Association of Circulating Branched-Chain Amino Acids with Gestational Diabetes Mellitus: A Meta-Analysis

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM.

METHODS

Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA).

RESULTS

A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers.

CONCLUSIONS

Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Maternal glycemic status during pregnancy and mid-childhood plasma amino acid profiles: findings from a multi-ethnic Asian birth cohort

BACKGROUND

Increasing maternal glycaemia across the continuum during pregnancy may predispose offspring to subsequent cardiometabolic risk later in life. However, evidence of long-term impacts of maternal glycemic status on offspring amino acid (AA) profiles is scarce. We aimed to investigate the association between maternal antenatal glycaemia and offspring mid-childhood amino acid (AA) profiles, which are emerging cardiometabolic biomarkers.

METHODS

Data were drawn from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) study, a multi-ethnic Asian birth cohort. A subset of 422 mother-child dyads from the GUSTO study, who was followed from early pregnancy to mid-childhood, was included. Mothers underwent an oral glucose tolerance test (OGTT) at 26-28 weeks gestation, with fasting and 2-h plasma glucose concentrations measured and gestational diabetes mellitus (GDM) diagnosed per WHO 1999 guidelines. Offspring fasting plasma samples were collected at mean age 6.1 years, from which AA profiles of nine AAs, alanine, glutamine, glycine, histidine, isoleucine, leucine, valine, phenylalanine, and tyrosine were measured. Total branched-chain amino acids (BCAAs) were calculated as the sum of isoleucine, leucine, and valine concentrations. Multi-variable linear regression was used to estimate the association of maternal glycemic status and offspring mid-childhood AA profiles adjusting for maternal age, ethnicity, maternal education, parity, family history of diabetes, ppBMI, child sex, age and BMI z-scores.

RESULTS

Approximately 20% of mothers were diagnosed with GDM. Increasing maternal fasting glucose was significantly associated with higher offspring plasma valine and total BCAAs, whereas higher 2-h glucose was significantly associated with higher histidine, isoleucine, valine, and total BCAAs. Offspring born to mothers with GDM had higher valine (standardized mean difference 0.27 SD; 95% CI: 0.01, 0.52), leucine (0.28 SD; 0.02, 0.53), and total BCAAs (0.26 SD; 0.01, 0.52) than their counterparts. Inconsistent associations were found between maternal GDM and other amino acids among offspring during mid-childhood.

CONCLUSIONS

Increasing maternal fasting and post-OGTT glucose concentrations at 26-28 weeks gestation were significantly associated with mid-childhood individual and total BCAAs concentrations. The findings suggest that elevated maternal glycaemia throughout pregnancy, especially GDM, may have persistent programming effects on offspring AA metabolism which were strongly associated with adverse cardiometabolic profiles at mid-childhood.

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.

Characterization of Maternal Circulating MicroRNAs in Obese Pregnancies and Gestational Diabetes Mellitus

Maternal obesity (MO) is expanding worldwide, contributing to the onset of Gestational Diabetes Mellitus (GDM). MO and GDM are associated with adverse maternal and foetal outcomes, with short- and long-term complications. Growing evidence suggests that MO and GDM are characterized by epigenetic alterations contributing to the pathogenesis of metabolic diseases. In this pilot study, plasma microRNAs (miRNAs) of obese pregnant women with/without GDM were profiled at delivery. Nineteen women with spontaneous singleton pregnancies delivering by elective Caesarean section were enrolled: seven normal-weight (NW), six obese without comorbidities (OB/GDM(-)), and six obese with GDM (OB/GDM(+)). miRNA profiling with miRCURY LNA PCR Panel allowed the analysis of the 179 most expressed circulating miRNAs in humans. Data acquisition and statistics (GeneGlobe and SPSS software) and Pathway Enrichment Analysis (PEA) were performed. Data analysis highlighted patterns of significantly differentially expressed miRNAs between groups: OB/GDM(-) vs. NW: n = 4 miRNAs, OB/GDM(+) vs. NW: n = 1, and OB/GDM(+) vs. OB/GDM(-): n = 14. For each comparison, PEA revealed pathways associated with oxidative stress and inflammation, as well as with nutrients and hormones metabolism. Indeed, miRNAs analysis may help to shed light on the complex epigenetic network regulating metabolic pathways in both the mother and the foeto-placental unit. Future investigations are needed to deepen the pregnancy epigenetic landscape in MO and GDM.

The Effects of Pregnancy on Amino Acid Levels and Nitrogen Disposition

Limited data are available on the effects of pregnancy on the maternal metabolome. Therefore, the objective of this study was to use metabolomics analysis to determine pathways impacted by pregnancy followed by targeted confirmatory analysis to provide more powerful conclusions about metabolic alterations during pregnancy. Forty-seven pregnant women, 18-50 years of age were included in this study, with each subject serving as their own control. Plasma samples were collected between 25 and 28 weeks gestation and again ≥3 months postpartum for metabolomics analysis utilizing an HILIC/UHPLC/MS/MS assay with confirmatory targeted specific concentration analysis for 10 of the significantly altered amino acids utilizing an LC/MS assay. Principle component analysis (PCA) on metabolomics data clearly separated pregnant and postpartum groups and identified outliers in a preliminary assessment. Of the 980 metabolites recorded, 706 were determined to be significantly different between pregnancy and postpartum. Pathway analysis revealed three significantly impacted pathways, arginine biosynthesis (p = 2 × 10-5 and FDR = 1 × 10-3), valine, leucine, and isoleucine metabolism (p = 2 × 10-5 and FDR = 2 × 10-3), and xanthine metabolism (p = 4 × 10-5 and FDR = 4 × 10-3). Of these we focused analysis on arginine biosynthesis and branched-chain amino acid (BCAA) metabolism due to their clinical importance and interconnected roles in amino acid metabolism. In the confirmational analysis, 7 of 10 metabolites were confirmed as significant and all 10 confirmed the direction of change of concentrations observed in the metabolomics analysis. The data support an alteration in urea nitrogen disposition and amino acid metabolism during pregnancy. These changes could also impact endogenous nitric oxide production and contribute to diseases of pregnancy. This study provides evidence for changes in both the ammonia-urea nitrogen and the BCAA metabolism taking place during pregnancy.

Relationship between Components, Intestinal Microbiota, and Mechanism of Hypoglycemic Effect of the Saggy Ink Cap Medicinal Mushroom (Coprinus Comatus, Agaricomycetes): A Review

Coprinus comatus is rich in a variety of nutrients, which has been reported to display a good hypoglycemic effect. However, there is no consensus on the hypoglycemic mechanism of this mushroom. Intestinal microbiota, a complex and intrinsic system, is closely related to metabolism. In this review, we discussed the potential relationship between certain components of C. comatus and intestinal microbiota to illustrate the possible hypoglycemic mechanism of C. comatus through intestinal microbiota. It will provide a new perspective for the study of hypoglycemic mechanism of C. comatus and promote the development and utilization of this mushroom.

Dynamic changes and early predictive value of branched-chain amino acids in gestational diabetes mellitus during pregnancy

OBJECTIVE

Branched-chain amino acids (BCAAs) are closely associated with type 2 diabetes mellitus, but their roles in gestational diabetes mellitus (GDM) are still controversial. This study aims to explore the dynamic changes of BCAAs during pregnancy and identify potential early biomarkers for GDM.

METHODS

This study is a nested case-control study involved 49 women with GDM and 50 age- and body mass index (BMI)-matched healthy pregnant women. The dynamic changes of valine (Val), isoleucine (Ile), and leucine (Leu) were detected in the first (8-12 weeks) and second trimesters (24-28 weeks) by liquid chromatography-mass spectrometry.

RESULTS

Serum Val, Ile, and Leu were higher in GDM patients than in controls in the first trimester. Compared with the first trimester, the serum Val, Ile, and Leu in GDM patients were decreased in the second trimester. In addition, Val, Ile, and Leu in the first trimester were the risk factors for GDM, and Ile presented a high predictive value for GDM. Ile + age (≥ 35) + BMI (≥ 24) exhibited the highest predictive value for GDM (AUC = 0.902, sensitivity = 93.9%, specificity = 80%).

CONCLUSION

Maternal serum Ile in the first trimester was a valuable biomarker for GDM. Ile combined with advanced maternal age and overweight may be used for the early prediction of GDM.

Differential analysis of the bacterial community in colostrum samples from women with gestational diabetes mellitus and obesity

Gestational Diabetes Mellitus (GDM) and obesity affect the functioning of multiple maternal systems and influence colonization of the newborn gastrointestinal through the breastmilk microbiota (BMM). It is currently unclear how GDM and obesity affect the human BMM composition. Here, we applied 16S-rRNA high-throughput sequencing to human colostrum milk to characterize BMM taxonomic changes in a cohort of 43 individuals classified in six subgroups according to mothers patho-physiological conditions (healthy control (n = 18), GDM (n = 13), or obesity (n = 12)) and newborn gender. Using various diversity indicators, including Shannon/Faith phylogenetic index and UniFrac/robust Aitchison distances, we evidenced that BMM composition was influenced by the infant gender in the obesity subgroup. In addition, the GDM group presented higher microbial diversity compared to the control group. Staphylococcus, Corynebacterium 1, Anaerococcus and Prevotella were overrepresented in colostrum from women with either obesity or GDM, compared to control samples. Finally, Rhodobacteraceae was distinct for GDM and 5 families (Bdellovibrionaceae, Halomonadaceae, Shewanellaceae, Saccharimonadales and Vibrionaceae) were distinct for obesity subgroups with an absolute effect size greater than 1 and a q-value ≤ 0.05. This study represents the first effort to describe the impact of maternal GDM and obesity on BMM.

A healthy dietary pattern with a low inflammatory potential reduces the risk of gestational diabetes mellitus

Purpose

An optimal diet for lowering the risk of gestational diabetes mellitus (GDM) is still to be defined, but may comprise of nutrient intakes, dietary patterns, diet quality, and eating frequency. This study was designed to investigate the contribution of diet in developing GDM in a comprehensive way.

Methods

The dietary intake of overweight or obese women, a risk group for GDM (n = 351), was assessed using 3-day food diaries and diet quality questionnaires in early pregnancy. Eating frequency and nutrient intakes were calculated, and dietary patterns identified using principal component analysis. The inflammatory potential of the diet was determined by calculating the dietary inflammatory index (DII^®) and energy-adjusted DII (E-DII™). GDM was diagnosed with an oral glucose tolerance test at 24–28 gestational weeks.

Results

Higher adherence to ‘healthier dietary pattern’ characterized by consumptions of vegetables and rye bread associated with a reduced risk of GDM (adjusted OR 0.27, 95% CI 0.11–0.70). Higher E-DII score, indicating pro-inflammatory diet, was associated with a 27% higher risk of GDM (adjusted OR 1.27; 95% CI 1.08–1.49) for each E-DII point. In the evaluation of nutrient intakes, total fat, saturated fatty acids (SFAs), and trans fatty acids were higher and fiber lower in women developing GDM compared to women not developing GDM (all p < 0.05). Intakes of total fat, SFAs, and trans fatty acids were also significant predictors for GDM (all p < 0.05).

Conclusions

The results emphasize the importance of an overall healthy diet and limitation of foods with SFAs, and other nutrients with a high inflammatory potential in reducing the risk of GDM.

Trial registration

ClinicalTrials.gov Identifier: NCT01922791, August 14, 2013.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00394-021-02749-z.

Branched chain amino acids-friend or foe in the control of energy substrate turnover and insulin sensitivity?

Branched chain amino acids (BCAA) and their derivatives are bioactive molecules with pleiotropic functions in the human body. Elevated fasting blood BCAA concentrations are considered as a metabolic hallmark of obesity, insulin resistance, dyslipidaemia, nonalcoholic fatty liver disease, type 2 diabetes and cardiovascular disease. However, since increased BCAA amount is observed both in metabolically healthy and obese subjects, a question whether BCAA are mechanistic drivers of insulin resistance and its morbidities or only markers of metabolic dysregulation, still remains open. The beneficial effects of BCAA on body weight and composition, aerobic capacity, insulin secretion and sensitivity demand high catabolic potential toward amino acids and/or adequate BCAA intake. On the opposite, BCAA-related inhibition of lipogenesis and lipolysis enhancement may preclude impairment in insulin sensitivity. Thereby, the following review addresses various strategies pertaining to the modulation of BCAA catabolism and the possible roles of BCAA in energy homeostasis. We also aim to elucidate mechanisms behind the heterogeneity of ramifications associated with BCAA modulation.

Metabolic biomarkers of small and large for gestational age newborns

BACKGROUND

Small for gestational age (SGA) and large for gestational age (LGA) newborns are at increased risk for developmental, metabolic and cardiovascular morbidities.

AIMS

To compare the metabolic biomarkers of SGA and LGA infants with those of appropriate for gestational age (AGA) newborns in order to shed more light on a possible pathogenesis of those morbidities.

STUDY DESIGN

An observational retrospective study.

SUBJECTS

70,809 term newborns divided into AGA, SGA, LGA, and severe subcategories (<3rd percentile or ≥97th percentile).

OUTCOME MEASURES

18 metabolites were measured by dried blood tandem mass spectrometry and compared in between groups in univariate and multivariate logistic regression.

RESULTS

SGA newborns had a significant likelihood for elevated methionine, proline, free carnitine, and reduced valine levels compared to AGA newborns (P < .0001). Severe SGA showed more apparent trends including elevated leucine. LGA newborns had a significant likelihood for low citrulline, glutamine, proline, tyrosine, and elevated leucine levels (P ≤ .0033). Severe LGA newborns showed the same trends, with the exception of citrulline and glutamine.

CONCLUSIONS

SGA and LGA newborns demonstrate distinct metabolic biomarkers in newborn screening. Most of the altered metabolites in the SGA group were elevated while those in the LGA group were decreased in comparison to AGA newborns. These trends were more apparent in the severe SGA subgroup while they mostly remained the same in the severe LGA subgroup. Whether these metabolic changes are involved with or can predict long-term outcome awaits further trials.

Establishment of clinical diagnostic models using glucose, lipid, and urinary polypeptides in gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) has many adverse outcomes that seriously threaten the short-term and long-term health of mothers and infants. This study comprehensively analyzed the clinical diagnostic value of GDM-related clinical indexes and urine polypeptide research results, and established comprehensive index diagnostic models.

METHODS

In this study, diagnostic values from the clinical indexes of serum triglyceride (TRIG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c), and 7 GDM-related urinary polypeptides were analyzed retrospectively. The multiple logistic regression equation, multilayer perceptron neural network model, radial basis function, and discriminant analysis function models of GDM-related indexes were established using machine language.

RESULTS

The results showed that HbA1c had the highest diagnostic value for GDM, with an area under the curve (AUC) of 0.769. When the cut-off value was 4.95, the diagnostic sensitivity and specificity were 70.5% and 70.0%, respectively. Among the seven GDM-related urinary polypeptides, human hemopexin (HEMO) had the highest diagnostic value, with an AUC of 0.690. When the cut-off value was 368.5, the sensitivity and specificity were 79.5% and 43.3%, respectively. The AUC of the multilayer perceptron neural network model was 0.942, followed by binary logistic regression (0.938), radial basis function model (0.909), and the discriminant analysis function model (0.908).

CONCLUSION

The establishment of a GDM diagnostic model combining blood glucose, blood lipid, and urine polypeptide indexes can lay a foundation for exploring machine language and artificial intelligence in diagnostic systems.

Differentiation of gestational diabetes mellitus by nuclear magnetic resonance-based metabolic plasma analysis

This study aimed to investigate the metabolic profile of gestational diabetes mellitus (GDM) at both antepartum and postpartum periods. Seventy pregnant women were divided into three groups: the normal glucose-tolerant group (NGT, n=35), the abnormal glucose-tolerant groups without insulin therapy (A1GDM, n=24) or with insulin therapy (A2GDM, n=11). Metabolic profiles of the plasma were acquired by proton nuclear magnetic resonance (¹H-NMR) spectroscopy and analyzed by multivariate statistical data analysis. The relationship between demographic parameters and the potential metabolite biomarkers was further explored. Group antepartum or postpartum showed similar metabolic trends. Compare with those of the NGT group, the levels of 2-hydroxybutyrate, lysine, acetate, glutamine, succinate, tyrosine, formate, and all three BCAAs (leucine, valine, isoleucine) in the A2GDM group were increased dramatically, and the levels of lysine, acetate, and formate in the A1GDM group were elevated significantly. The dramatically decreased levels of 3-methyl-2-oxovalerate and methanol were observed both in the A1GDM group and A2GDM group. Compare to the A1GDM group, the branched-chain amino acids (BCAAs) of leucine, valine, and isoleucine were increased dramatically in the A2GDM group. The levels of aromatic amino acids (AAAs), tyrosine and phenylalanine, were significantly increased in GDM women, consistent with the severity of GDM. Interference of amino acid metabolism and disturbance in energy metabolism occurred in women with different grades of GDM. Metabolic profiles could reflect the severity of GDM. Plasma BCAA concentrations showing strong positive correlations with weight and pre-delivery BMI. This study provides a new perspective to understand the pathogenesis and etiology of GDM, which may help the clinical management and treatment of GDM.

MicroRNA Signatures as Future Biomarkers for Diagnosis of Diabetes States

Diabetes results from the inability of pancreatic islets to maintain blood glucose concentrations within a normal physiological range. Clinical features are usually not observed until islets begin to fail and irreversible damage has occurred. Diabetes is generally diagnosed based on elevated glucose, which does not distinguish between type 1 and 2 diabetes. Thus, new diagnostic approaches are needed to detect different modes of diabetes before manifestation of disease. During prediabetes (pre-DM), islets undergo stress and release micro (mi) RNAs. Here, we review studies that have measured and tracked miRNAs in the blood for those with recent-onset or longstanding type 1 diabetes, obesity, pre-diabetes, type 2 diabetes, and gestational diabetes. We summarize the findings on miRNA signatures with the potential to stage progression of different modes of diabetes. Advances in identifying selective biomarker signatures may aid in early detection and classification of diabetic conditions and treatments to prevent and reverse diabetes.