Metabolic Syndrome Variables at Low Levels in Childhood Are Beneficially Associated With Adulthood Cardiovascular Risk: The Bogalusa Heart Study

In the community of Bogalusa, Louisiana, four cross-sectional surveys of children aged 4–17 years and four cross-sectional surveys of young adults aged 19–41 years, who had been previously examined as children, were conducted between 1982 and 2003. This panel design of repeated cross-sectional examinations conducted approximately every 3 years resulted in serial observations on the cohort from childhood to adulthood. The participation rate was ∼80% for children and ∼60% for the young adult cohort. A total of 1,474 subjects who had data on risk variables of metabolic syndrome as children (aged 4–17 years) and as young adults (aged 19–41 years) formed the study cohort for this report. The present study cohort consisted of 62.6% whites and 41.9% of males, which were representative of the Bogalusa population. The average follow-up period is 15.8 years, with a range of 5–21.1 years. Measurements on carotid artery IMT in adulthood were available in a subgroup of 138 subjects aged 25–41 years. All subjects in this study gave informed consent at each examination, and for those under 18 years of age, consent of a parent was also obtained. Study protocols were approved by the institutional review board of the Tulane University Medical Center.

All examinations followed the same protocols, and procedures for the general examination were described elsewhere (12). Antecubital venous blood was collected to obtain serum and plasma. Height and weight were measured twice to ±0.1 cm and to ±0.1 kg, respectively. As a measure of obesity, BMI (weight in kilograms divided by the square of height in meters) was used. Blood pressure levels were measured on the right arm of subjects in a relaxed sitting position in replicate by two randomly assigned nurses. The first and fourth Korotkoff phases were used to determine systolic (SBP) and diastolic pressure blood pressure. Means of replicate readings were used in all analyses.

From 1982 to 1986, HDL cholesterol and triglycerides were measured using chemical procedures in a Technicon Auto Analyzer II (Technicon Instrument, Tarrytown, NY) according to the laboratory manual of the Lipid Research Clinics Program (13). Since then, these variables were determined by enzymatic procedures (14,15) on the Abbott VP instrument (Abbott Laboratories, North Chicago, IL). Serum lipoprotein cholesterols were analyzed by a combination of heparin-calcium precipitation and agar-agarose gel electrophoresis procedures (16). Both chemical and enzymatic procedures met the performance requirements of the lipid standardization program of the Centers for Disease Control and Prevention, Atlanta, Georgia. The laboratory is being monitored for precision and accuracy of lipid measurements by the agency’s surveillance program since 1973. Measurements on Centers for Disease Control and Prevention–assigned quality control samples showed no consistent bias over time within or between surveys. Intraclass correlation coefficients, a measure of reproducibility of the entire process from blood collection to data processing, between the blind duplicate values ranged from 0.97 to 0.99 for total cholesterol, 0.92 to 0.98 for HDL cholesterol, and 0.97 to 0.99 for triglycerides. The ratio of total to HDL cholesterol was used as a measure of dyslipidemia and metabolic syndrome (17).

A commercial radioimmunoassay kit was used for measuring plasma immunoreactive insulin (Padebas Pharmacia, Piscataway, NJ). This insulin assay has 41% cross-reactivity with proinsulin, which is disproportionately low in nondiabetic subjects, and <0.2% cross-reactivity with C-peptide. The detection limit of insulin level was 2.0 μu/ml. Plasma glucose was measured by an enzymatic method using the Beckman Instant Glucose Analyzer (Beckman Instruments, Palo Alto, CA). Intraclass correlation coefficients between the blind duplicate values ranged from 0.94 to 0.98 for insulin and 0.86 to 0.98 for glucose. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated according to the homeostasis model assessment formula: HOMA-IR = fasting insulin (μu/ml) × fasting glucose (mmol/l)/22.5. This model is considered useful to assess insulin resistance in epidemiologic studies (18).

Carotid IMT of the far walls of the right and left common carotid artery, carotid bulb, and internal carotid artery was determined using a B-mode ultrasound scanner (Toshiba Sonolayer SSH160A) equipped with a 7.5-MHz linear array transducer by trained sonographers. Details of carotid artery measurements have been described elsewhere (19). The mean value of the three sites (three left and three right far wall measurements) was used for the present analysis.

All data analyses were performed using Statistical Analysis System (SAS). The criterion metabolic syndrome variables considered in the analyses were BMI, HOMA-IR, SBP, and the total–to–HDL cholesterol ratio in both childhood and adulthood. Nonfasting samples have been excluded from the analysis. Since the four components are age related, and data collection spanned >20 years, the four metabolic syndrome variables were adjusted for age (including higher order terms to allow for nonlinearity) for childhood and adulthood measurements separately by multiple regression analysis in race-sex groups by survey year. The residual values were then standardized by Z-transformation in race-sex groups by study year. The standardized regression residuals were used to determine the percentiles of the four variables for subsequent analyses.

Since the acceptable cutoff points for the metabolic syndrome components are not established for children, low levels were defined as values below the 25th percentiles of BMI, HOMA-IR, SBP, and total–to–HDL cholesterol ratio, as previously reported (20). Adulthood metabolic syndrome was defined as the clustering of adverse levels of these four components using the cutoff values above the 75th percentiles. In addition, the metabolic syndrome in adulthood as defined by the National Cholesterol Education Program Adult Treatment Panel III (21) was also used to compare the prevalence of the syndrome in adulthood. The ratio of observed to expected prevalence (O/E) was applied to estimate the degree of the clustering of favorable levels of risk variables in childhood. The expected prevalence of the cluster of the four risk variables was calculated by multiplying together all four individual prevalence rates, which were expected to be 25% each in this case. For the three risk variables cluster, the expected prevalence was calculated as 0.25³(1–0.25). Significance tests for O/E ratios were performed using generalized one-sample binomial tests (22). The association of the childhood clustering of risk variables at low levels with adulthood metabolic syndrome and parental histories of coronary heart disease, hypertension, and type 2 diabetes was examined by using the χ² test, adjusting for adulthood age and parental history. The association between childhood clustering of risk variables at low levels and adulthood metabolic syndrome, adjusting for parental histories, was tested using multivariate logistic regression models.

Table 1 shows the mean levels of risk variables related to the metabolic syndrome by race, sex, and age-group. In childhood, BMI was significantly higher in black females than in white females; sex difference (females > males) was noted in blacks only. Whites showed a significantly higher ratio of total to HDL cholesterol than blacks, irrespective of sex. In adulthood, significant race differences in SBP (black > white) and total–to–HDL cholesterol ratio (black < white) was seen in both sexes, but the race differences in BMI ((black > white) and HOMA-IR (black > white) were significant in females only. All variables showed significant sex differences in both races except for HOMA-IR.

In childhood, 9.0% of the cohort displayed clusters of three or four risk variables at the bottom quartiles. As shown in Fig. 1, the O/E ratios for any three and four risk variables clustering were significantly different from one (P < 0.01), indicating that there was a significantly higher frequency of clustering at low levels than expected by chance alone. The O/E ratio for four variables clustering was higher than those for three variables clustering. Further, for the specific combinations of three variables, clusters involving both BMI and HOMA-IR showed higher and significant O/E ratios than those without either of BMI or HOMA-IR.

The overall prevalence of the metabolic syndrome, as defined by top quartiles in our young adult study cohort, was 13.6%; it was higher in whites than in blacks (15.2 vs. 11.1%, P = 0.027). Children with clustering of three or more versus less than three risk variables at the bottom quartiles had significantly lower prevalence of the metabolic syndrome defined by top quartiles in adulthood (3.8 vs. 14.6%, P < 0.001). Further, according to the definition by the National Cholesterol Education Program Adult Treatment Panel III (21), the overall prevalence of the metabolic syndrome in our young adult study cohort was 12.1%; the prevalence was higher in whites than in blacks (14.5 vs. 8.2%, P < 0.001). Children with clustering of three or more versus less than three risk variables at the bottom quartiles had significantly lower prevalence of the metabolic syndrome in adulthood (4.6 vs. 12.9%, P = 0.005). The association between childhood clustering of risk variables at low levels and adulthood metabolic syndrome was also examined adjusting for parental histories of coronary heart disease, hypertension, and type 2 diabetes using multivariate logistic regression models. The odds ratio (0.29-fold, P = 0.008) for having metabolic syndrome in adulthood still remained significant, indicating that the influence of the clustering of risk variables at low levels in childhood on the metabolic syndrome in adulthood was independent of family history of CV diseases (surrogate markers of the genetic component).

Figure 2 illustrates the relationship between parental histories of CV diseases and the prevalence of clustering of risk variables at low levels in childhood. A positive parental history was defined as mother and/or father having the disease. The prevalence of clustering of three or more risk variables at the lowest quartiles in childhood was higher in subjects with negative parental histories of coronary heart disease (9.4 vs. 5.0%, P = 0.024), hypertension (10.5 vs. 6.6%, P = 0.012), and type 2 diabetes (9.1 vs. 6.4%, P = 0.166) than those with positive parental histories. Carotid IMT in adults was used as another surrogate measure of coronary atherosclerosis. As shown in Fig. 3, mean values of carotid IMT in adulthood decreased significantly (P for trend = 0.013) as the number of risk variables at the bottom quartiles in childhood increased.

In an attempt to control the epidemic of coronary heart disease, attention was focused primarily on the metabolic syndrome defined by adverse levels of major CV risk factors because it is a strong and consistent predictor of CV disease and type 2 diabetes (1–3). On the other hand, several longitudinal studies have demonstrated that the low risk factor profile has favorable effects on the development of CV disease, life expectancy, and health care costs (5–7). Based on data from the Bogalusa Heart Study cohort, our main finding is that the constellation of metabolic syndrome components at low levels occurs in childhood and is associated with low adulthood CV risk in terms of metabolic syndrome, carotid IMT, and parental histories of coronary heart disease, hypertension, and type 2 diabetes. These observations in a community-based cohort are noteworthy in that they are indicative of the beneficial consequence of having no metabolic syndrome condition (i.e., low-level clustering profile) in childhood on future CV risk. Further, the current study in youth extends and confirms the earlier statistical estimates and direct measurement (5,6) regarding the advantage of having low levels of major CV risk factors in adults.

The adverse levels of the metabolic syndrome components coexist more often than expected by chance alone (8–10). Clustering of risk variables at low levels represents the opposite side of the syndrome. In this study, the observed prevalence of three- and four-variable clusters at low levels in children was 1.7 and 4.1 times, respectively, more than the expected prevalence. The present and previous findings indicated that the metabolic syndrome variables are intercorrelated in terms of both continuous and categorical scale, even beginning from childhood. It is of note that the three-variable clusters involving both HOMA-IR and BMI showed higher O/E ratios compared with those without either of them. This is consistent with the notion that obesity and the attendant insulin resistance link other components of metabolic syndrome and play a pathogenic role in the development of the syndrome (20,23).

The prevalence of metabolic syndrome increases with age and is strongly associated with the CV risk (3,24). Using the top quartile definition, the prevalence of the metabolic syndrome was 13.6% in adults aged 19–41 years in this study, with whites having a higher prevalence than blacks (15.2 vs. 11.1%, P = 0.027). Further, according to the definition by the National Cholesterol Education Program Adult Treatment Panel III (21), the prevalence of the metabolic syndrome in our young adult study cohort was 12.1%; the prevalence was higher in whites than in blacks (14.5 vs. 8.2%, P < 0.001). The observed prevalence rate is similar to that from the Third National Health and Nutrition Examination Survey (6.7–13% in the 20- to 39-year age-groups) (24). In this study, children with three or more variables at the bottom quartiles displayed a significantly lower prevalence of metabolic syndrome later in adulthood, reflecting a phenomenon of “tracking at low levels.” An individual’s genetic diathesis may play an important role in maintaining the clustering at low levels from childhood to adulthood (25–27). On the other hand, the association between childhood clustering of risk variables at low levels and adulthood metabolic syndrome, independent of family history of CV diseases (surrogate markers of genetic susceptibility), also underscores the importance of lifestyles in early life for CV risk in adulthood.

Since CV disease aggregates in families, a positive parental history can be considered a useful and independent predictor of CV disease risk in the offspring (28,29). Further, a positive parental history of coronary heart disease and type 2 diabetes is associated with unfavorable risk factor status in their offspring from childhood to adulthood in the Bogalusa population (30,31). In the present study, adults who have a negative parental history of coronary heart disease and hypertension were more likely to have a favorable metabolic syndrome risk profile in childhood. These observations support the findings from other studies that middle-aged people without major CV risk factors are at lower risk for death from CV and non-CV causes and a greater life expectancy in the elderly population (5,6).

It is well established that CV risk factors are definable in childhood and are predictive of future CV risk (32). Autopsy studies have shown that both the presence and extent of atherosclerosic lesions in aorta and coronary arteries correlate positively and significantly with established risk factors in youth (33,34). Further, CV risk factor profile in childhood has been found to predict carotid artery thickness in adulthood in this and other populations (35,36). Since CV risk variables tend to track from childhood to adulthood (11,37), the tracking of the metabolic syndrome risk variables from childhood to adulthood is thought to play a role in the association between a favorable childhood clustering profile of risk variables and adulthood carotid IMT found in this study. By showing the advantage of having no metabolic syndrome in childhood on the carotid IMT in adulthood, the present study provides additional evidence for the association between the multiple risk factors status in childhood and future atherosclerosis.

In summary, these observations show that the condition of clustering of metabolic syndrome risk variables at low levels occurs in a significant proportion of individuals in childhood and is associated with lower CV risk in adulthood. This reinforces the benefit of health promotion and lifestyle modification in early life in order to sustain a lifetime low-risk profile.

This study was supported by grants HD-043820 and HD-47247-01 from the National Institute of Child Health and Human Development, HL-38844 from the National Heart, Lung, and Blood Institute, AG-16592 from the National Institute on Aging, and 0160261B from the American Heart Association.