Tracking of Cardiovascular Risk Factors

The Tromsø Study, 1979–1995

Tom Wilsgaard1, Bjarne K. Jacobsen1, Henrik Schirmer1, Inger Thune1, Maja-Lisa Løchen1, Inger Njølstad1 and Egil Arnesen1

1 From the Department of Epidemiology and Medical Statistics, Institute of Community Medicine, University of Tromsø, Tromsø, Norway.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 
Tracking of cardiovascular risk factors (blood pressure, body mass index (BMI), and serum lipids) has not been studied much in a general, adult population. No known study has compared tracking of these factors for both sexes. In the present study, 17,710 men and women aged 20–61 years at baseline attended two or three population-based health surveys in Tromsø, Norway, over 16 years (between 1979–1980 and 1994–1995). Tracking coefficients were estimated by using different methods, and possible predictors of tracking were found. There was a high degree of tracking for BMI (overall tracking coefficients: 0.85 for men, 0.80 for women). Relatively high (or moderate) tracking was found for systolic blood pressure (respective sex-specific coefficients: 0.52, 0.54), diastolic blood pressure (0.48, 0.48), high density lipoprotein cholesterol (0.55, 0.64), and total cholesterol (0.77, 0.65). The lowest coefficients were for triglycerides (0.43, 0.39). Analysis of tracking in the upper sextile confirmed these results. Although some baseline predictors were associated with tracking, the effects were relatively weak. When predictors for tracking in the upper sextile were assessed, significant associations were found with relatively strong effects. No major sex differences were observed in tracking. However, women were more likely than men to remain in the upper sextile of systolic and diastolic blood pressures and of BMI.

blood pressure; body mass index; cohort studies; lipids

Abbreviations: BMI, body mass index; GEE, generalized estimating equations; HDL, high density lipoprotein


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 
Biologic and lifestyle variables such as serum lipids, blood pressure, smoking habits, and body weight are all risk factors for cardiovascular diseases (1GoGoGo–4Go). If one assessment was representative of the long-term level of these risk factors, this measurement could predict disease occurrence. Tracking of a characteristic has been defined as either the stability of a certain variable over time (e.g., maintenance of a relative position within a distribution of values over time) or the predictability of later values from earlier measurements (5GoGo–7Go), and it is therefore of considerable interest. Most earlier studies have examined tracking of risk factor levels in childhood or adolescence into adulthood (8GoGoGoGoGo–13Go). The few existing papers concerning adults either did not assess sex differences (14Go, 15Go) or did not investigate a broad number of variables (12Go, 14Go, 15Go). Furthermore, few have analyzed large samples from a general population.

Tracking of cardiovascular risk factors may facilitate understanding of how a variable changes over time. Knowledge about tracking is important for several reasons, for example, to be able to identify in early adulthood persons at increased risk of cardiovascular disease. Assessment of sex differences in the stability of a certain variable over time may lead to a better understanding of sex differences in the incidence of cardiovascular diseases. Furthermore, knowledge of tracking of different characteristics provides the opportunity to investigate and compare the degree of tracking between different risk factors within a population.

The first purpose of this study was to address sex differences and degree of tracking of blood pressure, body mass index (BMI), and serum lipids in a general population of more than 18,000 persons examined two or three times over a period of 16 years. To quantify the tendency for subjects to maintain high levels of these variables over time, tracking was also assessed by focusing on the upper distributions of these risk factors. The second purpose was to investigate predictors of tracking (dichotomized), both in general and restricted to the upper distributions of risk factors.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 
Study population
The persons included in the study were men and women who participated in at least two of three population surveys carried out between 1979–1980 and 1994–1995 in the municipality of Tromsø, northern Norway (table 1). In 1979–1980, all men born between 1925 and 1959 and all women born between 1930 and 1959 were invited to participate in a health survey. The total number examined was 16,621 (78 percent attendance rate). Men born between 1925 and 1966 and women born between 1930 and 1966 were again invited to participate in a second health survey in 1986–1987. The invited population consisted of 27,416 men and women (75 percent attendance rate). All men and women aged >=25 years who lived in the area were invited to a third examination in 1994–1995. Of those persons who attended at least one of the previous two surveys, 19,351 were invited to this last survey; 16,542 were examined (85 percent attendance rate). The 18,372 men and women who had participated in at least two of the three surveys were eligible for the present study. Persons with missing measures of blood pressure, BMI, high density lipoprotein (HDL) cholesterol, total cholesterol, or tri-glycerides; with missing information about smoking habits; or with treatment for hypertension were excluded from the study (n = 156). Women who were pregnant at one of the examinations were also excluded (n = 506). Hence, data on 9,168 men and 8,542 women were included in the present analyses.


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TABLE 1. Number (percentage) of participants, by examination year and sex, in the Tromsø Study, Tromsø, Norway, 1979–1995

 

    Measurements
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 
At each survey, the weight, height, and blood pressure of all participants were measured. Blood samples were taken, and the subjects answered a questionnaire (variables of interest were "current smoker" (yes/no) and "treatment for hypertension" (yes/no)). The methods used at the three examinations were almost identical and are presented in detail elsewhere (16Go, 17Go). Height and weight were measured with subjects wearing light clothing and no shoes. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2).

Personnel trained by physicians and by listening to tape recordings of Korotkoff sounds, which were produced by the London School of Hygiene and Tropical Medicine (United Kingdom), measured blood pressure. In 1979–1980, after subjects rested for 4 minutes, two readings—separated by a 1-minute interval—were taken by using a standard stethoscope and mercury sphygmomanometer. The first and fifth Korotkoff phases represented systolic and diastolic blood pressures, respectively. In 1986–1987 and 1994–1995, blood pressure was recorded by using an automatic device (Dinamap Vital Signs Monitor 1846; Critikon Inc., Tampa, Florida). After participants rested for 2 minutes in a sitting position, three readings were taken on the upper right arm, separated by 2-minute intervals. In 1979–1980, the average of the two blood pressure readings was used whereas in 1986–1987 and 1994–1995, the average of the last two readings was used.

Blood pressures measured with the Dinamap device are slightly lower than those measured with a sphygmomanometer (Erkameter; ERKA, Bad Tölz, Germany), especially for diastolic blood pressure (18Go). However, there is a linear relation, with correlation coefficients of 0.9 for systolic blood pressure and 0.8 for diastolic blood pressure. Therefore, to adjust for the change in method, we transformed our Dinamap measurements into predicted values of Erkameter measurements.

The nonfasting blood samples were analyzed at the Department of Clinical Chemistry, University Hospital of Tromsø. The laboratory was standardized against the World Health Organization's Lipid Reference Laboratory in Prague (Czech Republic). In 1979–1980, total cholesterol was measured directly by using the enzymatic oxidase method and a commercially available kit (Boehringer-Mannheim, Mannheim, Germany). Triglyceride levels were enzymatically determined as glycerol (Boehringer 15725; Boehringer-Mannheim). In 1986–1987 and 1994–1995, total cholesterol and triglycerides were analyzed by using colorimetric methods and commercially available kits (CHOD-PAP for cholesterol, GPO-PAP for triglycerides; Boehringer-Mannheim). HDL levels were measured after precipitation of lower-density lipoproteins with heparin and manganese chloride.


    Analyses
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 
Tracking indices may be calculated by using different methods. In our study, we used two methods. The first was introduced by Twisk et al. (19Go) and is a multivariate linear regression model, as follows:

(1)

The tracking coefficient ß1 may be interpreted as the prediction of the dependent variable's initial value when the dependent variable changes at time t2 and t3.

The present model has several advantages compared with other tracking models. It handled missing values of the dependent variable, so a balanced data set was not required. All of the available longitudinal data could be used to calculate the tracking indices. Use of covariates, both time dependent and time independent, allowed us to adjust for possible confounders. The statistical technique used, which controls for dependencies between repeated observations in the same subject, is called generalized estimating equations (GEE) (20Go).

The second method presented the proportion of subjects who remained in the same sextile throughout the different examinations. We also classified the number of sextiles changed relative to the subject's initial examination. All proportions were compared with the proportions expected if all subjects were classified randomly to a sextile group at each examination. If the changes from one examination to the next had been random, we would have expected 38.9 percent of the subjects to have changed fewer than three sextiles. Comparisons between observed and expected proportions would have been valid only if the observed numbers were restricted to subjects who attended all three examinations. Hence, the number of participants included in these calculations was reduced to 5,014 men and 4,917 women.

To assess the tendency to maintain a high level of a certain risk factor over time, we used a method comparable to an ordinary multivariate logistic regression analysis. However, the difference was that GEE were used to control for dependencies between repeated observations of the same participant. Participants were dichotomized to a binary variable according to whether they belonged to the upper sextile of the specified risk factor. Tracking coefficients were given as odds ratios for participants belonging to the upper sextile at the initial examination who maintained this position at later examinations.

The same models—interpreted as an ordinary logistic regression analysis—were also used to investigate the predictors of tracking (dichotomized). Tracking was defined by two classification methods: first, tracking was present whenever participants maintained their baseline position in a sextile; second, tracking was present whenever subjects maintained their position in the upper sextile.

Two-sided p values of less than 0.05 were considered statistically significant. All statistical analyses were carried out by using the SAS software system (21Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 
Overall means and standard deviations, and means according to sextile groups of baseline characteristics, are presented in table 2. In every sextile, women had lower mean values of systolic and diastolic blood pressures, BMI, total cholesterol, and triglycerides than men did. Men had lower mean values of HDL cholesterol.


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TABLE 2. Means of baseline characteristics, by sextile group and sex, the Tromsø Study, Tromsø, Norway, 1979–1995

 
Table 3 shows the standardized regression coefficients that were interpreted as tracking coefficients according to age for blood pressure, BMI, and serum lipids. The coefficients were adjusted for age, blood pressure treatment, time of baseline examination (1979–1980 or 1986–1987), and time of follow-up examination (1986–1987 and/or 1994–1995). For systolic and diastolic blood pressures, and for BMI in women, the tracking coefficients tended to increase with age. The tracking coefficients for BMI were higher than for all other risk factors, and the ones for triglycerides were the lowest. There was no sex difference for systolic blood pressure, diastolic blood pressure, and triglycerides. Overall, the observed coefficients for systolic blood pressure, diastolic blood pressure, BMI, HDL cholesterol, total cholesterol, and triglycerides were 0.52, 0.48, 0.85, 0.55, 0.77, and 0.43 for men and 0.54, 0.48, 0.80, 0.64, 0.65, and 0.39 for women, respectively.


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TABLE 3. Tracking coefficients* (SE{dagger}) for cardiovascular risk factors, by age at baseline and sex, the Tromsø Study, Tromsø, Norway, 1979–1995

 
To study the influence of smoking habits, we stratified for smoking (never smoker, stopped smoking, started smoking, consistent smoker) in a separate set of analyses. The coefficients for subjects who changed their smoking habits tended to be lower than those for subjects who were either consistent smokers or never smokers, although the differences did not reach significance (results not shown in tables).

A higher-than-expected proportion of subjects did not change sextile over the three examinations (table 4). Changes of one (or two) sextile(s) represented participants who moved one (or two) sextile(s) away from the baseline sextile in at least one of the two examinations that followed the baseline examination. These proportions may be compared with what is expected given that each subject was randomly assigned to a sextile at each examination. For all risk factors, a significantly higher proportion of participants changed fewer than three sextiles, clearly indicating the presence of tracking. No major sex difference was observed, although tracking seemed to be higher for women than for men. BMI was an exception. The rank order of the risk factors, from highest to lowest, for which both men and women changed fewer than three sextiles was as follows: BMI, total cholesterol, HDL cholesterol, systolic blood pressure, diastolic blood pressure, and triglycerides. To more easily assess the stability of each risk factor over time and to use all available data, we calculated the proportions of participants who remained in the same sextile between two examinations. We found that 32.1, 31.6, 49.9, 36.2, 39.9, and 27.1 percent of the men remained in the same sextile of systolic blood pressure, diastolic blood pressure, BMI, HDL cholesterol, total cholesterol, and triglycerides, respectively. The corresponding estimates for women were 33.7, 32.1, 47.5, 36.3, 40.7, and 28.0.


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TABLE 4. Estimated probabilities of changing sextile group in a 16-year follow-up,* the Tromsø Study, Tromsø, Norway, 1979–1995

 
There were no strong baseline predictors for tracking in the same sextile for blood pressure, BMI, and serum lipids, although some were statistically significant (results not shown). A few sex differences were found, however. Tracking of BMI was significantly associated with age, HDL cholesterol, total cholesterol, triglycerides, and current smoking. For men, only triglycerides contributed statistically significantly. In addition, BMI at baseline had a significant association with tracking of systolic and diastolic blood pressures in women, but not in men.

Table 5 shows the tracking coefficients expressed as odds ratio estimates for participants in the upper sextile (for HDL, the lowest sextile) at baseline, relative to all other subjects, remaining in the upper sextile (lowest sextile for HDL) at later examinations. The coefficients for the youngest subjects (aged 20–24 years) tended to be the lowest, but no other age trend was observed. Consequently, the results were presented without any age stratification. The odds ratio for the BMI coefficient is notable because it is much higher than the odds ratios for the other coefficients. Participants in the upper sextile of baseline BMI had more than 30 times higher odds of remaining in this sextile at later examinations compared with participants in the other five sextiles. There was no significant sex difference in the odds ratio estimates for serum lipids. For blood pressures and BMI, the estimates for women were significantly higher (p <= 0.003). The proportion of participants who remained in the high-risk group (upper sextile) varied from 40 to 75 percent.


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TABLE 5. Percentage of subjects who remained in the upper sextile and odds ratio estimates for subjects in the upper sextile (relative to subjects in the other five sextiles) of being in the upper sextile at later examinations,* the Tromsø Study, Tromsø, Norway, 1979–1995

 
For cardiovascular risk factors, participants who maintained their position in the upper sextile (lower sextile for HDL cholesterol) at later examinations were categorized as tracking in the upper (lower) sextile. Table 6 shows the association between tracking and baseline variables. All baseline risk factors (except HDL cholesterol and, for women, current smoker) were significantly associated with tracking of systolic and diastolic blood pressures in the upper sextile. For tracking of BMI in the upper sextile, all variables listed in table 6 were significant predictors (p < 0.01). Significant predictors for tracking of HDL cholesterol in the lower sextile were BMI, triglycerides, current smoker, and age (and systolic blood pressure in women only). Systolic blood pressure in men and HDL cholesterol in women were not significant as baseline predictors for tracking in the upper sextile of total cholesterol. Except for age in men, all listed variables were significant predictors for tracking of triglycerides in the upper sextile. For most of the independent variables considered, direct relations were observed between the variable and the odds of remaining in the upper sextile. For some variables, an inverse relation was found, however. Current smokers had decreased odds for tracking of systolic blood pressure, diastolic blood pressure (in men), and BMI. Higher values of HDL cholesterol were associated with decreased odds for tracking of BMI and triglycerides.


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TABLE 6. Sex-specific odds ratio estimates of baseline predictors of remaining in the upper sextile relative to the baseline examination,{dagger} the Tromsø Study, Tromsø, Norway, 1979–1995

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 
In this paper, we have presented tracking coefficients and predictors for tracking of systolic blood pressure, diastolic blood pressure, BMI, HDL cholesterol, total cholesterol, and triglycerides in a population-based cohort study over a period of 16 years. For both men and women, significant tracking coefficients were found for all six cardiovascular risk factors. However, when the degree of tracking is evaluated, the magnitude rather than the significance of the coefficients should be used. Nonfasting triglycerides, for which our tracking coefficients were close to 0.30 in the youngest age groups, are hardly stable over time. Systolic and diastolic blood pressures and HDL cholesterol in men, for which the overall coefficients were at or just below 0.50, showed a moderate degree of tracking. For total cholesterol, HDL cholesterol in women, and BMI, the tracking coefficients were more than 0.50, indicating relatively high stability over time.

Tracking was strongest for BMI in both men and women, and there was no significant sex difference for tracking of blood pressure and triglycerides. For tracking in the upper sextile, a similar picture was observed; BMI had the highest level of tracking and triglycerides the lowest. Compared with men, women had a significantly higher level of tracking of systolic and diastolic blood pressures and of BMI in the upper sextile. Otherwise, there were no sex differences.

To understand the implications of the odd ratios presented in table 5, we compared them with the corresponding percentages of remaining in the upper sextile calculated from 2 x 2 tables (predictive values of the baseline measurement). An odds ratio of 38.3 (BMI in women) translates into a predictive value of 71 percent, whereas odds ratios of 10 and 5 correspond to predictive values of approximately 52 and 41 percent, respectively. These percentages indicate that, although the difference between odds ratios of 38 and 10 (BMI and cholesterol in women) is rather high, the difference between the corresponding predictive values (71 and 52 percent) is more moderate. Note that predictive values calculated from the odds ratios do not necessarily coincide with the percentages shown in table 5. The odds ratios were adjusted for dependencies between repeated observations and for covariates, whereas the predictive values in table 5 are unadjusted results. However, if we consider a predictive value of 51.3 percent (HDL cholesterol in women), the corresponding unadjusted odds ratio would be 9.8, almost equal to the odds ratio given in table 5. It is not straightforward to set a cutoff point for the level of the predictive value to classify tracking. In a general population, predictive values of about 50 percent may be moderate, however, although they are still important markers for later classification in high-risk groups, depending on the variable in question. In this study, HDL cholesterol and total cholesterol showed odds ratios of about 10, with corresponding predictive values of about 50 percent, whereas the predictive values for systolic blood pressure, diastolic blood pressure, and triglycerides ranged between 40 and 50 percent.

For tracking of the risk factors in the upper sextile (table 6), most of the baseline predictors had significant and relatively large effects. There were no clear sex differences, although the odds ratios for the baseline predictors tended to be stronger for women than for men.

Although the distributions of many of the variables examined in this study were quite different between men and women, a lack of sex difference in tracking was observed for some variables. This finding may be remarkable; however, a sex difference in baseline distribution does not necessarily imply that the stability of a variable over time should differ as well.

Comparisons of the tracking coefficients found in this study with those of other studies were hampered because both the methods used and the time span considered differed. To our knowledge, the Amsterdam Growth and Health Study (11Go), which introduced the GEE tracking coefficients, is the only one that has used a directly comparable method. However, that study had a mean baseline age of 13 years and included only 181 subjects. The tracking coefficients for systolic blood pressure, diastolic blood pressure, and total cholesterol were considered not to differ between the sexes and were estimated to be 0.43, 0.34 and 0.71, respectively. For HDL cholesterol, the coefficients were 0.51 and 0.65 for men and women, respectively. These results may be relevant to the results obtained for our youngest age group.

The correlation coefficient is the most frequently used measure of tracking. The advantage of the GEE tracking coefficient, which also ranges between -1 and 1, is that the GEE method uses all available data; allows for adjustment of covariates, both time dependent and independent; and accounts for the correlation among the repeated observations for a given subject. The GEE also handle missing values.

In a comparison with the traditional method, a correlation analysis between the first and the last surveys showed coefficients of 0.52, 0.46, 0.79, 0.50, 0.66, and 0.39 for systolic blood pressure, diastolic blood pressure, BMI, HDL cholesterol, total cholesterol, and triglycerides in men, respectively. For women, the respective coefficients were 0.60, 0.49, 0.80, 0.61, 0.68, and 0.38. These coefficients tended to be lower than the GEE tracking coefficients. In an earlier paper from the Tromsø Study (22Go), we found correlation coefficients of blood pressure lower but also still comparable to the present results. A total of 4,183 men aged 20–49 years at baseline were examined in 1974, 1979–1980, and 1986–1987, and the correlation coefficients between the first and last examinations were 0.46 and 0.40 for systolic and diastolic blood pressures, respectively. The Cardiovascular Risk in Young Finns study (13Go) presented tracking of serum lipids in subjects with an initial age of 18 years. The 12-year Spearman's correlation coefficients were 0.54, 0.73, and 0.49 for HDL cholesterol, total cholesterol, and triglycerides in men and 0.56, 0.51, and 0.37 in women, respectively. These estimates were somewhat higher than those found in our youngest age group. However, considering the small sample size—65 men and 51 women—the general impression is comparable to the Tromsø Study. The findings in the Framingham Study (23Go), which included 1,605 men and women aged 49–82 years, agree with those from the oldest age group in the Tromsø Study. The 8-year correlation coefficients for HDL cholesterol and total cholesterol were 0.68 and 0.69 for men and 0.60 and 0.61 for women, respectively. In the Dormont High School Study (24Go), tracking was assessed for systolic blood pressure, diastolic blood pressure, and weight. The mean baseline age was 34 years, and the respective 12-year correlation coefficients for 202 subjects were 0.38, 0.44, and 0.88 for men and 0.54, 0.54, and 0.81 for women.

Classification of the change in sextile group relative to the baseline examination (table 4) provided further and unadjusted information about the stability of the tracking variables (compared with the GEE method). Although the methods address the same questions, the interpretation is somewhat different, because the results shown in table 4 reflect exactly the proportions of movement between the sextile groups.

Several studies have shown results of tracking in the upper part of the distribution of risk factors (8Go, 11Go, 13Go, 14Go). However, it is difficult to compare these findings with the present results. Most studies show the frequency of subjects who remain (or the relative likelihood of remaining) in a high position between two examinations. In the Amsterdam Growth and Health Study (11Go), the odds ratios for subjects who were at risk at the age of 13 years to still be at risk 15 years later were 4.0, 4.8, 14.1, and 10.4 for systolic blood pressure, diastolic blood pressure, HDL cholesterol, and total cholesterol, respectively. No significant sex differences were found.

Although some studies have used baseline measurements as predictors for later values (13Go, 19Go, 25Go), to our knowledge no study has classified subjects who maintain their relative position over time (subjects who track) and then assessed predictors for tracking. Consequently, our results could not be compared with other studies.

There were some sources of bias in this study. People who were treated for hypertension in at least one of the examinations were not excluded from the analyses. However, in a separate set of analyses, we did exclude subjects with a history of treatment for hypertension. None of the results presented was altered significantly, and all conclusions remained unchanged. If we had excluded subjects who had a history of blood pressure treatment (1,339 men and women), we would have missed 16.5 percent of our cohort with a baseline age of more than 39 years.

The problem of measurement error may have had an impact on tracking. Single values of blood pressure and serum lipids may not reflect a person's true level as well as a single value of BMI, which may lead to underestimation of tracking, especially for systolic and diastolic blood pressures and for triglycerides. Since this study was nonfasting, it could have influenced the values of some of the variables. Measurements of diastolic blood pressure and triglycerides, in particular, may be associated with "time since last meal." However, including this variable in our models did not change the results.

Clinical implications for cardiovascular disease may be drawn from the present results. The stability of BMI over time indicates that we can, with a relatively high degree of certainty, identify participants who maintain their high BMI value. To improve the general health profile of persons later in life, a focus on physical activity and change in food habits during adolescence and young adulthood is therefore advocated. Although the predictability of initial values of blood pressure and serum lipids was lower than that of BMI, the implication of maintaining a high level should not be overlooked. Several studies have shown that a reduction in total cholesterol or an increase in HDL cholesterol predicts a reduction in cardiovascular disease (26Go, 27Go). Identification of subjects in the upper part of the total cholesterol distribution (lower part for HDL cholesterol) is therefore of clinical importance in preventive medicine. The same can be said for systolic and diastolic blood pressures, for which, in this study, the odds ratios were slightly lower than the odds ratio for cholesterol.

In conclusion, over a period of 16 years, we found a high degree of tracking of BMI for young and middle-aged men and women, in either the general BMI distribution or the upper part of the distribution. The stability of blood pressure, HDL cholesterol, total cholesterol, and triglycerides over time was more moderate, but still not negligible. Values from other baseline variables had a relatively low predictability of the overall tracking of a specified risk factor, whereas baseline predictors contributed a relatively strong association to tracking in the upper sextile. With the exception that women were more likely to stay in the upper sextile of systolic and diastolic blood pressures, no major sex differences were detected in this study.


    ACKNOWLEDGMENTS
 
This research was supported by grants from the Norwegian Council for Cardiovascular Disease. The study was carried out in cooperation with the National Health Screening Service, Oslo, Norway.


    NOTES
 
Correspondence to Tom Wilsgaard, Department of Epidemiology and Medical Statistics, Institute of Community Medicine, University of Tromsø, N-9037 Tromsø, Norway (e-mail: tom.wilsgaard{at}ism.uit.no).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 Measurements
 Analyses
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication May 23, 2000. Accepted for publication March 12, 2001.