Individual Metabolic Profiles Research Articles

Harnessing nucleotide metabolism and immunity in cancer: a tumour microenvironment perspective

The tumour microenvironment (TME) is a dynamic nexus where cancer cell metabolism and the immune system intricately converge, with nucleotide metabolism (NM) playing a pivotal role. This review explores the critical function of NM in cancer cell proliferation and its profound influence on the TME and immune landscape. NM is essential for DNA and RNA synthesis and is markedly upregulated in cancer cells to meet the demands of rapid growth. This metabolic rewiring fuels cancer progression, but also shapes the TME, impacting the function and viability of immune cells. The altered nucleotide milieu in the TME can suppress immune response, aiding cancer cell evasion from immune surveillance. Drug discoveries in the field of NM have revealed different therapeutic strategies, including inhibitors of nucleotide synthesis and drugs targeting salvage pathways, which are discussed thoroughly in this review. Furthermore, the emerging strategy of combining NM‐targeted therapies with immunotherapies is emphasised, particularly their effect on sensitising tumours to immune checkpoint inhibitors and enhancing overall treatment efficacy. The Human Genome Project paved the way for personalised medicine, countering the established ‘one size fits all’ approach to cancer treatment. Advances in understanding the TME and NM have spurred interest in personalised therapeutic strategies. This review highlights the potential of leveraging individual tumour metabolic profiles to guide treatment selection, aiming to optimise efficacy and minimise adverse effects. The strategic importance of targeting NM in cancer therapy and its synergistic potential with immunotherapies offers a path towards more effective and personalised cancer treatments.

Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications

The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential diagnostic applications. Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. We explore the fabrication and operational mechanisms of these transistors, the role of machine learning in metabolite analysis, and their potential clinical applications by analyzing practical human blood samples for hypernatremia syndrome. This synergy not only improves diagnostic precision but also holds potential for the development of personalized diagnostics, tailoring treatments for individual metabolic profiles.

The Role of Food Science in Combating Obesity and Related Metabolic Disorders

Obesity and related metabolic disorders have emerged as major global health challenges, affecting millions of individuals worldwide. The prevalence of these conditions has reached epidemic proportions, leading to increased morbidity, mortality, and healthcare costs. Food science plays a crucial role in addressing this complex issue by developing innovative strategies and interventions to promote healthier eating habits and prevent the development of obesity and its associated comorbidities. This comprehensive review explores the multifaceted role of food science in combating obesity and related metabolic disorders, focusing on key areas such as food formulation, processing, preservation, and fortification. We discuss the development of functional foods and ingredients that target specific metabolic pathways, as well as the application of novel technologies to enhance the nutritional quality and sensory appeal of food products. Additionally, we highlight the importance of consumer education and behavior change strategies in promoting healthy food choices and portion control. The review also examines the potential of personalized nutrition approaches, which leverage advances in nutrigenomics and metabolomics to tailor dietary interventions based on individual genetic and metabolic profiles. Furthermore, we explore the role of food science in developing sustainable and equitable food systems that ensure access to nutritious foods for all populations. By synthesizing the latest research findings and identifying gaps in current knowledge, this review provides valuable insights and recommendations for future research and policy initiatives aimed at harnessing the power of food science to combat obesity and related metabolic disorders on a global scale.

Metabolic remodelling in atrial fibrillation: manifestations, mechanisms and clinical implications.

Atrial fibrillation (AF) is a continually growing health-care burden that often presents together with metabolic disorders, including diabetes mellitus and obesity. Current treatments often fall short of preventing AF and its adverse outcomes. Accumulating evidence suggests that metabolic disturbances can promote the development of AF through structural and electrophysiological remodelling, but the underlying mechanisms that predispose an individual to AF are aetiology-dependent, thus emphasizing the need for tailored therapeutic strategies to treat AF that target an individual's metabolic profile. AF itself can induce changes in glucose, lipid and ketone metabolism, mitochondrial function and myofibrillar energetics (as part of a process referred to as 'metabolic remodelling'), which can all contribute to atrial dysfunction. In this Review, we discuss our current understanding of AF in the setting of metabolic disorders, as well as changes in atrial metabolism that are relevant to the development of AF. We also describe the potential of available and emerging treatment strategies to target metabolic remodelling in the setting of AF and highlight key questions and challenges that need to be addressed to improve outcomes in these patients.

Obesity, Dietary Patterns, and Hormonal Balance Modulation: Gender-Specific Impacts.

Understanding the intricate relationship between nutrition, hormonal balance, and gender-specific factors is crucial for developing targeted interventions to mitigate obesity-related endocrine disruptions and improve metabolic health. This narrative review examines the impact of various dietary patterns on hormonal regulation in both men and women, focusing on their effects on hormonal balance and metabolic health in the context of obesity. Calorie restriction, the Western diet, high-fat diets, low-CHO diets, plant-based diets, and the Mediterranean diet are analyzed in relation to their influence on obesity-related endocrine disruptions and metabolic health. Future research directions include investigating the specific mechanisms underlying dietary influences on hormonal regulation, addressing the gender-specific metabolic differences and body fat distribution, and exploring the dietary needs of individuals undergoing gender transition. Personalized dietary interventions tailored to individual metabolic and hormonal profiles are essential for optimizing health outcomes across the gender spectrum. By integrating gender-specific considerations into dietary recommendations, healthcare professionals can better support individuals in achieving optimal metabolic health and hormonal balance.

Relevance of pharmacogenetic analyses and therapeutic drug monitoring of antidepressants for an individualized treatment of peripartum psychopathology.

Psychiatric disorders burden the peripartum period, often requiring psychopharmacological treatment, including antidepressants. Efficacy and tolerability of antidepressants are influenced by the physiological changes of the peripartum and individual metabolic profiles, which in turn can be modified by pregnancy. The objective of this study is to assess the relationship between antidepressants' pharmacokinetic profiles during pregnancy and individual metabolic profiles, along with the efficacy of the treatment. In total 87 outpatients with diagnoses of bipolar disorder, major depression, anxiety, obsessive-compulsive disorder and post-traumatic stress disorder who required antidepressant treatment during pregnancy were recruited. Genotyping analysis of hepatic cytochrome P450 (CYPs) individual isoforms was performed. Antidepressants' blood concentrations and psychometric assessments were collected at five time points. Antidepressants' cord blood concentrations were assessed at birth. Sertraline showed greater stability in plasma concentrations and a lower placental penetrance index. Most of the antidepressants' concentrations below the therapeutic range were found in women with an extensive/ultrarapid metabolic profile. Antidepressants mainly metabolized by CYP2C19 were less frequently below the therapeutic range compared with antidepressants metabolized by CYP2D6. Pregnancy modulates cytochrome activity and drugs' pharmacokinetics. Genotyping analysis of CYPs isoforms and therapeutic drug monitoring might be used to guide clinicians in a well-tolerated treatment of psychiatric symptoms in pregnant women.

Unveiling the growing significance of metabolism in modulating immune cell function: exploring mechanisms and implications; a review

Immunometabolism has emerged as a rapidly growing field of research, holding significant promise for personalised medicine and precision immunotherapy. This review explores the intricate relationship between immune function and metabolic processes, emphasising their profound impact on various immune-related disorders. Understanding how metabolic dysregulation contributes to the pathogenesis of these disorders remains a critical research gap. Therefore, this review aims to bridge that gap by examining the key metabolic pathways involved and their specific implications in immune cell function. Key metabolic pathways, including glycolysis, mitochondrial metabolism, fatty acid metabolism, and amino acid metabolism, are discussed in the context of immune cell function. Dysregulation of these pathways can disrupt immune cell activation, differentiation, and overall function, contributing to disease pathogenesis. Understanding these metabolic alterations’ molecular mechanisms is essential for developing targeted therapeutic interventions. The review also emphasises the importance of personalised medicine in immune-related disorders. The unique metabolic profiles of individuals can influence treatment outcomes, highlighting the need for tailored approaches. Integrating metabolic profiling into clinical practice can enhance treatment efficacy and improve patient outcomes. Investigating the clinical significance of immunometabolism in diverse disease contexts will facilitate the translation of research findings into clinical practice. Moreover, refining treatment strategies based on individual metabolic profiles will contribute to advancing precision immunotherapy.

Integrating (Nutri-)Metabolomics into the One Health Tendency-The Key for Personalized Medicine Advancement.

Metabolomics is an advanced technology, still under development, with multiple research applications, especially in the field of health. Individual metabolic profiles, the functionality of the body, as well as its interaction with the environment, can be established using this technology. The body's response to various external factors, including the food consumed and the nutrients it contains, has increased researchers' interest in nutrimetabolomics. Establishing correlations between diet and the occurrence of various diseases, or even the development of personalized nutrition plans, could contribute to advances in precision medicine. The interdependence between humans, animals, and the environment is of particular importance today, with the dramatic emergence and spread of zoonotic diseases, food, water and soil contamination, and the degradation of resources and habitats. All these events have led to an increase in risk factors for functional diseases, burdening global health. Thus, this study aimed to highlight the importance of metabolomics, and, in particular, nutrimetabolomics, as a technical solution for a holistic, collaborative, and precise approach for the advancement of the One Health strategy.

Artificial intelligence to improve ischemia prediction in rubidium positron emission tomography

Abstract Funding Acknowledgements Type of funding sources: None. Background Patients are referred to coronary artery disease (CAD) testing based on their pre-test probability (PTP). The available, easy-to-use risk prediction tools use three variables only and their diagnostic accuracy is limited. Artificial intelligence-based tools incorporate multiple variables and could improve PTP assessment due to the resulting combinatorial power. The factors in the AI model are not seen as independent individual values, but are recognized as patterns derived by a combinatorial analysis of the individual metabolic profile of each patient. Purpose We aimed to compare the diagnostic accuracy of a novel, artificial intelligence-based tool with commonly used PTP tools to predict ischemia. Methods Consecutive patients (n = 2417) referred for Rubidium-Position Emission Tomography (PET) were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern-based algorithm (MPA) model incorporating symptoms, vitals, ECG and laboratory findings. Five risk categories (very low to very high risk) were defined (<5%, 5–15%, 15–50%, 50–85%, >85%). Ischemia was defined as summed difference score (SDS) ≥2 on PET. Receiver operator characteristics were calculated and compared using the DeLong method. Results Known CAD, ischemia and scar (detected on PET) were present in 46.3%, 37.1% and 24.6%, respectively. The MPA model was most accurate to assess PTP of ischemia (AUC: 0.758, ESC 2013: 0.661, ESC 2019: 0.673, ACC 2012: 0.585, ACC 2021: 0.667, p<0.0001 each) as depicted in figure 1. The MPA’s sensitivity and negative predictive value to rule-out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across risk groups, reduced the proportion of patients in the 15–85% range by 29% (ACC 2012) to 51% (ESC 2013), and was the only risk tool to correctly estimate ischemia prevalence in the very low risk group (table 1). Conclusion The MPA model significantly improved PTP assessment of ischemia. The MPA model has originally been derived on the endpoint coronary stenosis >50% and validated in three clinical studies (1–3). This study now confirms that the MPA model applied to the endpoint ischemia outperforms the current risk assessment tools and is most accurate to assess PTP. It enables clinicians to safely exclude ischemia based on readily available variables without advanced testing. It could be used to improve risk stratification of patients and to significantly reduce unnecessary non-invasive functional tests.

A Review Of The Methods And Approaches For Integrating Genetics And Metabolomics Into Personalized Nutrition

Personalized nutrition is emerging as a promising approach to optimize health outcomes by accounting for individual genetic and metabolic differences. This review examines the current methods and approaches for integrating genetics and metabolomics into personalized nutrition strategies. We discuss the importance of understanding an individual's genetic makeup, including single nucleotide polymorphisms (SNPs), and their effects on nutrient metabolism and health outcomes. Additionally, we explore the role of metabolomics in elucidating individual metabolic profiles and identifying biomarkers for personalized dietary interventions. We also discuss the application of computational and machine learning techniques in data integration and analysis, as well as the ethical and regulatory considerations surrounding personalized nutrition. The ultimate goal is to provide a comprehensive overview of the current state of the field and identify future research opportunities to improve the implementation of personalized nutrition for disease prevention and overall health promotion.

Comparison of cytotoxicity, saponin, and protoanemonin contents of medicinal plant extracts from Helleborus niger L. and Helleborus foetidus L.

BackgroundComplementary treatment options for tumors and hematological malignancies constitute a strong therapeutic need. Medicinal plant extracts from Helleborus niger L. and Helleborus foetidus L. are effective in corresponding clinical applications, but their composition and bioactivity still await further investigations. Due to their cytotoxicity, saponins and protoanemonin might be related to effectiveness of pharmaceutical extracts from H. niger and H. foetidus. PurposeThe goal of this study was to evaluate the cytotoxic and apoptosis-inducing capacity of different pharmaceutical extracts from H. niger and H. foetidus in selected in-vitro assays and to compare their protoanemonin and saponin concentration levels. MethodsAqueous extracts from H. niger and H. foetidus were obtained according to two different pharmacopoeial methods as stated in European Pharmacopoeia and German Homoeopathic Pharmacopoeia. Cytotoxic and apoptosis inducing activities of extracts were analyzed using tumor cell lines MOLT4, SK-UT-1b and HT-144, as well as cultured human PBMC. Protoanemonin and saponin concentrations were determined by HPLC-DAD analysis. ResultsSaponin and protoanemonin contents were different dependent on the pharmacopoieal method applied, as well as on the plant species and parts used for extract production. Maceration according to Ph. Eur. 1.3.1 yielded extracts with almost opposite protoanemonin and saponin contents from H. foetidus and H. niger (H. foetidus: low saponin level, high protoanemonin level; H. niger: vice versa). Fermented H. niger extracts showed saponin and protoanemonin contents ranging between those of the Ph. Eur. 1.3.1 extracts from both species. Both H. niger and H. foetidus extracts exhibited cytotoxicity in the selected cell line assays. H. foetidus extract was about 10 times more cytotoxic than H. niger extract. Investigated cell lines showed different responses towards the pharmaceutical extracts. Tumor cells were more prone to apoptosis induction than human PBMC cells. ConclusionsThe present investigation demonstrates that the contents of protoanemonin and saponins in the medicinal plant extracts from studied Helleborus sp. differ significantly. While both plant extracts were found to be cytotoxic against selected tumor cells, H. foetidus exhibited much stronger cytotoxic activities than H. niger. The individual metabolic profiles of saponins and protoanemonin in H. niger and H. foetidus could contribute to these differences in cytotoxic potential. To obtain defined plant extracts for medicinal purposes it is essential to use official manufacturing procedures and accurately specified plant species and parts.

Computed tomographic abdominal fat volume estimation - a handy tool to predict the risk of metabolic syndrome.

Abdominal obesity plays a significant role in the development of metabolic syndrome, with individual metabolic risk profiles for visceral and subcutaneous adipose tissues. This study aimed to calculate and correlate the subcutaneous, visceral, and total fat compartment volume in metabolic and non-metabolic syndrome patients. This was a cross-sectional study conducted on 112 patients categorized into Group A (with metabolic syndrome) and Group B (without metabolic syndrome). They were subjected to computed tomography (CT) study of the abdomen using a 128-slice MDCT scanner. Body mass index (BMI), visceral fat volume (VFV), subcutaneous fat volume (SFV), and total fat volume (TFV) were calculated and correlated with biochemical evidence of metabolic syndrome. The mean age of patients in Group A was 60.91 ± 12.23 years as compared to Group B, which was 50.12 ± 16.30 years. Overall, a male predominance was observed, i.e. 69 cases (61.6%). BMI was proven to be an inaccurate risk predictor. However, mean VFV, SFV, and TFV was statistically higher in patients with metabolic syndrome (p = 0.001), with visceral fat volume predicting a higher risk in females (p = 0.026). Abdominal CT is a commonly performed yet unexplored tool for the risk assessment of metabolic syndrome. Through the results obtained in this study, we have proven the need for calculating SFV, VFV, and TFV as predictors of metabolic syndrome in comparison to the conventional practice of BMI assessment. The radiologist can thus work with the clinician to effectively detect and treat this health condition.

Metabolic impact of infant formulas in young infants. An outlook from the urine metabolome

IntroductionAlthough breast milk is the ideal food source for newborns during the first six months of life, a high percentage of children receive infant formulas. There is evidence that specific diet habits may influence individual metabolic profile. Therefore, in newborns, such profile can be influenced by the use of infantile formulas given the composition differences that display compared to human milk. Up to now, there are no reports in the literature that address this issue. Objectivesthis work aims to compare the metabolic profile of full-term newborns that were feed with either breast milk (n = 32) or infantile formulas (n = 21). Methods: Metabolic profile was established based on urine analysis through gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (H-NMR). Resultsour results evidenced a more gluconeogenic profile in breast-fed infants characterized by elevation of Kreb's cycle intermediaries like fumaric, succinic and ketoglutaric acids compared to infants receiving infant formula. In addition, infant formula fed infants presented urinary excretion of metabolites derived from specific compounds present in this type of diet that were not observed in breast-fed infants, for instance D-glucitol, and 4-deoxytetronic. Moreover, in infant formula fed infants there was excretion of basal levels of metabolites of clinical relevance like 3-hydroxy-3-methyl-glutaric, 2-methyl-3-keto-valeric and 3,4-dihydroxybutyric. ConclusionThese results show the importance of understanding the metabolic impact of diet in newborn population in normal and pathological contexts.

High-throughput single cell metabolomics and cellular heterogeneity exploration by inertial microfluidics coupled with pulsed electric field-induced electrospray ionization-high resolution mass spectrometry

Single cell metabolomics can obtain the metabolic profiles of individual cells and reveal cellular heterogeneity. However, high-throughput single-cell mass spectrometry (MS) analysis under physiological conditions remains a great challenge due to the presence of complex matrix and extremely small cell volumes. Herein, a serpentine channel microfluidic device which was designed to achieve continuous cell separation and inertial focusing, was coupled with a pulsed electric field-induced electrospray ionization-high resolution MS (PEF-ESI-HRMS) to achieve high-throughput single cell analysis. The pulsed square wave electric field was applied to realize on-line cell disruption and induce electrospray ionization. Single cells were analyzed under near-physiological conditions at a throughput of up to 80 cells min−1. More than 900 features were detected and approximately 120 metabolites were tentatively identified from a single cell. Further, by continually analyzing more than 3000 MCF7 and HepG2 cells, discrimination of different cancer cells based on their individual metabolic profiles was achieved by using the principal component analysis. The PEF-ESI-HRMS method was also applied for the analysis of single yeast cells, and more than 40 metabolites were annotated. This method is versatile and has good robustness, which is promising for high-throughput single cell metabolomics analysis.

Empowering consumers to PREVENT diet-related diseases through OMICS sciences (PREVENTOMICS): protocol for a parallel double-blinded randomised intervention trial to investigate biomarker-based nutrition plans for weight loss

IntroductionPersonalised nutrition holds immense potential over conventional one-size-fits-all approaches for preventing and treating diet-related diseases, such as obesity. The current study aims to examine whether a personalised nutritional plan produces...

Incident heart failure and myocardial infarction in sodium-glucose cotransporter-2 vs. dipeptidyl peptidase-4 inhibitor users.

AimsThis study aimed to compare the rates of major cardiovascular adverse events in sodium‐glucose cotransporter‐2 inhibitors (SGLT2I) and dipeptidyl peptidase‐4 inhibitors (DPP4I) users in a Chinese population. SGLT2I and DPP4I are increasingly prescribed for type 2 diabetes mellitus patients. However, few population‐based studies are comparing their effects on incident heart failure or myocardial infarction.Methods and resultsThis was a population‐based retrospective cohort study using the electronic health record database in Hong Kong, including type 2 diabetes mellitus patients receiving either SGLT2I or DPP4I from 1 January 2015 to 31 December 2020. Propensity score matching was performed in a 1:1 ratio based on demographics, past comorbidities, and non‐SGLT2I/DPP4I medications with nearest neighbour matching (caliper = 0.1). Univariable and multivariable Cox models were used to identify significant predictors for new‐onset heart failure, new‐onset myocardial infarction, cardiovascular mortality, and all‐cause mortality. Sensitivity analyses with competing risk models and multiple propensity score matching approaches were conducted. A total of 41 994 patients (58.89% males, median admission age at 58 years old, interquartile range [IQR]: 51.2–65.3) were included with a median follow‐up of 5.6 years (IQR: 5.32–5.82). In the matched cohort, SGLT2I use was significantly associated with lower risks of new‐onset heart failure (hazard ratio [HR]: 0.73, 95% confidence interval [CI]: [0.66, 0.81], P < 0.0001), myocardial infarction (HR: 0.81, 95% CI: [0.73, 0.90], P < 0.0001), cardiovascular mortality (HR: 0.67, 95% CI: [0.53, 0.84], P < 0.001), and all‐cause mortality (HR: 0.26, 95% CI: [0.24, 0.29], P < 0.0001) after adjusting for significant demographics, past comorbidities, and non‐SGLT2I/DPP4I medications.ConclusionsSGLT2 inhibitors are protective against adverse cardiovascular events including new‐onset heart failure, myocardial infarction, cardiovascular mortality, and all‐cause mortality. The prescription of SGLT2I is preferred when taken into consideration individual cardiovascular and metabolic risk profiles in addition to drug–drug interactions.

Metabolic Status of Lean and Obese Zucker Rats Based on Untargeted and Targeted Metabolomics Analysis of Serum.

Obesity is growing worldwide epidemic. Animal models can provide some clues about the etiology, development, prevention, and treatment of obesity. We examined and compared serum metabolites between seven lean (L) and seven obese (O) female Zucker rats to investigate the individual serum metabolic profile. A combination of HPLC-UV, HPLC-ECD, and LC-MS revealed more than 400 peaks. The 50 highest quality peaks were selected as the focus of our study. Untargeted metabolomics analysis showed significantly higher mean peak heights for 20 peaks in L rats, generally distributed randomly, except for a cluster (peaks 44–50) where L showed stable dominancy over O. Only eight peaks were significantly higher in O rats. Peak height ratios between pairs of L and O rats were significantly higher at 199 positions in L rats and at 123 positions in O rats. Targeted metabolomics analysis showed significantly higher levels of methionine, cysteine, tryptophan, kynurenic acid, and cysteine/cystine ratio in L rats and significantly higher levels of cystine and tyrosine in O rats. These results contribute to a better understanding of systemic metabolic perturbations in the obese Zucker rat model, emphasizing the value of both whole metabolome and individual metabolic profiles in the design and interpretation of studies using animal models.

Spousal associations of serum metabolomic profiles by nuclear magnetic resonance spectroscopy

Phenotype-based assortative mating is well established in humans, with the potential for further convergence through a shared environment. To assess the correlation within infertile couples of physical, social, and behavioural characteristics and 155 circulating metabolic measures. Cross sectional study at a tertiary medical center of 326 couples undertaking IVF. Serum lipids, lipoprotein subclasses, and low-molecular weight metabolites as quantified by NMR spectroscopy (155 metabolic measures). Multivariable and quantile regression correlations within couples of metabolite profiles. Couples exhibited statistical correlations of varying strength for most physical, social, and behavioural characteristics including age, height, alcohol consumption, education, smoking status, physical activity, family history and ethnicity, with correlation coefficients ranging from 0.22 to 0.73. There was no evidence of within couple associations for BMI and weight, where the correlation coefficients were − 0.03 (95% CI − 0.14, 0.08) and 0.01 (95% CI − 0.10, 0.12), respectively. Within spousal associations of the metabolite measurements were all positive but with weak to modest magnitudes, with the median correlation coefficient across all 155 measures being 0.12 (range 0.01–0.37 and interquartile range 0.10–0.18). With just four having associations stronger than 0.3: docosahexaenoic acid (0.37, 95% CI 0.22, 0.52), omega-3 fatty acids (0.32, 95% CI 0.20, 0.43) histidine (0.32, 95% CI 0.23, 0.41) and pyruvate (0.32, 95% CI 0.22, 0.43). Infertile couples exhibit spousal similarities for a range of demographic and serum metabolite measures, supporting initial assortative mating, with diet-derived metabolites suggesting possible subsequent convergence of their individual metabolic profile.

Altered Follicular Fluid Metabolic Pattern Correlates with Female Infertility and Outcome Measures of In Vitro Fertilization.

Nearly 40–50% of infertility problems are estimated to be of female origin. Previous studies dedicated to the analysis of metabolites in follicular fluid (FF) produced contrasting results, although some valuable indexes capable to discriminate control groups (CTRL) from infertile females (IF) and correlate with outcome measures of assisted reproduction techniques were in some instances found. In this study, we analyzed in blind FF of 35 control subjects (CTRL = patients in which inability to obtain pregnancy was exclusively due to a male factor) and 145 IF (affected by: endometriosis, n = 19; polycystic ovary syndrome, n = 14; age-related reduced ovarian reserve, n = 58; reduced ovarian reserve, n = 29; unexplained infertility, n = 14; genetic infertility, n = 11) to determine concentrations of 55 water- and fat-soluble low molecular weight compounds (antioxidants, oxidative/nitrosative stress-related compounds, purines, pyrimidines, energy-related metabolites, and amino acids). Results evidenced that 27/55 of them had significantly different values in IF with respect to those measured in CTRL. The metabolic pattern of these potential biomarkers of infertility was cumulated (in both CTRL and IF) into a Biomarker Score index (incorporating the metabolic anomalies of FF), that fully discriminated CTRL (mean Biomarker Score value = 4.00 ± 2.30) from IF (mean Biomarker Score value = 14.88 ± 3.09, p < 0.001). The Biomarker Score values were significantly higher than those of CTRL in each of the six subgroups of IF. Posterior probability curves and ROC curve indicated that values of the Biomarker Score clustered CTRL and IF into two distinct groups, based on the individual FF metabolic profile. Furthermore, Biomarker Score values correlated with outcome measures of ovarian stimulation, in vitro fertilization, number and quality of blastocysts, clinical pregnancy, and healthy offspring. These results strongly suggest that the biochemical quality of FF deeply influences not only the effectiveness of IVF procedures but also the following embryonic development up to healthy newborns. The targeted metabolomic analysis of FF (using empowered Redox Energy Test) and the subsequent calculation of the Biomarker Score evidenced a set of 27 low molecular weight infertility biomarkers potentially useful in the laboratory managing of female infertility and to predict the success of assisted reproduction techniques.

Metabolites from midtrimester plasma of pregnant patients at high risk for preterm birth

There is an increased awareness regarding the association between exposure to environmental contaminants and adverse pregnancy outcomes including preterm birth. Whether an individual's metabolic profile can be utilized during pregnancy to differentiate the subset of patients who are ultimately destined to delivered preterm remains uncertain but could have MEANINGFUL clinical implications. We sought to objectively quantify metabolomic profiles of patients at high risk of preterm birth by evaluating midtrimester maternal plasma and to measure whether endogenous metabolites and exogenous environmental substances differ among those who ultimately deliver preterm compared with those who deliver at term. This was a case-control analysis from a prospective cohort of patients carrying a singleton, nonanomalous gestation who were at high risk of spontaneous preterm birth. Subjects with a plasma blood sample drawn at <28 weeks' gestation and no evidence of preterm labor at the time of enrollment were included. Metabolites were extracted from frozen samples, and metabolomic analysis was performed using liquid chromatography/mass spectrometry. The primary outcome was preterm birth at 16.0 to 36.9 weeks' gestation. A total of 42 patients met the inclusion criteria. Of these, 25 (59.5%) delivered preterm at <37 weeks' gestation, at a median of 30.14 weeks' gestation (interquartile range, 28.14-34.14). A total of 812 molecular features differed between preterm birth cases and term controls with a minimum fold change of 1.2 and P<.05. Of these, 570 of 812 (70.1%) were found in higher abundances in preterm birth cases; the other 242 of 812 (29.9%) were in higher abundance in term birth controls. The identity of the small molecule/compound represented by the molecular features differing statistically between preterm birth cases and term controls was identified as ranging from those involved with endogenous metabolic pathways (including lipid catabolism, steroids, and steroid-related molecules) to exogenous exposures (including avocadyne, diosgenin, polycyclic aromatic hydrocarbons, acetaminophen metabolites, aspartame, and caffeine). Random forest analyses evaluating the relative contribution of each of the top 30 compounds in differentiating preterm birth and term controls accurately classified 21 of 25 preterm birth cases (84%). Both endogenous metabolites and exogenous exposures differ in maternal plasma in the midtrimester among patients who ultimately delivered preterm compared with those who deliver at term.