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      Relation of Blood Pressure in Childhood to Self-Reported Hypertension in Adulthood : The International Childhood Cardiovascular Cohort Consortium

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          Blood pressure (BP) tracking (maintaining a BP percentile) across life is not well defined but is important in predicting which children will become hypertensive adults. We computed BP tracking in subjects with BP measured in childhood and adulthood and performed logistic regression to determine the ability of childhood BP to predict adult hypertension (N=5035, 46.7 years, 74.2% white, 17.7% black; 39.6% male). Prevalence of hypertension was 29%. Correlations between systolic BP for child and adolescent were r=0.48; adolescent and young adult r=0.40, and child and young adult r=0.24 (all p<0.0001). Participants self-reporting adult hypertension were less likely to be white (38.7% black, 27.6% white, 20.9% other; p<0.0001) and female (26.4% females, 32.9% male, p<0.0001). Participants with adult hypertension were more likely to have higher BP and adiposity by age 10 years and abnormal lipids and glucose by age 16 years. There was a graded increase in the frequency of self-reported adult hypertension across the BP change groups, even within the persistently normotensive group (X 2 < 0.0001) from 19% in children with a systolic BP % persistently below the median to 80% for individuals with elevated BP in both childhood and adolescence. Although our precision to predict which individual child is at risk of adult BP-related cardiovascular disease is weak, an increase in systolic BP and body mass index percentile from childhood to adolescence should signal a need for lifestyle intervention to prevent future sustained hypertension-related cardiovascular disease.

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          Tracking of serum lipid levels, blood pressure, and body mass index from childhood to adulthood: the Cardiovascular Risk in Young Finns Study.

          To examine tracking and predictiveness of childhood lipid levels, blood pressure, and body mass index for risk profile in adulthood and the best age to measure the childhood risk factor levels. Study subjects were participants of the longitudinal Cardiovascular Risk in Young Finns Study, started in 1980 (age 3, 6, 9, 12, 15, and 18 years). A total of 2204 subjects participated to the 27-year follow-up in 2007 (age, 30 to 45 years). In both sex groups and in all age groups, childhood risk factors were significantly correlated with levels in adulthood. The correlation coefficients for cholesterol levels and body mass index were 0.43 to 0.56 (P < .0001), and for blood pressure and triglyceride levels, they were 0.21 to 0.32 (P < .0001). To recognize children with abnormal adult levels, the National Cholesterol Education Program and the National High Blood Pressure Education Program cutoff points for lipid and blood pressure values and international cutoff points for overweight and obesity were used. Age seemed to affect associations. The best sensitivity and specificity rates were observed in 12- to 18-year-old subjects. Childhood blood pressure, serum lipid levels, and body mass index correlate strongly with values measured in middle age. These associations seemed to be stronger with increased age at measurements. Copyright © 2011 Mosby, Inc. All rights reserved.
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            Is Open Access

            Life Course Trajectories of Systolic Blood Pressure Using Longitudinal Data from Eight UK Cohorts

            Introduction Systolic blood pressure (SBP) is an important indicator of cardiovascular function as it has a strong, positive, and continuous relationship with cardiovascular disease (CVD) and mortality [1]. In prospective studies that measured SBP in adolescence or early adulthood, SBP has been shown to predict future risk of CVD with the same magnitude of association as that seen in studies measuring blood pressure (BP) in middle age [2]–[5]. Despite a tendency for SBP to track through life [6],[7], randomised controlled trials demonstrate that it is a highly modifiable risk factor [8]. Understanding the progression of SBP through life and factors affecting this progression is clearly important to determining the best methods for preventing future CVD. Textbook descriptions of age-related changes in BP are based on cross-sectional studies of different age groups [9]. These show marked increases in SBP in the first year of life (from ∼70 mm Hg to 95 mm Hg), followed by steady increases of 1–2 mm Hg per year to the late 20 s, after which the rise is more marked to age 80, when it begins to level off. Cross-sectional data cannot directly measure within-individual change, and thus the tempo of age-related BP may be misrepresented by the monotonic secular decline in BP over the last six decades [10]–[12], also called “cohort” effects, or by contextual changes related to specific historical periods. Whilst there are a small number of published population-based longitudinal studies on age-related BP changes in children [13] and adults [14]–[16], to date no study to our knowledge has repeat BP measurements from childhood to late adulthood, and it will be some decades before existing birth cohorts that do have repeat infant and childhood measurements reach middle and old age. An alternative to examining life course trajectories in a single cohort, and a potential improvement over cross-sectional analyses, is to compare data from multiple cohorts with repeated measurements that cover different and overlapping periods of life. We obtained longitudinal data on BP from eight UK Medical Research Council funded cohort studies, each covering different age periods. The objectives of this paper are to (1) describe the average unadjusted SBP trajectory in each cohort, (2) examine the potential to which these trajectories are modifiable by adjusting for a strong determinant of SBP, adiposity, as marked by concurrent body mass index (BMI) [8],[17], and (3) investigate gender differences in SBP trajectories. In addressing these questions, we also explore the methodological issues that arise when using multiple cohorts to investigate life course BP. Methods Study Population All cohorts included in these analyses were receiving funds from the UK Medical Research Council and had at least two repeat measurements of BP. Seven cohorts drawn from the general population, in the sense that they were sampled in a way that made them approximately representative of a geographical area, were included. These were the Caerphilly Prospective Study (CaPS), which includes only men [18], the Hertfordshire Ageing Study (HAS) [19], the Medical Research Council National Survey of Health and Development (NSHD) (1946 British birth cohort) [20], three cohorts from the West of Scotland Twenty-07 study (T-07) [21], and the Avon Longitudinal Study of Parents and Children (ALSPAC) [22]. The Whitehall II study (WHII) [23], which is an occupational cohort, was the eighth study included. All cohorts received ethical approval [18]–[23]. The NSHD is in most respects a nationally representative sample from England, Scotland, and Wales [20] while the other cohorts were sampled from single towns, cities, or counties in the UK. The T-07 study comprises three separate cohorts born 20 y apart (1932/1933, 1952/1953, 1972/1973) with measures across the three cohorts occurring at similar dates. Two sampling schemes were used in the T-07 study; for these analyses we used data from the regional sample [21] as it is population representative and was followed up at all waves of data collection. WHII is a prospective cohort study of civil servants aged 35 to 55 y working in the London-based offices of 20 Whitehall departments [23]. Blood Pressure Measurement Table 1 describes the BP measurement protocols in each cohort. All studies used trained nurses or fieldworkers, a seated posture, appropriate cuff sizes for arm circumference, and allowed at least 2 min rest prior to measurement. ALSPAC and HAS used an automated oscillometric (AO) device. CaPS, NSHD, T-07, and WHII used a manual random zero sphygmomanometer (MRZ) at earlier waves and switched to an AO device for the most recent waves of data collection (Table 1). To make the BP readings from separate waves within each cohort comparable, we used published equations [24] to convert the AO measurements at wave 5 in CaPS, wave 3 in NSHD, waves 4 and 5 in T-07, and wave 4 in WHII to an MRZ value. Failing to correct for a device switch leads to a biased trajectory by making the trajectory steeper between ages where the device was switched (see Text S1). However, while SBP tends to be higher when measured with an AO compared to MRZ device, a sensitivity analysis using the T-07 cohorts, which have three repeat measures with each type of device, suggested that the slopes of the trajectories were largely unaffected by device type (see Text S1). At least two readings were taken at each wave in all cohorts except CaPS. In the T-07 cohorts at wave 3, the first reading was taken with a MRZ and the second with an AO device. To maintain consistency between cohorts, we report results from the first reading. A sensitivity analysis using the second reading where available showed that this choice did not qualitatively alter our findings on the overall life course trajectory (see Text S1). 10.1371/journal.pmed.1000440.t001 Table 1 Blood pressure measurement protocols used in each cohort at each wave. Protocol Feature Study ALSPAC T-07 NSHD CaPS HAS WHII Posture Seated Seated Seated Seated Seated Seated Operator Trained field workers Nurse Nurse Waves 1 to 4: physician; wave 5: trained field worker Nurse Nurse Minimum rest before reading 2 min 5 min 5 min 5 min 5 min 5 min Number of readings Waves 1 to 6: 2 Wave 1: 2 Waves 1 to 3: 2 Wave 1: 1 Wave 1: 2 Waves 1 to 4: 2 Wave 2: 2 Wave 2: 2 Wave 2: 3 Wave 3: 2 Wave 3: 1 Wave 4: 2 Wave 4: 1 Wave 5: 3 Wave 5: 1 BP devicea Waves 1 to 6: AO(D) Wave 1: MRZ Wave 1: MRZ Wave 1: MRZ Wave 1: AO(D) Wave 1: MRZ Wave 2: MRZ Wave 2: MRZ Wave 2: MRZ; A(C) Wave 2: AO(D) Wave 2: MRZ Wave 3: MRZ; AO(O) Wave 3: AO(O) Wave 3: MRZ Wave 3: MRZ Wave 4: AO(O) Wave 4: MRZ Wave 4: AO(O) Wave 5: AO(O) Wave 5: AO(O) a MRZ, Hawksley MRZ (auscultatory); AO(D), AO (Dinamap); AO(O), AO (Omron); AO(C), AO (Copal UA-231). Blood Pressure Medication Nurses or trained field workers recorded any prescribed medications, and antihypertensive drugs (HypRx) were subsequently coded using the British National Formulary books. In ALSPAC, none of the children had taken antihypertensive medication. Medication data were unavailable for wave 2 in the youngest T-07 cohort (1972/1973) (mean age: 18.6 y), so we made the assumption that none were taking medication at this age given that none of this cohort were on HypRx at the first wave and fewer than 1.2% were on HypRx at wave 3. Cohort Characteristics At each wave, height and weight were measured and BMI was calculated (weight [in kilograms]/height [in meters]2). Adult socioeconomic position (SEP) was defined based on own occupation for men and on the husband's occupation for women or woman's own occupation where no data existed for the husband or the woman was unmarried. SEP was defined according to the Registrar General's classification system. Analysis Unadjusted life course pattern of SBP (aim 1) To describe the distribution of SBP across age and between cohorts, the observed median and 10th and 90th centiles were calculated at each wave. To estimate the mean SBP trajectory as a function of age, multilevel models were fitted to each cohort. Cubic and quadratic polynomials were used to describe non-linear trajectories (more details on the modelling strategy are in Text S2). To account for the influence of HypRx on the SBP trajectory, a constant of 10 mm Hg was added to SBP values that were observed while on treatment. This value was selected on the basis of previously reported estimates for the effect of medication on reducing BP [25]–[27], though our findings were robust to the choice of constant (see Text S1). The approach assumes that treatment effects are the same across age, period, and cohort, but has been shown to be a reasonable way of reducing treatment bias [25] and was adopted because we were interested in the age-related progression of SBP in the general population unaffected by the increasing prevalence of HypRx use. Likewise, excluding or censoring individuals on treatment would omit an important subgroup from the population we wish to describe. A sensitivity analysis also showed that our findings in the unadjusted models were unlikely to be biased under an assumption that the data are missing at random (see Text S3). Modifiability of trajectories (aim 2) As BMI generally increases with age, to see how these increases might influence the age-related SBP trajectory, models were fitted including BMI as a time-varying covariate, adjusting the SBP trajectory as if BMI remained at 23 kg/m2 through adult life. This value was chosen because it approximates the median BMI at the final age of the UK 1990 growth reference (age 23 y) [28] and is within recommendations for normal weight. For the cohorts where data collection began in childhood (ALSPAC and T-07 1972/1973), we adjusted the SBP trajectory to the median age- and sex-specific BMI values from the UK 1990 growth reference [28]. We also adjusted for baseline height differences in these cohorts using the same growth reference. Text S2 contains more details on these models. Gender differences (aim 3) Differences between the sexes were tested and described by fitting models that included sex interactions on all the fixed effects of each model; this was done for both the unadjusted and adjusted models. A restricted maximum likelihood algorithm was used for estimation, and Stata (version 10) was used for all analyses. Results Data are from 30,372 individuals comprising 102,583 data points (Table 2). Each cohort was overlapped to some extent by at least one other cohort with data at a similar age. The birth dates of the cohorts spanned the years 1918 to 1992, and BP data were collected over a 29-y period from 1979 to 2008 (Table 3). The oldest T-07 cohort (1932/1933) and the HAS and CaPS cohorts had the highest proportion of individuals working in manual occupations (Table 3) and were more likely to be from the manual social classes in childhood, reflecting secular changes in the UK labour market. WHII is predominantly a white collar cohort, with less than 10% employed in manual occupations and none in classes IV and V. 10.1371/journal.pmed.1000440.t002 Table 2 Number of participants and median age (years) at each wave in each population-based cohorts, stratified by sex. Sex Study Childhood Early Adulthood Mid Adulthood Late Adulthood Totals Male ALSPAC Age 7.5 9.8 10.6 11.7 12.8 15.3 n 4,139 3,762 3,666 3,455 3,299 2,426 20,747 T-07 1972/1973 Age 15.7 18.6 24 29.6 36.6 n 459 431 313 266 286 1,755 T-07 1952/1953 Age 36 40 44.5 49.7 57 n 409 373 322 305 312 1,721 NSHD Age 36 43 53 n 1,637 1,598 1,449 4,684 WHII Age 43 49 54.8 60.2 n 6,754 5,522 4,573 4,536 21,385 T-07 1932/1933 Age 55.9 59.1 63.5 68.8 75.9 n 443 400 318 241 189 1,591 CaPS Age 52.8 57.7 62.4 65.9 73.4 n 2,507 2,366 2,046 1,793 863 9,575 HAS Age 67.5 76.8 n 454 172 626 n (male) 62,084 Female ALSPAC Age 7.5 9.8 10.6 11.8 12.8 15.4 n 4,024 3,866 3,778 3,576 3,426 2,659 21,329 T-07 1972/1973 Age 15.7 18.6 24 29.6 36.6 n 489 475 347 303 335 1,949 T-07 1952/1953 Age 36.1 40.1 44.5 49.7 56.9 n 514 475 417 359 360 2,125 NSHD Age 36 43 53 n 1,649 1,590 1,477 4,716 WHII 44 50 55.6 60.8 2,707 2,044 1,620 1,625 7,996 T-07 1932/1933 Age 56 59.2 63.6 68.8 76 n 530 456 385 293 234 1,898 HAS Age 67.1 76 n 363 120 483 n (female) 40,496 n (all) 102,580 10.1371/journal.pmed.1000440.t003 Table 3 Cohort information and baseline characteristics by sex. Characteristic ALSPAC T-07 1972/1973 T-07 1952/1953 NSHD T-07 1932/1933 CaPS HAS WHII n malesa 4,876 478 428 1,841 456 2,951 454 6,892 n femalesa 4,815 514 529 1,820 542 — 363 3,413 Year(s) of birth 1991–1992 1972–1973 1952–1953 1946 1932–33 1918–1939 1920–1930 1930–1953 Age range, yearsb 7–16 15–37 34–60 36–53 55–77 44–83 63–81 35–75 Number of waves 7 5 5 3 5 5 2 4 Years of data collection 1998–2008 1987–2008 1987–2008 1982–1999 1987–2008 1979–2005 1994–2005 1985–2004 Population Children of women attending antenatal clinics in three health districts of Bristol.[22] Stratified sample from Central Clydeside, Greater Glasgow, Scotland [21] Stratified sample from Central Clydeside, Greater Glasgow, Scotland [21] UK representative [20] Stratified sample from Central Clydeside, Greater Glasgow, Scotland [21] All men aged 45–59 y living in Caerphilly, Wales [18] Permanent residents of North Hertfordshire [19] Civil servants based in 20 offices in Whitehall, London [23] Baseline characteristics Males BMI (kg/m2), median (IQR) 0.13 (UK90)c 0.07 (UK90)c 25.0 (22.8, 27.1) 24.6 (22.7, 26.7) 26.0 (23.6, 28.4) 26.1 (23.9, 28.2) 26.5 (24.1, 29.1) 24.3 (22.6, 26.2) Height (m), mean (sd) 0.19 (UK90)c −0.09 (UK90)c 1.74 (0.07) 1.75 (0.07) 1.71 (0.07) 1.71 (0.06) 1.72 (0.07) 1.76 (0.07) SEP, n (%) d I 644 (10.8) 40 (8.4) 47 (10.9) 246 (11.5) 23 (4.9) 117 (4.0) 30 (6.6) 2,647 (38.4) II 1,990 (33.5) 95 (19.8) 122 (28.2) 769 (35.9) 107 (22.6) 482 (16.5) 115 (25.3) 3,607 (52.3) III—non-manual 732 (12.3) 72 (15.0) 61 (14.1) 213 (9.9) 41 (8.7) 297 (10.2) 40 (8.8) 505 (7.3) III—manual 1,821 (30.6) 172 (35.9) 141 (32.6) 644 (30.0) 185 (39.1) 1,455 (49.8) 169 (37.1) 136 (2.0) IV 591 (9.9) 71 (14.8) 41 (9.5) 204 (9.5) 80 (16.9) 422 (14.4) 90 (19.8) 0 (0) V 170 (2.9) 29 (6.1) 20 (4.6) 68 (3.2) 37 (7.8) 150 (5.1) 11 (2.4) 0 (0) Females BMI (kg/m2), median (IQR) 0.13 (UK90)c 0.12 (UK90)c 23.2 (21.3, 26.0) 22.7 (20.9, 25.2) 25.0 (22.6, 28.1) 26.6 (24.0, 29.7) 24.0 (22.5, 28.0) Height (m), mean (sd) 0.14 (UK90)c −0.2 (UK90)c 1.60 (0.06) 1.62 (0.06) 1.59 (0.07) — 1.59 (0.06) 1.62 (0.07) SEP, n (%) d I 581 (10.4) 36 (7.0) 39 (7.7) 204 (9.9) 48 (8.5) 29 (8.1) 381 (11.2) II 1,894 (33.7) 99 (19.3) 125 (24.7) 715 (34.5) 11 (19.5) 74 (20.6) 1,336 (39.1) III—non-manual 684 (12.2) 68 (13.3) 88 (17.4) 253 (12.2) 97 (17.1) 52 (14.4) 991 (29.0) III—manual 1,711 (30.5) 196 (38.3) 168 (33.2) 615 (29.7) 170 (29.9) 126 (35.0) 705 (20.7) IV 583 (10.4) 74 (14.5) 69 (13.6) 213 (10.3) 91 (16.0) 57 (15.8) 0 (0) V 162 (2.9) 39 (7.6) 17 (3.4) 72 (3.5) 51 (9.0) 22 (6.1) 0 (0) a The number of participants with at least one BP measurement. b The 1st centile of wave 1 and 99th centile of the last data collection wave. c Referenced to the UK 1990 growth reference in z-score units [28]. d This is father's SEP for the ALSPAC and T-07 1972/1973 cohorts. IQR, inter-quartile range; sd, standard deviation. The prevalence of individuals on HypRx was similar in men and women, rising sharply from ∼40 y (Figure 1). For example, in the T-07 1932/1933 cohort, 13% were on medication at 55 y, and 62% at 75 y. For a given age, treatment was more prevalent in the more contemporary cohorts, although the pattern of uptake with age was consistent across cohorts. Figure 1 also shows the observed distribution of SBP with age in each cohort. The distribution widened with age in both sexes from the fourth to seventh decade, as illustrated by comparing waves 1 to 3 between the T-07 1952/1953 and 1932/1933 cohorts where the same measurement device was used. 10.1371/journal.pmed.1000440.g001 Figure 1 Observed SBP and prevalence of antihypertensive therapy. Observed median and 10th and 90th 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. Unadjusted SBP Life Course Trajectories Figure 2 shows the predicted mean SBP trajectories and annual SBP change estimated from the unadjusted models in each cohort, while Table 4 shows the coefficients for the age effects from these models. The steepest rises in SBP were in adolescence, reaching 5.2 mm Hg per year (95% CI: 5.1, 5.3) in ALSPAC boys and 3.6 mm Hg per year (95% CI: 3.5, 3.8) in ALSPAC girls from 14 to 15 y. From 15 to 30 y, the annual rate of change decreased in T-07 men from 0.9 mm Hg per year (95% CI: 0.6, 1.1) at age 15 y to 0.2 mm Hg per year (95% CI: −0.0, 0.4) at age 35 y. In T-07 women there was a relatively stable linear increase of 0.5 mm Hg per year (95% CI: 0.4, 0.6) from 15 to 35 y. In both men and women, there was then evidence of a midlife SBP acceleration beginning at ∼35 y, and reaching a velocity of ∼1.0 mm Hg per year by age 50 in both the T-07 1952/1953 and NSHD cohorts. The rate of SBP change reached a midlife peak at 55 y of 1.5 mm Hg per year (95% CI: 1.1, 1.9) in men and 1.4 mm Hg per year (95% CI: 1.1, 1.8) in women of the T-07 1932/1933 cohort. SBP increases then slowed and eventually began to decline at age 65, 66, and 70 y in the CaPS, HAS, and T-07 1932/1933 male cohorts, respectively, and at 77 y in the T-07 1932/1933 female cohort. SBP was already declining by age 65 y in the HAS female cohort. Compared to the population-based cohorts, SBP remained lower through midlife in the WHII occupational cohort, and the midlife acceleration in SBP occurred later (Figure 2). For example, at age 60 y, mean SBP in WHII was 17.5 mm Hg and 15.5 mm Hg lower than that of the T-07 1932/1933 cohort for men and women, respectively, and reached a velocity of 1 mm Hg per year at age 75 y in men and 65 y in women, compared with 50 y in the other cohorts. 10.1371/journal.pmed.1000440.g002 Figure 2 Predicted SBP from unadjusted models. 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. 10.1371/journal.pmed.1000440.t004 Table 4 Regression coefficients (standard errors) for the fixed effects from the main unadjusted multilevel models displayed in Figure 2. Group Study Intercept Agea Intercept Age Age2 Age3 Men ALSPAC 7 99.6 (0.21) 0.757 (0.211) 0.034 (0.060) 0.031 (0.005) T-07 1972/1973 15 113.8 (0.6) 0.897 (0.137) −0.018 (0.006) — T-07 1952/1953 35 123.2 (0.82) 0.32 (0.142) 0.019 (0.006) — NSHD 36 123.9 (0.39) 0.031 (0.096) 0.038 (0.005) — T-07 1932/1933 55 137.4 (1.2) 1.54 (0.220) −0.046 (0.010) — CaPS 44 135.0 (0.75) 1.27 (0.08) −0.03 (0.002) — HAS 64 158.1 (1.87) 0.40 (0.575) −0.126 (0.036) — WHII (occupational cohort) 35 125.4 (0.290) −0.340 (0.032) 0.015 (0.001) — Women ALSPAC 7 100.4 (0.22) 0.145 (0.218) 0.351 (0.060) −0.008 (0.005) T-07 1972/1973 15 107.0 (0.56) 0.63 (0.129) −0.005 (0.006) — T-07 1952/1953 35 115.2 (0.80) 0.86 (0.134) −0.007 (0.006) — NSHD 36 118.1 (0.42) 0.37 (0.104) 0.028 (0.006) — T-07 1932/1933 55 136.7 (1.01) 1.482 (0.203) −0.348 (0.01) — HAS 64 167.8 (2.6) −0.63 (0.84) −0.14 (0.054) — WHII (occupational cohort) 35 116.7 (0.494) 0.006 (0.054) 0.017 (0.001) — a This is the year that age was centred to in each model. We carried out several post hoc analyses to investigate possible reasons for the deceleration and decline seen in the older cohorts. An underestimation of the effect of HypRx was unlikely to have caused an artefact in the patterns we observed in old age (see Text S1). Excluding individuals who had ever taken HypRx in order to capture the trajectories in a healthy untreated sub-group explained a large part of the declining pattern observed at older ages (see Text S4). For example, in the T-07 1932/1933 cohort, the SBP of this non-medicated group continued to rise in a linear manner through old age, and in CaPS, the average untreated trajectory continued to rise for a longer period in later life and reached a plateau rather than exhibiting decline as in the primary analyses including treated individuals. The additional exclusion of those individuals who had suffered a myocardial infarction in this cohort further emphasised this plateau. Survivor bias (i.e., survival of those who are most healthy and least prone to common causes of premature mortality such as CVD) was unlikely to explain the general declining pattern of SBP, as the average trajectory of those who were still alive at the last wave of measurement still showed a deceleration and decline in the older population-based cohorts (see Text S4). BMI Adjusted Trajectories ALSPAC children were taller than the T-07 1972/1973 cohort at baseline when compared on the UK 1990 growth scale (+0.28 z and +0.16 z in ALSPAC versus T-07 boys and girls, respectively; Table 3). The median BMI (UK 1990 z-score) was similar in these cohorts, but individuals in the upper centiles of ALSPAC had a larger BMI than those in the upper centiles of T-07 1972/1973 (Figure 2). At age 15 y, SBP in ALSPAC was ∼10 mm Hg higher than in T-07. Adjusting the SBP trajectory to the UK 1990 growth reference made little difference to this cohort difference (Figure 3). 10.1371/journal.pmed.1000440.g003 Figure 3 Observed BMI (kg/m2) in each cohort. The lines represent the median, 10th and 90th 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 (2nd, 10th, 25th, 50th, 75th, 90th and 98th centiles). BMI increased through adult life in all cohorts, with steeper rises seen in early to mid adulthood (20 to 50 y) (Figure 3). Adjusting the SBP trajectory for BMI at each age in each cohort appeared to slow some of the SBP rise seen between 30 and 40 y, but the biggest impact of the BMI adjustment was on the intercept, shifting each cohort's mean SBP trajectory downwards (Figure 4). Table 5 shows the association between BMI and SBP in each cohort. Among adult cohorts there was a suggestion that the association was largest at 50 to 60 y. 10.1371/journal.pmed.1000440.g004 Figure 4 Predicted mean SBP and velocity after adjusting for BMI. 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 Methods and Text S2 for full details of this adjustment) in men (A) and women (B). The grey areas are the 95% CIs from unadjusted models. 10.1371/journal.pmed.1000440.t005 Table 5 Association (β) 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/metre2 in adult cohorts) and SBP (millimetres of mercury) in each cohort based on models including BMI as a time-updated covariate. Sex Study Age β 95% CI Male ALSPACa 7 to 16 2.29 2.12, 2.46 T-07 1972/1973a 15 to 37 3.63 3.01, 4.24 T-07 1952/1953 34 to 60 0.85 0.59, 1.11 NSHD 40 0.65 0.48, 0.82 50 1.07 0.90, 1.25 WHIIb 40 1.00 0.89, 1.12 50 1.18 1.09, 1.27 60 1.18 1.07, 1.29 70 0.93 0.67, 1.19 T-07 1932/1933 55 to 77 1.07 0.72, 1.43 HAS 63 to 81 0.56 0.04, 1.08 CaPSb 50 1.38 1.17, 1.59 70 0.92 0.72, 1.10 Female ALSPACa 7 to 16 2.53 2.36, 2.70 T-07 1972/1973a 15 to 37 2.73 2.18, 3.28 T-07 1952/1953 34 to 60 1.01 0.83, 1.20 NSHD 40 0.47 0.33, 0.61 50 0.76 0.63, 0.89 WHIIb 40 0.54 0.40, 0.69 50 0.82 0.72, 0.92 60 0.86 0.74, 0.99 70 0.61 0.33, 0.90 T-07 1932/1933b 60 1.08 0.81, 1.34 70 0.56 0.27, 0.84 HAS 63 to 81 0.49 −0.09, 1.08 a Beta is per z-score increase on the UK 1990 growth reference scale (28). b Results are presented at several ages in cohorts where there was evidence that the association between concurrent BMI and SBP differed across age. Sex Differences Figure 5 shows the sex differences in SBP in each cohort. The pattern of these differences was unaffected by adjustment for BMI (unadjusted not shown). From age 7 to 12 y, ALSPAC girls had a slightly higher SBP; boys caught up by age 13 and then overtook girls such that by age 15 y the mean SBP in boys was 5.0 mm Hg higher (95% CI: 4.5, 5.6). Similar sex differences were seen at age 15 y in the T-07 1972/1973 cohort. The maximum sex difference occurred at age 26 y (+8.2 mm Hg in men, 95% CI: 6.7, 9.8) in the T-07 1972/1973 cohort. Women experienced steeper early adulthood and midlife rises, and by the sixth decade there was no evidence of a sex difference in SBP in the T-07 1932/1933 cohort. However, in the HAS cohort, women had a higher SBP from 64 to 72 y, and men had a higher SBP from age 72 y. The pattern of sex difference in WHII followed the trends in the population-based cohorts. 10.1371/journal.pmed.1000440.g005 Figure 5 Mean sex difference in SBP (men minus women) (in millimetres of mercury) and 95% CIs. 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. Discussion Main Findings Our analysis describing the unadjusted pattern of SBP over life in population-based studies showed four chronological phases: (1) a rapid increase in SBP coinciding with peak adolescent growth, (2) more gentle increases in early adulthood, (3) a midlife acceleration beginning in the fourth decade, and (4) a period of deceleration in late adulthood, where increases in SBP slowed. These phases were not explained by increases in BMI through adulthood, but the deceleration in old age was less evident when restricting the analyses to individuals who had never taken antihypertensive therapy. Compared to the population-based cohorts, the occupational cohort (WHII) had a lower mean SBP, a shallower annual increase in midlife, and a later midlife acceleration. Men and women had different life course trajectories but a similar mean SBP by the seventh decade—females had lower increases during adolescence, but steeper rises from early to mid adulthood. Given population differences in SBP [29], our results may not be generalisable to populations with a different distribution of BP-related exposures. Comparison with Other Studies The mean level of SBP and annual increases found in childhood and adolescence were within the range seen in population-based cross-sectional data from the UK [30] and in longitudinal data from the US [13]. These increases are typically viewed as a concomitant of growth in size and stature. Cross-sectional [10],[31] and longitudinal data [14],[16],[32] in western populations also substantiate the gentle increases in SBP from early adulthood and the midlife acceleration in the fourth decade. In WHII, SBP was similar to that of the population-based cohorts at age 35 y, but the midlife increases occurred later, resulting in a lower mean SBP through to late adulthood. These differences are supported by the lower mortality rate in WHII compared to the general UK population [33]. WHII can be seen as a population sub-group nested within the UK population at large. Similar patterns of BP differences can also be seen in within-population comparisons from the US [34]. These unadjusted mean trajectories reflect both any underlying effects of ageing together with the distribution of lifetime BP influencing exposures in each cohort. To test how a strong determinant of BP might affect the shape of the trajectory, we adjusted for concurrent BMI at each age. The associations between BMI and SBP were similar in these observational studies to those reported in randomised controlled trials and Mendelian randomisation studies [8],[17]. We found that although mean SBP was lower after adjustment for BMI, BMI did not greatly influence the magnitude of age-related increases in SBP. One possible exception was the period around 30 to 40 y, where the rate slowed after adjustment—this was also the period of most rapid weight gain in our cohorts, which might explain this finding. Similar to our study, other population-based longitudinal studies in western societies have reported a deceleration and a decline of SBP in old age (>65 y) [16],[35]–[37]. Some cross-sectional studies did not show a decline [9],[38], although this may be partly obscured by secular decreases in BP [10],[11]. Our sensitivity and post hoc analyses (see Text S4) suggested that incorrect adjustment for HypRx in old age or survivor bias were unlikely to explain this general pattern in old age. However, some of the deceleration and decline was no longer evident after excluding individuals who had taken HypRx. This finding is in line with previous studies that have made similar exclusions in order to capture BP trajectories in healthy individuals [16],[39], and is supported by studies that have shown an association between SBP decline and higher mortality among the elderly [35]. A decline in SBP in old age has been linked to deteriorating health [35],[40], and a low BP to impaired cardiac output [41], suggesting decreasing BP is a feature of ageing in western populations. The deceleration and decline was not yet apparent in the WHII cohort, which may be a reflection of the better health and lower mortality of this cohort [33]. The steep declines in SBP in the HAS cohort were larger than those in other studies [16],[35]–[37], particularly in women, and were less reduced in the healthy sub-sample. These results should be interpreted with caution because of the small sample size and reliance on only one follow-up measure of BP. It is also possible that the trajectory in HAS is affected by systematic measurement differences between the two waves [42], and if so, errors in the estimation of the trajectory are likely to be exaggerated when based on only two observation points. Gender Differences Similar to our study, other longitudinal studies have shown boys to have greater SBP rises during adolescence [13] and women to have steeper rises from midlife [14],[16],[37]. The latter pattern has also been shown in aggregated cross-sectional data from almost all World Health Organization global sub-regions [38]. One exception to this pattern is in unacculturated societies such as the Yi in China [43], where gender differences were not observed. Heightened sodium sensitivity due to hormonal changes accompanying menopause is one proposed pathway to explain the gender dimorphism of BP around midlife [44]. The lack of gender differences in isolated populations where salt intake is low supports such a mechanism [43]. Interpretation and Implications Given the general similarity in measurement methods, birth years, and period of data collection, it seems reasonable to attribute some of the more favourable BP trends in WHII compared to the other cohorts to socially patterned and modifiable BP-related exposures such as lifestyle and diet [45] that act across the life course. Selection on the basis of being fit enough to work is also likely to mean that this cohort was healthier at baseline than the general population. And it is possible that either because of their higher SEP or as a result of work-based initiatives, participants in this cohort may be more likely to be treated earlier with antihypertensive therapy. This is suggested by the fact that the prevalence of HypRx use in this cohort in midlife was similar to that of other cohorts crossing the same age period, but the average BP in WHII was lower. However, some of the differences in WHII could be explained by variation in BP measurement, although given the differences in the shape of the trajectory, it seems unlikely that this would explain all of the disparity. Our study was not designed to answer how much of the increase in SBP with age is a natural physiological feature of ageing. There are a number of cross-sectional studies in isolated communities that show virtually no age-related increase in BP [43]. These communities typically have a predominantly vegetarian diet with very low salt content, physically arduous lifestyles, and very low or non-existent levels of obesity. One study also showed that individuals who had undergone a rural to urban migration from one of these isolated communities quickly went on to adapt the BP profiles of their adopted communities [43]. The stronger effect of age-related BMI increases on the intercept rather than slope of the SBP trajectory seen in our analysis might also reflect the importance of factors other than BMI, as shown by the opposing secular trends of falling BP but rising obesity levels seen in recent decades [10]–[12]. The trajectories in each cohort can be seen as a reflection of the dynamic aggregate of lifetime BP-related exposures. In this sense, one might expect the population-based trajectories to take a form more similar to that of WHII if the general population had similar lifetime exposures and ways of living. The midlife acceleration in SBP is interesting because of the transition from a period of flatter increases in early adulthood. Several studies suggest that individuals with a high SBP in midlife are at risk of more rapid arterial ageing, characterised by a stiffening of the large arteries [14],[46]. In contrast, the smaller changes seen in early adulthood may reflect more capacity for vascular repair or adaptation at this age [47],[48]. The delayed acceleration in WHII and widening distribution of SBP from 40 to 60 y might indicate that this is a point of transition when some individuals experience an earlier rise. Understanding the variation in midlife trajectories and factors driving this acceleration may be important for understanding the development and prevention of CVD risk. This is of particular importance given that a prolonged shift of just a few millimetres of mercury in SBP could remove a substantial burden of CVD at a population level [1]. Strengths and Limitations A key strength of our investigation is the use of individual-level longitudinal data and formal statistical modelling to describe SBP trajectories. The measurement protocols were relatively well standardised within each cohort, and we showed through a series of sensitivity analyses that our findings with regards to the slope of SBP were unlikely to be qualitatively affected by within- and between-cohort differences in the methods of BP measurement. However, comparisons of the mean SBP at overlapping ages between cohorts may still be subject to bias because of device differences and other unaccounted for methodological variation [42], hence the difficulty of attributing cohort or period effects. Thus, while we saw large differences in mean SBP at age 15 y between the ALSPAC and T-07 cohorts that were not explained by BMI or adolescent growth, we cannot rule out the contribution of device effects [42] because we could find no appropriate device conversion equations for younger populations. Despite the measurement issues, the use of multiple cohorts with longitudinal data to make inference on the change in BP over life seems viable when the methodology has been well standardised across waves. An extension to this approach is to join the trajectories from each cohort under certain assumptions and study the effect of lifetime exposures on the lifetime trajectory. This will require methodological development, but the use of this approach with commonly collected measures of biological function such as BP could provide a better and more dynamic understanding of when and how in the life course health is compromised. Conclusion We have described several lifetime phases in the age-related progression of SBP and have shown that the typical increases in BMI that accompany ageing are more strongly related to the mean BP than to the age-related changes in BP that we see in our population. We have also shown sex differences in BP change that are consistent with the hypothesis of a menopause-related effect on salt sensitivity. Lastly, our results provide some evidence that an occupational cohort with generally higher SEP than the general UK adult populations studied here has a slower midlife increase in BP and hence lower average levels in their 40s , 50 s, and 60 s. Whilst our study is unable to identify the key determinants of age-related increases in BP, it does suggest that these are modifiable, but perhaps not by exposures that largely influence BP through the increases in BMI that tend to accompany ageing. Further research should try to understand which factors affect this trajectory and when in the life course such factors exhibit most influence. Supporting Information Text S1 Sensitivity analysis of device and treatment effects. (0.04 MB PDF) Click here for additional data file. Text S2 More details on the multilevel models. (0.06 MB PDF) Click here for additional data file. Text S3 Missing data analyses. (0.02 MB PDF) Click here for additional data file. Text S4 Post hoc analysis examining reasons for the patterns of SBP in late life. (0.04 MB PDF) Click here for additional data file.
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              Childhood physical, environmental, and genetic predictors of adult hypertension: the cardiovascular risk in young Finns study.

              Hypertension is a major modifiable cardiovascular risk factor. The present longitudinal study aimed to examine the best combination of childhood physical and environmental factors to predict adult hypertension and furthermore whether newly identified genetic variants for blood pressure increase the prediction of adult hypertension. The study cohort included 2625 individuals from the Cardiovascular Risk in Young Finns Study who were followed up for 21 to 27 years since baseline (1980; age, 3-18 years). In addition to dietary factors and biomarkers related to blood pressure, we examined whether a genetic risk score based on 29 newly identified single-nucleotide polymorphisms enhances the prediction of adult hypertension. Hypertension in adulthood was defined as systolic blood pressure ≥ 130 mm Hg and/or diastolic blood pressure ≥ 85 mm Hg or medication for the condition. Independent childhood risk factors for adult hypertension included the individual's own blood pressure (P<0.0001), parental hypertension (P<0.0001), childhood overweight/obesity (P=0.005), low parental occupational status (P=0.003), and high genetic risk score (P<0.0001). Risk assessment based on childhood overweight/obesity status, parental hypertension, and parental occupational status was superior in predicting hypertension compared with the approach using only data on childhood blood pressure levels (C statistics, 0.718 versus 0.733; P=0.0007). Inclusion of both parental hypertension history and data on novel genetic variants for hypertension further improved the C statistics (0.742; P=0.015). Prediction of adult hypertension was enhanced by taking into account known physical and environmental childhood risk factors, family history of hypertension, and novel genetic variants. A multifactorial approach may be useful in identifying children at high risk for adult hypertension.

                Author and article information

                Ovid Technologies (Wolters Kluwer Health)
                April 15 2019
                April 15 2019
                [1 ]From the Heart Institute, Cincinnati Children’s Hospital Medical Center, and Department of Pediatrics, University of Cincinnati College of Medicine, OH (E.M.U., P.R.K.)
                [2 ]Tulane University, New Orleans, LA (L.B., W.C.)
                [3 ]Department of Epidemiology, College of Public Health, University of Iowa (T.L.B.)
                [4 ]Department of Pediatrics, University of Colorado School of Medicine, Aurora (S.D.)
                [5 ]The George Institute, Oxford University, England (T.D.)
                [6 ]Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (T.H., D.R.J.)
                [7 ]Department of Internal Medicine, University of Turku, Finland (M.J.)
                [8 ]Division of Medicine, Turku University Hospital, Finland (M.J.)
                [9 ]Murdoch Children’s Research Institute, Parkville, Victoria, Australia (M.J.)
                [10 ]Division of Public Health Sciences, Wake Forest University School of Medicin—Winston Salem, NC (R.P.)
                [11 ]Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (O.R.)
                [12 ]Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Finland (O.R.)
                [13 ]Division of Pediatric Cardiology, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis (J.S.)
                [14 ]Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia (A.V.).
                [15 ]Division of Biostatistics and Epidemiology Children’s Hospital Medical Center, and Department of Pediatrics, University of Cincinnati College of Medicine, OH (J.G.W.)
                [16 ]Division of Nephrology, University of Minnesota Medical School, Minneapolis (A.S.)
                © 2019


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