Published online before print June 16, 2008, doi: 10.1161/CIRCULATIONAHA.107.730366
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(Circulation. 2008;117:3171-3180.)
© 2008 American Heart Association, Inc.
Epidemiology |
Tracking of Blood Pressure From Childhood to Adulthood
A Systematic Review and Meta–Regression Analysis
From the Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Md.
Correspondence to Youfa Wang, MD, PhD, Associate Professor, Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205. E-mail ywang{at}jhsph.edu
Received October 8, 2007; accepted April 16, 2008.
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Abstract |
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Background— A large number of studies have examined the tracking of blood pressure (BP) from childhood to adulthood, but the reported findings are inconsistent and few systematic analyses have been conducted.
Methods and Results— We conducted a systematic search of PubMed for studies that examined the tracking of BP from childhood to adulthood published between January 1970 and July 2006. From 301 retrieved papers, 50 cohort studies met our inclusion criteria and provided 617 data points (Pearson/Spearman correlation coefficients) for systolic BP (SBP) and 547 data points for diastolic BP (DBP) for our meta-analysis. Information on sample characteristics and BP measurement protocols was extracted. Fisher z transformation and random-effects meta–regression analysis were conducted. The reported BP tracking correlation coefficients varied from –0.12 to 0.80 for SBP and from –0.16 to 0.70 for DBP, with an average of 0.38 for SBP and 0.28 for DBP. BP tracking varied significantly by baseline age and length of follow-up. The strength of BP tracking increased with baseline age by 0.012 for SBP (P<0.001) and 0.009 for DBP (P<0.001) and decreased with follow-up length by 0.008 for SBP (P<0.001) and 0.005 for DBP (P<0.001). BP tracking did not vary markedly across the number of BP measurements or race/population groups.
Conclusions— Data from diverse populations show that the evidence for BP tracking from childhood into adulthood is strong. Childhood BP is associated with BP in later life, and early intervention is important.
Key Words: blood pressure • child • epidemiology • meta-analysis
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Introduction |
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Over the past several decades, a large number of longitudinal studies have been conducted to assess blood pressure (BP) tracking over time among children and adults. These studies show that BP tracking correlations during childhood were positive but were substantially lower than those in adulthood.1–10 Studies also reported that BP tracking was stronger for shorter follow-up periods and in older children.11–14 Some studies, such as the Bogalusa Heart Study conducted in the United States, also investigated ethnic differences, which suggested mixed patterns.15–19 Explanations for the inconsistent findings and ethnic differences in the tracking are inconclusive. Furthermore, to the best of our knowledge, little is known about the differences in BP tracking patterns across populations worldwide.
Editorial p 3163
Clinical Perspective p 3180
The study of BP tracking from childhood to adulthood has many important public heath implications. As growing evidence indicates, hypertension, one of the major modifiable risk factors for cardiovascular disease, is established early in life.18–20 Recent studies also show that increased BP among children is related to the growing obesity epidemic.21,22 Although the evidence for BP tracking from childhood to adulthood is rich and continues to increase, reported findings from previous studies are inconsistent, and to the best of our knowledge, no systematic analyses have been conducted.
The present study aimed to systematically evaluate epidemiological evidence on BP tracking from childhood to adulthood. We first performed a systematic review of studies that examined BP tracking. Then, we conducted a meta–regression analysis to examine the predictors of BP tracking degree, which served 2 goals: (1) to summarize findings from different studies published over the past 4 decades and (2) to test the differences in tracking by sample and study characteristics such as race/population groups, length of follow-up, and BP measurement protocols.
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Methods |
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Literature Search Strategy
We conducted a comprehensive literature search of the PubMed database using related key words: child, adolescent, adult, tracking, persistence, stability, maintenance, consistency, blood pressure, hypertension, cohort, follow-up, longitudinal study, and cardiovascular disease. Related studies published between January 1970 and July 2006 were searched. Titles and abstracts of studies uncovered by the electronic searches were examined on screen first. Only studies that examined the tracking of BP from childhood to adulthood and that were published in English, Chinese, or Japanese were included. This initial screening resulted in 301 studies, which were then further examined to determine whether they met the criteria for inclusion in the meta-analysis described below. The full papers that met our selection criteria (see below) were reviewed carefully. Additional studies (n=6) identified in the course of reading or brought to our attention by colleagues and experts we consulted were included. Studies (n=10) were also identified by searching on authors who had contributed at least 1 relevant article, by using the "related articles" function in PubMed, and by hand searching of the cross-references from retrieved articles.
Study Selection Criteria
Cohort studies that examined BP tracking from childhood to adulthood and met the following criteria were included: (1) BP tracking correlation coefficients (Pearson or Spearman approach) were reported; (2) cohorts’ baseline ages were <18 years; (3) sample sizes were >50; and (4) studies were published in English, Chinese, or Japanese. Tracking (correlation) coefficients ranged between –1 and 1, which measures the degree of correlation between 2 repeated observations over time. A value of 1 indicates perfect positive tracking, and –1 means perfect negative tracking; a value of 0 shows no tracking. Multiple follow-up studies based on the same cohort with different follow-up periods were included. We defined "data points" as the reported BP tracking correlation coefficients for each subgroup included in a certain study, ie, a study might provide different BP tracking results by sex, baseline age, and follow-up. Note that some studies only reported systolic BP (SBP) or diastolic BP (DBP). Data points for SBP and DBP were analyzed separately.
Data Extraction
Using a standardized data extraction form, we extracted and tabulated related data. Information extracted included first author’s name, publication year, country of data collection, sample characteristics (eg, baseline age, sex, race/population, and sample size), length of follow-up, methods of BP measurement (eg, mercury sphygmomanometer), number of BP measurements, and outcome assessment (eg, correlation coefficient). On the basis of our review of previous studies and our research interest, we chose to include sex, baseline age, length of follow-up (years), number of BP measurements, publication year (which may also help indicate possible time change), and race/population as the potential predictors of BP tracking in the present meta-analysis. A data set based on this data extraction was created with a spreadsheet program (Microsoft Excel, Microsoft Co, Redmond, Wash). From 301 retrieved papers, 50 cohort studies met our inclusion criteria and provided 617 data points for SBP and 547 data points for DBP for the present meta-analysis. Appendix I in the online-only Data Supplement summarizes the 251 papers that were reviewed but were not included in the present meta-analysis.
Statistical Analysis
We first created scatterplots of BP tracking correlations by length of follow-up and baseline age, respectively. Next, we conducted locally weighted regression (LOWESS) as a nonparametric smoothing technique to show the relationships, with a bandwidth of 0.4.23 We also fit univariate models that assessed the association between BP tracking and length of follow-up. Using the Fisher z transformation, we converted Pearson or Spearman BP tracking correlation coefficients to z transforms to obtain approximate normality and then calculated a mean transformed correlation weighted by the sample sizes in these studies.16,24 We used a t test to compare the average transformed BP tracking correlation coefficients between men and women. Note that our findings with and without the Fisher z transformation were similar, and reported findings are based on the transformed data when relevant.
The homogeneity of the effect size among studies was tested with the Q test. Our tests indicated heterogeneity across the studies included in the present meta-analysis for SBP (Q=847.7, P<0.001) and DBP (Q=384.5, P<0.001). Thus, we fit random-effects models, which took into account the between-study variations, to study the factors that might affect BP tracking. These results are presented here but were more conservative than those based on our multiple linear regression models (online-only Data Supplement, Appendix II), which found that the number of BP measurements, publication year, and race/population were also significant predictors.
In addition, using general linear models, we computed least square means of BP tracking correlation coefficients by length of follow-up groupings (<5, 5 to 9, 10 to 14, and 
15 years) to control for baseline age. Similarly, we calculated least square means of BP tracking correlation coefficients by baseline age groups (<5, 5 to 9, 10 to 14, and 15 years old) after adjustment for length of follow-up. Trend tests were conducted with the Kruskal-Wallis test, a nonparametric approach.
Because some studies only reported BP tracking correlation coefficients for men and women combined instead of for each sex independently, we grouped the studies as men, women, and combined (both). To test the ethnic/population differences, we grouped the studies as European, American, Asian, and other populations. European was treated as the reference group. We further grouped American data points into 3 categories: general American (when the study sample could not be classified as black or white on the basis of the reported information); white (>80% of the study participants were white); and black. This allowed us to further test ethnic differences in the United States. In addition, if the centered publication year variable (ie, original publication year minus 1977) rather than the original publication year was included in the models, the intercepts changed, but the other effect estimates were not affected. We also assessed publication bias by plotting sample sizes against BP tracking correlations and by the Begg adjusted rank correlation test and the Egger regression asymmetry test.25–27
All analyses were performed with SAS version 9.1 (SAS Institute Inc, Cary, NC) and STATA release 9. Statistical significance was set at P<0.05.
The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.
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Results |
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Results of Systematic Review
Table 1 and Appendix III (online-only Data Supplement) summarize the main characteristics and findings of the 50 follow-up studies on BP tracking. The majority of the studies (n=29, 58%) were from the United States, 11 (22%) were from Europe, 6 (12%) were from Asia, and 4 (8%) were from other populations (including Australia, Canada, Israel, and New Zealand). All of these studies included men and women. Length of follow-up ranged from 0.5 to 47 years. The reported BP tracking correlation coefficients varied from –0.12 to 0.80 for SBP and from –0.16 to 0.70 for DBP, with a mean of 0.38 for SBP (SD=0.16) and 0.28 for DBP (SD=0.15). In men, the mean±SD of the SBP and DBP tracking correlation coefficients was 0.39±0.15 versus 0.29±0.16. In women, the corresponding figures were 0.38±0.15 versus 0.26±0.15. The t test indicated no sex difference in BP tracking. Of these studies, 6% recorded BP measurements once per visit, 22% twice, and 50%
3 times, whereas 22% did not provide detailed information. A total of 62.5% used a mercury manometer, 6.3% used a random-zero manometer, 8.3% used an ultrasound device, and 4.2% used an automated device to measure BP.
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Previous longitudinal data from diverse populations have shown different BP tracking patterns. The discrepancies may be due to differences in study design, baseline age, follow-up period, measuring instruments, intrasubject variability, characteristics of study samples, or analytic methods used. Nevertheless, most studies found significant BP tracking, and some found a weaker tracking for longer follow-up periods and a stronger tracking for SBP than for DBP, which was possibly due to the difficulties in measuring DBP in children and the changes in the recommendations on how to take DBP measurements over time.
Results of Meta-Analysis
Difference by Length of Follow-Up
Figure 1 shows the scatterplots and smoothed curves of SBP and DBP tracking correlation coefficients plotted against follow-up period. We observed linear relationships between the length of follow-up and SBP and DBP tracking correlation coefficient using the LOWESS approach. Our univariate models indicated a negative linear relationship between BP tracking and length of follow-up, which was similar for SBP and DBP (β=–0.007, P<0.001 and β=–0.005, P<0.001, respectively). Our predicted 5-year follow-up BP tracking correlation coefficient was 0.43 (95% confidence interval 0.30 to 0.59) for SBP and 0.32 (95% confidence interval 0.17 to 0.49) for DBP. The corresponding figures for 10-year follow-up were 0.40 (95% confidence interval 0.27 to 0.56) and 0.29 (95% confidence interval 0.17 to 0.44), respectively.
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Figure 2 shows the least square means of BP tracking correlations for length of follow-up after adjustment for baseline age. Least square means of tracking correlation coefficients for <5, 5 to 9, 10 to 14, and 15 years follow-up were 0.43, 0.38, 0.31, and 0.23 for SBP and 0.31, 0.30, 0.21, and 0.15 for DBP, respectively. A clear trend was found of weaker SBP and DBP tracking with longer follow-up (P<0.001 for both).
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Difference by Baseline Age
Figure 3 shows the scatterplots and smoothed curves of SBP and DBP tracking correlation coefficients plotted against baseline age. Near-linear relationships between baseline age and SBP and DBP tracking correlation coefficient were observed. SBP appeared to track with the increase in baseline age better than DBP. In cohorts with baseline ages of 8 years or younger, the strength of SBP tracking increased sharply with baseline age; among those with baseline age of 8 to 15 years, SBP tracking remained stable at 
0.40; and for those older than 15 years, SBP tracking increased again with age. The baseline age difference in DBP tracking differed slightly from that of SBP. Figure 4 shows the least square means of BP tracking correlation for baseline age groups adjusted for length of follow-up. SBP and DBP tracking correlations increased significantly with baseline age (both P<0.001, respectively), in particular because of the big difference between preschool children and their older counterparts. The age differences were small after age 5.
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Random-Effects Meta–Regression Analysis
Table 2 shows the predictors of BP tracking when we used the male group as the reference. The present analyses show that BP tracking varied significantly by baseline age and length of follow-up. The strength of BP tracking increased with baseline age by 0.012 for SBP (P<0.001) and 0.009 for DBP (P<0.001) and decreased with follow-up length by 0.008 for SBP (P<0.001) and 0.005 for DBP (P<0.001). Compared with 1 BP measurement per visit, 2 measurements increased observed BP tracking by 0.216 for SBP (P=0.070) and 0.122 for DBP (P=0.078). BP tracking did not vary markedly across race/population groups. No difference was found in BP tracking when we compared different BP measurement instruments with the mercury manometer, except for automated devices, which predicted higher BP tracking and which difference was significant for DBP (β=0.223, P<0.01). Although no significant sex difference was found in SBP tracking, women had weaker DBP tracking than men by 0.029 (P<0.01). We further examined the sex difference in BP tracking by excluding those data points for pooled men and women in the present models, and the results were similar. Table 3 shows the findings of the analysis stratified by sex, which indicates some sex differences in the effects of the predictors, such as baseline age.
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Publication Bias
We examined the potential publication bias by plotting sample sizes against SBP and DBP tracking correlation coefficients among studies in the present meta–regression analysis (Appendix IV, online-only Data Supplement). No evidence was found that suggested publication bias existed. Furthermore, neither the Begg adjusted rank correlation test nor the Egger regression asymmetry test was significant for SBP or DBP.
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Discussion |
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The tracking of BP over time has intrigued epidemiologists both as a measure of the genetic-environmental interaction and as a way of identifying high-risk individuals.28–30 A large number of studies have shown different degrees of BP tracking from childhood to adulthood. The present review and meta-analysis, based on cohort studies in diverse pediatric populations, demonstrate moderate BP tracking between childhood and adulthood. Overall, the average of reported tracking correlation was greater for SBP than for DBP. Sex, baseline age, and length of follow-up were significant predictors of BP tracking.
Sex Difference
Although the present findings indicate little sex difference in SBP tracking, men had a stronger DBP tracking than women. The average SBP tracking correlation was 0.39 in men versus 0.38 in women, whereas for DBP, it was 0.29 versus 0.26. Previous studies have reported conflicting findings. For example, the Dormont High School Follow-Up Study found that SBP tracking was stronger in men than women, 0.27 versus 0.24 over a 30-year follow-up.10 The Muscatine Study found that women had stronger DBP tracking than men in some age groups but found no sex difference in other age groups.8 A previous 1995 review found no sex difference in BP tracking from childhood to adulthood.31 Further research is needed to help explain the sex difference in BP tracking we observed.
Baseline Age
The present meta-analysis shows that BP tracking appears to increase with baseline age, ie, older children have a strong BP tracking into adulthood. Compared with cohorts whose baseline ages were <5 years, those aged 15 years had a stronger tracking correlation (0.18 versus 0.43 for SBP and 0.09 versus 0.32 for DBP). Several studies suggest that BP tracking starts at very young ages and increases with age.32,33 For example, 1 study measured BP in 1797 infants aged 4 days and then repeated the measurements at 6 weeks, 6 months, 1 year, and yearly thereafter until 4 years of age.32 During infancy, the tracking correlations were weak (most <0.20). As children grew older, the tracking became stronger.32 In the Bogalusa Study, children aged 10 to 14 years were found to have stronger BP tracking than those aged 5 to 9 years.18 Baseline age appears to be an important determinant of BP tracking. Difficulties in making accurate measurements of BP among younger children may result in poor BP tracking. This may also be true for the weaker tracking of DBP.
Length of Follow-Up
Our predicted 5-year follow-up tracking correlation coefficient was 0.42 for SBP and 0.32 for DBP. For 10-year follow-up, the corresponding figures were 0.38 and 0.29, respectively. For less than 5-year follow-up, age-adjusted means of tracking correlation were 0.43 for SBP and 0.31 for DBP, respectively. The US Dormont High School Follow-Up Study provided long-term BP tracking data in a cohort of 86 men and 116 women.10 Correlations for SBP were 0.42 in men versus 0.39 in women between baseline age 17 years and 34 years at the follow-up and 0.27 versus 0.24 between baseline age 17 years and 47 years at the follow-up.
Number of BP Measurements
The present meta-analysis using the random-effect models showed that taking multiple BP measurements per visit only increased BP tracking marginally, but our unreported analysis using linear regression analysis showed a clear relationship between the number of BP measurements and BP tracking correlation. The random-effect models may be more conservative. Because of BP variability, it has been argued that multiple visits are more important than multiple readings per visit in children and adolescents, and more than 3 measurements per visit may not be needed.34 We could not evaluate the impact of multiple visits on BP tracking in the present meta-analysis because of the limited available data.
Ethnic/Population Difference
Grouping the related published studies into several large ethnic and country categories, we attempted to examine the between-population group differences in BP tracking. Compared with Europeans, black and white Americans did not have stronger SBP tracking. The Bogalusa Heart Study found similar BP tracking in black and white ethnic groups, with SBP ranging from 0.38 to 0.50 for black children and from 0.36 to 0.49 for white children, whereas DBP tracking ranged from 0.19 to 0.41 for black children and from 0.26 to 0.42 for white children.18 However, some studies, such as the Child and Adolescent Trial for Cardiovascular Health cohort study (CATCH), have reported ethnic differences in BP tracking.29 Black children displayed stronger tracking than white children. It remains inconclusive what factors contributed to these differences.
BP tracking has been suggested to be related to genetic, biological, behavioral, environmental, and social determinants.19,35 Although the present study could not test these, we suspect that several factors may affect BP tracking from childhood to adulthood. First, previous studies have reported that lower birth weight is associated with higher subsequent SBP in children and adults, although findings have not been consistent in all populations.36–41 This association might be an example of "programming," a view that was supported in part by the existence of tracking of BP in children.36 A birth cohort study of 3634 men and women born in Britain in 1946 was conducted, and BP was measured at ages 36, 43, and 53 years. A consistent negative association between birth weight and SBP was noted from age 36 to 53 years. Among 25 000 United Kingdom men and women, those of small or disproportionate (thin or short) size at birth had high rates of high BP in middle age.42
Second, overweight and weight change are likely to affect BP tracking. Some studies found that tracking of SBP through adolescence to early adulthood was influenced by overweight and weight change.43 Accelerated weight gain in early childhood may increase the risk of elevated BP in later life.40 The influence of weight change on BP tracking emphasizes the importance of weight control in childhood and adolescence. Maintenance of normal weight gain in childhood may prevent clustering of hypertension and cardiovascular disease risk factors in adulthood. On the other hand, it is possible that the growing obesity epidemic might have weakened the BP tracking observed in some recent studies in which the study samples were affected.
An important strength of the present meta-analysis is that it was based on a large number of cohort studies conducted in different countries and published since the 1970s. A total of 617 data points for SBP and 547 data points for DBP provided in 50 studies were used. The systematic and quantitative assessment provides strong evidence to help quantify the degree of BP tracking from childhood to adulthood. In addition, we applied several statistical analysis approaches to examine the factors that may affect BP tracking. On the other hand, our meta-analysis has some limitations that are common to these types of studies, such as potential selection bias and the inability to study additional predictors or adjust for some potential confounders. For example, we did not have access to the original individual-level BP data. Individuals’ baseline BP levels and their treatment of elevated BP may affect BP tracking to older ages; however, these factors could not be tested in the present meta-analysis. Second, a large proportion of the related studies are based on data from the United States. The findings may not be generalizable to other populations that have different genetic backgrounds or environmental characteristics. Third, most of these studies are based on data published in the 1990s but not more recent studies, which used different statistical analyses (eg, did not report Spearman or Pearson correlation coefficients) and thus could not be included in the present meta-analysis. Fourth, because of the limited available information reported in the selected studies and the scope of the present study, we could not provide a through explanation of the observed between-population differences. In addition, one may be concerned because we used >1 data point from some cohort studies in which different follow-up periods or age groups were included, but such data in fact can help better test their influences on BP tracking. Our models controlled for the possible dependence between multiple data points from the same cohort.
In conclusion, based on studies from diverse populations, the present meta-analysis reinforces the concept that BP tracks from childhood to adulthood and that an elevated BP in childhood is likely to help predict adult hypertension. Future studies should focus on the determinants of BP tracking across sex and ethnic/population groups. Longitudinal studies are needed to identify the determinants of BP tracking in infancy, childhood, and adolescence. The persisting high rates of cardiovascular disease in developed countries and the growing cardiovascular disease and hypertension epidemic in developing countries, along with the growing global childhood obesity epidemic, all support the importance of continuing research and focusing attention on BP tracking.44,45
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Acknowledgments |
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We would like to thank Drs May Beydoun and Yiqing Song for their assistance in conducting some related statistical analysis.
Sources of Funding
The present study was supported in part by the Johns Hopkins University Bloomberg School of Public Health and the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK63383).
Disclosures
None.
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CLINICAL PERSPECTIVE
Previously, a large number of studies have examined blood pressure (BP) tracking over time among children and adults. Although rich evidence supports BP tracking from childhood to adulthood, the reported findings are inconsistent. No systematic analyses have been conducted to examine the consistency of findings from different studies or to test the differences across populations worldwide. Using a systematic search of PubMed for studies published between 1970 and 2006 that examined BP tracking from childhood to adulthood, we conducted a meta-analysis of findings from 50 cohort studies, which provided600 data points (ie, correlation coefficients) for systolic BP (SBP) and diastolic BP (DBP) tracking, respectively. BP tracking was stronger for SBP but did not vary significantly by the number of BP measurements taken per visit or across race/populations. SBP tracking did not vary significantly by sex, but women had weaker DBP tracking than men. The reported BP tracking coefficients varied from –0.12 to 0.80 for SBP and from –0.16 to 0.70 for DBP. The average was 0.38 for SBP and 0.28 for DBP. The strength of BP tracking increased with baseline age by 0.012 for SBP and 0.009 for DBP and decreased with follow-up by 0.008 for SBP and 0.005 for DBP. On the basis of studies from diverse populations, our meta-analysis reinforces the concepts that BP tracks from childhood to adulthood and that an elevated BP in childhood is likely to predict adult hypertension. Childhood BP is associated with BP in later life, and early intervention is important.
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The online-only Data Supplement, consisting of appendices, is available with this article at http://circ.ahajournals.org/cgi/content/full/CIRCULATIONAHA. 107.730366/DC1.
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Circulation 2008 117: 3161-3162.
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