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Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Background: The metabolites associated with a Mediterranean-style (AMED) diet may give insight into why AMED is robustly associated with protection against CVD. Previous investigations seeking to identify these have been limited by the use of modest sample sizes and targeted assays. Methods: Using samples from the Multi-Ethnic Study of Atherosclerosis (MESA), we conducted untargeted 1 H NMR spectroscopy (600 MHz) with internal and external annotation on the sera of ~3,698 participants, ages 45-84, who were free from overt CVD. We included data on baseline dietary intake (self-reported), and all incident CVD events (excluding stroke) over a 10-year period. From >100,000 spectral features, 845 significant associations (P-6 ) were identified via linear regression, and reduced to a putative list of 46 via elastic net regularized models. Hierarchical clustering identified 11 feature groups, from which cluster scores were constructed. The ‘mediate’ package in R used bootstrapping (1000 simulations) to partition the association between AMED and incident CVD into the effects of dietary intake on CVD which are mediated by the metabolomic cluster scores (the “indirect” / mediated effects) and those effects which are not (“direct effects”). All association analyses controlled for age, sex, ethnicity, data collection site and daily caloric intake. Results: AMED score was associated with reductions in the incidence of CVD (HR=0.95; 95% CI: 0.91 - 0.99; P=0.02). All metabolomic cluster scores were associated with AMED (all P-06 ; Table 1 ), with 6 significantly associated with incident CVD (all P-4 ) in the expected direction given their associations with AMED score. Four of these six significantly mediated the association of AMED score with incident CVD (PTable 1 ). Conclusions: These preliminary data suggest that specific molecules, if replicated in other studies, hold promise to identify the underlying pathways by which an AMED diet offers protection against CVD.

Genome-wide association studies (GWAS) have identified genetic loci associated with risk of Alzheimer’s disease (AD), but underlying mechanisms are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWAS using untargeted metabolic profiling (metabolomics) by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0x 10−5 to 1.3 x 10−44). We show that plasma LacCer concentrations are associated with cognitive performance in humans and concentrations of sphingomyelins, ceramides, and hexose-ceramides were altered in brain tissue from ABCA7 knock out mice, compared to wild type (WT) (P =0.049 to 1.4 x10−5). We then used Mendelian randomisation to show that the association of LacCer with AD risk is potentially causal. Our work suggests that risk for AD arising from functional variations in ABCA7 are mediated at least in part through ceramides. Modulation of their metabolism or downstream signalling may offer new therapeutic opportunities for AD.

Key Points Question What is the association of metabolomic biomarkers with lung cancer in women who have never smoked? Findings In this case-control study of 275 never-smoking women with lung cancer and 289 never-smoking cancer-free women in China, the metabolite 5-methyl-2-furoic acid was correlated with dietary soy consumption and pathways including 1-carbon metabolism, oxidative stress, nucleotide metabolism, and inflammation and was significantly associated with lower lung cancer risk. Meaning These findings suggest that certain metabolites and pathways are associated with lower lung cancer risk in never-smoking women and biological processes linked to air pollution may be associated with higher lung cancer risk in this population., This case-control study investigates the association between urinary metabolomic biomarkers and lung cancer risk among never-smoking women in China., Importance Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood. Objective To assess the association between metabolomics and lung cancer risk among never-smoking women. Design, Setting, and Participants This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women’s Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018. Exposures Untargeted ultra-high-performance liquid chromatography–tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured. Main Outcomes and Measures Incident lung cancer. Results Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P

Aims To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and results We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10−14 to 1.0 × 10−6 (discovery) and P = 5.6 × 10−10 to 1.1 × 10−2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P Conclusion Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.

Nephrotic syndrome with idiopathic membranous nephropathy as a major contributor, is characterized by proteinuria, hypoalbuminemia and oedema. Diagnosis is based on renal biopsy and the condition is treated using immunosuppressive drugs; however nephrotic syndrome treatment efficacy varies among patients. Multi-omic urine analyses can discover new markers of nephrotic syndrome that can be used to develop personalized treatments. For proteomics, a protease inhibitor (PI) is sometimes added at sample collection to conserve proteins but its impact on urine metabolic phenotyping needs to be evaluated. Urine from controls (n = 4) and idiopathic membranous nephropathy (iMN) patients (n = 6) were collected with and without PI addition and analysed using 1H NMR spectroscopy and UPLC-MS. PI-related data features were observed in the 1H NMR spectra but their removal followed by a median fold change normalisation, eliminated the PI contribution. PI-related metabolites in UPLC-MS data had limited effect on metabolic patterns specific to iMN. When using an appropriate data processing pipeline, PI-containing urine samples are appropriate for 1H NMR and MS metabolic profiling of patients with nephrotic syndrome.

Background: Never-smoking Chinese women have a high rate of lung cancer but the etiology is poorly understood. This study is the first to evaluate the untargeted urinary metabolome and risk of lung...

INTRODUCTION: Differences in the metabolite profiles between serum and plasma are incompletely understood. OBJECTIVES: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. METHODS: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG (1)H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. RESULTS: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophos-phatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC) (16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. CONCLUSIONS: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

Aim: Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events. Results: A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC–MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed. Conclusion: Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment.

Numerous metabolic profiling pipelines have been developed to characterize the composition of human biofluids and tissues, the vast majority of these being for studies in adults. To accommodate limited sample volume and to take into account the compositional differences between adult and infant biofluids, we developed and optimized sample handling and analytical procedures for studying urine from newborns. A robust pipeline for metabolic profiling using NMR spectroscopy was established, encompassing sample collection, preparation, spectroscopic measurement, and computational analysis. Longitudinal samples were collected from five infants from birth until 14 months of age. Methods of extraction and effects of freezing and sample dilution were assessed, and urinary contaminants from breakdown of polymers in a range of diapers and cotton wool balls were identified and compared, including propylene glycol, acrylic acid, and tert-butanol. Finally, assessment of urinary profiles obtained over the first few weeks of life revealed a dramatic change in composition, with concentrations of phenols, amino acids, and betaine altering systematically over the first few months of life. Therefore, neonatal samples require more stringent standardization of experimental design, sample handling, and analysis compared to that of adult samples to accommodate the variability and limited sample volume.

The lung cancer rate among never-smokers is the highest among Asian women, however its etiology and any relevant non-smoking related biomarkers are still unclear. Pre-diagnostic lung cancer-related metabolic biomarkers may provide novel insights into lung cancer mechanisms, and may contribute to the discovery of etiologic factors for the high lung cancer prevalence among Asian women. We evaluated the role of the urinary metabolome in lung cancer development among female never-smokers in China by conducting a nested case-control study of 275 lung cancer cases and 289 healthy controls from the Shanghai Women's Health Study, a prospective cohort comprised of 73,363 Chinese female never-smokers. Metabolic profiling of urinary chemical features was conducted using ultrahigh-performance liquid chromatography - tandem mass spectrometry (UPLC-MS) (39,409 spectral features) and 600 MHz 1H nuclear magnetic resonance (NMR) spectroscopy (386 features). Unconditional logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between each log-transformed metabolite level and lung cancer risk, adjusting for potential confounders such as age, body mass index, history of respiratory disease and passive smoking. Spearman correlation and linear regression were used to estimate associations between the most significant metabolites and pre-diagnosis dietary factors. Three detected UPLC-MS urinary metabolites were negatively associated with lung cancer risk with a false discovery rate of less than 10%: pos_2.61_127.0382m/z (OR = 0.57, 95% CI = 0.46-0.72, P = 1.98 x 10-6), neg_2.60_369.0408m/z (OR = 0.97, 95% CI = 0.96-0.98, P = 1.36 x 10-6), and pos_2.61_184.0325n (OR = 0.55, 95% CI = 0.43-0.71, P = 4.91 x 10-6). These were strongly correlated with each other (rho > 0.65, p < 0.0001). The significant metabolite (pos_2.61_127.0382m/z) was identified as 5-methyl-2-furoic acid and was moderately correlated with self-reported dietary intake of soy (rho = 0.21, p < 0.001). In conclusion, we identified a metabolite in urine (5-methyl-2-furoic acid) that provides support for the protective association of soy-based foods on lung cancer risk that was previously observed in this population of never-smoking women. Further studies are warranted to replicate these findings. Citation Format: Wei Jie Seow, Xiao-Ou Shu, Jeremy Nicholson, Elaine Holmes, Wei Hu, Qiuyin Cai, Yu-Tang Gao, Yong-Bing Xiang, Steve Moore, Bryan A. Bassig, Jason Yy Wong, Jinming Zhang, Bu-Tian Ji, Claire Boulange, Manuja Kaluarachchi, Kyrillos F. Adesina-Georgiadis, Anisha Wijeyesekera, Wei Zheng, Paul Elliot, Nathaniel Rothman, Qing Lan. Prospective study of untargeted urinary metabolomics and risk of lung cancer among female never-smokers in Shanghai, China [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4974.