Introduction: Background to metabolomics Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. Objectives of White Paper—expected treatment outcomes and metabolomics enabling tool for precision medicine We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Conclusions: Key scientific concepts and recommendations for precision medicine Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

The transition from 2D to 3D cell culture techniques is an important step in a trend towards better biomimetic tissue models. Microfluidics allows spatial control over fluids in micrometer-sized channels has become a valuable tool to further increase the physiological relevance of 3D cell culture by enabling spatially controlled co-cultures, perfusion flow and spatial control over of signaling gradients. This paper reviews most important developments in microfluidic 3D culture since 2012. Most efforts were exerted in the field of vasculature, both as a tissue on its own and as part of cancer models. We observe that the focus is shifting from tool building to implementation of specific tissue models. The next big challenge for the field is the full validation of these models and subsequently the implementation of these models in drug development pipelines of the pharmaceutical industry and ultimately in personalized medicine applications.

A hallmark of atopic eczema (AE) is skin barrier dysfunction. Lipids in the stratum corneum (SC), primarily ceramides, fatty acids, and cholesterol, are crucial for the barrier function, but their role in relation to AE is indistinct. Filaggrin is an epithelial barrier protein with a central role in the pathogenesis of AE. Nevertheless, the precise causes of AE-associated barrier dysfunction are largely unknown. In this study, a comprehensive analysis of ceramide composition and lipid organization in nonlesional SC of AE patients and control subjects was performed by means of mass spectrometry, infrared spectroscopy, and X-ray diffraction. In addition, the skin barrier and clinical state of the disease were examined. The level of ceramides with an extreme short chain length is drastically increased in SC of AE patients, which leads to an aberrant lipid organization and a decreased skin barrier function. Changes in SC lipid properties correlate with disease severity but are independent of filaggrin mutations. We demonstrate for the first time that changes in ceramide chain length and lipid organization are directly correlated with the skin barrier defects in nonlesional skin of AE patients. We envisage that these insights will provide a new therapeutic entry in therapy and prevention of AE.

Introduction: Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co‐metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid‐β deposition.Method: Serum levels of 20 primary and secondary BA metabolites from the AD Neuroimaging Initiative (n = 1562) were measured using targeted metabolomic profiling. We assessed the association of BAs with the “A/T/N” (amyloid, tau, and neurodegeneration) biomarkers for AD: cerebrospinal fluid (CSF) biomarkers, atrophy (magnetic resonance imaging), and brain glucose metabolism ([¹⁸F]FDG PET).Results: Of 23 BAs and relevant calculated ratios after quality control procedures, three BA signatures were associated with CSF Aβ_1‐42 (“A”) and three with CSF p‐tau181 (“T”) (corrected P < .05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t‐tau, glucose metabolism, and atrophy (“N”), respectively (corrected P < .05).Discussion: This is the first study to show serum‐based BA metabolites are associated with “A/T/N” AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association.