Evidence of different metabolic phenotypes in humans. Proc Natl Acad Sci U S A. 2008;105:1420-1424. [PMID: 18230739 DOI: 10.1073/pnas.0705685105]

Monitoring central nervous system tumour metabolism using cerebrospinal fluid

Central nervous system (CNS) tumours are the most common cancer cause of death in under 40s in the UK, largely because they persist and recur and sometimes metastasise during treatment. Therefore, longitudinal monitoring of patients during and following treatment must be undertaken to understand the course of the disease and alter treatment plans reactively. This monitoring must be specific, sensitive, rapid, low cost, simple, and accepted by the patient. Cerebrospinal fluid (CSF) examination obtained following lumbar puncture, already a routine part of treatment in paediatric cases, could be better utilised with improved biomarkers. In this review, we discuss the potential for metabolites in the CSF to be used as biomarkers of CNS tumour remission, progression, response to drugs, recurrence and metastasis. We confer the clinical benefits and risks of this approach and conclude that there are many potential advantages over other tests and the required instrumentation is already present in UK hospitals. On the other hand, the approach needs more research investment to find more metabolite biomarkers, better understand their relation to the tumour, and validate those biomarkers in a standardised assay in order for the assay to become a clinical reality.

Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls

Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop chemoresistance, leading to poor outcomes due to the disease's high heterogeneity. In this study, we used publicly available single-cell RNA sequencing data and machine learning to classify AML patients and healthy, monocytes, dendritic and progenitor cells population. We found that gene expression profiles of AML patients and healthy controls can be classified at the individual level with high accuracy (>70 %) when using progenitor cells, suggesting the existence of subject-specific single cell transcriptomics profiles. The analysis also revealed molecular determinants of patient heterogeneity (e.g. TPSD1, CT45A1, and GABRA4) which could support new strategies for patient stratification and personalized treatment in leukaemia.

Identification of Biochemical Determinants for Diagnosis and Prediction of Severity in 5q Spinal Muscular Atrophy Using 1H-Nuclear Magnetic Resonance Metabolic Profiling in Patient-Derived Biofluids

This study explores the potential of 1H-NMR spectroscopy-based metabolic profiling in various biofluids as a diagnostic and predictive modality to assess disease severity in individuals with 5q spinal muscular atrophy. A total of 213 biosamples (urine, plasma, and CSF) from 153 treatment-naïve patients with SMA across five German centers were analyzed using 1H-NMR spectroscopy. Prediction models were developed using machine learning algorithms which enabled the patients with SMA to be grouped according to disease severity. A quantitative enrichment analysis was employed to identify metabolic pathways associated with disease progression. The results demonstrate high sensitivity (84-91%) and specificity (91-94%) in distinguishing treatment-naïve patients with SMA from controls across all biofluids. The urinary and plasma profiles differentiated between early-onset (type I) and later-onset (type II/III) SMA with over 80% accuracy. Key metabolic differences involved alterations in energy and amino acid metabolism. This study suggests that 1H-NMR spectroscopy based metabolic profiling may be a promising, non-invasive tool to identify patients with SMA and for severity stratification, potentially complementing current diagnostic and prognostic strategies in SMA management.

CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration

Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI). Based on experimental observations, CODI generates synthetic data that integrate unrepresented sources of variation encountered in real-world applications into a given molecular fingerprint dataset. By augmenting a dataset with out-of-distribution variance, CODI enables an ML model to better generalize to samples beyond the seed training data, reducing the need for extensive experimental data collection. Using three independent longitudinal clinical studies and a case-control study, we demonstrate CODI's application to several classification tasks involving vibrational spectroscopy of human blood. We showcase our approach's ability to enable personalized fingerprinting for multiyear longitudinal molecular monitoring and enhance the robustness of trained ML models for improved disease detection. Our comparative analyses reveal that incorporating CODI into the classification workflow consistently leads to increased robustness against data variability and improved predictive accuracy.

A metabolome-wide Mendelian randomization study prioritizes causal circulating metabolites for reproductive disorders including primary ovarian insufficiency, polycystic ovary syndrome, and abnormal spermatozoa

BACKGROUND

Accumulating studies have highlighted the significant role of circulating metabolomics in the etiology of reproductive system disorders. However, the causal effects between genetically determined metabolites (GDMs) and reproductive diseases, including primary ovarian insufficiency (POI), polycystic ovary syndrome (PCOS), and abnormal spermatozoa (AS), still await thorough clarification.

METHODS

With the currently most comprehensive genome-wide association studies (GWAS) data of metabolomics, systematic two-sample Mendelian randomization (MR) analyses were conducted to disclose causal associations between 1,091 blood metabolites and 309 metabolite ratios with reproductive disorders. The inverse-variance weighted (IVW) method served as the primary analysis approach, and multiple effective MR methods were employed as complementary analyses including MR-Egger, weighted median, constrained maximum likelihood (cML-MA), contamination mixture method, robust adjusted profile score (MR-RAPS), and debiased inverse-variance weighted method. Heterogeneity and pleiotropy were assessed via MR-Egger intercept and Cochran's Q statistical analysis. Outliers were detected by Radial MR and MR-PRESSO methods. External replication and metabolic pathway analysis were also conducted.

RESULTS

Potential causal associations of 63 GDMs with POI were unearthed, and five metabolites with strong causal links to POI were emphasized. Two metabolic pathways related to the pathogenesis of POI were pinpointed. Suggestive causal effects of 70 GDMs on PCOS were detected, among which 7 metabolites stood out for strong causality with elevated PCOS risk. Four metabolic pathways associated with PCOS mechanisms were recognized. For AS, 64 GDMs as potential predictive biomarkers were identified, particularly highlighting two metabolites for their strong causal connections with AS. Three pathways underneath the AS mechanism were identified. Multiple assessments were conducted to further confirm the reliability and robustness of our causal inferences.

CONCLUSION

By extensively assessing the causal implications of circulating GDMs on reproductive system disorders, our study underscores the intricate and pivotal role of metabolomics in reproductive ill-health, laying a theoretical foundation for clinical strategies from metabolic insights.

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study

INTRODUCTION

Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.

OBJECTIVE

To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.

METHODS

We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.

RESULTS

499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.

CONCLUSIONS

Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Roles of blood metabolites in mediating the relationship between vitiligo and autoimmune diseases: Evidence from a Mendelian randomization study

OBJECTIVE

This study employed Mendelian Randomization (MR) to investigate the causal relationship between genetic susceptibility to vitiligo and the risk of various autoimmune diseases, along with the mediating role of blood metabolites.

METHODS

We performed two-sample MR analyses using aggregated genome-wide association studies (GWAS) data on 486 blood metabolites, vitiligo, and nine autoimmune diseases to investigate blood metabolites' causal effects on the susceptibility of vitiligo and the associations of vitiligo with nine autoimmune comorbidities. We also applied multivariable MR to unravel metabolites by which vitiligo influences the pathogenesis of autoimmune diseases.

RESULTS

Our findings indicate that vitiligo amplified the risk of several autoimmune diseases, including rheumatoid arthritis (OR 1.17; 95 % CI 1.08-1.27), psoriasis (OR 1.10; 95 % CI 1.04-1.17), type 1 diabetes (OR 1.41; 95 % CI 1.23-1.63), pernicious anemia (OR 1.23; 95 % CI 1.12-1.36), autoimmune hypothyroidism (OR 1.19; 95 % CI 1.11-1.26), alopecia areata (OR 1.22; 95 % CI 1.10-1.35), and autoimmune Addison's disease (OR 1.22; 95 % CI 1.12-1.33). Additionally, our analysis identified correlations with vitiligo for 14 known (nine risk, five protective) and seven uncharacterized serum metabolites. After adjusting for genetically predicted levels of histidine and pyruvate, the associations between vitiligo and these diseases were attenuated.

CONCLUSIONS

We substantiated vitiligo's influence on susceptibility to seven autoimmune diseases and conducted a thorough investigation of serum metabolites correlated with vitiligo. Histidine and pyruvate are potential mediators of vitiligo associated with autoimmune diseases.By combining metabolomics with genomics, we provide new perspectives on the etiology of vitiligo and its immune comorbidities.

Non-targeted metabolomics revealed novel links between serum metabolites and primary ovarian insufficiency: a Mendelian randomization study

Background

Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms.

Methods

Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis.

Results

Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weighted-median =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants.

Conclusion

By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention.

Integrated Metabolomics and Proteomics of Symptomatic and Early Presymptomatic States of Colitis

Colitis has a multifactorial pathogenesis with a strong cross-talk among microbiota, hypoxia, and tissue metabolism. Here, we aimed to characterize the molecular signature of the disease in symptomatic and presymptomatic stages of the inflammatory process at the tissue and fecal level. The study is based on two different murine models for colitis, and HR-MAS NMR on "intact" colon tissues and LC-MS/MS on colon tissue extracts were used to derive untargeted metabolomics and proteomics information, respectively. Solution NMR was used to derive metabolomic profiles of the fecal extracts. By combining metabolomic and proteomic analyses of the tissues, we found increased anaerobic glycolysis, accompanied by an altered citric acid cycle and oxidative phosphorylation in inflamed colons; these changes associate with inflammation-induced hypoxia taking place in colon tissues. Different colitis states were also characterized by significantly different metabolomic profiles of fecal extracts, attributable to both the dysbiosis characteristic of colitis as well as the dysregulated tissue metabolism. Strong and distinctive tissue and fecal metabolomic signatures can be detected before the onset of symptoms. Therefore, untargeted metabolomics of tissues and fecal extracts provides a comprehensive picture of the changes accompanying the disease onset already at preclinical stages, highlighting the diagnostic potential of global metabolomics for inflammatory diseases.

Exploring the Effects of Probiotic Treatment on Urinary and Serum Metabolic Profiles in Healthy Individuals

Probiotics are live microorganisms that confer health benefits when administered in adequate amounts. They are used to promote gut health and alleviate various disorders. Recently, there has been an increasing interest in the potential effects of probiotics on human physiology. In the presented study, the effects of probiotic treatment on the metabolic profiles of human urine and serum using a nuclear magnetic resonance (NMR)-based metabonomic approach were investigated. Twenty-one healthy volunteers were enrolled in the study, and they received two different dosages of probiotics for 8 weeks. During the study, urine and serum samples were collected from volunteers before and during probiotic supplementation. The results showed that probiotics had a significant impact on the urinary and serum metabolic profiles without altering their phenotypes. This study demonstrated the effects of probiotics in terms of variations of metabolite levels resulting also from the different probiotic posology. Overall, the results suggest that probiotic administration may affect both urine and serum metabolomes, although more research is needed to understand the mechanisms and clinical implications of these effects. NMR-based metabonomic analysis of biofluids is a powerful tool for monitoring host-gut microflora dynamic interaction as well as for assessing the individual response to probiotic treatment.

Practical considerations for rapid and quantitative NMR-based metabolomics

NMR is a key technology for metabolomics because of its robustness and reproducibility. Herein we discuss practical considerations that extend the utility of NMR spectroscopy. First, the long T1 spin relaxation times of small molecules limits high-throughput data acquisition because most experimental time is lost while waiting for signal recovery. In principle, the addition of a small amount of commercially-available paramagnetic gadolinium chelate allows cost-effective and efficient high-throughput mixture analysis with correct concentration determination. However, idle time caused by slow temperature regulation during sample exchanges, poses a next constraint. We show how, with proper care, NMR sample scanning times can be reduced additionally by a factor of two. Lastly, we describe how equidistant bucketing is a simple and fast procedure for metabolomic fingerprinting. The combination of these advancements help to make NMR metabolomics more versatile than it is today.

Phenylketonuria (PKU) Urinary Metabolomic Phenotype Is Defined by Genotype and Metabolite Imbalance: Results in 51 Early Treated Patients Using Ex Vivo 1H-NMR Analysis

Phenylketonuria (PKU) is a rare metabolic disorder caused by mutations in the phenylalanine hydroxylase gene. Depending on the severity of the genetic mutation, medical treatment, and patient dietary management, elevated phenylalanine (Phe) may occur in blood and brain tissues. Research has recently shown that high Phe not only impacts the central nervous system, but also other organ systems (e.g., heart and microbiome). This study used ex vivo proton nuclear magnetic resonance (¹H-NMR) analysis of urine samples from PKU patients (mean 14.9 ± 9.2 years, n = 51) to identify the impact of elevated blood Phe and PKU treatment on metabolic profiles. Our results found that 24 out of 98 urinary metabolites showed a significant difference (p < 0.05) for PKU patients compared to age-matched healthy controls (n = 51) based on an analysis of urinary metabolome. These altered urinary metabolites were related to Phe metabolism, dysbiosis, creatine synthesis or intake, the tricarboxylic acid (TCA) cycle, end products of nicotinamide-adenine dinucleotide degradation, and metabolites associated with a low Phe diet. There was an excellent correlation between the metabolome and genotype of PKU patients and healthy controls of 96.7% in a confusion matrix model. Metabolomic investigations may contribute to a better understanding of PKU pathophysiology.

Maternal metabolomic profiling and congenital heart disease risk in offspring: A systematic review of observational studies

Aetiological understanding and screening methods for congenital heart disease (CHD) are limited. Maternal metabolomic assessment offers the potential to identify risk factors and biomarkers. We performed a systematic review (PROSPERO CRD42022308452) investigating the association between fetal/childhood CHD and endogenous maternal metabolites. Ovid-MEDLINE, Ovid-EMBASE and Cochrane Library were searched between inception and 06/09/2022. Case control studies included analysing maternal blood or urine metabolites in pregnancy or postpartum where there was foetal/childhood CHD. Risk of bias assessment utilised the Scottish Intercollegiate Guidelines Network methodology checklist and narrative synthesis was performed. A total of 134 records were screened with eight eligible studies (n = 3242 pregnancies, n = 842 CHD-affected offspring). Five studies performed metabolomic analysis in pregnancy. Metabolites distinguishing case and control groups spanned lipid, glucose and amino-acid pathways, with the development of sensitive risk prediction models. No single metabolite consistently distinguished cases and controls across studies. Three studies performed targeted analysis postnatally with altered lipid and amino acid metabolites and raised homocysteine and markers of oxidative stress identified in cases. Included studies reported small sample sizes, analysing different biosamples at variable time points using differing techniques. At present, there is not enough evidence to confidently associate maternal metabolomic profiles with offspring CHD risk. However, several identified pathways warrant further investigation.

NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer

Background: Lung cancer can be detected by measuring the patient’s plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient’s metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Patients and methods: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86–100]) and a sensitivity of 92% (95% CI [81–98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77–97]) and an AUC value of 0.97 (95% CI [0.93–1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82–99]) and an AUC value of 0.97 (95% CI [0.93–1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor’s presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

Utilizing Amniotic Fluid Metabolomics to Monitor Fetal Well-Being: A Narrative Review of the Literature

Fetal and perinatal periods are critical phases for long-term development. Early diagnosis of maternal complications is challenging due to the great complexity of these conditions. In recent years, amniotic fluid has risen in a prominent position in the latest efforts to describe and characterize prenatal development. Amniotic fluid may provide real-time information on fetal development and metabolism throughout pregnancy as substances from the placenta, fetal skin, lungs, gastric fluid, and urine are transferred between the mother and the fetus. Applying metabolomics to monitor fetal well-being, in such a context, could help in the understanding, diagnosis, and treatment of these conditions and is a promising area of research. This review shines a spotlight on recent amniotic fluid metabolomics studies and their methods as an interesting tool for the assessment of many conditions and the identification of biomarkers. Platforms in use, such as proton nuclear magnetic resonance (¹H NMR) and ultra-high-performance liquid chromatography (UHPLC), have different merits, and a combinatorial approach could be valuable. Metabolomics may also be used in the quest for habitual diet-induced metabolic signals in amniotic fluid. Finally, analysis of amniotic fluid can provide information on exposure to exogenous substances by detecting the exact levels of metabolites carried to the fetus and associated metabolic effects.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Gut microbial response to host metabolic phenotypes

A large number of studies have proved that biological metabolic phenotypes exist objectively and are gradually recognized by humans. Gut microbes affect the host's metabolic phenotype. They directly or indirectly participate in host metabolism, physiology and immunity through changes in population structure, metabolite differences, signal transduction and gene expression. Obtaining comprehensive information and specific identification factors associated with gut microbiota and host metabolic phenotypes has become the focus of research in the field of gut microbes, and it has become possible to find new and effective ways to prevent or treat host metabolic diseases. In the future, precise treatment of gut microbes will become one of the new therapeutic strategies. This article reviews the content of gut microbes and carbohydrate, amino acid, lipid and nucleic acid metabolic phenotypes, including metabolic intermediates, mechanisms of action, latest research findings and treatment strategies, which will help to understand the relationship between gut microbes and host metabolic phenotypes and the current research status.

The lipidome of an omnivorous insect responds to diet composition and social environment

Lipids are biomolecules with essential roles in metabolic processes, signaling, and cellular architecture. In this study, we investigated changes in the lipidome of the house cricket Acheta domesticus subjected to diets of different nutritional composition (i.e., protein to carbohydrate ratio) and two distinct social environments (i.e., solitary or in groups). We measured relative abundances of 811 lipid species in whole-body cricket samples using flow injection analysis coupled to tandem mass spectrometry. We assessed differences in the relative abundances of lipid species induced by diet composition and social environment in female and male A. domesticus. Additionally, we performed a functional analysis of the lipids with significant differences using a recently developed database. We found that most differences in the relative abundances of lipid species were explained by sex alone. Furthermore, the lipidome of female A. domesticus was responsive to diet composition. Females fed with the balanced diet had an even higher abundance of lipids involved in lipid storage than their counterparts fed with a protein-rich diet. Interestingly, the male cricket lipidome was not responsive to diet composition. In addition, the social environment did not induce significant changes in the lipid profile neither in female nor in male crickets.

NMR-Based Metabolomics in Differential Diagnosis of Chronic Kidney Disease (CKD) Subtypes

Chronic Kidney Disease (CKD) is considered as a major public health problem as it can lead to end-stage kidney failure, which requires replacement therapy. A prompt and accurate diagnosis, along with the appropriate treatment, can delay CKD's progression, significantly. Herein, we sought to determine whether CKD etiology can be reflected in urine metabolomics during its early stage. This is achieved through the analysis of the urine metabolic fingerprint from 108 CKD patients by means of Nuclear Magnetic Resonance (NMR) spectroscopy metabolomic analysis. We report the first NMR-metabolomics data regarding the three most common etiologies of CKD: Chronic Glomerulonephritis (IgA and Membranous Nephropathy), Diabetic Nephropathy (DN) and Hypertensive Nephrosclerosis (HN). Analysis aided a moderate glomerulonephritis clustering, providing characterization of the metabolic fluctuations between the CKD subtypes and control disease. The urine metabolome of IgA Nephropathy reveals a specific metabolism, reflecting its different etiology or origin and is useful for determining the origin of the disease. In contrast, urine metabolomes from DN and HN patients did not reveal any indicative metabolic pattern, which is consistent with their fused clinical phenotype. These findings may contribute to improving diagnostics and prognostic approaches for CKD, as well as improving our understanding of its pathology.

Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.

Independent and Interactive Effects of Genetic Background and Sex on Tissue Metabolomes of Adipose, Skeletal Muscle, and Liver in Mice

Genetics play an important role in the development of metabolic diseases. However, the relative influence of genetic variation on metabolism is not well defined, particularly in tissues, where metabolic dysfunction that leads to disease occurs. We used inbred strains of laboratory mice to evaluate the impact of genetic variation on the metabolomes of tissues that play central roles in metabolic diseases. We chose a set of four common inbred strains that have different levels of susceptibility to obesity, insulin resistance, and other common metabolic disorders. At the ages used, and under standard husbandry conditions, these lines are not overtly diseased. Using global metabolomics profiling, we evaluated water-soluble metabolites in liver, skeletal muscle, and adipose from A/J, C57BL/6J, FVB/NJ, and NOD/ShiLtJ mice fed a standard mouse chow diet. We included both males and females to assess the relative influence of strain, sex, and strain-by-sex interactions on metabolomes. The mice were also phenotyped for systems level traits related to metabolism and energy expenditure. Strain explained more variation in the metabolite profile than did sex or its interaction with strain across each of the tissues, especially in liver. Purine and pyrimidine metabolism and pathways related to amino acid metabolism were identified as pathways that discriminated strains across all three tissues. Based on the results from ANOVA, sex and sex-by-strain interaction had modest influence on metabolomes relative to strain, suggesting that the tissue metabolome remains largely stable across sexes consuming the same diet. Our data indicate that genetic variation exerts a fundamental influence on tissue metabolism.

Serum NMR Profiling Reveals Differential Alterations in the Lipoproteome Induced by Pfizer-BioNTech Vaccine in COVID-19 Recovered Subjects and Naïve Subjects

¹H NMR spectra of sera have been used to define the changes induced by vaccination with Pfizer-BioNTech vaccine (2 shots, 21 days apart) in 10 COVID-19-recovered subjects and 10 COVID-19-naïve subjects at different time points, starting from before vaccination, then weekly until 7 days after second injection, and finally 1 month after the second dose. The data show that vaccination does not induce any significant variation in the metabolome, whereas it causes changes at the level of lipoproteins. The effects are different in the COVID-19-recovered subjects with respect to the naïve subjects, suggesting that a previous infection reduces the vaccine modulation of the lipoproteome composition.

Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients

Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress.

Metabolomic signatures for the longitudinal reduction of muscle strength over 10 years

Background

Skeletal muscles are essential components of the neuromuscular skeletal system that have an integral role in the structure and function of the synovial joints which are often affected by osteoarthritis (OA). The aim of this study was to identify the baseline metabolomic signatures for the longitudinal reduction of muscle strength over 10 years in the well-established community-based Tasmanian Older Adult Cohort (TASOAC).

Methods

Study participants were 50–79 year old individuals from the TASOAC. Hand grip, knee extension, and leg strength were measured at baseline, 2.6-, 5-, and 10-year follow-up points. Fasting serum samples were collected at 2.6-year follow-up point, and metabolomic profiling was performed using the TMIC Prime Metabolomics Profiling Assay. Generalized linear mixed effects model was used to identify metabolites that were associated with the reduction in muscle strength over 10 years after controlling for age, sex, and BMI. Significance level was defined at α=0.0004 after correction of multiple testing of 129 metabolites with Bonferroni method. Further, a genome-wide association study (GWAS) analysis was performed to explore if genetic factors account for the association between the identified metabolomic markers and the longitudinal reduction of muscle strength over 10 years.

Results

A total of 409 older adults (50% of them females) were included. The mean age was 60.93±6.50 years, and mean BMI was 27.12±4.18 kg/m² at baseline. Muscle strength declined by 0.09 psi, 0.02 kg, and 2.57 kg per year for hand grip, knee extension, and leg strength, respectively. Among the 143 metabolites measured, 129 passed the quality checks and were included in the analysis. We found that the elevated blood level of asymmetric dimethylarginine (ADMA) was associated with the reduction in hand grip (p=0.0003) and knee extension strength (p=0.008) over 10 years. GWAS analysis found that a SNP rs1125718 adjacent to WISP1gene was associated with ADMA levels (p=4.39*10^-8). Further, we found that the increased serum concentration of uric acid was significantly associated with the decline in leg strength over 10 years (p=0.0001).

Conclusion

Our results demonstrated that elevated serum ADMA and uric acid at baseline were associated with age-dependent muscle strength reduction. They might be novel targets to prevent muscle strength loss over time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13395-022-00286-9.

Evidence for distinct neuro-metabolic phenotypes in humans

Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however, remains largely unexplored - despite the potential for the discovery of novel behavioural and disease phenotypes. In this study, we measured 14 metabolite levels, normalised as ratios to total-creatine, with ¹H magnetic resonance spectroscopy (MRS) acquired from the bilateral anterior cingulate cortices of six healthy participants, repeatedly over a period of four months. ANOVA tests revealed statistically significant differences of 3 metabolites and 3 commonly used combinations (total-choline, glutamate + glutamine and total-N-acetylaspartate) between the participants, with scyllo-inositol (F=85, p=6e-26) and total-choline (F=39, p=1e-17) having the greatest discriminatory power. This was not attributable to structural differences. When predicting individuals from the repeated MRS measurements, a leave-one-out classification accuracy of 88% was achieved using a support vector machine based on scyllo-inositol and total-choline levels. Accuracy increased to 98% with the addition of total-N-acetylaspartate and myo-inositol - demonstrating the efficacy of combining MRS with machine learning and metabolomic methodology. These results provide evidence for the existence of neuro-metabolic phenotypes, which may be non-invasively measured using widely available 3 Tesla MRS. Establishing these phenotypes in a larger cohort and investigating their connection to brain health and function presents an important area for future study.

1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy

Background

5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of ¹H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA.

Results

Urine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool.

Conclusions

This study provides preliminary evidence for the use of ¹H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-02075-x.

Finger sweat analysis enables short interval metabolic biomonitoring in humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Respiratory Colonization and Short-Term Temporal Changes in the Urinary Metabolome of Children

The human metabolome may vary based on age, over time, and in the presence of viral carriage and bacterial colonization—a common scenario in children. We used nuclear magnetic resonance spectroscopy to identify and quantify urinary metabolites of children without signs or symptoms of respiratory illness. A urine sample and two nasopharyngeal swabs were collected to test for respiratory viral pathogens and colonization by Streptococcus pneumoniae (Sp). Urine samples were collected at the initial visit, 24 h post-enrollment, and 10–14 days post-enrollment. Of the 122 children enrolled, 24% had a virus detected and 19.7% had Sp detected. Intraclass correlation coefficients demonstrated greater within-subject versus between-subject variability for all metabolites detected. In linear mixed models adjusted for age, time, history of asthma, Sp, and viruses, 1-methylnicotinamide was increased by 50% in children with Sp and decreased by 35% in children with rhinovirus/enterovirus. Children with Sp had 83% higher levels of trimethylamine-N-oxide compared with those without Sp. However, when adjusting for multiple comparisons, the association was no longer statistically significant. In conclusion, there appear to be short-term changes within the urinary metabolome of healthy children, but levels of metabolites did not statistically differ in children with viral carriage or Sp detected.

Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study

Genes, sex, age, diet, lifestyle, gut microbiome, and multiple other factors affect human metabolomic profiles. Understanding metabolomic variation is critical in human nutrition research as metabolites that are sensitive to change versus those that are more stable might be more informative for a particular study design. This study aims to identify stable metabolomic regions and determine the genetic and environmental contributions to stability. Using a classic twin design, ¹H nuclear magnetic resonance (NMR) urinary metabolomic profiles were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate mixed models identified stable urinary metabolites with intraclass correlation coefficients ≥0.51. Longitudinal twin modeling measured the contribution of genetic and environmental influences to variation in the stable urinary NMR metabolome, comprising stable metabolites. The conservation of an individual’s stable urinary NMR metabolome over time was assessed by calculating conservation indices. In this study, 20% of the urinary NMR metabolome is stable over 2 months (intraclass correlation (ICC) 0.51–0.65). Common genetic and shared environmental factors contributed to variance in the stable urinary NMR metabolome over time. Using the stable metabolome, 91% of individuals had good metabolomic conservation indices ≥0.70. To conclude, this research identifies 20% of the urinary NMR metabolome as stable, improves our knowledge of the sources of metabolomic variation over time, and demonstrates the conservation of an individual’s urinary NMR metabolome.

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

Research Progress of Urine Biomarkers in the Diagnosis, Treatment, and Prognosis of Bladder Cancer

Bladder cancer (BC) is one of the most common tumor with high incidence. Relative to other cancers, BC has a high rate of recurrence, which results in increased mortality. As a result, early diagnosis and life-long monitoring are clinically significant for improving the long-term survival rate of BC patients. At present, the main methods of BC detection are cystoscopy and biopsy; however, these procedures can be invasive and expensive. This can lead to patient refusal and reluctance for monitoring. There are several BC biomarkers that have been approved by the FDA, but their sensitivity, specificity, and diagnostic accuracy are not ideal. More research is needed to identify suitable biomarkers that can be used for early detection, evaluation, and observation. There has been heavy research in the proteomics and genomics of BC and many potential biomarkers have been found. Although the advent of metabonomics came late, with the recent development of advanced analytical technology and bioinformatics, metabonomics has become a widely used diagnostic tool in clinical and biomedical research. It should be emphasized that despite progress in new biomarkers for BC diagnosis, there remains challenges and limitations in metabonomics research that affects its translation into clinical practice. In this chapter, the latest literature on BC biomarkers was reviewed.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.

Genetically Determined Levels of Serum Metabolites and Risk of Neuroticism: A Mendelian Randomization Study

BACKGROUND

Neuroticism is a strong predictor for a variety of social and behavioral outcomes, but the etiology is still unknown. Our study aims to provide a comprehensive investigation of causal effects of serum metabolome phenotypes on risk of neuroticism using Mendelian randomization (MR) approaches.

METHODS

Genetic associations with 486 metabolic traits were utilized as exposures, and data from a large genome-wide association study of neuroticism were selected as outcome. For MR analysis, we used the standard inverse-variance weighted (IVW) method for primary MR analysis and 3 additional MR methods (MR-Egger, weighted median, and MR pleiotropy residual sum and outlier) for sensitivity analyses.

RESULTS

Our study identified 31 metabolites that might have causal effects on neuroticism. Of the 31 metabolites, uric acid and paraxanthine showed robustly significant association with neuroticism in all MR methods. Using single nucleotide polymorphisms as instrumental variables, a 1-SD increase in uric acid was associated with approximately 30% lower risk of neuroticism (OR: 0.77; 95% CI: 0.62-0.95; PIVW = 0.0145), whereas a 1-SD increase in paraxanthine was associated with a 7% higher risk of neuroticism (OR: 1.07; 95% CI: 1.01-1.12; PIVW = .0145).

DISCUSSION

Our study suggested an increased level of uric acid was associated with lower risk of neuroticism, whereas paraxanthine showed the contrary effect. Our study provided novel insight by combining metabolomics with genomics to help understand the pathogenesis of neuroticism.

Effects of Probiotics Administration on Human Metabolic Phenotype

The establishment of the beneficial interactions between the host and its microbiota is essential for the correct functioning of the organism, since microflora alterations can lead to many diseases. Probiotics improve balanced microbial communities, exerting substantial health-promoting effects. Here we monitored the molecular outcomes, obtained by gut microflora modulation through probiotic treatment, on human urine and serum metabolic profiles, with a metabolomic approach. Twenty-two subjects were enrolled in the study and administered with two different probiotic types, both singularly and in combination, for 8 weeks. Urine and serum samples were collected before and during the supplementation and were analyzed by nuclear magnetic resonance (NMR) spectroscopy and statistical analyses. After eight weeks of treatment, probiotics deeply influence the urinary metabolic profiles of the volunteers, without significantly altering their single phenotypes. Anyway, bacteria supplementation tends to reduce the differences in metabolic phenotypes among individuals. Overall, the effects are recipient-dependent, and in some individuals, robust effects are already well visible after four weeks. Modifications in metabolite levels, attributable to each type of probiotic administration, were also monitored. Metabolomic analysis of biofluids turns out to be a powerful technique to monitor the dynamic interactions between the microflora and the host, and the individual response to probiotic assumption.

Effects of short- and long-term glucocorticoid-induced osteoporosis on plasma metabolome and lipidome of ovariectomized sheep

BACKGROUND

Understanding the metabolic and lipidomic changes that accompany bone loss in osteoporosis might provide insights about the mechanisms behind molecular changes and facilitate developing new drugs or nutritional strategies for osteoporosis prevention. This study aimed to examine the effects of short- or long-term glucocorticoid-induced osteoporosis on plasma metabolites and lipids of ovariectomized (OVX) sheep.

METHODS

Twenty-eight aged ewes were divided randomly into four groups: an OVX group, OVX in combination with glucocorticoids for two months (OVXG2), and OVX in combination with five doses of glucocorticoids (OVXG5) to induce bone loss, and a control group. Liquid chromatography-mass spectrometry untargeted metabolomic analysis was applied to monthly plasma samples to follow the progression of osteoporosis over five months.

RESULTS

The metabolite profiles revealed significant differences in the plasma metabolome of OVX sheep and OVXG when compared with the control group by univariate analysis. Nine metabolites were altered, namely 5-methoxytryptophan, valine, methionine, tryptophan, glutaric acid, 2-pyrrolidone-5-carboxylic acid, indole-3-carboxaldehyde, 5-hydroxylysine and malic acid. Similarly, fifteen lipids were perturbed from multiple lipid classes such as lysophoslipids, phospholipids and ceramides.

CONCLUSION

This study showed that OVX and glucocorticoid interventions altered the metabolite and lipid profiles of sheep, suggesting that amino acid and lipid metabolisms are potentially the main perturbed metabolic pathways regulating bone loss in OVX sheep.

Microbial and metabolomic analysis of gingival crevicular fluid in general chronic periodontitis patients: lessons for a predictive, preventive, and personalized medical approach

Objectives

General chronic periodontitis (GCP) is a bacterial inflammatory disease with complex pathology. Despite extensive studies published on the variation in the oral microbiota and metabolic profiles of GCP patients, information is lacking regarding the correlation between host-bacterial interactions and biochemical metabolism. This study aimed to analyze the oral microbiome, the oral metabolome, and the link between them and to identify potential molecules as useful biomarkers for predictive, preventive, and personalized medicine (PPPM) in GCP.

Methods

In this study, gingival crevicular fluid (GCF) samples were collected from patients with GCP (n = 30) and healthy controls (n = 28). The abundance of oral microbiota constituents was obtained by Illumina sequencing, and the relative level of metabolites was measured by gas chromatography-mass spectrometry. Full-mouth probing depth, clinical attachment loss, and bleeding on probing were recorded as indices of periodontal disease.

Results

The relative abundances of 7 phyla and 82 genera differed significantly between the GCP and healthy groups. Seventeen differential metabolites involved in different metabolism pathways were selected based on variable influence on projection values (VIP > 1) and P values (P < 0.05). Through Spearman's correlation analysis, microorganisms, metabolites in GCF, and clinical data together showed a clear trend, and clinical data regarding periodontitis can be reflected in the shift of the oral microbial community and the change in metabolites in GCF. A combination of citramalic acid and N-carbamylglutamate yielded satisfactory accuracy (AUC = 0.876) for the predictive diagnosis of GCP.

Conclusions

Dysbiosis in the polymicrobial community structure and changes in metabolism could be mechanisms underlying periodontitis. The differential microorganisms and metabolites in GCF between periodontitis patients and healthy individuals are possibly biomarkers, pointing to a potential strategy for the prediction, diagnosis, prognosis, and management of personalized periodontal therapy.

How could metabolomics change pediatric health?

In the last years, 'omics' technologies, and especially metabolomics, emerged as expanding scientific disciplines and promising technologies in the characterization of several pathophysiological processes.In detail, metabolomics, able to detect in a dynamic way the whole set of molecules of low molecular weight in cells, tissues, organs, and biological fluids, can provide a detailed phenotypic portray, representing a metabolic "snapshot."Thanks to its numerous strength points, metabolomics could become a fundamental tool in human health, allowing the exact evaluation of individual metabolic responses to pathophysiological stimuli including drugs, environmental changes, lifestyle, a great number of diseases and other epigenetics factors.Moreover, if current metabolomics data will be confirmed on larger samples, such technology could become useful in the early diagnosis of diseases, maybe even before the clinical onset, allowing a clinical monitoring of disease progression and helping in performing the best therapeutic approach, potentially predicting the therapy response and avoiding overtreatments. Moreover, the application of metabolomics in nutrition could provide significant information on the best nutrition regimen, optimal infantile growth and even in the characterization and improvement of commercial products' composition.These are only some of the fields in which metabolomics was applied, in the perspective of a precision-based, personalized care of human health.In this review, we discuss the available literature on such topic and provide some evidence regarding clinical application of metabolomics in heart diseases, auditory disturbance, nephrouropathies, adult and pediatric cancer, obstetrics, perinatal conditions like asphyxia, neonatal nutrition, neonatal sepsis and even some neuropsychiatric disorders, including autism.Our research group has been interested in metabolomics since several years, performing a wide spectrum of experimental and clinical studies, including the first metabolomics analysis of human breast milk. In the future, it is reasonable to predict that the current knowledge could be applied in daily clinical practice, and that sensible metabolomics biomarkers could be easily detected through cheap and accurate sticks, evaluating biofluids at the patient's bed, improving diagnosis, management and prognosis of sick patients and allowing a personalized medicine. A dream? May be I am a dreamer, but I am not the only one.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Effect of Estrogen Receptor Status on Circulatory Immune and Metabolomics Profiles of HER2-Positive Breast Cancer Patients Enrolled for Neoadjuvant Targeted Chemotherapy

HER2-positive breast cancer (BC) represents a heterogeneous cancer disease. In an attempt to identify new stratification models useful for prognosis and therapeutic strategy, we investigated the influence of estrogen receptor (ER) status on the host immune and metabolomics profile of HER2-positive BC patients enrolled for neoadjuvant targeted chemotherapy (NATC). The study enrolled 43 HER2-positive BC patients eligible for NATC based on the trastuzumab-paclitaxel combination. Baseline circulatory cytokines and ¹H NMR plasma metabolomics profiles were investigated. Differences in the immune cytokines and metabolomics profile as a function of the ER status, and their association with clinical outcomes were studied by multivariate and univariate analysis. Baseline metabolomics profiles were found to discriminate HER2-positive ER(+) from ER(−) BC patients. Within the ER(+) group an immune-metabolomics model, based on TNF-α and valine, predicted pathological complete response to NATC with 90.9% accuracy (AUROC = 0.92, p = 0.004). Moreover, metabolomics information integrated with IL-2 and IL-10 cytokine levels were prognostic of relapse with an accuracy of 95.5%. The results indicate that in HER2-positive BC patients the ER status influences the host circulatory immune-metabolomics profile. The baseline immune-metabolomics assessment in combination with ER status could represent an independent stratification tool able to predict NATC response and disease relapse of HER2-positive patients.

Systems and Precision Medicine in Necrotizing Soft Tissue Infections

Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.

DHA-Induced Perturbation of Human Serum Metabolome. Role of the Food Matrix and Co-Administration of Oat β-glucan and Anthocyanins

Docosahexaenoic acid (DHA) has been reported to have a positive impact on many diet-related disease risks, including metabolic syndrome. Although many DHA-enriched foods have been marketed, the impact of different food matrices on the effect of DHA is unknown. As well, the possibility to enhance DHA effectiveness through the co-administration of other bioactives has seldom been considered. We evaluated DHA effects on the serum metabolome administered to volunteers at risk of metabolic syndrome as an ingredient of three different foods. Foods were enriched with DHA alone or in combination with oat beta-glucan or anthocyanins and were administered to volunteers for 4 weeks. Serum samples collected at the beginning and end of the trial were analysed by NMR-based metabolomics. Multivariate and univariate statistical analyses were used to characterize modifications in the serum metabolome and to evaluate bioactive-bioactive and bioactive-food matrix interactions. DHA administration induces metabolome perturbation that is influenced by the food matrix and the co-presence of other bioactives. In particular, when co-administered with oat beta-glucan, DHA induces a strong rearrangement in the lipoprotein profile of the subjects. The observed modifications are consistent with clinical results and indicate that metabolomics represents a possible strategy to choose the most appropriate food matrices for bioactive enrichment.

Human beings as islands of stability: Monitoring body states using breath profiles

By checking the reproducibility of conventional mid-infrared Fourier spectroscopy of human breath in a small test study (15 individuals), we found that a set of volatile organic compounds (VOC) of the individual breath samples remains reproducible at least for 18 months. This set forms a unique individual’s “island of stability” (IOS) in a multidimensional VOC concentration space. The IOS stability can simultaneously be affected by various life effects as well as the onset of a disease. Reflecting the body state, they both should have different characteristics. Namely, they could be distinguished by different temporal profiles: In the case of life effects (beverage intake, physical or mental exercises, smoking etc.), there is a non-monotonic shift of the IOS position with the return to the steady state, whereas a progressing disease corresponds to a monotonic IOS shift. As a first step of proving these dependencies, we studied various life effects with the focus on the strength and characteristic time of the IOS shift. In general, our results support homeostasis on a long time scale of months, allostasis on scales of hours to weeks or until smoke quitting for smokers, as well as resilience in the case of recovery from a disease.

The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models. Part II: Results

The gut microbiota is increasingly implicated in the health and metabolism of its human host. The host's diet is a major component influencing the composition and function of the gut microbiota, and mounting evidence suggests that the composition and function of the gut microbiota influence the host's metabolic response to diet. This effect of the gut microbiota on personalized dietary response is a growing focus of precision nutrition research and may inform the effort to tailor dietary advice to the individual. Because the gut microbiota has been shown to be malleable to some extent, it may also allow for therapeutic alterations of the gut microbiota in order to alter response to certain dietary components. This article is the second in a 2-part review of the current research in the field of precision nutrition incorporating the gut microbiota into studies investigating interindividual variability in response to diet. Part I reviews the methods used by researchers to design and carry out such studies as well as analyze the results subsequently obtained. Part II reviews the findings of these studies and discusses the gaps in our current knowledge and directions for future research. The studies reviewed provide the current understanding in this field of research and a foundation from which we may build, utilizing and expanding upon the methods and results they present to inform future studies.

A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context

Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level.