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2. Association of dietary patterns with diabetes-related comorbidities varies among diabetes endotypes

Author

Roden, M., Al-Hasani, H., Belgardt, B., Lammert, E., Bönhof, G., Geerling, G., Herder, C., Icks, A., Jandeleit-Dahm, K., Kotzka, J., Kuß, O., Rathmann, W., Schlesinger, S., Schrauwen-Hinderling, V., Szendroedi, J., Trenkamp, S., Wagner, R., Weber, Katharina S., Schlesinger, Sabrina, Lang, Alexander, Straßburger, Klaus, Maalmi, Haifa, Zhu, Anna, Zaharia, Oana-Patricia, Strom, Alexander, Bönhof, Gidon J., Goletzke, Janina, Trenkamp, Sandra, Wagner, Robert, Buyken, Anette E., Lieb, Wolfgang, Roden, Michael, and Herder, Christian

Published
2024
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3. Association of C-Terminal Pro-Endothelin-1 with Mortality in the Population-Based KORA F4 Study

Author

Then C, Sujana C, Herder C, Then H, Heier M, Meisinger C, Peters A, Koenig W, Rathmann W, Maalmi H, Ritzel K, Roden M, Stumvoll M, Thorand B, and Seissler J

Subjects
endothelin, ct-proet-1, mortality, cardiovascular events, subclinical inflammation, intima-media thickness, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Cornelia Then,1,2 Chaterina Sujana,2– 4 Christian Herder,5– 7 Holger Then,8 Margit Heier,3,9 Christa Meisinger,10,11 Annette Peters,3,12 Wolfgang Koenig,12– 14 Wolfgang Rathmann,5,15 Haifa Maalmi,5,7 Katrin Ritzel,1 Michael Roden,5– 7 Michael Stumvoll,16 Barbara Thorand,2,3 Jochen Seissler1,2 1Department of Internal Medicine IV, University Hospital of Ludwigs-Maximilians-University Munich, Munich, Germany; 2German Center for Diabetes Research (DZD), Partner Munich-Neuherberg, Munich, Germany; 3Institute of Epidemiology, Helmholtz Zentrum Munich – German Research Center for Environmental Health (GmbH), Neuherberg, Germany; 4Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, Ludwigs-Maximilians-University Munich, Munich, Germany; 5German Center for Diabetes Research (DZD), Munich, Germany; 6Department of Endocrinology and Diabetology, Medical Faculty and University Hospital of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany; 7Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany; 8Freie Waldorfschule Augsburg, Augsburg, Germany; 9KORA Study Centre, University Hospital Augsburg, Augsburg, Germany; 10Independent Research Group Clinical Epidemiology, Helmholtz Zentrum Munich – German Research Center for Environmental Health (GmbH), Neuherberg, Germany; 11Chair of Epidemiology, University Hospital Augsburg, Augsburg, Germany; 12DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany; 13Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany; 14German Heart Center Munich, Technical University of Munich, Munich, Germany; 15Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany; 16Department of Medicine, University of Leipzig, Leipzig, GermanyCorrespondence: Cornelia Then, Medizinische Klinik und Poliklinik IV - Klinikum der Ludwig-Maximilians-Universität, Ziemssenstraße 1, München, 80336, Germany, Tel +4989440052111, Fax +4989440054956, Email cornelia.then@med.uni-muenchen.deIntroduction: Endothelin-1 and its prohormone C-terminal pro-endothelin-1 (CT-proET-1) have been linked to metabolic alterations, inflammatory responses and cardiovascular events in selected study populations. We analyzed the association of CT-proET-1 with cardiovascular events and mortality, carotid intima-media-thickness as surrogate for early atherosclerotic lesions, biomarkers of subclinical inflammation and adipokines in a population-based study.Methods: The cross-sectional and prospective analyses used data from the KORA F4 study with a median follow-up time of 9.1 (8.8– 9.4) years. Data on CT-proET-1 and mortality were available for 1554 participants, data on the other outcomes in subgroups (n = 596– 1554). The associations were estimated using multivariable linear regression and Cox proportional hazard models adjusted for sex, age, body mass index, estimated glomerular filtration rate, arterial hypertension, diabetes, low-density and high-density lipoprotein cholesterol, current and former smoking and physical activity. The Bonferroni method was used to correct for multiple testing.Results: In the fully adjusted model, CT-proET-1 was associated with cardiovascular (hazard ratio (HR) per standard deviation increase: 1.66; 95% confidence interval (CI): 1.10– 2.51; p = 0.017) and all-cause mortality (HR: 2.03; 95% CI 1.55– 2.67; p < 0.001), but not with cardiovascular events, and was inversely associated with the intima-media thickness (β: − 0.09 ± 0.03; p = 0.001). CT-proET-1 was positively associated with five out of ten biomarkers of subclinical inflammation and with two out of five adipokines after correction for multiple testing. After inclusion of biomarkers of subclinical inflammation in the Cox proportional hazard model, the association of CT-proET-1 with all-cause mortality persisted (p < 0.001).Conclusion: These results emphasize the complexity of endothelin-1 actions and/or indicator functions of CT-proET-1. CT-proET-1 is a risk marker for all-cause mortality, which is likely independent of vascular endothelin-1 actions, cardiovascular disease and inflammation.Keywords: endothelin, CT-proET-1, mortality, cardiovascular events, subclinical inflammation, intima-media thickness

Published
2022

4. Association of dietary patterns with diabetes-related comorbidities varies among diabetes endotypes

Author

Weber, Katharina S., primary, Schlesinger, Sabrina, additional, Lang, Alexander, additional, Straßburger, Klaus, additional, Maalmi, Haifa, additional, Zhu, Anna, additional, Zaharia, Oana-Patricia, additional, Strom, Alexander, additional, Bönhof, Gidon J., additional, Goletzke, Janina, additional, Trenkamp, Sandra, additional, Wagner, Robert, additional, Buyken, Anette E., additional, Lieb, Wolfgang, additional, Roden, Michael, additional, Herder, Christian, additional, Roden, M., additional, Al-Hasani, H., additional, Belgardt, B., additional, Lammert, E., additional, Bönhof, G., additional, Geerling, G., additional, Herder, C., additional, Icks, A., additional, Jandeleit-Dahm, K., additional, Kotzka, J., additional, Kuß, O., additional, Rathmann, W., additional, Schlesinger, S., additional, Schrauwen-Hinderling, V., additional, Szendroedi, J., additional, Trenkamp, S., additional, and Wagner, R., additional

Published
2024
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5. Avoiding Time-Related Biases: A Feasibility Study on Antidiabetic Drugs and Pancreatic Cancer Applying the Parametric g-Formula to a Large German Healthcare Database

Author

Börnhorst C, Reinders T, Rathmann W, Bongaerts B, Haug U, Didelez V, and Kollhorst B

Subjects
target trial emulation, electronic health data, parametric g-formula, time-related bias, time-dependent confounding, type-2 diabetes mellitus, Infectious and parasitic diseases, RC109-216
Abstract

Claudia Börnhorst,1 Tammo Reinders,1 Wolfgang Rathmann,2 Brenda Bongaerts,2 Ulrike Haug,1 Vanessa Didelez,1 Bianca Kollhorst1 1Leibniz Institute for Prevention Research and Epidemiology – BIPS, Department of Biometry and Data Management, Bremen, Germany; 2Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, GermanyCorrespondence: Bianca KollhorstLeibniz Institute for Prevention Research and Epidemiology – BIPS, Department of Biometry and Data Management, Achterstr. 30, Bremen, 28359, GermanyTel +49 421 21856980Email kollhorst@leibniz-bips.dePurpose: Investigating intended or unintended effects of sustained drug use is of high clinical relevance but remains methodologically challenging. This feasibility study aims to evaluate the usefulness of the parametric g-formula within a target trial for application to an extensive healthcare database in order to address various sources of time-related biases and time-dependent confounding.Patients and Methods: Based on the German Pharmacoepidemiological Research Database (GePaRD), we estimated the pancreatic cancer incidence comparing two hypothetical treatment strategies for type 2 diabetes mellitus (T2DM), i.e., (A) sustained metformin monotherapy vs (B) combination therapy with DPP-4 inhibitors after one year metformin monotherapy. We included 77,330 persons with T2DM who started metformin therapy at baseline between 2005 and 2011. Key aspects for avoiding time-related biases and time-dependent confounding were the emulation of a target trial over a 7-year follow-up period and application of the parametric g-formula.Results: Over the 7-year follow-up period, 652 out of the 77,330 study subjects had a diagnosis of pancreatic cancer. Assuming no unobserved confounding, we found evidence that the metformin/DPP-4i combination therapy increased the risk of pancreatic cancer compared to a sustained metformin monotherapy (risk ratio: 1.47; 95% bootstrap CI: 1.07– 1.94). The risk ratio decreased in sensitivity analyses addressing protopathic bias.Conclusion: While protopathic bias could not fully be ruled out, and computational challenges necessitated compromises in the analysis, the g-formula and target trial emulation proved useful: Self-inflicted biases were avoided, observed time-varying confounding was adjusted for, and the estimated risks have a clear causal interpretation.Keywords: target trial emulation, electronic health data, parametric g-formula, time-related bias, time-dependent confounding, type-2 diabetes mellitus

Published
2021

12. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants

Author

Zhou, B, Lu, Y, Hajifathalian, K, Bentham, J, Di Cesare, M, Danaei, G, Bixby, H, Cowan, MJ, Ali, MK, Taddei, C, Lo, WC, Reis-Santos, B, Stevens, GA, Riley, LM, Miranda, JJ, Bjerregaard, P, Rivera, JA, Fouad, HM, Ma, G, Mbanya, JC, McGarvey, ST, Mohan, V, Onat, A, Pilav, A, Ramachandran, A, Romdhane, HB, Paciorek, CJ, Bennett, JE, Ezzati, M, Abdeen, ZA, Abdul Kadir, K, Abu-Rmeileh, NM, Acosta-Cazares, B, Adams, R, Aekplakorn, W, Aguilar-Salinas, CA, Agyemang, C, Ahmadvand, A, Al-Othman, AR, Alkerwi, A, Amouyel, P, Amuzu, A, Andersen, LB, Anderssen, SA, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicová, M, Azizi, F, Balakrishna, N, Bandosz, P, Barbagallo, CM, Barceló, A, Batieha, AM, Baur, LA, Benet, M, Bernabe-Ortiz, A, Bharadwaj, S, Bhargava, SK, Bi, Y, Bjertness, E, Bjertness, MB, Björkelund, C, Blokstra, A, Bo, S, Boehm, BO, Boissonnet, CP, Bovet, P, Brajkovich, I, Breckenkamp, J, Brenner, H, Brewster, LM, Brian, GR, Bruno, G, Bugge, A, Cabrera de León, A, Can, G, Cândido, AP, Capuano, V, Carlsson, AC, Carvalho, MJ, Casanueva, FF, Casas, JP, Caserta, CA, Castetbon, K, Chamukuttan, S, Chaturvedi, N, Chen, CJ, Chen, F, Chen, S, Cheng, CY, Chetrit, A, Chiou, ST, Cho, Y, Chudek, J, Cifkova, R, Claessens, F, Concin, H, Cooper, C, Cooper, R, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, D'Arrigo, G, Dallongeville, J, Dankner, R, Dauchet, L, de Gaetano, G, De Henauw, S, Deepa, M, Dehghan, A, Deschamps, V, Dhana, K, Di Castelnuovo, AF, Djalalinia, S, Doua, K, Drygas, W, Du, Y, Dzerve, V, Egbagbe, EE, Eggertsen, R, El Ati, J, Elosua, R, Erasmus, RT, Erem, C, Ergor, G, Eriksen, L, Escobedo-de la Peña, J, Fall, CH, Farzadfar, F, Felix-Redondo, FJ, Ferguson, TS, Fernández-Bergés, D, Ferrari, M, Ferreccio, C, Feskens, EJ, Finn, JD, Föger, B, Foo, LH, Forslund, AS, Francis, DK, Franco Mdo, C, Franco, OH, Frontera, G, Furusawa, T, Gaciong, Z, Garnett, SP, Gaspoz, JM, Gasull, M, Gates, L, Geleijnse, JM, Ghasemian, A, Ghimire, A, Giampaoli, S, Gianfagna, F, Giovannelli, J, Giwercman, A, Gross, MG, González Rivas, JP, Gorbea, MB, Gottrand, F, Grafnetter, D, Grodzicki, T, Grøntved, A, Gruden, G, Gu, D, Guan, OP, Guerrero, R, Guessous, I, Guimaraes, AL, Gutierrez, L, Hambleton, IR, Hardy, R, Hari Kumar, R, Hata, J, He, J, Heidemann, C, Herrala, S, Hihtaniemi, IT, Ho, SY, Ho, SC, Hofman, A, Hormiga, CM, Horta, BL, Houti, L, Howitt, C, Htay, TT, Htet, AS, Htike, MM, Hu, Y, Hussieni, AS, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, AG, Ibrahim, MM, Ikeda, N, Ikram, MA, Irazola, VE, Islam, M, Iwasaki, M, Jacobs, JM, Jafar, T, Jamil, KM, Jasienska, G, Jiang, CQ, Jonas, JB, Joshi, P, Kafatos, A, Kalter-Leibovici, O, Kasaeian, A, Katz, J, Kaur, P, Kavousi, M, Keinänen-Kiukaanniemi, S, Kelishadi, R, Kengne, AP, Kersting, M, Khader, YS, Khalili, D, Khang, YH, Kiechl, S, Kim, J, Kolsteren, P, Korrovits, P, Kratzer, W, Kromhout, D, Kujala, UM, Kula, K, Kyobutungi, C, Laatikainen, T, Lachat, C, Laid, Y, Lam, TH, Landrove, O, Lanska, V, Lappas, G, Laxmaiah, A, Leclercq, C, Lee, J, Lehtimäki, T, Lekhraj, R, León-Muñoz, LM, Li, Y, Lim, WY, Lima-Costa, MF, Lin, HH, Lin, X, Lissner, L, Lorbeer, R, Lozano, JE, Luksiene, D, Lundqvist, A, Lytsy, P, Machado-Coelho, GL, Machi, S, Maggi, S, Magliano, DJ, Makdisse, M, Mallikharjuna Rao, K, Manios, Y, Manzato, E, Margozzini, P, Marques-Vidal, P, Martorell, R, Masoodi, SR, Mathiesen, EB, Matsha, TE, McFarlane, SR, McLachlan, S, McNulty, BA, Mediene-Benchekor, S, Meirhaeghe, A, Menezes, AM, Merat, S, Meshram, II, Mi, J, Miquel, JF, Mohamed, MK, Mohammad, K, Mohammadifard, N, Mohd Yusoff, MF, Møller, NC, Molnár, D, Mondo, CK, Morejon, A, Moreno, LA, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mota, J, Motta, J, Mu, TT, Muiesan, ML, Müller-Nurasyid, M, Mursu, J, Nagel, G, Námešná, J, Nang, EE, NangThetia, VB, Navarrete-Muñoz, EM, Ndiaye, NC, Nenko, I, Nervi, F, Nguyen, ND, Nguyen, QN, Nieto-Martínez, RE, Ning, G, Ninomiya, T, Noale, M, Noto, D, Nsour, MA, Ochoa-Avilés, AM, Oh, K, Ordunez, P, Osmond, C, Otero, JA, Owusu-Dabo, E, Pahomova, E, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pelletier, C, Peltonen, M, Peters, A, Peykari, N, Pham, ST, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubió, P, Porta, M, Portegies, ML, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Radisauskas, R, Rahman, M, Raitakari, O, Rao, SR, Ramke, J, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PF, Rigo, F, Robinson, SM, Robitaille, C, Rodríguez-Artalejo, F, Rodriguez-Perez Mdel, C, Rodríguez-Villamizar, LA, Rojas-Martinez, R, Ronkainen, K, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Russo Horimoto, RV, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sánchez-Abanto, J, Santos, D, dos Santos, RN, Santos, R, Saramies, JL, Sardinha, LB, Sarrafzadegan, N, Saum, KU, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Sein, AA, Sharma, SK, Shaw, JE, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, So, HK, Sobngwi, E, Söderberg, S, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Stathopoulou, MG, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stöckl, D, Stokwiszewski, J, Stronks, K, Strufaldi, MW, Sun, CA, Sundström, J, Sung, YT, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tamosiunas, A, Tang, L, Tarawneh, M, Tarqui-Mamani, CB, Taylor, A, Theobald, H, Thijs, L, Thuesen, BH, Tolonen, HK, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OT, Tulloch-Reid, MK, Tuomainen, TP, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FA, Ulmer, H, Uusitalo, HM, Valdivia, G, Valvi, D, van Rossem, L, van Valkengoed, IG, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, WM, Verstraeten, R, Viet, L, Vioque, J, Virtanen, JK, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Vrijheid, M, Wade, AN, Wagner, A, Walton, J, Wan Mohamud, WN, Wang, F, Wang, MD, Wang, Q, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wijga, AH, Wilks, RJ, Willeit, J, Wilsgaard, T, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, FC, Wu, SL, Xu, H, Yan, W, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, Zheng, Y, and Zuñiga Cisneros, J

Published
2016
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14. Verletzungshäufigkeit und -muster beim Rennradfahren

Author

Ueblacker, P., Rathmann, W., Rueger, J.M., and Püschel, K.

Abstract

Zusammenfassung: Hintergrund: Über Risiken und Verletzungen des Radsports ist in der Literatur wenig berichtet. Ziel vorliegender Studie war es, alle Verletzten der 182 Profi- und 18.788 Amateurradfahrer der Hamburger „Cyclassics“ 2006 zu erfassen. Patienten und Methode: Die Verletzten wurden durch Daten des Rettungsdienstes, des Veranstalters und der Kliniken erfasst und in einem Fragebogen befragt. Ergebnisse: 70 Verletzte mit 193 Verletzungen wurden verzeichnet, die Verletzungsrate betrug 0,37%. Das mittlere Alter lag bei 44 (19–72) Jahren. Die Extremitäten waren in 94,4% der Fälle betroffen, häufigste Lokalisation war in 54,7% der Schultergürtel (32 Frakturen wurden registriert). Der „mittlere Abbreviated Injury Score“ (MAIS) betrug 1,34±0,73 (Spanne 1–4), der „Injury Severity Score“ (ISS) 2,86±3,61 (Spanne 1–20). 10% der Teilnehmer erlitten ernste Verletzungen (AIS≥3), statistisch signifikant häufiger bei Frauen als bei Männern (p<0,01). Bezogen auf 100.000 km ereigneten sich in der 55-km-Distanz die häufigsten Unfälle (p<0,01). 84,4% der Unfälle fanden im Pulk statt. Das mittlere Tempo zum Unfallzeitpunkt war 37,3 (0–57) km/h. Schlussfolgerung: Die Unfälle ereigneten sich eher bei unerfahrenen Fahrern, auf der 55-km-Distanz sowie im Bereich bekannter Gefahrenpunkte.

Published
2024
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15. Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331 288 participants

Author

Danaei, G, Fahimi, S, Lu, Y, Zhou, B, Hajifathalian, K, Di Cesare, M, Lo, WC, Reis-Santos, B, Cowan, MJ, Shaw, JE, Bentham, J, Lin, JK, Bixby, H, Magliano, D, Bovet, P, Miranda, JJ, Khang, YH, Stevens, GA, Riley, LM, Ali, MK, Ezzati, M, Abdeen, ZA, Kadir, KA, Abu-Rmeileh, M, Acosta-Cazares, B, Aekplakorn, W, Aguilar-Salinas, CA, Ahmadvand, A, Al Nsour, M, Alkerwi, A, Amouyel, P, Andersen, LB, Anderssen, SA, Andrade, DS, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicová, M, Balakrishna, N, Bandosz, P, Barbagallo, CM, Barceló, A, Batieha, AM, Baur, LA, Ben Romdhane, H, Bernabe-Ortiz, A, Bhargava, SK, Bi, Y, Bjerregaard, P, Björkelund, C, Blake, M, Blokstra, A, Bo, S, Boehm, BO, Boissonnet, CP, Brajkovich, I, Breckenkamp, J, Brewster, LM, Brian, GR, Bruno, G, Bugge, A, Cabrera de León, A, Can, G, Cândido, AP, Capuano, V, Carvalho, MJ, Casanueva, FF, Caserta, CA, Castetbon, K, Chamukuttan, S, Chaturvedi, N, Chen, CJ, Chen, F, Chen, S, Cheng, CY, Chetrit, A, Chiou, ST, Cho, Y, Chudek, J, Cifkova, R, Claessens, F, Concin, H, Cooper, C, Cooper, R, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, D'Arrigo, G, Dallongeville, J, Dankner, R, Dauchet, L, de Gaetano, G, De Henauw, S, Deepa, M, Dehghan, A, Dhana, K, Di Castelnuovo, AF, Djalalinia, S, Doua, K, Drygas, W, Du, Y, Egbagbe, EE, Eggertsen, R, El Ati, J, Elosua, R, Erasmus, RT, Erem, C, Ergor, G, Eriksen, L, Escobedo-de la Peña, J, Fall, CH, Farzadfar, F, Felix-Redondo, FJ, Ferguson, TS, Fernández-Bergés, D, Ferrari, M, Ferreccio, C, Finn, JD, Föger, B, Foo, LH, Fouad, HM, Francis, DK, Franco Mdo, C, Frontera, G, Furusawa, T, Gaciong, Z, Galbarczyk, A, Garnett, SP, Gaspoz, JM, Gasull, M, Gates, L, Geleijnse, JM, Ghasemain, A, Giampaoli, S, Gianfagna, F, Giovannelli, J, Gonzalez Gross, M, González Rivas, JP, Gorbea, MB, Gottrand, F, Grant, JF, Grodzicki, T, Grøntved, A, Gruden, G, Gu, D, Guan, OP, Guerrero, R, Guessous, I, Guimaraes, AL, Gutierrez, L, Hardy, R, Hari Kumar, R, Heidemann, C, Hihtaniemi, IT, Ho, SY, Ho, SC, Hofman, A, Horimoto, AR, Hormiga, CM, Horta, BL, Houti, L, Hussieni, AS, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, AG, Ibrahim, MM, Ikeda, N, Ikram, MA, Irazola, VE, Islam, M, Iwasaki, M, Jacobs, JM, Jafar, T, Jasienska, G, Jiang, CQ, Jonas, JB, Joshi, P, Kafatos, A, Kalter-Leibovici, O, Kasaeian, A, Katz, J, Kaur, P, Kavousi, M, Kelishadi, R, Kengne, AP, Kersting, M, Khader, YS, Kiechl, S, Kim, J, Kiyohara, Y, Kolsteren, P, Korrovits, P, Koskinen, S, Kratzer, W, Kromhout, D, Kula, K, Kurjata, P, Kyobutungi, C, Lachat, C, Laid, Y, Lam, TH, Lanska, V, Lappas, G, Laxmaiah, A, Leclercq, C, Lee, J, Lehtimäki, T, Lekhraj, R, León-Muñoz, LM, Li, Y, Lim, WY, Lima-Costa, MF, Lin, HH, Lin, X, Lissner, L, Lorbeer, R, Lozano, JE, Lundqvist, A, Lytsy, P, Ma, G, Machado-Coelho, GL, Machi, S, Maggi, S, Makdisse, M, Mallikharjuna v, K, Manios, Y, Manzato, E, Margozzini, P, Marques-Vidal, P, Martorell, R, Masoodi, SR, Matsha, TE, Mbanya, JC, McFarlane, SR, McGarvey, ST, McLachlan, S, McNulty, BA, Mediene-Benchekor, S, Meirhaeghe, A, Menezes, AM, Merat, S, Meshram, II, Mi, J, Miquel, JF, Mohamed, MK, Mohammad, K, Mohan, V, Mohd Yusoff, MF, Møller, NC, Molnar, D, Mondo, CK, Moreno, LA, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mota, J, Muiesan, ML, Müller-Nurasyid, M, Mursu, J, Nagel, G, Námešná, J, Nang, EE, Nangia, VB, Navarrete-Muñoz, EM, Ndiaye, NC, Nervi, F, Nguyen, ND, Nieto-Martínez, RE, Alvarado, L, Ning, G, Ninomiya, T, Noale, M, Noto, D, Ochoa-Avilés, M, Oh, K, Onat, A, Osmond, C, Otero, JA, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pereira, AC, Peters, A, Peykari, N, Pilav, A, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubió, P, Porta, M, Portegies, ML, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Raitakari, O, Ramachandra Rao, S, Ramachandran, A, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PF, Rigo, F, Robinson, SM, Robitaille, C, Rodríguez, LA, Rodríguez-Artalejo, F, del Cristo Rodriguez-Perez, M, Rojas-Martinez, R, Romaguera, D, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sánchez-Abanto, J, Santos, RN, Santos, R, Sardinha, LB, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stöckl, D, Stokwiszewski, J, Strufaldi, MW, Sun, CA, Sundström, J, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tarawneh, M, Tarqui-Mamani, CB, Thijs, L, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OT, Tulloch-Reid, MK, Tuomainen, TP, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FM, Ulmer, H, Valdivia, G, van Valkengoed, IG, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, M, Vioque, J, Virtanen, J, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Wade, AN, Wagner, A, Walton, J, Mohamud, WN, Wang, MD, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wilks, RJ, Willeit, J, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, AG, Wu, FC, Wu, SL, Xu, H, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, and Zheng, Y

Published
2015
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16. Fracture risk in patients with type 2 diabetes under different antidiabetic treatment regimens: a retrospective database analysis in primary care

Author

Pscherer S, Kostev K, Dippel FW, and Rathmann W

Subjects
type 2 diabetes, fracture risk, insulin treatment, oral antidiabetic medication, primary care, Specialties of internal medicine, RC581-951
Abstract

S Pscherer,1 K Kostev,2 FW Dippel,3 W Rathmann4 1Department of Diabetology, Klinikum Traunstein, Kliniken Südostbayern AG, Traunstein, 2Epidemiology Department, IMS Health, Frankfurt, 3Sanofi-Aventis Deutschland GmbH, Berlin, 4German Diabetes Center, Institute for Biometrics and Epidemiology, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany Aim: Type 2 diabetes is associated with an increased risk of fractures. There are a few studies on the effects of diabetes treatment on fracture risk. The aim was to investigate the fracture risk related to various types of insulin therapy in primary care practices. Methods: Data from 105,960 type 2 diabetes patients from 1,072 general and internal medicine practices in Germany were retrospectively analyzed (Disease Analyzer database; 01/2000–12/2013). Fracture risk of the following therapies was compared using multivariate logistic regression models adjusting for age, sex, diabetes care, comorbidity, and glycemic control (HbAlc): 1) incident insulin therapy versus oral antidiabetic drugs, 2) basal-supported oral therapy versus supplementary insulin therapy versus conventional insulin therapy, and 3) insulin glargine versus insulin detemir versus NPH insulin. Results: There was a lower odds of having incident fractures in the oral antidiabetic drug group compared to incident insulin users, although not significant (odds ratio [OR]; 95% confidence interval: 0.87; 0.72–1.06). There were increased odds for conventional insulin therapy (OR: 1.59; 95% CI [confidence interval] 0.89–2.84) and supplementary insulin therapy (OR: 1.20; 0.63–2.27) compared to basal-supported oral therapy, which was not significant as well. Overall, there was no significant difference in fracture risk for basal insulins (glargine, detemir, NPH insulin). After a treatment duration ≥2 years, insulin glargine showed a lower odds of having ≥1 fracture compared to NPH users (OR: 0.78; 0.65–0.95) (detemir vs NPH insulin: OR: 1.03; 0.79–1.36). Conclusion: Long-standing therapy with insulin glargine was associated with a lower odds of having any fractures compared to NPH insulin. Further studies are required to investigate whether the lower chance is due to a reduced frequency of hypoglycemia. Keywords: type 2 diabetes, fracture risk, insulin treatment, oral antidiabetic medication, primary care

Published
2016

19. Glycemic control after initiating basal insulin therapy in patients with type 2 diabetes: a primary care database analysis

Author

Kostev K, Dippel FW, and Rathmann W

Subjects
Specialties of internal medicine, RC581-951
Abstract

Karel Kostev,1 Franz W Dippel,2 Wolfgang Rathmann3 1IMS Health, Frankfurt, 2Sanofi-Aventis Deutschland GmbH, Berlin, 3Institute of Biometrics and Epidemiology, German Diabetes Center, Düsseldorf, Germany Background: When target glycated hemoglobin (HbA1C) levels are not reached, basal insulin therapy should be considered in type 2 diabetes. The objective of this report was to describe the predictors of glycemic control (strict criterion: HbA1c ≤6.5%) during the first year after initiating basal insulin therapy in primary care.Methods: The study applied a retrospective approach using a nationwide database in Germany (Disease Analyzer, IMS Health, January 2008 to December 2011, including 1,024 general and internal medicine practices). Potential predictors of glycemic control considered were age, sex, duration of diabetes, type of basal insulin, comedication with short-acting insulin, baseline HbA1c, previous oral antidiabetic drugs, diabetologist care, private health insurance, macrovascular and microvascular comorbidity, and concomitant medication. Multivariable logistic regression models were fitted with glycemic control as the dependent variable.Results: A total of 4,062 type 2 diabetes patients started basal insulin (mean age 66 years, males 53%, diabetes duration 4.8 years, mean HbA1c 8.8%), of whom 295 (7.2%) achieved an HbA1c ≤6.5% during the one-year follow-up. Factors positively associated with HbA1c ≤6.5% in logistic regression were male sex (odds ratio 1.59, 95% confidence interval 1.23–2.04), insulin glargine (reference neutral protamine Hagedorn; odds ratio 1.43, 95% confidence interval 1.09–1.88), short-acting insulin (odds ratio 1.33, 95% confidence interval 1.01–1.76), and prior treatment with metformin, dipeptidyl peptidase-4 inhibitors, and diuretics. Lipid-lowering drugs were associated with a lower odds of reaching the glycemic target.Conclusion: Few type 2 diabetes patients (7%) reached the glycemic target (HbA1c ≤6.5%) after one year of basal insulin therapy. Achievement of the glycemic target was associated with type of basal insulin, additional short-acting insulins, previous antidiabetic medication, and other comedication, eg, diuretics or lipid-lowering drugs.Keywords: insulin initiation, type 2 diabetes, glycemic control, basal insulin, primary care

Published
2015

22. Metabolic flexibility and oxidative capacity independently associate with insulin sensitivity in individuals with newly diagnosed type 2 diabetes

Author

Apostolopoulou, Maria, Strassburger, Klaus, Herder, Christian, Knebel, Birgit, Kotzka, Jörg, Szendroedi, Julia, Roden, Michael, Roden, M., Buyken, A. E., Eckel, J., Geerling, G., Al-Hasani, H., Herder, C., Icks, A., Kotzka, J., Kuss, O., Lammert, E., Lundbom, J., Müssig, K., Nowotny, P., Rathmann, W., Szendroedi, J., Ziegler, D., and for the GDS group

Published
2016
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23. Longitudinal multivariable trajectory risk clusters and sex-specific association with MRI-derived cardiac function and structure in a population-based sample

Author

Lorbeer, R., primary, Rospleszcz, S., additional, Schlett, C., additional, Rado, S., additional, Thorand, B., additional, Meisinger, C., additional, Rathmann, W., additional, Heier, M., additional, Vasan, R., additional, Bamberg, F., additional, Peters, A., additional, and Lieb, W., additional

Published
2022
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24. Socioeconomic Factors Associated With Glycemic Measurement and Poor HbA1c Control in People With Type 2 Diabetes: The Global DISCOVER Study

Author

Gomes, MB, Tang, F, Chen, H, Cid-Ruzafa, J, Fenici, P, Khunti, K, Rathmann, W, Shestakova, MV, Surmont, F, Watada, H, Medina, J, Shimomura, I, Saraiva, GL, Cooper, A, Nicolucci, A, Gomes, MB, Tang, F, Chen, H, Cid-Ruzafa, J, Fenici, P, Khunti, K, Rathmann, W, Shestakova, MV, Surmont, F, Watada, H, Medina, J, Shimomura, I, Saraiva, GL, Cooper, A, and Nicolucci, A

Abstract

DISCOVER is a 3-year observational study program of 15,983 people with type 2 diabetes initiating second-line glucose-lowering therapy in 38 countries. We investigated the association between socioeconomic status and both the availability of a baseline glycated hemoglobin (HbA1c) measurement and poor glycemic control (HbA1c level ≥ 9.0%) in participants enrolled in DISCOVER. Factors associated with a lack of baseline HbA1c measurement or an HbA1c level ≥ 9.0% were assessed using three-level hierarchical logistic models. Overall, 19.1% of participants did not have a baseline HbA1c measurement recorded. Lower-middle country income (vs. high) and primary/no formal education (vs. university education) were independently associated with a reduced likelihood of having a baseline HbA1c measurement (odds ratio [95% confidence interval]: 0.11 [0.03-0.49] and 0.81 [0.66-0.98], respectively. Of the participants with an available HbA1c measurement, 26.9% had an HbA1c level ≥ 9.0%; 68.7% of these individuals were from lower- or upper-middle-income countries. Factors associated with an increased likelihood of poor glycemic control included low country income, treatment at a site with public and/or governmental funding (vs. private funding) and having public or no health insurance (vs. private). A substantial proportion of DISCOVER participants did not have an HbA1c measurement; more than one-quarter of these participants had poorly controlled type 2 diabetes. Both individual- and country-level socioeconomic factors are associated with the quality of care regarding glycemic control. Awareness of these factors could help improve the management of patients with type 2 diabetes.

Published
2022

25. Global use of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes. Results from DISCOVER

Author

Arnold, SV, Tang, F, Cooper, A, Chen, H, Gomes, MB, Rathmann, W, Shimomura, I, Vora, J, Watada, H, Khunti, K, Kosiborod, M, Arnold, SV, Tang, F, Cooper, A, Chen, H, Gomes, MB, Rathmann, W, Shimomura, I, Vora, J, Watada, H, Khunti, K, and Kosiborod, M

Abstract

BACKGROUND: Despite strong evidence of benefit, uptake of newer glucose-lowering medications that reduce cardiovascular risk has been low. We sought to examine global trends and predictors of use of SGLT2i and GLP-1 RA in patients with type 2 diabetes. METHODS: DISCOVER is a global, prospective, observational study of patients with diabetes enrolled from 2014-16 at initiation of second-line glucose-lowering therapy and followed for 3 years. We used hierarchical logistic regression to examine factors associated with use of either an SGLT2i or GLP-1 RA at last follow-up and to assess country-level variability. RESULTS: Among 14,576 patients from 37 countries, 1579 (10.8%) were started on an SGLT2i (1275; 8.7%) or GLP-1 RA (318; 2.2%) at enrollment, increasing to 16.1% at end of follow-up, with large variability across countries (range 0-62.7%). Use was highest in patients treated by cardiologists (26.1%) versus primary care physicians (10.4%), endocrinologists (16.9%), and other specialists (22.0%; p < 0.001). Coronary artery disease (OR 1.29, 95% CI 1.08-1.54) was associated with greater use of SGLT2i or GLP-1 RA while peripheral artery disease (OR 0.73, 95% CI 0.54-1.00) and chronic kidney disease (OR 0.73, 95% CI 0.58-0.94) were associated with lower use (OR 0.73, 95% CI 0.54-1.00). The country-level median odds ratio was 3.48, indicating a very large amount of variability in the use of SGLT2i or GLP-1 RA independent of patient demographic and clinical factors. CONCLUSIONS: Global use of glucose-lowering medications with established cardiovascular benefits has increased over time but remains suboptimal, particularly in sub-groups most likely to benefit. Substantial country-level variability exists independent of patient factors, suggesting structural barriers may limit more widespread use of these medications.

Published
2022

26. DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases

Author

Wielscher, M. (Matthias), Mandaviya, P. R. (Pooja R.), Kuehnel, B. (Brigitte), Joehanes, R. (Roby), Mustafa, R. (Rima), Robinson, O. (Oliver), Zhang, Y. (Yan), Bodinier, B. (Barbara), Walton, E. (Esther), Mishra, P. P. (Pashupati P.), Schlosser, P. (Pascal), Wilson, R. (Rory), Tsai, P.-C. (Pei-Chien), Palaniswamy, S. (Saranya), Marioni, R. E. (Riccardo E.), Fiorito, G. (Giovanni), Cugliari, G. (Giovanni), Karhunen, V. (Ville), Ghanbari, M. (Mohsen), Psaty, B. M. (Bruce M.), Loh, M. (Marie), Bis, J. C. (Joshua C.), Lehne, B. (Benjamin), Sotoodehnia, N. (Nona), Deary, I. J. (Ian J.), Chadeau-Hyam, M. (Marc), Brody, J. A. (Jennifer A.), Cardona, A. (Alexia), Selvin, E. (Elizabeth), Smith, A. K. (Alicia K.), Miller, A. H. (Andrew H.), Torres, M. A. (Mylin A.), Marouli, E. (Eirini), Gao, X. (Xin), van Meurs, J. B. (Joyce B. J.), Graf-Schindler, J. (Johanna), Rathmann, W. (Wolfgang), Koenig, W. (Wolfgang), Peters, A. (Annette), Weninger, W. (Wolfgang), Farlik, M. (Matthias), Zhang, T. (Tao), Chen, W. (Wei), Xia, Y. (Yujing), Teumer, A. (Alexander), Nauck, M. (Matthias), Grabe, H. J. (Hans J.), Doerr, M. (Macus), Lehtimaki, T. (Terho), Guan, W. (Weihua), Milani, L. (Lili), Tanaka, T. (Toshiko), Fisher, K. (Krista), Waite, L. L. (Lindsay L.), Kasela, S. (Silva), Vineis, P. (Paolo), Verweij, N. (Niek), van der Harst, P. (Pim), Iacoviello, L. (Licia), Sacerdote, C. (Carlotta), Panico, S. (Salvatore), Krogh, V. (Vittorio), Tumino, R. (Rosario), Tzala, E. (Evangelia), Matullo, G. (Giuseppe), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Colicino, E. (Elena), Baccarelli, A. A. (Andrea A.), Kahonen, M. (Mika), Herzig, K.-H. (Karl-Heinz), Li, S. (Shengxu), BIOS consortium, Conneely, K. N. (Karen N.), Kooner, J. S. (Jaspal S.), Kottgen, A. (Anna), Heijmans, B. T. (Bastiaan T.), Deloukas, P. (Panos), Relton, C. (Caroline), Ong, K. K. (Ken K.), Bell, J. T. (Jordana T.), Boerwinkle, E. (Eric), Elliott, P. (Paul), Brenner, H. (Hermann), Beekman, M. (Marian), Levy, D. (Daniel), Waldenberger, M. (Melanie), Chambers, J. C. (John C.), Dehghan, A. (Abbas), Järvelin, M.-R. (Marjo-Riitta), Wielscher, M. (Matthias), Mandaviya, P. R. (Pooja R.), Kuehnel, B. (Brigitte), Joehanes, R. (Roby), Mustafa, R. (Rima), Robinson, O. (Oliver), Zhang, Y. (Yan), Bodinier, B. (Barbara), Walton, E. (Esther), Mishra, P. P. (Pashupati P.), Schlosser, P. (Pascal), Wilson, R. (Rory), Tsai, P.-C. (Pei-Chien), Palaniswamy, S. (Saranya), Marioni, R. E. (Riccardo E.), Fiorito, G. (Giovanni), Cugliari, G. (Giovanni), Karhunen, V. (Ville), Ghanbari, M. (Mohsen), Psaty, B. M. (Bruce M.), Loh, M. (Marie), Bis, J. C. (Joshua C.), Lehne, B. (Benjamin), Sotoodehnia, N. (Nona), Deary, I. J. (Ian J.), Chadeau-Hyam, M. (Marc), Brody, J. A. (Jennifer A.), Cardona, A. (Alexia), Selvin, E. (Elizabeth), Smith, A. K. (Alicia K.), Miller, A. H. (Andrew H.), Torres, M. A. (Mylin A.), Marouli, E. (Eirini), Gao, X. (Xin), van Meurs, J. B. (Joyce B. J.), Graf-Schindler, J. (Johanna), Rathmann, W. (Wolfgang), Koenig, W. (Wolfgang), Peters, A. (Annette), Weninger, W. (Wolfgang), Farlik, M. (Matthias), Zhang, T. (Tao), Chen, W. (Wei), Xia, Y. (Yujing), Teumer, A. (Alexander), Nauck, M. (Matthias), Grabe, H. J. (Hans J.), Doerr, M. (Macus), Lehtimaki, T. (Terho), Guan, W. (Weihua), Milani, L. (Lili), Tanaka, T. (Toshiko), Fisher, K. (Krista), Waite, L. L. (Lindsay L.), Kasela, S. (Silva), Vineis, P. (Paolo), Verweij, N. (Niek), van der Harst, P. (Pim), Iacoviello, L. (Licia), Sacerdote, C. (Carlotta), Panico, S. (Salvatore), Krogh, V. (Vittorio), Tumino, R. (Rosario), Tzala, E. (Evangelia), Matullo, G. (Giuseppe), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Colicino, E. (Elena), Baccarelli, A. A. (Andrea A.), Kahonen, M. (Mika), Herzig, K.-H. (Karl-Heinz), Li, S. (Shengxu), BIOS consortium, Conneely, K. N. (Karen N.), Kooner, J. S. (Jaspal S.), Kottgen, A. (Anna), Heijmans, B. T. (Bastiaan T.), Deloukas, P. (Panos), Relton, C. (Caroline), Ong, K. K. (Ken K.), Bell, J. T. (Jordana T.), Boerwinkle, E. (Eric), Elliott, P. (Paul), Brenner, H. (Hermann), Beekman, M. (Marian), Levy, D. (Daniel), Waldenberger, M. (Melanie), Chambers, J. C. (John C.), Dehghan, A. (Abbas), and Järvelin, M.-R. (Marjo-Riitta)

Abstract

We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD.

Published
2022

27. Inflammatory markers are associated with cardiac autonomic dysfunction in recent-onset type 2 diabetes

Author

Herder, Christian, Schamarek, Imke, Nowotny, Bettina, Carstensen-Kirberg, Maren, Straburger, Klaus, Nowotny, Peter, Kannenberg, Julia M, Strom, Alexander, Püttgen, Sonja, Müssig, Karsten, Szendroedi, Julia, Roden, Michael, Ziegler, Dan, Buyken, A. E., Eckel, J., Geerling, G., Al-Hasani, H., Icks, A., Kotzka, J., Kuss, O., Lammert, E., Lundbom, J., and Rathmann, W.

Published
2017
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28. HbA1c for diagnosis of type 2 diabetes. Is there an optimal cut point to assess high risk of diabetes complications, and how well does the 6.5% cutoff perform?

Author

Kowall B and Rathmann W

Subjects
Specialties of internal medicine, RC581-951
Abstract

Bernd Kowall, Wolfgang Rathmann Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany Abstract: Glycated hemoglobin (HbA1c) has recently been recommended for the diagnosis of type 2 diabetes mellitus (T2DM) by leading diabetes organizations and by the World Health Organization. The most important reason to define T2DM is to identify subjects with high risk of diabetes complications who may benefit from treatment. This review addresses two questions: 1) to assess from existing studies whether there is an optimal HbA1c threshold to predict diabetes complications and 2) to assess how well the recommended 6.5% cutoff of HbA1c predicts diabetes complications. HbA1c cutoffs derived from predominantly cross-sectional studies on retinopathy differ widely from 5.2%–7.8%, and among other reasons, this is due to the heterogeneity of statistical methods and differences in the definition of retinopathy. From the few studies on other microvascular complications, HbA1c thresholds could not be identified. HbA1c cutoffs make less sense for the prediction of cardiovascular events (CVEs) because CVE risks depend on various strong risk factors (eg, hypertension, smoking); subjects with low HbA1c levels but high values of CVE risk factors were shown to be at higher CVE risk than subjects with high HbA1c levels and low values of CVE risk factors. However, the recommended 6.5% threshold distinguishes well between subjects with and subjects without retinopathy, and this distinction is particularly strong in severe retinopathy. Thus, in existing studies, the prevalence of any retinopathy was 2.5 to 4.5 times as high in persons with HbA1c-defined T2DM as in subjects with HbA1c

Published
2013

41. A panel of six biomarkers significantly improves the prediction of type 2 diabetes in the MONICA/KORA study population

Author

Thorand, B., Zierer, A., Büyüközkan, M., Krumsiek, J., Bauer, A., Schederecker, F., Sudduth-Klinger, J., Meisinger, C., Grallert, H., Rathmann, W., Roden, M., Peters, A., Koenig, W., Herder, C., and Huth, C.

Subjects
Biomarkers, Cohort Analysis, Risk Prediction Model, Type 2
Abstract

CONTEXT: Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most. OBJECTIVE: To assess whether novel biomarkers improve the prediction of type 2 diabetes beyond non-invasive standard clinical risk factors alone or in combination with HbA1c. DESIGN AND METHODS: We used a population-based case-cohort study for discovery (689 incident cases and 1,850 non-cases) and an independent cohort study (n=262 incident cases, 2,549 non-cases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the non-invasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C-index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c). RESULTS: Interleukin-1 receptor antagonist, insulin growth factor binding protein-2, soluble E-selectin, decorin, adiponectin, and high density lipoprotein-cholesterol were selected as most relevant. The simultaneous addition of these six biomarkers significantly improved the predictive performance in both the discovery (C-index [95% CI]: 0.053 [0.039-0.066]; cfNRI [95% CI]: 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies. CONCLUSION: The addition of six biomarkers significantly improved the prediction of type 2 diabetes when added to a non-invasive clinical model or to a clinical model plus HbA1c.

Published
2021

42. Association between type 2 diabetes and chronic low back pain in general practices in Germany

Author

Jacob L, Rathmann W, Koyanagi A, Haro JM, and Kostev K

Subjects
low back pain, epidemiology, cohort studies, type 2 diabetes
Abstract

INTRODUCTION: There are conflicting results on the association between type 2 diabetes and chronic low back pain (CLBP). Therefore, the goal was to investigate the relationship between type 2 diabetes and CLBP in individuals followed in general practices in Germany. RESEARCH DESIGN AND METHODS: Adults diagnosed for the first time with type 2 diabetes in 809 general practices in Germany between 2005 and 2018 (index date) were included. Adults without type 2 diabetes were matched (1:1) to those with type 2 diabetes by sex, age, index year, and the annual number of medical consultations (index date: a randomly selected visit date). The association between type 2 diabetes and the 10-year incidence of CLBP was analyzed in conditional Cox regression models adjusted for a wide range of comorbidities, including hypertension, lipid metabolism disorders, and obesity. RESULTS: There were 139 002 individuals included in this study (women: 58.0%; mean (SD) age 62.5 (13.4) years). There was a positive association between type 2 diabetes and the incidence of CLBP in the overall sample (HR=1.23, 95% CI: 1.13 to 1.35). Sex-stratified analyses showed a higher risk of CLBP in women (HR=1.68, 95% CI: 1.43 to 1.90) and a lower risk in men with than in their counterparts without type 2 diabetes (HR=0.83, 95% CI: 0.71 to 0.97). CONCLUSIONS: Newly diagnosed type 2 diabetes was associated with an increased risk of CLBP. There were important sex differences in the type 2 diabetes-CLBP relationship, and more research is warranted to investigate the underlying factors explaining these differences.

Published
2021

43. Prevalence and progression of chronic kidney disease among patients with type 2 diabetes: Insights from the DISCOVER study

Author

Khunti, K, Charbonnel, B, Chen, H, Cherney, DZ, Cooper, A, Fenici, P, Gomes, MB, Hammar, N, Heerspink, HJL, Ji, L, Medina, J, Nicolucci, A, Ramirez, L, Rathmann, W, Shestakova, M, Shimomura, I, Tang, F, Watada, H, Kosiborod, M, Khunti, K, Charbonnel, B, Chen, H, Cherney, DZ, Cooper, A, Fenici, P, Gomes, MB, Hammar, N, Heerspink, HJL, Ji, L, Medina, J, Nicolucci, A, Ramirez, L, Rathmann, W, Shestakova, M, Shimomura, I, Tang, F, Watada, H, and Kosiborod, M

Abstract

We report the prevalence and change in severity of chronic kidney disease (CKD) in DISCOVER, a global, 3-year, prospective, observational study of patients with type 2 diabetes (T2D) initiating second-line glucose-lowering therapy. CKD stages were defined according to estimated glomerular filtration rate (eGFR). Overall, 7843 patients from 35 countries had a baseline serum creatinine measurement. Of these (56.7% male; mean age: 58.1 years; mean eGFR: 87.5 mL/min/1.73 m2 ), baseline prevalence estimates for stage 0-1, 2, 3 and 4-5 CKD were 51.4%, 37.7%, 9.4% and 1.4%, respectively. A total of 5819 patients (74.2%) also had at least one follow-up serum creatinine measurement (median time between measurements: 2.9 years, interquartile range: 1.9-3.0 years). Mean eGFR decreased slightly to 85.7 mL/min/1.73 m2 over follow-up. CKD progression (increase of ≥1 stage) occurred in 15.7% of patients, and regression (decrease of ≥1 stage) in 12.0%. In summary, a substantial proportion of patients with T2D developed CKD or had CKD progression after the initiation of second-line therapy. Renal function should be regularly monitored in these patients, to ensure early CKD diagnosis and treatment.

Published
2021

44. Associations between second-line glucose-lowering combination therapies with metformin and HbA1c, body weight, quality of life, hypoglycaemic events and glucose-lowering treatment intensification: The DISCOVER study

Author

Khunti, K, Charbonnel, B, Cooper, A, Gomes, MB, Ji, L, Leigh, P, Nicolucci, A, Rathmann, W, Shestakova, MV, Siddiqui, A, Tang, F, Watada, H, Chen, H, Khunti, K, Charbonnel, B, Cooper, A, Gomes, MB, Ji, L, Leigh, P, Nicolucci, A, Rathmann, W, Shestakova, MV, Siddiqui, A, Tang, F, Watada, H, and Chen, H

Abstract

AIM: To explore the effects of second-line combination therapies with metformin on body weight, HbA1c and health-related quality of life, as well as the risks of hypoglycaemia and further treatment intensification in the DISCOVER study, a 3-year, prospective, global observational study of patients with type 2 diabetes initiating second-line glucose-lowering therapy. MATERIALS AND METHODS: Adjusted changes from baseline in weight, HbA1c and 36-item Short Form Health Survey version 2 (SF-36v2) summary scores at 6, 12, 24 and 36 months were assessed using linear mixed models. Risk of hypoglycaemia and further intensification were assessed using interval censored analyses. RESULTS: At baseline, 7613 patients received metformin in combination with a sulphonylurea (SU; 40.9%), a dipeptidyl peptidase-4 (DPP-4) inhibitor (48.3%), a sodium-glucose co-transporter-2 (SGLT-2) inhibitor (8.3%) or a glucagon-like peptide-1 (GLP-1) receptor agonist (2.4%). After 36 months, all combinations showed similar reductions in HbA1c (0.8%-1.0%), however, metformin plus a DPP-4 inhibitor, an SGLT-2 inhibitor or a GLP-1 receptor agonist was associated with greater weight loss (1.9, 2.9 and 5.0 kg, respectively) than metformin plus an SU (1.3 kg, P < .0001). Proportions of further treatment intensification were similar across combinations (19.9%-26.2%). Patients prescribed metformin plus an SU more often reported one or more hypoglycaemic events (11.9%) than other combinations (3.9%-6.4%, P < .0001). SF-36v2 summary scores were typically lowest among patients prescribed metformin and an SU. CONCLUSIONS: Combinations of metformin with an SU were associated with the lowest weight reduction, highest risk of hypoglycaemia and lower SF-36v2 scores.

Published
2021

45. Inappropriate intensification of glucose-lowering treatment in older patients with type 2 diabetes: the global DISCOVER study

Author

Bongaerts, B, Arnold, S, Charbonnel, BH, Chen, H, Cooper, A, Fenici, P, Gomes, M, Ji, L, Khunti, K, Kosiborod, M, Medina, J, Nicolucci, A, Shestakova, M, Shimomura, I, Tang, F, Watada, H, Rathmann, W, Bongaerts, B, Arnold, S, Charbonnel, BH, Chen, H, Cooper, A, Fenici, P, Gomes, M, Ji, L, Khunti, K, Kosiborod, M, Medina, J, Nicolucci, A, Shestakova, M, Shimomura, I, Tang, F, Watada, H, and Rathmann, W

Abstract

INTRODUCTION: Although individualized target glycated hemoglobin (HbA1c) levels are recommended in older people with type 2 diabetes, studies report high levels of potential overtreatment. We aimed to investigate the proportion of older patients (aged ≥65 years) who potentially received an inappropriately intensive treatment (HbA1c level <7.0% (53.0 mmol/mol)) in a global study. Factors associated with intensive glycemic management and using glucose-lowering medications associated with a high risk of hypoglycemia (high-risk medications (insulin, sulfonylureas, and meglitinides)) were also assessed. RESEARCH DESIGN AND METHODS: DISCOVER is a 3-year observational study program of 15 992 people with type 2 diabetes initiating second-line glucose-lowering therapy in 38 countries. Data were collected at baseline (initiation of second-line therapy) and at 6, 12, and 24 months. Factors associated with an inappropriately intensive treatment or using high-risk medications were assessed using a hierarchical regression model. RESULTS: Of the 3344 older patients with baseline HbA1c data in our analytic cohort, 23.5% received inappropriate treatment intensification. Among those who had follow-up HbA1c data, 55.2%, 54.2%, and 53.5% were inappropriately tightly controlled at 6, 12, and 24 months, respectively, with higher proportions in high-income than in middle-income countries. The proportion of patients receiving high-risk medications was higher in middle-income countries than in high-income countries. Gross national income (per US$5000 increment) was associated with increased odds of inappropriately intensive treatment but with decreased odds of receiving high-risk medications. CONCLUSIONS: A large proportion of older DISCOVER patients received an inappropriately intensive glucose-lowering treatment across the 2 years of follow-up, with substantial regional variation. The use of high-risk medications in these patients is particularly concerning.

Published
2021

46. DNA methylation and lipid metabolism:an EWAS of 226 metabolic measures

Author

Gomez-Alonso, M. d. (Monica del C.), Kretschmer, A. (Anja), Wilson, R. (Rory), Pfeiffer, L. (Liliane), Karhunen, V. (Ville), Seppala, I. (Ilkka), Zhang, W. (Weihua), Mittelstrass, K. (Kirstin), Wahl, S. (Simone), Matias-Garcia, P. R. (Pamela R.), Prokisch, H. (Holger), Horn, S. (Sacha), Meitinger, T. (Thomas), Serrano-Garcia, L. R. (Luis R.), Sebert, S. (Sylvain), Raitakari, O. (Olli), Loh, M. (Marie), Rathmann, W. (Wolfgang), Mueller-Nurasyid, M. (Martina), Herder, C. (Christian), Roden, M. (Michael), Hurme, M. (Mikko), Jarvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), Kooner, J. S. (Jaspal S.), Peters, A. (Annette), Lehtimaki, T. (Terho), Chambers, J. C. (John C.), Gieger, C. (Christian), Kettunen, J. (Johannes), Waldenberger, M. (Melanie), Gomez-Alonso, M. d. (Monica del C.), Kretschmer, A. (Anja), Wilson, R. (Rory), Pfeiffer, L. (Liliane), Karhunen, V. (Ville), Seppala, I. (Ilkka), Zhang, W. (Weihua), Mittelstrass, K. (Kirstin), Wahl, S. (Simone), Matias-Garcia, P. R. (Pamela R.), Prokisch, H. (Holger), Horn, S. (Sacha), Meitinger, T. (Thomas), Serrano-Garcia, L. R. (Luis R.), Sebert, S. (Sylvain), Raitakari, O. (Olli), Loh, M. (Marie), Rathmann, W. (Wolfgang), Mueller-Nurasyid, M. (Martina), Herder, C. (Christian), Roden, M. (Michael), Hurme, M. (Mikko), Jarvelin, M.-R. (Marjo-Riitta), Ala-Korpela, M. (Mika), Kooner, J. S. (Jaspal S.), Peters, A. (Annette), Lehtimaki, T. (Terho), Chambers, J. C. (John C.), Gieger, C. (Christian), Kettunen, J. (Johannes), and Waldenberger, M. (Melanie)

Abstract

Background: The discovery of robust and trans-ethnically replicated DNA methylation markers of metabolic phenotypes, has hinted at a potential role of epigenetic mechanisms in lipid metabolism. However, DNA methylation and the lipid compositions and lipid concentrations of lipoprotein sizes have been scarcely studied. Here, we present an epigenome-wide association study (EWAS) (N = 5414 total) of mostly lipid-related metabolic measures, including a fine profiling of lipoproteins. As lipoproteins are the main players in the different stages of lipid metabolism, examination of epigenetic markers of detailed lipoprotein features might improve the diagnosis, prognosis, and treatment of metabolic disturbances. Results: We conducted an EWAS of leukocyte DNA methylation and 226 metabolic measurements determined by nuclear magnetic resonance spectroscopy in the population-based KORA F4 study (N = 1662) and replicated the results in the LOLIPOP, NFBC1966, and YFS cohorts (N = 3752). Follow-up analyses in the discovery cohort included investigations into gene transcripts, metabolic-measure ratios for pathway analysis, and disease endpoints. We identified 161 associations (p value < 4.7 × 10⁻¹⁰), covering 16 CpG sites at 11 loci and 57 metabolic measures. Identified metabolic measures were primarily medium and small lipoproteins, and fatty acids. For apolipoprotein B-containing lipoproteins, the associations mainly involved triglyceride composition and concentrations of cholesterol esters, triglycerides, free cholesterol, and phospholipids. All associations for HDL lipoproteins involved triglyceride measures only. Associated metabolic measure ratios, proxies of enzymatic activity, highlight amino acid, glucose, and lipid pathways as being potentially epigenetically implicated. Five CpG sites in four genes were associated with differential expression of transcripts in blood or adipose tissue. CpG sites in ABCG1 and PHGDH showed associations with metabolic measures

Published
2021

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