Associations of plasma proteomics with type 2 diabetes and related traits: results from the longitudinal KORA S4/F4/FF4 Study

被引:19
作者
Luo, Hong [1 ,2 ]
Bauer, Alina [1 ]
Nano, Jana [1 ,2 ]
Petrera, Agnese [3 ]
Rathmann, Wolfgang [4 ,5 ]
Herder, Christian [5 ,6 ,7 ,8 ]
Hauck, Stefanie M. [3 ,9 ]
Sun, Benjamin B. [10 ]
Hoyer, Annika [11 ]
Peters, Annette [1 ,2 ,9 ]
Thorand, Barbara [1 ,9 ]
机构
[1] German Res Ctr Environm Hlth GmbH, Inst Epidemiol, Helmholtz Zentrum Munchen, Neuherberg, Germany
[2] Ludwig Maximilians Univ Munchen, Fac Med, Pettenkofer Sch Publ Hlth, Inst Med Informat Proc Biometry & Epidemiol IBE, Munich, Germany
[3] German Res Ctr Environm Hlth GmbH, Helmholtz Zentrum Munchen, Metabol & Prote Core, Neuherberg, Germany
[4] Heinrich Heine Univ Dusseldorf, Inst Biometr & Epidemiol, German Diabet Ctr, Leibniz Ctr Diabet Res, Dusseldorf, Germany
[5] German Ctr Diabet Res DZD, Partner Dusseldorf, Neuherberg, Germany
[6] Heinrich Heine Univ Dusseldorf, Inst Clin Diabetol, German Diabet Ctr, Leibniz Ctr Diabet Res, Dusseldorf, Germany
[7] Heinrich Heine Univ Dusseldorf, Med Fac, Dept Endocrinol & Diabetol, Dusseldorf, Germany
[8] Heinrich Heine Univ Dusseldorf, Univ Hosp Dusseldorf, Dusseldorf, Germany
[9] German Ctr Diabet Res DZD, Partner Munchen Neuherberg, Neuherberg, Germany
[10] Biogen Inc, Translat Sci Res & Dev, Cambridge, MA USA
[11] Bielefeld Univ, Med Sch OWL, Biostat & Med Biometry, Bielefeld, Germany
关键词
Cohort study; Mendelian randomisation; Proteomics; Traits of glucose and insulin; Type; 2; diabetes; INSULIN SENSITIVITY; BIOMARKERS; MONICA/KORA; POPULATION; METABOLISM; PREDICTION; MELLITUS; IMPROVES; RISK; BONE;
D O I
10.1007/s00125-023-05943-2
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Aims/hypothesis This study aimed to elucidate the aetiological role of plasma proteins in glucose metabolism and type 2 diabetes development.Methods We measured 233 proteins at baseline in 1653 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort study (median follow-up time: 13.5 years). We used logistic regression in the cross-sectional analysis (n=1300), and Cox regression accounting for interval-censored data in the longitudinal analysis (n=1143). We further applied two-level growth models to investigate associations with repeatedly measured traits (fasting glucose, 2 h glucose, fasting insulin, HOMA-B, HOMA-IR, HbA(1c)), and two-sample Mendelian randomisation analysis to investigate causal associations. Moreover, we built prediction models using priority-Lasso on top of Framingham-Offspring Risk Score components and evaluated the prediction accuracy through AUC.Results We identified 14, 24 and four proteins associated with prevalent prediabetes (i.e. impaired glucose tolerance and/or impaired fasting glucose), prevalent newly diagnosed type 2 diabetes and incident type 2 diabetes, respectively (28 overlapping proteins). Of these, IL-17D, IL-18 receptor 1, carbonic anhydrase-5A, IL-1 receptor type 2 (IL-1RT2) and matrix extracellular phosphoglycoprotein were novel candidates. IGF binding protein 2 (IGFBP2), lipoprotein lipase (LPL) and paraoxonase 3 (PON3) were inversely associated while fibroblast growth factor 21 was positively associated with incident type 2 diabetes. LPL was longitudinally linked with change in glucose-related traits, while IGFBP2 and PON3 were linked with changes in both insulin-and glucose-related traits. Mendelian randomisation analysis suggested causal effects of LPL on type 2 diabetes and fasting insulin. The simultaneous addition of 12 priority-Lasso-selected biomarkers (IGFBP2, IL-18, IL-17D, complement component C1q receptor, V-set and immunoglobulin domain-containing protein 2, IL-1RT2, LPL, CUB domain-containing protein 1, vascular endothelial growth factor D, PON3, C-C motif chemokine 4 and tartrate-resistant acid phosphatase type 5) significantly improved the predictive performance (?AUC 0.0219; 95% CI 0.0052, 0.0624).Conclusions/interpretation We identified new candidates involved in the development of derangements in glucose metabolism and type 2 diabetes and confirmed previously reported proteins. Our findings underscore the importance of proteins in the pathogenesis of type 2 diabetes and the identified putative proteins can function as potential pharmacological targets for diabetes treatment and prevention.
引用
收藏
页码:1655 / 1668
页数:14
相关论文
共 48 条
[1]  
Bankul A., 2023, MINERVA ENDOCRINOL, V48, P35, DOI [10.23736/S0391-1977.20.03271-X, DOI 10.23736/S0391-1977.20.03271-X]
[2]   In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study [J].
Beijer, Kristina ;
Nowak, Christoph ;
Sundstrom, Johan ;
Arnlov, Johan ;
Fall, Tove ;
Lind, Lars .
DIABETOLOGIA, 2019, 62 (11) :1998-2006
[3]   Identification of novel biomarkers to monitor β-cell function and enable early detection of type 2 diabetes risk [J].
Belongie, Kirstine J. ;
Ferrannini, Ele ;
Johnson, Kjell ;
Andrade-Gordon, Patricia ;
Hansen, Michael K. ;
Petrie, John R. .
PLOS ONE, 2017, 12 (08)
[4]   FGF21 Regulates Metabolism Through Adipose-Dependent and -Independent Mechanisms [J].
BonDurant, Lucas ;
Ameka, Magdalene ;
Naber, Meghan ;
Markan, Kathleen ;
Idiga, Sharon ;
Acevedo, Michael ;
Walsh, Susan ;
Ornitz, David ;
Potthoff, Matthew .
CELL METABOLISM, 2017, 25 (04) :935-+
[5]   Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression [J].
Bowden, Jack ;
Smith, George Davey ;
Burgess, Stephen .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2015, 44 (02) :512-525
[6]   Association of lipoprotein lipase (LPL) single nucleotide polymorphisms with type 2 diabetes mellitus [J].
Cho, Yoon Shin ;
Go, Min Jin ;
Han, Hye Ree ;
Cha, Seung-Hun ;
Kim, Hung-Tae ;
Min, Haesook ;
Shin, Hyoung Doo ;
Park, Chan ;
Han, Bok-Ghee ;
Cho, Nam Han ;
Shin, Chol ;
Kimm, Kuchan ;
Oh, Bermseok .
EXPERIMENTAL AND MOLECULAR MEDICINE, 2008, 40 (05) :523-532
[7]   A paradigm of integrative physiology, the crosstalk between bone and energy metabolisms [J].
Confavreux, Cyrille B. ;
Levine, Robert L. ;
Karsenty, Gerard .
MOLECULAR AND CELLULAR ENDOCRINOLOGY, 2009, 310 (1-2) :21-29
[8]   A proteomic signature that reflects pancreatic beta-cell function [J].
Curran, Aoife M. ;
Scott-Boyer, Marie Pier ;
Kaput, Jim ;
Ryan, Miriam F. ;
Drummond, Elaine ;
Gibney, Eileen R. ;
Gibney, Michael J. ;
Roche, Helen M. ;
Brennan, Lorraine .
PLOS ONE, 2018, 13 (08)
[9]   Serum sTWEAK Concentrations and Risk of Developing Type 2 Diabetes in a High Cardiovascular Risk Population: A Nested Case-Control Study [J].
Diaz-Lopez, Andres ;
Chacon, Matilde R. ;
Bullo, Monica ;
Maymo-Masip, Elsa ;
Martinez-Gonzalez, Miguel A. ;
Estruch, Ramon ;
Vendrell, Joan ;
Basora, Josep ;
Diez-Espino, Javier ;
Covas, Maria-Isabel ;
Salas-Salvado, Jordi .
JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM, 2013, 98 (08) :3482-3490
[10]   Deciphering the Plasma Proteome of Type 2 Diabetes [J].
Elhadad, Mohamed A. ;
Jonasson, Christian ;
Huth, Cornelia ;
Wilson, Rory ;
Gieger, Christian ;
Matias, Pamela ;
Grallert, Harald ;
Graumann, Johannes ;
Gailus-Durner, Valerie ;
Rathmann, Wolfgang ;
von Toerne, Christine ;
Hauck, Stefanie M. ;
Koenig, Wolfgang ;
Sinner, Moritz F. ;
Oprea, Tudor I. ;
Suhre, Karsten ;
Thorand, Barbara ;
Hveem, Kristian ;
Peters, Annette ;
Waldenberger, Melanie .
DIABETES, 2020, 69 (12) :2766-2778