Time trends in preeclampsia and gestational diabetes in Denmark and Alberta, Canada, 2005-2018-A population-based cohort study.

INTRODUCTION: Preeclampsia and gestational diabetes mellitus share risk factors such as obesity and increased maternal age, which have become more prevalent in recent decades. We examined changes in the prevalence of preeclampsia and gestational diabetes between 2005 and 2018 in Denmark and Alberta, Canada, and investigated whether the observed trends can be explained by changes in maternal age, parity, multiple pregnancy, comorbidity, and body mass index (BMI) over time. MATERIAL AND METHODS: This study was a register-based cohort study conducted using data from the Danish National Health Registers and the provincial health registers of Alberta, Canada. We included in the study cohort all pregnancies in 2005-2018 resulting in live-born infants and used binomial regression to estimate mean annual increases in the prevalence of preeclampsia and gestational diabetes in the two populations across the study period, adjusted for maternal characteristics. RESULTS: The study cohorts included 846 127 (Denmark) and 706 728 (Alberta) pregnancies. The prevalence of preeclampsia increased over the study period in Denmark (2.5% to 2.9%) and Alberta (1.7% to 2.5%), with mean annual increases of 0.03 (95% confidence interval [CI] 0.02-0.04) and 0.06 (95% CI 0.05-0.07) percentage points, respectively. The prevalence of gestational diabetes also increased in Denmark (1.9% to 4.6%) and Alberta (3.9% to 9.2%), with average annual increases of 0.20 (95% CI 0.19-0.21) and 0.44 (95% CI 0.42-0.45) percentage points. Changes in the distributions of maternal age and BMI contributed to increases in the prevalence of both conditions but could not explain them entirely. CONCLUSIONS: The prevalence of both preeclampsia and gestational diabetes increased significantly from 2005 to 2018, which portends future increases in chronic disease rates among affected women. Increasing demand for long-term follow up and care will amplify the existing pressure on healthcare systems.

Reducing readmission rates for individuals discharged from acute psychiatric care in Alberta using peer and text message support: Protocol for an innovative supportive program.

BACKGROUND: Individuals discharged from inpatient psychiatry units have the highest readmission rates of all hospitalized patients. These readmissions are often due to unmet need for mental health care compounded by limited human resources. Reducing the need for hospital admissions by providing alternative effective care will mitigate the strain on the healthcare system and for people with mental illnesses and their relatives. We propose implementation and evaluation of an innovative program which augments Mental Health Peer Support with an evidence-based supportive text messaging program developed using the principles of cognitive behavioral therapy. METHODS: A pragmatic stepped-wedge cluster-randomized trial, where daily supportive text messages (Text4Support) and mental health peer support are the interventions, will be employed. We anticipate recruiting 10,000 participants at the point of their discharge from 9 acute care psychiatry sites and day hospitals across four cities in Alberta. The primary outcome measure will be the number of psychiatric readmissions within 30 days of discharge. We will also evaluate implementation outcomes such as reach, acceptability, fidelity, and sustainability. Our study will be guided by the Consolidated Framework for Implementation Research, and the Reach-Effectiveness-Adoption-Implementation-Maintenance framework. Data will be extracted from administrative data, surveys, and qualitative methods. Quantitative data will be analysed using machine learning. Qualitative interviews will be transcribed and analyzed thematically using both inductive and deductive approaches. CONCLUSIONS: To our knowledge, this will be the first large-scale clinical trial to assess the impact of a daily supportive text message program with and without mental health peer support for individuals discharged from acute psychiatric care. We anticipate that the interventions will generate significant cost-savings by reducing readmissions, while improving access to quality community mental healthcare and reducing demand for acute care. It is envisaged that the results will shed light on the effectiveness, as well as contextual barriers and facilitators to implementation of automated supportive text message and mental health peer support interventions to reduce the psychological treatment and support gap for patients who have been discharged from acute psychiatric care. TRIAL REGISTRATION: clinicaltrials.gov, NCT05133726 . Registered 24 November 2021.

Computational Prediction of Electron Ionization Mass Spectra to Assist in GC/MS Compound Identification.

We describe a tool, competitive fragmentation modeling for electron ionization (CFM-EI) that, given a chemical structure (e.g., in SMILES or InChI format), computationally predicts an electron ionization mass spectrum (EI-MS) (i.e., the type of mass spectrum commonly generated by gas chromatography mass spectrometry). The predicted spectra produced by this tool can be used for putative compound identification, complementing measured spectra in reference databases by expanding the range of compounds able to be considered when availability of measured spectra is limited. The tool extends CFM-ESI, a recently developed method for computational prediction of electrospray tandem mass spectra (ESI-MS/MS), but unlike CFM-ESI, CFM-EI can handle odd-electron ions and isotopes and incorporates an artificial neural network. Tests on EI-MS data from the NIST database demonstrate that CFM-EI is able to model fragmentation likelihoods in low-resolution EI-MS data, producing predicted spectra whose dot product scores are significantly better than full enumeration "bar-code" spectra. CFM-EI also outperformed previously reported results for MetFrag, MOLGEN-MS, and Mass Frontier on one compound identification task. It also outperformed MetFrag in a range of other compound identification tasks involving a much larger data set, containing both derivatized and nonderivatized compounds. While replicate EI-MS measurements of chemical standards are still a more accurate point of comparison, CFM-EI's predictions provide a much-needed alternative when no reference standard is available for measurement. CFM-EI is available at https://sourceforge.net/projects/cfm-id/ for download and http://cfmid.wishartlab.com as a web service.

CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra.

CFM-ID is a web server supporting three tasks associated with the interpretation of tandem mass spectra (MS/MS) for the purpose of automated metabolite identification: annotation of the peaks in a spectrum for a known chemical structure; prediction of spectra for a given chemical structure and putative metabolite identification--a predicted ranking of possible candidate structures for a target spectrum. The algorithms used for these tasks are based on Competitive Fragmentation Modeling (CFM), a recently introduced probabilistic generative model for the MS/MS fragmentation process that uses machine learning techniques to learn its parameters from data. These algorithms have been extensively tested on multiple datasets and have been shown to out-perform existing methods such as MetFrag and FingerId. This web server provides a simple interface for using these algorithms and a graphical display of the resulting annotations, spectra and structures. CFM-ID is made freely available at http://cfmid.wishartlab.com.

HMDB 3.0--The Human Metabolome Database in 2013.

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40,000 (a 600% increase). This enormous expansion is a result of the inclusion of both 'detected' metabolites (those with measured concentrations or experimental confirmation of their existence) and 'expected' metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data.

AIMS: Patients visiting the emergency department (ED) or hospitalized for heart failure (HF) are at increased risk for subsequent adverse outcomes, however effective risk stratification remains challenging. We utilized a machine-learning (ML)-based approach to identify HF patients at risk of adverse outcomes after an ED visit or hospitalization using a large regional administrative healthcare data system. METHODS AND RESULTS: Patients visiting the ED or hospitalized with HF between 2002-2016 in Alberta, Canada were included. Outcomes of interest were 30-day and 1-year HF-related ED visits, HF hospital readmission or all-cause mortality. We applied a feature extraction method using deep feature synthesis from multiple sources of health data and compared performance of a gradient boosting algorithm (CatBoost) with logistic regression modelling. The area under receiver operating characteristic curve (AUC-ROC) was used to assess model performance. We included 50,630 patients with 93,552 HF ED visits/hospitalizations. At 30-day follow-up in the holdout validation cohort, the AUC-ROC for the combined endpoint of HF ED visit, HF hospital readmission or death for the Catboost and logistic regression models was 74.16 (73.18-75.11) versus 62.25 (61.25-63.18), respectively. At 1-year follow-up corresponding values were 76.80 (76.1-77.47) versus 69.52 (68.77-70.26), respectively. AUC-ROC values for the endpoint of all-cause death alone at 30-days and 1-year follow-up were 83.21 (81.83-84.41) versus 69.53 (67.98-71.18), and 85.73 (85.14-86.29) versus 69.40 (68.57-70.26), for the CatBoost and logistic regression models, respectively. CONCLUSIONS: ML-based modelling with deep feature synthesis provided superior risk stratification for HF patients at 30-days and 1-year follow-up after an ED visit or hospitalization using data from a large administrative regional healthcare system.

HMDB: a knowledgebase for the human metabolome.

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network.

Acute ischemic stroke is one of the leading causes of death and long-term disability worldwide. It occurs when a blood clot blocks an artery that supplies blood to the brain tissue. Segmentation of acute ischemic stroke lesions plays a vital role to improve diagnosis, outcome assessment, and treatment planning. The current standard approach of ischemic stroke lesion segmentation is simply thresholding the Computed Tomography Perfusion (CTP) maps, i.e., quantitative feature maps created by summarizing CTP time sequence scans. However, this approach is not precise enough (its Dice similarity score is only around 50%) to be used in practice. Numerous machine learning-based techniques have recently been proposed to improve the accuracy of ischemic stroke lesion segmentation. Although they have achieved remarkable results, they still need to be improved before they can be used in actual practice. This paper presents a novel deep learning-based technique, MutiRes U-Net, for the segmentation of ischemic stroke lesions in CTP maps. MultiRes U-Net is a modified version of the original U-Net that is re-designed to be robust to segment the objects in different scales and unusual appearances. Additionally, in this paper, we propose to enrich the input CTP maps by using their contra-lateral and corresponding Tmax images. We evaluated the proposed method using the ISLES challenge 2018 dataset. As compared to the state-of-the-art methods, the results show an improvement in segmentation task accuracy. The dice similarity score (DSC) was 68%, the Jaccard score was 57.13%, and the mean absolute volume error was 22.62(ml).

Learning to predict relapse in invasive ductal carcinomas based on the subcellular localization of junctional proteins.

The complexity of breast cancer biology makes it challenging to analyze large datasets of clinicopathologic and molecular attributes, toward identifying the key prognostic features and producing systems capable of predicting which patients are likely to relapse. We applied machine-learning techniques to analyze a set of well-characterized primary breast cancers, which specified the abundance and localization of various junctional proteins. We hypothesized that disruption of junctional complexes would lead to the cytoplasmic/nuclear redistribution of the protein components and their potential interactions with growth-regulating molecules, which would promote relapse, and that machine-learning techniques could use the subcellular locations of these proteins, together with standard clinicopathological data, to produce an efficient prognostic classifier. We used immunohistochemistry to assess the expression and subcellular distribution of six junctional proteins, in addition to a panel of eight standard clinical features and concentrations of four "growth-regulating" proteins, to produce a database involving 36 features, over 66 primary invasive ductal breast carcinomas. A machine-learning system was applied to this clinicopathologic dataset to produce a decision-tree classifier that could predict whether a novel breast cancer patient would relapse. We show that this decision-tree classifier, which incorporates a combination of only four features (nuclear alpha- and beta-catenin levels, the total level of PTEN and the number of involved axillary lymph nodes), is able to correctly classify patient outcomes essentially 80% of the time. Further, this classifier is significantly better than classifiers based on any subgroup of these 36 features. This study demonstrates that autonomous machine-learning techniques are able to generate simple and efficient decision-tree prognostic classifiers from a wide variety of clinical, pathologic and biomarker data, and unlike other analytic methods, suggest testable biologic relationships among explicitly identified key variables. The decision-tree classifier resulting from these analytic methods is sufficiently simple and should be widely applicable to a spectrum of clinical cancer settings. Further, the subcellular distribution of junctional proteins, which influences growth regulatory pathways involved in locoregional and metastatic relapse of breast cancer, helped to identify which patients would relapse while their total concentration did not. This emphasizes the need to evaluate the subcellular distribution of junctional proteins in assessing their contribution to tumor progression.

Closing the Psychological Treatment Gap During the COVID-19 Pandemic With a Supportive Text Messaging Program: Protocol for Implementation and Evaluation.

BACKGROUND: Coronavirus disease (COVID-19) has spread globally with far-reaching, significant, and unprecedented impacts on health and everyday life. Threats to mental health, psychological safety, and well-being are now emerging, increasing the impact of this virus on world health. Providing support for these challenges is difficult because of the high number of people requiring support in the context of a need to maintain physical distancing. This protocol describes the use of SMS text messaging (Text4Hope) as a convenient, cost-effective, and accessible population-level mental health intervention. This program is evidence-based, with prior research supporting good outcomes and high user satisfaction. OBJECTIVE: The project goal is to implement a program of daily supportive SMS text messaging (Text4Hope) to reduce distress related to the COVID-19 crisis, initially among Canadians. The prevalence of stress, anxiety, and depressive symptoms; the demographic correlates of the same; and the outcomes of the Text4Hope intervention in mitigating distress will be evaluated. METHODS: Self-administered anonymous online questionnaires will be used to assess stress (Perceived Stress Scale), anxiety (Generalized Anxiety Disorder-7 scale [GAD-7]), and depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]). Data will be collected at baseline (onset of SMS text messaging), the program midpoint (6 weeks), and the program endpoint (12 weeks). RESULTS: Data analysis will include parametric and nonparametric techniques, focusing on primary outcomes (ie, stress, anxiety, and depressive symptoms) and metrics of use, including the number of subscribers and user satisfaction. Given the large size of the data set, machine learning and data mining methods will also be used. CONCLUSIONS: This COVID-19 project will provide key information regarding prevalence rates of stress, anxiety, and depressive symptoms during the pandemic; demographic correlates of distress; and outcome data related to this scalable population-level intervention. Information from this study will be valuable for practitioners and useful for informing policy and decision making regarding psychological interventions during the pandemic. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19292.

Improving subcellular localization prediction using text classification and the gene ontology.

MOTIVATION: Each protein performs its functions within some specific locations in a cell. This subcellular location is important for understanding protein function and for facilitating its purification. There are now many computational techniques for predicting location based on sequence analysis and database information from homologs. A few recent techniques use text from biological abstracts: our goal is to improve the prediction accuracy of such text-based techniques. We identify three techniques for improving text-based prediction: a rule for ambiguous abstract removal, a mechanism for using synonyms from the Gene Ontology (GO) and a mechanism for using the GO hierarchy to generalize terms. We show that these three techniques can significantly improve the accuracy of protein subcellular location predictors that use text extracted from PubMed abstracts whose references are recorded in Swiss-Prot.

HMDB: the Human Metabolome Database.

The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the HMDB also contains an extensive collection of experimental metabolite concentration data compiled from hundreds of mass spectra (MS) and Nuclear Magnetic resonance (NMR) metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, reference metabolites. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. The HMDB is available at: www.hmdb.ca.

The human cerebrospinal fluid metabolome.

With continuing improvements in analytical technology and an increased interest in comprehensive metabolic profiling of biofluids and tissues, there is a growing need to develop comprehensive reference resources for certain clinically important biofluids, such as blood, urine and cerebrospinal fluid (CSF). As part of our effort to systematically characterize the human metabolome we have chosen to characterize CSF as the first biofluid to be intensively scrutinized. In doing so, we combined comprehensive NMR, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography (LC) Fourier transform-mass spectrometry (FTMS) methods with computer-aided literature mining to identify and quantify essentially all of the metabolites that can be commonly detected (with today's technology) in the human CSF metabolome. Tables containing the compounds, concentrations, spectra, protocols and links to disease associations that we have found for the human CSF metabolome are freely available at http://www.csfmetabolome.ca.

BASys: a web server for automated bacterial genome annotation.

BASys (Bacterial Annotation System) is a web server that supports automated, in-depth annotation of bacterial genomic (chromosomal and plasmid) sequences. It accepts raw DNA sequence data and an optional list of gene identification information and provides extensive textual annotation and hyperlinked image output. BASys uses >30 programs to determine approximately 60 annotation subfields for each gene, including gene/protein name, GO function, COG function, possible paralogues and orthologues, molecular weight, isoelectric point, operon structure, subcellular localization, signal peptides, transmembrane regions, secondary structure, 3D structure, reactions and pathways. The depth and detail of a BASys annotation matches or exceeds that found in a standard SwissProt entry. BASys also generates colorful, clickable and fully zoomable maps of each query chromosome to permit rapid navigation and detailed visual analysis of all resulting gene annotations. The textual annotations and images that are provided by BASys can be generated in approximately 24 h for an average bacterial chromosome (5 Mb). BASys annotations may be viewed and downloaded anonymously or through a password protected access system. The BASys server and databases can also be downloaded and run locally. BASys is accessible at http://wishart.biology.ualberta.ca/basys.

Improved A1C Levels in Type 1 Diabetes with Smartphone App Use.

OBJECTIVES: Smartphones are a potentially useful tool in diabetes care. We have developed an application (app) linked to a website, Intelligent Diabetes Management (IDM), which serves as both an insulin bolus calculator and an electronic diabetes diary. We have prospectively studied whether patients using this app improved control of their glucose levels. METHODS: Patients with type 1 diabetes were recruited. There was a 4-week observation period, midway during which we offered to review the participants' records. The app was then downloaded and participants' diabetes regimens entered on the synchronized IDM website. At 2, 4, 8, 12 and 16 weeks of the active phase, their records were reviewed online, and feedback was provided electronically. The primary endpoint was change in levels of glycated hemoglobin (A1C). RESULTS: Of the 31 patients recruited, 18 completed the study. These 18 made 572±98 entries per person on the IDM system over the course of the study (≈5.1/day). Their ages were 40.0±13.9 years, the durations of their diabetes were 27.3±14.9 years and 44% used insulin pumps. The median A1C level fell from 8.1% (7.5 to 9.0, IQ range) to 7.8% (6.9 to 8.3; p<0.001). During the observation period, glucose records were reviewed for 50% of the participants. In the active phase, review of the glucose diaries took less time on the IDM website than using personal glucose records in the observation period, median 6 minutes (5 to 7.5 IQ range) vs. 10 minutes (7.5 to 10.5 IQ range; p<0.05). CONCLUSIONS: Our smartphone app enables online review of glucose records, requires less time for clinical staff and is associated with improved glucose control.

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

The human serum metabolome.

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.

Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles.

Metabolomics may have the capacity to revolutionize disease diagnosis through the identification of scores of metabolites that vary during environmental, pathogenic, or toxicological insult. NMR spectroscopy has become one of the main tools for measuring these changes since an NMR spectrum can accurately identify metabolites and their concentrations. The predominant approach in analyzing NMR data has been through the technique of spectral binning. However, identification of spectral areas in an NMR spectrum is insufficient for diagnostic evaluation, since it is unknown whether areas of interest are strictly caused by metabolic changes or are simply artifacts. In this paper, we explore differences in gender, diurnal variation, and age in a human population. We use the example of gender differences to compare traditional spectral binning techniques (NMR spectral areas) to novel targeted profiling techniques (metabolites and their concentrations). We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences in a healthy population. Metabolites relating to mitochondrial energy metabolism were found to differentiate gender and age. Dietary components and some metabolites related to circadian rhythms were found to differentiate time of day urine collection. The mechanisms by which these differences arise will be key to the discovery of new diagnostic tests and new understandings of the mechanism of disease.