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The primary therapeutic approach for managing hyperglycemia today is diet therapy. Lipids are not only a source of nutrients but also play a role in initiating adipocyte differentiation in the fetus, which may explain the development of fetal macrosomia and future metabolic disorders in children born to mothers with gestational diabetes mellitus (GDM). Alterations in the maternal blood lipid profile, influenced by adherence to a healthy diet in mothers with GDM and the occurrence of fetal macrosomia, represent a complex and not fully understood process. The aim of this study was to examine the characteristics of the blood plasma lipid profile in pregnant women with GDM across all trimesters based on adherence to diet therapy. The clinical part of the study followed a case-control design, including 110 women: 80 in the control group, 20 in a GDM group adhering to the diet, and 10 in a GDM group not adhering to the diet. The laboratory part was conducted as a longitudinal dynamic study, with venous blood samples collected at three time points: 11–13, 24–26, and 30–32 weeks of pregnancy. A significant impact of diet therapy on the composition of blood lipids throughout pregnancy was demonstrated, starting as early as the first trimester. ROC analysis indicated high effectiveness of the models developed, with an AUC of 0.98 for the 30- to 32-week model and sensitivity and specificity values of 1 and 0.9, respectively. An association was found between dietary habits, maternal blood lipid composition at 32 weeks, and newborn weight. The changes in lipid profiles during macrosomia development and under diet therapy were found to be diametrically opposed, confirming at the molecular level that diet therapy can normalize not only carbohydrate metabolism but also lipid metabolism in both the mother and fetus. Based on the data obtained, it is suggested that after further validation, the developed models could be used to improve the prognosis of macrosomia by analyzing blood plasma lipid profiles at various stages of pregnancy. [ABSTRACT FROM AUTHOR]

Preeclampsia (PE) is a complex and multifaceted obstetric syndrome characterized by several distinct molecular subtypes. It complicates up to 5% of pregnancies and significantly contributes to maternal and newborn morbidity, thereby diminishing the long-term quality of life for affected women. Due to the widespread dissatisfaction with the effectiveness of existing approaches for assessing PE risk, there is a pressing need for ongoing research to identify newer, more accurate predictors. This study aimed to investigate early changes in the maternal serum proteome and associated signaling pathways. The levels of 125 maternal serum proteins at 11–13 weeks of gestation were quantified using liquid chromatography–multiple reaction monitoring mass spectrometry (LC-MRM MS) with the BAK-125 kit. Ten serum proteins emerged as potential early markers for PE: Apolipoprotein M (APOM), Complement C1q subcomponent subunit B (C1QB), Lysozyme (LYZ), Prothrombin (F2), Albumin (ALB), Zinc-alpha-2-glycoprotein (AZGP1), Tenascin-X (TNXB), Alpha-1-antitrypsin (SERPINA1), Attractin (ATRN), and Apolipoprotein A-IV (APOA4). Notably, nine of these proteins have previously been associated with PE in prior research, underscoring the consistency and reliability of our findings. These proteins play key roles in critical molecular processes, including complement and coagulation cascades, platelet activation, and insulin-like growth factor pathways. To improve the early prediction of PE, a highly effective Support Vector Machine (SVM) model was developed, analyzing 19 maternal serum proteins from the first trimester. This model achieved an area under the curve (AUC) of 0.91, with 87% sensitivity and 95% specificity, and a hazard ratio (HR) of 13.5 (4.6–40.8) with p < 0.001. These findings demonstrate that serum protein-based SVM models possess significantly higher predictive power compared to the routine first-trimester screening test, highlighting their superior utility in the early detection and risk stratification of PE. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is a complex disorder that impacts both the endocrine and metabolic systems, often resulting in infertility, obesity, insulin resistance, and cardiovascular complications. The aim of this study is to investigate the role of intestinal flora and its metabolites, particularly short-chain fatty acids (SCFAs), in the development of PCOS, and to assess the effects of metformin therapy on these components. SCFA levels in fecal and blood samples from women with PCOS ( n = 69 ) and healthy controls ( n = 18 ) were analyzed using Gas Chromatography–Mass Spectrometry (GC/MS) for precise measurement. Fecal microbiota were quantitatively detected by real-time polymerase chain reaction (PCR). To assess the efficacy of six months of metformin treatment, changes in the microbiota and SCFAs in the PCOS group ( n = 69 ) were also evaluated. The results revealed that women with PCOS exhibited a significant reduction in beneficial bacteria (namely, the C. leptum group and Prevotella spp.) alongside a notable overgrowth of opportunistic microorganisms (C. perfringens, C. difficile, Staphylococcus spp., and Streptococcus spp.). An overproduction of acetic acid (AA, FC = 0.47 , p < 0.05 ) and valeric acid (VA, FC = 0.54 , p < 0.05 ) suggests a link between elevated SCFAs and the development of obesity and PCOS. Interestingly, AA in the bloodstream might offer a protective effect against PCOS by ameliorating key symptoms such as high body mass index ( r = − 0.33 , p = 0.02 ), insulin resistance ( r = − 0.39 , p = 0.02 ), and chronic inflammation. Although serum SCFA levels showed non-significant changes following metformin treatment ( p > 0.05 ), the normalization of AA in the gut underscores that metformin exerts a more pronounced effect locally within the gastrointestinal tract. Furthermore, the study identified the most effective model for predicting the success of metformin therapy, based on serum concentrations of butyric acid (BA) and VA, achieving a 91% accuracy rate, 100% sensitivity, and 80% specificity. These promising findings highlight the potential for developing targeted interventions and personalized treatments, ultimately improving clinical outcomes for women with PCOS. [ABSTRACT FROM AUTHOR]

Hypoxic–ischemic encephalopathy (HIE) is a severe neurological disorder caused by perinatal asphyxia with significant consequences. Early recognition and intervention are crucial, with therapeutic hypothermia (TH) being the primary treatment, but its efficacy depends on early initiation of treatment. Accurately assessing the HIE severity in neonatal care poses challenges, but omics approaches have made significant contribution to understanding its complex pathophysiology. Our study further explores the impact of HIE on the blood metabolome over time and investigated changes associated with hypothermia's therapeutic effects. Using a rat model of hypoxic–ischemic brain injury, we comprehensively analyzed dried blood spot samples for fat-soluble compounds using HPLC-MS. Our research shows significant changes in the blood metabolome after HIE, with a particularly rapid recovery of lipid metabolism observed. Significant changes in lipid metabolites were observed after 3 h of HIE, including increases in ceramides, carnitines, certain fatty acids, phosphocholines, and phosphoethanolamines, while sphingomyelins and N-acylethanolamines (NAEs) decreased (p < 0.05). Furthermore, NAEs were found to be significant features in the OPLS-DA model for HIE diagnosis, with an area under the curve of 0.812. TH showed a notable association with decreased concentrations of ceramides. Enrichment analysis further corroborated these observations, showing modulation in several key metabolic pathways, including arachidonic acid oxylipin metabolism, eicosanoid metabolism via lipooxygenases, and leukotriene C4 synthesis deficiency. Our study reveals dynamic changes in the blood metabolome after HIE and the therapeutic effects of hypothermia, which improves our understanding of the pathophysiology of HIE and could lead to the development of new rapid diagnostic approaches for neonatal HIE. [ABSTRACT FROM AUTHOR]

Endometriosis is a prevalent chronic inflammatory disease characterized by a considerable delay between initial symptoms and diagnosis through surgery. The pressing need for a timely, non-invasive diagnostic solution underscores the focus of current research efforts. This study examines the diagnostic potential of the menstrual blood lipidome. The lipid profile of 39 samples (23 women with endometriosis and 16 patients in a control group) was acquired using reverse-phase high-performance liquid chromatography–mass spectrometry with LipidMatch processing and identification. Profiles were normalized based on total ion counts. Significant differences in lipids were determined using the Mann–Whitney test. Lipids for the diagnostic model, based on logistic regression, were selected using a combination of variance importance projection filters and Akaike information criteria. Levels of ceramides, sphingomyelins, cardiolipins, triacylglycerols, acyl- and alkenyl-phosphatidylethanolamines, and alkenyl-phosphatidylcholines increased, while acyl- and alkyl-phosphatidylcholines decreased in cases of endometriosis. Plasmenylphosphatidylethanolamine PE P-16:0/18:1 and cardiolipin CL 16:0_18:0_22:5_22:6 serve as marker lipids in the diagnostic model, exhibiting a sensitivity of 81% and specificity of 85%. The diagnostic approach based on dried spots of menstrual blood holds promise as an alternative to traditional non-invasive methods for endometriosis screening. [ABSTRACT FROM AUTHOR]

Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood spots in the context of the Rice–Vannucci model of hypoxic–ischemic encephalopathy (HIE) in rats. The quality of normalization was assessed through the performance of Orthogonal Partial Least Squares (OPLS) models built on the training datasets; the sensitivity and specificity of these models were calculated by application to validation datasets. PQN, MRN, and VSN demonstrated a higher diagnostic quality of OPLS models than the other methods studied. The OPLS model based on VSN demonstrated superior performance (86% sensitivity and 77% specificity). After VSN, the VIP-identified potential biomarkers notably diverged from those identified using other normalization methods. Glycine consistently emerged as the top marker in six out of seven models, aligning perfectly with our prior research findings. Likewise, alanine exhibited a similar pattern. Notably, VSN uniquely highlighted pathways related to the oxidation of brain fatty acids and purine metabolism. Our findings underscore the widespread utility of VSN in metabolomics, suggesting its potential for use in large-scale and cross-study investigations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has raised questions about indirect impact in pregnant women on the development of their future children. Investigating the characteristics of lipid metabolism in the "mother–placenta–fetus" system can give information about the pathophysiology of COVID-19 infection during pregnancy. A total of 234 women were included in study. Maternal plasma, cord blood, and amniotic fluid lipidome were analyzed using HPLC-MS/MS. Differences in lipid profile were searched by Mann–Whitney and Kruskall–Wallis test, and diagnostic model based on logistic regression were built by AIC. Elevated levels of lysophospholipids, triglycerides, sphingomyelins, and oxidized lipids were registered in patients' maternal and cord plasma after COVID-19 infection. An increase in maternal plasma sphingomyelins and oxidized lipids was observed in cases of infection during the second trimester. In amniotic fluid, compared to the control group, nine lipids were reduced and six were elevated. Levels of phosphoglycerides, lysophosphoglycerides, and phosphatidylinositols decreased during infection in the second and third trimesters of pregnancy. A health diagnostic model for newborns based on maternal plasma was developed for each group and exhibited good diagnostic value (AUC > 0.85). Maternal and cord plasma's lipidome changes during delivery, which are associated with COVID-19 infection during pregnancy, are synergistic. The most significant disturbances occur with infections in the second trimester of pregnancy. [ABSTRACT FROM AUTHOR]

Follicular fluid is an important component of follicle growth and development. Negative effects of COVID-19 on follicular function are still open. The aim of this work was to study the features of the lipid profile of follicular fluid and evaluate the results of the in vitro fertilization (IVF) program in women after COVID-19 to identify biomarkers with prognostic potential. The study involved samples of follicular fluid collected from 237 women. Changes in the lipid composition of the follicular fluid of patients who underwent COVID-19 in mild and severe forms before entering the IVF program and women who did not have COVID-19 were studied by mass spectrometry. Several lipids were identified that significantly changed their level. On the basis of these findings, models were developed for predicting the threat of miscarriage in patients who had a severe course of COVID-19 and models for predicting the success of the IVF procedure, depending on the severity of COVID-19. Of practical interest is the possibility of using the developed predictive models in working with patients who have undergone COVID-19 before entering the IVF program. The results of the study suggest that the onset of pregnancy and its outcome after severe COVID-19 may be associated with changes in lipid metabolism in the follicular fluid. [ABSTRACT FROM AUTHOR]