Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity Q_Y² of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography–mass spectrometry data from humans and the model organism C. elegans.

Background: Gut microbes influence animal health and thus, are potential targets for interventions that slow aging. Live E. coli provides the nematode worm Caenorhabditis elegans with vital micronutrients, such as folates that cannot be synthesized by animals. However, the microbe also limits C. elegans lifespan. Understanding these interactions may shed light on how intestinal microbes influence mammalian aging. Results: Serendipitously, we isolated an E. coli mutant that slows C. elegans aging. We identified the disrupted gene to be aroD, which is required to synthesize aromatic compounds in the microbe. Adding back aromatic compounds to the media revealed that the increased C. elegans lifespan was caused by decreased availability of para-aminobenzoic acid, a precursor to folate. Consistent with this result, inhibition of folate synthesis by sulfamethoxazole, a sulfonamide, led to a dose-dependent increase in C. elegans lifespan. As expected, these treatments caused a decrease in bacterial and worm folate levels, as measured by mass spectrometry of intact folates. The folate cycle is essential for cellular biosynthesis. However, bacterial proliferation and C. elegans growth and reproduction were unaffected under the conditions that increased lifespan. Conclusions: In this animal:microbe system, folates are in excess of that required for biosynthesis. This study suggests that microbial folate synthesis is a pharmacologically accessible target to slow animal aging without detrimental effects.

Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires 10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study.

Objective: Inflammation and metabolism are closely interlinked. Both undergo significant dysregulation following surgery for congenital heart disease, contributing to organ failure and morbidity. In this study, we combined cytokine and metabolic profiling to examine the effect of postoperative tight glycemic control compared with conventional blood glucose management on metabolic and inflammatory outcomes in children undergoing congenital heart surgery. The aim was to evaluate changes in key metabolites following congenital heart surgery and to examine the potential of metabolic profiling for stratifying patients in terms of expected clinical outcomes. Design: Laboratory and clinical study. Setting: University Hospital and Laboratory. Patients: Of 28 children undergoing surgery for congenital heart disease, 15 underwent tight glycemic control postoperatively and 13 were treated conventionally. Interventions: Metabolic profiling of blood plasma was undertaken using proton nuclear magnetic resonance spectroscopy. A panel of metabolites was measured using a curve-fitting algorithm. Inflammatory cytokines were measured by enzyme-linked immunosorbent assay. The data were assessed with respect to clinical markers of disease severity (Risk Adjusted Congenital heart surgery score-1, Pediatric Logistic Organ Dysfunction, inotrope score, duration of ventilation and pediatric ICU-free days). Measurements and Main Results: Changes in metabolic and inflammatory profiles were seen over the time course from surgery to recovery, compared with the preoperative state. Tight glycemic control did not significantly alter the response profile. We identified eight metabolites (3-D-hydroxybutyrate, acetone, acetoacetate, citrate, lactate, creatine, creatinine, and alanine) associated with surgical and disease severity. The strength of proinflammatory response, particularly interleukin-8 and interleukin-6 concentrations, inversely correlated with PICU-free days at 28 days. The interleukin-6/interleukin-10 ratio directly correlated with plasma lactate. Conclusions: This is the first report on the metabolic response to cardiac surgery in children. Using nuclear magnetic resonance to monitor the patient journey, we identified metabolites whose concentrations and trajectory appeared to be associated with clinical outcome. Metabolic profiling could be useful for patient stratification and directing investigations of clinical interventions.

Summary: As large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC and visualization of metabolic phenotyping datasets, either interactively, or in automated pipelines. Availability and implementation: The nPYc-Toolbox is implemented in Python, and is freely available from the Python package index https://pypi.org/project/nPYc/, source is available at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation can be found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials at https://github.com/phenomecentre/nPYc-toolbox-tutorials.