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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Hypertens. Author manuscript; available in PMC 2010 September 1.
Published in final edited form as:
PMCID: PMC2886129

Relation of Blood Pressure and Body Mass Index During Childhood to Cardiovascular Risk Factor Levels in Young Adults



Adult obesity and hypertension are leading causes of cardiovascular morbidity/mortality. Although childhood body mass index and blood pressure track into adulthood, how they influence adult cardiovascular risk independent of each other is not well defined.


Participants were from two longitudinal studies with a baseline evaluation at mean age 13 and a follow-up at mean age 24. Regression models using childhood blood pressure and body mass index to predict young adult cardiovascular risk factors were performed.


In univariate analysis, childhood body mass index predicted young adult blood pressure, lipids, glucose, insulin, and insulin resistance, while childhood blood pressure predicted young adult blood pressure, lipids and glucose. In a multivariable regression model (adjusted for age, sex and race) which included change in body mass index and blood pressure from age 13 to 24, body mass index predicted all young adult risk factors except blood pressure and glucose. Baseline systolic blood pressure predicted young adult blood pressure, cholesterol, triglycerides and glucose while baseline diastolic blood pressure predicted young adult blood pressure, body mass index and glucose.


The results from this study show that 1) blood pressure and body mass index act independently in children to influence future cardiovascular risk factors, and 2) the combination of high blood pressure and body mass index in childhood have an additive effect in predicting the highest levels of young adult cardiovascular risk. Thus, there should be a focus on treating hypertension in overweight and obese children, in addition to attempting to reduce weight.

Keywords: adolescents, young adults, risk factors, blood pressure, body mass index


Obesity and hypertension are prevalent in childhood and adolescence. Studies have shown that 3-5% of school-aged children are hypertensive and that levels of blood pressure in children are rising[1, 2]. Data from NHANES have shown an increase of 1.4 mmHg in mean systolic blood pressure (SBP) and 3.3 mmHg in diastolic blood pressure (DBP) between the 1988-1994 and 1999-2000 surveys [3, 4].The prevalence of pre-hypertension (blood pressure between the 90th and 95th percentiles for age, sex and height[4]) increased from 7.7% to 10% in children aged 8-17 years, [5] suggesting that hypertension will continue to be an issue in children and adolescents in future decades. Even more striking data have been developed for obesity. NHANES has shown an increase in the percentage of overweight adolescents (body mass index (BMI) greater than or equal to the 85th percentile) from 26% to 31% between 1988-1994 and 1999-2000. [6] The percentage of obese adolescents (BMI greater than 95th percentile) increased from 4.6% in 1966 to 15.5% in 2000 [7] with a further rise in 2004 to 17.4%. [8]

Both BMI and SBP in adolescence have been shown to predict adverse levels of cardiovascular risk factors in adulthood when analyzed separately [9-12]; however, the studies that have compared the independent effects of adolescent fatness versus BP in the same multivariable analyses have provided conflicting results. The Bogalusa and Muscatine studies showed that childhood blood pressure and ponderosity independently predicted adult blood pressure [13, 14], but data from the Fels Longitudinal Study [15] and a cohort of Boston children [16] found that BMI was not a significant predictor of adult SBP when childhood SBP was considered. In contrast, childhood BMI but not SBP has been shown to predict adult left ventricular mass index [17] and carotid artery intima media thickness [18].

There are few studies relating childhood BMI or blood pressure to other adult cardiovascular risk factors or the metabolic syndrome. Data from Bogalusa showed that childhood BMI and fasting insulin but not SBP predict adult clustering of BMI, fasting insulin, blood pressure and cholesterol/HDL or triglyceride/HDL. [19] The NHLBI Growth and Health Study showed that BMI but not SBP in girls at age 10 predicted development of the metabolic syndrome at age 19. [20] In contrast, in The Fels Longitudinal Study BMI did not predict the metabolic syndrome independent of childhood SBP [15].

The strong influence of BMI on blood pressure during childhood [4] suggests that the relation between childhood blood pressure and adult cardiovascular risk is, in large part, mediated via body fatness. However, cohort studies in adult French and Swedish subjects have shown that the adverse effect of BMI on cardiovascular events is dependent, in part, on elevated blood pressure [21, 22]. In the present report data from a longitudinal study of obesity, insulin resistance and cardiovascular risk were used to determine the independent influence of BMI and SBP or DBP at mean age 13 not only on factors associated with cardiovascular risk (SBP, BMI, cholesterol, HDL-C, LDL-C, triglycerides) but also on insulin resistance (fasting insulin, fasting glucose and insulin clamp measure of insulin resistance) at mean age 24.


This study was approved by the Human Subjects Committee at the University of Minnesota. Consent was obtained from all participants and their parents/guardians.

Participants were recruited for two longitudinal studies beginning in childhood. The first cohort (The Sodium-Potassium Blood Pressure Trial in Children (NAKS)) [23] was recruited at age 11-15 years (mean age 13), after blood pressure screening of 5th-8th grade students, into a four-year intervention trial to study the effect of potassium supplementation or sodium restriction on blood pressure. All students whose systolic blood pressure was above the 85th percentile after two separate measurements were invited to enroll. The participants were reexamined nine years after completion of the trial as part of a study on young adult cardiovascular risk and insulin resistance [24]. Data for the present report are from 156 participants (of 243 total participants) who had both baseline and young adult examinations. There were no significant differences in baseline variables between those who were or were not reexamined as young adults. Despite recruiting participants with blood pressure ≥85th percentile, the mean blood pressure at baseline was 111±10/64±10, corresponding to a mean BP percentile (for age, sex, and height) of 56%/51%. The mean blood pressure at age 24 was 115±10/70±12.

The second cohort also was recruited after blood pressure screening of students in 5th-8th grades (age 11-15 years, mean 13 years)) for participation in a study of insulin resistance [25]. Participants were randomly selected with stratification for blood pressure (half from upper 25% and half from lower 75% of systolic blood pressure). Data for the present report were from the 186 participants (of 368 total participants) who had both baseline and young adult examinations. Mean blood pressure of the participants at baseline was 108±9/56±13, corresponding to a mean BP percentile of 43%/32%. Mean blood pressure at age 24 was 110±10/66±10. There were no significant differences in baseline variables between those who were or were not examined as young adults, with the exception that participants not examined were slightly younger (age 12.6 ± 1.3 vs 13.2 ± 1.1 years) and shorter (height 160 ± 9.5 vs 164 ± 9.2 cm), both p <0.0001.

Participants in both studies were seen in the same dedicated clinic and in the University of Minnesota General Clinical Research Center. No participant had any chronic illnesses nor was any taking any medications. Measurements were made with participants in a study gown and without shoes. Blood pressure was measured twice in the right arm with a random zero sphygmomanometer with participants seated. The mean of the two measurements (systolic and 5th phase Korotkoff diastolic) was used for analysis. Height was measured with a wall-mounted stadiometer. Weight was measured by a balance scale. Body mass index (BMI) was calculated as weight (kilograms) divided by height (m2).

Insulin sensitivity was measured with the euglycemic hyperinsulinemic clamp (as described previously [25]) at baseline and young adult in the insulin study group and only as young adults in the NaKs group. Fasting blood samples were obtained prior to beginning the clamp for measurement of glucose, insulin, total cholesterol, high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C) and triglycerides. Plasma glucose was measured at baseline and every 5 minutes during the clamp. The insulin infusion was started at time 0 and continued at 1 mU/kg per minute for 3 hours. An infusion of 20% glucose was started at time 0 and adjusted, based on plasma glucose levels, to maintain plasma glucose at 5.6 mmol/L. Insulin sensitivity was determined from the amount of glucose administered over the final 40 minutes of the euglycemic clamp and was expressed as Mlbm (i.e., glucose use/kg lean body mass [LBM] per minute).LBM was calculated by the skinfold method of Slaughter [26] at the baseline visit and by dual emission x-ray absorptiometry (DEXA) at the young adult visit. The baseline LBM values were adjusted to the DEXA values according to equations derived from siblings of the present cohort, as previously reported [27]. Higher values of Mlbm correspond to greater insulin sensitivity.

Blood samples were analyzed for glucose immediately at the bedside with a Beckman Glucose Analyzer II (Beckman Instruments Inc., Fullerton, CA). Serum lipids and insulin were determined in the laboratory of the Fairview-University Medical Center, as previously reported [25]. The age 13 insulin measurement was done by radioimmunoassay (RIA, Diagnostic Products Corporation, Los Angeles, CA). The age 24 measurement was done by chemiluminescence solid phase immunometric assay (Immulite, Diagnostic Products Corporation, Los Angeles, CA). After analysis of 26 samples with RIA insulin <80mμ/L measured both ways, insulin values were recalibrated from the RIA to the Immulite method, as follows: Immulite insulin value = -2.195 + 0.9349 × RIA insulin value (r=0.98; p ≤ 0.0001).

All analyses were performed using SAS software. Pearson's partial correlation of BMI and SBP was calculated with adjustment for race, sex and age.

Linear regression models were constructed using Proc GLM. We first ran univariate models using baseline SBP, DBP or BMI to predict the young adult CV risk factors levels (SBP, DBP, BMI, fasting insulin, fasting glucose, mlbm, total cholesterol, LDL-C, HDL-C, and triglycerides). Three multivariable models were run: 1.) baseline SBP (or DBP) and BMI as independent variables; 2.) baseline SBP (or DBP), change in SBP (or DBP), baseline BMI, and change in BMI; and 3.) model 2 with the addition of the baseline level of the outcome variable (for lipids and insulin resistance measures). Change in BMI or blood pressure was calculated as the young adult value minus the baseline value. All multivariable models were adjusted for age, sex, and race. Race was collapsed to white or nonwhite.

ANOVA was used to compare SBP/BMI groups defined by a median split in both variables. We developed a cardiovascular risk score by assigning a value of 1 to each subject's individual young adult CV risk factor (cholesterol, HDL-C, LDL-C, triglycerides, fasting glucose, fasting insulin and Mlbm) if the subject's value exceeded the cohort's 75th percentile (<25th percentile for HDL-C) or a zero if the value was less than/equal to the 75th percentile (≥ 25th percentile for HDL-C). The sum of the individual factors was the cumulative risk score (maximum score = 7). This analysis was also performed with DBP/BMI groups.

Logistic regression was used to test baseline BMI and SBP (or DBP) as predictors of adult metabolic syndrome. The ATP III definition of metabolic syndrome was used[28] for participants at age 24 (3 or more of the following: fasting glucose > 5.5 mmol/L; triglycerides > 1.68 mmol/L, HDL-C < 1.28 mmol/L (women) or 1.02 mmol/L (men); waist circumference > 88 cm (women) or > 102 cm (men); SBP > 130 mmHg). P values < 0.05 were considered significant.


Clinical and laboratory values are shown in Table 1. Of the 342 participants, 179 were males (52%) and 296 (87%) were white - the rest being black or Hispanic. At baseline 41% were overweight or obese. Males had slightly, but significantly, lower BMI and DBP, higher fasting glucose, and greater insulin sensitivity than females. By young adulthood, the percentage of overweight or obese participants had reached 57%, and males expressed significantly greater cardiovascular risk, with higher SBP, DBP, cholesterol, LDL-C, triglycerides, and fasting glucose and lower insulin sensitivity and HDL-C than females. There were also ethnic differences. At baseline, whites had significantly lower HDL-C (1.18 ± 0.28 v. 1.35 ± 0.28 mmol/L, p = 0.0004) and higher triglycerides (1.09 ± 0.62 v. 0.89 ± 0.44 mmol/L, p = 0.048) and fasting glucose (5.00 ± 0.47 v. 4.83 ± 0.43 mmol/L, p = 0.03) compared to non-whites. At young adulthood, HDL-C remained lower (1.17 ± 0.29 v. 1.29 ± 0.30 mmol/L, p = 0.008) and triglycerides remained higher (1.37 ± 1.20 v. 1.00 ± 0.56 mmol/L, p = 0.0008) in whites.

Table 1
Baseline and Young Adult Clinical and Laboratory Values by Sex (mean ± SD)

Changes in clinical and laboratory values in subjects who remained in three weight categories (lean, overweight, obese) from baseline to young adulthood were also examined. In 131 subjects who were lean as children and young adults SBP, DBP, BMI, cholesterol and waist circumference increased and insulin decreased significantly. In 30 subjects who were overweight at both visits DBP, BMI, cholesterol, LDL-C and waist circumference increased significantly. In 53 subjects who were obese at both visits DBP, BMI, cholesterol, glucose, and waist circumference increased significantly while HDL-C decreased significantly. Thus, the increase in levels of the risk factors at age 24 are most likely related to increasing levels of BMI occurring at all levels of BMI.

The correlations between BMI and SBP are presented in Table 2. SBP and BMI were significantly related at both baseline and young adult. Baseline SBP was significantly related to young adult SBP and, less strongly, to young adult BMI, whereas baseline BMI was strongly related to young adult BMI and not related to young adult SBP. Results were similar when boys and girls were examined separately (data not shown).Baseline DBP was significantly related to young adult DBP (r=0.36, p < 0.001). There was no significant correlation between DBP and BMI at baseline in the entire cohort or when males and females were examined separately. There was a significant correlation between DBP and BMI at young adulthood in males only (r = 0.26, p = 0.0004).

Table 2
Pearson Correlations among childhood and adult SBP and BMI

Baseline BMI and SBP were first investigated as continuous variables in univariate linear regression models. Baseline BMI predicted all adult risk factor levels. Baseline SBP predicted all except fasting insulin and insulin sensitivity. In multivariable model 1, baseline BMI predicted all adult risk factor levels except SBP, while baseline SBP predicted only SBP (see Table 3). In model 2 (baseline plus change from baseline), baseline SBP predicted adult SBP, cholesterol, triglycerides and fasting glucose; baseline BMI predicted all the adult risk factors except SBP and fasting glucose; change in BMI predicted SBP, total cholesterol, HDL-C, LDL-C, triglycerides, insulin sensitivity, fasting insulin and fasting glucose; while change in SBP predicted only BMI. With the addition of the baseline CV risk factor to model 2, prediction of the adult risk factors was somewhat attenuated. Baseline SBP still significantly predicted adult SBP, cholesterol, LDL and fasting glucose but triglycerides were no longer predicted; baseline BMI predicted only adult BMI and fasting insulin; change in SBP predicted adult BMI and fasting glucose; and change in BMI predicted all CV risk factors except fasting insulin.

Table 3
Results of multivariable models using childhood SBP to predict CV risk factor levels in young adults (Beta coefficient ± SE).

Models were also run in each gender separately. The results for model 1in males and females were virtually identical to the combined cohort. The same was true in model 2 for change in BMI and change in SBP However, there were slightly different results between the genders for baseline SBP and BMI. In females baseline SBP predicted young adult SBP, cholesterol, and LDL-C; and baseline BMI predicted adult BMI, HDL-C, triglycerides, and insulin. In males baseline SBP predicted young adult SBP, HDL-C, and glucose, and baseline BMI predicted all young adult values but glucose and insulin resistance; change in BMI.

When DBP was substituted for SBP (Table 4), baseline DBP was associated only with young adult DBP and glucose in univariate analysis. In multivariable model 1, DBP predicted young adult DBP, insulin, and glucose. In multivariable model 2, baseline DBP predicted adult DBP, BMI, cholesterol, triglycerides, and insulin while change in DBP predicted BMI and triglcyerides. The results for baseline BMI and change in BMI were not different from the models using SBP. Addition of the baseline level of the CV risk factor to model 2 largely eliminated the effect of baseline DBP and BMI except for fasting glucose.

Table 4
Results of multivariable models using childhood DBP and BMI to predict CV risk factor levels in young adults (Beta coefficient ± SE).

BMI and SBP were also studied as categorical variables. Participants were divided into 4 groups based on whether their baseline values for SBP and BMI were above (arbitrarily defined as “high”) or equal/below (arbitrarily defined as “low”) the cohort median for SBP (109 mmHg) and BMI (21 kg/m2) as follows: High BMI/ High SBP, High BMI/Low SBP, Low BMI/High SBP, and Low BMI/Low SBP. The mean ± SD SBP and BMI values of each group at age 13 were: 117 ± 6 mmHg/25.5 ± 3.7 kg/m2, 103 ± 4 mmHg/25 ± 4.2 kg/m2, 116 ± 5 mmHg/19 + 1.4 kg/m2, and 100 ± 5.8 mmHg/18.4 + 1.6 kg/m2. The mean values for each variable at young adulthood by group are shown in Table 5. In general, the high BMI groups had significantly higher levels of the risk factors than the low BMI groups. The only significant difference between the SBP groups (other than SBP) was in insulin sensitivity between the high SBP/low BMI group and the low SBP/low BMI group (0.61 v. 0.72 mmol/kg/min, p = 0.01), Repeating the categorical analysis with DBP instead of SBP revealed largely the same pattern of results with the high BMI groups showing significantly worse levels of CV risk factors than low BMI groups. Significant differences between the high BMI/high DBP group and the high BMI/low DBP groups were seen in HDL-C levels (1.21 v. 1.10 mmol/L, p = 0.03), triglycerides (1.63 v. 1.30 mmol/L, p = 0.02), and fasting insulin (107.65 v. 66.67 pmol/L, p = 0.001). Figure 1 shows the young adult CV risk score by baseline BMI/SBP category. The risk score increased progressively from the low BMI/low SBP group to the high BMI/high SBP group, with no evidence of interaction. Inclusion of the baseline risk score (based on baseline levels of the CV risk factors) into the model did not change the results.

Figure 1
CV risk score at age 24 by age 13 BMI-SBP group
Table 5
Values of CV risk factors at age 24 (mean ± SE) according to BMI-SBP group at age 13

Finally, logistic regression was used to determine whether baseline SBP or DBP and BMI are predictors of developing the metabolic syndrome as a young adult. Metabolic syndrome was diagnosed in 53 young adult participants (50% male, 94% white) with the frequencies of the five constituent variables as follows: low HDL (94%), high waist circumference (83%), high triglycerides (72%), high glucose (62 %) and high blood pressure (17%). By regression analysis, baseline BMI (and change in BMI) but not baseline SBP (or change in SBP) significantly predicted the development of the metabolic syndrome. Substitution of DBP for SBP did not change the results.


In this report we have shown that childhood BP, independent of BMI, predicts young adult CV risk. Although the predictive effect of BP is generally weaker than BMI, SBP was found not only to predict future BP, but also triglycerides, cholesterol and glucose, and DBP predicted glucose and BMI. As is well known from previous studies, childhood BMI predicted a wide variety of young adult CV risk factors (lipids, BMI, fasting insulin, fasting glucose, and insulin resistance) but did not predict SBP or DBP.

A number of studies have shown that both childhood BP and BMI, when considered separately, predict future adult CV risk factors. A few studies have included BP with BMI and found an independent influence of child BP on adult BP [12-14, 16]. However, the independent influence of childhood BP on other cardiovascular risk factors and, in particular, on insulin resistance has not been well studied.

The present study extends those findings to show that childhood blood pressure not only predicts young adult blood pressure but also predicts other measures of adult CV risk independent of childhood BMI. In addition, the study shows that the CV risk factors predicted by childhood blood pressure differ between males and females. This may be related to changes in hormonal level or body composition that occurs during the maturational period between ages 13 and 24. Previous studies in this cohort on the natural history of the risk factors during adolescence have shown differences in the developmental changes in blood pressure, body fatness and lipids between the genders[29], emphasizing the important differences in early risk and in potential mechanisms for influencing these risk factors.

The specific mechanisms associated with these relations have not been defined, but it seems likely they are related through some common pathway, rather than a direct etiologic effect of blood pressure on the other risk factors. In this regard, one such potential candidate pathway might be mediated via insulin resistance. Insulin resistance may cause hypertension through several mechanisms, including stimulation of renal sodium reabsorption and sympathetic nervous system activity [30], and it has also been linked to dyslipidemia [31, 32]. Childhood BP in the present study did not predict young adult insulin resistance. However, prior analyses in the present cohort have shown that insulin resistance at age 13 and change in insulin resistance during adolescence predict SBP, triglycerides and an overall risk factor score in early young adulthood independent of BMI in both males and females [33]. These changes also were predicted when fasting insulin was substituted for insulin resistance in the prediction equations. The natural history studies in this cohort during adolescence, showing a significant increase of insulin resistance in males and no change in females, are compatible with early initiation of an ongoing adverse effect of insulin resistance on risk [29].

An alternative explanation linking BP to future dyslipidemia and abnormal glucose metabolism could be oxidative stress. A recent study in otherwise healthy adults (defined as normal BMI, lipids, glucose, and insulin) showed that subjects with high-normal BP had evidence of increased oxidative stress and endothelial activation compared to those with normal BP [34]. Subjects with pre-eclampsia [35], prehypertension [36, 37], or hypertension [38] have all been shown to have increased oxidative stress compared to normotensives. Thus far, human studies have been cross-sectional in nature and, therefore, have not been able to explore the etiologic relation between oxidative stress and elevated blood pressure. However, studies in rats have demonstrated the development of hypertension after the induction of oxidative stress [39]. Oxidative stress has also been associated with diabetes, dyslipidemia, and insulin resistance. While a cause-effect relationship has not been documented in humans thus far, in mice it has been shown that reactive oxygen species increase before the development of insulin resistance [40]. Prior studies in this cohort at mean age 15 have shown a significant relation between oxidative stress and both BMI and insulin resistance[41]; however, a recent further analysis (unpublished) of these data did not show a significant correlation between either SBP (r=0.5, p=0.34) or DBP (5=0.5, p=0.39) and 8-iso PGF.

In contrast to the relation of blood pressure to the individual CV risk factors in young adulthood, a significant relation between blood pressure at baseline and the metabolic syndrome in young adulthood was not observed. This is in contrast to findings from the Fels Longitudinal Study where adolescent SBP but not BMI predicted the presence of the adult metabolic syndrome [15]. However, it is consistent with epidemiologic studies in children showing that SBP has the loosest connection to the metabolic syndrome of the five factors that define the syndrome [42] and with the low incidence of high blood pressure in the subjects with metabolic syndrome in the present study. These findings also are consistent with prior studies questioning the utility of the diagnosis of metabolic syndrome in children, because of fluctuations in the levels of the component variables and prevalence of the syndrome prior to adulthood [43]. Thus, the data from the present study highlight the importance of considering the risk factors in addition to the clustering effect.

An additive effect of SBP and BMI on future CV risk was also observed. Although it is generally recognized that BMI is the strongest correlate of blood pressure in children, these results further confirm the independent effect of blood pressure on CV risk. This result is especially notable because the results were based on a median split of SBP and BMI, rather than dividing the cohort into hypertensive or obese categories. This permitted an evaluation of CV risk at an early stage of its development. Given that both SBP and BMI track into and through adulthood, it could be expected that this effect will strengthen as the participants age and develop overt hypertension and obesity.

The significant relation of childhood BMI to future cardiovascular risk is well documented. Data from Bogalusa showed that both adolescent BMI and change in BMI predicted adult lipids, SBP, DBP, fasting insulin and fasting glucose [11] and that childhood BMI but not blood pressure predicted the clustering of adult risk factors [19]. Data from the Minneapolis Blood Pressure Study showed that change in BMI from adolescence to young adulthood was significantly correlated with adult HDL, SBP and fasting insulin [10]. The linkage between childhood BMI status and adult CV risk has important implications for pre-adult intervention, because of the well-documented relation in adults between BMI and CV morbidity and mortality. [44-46] However, the mechanisms controlling this relation are not clear, particularly with regard to the role of obesity-related CV risk factors, and risk is known to vary among obese individuals. [47]. Studies have shown that abdominal fat, in particular visceral fat, predicts the presence of CV risk factors better than BMI [48, 49], perhaps as a result of the significant relation between visceral fat and insulin resistance. [50] Results from the present study also show the very strong tracking effect of BMI from childhood to young adulthood and the strong association of BMI to the CV risk factors. However, this study also shows that, in addition to the BMI association, the presence of other risk factors in childhood and, in particular, blood pressure may contribute to establishment of adult CV risk.

In that regard, some prior studies have suggested that hypertension and other risk factors play a major role in BMI related events, and in the present study blood pressure had an additive effect to BMI in predicting CV risk. A large population-based study in France with 25 years of follow-up showed that overweight status (BMI ≥ 25 kg/m2) was not associated with CV mortality, even in overweight subjects who also had diabetes, unless hypertension also was present. [22] A study of Swedish men with 23 years of follow-up found that BMI was not associated with CV death after adjustment for hypertension, dyslipidemia and diabetes. [21] In a study of American women referred for evaluation of suspected myocardial ischemia, overweight or obese status was not independently related to CV mortality over the next 3 years. [51] The independent adverse effect of blood pressure in obesity is also suggested from childhood studies showing carotid intima media thickness to be higher in hypertensive children compared to normotensive controls, matched for BMI[52] and showing left ventricular mass index to be significantly related to 24 hour systolic blood pressure after adjustment for body fatness. [53]

In summary, not only is childhood blood pressure, independent of BMI, related to the development of future CV risk factors, but, in addition, BMI and blood pressure in adolescents have an additive effect on the CV risk factors as calculated from a young adult composite CV risk score. Because elevated risk factors are associated with development of CV disease, the results from this study showing that the combination of hypertension and obesity in childhood predicts the highest levels of the young adult CV risk factors, emphasize the importance of treating hypertension in overweight and obese children, in addition to attempting to effect active weight loss.


This work was presented in part at the ASPN/PAS 2008 annual meeting.

Source of Funding: These studies were supported by grants HL 34659, HL 52851 and M01RR00400 from the National Institute of Health.


Conflicts of Interest/Disclosure Statement: none

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1. Moore W, Stephens A, Wilson T, Wilson W, Eichner J. Body Mass Index and Blood Pressure Screening in a Rural Public School System: the Healthy Kids Project. Preventing Chronic Disease. [serial online] 2006. October: p. available from: [PMC free article] [PubMed]
2. Sorof J, Lai D, Turner J, Poffenbarger T, Portman R. Overweight, Ethnicity, and the Prevalence of Hypertension in School-Aged Children. Pediatrics. 2004;113(3):475–482. [PubMed]
3. Muntner P, He J, Cutler J, Wildman R, Whelton P. Trends in Blood Pressure Among Children and Adolescents. Journal of the American Medical Association. 2004;291(17):2107–2113. [PubMed]
4. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics. 2000;114:555–576. [PubMed]
5. Din-Dzietham R, Liu Y, Bielo MV, Shamsa F. High Blood Pressure Trends in Children and Adolescents in National Surverys, 1963-2002. Circulation. 2007;116:1488–1496. [PubMed]
6. de Ferranti S, Gauvreau K, Ludwig D, Newburger J, Rifai N. Inflammation and Changes in Metabolic Syndrome Abnormalities in US Adolescents: Findings from the 1988-1994 and 1999-2000 National Health and Nutrition Examination Surveys. Clinical Chemistry. 2006;52(7):1325–1330. [PubMed]
7. Ogden C, Flegal K, Carroll M, Johnson C. Prevalence and Trends in Overweight Among US Children and Adolescents, 1999-2000. Journal of the American Medical Association. 2002;288(14):1728–1732. [PubMed]
8. Ogden C, Carroll M, Curtin L, McDowell M, Tabak C, Flegal K. Prevalence of Overweight and Obesity in the United States, 1999-2004. Journal of the American Medical Association. 2006;295(13):1549–1555. [PubMed]
9. Freedman D, Khan L, Dietz W, Srinivasan S, Berenson G. Relationship of Childhood Obesity to Coronary Heart Disease Risk Factors in Adulthood: the Bogalusa Heart Study. Pediatrics. 2001;2001(108):3. [PubMed]
10. Sinaiko A, Donahue R, Jacobs D, Prineas R. Relation of weight and rate of increase in weight during childhood and adolescence to body size, blood pressure, fasting insulin, and lipids in young adults. The Minneapolis Children's Blood Pressure Study. Circulation. 1999;99(11):1471–6. [PubMed]
11. Srinivasan S, Bao W, Wattigney W, Berenson G. Adolescent Overweight is Associated with Adult Overweight and Related Multiple Cardiovascular Risk Factors: The Bogalusa Heart Study. Metabolism. 1996;45(2):235–240. [PubMed]
12. Srinivasan S, Myers L, Berenson G. Changes in Metabolic Syndrome Variables Since Childhood in Prehypertensive and Hypertensive Subjects. Hypertension. 2006;48:33–39. [PubMed]
13. Lauer R, Clarke W. Childhood Risk Factors for High Adult Blood Pressure: The Muscatine Study. Pediatrics. 1989;84(4):633–641. [PubMed]
14. Shear C, Burke G, Freedman D, Berenson G. Value of Childhood Blood Pressure Measurements and Family History in Predicting Future Blood Pressure Status: Results From 8 Years of Follow-Up in the Bogalusa Heart Study. Pediatrics. 1986;77(6):862–869. [PubMed]
15. Sun S, Grave G, Siervogel R, Pickoff A, Arslanian S, Daniels S. Systolic Blood Pressure in Childhood Predicts Hypertension and Metabolic Syndrome Later in Life. Pediatrics. 2007;119(2):237–246. [PubMed]
16. Cook N, Gillman M, Rosner B, Taylor J, Hennekens C. Prediction of Young Adult Blood Pressure from Childhood Blood Pressure, Height, and Weight. Journal of Clinical Epidemiology. 1997;50(5):571–579. [PubMed]
17. Li X, Li S, Ulusoy E, Chen W, Srinivasan S, Berenson G. Childhood Adiposity as a Predictor of Cardiac Mass in Adulthood: The Bogalusa Heart Study. Circulation. 2004;110:3488–3492. [PubMed]
18. Li S, Chen W, Srinivasan S, Bond M, Tang R, Urbina E, Berenson G. Childhood Cardiovascular Risk Factors and Carotid Vascular Changes in Adulthood: the Bogalusa Heart Study. Journal of the American Medical Association. 2003;290(17):2271–2276. [PubMed]
19. Srinivasan S, Myers L, Berenson G. Predictability of Childhood Adiposity and Insulin for Developing Insulin Resistance Syndrome (Syndrome X) in Young Adulthood: The Bogalusa Heart Study. Diabetes. 2002;51:204–209. [PubMed]
20. Morrison J, Friedman L, Harlan W, Harlan L, Barton B, Schreiber G, Klein D. Development of the Metabolic Syndrome in Black and White Adolescent Girls: A Longitudinal Assessment. Pediatrics. 2005;116(5):1178–1182. [PubMed]
21. Jonsson S, Hedblad B, Engstrom G, Nilsson P, Berglund G, Janzon L. Influence of obesity on cardiovascular risk. Twenty-three year follow-up of 22,025 men from an urban Swedish population. International Journal of Obesity. 2002;26:1046–1053. [PubMed]
22. Thomas F, Bean K, Pannier B, Oppert JM, Guize L, Benetos A. Cardiovascular mortality in overweight subjects: The key role of associated risk factors. Hypertension. 2005;46:654–659. [PubMed]
23. Sinaiko AR, Gomez-Marin O, Prineas RJ. Effect of low sodium diet or potassium supplementation on adolescent blood pressure. Hypertesnion. 1993;21:989–994. [PubMed]
24. Sivanandam S, Sinaiko A, Jacobs D, Steffen L, Moran A, Steinberger J. Relation of Increase in Adiposity to Increase in Left Ventricular Mass from Childhood to Young Adulthood. American Journal of Cardiology. 2006;98:411–415. [PubMed]
25. Sinaiko A, Jacobs D, Steinberger J, Moran A, Luepker R, Rocchini A, Prineas R. Insulin resistance syndrome in childhood: associations of the euglycemic insulin clamp and fasting insulin with fatness and other risk factors. Journal of Pediatrics. 2001;139(5):700–707. [PubMed]
26. Slaughter MH, Lohman TG, Baileau RA, Horswill CA, Stillman RJ, Van Loan MD, Bemben DA. Skinfold equations for estimation of body fatness in children and youth. Human Biology. 1988;60:709–723. [PubMed]
27. Steinberger J, Jacobs D, Raatz S, Moran A, Hong C, Sinaiko A. Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents. International Journal of Obesity (London) 2005;29(11):1346–1352. [PubMed]
28. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, et al. Diagnosis and Management of the Metabolic Syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement: Executive Summary. Circulation. 2005;112(17):e285–290. [PubMed]
29. Moran A, Jacobs DR, Jr, Steinberger J, Steffen LM, Pankow JS, Hong CP, Sinaiko AR. Changes in Insulin Resistance and Cardiovascular Risk During Adolescence: Establishment of Differential Risk in Males and Females. Circulation. 2008;117(18):2361–2368. [PubMed]
30. Daly PA, Landsberg L. Hypertension in Obesity and NIDDM: Role of Insulin and Sympathetic Nervous System. Diabetes Care. 1991;14(3):240–8. [PubMed]
31. Jeppesen J, Hollenbeck CB, Zhou MY, Coulston AM, Jones C, Chen YD, R GM. Relation between insulin resistance, hyperinsulinemia, postheparin plasma lipoprotein lipase activity, and postprandial lipemia. Arteriosclerosis, Thrombosis and Vascular Biology. 1995;15:320–4. [PubMed]
32. Majali KA, Cooper MB, Staels B, Lue G, Taskinen MR, Betteridge DJ. The effect of sensitization to insulin with pioglitazone on fasting and postprandial lipid metabolism, lipoprotein modification by lipases, and lipid transfer activities in type 2 diabetic patients. Diabetologia. 2006;49(527-37) [PubMed]
33. Sinaiko A, Steinberger J, Moran A, Hong C, Prineas R, Jacobs D. Influence of Insulin Resistance and Body Mass Index at Age 13 on Systolic Blood Pressure, Triglycerides, and High-Density Lipoprotein Cholesterol at Age 19. Hypertension. 2006;48:730–736. [PubMed]
34. Bo S, Gambino R, Gentile L, Pagano G, Rosato R, Saracco GM, Cassador M, et al. High-normal blood pressure in associated with a cluster of cardiovascular and metabolic risk factors: a population-based study. Journal of Hypertension. 2009;27(1):102–8. [PubMed]
35. Raijmakers MTM, Dechend R, Poston L. Oxidative Stress and Preeclampsia: Rationale for Antioxidant Clinical Trials. Hypertension. 2004;44(4):374–380. [PubMed]
36. Chrysohoou C, Panagiotakos DB, Pitsavos C, Skoumas J, Economou M, Papadimitriou L, Stefanadis C. The association between pre-hypertension status and oxidative stress markers related to atherosclerotic disease: The ATTICA study. Atherosclerosis. 2007;192(1):169–176. [PubMed]
37. Sathiyapriya V, Nandeesha H, Bobby Z, Selvaraj N, Pavithran P. Perturbation of oxidant antioxidant status in non-obese prehypertensive male subjects. Journal of Human Hypertension. 2007;21(2):176–178. [PubMed]
38. Fortuno A, Olivan S, Beloqui O, San Jose G, Moreno MU, Diez J, Zalba G. Association of increased phagocytic NADPH oxidase-dependent superoxide production with diminshed nitric oxide generation in essential hypertension. J Hypertens. 2004;22:2169–2175. [PubMed]
39. Vaziri ND, Wang XQ, Oveisi F, Rad B. Induction of Oxidative Stress by Glutathione Depletion Causes Severe Hypertension in Normal Rats. Hypertension. 2000;36(1):142–146. [PubMed]
40. Houstis N, Rosen ED, Lander ES. Reactive oxygen species have a causal role in multiple forms of insulin resistance. Nature. 2006;440(7086):944–948. [PubMed]
41. Sinaiko A, Steinberger J, Moran A, Prineas R, Vessby B, Basu S, Tracy R, et al. Relation of body mass index and insulin resistance to cardiovascular risk factors, inflammatory factors, and oxidative stress during adolescence. Circulation. 2005;111(15):1985–1991. [PubMed]
42. Cook S, Auinger P, Li C, Ford ES. Metabolic Syndrome Rates in United States Adolescents, from the National Health and Nutrition Examination Survey, 1999-2002. The Journal of Pediatrics. 2008;152(2):165–170.e2. [PubMed]
43. Goodman E, Daniels SR, Meigs JB, Dolan LM. Instability in the Diagnosis of Metabolic Syndrome in Adolescents. Circulation. 2007;115(17):2316–2322. [PMC free article] [PubMed]
44. Kenchaiah S, Evan JC, Levy D, Wilson PWF, Benjamin EJ, Larson MG, Kannel WB, et al. Obesity and the Risk of Heart Failure. New England Journal of Medicine. 2002;347(5):305–313. [PubMed]
45. Li TY, Rana JS, Manson JE, Willet WC, Stampfer MJ, Colditz GA, Rexrode KM, et al. Obesity as Compared with Physical Activity in Predicting Risk of Coronary Heart Disease in Women. Circulation. 2006;113:499–506. [PMC free article] [PubMed]
46. Wilson PWF, D'Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight and Obesity as Determinants of Cardiovascular Risk: The Framingham Experience. Archives of Internal Medicine. 2002;162:1867–1872. [PubMed]
47. McLaughlin T, Abbasi F, Lamendola C, Reaven G. Heterogeneity on the Prevalence of Risk Factors for Cardiovascular Disease and Type 2 Diabetes Mellitus in Obese Individuals. Archives of Internal Medicine. 2007;2007(167):642–648. [PubMed]
48. Nagaretani H, Nakamura T, Funahashi T, Kotani K, Miyanaga M, Tokunaga K, Takahashi M, et al. Visceral fat is a major contributor for multiple risk factor clustering in Japanese men with impaired glucose tolerance. Diabetes Care. 2001;24:2127–2133. [PubMed]
49. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, et al. Abdominal Visceral and Subcutaneous Adipose Tissue Compartments: Association With Metabolic Risk Factors in the Framingham Heart Study. Circulation. 2007;116(1):39–48. [PubMed]
50. Wajchenberg BL. Subcutaneous and Visceral Adipose Tissue: Their Relation to the Metabolic Syndrome. Endocr Rev. 2000;21(6):697–738. [PubMed]
51. Kip KE, Marroquin OC, Kelley DE, Johnson D, Kelsey SF, Shaw LJ, Rogers WJ, et al. Clinical Importance of Obesity Versus the Metabolic Syndrome in Cardiovascular Risk in Women. A report from the Women's Ischemia Syndrome Evaluation (WISE) Study. Circulation. 2004;109:706–713. [PubMed]
52. Lande M, Carson N, Roy J, Meagher C. Effects of childhood primary hypertension on carotid intima media thickness: a matched controlled study. Circulation. 2006;48(1):40–44. [PubMed]
53. Maggio ABR, Aggoun Y, Marchand LM, Martin XE, Herrmann F, Beghetti M, Farpour-Lambert NJ. Associations among Obesity, Blood Pressure, and Left Ventricular Mass. The Journal of Pediatrics. 2008;152(4):489–493. [PubMed]