Life Course Trajectories of Systolic Blood Pressure Using Longitudinal Data from Eight UK Cohorts
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{"title"=>"Life course trajectories of systolic blood pressure using longitudinal data from eight UK cohorts", "type"=>"journal", "authors"=>[{"first_name"=>"Andrew K.", "last_name"=>"Wills", "scopus_author_id"=>"26323396400"}, {"first_name"=>"Debbie A.", "last_name"=>"Lawlor", "scopus_author_id"=>"7102452820"}, {"first_name"=>"Fiona E.", "last_name"=>"Matthews", "scopus_author_id"=>"7005722061"}, {"first_name"=>"Avan Aihie", "last_name"=>"Sayer", "scopus_author_id"=>"7006450580"}, {"first_name"=>"Eleni", "last_name"=>"Bakra", "scopus_author_id"=>"41960903000"}, {"first_name"=>"Yoav", "last_name"=>"Ben-Shlomo", "scopus_author_id"=>"7006649063"}, {"first_name"=>"Michaela", "last_name"=>"Benzeval", "scopus_author_id"=>"6603373510"}, {"first_name"=>"Eric", "last_name"=>"Brunner", "scopus_author_id"=>"24576354300"}, {"first_name"=>"Rachel", "last_name"=>"Cooper", "scopus_author_id"=>"35354537000"}, {"first_name"=>"Mika", "last_name"=>"Kivimaki", "scopus_author_id"=>"7004391239"}, {"first_name"=>"Diana", "last_name"=>"Kuh", "scopus_author_id"=>"8329745200"}, {"first_name"=>"Graciela", "last_name"=>"Muniz-Terrera", "scopus_author_id"=>"35206359500"}, {"first_name"=>"Rebecca", "last_name"=>"Hardy", "scopus_author_id"=>"55619306030"}], "year"=>2011, "source"=>"PLoS Medicine", "identifiers"=>{"pui"=>"362049923", "sgr"=>"79959778973", "pmid"=>"21695075", "scopus"=>"2-s2.0-79959778973", "isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)", "doi"=>"10.1371/journal.pmed.1000440", "issn"=>"15491277"}, "id"=>"4c19795e-1364-337e-b9e6-fdd3f7a9ed96", "abstract"=>"BACKGROUND:Much of our understanding of the age-related progression of systolic blood pressure (SBP) comes from cross-sectional data, which do not directly capture within-individual change. We estimated life course trajectories of SBP using longitudinal data from seven population-based cohorts and one predominantly white collar occupational cohort, each from the United Kingdom and with data covering different but overlapping age periods.\\n\\nMETHODS AND FINDINGS:Data are from 30,372 individuals and comprise 102,583 SBP observations spanning from age 7 to 80+y. Multilevel models were fitted to each cohort. Four life course phases were evident in both sexes: a rapid increase in SBP coinciding with peak adolescent growth, a more gentle increase in early adulthood, a midlife acceleration beginning in the fourth decade, and a period of deceleration in late adulthood where increases in SBP slowed and SBP eventually declined. These phases were still present, although at lower levels, after adjusting for increases in body mass index though adulthood. The deceleration and decline in old age was less evident after excluding individuals who had taken antihypertensive medication. Compared to the population-based cohorts, the occupational cohort had a lower mean SBP, a shallower annual increase in midlife, and a later midlife acceleration. The maximum sex difference was found at age 26 (+8.2 mm Hg higher in men, 95% CI: 6.7, 9.8); women then experienced steeper rises and caught up by the seventh decade.\\n\\nCONCLUSIONS:Our investigation shows a general pattern of SBP progression from childhood in the UK, and suggests possible differences in this pattern during adulthood between a general population and an occupational population. Please see later in the article for the Editors' Summary.", "link"=>"http://www.mendeley.com/research/life-course-trajectories-systolic-blood-pressure-using-longitudinal-data-eight-uk-cohorts-1", "reader_count"=>77, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Librarian"=>1, "Researcher"=>16, "Student > Doctoral Student"=>2, "Student > Ph. D. 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Student"=>13, "Student > Postgraduate"=>2, "Other"=>9, "Student > Master"=>17, "Student > Bachelor"=>1, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Agricultural and Biological Sciences"=>3, "Arts and Humanities"=>1, "Business, Management and Accounting"=>1, "Computer Science"=>2, "Decision Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Nursing and Health Professions"=>3, "Mathematics"=>2, "Medicine and Dentistry"=>41, "Psychology"=>4, "Social Sciences"=>7}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>41}, "Social Sciences"=>{"Social Sciences"=>7}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>4}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>7}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"United States"=>1, "Norway"=>1, "Denmark"=>1, "United Kingdom"=>2, "Germany"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/766250"], "description"=>"a<p>This is the year that age was centred to in each model.</p>", "links"=>[], "tags"=>["coefficients", "unadjusted", "multilevel", "models", "displayed"], "article_id"=>436608, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.t004", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regression_coefficients_standard_errors_for_the_fixed_effects_from_the_main_unadjusted_multilevel_models_displayed_in_Figure_2_/436608", "title"=>"Regression coefficients (standard errors) for the fixed effects from the main unadjusted multilevel models displayed in Figure 2.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:50:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/766349"], "description"=>"a<p>The number of participants with at least one BP measurement.</p>b<p>The 1<sup>st</sup> centile of wave 1 and 99<sup>th</sup> centile of the last data collection wave.</p>c<p>Referenced to the UK 1990 growth reference in <i>z-</i>score units <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000440#pmed.1000440-Cole1\" target=\"_blank\">[28]</a>.</p>d<p>This is father's SEP for the ALSPAC and T-07 1972/1973 cohorts.</p><p>IQR, inter-quartile range; sd, standard deviation.</p>", "links"=>[], "tags"=>["baseline", "characteristics"], "article_id"=>436708, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.t003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cohort_information_and_baseline_characteristics_by_sex_/436708", "title"=>"Cohort information and baseline characteristics by sex.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:51:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/765871"], "description"=>"<p>The lines represent the median, 10<sup>th</sup> and 90<sup>th</sup> centile at each wave in each cohort in men (A) and women (B). The grey lines from 0 to 23 y are centiles from the UK 1990 growth reference (see reference 26) spaced approximately 2/3 of a standard deviation apart (2<sup>nd</sup>, 10<sup>th</sup>, 25<sup>th</sup>, 50<sup>th</sup>, 75<sup>th</sup>, 90<sup>th</sup> and 98<sup>th</sup> centiles).</p>", "links"=>[], "tags"=>["bmi"], "article_id"=>436230, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.g003", "stats"=>{"downloads"=>3, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Observed_BMI_kg_m_2_in_each_cohort_/436230", "title"=>"Observed BMI (kg/m<sup>2</sup>) in each cohort.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:43:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/765685"], "description"=>"<p>Observed median and 10<sup>th</sup> and 90<sup>th</sup> centiles for SBP (in millimetres of mercury) at each wave in each cohort (for the CaPS and WHII cohorts this is the median in 10-y intervals to allow for the wide age distribution at each wave) and the prevalence (percent) of HypRx use (filled circles) in men (A) and women (B). Individual SBP data points are also plotted. Data presented here do not include an added constant to account for BP medication.</p>", "links"=>[], "tags"=>["sbp", "prevalence", "antihypertensive"], "article_id"=>436040, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Observed_SBP_and_prevalence_of_antihypertensive_therapy_/436040", "title"=>"Observed SBP and prevalence of antihypertensive therapy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:40:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/765774"], "description"=>"<p>Predicted mean SBP trajectories (in millimetres of mercury) and velocities (millimetres of mercury per year) estimated from unadjusted multilevel models in men (A) and women (B) in each cohort. The thin lines are the 95% CIs.</p>", "links"=>[], "tags"=>["sbp", "unadjusted"], "article_id"=>436138, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_SBP_from_unadjusted_models_/436138", "title"=>"Predicted SBP from unadjusted models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/766300"], "description"=>"a<p>MRZ, Hawksley MRZ (auscultatory); AO(D), AO (Dinamap); AO(O), AO (Omron); AO(C), AO (Copal UA-231).</p>", "links"=>[], "tags"=>["protocols", "cohort"], "article_id"=>436658, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.t001", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Blood_pressure_measurement_protocols_used_in_each_cohort_at_each_wave_/436658", "title"=>"Blood pressure measurement protocols used in each cohort at each wave.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:50:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/766394"], "description"=>"<p>Number of participants and median age (years) at each wave in each population-based cohorts, stratified by sex.</p>", "links"=>[], "tags"=>["median", "population-based", "stratified"], "article_id"=>436751, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.t002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_participants_and_median_age_years_at_each_wave_in_each_population_based_cohorts_stratified_by_sex_/436751", "title"=>"Number of participants and median age (years) at each wave in each population-based cohorts, stratified by sex.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:52:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/384732", "https://ndownloader.figshare.com/files/384798", "https://ndownloader.figshare.com/files/384851", "https://ndownloader.figshare.com/files/384908"], "description"=>"<div><h3>Background</h3><p>Much of our understanding of the age-related progression of systolic blood pressure (SBP) comes from cross-sectional data, which do not directly capture within-individual change. We estimated life course trajectories of SBP using longitudinal data from seven population-based cohorts and one predominantly white collar occupational cohort, each from the United Kingdom and with data covering different but overlapping age periods.</p><h3>Methods and Findings</h3><p>Data are from 30,372 individuals and comprise 102,583 SBP observations spanning from age 7 to 80+y. Multilevel models were fitted to each cohort. Four life course phases were evident in both sexes: a rapid increase in SBP coinciding with peak adolescent growth, a more gentle increase in early adulthood, a midlife acceleration beginning in the fourth decade, and a period of deceleration in late adulthood where increases in SBP slowed and SBP eventually declined. These phases were still present, although at lower levels, after adjusting for increases in body mass index though adulthood. The deceleration and decline in old age was less evident after excluding individuals who had taken antihypertensive medication. Compared to the population-based cohorts, the occupational cohort had a lower mean SBP, a shallower annual increase in midlife, and a later midlife acceleration. The maximum sex difference was found at age 26 (+8.2 mm Hg higher in men, 95% CI: 6.7, 9.8); women then experienced steeper rises and caught up by the seventh decade.</p><h3>Conclusions</h3><p>Our investigation shows a general pattern of SBP progression from childhood in the UK, and suggests possible differences in this pattern during adulthood between a general population and an occupational population.</p><h3></h3><p><em>Please see later in the article for the Editors' Summary</em></p></div>", "links"=>[], "tags"=>["trajectories", "systolic", "longitudinal", "uk", "cohorts"], "article_id"=>135954, "categories"=>["Biological Sciences", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000440.s001", "https://dx.doi.org/10.1371/journal.pmed.1000440.s002", "https://dx.doi.org/10.1371/journal.pmed.1000440.s003", "https://dx.doi.org/10.1371/journal.pmed.1000440.s004"], "stats"=>{"downloads"=>8, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Life_Course_Trajectories_of_Systolic_Blood_Pressure_Using_Longitudinal_Data_from_Eight_UK_Cohorts/135954", "title"=>"Life Course Trajectories of Systolic Blood Pressure Using Longitudinal Data from Eight UK Cohorts", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-06-14 01:39:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/765983"], "description"=>"<p>Coloured and black lines are the predicted mean SBP trajectory (in millimetres of mercury) and velocity (millimetres of mercury per year) after adjusting for BMI as a time-varying covariate (see <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000440#s3\" target=\"_blank\">Methods</a> and <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000440#pmed.1000440.s002\" target=\"_blank\">Text S2</a> for full details of this adjustment) in men (A) and women (B). The grey areas are the 95% CIs from unadjusted models.</p>", "links"=>[], "tags"=>["sbp", "velocity", "adjusting"], "article_id"=>436341, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.g004", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_mean_SBP_and_velocity_after_adjusting_for_BMI_/436341", "title"=>"Predicted mean SBP and velocity after adjusting for BMI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:45:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/766087"], "description"=>"<p>Estimated from multilevel models adjusting for current BMI (all cohorts) and with additional adjustment for baseline height in the child cohorts (ALSPAC and T-07 1972/1973). Positive values indicate a higher SBP in males.</p>", "links"=>[], "tags"=>["sbp", "minus", "millimetres"], "article_id"=>436444, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.g005", "stats"=>{"downloads"=>2, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_sex_difference_in_SBP_men_minus_women_in_millimetres_of_mercury_and_95_CIs_/436444", "title"=>"Mean sex difference in SBP (men minus women) (in millimetres of mercury) and 95% CIs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:47:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/766201"], "description"=>"a<p>Beta is per <i>z</i>-score increase on the UK 1990 growth reference scale (28).</p>b<p>Results are presented at several ages in cohorts where there was evidence that the association between concurrent BMI and SBP differed across age.</p>", "links"=>[], "tags"=>["concurrent", "bmi", "uk", "1990", "units", "cohorts", "began", "adolescence", "sbp", "cohort", "models", "time-updated"], "article_id"=>436543, "categories"=>["Computational Biology", "Biotechnology"], "users"=>["Andrew K. Wills", "Debbie A. Lawlor", "Fiona E. Matthews", "Avan Aihie Sayer", "Eleni Bakra", "Yoav Ben-Shlomo", "Michaela Benzeval", "Eric Brunner", "Rachel Cooper", "Mika Kivimäki", "Diana Kuh", "Graciela Muniz-Terrera", "Rebecca Hardy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000440.t005", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Association_between_concurrent_BMI_per_z_score_increase_in_UK_1990_growth_reference_units_in_cohorts_where_data_collection_began_in_childhood_or_adolescence_and_per_kilogram_metre_2_in_adult_cohorts_and_SBP_millimetres_of_mercury_in_each_cohort_based_on_/436543", "title"=>"Association (β) between concurrent BMI (per <i>z-</i>score increase in UK 1990 growth reference units in cohorts where data collection began in childhood or adolescence and per kilogram/metre<sup>2</sup> in adult cohorts) and SBP (millimetres of mercury) in each cohort based on models including BMI as a time-updated covariate.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:49:03"}

PMC Usage Stats | Further Information

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