Potential biomarkers identified in plasma of patients with gestational diabetes mellitus

A closer look at Galectin-3: its association with gestational diabetes mellitus revealed by systematic review and meta-analysis

Purpose

Gestational diabetes mellitus (GDM) represents a significant metabolic disorder that affects pregnant women worldwide and has negative consequences for both the mother and her offspring. This research aims to investigate the relation between circulating levels of Galectin-3 and the incidence of GDM, and to evaluate its potential as a biomarker for monitoring and early detection of the disease.

Methods

A thorough search of the literature has been performed using databases such as Scopus, Web of science, Embase, Cochrane Library and PubMed. The standardized mean difference (SMD) and corresponding confidence intervals (CIs) were used to compute the effect size from individual records and pooled using the Random-effect model.

Results

Our meta-analysis synthesized data from 9 studies, encompassing 1,286 participants (533 GDM patients and 753 healthy pregnant controls). The findings demonstrated a considerable increase in Galectin-3 levels among individuals diagnosed with GDM as compared to the healthy control (SMD = 0.929; CI: 0.179-1.679; p = 0.015), with observed heterogeneity (I2 = 87%; p < 0.001). Subgroup analyses revealed the influence of factors such as age, BMI, study design, and sample type on Galectin-3 levels. A meta-regression analysis further identified trends indicating that levels of Galectin-3 are linked to gestational age, specific geographical areas, and sample size.

Conclusion

Increased levels of Galectin-3 exhibit a significant association with GDM, indicating its prospective utility as a biomarker for early detection and risk assessment. Further research is warranted to elucidate its regulation and clinical implications in GDM management.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01461-z.

Proteomic analysis of plasma total exosomes and placenta-derived exosomes in patients with gestational diabetes mellitus in the first and second trimesters

Gestational diabetes mellitus (GDM) is the first spontaneous hyperglycemia during pregnancy. Early diagnosis and intervention are important for the management of the disease. This study compared and analyzed the proteins of total plasma exosomes (T-EXO) and placental-derived exosomes (PLAP-EXO) in pregnant women who subsequently developed GDM (12-16 weeks), GDM patients (24-28 weeks) and their corresponding controls to investigate the pathogenesis and biomarkers of GDM associated with exosomes. The exosomal proteins were extracted and studied by proteomics approach, then bioinformatics analysis was applied to the differentially expressed proteins (DEPs) between the groups. At 12-16 and 24-28 weeks of gestation, 36 and 21 DEPs were identified in T-EXO, while 34 and 20 DEPs were identified in PLAP-EXO between GDM and controls, respectively. These proteins are mainly involved in complement pathways, immunity, inflammation, coagulation and other pathways, most of them have been previously reported as blood or exosomal proteins associated with GDM. The findings suggest that the development of GDM is a progressive process and that early changes promote the development of the disease. Maternal and placental factors play a key role in the pathogenesis of GDM. These proteins especially Hub proteins have the potential to become predictive and diagnostic biomarkers for GDM.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.

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.

Analysis, validation, and discussion of key genes in placenta of patients with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common complication during pregnancy, which can have harmful health consequences for both the mother and the fetus. Given the placenta's crucial role as an endocrine organ during pregnancy, exploring and validating key genes in the placenta hold significant potential in the realm of GDM prevention and treatment. In this study, differentially expressed genes (DEGs) were identified from two databases, GSE70493 and PRJNA646212, and verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in placenta tissues. DEGs expression was detected in normal or high-glucose-treated HTR8/SVneo cells. We also investigated the relationship between DEGs and glucose levels in GDM patients. By selecting the intersection of the two databases, we screened 20 DEGs, which were validated in GDM patients. We observed an up-regulation of SLAMF, ALDH1A2, and CHI3L2, and a down-regulation of HLA-E, MYH11, HLA-DRB5, ITGAX, GZMB, NAIP, TMEM74B, RANBP3L, PAEP, WT-1, and CEP170. We conducted further investigations into the expression of DEGs in HTR8/SVneo cells exposed to high glucose, revealing a significant upregulation in the expression of SERPINA3, while the expressions of HLA-E, BCL6, NAIP, PAEP, MUC16, WT-1, and CEP170 were decreased. Moreover, some DEGs were confirmed to have a positive or negative correlation with blood glucose levels of GDM patients through correlation analysis. The identified DEGs are anticipated to exert potential implications in the prevention and management of GDM, thereby offering potential benefits for improving pregnancy outcomes and long-term prognosis of fetuses among individuals affected by GDM.

A study of genetic heterogeneity in autism spectrum disorders based on plasma proteomic and metabolomic analysis: multiomics study of autism heterogeneity

Genetic heterogeneity poses a challenge to research and clinical translation of autism spectrum disorder (ASD). In this study, we conducted a plasma proteomic and metabolomic study of children with ASD with and without risk genes (de novo mutation) and controls to explore the impact of genetic heterogeneity on the search for biomarkers for ASD. In terms of the proteomic and metabolomic profiles, the groups of children with ASD carrying and those not carrying de novo mutation tended to cluster and overlap, and integrating them yielded differentially expressed proteins and differential metabolites that effectively distinguished ASD from controls. The mechanisms associated with them focus on several common and previously reported mechanisms. Proteomics results highlight the role of complement, inflammation and immunity, and cell adhesion. The main pathways of metabolic perturbations include amino acid, vitamin, glycerophospholipid, tryptophan, and glutamates metabolic pathways and solute carriers-related pathways. Integrating the two omics analyses revealed that L-glutamic acid and malate dehydrogenase may play key roles in the pathogenesis of ASD. These results suggest that children with ASD may have important underlying common mechanisms. They are not only potential therapeutic targets for ASD but also important contributors to the study of biomarkers for the disease.

Inhibiting NLRP3 Inflammasome Activation by CY-09 Helps to Restore Cerebral Glucose Metabolism in 3×Tg-AD Mice

The reduction of the cerebral glucose metabolism is closely related to the activation of the NOD-like receptor protein 3 (NLRP3) inflammasome in Alzheimer’s disease (AD); however, its underlying mechanism remains unclear. In this paper, 18F-flurodeoxyglucose positron emission tomography was used to trace cerebral glucose metabolism in vivo, along with Western blotting and immunofluorescence assays to examine the expression and distribution of associated proteins. Glucose and insulin tolerance tests were carried out to detect insulin resistance, and the Morris water maze was used to test the spatial learning and memory ability of the mice. The results show increased NLRP3 inflammasome activation, elevated insulin resistance, and decreased glucose metabolism in 3×Tg-AD mice. Inhibiting NLRP3 inflammasome activation using CY-09, a specific inhibitor for NLRP3, may restore cerebral glucose metabolism by increasing the expression and distribution of glucose transporters and enzymes and attenuating insulin resistance in AD mice. Moreover, CY-09 helps to improve AD pathology and relieve cognitive impairment in these mice. Although CY-09 has no significant effect on ferroptosis, it can effectively reduce fatty acid synthesis and lipid peroxidation. These findings provide new evidence for NLRP3 inflammasome as a therapeutic target for AD, suggesting that CY-09 may be a potential drug for the treatment of this disease.

Circulating lncRNAs NONHSAT054669.2 and ENST00000525337 can be used as early biomarkers of gestational diabetes mellitus

Early diagnosis can help prevent and reduce the adverse effects of gestational diabetes mellitus (GDM). This study intended to investigate key circulating long non-coding RNAs (lncRNAs) as novel biomarkers for diagnosis of GDM at the early stages. First, lncRNA microarray analysis was conducted for plasma samples of GDM women before delivery and 48 h after delivery. The expression of differentially expressed lncRNAs in clinical samples at different trimesters was randomly validated by quantitative polymerase chain reaction (PCR). Moreover, the correlation between lncRNA expression and oral glucose tolerance test (OGTT) level in GDM women during the second trimester was analyzed, followed by evaluating the diagnostic value of key lncRNAs during different trimesters using receiver operating characteristic (ROC) curve. Higher NONHSAT054669.2 expression and lower ENST00000525337 expression were revealed in GDM women before delivery relative to 48 h after delivery (P < 0.05). The expression of NONHSAT054669.2 and ENST00000525337 in GDM women during the first and second trimesters was dramatically higher than pregnant women (P < 0.05) with normal glucose tolerance (NGT). During the second trimester, NONHSAT054669.2 expression was positively related to OGTT level at 1 h (r = 0.41455, P < 0.001). Furthermore, ROC curve analysis revealed that ENST00000525337 alone, NONHSAT054669.2 alone, and their combination had high diagnostic value for GDM during the first (area under the ROC curve (AUC) = 0.979, 0.956, and 0.984, respectively) and second (AUC = 0.829, 0.809, and 0.838, respectively) trimesters (all P < 0.001). The plasma level of NONHSAT054669.2 and ENST00000525337 may be applied as novel diagnostic biomarkers for early diagnosis of GDM.

Glucose Metabolism and Metabolomic Changes in Response to Prolonged Fasting in Individuals with Obesity, Type 2 Diabetes and Non-Obese People-A Cohort Trial

Metabolic regulation of glucose can be altered by fasting periods. We examined glucose metabolism and metabolomics profiles after 12 h and 36 h fasting in non-obese and obese participants and people with type 2 diabetes using oral glucose tolerance (OGTT) and intravenous glucose tolerance testing (IVGTT). Insulin sensitivity was estimated by established indices and mass spectrometric metabolomics was performed on fasting serum samples. Participants had a mean age of 43 ± 16 years (62% women). Fasting levels of glucose, insulin and C-peptide were significantly lower in all cohorts after 36 h compared to 12 h fasting (p < 0.05). In non-obese participants, glucose levels were significantly higher after 36 h compared to 12 h fasting at 120 min of OGTT (109 ± 31 mg/dL vs. 79 ± 18 mg/dL; p = 0.001) but insulin levels were lower after 36 h of fasting at 30 min of OGTT (41.2 ± 34.1 mU/L after 36 h vs. 56.1 ± 29.7 mU/L; p < 0.05). In contrast, no significant differences were observed in obese participants or people with diabetes. Insulin sensitivity improved in all cohorts after 36 h fasting. In line, metabolomics revealed subtle baseline differences and an attenuated metabolic response to fasting in obese participants and people with diabetes. Our data demonstrate an improved insulin sensitivity after 36 h of fasting with higher glucose variations and reduced early insulin response in non-obese people only.

Esculentoside A alleviates cognitive deficits and amyloid pathology through peroxisome proliferator-activated receptor γ-dependent mechanism in an Alzheimer's disease model

BACKGROUND

Alzheimer's disease (AD) is characterized clinically by cognitive deficits and pathologically by amyloid-β (Aβ) deposition and tau aggregation, as well as the brain atrophy. Esculentoside A (EsA), a neuroprotective saponin, is isolated from Phytolacca esculenta and shows potent health-promoting effects in a variety of experimental models. However, there are minimal reports on the effects of EsA on triple transgenic AD mice.

PURPOSE

The current research aimed at investigating the protective effects and underlying mechanisms of EsA on the mitigation of cognitive deficits and pathology in triple transgenic AD mice.

METHODS

Triple transgenic AD mice (3 × Tg-AD) of 8 months old received intraperitoneal treatment of 5 or 10 mg/kg EsA for 8 consecutive weeks. Morris water maze test and open field test were made to evaluate the cognitive function and degree of anxiety of the mice. Liquid chromatography with tandem mass spectrometry analysis was performed to characterize and to quantify EsA in the blood and brain of mice. Immunofluorescence assay and Western blot were adopted to measure the levels of peroxisome proliferator-activated receptor gamma (PPARγ) and key proteins in Aβ pathology, ER stress- and apoptosis-associated pathways. The combination of EsA with PPARγ were theoretically calculated by molecular docking programs and experimentally confirmed by the bio-layer interferometry technology.

RESULTS

Supplemental EsA could improve the cognitive deficits of 3 × Tg-AD mice. EsA penetrated the brain-blood barrier to exert a strong effect on AD mice, evidenced as decreasing Aβ generation, reducing the degrees of oxidative and ER stress, and mitigating neuronal apoptosis through the increase of PPARγ expression. In the culture of primary neurons, addition of PPARγ inhibitor GW9662 eliminated the effects of EsA on AD pathologies. Direct combination of EsA with PPARγ were demonstrated by molecular docking programs and bio-layer interferometry technology.

CONCLUSIONS

For the first time, these outcomes revealed that EsA could penetrate the brain-blood barrier to exert a strong effect on ameliorating cognitive deficits in 3 × Tg-AD mice and exert neuroprotective effects toward AD pathology via PPARγ-dependent mechanism.

Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnant women. The early detection of GDM provides an opportunity for the effective treatment of hyperglycemia in pregnancy, thus decreasing the risk of adverse perinatal outcomes for mothers and newborns. Metabolomics, an emerging technique, offers a novel point of view in understanding the onset and development of diseases and has been repeatedly used in various gestational periods in recent studies of GDM. Moreover, metabolomics provides varied opportunities in the different diagnoses of GDM from prediabetes or predisposition to diabetes, the diagnosis of GDM at a gestational age several weeks earlier than that used in the traditional method, and the assessment of prognosis considering the physiologic subtypes of GDM and clinical indexes. Longitudinal metabolomics truly facilitates the dynamic monitoring of metabolic alterations over the course of pregnancy. Herein, we review recent advancements in metabolomics and summarize evidence from studies on the application of metabolomics in GDM, highlighting the aspects of the diagnosis and differential diagnoses of GDM in an early stage. We also discuss future study directions concerning the physiologic subtypes, prognosis, and limitations of metabolomics.