Impact of Pre-analytic Blood Sample Collection Factors on Metabolomics

Effects of short-term moderate intensity exercise on the serum metabolome in older adults: a pilot randomized controlled trial

BACKGROUND

We previously reported changes in the serum metabolome associated with impaired myocardial relaxation in an asymptomatic older community cohort. In this prospective parallel-group randomized control pilot trial, we subjected community adults without cardiovascular disease to exercise intervention and evaluated the effects on serum metabolomics.

METHODS

Between February 2019 to November 2019, thirty (83% females) middle-aged adults (53 ± 4 years) were randomized with sex stratification to either twelve weeks of moderate-intensity exercise training (Intervention) (n = 15) or Control (n = 15). The Intervention group underwent once-weekly aerobic and strength training sessions for 60 min each in a dedicated cardiac exercise laboratory for twelve weeks (ClinicalTrials.gov: NCT03617653). Serial measurements were taken pre- and post-intervention, including serum sampling for metabolomic analyses.

RESULTS

Twenty-nine adults completed the study (Intervention n = 14; Control n = 15). Long-chain acylcarnitine C20:2-OH/C18:2-DC was reduced in the Intervention group by a magnitude of 0.714 but increased in the Control group by a magnitude of 1.742 (mean difference -1.028 age-adjusted p = 0.004). Among Controls, alanine correlated with left ventricular mass index (r = 0.529, age-adjusted p = 0.018) while aspartate correlated with Lateral e' (r = -764, age-adjusted p = 0.016). C20:3 correlated with E/e' ratio fold-change in the Intervention group (r = -0.653, age-adjusted p = 0.004). Among Controls, C20:2/C18:2 (r = 0.795, age-adjusted p = 0.005) and C20:2-OH/C18:2-DC fold-change (r = 0.742, age-adjusted p = 0.030) correlated with change in E/A ratio.

CONCLUSIONS

Corresponding relationships between serum metabolites and cardiac function in response to exercise intervention provided pilot observations. Future investigations into cellular fuel oxidation or central carbon metabolism pathways that jointly impact the heart and related metabolic systems may be critical in preventive trials.

Longitudinal aortic strain, ventriculo-arterial coupling and fatty acid oxidation: novel insights into human cardiovascular aging

Aging-induced aortic stiffness has been associated with altered fatty acid metabolism. We studied aortic stiffness using cardiac magnetic resonance (CMR)-assessed ventriculo-arterial coupling (VAC) and novel aortic (AO) global longitudinal strain (GLS) combined with targeted metabolomic profiling. Among community older adults without cardiovascular disease, VAC was calculated as aortic pulse wave velocity (PWV), a marker of arterial stiffness, divided by left ventricular (LV) GLS. AOGLS was the maximum absolute strain measured by tracking the phasic distance between brachiocephalic artery origin and aortic annulus. In 194 subjects (71 ± 8.6 years; 88 women), AOGLS (mean 5.6 ± 2.1%) was associated with PWV (R = -0.3644, p < 0.0001), LVGLS (R = 0.2756, p = 0.0001) and VAC (R = -0.3742, p <0.0001). Stiff aorta denoted by low AOGLS <4.26% (25th percentile) was associated with age (OR 1.13, 95% CI 1.04-1.24, p = 0.007), body mass index (OR 1.12, 95% CI 1.01-1.25, p = 0.03), heart rate (OR 1.04, 95% CI 1.01-1.06, p = 0.011) and metabolites of medium-chain fatty acid oxidation: C8 (OR 1.005, p = 0.026), C10 (OR 1.003, p = 0.036), C12 (OR 1.013, p = 0.028), C12:2-OH/C10:2-DC (OR 1.084, p = 0.032) and C16-OH (OR 0.82, p = 0.006). VAC was associated with changes in long-chain hydroxyl and dicarboxyl carnitines. Multivariable models that included acyl-carnitine metabolites, but not amino acids, significantly increased the discrimination over clinical risk factors for prediction of AOGLS (AUC [area-under-curve] 0.73 to 0.81, p = 0.037) and VAC (AUC 0.78 to 0.87, p = 0.0044). Low AO GLS and high VAC were associated with altered medium-chain and long-chain fatty acid oxidation, respectively, which may identify early metabolic perturbations in aging-associated aortic stiffening. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02791139.

Characteristics of pulmonary artery strain assessed by cardiovascular magnetic resonance imaging and associations with metabolomic pathways in human ageing

Background

Pulmonary artery (PA) strain is associated with structural and functional alterations of the vessel and is an independent predictor of cardiovascular events. The relationship of PA strain to metabolomics in participants without cardiovascular disease is unknown.

Methods

In the current study, community-based older adults, without known cardiovascular disease, underwent simultaneous cine cardiovascular magnetic resonance (CMR) imaging, clinical examination, and serum sampling. PA global longitudinal strain (GLS) analysis was performed by tracking the change in distance from the PA bifurcation to the pulmonary annular centroid, using standard cine CMR images. Circulating metabolites were measured by cross-sectional targeted metabolomics analysis.

Results

Among n = 170 adults (mean age 71 ± 6.3 years old; 79 women), mean values of PA GLS were 16.2 ± 4.4%. PA GLS was significantly associated with age (β = -0.13, P = 0.017), heart rate (β = -0.08, P = 0.001), dyslipidemia (β = -2.37, P = 0.005), and cardiovascular risk factors (β = -2.49, P = 0.001). Alanine (β = -0.007, P = 0.01) and proline (β = -0.0009, P = 0.042) were significantly associated with PA GLS after adjustment for clinical risk factors. Medium and long-chain acylcarnitines were significantly associated with PA GLS (C12, P = 0.027; C12-OH/C10-DC, P = 0.018; C14:2, P = 0.036; C14:1, P = 0.006; C14, P = 0.006; C14-OH/C12-DC, P = 0.027; C16:3, P = 0.019; C16:2, P = 0.006; C16:1, P = 0.001; C16:2-OH, P = 0.016; C16:1-OH/C14:1-DC, P = 0.028; C18:1-OH/C16:1-DC, P = 0.032).

Conclusion

By conventional CMR, PA GLS was associated with aging and vascular risk factors among a contemporary cohort of older adults. Metabolic pathways involved in PA stiffness may include gluconeogenesis, collagen synthesis, and fatty acid oxidation.

Metabolites and MRI-Derived Markers of AD/ADRD Risk in a Puerto Rican Cohort

Objective

Several studies have examined metabolomic profiles in relation to Alzheimer's disease and related dementia (AD/ADRD) risk; however, few studies have focused on minorities, such as Latinos, or examined Magnetic-Resonance Imaging (MRI)-based outcomes.

Methods

We used multiple linear regression, adjusted for covariates, to examine the association between metabolite concentration and MRI-derived brain age deviation. Metabolites were measured at baseline with untargeted metabolomic profiling (Metabolon, Inc). Brain age deviation (BAD) was calculated at wave 4 (~ 9 years from Boston Puerto Rican Health Study (BPRHS) baseline) as chronologic age, minus MRI-estimated brain age, representing the rate of biological brain aging relative to chronologic age. We also examined if metabolites associated with BAD were similarly associated with hippocampal volume and global cognitive function at wave 4 in the BPRHS.

Results

Several metabolites, including isobutyrylcarnitine, propionylcarnitine, phenylacetylglutamine, phenylacetylcarnitine (acetylated peptides), p-cresol-glucuronide, phenylacetylglutamate, and trimethylamine N-oxide (TMAO) were inversely associated with brain age deviation. Taurocholate sulfate, a bile salt, was marginally associated with better brain aging. Most metabolites with negative associations with brain age deviation scores also were inversely associations with hippocampal volumes and wave 4 cognitive function.

Conclusion

The metabolites identifiedin this study are generally consistent with prior literature and highlight the role of BCAA, TMAO and microbially derived metabolites in cognitive decline.

Associations of Vitamins and Related Cofactor Metabolites with Mammographic Breast Density in Premenopausal Women

BACKGROUND

Identifying biological drivers of mammographic breast density (MBD), a strong risk factor for breast cancer, could provide insight into breast cancer etiology and prevention. Studies on dietary factors and MBD have yielded conflicting results. There are, however, very limited data on the associations of dietary biomarkers and MBD.

OBJECTIVE

We aimed to investigate the associations of vitamins and related cofactor metabolites with MBD in premenopausal women.

METHODS

We measured 37 vitamins and related cofactor metabolites in fasting plasma samples of 705 premenopausal women recruited during their annual screening mammogram at the Washington University School of Medicine, St. Louis, MO. Volpara was used to assess volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV). We estimated the least square means of VPD, DV, and NDV across quartiles of each metabolite, as well as the regression coefficient of a metabolite in continuous scale from multiple covariate-adjusted linear regression. We corrected for multiple testing using the Benjamini-Hochberg procedure to control the false discover rate (FDR) at a 5% level.

RESULTS

Participants' mean VPD was 10.5%. Two vitamin A metabolites (β-cryptoxanthin and carotene diol 2) were positively associated, and one vitamin E metabolite (γ-tocopherol) was inversely associated with VPD. The mean VPD increased across quartiles of β-cryptoxanthin (Q1 = 7.2%, Q2 = 7.7%, Q3 = 8.4%%, Q4 = 9.2%; P-trend = 1.77E-05, FDR P value = 1.18E-03). There was a decrease in the mean VPD across quartiles of γ-tocopherol (Q1 = 9.4%, Q2 = 8.1%, Q3 = 8.0%, Q4 = 7.8%; P -trend = 4.01E-03, FDR P value = 0.04). Seven metabolites were associated with NDV: 3 vitamin E (γ-CEHC glucuronide, δ-CEHC, and γ-tocopherol) and 1 vitamin C (gulonate) were positively associated, whereas 2 vitamin A (carotene diol 2 and β-cryptoxanthin) and 1 vitamin C (threonate) were inversely associated with NDV. No metabolite was significantly associated with DV.

CONCLUSION

We report novel associations of vitamins and related cofactor metabolites with MBD in premenopausal women.

Exploring Optimal Biomarker Sources: A Comparative Analysis of Exosomes and Whole Plasma in Fasting and Non-Fasting Conditions for Liquid Biopsy Applications

The study of liquid biopsy with plasma samples is being conducted to identify biomarkers for clinical use. Exosomes, containing nucleic acids and metabolites, have emerged as possible sources for biomarkers. To evaluate the effectiveness of exosomes over plasma, we analyzed the small non-coding RNAs (sncRNAs) and metabolites extracted from exosomes in comparison to those directly extracted from whole plasma under both fasting and non-fasting conditions. We found that sncRNA profiles were not affected by fasting in either exosome or plasma samples. Our results showed that exosomal sncRNAs were found to have more consistent profiles. The plasma miRNA profiles contained high concentrations of cell-derived miRNAs that were likely due to hemolysis. We determined that certain metabolites in whole plasma exhibited noteworthy concentration shifts in relation to fasting status, while others did not. Here, we propose that (1) fasting is not required for a liquid biopsy study that involves both sncRNA and metabolomic profiling, as long as metabolites that are not influenced by fasting status are selected, and (2) the utilization of exosomal RNAs promotes robust and consistent findings in plasma samples, mitigating the impact of batch effects derived from hemolysis. These findings advance the optimization of liquid biopsy methodologies for clinical applications.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Plasma Metabolomic Signature of Early Abuse in Middle-Aged Women

OBJECTIVE

Metabolomic profiling may provide insights into biological mechanisms underlying the strong epidemiologic links observed between early abuse and cardiometabolic disorders in later life.

METHODS

We examined the associations between early abuse and midlife plasma metabolites in two nonoverlapping subsamples from the Nurses' Health Study II, comprising 803 (mean age = 40 years) and 211 women (mean age = 61 years). Liquid chromatography-tandem mass spectrometry assays were used to measure metabolomic profiles, with 283 metabolites consistently measured in both subsamples. Physical and sexual abuse before age 18 years was retrospectively assessed by validated questions integrating type/frequency of abuse. Analyses were conducted in each sample and pooled using meta-analysis, with multiple testing adjustment using the q value approach for controlling the positive false discovery rate.

RESULTS

After adjusting for age, race, menopausal status, body size at age 5 years, and childhood socioeconomic indicators, more severe early abuse was consistently associated with five metabolites at midlife (q value < 0.20 in both samples), including lower levels of serotonin and C38:3 phosphatidylethanolamine plasmalogen and higher levels of alanine, proline, and C40:6 phosphatidylethanolamine. Other metabolites potentially associated with early abuse (q value < 0.05 in the meta-analysis) included triglycerides, phosphatidylcholine plasmalogens, bile acids, tyrosine, glutamate, and cotinine. The association between early abuse and midlife metabolomic profiles was partly mediated by adulthood body mass index (32% mediated) and psychosocial distress (13%-26% mediated), but not by other life-style factors.

CONCLUSIONS

Early abuse was associated with distinct metabolomic profiles of multiple amino acids and lipids in middle-aged women. Body mass index and psychosocial factors in adulthood may be important intermediates for the observed association.

Sphingolipid metabolism plays a key role in diabetic peripheral neuropathy

INTRODUCTION

As the most common chronic complication of diabetes mellitus (DM), diabetic peripheral neuropathy (DPN) seriously affects the quality of life of DM patients. So, it is of great significance for the diagnosis and treatment of DPN. In recent years, there have been numerous studies on pathogenesis and biomarkers of DM, but there are few studies on the biomarkers of DPN.

OBJECTIVES

This research is intended to identify abnormal metabolic pathways, search for potential biomarkers of DPN, and provide a metabolic basis for the diagnosis and mechanism of DPN.

METHODS

Serum samples from 23 healthy controls (HC), 42 DM patients and 30 DPN patients and urine samples from 42 HC, 40 DM patients, and 30 DPN patients were collected. UPLC-Q-TOF/MS was used to analyze the samples. Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least squares discriminant analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). The biomarkers were then enriched and pathway analyzed.

RESULTS

12 potential DPN biomarkers were identified from patient's serum. 11 potential DPN biomarkers were identified from the patient's urine. Among them, the diagnostic ability of gluconic acid, lipoic acid, sphinganine, bilirubin, sphingosine and 4-hydroxybenzoic acid was increased by ROC analysis. Potential biomarkers suggest that the disorder of DPN metabolism may be linked to sphingolipid metabolism.

CONCLUSIONS

This research laid a theoretical foundation for the diagnosis and pathogenesis of DPN.

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.

Presurgical blood metabolites and risk of postsurgical pelvic pain in young patients with endometriosis

OBJECTIVE

To identify metabolites in presurgical blood associated with risk of persistent postsurgical pelvic pain 1 year after endometriosis surgery in adolescent and young adult patients.

DESIGN

Prospective observational study within the Women's Health Study: From Adolescence to Adulthood, a US-based longitudinal cohort of adolescents and women enrolled from 2012-2018.

SETTING

Two tertiary care hospitals.

PATIENT(S)

Laparoscopically confirmed endometriosis patients (n = 180) with blood collected before their endometriosis surgery. Of these, 77 patients additionally provided blood samples 5 weeks to 6 months after their surgery. We measured plasma metabolites using liquid chromatography tandem mass spectrometry, and a total of 390 known metabolites were included in our analysis.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Persistent postsurgical pelvic pain, defined as severe, life-impacting pelvic pain 1 year after endometriosis surgery.

RESULT(S)

Most patients (>95%) were at stage I/II of the revised American Society for Reproductive Medicine classification. Their average age at diagnosis was 18.7 years, with 36% reporting persistent postsurgical pelvic pain. Of the 21 metabolites in presurgical blood that were associated with risk of persistent postsurgical pelvic pain, 19 metabolites, which were mainly lipid metabolites, were associated with increased risk. Only 2 metabolites-pregnenolone sulfate (odds ratio = 0.64, 95% confidence interval = 0.44-0.92) and fucose (odds ratio = 0.69, 95% confidence interval = 0.47-0.97)-were associated with decreased risk. Metabolite set enrichment analysis revealed that higher levels of lysophosphatidylethanolamines (false discovery rate = 0.01) and lysophosphatidylcholines (false discovery rate = 0.01) in presurgical blood were associated with increased risk of persistent postsurgical pelvic pain.

CONCLUSION(S)

Our results suggest that dysregulation of multiple groups of lipid metabolites may play a role in the persistence of pelvic pain postsurgery among young endometriosis patients.

Prediction and evaluation of the effect of pre-centrifugation sample management on the measurable untargeted LC-MS plasma metabolome

Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 °C; classification rate 87%) and time (5-210 min; Q² = 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5-210 min), and the variation at 4 °C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of pre-centrifugation temperature (classification rate 94%) and delay time (Q² = 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed.

Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease

Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9-63.2% (coefficient of variation) and 0.6-99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16-70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.

Longitudinal Plasma Metabolomics Profile in Pregnancy-A Study in an Ethnically Diverse U.S. Pregnancy Cohort

Amino acids, fatty acids, and acylcarnitine metabolites play a pivotal role in maternal and fetal health, but profiles of these metabolites over pregnancy are not completely established. We described longitudinal trajectories of targeted amino acids, fatty acids, and acylcarnitines in pregnancy. We quantified 102 metabolites and combinations (37 fatty acids, 37 amino acids, and 28 acylcarnitines) in plasma samples from pregnant women in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies—Singletons cohort (n = 214 women at 10–14 and 15–26 weeks, 107 at 26–31 weeks, and 103 at 33–39 weeks). We used linear mixed models to estimate metabolite trajectories and examined variation by body mass index (BMI), race/ethnicity, and fetal sex. After excluding largely undetected metabolites, we analyzed 77 metabolites and combinations. Levels of 13 of 15 acylcarnitines, 7 of 25 amino acids, and 18 of 37 fatty acids significantly declined over gestation, while 8 of 25 amino acids and 10 of 37 fatty acids significantly increased. Several trajectories appeared to differ by BMI, race/ethnicity, and fetal sex although no tests for interactions remained significant after multiple testing correction. Future studies merit longitudinal measurements to capture metabolite changes in pregnancy, and larger samples to examine modifying effects of maternal and fetal characteristics.

Fine particulate matter exposure and perturbation of serum metabolome: A longitudinal study in Baoding, China

Metabolomics represents a powerful tool for measuring environmental exposures and biological responses to unveil potential mechanisms. Few studies have investigated the effects of exposure to fine particulate matter (PM_2.5) longitudinally on serum metabolomics in regions with high-level PM_2.5. Therefore, we examined the changes of serum metabolomics corresponding to individual PM_2.5 exposure levels in spring and autumn among 63 healthy college students in Baoding city, Hebei, China. The metabolic profiling was determined by ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The average level of individual PM_2.5 in the spring was 1.82-fold higher than in the autumn (240 μg/m³ vs 132 μg/m³). Males were exposed to a higher level of PM_2.5 than females in the spring. Metabolic profiling was clearly separated by orthogonal partial least square-discriminant analysis in males but not in females. In the analysis of the associations between the metabolome and PM_2.5 of the two seasons, the changes of 14 serum metabolites were significantly associated with PM_2.5 in males. The metabolites related to heme metabolism (bilirubin, biliverdin), energy metabolism and oxidative stress (2-Octenoylcarnitine, N-Heptanoylglycine, and acetylcysteine), phospholipid metabolism (lysophosphatidic acid, phospholipid acid, and lysophosphatidylethanolamine), and tryptophan metabolism (N-Acetylserotonin, indolepyruvate, and melatonin) were decreased in the range of 2.16%-6.80% for each 10 μg/m³ increase of PM_2.5, while thyrotropin-releasing hormone, glutathione, and phosphatidylethanolamine related to energy metabolism and oxidative stress, and phospholipid metabolism were increased in the range of 2.95%-4.90% for each 10 μg/m³ increase of PM_2.5. This longitudinal study suggests that higher PM_2.5 exposure may induce perturbations in serum metabolic signaling related to oxidative stress and inflammation, and males may be more prone to these metabolic perturbations.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Influence of pregnancy and non-fasting conditions on the plasma metabolome in a rat prenatal toxicity study

The current parameters for determining maternal toxicity (e.g. clinical signs, food consumption, body weight development) lack specificity and may underestimate the extent of effects of test compounds on the dams. Previous reports have highlighted the use of plasma metabolomics for an improved and mechanism-based identification of maternal toxicity. To establish metabolite profiles of healthy pregnancies and evaluate the influence of food consumption as a confounding factor, metabolite profiling of rat plasma was performed by gas- and liquid-chromatography-tandem mass spectrometry techniques. Metabolite changes in response to pregnancy, food consumption prior to blood sampling (non-fasting) as well as the interaction of both conditions were studied. In dams, both conditions, non-fasting and pregnancy, had a marked influence on the plasma metabolome and resulted in distinct individual patterns of changed metabolites. Non-fasting was characterized by increased plasma concentrations of amino acids and diet related compounds and lower levels of ketone bodies. The metabolic profile of pregnant rats was characterized by lower amino acids and glucose levels and higher concentrations of plasma fatty acids, triglycerides and hormones, capturing the normal biochemical changes undergone during pregnancy. The establishment of metabolic profiles of pregnant non-fasted rats serves as a baseline to create metabolic fingerprints for prenatal and maternal toxicity studies.

A Comparison of Serum and Plasma Blood Collection Tubes for the Integration of Epidemiological and Metabolomics Data

Blood is a rich biological sample routinely collected in clinical and epidemiological studies. With advancements in high throughput -omics technology, such as metabolomics, epidemiology can now delve more deeply and comprehensively into biological mechanisms involved in the etiology of diseases. However, the impact of the blood collection tube matrix of samples collected needs to be carefully considered to obtain meaningful biological interpretations and understand how the metabolite signatures are affected by different tube types. In the present study, we investigated whether the metabolic profile of blood collected as serum differed from samples collected as ACD plasma, citrate plasma, EDTA plasma, fluoride plasma, or heparin plasma. We identified and quantified 50 metabolites present in all samples utilizing nuclear magnetic resonance (NMR) spectroscopy. The heparin plasma tubes performed the closest to serum, with only three metabolites showing significant differences, followed by EDTA which significantly differed for five metabolites, and fluoride tubes which differed in eleven of the fifty metabolites. Most of these metabolite differences were due to higher levels of amino acids in serum compared to heparin plasma, EDTA plasma, and fluoride plasma. In contrast, metabolite measurements from ACD and citrate plasma differed significantly for approximately half of the metabolites assessed. These metabolite differences in ACD and citrate plasma were largely due to significant interfering peaks from the anticoagulants themselves. Blood is one of the most banked samples and thus mining and comparing samples between studies requires understanding how the metabolite signature is affected by the different media and different tube types.

Associations of depression status with plasma levels of candidate lipid and amino acid metabolites: a meta-analysis of individual data from three independent samples of US postmenopausal women

Recent animal and small clinical studies have suggested depression is related to altered lipid and amino acid profiles. However, this has not been examined in a population-based sample, particularly in women. We identified multiple metabolites associated with depression as potential candidates from prior studies. Cross-sectional data from three independent samples of postmenopausal women were analyzed, including women from the Women's Health Initiative-Observational Study (WHI-OS, n = 926), the WHI-Hormone Trials (WHI-HT; n = 1,325), and the Nurses' Health Study II Mind-Body Study (NHSII-MBS; n = 218). Positive depression status was defined as having any of the following: elevated depressive symptoms, antidepressant use, or depression history. Plasma metabolites were measured using liquid chromatography-tandem mass spectrometry (21 phosphatidylcholines (PCs), 7 lysophosphatidylethanolamines, 5 ceramides, 3 branched chain amino acids, and 9 neurotransmitters). Associations between depression status and metabolites were evaluated using multivariable linear regression; results were pooled by random-effects meta-analysis with multiple testing adjustment using the false discovery rate (FDR). Prevalence rates of positive depression status were 24.4% (WHI-OS), 25.7% (WHI-HT), and 44.7% (NHSII-MBS). After multivariable adjustment, positive depression status was associated with higher levels of glutamate and PC 36 : 1/38 : 3, and lower levels of tryptophan and GABA-to-glutamate and GABA-to-glutamine ratio (FDR-p < 0.05). Positive associations with LPE 18 : 0/18 : 1 and inverse associations with valine and serotonin were also observed, although these associations did not survive FDR adjustment. Associations of positive depression status with several candidate metabolites including PC 36 : 1/38 : 3 and amino acids involved in neurotransmission suggest potential depression-related metabolic alterations in postmenopausal women, with possible implications for later chronic disease.

Exacerbation of cardiovascular ageing by diabetes mellitus and its associations with acyl-carnitines

Objective: To demonstrate differences in cardiovascular structure and function between diabetic and non-diabetic older adults. To investigate associations between acyl-carnitines and cardiovascular function as indexed by imaging measurements.

Methods: A community-based cohort of older adults without cardiovascular disease underwent current cardiovascular imaging and metabolomics acyl-carnitines profiling based on current and archived sera obtained fifteen years prior to examination.

Results: A total of 933 participants (women 56%, n=521) with a mean age 63±13 years were studied. Old diabetics compared to old non-diabetics had lower myocardial relaxation (0.8±0.2 vs 0.9±0.3, p=0.0039); lower left atrial conduit strain (12±4.3 vs 14±4.1, p=0.045), lower left atrial conduit strain rate (-1.2±0.4 vs -1.3±0.5, p=0.042) and lower ratio of left atrial conduit strain to left atrial booster strain (0.5±0.2 vs 0.7±0.3, p=0.0029). Higher levels of archived short chain acyl-carnitine were associated with present-day impairments in myocardial relaxation (C5:1; OR 1.03, p=0.011), worse left atrial conduit strain function (C5:1; OR 1.03, p=0.037). Increases in hydroxylated acyl-carnitines were associated with worse left atrial conduit strain [(C4-OH; OR 1.05, p=0.0017), (C16:2-OH; OR 1.18, p=0.037)]. Current, archived and changes in long chain acyl-carnitines were associated with cardiovascular functions [(C16; OR 1.02, p=0.002), (C20:3; OR 1.01, p=0.014), (C14:3; OR 1.12, p=0.033), (C18:1; OR 1.01, p=0.018), (C18:2; OR 1.01, p=0.028), (C20:4; OR 1.10, p=0.038)] (all p<0.05).

Conclusion: Older diabetic adults had significant impairments in left ventricular myocardial relaxation and left atrial strain, compared to older non-diabetic adults. Short chain and long chain, di-carboxyl and hydroxylated acyl-carnitines were associated with these cardiovascular functional differences.

Associations between dietary amino acid intakes and blood concentration levels

BACKGROUND AND AIMS

Emerging evidence suggests a role of amino acids (AAs) in the development of various diseases including renal failure, liver cirrhosis, diabetes and cancer. However, mechanistic pathways and the effects of dietary AA intakes on circulating levels and disease outcomes are unclear. We aimed to compare protein and AA intakes, with their respective blood concentrations in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

METHODS

Dietary protein and AA intakes were assessed via the EPIC dietary questionnaires (DQ) and 24-h dietary recalls (24-HDR). A subsample of 3768 EPIC participants who were free of cancer had blood AA concentrations measured. To investigate how circulating levels relate to their respective intakes, dietary AA intake was examined in quintiles and ANOVA tests were run. Pearson correlations were examined for continous associations between intakes and blood concentrations.

RESULTS

Dietary AA intakes (assessed with the DQ) and blood AA concentrations were not strongly correlated (-0.15 ≤ r ≤ 0.17) and the direction of the correlations depended on AA class: weak positive correlations were found for most essential AAs (isoleucine, leucine, lysine, methionine, threonine, tryptophan, and valine) and conditionally essential AAs (arginine and tyrosine), while negative associations were found for non-essential AAs. Similar results were found when using the 24-HDR. When conducting ANOVA tests for essential AAs, higher intake quintiles were linked to higher blood AA concentrations, except for histidine and phenylalanine. For non-essential AAs and glycine, an inverse relationship was observed. Conditionally-essential AAs showed mixed results.

CONCLUSIONS

Weak positive correlations and dose responses were found between most essential and conditionally essential AA intakes, and blood concentrations, but not for the non-essential AAs. These results suggest that intake of dietary AA might be related to physiological AA status, particularly for the essential AAs. However, these results should be further evaluated and confirmed in large-scale prospective studies.

Amino acid differences between diabetic older adults and non-diabetic older adults and their associations with cardiovascular function

BACKGROUND

Ageing and insulin resistant states such as diabetes mellitus frequently coexist and increase the risk of cardiovascular disease development among older adults. Here we investigate metabolic differences in amino acid profiles between ageing and diabetes mellitus, and their associations with cardiovascular function.

METHODS

In a group of community older adults we performed echocardiography, cardiac magnetic resonance imaging as well as cross sectional and longitudinal metabolomics profiling based on current and archived sera obtained fifteen years prior to examination.

RESULTS

We studied a total of 515 participants (women 50%, n = 255) with a mean age 73 (SD = 4.3) years. Diabetics had higher alanine (562 vs 448, p < 0.0001), higher glutamate (107 vs 95, p = 0.016), higher proline (264 vs 231, p = 0.008) and lower arginine (107 vs 117, p = 0.043), lower citrulline (30 vs 38, p = 0.006) levels (μM) compared to non-diabetics. Over time, changes in amino acid profiles differentiated diabetic older adults from non-diabetic older adults, with greater accumulation of alanine (p = 0.002), proline (p = 0.008) and (non-significant) trend towards greater accumulation of glycine (p = 0.057) among the older diabetics compared to the older non-diabetics. However, independent of diabetes status, amino acids were associated with cardiovascular functions in ageing, [archived valine (p = 0.011), leucine (p = 0.011), archived isoleucine (p = 0.0006), archived serine (p = 0.008), archived glycine (p = 0.006) methionine (p = 0.003)] which were associated with impairments in E/A ratio.

CONCLUSION

Markers of branched chain amino acids and one ‑carbon metabolism pathways were associated with changes in cardiovascular function in older adults regardless of diabetes status. However, nitrogen handling pathways were specifically altered among older adults with diabetes. These findings broaden our understanding into specific amino acid pathways that may be altered between diabetic and non-diabetic older adults, and their relevance to cardiovascular function in ageing.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02791139.

Alteration of lipids and amino acids in plasma distinguish schizophrenia patients from controls: A targeted metabolomics study

BACKGROUND

Schizophrenia (SCZ) is a serious psychiatric disorder. Metabolite disturbance is an important pathogenic factor in schizophrenic patients. In this study, we aim to identify plasma lipid and amino acid biomarkers for SCZ using targeted metabolomics.

METHODS

Plasma from 76 SCZ patients and 50 matched controls were analyzed using the LC/MS-based multiple reaction monitoring (MRM) metabolomics approach. A total of 182 targeted metabolites, including 22 amino acids and 160 lipids or lipid-related metabolites were observed. We used binary logistic regression analysis to determine whether the lipid and amino acid biomarkers could discriminate SCZ patients from controls. The area under the curve (AUC) from receiver operation characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the biomarkers panel.

RESULTS

We identified 19 significantly differentially expressed metabolites between the SCZ patients and the controls (false discovery rate < 0.05), including one amino acid and 18 lipids or lipid-related metabolites. The binary logistic regression-selected panel showed good diagnostic performance in the drug-naïve group (AUC = 0.936) and all SCZ patients (AUC = 0.948), especially in the drug-treated group (AUC = 0.963).

CONCLUSIONS

Plasma lipids and amino acids showed significant dysregulation in SCZ, which could effectively discriminate SCZ patients from controls. The LC/MS/MS-based approach provides reliable data for the objective diagnosis of SCZ.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies

BACKGROUND

Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results.

AIM OF REVIEW

In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.

Impact of Pre-blood Collection Factors on Plasma Metabolomic Profiles

Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted.

Prediagnostic plasma polyunsaturated fatty acids and the risk of amyotrophic lateral sclerosis

OBJECTIVE

To examine the association between prediagnostic plasma polyunsaturated fatty acids levels (PUFA) and amyotrophic lateral sclerosis (ALS).

METHODS

We identified 275 individuals who developed ALS while enrolled in 5 US prospective cohorts, and randomly selected 2 controls, alive at the time of the case diagnosis, matched on cohort, birth year, sex, ethnicity, fasting status, and time of blood draw. We measured PUFA, expressed as percentages of total fatty acids, using gas liquid chromatography and used conditional logistic regression to estimate risk ratios (RR) and 95% confidence intervals (CI) for the association between PUFA and ALS.

RESULTS

There was no association between total, n-3, and n-6 PUFA, eicosapentaenoic acid, or docosapentaenoic acid levels and ALS. Higher plasma α-linolenic acid (ALA) in men was associated with lower risk of ALS in age- and matching factor-adjusted analyses (top vs bottom quartile: RR = 0.21 [95% CI 0.07, 0.58], p for trend = 0.004). In women, higher plasma arachidonic acid was associated with higher risk (top vs bottom quartile: RR = 1.65 [95% CI 0.99, 2.76], p for trend = 0.052). Multivariable adjustment, including correlated PUFA, did not change the findings for ALA and arachidonic acid. In men and women combined, higher plasma docosahexaenoic acid (DHA) was associated with higher risk of ALS (top vs bottom quartile: RR = 1.56 [95% CI 1.01, 2.41], p for trend = 0.054), but in multivariable models the association was only evident in men.

CONCLUSIONS

The majority of individual PUFAs were not associated with ALS. In men, ALA was inversely and DHA was positively related to risk of ALS, while in women arachidonic acid was positively related. These findings warrant confirmation in future studies.

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case-control sets from EPIC

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD ) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD  = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD  = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR1SD  = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD  = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer.

Associations between Blood Metabolic Profile at 7 Years Old and Eating Disorders in Adolescence: Findings from the Avon Longitudinal Study of Parents and Children

Eating disorders are severe illnesses characterized by both psychiatric and metabolic factors. We explored the prospective role of metabolic risk in eating disorders in a UK cohort (n = 2929 participants), measuring 158 metabolic traits in non-fasting EDTA-plasma by nuclear magnetic resonance. We associated metabolic markers at 7 years (exposure) with risk for anorexia nervosa and binge-eating disorder (outcomes) at 14, 16, and 18 years using logistic regression adjusted for maternal education, child’s sex, age, body mass index, and calorie intake at 7 years. Elevated very low-density lipoproteins, triglycerides, apolipoprotein-B/A, and monounsaturated fatty acids ratio were associated with lower odds of anorexia nervosa at age 18, while elevated high-density lipoproteins, docosahexaenoic acid and polyunsaturated fatty acids ratio, and fatty acid unsaturation were associated with higher risk for anorexia nervosa at 18 years. Elevated linoleic acid and n-6 fatty acid ratios were associated with lower odds of binge-eating disorder at 16 years, while elevated saturated fatty acid ratio was associated with higher odds of binge-eating disorder. Most associations had large confidence intervals and showed, for anorexia nervosa, different directions across time points. Overall, our results show some evidence for a role of metabolic factors in eating disorders development in adolescence.

Metabolomics to reveal biomarkers and pathways of preterm birth: a systematic review and epidemiologic perspective

INTRODUCTION

Most known risk factors for preterm birth, a leading cause of infant morbidity and mortality, are not modifiable. Advanced molecular techniques are increasingly being applied to identify biomarkers and pathways important in disease development and progression.

OBJECTIVES

We review the state of the literature and assess it from an epidemiologic perspective.

METHODS

PubMed, Embase, CINAHL, and Cochrane Central were searched on January 31, 2019 for original articles published after 1998 that utilized an untargeted metabolomic approach to identify markers of preterm birth. Eligible manuscripts were peer-reviewed and included original data from untargeted metabolomics analyses of maternal tissue derived from human studies designed to determine mechanisms and predictors of preterm birth.

RESULTS

Of 2823 results, 14 articles met the inclusion requirements. There was little consistency in study design, outcome definition, type of biospecimen, or the inclusion of covariates and confounding factors, and few consistent associations with metabolites were identified in this review.

CONCLUSION

Studies to date on metabolomic predictors of preterm birth are highly heterogeneous in both methodology and resulting metabolite identification. There is an urgent need for larger studies in well-defined populations, to determine biomarkers predictive of preterm birth, and to reveal mechanisms and targets for development of intervention strategies.

A Metabolomics Analysis of Body Mass Index and Postmenopausal Breast Cancer Risk

Background

Elevated body mass index (BMI) is associated with increased risk of postmenopausal breast cancer. The underlying mechanisms, however, remain elusive.

Methods

In a nested case-control study of 621 postmenopausal breast cancer case participants and 621 matched control participants, we measured 617 metabolites in prediagnostic serum. We calculated partial Pearson correlations between metabolites and BMI, and then evaluated BMI-associated metabolites (Bonferroni-corrected α level for 617 statistical tests = P < 8.10 × 10-5) in relation to invasive breast cancer. Odds ratios (ORs) of breast cancer comparing the 90th vs 10th percentile (modeled on a continuous basis) were estimated using conditional logistic regression while controlling for breast cancer risk factors, including BMI. Metabolites with the lowest P values (false discovery rate < 0.2) were mutually adjusted for one another to determine those independently associated with breast cancer risk.

Results

Of 67 BMI-associated metabolites, two were independently associated with invasive breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR = 1.65, 95% confidence interval [CI] = 1.22 to 2.22) and 3-methylglutarylcarnitine (OR = 1.67, 95% CI = 1.21 to 2.30). Four metabolites were independently associated with estrogen receptor-positive (ER+) breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR = 1.84, 95% CI = 1.27 to 2.67), 3-methylglutarylcarnitine (OR = 1.91, 95% CI = 1.23 to 2.96), allo-isoleucine (OR = 1.76, 95% CI = 1.23 to 2.51), and 2-methylbutyrylcarnitine (OR = 1.89, 95% CI = 1.22 to 2.91). In a model without metabolites, each 5 kg/m2 increase in BMI was associated with a 14% higher risk of breast cancer (OR = 1.14, 95% CI = 1.01 to 1.28), but adding 16a-hydroxy-DHEA-3-sulfate and 3-methylglutarylcarnitine weakened this association (OR = 1.06, 95% CI = 0.93 to 1.20), with the logOR attenuating by 57.6% (95% CI = 21.8% to 100.0+%).

Conclusion

These four metabolites may signal metabolic pathways that contribute to breast carcinogenesis and that underlie the association of BMI with increased postmenopausal breast cancer risk. These findings warrant further replication efforts.

Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review

Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.

A pilot study of the effect of phospholipid curcumin on serum metabolomic profile in patients with non-alcoholic fatty liver disease: a randomized, double-blind, placebo-controlled trial

BACKGROUND/OBJECTIVES

Curcumin, a natural polyphenol compound in the spice turmeric, has been found to have potent anti-oxidative and anti-inflammatory activity. Curcumin may treat non-alcoholic fatty liver disease (NAFLD) through its beneficial effects on biomarkers of oxidative stress (OS) and inflammation, which are considered as two feature of this disease. However, the effects of curcumin on NAFLD have been remained poorly understood. This investigation evaluated the effects of administrating curcumin on metabolic status in NAFLD patients.

SUBJECTS/METHODS

Fifty-eight NAFLD patients participated in a randomized, double-blind, placebo-controlled parallel design of study. The subjects were allocated randomly into two groups, which either received 250 mg phospholipid curcumin or placebo, one capsule per day for a period of 8 weeks. Fasting blood samples were taken from each subject at the start and end of the study period. Subsequently, metabolomics analysis was performed for serum samples using NMR.

RESULTS

Compared with the placebo, supplementing phospholipid curcumin resulted in significant decreases in serum including 3- methyl-2-oxovaleric acid, 3-hydroxyisobutyrate, kynurenine, succinate, citrate, α-ketoglutarate, methylamine, trimethylamine, hippurate, indoxyl sulfate, chenodeoxycholic acid, taurocholic acid, and lithocholic acid. This profile of metabolic biomarkers could distinguish effectively NAFLD subjects who were treated with curcumin and placebo groups, achieving value of 0.99 for an area under receiver operating characteristic curve (AUC).

CONCLUSIONS

Characterizing the serum metabolic profile of the patients with NAFLD at the end of the intervention using NMR-based metabolomics method indicated that the targets of curcumin treatment included some amino acids, TCA cycle, bile acids, and gut microbiota.

Metabolite Profiling of Clinical Cancer Biofluid Samples by NMR Spectroscopy

Metabolomics is a comprehensive characterization of the small polar molecules (metabolites) in different biological systems. One of the analytical platforms commonly used to study metabolic alterations in biofluid samples is proton nuclear magnetic resonance (¹H NMR) spectroscopy. NMR spectroscopy is very specific, quantitative, and highly reproducible. Moreover, sample preparation for NMR experiments is very simple and straightforward, and this gives NMR spectroscopy a distinct advantage over other metabolic profiling methods. It has already been shown that ¹H NMR-based profiling of biological fluids can be effective in differentiating benign from malignant lesions and in investigating the efficacy of specific cancer treatments. Therefore, ¹H NMR spectroscopy may become a promising tool for early noninvasive diagnosis and rapid assessment of treatment effects in cancer patients. Here, we describe a detailed protocol for ¹H NMR metabolite profiling in serum, plasma, and urine samples, including sample collection procedures, sample preparation for ¹H NMR experiments, spectral acquisition and processing, and quantitative profiling of ¹H NMR spectra. We also discuss several aspects of appropriate study design and some multivariate statistical methods that are commonly used to analyze metabolomics datasets.

Metabolites Associated With the Risk of Incident Venous Thromboembolism: A Metabolomic Analysis

Background Venous thromboembolism ( VTE ) is a complex thrombotic disorder that constitutes a major source of mortality and morbidity. To improve understanding of the cause of VTE , we conducted a metabolomic analysis in a case-control study including 240 incident VTE cases and 6963 controls nested within 3 large prospective population-based cohorts, the Nurses' Health Study, the Nurses' Health Study II , and the Health Professionals Follow-Up Study. Methods and Results For each individual, we measured 211 metabolites and collected detailed information on lifestyle factors. We performed logistic regression and enrichment analysis to identify metabolites and biological categories associated with incident VTE risk, accounting for key confounders, such as age, sex, smoking, alcohol consumption, body mass index, and comorbid diseases (eg, cancers). We performed analyses of all VTEs and separate analyses of pulmonary embolism. Using the basic model controlling for age, sex, and primary disease, we identified 60 nominally significant VTE - or pulmonary embolism-associated metabolites ( P<0.05). These metabolites were enriched for diacylglycerols ( P_permutation<0.05). However, after controlling for multiple testing, only 1 metabolite (C5 carnitine; odds ratio, 1.25; 95% confidence interval, 1.10-1.41; P_corrected=0.03) remained significantly associated with VTE . After further adjustment for body mass index, no metabolites were significantly associated with disease after accounting for multiple testing, and no metabolite classes were enriched for nominally significant associations. Conclusions Although our findings suggest that circulating metabolites may influence the risk of incident VTE , the associations we observed were confounded by body mass index. Larger studies involving additional individuals and with broader metabolomics coverage are needed to confirm our findings.

Impact of post-collection freezing delay on the reliability of serum metabolomics in samples reflecting the California mid-term pregnancy biobank

BACKGROUND

Population-based biorepositories are important resources, but sample handling can affect data quality.

OBJECTIVE

Identify metabolites of value for clinical investigations despite extended postcollection freezing delays, using protocols representing a California mid-term pregnancy biobank.

METHODS

Blood collected from non-pregnant healthy female volunteers (n = 20) underwent three handling protocols after 30 min clotting at room temperature: (1) ideal-samples frozen (- 80 °C) within 2 h of collection; (2) delayed freezing-samples held at room temperature for 3 days, then 4 °C for 9 days, the median times for biobank samples, and then frozen; (3) delayed freezing with freeze-thaw-the delayed freezing protocol with a freeze-thaw cycle simulating retrieved sample sub-aliquoting. Mass spectrometry-based untargeted metabolomic analyses of primary metabolism and complex lipids and targeted profiling of oxylipins, endocannabinoids, ceramides/sphingoid-bases, and bile acids were performed. Metabolite concentrations and intraclass correlation coefficients (ICC) were compared, with the ideal protocol as the reference.

RESULTS

Sixty-two percent of 428 identified compounds had good to excellent ICCs, a metric of concordance between measurements of samples handled with the different protocols. Sphingomyelins, phosphatidylcholines, cholesteryl esters, triacylglycerols, bile acids and fatty acid diols were the least affected by non-ideal handling, while sugars, organic acids, amino acids, monoacylglycerols, lysophospholipids, N-acylethanolamides, polyunsaturated fatty acids, and numerous oxylipins were altered by delayed freezing. Freeze-thaw effects were assay-specific with lipids being most stable.

CONCLUSIONS

Despite extended post-collection freezing delays characteristic of some biobanks of opportunistically collected clinical samples, numerous metabolomic compounds had both stable levels and good concordance.

Roux-en-Y gastric bypass surgery alters serum metabolites and fatty acids in patients with morbid obesity

AIM

Bariatric surgery induces significant weight loss, increases insulin sensitivity, and reduces mortality, but the underlying mechanisms are not clear. It was hypothesized that Roux-en-Y gastric bypass (RYGB) surgery improves metabolic profile along with weight loss. The objective of this pilot study was to evaluate changes in serum metabolites and fatty acids (FA) at 2 weeks and 6 months after RYGB.

MATERIALS AND METHODS

Serum samples were collected pre-surgery, at 2 weeks and 6 months post-surgery from 20 patients undergoing RYGB surgery. Serum non-esterified free FA (NEFA) were measured. Serum metabolites and FA were measured using nuclear magnetic resonance spectroscopy and improved direct fatty acid methyl ester synthesis and the gas chromatography/mass spectrometry method, respectively, in subjects who completed follow-up at 6 months (n = 8).

RESULTS

Mean (standard deviation) percent total weight loss was 6.70% (1.7) and 24.91% (6.63) at 2 weeks (n = 15) and 6 months (n = 8) post-surgery, respectively. NEFA were significantly reduced at 6 months post-surgery (P = 0.001, n = 8). Serum branched chain amino acids, 2-aminobutyrate, butyrate, 2-hydroxybutyrate, 3-hydroxybutyrate, acetone, 2-methylglutarate, and 2-oxoisocaproate were significantly reduced, while serum alanine, glycine, pyruvate, and taurine were significantly elevated at 6 months post-surgery compared with pre-surgery (n = 8, P < 0.05). Also, serum FA C10:0, C13:0, C14:0, C15:0, and C18:0 increased significantly (n = 8, P < 0.05) by 6 months post-surgery.

CONCLUSIONS

Changes in serum metabolites and FA at 6 months post-RYGB surgery in this pilot study with limited number of participants are suggestive of metabolic improvement; larger studies are warranted for confirmation.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Quantitative metabolomics comparison of traditional blood draws and TAP capillary blood collection

INTRODUCTION

Mass spectrometry and computational biology have advanced significantly in the past ten years, bringing the field of metabolomics a step closer to personalized medicine applications. Despite these analytical advancements, collection of blood samples for routine clinical analysis is still performed through traditional blood draws.

OBJECTIVE

TAP capillary blood collection has been recently introduced for the rapid, painless draw of small volumes of blood (~ 100 μL), though little is known about the comparability of metabolic phenotypes of blood drawn via traditional venipuncture and TAP devices.

METHODS

UHPLC-MS-targeted metabolomics analyses were performed on blood drawn traditionally or through TAP devices from 5 healthy volunteers. Absolute quantitation of 45 clinically-relevant metabolites was calculated against stable heavy isotope-labeled internal standards.

RESULTS

Ranges for 39 out of 45 quantified metabolites overlapped between drawing methods. Pyruvate and succinate were over threefold higher in the TAP samples than in traditional blood draws. No significant changes were observed for other carboxylates, glucose or lactate. TAP samples were characterized by increases in reduced glutathione and decreases in urate and cystine, markers of oxidation of purines and cysteine-overall suggesting decreased oxidation during draws. The absolute levels of bile acids and acyl-carnitines, as well as almost all amino acids, perfectly correlated among groups (Spearman r ≥ 0.95).

CONCLUSION

Though further more extensive studies will be mandatory, this pilot suggests that TAP-derived blood may be a logistically-friendly source of blood for large scale metabolomics studies-especially those addressing amino acids, glycemia and lactatemia as well as bile acids, acyl-carnitine levels.

Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative)

BACKGROUND

The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data.

CONTENT

This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices.

SUMMARY

Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

An Integrative Transcriptomic and Metabolomic Study of Lung Function in Children With Asthma

BACKGROUND

Single omic analyses have provided some insight into the basis of lung function in children with asthma, but the underlying biologic pathways are still poorly understood.

METHODS

Weighted gene coexpression network analysis (WGCNA) was used to identify modules of coregulated gene transcripts and metabolites in blood among 325 children with asthma from the Genetic Epidemiology of Asthma in Costa Rica study. The biology of modules associated with lung function as measured by FEV₁, the FEV₁/FVC ratio, bronchodilator response, and airway responsiveness to methacholine was explored. Significantly correlated gene-metabolite module pairs were then identified, and their constituent features were analyzed for biologic pathway enrichments.

RESULTS

WGCNA clustered 25,060 gene probes and 8,185 metabolite features into eight gene modules and eight metabolite modules, where four and six, respectively, were associated with lung function (P ≤ .05). The gene modules were enriched for immune, mitotic, and metabolic processes and asthma-associated microRNA targets. The metabolite modules were enriched for lipid and amino acid metabolism. Integration of correlated gene-metabolite modules expanded the single omic findings, linking the FEV₁/FVC ratio with ORMDL3 and dysregulated lipid metabolism. This finding was replicated in an independent population.

CONCLUSIONS

The results of this hypothesis-generating study suggest a mechanistic basis for multiple asthma genes, including ORMDL3, and a role for lipid metabolism. They demonstrate that integrating multiple omic technologies may provide a more informative picture of asthmatic lung function biology than single omic analyses.

Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Little is known about how pre-diagnostic metabolites in blood relate to risk of prostate cancer. We aimed to investigate the prospective association between plasma metabolite concentrations and risk of prostate cancer overall, and by time to diagnosis and tumour characteristics, and risk of death from prostate cancer.

METHODS

In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition, pre-diagnostic plasma concentrations of 122 metabolites (including acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose and sphingolipids) were measured using targeted mass spectrometry (AbsoluteIDQ p180 Kit) and compared between 1077 prostate cancer cases and 1077 matched controls. Risk of prostate cancer associated with metabolite concentrations was estimated by multi-variable conditional logistic regression, and multiple testing was accounted for by using a false discovery rate controlling procedure.

RESULTS

Seven metabolite concentrations, i.e. acylcarnitine C18:1, amino acids citrulline and trans-4-hydroxyproline, glycerophospholipids PC aa C28:1, PC ae C30:0 and PC ae C30:2, and sphingolipid SM (OH) C14:1, were associated with prostate cancer (p < 0.05), but none of the associations were statistically significant after controlling for multiple testing. Citrulline was associated with a decreased risk of prostate cancer (odds ratio (OR1SD) = 0.73; 95% confidence interval (CI) 0.62-0.86; p trend = 0.0002) in the first 5 years of follow-up after taking multiple testing into account, but not after longer follow-up; results for other metabolites did not vary by time to diagnosis. After controlling for multiple testing, 12 glycerophospholipids were inversely associated with advanced stage disease, with risk reduction up to 46% per standard deviation increase in concentration (OR1SD = 0.54; 95% CI 0.40-0.72; p trend = 0.00004 for PC aa C40:3). Death from prostate cancer was associated with higher concentrations of acylcarnitine C3, amino acids methionine and trans-4-hydroxyproline, biogenic amine ADMA, hexose and sphingolipid SM (OH) C14:1 and lower concentration of glycerophospholipid PC aa C42:4.

CONCLUSIONS

Several metabolites, i.e. C18:1, citrulline, trans-4-hydroxyproline, three glycerophospholipids and SM (OH) C14:1, might be related to prostate cancer. Analyses by time to diagnosis indicated that citrulline may be a marker of subclinical prostate cancer, while other metabolites might be related to aetiology. Several glycerophospholipids were inversely related to advanced stage disease. More prospective data are needed to confirm these associations.

Serum metabolomic profiling of prostate cancer risk in the prostate, lung, colorectal, and ovarian cancer screening trial

Background:

Two recent metabolomic analyses found serum lipid, energy, and other metabolites related to aggressive prostate cancer risk up to 20 years prior to diagnosis.

Methods:

We conducted a serum metabolomic investigation of prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial that included annual serum total prostate-specific antigen measurement and digital rectal examination. This nested study included 380 cases diagnosed post-screening and 380 controls individually matched to cases on age, race, study centre, and blood-collection date (median time to diagnosis, 10 years (range 4.4–17 years)). Sera were analysed on a high-resolution accurate mass platform of ultrahigh-performance liquid and gas chromatography/mass spectroscopy that identified 695 known metabolites. Logistic regression conditioned on the matching factors estimated odds ratios (OR) and 95% confidence intervals of risk associated with an 80th percentile increase in the log-metabolite signal.

Results:

Twenty-seven metabolites were associated with prostate cancer at P<0.05. Pyroglutamine, gamma-glutamylphenylalanine, phenylpyruvate, N-acetylcitrulline, and stearoylcarnitine showed the strongest metabolite-risk signals (ORs=0.53, 0.51, 0.46, 0.58, and 1.74, respectively; 0.001⩽P⩽0.006). Findings were similar for aggressive disease (peptide chemical class, P=0.03). None of the P-values were below the threshold of Bonferroni correction, however.

Conclusions:

A unique metabolomic profile associated with post-screening prostate cancer is identified that differs from that in a previously studied, unscreened population.