001     148300
005     20240229121831.0
024 7 _ |a 10.3390/jcm8122075
|2 doi
024 7 _ |a pmid:31783601
|2 pmid
024 7 _ |a altmetric:71338958
|2 altmetric
037 _ _ |a DKFZ-2019-02864
041 _ _ |a eng
082 _ _ |a 610
100 1 _ |a Pletsch-Borba, Laura
|0 0000-0003-4872-6452
|b 0
|e First author
245 _ _ |a Biomarkers of Vascular Injury and Type 2 Diabetes: A Prospective Study, Systematic Review and Meta-Analysis.
260 _ _ |a Basel
|c 2019
|b MDPI
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1627483136_3141
|2 PUB:(DE-HGF)
|x Review Article
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Data on biomarkers of vascular injury and type 2 diabetes (T2D) risk from prospective studies are lacking. We evaluated seven biomarkers of vascular injury in relation to T2D. Additionally, a meta-analysis was performed. From the EPIC-Heidelberg cohort, 2224 participants were followed-up from baseline for 16 (median) years. E-Selectin, P-Selectin, intercellular adhesion molecule 3 (ICAM3), thrombomodulin, thrombopoietin, glycoprotein IIb/IIIa and fibrinogen levels were measured in baseline blood samples. The systematic review and meta-analysis included prospective studies identified through MEDLINE and Web of Science that investigated the association between mentioned biomarkers and T2D. The study population included 55% women, median age was 50 years, and 163 developed T2D. ICAM3 was associated with lower T2D risk (fully adjusted HRhighest vs. lowest tertile 0.62 (95% CI: 0.43, 0.91)), but no other studies on ICAM3 were identified. Overall, fifteen studies were included in the systematic review and meta-analysis (6,171 cases). E-Selectin was associated with higher T2D risk HRper SD: 1.34 (95% CI: 1.16, 1.54; I2 = 63%, n = 9 studies), while thrombomodulin was associated with lower risk HRper SD: 0.82 (95% CI: 0.71, 0.95; I2 = 0%, n = 2 studies). In the EPIC-Heidelberg, ICAM3 was associated with lower T2D risk. The meta-analysis showed a consistent positive association between E-Selectin and T2D. It was also suggestive of an inverse association between thrombomodulin and T2D, although further studies are needed to corroborate this finding.
536 _ _ |a 323 - Metabolic Dysfunction as Risk Factor (POF3-323)
|0 G:(DE-HGF)POF3-323
|c POF3-323
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed,
700 1 _ |a Watzinger, Cora
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Turzanski Fortner, Renée
|0 P:(DE-He78)74a6af8347ec5cbd4b77e562e10ca1f2
|b 2
|u dkfz
700 1 _ |a Katzke, Verena
|0 P:(DE-He78)fb68a9386399d72d84f7f34cfc6048b4
|b 3
|u dkfz
700 1 _ |a Schwingshackl, Lukas
|b 4
700 1 _ |a Sowah, Solomon
|0 P:(DE-He78)b4004ee4b650b575447f59a4a0471312
|b 5
|u dkfz
700 1 _ |a Hüsing, Anika
|0 P:(DE-He78)6519c85d61a3def7974665471b8a4f74
|b 6
|u dkfz
700 1 _ |a Johnson, Theron
|0 P:(DE-He78)79ab945544e5bc017a2317b6146ed3aa
|b 7
|u dkfz
700 1 _ |a Groß, Marie-Luise
|b 8
700 1 _ |a González Maldonado, Sandra
|0 P:(DE-He78)5b69eb65801a144c299d2aee312aefa8
|b 9
|u dkfz
700 1 _ |a Hoffmeister, Michael
|0 P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f
|b 10
|u dkfz
700 1 _ |a Bugert, Peter
|b 11
700 1 _ |a Kaaks, Rudolf
|0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
|b 12
|u dkfz
700 1 _ |a Grafetstätter, Mirja
|0 P:(DE-He78)9a183bfc8348c1db81a0ecf1458d0708
|b 13
|u dkfz
700 1 _ |a Kühn, Tilman
|0 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
|b 14
|e Last author
|u dkfz
773 _ _ |a 10.3390/jcm8122075
|g Vol. 8, no. 12, p. 2075 -
|0 PERI:(DE-600)2662592-1
|n 12
|p 2075
|t Journal of Clinical Medicine
|v 8
|y 2019
|x 2077-0383
909 C O |p VDB
|o oai:inrepo02.dkfz.de:148300
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 0000-0003-4872-6452
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 1
|6 P:(DE-HGF)0
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 2
|6 P:(DE-He78)74a6af8347ec5cbd4b77e562e10ca1f2
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 3
|6 P:(DE-He78)fb68a9386399d72d84f7f34cfc6048b4
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 P:(DE-He78)b4004ee4b650b575447f59a4a0471312
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 6
|6 P:(DE-He78)6519c85d61a3def7974665471b8a4f74
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 7
|6 P:(DE-He78)79ab945544e5bc017a2317b6146ed3aa
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 9
|6 P:(DE-He78)5b69eb65801a144c299d2aee312aefa8
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 10
|6 P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 12
|6 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 13
|6 P:(DE-He78)9a183bfc8348c1db81a0ecf1458d0708
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 14
|6 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
913 1 _ |a DE-HGF
|b Gesundheit
|l Herz-Kreislauf-Stoffwechselerkrankungen
|1 G:(DE-HGF)POF3-320
|0 G:(DE-HGF)POF3-323
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-300
|4 G:(DE-HGF)POF
|v Metabolic Dysfunction as Risk Factor
|x 0
914 1 _ |y 2019
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b J CLIN MED : 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b J CLIN MED : 2017
920 1 _ |0 I:(DE-He78)C020-20160331
|k C020
|l C020 Epidemiologie von Krebs
|x 0
920 1 _ |0 I:(DE-He78)C070-20160331
|k C070
|l C070 Klinische Epidemiologie und Alternf.
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C020-20160331
980 _ _ |a I:(DE-He78)C070-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21