Longitudinal Metabolomics Research Articles

Intermittent fasting for systemic triglyceride metabolic reprogramming (IFAST): Design and methods of a prospective, randomized, controlled trial

BackgroundCaloric restriction prolongs lifespan in model organisms and improves metrics of aging-related diseases in humans, but daily compliance is challenging. Intermittent fasting improves metrics of lipid and glucose metabolism in the setting of weight loss but whether these metrics are improved independent of weight loss is not known. MethodsWe seek to address this gap with IFAST, a single-center, three-arm, prospective, randomized, controlled clinical trial. Eligible study participants are adults with no chronic medical conditions beyond prediabetes or overweight but who are at high risk for type 2 diabetes mellitus (T2D), defined as having a history of gestational diabetes or a first-degree relative with T2D. Participants will be randomized in a 1:2:2 schema to either a control group, a fasting group, or a fasting/weight maintenance group. The fasting groups will complete a 24-h fast one day per week for 12 weeks. The key mechanistic endpoint is change in triglyceride composition (defined by carbon content and degree of saturation) as measured by longitudinal metabolomics. The key safety endpoint is percent change from baseline in bone volume fraction (BV/TV; high-resolution peripheral quantitative CT) at the radius in the fasting group. Secondary endpoints include measures of insulin sensitivity (hyperinsulinemic-euglycemic clamp), clinical lipid profiling, systemic inflammation markers, hunger assessment, bone density, and bone microarchitecture with high-resolution peripheral quantitative CT. ConclusionIFAST will investigate intrinsic metabolic benefits of intermittent fasting beyond weight loss. Trial registrationClinicalTrials.gov ID NCT05722873.

Methods for joint modelling of longitudinal omics data and time-to-event outcomes: Applications to lysophosphatidylcholines in connection to aging and mortality in the Long Life Family Study.

Studying relationships between longitudinal changes in omics variables and risks of events requires specific methodologies for joint analyses of longitudinal and time-to-event outcomes. We applied two such approaches (joint models [JM], stochastic process models [SPM]) to longitudinal metabolomics data from the Long Life Family Study focusing on understudied associations of longitudinal changes in lysophosphatidylcholines (LPC) with mortality and aging-related outcomes (23 LPC species, 5,790 measurements of each in 4,011 participants, 1,431 of whom died during follow-up). JM analyses found that higher levels of the majority of LPC species were associated with lower mortality risks, with the largest effect size observed for LPC 15:0/0:0 (hazard ratio: 0.715, 95% CI (0.649, 0.788)). SPM applications to LPC 15:0/0:0 revealed how the association found in JM reflects underlying aging-related processes: decline in robustness to deviations from optimal LPC levels, better ability of males' organisms to return to equilibrium LPC levels (which are higher in females), and increasing gaps between the optimum and equilibrium levels leading to increased mortality risks with age. Our results support LPC as a biomarker of aging and related decline in robustness/resilience, and call for further exploration of factors underlying age-dynamics of LPC in relation to mortality and diseases.

Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.

Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications. Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health. We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode. The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state. The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications-when markers are present in both fasting and dynamic states.

Temporal changes in plasma metabolic signatures to predict immune response of antiretroviral therapy among people living with HIV.

Antiretroviral therapy (ART) is an effective treatment for people living with HIV (PLHIVs), requiring an extended period to achieve immune reconstitution. Metabolic alterations induced by ART are crucial for predicting long-term therapeutic responses, yet comprehensive investigation through large-scale clinical studies is still lacking. Here, we collected plasma samples from 108 PLHIVs to the untargeted plasma metabolomics study, based on the longitudinal metabolomics design. Cross-sectional analyzes were performed at pre- and post-ART to explore the metabolic transformation induced by the therapy. Subsequently, delta values between pre- and post-ART measurements were calculated to quantify metabolic alterations. Then, the optimal set of metabolic traits and clinical signatures were further identified and applied to construct random forest model for predicting the future therapeutic responses to ART. We found distinct ART-induced metabolic transformation among PLHIVs. After confounder-adjustments, five metabolites exhibited significant associations with future immune response: tetracosatetraenoic acid (24:4n-6) (pre-ART) (odds ratio [OR]: 0.978, 95% confidence interval [CI]: 0.955~0.997), 1-(3,4-dihydroxyphenyl)-5-hydroxy-3-decanone (pre-ART) (OR: 1.298, 95% CI: 1.061~1.727), beta-PC-M6 (change) (OR: 0.967, 95% CI: 0.938~0.993), d-Galactaro-1,4-lactone (change) (OR: 1.032, 95% CI: 1.007~1.063), Annuionone C (change) (OR: 1.100, 95% CI: 1.030~1.190). The addition of plasma metabolites to clinical markers accurately predicted immune response to ART with an area under curve of 0.91. Notably, most disrupted metabolites were significantly correlated with blood lipids, suggesting that metabolic transformation might contribute to dyslipidemia among PLHIVs. This study highlights the distinct metabolic transformation post-ART among PLHIVs and reveals the potential role of metabolic transformation as key determinants of ART efficacy.

Longitudinal metabolomics of human plasma reveal metabolic dynamics and predictive markers of antituberculosis drug-induced liver injury

Tuberculosis (TB) remains the second leading cause of death from a single infectious agent and long-term medication could lead to antituberculosis drug-induced liver injury (ATB-DILI). We established a prospective longitudinal cohort of ATB-DILI with multiple timepoint blood sampling and used untargeted metabolomics to analyze the metabolic profiles of 107 plasma samples from healthy controls and newly diagnosed TB patients who either developed ATB-DILI within 2 months of anti-TB treatment (ATB-DILI subjects) or completed their treatment without any adverse drug reaction (ATB-Ctrl subjects). The untargeted metabolome revealed that 77 metabolites (of 895 total) were significantly changed with ATB-DILI progression. Among them, levels of multiple fatty acids and bile acids significantly increased over time in ATB-DILI subjects. Meanwhile, metabolites of the same class were highly correlated with each other and pathway analysis indicated both fatty acids metabolism and bile acids metabolism were up-regulated with ATB-DILI progression. The targeted metabolome further validated that 5 fatty acids had prediction capability at the early stage of the disease and 6 bile acids had a better diagnostic performance when ATB-DILI occurred. These findings provide evidence indicating that fatty acids metabolism and bile acids metabolism play a vital role during ATB-DILI progression. Our report adds a dynamic perspective better to understand the pathological process of ATB-DILI in clinical settings.

Methionine as a regulator of bone remodeling with fasting.

Caloric restriction improves metabolic health but is often complicated by bone loss. We studied bone parameters in humans during a 10-day fast and identified candidate metabolic regulators of bone turnover. Pro-collagen 1 intact N-terminal pro-peptide (P1NP), a bone formation marker, decreased within 3 days of fasting. Whereas dual-energy x-ray absorptiometry measures of bone mineral density were unchanged after 10 days of fasting, high-resolution peripheral quantitative CT demonstrated remodeling of bone microarchitecture. Pathway analysis of longitudinal metabolomics data identified one-carbon metabolism as fasting dependent. In cultured osteoblasts, we tested the functional significance of one-carbon metabolites modulated by fasting, finding that methionine - which surged after 3 days of fasting - affected markers of osteoblast cell state in a concentration-dependent manner, in some instances exhibiting a U-shaped response with both low and high concentrations driving putative antibone responses. Administration of methionine to mice for 5 days recapitulated some fasting effects on bone, including a reduction in serum P1NP. In conclusion, a 10-day fast in humans led to remodeling of bone microarchitecture, potentially mediated by a surge in circulating methionine. These data support an emerging model that points to a window of optimal methionine exposure for bone health.

The longitudinal biochemical profiling of TBI in a drop weight model of TBI

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic pathways and unique biochemical profiles associated with TBI. Herein, we employed a longitudinal metabolomics approach to study TBI in a weight drop mouse model to reveal metabolic changes associated with TBI pathogenesis, severity, and secondary injury. Using proton nuclear magnetic resonance (1H NMR) spectroscopy, we biochemically profiled post-mortem brain from mice that suffered mild TBI (N = 25; 13 male and 12 female), severe TBI (N = 24; 11 male and 13 female) and sham controls (N = 16; 11 male and 5 female) at baseline, day 1 and day 7 following the injury. 1H NMR-based metabolomics, in combination with bioinformatic analyses, highlights a few significant metabolites associated with TBI severity and perturbed metabolism related to the injury. We report that the concentrations of taurine, creatinine, adenine, dimethylamine, histidine, N-Acetyl aspartate, and glucose 1-phosphate are all associated with TBI severity. Longitudinal metabolic observation of brain tissue revealed that mild TBI and severe TBI lead distinct metabolic profile changes. A multi-class model was able to classify the severity of injury as well as time after TBI with estimated 86% accuracy. Further, we identified a high degree of correlation between respective hemisphere metabolic profiles (r > 0.84, p < 0.05, Pearson correlation). This study highlights the metabolic changes associated with underlying TBI severity and secondary injury. While comprehensive, future studies should investigate whether: (a) the biochemical pathways highlighted here are recapitulated in the brain of TBI sufferers and (b) if the panel of biomarkers are also as effective in less invasively harvested biomatrices, for objective and rapid identification of TBI severity and prognosis.

Phase I clinical trial of intracerebroventricular transplantation of allogeneic neural stem cells in people with progressive multiple sclerosis

We report the analysis of 1 year of data from the first cohort of 15 patients enrolled in an open-label, first-in-human, dose-escalation phase I study (ClinicalTrials.gov: NCT03282760, EudraCT2015-004855-37) to determine the feasibility, safety, and tolerability of the transplantation of allogeneic human neural stem/progenitor cells (hNSCs) for the treatment of secondary progressive multiple sclerosis. Participants were treated with hNSCs delivered via intracerebroventricular injection in combination with an immunosuppressive regimen. No treatment-related deaths nor serious adverse events (AEs) were observed. All participants displayed stability of clinical and laboratory outcomes, as well as lesion load and brain activity (MRI), compared with the study entry. Longitudinal metabolomics and lipidomics of biological fluids identified time- and dose-dependent responses with increased levels of acyl-carnitines and fatty acids in the cerebrospinal fluid (CSF). The absence of AEs and the stability of functional and structural outcomes are reassuring and represent a milestone for the safe translation of stem cells into regenerative medicines.

Reduction in metabolic noise reveals rejuvenation following transient severe caloric restriction.

Among land vertebrates, the laying hen stands out due to its great reproductive efficiency: producing an egg daily all year long. This production rate makes the laying hen a special model animal to study the general process of reproduction and aging. One unique aspect of hens is their ability to undergo reproductive plasticity and to rejuvenate their reproductive tract during molting, a standard industrial feed restriction protocol for transiently pausing reproduction, followed by improved laying efficiency almost to peak production. Here we use longitudinal metabolomics, immunology, and physiological assays to show that molting promotes reproduction, compresses morbidity, and restores youthfulness when applied to old hens. We identified circulating metabolic biomarkers that quantitatively predict the reproduction and age of individuals. Lastly, we introduce metabolic noise, a robust, unitless, and quantifiable measure for heterogeneity of the complete metabolome as a general marker that can indicate the rate of aging of a population. Indeed, metabolic noise increased with age in control hens, whereas molted hens exhibited reduced noise following molting, indicating systemic rejuvenation. Our results suggest that metabolic noise can be used as a quick and universal proxy for assessing successful aging treatments, accelerating the timeline for drug development.

Longitudinal metabolomics integrated with machine learning identifies novel biomarkers of gestational diabetes mellitus

BackgroundEvidence from longitudinal studies is crucial to enhance our understanding of the role of metabolites in the progression of gestational diabetes mellitus (GDM). Herein, a longitudinal untargeted metabolomic study was conducted to reveal the metabolomic profiles and biomarkers associated with the progression of GDM, and characterize the changing patterns of metabolites. MethodsWe collected serum samples at three trimesters from 30 patients with GDM and 30 healthy Chinese pregnant women with pre-pregnancy BMI, age, and parity matched, and untargeted metabolomic analysis was performed, followed by machine learning approaches that integrated bootstrap and LASSO. Cluster analysis was conducted to elucidate the patterns of metabolite changes. Pathway analyses were conducted to gain insights into the underlying pathways involved. ResultsA total of 32 metabolites, mainly belonging to amino acid and its derivatives, were significantly associated with GDM across three trimesters, and were clustered into three distinct patterns. Metabolites belonging to phosphatidylcholines, lysophosphatidylcholines, lysophosphatidic acids, and lysophosphatidylethanolamines were consistently upregulated, and 2,3-Dihydroxypropyl dihydrogen phosphate was downregulated in GDM group. Amino acid-related, glycerophospholipid, and vitamin B6 metabolism were enriched in multiple trimesters. The levels of allantoic acid, which was positively correlated with blood glucose, was consistently higher in GDM patients and exhibited good discriminatory ability for GDM in the early and mid-pregnancy. ConclusionWe identified and characterized distinct patterns of metabolites associated with GDM throughout pregnancy, and found that allantoic acid was a potential biomarker for early diagnosis of GDM.

Longitudinal metabolomics analysis reveals the acute effect of cysteine and NAC included in the combined metabolic activators.

Growing evidence suggests that the depletion of plasma NAD+ and glutathione (GSH) may play an important role in the development of metabolic disorders. The administration of Combined Metabolic Activators (CMA), consisting of GSH and NAD+ precursors, has been explored as a promising therapeutic strategy to target multiple altered pathways associated with the pathogenesis of the diseases. Although studies have examined the therapeutic effect of CMA that contains N-acetyl-l-cysteine (NAC) as a metabolic activator, a system-wide comparison of the metabolic response to the administration of CMA with NAC and cysteine remains lacking. In this placebo-controlled study, we studied the acute effect of the CMA administration with different metabolic activators, including NAC or cysteine with/without nicotinamide or flush free niacin, and performed longitudinal untargeted-metabolomics profiling of plasma obtained from 70 well-characterized healthy volunteers. The time-series metabolomics data revealed the metabolic pathways affected after the administration of CMAs showed high similarity between CMA containing nicotinamide and NAC or cysteine as metabolic activators. Our analysis also showed that CMA with cysteine is well-tolerated and safe in healthy individuals throughout the study. Last, our study systematically provided insights into a complex and dynamics landscape involved in amino acid, lipid and nicotinamide metabolism, reflecting the metabolic responses to CMA administration containing different metabolic activators.

Longitudinal study investigating serum metabolites and their association with type 2 diabetes risk in a Korean population.

The lack of longitudinal metabolomics data and the statistical techniques to analyse them has limited the understanding of the metabolite levels related to type 2 diabetes (T2D) onset. Thus, we carried out logistic regression analysis and simultaneously proposed new approaches based on residuals of multiple logistic regression and geometric angle-based clustering for the analysis in T2D onset-specific metabolic changes. We used the sixth, seventh and eighth follow-up data from 2013, 2015 and 2017 among the Korea Association REsource (KARE) cohort data. Semi-targeted metabolite analysis was performed using ultraperformance liquid chromatography/triple quadrupole-mass spectrometry systems. As the results from the multiple logistic regression and a single metabolite in a logistic regression analysis varied dramatically, we recommend using models that consider potential multicollinearity among metabolites. The residual-based approach particularly identified neurotransmitters or related precursors as T2D onset-specific metabolites. By using geometric angle-based pattern clustering studies, ketone bodies and carnitines are observed as disease-onset specific metabolites and separated from others. To treat patients with early-stage insulin resistance and dyslipidaemia when metabolic disorders are still reversible, our findings may contribute to a greater understanding of how metabolomics could be used in disease intervention strategies during the early stages of T2D.

Metabolic changes induced by T cell-mediated cutaneous vaccinia virus clearance

Abstract Viral infections induce prominent alterations to the host metabolism. Vaccinia virus (VACV) has been used as a live vaccine preventing smallpox and monkeypox. However, much remains unknown about the metabolic regulations of the adaptive immune response mounted to VACV infection in the skin. To survey the metabolic changes during VACV clearance, we performed a longitudinal metabolomics study based on our cutaneous VACV infection murine model. We adoptively transferred OT-I CD8 +T cells into Rag−/−mice, then infected one ear with VACV expressing SIINFEKL and VACV lacking SIINFEKL in the contralateral ear. Ears were harvested on 0,5,6,7,8,10 days post infection (dpi), which were homogenized and methanol extracted prior to untargeted mass spectrometry analyses. Metabolomics profiling revealed that VACV infection induced significant metabolic changes over time in the ear tissue. Interestingly, the metabolic profile after viral clearance was still distinct from uninfected control group. The levels of phenylalanine (adj. p=0.032), oleic acid (adj. p=0.011), S-lactoylglutathione (adj. p=0.022), and hypoxanthine (adj. p=0.039) were significantly altered between samples undergoing T cells-mediated killing and those without. Furthermore, glutamic acid level was significantly different (adj. p=0.036) between groups with and without T cells, suggesting T cell utilization during VACV clearance. Overall, our results indicated that VACV infection caused significant and persistent metabolic perturbation in the skin tissue environment. Antiviral T cells that mediate VACV clearance may require specific metabolites or metabolic pathways to regulate cytotoxic functions.

Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic

Background/ObjectiveThis observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.Subjects/MethodsFirstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25–74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24–39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.ResultsThe four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.ConclusionsAge-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

Tumour catabolism independent of malnutrition and inflammation in upper GI cancer patients revealed by longitudinal metabolomics.

The detrimental impact of malnutrition and cachexia in cancer patients subjected to surgical resection is well established. However, how systemic and local metabolic alterations in cancer patients impact the serum metabolite signature, thereby leading to cancer-specific differences, is poorly defined. In order to implement metabolomics as a potential tool in clinical diagnostics and disease follow-up, targeted metabolite profiling based on quantitative measurements is essential. We hypothesized that the quantitative metabolic profile assessed by 1 H nuclear magnetic resonance (NMR) spectroscopy can be used to identify cancer-induced catabolism and potentially distinguish between specific tumour entities. Importantly, to prove tumour dependency and assess metabolic normalization, we additionally analysed the metabolome of patients' sera longitudinally post-surgery in order to assess metabolic normalization. Forty two metabolites in sera of patients with tumour entities known to cause malnutrition and cachexia, namely, upper gastrointestinal cancer and pancreatic cancer, as well as sera of healthy controls, were quantified by 1 H NMR spectroscopy. Comparing serum metabolites of patients with gastrointestinal cancer with healthy controls and pancreatic cancer patients, we identified at least 15 significantly changed metabolites in each comparison. Principal component and pathway analysis tools showed a catabolic signature in preoperative upper gastrointestinal cancer patients. The most specifically upregulated metabolite group in gastrointestinal cancer patients was ketone bodies (3-hydroxybutyrate, P<0.0001; acetoacetate, P<0.0001; acetone, P<0.0001; false discovery rate [FDR] adjusted). Increased glycerol levels (P<0.0001), increased concentration of the ketogenic amino acid lysine (P=0.03) and a significant correlation of 3-hydroxybutyrate levels with branched-chained amino acids (leucine, P=0.02; isoleucine, P=0.04 [FDR adjusted]) suggested that ketone body synthesis was driven by lipolysis and amino acid breakdown. Interestingly, the catabolic signature was independent of the body mass index, clinically assessed malnutrition using the nutritional risk screening score, and systemic inflammation assessed by CRP and leukocyte count. Longitudinal measurements and principal component analyses revealed a quick normalization of key metabolic alterations seven days post-surgery, including ketosis. Together, the quantitative metabolic profile obtained by 1 H NMR spectroscopy identified a tumour-induced catabolic signature specific to upper gastrointestinal cancer patients and enabled monitoring restoration of metabolic homeostasis after surgery. This approach was critical to identify the obtained metabolic profile as an upper gastrointestinal cancer-specific signature independent of malnutrition and inflammation.

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

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

Longitudinal Metabolomics and Lipidomics Analyses Reveal Alterations Associated with Envenoming by Bothrops asper and Daboia russelii in an Experimental Murine Model.

Longitudinal metabolomics and lipidomics analyses were carried out on the blood plasma of mice injected intramuscularly with venoms of the viperid species Bothrops asper or Daboia russelii. Blood samples were collected 1, 3, 6, and 24 h after venom injection, and a control group of non-envenomed mice was included. Significant perturbations in metabolomics and lipidomics were observed at 1, 3, and 6 h, while values returned close to those of control mice by 24 h, hence reflecting a transient pattern of metabolic disturbance. Both venoms induced significant changes in amino acids, as well as in several purines and pyrimidines, and in some metabolites of the tricarboxylic acid cycle. KEGG analysis of metabolic pathways that showed those with the greatest change included aminoacyl tRNA synthesis and amino acid biosynthesis and metabolism pathways. With regard to lipid metabolism, there was an increase in triglycerides and some acyl carnitines and a concomitant drop in the levels of some phospholipids. In addition, envenomed mice had higher levels of cortisol, heme, and some oxidative stress markers. The overall pattern of metabolic changes in envenomed mice bears similarities with the patterns described in several traumatic injuries, thus underscoring a metabolic response/adaptation to the injurious action of the venoms.

Evaluation of the Use of Saliva Metabolome as a Surrogate of Blood Metabolome in Assessing Internal Exposures to Traffic-Related Air Pollution.

In the omics era, saliva, a filtrate of blood, may serve as an alternative, noninvasive biospecimen to blood, although its use for specific metabolomic applications has not been fully evaluated. We demonstrated that the saliva metabolome may provide sensitive measures of traffic-related air pollution (TRAP) and associated biological responses via high-resolution, longitudinal metabolomics profiling. We collected 167 pairs of saliva and plasma samples from a cohort of 53 college student participants and measured corresponding indoor and outdoor concentrations of six air pollutants for the dormitories where the students lived. Grand correlation between common metabolic features in saliva and plasma was moderate to high, indicating a relatively consistent association between saliva and blood metabolites across subjects. Although saliva was less associated with TRAP compared to plasma, 25 biological pathways associated with TRAP were detected via saliva and accounted for 69% of those detected via plasma. Given the slightly higher feature reproducibility found in saliva, these findings provide some indication that the saliva metabolome offers a sensitive and practical alternative to blood for characterizing individual biological responses to environmental exposures.

Longitudinal profiling of metabolic ageing trends in two population cohorts of young adults.

Quantification of metabolic changes over the human life course is essential to understanding ageing processes. Yet longitudinal metabolomics data are rare and long gaps between visits can introduce biases that mask true trends. We introduce new ways to process quantitative time-series population data and elucidate metabolic ageing trends in two large cohorts. Eligible participants included 1672 individuals from the Cardiovascular Risk in Young Finns Study and 3117 from the Northern Finland Birth Cohort 1966. Up to three time points (ages 24-49 years) were analysed by nuclear magnetic resonance metabolomics and clinical biochemistry (236 measures). Temporal trends were quantified as median change per decade. Sample quality was verified by consistency of shared biomarkers between metabolomics and clinical assays. Batch effects between visits were mitigated by a new algorithm introduced in this report. The results below satisfy multiple testing threshold of P < 0.0006. Women gained more weight than men (+6.5% vs +5.0%) but showed milder metabolic changes overall. Temporal sex differences were observed for C-reactive protein (women +5.1%, men +21.1%), glycine (women +5.2%, men +1.9%) and phenylalanine (women +0.6%, men +3.5%). In 566 individuals with ≥+3% weight gain vs 561 with weight change ≤-3%, divergent patterns were observed for insulin (+24% vs -10%), very-low-density-lipoprotein triglycerides (+32% vs -6%), high-density-lipoprotein2 cholesterol (-6.5% vs +4.7%), isoleucine (+5.7% vs -6.0%) and C-reactive protein (+25% vs -22%). We report absolute and proportional trends for 236 metabolic measures as new reference material for overall age-associated and specific weight-driven changes in real-world populations.

Longitudinal metabolomics profiling of serum amino acids in rotenone-induced Parkinson's mouse model.

Recently, the detailed etiology and pathogenesis of Parkinson's disease (PD) have not been fully clarified yet. Increasing evidences suggested that the disturbance of peripheral branched-chain amino acids (BCAAs) metabolism can promote the occurrence and progression of neurodegenerative diseases through neuroinflammatory signaling. Although there are several studies on the metabolomics of PD, longitudinal study of metabolic pathways is still lacking. Therefore, the purpose of the present study was to determine the longitudinal alterations in serum amino acid profiles in PD mouse model. Gas chromatography-mass spectrometry (GC-MS) was applied to detect serum amino acid concentrations in C57BL/6 mice after 0, 3 and 4weeks of oral administration with rotenone. Then the data were analysed by principal component analysis (PCA) and orthogonal projection to latent structures (OPLS) analysis. Finally, the correlations between different kinds of serum amino acids and behaviors in rotenone-treated mice were also explored. Compared with 0-week mice, the levels of L-isoleucine and L-leucine were down-regulated in 3-week and 4-week mice, especially in 4-week mice. Moreover, the comprehensive analysis showed that L-isoleucine and L-leucine were negatively correlated with pole-climbing time and positively correlated with fecal weight and water content of PD mice. These results not only suggested that L-isoleucine and L-leucine may be potential biomarkers, but also pointed out the possibility of treating PD by intervening in the circulating amino acids metabolism.