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1. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

Author

't Hart, L.M., Abdalla, M., Adam, J., Adamski, J., Adragni, K., Allin, K.H., Arumugam, M., Atabaki Pasdar, N., Baltauss, T., Banasik, K.B., Baum, P., Bell, J.D., Bergstrom, M., Beulens, J.W., Bianzano, S., Bizzotto, R., Bonneford, A., Brorsson, C.A.B., Brown, A.A., Brunak, S.B., Cabrelli, L., Caiazzo, R., Canouil, M., Dale, M., Davtian, D., Dawed, A.Y., De Masi, F.M., de Preville, N., Dekkers, K.F., Dermitzakis, E.T., Deshmukh, H.A., Dings, C., Donnelly, L., Dutta, A., Ehrhardt, B., Elders, P.J.M., Engel Thomas, C.E.T., Engelbrechtsen, L., Eriksen, R.G., Eriksen, R.E., Fan, Y., Fernandez, J., Ferrer, J., Fitipaldi, H., Forgie, I.M., Forman, A., Franks, P.W., Frau, F., Fritsche, A., Froguel, P., Frost, G., Gassenhuber, J., Giordano, G.N., Giorgino, T., Gough, S., Graefe-Mody, U., Grallert, H., Grempler, R., Groeneveld, L., Groop, L., Gudmundsdóttir, V.G., Gupta, R.G., Haid, M., Hansen, T., Hansen, T.H., Hattersley, A.T., Haussler, R.S., Heggie, A.J., Hennige, A.M., Hill, A.V., Holl, R.W., Hong, M.-G., Hudson, M., Jablonka, B., Jennison, C., Jiao, J., Johansen, J.J., Jones, A.G., Jonsson, A., Karaderi, T.K., Kaye, J., Klintenberg, M., Koivula, R.W., Kokkola, T., Koopman, A.D.M., Kurbasic, A, Kuulasmaa, T., Laakso, M., Lehr, T., Loftus, H., Lundbye Allesøe, R.L.A, Mahajan, A., Mari, A., Mazzoni, G.M., McCarthy, M.I., McDonald, T.J., McEvoy, D., McRobert, N., McVittie, I., Mourby, M., Musholt, P., Mutie, P, Nice, R., Nicolay, C., Nielsen, A.M.N., Nilsson, B.N., Palmer, C.N., Pattou, F., Pavo, I., Pearson, E.R., Pedersen, O., Pedersen, H.K.P., Perry, M.H., Pomares-Millan, H., Ramisch, A., Rasmussen, S.R., Raverdi, V., Ridderstrale, M., Robertson, N., Roderick, R.C., Rodriquez, M., Ruetten, H., Rutters, F., Sackett, W., Scherer, N., Schwenk, J.M., Shah, N., Sharma, S., Sihinevich, I., Sondertoft, N.B., Staerfeldt, H., Steckel-Hamann, B., Teare, H., Thomas, M.K., Thomas, E.L., Thomsen, H.S., Thorand, B., Thorne, C.E., Tillner, J., Troen Lundgaard, A.T.L., Troll, M., Tsirigos, K.D.T., Tura, A., Uhlen, M., van Leeuwen, N., van Oort, S., Verkindt, H., Vestergaard, H., Viñuela, A., Vogt, J.K, Wad Sackett, P.W.S, Wake, D., Walker, M., Wesolowska-Andersen, A., Whitcher, B., White, M.W., Wu, H., Dawed, Adem Y, Mari, Andrea, Brown, Andrew, McDonald, Timothy J, Li, Lin, Wang, Shuaicheng, Hong, Mun-Gwan, Sharma, Sapna, Robertson, Neil R, Mahajan, Anubha, Wang, Xuan, Walker, Mark, Gough, Stephen, Hart, Leen M ‘t, Zhou, Kaixin, Forgie, Ian, Ruetten, Hartmut, Pavo, Imre, Bhatnagar, Pallav, Jones, Angus G, and Pearson, Ewan R

Published
2023
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7. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

Author

Dawed, Adem Y, primary, Mari, Andrea, additional, Brown, Andrew, additional, McDonald, Timothy J, additional, Li, Lin, additional, Wang, Shuaicheng, additional, Hong, Mun-Gwan, additional, Sharma, Sapna, additional, Robertson, Neil R, additional, Mahajan, Anubha, additional, Wang, Xuan, additional, Walker, Mark, additional, Gough, Stephen, additional, Hart, Leen M ‘t, additional, Zhou, Kaixin, additional, Forgie, Ian, additional, Ruetten, Hartmut, additional, Pavo, Imre, additional, Bhatnagar, Pallav, additional, Jones, Angus G, additional, Pearson, Ewan R, additional, 't Hart, L.M., additional, Abdalla, M., additional, Adam, J., additional, Adamski, J., additional, Adragni, K., additional, Allin, K.H., additional, Arumugam, M., additional, Atabaki Pasdar, N., additional, Baltauss, T., additional, Banasik, K.B., additional, Baum, P., additional, Bell, J.D., additional, Bergstrom, M., additional, Beulens, J.W., additional, Bianzano, S., additional, Bizzotto, R., additional, Bonneford, A., additional, Brorsson, C.A.B., additional, Brown, A.A., additional, Brunak, S.B., additional, Cabrelli, L., additional, Caiazzo, R., additional, Canouil, M., additional, Dale, M., additional, Davtian, D., additional, Dawed, A.Y., additional, De Masi, F.M., additional, de Preville, N., additional, Dekkers, K.F., additional, Dermitzakis, E.T., additional, Deshmukh, H.A., additional, Dings, C., additional, Donnelly, L., additional, Dutta, A., additional, Ehrhardt, B., additional, Elders, P.J.M., additional, Engel Thomas, C.E.T., additional, Engelbrechtsen, L., additional, Eriksen, R.G., additional, Eriksen, R.E., additional, Fan, Y., additional, Fernandez, J., additional, Ferrer, J., additional, Fitipaldi, H., additional, Forgie, I.M., additional, Forman, A., additional, Franks, P.W., additional, Frau, F., additional, Fritsche, A., additional, Froguel, P., additional, Frost, G., additional, Gassenhuber, J., additional, Giordano, G.N., additional, Giorgino, T., additional, Gough, S., additional, Graefe-Mody, U., additional, Grallert, H., additional, Grempler, R., additional, Groeneveld, L., additional, Groop, L., additional, Gudmundsdóttir, V.G., additional, Gupta, R.G., additional, Haid, M., additional, Hansen, T., additional, Hansen, T.H., additional, Hattersley, A.T., additional, Haussler, R.S., additional, Heggie, A.J., additional, Hennige, A.M., additional, Hill, A.V., additional, Holl, R.W., additional, Hong, M.-G., additional, Hudson, M., additional, Jablonka, B., additional, Jennison, C., additional, Jiao, J., additional, Johansen, J.J., additional, Jones, A.G., additional, Jonsson, A., additional, Karaderi, T.K., additional, Kaye, J., additional, Klintenberg, M., additional, Koivula, R.W., additional, Kokkola, T., additional, Koopman, A.D.M., additional, Kurbasic, A, additional, Kuulasmaa, T., additional, Laakso, M., additional, Lehr, T., additional, Loftus, H., additional, Lundbye Allesøe, R.L.A, additional, Mahajan, A., additional, Mari, A., additional, Mazzoni, G.M., additional, McCarthy, M.I., additional, McDonald, T.J., additional, McEvoy, D., additional, McRobert, N., additional, McVittie, I., additional, Mourby, M., additional, Musholt, P., additional, Mutie, P, additional, Nice, R., additional, Nicolay, C., additional, Nielsen, A.M.N., additional, Nilsson, B.N., additional, Palmer, C.N., additional, Pattou, F., additional, Pavo, I., additional, Pearson, E.R., additional, Pedersen, O., additional, Pedersen, H.K.P., additional, Perry, M.H., additional, Pomares-Millan, H., additional, Ramisch, A., additional, Rasmussen, S.R., additional, Raverdi, V., additional, Ridderstrale, M., additional, Robertson, N., additional, Roderick, R.C., additional, Rodriquez, M., additional, Ruetten, H., additional, Rutters, F., additional, Sackett, W., additional, Scherer, N., additional, Schwenk, J.M., additional, Shah, N., additional, Sharma, S., additional, Sihinevich, I., additional, Sondertoft, N.B., additional, Staerfeldt, H., additional, Steckel-Hamann, B., additional, Teare, H., additional, Thomas, M.K., additional, Thomas, E.L., additional, Thomsen, H.S., additional, Thorand, B., additional, Thorne, C.E., additional, Tillner, J., additional, Troen Lundgaard, A.T.L., additional, Troll, M., additional, Tsirigos, K.D.T., additional, Tura, A., additional, Uhlen, M., additional, van Leeuwen, N., additional, van Oort, S., additional, Verkindt, H., additional, Vestergaard, H., additional, Viñuela, A., additional, Vogt, J.K, additional, Wad Sackett, P.W.S, additional, Wake, D., additional, Walker, M., additional, Wesolowska-Andersen, A., additional, Whitcher, B., additional, White, M.W., additional, and Wu, H., additional

Published
2023
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8. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Author

Wesolowska-Andersen A, Brorsson CA, Bizzotto R, Mari A, Tura A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Prehn C, Artati A, Hong MG, Musholt PB, Kurbasic A, De Masi F, Tsirigos K, Pedersen HK, Gudmundsdottir V, Thomas CE, Banasik K, Jennison C, Jones A, Kennedy G, Bell J, Thomas L, Frost G, Thomsen H, Allin K, Hansen TH, Vestergaard H, Hansen T, Rutters F, Elders P, t'Hart L, Bonnefond A, Canouil M, Brage S, Kokkola T, Heggie A, McEvoy D, Hattersley A, McDonald T, Teare H, Ridderstrale M, Walker M, Forgie I, Giordano GN, Froguel P, Pavo I, Ruetten H, Pedersen O, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, Pearson E, McCarthy MI, Brunak S, and ID Consortium

Subjects
General Economics, Econometrics and Finance
Published
2022
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11. Genetic analysis of blood molecular phenotypes reveals regulatory networks affecting complex traits: a DIRECT study

Author

Petra J. M. Elders, Andrea Mari, Femke Rutters, Musholt P, Caiazzo R, Ana Viñuela, Harriet Teare, Fernandez J, H Grallert, McEvoy D, Kristine H. Allin, Pattou F, Jochen M. Schwenk, Pavo I, Mourby M, Dupuis T, Giuseppe N. Giordano, Tarja Kokkola, Andrew T. Hattersley, Adam J, Jagadish Vangipurapu, Ian M Forgie, Anubha Mahajan, Cédric Howald, Caroline Brorsson, Adamski J, Henrik Vestergaard, Gary Frost, Emmanouil T. Dermitzakis, Thomas Willum Hansen, Alison Heggie, Deborah Penet, Sapna Sharma, McDonald Tj, Mark Haid, De Masi F, Raverdy, Bernd Jablonka, Paul W. Franks, Robert W. Koivula, Andrew A. Brown, Søren Brunak, Mark I. McCarthy, Konstantinos D. Tsirigos, Angus G. Jones, Ridderstrale M, Mun-Gwan Hong, E R Pearson, Markku Laakso, Birgitte Nilsson, Davtian D, T’Hart Lm, Walker M, Oluf Pedersen, Ruetten H, Henna Cederberg, and Luciana Romano

Subjects
Genetics, Pleiotropy, Genetic variation, Genome-wide association study, Allelic heterogeneity, Biology, Genetic analysis, Gene, Phenotype, Genetic association
Abstract

Genetic variants identified by genome-wide association studies can contribute to disease risk by altering the production and abundance of mRNA, proteins and other molecules. However, the interplay between molecular intermediaries that define the pathway from genetic variation to disease is not well understood. Here, we evaluated the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3,029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant was associated with multiple molecular phenotypes over multiple genomic regions. We find varying proportions of shared genetic regulation across phenotypes, highest between expression and proteins (66.6%). We were able to recapitulate a substantial proportion of gene expression genetic regulation in a diverse set of 44 tissues, with a median of 88% shared associations for blood expression and 22.3% for plasma proteins. Finally, the genetic and molecular associations were represented in networks including 2,828 known GWAS variants. One sub-network shows the trans relationship between rs149007767 and RTEN, and identifies GRB10 and IKZF1 as candidates mediating genes. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants across different molecular phenotypes.

Published
2021
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15. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, N, Ohlsson, M, Vinuela, A, Frau, F, Pomares-Millan, H, Haid, M, Jones, AG, Thomas, EL, Koivula, RW, Kurbasic, A, Mutie, PM, Fitipaldi, H, Fernandez, J, Dawed, AY, Giordano, GN, Forgie, IM, McDonald, TJ, Rutters, F, Cederberg, H, Chabanova, E, Dale, M, Masi, FD, Thomas, CE, Allin, KH, Hansen, TH, Heggie, A, Hong, M-G, Elders, PJM, Kennedy, G, Kokkola, T, Pedersen, HK, Mahajan, A, McEvoy, D, Pattou, F, Raverdy, V, Haussler, RS, Sharma, S, Thomsen, HS, Vangipurapu, J, Vestergaard, H, 't Hart, LM, Adamski, J, Musholt, PB, Brage, S, Brunak, S, Dermitzakis, E, Frost, G, Hansen, T, Laakso, M, Pedersen, O, Ridderstrale, M, Ruetten, H, Hattersley, AT, Walker, M, Beulens, JWJ, Mari, A, Schwenk, JM, Gupta, R, McCarthy, MI, Pearson, ER, Bell, JD, Pavo, I, Franks, PW, Epidemiology and Data Science, General practice, APH - Health Behaviors & Chronic Diseases, Amsterdam Reproduction & Development (AR&D), ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Aging & Later Life, Atabaki-Pasdar, Naeimeh [0000-0001-7229-1888], Ohlsson, Mattias [0000-0003-1145-4297], Viñuela, Ana [0000-0003-3771-8537], Pomares-Millan, Hugo [0000-0001-9245-4576], Haid, Mark [0000-0001-6118-1333], Jones, Angus G. [0000-0002-0883-7599], Thomas, E. Louise [0000-0003-4235-4694], Koivula, Robert W. [0000-0002-1646-4163], Kurbasic, Azra [0000-0002-1910-2619], Fitipaldi, Hugo [0000-0001-5352-2134], Dawed, Adem Y. [0000-0003-0224-2428], Forgie, Ian M. [0000-0002-8800-6145], Cederberg, Henna [0000-0003-2901-9373], Dale, Matilda [0000-0002-5788-7744], Masi, Federico De [0000-0003-4859-4170], Thomas, Cecilia Engel [0000-0001-6201-6380], Allin, Kristine H. [0000-0002-6880-5759], Hansen, Tue H. [0000-0001-5948-8993], Elders, Petra J. M. [0000-0002-5907-7219], Kennedy, Gwen [0000-0002-9856-3236], Kokkola, Tarja [0000-0002-3303-3912], Pedersen, Helle Krogh [0000-0001-9609-7377], Mahajan, Anubha [0000-0001-5585-3420], McEvoy, Donna [0000-0003-1546-5567], Häussler, Ragna S. [0000-0003-1664-8875], Vangipurapu, Jagadish [0000-0001-6657-2659], Vestergaard, Henrik [0000-0003-3090-269X], ‘t Hart, Leen M. [0000-0003-4401-2938], Brage, Soren [0000-0002-1265-7355], Frost, Gary [0000-0003-0529-6325], Hansen, Torben [0000-0001-8748-3831], Hattersley, Andrew T. [0000-0001-5620-473X], Mari, Andrea [0000-0002-1436-5591], Schwenk, Jochen M. [0000-0001-8141-8449], Gupta, Ramneek [0000-0001-6841-6676], McCarthy, Mark I. [0000-0002-4393-0510], Pearson, Ewan R. [0000-0001-9237-8585], Bell, Jimmy D. [0000-0003-3804-1281], Franks, Paul W. [0000-0002-0520-7604], Apollo - University of Cambridge Repository, HUS Abdominal Center, Clinicum, Department of Medicine, Endokrinologian yksikkö, Jones, Angus G [0000-0002-0883-7599], Thomas, E Louise [0000-0003-4235-4694], Koivula, Robert W [0000-0002-1646-4163], Dawed, Adem Y [0000-0003-0224-2428], Forgie, Ian M [0000-0002-8800-6145], Allin, Kristine H [0000-0002-6880-5759], Hansen, Tue H [0000-0001-5948-8993], Elders, Petra JM [0000-0002-5907-7219], Häussler, Ragna S [0000-0003-1664-8875], 't Hart, Leen M [0000-0003-4401-2938], Hattersley, Andrew T [0000-0001-5620-473X], Schwenk, Jochen M [0000-0001-8141-8449], McCarthy, Mark I [0000-0002-4393-0510], Pearson, Ewan R [0000-0001-9237-8585], Bell, Jimmy D [0000-0003-3804-1281], Franks, Paul W [0000-0002-0520-7604], and IMI

Subjects
Male, Proteomics, Oral Glucose Suppression Test, Biochemistry, Machine Learning, Fats, Database and Informatics Methods, Endocrinology, Medicine and Health Sciences, Insulin, Prospective Studies, 11 Medical and Health Sciences, GLOBAL EPIDEMIOLOGY, INSULIN SENSITIVITY, Proteomic Databases, Liver Diseases, Middle Aged, Lipids, Medicine, Female, Life Sciences & Biomedicine, Research Article, Computer and Information Sciences, Endocrine Disorders, BIOMARKERS, Gastroenterology and Hepatology, Research and Analysis Methods, Risk Assessment, Diabetes Complications, Medicine, General & Internal, SDG 3 - Good Health and Well-being, Artificial Intelligence, General & Internal Medicine, NAFLD, Diabetes Mellitus, Humans, Metabolomics, Diabetic Endocrinology, Pharmacology, Science & Technology, Models, Statistical, Reproducibility of Results, Biology and Life Sciences, ALCOHOLIC STEATOHEPATITIS, Hormones, Pharmacologic-Based Diagnostics, Fatty Liver, Metabolism, Biological Databases, 3121 General medicine, internal medicine and other clinical medicine, Metabolic Disorders
Abstract

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (, In a modelling study, Naeimeh Atabaki-Pasdar and colleagues apply machine learning techniques to develop models to predict non-alcoholic fatty liver disease diagnosis using multi-omic and clinical data from individuals with and without type 2 diabetes in the IMI DIRECT cohorts., Author summary Why was this study done? Globally, about 1 in 4 adults have non-alcoholic fatty liver disease (NAFLD), which adversely affects energy homeostasis (in particular blood glucose concentrations), blood detoxification, drug metabolism, and food digestion. Although numerous noninvasive tests to detect NAFLD exist, these typically include inaccurate blood-marker tests or expensive imaging methods. The purpose of this work was to develop accurate noninvasive methods to aid in the clinical prediction of NAFLD. What did the researchers do and find? The analyses applied machine learning methods to data from the deep-phenotyped IMI DIRECT cohorts (n = 1,514) to identify sets of highly informative variables for the prediction of NAFLD. The criterion measure was liver fat quantified from MRI. We developed a total of 18 prediction models that ranged from very inexpensive models of modest accuracy to more expensive biochemistry- and/or omics-based models with high accuracy. We found that models using measures commonly collected in either clinical settings or research studies proved adequate for the prediction of NAFLD. The addition of detailed omics data significantly improved the predictive utility of these models. We also found that of all omics markers, proteomic markers yielded the highest predictive accuracy when appropriately combined. What do these findings mean? We envisage that these new approaches to predicting fatty liver may be of clinical value when screening at-risk populations for NAFLD. The identification of specific molecular features that underlie the development of NAFLD provides novel insights into the disease’s etiology, which may lead to the development of new treatments.

Published
2020
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16. Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time:An IMI-DIRECT study

Author

Obura, M., Beulens, J. W. J., Slieker, R., Koopman, A. D. M., Hoekstra, T., Nijpels, G., Elders, P., Dekker, J. M., Koivula, R. W., Kurbasic, A., Laakso, M., Hansen, T. H., Ridderstråle, M., Hansen, T., Pavo, I., Forgie, I., Jablonka, B., Ruetten, H., Mari, A., McCarthy, M. I., Walker, M., McDonald, T. J., Perry, M. H., Pearson, E. R., Franks, P. W., 't Hart, L. M., Rutters, F., Obura, M., Beulens, J. W. J., Slieker, R., Koopman, A. D. M., Hoekstra, T., Nijpels, G., Elders, P., Dekker, J. M., Koivula, R. W., Kurbasic, A., Laakso, M., Hansen, T. H., Ridderstråle, M., Hansen, T., Pavo, I., Forgie, I., Jablonka, B., Ruetten, H., Mari, A., McCarthy, M. I., Walker, M., McDonald, T. J., Perry, M. H., Pearson, E. R., Franks, P. W., 't Hart, L. M., and Rutters, F.

Abstract

Aim To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration.Methods We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months.Results At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (beta = 0.36, 95% CI 0.13-0.58), Subgroup 3 (beta = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (beta = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months.Conclusions Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

Published
2021

19. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes : an IMI DIRECT study

Author

Koivula, R. W., Atabaki-Pasdar, N., Giordano, G. N., White, T., Adamski, J., Bell, J. D., Beulens, J., Brage, S., Brunak, S., De Masi, F., Dermitzakis, E. T., Forgie, I. M., Frost, G., Hansen, T., Hansen, T. H., Hattersley, A., Kokkola, T., Kurbasic, A., Laakso, M., Mari, A., McDonald, T. J., Pedersen, O., Rutters, F., Schwenk, Jochen M., Teare, H. J. A., Thomas, E. L., Vinuela, A., Mahajan, A., McCarthy, M. I., Ruetten, H., Walker, M., Pearson, E., Pavo, I., Franks, P. W., Consortium, for the IMI DIRECT, Koivula, R. W., Atabaki-Pasdar, N., Giordano, G. N., White, T., Adamski, J., Bell, J. D., Beulens, J., Brage, S., Brunak, S., De Masi, F., Dermitzakis, E. T., Forgie, I. M., Frost, G., Hansen, T., Hansen, T. H., Hattersley, A., Kokkola, T., Kurbasic, A., Laakso, M., Mari, A., McDonald, T. J., Pedersen, O., Rutters, F., Schwenk, Jochen M., Teare, H. J. A., Thomas, E. L., Vinuela, A., Mahajan, A., McCarthy, M. I., Ruetten, H., Walker, M., Pearson, E., Pavo, I., Franks, P. W., and Consortium, for the IMI DIRECT

Abstract

Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially support the mechanis, QC 20200626

Published
2020
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20. A reference map of potential determinants for the human serum metabolome

Author

Bar, N., Korem, T., Weissbrod, O., Zeevi, D., Rothschild, D., Leviatan, S., Kosower, N., Lotan-Pompan, M., Weinberger, A., Le Roy, C. I., Menni, C., Visconti, A., Falchi, M., Spector, T. D., Vestergaard, H., Arumugam, M., Hansen, T., Allin, K., Hong, Mun-Gwan, Schwenk, Jochen M., Häussler, Ragna S., Dale, Matilda, Giorgino, T., Rodriquez, M., Perry, M., Nice, R., McDonald, T., Hattersley, A., Jones, A., Graefe-Mody, U., Baum, P., Grempler, R., Thomas, C. E., Masi, F. D., Brorsson, C. A., Mazzoni, G., Allesøe, R., Rasmussen, S., Gudmundsdóttir, V., Nielsen, A. M., Banasik, K., Tsirigos, K., Nilsson, B., Pedersen, H., Brunak, S., Karaderi, T., Lundgaard, A. T., Johansen, J., Gupta, R., Sackett, P. W., Tillner, J., Lehr, T., Scherer, N., Dings, C., Sihinevich, I., Loftus, H., Cabrelli, L., McEvoy, D., Mari, A., Bizzotto, R., Tura, A., ’t Hart, L., Dekkers, K., Leeuwen, N., Slieker, R., Rutters, F., Beulens, J., Nijpels, G., Koopman, A., Oort, S., Groeneveld, L., Groop, L., Elders, P., Viñuela, A., Ramisch, A., Dermitzakis, E., Ehrhardt, B., Jennison, C., Froguel, P., Canouil, M., Boneford, A., McVittie, I., Wake, D., Frau, F., Staerfeldt, H. -H, Adragni, K., Thomas, M., Wu, H., Pavo, I., Steckel-Hamann, B., Thomsen, H., Giordano, G. N., Fitipaldi, H., Ridderstråle, M., Kurbasic, A., Pasdar, N. A., Pomares-Millan, H., Mutie, P., Koivula, R., McRobert, N., McCarthy, M., Wesolowska-Andersen, A., Mahajan, A., Abdalla, M., Fernandez, J., Holl, R., Heggie, A., Deshmukh, H., Hennige, A., Bianzano, S., Thorand, B., Sharma, S., Grallert, H., Adam, J., Troll, M., Fritsche, A., Hill, A., Thorne, C., Hudson, M., Kuulasmaa, T., Vangipurapu, J., Laakso, M., Cederberg, H., Kokkola, T., Jiao, Y., Gough, S., Robertson, N., Verkindt, H., Raverdi, V., Caiazzo, R., Pattou, F., White, M., Donnelly, L., Brown, A., Palmer, C., Davtian, D., Dawed, A., Forgie, I., Pearson, E., Ruetten, H., Musholt, P., Bell, J., Thomas, E. L., Whitcher, B., Haid, M., Nicolay, C., Mourby, M., Kaye, J., Shah, N., Teare, H., Frost, G., Jablonka, B., Uhlen, M., Eriksen, R., Vogt, J., Dutta, A., Jonsson, A., Engelbrechtsen, L., Forman, A., Sondertoft, N., de Preville, N., Baltauss, T., Walker, M., Gassenhuber, J., Klintenberg, M., Bergstrom, M., Ferrer, J., Adamski, J., Franks, P. W., Pedersen, O., Segal, E., consortium, The IMI DIRECT, Bar, N., Korem, T., Weissbrod, O., Zeevi, D., Rothschild, D., Leviatan, S., Kosower, N., Lotan-Pompan, M., Weinberger, A., Le Roy, C. I., Menni, C., Visconti, A., Falchi, M., Spector, T. D., Vestergaard, H., Arumugam, M., Hansen, T., Allin, K., Hong, Mun-Gwan, Schwenk, Jochen M., Häussler, Ragna S., Dale, Matilda, Giorgino, T., Rodriquez, M., Perry, M., Nice, R., McDonald, T., Hattersley, A., Jones, A., Graefe-Mody, U., Baum, P., Grempler, R., Thomas, C. E., Masi, F. D., Brorsson, C. A., Mazzoni, G., Allesøe, R., Rasmussen, S., Gudmundsdóttir, V., Nielsen, A. M., Banasik, K., Tsirigos, K., Nilsson, B., Pedersen, H., Brunak, S., Karaderi, T., Lundgaard, A. T., Johansen, J., Gupta, R., Sackett, P. W., Tillner, J., Lehr, T., Scherer, N., Dings, C., Sihinevich, I., Loftus, H., Cabrelli, L., McEvoy, D., Mari, A., Bizzotto, R., Tura, A., ’t Hart, L., Dekkers, K., Leeuwen, N., Slieker, R., Rutters, F., Beulens, J., Nijpels, G., Koopman, A., Oort, S., Groeneveld, L., Groop, L., Elders, P., Viñuela, A., Ramisch, A., Dermitzakis, E., Ehrhardt, B., Jennison, C., Froguel, P., Canouil, M., Boneford, A., McVittie, I., Wake, D., Frau, F., Staerfeldt, H. -H, Adragni, K., Thomas, M., Wu, H., Pavo, I., Steckel-Hamann, B., Thomsen, H., Giordano, G. N., Fitipaldi, H., Ridderstråle, M., Kurbasic, A., Pasdar, N. A., Pomares-Millan, H., Mutie, P., Koivula, R., McRobert, N., McCarthy, M., Wesolowska-Andersen, A., Mahajan, A., Abdalla, M., Fernandez, J., Holl, R., Heggie, A., Deshmukh, H., Hennige, A., Bianzano, S., Thorand, B., Sharma, S., Grallert, H., Adam, J., Troll, M., Fritsche, A., Hill, A., Thorne, C., Hudson, M., Kuulasmaa, T., Vangipurapu, J., Laakso, M., Cederberg, H., Kokkola, T., Jiao, Y., Gough, S., Robertson, N., Verkindt, H., Raverdi, V., Caiazzo, R., Pattou, F., White, M., Donnelly, L., Brown, A., Palmer, C., Davtian, D., Dawed, A., Forgie, I., Pearson, E., Ruetten, H., Musholt, P., Bell, J., Thomas, E. L., Whitcher, B., Haid, M., Nicolay, C., Mourby, M., Kaye, J., Shah, N., Teare, H., Frost, G., Jablonka, B., Uhlen, M., Eriksen, R., Vogt, J., Dutta, A., Jonsson, A., Engelbrechtsen, L., Forman, A., Sondertoft, N., de Preville, N., Baltauss, T., Walker, M., Gassenhuber, J., Klintenberg, M., Bergstrom, M., Ferrer, J., Adamski, J., Franks, P. W., Pedersen, O., Segal, E., and consortium, The IMI DIRECT

Abstract

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites., QC 20211001

Published
2020
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24. P5990In vivo tracking of long-term survival of xenogeneic porcine mesenchymal stem cells seeded on tissue-engineered heart valve implanted in sheep

Author

Gyongyosi, M, primary, Lukovic, D, additional, Pavo, N, additional, Gugerell, A, additional, Winkler, J, additional, Spannbauer, A, additional, Pavo, I J, additional, Michel-Behnke, I, additional, Emmert, M Y, additional, Hoerstrup, S P, additional, Marian, T, additional, Balkay, L, additional, Trencsenyi, G, additional, Wu, J C, additional, and Zlabinger, K, additional

Published
2019
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26. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Wilman, H. R., Parisinos, C. A., Atabaki-Pasdar, N., Kelly, M., Thomas, E. L., Neubauer, S., Jennison, C., Ehrhardt, B., Baum, P., Schoelsch, C., Freijer, J., Grempler, R., Graefe-Mody, U., Hennige, A., Dings, C., Lehr, T., Scherer, N., Sihinecich, I., Pattou, F., Raverdi, V., Caiazzo, R., Torres, F., Verkindt, H., Mari, A., Tura, A., Giorgino, T., Bizzotto, Froguel, P., Bonneford, A., Canouil, M., Dhennin, V., Brorsson, C., Brunak, S., De Masi, F., Gudmundsdóttir, V., Pedersen, H., Banasik, K., Thomas, C., Sackett, P., Staerfeldt, H. -H, Lundgaard, A., Nilsson, B., Nielsen, A., Mazzoni, G., Karaderi, T., Rasmussen, S., Johansen, J., Allesøe, R., Fritsche, A., Thorand, B., Adamski, J., Grallert, H., Haid, M., Sharma, S., Troll, M., Adam, J., Ferrer, J., Eriksen, H., Frost, G., Häussler, Ragna S., Hong, Mun-Gwan, Schwenk, Jochen M., Uhlén, Mathias, Nicolay, C., Pavo, I., Steckel-Hamann, B., Thomas, M., Adragni, K., Wu, H., Hart, L., Roderick, S., van Leeuwen, N., Dekkers, K., Frau, F., Gassenhuber, J., Jablonka, B., Musholt, P., Ruetten, H., Tillner, J., Baltauss, T., Bernard Poenaru, O., de Preville, N., Rodriquez, M., Arumugam, M., Allin, K., Engelbrechtsen, L., Hansen, T., Forman, A., Jonsson, A., Pedersen, O., Dutta, A., Vogt, J., Vestergaard, H., Laakso, M., Kokkola, T., Kuulasmaa, T., Franks, P., Giordano, N., Pomares-Millan, H., Fitipaldi, H., Mutie, P., Klintenberg, M., Bergstrom, M., Groop, L., Ridderstrale, M., Atabaki Pasdar, N., Deshmukh, H., Heggie, A., Wake, D., McEvoy, D., McVittie, I., Walker, M., Hattersley, A., Hill, A., Jones, A., McDonald, T., Perry, M., Nice, R., Hudson, M., Thorne, C., Dermitzakis, E., Viñuela, A., Cabrelli, L., Loftus, H., Dawed, A., Donnelly, L., Forgie, I., Pearson, E., Palmer, C., Brown, A., Koivula, R., Wesolowska-Andersen, A., Abdalla, M., McRobert, N., Fernandez, J., Jiao, Y., Robertson, N., Gough, S., Kaye, J., Mourby, M., Mahajan, A., McCarthy, M., Shah, N., Teare, H., Holl, R., Koopman, A., Rutters, F., Beulens, J., Groeneveld, L., Bell, J., Thomas, L., Whitcher, B., Hingorani, A. D., Patel, R. S., Hemingway, H., Franks, P. W., Bell, J. D., Banerjee, R., Yaghootkar, H., Wilman, H. R., Parisinos, C. A., Atabaki-Pasdar, N., Kelly, M., Thomas, E. L., Neubauer, S., Jennison, C., Ehrhardt, B., Baum, P., Schoelsch, C., Freijer, J., Grempler, R., Graefe-Mody, U., Hennige, A., Dings, C., Lehr, T., Scherer, N., Sihinecich, I., Pattou, F., Raverdi, V., Caiazzo, R., Torres, F., Verkindt, H., Mari, A., Tura, A., Giorgino, T., Bizzotto, Froguel, P., Bonneford, A., Canouil, M., Dhennin, V., Brorsson, C., Brunak, S., De Masi, F., Gudmundsdóttir, V., Pedersen, H., Banasik, K., Thomas, C., Sackett, P., Staerfeldt, H. -H, Lundgaard, A., Nilsson, B., Nielsen, A., Mazzoni, G., Karaderi, T., Rasmussen, S., Johansen, J., Allesøe, R., Fritsche, A., Thorand, B., Adamski, J., Grallert, H., Haid, M., Sharma, S., Troll, M., Adam, J., Ferrer, J., Eriksen, H., Frost, G., Häussler, Ragna S., Hong, Mun-Gwan, Schwenk, Jochen M., Uhlén, Mathias, Nicolay, C., Pavo, I., Steckel-Hamann, B., Thomas, M., Adragni, K., Wu, H., Hart, L., Roderick, S., van Leeuwen, N., Dekkers, K., Frau, F., Gassenhuber, J., Jablonka, B., Musholt, P., Ruetten, H., Tillner, J., Baltauss, T., Bernard Poenaru, O., de Preville, N., Rodriquez, M., Arumugam, M., Allin, K., Engelbrechtsen, L., Hansen, T., Forman, A., Jonsson, A., Pedersen, O., Dutta, A., Vogt, J., Vestergaard, H., Laakso, M., Kokkola, T., Kuulasmaa, T., Franks, P., Giordano, N., Pomares-Millan, H., Fitipaldi, H., Mutie, P., Klintenberg, M., Bergstrom, M., Groop, L., Ridderstrale, M., Atabaki Pasdar, N., Deshmukh, H., Heggie, A., Wake, D., McEvoy, D., McVittie, I., Walker, M., Hattersley, A., Hill, A., Jones, A., McDonald, T., Perry, M., Nice, R., Hudson, M., Thorne, C., Dermitzakis, E., Viñuela, A., Cabrelli, L., Loftus, H., Dawed, A., Donnelly, L., Forgie, I., Pearson, E., Palmer, C., Brown, A., Koivula, R., Wesolowska-Andersen, A., Abdalla, M., McRobert, N., Fernandez, J., Jiao, Y., Robertson, N., Gough, S., Kaye, J., Mourby, M., Mahajan, A., McCarthy, M., Shah, N., Teare, H., Holl, R., Koopman, A., Rutters, F., Beulens, J., Groeneveld, L., Bell, J., Thomas, L., Whitcher, B., Hingorani, A. D., Patel, R. S., Hemingway, H., Franks, P. W., Bell, J. D., Banerjee, R., and Yaghootkar, H.

Abstract

Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10−8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart di, QC 20191128

Published
2019
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27. Discovery of Biomarkers for Glycaemic Deterioration before and after the Onset of Type 2 Diabetes: Descriptive Characteristics of the Epidemiological Studies within the IMI DIRECT Consortium

Author

Koivula, RW, Forgie, IM, Kurbasic, A, Vinuela, A, Heggie, A, Giordano, GN, Hansen, TH, Hudson, M, Koopman, ADM, Rutters, F, Siloaho, M, Allin, KH, Brage, S, Brorsson, CA, Dawed, AY, De Masi, F, Groves, CJ, Kokkola, T, Mahajan, A, Perry, MH, Rauh, SP, Ridderstrale, M, Teare, HJA, Thomas, EL, Tura, A, Vestergaard, H, White, T, Adamski, J, Bell, JD, Beulens, JW, Brunak, S, Dermitzakis, ET, Froguel, P, Frost, G, Gupta, R, Hansen, T, Hattersley, A, Jablonka, B, Kaye, J, Laakso, M, McDonald, TJ, Pedersen, O, Schwenk, JM, Pavo, I, Mari, A, McCarthy, MI, Ruetten, H, Walker, M, Pearson, E, Franks, PW, Koivula, RW, Forgie, IM, Kurbasic, A, Vinuela, A, Heggie, A, Giordano, GN, Hansen, TH, Hudson, M, Koopman, ADM, Rutters, F, Siloaho, M, Allin, KH, Brage, S, Brorsson, CA, Dawed, AY, De Masi, F, Groves, CJ, Kokkola, T, Mahajan, A, Perry, MH, Rauh, SP, Ridderstrale, M, Teare, HJA, Thomas, EL, Tura, A, Vestergaard, H, White, T, Adamski, J, Bell, JD, Beulens, JW, Brunak, S, Dermitzakis, ET, Froguel, P, Frost, G, Gupta, R, Hansen, T, Hattersley, A, Jablonka, B, Kaye, J, Laakso, M, McDonald, TJ, Pedersen, O, Schwenk, JM, Pavo, I, Mari, A, McCarthy, MI, Ruetten, H, Walker, M, Pearson, E, and Franks, PW

Abstract

Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up).

Published
2019

28. Clinical profiles of post‐load glucose subgroups and their association with glycaemic traits over time: An IMI‐DIRECT study.

Author

Obura, M., Beulens, J. W. J., Slieker, R., Koopman, A. D. M., Hoekstra, T., Nijpels, G., Elders, P., Dekker, J. M., Koivula, R. W., Kurbasic, A., Laakso, M., Hansen, T. H., Ridderstråle, M., Hansen, T., Pavo, I., Forgie, I., Jablonka, B., Ruetten, H., Mari, A., and McCarthy, M. I.

Subjects
BLOOD sugar, BLOOD sugar monitoring, C-peptide, CLASSIFICATION, CONFIDENCE intervals, CARBOHYDRATE content of food, GLUCOSE tolerance tests, GLYCEMIC index, INSULIN, INSULIN resistance, TYPE 2 diabetes, PATIENTS, DISEASE risk factors
Abstract

Aim: To examine the hypothesis that, based on their glucose curves during a seven‐point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. Methods: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI‐DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven‐point oral glucose tolerance test at baseline and follow‐up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. Results: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1–4. Participants in Subgroups 2–4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13–0.58), Subgroup 3 (β = 0.30; 95% CI 0.10–0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04–0.32), compared to Subgroup 1. The same was observed for C‐peptide and insulin. Five subgroups were identified at follow‐up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. Conclusions: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified. [ABSTRACT FROM AUTHOR]

Published
2021
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35. Meta-Analysis of Cell-based CaRdiac stUdiEs (ACCRUE) in patients with acute myocardial infarction based on individual patient data

Author

Gyongyosi, M, Wojakowski, W, Lemarchand, P, Lunde, K, Tendera, M, Bartunek, J, Marban, E, Assmus, B, Henry, T D, Traverse, J H, Moye, L A, Surder, D, Corti, R, Huikuri, H, Miettinen, J, Wohrle, J, Obradovic, S, Roncalli, J, Malliaras, K, Pokushalov, E, Romanov, A, Kastrup, J, Bergmann, M W, Atsma, D E, Diederichsen, A, Edes, I, Benedek, I, Benedek, T, Pejkov, H, Nyolczas, N, Pavo, N, Bergler-Klein, J, Pavo, I J, Sylven, C, Berti, S, Navarese, E P, Maurer, G, Gyongyosi, M, Wojakowski, W, Lemarchand, P, Lunde, K, Tendera, M, Bartunek, J, Marban, E, Assmus, B, Henry, T D, Traverse, J H, Moye, L A, Surder, D, Corti, R, Huikuri, H, Miettinen, J, Wohrle, J, Obradovic, S, Roncalli, J, Malliaras, K, Pokushalov, E, Romanov, A, Kastrup, J, Bergmann, M W, Atsma, D E, Diederichsen, A, Edes, I, Benedek, I, Benedek, T, Pejkov, H, Nyolczas, N, Pavo, N, Bergler-Klein, J, Pavo, I J, Sylven, C, Berti, S, Navarese, E P, and Maurer, G

Abstract

RATIONALE: The meta-Analysis of Cell-based CaRdiac study is the first prospectively declared collaborative multinational database, including individual data of patients with ischemic heart disease treated with cell therapy. OBJECTIVE: We analyzed the safety and efficacy of intracoronary cell therapy after acute myocardial infarction (AMI), including individual patient data from 12 randomized trials (ASTAMI, Aalst, BOOST, BONAMI, CADUCEUS, FINCELL, REGENT, REPAIR-AMI, SCAMI, SWISS-AMI, TIME, LATE-TIME; n=1252). METHODS AND RESULTS: The primary end point was freedom from combined major adverse cardiac and cerebrovascular events (including all-cause death, AMI recurrance, stroke, and target vessel revascularization). The secondary end point was freedom from hard clinical end points (death, AMI recurrence, or stroke), assessed with random-effects meta-analyses and Cox regressions for interactions. Secondary efficacy end points included changes in end-diastolic volume, end-systolic volume, and ejection fraction, analyzed with random-effects meta-analyses and ANCOVA. We reported weighted mean differences between cell therapy and control groups. No effect of cell therapy on major adverse cardiac and cerebrovascular events (14.0% versus 16.3%; hazard ratio, 0.86; 95% confidence interval, 0.63-1.18) or death (1.4% versus 2.1%) or death/AMI recurrence/stroke (2.9% versus 4.7%) was identified in comparison with controls. No changes in ejection fraction (mean difference: 0.96%; 95% confidence interval, -0.2 to 2.1), end-diastolic volume, or systolic volume were observed compared with controls. These results were not influenced by anterior AMI location, reduced baseline ejection fraction, or the use of MRI for assessing left ventricular parameters. CONCLUSIONS: This meta-analysis of individual patient data from randomized trials in patients with recent AMI revealed that intracoronary cell therapy provided no benefit, in terms of clinical events or changes in left ventricular fun

Published
2015

36. Patient compliance with alendronate, risedronate and raloxifene for the treatment of osteoporosis in postmenopausal women

Author

Ringe, J. D. Christodoulakos, G. E. Mellstroem, D. Petto, H. and Nickelsen, T. Marin, F. Pavo, I.

Abstract

Objectives: The aim was to investigate patient compliance with different osteoporosis medications commonly prescribed in clinical practice, to determine risk factors associated with discontinuation and to evaluate quality of changes. Research design and methods: We conducted a 1-year observational study of patients of age >= 60 years in a clinical setting at 917 sites in 10 European countries (Germany, Greece, UK, Netherlands, Romania, Norway, Finland, Estonia), Lebanon and South Africa. data, concomitant diseases, the reasons for intervention, educational, socio-economical and disease knowledge were captured at Self-reported compliance, discontinuation data health status were collected. Main outcome measures: Out of 5198 patients, 3490 (67.1%) patients received 60 daily raloxifene (RAL), 452 (8.7%) 10 mg daily alendronate (AQD), 769 (14.8%) 70 mg once weekly alendronate (AQW) and 487 (9.4%) 5 mg daily risedronate (RIS). Among patients completing the study (4231, 81%), the percentage of patients with high compliance was 80% (RAL), 79% (AOD), 65% (AQM and 76% (RIS). The discontinuation due to side effects was highest on AQW (7.0%), followed by AQD (6.4%), RAL (3.8%) and RIS (3.4%). The discontinuation-rate was higher for patients with a history of surgical menopause, increased age, lack of knowledge about medical prevention of osteoporosis and thin frame as a reason for intervention. The EQ-5D weighted index showed the highest improvement for RIS (0.13), followed by RAL (0.11), ADD (0.08) and AQW (0,07). Conclusions: Data from this non-interventional observational study indicate moderate overall compliance and discontinuation rate with the prescribed osteoporosis medications.

Published
2007

37. Comparing raloxifene with continuous combined estrogen-progestin therapy in postmenopausal women: Review of Euralox 1

Author

Neven, P. Quail, D. Marin, F. Creatsas, G. Depypere, H. Rechberger, T. Liu-Léage, S. Pavo, I. Schmitt, H. Nickelsen, T.

Abstract

Objectives: To review the main findings of the Euralox 1 study - a multicentre, randomised, double-blind study conducted in 1008 healthy postmenopausal women allocated to raloxifene (n = 495) or continuous combined estrogen-progestin therapy (ccEPT; n = 513) for 6 months - and provide an overview of the risks and benefits of raloxifene and ccEPT. Methods: A review is provided of previously published findings on uterine safety (bleeding rates and changes in endometrial thickness and uterine volume), gynaecological adjudication, cardiovascular risk (lipids, fibrinogen), adverse events, compliance, treatment satisfaction and quality of life. New data on biochemical markers of bone turnover (serum N-telopeptides and C-terminal telopeptides of type I collagen; NTX and CTX) assessed before and after 6 months' treatment are presented. Results: Raloxifene caused less uterine bleeding than ccEPT and, unlike ccEPT, did not alter endometrial thickness or uterine volume. Serum CTX and NTX levels were reduced in both treatment groups, but the reduction was significantly greater with ccEPT. The two treatments had differential effects on lipids and fibrinogen levels; raloxifene had more favourable effects on serum HDL, the LDL/HDL ratio, and plasma fibrinogen. Raloxifene was associated with fewer adverse events or discontinuations, and this was associated with higher treatment satisfaction and better self-reported compliance. Conclusions: The clinical risk-benefit profile of raloxifene derived from the intermediate endpoints of this study suggests that it may be a better alternative to ccEPT for preventing long-term postmenopausal health risks in healthy postmenopausal women who are not suffering from vasomotor symptoms. © 2005 Elsevier Ireland Ltd. All rights reserved.

Published
2005

40. Patient compliance with alendronate, risedronate and raloxifene for the treatment of osteoporosis in postmenopausal women.

Author

Ringe JD, Christodoulakos GE, Mellström D, Petto H, Nickelsen T, Marín F, Pavo I, Ringe, J D, Christodoulakos, G E, Mellström, D, Petto, H, Nickelsen, T, Marín, F, and Pavo, I

Abstract

Objectives: The aim was to investigate patient compliance with different osteoporosis medications commonly prescribed in clinical practice, to determine risk factors associated with discontinuation and to evaluate quality of life changes.Research Design and Methods: We conducted a 1-year observational study of patients of age > or = 60 years in a clinical setting at 917 sites in 10 European countries (Germany, Greece, UK, Sweden, Netherlands, Romania, Norway, Finland, Denmark, Estonia), Lebanon and South Africa. Demographic data, concomitant diseases, the reasons for intervention, educational, socio-economical status and disease knowledge were captured at baseline. Self-reported compliance, discontinuation data and health status were collected.Main Outcome Measures: Out of 5198 patients, 3490 (67.1%) patients received 60 mg daily raloxifene (RAL), 452 (8.7%) 10 mg daily alendronate (AQD), 769 (14.8%) 70 mg once weekly alendronate (AQW) and 487 (9.4%) 5 mg daily risedronate (RIS). Among patients completing the study (4231, 81%), the percentage of patients with high compliance was 80% (RAL), 79% (AQD), 65% (AQW) and 76% (RIS). The discontinuation due to side effects was highest on AQW (7.0%), followed by AQD (6.4%), RAL (3.8%) and RIS (3.4%). The discontinuation-rate was higher for patients with a history of surgical menopause, increased age, lack of knowledge about medical prevention of osteoporosis and thin frame as a reason for intervention. The EQ-5D weighted index showed the highest improvement for RIS (0.13), followed by RAL (0.11), AQD (0.08) and AQW (0.07).Conclusions: Data from this non-interventional observational study indicate moderate overall compliance and discontinuation rate with the prescribed osteoporosis medications. [ABSTRACT FROM AUTHOR]

Published
2007
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41. Saturday, 17 July 2010

Author

Dimova, I., primary, Hlushchuk, R., additional, Makanya, A., additional, Djonov, V., additional, Theurl, M., additional, Schgoer, W., additional, Albrecht, K., additional, Beer, A., additional, Patsch, J. R., additional, Schratzberger, P., additional, Mahata, S., additional, Kirchmair, R., additional, Didie, M., additional, Christalla, P., additional, Rau, T., additional, Eschenhagen, T., additional, Schumacher, U., additional, Lin, Q., additional, Zenke, M., additional, Zimmmermann, W., additional, Hoch, M., additional, Fischer, P., additional, Stapel, B., additional, Missol-Kolka, E., additional, Erschow, S., additional, Scherr, M., additional, Drexler, H., additional, Hilfiker-Kleiner, D., additional, Diebold, I., additional, Petry, A., additional, Kennel, P., additional, Djordjevic, T., additional, Hess, J., additional, Goerlach, A., additional, Castellano, J., additional, Aledo, R., additional, Sendra, J., additional, Costales, P., additional, Badimon, L., additional, Llorente-Cortes, V., additional, Dworatzek, E., additional, Mahmoodzadeh, S., additional, Regitz-Zagrosek, V., additional, Posa, A., additional, Varga, C., additional, Berko, A., additional, Veszelka, M., additional, Szablics, P., additional, Vari, B., additional, Pavo, I., additional, Laszlo, F., additional, Brandenburger, M., additional, Wenzel, J., additional, Bogdan, R., additional, Richardt, D., additional, Reppel, M., additional, Hescheler, J., additional, Terlau, H., additional, Dendorfer, A., additional, Heijman, J., additional, Rudy, Y., additional, Westra, R., additional, Volders, P., additional, Rasmusson, R., additional, Bondarenko, V., additional, Ertas Gokhan, M. D., additional, Ural Ertan, M. D., additional, Karaoz Erdal, P. H. D., additional, Aksoy Ayca, P. H. D., additional, Kilic Teoman, M. D., additional, Kozdag Guliz, M. D., additional, Vural Ahmet, M. D., additional, Ural Dilek, M. D., additional, Poulet, C., additional, Christ, T., additional, Wettwer, E., additional, Ravens, U., additional, Van Der Pouw Kraan, C., additional, Schirmer, S., additional, Fledderus, J., additional, Moerland, P., additional, Leyen, T., additional, Piek, J., additional, Van Royen, N., additional, Horrevoets, A., additional, Fleissner, F., additional, Jazbutyte, V., additional, Fiedler, J., additional, Galuppo, P., additional, Mayr, M., additional, Ertl, G., additional, Bauersachs, J., additional, Thum, T., additional, Protze, S., additional, Bussek, A., additional, Li, F., additional, Hoo, R., additional, Lam, K., additional, Xu, A., additional, Subramanian, P., additional, Karshovska, E., additional, Megens, R., additional, Akhtar, S., additional, Heyll, K., additional, Jansen, Y., additional, Weber, C., additional, Schober, A., additional, Zafeiriou, M., additional, Noack, C., additional, Renger, A., additional, Dietz, R., additional, Zelarayan, L., additional, Bergmann, M., additional, Meln, I., additional, Malashicheva, A., additional, Anisimov, S., additional, Kalinina, N., additional, Sysoeva, V., additional, Zaritskey, A., additional, Barbuti, A., additional, Scavone, A., additional, Mazzocchi, N., additional, Crespi, A., additional, Capilupo, D., additional, Difrancesco, D., additional, Qian, L., additional, Shim, W., additional, Gu, Y., additional, Mohammed, S., additional, Wong, P., additional, Zafiriou, M., additional, Schaeffer, H., additional, Kovacs, P., additional, Simon, J., additional, Varro, A., additional, Athias, P., additional, Wolf, J., additional, Bouchot, O., additional, Vandroux, D., additional, Mathe, A., additional, De Carvalho, A., additional, Laurent, G., additional, Rainer, P., additional, Huber, M., additional, Edelmann, F., additional, Stojakovic, T., additional, Trantina-Yates, A., additional, Trauner, M., additional, Pieske, B., additional, Von Lewinski, D., additional, De Jong, A., additional, Maass, A., additional, Oberdorf-Maass, S., additional, Van Gelder, I., additional, Lin, Y., additional, Li, J., additional, Wang, F., additional, He, Y., additional, Li, X., additional, Xu, H., additional, Yang, X., additional, Coppini, R., additional, Ferrantini, C., additional, Ferrara, C., additional, Rossi, A., additional, Mugelli, A., additional, Poggesi, C., additional, Cerbai, E., additional, Rozmaritsa, N., additional, Voigt, N., additional, Dobrev, D., additional, Kienitz, M.-C., additional, Zoidl, G., additional, Bender, K., additional, Pott, L., additional, Kohajda, Z., additional, Kristof, A., additional, Virag, L., additional, Jost, N., additional, Trafford, A., additional, Prnjavorac, B., additional, Mujaric, E., additional, Jukic, J., additional, Abduzaimovic, K., additional, Brack, K., additional, Patel, V., additional, Coote, J., additional, Ng, G., additional, Wilders, R., additional, Van Ginneken, A., additional, Verkerk, A., additional, Xaplanteris, P., additional, Vlachopoulos, C., 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R., additional, De Almeida, A., additional, Van Oort, R., additional, Lohmann, H., additional, Saes, M., additional, Messer, A., additional, Copeland, O., additional, Leung, M., additional, Matthes, F., additional, Steinbrecher, J., additional, Salinas-Riester, G., additional, Opitz, L., additional, Hasenfuss, G., additional, Lehnart, S., additional, Caracciolo, G., additional, Eleid, M., additional, Carerj, S., additional, Chandrasekaran, K., additional, Khandheria, B., additional, Sengupta, P., additional, Riaz, I., additional, Tyng, L., additional, Dou, Y., additional, Seymour, A., additional, Dyer, C., additional, Griffin, S., additional, Haswell, S., additional, Greenman, J., additional, Yasushige, S., additional, Amorim, P., additional, Nguyen, T., additional, Schwarzer, M., additional, Mohr, F., additional, Doenst, T., additional, Popin Sanja, S., additional, Lalosevic, D., additional, Capo, I., additional, Momcilov Popin, T., additional, Astvatsatryan, A., additional, Senan, 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Szczepanski, W., additional, Mateuszuk, L., additional, Szymura-Oleksiak, J., additional, Chlopicki, S., additional, Sulicka, J., additional, Strach, M., additional, Kierzkowska, I., additional, Surdacki, A., additional, Mikolajczyk, T., additional, Balwierz, W., additional, Guzik, T., additional, Dmitriev, V., additional, Oschepkova, E., additional, Polovitkina, O., additional, Titov, V., additional, Rogoza, A., additional, Shakur, R., additional, Metcalfe, S., additional, Bradley, J., additional, Demyanets, S., additional, Kaun, C., additional, Kastl, S., additional, Pfaffenberger, S., additional, Huk, I., additional, Maurer, G., additional, Huber, K., additional, Wojta, J., additional, Eriksson, O., additional, Aberg, M., additional, Siegbahn, A., additional, Niccoli, G., additional, Sgueglia, G., additional, Conte, M., additional, Giubilato, S., additional, Cosentino, N., additional, Ferrante, G., additional, Crea, F., additional, Ilisei, D., additional, Leon, M., additional, Mitu, 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Dos Santos, P., additional, Fusco, A., additional, Sorriento, D., additional, Cervero, P., additional, Feliciello, A., additional, Barnucz, E., additional, Kozichova, K., additional, Hlavackova, M., additional, Neckar, J., additional, Kolar, F., additional, Novakova, O., additional, Novak, F., additional, Barsanti, C., additional, Abraham, N., additional, Muntean, D., additional, Mirica, S., additional, Duicu, O., additional, Raducan, A., additional, Hancu, M., additional, Fira-Mladinescu, O., additional, Ordodi, V., additional, Voelkl, J., additional, Haubner, B., additional, Neely, G., additional, Moriell, C., additional, Seidl, S., additional, Pachinger, O., additional, Penninger, J., additional, and Metzler, B., additional

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