Special Issue "Metabolomics in Epidemiological Studies"

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editor

Dr. Krista Zanetti
E-Mail Website
Guest Editor
Division of Cancer Control and Population Sciences, National Cancer Institute, USA
Interests: metabolomics; epidemiology; 'Omics data integration; cancer biomarker discovery

Special Issue Information

Dear Colleagues,

Metabolomics is the large-scale study of small molecule metabolites. When employed to examine biological samples, metabolomics affords epidemiologists and other investigators a means to identify novel biomarkers of disease risk, diagnosis, and prognosis. Using metabolomics, investigators can also evaluate the biological effects of environmental exposures, including both chemical and lifestyle exposures. In recent years, the application of metabolite profiling to epidemiological studies has become possible due to technological advancements in the field, allowing for large-scale population-based investigations. Although metabolomics has great potential for use in epidemiological studies, there is still a need for analytical methods to address quality control, data management, and data integration. This Special Issue highlights the use of metabolomics in epidemiological research. Specific areas include, but not limited to, are the identification of disease biomarkers and biomarkers of exposure, data integration across studies and laboratory platforms, data management, quality control, and statistical, bioinformatic, and analytical methods for large-scale studies.

Dr. Krista Zanetti
Guest Editor

Manuscript Submission Information

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Keywords

  • Epidemiology
  • Metabolite biomarkers
  • Data integration
  • Data management
  • Quality control
  • Statistical methods
  • Bioinformatic methods

Published Papers (15 papers)

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Research

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Open AccessArticle
Associations between Blood Metabolic Profile at 7 Years Old and Eating Disorders in Adolescence: Findings from the Avon Longitudinal Study of Parents and Children
Metabolites 2019, 9(9), 191; https://doi.org/10.3390/metabo9090191 - 19 Sep 2019
Abstract
Eating disorders are severe illnesses characterized by both psychiatric and metabolic factors. We explored the prospective role of metabolic risk in eating disorders in a UK cohort (n = 2929 participants), measuring 158 metabolic traits in non-fasting EDTA-plasma by nuclear magnetic resonance. [...] Read more.
Eating disorders are severe illnesses characterized by both psychiatric and metabolic factors. We explored the prospective role of metabolic risk in eating disorders in a UK cohort (n = 2929 participants), measuring 158 metabolic traits in non-fasting EDTA-plasma by nuclear magnetic resonance. We associated metabolic markers at 7 years (exposure) with risk for anorexia nervosa and binge-eating disorder (outcomes) at 14, 16, and 18 years using logistic regression adjusted for maternal education, child’s sex, age, body mass index, and calorie intake at 7 years. Elevated very low-density lipoproteins, triglycerides, apolipoprotein-B/A, and monounsaturated fatty acids ratio were associated with lower odds of anorexia nervosa at age 18, while elevated high-density lipoproteins, docosahexaenoic acid and polyunsaturated fatty acids ratio, and fatty acid unsaturation were associated with higher risk for anorexia nervosa at 18 years. Elevated linoleic acid and n-6 fatty acid ratios were associated with lower odds of binge-eating disorder at 16 years, while elevated saturated fatty acid ratio was associated with higher odds of binge-eating disorder. Most associations had large confidence intervals and showed, for anorexia nervosa, different directions across time points. Overall, our results show some evidence for a role of metabolic factors in eating disorders development in adolescence. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Differences in Pregnancy Metabolic Profiles and Their Determinants between White European and South Asian Women: Findings from the Born in Bradford Cohort
Metabolites 2019, 9(9), 190; https://doi.org/10.3390/metabo9090190 - 18 Sep 2019
Abstract
There is widespread metabolic disruption in women upon becoming pregnant. South Asians (SA) compared to White Europeans (WE) have more fat mass and are more insulin-resistant at a given body mass index (BMI). Whether these are reflected in other gestational metabolomic differences is [...] Read more.
There is widespread metabolic disruption in women upon becoming pregnant. South Asians (SA) compared to White Europeans (WE) have more fat mass and are more insulin-resistant at a given body mass index (BMI). Whether these are reflected in other gestational metabolomic differences is unclear. Our aim was to compare gestational metabolic profiles and their determinants between WE and SA women. We used data from a United Kingdom (UK) cohort to compare metabolic profiles and associations of maternal age, education, parity, height, BMI, tricep skinfold thickness, gestational diabetes (GD), pre-eclampsia, and gestational hypertension with 156 metabolic measurements in WE (n = 4072) and SA (n = 4702) women. Metabolic profiles, measured in fasting serum taken between 26–28 weeks gestation, were quantified by nuclear magnetic resonance. Distributions of most metabolic measures differed by ethnicity. WE women had higher levels of most lipoprotein subclasses, cholesterol, glycerides and phospholipids, monosaturated fatty acids, and creatinine but lower levels of glucose, linoleic acid, omega-6 and polyunsaturated fatty acids, and most amino acids. Higher BMI and having GD were associated with higher levels of several lipoprotein subclasses, triglycerides, and other metabolites, mostly with stronger associations in WEs. We have shown differences in gestational metabolic profiles between WE and SA women and demonstrated that associations of exposures with these metabolites differ by ethnicity. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Multiplatform Urinary Metabolomics Profiling to Discriminate Cachectic from Non-Cachectic Colorectal Cancer Patients: Pilot Results from the ColoCare Study
Metabolites 2019, 9(9), 178; https://doi.org/10.3390/metabo9090178 - 06 Sep 2019
Abstract
Cachexia is a multifactorial syndrome that is characterized by loss of skeletal muscle mass in cancer patients. The biological pathways involved remain poorly characterized. Here, we compare urinary metabolic profiles in newly diagnosed colorectal cancer patients (stage I–IV) from the ColoCare Study in [...] Read more.
Cachexia is a multifactorial syndrome that is characterized by loss of skeletal muscle mass in cancer patients. The biological pathways involved remain poorly characterized. Here, we compare urinary metabolic profiles in newly diagnosed colorectal cancer patients (stage I–IV) from the ColoCare Study in Heidelberg, Germany. Patients were classified as cachectic (n = 16), pre-cachectic (n = 13), or non-cachectic (n = 23) based on standard criteria on weight loss over time at two time points. Urine samples were collected pre-surgery, and 6 and 12 months thereafter. Fat and muscle mass area were assessed utilizing computed tomography scans at the time of surgery. N = 152 compounds were detected using untargeted metabolomics with gas chromatography–mass spectrometry and n = 154 features with proton nuclear magnetic resonance spectroscopy. Thirty-four metabolites were overlapping across platforms. We calculated differences across groups and performed discriminant and overrepresentation enrichment analysis. We observed a trend for 32 compounds that were nominally significantly different across groups, although not statistically significant after adjustment for multiple testing. Nineteen compounds could be identified, including acetone, hydroquinone, and glycine. Comparing cachectic to non-cachectic patients, higher levels of metabolites such as acetone (Fold change (FC) = 3.17; p = 0.02) and arginine (FC = 0.33; p = 0.04) were observed. The two top pathways identified were glycerol phosphate shuttle metabolism and glycine and serine metabolism pathways. Larger subsequent studies are needed to replicate and validate these results. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)
Metabolites 2019, 9(7), 145; https://doi.org/10.3390/metabo9070145 - 17 Jul 2019
Abstract
The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting [...] Read more.
The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Fine-Mapping of the Human Blood Plasma N-Glycome onto Its Proteome
Metabolites 2019, 9(7), 122; https://doi.org/10.3390/metabo9070122 - 26 Jun 2019
Abstract
Most human proteins are glycosylated. Attachment of complex oligosaccharides to the polypeptide part of these proteins is an integral part of their structure and function and plays a central role in many complex disorders. One approach towards deciphering this human glycan code is [...] Read more.
Most human proteins are glycosylated. Attachment of complex oligosaccharides to the polypeptide part of these proteins is an integral part of their structure and function and plays a central role in many complex disorders. One approach towards deciphering this human glycan code is to study natural variation in experimentally well characterized samples and cohorts. High-throughput capable large-scale methods that allow for the comprehensive determination of blood circulating proteins and their glycans have been recently developed, but so far, no study has investigated the link between both traits. Here we map for the first time the blood plasma proteome to its matching N-glycome by correlating the levels of 1116 blood circulating proteins with 113 N-glycan traits, determined in 344 samples from individuals of Arab, South-Asian, and Filipino descent, and then replicate our findings in 46 subjects of European ancestry. We report protein-specific N-glycosylation patterns, including a correlation of core fucosylated structures with immunoglobulin G (IgG) levels, and of trisialylated, trigalactosylated, and triantennary structures with heparin cofactor 2 (SERPIND2). Our study reveals a detailed picture of protein N-glycosylation and suggests new avenues for the investigation of its role and function in the associated complex disorders. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Identifying Metabolomic Profiles of Insulinemic Dietary Patterns
Metabolites 2019, 9(6), 120; https://doi.org/10.3390/metabo9060120 - 24 Jun 2019
Cited by 1
Abstract
The food-based empirical dietary index for hyperinsulinemia (EDIH) score assesses the insulinemic potential of diet. This cross-sectional study evaluated associations between EDIH scores from food frequency questionnaires with c-peptide concentrations and with 448 metabolites, from fasting plasma samples, in multivariable linear regression analyses. [...] Read more.
The food-based empirical dietary index for hyperinsulinemia (EDIH) score assesses the insulinemic potential of diet. This cross-sectional study evaluated associations between EDIH scores from food frequency questionnaires with c-peptide concentrations and with 448 metabolites, from fasting plasma samples, in multivariable linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Using a robust two-stage study design, discovery of metabolite associations was conducted among 1109 Women’s Health Initiative (WHI) Hormone Therapy (HT) trial participants and results replicated in an independent dataset of 810 WHI Observational Study (OS) participants. In both discovery and replication datasets, statistical significance was based on the false-discovery rate adjusted P < 0.05. In the multivariable-adjusted analyses, EDIH was significantly associated with c-peptide concentrations among 919 women (HT & OS) with c-peptide data. On average, c-peptide concentrations were 18% higher (95% CI, 6%, 32%; P-trend < 0.0001) in EDIH quintile 5 compared to quintile 1. Twenty-six metabolites were significantly associated with EDIH in the discovery dataset, and 19 of these were replicated in the validation dataset. Nine metabolites were found to decrease in abundance with increasing EDIH scores and included: C14:0 CE, C16:1 CE, C18:1 CE, C18:3 CE, C20:3 CE, C20:5 CE, C36:1 PS plasmalogen, trigonelline, and eicosapentanoate, whereas the 10 metabolites observed to increase with increasing EDIH scores were: C18:2 SM, C36:3 DAG, C36:4 DAG-A, C51:3 TAG, C52:3 TAG, C52:4, TAG, C54:3 TAG, C54:4 TAG, C54:6 TAG, and C10:2 carnitine. Cholesteryl esters, phospholipids, acylglycerols, and acylcarnitines may constitute circulating metabolites that are associated with insulinemic dietary patterns. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics
Metabolites 2019, 9(6), 109; https://doi.org/10.3390/metabo9060109 - 08 Jun 2019
Abstract
Kit-based assays, such as AbsoluteIDQTM p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by [...] Read more.
Kit-based assays, such as AbsoluteIDQTM p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving LipidyzerTM platform, analyzing 223 samples in parallel to the AbsoluteIDQTM. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Urate and Nonanoate Mark the Relationship between Sugar-Sweetened Beverage Intake and Blood Pressure in Adolescent Girls: A Metabolomics Analysis in the ELEMENT Cohort
Metabolites 2019, 9(5), 100; https://doi.org/10.3390/metabo9050100 - 17 May 2019
Cited by 1
Abstract
We sought to identify metabolites that mark the relationship of sugar-sweetened beverage (SSB) intake with adiposity and metabolic risk among boys (n = 114) and girls (n = 128) aged 8–14 years. We conducted the analysis in three steps: (1) linear [...] Read more.
We sought to identify metabolites that mark the relationship of sugar-sweetened beverage (SSB) intake with adiposity and metabolic risk among boys (n = 114) and girls (n = 128) aged 8–14 years. We conducted the analysis in three steps: (1) linear regression to examine associations of SSB intake (quartiles) with adiposity, glycemia, lipids, and blood pressure (BP); (2) least absolute shrinkage and selection operator (LASSO) regression to identify SSB-associated metabolites from an untargeted dataset of 938 metabolites; and (3) linear regression to determine whether SSB-related metabolites are also associated with adiposity and metabolic risk. In girls, SSB intake was associated with marginally higher BP (Q2 vs, Q1: 1.11 [−3.90, 6.13], Q3 vs. Q1: 1.16 [−3.81, 6.13], Q4 vs. Q1: 4.65 [−0.22, 9.53] mmHg systolic blood pressure (SBP); P-trend = 0.07). In boys, SSB intake corresponded with higher C-peptide insulin resistance (Q2 vs. Q1: 0.06 [−0.06, 0.19], Q3 vs. Q1: 0.01 [−0.12, 0.14], Q4 vs. Q1: 0.17 [0.04, 0.30] ng/mL; P-trend = 0.03) and leptin (P-trend = 0.02). LASSO identified 6 annotated metabolites in girls (5-methyl-tetrohydrofolate, phenylephrine, urate, nonanoate, deoxyuridine, sn-glycero-3-phosphocholine) and 3 annotated metabolites in boys (2-piperidinone, octanoylcarnitine, catechol) associated with SSB intake. Among girls, urate and nonanoate marked the relationship of SSB intake with BP. None of the SSB-associated metabolites were related to health outcomes in boys. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
Open AccessArticle
Metabolites Associated with Vigor to Frailty Among Community-Dwelling Older Black Men
Metabolites 2019, 9(5), 83; https://doi.org/10.3390/metabo9050083 - 30 Apr 2019
Abstract
Black versus white older Americans are more likely to experience frailty, a condition associated with adverse health outcomes. To reduce racial disparities in health, a complete understanding of the pathophysiology of frailty is needed. Metabolomics may further our understanding by characterizing differences in [...] Read more.
Black versus white older Americans are more likely to experience frailty, a condition associated with adverse health outcomes. To reduce racial disparities in health, a complete understanding of the pathophysiology of frailty is needed. Metabolomics may further our understanding by characterizing differences in the body during a vigorous versus frail state. We sought to identify metabolites and biological pathways associated with vigor to frailty among 287 black men ages 70–81 from the Health, Aging, and Body Composition study. Using liquid chromatography-mass spectrometry, 350 metabolites were measured in overnight-fasting plasma. The Scale of Aging Vigor in Epidemiology (SAVE) measured vigor to frailty based on weight change, strength, energy, gait speed, and physical activity. Thirty-seven metabolites correlated with SAVE scores (p < 0.05), while adjusting for age and site. Fourteen metabolites remained significant after multiple comparisons adjustment (false discovery rate < 0.30). Lower values of tryptophan, methionine, tyrosine, asparagine, C14:0 sphingomyelin, and 1-methylnicotinamide, and higher values of glucoronate, N-carbamoyl-beta-alanine, isocitrate, creatinine, C4-OH carnitine, cystathionine, hydroxyphenylacetate, and putrescine were associated with frailer SAVE scores. Pathway analyses identified nitrogen metabolism, aminoacyl-tRNA biosynthesis, and the citric acid cycle. Future studies need to confirm these SAVE-associated metabolites and pathways that may indicate novel mechanisms involved in the frailty syndrome. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
The Effect of Pre-Analytical Conditions on Blood Metabolomics in Epidemiological Studies
Metabolites 2019, 9(4), 64; https://doi.org/10.3390/metabo9040064 - 03 Apr 2019
Cited by 1
Abstract
Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic [...] Read more.
Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (n = 151 traits). Correlations and differences in mean of metabolite concentrations were compared between reference (pre-storage: 4 °C, 1.5 h; post-storage: no buffer addition delay or NMR analysis delay) and four pre-storage blood processing conditions, where samples were incubated at (i) 4 °C, 24 h; (ii) 4 °C, 48 h; (iii) 21 °C, 24 h; and (iv) 21 °C, 48 h, before centrifugation; and two post-storage sample processing conditions in which samples thawed overnight (i) then left for 24 h before addition of sodium buffer followed by immediate NMR analysis; and (ii) addition of sodium buffer, then left for 24 h before NMR profiling. We used multilevel linear regression models and Spearman’s rank correlation coefficients to analyse the data. Most metabolic traits had high rank correlation and minimal differences in mean concentrations between samples subjected to reference and the different conditions tested, that may commonly occur in studies. However, glycolysis metabolites, histidine, acetate and diacylglycerol concentrations may be compromised and this could bias results in association/causal analyses. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessArticle
Metabolomics and Communication Skills Development in Children; Evidence from the Ages and Stages Questionnaire
Metabolites 2019, 9(3), 42; https://doi.org/10.3390/metabo9030042 - 05 Mar 2019
Cited by 2
Abstract
We hypothesized metabolomic profiling could be utilized to identify children who scored poorly on the communication component of the Ages and Stages Questionnaire (ASQ); which assesses development in childhood, and to provide candidate biomarkers for autism spectrum disorders (ASD). In a population of [...] Read more.
We hypothesized metabolomic profiling could be utilized to identify children who scored poorly on the communication component of the Ages and Stages Questionnaire (ASQ); which assesses development in childhood, and to provide candidate biomarkers for autism spectrum disorders (ASD). In a population of three-year-old children, 15 plasma metabolites, were significantly (p < 0.05) different between children who were categorized as having communication skills that were “on schedule” (n = 365 (90.6%)) as compared to those “requiring further monitoring/evaluation” (n = 38 (9.4%)) according to multivariable regression models. Five of these metabolites, including three endocannabinoids, were also dysregulated at age one (n = 204 “on schedule”, n = 24 “further monitoring/evaluation”) in the same children. Stool metabolomic profiling identified 11 significant metabolites. Both the plasma and stool results implicated a role for tryptophan and tyrosine metabolism; in particular, higher levels of N-formylanthranilic acid were associated with an improved communication score in both biosample types. A model based on the significant plasma metabolites demonstrated high sensitivity (88.9%) and specificity (84.5%) for the prediction of autism by age 8. These results provide evidence that ASQ communication score and metabolomic profiling of plasma and/or stool may provide alternative approaches for early diagnosis of ASD, as well as insights into the pathobiology of these conditions. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Review

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Open AccessReview
Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review
Metabolites 2019, 9(8), 156; https://doi.org/10.3390/metabo9080156 - 25 Jul 2019
Abstract
Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, [...] Read more.
Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessReview
Statistical Workflow for Feature Selection in Human Metabolomics Data
Metabolites 2019, 9(7), 143; https://doi.org/10.3390/metabo9070143 - 12 Jul 2019
Cited by 2
Abstract
High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of [...] Read more.
High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Open AccessReview
Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective
Metabolites 2019, 9(6), 117; https://doi.org/10.3390/metabo9060117 - 18 Jun 2019
Cited by 2
Abstract
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. [...] Read more.
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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Other

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Open AccessProtocol
A Single Visualization Technique for Displaying Multiple Metabolite–Phenotype Associations
Metabolites 2019, 9(7), 128; https://doi.org/10.3390/metabo9070128 - 02 Jul 2019
Abstract
To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community [...] Read more.
To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically. Full article
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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