Mohamed A Elhadad1,2,3, Rory Wilson4,5, Shaza B Zaghlool6, Cornelia Huth5,7, Christian Gieger4,5,7, Harald Grallert4,5,7, Johannes Graumann8,9, Wolfgang Rathmann7,10, Wolfgang Koenig11,12,13, Moritz F Sinner11,14, Kristian Hveem15,16, Karsten Suhre6, Barbara Thorand5,7, Christian Jonasson15,16, Melanie Waldenberger17,18,19, Annette Peters20,21,22,23. 1. Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. mohamed.elhadad@helmholtz-muenchen.de. 2. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. mohamed.elhadad@helmholtz-muenchen.de. 3. German Research Center for Cardiovascular Disease (DZHK), Partner site Munich Heart Alliance, Munich, Germany. mohamed.elhadad@helmholtz-muenchen.de. 4. Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 5. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 6. Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar. 7. German Center for Diabetes Research (DZD), München-Neuherberg, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany. 8. Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, 61231, Bad Nauheim, Germany. 9. The German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany. 10. Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany. 11. German Research Center for Cardiovascular Disease (DZHK), Partner site Munich Heart Alliance, Munich, Germany. 12. Deutsches Herzzentrum München, Technische Universität München, Munich, Germany. 13. Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany. 14. Department of Medicine I, University Hospital Munich, Ludwig-Maximilians-University, Munich, Germany. 15. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. 16. HUNT Research Center, Department of Public Health, NTNU - Norwegian University of Science and Technology, Levanger, Norway. 17. Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. waldenberger@helmholtz-muenchen.de. 18. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. waldenberger@helmholtz-muenchen.de. 19. German Research Center for Cardiovascular Disease (DZHK), Partner site Munich Heart Alliance, Munich, Germany. waldenberger@helmholtz-muenchen.de. 20. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. peters@helmholtz-muenchen.de. 21. German Research Center for Cardiovascular Disease (DZHK), Partner site Munich Heart Alliance, Munich, Germany. peters@helmholtz-muenchen.de. 22. German Center for Diabetes Research (DZD), München-Neuherberg, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany. peters@helmholtz-muenchen.de. 23. Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany. peters@helmholtz-muenchen.de.
Abstract
BACKGROUND: The metabolic syndrome (MetS), defined by the simultaneous clustering of cardio-metabolic risk factors, is a significant worldwide public health burden with an estimated 25% prevalence worldwide. The pathogenesis of MetS is not entirely clear and the use of molecular level data could help uncover common pathogenic pathways behind the observed clustering. METHODS: Using a highly multiplexed aptamer-based affinity proteomics platform, we examined associations between plasma proteins and prevalent and incident MetS in the KORA cohort (n = 998) and replicated our results for prevalent MetS in the HUNT3 study (n = 923). We applied logistic regression models adjusted for age, sex, smoking status, and physical activity. We used the bootstrap ranking algorithm of least absolute shrinkage and selection operator (LASSO) to select a predictive model from the incident MetS associated proteins and used area under the curve (AUC) to assess its performance. Finally, we investigated the causal effect of the replicated proteins on MetS using two-sample Mendelian randomization. RESULTS: Prevalent MetS was associated with 116 proteins, of which 53 replicated in HUNT. These included previously reported proteins like leptin, and new proteins like NTR domain-containing protein 2 and endoplasmic reticulum protein 29. Incident MetS was associated with 14 proteins in KORA, of which 13 overlap the prevalent MetS associated proteins with soluble advanced glycosylation end product-specific receptor (sRAGE) being unique to incident MetS. The LASSO selected an eight-protein predictive model with an (AUC = 0.75; 95% CI = 0.71-0.79) in KORA. Mendelian randomization suggested causal effects of three proteins on MetS, namely apolipoprotein E2 (APOE2) (Wald-Ratio = - 0.12, Wald-p = 3.63e-13), apolipoprotein B (APOB) (Wald-Ratio = - 0.09, Wald-p = 2.54e-04) and proto-oncogene tyrosine-protein kinase receptor (RET) (Wald-Ratio = 0.10, Wald-p = 5.40e-04). CONCLUSIONS: Our findings offer new insights into the plasma proteome underlying MetS and identify new protein associations. We reveal possible casual effects of APOE2, APOB and RET on MetS. Our results highlight protein candidates that could potentially serve as targets for prevention and therapy.
BACKGROUND: The metabolic syndrome (MetS), defined by the simultaneous clustering of cardio-metabolic risk factors, is a significant worldwide public health burden with an estimated 25% prevalence worldwide. The pathogenesis of MetS is not entirely clear and the use of molecular level data could help uncover common pathogenic pathways behind the observed clustering. METHODS: Using a highly multiplexed aptamer-based affinity proteomics platform, we examined associations between plasma proteins and prevalent and incident MetS in the KORA cohort (n = 998) and replicated our results for prevalent MetS in the HUNT3 study (n = 923). We applied logistic regression models adjusted for age, sex, smoking status, and physical activity. We used the bootstrap ranking algorithm of least absolute shrinkage and selection operator (LASSO) to select a predictive model from the incident MetS associated proteins and used area under the curve (AUC) to assess its performance. Finally, we investigated the causal effect of the replicated proteins on MetS using two-sample Mendelian randomization. RESULTS: Prevalent MetS was associated with 116 proteins, of which 53 replicated in HUNT. These included previously reported proteins like leptin, and new proteins like NTR domain-containing protein 2 and endoplasmic reticulum protein 29. Incident MetS was associated with 14 proteins in KORA, of which 13 overlap the prevalent MetS associated proteins with soluble advanced glycosylation end product-specific receptor (sRAGE) being unique to incident MetS. The LASSO selected an eight-protein predictive model with an (AUC = 0.75; 95% CI = 0.71-0.79) in KORA. Mendelian randomization suggested causal effects of three proteins on MetS, namely apolipoprotein E2 (APOE2) (Wald-Ratio = - 0.12, Wald-p = 3.63e-13), apolipoprotein B (APOB) (Wald-Ratio = - 0.09, Wald-p = 2.54e-04) and proto-oncogene tyrosine-protein kinase receptor (RET) (Wald-Ratio = 0.10, Wald-p = 5.40e-04). CONCLUSIONS: Our findings offer new insights into the plasma proteome underlying MetS and identify new protein associations. We reveal possible casual effects of APOE2, APOB and RET on MetS. Our results highlight protein candidates that could potentially serve as targets for prevention and therapy.
Authors: Ashkan Afshin; Mohammad H Forouzanfar; Marissa B Reitsma; Patrick Sur; Kara Estep; Alex Lee; Laurie Marczak; Ali H Mokdad; Maziar Moradi-Lakeh; Mohsen Naghavi; Joseph S Salama; Theo Vos; Kalkidan H Abate; Cristiana Abbafati; Muktar B Ahmed; Ziyad Al-Aly; Ala’a Alkerwi; Rajaa Al-Raddadi; Azmeraw T Amare; Alemayehu Amberbir; Adeladza K Amegah; Erfan Amini; Stephen M Amrock; Ranjit M Anjana; Johan Ärnlöv; Hamid Asayesh; Amitava Banerjee; Aleksandra Barac; Estifanos Baye; Derrick A Bennett; Addisu S Beyene; Sibhatu Biadgilign; Stan Biryukov; Espen Bjertness; Dube J Boneya; Ismael Campos-Nonato; Juan J Carrero; Pedro Cecilio; Kelly Cercy; Liliana G Ciobanu; Leslie Cornaby; Solomon A Damtew; Lalit Dandona; Rakhi Dandona; Samath D Dharmaratne; Bruce B Duncan; Babak Eshrati; Alireza Esteghamati; Valery L Feigin; João C Fernandes; Thomas Fürst; Tsegaye T Gebrehiwot; Audra Gold; Philimon N Gona; Atsushi Goto; Tesfa D Habtewold; Kokeb T Hadush; Nima Hafezi-Nejad; Simon I Hay; Masako Horino; Farhad Islami; Ritul Kamal; Amir Kasaeian; Srinivasa V Katikireddi; Andre P Kengne; Chandrasekharan N Kesavachandran; Yousef S Khader; Young-Ho Khang; Jagdish Khubchandani; Daniel Kim; Yun J Kim; Yohannes Kinfu; Soewarta Kosen; Tiffany Ku; Barthelemy Kuate Defo; G Anil Kumar; Heidi J Larson; Mall Leinsalu; Xiaofeng Liang; Stephen S Lim; Patrick Liu; Alan D Lopez; Rafael Lozano; Azeem Majeed; Reza Malekzadeh; Deborah C Malta; Mohsen Mazidi; Colm McAlinden; Stephen T McGarvey; Desalegn T Mengistu; George A Mensah; Gert B M Mensink; Haftay B Mezgebe; Erkin M Mirrakhimov; Ulrich O Mueller; Jean J Noubiap; Carla M Obermeyer; Felix A Ogbo; Mayowa O Owolabi; George C Patton; Farshad Pourmalek; Mostafa Qorbani; Anwar Rafay; Rajesh K Rai; Chhabi L Ranabhat; Nikolas Reinig; Saeid Safiri; Joshua A Salomon; Juan R Sanabria; Itamar S Santos; Benn Sartorius; Monika Sawhney; Josef Schmidhuber; Aletta E Schutte; Maria I Schmidt; Sadaf G Sepanlou; Moretza Shamsizadeh; Sara Sheikhbahaei; Min-Jeong Shin; Rahman Shiri; Ivy Shiue; Hirbo S Roba; Diego A S Silva; Jonathan I Silverberg; Jasvinder A Singh; Saverio Stranges; Soumya Swaminathan; Rafael Tabarés-Seisdedos; Fentaw Tadese; Bemnet A Tedla; Balewgizie S Tegegne; Abdullah S Terkawi; J S Thakur; Marcello Tonelli; Roman Topor-Madry; Stefanos Tyrovolas; Kingsley N Ukwaja; Olalekan A Uthman; Masoud Vaezghasemi; Tommi Vasankari; Vasiliy V Vlassov; Stein E Vollset; Elisabete Weiderpass; Andrea Werdecker; Joshua Wesana; Ronny Westerman; Yuichiro Yano; Naohiro Yonemoto; Gerald Yonga; Zoubida Zaidi; Zerihun M Zenebe; Ben Zipkin; Christopher J L Murray Journal: N Engl J Med Date: 2017-06-12 Impact factor: 91.245
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