Mealtime, Temporal, and Daily Variability of the Human Urinary and Plasma Metabolomes in a Tightly Controlled Environment
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{"title"=>"Mealtime, temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment", "type"=>"journal", "authors"=>[{"first_name"=>"Kyoungmi", "last_name"=>"Kim", "scopus_author_id"=>"55081722200"}, {"first_name"=>"Christine", "last_name"=>"Mall", "scopus_author_id"=>"55817380300"}, {"first_name"=>"Sandra L.", "last_name"=>"Taylor", "scopus_author_id"=>"36984012800"}, {"first_name"=>"Stacie", "last_name"=>"Hitchcock", "scopus_author_id"=>"56150370500"}, {"first_name"=>"Chen", "last_name"=>"Zhang", "scopus_author_id"=>"56714192800"}, {"first_name"=>"Hiromi I.", "last_name"=>"Wettersten", "scopus_author_id"=>"55583613900"}, {"first_name"=>"A. 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To address these issues and to measure the variability of urinary and plasma metabolomes throughout the day, we have undertaken a comprehensive inpatient study in which we have performed non-targeted metabolomics analysis of blood and urine in 26 volunteers (13 healthy subjects with no known disease and 13 healthy subjects with autosomal dominant polycystic kidney disease not taking medication). These individuals were evaluated in a clinical research facility on two separate occasions, over three days, while on a standardized, weight-based diet. Subjects provided pre- and post-prandial blood and urine samples at the same time of day, and all samples were analyzed by \"fast lane\" LC-MS-based global metabolomics. The largest source of variability in blood and urine metabolomes was attributable to technical issues such as sample preparation and analysis, and less variability was due to biological variables, meals, and time of day. Higher metabolome variability was observed after the morning as compared to the evening meal, yet day-to-day variability was minimal and urine metabolome variability was greater than that of blood. Thus we suggest that blood and urine are suitable biofluids for metabolomics studies, though nontargeted mass spectrometry alone may not offer sufficient precision to reveal subtle changes in the metabolome. Additional targeted analyses may be needed to support the data from nontargeted mass spectrometric analyses. In light of these findings, future metabolomics studies should consider these sources of variability to allow for appropriate metabolomics testing and reliable clinical translation of metabolomics data.", "link"=>"http://www.mendeley.com/research/mealtime-temporal-daily-variability-human-urinary-plasma-metabolomes-tightly-controlled-environment", "reader_count"=>57, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>7, "Student > Bachelor"=>7, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>4, "Student > Ph. D. 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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1360320"], "description"=>"<p>Study Design: Times of urine and blood collection and meals.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "times", "urine"], "article_id"=>913026, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.t001", "stats"=>{"downloads"=>10, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Study_Design_Times_of_urine_and_blood_collection_and_meals_/913026", "title"=>"Study Design: Times of urine and blood collection and meals.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360330", "https://ndownloader.figshare.com/files/1360331", "https://ndownloader.figshare.com/files/1360332", "https://ndownloader.figshare.com/files/1360333", "https://ndownloader.figshare.com/files/1360334", "https://ndownloader.figshare.com/files/1360335", "https://ndownloader.figshare.com/files/1360336", "https://ndownloader.figshare.com/files/1360337", "https://ndownloader.figshare.com/files/1360338", "https://ndownloader.figshare.com/files/1360339"], "description"=>"<div><p>While metabolomics has tremendous potential for diagnostic biomarker and therapeutic target discovery, its utility may be diminished by the variability that occurs due to environmental exposures including diet and the influences of the human circadian rhythm. For successful translation of metabolomics findings into the clinical setting, it is necessary to exhaustively define the sources of metabolome variation. To address these issues and to measure the variability of urinary and plasma metabolomes throughout the day, we have undertaken a comprehensive inpatient study in which we have performed non-targeted metabolomics analysis of blood and urine in 26 volunteers (13 healthy subjects with no known disease and 13 healthy subjects with autosomal dominant polycystic kidney disease not taking medication). These individuals were evaluated in a clinical research facility on two separate occasions, over three days, while on a standardized, weight-based diet. Subjects provided pre- and post-prandial blood and urine samples at the same time of day, and all samples were analyzed by “fast lane” LC-MS-based global metabolomics. The largest source of variability in blood and urine metabolomes was attributable to technical issues such as sample preparation and analysis, and less variability was due to biological variables, meals, and time of day. Higher metabolome variability was observed after the morning as compared to the evening meal, yet day-to-day variability was minimal and urine metabolome variability was greater than that of blood. Thus we suggest that blood and urine are suitable biofluids for metabolomics studies, though nontargeted mass spectrometry alone may not offer sufficient precision to reveal subtle changes in the metabolome. Additional targeted analyses may be needed to support the data from nontargeted mass spectrometric analyses. In light of these findings, future metabolomics studies should consider these sources of variability to allow for appropriate metabolomics testing and reliable clinical translation of metabolomics data.</p></div>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "variability", "urinary", "plasma", "metabolomes", "controlled"], "article_id"=>913036, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0086223.s001", "https://dx.doi.org/10.1371/journal.pone.0086223.s002", "https://dx.doi.org/10.1371/journal.pone.0086223.s003", "https://dx.doi.org/10.1371/journal.pone.0086223.s004", "https://dx.doi.org/10.1371/journal.pone.0086223.s005", "https://dx.doi.org/10.1371/journal.pone.0086223.s006", "https://dx.doi.org/10.1371/journal.pone.0086223.s007", "https://dx.doi.org/10.1371/journal.pone.0086223.s008", "https://dx.doi.org/10.1371/journal.pone.0086223.s009", "https://dx.doi.org/10.1371/journal.pone.0086223.s010"], "stats"=>{"downloads"=>23, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mealtime_Temporal_and_Daily_Variability_of_the_Human_Urinary_and_Plasma_Metabolomes_in_a_Tightly_Controlled_Environment_/913036", "title"=>"Mealtime, Temporal, and Daily Variability of the Human Urinary and Plasma Metabolomes in a Tightly Controlled Environment", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360310"], "description"=>"<p>Distribution of the relative proportion of total variance explained by each factor (meal, subject, residual) across all metabolites. Data of Hours +1 (pre-) and +3 (post- breakfast) and Hours +9 (pre-) and +11 (post-dinner) in each day were used in the analysis to estimate the proportion of variation attributable to meal effects for each of individual metabolites separately.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "components", "urine", "metabolites"], "article_id"=>913016, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.g001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variance_components_analysis_in_urine_A_and_blood_B_metabolites_for_meal_effects_/913016", "title"=>"Variance components analysis in urine (A) and blood (B) metabolites for meal effects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360311"], "description"=>"<p>Distribution of the relative proportion of total variance explained by each factor (time of day in hour, subject, residual) across all metabolites. For time of day effects, data measured at hour +1, +3, +7, +9, +11, and +14 on Day 1 were used in the analysis to estimate the proportion of variation attributable to time of day effects for each of individual metabolites separately.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "components", "urine", "metabolites"], "article_id"=>913017, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.g002", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variance_components_analysis_in_urine_A_and_blood_B_metabolites_for_time_of_day_effects_/913017", "title"=>"Variance components analysis in urine (A) and blood (B) metabolites for time of day effects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360313"], "description"=>"<p>Variability in intensity values (log<sub>2</sub> transformed) of selected metabolites in healthy volunteers is graphed as a function of indicated time (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086223#pone.0086223.s008\" target=\"_blank\">Table S2</a>). Each colored line represents one subject.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "variability"], "article_id"=>913019, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Temporal_variability_of_selected_metabolites_/913019", "title"=>"Temporal variability of selected metabolites.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360314"], "description"=>"<p>There were a total of 294 metabolites identified in urine and 121 in blood.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "changed", "metabolites", "urine"], "article_id"=>913020, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.t006", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_and_percentage_of_significantly_changed_metabolites_FDR_lt_0_05_in_blood_and_urine_by_three_factors_meal_day_hour_/913020", "title"=>"Number and percentage (%) of significantly changed metabolites (FDR<0.05) in blood and urine by three factors (meal, day, hour).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360315"], "description"=>"a<p>Meal CVs were calculated using pre and post breakfast samples from each day.</p>b<p>Hour CVs were calculated using all values from Day 1.</p>c<p>Day CVs were calculated pre-breakfast values from each day.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "metabolites"], "article_id"=>913021, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.t005", "stats"=>{"downloads"=>5, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coefficients_of_variation_for_blood_metabolites_representing_the_full_range_of_variability_/913021", "title"=>"Coefficients of variation for blood metabolites representing the full range of variability.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360316"], "description"=>"<p>Pearson's correlation coefficients of plasma metabolite intensities between hours of collection on Day 1.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "coefficients", "plasma", "metabolite", "intensities", "hours"], "article_id"=>913022, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.t004", "stats"=>{"downloads"=>4, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pearson_s_correlation_coefficients_of_plasma_metabolite_intensities_between_hours_of_collection_on_Day_1_/913022", "title"=>"Pearson's correlation coefficients of plasma metabolite intensities between hours of collection on Day 1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:12:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360317"], "description"=>"<p>Pearson's correlation coefficients of urinary metabolite intensities between hours of collection on Day 1.</p>", "links"=>[], "tags"=>["Analytical chemistry", "Clinical genetics", "Autosomal dominant", "Polycystic kidney disease", "Clinical research design", "Survey research", "Metabolic disorders", "oncology", "Cancer detection and diagnosis", "urology", "Bladder and ureteric disorders", "Bladder cancer and urothelial neoplasias of the urinary tract", "Genitourinary cancers", "coefficients", "urinary", "metabolite", "intensities", "hours"], "article_id"=>913023, "categories"=>["Medicine", "Chemistry"], "users"=>["Kyoungmi Kim", "Christine Mall", "Sandra L. Taylor", "Stacie Hitchcock", "Chen Zhang", "Hiromi I. Wettersten", "A. Daniel Jones", "Arlene Chapman", "Robert H. Weiss"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0086223.t003", "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pearson_s_correlation_coefficients_of_urinary_metabolite_intensities_between_hours_of_collection_on_Day_1_/913023", "title"=>"Pearson's correlation coefficients of urinary metabolite intensities between hours of collection on Day 1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:12:58"}

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Relative Metric

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