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

Potential Causal Association between Plasma Metabolites, Immunophenotypes, and Female Reproductive Disorders: A Two-Sample Mendelian Randomization Analysis

BACKGROUND

While extensive research highlighted the involvement of metabolism and immune cells in female reproductive diseases, causality remains unestablished.

METHODS

Instrumental variables for 486 circulating metabolites (N = 7824) and 731 immunophenotypes (N = 3757) were derived from a genome-wide association study (GWAS) meta-analysis. FinnGen contributed data on 14 female reproductive disorders. A bidirectional two-sample Mendelian randomization study was performed to determine the relationships between exposures and outcomes. The robustness of results, potential heterogeneity, and horizontal pleiotropy were examined through sensitivity analysis.

RESULTS

High levels of mannose were found to be causally associated with increased risks of gestational diabetes (GDM) (OR [95% CI], 6.02 [2.85-12.73], p = 2.55 × 10-6). A genetically predicted elevation in the relative count of circulating CD28-CD25++CD8+ T cells was causally related to increased female infertility risk (OR [95% CI], 1.26 [1.14-1.40], p = 1.07 × 10-5), whereas a high absolute count of NKT cells reduced the risk of ectopic pregnancy (OR [95% CI], 0.87 [0.82-0.93], p = 5.94 × 10-6). These results remained consistent in sensitivity analyses.

CONCLUSIONS

Our study supports mannose as a promising GDM biomarker and intervention target by integrating metabolomics and genomics.

The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis

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.

Postpartum defects in inflammatory response after gestational diabetes precede progression to type 2 diabetes: a nested case-control study within the SWIFT study

BACKGROUND

Gestational diabetes (GDM) is a distinctive form of diabetes that first presents in pregnancy. While most women return to normoglycemia after delivery, they are nearly ten times more likely to develop type 2 diabetes than women with uncomplicated pregnancies. Current prevention strategies remain limited due to our incomplete understanding of the early underpinnings of progression.

AIM

To comprehensively characterize the postpartum profiles of women shortly after a GDM pregnancy and identify key mechanisms responsible for the progression to overt type 2 diabetes using multi-dimensional approaches.

METHODS

We conducted a nested case-control study of 200 women from the Study of Women, Infant Feeding and Type 2 Diabetes After GDM Pregnancy (SWIFT) to examine biochemical, proteomic, metabolomic, and lipidomic profiles at 6-9 weeks postpartum (baseline) after a GDM pregnancy. At baseline and annually up to two years, SWIFT administered research 2-hour 75-gram oral glucose tolerance tests. Women who developed incident type 2 diabetes within four years of delivery (incident case group, n = 100) were pair-matched by age, race, and pre-pregnancy body mass index to those who remained free of diabetes for at least 8 years (control group, n = 100). Correlation analyses were used to assess and integrate relationships across profiling platforms.

RESULTS

At baseline, all 200 women were free of diabetes. The case group was more likely to present with dysglycemia (e.g., impaired fasting glucose levels, glucose tolerance, or both). We also detected differences between groups across all omic platforms. Notably, protein profiles revealed an underlying inflammatory response with perturbations in protease inhibitors, coagulation components, extracellular matrix components, and lipoproteins, whereas metabolite and lipid profiles implicated disturbances in amino acids and triglycerides at individual and class levels with future progression. We identified significant correlations between profile features and fasting plasma insulin levels, but not with fasting glucose levels. Additionally, specific cross-omic relationships, particularly among proteins and lipids, were accentuated or activated in the case group but not the control group.

CONCLUSIONS

Overall, we applied orthogonal, complementary profiling techniques to uncover an inflammatory response linked to elevated triglyceride levels shortly after a GDM pregnancy, which is more pronounced in women who progress to overt diabetes.

Lower serum branched-chain amino acid catabolic intermediates are predictive signatures specific to patients with diabetic foot

Diabetic foot (DF) is one of the serious chronic complications of diabetes. Accurate prediction of the risk of DF may take timely intervention measures to prevent its occurrence. The understanding of metabolomic changes in the progression of diabetes to DF may reveal new targets for interventions. We hypothesized that changes in metabolic pathways during DF would lead to changes in the metabolic profile, which could be predictive signature specific to it. In the present study, 43 participants with type 2 diabetes mellitus (T2DM), 32 T2DM participants with DF (T2DM-F), and 36 healthy subjects were enrolled and their serum samples were used for targeted and nonpolar metabolic analysis with liquid chromatography-tandem mass spectrometry. Differential metabolites related to T2DM-F were discovered in metabolomic analysis. Lasso machine learning regression model, random forest algorithm, causal mediation analysis, disease risk assessment, and clinical decision model were carried out. T2DM and T2DM-F groups could be distinguished with the healthy control group. The differential metabolites were all enriched in alpha-linolenic acid and linoleic acid metabolic pathways including arachidonic acid, docosapentaenoic-acid 22N-6, and docosahexaenoic-acid, which were significantly lower in the T2DM and T2DM-F groups compared with the healthy control group. The differential metabolites in T2DM-F vs T2DM groups were enriched to branched-chain amino acid (BCAA) catabolic pathways involving in methylmalonic acid, succinic acid, 3-methyl-2-oxovaleric acid, and ketoleucine, which were the BCAA catabolic intermediates and significantly lower in the T2DM-F compared with the T2DM group except for succinic acid. We reveal a new set of predictive signatures and associate the lower BCAA catabolic intermediates with the progression from T2DM to T2DM-F.

Formate Might Be a Novel Potential Serum Metabolic Biomarker for Type 2 Diabetic Peripheral Neuropathy

Background

As one of the most frequent complications of type 2 diabetes mellitus (T2DM), diabetic peripheral neuropathy (DPN) shows a profound impact on 50% of patients with symptoms of neuropathic pain, numbness and other paresthesia. No valid serum biomarkers for the prediction of DPN have been identified in the clinic so far. This study is to investigate the potential serum biomarkers for DPN firstly based on 1H-Nuclear Magnetic Resonance (1H-NMR)-based metabolomics technique.

Methods

Thirty-six patients enrolled in this study were divided into two groups: 18 T2DM patients without DPN (T2DM group) and 18 T2DM patients with DPN (DPN group). Serum metabolites were measured via 1H-NMR spectroscopy. Bioinformatic approaches including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), independent sample t-test, Fisher's test, Pearson and Spearman correlation analysis, Stepwise multiple linear regression analysis and receiver operating characteristic (ROC) curve analysis were used to identify the potential altered serum biomarkers.

Results

A total of 20 metabolites were obtained and further analyzed. Formate was identified as the only potential biomarker that decreased in the DPN group with statistical significance after multiple comparisons (p<0.05). Formate also displayed a negative relationship with some elevated clinical markers in DPN. ROC curve analysis showed a good discriminative ability for formate in DPN with an area under the curve (AUC) value of 0.981.

Conclusion

Formate could be considered a potential serum metabolic biomarker for DPN. The reduced level of formate in DPN may be associated with mitochondrial dysfunction and gut microbiota alteration. Monitoring the level of serum formate would be an important strategy for the early diagnosis of DPN and a supplement of formate may be a promising treatment for DPN in the future.

Gut microbiome interacts with pregnancy hormone metabolites in gestational diabetes mellitus

Introduction

Change in the composition of intestinal microbiota is associated with metabolic disorders such as gestational diabetes mellitus (GDM).

Methods

To understand how the microbiota impacts the development of gestational diabetes mellitus, we profiled the intestinal microbiome of 54 pregnant women, including 27 GDM subjects, by employing 16S rRNA gene sequencing. Additionally, we conducted targeted metabolomics assays to validate the identified pathways with overrepresented metabolites.

Results

We evaluated the patterns of changing abundances of operational taxonomic units (OTU) between GDM and the healthy counterparts over three timepoints. Based on the significant OTUs, we inferred 132 significantly altered metabolic pathways in GDM. And identified two overrepresented metabolites of pregnancy hormone, butyrate and mevalonate, as potential intermediary metabolites of intestinal microbiota in GDM. Finally, we validated the impacts of the intestinal microbiota on GDM by demonstrating consistent changes of the serum levels of progesterone, estradiol, butyrate, and mevalonate in an independent cohort.

Discussion

Our findings confirm that alterations in the microbiota play a role in the development of GDM by impacting the metabolism of pregnancy hormones. This provides a novel perspective on the pathogenesis of GDM and introduces potential biomarkers that can be used for early diagnosis and prevention of the disease.

Genetic variants in epoxyeicosatrienoic acid processing and degradation pathways are associated with gestational diabetes mellitus

AIM

To explore the genetic effects of CYP2C8, CYP2C9, CYP2J2, and EPHX2, the key genes involved in epoxyeicosatrienoic acid processing and degradation pathways in gestational diabetes mellitus (GDM) and metabolic traits in Chinese pregnant women.

METHODS

A total of 2548 unrelated pregnant women were included, of which 938 had GDM and 1610 were considered as controls. Common variants were genotyped using the Infinium Asian Screening Array. Association studies of single nucleotide polymorphisms (SNPs) with GDM and related traits were performed using logistic regression and multivariable linear regression analyses. A genetic risk score (GRS) model based on 12 independent target SNPs associated with GDM was constructed. Logistic regression was used to estimate odds ratios and 95% confidence intervals, adjusting for potential confounders including age, pre-pregnancy body mass index, history of polycystic ovarian syndrome, history of GDM, and family history of diabetes, with GRS entered both as a continuous variable and categorized groups. The relationship between GRS and quantitative traits was also evaluated.

RESULTS

The 12 SNPs in CYP2C8, CYP2C9, CYP2J2, and EPHX2 were significantly associated with GDM after adjusting for covariates (all P < 0.05). The GRS generated from these SNPs significantly correlated with GDM. Furthermore, a significant interaction between CYP2J2 and CYP2C8 in GDM (P_Interaction = 0.014, OR_Interaction= 0.61, 95%CI 0.41-0.90) was observed.

CONCLUSION

We found significant associations between GDM susceptibility and 12 SNPs of the four genes involved in epoxyeicosatrienoic acid processing and degradation pathways in a Chinese population. Subjects with a higher GRS showed higher GDM susceptibility with higher fasting plasma glucose and area under the curve of glucose and poorer β-cell function.

Maternal microbiota and gestational diabetes: impact on infant health

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that has been associated with an increased risk of obesity and diabetes in the offspring. Pregnancy is accompanied by tightly regulated changes in the endocrine, metabolic, immune, and microbial systems, and deviations from these changes can alter the mother's metabolism resulting in adverse pregnancy outcomes and a negative impact on the health of her infant. Maternal microbiomes are significant drivers of mother and child health outcomes, and many microbial metabolites are likely to influence the host health. This review discusses the current understanding of how the microbiota and microbial metabolites may contribute to the development of GDM and how GDM-associated changes in the maternal microbiome can affect infant's health. We also describe microbiota-based interventions that aim to improve metabolic health and outline future directions for precision medicine research in this emerging field.

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BACKGROUND

Metabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.

METHODS

Within a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.

RESULTS

A total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference β: 0.07; 95% CI: 0.02, 0.11) and 38:6 (β: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.

CONCLUSIONS

Our data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia

INTRODUCTION

Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M.

METHODS

Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model.

RESULTS

We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR <  0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P _{Hosmer-Lemeshow test} = 0.897).

CONCLUSION

The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.

Noninvasive detection and interpretation of gastrointestinal diseases by collaborative serum metabolite and magnetically controlled capsule endoscopy

Gastrointestinal diseases are complex diseases that occur in the gastrointestinal tract. Common gastrointestinal diseases include chronic gastritis, peptic ulcers, inflammatory bowel disease, and gastrointestinal tumors. These diseases may manifest a long course, difficult treatment, and repeated attacks.

Gastroscopy and mucosal biopsy are the gold standard methods for diagnosing gastric and duodenal diseases, but they are invasive procedures and carry risks due to the necessity of sedation and anesthesia. Recently, several new approaches have been developed, including serological examination and magnetically controlled capsule endoscopy (MGCE). However, serological markers lack lesion information, while MGCE images lack molecular information.

This study proposes combining these two technologies in a collaborative noninvasive diagnostic scheme as an alternative to the standard procedures. We introduce an interpretable framework for the clinical diagnosis of gastrointestinal diseases. Based on collected blood samples and MGCE records of patients with gastrointestinal diseases and comparisons with normal individuals, we selected serum metabolite signatures by bioinformatic analysis, captured image embedding signatures by convolutional neural networks, and inferred the location-specific associations between these signatures.

Our study successfully identified five key metabolite signatures with functional relevance to gastrointestinal disease. The combined signatures achieved discrimination AUC of 0.88. Meanwhile, the image embedding signatures showed different levels of validation and testing accuracy ranging from 0.7 to 0.9 according to different locations in the gastrointestinal tract as explained by their specific associations with metabolite signatures. Overall, our work provides a new collaborative noninvasive identification pipeline and candidate metabolite biomarkers for image auxiliary diagnosis. This method should be valuable for the noninvasive detection and interpretation of gastrointestinal and other complex diseases.

Common single-nucleotide polymorphisms combined with a genetic risk score provide new insights regarding the etiology of gestational diabetes mellitus

AIMS

Few studies have constructed a genetic risk score (GRS) to predict the risk of gestaional diabetes mellitus (GDM). We tested the hypothesis that single-nucleotide polymorphisms (SNPs) confirmed for diabetes and obesity and the GRS are associated with GDM.

METHODS

We conducted a case-control study comprising 971 GDM cases and 1682 controls from the University of Hong Kong Shenzhen Hospital. A total of 1448 SNPs reported with type 2 diabetes (T2D), type 1 diabetes (T1D), and obesity were selected and the GRS based on SNPs associated with GDM was created.

RESULTS

We confirmed that rs10830963 (OR = 1.41,95% CI = 1.25, 1.59) in MTNR1B and rs2206734 (OR = 1.38, 95% CI = 1.22, 1.55) in CDKAL1 were strongly associated with the risk of GDM. Compared with participants with GRS based on T2D SNPs in the low tertile, the ORs of GDM across increasing GRS tertiles were 1.63 (95% CI 1.29, 2.06) and 2.72 (95% CI 2.18, 3.38) in the middle and high tertile, respectively. The positive associations between the GRS and the risk of GDM were also observed in GRS based on obesity/waist-to-hip ratio (WHR)/body mass index (BMI) SNPs. The resulting GRS for each allele increase was significantly associated with higher glycemic indices and lower HOMA-B values for GRS based on T2D SNPs, but not for GRS based on T1D SNPs and GRS based on obesity/WHR/BMI SNPs.

CONCLUSION

These findings indicate that GDM may share a common genetic background with T2D and obesity and that SNPs associated with insulin secretion defects have a vital role in the development of GDM.

Changes in the Gut Metabolic Profile of Gestational Diabetes Mellitus Rats Following Probiotic Supplementation

The roles of gut microbiota and metabolomics in women with gestational diabetes mellitus (GDM) are not well understood. This study investigated the gut metabolomic profiling of GDM rats and GDM rats treated with probiotic supplements. Associations between gut metabolites and microbiota were also studied in GDM rats. Liquid chromatography–mass spectrometry was used to detect gut metabolites in GDM rats and GDM rats treated with probiotic supplements of 0.5 g (low-dose group) or 1 g (high-dose group) for 15 days. Each gram of probiotic supplement contained 5 × 107 colony-forming units (CFU) of Lactobacillus rhamnosus LGG and 1 × 108 CFU of Bifidobacterium animalis subspecies lactis Bb12. The association between gut metabolites and microbiota in GDM rats was investigated using Spearman’s correlation. Finally, 10 rats in the normal pregnant group, eight rats in the GDM model group, eight GDM rats in the low-dose probiotics group, and nine GDM rats in the high-dose probiotics group were further studied. Serum parameters and pancreatic and colon histology were significantly changed in GDM rats, and these were restored using probiotic supplements. In total, 999 gut metabolites were detected in the feces, and GDM rats were distinguished from normal rats. The levels of 44 metabolites were increased in GDM rats, and they were alleviated using probiotic supplements. Changes in metabolites in GDM rats were associated with amino acids and bile acids metabolism signaling pathways. Furthermore, changes in metabolites after probiotic supplementation were associated with porphyrin and chlorophyll metabolism pathways. We found that the Allobaculum genus displayed strong positive correlations, whereas the Bryobacter and Gemmatimonas genera displayed strong negative correlations with metabolisms of amino acids and bile acids in GDM rats. The Lactobacillus and Bifidobacterium genera were positively correlated with gut metabolites. Overall, our results showed that metabolism signaling pathways of amino acids and bile acids are associated with the development of GDM. Probiotic supplements alleviate the pathology of GDM through the metabolism pathways of amino acids, bile acids, porphyrin, and chlorophyll.

Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

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.