Yoav Ben-Shlomo1, Melissa Spears2, Chris Boustred2, Margaret May2, Simon G Anderson3, Emelia J Benjamin4, Pierre Boutouyrie5, James Cameron6, Chen-Huan Chen7, J Kennedy Cruickshank8, Shih-Jen Hwang9, Edward G Lakatta10, Stephane Laurent5, João Maldonado11, Gary F Mitchell12, Samer S Najjar13, Anne B Newman14, Mitsuru Ohishi15, Bruno Pannier16, Telmo Pereira17, Ramachandran S Vasan18, Tomoki Shokawa19, Kim Sutton-Tyrell14, Francis Verbeke20, Kang-Ling Wang7, David J Webb21, Tine Willum Hansen22, Sophia Zoungas23, Carmel M McEniery24, John R Cockcroft25, Ian B Wilkinson24. 1. School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom. Electronic address: y.ben-shlomo@bristol.ac.uk. 2. School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom. 3. Institute of Cardiovascular Sciences, University of Manchester, United Kingdom. 4. National Heart Lung and Blood Institute and Boston University's Framingham Heart Study, Cardiology Section, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts. 5. INSERM U 970, Paris-Descartes University, Hopital Europeen Georges Pompidou, Assistance Publique Hopitaux de Paris, Paris, France. 6. Monash Cardiovascular Research Centre, MonashHEART and Monash University Department of Medicine (MMC), Melbourne, Australia. 7. School of Medicine, National Yang-Ming University, Taipei, Taiwan. 8. King's College & King's Health Partners, St. Thomas' & Guy's Hospital, London, United Kingdom. 9. Branch of Population Sciences, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, Maryland. 10. Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland. 11. Instituto de Investigação e Formação Cardiovascular, Penacova, Portugal. 12. Cardiovascular Engineering, Inc., Norwood, Massachusetts. 13. Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland; MedStar Heart Research Institute, Washington, DC. 14. Center for Aging and Population Health, Pittsburgh, Pennsylvania. 15. Department of Geriatric Medicine, Osaka University, Osaka, Japan. 16. Centre d'Investigations Preventives et Cliniques, Paris, France. 17. Escola Superior de Tecnologia da Saúde de Coimbra, Coimbra, Portugal. 18. National Heart Lung and Blood Institute and Boston University's Framingham Heart Study, Department of Medicine, Boston University, Boston, Massachusetts. 19. Department of Molecular and Internal Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan. 20. Department of Nephrology, Ghent University Hospital, Ghent, Belgium. 21. University/BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom. 22. Research Center for Prevention and Health, Glostrup Hospital, Glostrup and Steno Diabetes Center, Glostrup, Denmark. 23. School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. 24. Clinical Pharmacology Unit, University of Cambridge, Cambridge, United Kingdom. 25. Wales Heart Research Institute, Cardiff, United Kingdom.
Abstract
OBJECTIVES: The goal of this study was to determine whether aortic pulse wave velocity (aPWV) improves prediction of cardiovascular disease (CVD) events beyond conventional risk factors. BACKGROUND: Several studies have shown that aPWV may be a useful risk factor for predicting CVD, but they have been underpowered to examine whether this is true for different subgroups. METHODS: We undertook a systematic review and obtained individual participant data from 16 studies. Study-specific associations of aPWV with CVD outcomes were determined using Cox proportional hazard models and random effect models to estimate pooled effects. RESULTS: Of 17,635 participants, a total of 1,785 (10%) had a CVD event. The pooled age- and sex-adjusted hazard ratios (HRs) per 1-SD change in loge aPWV were 1.35 (95% confidence interval [CI]: 1.22 to 1.50; p < 0.001) for coronary heart disease, 1.54 (95% CI: 1.34 to 1.78; p < 0.001) for stroke, and 1.45 (95% CI: 1.30 to 1.61; p < 0.001) for CVD. Associations stratified according to sex, diabetes, and hypertension were similar but decreased with age (1.89, 1.77, 1.36, and 1.23 for age ≤50, 51 to 60, 61 to 70, and >70 years, respectively; pinteraction <0.001). After adjusting for conventional risk factors, aPWV remained a predictor of coronary heart disease (HR: 1.23 [95% CI: 1.11 to 1.35]; p < 0.001), stroke (HR: 1.28 [95% CI: 1.16 to 1.42]; p < 0.001), and CVD events (HR: 1.30 [95% CI: 1.18 to 1.43]; p < 0.001). Reclassification indices showed that the addition of aPWV improved risk prediction (13% for 10-year CVD risk for intermediate risk) for some subgroups. CONCLUSIONS: Consideration of aPWV improves model fit and reclassifies risk for future CVD events in models that include standard risk factors. aPWV may enable better identification of high-risk populations that might benefit from more aggressive CVD risk factor management.
OBJECTIVES: The goal of this study was to determine whether aortic pulse wave velocity (aPWV) improves prediction of cardiovascular disease (CVD) events beyond conventional risk factors. BACKGROUND: Several studies have shown that aPWV may be a useful risk factor for predicting CVD, but they have been underpowered to examine whether this is true for different subgroups. METHODS: We undertook a systematic review and obtained individual participant data from 16 studies. Study-specific associations of aPWV with CVD outcomes were determined using Cox proportional hazard models and random effect models to estimate pooled effects. RESULTS: Of 17,635 participants, a total of 1,785 (10%) had a CVD event. The pooled age- and sex-adjusted hazard ratios (HRs) per 1-SD change in loge aPWV were 1.35 (95% confidence interval [CI]: 1.22 to 1.50; p < 0.001) for coronary heart disease, 1.54 (95% CI: 1.34 to 1.78; p < 0.001) for stroke, and 1.45 (95% CI: 1.30 to 1.61; p < 0.001) for CVD. Associations stratified according to sex, diabetes, and hypertension were similar but decreased with age (1.89, 1.77, 1.36, and 1.23 for age ≤50, 51 to 60, 61 to 70, and >70 years, respectively; pinteraction <0.001). After adjusting for conventional risk factors, aPWV remained a predictor of coronary heart disease (HR: 1.23 [95% CI: 1.11 to 1.35]; p < 0.001), stroke (HR: 1.28 [95% CI: 1.16 to 1.42]; p < 0.001), and CVD events (HR: 1.30 [95% CI: 1.18 to 1.43]; p < 0.001). Reclassification indices showed that the addition of aPWV improved risk prediction (13% for 10-year CVD risk for intermediate risk) for some subgroups. CONCLUSIONS: Consideration of aPWV improves model fit and reclassifies risk for future CVD events in models that include standard risk factors. aPWV may enable better identification of high-risk populations that might benefit from more aggressive CVD risk factor management.
Authors: Carmel M McEniery; Michael Spratt; Margaret Munnery; John Yarnell; Gordon D Lowe; Ann Rumley; John Gallacher; Yoav Ben-Shlomo; John R Cockcroft; Ian B Wilkinson Journal: Hypertension Date: 2010-06-07 Impact factor: 10.190
Authors: Carmel M McEniery; Ian R Hall; Ahmad Qasem; Ian B Wilkinson; John R Cockcroft Journal: J Am Coll Cardiol Date: 2005-10-10 Impact factor: 24.094
Authors: Kennedy Cruickshank; Lisa Riste; Simon G Anderson; John S Wright; Graham Dunn; Ray G Gosling Journal: Circulation Date: 2002-10-15 Impact factor: 29.690
Authors: Samer S Najjar; Angelo Scuteri; Veena Shetty; Jeanette G Wright; Denis C Muller; Jerome L Fleg; Harold P Spurgeon; Luigi Ferrucci; Edward G Lakatta Journal: J Am Coll Cardiol Date: 2008-04-08 Impact factor: 24.094
Authors: Samuli Ripatti; Emmi Tikkanen; Marju Orho-Melander; Aki S Havulinna; Kaisa Silander; Amitabh Sharma; Candace Guiducci; Markus Perola; Antti Jula; Juha Sinisalo; Marja-Liisa Lokki; Markku S Nieminen; Olle Melander; Veikko Salomaa; Leena Peltonen; Sekar Kathiresan Journal: Lancet Date: 2010-10-23 Impact factor: 79.321
Authors: Kang-Ling Wang; Hao-Min Cheng; Shao-Yuan Chuang; Harold A Spurgeon; Chih-Tai Ting; Edward G Lakatta; Frank C P Yin; Pesus Chou; Chen-Huan Chen Journal: J Hypertens Date: 2009-03 Impact factor: 4.844
Authors: O Cseprekál; J Egresits; Á Tabák; J Nemcsik; Z Járai; L Babos; E Fodor; K Farkas; G Godina; K I Kárpáthi; L Kerkovits; A Marton; Z Nemcsik-Bencze; Z Németh; L Sallai; I Kiss; A Tislér Journal: J Hum Hypertens Date: 2015-10-01 Impact factor: 3.012
Authors: Egidio Imbalzano; Marco Vatrano; Giuseppe Mandraffino; Lorenzo Ghiadoni; Sebastiano Gangemi; Rosa Maria Bruno; Vincenzo Antonio Ciconte; Nevena Paunovic; Rossella Costantino; Enrico Maria Mormina; Roberto Ceravolo; Antonino Saitta; Giuseppe Dattilo Journal: Int J Cardiovasc Imaging Date: 2015-08-04 Impact factor: 2.357
Authors: Michal Schäfer; Cynthia Myers; R Dale Brown; Maria G Frid; Wei Tan; Kendall Hunter; Kurt R Stenmark Journal: Curr Hypertens Rep Date: 2016-01 Impact factor: 5.369
Authors: Graziela Z Kalil; Ana Recober; Ann Hoang-Tienor; Miriam Bridget Zimmerman; William G Haynes; Gary L Pierce Journal: Obesity (Silver Spring) Date: 2016-02-05 Impact factor: 5.002
Authors: Julio A Chirinos; Mayank Sardana; Amer Ahmed Syed; Maheshwara R Koppula; Swapna Varakantam; Izzah Vasim; Harold G Oldland; Timothy S Phan; Nadja E A Drummen; Cees Vermeer; Raymond R Townsend; Scott R Akers; Wen Wei; Edward G Lakatta; Olga V Fedorova Journal: J Am Soc Hypertens Date: 2018-06-30
Authors: Julia O Totosy de Zepetnek; Masae Miyatani; Maggie Szeto; Lora M Giangregorio; B Catharine Craven Journal: J Spinal Cord Med Date: 2017-09-04 Impact factor: 1.985
Authors: James H Stein; Rebecca Stern; Jodi H Barnet; Claudia E Korcarz; Erika W Hagen; Terry Young; Paul E Peppard Journal: Sleep Breath Date: 2015-04-26 Impact factor: 2.816