Literature DB >> 23232644

Prediction of blood pressure changes over time and incidence of hypertension by a genetic risk score in Swedes.

Cristiano Fava1, Marketa Sjögren, Martina Montagnana, Elisa Danese, Peter Almgren, Gunnar Engström, Peter Nilsson, Bo Hedblad, Gian Cesare Guidi, Pietro Minuz, Olle Melander.   

Abstract

Recent Genome-Wide Association Studies (GWAS) have pinpointed different single nucleotide polymorphisms consistently associated with blood pressure (BP) and hypertension prevalence. However, little data exist regarding single nucleotide polymorphisms predicting BP variation over time and hypertension incidence. The aim of this study was to confirm the association of a genetic risk score (GRS), based on 29 independent single nucleotide polymorphisms, with cross-sectional BP and hypertension prevalence and to challenge its prediction of BP change over time and hypertension incidence in >17 000 middle-aged Swedes participating in a prospective study, the Malmö Preventive Project, investigated at baseline and over a 23-year average period of follow-up. The GRS was associated with higher systolic and diastolic BP values both at baseline (β ± SEM, 0.968 ± 0.102 mm Hg and 0.585 ± 0.064 mm Hg; P<1E-19 for both) and at reinvestigation (β ± SEM, 1.333 ± 0.161 mm Hg and 0.724 ± 0.086 mm Hg; P<1E-15 for both) and with increased hypertension prevalence (odds ratio [95% CI], 1.192 [1.140-1.245] and 1.144 [1.107-1.183]; P<1E-15 for both). The GRS was positively associated with change (Δ) in BP (β ± SEM, 0.033 ± 0.008 mm Hg/y and 0.023 ± 0.004 mm Hg/y; P<1E-04 for both) and hypertension incidence (odds ratio [95% CI], 1.110 [1.065-1.156]; P=6.7 E-07), independently from traditional risk factors. The relative weight of the GRS was lower in magnitude than obesity or prehypertension, but comparable with diabetes mellitus or a positive family history of hypertension. A C-statistics analysis does not show any improvement in the prediction of incident hypertension on top of traditional risk factors. Our data from a large cohort study show that a GRS is independently associated with BP increase and incidence of hypertension.

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Year:  2012        PMID: 23232644     DOI: 10.1161/HYPERTENSIONAHA.112.202655

Source DB:  PubMed          Journal:  Hypertension        ISSN: 0194-911X            Impact factor:   10.190


  47 in total

1.  A genetic risk score for hypertension is associated with risk of thoracic aortic aneurysm.

Authors:  A Tagetti; S Bonafini; T Ohlsson; G Engström; P Almgren; P Minuz; G Smith; O Melander; C Fava
Journal:  J Hum Hypertens       Date:  2019-01-18       Impact factor: 3.012

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3.  Influence of fat intake and BMI on the association of rs1799983 NOS3 polymorphism with blood pressure levels in an Iberian population.

Authors:  Leticia Goni; Marta Cuervo; Fermín I Milagro; J Alfredo Martínez
Journal:  Eur J Nutr       Date:  2016-03-19       Impact factor: 5.614

4.  Prediction model and assessment of probability of incident hypertension: the Rural Chinese Cohort Study.

Authors:  Bingyuan Wang; Yu Liu; Xizhuo Sun; Zhaoxia Yin; Honghui Li; Yongcheng Ren; Yang Zhao; Ruiyuan Zhang; Ming Zhang; Dongsheng Hu
Journal:  J Hum Hypertens       Date:  2020-02-27       Impact factor: 3.012

5.  Is the association of hypertension with cardiovascular events stronger among the lean and normal weight than among the overweight and obese? The multi-ethnic study of atherosclerosis.

Authors:  Laura A Colangelo; Thanh-Huyen T Vu; Moyses Szklo; Gregory L Burke; Christopher Sibley; Kiang Liu
Journal:  Hypertension       Date:  2015-06-15       Impact factor: 10.190

Review 6.  Progress and future aspects in genetics of human hypertension.

Authors:  Qi Zhao; Tanika N Kelly; Changwei Li; Jiang He
Journal:  Curr Hypertens Rep       Date:  2013-12       Impact factor: 5.369

7.  Predicting changes in systolic blood pressure using longitudinal patient records.

Authors:  John Wes Solomon; Rodney D Nielsen
Journal:  J Biomed Inform       Date:  2015-07-22       Impact factor: 6.317

8.  Predictors of hypertension awareness, treatment and control in South Africa: results from the WHO-SAGE population survey (Wave 2).

Authors:  Lisa Jayne Ware; Glory Chidumwa; Karen Charlton; Aletta Elisabeth Schutte; Paul Kowal
Journal:  J Hum Hypertens       Date:  2018-10-31       Impact factor: 3.012

9.  Genetic Susceptibility, Dietary Protein Intake, and Changes of Blood Pressure: The POUNDS Lost Trial.

Authors:  Dianjianyi Sun; Tao Zhou; Xiang Li; Yoriko Heianza; Zhaoxia Liang; George A Bray; Frank M Sacks; Lu Qi
Journal:  Hypertension       Date:  2019-10-28       Impact factor: 10.190

10.  Developing and validating a new precise risk-prediction model for new-onset hypertension: The Jichi Genki hypertension prediction model (JG model).

Authors:  Hiroshi Kanegae; Takamitsu Oikawa; Kenji Suzuki; Yukie Okawara; Kazuomi Kario
Journal:  J Clin Hypertens (Greenwich)       Date:  2018-03-31       Impact factor: 3.738

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