Amino acid profiling in the gestational diabetes mellitus

Correlation between newborn weight and serum BCAAs in pregnant women with diabetes

BACKGROUND

Branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential amino acids for mammals. Maternal BCAAs during pregnancy have been associated with newborn development. Meanwhile, BCAAs have been tightly linked with insulin resistance and diabetes in recent years. Diabetes in pregnancy is a common metabolic disorder. The current study aims to assess the circulating BCAA levels in pregnant women with diabetes and their relationship with neonatal development.

METHODS

The serum concentrations of BCAAs and their corresponding branched-chain α-keto acids (BCKAs) catabolites in 33 pregnant women with normal glucose tolerance, 16 pregnant women with type 2 diabetes before pregnancy (PDGM), and 15 pregnant women with gestational diabetes mellitus (GDM) were determined using a liquid chromatography system coupled to a mass spectrometer. The data were tested for normal distribution and homogeneity of variance before statistical analysis. Correlations were computed with the Pearson correlation coefficient.

RESULTS

The maternal serum BCAAs and BCKAs levels during late pregnancy were higher in women with PGDM than those in healthy women. Meanwhile, the circulating BCAAs and BCKAs showed no significant changes in women with GDM compared with those in healthy pregnant women. Furthermore, the circulating BCAA and BCKA levels in women with PGDM were positively correlated with the weight of the newborn. The circulating leucine level in women with GDM was positively correlated with the weight of the newborn. BCAA and BCKA levels in healthy pregnant women showed no correlation with newborn weight.

CONCLUSIONS

The serum BCAAs in pregnant women with diabetes, which was elevated in PGDM but not GDM, were positively correlated with newborn weight. These findings highlight potential approaches for early identification of high-risk individuals and interventions to reduce the risk of adverse pregnancy outcomes.

Amino Acids Profile in Children with Acute Brucellosis

BACKGROUND

Many new cases of brucella infections are seen in Turkey every year, especially in March, April, and May, due to the consumption of local unpasteurized cheese. Amino acids profiles have not been studied in brucellosis so far.

AIMS

The amino acid profiles may be affected by infectious diseases. Our study aims to evaluate the plasma amino acid profile in the progression of acute brucellosis.

METHODS

Plasma amino acid profile was performed by an 8045 LC-MS / MS device (Shimadzu 8045, Japan) using JASEM amino acid kit.

RESULTS

Analysis of 45 amino acid profiles was made and results profiles showed significant differences in concentrations and types of amino acids in brucella patients. We observed a significant difference in terms of alanine, arginine, aspartic acid, glutamine, glutamic acid, glycine, isoleucine, ornithine, phenylalanine, proline, tyrosine, valine, alpha-aminoadipic acid, alpha-amino-pimelic acid, argininosuccinic acid, gamma-aminobutyric acid, thiaproline, 1-methylhistidine, 3-methylhistidine, hydroxylysine, hydroxyproline, cystine, serotonin, ethanolamine, and taurine (p-value < 0.05 for each). No significant differences were determined regarding asparagine, citrulline, histidine, leucine, alloisoleucine, lysine, methionine, serine, threonine, tryptophan, anserine, alpha aminobutyric acid, beta aminoisobutyric acid, beta-alanine, cystathionine, histamine, and 5-oh-trp (p-value > 0.05 for all).

CONCLUSION

Patients with brucellosis have a specific profile of amino acids which may reflect sequelae of pathological and metabolic biochemical changes in the disease process due to the growth of Brucella spp. in the human body leading to an imbalance of amino acid levels.

Profile of plasma free amino acids, carnitine and acylcarnitines, and JAK2v617f mutation as potential metabolic markers in children with type 1 diabetic nephropathy

Fifty diabetic nephropathy (DN) children with type 1 diabetes mellitus (T1DM) and 50 healthy matched controls were included. Chromatographic assays of 14 amino acids, free carnitine and 27 carnitine esters using high-performance liquid chromatography/electrospray ionization-mass spectroscopy, and genetic testing for JAK2v617f mutation using real-time PCR were performed. Patients had significantly lower levels of tyrosine, branched-chain amino acids (BCAAs), and BCAA/AAA (aromatic chain amino acids) ratios, glycine, arginine, ornithine, free carnitine and some carnitine esters (C5, 6, 12 and 16) and higher phenylalanine, phenylalanine/tyrosine ratio and C18 compared with the controls and in the macro-albuminuria vs. the microalbuminuria group (p < 0.05 for all) except for free carnitine. Plasma carnitine was negatively correlated with eGFR (r = -0.488, p = 0.000). There were significant positive correlations between tyrosine with UACR ratio (r = 0.296, p = 0.037). The plasma BCAA/AAA ratio showed significant negative correlations with UACR (r = -0.484, p = 0.000). There was a significantly higher frequency of the JAK2V617F gene mutation in diabetic nephropathy patients compared with the control group and in macro-albuminuria than the microalbuminuria group (p = 0.000) for both. When monitoring children with T1DM, plasma free amino acids and acylcarnitine profiles should be considered, especially if they have tested positive for JAK2V617F for the early diagnosis of DN.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Branched-chain and aromatic amino acid levels response to an oral glucose load associated with gestational diabetes mellitus

Serum branched chain amino acids (BCAAs) and aromatic amino acids (AAAs) are associated with obesity, insulin resistance and type 2 diabetes mellitus (T2DM). We investigated the levels of these amino acids in women with gestational diabetes mellitus (GDM) and examined their changes in response to an oral glucose tolerance test (OGTT). 110 women were enrolled and underwent a 75-g OGTT during their second trimester; 43 women were diagnosed with GDM and 67 women did not have GDM (non-GDM women). During the OGTT, fasting, 1-h, and 2-h blood samples were obtained. BCAA and AAA levels were measured by liquid chromatography-tandem mass spectrometry. The differences in BCAA and AAA levels between GDM and non-GDM women were not evident during fasting but became significant after glucose loading. Glucose ingestion decreased the levels of BCAAs and AAAs in both groups. Notably, GDM women showed a delayed and blunted decrease in these amino acids compared to non-GDM women. The risks of 2-h changes in BCAAs and AAAs for GDM women were significant. We identified that the differences in BCAA and AAA levels between GDM women and controls, which were not evident during fasting, could be provoked by performing an OGTT.

Analysis of plasma free amino acids in diabetic rat and the intervention of Ginkgo biloba leaves extract using hydrophilic interaction liquid chromatography coupled with tandem mass-spectrometry

Amino acids (AAs) are important metabolites that are related with diabetes. However, their roles in the initiation and development of diabetes mellitus (DM), especially in the treatment of Ginkgo biloba leaves extract (GBE) have not been fully explored. Thus, we investigated the roles that AAs played in the progression and GBE supplementation of DM rat induced by streptozotocin. The rats were randomly divided into a normal control group treated with drug-free solution, a normal control group treated with GBE, a DM group treated with drug-free solution, and DM group treated with GBE; and maintained on this protocol for 9 weeks. Rat plasma was collected from the sixth week to the ninth week and then analyzed with the optimized hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry method. A total of 17 AAs with differential levels were monitored to indicate dysfunction of AAs metabolism to confirm the occurrence and development of DM. Treatment with GBE partially reversed the changes seen in seven AAs including leucine, isoleucine, tyrosine, glutamic acid, asparagines, lysine and alanine in DM rats, indicating that GBE could prevent the occurrence and development of DM by acting on AAs metabolism. The improvement of those AAs metabolism disorders may play a considerable role in the treatment of GBE on the occurrence and development of DM. Those findings potentially promote the understanding of the pathogenic progression of DM and reveal the therapeutic mechanism of GBE against DM.

Micronutrients of the one-carbon metabolism cycle are altered in mothers and neonates by gestational diabetes and are associated with weight, height and head circumference at birth

While several studies have previously described the levels of one-carbon metabolism-related micronutrients in women with gestational diabetes mellitus (GDM) and their neonates, the results in these literature reports have been contradictory. We hypothesized that the concentrations of micronutrients involved in the one-carbon cycle are altered in pregnant women and their neonates by GDM, and that these changes could further modify the neonatal anthropometry. Micronutrient levels were measured in 123 pregnant women with normal glucose levels (M-ND) and their neonates (N-ND), as well as in 54 pregnant women with gestational diabetes (M-GDM) and their neonates (M-GDM). Folate and vitamin B12 levels were measured via competitive ELISA, and betaine, choline, and glycine levels were measured via ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS/MS). Vitamin B12 and Glycine were found to be higher in M-GDM compared to M-ND. N-GDM had higher levels of folic acid and vitamin B12 and lower levels of betaine and choline compared to N-ND. In general, neonates presented with high concentrations of micronutrients compared to their mothers, and the fetus/maternal ratio of micronutrients was higher among the N-ND as compared to the N-GDM. Micronutrients were also variably associated with anthropometric measurements. The association of betaine with neonatal anthropometry in N-GDM is highlighted. In summary, our results implicate a potential role of GDM in altering the levels of one-carbon metabolism-related micronutrients among pregnant women and their neonates. Likewise, our results also elucidate a potential association between the concentrations of micronutrients and the weight, height, and head circumference of neonates.

Comparison of Diagnostic Values of Maternal Arginine Concentration for Different Pregnancy Complications: A Systematic Review and Meta-Analysis

Abnormal arginine metabolism contributes to the development of intrauterine growth restriction (IUGR), preeclampsia (PE), and gestational diabetes mellitus (GDM), which increase the health burden of mothers and induce adverse birth outcomes. However, associations between maternal arginine concentration and different pregnancy complications have not been systematically compared. The PubMed, ScienceDirect, and Web of Science databases were searched for peer-reviewed publications to evaluate the diagnostic value of plasma arginine concentration in complicated pregnancies. Standardized mean difference (SMD) of the arginine concentration was pooled by a random effects model. The results show that increased maternal arginine concentrations were observed in IUGR (SMD: 0.48; 95% CI: 0.20, 0.76; I² = 47.0%) and GDM (SMD: 0.46; 95% CI: 0.11, 0.81; I² = 82.3%) cases but not in PE patients (SMD: 0.21; 95% CI: −0.04, 0.47; I² = 80.3%) compared with the normal cohorts. Subgroup analyses indicated that the non-fasting circulating arginine concentration in third trimester was increased significantly in GDM and severe IUGR pregnancies, but the change mode was dependent on ethnicity. Additionally, only severe PE persons were accompanied by higher plasma arginine concentrations. These findings suggest that maternal arginine concentration is an important reference for assessing the development of pregnancy complications.

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.

Early markers of gestational diabetes mellitus: what we know and which way forward?

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.

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.

Mass spectrometry with derivatization method for concurrent measurement of amino acids and acylcarnitines in plasma of diabetic type 2 patients with diabetic nephropathy

BACKGROUND

Amino acids (AAs) and acylcarnitines play a key role in metabolic disease and can be used as biomarkers of various diseases such as malignancies, type 2 diabetes (T2D), insulin resistance, and cardiovascular diseases, therefore, designing an accurate and simple laboratory method that simultaneously measure both groups of substances, could improve the process of analytes quantification. In this research, a flow injection tandem mass spectrometry (FI-MS/MS) method for simultaneous measurement of AAs and acylcarnitines in addition to results of validation is explained.

METHODS

Samples were mixed with internal standards and after derivatization (with butanolic-HCL), AAs, and acylcarnitines were quantified by tandem mass spectrometry (SCIEX API 3200). Analytical performance studies were designed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines including precision, accuracy, linearity, and limit of detection-quantification (LOD-LOQ) experiments. Samples from patients with T2D in different stages of kidney disease were also analyzed to ensure the clinical usage of the method.

RESULTS

Performance evaluation of the method demonstrated adequate results. The mean of estimated inter-assay precision (reported as a coefficient variation) for AAs and acylcarnitines were less than 8.7% and 12.3%, the estimated mean bias was below 8.8% and 10.2% respectively. LOD of analytes ranged between 0.6-10 μmol per liter (μmol/L) for AAs and 0.02-1 μmol/L for acylcarnitines. LOQ analytes showed a range of 2-25 μmol/L and 0.05-5 μmol/L for AAs and carnitine/acylcarnitines respectively. In diabetic patients sample analysis, a significant increase in acylcarnitines (C2, C4, C5DC, C6, C8, C10, C14) and citrulline with a significant decrease in valine were seen in patients with severely increased albuminuria.

CONCLUSION

FI-MS/MS method with pre-injection derivatization with butanolic-HCL can be used for concurrent measurement of AAs and carnitine/acylcarnitines in a short time and it satisfies the analytical performance requirements. This method is applied for AAs and carnitine/acylcarnitines measurement in patient with T2DM and results show some of the acylcarnitines and AAs can be involved in diabetic nephropathy development.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40200-021-00786-3.

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.

Associations of Arginine with Gestational Diabetes Mellitus in a Follow-Up Study

In the reported study we applied the targeted metabolomic profiling employing high pressure liquid chromatography coupled with triple quadrupole tandem mass spectrometry (HPLC–MS/MS) to understand the pathophysiology of gestational diabetes mellitus (GDM), early identification of women who are at risk of developing GDM, and the differences in recovery postpartum between these women and normoglycemic women. We profiled the peripheral blood from patients during the second trimester of pregnancy and three months, and one year postpartum. In the GDM group Arg, Gln, His, Met, Phe and Ser were downregulated with statistical significance in comparison to normoglycemic (NGT) women. From the analysis of the association of all amino acid profiles of GDM and NGT women, several statistical models predicting diabetic status were formulated and compared with the literature, with the arginine-based model as the most promising of the screened ones (area under the curve (AUC) = 0.749). Our research results have shed light on the critical role of arginine in the development of GDM and may help in precisely distinguishing between GDM and NGT and earlier detection of GDM but also in predicting women with the increased type 2 diabetes mellitus (T2DM) risk.

Endocrinology and Metabolism Research Institute from inception to maturity: an overview of 25-year activity

Endocrinology and Metabolism Research Institute (EMRI) was founded in 1993. EMRI progressed step by step from inception and reached to its maturation during the past 25 years. EMRI has expanded and progressed in different aspects including human resources and infrastructures (laboratories and new technologies) and has obtained the first rank in the country in endocrinology research. It has also collaborated with regional and international organizations such as World Health Organization (WHO), International Osteoporosis Foundation (IOF), and American Association of Clinical Endocrinologists (AACE). This article provides an overview of EMRI activities during a quarter of a century.

Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery

Background

Pregnant women with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM) are at increased risks of pre-term labor, hypertension and preeclampsia. In this study, metabolic profiling of blood samples collected from GDM, T2DM and control pregnant women was undertaken to identify potential diagnostic biomarkers in GDM/T2DM and compared to pregnancy outcome.

Methods

Sixty-seven pregnant women (21 controls, 32 GDM, 14 T2DM) in their second trimester underwent targeted metabolomics of plasma samples using tandem mass spectrometry with the Biocrates MxP^® Quant 500 Kit. Linear regression models were used to identify the metabolic signature of GDM and T2DM, followed by generalized linear model (GLMNET) and Receiver Operating Characteristic (ROC) analysis to determine best predictors of GDM, T2DM and pre-term labor.

Results

The gestational age at delivery was 2 weeks earlier in T2DM compared to GDM and controls and correlated negatively with maternal HbA1C and systolic blood pressure and positively with serum albumin. Linear regression models revealed elevated glutamate and branched chain amino acids in GDM + T2DM group compared to controls. Regression models also revealed association of lower levels of triacylglycerols and diacylglycerols containing oleic and linoleic fatty acids with pre-term delivery. A generalized linear model ROC analyses revealed that that glutamate is the best predictors of GDM compared to controls (area under curve; AUC = 0.81). The model also revealed that phosphatidylcholine diacyl C40:2, arachidonic acid, glycochenodeoxycholic acid, and phosphatidylcholine acyl-alkyl C34:3 are the best predictors of GDM + T2DM compared to controls (AUC = 0.90). The model also revealed that the triacylglycerols C17:2/36:4 and C18:1/34:1 are the best predictors of pre-term delivery (≤ 37 weeks) (AUC = 0.84).

Conclusions

This study highlights the metabolite alterations in women in their second trimester with diabetes mellitus and identifies predictive indicators of pre-term delivery. Future studies to confirm these associations in other cohorts and investigate their functional relevance and potential utilization for targeted therapies are warranted.

Diabetes Leads to Alterations in Normal Metabolic Transitions of Pregnancy as Revealed by Time-Course Metabolomics

Women with diabetes during pregnancy are at increased risk of poor maternal and neonatal outcomes. Despite this, the effects of pre-gestational (PGDM) or gestational diabetes (GDM) on metabolism during pregnancy are not well understood. In this study, we utilized metabolomics to identify serum metabolic changes in women with and without diabetes during pregnancy and the cord blood at birth. We observed elevations in tricarboxylic acid (TCA) cycle intermediates, carbohydrates, ketones, and lipids, and a decrease in amino acids across gestation in all individuals. In early gestation, PGDM had elevations in branched-chain amino acids and sugars compared to controls, whereas GDM had increased lipids and decreased amino acids during pregnancy. In both GDM and PGDM, carbohydrate and amino acid pathways were altered, but in PGDM, hemoglobin A1c and isoleucine were significantly increased compared to GDM. Cord blood from GDM and PGDM newborns had similar increases in carbohydrates and choline metabolism compared to controls, and these alterations were not maternal in origin. Our results revealed that PGDM and GDM have distinct metabolic changes during pregnancy. A better understanding of diabetic metabolism during pregnancy can assist in improved management and development of therapeutics and help mitigate poor outcomes in both the mother and newborn.

Prenatal Amino Acid Supplementation to Improve Fetal Growth: A Systematic Review and Meta-Analysis

Aberrant fetal growth remains a leading cause of perinatal morbidity and mortality and is associated with a risk of developing non-communicable diseases later in life. We performed a systematic review and meta-analysis combining human and animal studies to assess whether prenatal amino acid (AA) supplementation could be a promising approach to promote healthy fetal growth. PubMed, Embase, and Cochrane libraries were searched to identify studies orally supplementing the following AA groups during gestation: (1) arginine family, (2) branched chain (BCAA), and (3) methyl donors. The primary outcome was fetal/birth weight. Twenty-two human and 89 animal studies were included in the systematic review. The arginine family and, especially, arginine itself were studied the most. Our meta-analysis showed beneficial effects of arginine and (N-Carbamyl) glutamate (NCG) but not aspartic acid and citrulline on fetal/birth weight. However, no effects were reported when an isonitrogenous control diet was included. BCAA and methyl donor supplementation did not affect fetal/birth weight. Arginine family supplementation, in particular arginine and NCG, improves fetal growth in complicated pregnancies. BCAA and methyl donor supplementation do not seem to be as promising in targeting fetal growth. Well-controlled research in complicated pregnancies is needed before ruling out AA supplements or preferring arginine above other AAs.

Amino acids levels in early pregnancy predict subsequent gestational diabetes

BACKGROUND

We aimed to estimate the performance of amino acids levels in predicting the risk of subsequent gestational diabetes mellitus (GDM).

METHODS

A total of 431 women at 12 to 16 weeks of gestation in the Department of Obstetrics and Gynecology of the Second Affiliated Hospital of Soochow University were recruited. High-performance liquid chromatography electrospray tandem mass spectrometry was used to measure amino acids levels in maternal blood at 12 to 16 weeks of gestation. At 24 to 28 weeks of gestation, all participants were administered 75-g oral glucose tolerance tests for the diagnosis of GDM.

RESULTS

Alanine, isoleucine, and tyrosine levels in early pregnancy were significantly different between women who developed GDM and those who remained normal glucose tolerant. Logistic regressions showed that after adjustments for age, parity, body mass index, family history of diabetes, γ-glutamyltranspeptidase, triglycerides, fasting glucose and fasting insulin levels, alanine (odds ratio [OR], 1.46; 95% CI, 1.05-2.04; P = .027), isoleucine (OR, 1.48; 95% CI, 1.12-1.96; P = .0062), and tyrosine (OR, 1.46; 95% CI, 1.07-2.03; P = .020) levels in early pregnancy were independently associated with subsequent GDM. The addition of isoleucine and tyrosine into the conventional model improved the area under curve from 0.692 to 0.737 (P = .036) and significantly increased the net reclassification improvement (+13.7%, P = .0025).

CONCLUSIONS

The present study suggests that elevated isoleucine, tyrosine, and alanine levels are independently and significantly associated with subsequent incidence of GDM. New models including conventional risk factors, isoleucine and tyrosine levels in early pregnancy might help physicians identify high-risk population of GDM.

Divergent Changes in Serum Branched-Chain Amino Acid Concentrations and Estimates of Insulin Resistance throughout Gestation in Healthy Women

BACKGROUND

Branched-chain amino acid (BCAA) concentrations in the blood have been correlated with insulin resistance, but this relation throughout gestation (period in which insulin resistance typically increases) is unclear.

OBJECTIVE

The objective of this study was to determine the associations between changes in BCAA concentrations and estimates of insulin resistance throughout gestation.

METHODS

Serum BCAA (Leu, Ile, Val) concentrations and insulin resistance/sensitivity [i.e., homeostatic model assessment-2 of insulin resistance (HOMA2-IR), estimated metabolic clearance rate (MCR) of glucose, and estimated first- and second-phase insulin responses] were assessed at early (EP; 8.5 ± 0.2 wk) and/or late (LP; 29.2 ± 0.8 wk) pregnancy in 53 healthy women from the Glowing cohort. Adjusted Spearman correlations were used to evaluate the association between BCAA and insulin resistance/sensitivity measures at EP and LP, adjusted for body fat percentage and gestational weight gain (GWG). A multiple linear regression analysis was used to assess the association between changes in HOMA2-IR and BCAAs throughout gestation. Groups were made post hoc based on the mean percentage change (10% decrease) in Leu throughout gestation, creating a group with a ≥10% decrease in LeuLP-EP (BELOW) and a <10% decrease in LeuLP-EP (ABOVE), and Student's t tests were performed to assess differences between groups.

RESULTS

Leu and Ile concentrations positively correlated with HOMA2-IR at both time points, but these relations at EP disappeared/weakened when adjusted for body fat percentage. From EP to LP, the change in Leu (LeuLP-EP) was negatively associated with the change in HOMA2-IR (HOMA2-IRLP-EP) (β = -0.037, P = 0.006). MCR was lower in the BELOW group compared with the ABOVE group, whereas there was no difference in HOMA2-IR between groups.

CONCLUSIONS

In this pregnancy cohort, BCAA concentrations decreased throughout gestation, whereas the mean insulin resistance did not change. These data do not support a connection between changes in blood BCAA concentrations and estimates of insulin resistance in pregnant women. This trial is registered at clinicaltrials.gov as NCT01131117.

Metabolites and metabolic pathways associated with glucocorticoid resistance in pregnant African-American women

Glucocorticoid resistance (GR) is associated with exposure to chronic stress and an increased risk of metabolic and inflammatory disorders in both animal and human populations. Studies on ethnic disparities highlight the African-American (AA) population as having a high propensity to both GR and chronic stress exposure. Glucocorticoids and inflammation play a very important role in pregnancy outcome and fetal development. To date, however, the metabolites and metabolic pathways associated with GR during pregnancy have not been identified, obscuring the mechanisms by which adverse health consequences arise, and thus impeding targeted therapeutic intervention. The objective of this study was to perform untargeted high-resolution metabolomics (HRM) profiling on 273 pregnant AA women, to identify metabolites and metabolic pathways associated with GR during the first trimester of pregnancy and to evaluate their cross-sectional association with birth outcomes and psychosocial variables related to chronic stress exposure. For this study, GR was determined by the concentration of dexamethasone required for 50% inhibition (Dex IC50) of the cytokine tumor-necrosis factor alpha (TNF-alpha) release in vitro in response to a standard dose of lipopolysaccharide. The results for Metabolome-Wide Association Studies (MWAS) and pathway enrichment analysis for serum metabolic associations with Dex IC50, showed energy (nicotinamide and TCA cycle), amino acid, and glycosphingolipid metabolism as top altered pathways. Bioinformatic analysis showed that GR, as indicated by elevated Dex IC50 in the pregnant women, was associated with increased inflammatory metabolites, oxidative stress related metabolites, increased demand for functional amino acids to support growth and development, and disruption in energy-related metabolites. If confirmed in future studies, targeting these physiologically significant metabolites and metabolic pathways may lead to future assessment and intervention strategies to prevent inflammatory and metabolic complications observed in pregnant populations.

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GR is associated with chronic stress and is a risk factor for adverse health outcomes, especially among African Americans.

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Metabolites and metabolic pathways associated with GR relate to energy production, amino acid metabolism, and inflammation.

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Findings provide a foundation for future studies investigating risk factors in this health disparity population.

Exploration of the microbiota and metabolites within body fluids could pinpoint novel disease mechanisms

Thanks to the emergence and recent advances in high-throughput sequencing technologies, it is becoming more evident every day that changes in the microbiome composition are linked to a myriad of health conditions. Despite this, the mechanisms of host-microbiota signalling remain largely unknown. The microbiome has an extensive metabolic activity that leads to the generation of a large number of compounds that are likely to influence host health. Therefore, the microbiome-host cross-talk is in part mediated by microbial-derived metabolites. Unlike metagenomics, which only provides information about microbial genes and thus the microbiome functional potential, metabolic phenotyping is well suited to capture their actual metabolic activity. Here, we provide an overview of these approaches and propose an integration of metagenomics, as a microbiome compositional readout, with faecal and plasma/urine metabolomics, as a functional readout, to unravel novel mechanisms linking the microbiome to host health and disease.

Omics-based biomarkers in the diagnosis of diabetes

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.

Understanding glycaemic control and current approaches for screening antidiabetic natural products from evidence-based medicinal plants

Type 2 Diabetes Mellitus has reached epidemic proportions as a result of over-nutrition and increasingly sedentary lifestyles. Current therapies, although effective, are not without limitations. These limitations, the alarming increase in the prevalence of diabetes, and the soaring cost of managing diabetes and its complications underscores an urgent need for safer, more efficient and affordable alternative treatments. Over 1200 plant species are reported in ethnomedicine for treating diabetes and these represents an important and promising source for the identification of novel antidiabetic compounds. Evaluating medicinal plants for desirable bioactivity goes hand-in-hand with methods in analytical biochemistry for separating and identifying lead compounds. This review aims to provide a comprehensive summary of current methods used in antidiabetic plant research to form a useful resource for researchers beginning in the field. The review summarises the current understanding of blood glucose regulation and the general mechanisms of action of current antidiabetic medications, and combines knowledge on common experimental approaches for screening plant extracts for antidiabetic activity and currently available analytical methods and technologies for the separation and identification of bioactive natural products. Common in vivo animal models, in vitro models, in silico methods and biochemical assays used for testing the antidiabetic effects of plants are discussed with a particular emphasis on in vitro methods such as cell-based bioassays for screening insulin secretagogues and insulinomimetics. Enzyme inhibition assays and molecular docking are also highlighted. The role of metabolomics, metabolite profiling, and dereplication of data for the high-throughput discovery of novel antidiabetic agents is reviewed. Finally, this review also summarises sample preparation techniques such as liquid-liquid extraction, solid phase extraction, and supercritical fluid extraction, and the critical function of nuclear magnetic resonance and high resolution liquid chromatography-mass spectrometry for the dereplication, putative identification and structure elucidation of natural compounds from evidence-based medicinal plants.

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

CONTEXT

Recently, the relationship between branched-chain amino acids (BCAAs) and diabetes mellitus (DM) has attracted worldwide attention. However, the results related to plasma BCAAs concentrations and gestational diabetes mellitus (GDM) lack statistical power due to the small sample size of a single article.

OBJECTIVES

This study quantitatively summarized current observational studies to evaluate the association between plasma BCAAs concentration levels and GDM.

METHODS

A systematic search was performed to select eligible publications using PubMed and EMBASE databases until July 23, 2018. The references of relevant articles were also manually searched. The quality evaluation of included studies was according to the guidelines of the Newcastle-Ottawa Scale (NOS). Data were analyzed with Review Manager 5.3 and STATA 14.0 software. In total, seven articles (including eight studies) involving 432 subjects were included.

RESULTS

The results showed that all three-individual plasma BCAAs concentration levels in the GDM group were higher than those in the control group (leucine: SMD = 3.76, 95% CI: 1.70 - 5.82, P (SMD) < 0.001; isoleucine: SMD = 3.15, 95% CI: 1.42 - 4.87, P (SMD) < 0.001; valine: SMD = 2.77, 95% CI: 1.21 - 4.32, P (SMD) = 0.001), and the differences were statistically significant. In addition, subgroup analysis indicated that age, body mass index (BMI), publication year, and ethnicity were positively associated with plasma BCAAs concentrations in GDM.

CONCLUSIONS

Plasma BCAAs, as potential biomarkers, might be associated with GDM risk, which provides useful information for the prevention and early diagnosis of GDM.

Impact of Arginine Nutrition and Metabolism during Pregnancy on Offspring Outcomes

By serving as a precursor for the synthesis of nitric oxide, polyamines, and other molecules with biological importance, arginine plays a key role in pregnancy and fetal development. Arginine supplementation is a potential therapy for treating many human diseases. An impaired arginine metabolic pathway during gestation might produce long-term morphological or functional changes in the offspring, namely, developmental programming to increase vulnerability to developing a variety of non-communicable diseases (NCDs) in later life. In contrast, reprogramming is a strategy that shifts therapeutic interventions from adulthood to early-life, in order to reverse the programming processes, which might counterbalance the rising epidemic of NCDs. This review presented the role of arginine synthesis and metabolism in pregnancy. We also provided evidence for the links between an impaired arginine metabolic pathway and the pathogenesis of compromised pregnancy and fetal programming. This was followed by reprogramming strategies targeting the arginine metabolic pathway, to prevent the developmental programming of NCDs. Despite emerging evidence from experimental studies showing that targeting the arginine metabolic pathway has promise as a reprogramming strategy in pregnancy to prevent NCDs in the offspring, these results need further clinical application.

Microbiome Alteration in Type 2 Diabetes Mellitus Model of Zebrafish

Understanding the gut microbiota in metabolic disorders, including type 2 diabetes mellitus (T2DM), is now gaining importance due to its potential role in disease risk and progression. We previously established a zebrafish model of T2DM, which shows glucose intolerance with insulin resistance and responds to anti-diabetic drugs. In this study, we analysed the gut microbiota of T2DM zebrafish by deep sequencing the 16S rRNA V3-V4 hypervariable regions, and imputed a functional profile using predictive metagenomic tools. While control and T2DM zebrafish were fed with the same kind of feed, the gut microbiota in T2DM group was less diverse than that of the control. Predictive metagenomics profiling using PICRUSt revealed functional alternation of the KEGG pathways in T2DM zebrafish. Several amino acid metabolism pathways (arginine, proline, and phenylalanine) were downregulated in the T2DM group, similar to what has been previously reported in humans. In summary, we profiled the gut microbiome in T2DM zebrafish, which revealed functional similarities in gut bacterial environments between these zebrafish and T2DM affected humans. T2DM zebrafish can become an alternative model organism to study host-bacterial interactions in human obesity and related diseases.

Simultaneous determination of amino acids, purines and derivatives in serum by ultrahigh-performance liquid chromatography/tandem mass spectrometry

RATIONALE

Amino acids, purines and derivatives play a key role in physiological and pathological processes, such as the development of gestational diabetes mellitus. However, little literature has reported the simultaneous quantification of amino acids and purines. Therefore, a reliable and robust method for the determination of amino acids and purines is necessary.

METHODS

A rapid and simple method based on ultrahigh-performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS) was developed to determine the concentrations of twenty amino acids or derivatives and three purines or derivatives in serum.

RESULTS

The method was validated for linearity of calibration curve, limit of detection, limit of quantification, inter-day and intra-day precision, recovery, stability and matrix effect. The correlation coefficients of calibration curves were higher than 0.993 except for isoleucine. The recoveries ranged from 78.4% to 128.0% for three different concentrations of the spiked analytes. The matrix effect value ranged from -7.3% to 9.2%.

CONCLUSIONS

A rapid and simple UHPLC/MS/MS method for the simultaneous measurement of amino acids, purines and derivatives in serum samples was developed. The method was successfully applied to determine and compare the concentrations of analytes between gestational diabetes mellitus samples and controls.

The Initial Oral Microbiota of Neonates Among Subjects With Gestational Diabetes Mellitus

Objective: The objective was to investigate the potential effect of gestational diabetes mellitus on the initial neonatal oral microbiome community structure. Methods: Oral samples were collected from 20 full-term, vaginally delivered newborns with sterile swabs. Nine of them had mothers diagnosed with gestational diabetes mellitus (GDM group), while 11 had non-diabetic mothers (NDM group). The oral microbiota was analyzed using multi-barcode 16S rRNA sequencing on Illumina MiSeq system. Results: The results showed that the birth weight, gestational age and gestational weight gain were significantly higher in NDM group. There was a significant correlation between gestational age and birth weight. Neonatal oral microbiome was composed of five dominant phyla from Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Tenericutes. Compared to NDM group, a higher alpha diversity and reduction of phylum Firmicutes were observed in GDM group. Genus Lactobacillus dominated in NDM group, while Alistipes, Streptococcus, and Faecalibacterium were overabundant in GDM group. Additionally, carbohydrate metabolism increased in NDM group, whereas amino acid metabolism, vitamin metabolism and lipopolysaccharide biosynthesis were more abundant in GDM group. Conclusions: This study showed a distinct oral microbiota profile in neonates born to mothers with GDM, which indicated that maternal diabetes status played an important role in neonatal initial oral microbiota.

The Role of Inflammation in the Development of GDM and the Use of Markers of Inflammation in GDM Screening

Gestational diabetes mellitus is a hyperglycaemic state first recognised in pregnancy. GDM affects both mother and child. Women with GDM and their new-borns are at risk of developing type 2 diabetes in the future. The screening and diagnostic criteria for GDM are inconsistent and thus novel biomarkers of GDM are required to strengthen the screening and diagnostic processes in GDM. Chronic low-grade inflammation is linked to the majority of the well-established risk factors of GDM such as old age, obesity and PCOS. This review provides an overview of the present knowledge on the pathology of GDM, the screening criteria applied, the role of inflammation in the development of GDM and the use of markers of inflammation namely cytokines, oxidative stress markers, lipids, amino acids and iron markers in screening and diagnosis of GDM.

Development of Multimarker Diagnostic Models from Metabolomics Analysis for Gestational Diabetes Mellitus (GDM)

Although metabolomics are desirable to understand the pathophysiology of gestational diabetes mellitus (GDM), comprehensive metabolomic studies of GDM are rare. We aimed to offer a holistic view of metabolites alteration in GDM patients and investigate the possible multimarker models for GDM diagnosis. Biochemical parameters and perinatal data of 131 GDM cases and 138 controls were collected. Fasting serum samples at 75 g oral glucose tolerance test were used for metabolites by ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry, ultra performance liquid chromatography-triple triple-quadrupole-mass spectrometry and gas chromatography- time-of- flight mass spectrometry platforms. Significant changes were observed in free fatty acids, bile acids, branched chain amino acids, organic acids, lipids and organooxygen compounds between two groups. In receiver operating characteristic (ROC) analysis, different combinations of candidate biomarkers and metabolites in multimarker models achieved satisfactory discriminative abilities for GDM, with the values of area under the curve (AUC) ranging from 0.721 to 0.751. Model consisting of body mass index (BMI), retinol binding protein 4 (RBP4), n-acetylaspartic acid and C16:1 (cis-7) manifested the best discrimination [AUC 0.751 (95% CI: 0.693-0.809), p < 0.001], followed by model consisting of BMI, Cystatin C, acetylaspartic acid and 6,7-diketoLCA [AUC 0.749 (95% CI: 0.691-0.808), p < 0.001]. Metabolites alteration reflected disorders of glucose metabolism, lipid metabolism, amino acid metabolism, bile acid metabolism as well as intestinal flora metabolism in GDM state. Multivariate models combining clinical markers and metabolites have the potential to differentiate GDM subjects from healthy controls.