A Network Analysis of Biomarkers for Type 2 Diabetes
Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We co...
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| Veröffentlicht in: | Diabetes (New York, N.Y.) N.Y.), 2019-02, Vol.68 (2), p.281-290 |
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| creator | Huang, Tianyi Glass, Kimberly Zeleznik, Oana A Kang, Jae H Ivey, Kerry L Sonawane, Abhijeet R Birmann, Brenda M Hersh, Craig P Hu, Frank B Tworoger, Shelley S |
| description | Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We conducted a secondary analysis of 27 plasma biomarkers representing glucose metabolism, inflammation, adipokines, endothelial dysfunction, IGF axis, and iron store plus age and BMI at blood collection from an existing case-control study nested in the Nurses' Health Study (NHS), including 1,303 incident diabetes case subjects and 1,627 healthy women. A correlation network was constructed based on pairwise Spearman correlations of the above factors that were statistically different between case and noncase subjects using permutation tests (
< 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed 10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA
and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus |
| doi_str_mv | 10.2337/db18-0892 |
| format | Article |
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< 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed <5, 5-10, and >10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA
and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus <5 years after blood collection, with consistent differential correlations of insulin and HbA
C-peptide was the most highly connected node in the early-stage network, whereas leptin was the hub for mid- or late-stage networks. Our results suggest that perturbations of the diabetes-related biomarker network may occur decades prior to clinical recognition. In addition to the persistent dysregulation between insulin and HbA
, our results highlight the central role of the leptin system in diabetes development.</description><identifier>ISSN: 0012-1797</identifier><identifier>EISSN: 1939-327X</identifier><identifier>DOI: 10.2337/db18-0892</identifier><identifier>PMID: 30409783</identifier><language>eng</language><publisher>United States: American Diabetes Association</publisher><subject>Adiponectin ; Adiponectin - blood ; Adult ; Biomarkers ; Biomarkers - blood ; Biomarkers - metabolism ; Blood ; Blood Glucose - analysis ; C-Peptide - blood ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - blood ; Diabetes Mellitus, Type 2 - metabolism ; Etiology ; Female ; Glucose metabolism ; Glycated Hemoglobin A - analysis ; Humans ; Insulin ; Insulin - blood ; Insulin-like growth factors ; Leptin - blood ; Metabolism ; Middle Aged ; Receptors, Leptin - blood ; Surveys and Questionnaires</subject><ispartof>Diabetes (New York, N.Y.), 2019-02, Vol.68 (2), p.281-290</ispartof><rights>2018 by the American Diabetes Association.</rights><rights>Copyright American Diabetes Association Feb 1, 2019</rights><rights>2018 by the American Diabetes Association. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c403t-a9b9415051d5b7ef3c4a07c011a3fcca8ee78b8165b276201294be490216243a3</citedby><cites>FETCH-LOGICAL-c403t-a9b9415051d5b7ef3c4a07c011a3fcca8ee78b8165b276201294be490216243a3</cites><orcidid>0000-0002-6986-7046 ; 0000-0001-8420-9167</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341308/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341308/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30409783$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Huang, Tianyi</creatorcontrib><creatorcontrib>Glass, Kimberly</creatorcontrib><creatorcontrib>Zeleznik, Oana A</creatorcontrib><creatorcontrib>Kang, Jae H</creatorcontrib><creatorcontrib>Ivey, Kerry L</creatorcontrib><creatorcontrib>Sonawane, Abhijeet R</creatorcontrib><creatorcontrib>Birmann, Brenda M</creatorcontrib><creatorcontrib>Hersh, Craig P</creatorcontrib><creatorcontrib>Hu, Frank B</creatorcontrib><creatorcontrib>Tworoger, Shelley S</creatorcontrib><title>A Network Analysis of Biomarkers for Type 2 Diabetes</title><title>Diabetes (New York, N.Y.)</title><addtitle>Diabetes</addtitle><description>Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We conducted a secondary analysis of 27 plasma biomarkers representing glucose metabolism, inflammation, adipokines, endothelial dysfunction, IGF axis, and iron store plus age and BMI at blood collection from an existing case-control study nested in the Nurses' Health Study (NHS), including 1,303 incident diabetes case subjects and 1,627 healthy women. A correlation network was constructed based on pairwise Spearman correlations of the above factors that were statistically different between case and noncase subjects using permutation tests (
< 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed <5, 5-10, and >10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA
and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus <5 years after blood collection, with consistent differential correlations of insulin and HbA
C-peptide was the most highly connected node in the early-stage network, whereas leptin was the hub for mid- or late-stage networks. Our results suggest that perturbations of the diabetes-related biomarker network may occur decades prior to clinical recognition. In addition to the persistent dysregulation between insulin and HbA
, our results highlight the central role of the leptin system in diabetes development.</description><subject>Adiponectin</subject><subject>Adiponectin - blood</subject><subject>Adult</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Biomarkers - metabolism</subject><subject>Blood</subject><subject>Blood Glucose - analysis</subject><subject>C-Peptide - blood</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - blood</subject><subject>Diabetes Mellitus, Type 2 - metabolism</subject><subject>Etiology</subject><subject>Female</subject><subject>Glucose metabolism</subject><subject>Glycated Hemoglobin A - analysis</subject><subject>Humans</subject><subject>Insulin</subject><subject>Insulin - blood</subject><subject>Insulin-like growth factors</subject><subject>Leptin - blood</subject><subject>Metabolism</subject><subject>Middle Aged</subject><subject>Receptors, Leptin - blood</subject><subject>Surveys and Questionnaires</subject><issn>0012-1797</issn><issn>1939-327X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkU9LwzAchoMobk4PfgEpeNFD9Zc_bZqLMOdfGHqZ4C0kXardumYmrbJvb8rmUMnhd8jDy8P7InSM4YJQyi-nGmcxZILsoD4WVMSU8Ndd1AfAJMZc8B468H4GAGl4-6hHgYHgGe0jNoyeTPNl3Twa1qpa-dJHtoiuS7tQbm6cjwrroslqaSIS3ZRKm8b4Q7RXqMqbo80doJe728noIR4_3z-OhuM4Z0CbWAktGE4gwdNEc1PQnCngOWCsaJHnKjOGZzrDaaIJT0mQFUwbJoDglDCq6ABdrXOXrV6YaW7qxqlKLl0Z5FbSqlL-_anLd_lmP2VKGaaQhYCzTYCzH63xjVyUPjdVpWpjWy8JpoTwBEMa0NN_6My2LlTSUVmoS-DQ9QCdr6ncWe-dKbYyGGS3hey2kN0WgT35bb8lf8qn32PJgdI</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Huang, Tianyi</creator><creator>Glass, Kimberly</creator><creator>Zeleznik, Oana A</creator><creator>Kang, Jae H</creator><creator>Ivey, Kerry L</creator><creator>Sonawane, Abhijeet R</creator><creator>Birmann, Brenda M</creator><creator>Hersh, Craig P</creator><creator>Hu, Frank B</creator><creator>Tworoger, Shelley S</creator><general>American Diabetes Association</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-6986-7046</orcidid><orcidid>https://orcid.org/0000-0001-8420-9167</orcidid></search><sort><creationdate>20190201</creationdate><title>A Network Analysis of Biomarkers for Type 2 Diabetes</title><author>Huang, Tianyi ; Glass, Kimberly ; Zeleznik, Oana A ; Kang, Jae H ; Ivey, Kerry L ; Sonawane, Abhijeet R ; Birmann, Brenda M ; Hersh, Craig P ; Hu, Frank B ; Tworoger, Shelley S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c403t-a9b9415051d5b7ef3c4a07c011a3fcca8ee78b8165b276201294be490216243a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adiponectin</topic><topic>Adiponectin - blood</topic><topic>Adult</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Biomarkers - metabolism</topic><topic>Blood</topic><topic>Blood Glucose - analysis</topic><topic>C-Peptide - blood</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetes Mellitus, Type 2 - blood</topic><topic>Diabetes Mellitus, Type 2 - metabolism</topic><topic>Etiology</topic><topic>Female</topic><topic>Glucose metabolism</topic><topic>Glycated Hemoglobin A - analysis</topic><topic>Humans</topic><topic>Insulin</topic><topic>Insulin - blood</topic><topic>Insulin-like growth factors</topic><topic>Leptin - blood</topic><topic>Metabolism</topic><topic>Middle Aged</topic><topic>Receptors, Leptin - blood</topic><topic>Surveys and Questionnaires</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Tianyi</creatorcontrib><creatorcontrib>Glass, Kimberly</creatorcontrib><creatorcontrib>Zeleznik, Oana A</creatorcontrib><creatorcontrib>Kang, Jae H</creatorcontrib><creatorcontrib>Ivey, Kerry L</creatorcontrib><creatorcontrib>Sonawane, Abhijeet R</creatorcontrib><creatorcontrib>Birmann, Brenda M</creatorcontrib><creatorcontrib>Hersh, Craig P</creatorcontrib><creatorcontrib>Hu, Frank B</creatorcontrib><creatorcontrib>Tworoger, Shelley S</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Diabetes (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Tianyi</au><au>Glass, Kimberly</au><au>Zeleznik, Oana A</au><au>Kang, Jae H</au><au>Ivey, Kerry L</au><au>Sonawane, Abhijeet R</au><au>Birmann, Brenda M</au><au>Hersh, Craig P</au><au>Hu, Frank B</au><au>Tworoger, Shelley S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Network Analysis of Biomarkers for Type 2 Diabetes</atitle><jtitle>Diabetes (New York, N.Y.)</jtitle><addtitle>Diabetes</addtitle><date>2019-02-01</date><risdate>2019</risdate><volume>68</volume><issue>2</issue><spage>281</spage><epage>290</epage><pages>281-290</pages><issn>0012-1797</issn><eissn>1939-327X</eissn><abstract>Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We conducted a secondary analysis of 27 plasma biomarkers representing glucose metabolism, inflammation, adipokines, endothelial dysfunction, IGF axis, and iron store plus age and BMI at blood collection from an existing case-control study nested in the Nurses' Health Study (NHS), including 1,303 incident diabetes case subjects and 1,627 healthy women. A correlation network was constructed based on pairwise Spearman correlations of the above factors that were statistically different between case and noncase subjects using permutation tests (
< 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed <5, 5-10, and >10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA
and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus <5 years after blood collection, with consistent differential correlations of insulin and HbA
C-peptide was the most highly connected node in the early-stage network, whereas leptin was the hub for mid- or late-stage networks. Our results suggest that perturbations of the diabetes-related biomarker network may occur decades prior to clinical recognition. In addition to the persistent dysregulation between insulin and HbA
, our results highlight the central role of the leptin system in diabetes development.</abstract><cop>United States</cop><pub>American Diabetes Association</pub><pmid>30409783</pmid><doi>10.2337/db18-0892</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-6986-7046</orcidid><orcidid>https://orcid.org/0000-0001-8420-9167</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adiponectin Adiponectin - blood Adult Biomarkers Biomarkers - blood Biomarkers - metabolism Blood Blood Glucose - analysis C-Peptide - blood Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - blood Diabetes Mellitus, Type 2 - metabolism Etiology Female Glucose metabolism Glycated Hemoglobin A - analysis Humans Insulin Insulin - blood Insulin-like growth factors Leptin - blood Metabolism Middle Aged Receptors, Leptin - blood Surveys and Questionnaires |
| title | A Network Analysis of Biomarkers for Type 2 Diabetes |
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