In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Human urine, which is rich in metabolites, provides valuable approaches for biomarker measurement. Maintaining the stability of metabolites in urine is critical for accurate and reliable research results and subsequent interpretation. In this study, the effect of storage temperature (4, 22, and 40 °C), storage time (24 and 48 h), and use of preservatives (boric acid (BA), thymol) and para-aminobenzoic acid (PABA) on urinary metabolites in the pooled urine samples from 20 participants was systematically investigated using large-scale targeted liquid chromatography tandem mass spectrometry (LC-MS/MS)-based metabolomics. Statistical analysis of 158 reliably detected metabolites showed that metabolites in urine with no preservative remained stable at 4 °C for 24 and 48 h as well as at 22 °C for 24 h, but significant metabolite differences were observed in urine stored at 22 °C for 48 h and at 40 °C. The mere addition of BA caused metabolite changes. Thymol was observed to be effective in maintaining metabolite stability in urine in all the conditions designed, most likely due to the inhibitory effect of thymol on urine microbiota. Our results provide valuable urine preservation guidance during sample storage, which is essential for obtaining reliable, accurate, and reproducible analytical results from urine samples.

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material®s (SRM®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

In Mexican Americans, metabolic conditions, such as obesity and type 2 diabetes (T2DM), are not necessarily associated with an increase in mortality; this is the so-called Hispanic paradox. In this cross-sectional analysis, we used a metabolomic analysis to look at the mechanisms behind the Hispanic paradox. To do this, we examined dietary intake and body mass index (BMI; kg/m2) in men and women and their effects on serum metabolomic fingerprints in 70 Mexican Americans (26 men, 44 women). Although having different BMI values, the participants had many similar anthropometric and biochemical parameters, such as systolic and diastolic blood pressure, total cholesterol, and LDL cholesterol, which supported the paradox in these subjects. Plasma metabolomic phenotypes were measured using liquid chromatography tandem mass spectrometry (LC-MS/MS). A two-way ANOVA assessing sex, BMI, and the metabolome revealed 23 significant metabolites, such as 2-pyrrolidinone (p = 0.007), TMAO (p = 0.014), 2-aminoadipic acid (p = 0.019), and kynurenine (p = 0.032). Pathway and enrichment analyses discovered several significant metabolic pathways between men and women, including lysine degradation, tyrosine metabolism, and branch-chained amino acid (BCAA) degradation and biosynthesis. A log-transformed OPLS-DA model was employed and demonstrated a difference due to BMI in the metabolomes of both sexes. When stratified for caloric intake (<2200 kcal/d vs. >2200 kcal/d), a separate OPLS-DA model showed clear separation in men, while females remained relatively unchanged. After accounting for caloric intake and BMI status, the female metabolome showed substantial resistance to alteration. Therefore, we provide a better understanding of the Mexican-American metabolome, which may help demonstrate how this population-particularly women-possesses a longer life expectancy despite several comorbidities, and reveal the underlying mechanisms of the Hispanic paradox.

Menopause-associated asthma impacts a subset of women, tends to be more severe, and is less responsive to current treatments. We recently developed a model of menopause-associated asthma using 4-Vinylcyclohexene Diepoxide (VCD) and house dust mites (HDM). The goal of this study was to uncover potential biomarkers and drivers of menopause-onset asthma by assessing serum and bronchoalveolar lavage fluid (BALF) samples from mice with and without menopause and HDM challenge by large-scale targeted metabolomics. Female mice were treated with VCD/HDM to model menopause-associated asthma, and serum and BALF samples were processed for large-scale targeted metabolomic assessment. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to examine metabolites of potential biological significance. We identified over 50 individual metabolites, impacting 46 metabolic pathways, in the serum and BALF that were significantly different across the four study groups. In particular, glutamate, GABA, phosphocreatine, and pyroglutamic acid, which are involved in glutamate/glutamine, glutathione, and arginine and proline metabolisms, were significantly impacted in the menopausal HDM-challenged mice. Additionally, several metabolites had significant correlations with total airway resistance including glutamic acid, histamine, uridine, cytosine, cytidine, and acetamide. Using metabolic profiling, we identified metabolites and metabolic pathways that may aid in discriminating potential biomarkers for and drivers of menopause-associated asthma.