acid, L-glutamic acid, phenylacetic acid, pantothenic acid, and xanthine were significantly higher and the levels of 1,5-anhydro-D-glucitol, calcitriol, and 4-oxoproline were significantly lower than those in the control group. These metabolites were involved in multiple metabolic pathways, including those for amino acid, carbohydrate, lipid, energy, nucleotide, cofactor, and vitamin metabolism. CONCLUSIONS We identified significant differentially expressed metabolites associated with the risk of GDM, providing insight into the mechanisms underlying GDM in early pregnancy and candidate predictive markers.
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Affiliation(s)
- Mengyuan Tian
- Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Shujuan Ma
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, China
| | - Yiping You
- Department of Obstetrics, Hunan Provincial Maternal and Child Health Hospital, Changsha, China
| | - Sisi Long
- Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Jiayue Zhang
- Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Chuhao Guo
- Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Xiaolei Wang
- Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Key Laboratory of Clinical Epidemiology, Changsha, China
| | - Hongzhuan Tan
- Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Key Laboratory of Clinical Epidemiology, Changsha, China
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Scott HD, Buchan M, Chadwick C, Field CJ, Letourneau N, Montina T, Leung BMY, Metz GAS. Metabolic dysfunction in pregnancy: Fingerprinting the maternal metabolome using proton nuclear magnetic resonance spectroscopy. Endocrinol Diabetes Metab 2021; 4:e00201. [PMID: 33532625 PMCID: PMC7831222 DOI: 10.1002/edm2.201] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 09/30/2020] [Accepted: 10/24/2020] [Indexed: 12/28/2022] Open
Abstract
Aims Maternal metabolic disorders place the mother at risk for negative pregnancy outcomes with potentially long-term health impacts for the child. Metabolic syndrome, a cluster of features associated with increased risk of metabolic disorders, such as cardiovascular disease, diabetes and stroke, affects roughly one in five Canadians. Metabolomics is a relatively new technique that may be a useful tool to identify women at risk of metabolic disorders. This study set out to characterize urinary metabolic biomarkers of pregnant women with obesity and of pregnant women who later developed gestational diabetes mellitus (pre-GDM), compared to controls. Methods and Materials Second trimester urine samples were collected through the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort and examined with 1H nuclear magnetic resonance (NMR) spectroscopy. Multivariate analysis was used to examine group differences, and machine learning feature selection tools identified the metabolites contributing to separation. Results Obesity and pre-GDM metabolomes were distinct from controls and from each other. In each comparison, the glycine, serine and threonine pathways were the most impacted. Pantothenate, formic acid and glycine were downregulated by obesity, while formic acid, dimethylamine and galactose were downregulated in pre-GDM. The three most impacted metabolites for the comparison of obesity versus pre-GDM groups were upregulated creatine/caffeine, downregulated sarcosine/dimethylamine and upregulated maltose/sucrose in individuals who later developed GDM. Conclusion These findings suggest a role for urinary metabolomics in the prediction of GDM and metabolic marker identification for potential diagnostics and prognostics in women at risk.
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Affiliation(s)
- Hannah D. Scott
- Canadian Centre for Behavioural NeuroscienceDepartment of NeuroscienceUniversity of LethbridgeLethbridgeABCanada
| | - Marrissa Buchan
- Canadian Centre for Behavioural NeuroscienceDepartment of NeuroscienceUniversity of LethbridgeLethbridgeABCanada
- Department of Chemistry and BiochemistryUniversity of LethbridgeLethbridgeABCanada
| | - Caylin Chadwick
- Canadian Centre for Behavioural NeuroscienceDepartment of NeuroscienceUniversity of LethbridgeLethbridgeABCanada
| | - Catherine J. Field
- Department of Agriculture, Food and Nutritional ScienceUniversity of AlbertaEdmontonABCanada
| | - Nicole Letourneau
- Faculty of Nursing and Cumming School of MedicineUniversity of CalgaryCalgaryABCanada
| | - Tony Montina
- Department of Chemistry and BiochemistryUniversity of LethbridgeLethbridgeABCanada
- Southern Alberta Genome Sciences CentreUniversity of LethbridgeLethbridgeABCanada
| | - Brenda M. Y. Leung
- Public Health ProgramFaculty of Health SciencesUniversity of LethbridgeLethbridgeABCanada
| | - Gerlinde A. S. Metz
- Canadian Centre for Behavioural NeuroscienceDepartment of NeuroscienceUniversity of LethbridgeLethbridgeABCanada
- Southern Alberta Genome Sciences CentreUniversity of LethbridgeLethbridgeABCanada
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Nagaoka K, Mei H, Guo Y, Han J, Konno H, Moriwaki H, Soloshonok VA. Michael addition reactions of chiral glycine Schiff base Ni (II)‐complex with 1‐(1‐phenylsulfonyl)benzene. Chirality 2020; 32:885-893. [DOI: 10.1002/chir.23203] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 02/11/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Keita Nagaoka
- School of Chemistry and Chemical Engineering, State of Key Laboratory of CoordinationNanjing University Nanjing China
- Department of Biological Engineering, Graduate School of Science and EngineeringYamagata University Yamagata Japan
| | - Haibo Mei
- School of Chemistry and Chemical Engineering, State of Key Laboratory of CoordinationNanjing University Nanjing China
| | - Yunjie Guo
- School of Chemistry and Chemical Engineering, State of Key Laboratory of CoordinationNanjing University Nanjing China
| | - Jianlin Han
- School of Chemistry and Chemical Engineering, State of Key Laboratory of CoordinationNanjing University Nanjing China
| | - Hiroyuki Konno
- Department of Biological Engineering, Graduate School of Science and EngineeringYamagata University Yamagata Japan
| | | | - Vadim A. Soloshonok
- Department of Organic Chemistry I, Faculty of ChemistryUniversity of the Basque Country UPV/EHU San Sebastián Spain
- IKERBASQUE, Basque Foundation for Science Bilbao Spain
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Rezaei Tavirani M, Zamanian Azodi M, Rostami-Nejad M, Morravej H, Razzaghi Z, Okhovatian F, Rezaei-Tavirani M. Introducing Serine as Cardiovascular Disease Biomarker Candidate via Pathway Analysis. Galen Med J 2020; 9:e1696. [PMID: 34466570 PMCID: PMC8343801 DOI: 10.31661/gmj.v9i0.1696] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 10/01/2019] [Accepted: 12/03/2019] [Indexed: 11/25/2022] Open
Abstract
Background: The rate of death due to cardiovascular disease (CVD) is growing. Investigations about CVD that leading to introduce varieties of metabolites is available. The monitoring of these metabolites to find effective ones in the future of clinic applications is the main aim of this study. Materials and Methods: Numbers of 34 metabolites for the CVD are extracted from literature and designated for interaction determinations by MetScape V 3.1.3. The compound-reaction-enzyme-gene network was constructed and the pathways were analyzed. Based on the presence of metabolites in the pathways the critical compounds were determined. Results: Pathway analysis revealed 18 disturbed pathways related to the CVD. glycerophospholipid metabolism pathway including 27 compounds is related to the 9 queried metabolites. L-Serine which was communed between 5 pathways and also was presented in the largest pathway was identified as the critical compound. Conclusion: It can be concluded that L-Serine is a proper biomarker candidate for CVD diagnosis and also patients follow up approaches.
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Affiliation(s)
- Mostafa Rezaei Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mona Zamanian Azodi
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Correspondence to: Mona Zamanian Azodi, Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Telephone Number: +982122714248 Email Address:
| | - Mohammad Rostami-Nejad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamideh Morravej
- Skin Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Razzaghi
- Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farshad Okhovatian
- Physiotherapy Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Majid Rezaei-Tavirani
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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43
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Perng W, Ringham BM, Smith HA, Michelotti G, Kechris KM, Dabelea D. A prospective study of associations between in utero exposure to gestational diabetes mellitus and metabolomic profiles during late childhood and adolescence. Diabetologia 2020; 63:296-312. [PMID: 31720734 PMCID: PMC8327857 DOI: 10.1007/s00125-019-05036-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 10/08/2019] [Indexed: 12/25/2022]
Abstract
AIMS/HYPOTHESIS This study aimed to: (1) identify metabolite patterns during late childhood that differ with respect to exposure to maternal gestational diabetes mellitus (GDM); (2) examine the persistence of GDM/metabolite associations 5 years later, during adolescence; and (3) investigate the associations of metabolite patterns with adiposity and metabolic biomarkers from childhood through adolescence. METHODS This study included 592 mother-child pairs with information on GDM exposure (n = 92 exposed), untargeted metabolomics data at age 6-14 years (T1) and at 12-19 years (T2), and information on adiposity and metabolic risk biomarkers at T1 and T2. We first consolidated 767 metabolites at T1 into factors (metabolite patterns) via principal component analysis (PCA) and used multivariable regression to identify factors that differed by GDM exposure, at α = 0.05. We then examined associations of GDM with individual metabolites within factors of interest at T1 and T2, and investigated associations of GDM-related factors at T1 with adiposity and metabolic risk throughout T1 and T2 using mixed-effects linear regression models. RESULTS Of the six factors retained from PCA, GDM exposure was associated with greater odds of being in quartile (Q)4 (vs Q1-3) of 'Factor 4' at T1 after accounting for age, sex, race/ethnicity, maternal smoking habits during pregnancy, Tanner stage, physical activity and total energy intake, at α = 0.05 (OR 1.78 [95% CI 1.04, 3.04]; p = 0.04). This metabolite pattern comprised phosphatidylcholines, diacylglycerols and phosphatidylethanolamines. GDM was consistently associated with elevations in a subset of individual compounds within this pattern at T1 and T2. While this metabolite pattern was not related to the health outcomes in boys, it corresponded with greater adiposity and a worse metabolic profile among girls throughout the follow-up period. Each 1-unit increment in Factor 4 corresponded with 0.17 (0.08, 0.25) units higher BMI z score, 8.83 (5.07, 12.59) pmol/l higher fasting insulin, 0.28 (0.13, 0.43) units higher HOMA-IR, and 4.73 (2.15, 7.31) nmol/l higher leptin. CONCLUSIONS/INTERPRETATION Exposure to maternal GDM was nominally associated with a metabolite pattern characterised by elevated serum phospholipids in late childhood and adolescence at α = 0.05. This metabolite pattern was associated with greater adiposity and metabolic risk among female offspring throughout the late childhood-to-adolescence transition. Future studies are warranted to confirm our findings.
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Affiliation(s)
- Wei Perng
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Room 208, 12474 E. 19th Ave, Aurora, CO, 80045, USA.
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
| | - Brandy M Ringham
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Room 208, 12474 E. 19th Ave, Aurora, CO, 80045, USA
| | - Harry A Smith
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Room 208, 12474 E. 19th Ave, Aurora, CO, 80045, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Katerina M Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Room 208, 12474 E. 19th Ave, Aurora, CO, 80045, USA
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Mokkala K, Vahlberg T, Pellonperä O, Houttu N, Koivuniemi E, Laitinen K. Distinct Metabolic Profile in Early Pregnancy of Overweight and Obese Women Developing Gestational Diabetes. J Nutr 2020; 150:31-37. [PMID: 31529056 DOI: 10.1093/jn/nxz220] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 07/11/2019] [Accepted: 08/20/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Reliable biomarkers for gestational diabetes mellitus (GDM) would be beneficial in the early prevention of adverse metabolic outcomes during pregnancy and beyond. OBJECTIVES The objective of this study was to investigate whether the early pregnancy serum metabolic profile differs in women developing GDM from those remaining healthy. Furthermore, we evaluated the potential of these metabolites to act as predictive markers for GDM. METHODS This was a prospective study investigating overweight and obese [prepregnancy BMI (in kg/m2) ≥25 and >30, respectively] pregnant women (prepregnancy median BMI: 28.5; IQR: 26.4-31.5; n = 357). Fasting serum samples were analyzed with a targeted NMR approach in early pregnancy (median: 14.3 weeks of gestation). GDM was diagnosed on the basis of a 2-h, 75-g oral-glucose-tolerance test at a median of 25.7 weeks of gestation. RESULTS In early pregnancy, 78 lipid metabolites differed in women who later developed GDM (n = 82) compared with those who remained healthy (n = 275) (ANCOVA, adjusted for confounding factors and corrected for multiple comparisons; false discovery rate <0.05). Higher concentrations of several-sized VLDL particles and medium- and small-sized HDL particles, and lower concentrations of very large-sized HDL particles, were detected in women developing GDM. Furthermore, concentrations of amino acids including 2 branched-chain amino acids, isoleucine and leucine, and GlycA, a marker for low-grade inflammation, were higher in women who developed GDM. Receiver operating characteristic analysis revealed that the most predictive marker for GDM was a higher concentration of small-sized HDL particles (AUC: 0.71; 95% CI: 0.67, 0.77; P < 0.001). CONCLUSIONS We identified a distinct early pregnancy metabolomic profile especially attributable to small HDL particles in women developing GDM. The aberrant metabolic profile could represent a novel way to allow early identification of this most common medical condition affecting pregnant women. This trial was registered at clinicaltrials.gov as NCT01922791.
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Affiliation(s)
- Kati Mokkala
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Tero Vahlberg
- Department of Clinical Medicine, Biostatistics, University of Turku, Turku, Finland
| | - Outi Pellonperä
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Noora Houttu
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Ella Koivuniemi
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Kirsi Laitinen
- Institute of Biomedicine, University of Turku, Turku, Finland
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45
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Gan WZ, Ramachandran V, Lim CSY, Koh RY. Omics-based biomarkers in the diagnosis of diabetes. J Basic Clin Physiol Pharmacol 2019; 31:/j/jbcpp.ahead-of-print/jbcpp-2019-0120/jbcpp-2019-0120.xml. [PMID: 31730525 DOI: 10.1515/jbcpp-2019-0120] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 10/07/2019] [Indexed: 02/06/2023]
Abstract
Diabetes mellitus (DM) is a group of metabolic diseases related to the dysfunction of insulin, causing hyperglycaemia and life-threatening complications. Current early screening and diagnostic tests for DM are based on changes in glucose levels and autoantibody detection. This review evaluates recent studies on biomarker candidates in diagnosing type 1, type 2 and gestational DM based on omics classification, whilst highlighting the relationship of these biomarkers with the development of diabetes, diagnostic accuracy, challenges and future prospects. In addition, it also focuses on possible non-invasive biomarker candidates besides common blood biomarkers.
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Affiliation(s)
- Wei Zien Gan
- Division of Applied Biomedical Science and Biotechnology, School of Health Sciences, International Medical University, 57000 Kuala Lumpur, Malaysia
| | - Valsala Ramachandran
- Division of Applied Biomedical Science and Biotechnology, School of Health Sciences, International Medical University, 57000 Kuala Lumpur, Malaysia
| | - Crystale Siew Ying Lim
- Department of Biotechnology, Faculty of Applied Sciences, UCSI University Kuala Lumpur, 56000 Kuala Lumpur, Malaysia
| | - Rhun Yian Koh
- Division of Applied Biomedical Science and Biotechnology, School of Health Sciences, International Medical University, 57000 Kuala Lumpur, Malaysia, Phone: +60327317207
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Li G, Gao W, Xu Y, Xie M, Tang S, Yin P, Guo S, Chu S, Sultana S, Cui S. Serum metabonomics study of pregnant women with gestational diabetes mellitus based on LC-MS. Saudi J Biol Sci 2019; 26:2057-2063. [PMID: 31889794 PMCID: PMC6923470 DOI: 10.1016/j.sjbs.2019.09.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 09/11/2019] [Accepted: 09/12/2019] [Indexed: 12/12/2022] Open
Abstract
Objective Through metabolomics method, the objective of the paper is to differentially screen serum metabolites of GDM patients and healthy pregnant women, to explore potential biomarkers of GDM and analyze related pathways, and to explain the potential mechanism and biological significance of GDM. Methods The serum samples from 30 GDM patients and 30 healthy pregnant women were selected to conduct non-targeted metabolomics study by liquid chromatography-mass spectrometry. The differential metabolites between the two groups were searched and the metabolic pathway was analyzed by KEGG database. Results Multivariate statistical analysis found that serum metabolism in GDM patients was different significantly from healthy pregnant women, 36 differential metabolites and corresponding metabolic pathways were identified in serum, which involved several metabolic ways like, fatty acid metabolism, butyric acid metabolism, bile secretion, and amino acid metabolism. Conclusion The discovery of these biomarkers provided a new theoretical basis and experimental basis for further study of the early diagnosis and pathogenesis of GDM. At the same time, LC-MS-based serum metabolomics methods also showed great application values in disease diagnosis and mechanism research.
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Affiliation(s)
- Genxia Li
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Wanli Gao
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yajuan Xu
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Mingkun Xie
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Suhua Tang
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Pan Yin
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shuhua Guo
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shuhui Chu
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shaima Sultana
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shihong Cui
- Obstetrics Department, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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Holm LJ, Buschard K. L-serine: a neglected amino acid with a potential therapeutic role in diabetes. APMIS 2019; 127:655-659. [PMID: 31344283 PMCID: PMC6851881 DOI: 10.1111/apm.12987] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 07/19/2019] [Indexed: 12/13/2022]
Abstract
L-serine is classified as a non-essential amino acid; however, L-serine is indispensable having a central role in a broad range of cellular processes. Growing evidence suggests a role for L-serine in the development of diabetes mellitus and its related complications, with L-serine being positively correlated to insulin secretion and sensitivity. L-serine metabolism is altered in type 1, type 2, and gestational diabetes, and L-serine supplementations improve glucose homeostasis and mitochondrial function, and reduce neuronal death. Additionally, L-serine lowers the incidence of autoimmune diabetes in NOD mice. Dietary supplementations of L-serine are generally regarded as safe (GRAS) by the FDA. Therefore, we believe that L-serine should be considered as an emerging therapeutic option in diabetes, although work remains in order to fully understand the role of L-serine in diabetes.
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Affiliation(s)
- Laurits J Holm
- The Bartholin Institute, Department of Pathology, Rigshospitalet, Copenhagen N, Denmark
| | - Karsten Buschard
- The Bartholin Institute, Department of Pathology, Rigshospitalet, Copenhagen N, Denmark
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Wen L, Wu Y, Yang Y, Han TL, Wang W, Fu H, Zheng Y, Shan T, Chen J, Xu P, Jin H, Lin L, Liu X, Qi H, Tong C, Baker P. Gestational Diabetes Mellitus Changes the Metabolomes of Human Colostrum, Transition Milk and Mature Milk. Med Sci Monit 2019; 25:6128-6152. [PMID: 31418429 PMCID: PMC6708282 DOI: 10.12659/msm.915827] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Background Gestational diabetes mellitus (GDM) is a pregnancy complication that is diagnosed by the novel onset of abnormal glucose intolerance. Our study aimed to investigate the changes in human breast milk metabolome over the first month of lactation and how GDM affects milk metabolome. Material/Methods Colostrum, transition milk, and mature milk samples from women with normal uncomplicated pregnancies (n=94) and women with GDM-complicated pregnancies (n=90) were subjected to metabolomic profiling by the use of gas chromatography-mass spectrometry (GC-MS). Results For the uncomplicated pregnancies, there were 59 metabolites that significantly differed among colostrum, transition milk, and mature milk samples, while 58 metabolites differed in colostrum, transition milk, and mature milk samples from the GDM pregnancies. There were 28 metabolites that were found to be significantly different between women with normal pregnancies and women with GDM pregnancies among colostrum, transition milk, and mature milk samples. Conclusions The metabolic profile of human milk is dynamic throughout the first months of lactation. High levels of amino acids in colostrum and high levels of saturated fatty acids and unsaturated fatty acids in mature milk, which may be critical for neonatal development in the first month of life, were features of both normal and GDM pregnancies.
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Affiliation(s)
- Li Wen
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Yue Wu
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Yang Yang
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Ting-Li Han
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, New Zealand.,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Wenling Wang
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland).,Department of Obstetrics, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, Gansu, China (mainland)
| | - Huijia Fu
- Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,Department of Reproduction Health and Infertility, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland)
| | - Yangxi Zheng
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Tengfei Shan
- Department of Obstetrics and Gynecology, The First People's Hospital of Yuhang District, Hangzhou, Zhejiang, China (mainland)
| | - Jianjun Chen
- Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,Institute of Life Sciences, Chongqing Medical University, Chongqing, China (mainland)
| | - Ping Xu
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Huili Jin
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Li Lin
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Xiyao Liu
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Hongbo Qi
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Chao Tong
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).,Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing, China (mainland)
| | - Philip Baker
- Ministry of Education of China International Collaborative Joint Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China (mainland).,Liggins Institute, University of Auckland, Auckland, New Zealand.,College of Life Sciences, University of Leicester, Leicester, United Kingdom
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49
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Shen L, Zhao D, Chen Y, Zhang K, Chen X, Lin J, Li C, Iqbal J, Zhao Y, Liang Y, Wei Y, Feng C. Comparative Proteomics Analysis of Serum Proteins in Gestational Diabetes during Early and Middle Stages of Pregnancy. Proteomics Clin Appl 2019; 13:e1800060. [DOI: 10.1002/prca.201800060] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 05/26/2019] [Indexed: 12/18/2022]
Affiliation(s)
- Liming Shen
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Danqing Zhao
- Department of Obstetrics and GynecologyAffiliated Hospital of Guizhou Medical University Guiyang 550004 P. R. China
| | - Youjiao Chen
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Kaoyuan Zhang
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Xinqian Chen
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Jing Lin
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Cuihua Li
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Javed Iqbal
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Yuxi Zhao
- College of Life Science and OceanographyShenzhen University Shenzhen 518060 P. R. China
| | - Yi Liang
- School of Public HealthGuizhou Medical University Guiyang 550025 P. R. China
| | - Yan Wei
- School of Public HealthGuizhou Medical University Guiyang 550025 P. R. China
| | - Chengyun Feng
- Maternal and Child Health Hospital of Baoan Shenzhen 518100 P. R. China
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Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel. Metabolites 2019; 9:metabo9050090. [PMID: 31067710 PMCID: PMC6572582 DOI: 10.3390/metabo9050090] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 04/24/2019] [Accepted: 04/30/2019] [Indexed: 12/27/2022] Open
Abstract
The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.
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