Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting
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{"title"=>"Metabolic footprint of diabetes: A multiplatform metabolomics study in an epidemiological setting", "type"=>"journal", "authors"=>[{"first_name"=>"Karsten", "last_name"=>"Suhre", "scopus_author_id"=>"56188100500"}, {"first_name"=>"Christa", "last_name"=>"Meisinger", "scopus_author_id"=>"35242861500"}, {"first_name"=>"Angela", "last_name"=>"Döring", "scopus_author_id"=>"7006020461"}, {"first_name"=>"Elisabeth", "last_name"=>"Altmaier", "scopus_author_id"=>"36553850700"}, {"first_name"=>"Petra", "last_name"=>"Belcredi", "scopus_author_id"=>"18933300600"}, {"first_name"=>"Christian", "last_name"=>"Gieger", "scopus_author_id"=>"55138342700"}, {"first_name"=>"David", "last_name"=>"Chang", "scopus_author_id"=>"37116655100"}, {"first_name"=>"Michael V.", "last_name"=>"Milburn", "scopus_author_id"=>"57197977770"}, {"first_name"=>"Walter E.", "last_name"=>"Gall", "scopus_author_id"=>"15070453100"}, {"first_name"=>"Klaus M.", "last_name"=>"Weinberger", "scopus_author_id"=>"6701769526"}, {"first_name"=>"Hans Werner", "last_name"=>"Mewes", "scopus_author_id"=>"7006821782"}, {"first_name"=>"Martin Hrabé", "last_name"=>"Angelis", "scopus_author_id"=>"57199402844"}, {"first_name"=>"H. Erich", "last_name"=>"Wichmann", "scopus_author_id"=>"7102574024"}, {"first_name"=>"Florian", "last_name"=>"Kronenberg", "scopus_author_id"=>"55800251500"}, {"first_name"=>"Jerzy", "last_name"=>"Adamski", "scopus_author_id"=>"7004973111"}, {"first_name"=>"Thomas", "last_name"=>"Illig", "scopus_author_id"=>"6701823086"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"21085649", "doi"=>"10.1371/journal.pone.0013953", "sgr"=>"78649735772", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-78649735772", "issn"=>"19326203", "pui"=>"360084571"}, "id"=>"e2fa05b2-7bf7-3427-83e7-6e8e1cec1c2a", "abstract"=>"BACKGROUND Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study. METHODOLOGY/PRINCIPAL FINDINGS 40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid). CONCLUSIONS/SIGNIFICANCE Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.", "link"=>"http://www.mendeley.com/research/metabolic-footprint-diabetes-multiplatform-metabolomics-study-epidemiological-setting", "reader_count"=>312, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>19, "Researcher"=>87, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>79, "Student > Postgraduate"=>9, "Student > Master"=>33, "Other"=>18, "Student > Bachelor"=>23, "Lecturer"=>11, "Lecturer > Senior Lecturer"=>2, "Professor"=>14}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>19, "Researcher"=>87, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>79, "Student > Postgraduate"=>9, "Student > Master"=>33, "Other"=>18, "Student > Bachelor"=>23, "Lecturer"=>11, "Lecturer > Senior Lecturer"=>2, "Professor"=>14}, "reader_count_by_subject_area"=>{"Unspecified"=>15, "Agricultural and Biological Sciences"=>123, "Arts and Humanities"=>1, "Veterinary Science and Veterinary Medicine"=>2, "Business, Management and Accounting"=>1, "Chemical Engineering"=>1, "Chemistry"=>31, "Computer Science"=>6, "Engineering"=>3, "Environmental Science"=>5, "Biochemistry, Genetics and Molecular Biology"=>37, "Nursing and Health Professions"=>4, "Mathematics"=>2, "Medicine and Dentistry"=>67, "Neuroscience"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>6, "Psychology"=>3, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>67}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>15}, "Environmental Science"=>{"Environmental Science"=>5}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>6}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>31}, "Neuroscience"=>{"Neuroscience"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>123}, "Computer Science"=>{"Computer Science"=>6}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>37}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>4, "United Kingdom"=>3, "Malaysia"=>1, "Switzerland"=>3, "Portugal"=>1, "Spain"=>4, "New Zealand"=>1, "Netherlands"=>3, "Qatar"=>1, "Finland"=>1, "Denmark"=>1, "Brazil"=>1, "Germany"=>6}, "group_count"=>21}

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  • {"files"=>["https://ndownloader.figshare.com/files/819435"], "description"=>"<p>Medium chain-length fatty acids and arachidonate are on average lower in the diabetes group, while long chain fatty acids are higher; shown are all fatty acids that associate with diabetes in at least one ratio pair combination with a q-value <5% and a p-gain >1.</p>", "links"=>[], "tags"=>["chain-length", "fatty", "acids", "arachidonate", "shown", "q-value", "p-gain"], "article_id"=>489773, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.t004", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Medium_chain_length_fatty_acids_and_arachidonate_are_on_average_lower_in_the_diabetes_group_while_long_chain_fatty_acids_are_higher_shown_are_all_fatty_acids_that_associate_with_diabetes_in_at_least_one_ratio_pair_combination_with_a_q_value_lt_5_and_a_p_/489773", "title"=>"Medium chain-length fatty acids and arachidonate are on average lower in the diabetes group, while long chain fatty acids are higher; shown are all fatty acids that associate with diabetes in at least one ratio pair combination with a q-value <5% and a p-gain >1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-11 02:42:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/819217"], "description"=>"<p>The coverage of the metabolome's diversity allows the detection of systemic metabolic imbalances, thereby providing a disease specific picture of human physiology.</p>", "links"=>[], "tags"=>["systemic", "metabolic", "markers"], "article_id"=>489577, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.g003", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_systemic_view_of_metabolic_markers_that_associate_with_diabetes_in_this_study_/489577", "title"=>"A systemic view of metabolic markers that associate with diabetes in this study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-11 02:39:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/819147"], "description"=>"<p>Lower 1,5-AG concentrations at higher glucose levels in participants with diabetes when compared to the control group display the current role of 1,5-AG as a marker for glycemic control in patients with diabetes.</p>", "links"=>[], "tags"=>["glucose", "metabolon"], "article_id"=>489498, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_1_5_AG_and_glucose_measured_on_Metabolon_platform_/489498", "title"=>"1,5-AG and glucose (measured on Metabolon platform).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-11 02:38:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/819374"], "description"=>"<p>Difference of average branched chain amino acids (BCAA) concentrations between the diabetes and the control group; a positive value %-change indicates higher concentrations in the diabetes group; where metabolites that were measured on more than one platform, results from these platforms are presented separately.</p>", "links"=>[], "tags"=>["branched", "amino", "acids", "concentrations", "indicates", "higher", "metabolites", "platforms", "presented"], "article_id"=>489717, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.t005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Difference_of_average_branched_chain_amino_acids_BCAA_concentrations_between_the_diabetes_and_the_control_group_a_positive_value_change_indicates_higher_concentrations_in_the_diabetes_group_where_metabolites_that_were_measured_on_more_than_one_platform_r/489717", "title"=>"Difference of average branched chain amino acids (BCAA) concentrations between the diabetes and the control group; a positive value %-change indicates higher concentrations in the diabetes group; where metabolites that were measured on more than one platform, results from these platforms are presented separately.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-11 02:41:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/819616"], "description"=>"<p>*determined at baseline 2004/2005, about 1–2 years prior to sampling for metabolomics.</p><p>**mean [standard deviation].</p>", "links"=>[], "tags"=>["diabetic"], "article_id"=>489972, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_the_diabetic_and_the_control_group_/489972", "title"=>"Characteristics<sup>**</sup> of the diabetic and the control group.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-11 02:46:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/819572"], "description"=>"<p>List of selected metabolites that associate with diabetes at q-values <0.05 using log-scaled metabolite concentrations and assuming a linear model; %change is the increase or decrease of the mean in the diabetes group with respect to the control group; eta<sup>2</sup> is the proportion of the total variance that can be explained by the factor “diabetes” in the linear model; N is the number of valid data points that entered the analysis; the platform on which the corresponding metabolite was measured is indicated by the first letter of the provider: B = Biocrates, C = Chenomx, M = Metabolon.</p>", "links"=>[], "tags"=>["metabolites", "q-values", "log-scaled", "metabolite", "concentrations", "assuming", "linear", "variance", "explained", "points", "entered", "corresponding", "was", "indicated"], "article_id"=>489904, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_selected_metabolites_that_associate_with_diabetes_at_q_values_2_is_the_proportion_of_the_total_variance_that_can_be_explained_by_the_factor_8220_diabetes_8221_in_the_linear_model_N_is_the_number_of_valid_data_points_that_entered_the_analysis_the_/489904", "title"=>"List of selected metabolites that associate with diabetes at q-values <0.05 using log-scaled metabolite concentrations and assuming a linear model; %change is the increase or decrease of the mean in the diabetes group with respect to the control group; eta<sup>2</sup> is the proportion of the total variance that can be explained by the factor “diabetes” in the linear model; N is the number of valid data points that entered the analysis; the platform on which the corresponding metabolite was measured is indicated by the first letter of the provider: B = Biocrates, C = Chenomx, M = Metabolon.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-11 02:45:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/819081"], "description"=>"<p>The identity of the individual metabolites that are measured on each platform is provided in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013953#pone.0013953.s003\" target=\"_blank\">Table S2</a>. Note that the metabolites metabolites that are quantified uniquely on the Biocrates platform carry specific information on the lipid side-chain composition of the different phospholipid classes (sometimes also referred to as lipidomics). The Metabolon platform, in contrast, provides a wider non-targeted, but semi-quantitative coverage of the general metabolome. NMR presently allows quantifying only a smaller set of metabolites, but this at a much higher degree of reproducibility, faster, and without specific sample preparation.</p>", "links"=>[], "tags"=>["diagram", "metabolites", "platforms"], "article_id"=>489436, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Venn_diagram_showing_the_number_of_metabolites_common_to_all_three_platforms_to_two_platforms_and_metabolites_detected_specifically_by_one_platform_/489436", "title"=>"Venn diagram showing the number of metabolites common to all three platforms, to two platforms and metabolites detected specifically by one platform.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-11 02:37:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/819307"], "description"=>"<p>Overview of findings.</p>", "links"=>[], "tags"=>["diabetes and endocrinology", "public health and epidemiology", "biochemistry/bioinformatics", "nephrology/chronic kidney disease"], "article_id"=>489656, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.t006", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_findings_/489656", "title"=>"Overview of findings.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-11 02:40:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/819495"], "description"=>"<p>List of selected metabolite concentration ratios that associate with diabetes at q-values <0.05 and that display a significant increase in the strength of association (p-gain>241) after Bonferroni correction; based on log-scaled metabolite concentration ratios and assuming a linear model.</p>", "links"=>[], "tags"=>["metabolite", "ratios", "q-values", "bonferroni", "log-scaled", "assuming", "linear"], "article_id"=>489843, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013953.t003", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_selected_metabolite_concentration_ratios_that_associate_with_diabetes_at_q_values_lt_0_05_and_that_display_a_significant_increase_in_the_strength_of_association_p_gain_gt_241_after_Bonferroni_correction_based_on_log_scaled_metabolite_concentratio/489843", "title"=>"List of selected metabolite concentration ratios that associate with diabetes at q-values <0.05 and that display a significant increase in the strength of association (p-gain>241) after Bonferroni correction; based on log-scaled metabolite concentration ratios and assuming a linear model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-11 02:44:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/408224", "https://ndownloader.figshare.com/files/408263", "https://ndownloader.figshare.com/files/408271", "https://ndownloader.figshare.com/files/408289", "https://ndownloader.figshare.com/files/408300", "https://ndownloader.figshare.com/files/408317", "https://ndownloader.figshare.com/files/408340"], "description"=>"<div><h3>Background</h3><p>Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.</p><h3>Methodology/Principal Findings</h3><p>40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).</p><h3>Conclusions/Significance</h3><p>Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.</p></div>", "links"=>[], "tags"=>["metabolic", "footprint", "multiplatform", "metabolomics", "epidemiological"], "article_id"=>140644, "categories"=>["Chemistry", "Medicine", "Biochemistry", "Biotechnology"], "users"=>["Karsten Suhre", "Christa Meisinger", "Angela Döring", "Elisabeth Altmaier", "Petra Belcredi", "Christian Gieger", "David Chang", "Michael V. Milburn", "Walter E. Gall", "Klaus M. Weinberger", "Hans-Werner Mewes", "Martin Hrabé de Angelis", "H.-Erich Wichmann", "Florian Kronenberg", "Jerzy Adamski", "Thomas Illig"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0013953.s001", "https://dx.doi.org/10.1371/journal.pone.0013953.s002", "https://dx.doi.org/10.1371/journal.pone.0013953.s003", "https://dx.doi.org/10.1371/journal.pone.0013953.s004", "https://dx.doi.org/10.1371/journal.pone.0013953.s005", "https://dx.doi.org/10.1371/journal.pone.0013953.s006", "https://dx.doi.org/10.1371/journal.pone.0013953.s007"], "stats"=>{"downloads"=>31, "page_views"=>36, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Metabolic_Footprint_of_Diabetes_A_Multiplatform_Metabolomics_Study_in_an_Epidemiological_Setting/140644", "title"=>"Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-11-11 00:10:44"}

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  • {"unique-ip"=>"18", "full-text"=>"26", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
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  • {"unique-ip"=>"23", "full-text"=>"32", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"22", "full-text"=>"39", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"29", "full-text"=>"36", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}

Relative Metric

{"start_date"=>"2010-01-01T00:00:00Z", "end_date"=>"2010-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Medicine and health sciences", "average_usage"=>[277, 558, 715, 845, 963, 1070, 1166, 1258, 1336, 1410, 1485, 1561, 1638, 1707, 1767, 1834, 1896, 1960, 2030, 2096, 2165, 2229, 2300, 2368, 2425, 2489, 2553, 2617, 2679, 2743, 2814, 2873, 2941, 2995, 3050, 3111, 3169, 3235, 3300, 3363, 3428, 3487, 3548, 3605, 3670, 3726, 3785, 3844]}, {"subject_area"=>"/Medicine and health sciences/Pharmacology", "average_usage"=>[275, 539, 709, 846, 991, 1098, 1213, 1311, 1390, 1473, 1556, 1638, 1713, 1783, 1859, 1935, 2005, 2083, 2145, 2207, 2275, 2352, 2444, 2514, 2592, 2657, 2732, 2801, 2879, 2947, 3031, 3109, 3181, 3241, 3312, 3385, 3454, 3502, 3600, 3665, 3726, 3782, 3829, 3901, 3967, 4014, 4055, 4156, 4196]}]}
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