Temporal intra-individual variation of immunological biomarkers in type 1 diabetes patients: implications for future use in cross-sectional assessment
Multiple immune parameters such as frequencies of autoreactive CD4(+), CD8(+) T-cells and CD4(+)CD25(+)Foxp3(+) T-cells have been explored as biomarkers in human T1D. However, intra-individual temporal variation of these parameters has not been assessed systematically over time. We determined the va...
Gespeichert in:
Veröffentlicht in: | PloS one 2013-11, Vol.8 (11), p.e79383-e79383 |
---|---|
Hauptverfasser: | , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | e79383 |
---|---|
container_issue | 11 |
container_start_page | e79383 |
container_title | PloS one |
container_volume | 8 |
creator | Sarikonda, Ghanashyam Pettus, Jeremy Sachithanantham, Sowbarnika Phatak, Sonal Miller, Jacqueline F Ganesan, Lakshmi Chae, Ji Mallios, Ronna Edelman, Steve Peters, Bjoern von Herrath, Matthias |
description | Multiple immune parameters such as frequencies of autoreactive CD4(+), CD8(+) T-cells and CD4(+)CD25(+)Foxp3(+) T-cells have been explored as biomarkers in human T1D. However, intra-individual temporal variation of these parameters has not been assessed systematically over time. We determined the variation in each of these parameters in a cohort of T1D and healthy donors (HDs), at monthly intervals for one year. Despite low intra- and inter-assay co-efficient of variation (CV), mean CVs for each of the immune parameters were 119.1% for CD4(+) T-cell-derived IFN-γ, 50.44% for autoreactive CD8(+) T-cells, and 31.24% for CD4(+)CD25(+)Foxp3(+) T-cells. Further, both HDs and T1D donors had similar CVs. The variation neither correlated with BMI, age, disease duration or insulin usage, nor were there detectable cyclical patterns of variation. However, averaging results from multiple visits for an individual provided a better estimate of the CV between visits. Based on our data we predict that by averaging values from three visits a treatment effect on these parameters with a 50% effect size could be detected with the same power using 1.8-4-fold fewer patients within a trial compared to using values from a single visit. Thus, our present data contribute to a more robust, accurate endpoint design for future clinical trials in T1D and aid in the identification of truly efficacious therapies. |
doi_str_mv | 10.1371/journal.pone.0079383 |
format | Article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1448416386</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A478425958</galeid><doaj_id>oai_doaj_org_article_e274ab2f8a83471eba26a97fc2f8c3bd</doaj_id><sourcerecordid>A478425958</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-703e08f1fc1dc2b333c6b91c4a610519cc76f1be90335db8330338349d9751bb3</originalsourceid><addsrcrecordid>eNqNk9tu1DAQhiMEoqXwBggiISG4yOJTTlwgVRWHlSpVgsKtZTuTXS9JHOxkRV-E52Wyu602qBcoF7bG3_-PZ-KJoueULCjP6buNG32nmkXvOlgQkpe84A-iU1pylmSM8IdH-5PoSQgbQlJeZNnj6IQJxjgKTqM_19D2zqsmtt3gVWK7ym5tNWJgq7xVg3Vd7OrYtu3YucatrMEjbV2r_E_wAWXxcNNDTOPKKg0DhLhHFXRDeI-qvkHBZBLi2vm4HofRQzwGmITGuxCSAGYC0FaFACG0qH0aPapVE-DZYT2Lvn_6eH3xJbm8-ry8OL9MTFayIckJB1LUtDa0Mkxzzk2mS2qEyihJaWlMntVUQ0k4TytdcI6bgouyKvOUas3Popd7375xQR5aGiQVohA0w24hsdwTlVMb2XuLhd9Ip6zcBZxfSeUHaxqQwHKhNKsLhSlyClqxTJV5bTBkuK7Q68Mh26hbqAxMLW9mpvOTzq7lym0lL2hOBEODNwcD736NEAbZ2mCgaVQHbpzunRYp4UJM9371D3p_dQdqpbAA29UO85rJVJ6LvBAsLdMCqcU9FH4VtNbg-6stxmeCtzMBMgP8HlZqDEEuv339f_bqx5x9fcSuQTXDOrhm3D2wOSj24O6JeajvmkyJnMbnthtyGh95GB-UvTj-QXei23nhfwFglxi0</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1448416386</pqid></control><display><type>article</type><title>Temporal intra-individual variation of immunological biomarkers in type 1 diabetes patients: implications for future use in cross-sectional assessment</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Sarikonda, Ghanashyam ; Pettus, Jeremy ; Sachithanantham, Sowbarnika ; Phatak, Sonal ; Miller, Jacqueline F ; Ganesan, Lakshmi ; Chae, Ji ; Mallios, Ronna ; Edelman, Steve ; Peters, Bjoern ; von Herrath, Matthias</creator><contributor>Dotta, Francesco</contributor><creatorcontrib>Sarikonda, Ghanashyam ; Pettus, Jeremy ; Sachithanantham, Sowbarnika ; Phatak, Sonal ; Miller, Jacqueline F ; Ganesan, Lakshmi ; Chae, Ji ; Mallios, Ronna ; Edelman, Steve ; Peters, Bjoern ; von Herrath, Matthias ; Dotta, Francesco</creatorcontrib><description>Multiple immune parameters such as frequencies of autoreactive CD4(+), CD8(+) T-cells and CD4(+)CD25(+)Foxp3(+) T-cells have been explored as biomarkers in human T1D. However, intra-individual temporal variation of these parameters has not been assessed systematically over time. We determined the variation in each of these parameters in a cohort of T1D and healthy donors (HDs), at monthly intervals for one year. Despite low intra- and inter-assay co-efficient of variation (CV), mean CVs for each of the immune parameters were 119.1% for CD4(+) T-cell-derived IFN-γ, 50.44% for autoreactive CD8(+) T-cells, and 31.24% for CD4(+)CD25(+)Foxp3(+) T-cells. Further, both HDs and T1D donors had similar CVs. The variation neither correlated with BMI, age, disease duration or insulin usage, nor were there detectable cyclical patterns of variation. However, averaging results from multiple visits for an individual provided a better estimate of the CV between visits. Based on our data we predict that by averaging values from three visits a treatment effect on these parameters with a 50% effect size could be detected with the same power using 1.8-4-fold fewer patients within a trial compared to using values from a single visit. Thus, our present data contribute to a more robust, accurate endpoint design for future clinical trials in T1D and aid in the identification of truly efficacious therapies.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0079383</identifier><identifier>PMID: 24223938</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Allergies ; Alzheimer's disease ; Autoimmunity ; Biological markers ; Biomarkers ; Biomarkers - metabolism ; Blood & organ donations ; Body mass ; Case-Control Studies ; CD25 antigen ; CD4 antigen ; CD4-Positive T-Lymphocytes - cytology ; CD4-Positive T-Lymphocytes - immunology ; CD4-Positive T-Lymphocytes - metabolism ; CD8 antigen ; Cell Count ; Clinical trials ; Consent ; Cross-Sectional Studies ; Cytokines ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (insulin dependent) ; Diabetes Mellitus, Type 1 - blood ; Diabetes Mellitus, Type 1 - immunology ; Female ; Foxp3 protein ; Humans ; Hydrogen sulfide ; Immunology ; Insulin ; Interferon-gamma - biosynthesis ; Longitudinal Studies ; Lymphocytes T ; Male ; Medical research ; Middle Aged ; Parameters ; Patients ; Peptides ; T cells ; Temporal variations ; Time Factors ; Type 1 diabetes ; Variation ; γ-Interferon</subject><ispartof>PloS one, 2013-11, Vol.8 (11), p.e79383-e79383</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Sarikonda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2013 Sarikonda et al 2013 Sarikonda et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-703e08f1fc1dc2b333c6b91c4a610519cc76f1be90335db8330338349d9751bb3</citedby><cites>FETCH-LOGICAL-c692t-703e08f1fc1dc2b333c6b91c4a610519cc76f1be90335db8330338349d9751bb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3817042/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3817042/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2929,23871,27929,27930,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24223938$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Dotta, Francesco</contributor><creatorcontrib>Sarikonda, Ghanashyam</creatorcontrib><creatorcontrib>Pettus, Jeremy</creatorcontrib><creatorcontrib>Sachithanantham, Sowbarnika</creatorcontrib><creatorcontrib>Phatak, Sonal</creatorcontrib><creatorcontrib>Miller, Jacqueline F</creatorcontrib><creatorcontrib>Ganesan, Lakshmi</creatorcontrib><creatorcontrib>Chae, Ji</creatorcontrib><creatorcontrib>Mallios, Ronna</creatorcontrib><creatorcontrib>Edelman, Steve</creatorcontrib><creatorcontrib>Peters, Bjoern</creatorcontrib><creatorcontrib>von Herrath, Matthias</creatorcontrib><title>Temporal intra-individual variation of immunological biomarkers in type 1 diabetes patients: implications for future use in cross-sectional assessment</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Multiple immune parameters such as frequencies of autoreactive CD4(+), CD8(+) T-cells and CD4(+)CD25(+)Foxp3(+) T-cells have been explored as biomarkers in human T1D. However, intra-individual temporal variation of these parameters has not been assessed systematically over time. We determined the variation in each of these parameters in a cohort of T1D and healthy donors (HDs), at monthly intervals for one year. Despite low intra- and inter-assay co-efficient of variation (CV), mean CVs for each of the immune parameters were 119.1% for CD4(+) T-cell-derived IFN-γ, 50.44% for autoreactive CD8(+) T-cells, and 31.24% for CD4(+)CD25(+)Foxp3(+) T-cells. Further, both HDs and T1D donors had similar CVs. The variation neither correlated with BMI, age, disease duration or insulin usage, nor were there detectable cyclical patterns of variation. However, averaging results from multiple visits for an individual provided a better estimate of the CV between visits. Based on our data we predict that by averaging values from three visits a treatment effect on these parameters with a 50% effect size could be detected with the same power using 1.8-4-fold fewer patients within a trial compared to using values from a single visit. Thus, our present data contribute to a more robust, accurate endpoint design for future clinical trials in T1D and aid in the identification of truly efficacious therapies.</description><subject>Adult</subject><subject>Allergies</subject><subject>Alzheimer's disease</subject><subject>Autoimmunity</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Biomarkers - metabolism</subject><subject>Blood & organ donations</subject><subject>Body mass</subject><subject>Case-Control Studies</subject><subject>CD25 antigen</subject><subject>CD4 antigen</subject><subject>CD4-Positive T-Lymphocytes - cytology</subject><subject>CD4-Positive T-Lymphocytes - immunology</subject><subject>CD4-Positive T-Lymphocytes - metabolism</subject><subject>CD8 antigen</subject><subject>Cell Count</subject><subject>Clinical trials</subject><subject>Consent</subject><subject>Cross-Sectional Studies</subject><subject>Cytokines</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (insulin dependent)</subject><subject>Diabetes Mellitus, Type 1 - blood</subject><subject>Diabetes Mellitus, Type 1 - immunology</subject><subject>Female</subject><subject>Foxp3 protein</subject><subject>Humans</subject><subject>Hydrogen sulfide</subject><subject>Immunology</subject><subject>Insulin</subject><subject>Interferon-gamma - biosynthesis</subject><subject>Longitudinal Studies</subject><subject>Lymphocytes T</subject><subject>Male</subject><subject>Medical research</subject><subject>Middle Aged</subject><subject>Parameters</subject><subject>Patients</subject><subject>Peptides</subject><subject>T cells</subject><subject>Temporal variations</subject><subject>Time Factors</subject><subject>Type 1 diabetes</subject><subject>Variation</subject><subject>γ-Interferon</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tu1DAQhiMEoqXwBggiISG4yOJTTlwgVRWHlSpVgsKtZTuTXS9JHOxkRV-E52Wyu602qBcoF7bG3_-PZ-KJoueULCjP6buNG32nmkXvOlgQkpe84A-iU1pylmSM8IdH-5PoSQgbQlJeZNnj6IQJxjgKTqM_19D2zqsmtt3gVWK7ym5tNWJgq7xVg3Vd7OrYtu3YucatrMEjbV2r_E_wAWXxcNNDTOPKKg0DhLhHFXRDeI-qvkHBZBLi2vm4HofRQzwGmITGuxCSAGYC0FaFACG0qH0aPapVE-DZYT2Lvn_6eH3xJbm8-ry8OL9MTFayIckJB1LUtDa0Mkxzzk2mS2qEyihJaWlMntVUQ0k4TytdcI6bgouyKvOUas3Popd7375xQR5aGiQVohA0w24hsdwTlVMb2XuLhd9Ip6zcBZxfSeUHaxqQwHKhNKsLhSlyClqxTJV5bTBkuK7Q68Mh26hbqAxMLW9mpvOTzq7lym0lL2hOBEODNwcD736NEAbZ2mCgaVQHbpzunRYp4UJM9371D3p_dQdqpbAA29UO85rJVJ6LvBAsLdMCqcU9FH4VtNbg-6stxmeCtzMBMgP8HlZqDEEuv339f_bqx5x9fcSuQTXDOrhm3D2wOSj24O6JeajvmkyJnMbnthtyGh95GB-UvTj-QXei23nhfwFglxi0</recordid><startdate>20131104</startdate><enddate>20131104</enddate><creator>Sarikonda, Ghanashyam</creator><creator>Pettus, Jeremy</creator><creator>Sachithanantham, Sowbarnika</creator><creator>Phatak, Sonal</creator><creator>Miller, Jacqueline F</creator><creator>Ganesan, Lakshmi</creator><creator>Chae, Ji</creator><creator>Mallios, Ronna</creator><creator>Edelman, Steve</creator><creator>Peters, Bjoern</creator><creator>von Herrath, Matthias</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20131104</creationdate><title>Temporal intra-individual variation of immunological biomarkers in type 1 diabetes patients: implications for future use in cross-sectional assessment</title><author>Sarikonda, Ghanashyam ; Pettus, Jeremy ; Sachithanantham, Sowbarnika ; Phatak, Sonal ; Miller, Jacqueline F ; Ganesan, Lakshmi ; Chae, Ji ; Mallios, Ronna ; Edelman, Steve ; Peters, Bjoern ; von Herrath, Matthias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-703e08f1fc1dc2b333c6b91c4a610519cc76f1be90335db8330338349d9751bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Adult</topic><topic>Allergies</topic><topic>Alzheimer's disease</topic><topic>Autoimmunity</topic><topic>Biological markers</topic><topic>Biomarkers</topic><topic>Biomarkers - metabolism</topic><topic>Blood & organ donations</topic><topic>Body mass</topic><topic>Case-Control Studies</topic><topic>CD25 antigen</topic><topic>CD4 antigen</topic><topic>CD4-Positive T-Lymphocytes - cytology</topic><topic>CD4-Positive T-Lymphocytes - immunology</topic><topic>CD4-Positive T-Lymphocytes - metabolism</topic><topic>CD8 antigen</topic><topic>Cell Count</topic><topic>Clinical trials</topic><topic>Consent</topic><topic>Cross-Sectional Studies</topic><topic>Cytokines</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (insulin dependent)</topic><topic>Diabetes Mellitus, Type 1 - blood</topic><topic>Diabetes Mellitus, Type 1 - immunology</topic><topic>Female</topic><topic>Foxp3 protein</topic><topic>Humans</topic><topic>Hydrogen sulfide</topic><topic>Immunology</topic><topic>Insulin</topic><topic>Interferon-gamma - biosynthesis</topic><topic>Longitudinal Studies</topic><topic>Lymphocytes T</topic><topic>Male</topic><topic>Medical research</topic><topic>Middle Aged</topic><topic>Parameters</topic><topic>Patients</topic><topic>Peptides</topic><topic>T cells</topic><topic>Temporal variations</topic><topic>Time Factors</topic><topic>Type 1 diabetes</topic><topic>Variation</topic><topic>γ-Interferon</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sarikonda, Ghanashyam</creatorcontrib><creatorcontrib>Pettus, Jeremy</creatorcontrib><creatorcontrib>Sachithanantham, Sowbarnika</creatorcontrib><creatorcontrib>Phatak, Sonal</creatorcontrib><creatorcontrib>Miller, Jacqueline F</creatorcontrib><creatorcontrib>Ganesan, Lakshmi</creatorcontrib><creatorcontrib>Chae, Ji</creatorcontrib><creatorcontrib>Mallios, Ronna</creatorcontrib><creatorcontrib>Edelman, Steve</creatorcontrib><creatorcontrib>Peters, Bjoern</creatorcontrib><creatorcontrib>von Herrath, Matthias</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection (ProQuest)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sarikonda, Ghanashyam</au><au>Pettus, Jeremy</au><au>Sachithanantham, Sowbarnika</au><au>Phatak, Sonal</au><au>Miller, Jacqueline F</au><au>Ganesan, Lakshmi</au><au>Chae, Ji</au><au>Mallios, Ronna</au><au>Edelman, Steve</au><au>Peters, Bjoern</au><au>von Herrath, Matthias</au><au>Dotta, Francesco</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Temporal intra-individual variation of immunological biomarkers in type 1 diabetes patients: implications for future use in cross-sectional assessment</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-11-04</date><risdate>2013</risdate><volume>8</volume><issue>11</issue><spage>e79383</spage><epage>e79383</epage><pages>e79383-e79383</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Multiple immune parameters such as frequencies of autoreactive CD4(+), CD8(+) T-cells and CD4(+)CD25(+)Foxp3(+) T-cells have been explored as biomarkers in human T1D. However, intra-individual temporal variation of these parameters has not been assessed systematically over time. We determined the variation in each of these parameters in a cohort of T1D and healthy donors (HDs), at monthly intervals for one year. Despite low intra- and inter-assay co-efficient of variation (CV), mean CVs for each of the immune parameters were 119.1% for CD4(+) T-cell-derived IFN-γ, 50.44% for autoreactive CD8(+) T-cells, and 31.24% for CD4(+)CD25(+)Foxp3(+) T-cells. Further, both HDs and T1D donors had similar CVs. The variation neither correlated with BMI, age, disease duration or insulin usage, nor were there detectable cyclical patterns of variation. However, averaging results from multiple visits for an individual provided a better estimate of the CV between visits. Based on our data we predict that by averaging values from three visits a treatment effect on these parameters with a 50% effect size could be detected with the same power using 1.8-4-fold fewer patients within a trial compared to using values from a single visit. Thus, our present data contribute to a more robust, accurate endpoint design for future clinical trials in T1D and aid in the identification of truly efficacious therapies.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24223938</pmid><doi>10.1371/journal.pone.0079383</doi><tpages>e79383</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2013-11, Vol.8 (11), p.e79383-e79383 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_1448416386 |
source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Adult Allergies Alzheimer's disease Autoimmunity Biological markers Biomarkers Biomarkers - metabolism Blood & organ donations Body mass Case-Control Studies CD25 antigen CD4 antigen CD4-Positive T-Lymphocytes - cytology CD4-Positive T-Lymphocytes - immunology CD4-Positive T-Lymphocytes - metabolism CD8 antigen Cell Count Clinical trials Consent Cross-Sectional Studies Cytokines Diabetes Diabetes mellitus Diabetes mellitus (insulin dependent) Diabetes Mellitus, Type 1 - blood Diabetes Mellitus, Type 1 - immunology Female Foxp3 protein Humans Hydrogen sulfide Immunology Insulin Interferon-gamma - biosynthesis Longitudinal Studies Lymphocytes T Male Medical research Middle Aged Parameters Patients Peptides T cells Temporal variations Time Factors Type 1 diabetes Variation γ-Interferon |
title | Temporal intra-individual variation of immunological biomarkers in type 1 diabetes patients: implications for future use in cross-sectional assessment |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-11T14%3A05%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Temporal%20intra-individual%20variation%20of%20immunological%20biomarkers%20in%20type%201%20diabetes%20patients:%20implications%20for%20future%20use%20in%20cross-sectional%20assessment&rft.jtitle=PloS%20one&rft.au=Sarikonda,%20Ghanashyam&rft.date=2013-11-04&rft.volume=8&rft.issue=11&rft.spage=e79383&rft.epage=e79383&rft.pages=e79383-e79383&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0079383&rft_dat=%3Cgale_plos_%3EA478425958%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1448416386&rft_id=info:pmid/24223938&rft_galeid=A478425958&rft_doaj_id=oai_doaj_org_article_e274ab2f8a83471eba26a97fc2f8c3bd&rfr_iscdi=true |