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A prospective study of familial aggregation of blood pressure in young children
J CIin EpidemiolVol. 45, No. 9, PP. 959-969, 1992 Printed in Great Britain. All rights reserved
0895-4356/9255.00+ 0.00 Copyright Q 1992Pergamon Press Ltd
A PROSPECTIVE STUDY OF FAMILIAL AGGREGATION OF BLOOD PRESSURE IN YOUNG CHILDREN DAWN K. WILSON,‘* LISAM. KLESGES,~ ROBERTC. KLESGES,~ LINDAH. EcK,~
CATHERINE A. HACKETT-RENNER,’ BRUCES. ALPERT’and EDITH T. DALTON~ Departments of ‘Pediatrics and ‘Biostatistics and Epidemiology, University of Tennessee, Memphis and )Center For Applied Psychological Research, Department of Psychology, Memphis State University, TN, U.S.A. (Received 4 March 195’2)
Abstract-In a prospective study, we evaluated familial aggregation of blood pressure in a sample of 175 normotensive familes with children 3 to 6 yr old. Systolic and diastolic blood pressure measurements of parents and children were correlated at 1, 2, and 3 yr intervals. Spearman rank-order correlation coefficients between parents and children were significant for mother-son pairs. In particular, mother-son blood pressure correlation coefficients were significant for systolic blood pressure across all 3 yr and for diastolic blood pressure during yr 2. Further analyses were performed adjusting for body mass index (BMI), age, physical activity, sodium intake, potassium intake, and parental
smoking status, and alcohol use. The Speannan correlation coefficients for mother-son pairs remained significant for yr I and 3 after adjusting for these blood pressure correlates. These results are consistent with cross-sectional studies and suggest that both genetic and environmental contributions to blood pressure status are important in young children. Blood pressure aggregation
Physical activity
dietary intake
INTRODUCTION
Hypertension currently affects 30% of adults 18 to 74 years of age in the U.S. [I]. Elevated blood pressure levels in children may be a marker for indentifying those who are at risk for developing hypertension in early adulthood [2]. Genetic and environmental contributions to children’s blood pressure status are not fully known. Probably the most consistent predictor of children’s blood pressure is family history of hypertension either in parents or grandparents (viz. familial aggregation of blood pressure) [3-II]. Further evidence that genetic factors are predictive of blood pressure is demonstrated by studies in *All correspondence to be addressed to: Dr D. K. Wilson, University of Tennessee, The Health Science Center, Le Bonheur Childrens Center, 848 Adams Ave, Memphis, TN 38103, U.S.A. 959
Children
Familial
which parent-biologic child correlations are significantly greater than zero, while parentadoptee correlations are negligible [12, 131. There has been a lack of consistency in the literature concerning patterns of familial transmission of blood pressure among normotensives. Some studies have demonstrated that children of either sex resemble mothers and fathers equally [9, 121.In contrast, other investigators have found that only same-sex parentchild relationships were significantly correlated [15,16]. Still other researchers have reported that opposite-sex parent-child correlations were of greater magnitude than same-sex relationships [4, 10, 171.A number of investigators have also examined the contribution of environmental factors in familial aggregation of blood pressure among parents and offspring (e.g. [3,7, 151. Voors et al. [3] examined the role of
DAWN K. WILSONet al.
960
demographic, anthropometric, and dietary variables (e.g. cholesterol, trigylcerides) and demonstrated that only anthropometric variables were significant predictors of blood pressure variability among family members. Havlik et al. [7] found the strongest predictors of blood pressure among both men and women in the Framingham Heart Study were weight, heart rate, total serum proteins, and alcohol consumption, respectively. To understand more fully the nature of blood pressure correlates among parents and children, both genetic and environmental factors were incorporated into our study design. A longitudinal design was implemented and only children within a narrow age range (3-6 yr) were included in the study. Thus, one purpose of the present study was to determine the degree of familial aggregation of blood pressure over a 3 yr period in young children. A second purpose of the study was to examine the role of environmental factors in contributing to the familial aggregation of blood pressure in this population.
METHODS
Recruitment
Participants were recruited through local pediatricians, day-care centers, and churches in Memphis, TN. Parents who expressed interest in the study were contacted by telephone. Criteria for inclusion in the study included that the child participant: (1) was the natural, biologic offspring of his/her parents; (2) had no physical handicap or condition that could effect blood pressure, dietary intake, or physical activity; (3) had parents who were married; (4) had parents without hypertension or cardiovascular disease and; (5) had a family whose plans were to stay in the metropolitan area in the coming year. *Two of the families had more than one child aged 3-6 yr. For the purpose of this paper only one child was chosen at random to be included in the data analyses. TThere is overlap between mothers and fathers who were eliminated for these exclusionary criteria. For example, in some families both the mother and the father were diagnosed as hypertensive, and therefore eliminated, while in other families only one parent may have been diagnosed as hypertensive, and therefore eliminated. $The reduced sample size for mothers during yr 2 was primarily a result of missing data on the physical activity questionnaire. Due to technical error this missing information was not detected until well after the yr 2 assessment occurred.
Table 1. Sample exclusion and missing data across 3 yr of study Year 1 Year 2 Available families Fathers-BP medication Fathers-hypertensive Mothers-BP medication Mothers-hypertensive Mothers-pregnant Children-missing data Fathers-missing data Mothers-missing data
216 14 20 14 5 8 12 12 9
Year 3
180 12 14 12 2 3 13 38 66
160 12 I 5 2 7 2 5 7
Subjects
The initial sample of subjects were 216 Caucasian families with one child aged 3-6 yr.* Only parents who were not classified as hypertensive according to baseline blood pressure readings, who were not currently taking medications, who had no medical history of cardiovascular disease, and who were not pregnant were included in the data analyses (see Table I).? Hypertension was defined as a systolic blood pressure 2 160 mmHg and/or a diastolic blood pressure 290 mmHg. A total of 36 families dropped out of the study during yr 2 and an additional 20 families dropped during yr 3 (see Table 1 for available families each yr). Subjects lost to attrition had similar systolic and diastolic blood pressures as those who stayed in the study (p > 0.05, for both). The remaining sample size for each group of family members is presented in Table 2.$ Measures Blood pressure. Volunteers who met the inclusion criteria were scheduled for a laboratory visit. In the laboratory, subjects provided demographic and background information and then participated in the blood pressure assessment. Blood pressure measurements were recorded during the laboratory visit. Mother, father, and child were seated with the technician in a quiet room. Subjects remained seated for 5 min before blood pressure measurements were taken. Blood pressures were obtained with a Dinamap 1846SX monitor, positioned so that the readout was not visible to the subject. This Table 2. Number of subjects reflected in data analyses across 3 yr of study Boys Girls Fathers Mothers
Year 1
Year 2
Year 3
110 94 175 182
92 75 104 89
89 69 115 133
Blood PressureCorrelates blood pressure apparatus was used to eliminate terminal digit bias and inter-observer differences. Each subject was seated in a relaxed position with legs uncrossed. Care was taken to select the proper size cuff which was then applied firmly to the left arm with the bladder centered over the brachial artery. The cuff was unconstricted by clothing and the subject’s arm was placed on a table at the level of the heart in a comfortable posture. No talking was permitted during blood pressure measurement. Systolic and diastolic blood pressures were recorded four times, as recommended by the American Heart Association [ 181. Readings were taken at 1-min intervals by a carefully trained technician. The first reading was discarded, and the average of the second, third, and fourth readings was used in the data analyses. Physical activity. Parents were administered the Baecke physical activity questionnaire [ 191, a factor analyzed scale comprising 16 items that represent physical activity at work, during sports, and during nonsport leisure-time. These items yield scores ranging from 1 (highly sedentary) to 5 (highly active). The Baecke questionnaire has been used extensively in recent years and has demonstrated an acceptable degree of reliability and construct validity [20,21]. Previous research has demonstrated that the test-retest reliabilities of the work, sport, and leisure-time subscales were 0.88, 0.81 and 0.74, respectively [19]. In terms of construct validity, level of education was inversely associated with the work index (r = -0.56, males; r = -0.25, females, p < 0.001 for both) and positively associated with the leisure-time index in both males and females (r = 0.38, r = 0.34, respectively, p < 0.001 for both); and lean body mass was positively related to the work index in males (/I = 1.36, p < O.OOl),but was not related to the leisure-time index in either sex [19]. Parents assessed their child’s physical activity level with the Energy Balance Questionnaire [22]. This questionnaire contains a series of three questions that ask parents to rate their child’s structured, leisure, and aerobic activities (e.g. stairs climbed per week, minutes of exercise that makes one sweat or become winded) as compared with other children their child’s age and sex. Epidemiologists have suggested that physical activity is most reliably assessed with simple, single-item questions that measure vigorous and aerobic activities such as those contained in the Energy Balance Questionnaire [23].
961
These investigators have also advocated that assessing physical activity in children relative to age- and sex-matched peers improves upon the accuracy of these measures [23]. Dietary intake. Dietary intake was assessed with the Willett’s Food Frequency Questionnaire (FFQ). This questionnaire assesses intake over a previous I-yr period [24,25] and has been modified for use with children [26-281. Previous research has shown that the FFQ was positively correlated (r = 0.60, n = 27) with a diet record which was completed during a I-yr period [24]. For the present study, each parent individually completed their own FFQ. Both parents and their child worked together to complete the child’s FFQ in the laboratory where food models were available to assist in determining serving sizes. Trained nutritionists were available if parents had questions. Questionnaires were checked for completeness so that missing data could be minimized. The questionnaires were analyzed for sodium and potassium intake with a computer program designed to allow for adjustment of serving sizes. Smoking and alcohol use. Questions designed to assess tobacco and alcohol use in adults were obtained from the Health and Daily Living Questionnaire [29,30]. All parents were asked to indicate with a yes or no answer whether they currently smoked tobacco (cigarettes, cigars, or pipes). They were also asked to indicate with a yes or no answer if they currently drank any alcoholic beverages (wine, beer, or liquor). Design and analyses
Blood pressure measurements were obtained at 1-yr intervals over a 3 yr time period. Blood pressure data were analyzed using a multiple regression of residuals approach. The major dependent variables of interest were systolic and diastolic blood pressure. The major independent variables (control variables) of interest were body mass index (BMI), age, physical activity, sodium intake, potassium intake, and parental smoking status, and alcohol use. The smoking and alcohol use questions were coded as 0 = no and 1 = yes. For each year, hierarchical linear regression models were analyzed separately for boys, girls, fathers, and mothers and the residuals for each were calculated. These residuals reflect blood pressures adjusted for the independent variables and therefore represent the component of blood pressure not explained by these factors. The normality of residuals was scrutinized for all models. The data were
DAWN K. WILSONet al.
962
Table 3. Baseline means (&SD) and frequencies of sample measures and characteristics Variables BMI (kg/m’) Age (Jr) Systolic BP (mmHg) Diastolic BP (mmHg) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Work activity Leisure activity Sport activity Smoke (X yes) Alcohol (% yes)
Boys
Girls
Fathers
Mothers
16.1 -+ 1.4 4.5 f 0.5 103.0 f 9.0 61.5 + 6.2 1780.4 f 563.3 2891.5 & 900.0 3.2 + 0.7 3.3 f 0.7 3.0 & 0.8 -
16.0 f 1.2 4.4 f 0.5 101.8 f 7.9 61.5 f 5.9 1627.1 f 503.3 2635.8 f 870.1 3.2 & 0.5 3.1 f 0.7 3.0 f 0.7 -
26.9 f 4.2 34.3 f 4.8 126.6 f 11.9 75.3 f 6.8 1816.2 f 687.5 2984.4 f 1034.6 2.6 f 0.7 2.3 f 0.6 2.6 f 0.7 24% 66%
24.4 f 4.8 32.8 & 4.0 112.9 f 10.6 7l.Ok6.3 1781.5 + 621.1 2846.2 f 1006.6 2.8 + 0.5 2.5 + 0.6 2.1 f 0.6 11% 64%
Table 4. Spearman rank-order correlations of blood pressure measurements for parents and children Son DBP Time Year 1 Year 2 Year 3
Daughter DBP
Mother DBP
Father DBP
0.18 0.24* 0.19
0.13 0.05 0.18
Mother DBP -0.02 0.11 0.15
Son SBP
Father DBP
Mother SBP
0.21’ 0.03 0.25
0.32*** 0.34** 0.49**+
Father SBP 0.12 0.05 -0.03
Daughter SBP Mother SBP
Father SBP
0.02 0.10 0.23
0.08 0.03 0.12
SBP = systolic blood pressure (BP); DBP = diastolic BP. **p < 0.001. ‘p < 0.05; **p < 0.01; ??
generally normally distributed except for several outlying observations. Specifically, there was 1 standardized residual for fathers, 1 for mothers, 1 for girls, and 3 for boys that were outside a range of + 3 SD (none exceeded + 4 SD). These data were checked for coding errors but none were discovered so they appeared to be reasonable observations. Thus, a nonparametric Spearman rank-order correlation was used to reduce the influence of these outliers in the analyses. Spearman rank-order correlation coefficients were calculated for both unadjusted and adjusted (residual) blood pressures for mother-daughter, mother-son, fatherdaughter, and father-son pairings. RESULTS
Baseline measures and sample characteristics are presented in Table 3.*
*The average levels of potassium obtained from the FFQ in parents are consistent with the average level previously reported by Willett et al. [25] i.e. 3076 mg/24 hr. The average levels of sodium obtained from the FFQ in parents was somewhat lower than levels previously reported in adults [31] i.e. 4025 mg/24 hr (as reflected by urinary sodium excretion). These parents appeared to be very health conscious and could have consumed less sodium than the average person because of being aware of the relationship between sodium intake and high blood pressure.
Spearman rank-order correlation coefficients were performed to assess the degree of association between parents’ and childrens’ unadjusted blood pressure measurements (Table 4). The results demonstrated that mother-son correlation coefficients were significant for systolic blood pressure across all 3 yr and for diastolic blood pressure during yr 2. For fatherdaughter pairs, the correlation coefficient was significant for diastolic blood pressure during yr 1. Correlation coefficients were not significant for any of the remaining comparisons. The blood pressure regression equations for boys and girls are given in Appendix A. For boys, the regression models for systolic blood pressure were significant for yr 2 [F(7,102) = 0.84, p < 0.56; F(7,84) = 3.60, p < 0.01; F(7,81) = 1.88, p < 0.08, for yr 1, 2, and 3, respectively]; and for diastolic blood pressure were not significant for any of the 3 yr [F(7,102) = 1.09, p < 0.38; F(7,84) = 1.96, p =0.07; F(7,81) = 1.81, p ~0.10, for yr 1, 2, and 3, respectively]. These regression models ranged from 5.4 to 23.1% in accounting for the total variability in blood pressure. For girls, the regression models for systolic blood pressure were significant for yr 1 and 2 [F(7,86) = 2.45, p < 0.03; F(7,67) = 2.81, p ~0.02; F(7,61)= 1.81, p ~0.11, for yr 1, 2,
Blood Pressure Correlates
963
Table 5. Spearman rank-order correlations of residuals of blood pressure measurements for parents and children Daughter DBP
Son DBP Time Year 1 Year 2 Year 3
Mother DBP 0.19 0.22 0.12
Mother DBP
Father DBP
0.01 -0.02 0.14
0.18 0.01 0.24
Father DBP 0.07 -0.15 0.08
Son SBP Mother SBP 0.34*** 0.13 0.40***
Daughter SBP
Father SBP
Mother SBP
0.16 0.32* -0.06
0.12 0.07 0.22
Father SBP 0.04 0.51*** 0.17
For abbreviations see legend to Table 4. *p < 0.05; **p < 0.01; ***p < 0.001.
and 3, respectively]; and for diastolic blood pressure were not significant for any of the 3 yr [F(7,86) = 0.86, p c 0.54; F(7,67) = 1.78, p < 0.11; F(7,61) = 0.65, p < 0.72, for yr 1, 2 and 3, respectively]. These regression models ranged from 6.6 to 22.7% in accounting for the total variability in blood pressure. The single most significant factor in the regression models for children was BMI, however, age, and dietary intake were also significant factors in some of the models. The blood pressure regression equations for fathers and mothers are also presented in Appendix A. For fathers, the regression models for systolic blood pressure were significant for all 3 yr [F(9,165) = 3.29, p < 0.01; F(9,94) = 3.49, p
Table 5 presents the Spearman rank-order correlation coefficients for residual blood pressures between parents and children after adjusting for variables in the regression equations. The mother-son correlation coefficients for systolic blood pressure remained significant during yr 1 and 3. For yr 2, the mother-son correlation coefficients were no longer significant for either systolic or diastolic blood pressure. Instead, fathers demonstrated significant positive correlations for systolic blood pressure with both daughters and sons. Correlation coefficients were not significant for any of the remaining comparisons.
DISCUSSION
The results of this study indicate that familial aggregation of blood pressure occurs in children at very young ages. Specifically, positive relationships were obtained between mothers and sons for systolic blood pressure across all 3 yr and for diastolic blood pressure during yr 2. Fathers were significantly related to daughters for diastolic blood pressure during yr 2. After statistically adjusting for other important variables, the correlation coefficients remained significant for mother-son pairs during yr 1 and 3, and, fathers demonstrated significant correlations with both sons and daughters for systolic blood pressure during yr 2. Overall, these findings suggest that blood pressure aggregates most strongly between mother-son pairs, while appearing less stable for father-child pairings.* Although familial aggregation of blood pressure is well-documented, the nature of the relationship remains unclear. The findings in the present study are, in part, consistent with the results of other studies [lo] that also investigated familial aggregation of blood pressure in young children (ages 5 and older). Miall et al. [lo] demonstrated higher regression coefficients of systolic and diastolic blood pressure for opposite-sex relatives (e.g. mother-son) than for same-sex relatives. Miall et al. also evaluated the
964
DAWNK. WIUON er al.
effects of age on the resemblance of blood pressure between first-degree relatives. The results suggested that relatives of the same-sex became more alike in blood pressure with increasing age, while those of opposite-sex became less alike. Thus, Miall et af.‘s findings suggest that genetics may play a stronger role in determining blood pressure in very young children, although the specific genetic mechanism is unknown. As children age, environmental factors (e.g. same-sex role models of diet and stress management) may moderate this resemblance and same-sex blood pressure relationships may be more likely observed. If blood pressure in younger children is more genetically determined, perhaps through the X chromosome, this would explain the higher correlations between mother-son pairings in the present study. These results lend some support for a genetic determination of blood pressure in children at younger ages than previously studied. The magnitude of correlations between parents and children in the present study compare favorably with those found in previous studies. Other investigators have found correlations between parent-child blood pressure measurements which ranged from 0.15 to 0.48. [ 10, 12, 161. The present study demonstrated correlations in the range of 0.21 to 0.51. Other investigators have reported small to moderate correlations between same-sex parent-child blood pressure measurements, however, the findings in this study do not suggest such a relationship. Same-sex parent-child correlations did not significantly deviate from zero in the present study except in one instance. One explanation for this finding may be that cdildren in the present study were younger in age than those studied in previous investigations. The regression analyses demonstrated that BMI was consistently a strong predictor of blood pressure in parents and children. In addition, age, physical activity, dietary intake, and parental smoking status, and alcohol use were important predictors of blood pressure measurements. Our results suggested that even after controlling for these factors several pairwise correlations between mothers and sons remained significant. Thus, other factors (e.g. BMI, environmental variables) played a significant role in only weakening some of the associations between parents and children. In summary, these results suggest that both genetic and environmental contributions to
blood pressure status are important in young children. It is important to note that although familial aggregation of blood pressure was observed in the present study, blood pressure remains a multi-faceted phenomenon. A number of other factors which have not been investigated fully in young children were not included in this study. For example, physiological factors such as catecholamines and the renin-angiotensinaldosterone system may exert significant influences on blood pressure. Future investigations of physiological influences may greatly add to our understanding of the development of elevated blood pressure during growth periods in childhood. In addition, studying metabolic measures such as salt sensitivity and membrane sodium transport could further our knowledge regarding the role of genetics in blood pressure regulation. Acknowledgement-This project was funded by the National Institutes of Health (Grant No. HL-36553) to the author Robert C. Klesges, Ph.D.
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(Appendix overleaf)
1.86 3.90 -0.01 0.01 -2.20 1.65 -0.79 58.40
1.13 0.39 0.01 0.01 -0.76 0.24 -1.10 80.52
BMI (kg/m*) Age (Yr) Sodium (mg/24 hr) Potassium (m&24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (k&d )
‘p c 0.05;** < 0.01; *+* < 0.001.
Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
0.66
1.28 1.55 -0.01 0.01 -0.89 -1.06 0.88 79.27
BMI (kg/m*) Age W) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
0.39 1.58 0.01 0.01 1.85 1.52 1.57 11.09
1.34 1.40 11.98
1.43
0.56 1.65 0.01 0.01
2.06 0.01 0.01 I .51 1.81 1.37 15.19
SE
Coefficient
Variables
2
I
2.86** 0.24
1.02 0.39 -0.41 0.16 -0.70 7.26***
1.26 0.19 0.21 0.03 0.61
3.29** 2.36* -0.32 1.00 -1.54 1.23 -0.56 4.88***
1.95, 0.75 -0.09 0.09 -0.57 -0.59 0.64 5.22***
t Value
9.18 0.08
11.43 6.20 0.12 1.17 2.15 1.71 0.37
3.60 0.55 0.01 0.01 0.32 0.34 0.40
% Variance
Year 3
Year
Year
Table Al. Regression models of systolic blood pressure measurements for boys
Regression Models
0.64 0.88 -0.01 0.01 0.49 1.19 -2.12 44.47
0.78 3.26 0.01 -0.01 -1.47 2.50 -0.93 35.24
0.39 1.35 -0.01 0.01 0.81 -1.40 0.03 53.56
Coefficient
0.28 1.11 0.01 0.01 1.30 I .07 1.10 7.78
0.48 1.39 0.01 0.01 1.20 1.13 1.18 10.07
0.01 0.01 1.08 I .25 0.95 10.45
1.42
0.45
SE
tp d 0.10; ?? p < 0.05; **p < 0.01; ***p < 0.001.
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m*) Age W) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
Variables
Year
Year
Year
0.72 0.88 1.78 0.20 0.54 1.22 0.01
3 6.22 0.78 0.15 0.82 0.17 1.51 7.00
2 3.08 6.13 0.24 0.53 1.75 5.52 0.73
I
% Variance
for boys
2.32* 0.80 -0.35 0.82 0.38 1.12 -2.47. 5.71***
1.63t 2.34* 0.45 -0.67 -1.22 2.21* -0.79 3.50”
0.86 0.95 - 1.34 0.45 0.74 -1.12 0.03 5.13***
r Value
Table A2. Regression models of diastolic blood pressure measurements
of Blood Pressure Measurements
APPENDIX
0.67 1.51 0.01 0.01 1.91 1.25 1.29 15.09
0.42 1.I3 0.01 0.01 2.44 1.91 1.72 12.73 0.62 1.75 0.01 0.01 2.64 2.21
1.80 3.12 0.01 -0.01 -2.13 0.13 I .26 61.46
0.09 4.45 0.01 -0.01 -2.16 2.87 1.43 75.84
1.24 1.I3 0.01 0.01 0.23 2.20 0.01 56.22
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity
“p < 0.05; **p < 0.01; ***p < 0.001.
13.52
1.93
SE
Coefficient
Year 3 6.12 1.57 0.94 0.34 0.01 1.59 0.01
Year 2 0.07 8.98 6.52 8.27 1.86 3.26 1.02
1.72 4.14 1.84 2.12 1.34 0.01 I.10
Year I
% Variance
1.99* 0.99 0.76 0.46 0.09 0.99 0.01 4.16***
0.22 2.51* 2.16; - 2.46” -1.13 1.50 0.83 5.96***
2.68** 2.07* 1.27 - 1.37 -1.08 0.10 0.98 4.07**+
r Value
Table A3. Regression models of svstolic blood messme measurements for girls
Variables
0.54 -0.03 0.01 0.01 -0.58 1.29 -0.93 44.88
-0.34 I .37 0.01 -0.01 -0.60 1.92 0.91 53.89
0.51 0.14 -0.01 0.01 -2.79 0.53 0.24 56.74
0.41 1.16 0.01 0.01 1.76 1.47 1.28 8.98
0.32 1.33 0.01 0.01 1.88 1.47 1.33 9.19
0.52 1.17 0.01 0.01 1.53 0.97 1.00 11.75
SE
tp d 0.10; *p < 0.05; **p < 0 .01.7 ***p < 0.001.
BMI (kg/m’) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m’) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
Coefficient
2.70 0.01 0.57 0.16 0.18 1.24 0.85
Year 3
1.62 1.56 5.97 8.50 0.15 2.46 0.70
Year 2
1.11 0.02 0.15 1.25 3.12 0.35 0.06
Year I
% Variance
1.30 -0.03 0.59 0.31 -0.33 0.88 -0.72 5.00***
-1.05 I .03 2.06* - 2.49* -0.32 1.30 0.69 5.50***
0.98 0.12 -0.36 1.04 - 1.82t 0.55 0.24 4.83***
r Value
Table A4. Regression models of diastolic blood pressure measurements for girls Variables
0.25 0.25 0.01 0.01 2.83 2.37 1.77 I .86 1.36 13.16
0.27 0.24 0.01 0.01 3.00 2.37 1.76
I .02
-0.02 0.01 0.01 -0.50 0.27 0.11 -3.27 0.14 99.29
1.25 -0.32 0.01 -0.01 1.08 - 1.38 -1.74 -0.16 1.46 98.99
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept -
?? p < 0.05; ?? *p < 0.01; ***jJ < 0.001.
1.61 14.46
1.92
Year 3 17.11 1.60 1.07 0.07 0.12 0.32 0.92 0.01 0.77
Year 2 14.79 0.01 1.15 0.01 0.03 0.01 0.01 3.19 0.01
466*** -1.31 1.07 -0.27 0.36 -0.58 -0.99 -0.89 0.90 6.85++*
-K+ 0.10 7.54***
4.04**+ -0.09 I .05 0.99 -0.18 0.11
4.32*** -2.19* -0.08 0.53 -0.30 -0.63 0.57 -0.67 1.27 10.27***
activity
61.29
1.01 9.01
1.10
0.25
1.48 1.10 1.19
0.20
0.43 0.87
1.87 0.85
-0.99 -1.05 -0.61 -1.07
Year 3 8.61 0.84 0.40 0.23
Year 2 4.26 15.35 1.66 0.17 0.98 0.01 0.01 2.24 2.04
0.17 0.15 0.01 0.01
0.16 0.16 0.01 0.01 1.79 1.50 1.12 1.18 0.86 8.32
0.13 0.11 0.01 0.01 1.29 1.10 0.84 0.95 0.73 6.60
0.53 0.14 -0.01 0.01
0.33 0.65 0.01 -0.01 - 1.73 0.05 0.03 -1.73 1.20 40.87
0.40 0.13 -0.01 -0.01 -0.13 -1.19 0.37 - 1.12 0.38 62.71
‘p < 0.05; **p < 0.01; ***p < 0.001.
Intercept
Sport
Alcohol Work activity J_eisure activity
Smoke
BMI (kg/m*) Age (Yr) (mg/24 hr) Potassmm Sodium (mg/24 hr)
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Lesiure activity Sport activity Intercept
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
BMI (kg/m*) Age olr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity
10.18 2.82 0.01 0.17 0.05 0.24 0.20 0.27 0.97
-1.86 6.80’+*
-0.51
-0.67 -1.06
0.46
3.15.. 0.94 -0.65 0.49
-0.48 -0.96 0.03 0.03 -1.47 1.40 4.91+**
1.26
2.05* 4.13***
3.20** 1.17 -0.17 -0.36 -0.10 -1.09 0.44 -1.18 0.52 9x***
f Value
0.21 0.19 0.01 0.01 2.13 1.18 1.39 1.56 I .20 10.91
Year I 5.84 0.82 0.02 0.08 0.01 0.71 0.12 0.84 0.16
% Variance
0.89 -0.41 -0.01 0.01 -0.64 -1.15 0.80 -1.05 1.53 112.01
SE
BMI kdm*) AgeW
Coefficient
Variables
Year I
I Value
Table A6. Regression models of diastolic blood pressure measurements for fathers
% Variance
Coefficient
Variables
SE
Table AS. Regression models of systolic blood pressure measurements for fathers
tp < 0.10; ?? p io.05;
BMI (kg/m*) Age (ur) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercent
Sodium (me/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity sport activity Intercept
AgcQ
BMI @g/m*)
BMI (kg/m’) Age (Yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
0.19 0.22 0.01 0.01 2.91 1.97 1.93 1.75 1.99 10.89
1.68 12.68
**p < 0.01; ***p < 0.001.
3.85 0.98 -0.86 -0.24 -0.17 81.81
0.01 -0.01
1.18 0.09
4.59 2.29 2.03
1.43
0.01
4.58 -0.69 -0.65 3.23 0.84 66.77
0.21 0.26 0.01
0.15 0.18 0.01 0.01 2.34 1.53 1.46 1.23 1.22 9.04
-0.01
1.23 0.01 0.01
0.91 0.04 0.01 -0.01 -5.91 3.04 0.33 -0.76 -1.72 90.88
0.02
0.01
Year 3 24.12 0.15 0.78 0.78 1.41 0.20 0.16
Year 2 30.51 0.01 4.05 1.86 1.25 0.12 0.13 6.04 0.31
17.61 0.03 2.24 1.16 3.59 2.25 0.03 0.22 1.15
Year 1
% Variance
-0.14 -0.09 7.51***
-0.44
6.25*** 0.43 0.98 - 1.56 1.33 0.49
5.89*** 0.04 I .83t - 1.22 1.00 -0.30 -0.32 2.25* 0.50 5.27***
-2.53* I.992 0.23 -0.61 - 1.42 10.05**
I .!w - 1.42
6.06*** 0.24
f Value
tp c 0.10; ?? p co.05;
MI (kg/m2) Age (Yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity sport activity Intercept
BMI (kg/m2) Age (Yr) Sodium (mg/U hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
BMI Wm’) Age br) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity sport activity Intercept
0.14 0.16 0.01 0.01 2.13 1.45 1.41 I .28 1.46 7.97
0.14 0.17 0.01 0.01 3.10 1.54 1.37 0.97 1.14 8.55
0.10 0.11 0.01 0.01 I .49 0.98 0.93 0.79 0.78 5.78
SE
**p < 0.01; ?? **p < 0.001.
0.36 0.13 0.01 -0.01 3.79 1.29 1.36 -0.75 0.26 47.80
0.39 0.03 0.01 -0.01 4.96 -0.13 0.36 I .69 1.12 46.11
0.31 0.06 0.01 -0.01 -3.48 1.58 -0.21 -1.09 -1.10 66.06
Coefficient
Year 3 5.38 0.58 1.76 0.75 2.52 0.64 0.75 0.28 0.03
Year 2 8.74 0.03 0.46 0.01 3.14 0.01 0.09 3.71 1.21
Year I 5.59 0.19 0.78 0.52 3.05 1.50 0.03 1.11 1.14
% Variance
2.64+* 0.85 1.48 -0.96 1.78t 0.89 0.97 -0.59 0.17 6.00***
2.75” 0.14 0.61 -0.08 1.60 -0.08 0.26 1.74t 0.98 5.39**+
3.19** 0.57 1.16 -0.94 -2.33* 1.62t -0.23 - 1.39 -1.41 11.42***
f Value
Variables
SE
Variables
coefficient
Table A8. Regression models of diastolic blood pressure measurements for mothers
Table A7. Regression models of systolic blood pressure measurements for mothers
0895-4356/9255.00+ 0.00 Copyright Q 1992Pergamon Press Ltd
A PROSPECTIVE STUDY OF FAMILIAL AGGREGATION OF BLOOD PRESSURE IN YOUNG CHILDREN DAWN K. WILSON,‘* LISAM. KLESGES,~ ROBERTC. KLESGES,~ LINDAH. EcK,~
CATHERINE A. HACKETT-RENNER,’ BRUCES. ALPERT’and EDITH T. DALTON~ Departments of ‘Pediatrics and ‘Biostatistics and Epidemiology, University of Tennessee, Memphis and )Center For Applied Psychological Research, Department of Psychology, Memphis State University, TN, U.S.A. (Received 4 March 195’2)
Abstract-In a prospective study, we evaluated familial aggregation of blood pressure in a sample of 175 normotensive familes with children 3 to 6 yr old. Systolic and diastolic blood pressure measurements of parents and children were correlated at 1, 2, and 3 yr intervals. Spearman rank-order correlation coefficients between parents and children were significant for mother-son pairs. In particular, mother-son blood pressure correlation coefficients were significant for systolic blood pressure across all 3 yr and for diastolic blood pressure during yr 2. Further analyses were performed adjusting for body mass index (BMI), age, physical activity, sodium intake, potassium intake, and parental
smoking status, and alcohol use. The Speannan correlation coefficients for mother-son pairs remained significant for yr I and 3 after adjusting for these blood pressure correlates. These results are consistent with cross-sectional studies and suggest that both genetic and environmental contributions to blood pressure status are important in young children. Blood pressure aggregation
Physical activity
dietary intake
INTRODUCTION
Hypertension currently affects 30% of adults 18 to 74 years of age in the U.S. [I]. Elevated blood pressure levels in children may be a marker for indentifying those who are at risk for developing hypertension in early adulthood [2]. Genetic and environmental contributions to children’s blood pressure status are not fully known. Probably the most consistent predictor of children’s blood pressure is family history of hypertension either in parents or grandparents (viz. familial aggregation of blood pressure) [3-II]. Further evidence that genetic factors are predictive of blood pressure is demonstrated by studies in *All correspondence to be addressed to: Dr D. K. Wilson, University of Tennessee, The Health Science Center, Le Bonheur Childrens Center, 848 Adams Ave, Memphis, TN 38103, U.S.A. 959
Children
Familial
which parent-biologic child correlations are significantly greater than zero, while parentadoptee correlations are negligible [12, 131. There has been a lack of consistency in the literature concerning patterns of familial transmission of blood pressure among normotensives. Some studies have demonstrated that children of either sex resemble mothers and fathers equally [9, 121.In contrast, other investigators have found that only same-sex parentchild relationships were significantly correlated [15,16]. Still other researchers have reported that opposite-sex parent-child correlations were of greater magnitude than same-sex relationships [4, 10, 171.A number of investigators have also examined the contribution of environmental factors in familial aggregation of blood pressure among parents and offspring (e.g. [3,7, 151. Voors et al. [3] examined the role of
DAWN K. WILSONet al.
960
demographic, anthropometric, and dietary variables (e.g. cholesterol, trigylcerides) and demonstrated that only anthropometric variables were significant predictors of blood pressure variability among family members. Havlik et al. [7] found the strongest predictors of blood pressure among both men and women in the Framingham Heart Study were weight, heart rate, total serum proteins, and alcohol consumption, respectively. To understand more fully the nature of blood pressure correlates among parents and children, both genetic and environmental factors were incorporated into our study design. A longitudinal design was implemented and only children within a narrow age range (3-6 yr) were included in the study. Thus, one purpose of the present study was to determine the degree of familial aggregation of blood pressure over a 3 yr period in young children. A second purpose of the study was to examine the role of environmental factors in contributing to the familial aggregation of blood pressure in this population.
METHODS
Recruitment
Participants were recruited through local pediatricians, day-care centers, and churches in Memphis, TN. Parents who expressed interest in the study were contacted by telephone. Criteria for inclusion in the study included that the child participant: (1) was the natural, biologic offspring of his/her parents; (2) had no physical handicap or condition that could effect blood pressure, dietary intake, or physical activity; (3) had parents who were married; (4) had parents without hypertension or cardiovascular disease and; (5) had a family whose plans were to stay in the metropolitan area in the coming year. *Two of the families had more than one child aged 3-6 yr. For the purpose of this paper only one child was chosen at random to be included in the data analyses. TThere is overlap between mothers and fathers who were eliminated for these exclusionary criteria. For example, in some families both the mother and the father were diagnosed as hypertensive, and therefore eliminated, while in other families only one parent may have been diagnosed as hypertensive, and therefore eliminated. $The reduced sample size for mothers during yr 2 was primarily a result of missing data on the physical activity questionnaire. Due to technical error this missing information was not detected until well after the yr 2 assessment occurred.
Table 1. Sample exclusion and missing data across 3 yr of study Year 1 Year 2 Available families Fathers-BP medication Fathers-hypertensive Mothers-BP medication Mothers-hypertensive Mothers-pregnant Children-missing data Fathers-missing data Mothers-missing data
216 14 20 14 5 8 12 12 9
Year 3
180 12 14 12 2 3 13 38 66
160 12 I 5 2 7 2 5 7
Subjects
The initial sample of subjects were 216 Caucasian families with one child aged 3-6 yr.* Only parents who were not classified as hypertensive according to baseline blood pressure readings, who were not currently taking medications, who had no medical history of cardiovascular disease, and who were not pregnant were included in the data analyses (see Table I).? Hypertension was defined as a systolic blood pressure 2 160 mmHg and/or a diastolic blood pressure 290 mmHg. A total of 36 families dropped out of the study during yr 2 and an additional 20 families dropped during yr 3 (see Table 1 for available families each yr). Subjects lost to attrition had similar systolic and diastolic blood pressures as those who stayed in the study (p > 0.05, for both). The remaining sample size for each group of family members is presented in Table 2.$ Measures Blood pressure. Volunteers who met the inclusion criteria were scheduled for a laboratory visit. In the laboratory, subjects provided demographic and background information and then participated in the blood pressure assessment. Blood pressure measurements were recorded during the laboratory visit. Mother, father, and child were seated with the technician in a quiet room. Subjects remained seated for 5 min before blood pressure measurements were taken. Blood pressures were obtained with a Dinamap 1846SX monitor, positioned so that the readout was not visible to the subject. This Table 2. Number of subjects reflected in data analyses across 3 yr of study Boys Girls Fathers Mothers
Year 1
Year 2
Year 3
110 94 175 182
92 75 104 89
89 69 115 133
Blood PressureCorrelates blood pressure apparatus was used to eliminate terminal digit bias and inter-observer differences. Each subject was seated in a relaxed position with legs uncrossed. Care was taken to select the proper size cuff which was then applied firmly to the left arm with the bladder centered over the brachial artery. The cuff was unconstricted by clothing and the subject’s arm was placed on a table at the level of the heart in a comfortable posture. No talking was permitted during blood pressure measurement. Systolic and diastolic blood pressures were recorded four times, as recommended by the American Heart Association [ 181. Readings were taken at 1-min intervals by a carefully trained technician. The first reading was discarded, and the average of the second, third, and fourth readings was used in the data analyses. Physical activity. Parents were administered the Baecke physical activity questionnaire [ 191, a factor analyzed scale comprising 16 items that represent physical activity at work, during sports, and during nonsport leisure-time. These items yield scores ranging from 1 (highly sedentary) to 5 (highly active). The Baecke questionnaire has been used extensively in recent years and has demonstrated an acceptable degree of reliability and construct validity [20,21]. Previous research has demonstrated that the test-retest reliabilities of the work, sport, and leisure-time subscales were 0.88, 0.81 and 0.74, respectively [19]. In terms of construct validity, level of education was inversely associated with the work index (r = -0.56, males; r = -0.25, females, p < 0.001 for both) and positively associated with the leisure-time index in both males and females (r = 0.38, r = 0.34, respectively, p < 0.001 for both); and lean body mass was positively related to the work index in males (/I = 1.36, p < O.OOl),but was not related to the leisure-time index in either sex [19]. Parents assessed their child’s physical activity level with the Energy Balance Questionnaire [22]. This questionnaire contains a series of three questions that ask parents to rate their child’s structured, leisure, and aerobic activities (e.g. stairs climbed per week, minutes of exercise that makes one sweat or become winded) as compared with other children their child’s age and sex. Epidemiologists have suggested that physical activity is most reliably assessed with simple, single-item questions that measure vigorous and aerobic activities such as those contained in the Energy Balance Questionnaire [23].
961
These investigators have also advocated that assessing physical activity in children relative to age- and sex-matched peers improves upon the accuracy of these measures [23]. Dietary intake. Dietary intake was assessed with the Willett’s Food Frequency Questionnaire (FFQ). This questionnaire assesses intake over a previous I-yr period [24,25] and has been modified for use with children [26-281. Previous research has shown that the FFQ was positively correlated (r = 0.60, n = 27) with a diet record which was completed during a I-yr period [24]. For the present study, each parent individually completed their own FFQ. Both parents and their child worked together to complete the child’s FFQ in the laboratory where food models were available to assist in determining serving sizes. Trained nutritionists were available if parents had questions. Questionnaires were checked for completeness so that missing data could be minimized. The questionnaires were analyzed for sodium and potassium intake with a computer program designed to allow for adjustment of serving sizes. Smoking and alcohol use. Questions designed to assess tobacco and alcohol use in adults were obtained from the Health and Daily Living Questionnaire [29,30]. All parents were asked to indicate with a yes or no answer whether they currently smoked tobacco (cigarettes, cigars, or pipes). They were also asked to indicate with a yes or no answer if they currently drank any alcoholic beverages (wine, beer, or liquor). Design and analyses
Blood pressure measurements were obtained at 1-yr intervals over a 3 yr time period. Blood pressure data were analyzed using a multiple regression of residuals approach. The major dependent variables of interest were systolic and diastolic blood pressure. The major independent variables (control variables) of interest were body mass index (BMI), age, physical activity, sodium intake, potassium intake, and parental smoking status, and alcohol use. The smoking and alcohol use questions were coded as 0 = no and 1 = yes. For each year, hierarchical linear regression models were analyzed separately for boys, girls, fathers, and mothers and the residuals for each were calculated. These residuals reflect blood pressures adjusted for the independent variables and therefore represent the component of blood pressure not explained by these factors. The normality of residuals was scrutinized for all models. The data were
DAWN K. WILSONet al.
962
Table 3. Baseline means (&SD) and frequencies of sample measures and characteristics Variables BMI (kg/m’) Age (Jr) Systolic BP (mmHg) Diastolic BP (mmHg) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Work activity Leisure activity Sport activity Smoke (X yes) Alcohol (% yes)
Boys
Girls
Fathers
Mothers
16.1 -+ 1.4 4.5 f 0.5 103.0 f 9.0 61.5 + 6.2 1780.4 f 563.3 2891.5 & 900.0 3.2 + 0.7 3.3 f 0.7 3.0 & 0.8 -
16.0 f 1.2 4.4 f 0.5 101.8 f 7.9 61.5 f 5.9 1627.1 f 503.3 2635.8 f 870.1 3.2 & 0.5 3.1 f 0.7 3.0 f 0.7 -
26.9 f 4.2 34.3 f 4.8 126.6 f 11.9 75.3 f 6.8 1816.2 f 687.5 2984.4 f 1034.6 2.6 f 0.7 2.3 f 0.6 2.6 f 0.7 24% 66%
24.4 f 4.8 32.8 & 4.0 112.9 f 10.6 7l.Ok6.3 1781.5 + 621.1 2846.2 f 1006.6 2.8 + 0.5 2.5 + 0.6 2.1 f 0.6 11% 64%
Table 4. Spearman rank-order correlations of blood pressure measurements for parents and children Son DBP Time Year 1 Year 2 Year 3
Daughter DBP
Mother DBP
Father DBP
0.18 0.24* 0.19
0.13 0.05 0.18
Mother DBP -0.02 0.11 0.15
Son SBP
Father DBP
Mother SBP
0.21’ 0.03 0.25
0.32*** 0.34** 0.49**+
Father SBP 0.12 0.05 -0.03
Daughter SBP Mother SBP
Father SBP
0.02 0.10 0.23
0.08 0.03 0.12
SBP = systolic blood pressure (BP); DBP = diastolic BP. **p < 0.001. ‘p < 0.05; **p < 0.01; ??
generally normally distributed except for several outlying observations. Specifically, there was 1 standardized residual for fathers, 1 for mothers, 1 for girls, and 3 for boys that were outside a range of + 3 SD (none exceeded + 4 SD). These data were checked for coding errors but none were discovered so they appeared to be reasonable observations. Thus, a nonparametric Spearman rank-order correlation was used to reduce the influence of these outliers in the analyses. Spearman rank-order correlation coefficients were calculated for both unadjusted and adjusted (residual) blood pressures for mother-daughter, mother-son, fatherdaughter, and father-son pairings. RESULTS
Baseline measures and sample characteristics are presented in Table 3.*
*The average levels of potassium obtained from the FFQ in parents are consistent with the average level previously reported by Willett et al. [25] i.e. 3076 mg/24 hr. The average levels of sodium obtained from the FFQ in parents was somewhat lower than levels previously reported in adults [31] i.e. 4025 mg/24 hr (as reflected by urinary sodium excretion). These parents appeared to be very health conscious and could have consumed less sodium than the average person because of being aware of the relationship between sodium intake and high blood pressure.
Spearman rank-order correlation coefficients were performed to assess the degree of association between parents’ and childrens’ unadjusted blood pressure measurements (Table 4). The results demonstrated that mother-son correlation coefficients were significant for systolic blood pressure across all 3 yr and for diastolic blood pressure during yr 2. For fatherdaughter pairs, the correlation coefficient was significant for diastolic blood pressure during yr 1. Correlation coefficients were not significant for any of the remaining comparisons. The blood pressure regression equations for boys and girls are given in Appendix A. For boys, the regression models for systolic blood pressure were significant for yr 2 [F(7,102) = 0.84, p < 0.56; F(7,84) = 3.60, p < 0.01; F(7,81) = 1.88, p < 0.08, for yr 1, 2, and 3, respectively]; and for diastolic blood pressure were not significant for any of the 3 yr [F(7,102) = 1.09, p < 0.38; F(7,84) = 1.96, p =0.07; F(7,81) = 1.81, p ~0.10, for yr 1, 2, and 3, respectively]. These regression models ranged from 5.4 to 23.1% in accounting for the total variability in blood pressure. For girls, the regression models for systolic blood pressure were significant for yr 1 and 2 [F(7,86) = 2.45, p < 0.03; F(7,67) = 2.81, p ~0.02; F(7,61)= 1.81, p ~0.11, for yr 1, 2,
Blood Pressure Correlates
963
Table 5. Spearman rank-order correlations of residuals of blood pressure measurements for parents and children Daughter DBP
Son DBP Time Year 1 Year 2 Year 3
Mother DBP 0.19 0.22 0.12
Mother DBP
Father DBP
0.01 -0.02 0.14
0.18 0.01 0.24
Father DBP 0.07 -0.15 0.08
Son SBP Mother SBP 0.34*** 0.13 0.40***
Daughter SBP
Father SBP
Mother SBP
0.16 0.32* -0.06
0.12 0.07 0.22
Father SBP 0.04 0.51*** 0.17
For abbreviations see legend to Table 4. *p < 0.05; **p < 0.01; ***p < 0.001.
and 3, respectively]; and for diastolic blood pressure were not significant for any of the 3 yr [F(7,86) = 0.86, p c 0.54; F(7,67) = 1.78, p < 0.11; F(7,61) = 0.65, p < 0.72, for yr 1, 2 and 3, respectively]. These regression models ranged from 6.6 to 22.7% in accounting for the total variability in blood pressure. The single most significant factor in the regression models for children was BMI, however, age, and dietary intake were also significant factors in some of the models. The blood pressure regression equations for fathers and mothers are also presented in Appendix A. For fathers, the regression models for systolic blood pressure were significant for all 3 yr [F(9,165) = 3.29, p < 0.01; F(9,94) = 3.49, p
Table 5 presents the Spearman rank-order correlation coefficients for residual blood pressures between parents and children after adjusting for variables in the regression equations. The mother-son correlation coefficients for systolic blood pressure remained significant during yr 1 and 3. For yr 2, the mother-son correlation coefficients were no longer significant for either systolic or diastolic blood pressure. Instead, fathers demonstrated significant positive correlations for systolic blood pressure with both daughters and sons. Correlation coefficients were not significant for any of the remaining comparisons.
DISCUSSION
The results of this study indicate that familial aggregation of blood pressure occurs in children at very young ages. Specifically, positive relationships were obtained between mothers and sons for systolic blood pressure across all 3 yr and for diastolic blood pressure during yr 2. Fathers were significantly related to daughters for diastolic blood pressure during yr 2. After statistically adjusting for other important variables, the correlation coefficients remained significant for mother-son pairs during yr 1 and 3, and, fathers demonstrated significant correlations with both sons and daughters for systolic blood pressure during yr 2. Overall, these findings suggest that blood pressure aggregates most strongly between mother-son pairs, while appearing less stable for father-child pairings.* Although familial aggregation of blood pressure is well-documented, the nature of the relationship remains unclear. The findings in the present study are, in part, consistent with the results of other studies [lo] that also investigated familial aggregation of blood pressure in young children (ages 5 and older). Miall et al. [lo] demonstrated higher regression coefficients of systolic and diastolic blood pressure for opposite-sex relatives (e.g. mother-son) than for same-sex relatives. Miall et al. also evaluated the
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DAWNK. WIUON er al.
effects of age on the resemblance of blood pressure between first-degree relatives. The results suggested that relatives of the same-sex became more alike in blood pressure with increasing age, while those of opposite-sex became less alike. Thus, Miall et af.‘s findings suggest that genetics may play a stronger role in determining blood pressure in very young children, although the specific genetic mechanism is unknown. As children age, environmental factors (e.g. same-sex role models of diet and stress management) may moderate this resemblance and same-sex blood pressure relationships may be more likely observed. If blood pressure in younger children is more genetically determined, perhaps through the X chromosome, this would explain the higher correlations between mother-son pairings in the present study. These results lend some support for a genetic determination of blood pressure in children at younger ages than previously studied. The magnitude of correlations between parents and children in the present study compare favorably with those found in previous studies. Other investigators have found correlations between parent-child blood pressure measurements which ranged from 0.15 to 0.48. [ 10, 12, 161. The present study demonstrated correlations in the range of 0.21 to 0.51. Other investigators have reported small to moderate correlations between same-sex parent-child blood pressure measurements, however, the findings in this study do not suggest such a relationship. Same-sex parent-child correlations did not significantly deviate from zero in the present study except in one instance. One explanation for this finding may be that cdildren in the present study were younger in age than those studied in previous investigations. The regression analyses demonstrated that BMI was consistently a strong predictor of blood pressure in parents and children. In addition, age, physical activity, dietary intake, and parental smoking status, and alcohol use were important predictors of blood pressure measurements. Our results suggested that even after controlling for these factors several pairwise correlations between mothers and sons remained significant. Thus, other factors (e.g. BMI, environmental variables) played a significant role in only weakening some of the associations between parents and children. In summary, these results suggest that both genetic and environmental contributions to
blood pressure status are important in young children. It is important to note that although familial aggregation of blood pressure was observed in the present study, blood pressure remains a multi-faceted phenomenon. A number of other factors which have not been investigated fully in young children were not included in this study. For example, physiological factors such as catecholamines and the renin-angiotensinaldosterone system may exert significant influences on blood pressure. Future investigations of physiological influences may greatly add to our understanding of the development of elevated blood pressure during growth periods in childhood. In addition, studying metabolic measures such as salt sensitivity and membrane sodium transport could further our knowledge regarding the role of genetics in blood pressure regulation. Acknowledgement-This project was funded by the National Institutes of Health (Grant No. HL-36553) to the author Robert C. Klesges, Ph.D.
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Voors AW, Webber LS, Berenson GS. Time course studies of blood pressure in children-the Bogalusa heart study. Am J Epidemiol 1979; 109: 320-334. Bengtsson B, Thulin T, Schersten B. Familial resemblance in casual blood pressure-a maternal effect? Clin Sci 1979; 57: 279-281. Clark WR, Schrott HB, Burns TL, Sing CF. Lauer RM. Aggregation of blood pressure in the families of children with labile high systolic blood pressure: the Muscatine study. Am J Epidemiol 1986; 123: 67-80. Conner SL, Conner WE, Henry H, Sexton G, Keenan EJ. The effects of familial relationships, age, body weight, and diet on blood pressure and the 24 hour urinary excretion of sodium, potassium and creatinine in men, women, and child& of randomly selected families. Circulation 1984: 70: 76-85. Havlik RJ, Garrison RJ; Feinleib M, Kannel WB, Castelli WP, McNamara PM. Blood pressure aggregation in families. Am J bidemiol 1979: 110: 3Ok312. hayes CB, Tyroler HA, &se1 JC. Family aggregation of blood pressure in Evans County, Georgia. Arch Intern Med 1971; 128: 965-975. Johnson BC, Epstein FH, Kjelsberg MO. Distributions and familial studies of blood pressure and serum cholesterol levels in a total communitvTecumseh, Michigan. J Chron Dis 1965; 18: 147-i60. Miall WE. Heneage P. Khosla T. Love11HG. Moore F. Factors influencing the degree of resemblance in arterial pressure of close relatives. Clin Sci 1967; 33: 271-283.
Blood Pressure Correlates 11. Miall WE, Oldham PD. The hereditary factor in arterial blood pressure. Br Med J 1963; I: 75-80. 12. Tseng WP. Blood pressure and hypertension in an agricultural and a fishing population in Taiwan. Am J Epidemiol 1967; 86: 513-525. 13. Biron P, Mongeau JG. Familial aggregation of blood pressure and its components. Pediatr Clin North Am 1978; 25: 29-33. 14. Biron P, Mongeau, JG, Bertrand D. Familial aggregation of blood pressure in adopted and natural children. In: Oglesby P, Ed. Epidemiology and Control 01 Hypertension. New York: Stratton; 1975: 397-403. 15. Patterson TL, Kaplan RM, Sallis JF, Nader PR. Aggregation of blood pressure in Anglo-American and Mexican-American families. Prev MerJ 1987; 16: 616-625. 16. Staessen J, Bulpitt CJ, Fagard R, Joosens JW, Lijnen P, Amery A. Familial aggregation of blood pressure, anthropometric characteristics and urinary excretion of sodium and potassium-a population study in two Belgian towns. J Chron Dis 1985; 38: 3977407. 17. Beaglehole R, Salmond CE, Prior IAM. Blood pressure studies in Polynesian children. In: Oglesby P, Ed. Epidemiology and Control of Hypertension. New York: Stratton; 1975: 407-417. 18. Forhlich ED, Grim C, Labarthe DR, Maxwell MH, Perloff D, Weidman WH. Recommendations for Human Blood Pressure Determination by Sphygmomanometers: Report of a Special Task Force Appointed by the Steering Committee, American Heart Association. American Heart Association National Center, Dallas; 1987. 19. Baecke JA, Burema J, Fritters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 1982; 36: 932-942. 20. Washburn RA, Montoye HJ. The assessment of physical activity by questionnaire. Am J Epidemiol 1986; 123: 563-576. 21. Baecke JA, van Stavem WA, Burema J. Food consumption, habitual physical activity, and body fatness in young Dutch adults. Ant J Clin Nutr 1983; 37: 278-286.
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Klesges RC, Haddock CK, Eck LH. A multimethod approach to the measurement of childhood physical activity and its relationship to blood pressure and body weight. J Pediatr 1990; 116: 888-893. LaPorte RE, Adams LL, Savage DD, Brenes G, Dearwater S, Clark T. The spectrum of physical activity, cardiovascular disease and health: an epidemiological perspective. Am J Epidemiol 1984; 120: 507-517. Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH, Speizer FE. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 1985; 122: 51-65. Willett WC. Revnolds RD. Cottrell-Hoehner S. Sampson L,’ Browne ML. Validation of a semi: quantitative food frequency questionnaire: Comparison with a l-year diet record. J Am Diet Assoc 1987; 87: 43-47. Treiber FA, Leonard SB, Frank G, Musante L, Davis H, Strong WB, Levy M. Dietary assessment instruments for preschool children: reliability of parental responses to the 24-hour recall and a food frequency questionnaire. J Am Diet Assoc 1990; 90: 814-820. Eck LH, Klesges RC, Hanson CL, White J. Reporting retrospective dietary intake by food frequency questionnaire. J Am Diet Assoc 1991; 91: 606-608. Gutin B, Basch C, Shea S, Content0 I, DeLozier M, Rips J, Irigoyen M, Zybert P. Blood pressure, fitness, and fatness in 5- and 6-year-old children. JAMA 1990; 264: 1123-l 127. Holahan CJ, Moos RH. Personality, coping, and family resources in stress resistance: a longitudinal analysis. J Pers Sot Psycho1 1986; 51: 389-395. Moos RH, Cronkite RC, Billings AC, Finney JW. Health and daily living form manual. Social Ecology Laboratory, Department of Psychiatry and Behavioral Sciences, Standford University, School of Medicine, CA; 1982. INTERSALT Cooperative Research Group. INTERSALT: and international study of electrolyte excretion and blood pressure. Results for 24 hour urinary sodium and potassium excretion. Br Med J 1988; 297: 319-328.
(Appendix overleaf)
1.86 3.90 -0.01 0.01 -2.20 1.65 -0.79 58.40
1.13 0.39 0.01 0.01 -0.76 0.24 -1.10 80.52
BMI (kg/m*) Age (Yr) Sodium (mg/24 hr) Potassium (m&24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (k&d )
‘p c 0.05;** < 0.01; *+* < 0.001.
Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
0.66
1.28 1.55 -0.01 0.01 -0.89 -1.06 0.88 79.27
BMI (kg/m*) Age W) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
0.39 1.58 0.01 0.01 1.85 1.52 1.57 11.09
1.34 1.40 11.98
1.43
0.56 1.65 0.01 0.01
2.06 0.01 0.01 I .51 1.81 1.37 15.19
SE
Coefficient
Variables
2
I
2.86** 0.24
1.02 0.39 -0.41 0.16 -0.70 7.26***
1.26 0.19 0.21 0.03 0.61
3.29** 2.36* -0.32 1.00 -1.54 1.23 -0.56 4.88***
1.95, 0.75 -0.09 0.09 -0.57 -0.59 0.64 5.22***
t Value
9.18 0.08
11.43 6.20 0.12 1.17 2.15 1.71 0.37
3.60 0.55 0.01 0.01 0.32 0.34 0.40
% Variance
Year 3
Year
Year
Table Al. Regression models of systolic blood pressure measurements for boys
Regression Models
0.64 0.88 -0.01 0.01 0.49 1.19 -2.12 44.47
0.78 3.26 0.01 -0.01 -1.47 2.50 -0.93 35.24
0.39 1.35 -0.01 0.01 0.81 -1.40 0.03 53.56
Coefficient
0.28 1.11 0.01 0.01 1.30 I .07 1.10 7.78
0.48 1.39 0.01 0.01 1.20 1.13 1.18 10.07
0.01 0.01 1.08 I .25 0.95 10.45
1.42
0.45
SE
tp d 0.10; ?? p < 0.05; **p < 0.01; ***p < 0.001.
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m*) Age W) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
Variables
Year
Year
Year
0.72 0.88 1.78 0.20 0.54 1.22 0.01
3 6.22 0.78 0.15 0.82 0.17 1.51 7.00
2 3.08 6.13 0.24 0.53 1.75 5.52 0.73
I
% Variance
for boys
2.32* 0.80 -0.35 0.82 0.38 1.12 -2.47. 5.71***
1.63t 2.34* 0.45 -0.67 -1.22 2.21* -0.79 3.50”
0.86 0.95 - 1.34 0.45 0.74 -1.12 0.03 5.13***
r Value
Table A2. Regression models of diastolic blood pressure measurements
of Blood Pressure Measurements
APPENDIX
0.67 1.51 0.01 0.01 1.91 1.25 1.29 15.09
0.42 1.I3 0.01 0.01 2.44 1.91 1.72 12.73 0.62 1.75 0.01 0.01 2.64 2.21
1.80 3.12 0.01 -0.01 -2.13 0.13 I .26 61.46
0.09 4.45 0.01 -0.01 -2.16 2.87 1.43 75.84
1.24 1.I3 0.01 0.01 0.23 2.20 0.01 56.22
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity
“p < 0.05; **p < 0.01; ***p < 0.001.
13.52
1.93
SE
Coefficient
Year 3 6.12 1.57 0.94 0.34 0.01 1.59 0.01
Year 2 0.07 8.98 6.52 8.27 1.86 3.26 1.02
1.72 4.14 1.84 2.12 1.34 0.01 I.10
Year I
% Variance
1.99* 0.99 0.76 0.46 0.09 0.99 0.01 4.16***
0.22 2.51* 2.16; - 2.46” -1.13 1.50 0.83 5.96***
2.68** 2.07* 1.27 - 1.37 -1.08 0.10 0.98 4.07**+
r Value
Table A3. Regression models of svstolic blood messme measurements for girls
Variables
0.54 -0.03 0.01 0.01 -0.58 1.29 -0.93 44.88
-0.34 I .37 0.01 -0.01 -0.60 1.92 0.91 53.89
0.51 0.14 -0.01 0.01 -2.79 0.53 0.24 56.74
0.41 1.16 0.01 0.01 1.76 1.47 1.28 8.98
0.32 1.33 0.01 0.01 1.88 1.47 1.33 9.19
0.52 1.17 0.01 0.01 1.53 0.97 1.00 11.75
SE
tp d 0.10; *p < 0.05; **p < 0 .01.7 ***p < 0.001.
BMI (kg/m’) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m’) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
BMI (kg/m2) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Structured activity Leisure activity Aerobic activity Intercept
Coefficient
2.70 0.01 0.57 0.16 0.18 1.24 0.85
Year 3
1.62 1.56 5.97 8.50 0.15 2.46 0.70
Year 2
1.11 0.02 0.15 1.25 3.12 0.35 0.06
Year I
% Variance
1.30 -0.03 0.59 0.31 -0.33 0.88 -0.72 5.00***
-1.05 I .03 2.06* - 2.49* -0.32 1.30 0.69 5.50***
0.98 0.12 -0.36 1.04 - 1.82t 0.55 0.24 4.83***
r Value
Table A4. Regression models of diastolic blood pressure measurements for girls Variables
0.25 0.25 0.01 0.01 2.83 2.37 1.77 I .86 1.36 13.16
0.27 0.24 0.01 0.01 3.00 2.37 1.76
I .02
-0.02 0.01 0.01 -0.50 0.27 0.11 -3.27 0.14 99.29
1.25 -0.32 0.01 -0.01 1.08 - 1.38 -1.74 -0.16 1.46 98.99
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept -
?? p < 0.05; ?? *p < 0.01; ***jJ < 0.001.
1.61 14.46
1.92
Year 3 17.11 1.60 1.07 0.07 0.12 0.32 0.92 0.01 0.77
Year 2 14.79 0.01 1.15 0.01 0.03 0.01 0.01 3.19 0.01
466*** -1.31 1.07 -0.27 0.36 -0.58 -0.99 -0.89 0.90 6.85++*
-K+ 0.10 7.54***
4.04**+ -0.09 I .05 0.99 -0.18 0.11
4.32*** -2.19* -0.08 0.53 -0.30 -0.63 0.57 -0.67 1.27 10.27***
activity
61.29
1.01 9.01
1.10
0.25
1.48 1.10 1.19
0.20
0.43 0.87
1.87 0.85
-0.99 -1.05 -0.61 -1.07
Year 3 8.61 0.84 0.40 0.23
Year 2 4.26 15.35 1.66 0.17 0.98 0.01 0.01 2.24 2.04
0.17 0.15 0.01 0.01
0.16 0.16 0.01 0.01 1.79 1.50 1.12 1.18 0.86 8.32
0.13 0.11 0.01 0.01 1.29 1.10 0.84 0.95 0.73 6.60
0.53 0.14 -0.01 0.01
0.33 0.65 0.01 -0.01 - 1.73 0.05 0.03 -1.73 1.20 40.87
0.40 0.13 -0.01 -0.01 -0.13 -1.19 0.37 - 1.12 0.38 62.71
‘p < 0.05; **p < 0.01; ***p < 0.001.
Intercept
Sport
Alcohol Work activity J_eisure activity
Smoke
BMI (kg/m*) Age (Yr) (mg/24 hr) Potassmm Sodium (mg/24 hr)
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Lesiure activity Sport activity Intercept
BMI (kg/m*) Age (yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
BMI (kg/m*) Age olr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity
10.18 2.82 0.01 0.17 0.05 0.24 0.20 0.27 0.97
-1.86 6.80’+*
-0.51
-0.67 -1.06
0.46
3.15.. 0.94 -0.65 0.49
-0.48 -0.96 0.03 0.03 -1.47 1.40 4.91+**
1.26
2.05* 4.13***
3.20** 1.17 -0.17 -0.36 -0.10 -1.09 0.44 -1.18 0.52 9x***
f Value
0.21 0.19 0.01 0.01 2.13 1.18 1.39 1.56 I .20 10.91
Year I 5.84 0.82 0.02 0.08 0.01 0.71 0.12 0.84 0.16
% Variance
0.89 -0.41 -0.01 0.01 -0.64 -1.15 0.80 -1.05 1.53 112.01
SE
BMI kdm*) AgeW
Coefficient
Variables
Year I
I Value
Table A6. Regression models of diastolic blood pressure measurements for fathers
% Variance
Coefficient
Variables
SE
Table AS. Regression models of systolic blood pressure measurements for fathers
tp < 0.10; ?? p io.05;
BMI (kg/m*) Age (ur) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercent
Sodium (me/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity sport activity Intercept
AgcQ
BMI @g/m*)
BMI (kg/m’) Age (Yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
0.19 0.22 0.01 0.01 2.91 1.97 1.93 1.75 1.99 10.89
1.68 12.68
**p < 0.01; ***p < 0.001.
3.85 0.98 -0.86 -0.24 -0.17 81.81
0.01 -0.01
1.18 0.09
4.59 2.29 2.03
1.43
0.01
4.58 -0.69 -0.65 3.23 0.84 66.77
0.21 0.26 0.01
0.15 0.18 0.01 0.01 2.34 1.53 1.46 1.23 1.22 9.04
-0.01
1.23 0.01 0.01
0.91 0.04 0.01 -0.01 -5.91 3.04 0.33 -0.76 -1.72 90.88
0.02
0.01
Year 3 24.12 0.15 0.78 0.78 1.41 0.20 0.16
Year 2 30.51 0.01 4.05 1.86 1.25 0.12 0.13 6.04 0.31
17.61 0.03 2.24 1.16 3.59 2.25 0.03 0.22 1.15
Year 1
% Variance
-0.14 -0.09 7.51***
-0.44
6.25*** 0.43 0.98 - 1.56 1.33 0.49
5.89*** 0.04 I .83t - 1.22 1.00 -0.30 -0.32 2.25* 0.50 5.27***
-2.53* I.992 0.23 -0.61 - 1.42 10.05**
I .!w - 1.42
6.06*** 0.24
f Value
tp c 0.10; ?? p co.05;
MI (kg/m2) Age (Yr) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity sport activity Intercept
BMI (kg/m2) Age (Yr) Sodium (mg/U hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity Sport activity Intercept
BMI Wm’) Age br) Sodium (mg/24 hr) Potassium (mg/24 hr) Smoke Alcohol Work activity Leisure activity sport activity Intercept
0.14 0.16 0.01 0.01 2.13 1.45 1.41 I .28 1.46 7.97
0.14 0.17 0.01 0.01 3.10 1.54 1.37 0.97 1.14 8.55
0.10 0.11 0.01 0.01 I .49 0.98 0.93 0.79 0.78 5.78
SE
**p < 0.01; ?? **p < 0.001.
0.36 0.13 0.01 -0.01 3.79 1.29 1.36 -0.75 0.26 47.80
0.39 0.03 0.01 -0.01 4.96 -0.13 0.36 I .69 1.12 46.11
0.31 0.06 0.01 -0.01 -3.48 1.58 -0.21 -1.09 -1.10 66.06
Coefficient
Year 3 5.38 0.58 1.76 0.75 2.52 0.64 0.75 0.28 0.03
Year 2 8.74 0.03 0.46 0.01 3.14 0.01 0.09 3.71 1.21
Year I 5.59 0.19 0.78 0.52 3.05 1.50 0.03 1.11 1.14
% Variance
2.64+* 0.85 1.48 -0.96 1.78t 0.89 0.97 -0.59 0.17 6.00***
2.75” 0.14 0.61 -0.08 1.60 -0.08 0.26 1.74t 0.98 5.39**+
3.19** 0.57 1.16 -0.94 -2.33* 1.62t -0.23 - 1.39 -1.41 11.42***
f Value
Variables
SE
Variables
coefficient
Table A8. Regression models of diastolic blood pressure measurements for mothers
Table A7. Regression models of systolic blood pressure measurements for mothers