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1. Circulating Tissue Factor-Positive Procoagulant Microparticles in Patients with Type 1 Diabetes

Author

Zhang C, Ou Q, Gu Y, Cheng G, Du R, Yuan L, Cordiner RLM, Kang D, Zhang J, Huang Q, Yu C, Kang L, Wang X, Sun X, Mo X, Tian H, Pearson ER, Meng W, and Li S

Subjects
microparticles, diabetic retinopathy, RC581-951, Specialties of internal medicine, tissue factor, type 1 diabetes mellitus
Abstract

Chenghui Zhang,1,2,* Qing Ou,1,* Yan Gu,1,* Gaiping Cheng,3 Rong Du,1,2 Li Yuan,1 Ruth LM Cordiner,4 Deying Kang,5 Jiaying Zhang,6 Qiaorong Huang,7 Chuan Yu,8 Li Kang,9 Xuan Wang,4,10 Xin Sun,5 Xianming Mo,7 Haoming Tian,1 Ewan R Pearson,4 Wentong Meng,7 Sheyu Li1,4 1Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 2Department of Endocrinology and Metabolism, Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region, Chengdu 610041, People’s Republic of China; 3Department of Clinical Nutrition, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 4Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 9SY, Scotland, UK; 5Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 6Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 7Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 8Department of Health-Related Social and Behavioral Science, West China School of Public Health, Sichuan University, Chengdu 610041, People’s Republic of China; 9Division of Systems Medicine, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 9SY, Scotland, UK; 10Science for Life Laboratory, Department of Medical Cell Biology, Uppsala University, Uppsala 75123, Sweden*These authors contributed equally to this workCorrespondence: Sheyu LiDepartment of Endocrinology and Metabolism, West China Hospital, Sichuan University, 37# Guoxue Road, Chengdu 610041, ChinaTel +86-13194874843Fax +86-28-85422982Email lisheyu@gmail.comWentong MengLaboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guoxue Road, Chengdu, Sichuan 610041, People’s Republic of ChinaTel +86-18980601256Fax +86-28-85422982Email mwentong@sina.comAim: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM).Methods: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF+PS+MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF+PS+MPs and PS+MPs (TF+PS+MPs/PS+MPs).Results: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS+MPs, TF+PS+MPs and TF+PS+MPs/PS+MPs were elevated in patients with T1DM (PS+MPs: 1078.5 ± 158.08 vs 686.84 ± 122.04/μL, P

Published
2020

2. Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes: a joint modeling approach

Author

Dennis JM, Shields BM, Jones AG, Pearson ER, Hattersley AT, and Henley WE

Subjects
Glycated Hemoglobin A, type 2, Drug-Related Side Effects and Adverse Reactions, Joint model, diabetes mellitus, lcsh:RC109-216, Precision Medicine, Hypoglycemia, lcsh:Infectious and parasitic diseases
Abstract

John M Dennis,1 Beverley M Shields,2 Angus G Jones,2 Ewan R Pearson,3 Andrew T Hattersley,2 William E Henley1 On behalf of the MASTERMIND consortium 1Health Statistics Group, University of Exeter Medical School, Exeter, UK; 2National Institute for Health Research Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK; 3Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK Objective: Precision medicine drug therapy seeks to maximize efficacy and minimize harm for individual patients. This will be difficult if drug response and side effects are positively associated, meaning that patients likely to respond best are at increased risk of side effects. We applied joint longitudinal–survival models to evaluate associations between drug response (longitudinal outcome) and the risk of side effects (survival outcome) for patients initiating type 2 diabetes therapy. Study design and setting: Participants were randomized to metformin (MFN), sulfonylurea(SU), or thiazolidinedione (TZD) therapy in the A Diabetes Outcome Progression Trial (ADOPT) drug efficacy trial (n=4,351). Joint models were parameterized for 1) current HbA1c response (change from baseline in HbA1c) and 2) cumulative HbA1c response (total HbA1c change). Results: With MFN, greater HbA1c response did not increase the risk of gastrointestinal events (HR per 1% absolute greater current response 0.82 [95% CI 0.67, 1.01]; HR per 1% higher cumulative response 0.90 [95% CI 0.81, 1.00]). With SU, greater current response was associated with an increased risk of hypoglycemia (HR 1.41 [95% CI 1.04, 1.91]). With TZD, greater response was associated with an increased risk of edema (current HR 1.45 [95% CI 1.05, 2.01]; cumulative 1.22 [95% CI 1.07, 1.38]) but not fracture. Conclusion: Joint modeling provides a useful framework to evaluate the association between response to a drug and the risk of developing side effects. There may be great potential for widespread application of joint modeling to evaluate the risks and benefits of both new and established medications. Keywords: diabetes mellitus, type 2, drug-related side effects, HbA1c, hypoglycemia, joint model, precision medicine, thiazolidinediones, metformin, sulfonylurea compounds, ADOPT, edema

Published
2018

3. 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

4. What to do with diabetes therapies when HbA1c lowering is inadequate: add, switch, or continue? A MASTERMIND study

Author

McGovern, AP, Dennis, JM, Shields, BM, Hattersley, AT, Pearson, ER, Jones, AG, Farmer, AJ, and Mastermind Consortium

Abstract

BACKGROUND:It is unclear what to do when people with type 2 diabetes have had no or a limited glycemic response to a recently introduced medication. Intra-individual HbA1c variability can obscure true response. Some guidelines suggest stopping apparently ineffective therapy, but no studies have addressed this issue. METHODS:In a retrospective cohort analysis using the UK Clinical Practice Research Datalink (CPRD), we assessed the outcome of 55,530 patients with type 2 diabetes starting their second or third non-insulin glucose-lowering medication, with a baseline HbA1c > 58 mmol/mol (7.5%). For those with no HbA1c improvement or a limited response at 6 months (HbA1c fall

Published
2019

5. Pharmacogenetics in type 2 diabetes: influence on response to oral hypoglycemic agents

Author

Dawed AY, Zhou K, and Pearson ER

Subjects
response, lcsh:Therapeutics. Pharmacology, lcsh:RM1-950, pharmacodynamics, type 2 diabetes, oral hypoglycaemic agents, pharmacokinetics, pharmacogenetics
Abstract

Adem Yesuf Dawed, Kaixin Zhou, Ewan Robert PearsonDivision of Cardiovascular and Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, Scotland, UK Abstract: Type 2 diabetes is one of the leading causes of morbidity and mortality, consuming a significant proportion of public health spending. Oral hypoglycemic agents (OHAs) are the frontline treatment approaches after lifestyle changes. However, huge interindividual variation in response to OHAs results in unnecessary treatment failure. In addition to nongenetic factors, genetic factors are thought to contribute to much of such variability, highlighting the importance of the potential of pharmacogenetics to improve therapeutic outcome. Despite the presence of conflicting results, significant progress has been made in an effort to identify the genetic markers associated with pharmacokinetics, pharmacodynamics, and ultimately therapeutic response and/or adverse outcomes to OHAs. As such, this article presents a comprehensive review of current knowledge on pharmacogenetics of OHAs and provides insights into knowledge gaps and future directions. Keywords: pharmacogenetics, type 2 diabetes, oral hypoglycemic agents, pharmacokinetics, pharmacodynamics, response

Published
2016

6. Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity

Author

D. L. Cousminer, D. J. Berry, N. J. Timpson, W. Ang, E. Thiering, E. M. Byrne, H. R. Taal, V. Huikari, J. P. Bradfield, M. Kerkhof, M. M. Groen Blokhuis, E. Kreiner Moller, M. Marinelli, C. Holst, J. T. Leinonen, J. R. B. Perry, I. Surakka, O. Pietilainen, J. Kettunen, V. Anttila, M. Kaakinen, U. Sovio, A. Pouta, S. Das, V. Lagou, C. Power, I. Prokopenko, D. M. Evans, J. P. Kemp, B. St Pourcain, S. Ring, A. Palotie, E. Kajantie, C. Osmond, T. Lehtimaki, J. S. Viikari, M. Kahonen, N. M. Warrington, S. J. Lye, L. J. Palmer, C. M. T. Tiesler, C. Flexeder, G. W. Montgomery, S. E. Medland, A. Hofman, H. Hakonarson, M. Guxens, M. Bartels, V. Salomaa, J. M. Murabito, J. Kaprio, T. I. A. Sorensen, F. Ballester, H. Bisgaard, D. I. Boomsma, G. H. Koppelman, S. F. A. Grant, V. W. V. Jaddoe, N. G. Martin, J. Heinrich, C. E. Pennell, O. T. Raitakari, J. G. Eriksson, G. D. Smith, E. Hypponen, M. R. Jarvelin, M. I. McCarthy, S. Ripatti, E. Widen, Adair LS, Ang W, Atalay M, van Beijsterveldt T, Bergen N, Benke K, Berry DJ, Boomsma DI, Bradfield JP, Charoen P, Coin L, Cooper C, Cousminer DL, Das S, Davis OS, Dedoussis GV, Elliott P, Estivill X, Evans DM, Feenstra B, Flexeder C, Frayling T, Freathy RM, Gaillard R, Geller F, Gillman M, Grant SF, Groen Blokhuis M, Goh LK, Guxens M, Hakonarson H, Hattersley AT, Haworth CM, Hadley D, Hedebrand J, Heinrich J, Hinney A, Hirschhorn JN, Hocher B, Holloway JW, Holst C, Hottenga JJ, Horikoshi M, Huikari V, Hypponen E, Iñiguez C, Jaddoe VW, Jarvelin MR, Kaakinen M, Kilpeläinen TO, Kirin M, Kowgier M, Lakka HM, Lakka TA, Lange LA, Lawlor DA, Lehtimäki T, Lewin A, Lindgren C, Lindi V, Maggi R, Marsh J, McCarthy MI, Melbye M, Middeldorp C, Millwood I, Mohlke KL, Mook Kanamori DO, Murray JC, Nivard M, Nohr EA, Ntalla I, Oken E, Ong KK, O'Reilly PF, Palmer LJ, Panoutsopoulou K, Pararajasingham J, Pearson ER, Pennell CE, Power C, Price TS, Prokopenko I, Raitakari OT, Rodriguez A, Salem RM, Saw SM, Scherag A, Sebert S, Siitonen N, Simell O, Sørensen TI, Sovio U, Pourcain BS, Strachan DP, Sunyer J, Taal HR, Teo YY, Thiering E, Tiesler C, Timpson NJ, Uitterlinden AG, Valcárcel B, Warrington NM, White S, Widén E, Willemsen G, Wilson JF, Yaghootkar H, Zeggini E, Elks CE, Perry JR, Sulem P, Chasman DI, Franceschini N, He C, Lunetta KL, Visser JA, Byrne EM, Gudbjartsson DF, Esko T, Koller DL, Kutalik Z, Lin P, Mangino M, Marongiu M, McArdle PF, Smith AV, Stolk L, van Wingerden SH, Zhao JH, Albrecht E, Corre T, Ingelsson E, Hayward C, Magnusson PK, Smith EN, Ulivi S, Warrington M, Zgaga L, Alavere H, Amin N, Aspelund T, Bandinelli S, Barroso I, Berenson GS, Bergmann S, Blackburn H, Boerwinkle E, Buring JE, Busonero F, Campbell H, Chanock SJ, Chen W, Cornelis MC, Couper D, Coviello AD, de Faire U, de Geus EJ, Deloukas P, Döring A, Davey Smith G, Easton DF, Eiriksdottir G, Emilsson V, Eriksson J, Ferrucci L, Folsom AR, Foroud T, Garcia M, GASPARINI, PAOLO, Gieger C, Gudnason V, Hall P, Hankinson SE, Ferreli L, Heath AC, Hernandez DG, Hofman A, Hu FB, Illig T, Järvelin MR, Johnson AD, Karasik D, Khaw KT, Kiel DP, Kolcic I, Kraft P, Launer LJ, Laven JS, Li S, Liu J, Levy D, Martin NG, McArdle WL, Mooser V, Murray SS, Nalls MA, Navarro P, Nelis M, Ness AR, Northstone K, Oostra BA, Peacock M, Palotie A, Paré G, Parker AN, Pedersen NL, Peltonen L, Pharoah P, Polasek O, Plump AS, Pouta A, Porcu E, Rafnar T, Rice JP, Ring SM, Rivadeneira F, Rudan I, Sala C, Salomaa V, Sanna S, Schlessinger D, Schork NJ, Scuteri A, Segrè AV, Shuldiner AR, Soranzo N, Srinivasan SR, Tammesoo ML, Tikkanen E, Toniolo D, Tsui K, Tryggvadottir L, Tyrer J, Uda M, van Dam RM, van Meurs JB, Vollenweider P, Waeber G, Wareham NJ, Waterworth DM, Weedon MN, Wichmann HE, Wright AF, Young L, Zhai G, Zhuang WV, Bierut LJ, Boyd HA, Crisponi L, Demerath EW, van Duijn CM, Econs MJ, Harris TB, Hunter DJ, Loos RJ, Metspalu A, Montgomery GW, Ridker PM, Spector TD, Streeten EA, Stefansson K, Thorsteinsdottir U, Widen E, Murabito JM, Murray A., D'ADAMO, ADAMO PIO, Cousminer, Diana L, Berry, Diane J, Timpson, Nicholas J, Ang, Wei, Hyppönen, Elina, Widen, Elisabéth, ReproGen Consortium, Early Growth Genetics (EGG) Consortium, Pediatrics, Epidemiology, Internal Medicine, D. L., Cousminer, D. J., Berry, N. J., Timpson, W., Ang, E., Thiering, E. M., Byrne, H. R., Taal, V., Huikari, J. P., Bradfield, M., Kerkhof, M. M., Groen Blokhui, E., Kreiner Moller, M., Marinelli, C., Holst, J. T., Leinonen, J. R. B., Perry, I., Surakka, O., Pietilainen, J., Kettunen, V., Anttila, M., Kaakinen, U., Sovio, A., Pouta, S., Da, V., Lagou, C., Power, I., Prokopenko, D. M., Evan, J. P., Kemp, B., St Pourcain, S., Ring, A., Palotie, E., Kajantie, C., Osmond, T., Lehtimaki, J. S., Viikari, M., Kahonen, N. M., Warrington, S. J., Lye, L. J., Palmer, C. M. T., Tiesler, C., Flexeder, G. W., Montgomery, S. E., Medland, A., Hofman, H., Hakonarson, M., Guxen, M., Bartel, V., Salomaa, J. M., Murabito, J., Kaprio, T. I. A., Sorensen, F., Ballester, H., Bisgaard, D. I., Boomsma, G. H., Koppelman, S. F. A., Grant, V. W. V., Jaddoe, N. G., Martin, J., Heinrich, C. E., Pennell, O. T., Raitakari, J. G., Eriksson, G. D., Smith, E., Hypponen, M. R., Jarvelin, M. I., Mccarthy, S., Ripatti, E., Widen, Adair, L, Ang, W, Atalay, M, van Beijsterveldt, T, Bergen, N, Benke, K, Berry, Dj, Boomsma, Di, Bradfield, Jp, Charoen, P, Coin, L, Cooper, C, Cousminer, Dl, Das, S, Davis, O, Dedoussis, Gv, Elliott, P, Estivill, X, Evans, Dm, Feenstra, B, Flexeder, C, Frayling, T, Freathy, Rm, Gaillard, R, Geller, F, Gillman, M, Grant, Sf, Groen Blokhuis, M, Goh, Lk, Guxens, M, Hakonarson, H, Hattersley, At, Haworth, Cm, Hadley, D, Hedebrand, J, Heinrich, J, Hinney, A, Hirschhorn, Jn, Hocher, B, Holloway, Jw, Holst, C, Hottenga, Jj, Horikoshi, M, Huikari, V, Hypponen, E, Iñiguez, C, Jaddoe, Vw, Jarvelin, Mr, Kaakinen, M, Kilpeläinen, To, Kirin, M, Kowgier, M, Lakka, Hm, Lakka, Ta, Lange, La, Lawlor, Da, Lehtimäki, T, Lewin, A, Lindgren, C, Lindi, V, Maggi, R, Marsh, J, Mccarthy, Mi, Melbye, M, Middeldorp, C, Millwood, I, Mohlke, Kl, Mook Kanamori, Do, Murray, Jc, Nivard, M, Nohr, Ea, Ntalla, I, Oken, E, Ong, Kk, O'Reilly, Pf, Palmer, Lj, Panoutsopoulou, K, Pararajasingham, J, Pearson, Er, Pennell, Ce, Power, C, Price, T, Prokopenko, I, Raitakari, Ot, Rodriguez, A, Salem, Rm, Saw, Sm, Scherag, A, Sebert, S, Siitonen, N, Simell, O, Sørensen, Ti, Sovio, U, Pourcain, B, Strachan, Dp, Sunyer, J, Taal, Hr, Teo, Yy, Thiering, E, Tiesler, C, Timpson, Nj, Uitterlinden, Ag, Valcárcel, B, Warrington, Nm, White, S, Widén, E, Willemsen, G, Wilson, Jf, Yaghootkar, H, Zeggini, E, Elks, Ce, Perry, Jr, Sulem, P, Chasman, Di, Franceschini, N, He, C, Lunetta, Kl, Visser, Ja, Byrne, Em, Gudbjartsson, Df, Esko, T, Koller, Dl, Kutalik, Z, Lin, P, Mangino, M, Marongiu, M, Mcardle, Pf, Smith, Av, Stolk, L, van Wingerden, Sh, Zhao, Jh, Albrecht, E, Corre, T, Ingelsson, E, Hayward, C, Magnusson, Pk, Smith, En, Ulivi, S, Warrington, M, Zgaga, L, Alavere, H, Amin, N, Aspelund, T, Bandinelli, S, Barroso, I, Berenson, G, Bergmann, S, Blackburn, H, Boerwinkle, E, Buring, Je, Busonero, F, Campbell, H, Chanock, Sj, Chen, W, Cornelis, Mc, Couper, D, Coviello, Ad, D'Adamo, ADAMO PIO, de Faire, U, de Geus, Ej, Deloukas, P, Döring, A, Davey Smith, G, Easton, Df, Eiriksdottir, G, Emilsson, V, Eriksson, J, Ferrucci, L, Folsom, Ar, Foroud, T, Garcia, M, Gasparini, Paolo, Gieger, C, Gudnason, V, Hall, P, Hankinson, Se, Ferreli, L, Heath, Ac, Hernandez, Dg, Hofman, A, Hu, Fb, Illig, T, Järvelin, Mr, Johnson, Ad, Karasik, D, Khaw, Kt, Kiel, Dp, Kolcic, I, Kraft, P, Launer, Lj, Laven, J, Li, S, Liu, J, Levy, D, Martin, Ng, Mcardle, Wl, Mooser, V, Murray, S, Nalls, Ma, Navarro, P, Nelis, M, Ness, Ar, Northstone, K, Oostra, Ba, Peacock, M, Palotie, A, Paré, G, Parker, An, Pedersen, Nl, Peltonen, L, Pharoah, P, Polasek, O, Plump, A, Pouta, A, Porcu, E, Rafnar, T, Rice, Jp, Ring, Sm, Rivadeneira, F, Rudan, I, Sala, C, Salomaa, V, Sanna, S, Schlessinger, D, Schork, Nj, Scuteri, A, Segrè, Av, Shuldiner, Ar, Soranzo, N, Srinivasan, Sr, Tammesoo, Ml, Tikkanen, E, Toniolo, D, Tsui, K, Tryggvadottir, L, Tyrer, J, Uda, M, van Dam, Rm, van Meurs, Jb, Vollenweider, P, Waeber, G, Wareham, Nj, Waterworth, Dm, Weedon, Mn, Wichmann, He, Wright, Af, Young, L, Zhai, G, Zhuang, Wv, Bierut, Lj, Boyd, Ha, Crisponi, L, Demerath, Ew, van Duijn, Cm, Econs, Mj, Harris, Tb, Hunter, Dj, Loos, Rj, Metspalu, A, Montgomery, Gw, Ridker, Pm, Spector, Td, Streeten, Ea, Stefansson, K, Thorsteinsdottir, U, Widen, E, Murabito, Jm, Murray, A., Hedebrand, Johannes (Beitragende*r), Hinney, Anke (Beitragende*r), Biological Psychology, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, EMGO+ - Lifestyle, Overweight and Diabetes, Neuroscience Campus Amsterdam - Brain Imaging Technology, Neuroscience Campus Amsterdam - Brain Mechanisms in Health & Disease, Faculteit Medische Wetenschappen/UMCG, Groningen Research Institute of Pharmacy, and Groningen Research Institute for Asthma and COPD (GRIAC)

Subjects
Male, Netherlands Twin Register (NTR), Genetic Linkage, Medizin, Gene Expression, Genome-wide association study, VARIANTS, Body Mass Index, 0302 clinical medicine, genetic linkage, Transforming Growth Factor beta, Neoplasms, molecular biology, genetics, Child, Genetics (clinical), Adiposity, 2. Zero hunger, 0303 health sciences, adiposity, Mitogen-Activated Protein Kinase 3, Association Studies Articles, Age Factors, ACHONDROPLASIA, General Medicine, Genome-Wide Association Study, pubertal height growth, pubertal timing, Phenotype, OBESITY, Menarche, body height, Female, Signal Transduction, medicine.medical_specialty, age factors, CHROMOSOME 16P11.2, Adolescent, BIRTH, Quantitative Trait Loci, 030209 endocrinology & metabolism, Context (language use), Biology, Childhood obesity, MENARCHE, Young Adult, 03 medical and health sciences, AGE, SDG 3 - Good Health and Well-being, Prepuberty, Internal medicine, Genetics, medicine, Humans, Molecular Biology, 030304 developmental biology, Sign, FACTOR RECEPTOR-3, MUTATIONS, Puberty, ta3121, medicine.disease, Obesity, Body Height, Genetic architecture, Endocrinology, POPULATION COHORT, gene expression, Body mass index, Follow-Up Studies
Abstract

The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P

Published
2013

7. Early growth genetics C. New loci associated with birgh weight identify genetic links between intrauterine growth and adult height and metabolism

Author

Horikoshi, M, Yaghootkar, H, Mook, Dennis, Sovio, U, Taal, Rob, Hennig, BJ, Bradfield, JP, St Pourcain, B, Evans, DM, Charoen, P, Kaakinen, M, Cousminer, DL, Lehtimaki, T, Kreiner-Moller, E, Warrington, NM, Bustamante, M, Feenstra, B, Berry, DJ, Thiering, E, Pfab, T, Barton, SJ, Shields, BM, Kerkhof, M, Leeuwen, Elisa, Fulford, AJ, Kutalik, Z, Zhao, JH, den Hoed, M, Mahajan, A, Lindi, V, Goh, LK, Hottenga, JJ (Jouke Jan), Wu, Fenny, Raitakari, OT, Harder, M, Meirhaeghe, A, Ntalla, I, Salem, RM, Jameson, KA, Zhou, K, Monies, D, Lagou, V, Kirin, M, Heikkinen, J, Adair, LS, Alkuraya, FS, Al-Odaib, A, Amouyel, P, Andersson, EAS, Bennett, AJ, Blakemore, AIF, Buxton, JL, Dallongeville, J, Das, Suman, de Geus, EJC, Estivill, X, Flexeder, C, Froguel, P, Geller, F, Godfrey, KM, Gottrand, F, Groves, CJ, Hansen, T, Hirschhorn, JN, Hofman, Bert, Hollegaard, MV, Hougaard, DM, Hypponen, E, Inskip, H, Isaacs, Aaron, Jorgensen, T, Kanaka-Gentenbein, C, Kemp, JP, Kiess, W, Kipelainen, TO, Klopp, N, Knight, BA, Kuzawa, CW, McMahon, G, Newnham, JP, Niinikoski, H, Oostra, Ben, Pedersen, L, Postma, DS, Ring, SM, Rivadeneira, Fernando, Robertson, NR, Sebert, S, Simell, O, Slowinski, T, Tiesler, CMT, Tonjes, A, Vaag, A, Viikari, JS, Vink, JM, Vissing, NH, Wareham, NJ, Willemsen, G, Witte, DR, Zhang, H, Zhao, J, Wilson, JF, Stumvoll, M, Prentice, AM, Meyer, BF, Pearson, ER, Boreham, CA, Cooper, C, Gilman, MW, Dedoussis, GV, Moreno, LA, Pedersen, O, Saarinen, M, Mohlke, KL, Boomsma, DI, Saw, SM, Lakka, TA, Korner, A, Loos, RJ, Ong, KK, Vollenweider, P, Duijn, Cornelia, Koppelman, GH, Hattersley, AT, Holloway, JW, Hocher, B, Heinrich, J, Power, C, Melbye, M, Guxens Junyent, Monica, Pennell, CE, Bonnelykke, K, Bisgaard, H, Eriksson, JG, Widen, E, Hakonarson, H, Uitterlinden, André, Pouta, A, Lawlor, DA, Smith, GD, Frayling, TM, McCarthy, M, Grant, SFA, Jaddoe, Vincent, Jarvelin, MR, Timpson, NJ, Prokopenko, I, Freathy, RM, Erasmus MC other, Epidemiology, Medical Microbiology & Infectious Diseases, Clinical Genetics, Internal Medicine, and Hematology

Published
2013

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