The authors have declared that no competing interests exist.
Conceived and designed the experiments: VTWP JLK CIA. Performed the experiments: VTWP. Analyzed the data: VTWP. Contributed reagents/materials/analysis tools: JLK CIA. Wrote the paper: VTWP JLK CIA.
Metabolic syndrome (MetS) is a constellation of metabolic aberrations that collectively increase the risk for cardiovascular disease and type 2 diabetes. Greater understanding of MetS developments may provide insight into targeted prevention strategies for individuals at greatest risk. The purpose of this study was to i) identify distinct patterns of longitudinal MetS development and; ii) develop a character profile that differentiates groups by level of MetS risk.
Data from the Coronary Artery Risk Development in Young Adults (CARDIA) study (n = 3 804; 18–30 y) was obtained by limited access application from the National Heart, Lung, and Blood Institute and used for this analysis. MetS, as defined by the Harmonized criteria, was assessed over a 20 year follow-up period. Group-level trajectory analysis identified 4 distinct groups with varying rates of component development [No (23.8% of sample); Low (33.5%); Moderate (35.3%); and High MetS (7.4%)]. After adjusting for covariates, individuals in the At-Risk groups (Low, Moderate and High MetS) were more likely to be of black ethnicity (1.37, 1.14–1.66), have a family history of cardiovascular disease (1.61, 1.31–1.97) and history of dieting (1.69, 1.20–2.39) when compared to the No Risk trajectory group (No MetS). Conversely, increasing baseline education (0.76, 0.65–0.89) and aerobic fitness (0.55, 0.47–0.64) was inversely associated with At-Risk group membership.
Results suggest distinct profiles of MetS development that can be identified by baseline risk factors. Further research is necessary to understand the clinical implication of intermediate MetS development groups with respect to overall cardiometabolic risk.
Metabolic syndrome (MetS) is a constellation of cardiovascular risk factors that collectively increases the risk and burden of chronic disease, particularly type 2 diabetes
Previous research into the longitudinal development of MetS components and their medical sequalae has been hampered by two distinct limitations. First, research into the development of MetS has been focused primarily on the five individual MetS components (hypertension, hypertriglyceridemia, low high-density lipoprotein-cholesterol (HDL-C), elevated waist circumference (WC) and high fasting plasma glucose), with little evaluation of both the overall pattern of development, nor their association with known primordial risk factors such as gender, ethnicity, physical inactivity, and diet. Secondly, conventional approaches to exploring patterns of MetS development are typically based on parametric assumptions, whereby it has been assumed that all individuals with MetS develop the condition in a similar manner. Such efforts have subsequently led to strategic, yet arbitrary categorizations of study samples that may not reflect the full range of variation in MetS component combinations possible
Data for this analysis were obtained through a limited access dataset obtained from the National Heart, Lung, and Blood Institute of the National Institutes of Health. The CARDIA study was approved by institutional review boards at all study locations. This research was reviewed and approved by the Human Participants Review Sub-Committee of York University. Written Informed Consent was provided by all CARDIA study participants.
The Coronary Artery Disease Risk Development in Young Adults (CARDIA) study is an ongoing, multicenter, longitudinal study designed to track the development of CVD risk factors in a U.S. cohort of apparently healthy 18–30 year old participants. Details of the scientific rationale, eligibility requirements, recruitment process and baseline characteristics of the CARDIA participants have been published elsewhere
The procedures for each examination differed marginally, as efforts were made to ensure that emerging cardiovascular risk factors were captured. However, each examination consistently included physical measurements (e.g. blood serum chemistry, blood pressure, and anthropometry), lifestyle factors (e.g. physical activity (PA), dietary habits, tobacco and alcohol use, behavioural/psychological assessment), a medical history and socioeconomic status (SES) profile.
Data was limited to participants who attended at least two follow-up examinations, were non-pregnant, free from MetS at baseline, and had complete data for all five MetS components. Of the initial 5 115 individuals enrolled at baseline, 3 804 apparently healthy participants completed at least two follow-up examinations (males: n = 1 694; females: n = 2 110). Collectively, 2 597 attended all five examinations, while 718 completed only four visits and 489 attended only three.
All outcome variables were collected according to a standardized protocol and processed at central laboratories. Participants were asked to fast for at least 12-hours and refrain from engaging in any heavy physical activity or tobacco use in the 2-hours prior to testing
For the purpose of the current analysis, MetS was operationalized by the Harmonized criteria
To provide further insight into the relationship between MetS and global cardiovascular risk, the broader framework of the revised
Covariates included both non-modifiable (sex, ethnicity and family history of CHD) and modifiable risk factors (physical activity, cardiorespiratory fitness, diet, smoking, alcohol consumption, and education). All non-modifiable risk factors were collected by self-report using standardized questionnaires.
Because half of the selected participants were between the ages of 18 and 24 years at baseline, self-reported highest level of education (less than high school education, high school graduate, college or university degree, and postgraduate studies) was re-assessed at each follow-up interval.
Baseline PA was based on an interview-administered questionnaire derived from the Minnesota Leisure Time Physical Activity Questionnaire. Subsequently, the CARDIA Physical Activity Questionnaire provides an estimate of 12-month PA frequency based on participation in 13 activities. Because participants were not asked to report the duration of PA, separate heavy and moderate intensity activity scores (expressed in exercise units; EU) were computed based on the product of the frequency of activity participation and activity intensity
Aerobic fitness was estimated by the modified Balke treadmill test
Usual alcohol consumption was estimated using the CARDIA Alcohol Use Questionnaire, which asks participants to report their typical weekly consumption of wine/beer/liquor over the past year. Consistent with Center for Disease Control (CDC) guidelines, a standard alcoholic drink (with 13.7 g of pure alcohol)
Trajectory modelling
Once all participants had been assigned to a trajectory group, group-level profiles (e.g. modifiable and non-modifiable characteristics) were evaluated by Chi-square and analysis of variance, as appropriate. To assess potential baseline predictors of MetS, the pattern of individual MetS components was plotted separately for each group across the 20-years of follow-up. Additionally, measures of metabolic and cardiovascular risk (MetS development, Framingham Risk, ATP III-R risk and treatment groups) were assessed both at baseline and compared by MetS group at year 20.
To identify factors associated with higher risk group membership, groups with similar risk profiles and trajectories were then aggregated into either “no risk” or “at-risk” groups. Logistic regression was then used to identify variables that could predict group membership and identify targets for lifestyle-based intervention. Model 1 adjusted for non-modifiable risk factors (i.e. sex, ethnicity, and family history of CVD), and Model 2 additionally adjusted for modifiable and behavioural risk factors (i.e. additional adjustment for education, smoking, drinking, physical activity score, current engagement in weight reducing diets, and fast food consumption). After assessing collinearity and confirming independence (Tolerance<0.25; VIF<4), a further model adjusted for both non-modifiable and modifiable risk factors as well as aerobic fitness (Model 3). All statistical analyses were conducted using SAS version 9.3 (Cary, NC, U.S.A.) with statistical significance set at alpha <0.05.
The 20-year likelihood of developing MetS was found to follow two distinct trajectories. Group 1 (MetS: n = 3 078, 80.9% of the sample) demonstrated a steady low-probability of developing MetS, while group 2 (MetS: n = 726, 19.1%) increased linearly over time, reaching approximately 80% likelihood by the final year (
Two distinct groups were observed and was denoted as Low Probability (n = 3 078) and High Probability (n = 746).
When trajectory models were developed with the number of MetS
Trajectories were determined by PROC TRAJ censored normal modelling in 3 804 apparently healthy young adults. Four trajectories were observed and denoted as No MetS (No MetS: n = 906), Low MetS (n = 1 273), Moderate MetS (n = 1 342), and High MetS (High MetS: n = 283).
Non-Modifiable Risk Factors | ||||||||
Sex (% Male) |
41.5 | 39.7 | 49.0 | 47.4 | 42.1 | 41.3 | 45.6 | 44.7 |
Ethnicity (% Black) |
36.8 | 33.1 | 47.8 | 45.2 | 57 | 55.1 | 58.3 | 59.1 |
Family History of CHD (%) |
19.7 | 28.3 | 30.9 | 36.4 | ||||
Modifiable Risk Factors | ||||||||
Education (% ≤ High School Graduates) |
4.8 | 28.6 |
6.8 | 36.5 |
9.0 | 46.9 |
9.7 | 50.0 |
Smoking (% Current) |
22.0 | 13.9 |
27.2 | 17.1 |
32.1 | 23.8 |
30.9 | 19.7 |
Alcohol (% Non-drinker) |
71.5 | 70.1 | 70.1 | 70.2 | 72.5 | 78.3 |
80.5 | 84.9 |
Physical Activity Level (% Inactive) |
27.7 | 34.8 |
30.3 | 39.7 |
35.5 | 52.1 |
37.5 | 60.8 |
Aerobic Fitness (% Unfit) |
11.0 | 9.8 |
8.9 | 24.3 |
22.5 | 50.1 |
38.9 | 71.3 |
Fast Food Consumption (% more than 2 times/week) |
25.7 | 17.0 |
28.0 | 23.8 |
33.4 | 26.0 |
30.0 | 30.4 |
Currently on a Weight Reducing Diet (%) |
5.6 | 25.9 |
7.8 | 33.2 |
9.7 | 35.5 |
13.6 | 38.0 |
MetS Components | ||||||||
Systolic Blood Pressure (mmHg) |
106.6±9.4 | 109.3±11.1 |
109.5±9.8 | 116.4±13.7 |
111.7±11.5 | 119.9±16.7 |
115.1±10.5 | 124.9±16.5 |
Diastolic Blood Pressure(mmHg) |
66.5±8.6 | 66.6±8.2 | 68.0±9.0 | 72.2±10.5 |
69.1±10.0 | 76.6±11.9 |
72.1±9.9 | 81.4±11.1 |
Waist Circumference (cm) |
77.5±5.3 | 87.1±7.4 |
79.3±6.9 | 93.0±9.6 |
83.5±8.6 | 101.4±12.8 |
91.1±9.2 | 114.4±12.2 |
Waist Circumference (cm) |
67.8±5.4 | 75.3±6.9 |
70.1±6.3 | 83.9±10.7 |
78.0±11.4 | 95.8±13.1 |
87.4±12.4 | 107.9±11.5 |
Fasting Plasma Glucose mmol/L) |
4.4±0.4 | 4.9±0.4 |
4.5±0.5 | 5.2±0.8 |
4.6±0.8 | 5.6±1.4 |
4.8±0.7 | 6.9±2.9 |
Serum Triglycerides (mmol/L) |
0.6±0.3 | 0.8±0.3 |
0.7±0.4 | 1.0±0.6 |
0.9±0.5 | 1.5±1.0 |
1.2±0.6 | 2.1±1.3 |
HDL-C (mmol/L) |
1.5±0.1 | 1.5±0.2 | 1.4±0.1 | 1.3±0.2 | 1.1±0.1 | 0.9±0.1 | 1.1±0.1 | 1.0±0.1 |
HDL-C (mmol/L) |
1.6±0.1 | 1.9±0.2 | 1.5±0.1 | 1.6±0.1 | 1.3±0.1 | 1.3±0.2 | 1.1±0.1 | 1.1±0.1 |
Health Risk | ||||||||
Obesity (% Body Mass Index ≥30 kg/m2) |
0.3 | 5.4 |
2.3 | 23.6 |
17.3 | 45.7 |
42.4 |
66.1 |
Develop Metabolic Syndrome (%) |
– | 0 |
– | 5.6 |
– | 40.5 |
– | 93.0 |
No MetS Components (%) |
91.1 | 86.1 |
83.7 | 27.1 |
41.3 | 3.3 |
19.2 | 0.0 |
Framingham Risk Score | 1.0±0.2 | 1.8±2.0 |
1.1±0.4 | 2.2±2.6 |
1.1±0.5 | 2.5±3.2 |
1.1±0.6 | 2.5±3.4 |
ATP-III-R Group (% High Risk) |
0 | 0 | 0.5 | 3.2 |
0.2 | 10.8 |
0.1 | 33.5 |
ATP-III-R Treatment (% Drug Therapy) |
0.2 | 0.8 | 0.5 | 3.4 |
1.5 | 8.5 |
0.4 | 24.9 |
Data presented as group means ± SD unless stated otherwise;
*Significance observed between baseline and Year 20, within each trajectory group; p<0.05;
Significance observed between trajectory groups at baseline; p<0.05;
Significance observed between trajectory groups at Year 20; p<0.05;
Presented Year 20 data reflects those who attended that specific exam; Abbreviations: CHD; Coronary Heart Disease, HDL-C; High Density Lipoprotein Cholesterol; MetS, Metabolic Syndrome; ATP III-R, Revised Third Report of National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol.
Potential gender and ethnic differences within the cohort were also assessed, and gender- and ethnic-specific models were developed (
Baseline characteristics between each of the trajectory groups are presented in
Mean differences in the MetS components observed at baseline persisted through year 20, wherein the High MetS group presented significantly higher levels of all five MetS components (
Consistent with estimated trajectory patterns, higher risk trajectory groups corresponded to a growing prevalence of MetS by year 20 (No: 0%; Low: 5.6%; Moderate: 40.5%; High: 93.0%). However, the short-term risk of CVD (as predicted by the Framingham Risk Algorithm) among CARDIA participants at baseline was low (Baseline: 1.0 to 1.1% 10-year risk vs. Year 20: 1.8 to 2.5% 10-year risk). By contrast, group differences were observed when ATP III-R global risk strata were applied. In general, increasing risk trajectory corresponded with greater cardiometabolic risk, with 25% of all High MetS members eligible for lipid-lowering therapy by year 20.
To identify baseline predictors associated with membership in at-risk trajectory groups, logistic regression was used to compare at-risk trajectory groups (Low, Moderate and High MetS) to the apparent no risk (No MetS) group (
Model 1 | Model 2 | Model 3 | |
Group Predictors |
Odds Ratio | Odds Ratio | Odds Ratio |
(95% CI) | (95% CI) | (95% CI) | |
No vs At-Risk | |||
Gender (Female vs Male) | 0.81 (0.70–0.95) | 0.85 (0.71–1.01) | 0.86 (0.72–1.04) |
Ethnicity (Black vs White) | 1.93 (1.65–2.25) | 1.66 (1.38–1.98) | 1.37 (1.14–1.66) |
Family History of CVD (yes vs no) | 1.74 (1.44–2.09) | 1.71 (1.40–2.09) | 1.61 (1.31–1.97) |
Education | - | 0.72 (0.61–0.83) | 0.76 (0.65–0.89) |
Smoking | - | 1.10 (0.99–1.21) | 1.06 (0.96–1.18) |
Drinking | - | 1.00 (0.85–1.16) | 0.99 (0.84–1.16) |
PA Level | - | 0.88 (0.79–0.98) | 0.98 (0.88–1.09) |
Currently Dieting | - | 1.92 (1.37–2.70) | 1.69 (1.20–2.39) |
Fast Food Consumption | - | 1.04 (1.00–1.08) | 1.03 (0.99–1.08) |
Fitness | - | - | 0.55 (0.47–0.64) |
*Logistic Regression modelled with No MetS group as referent (Model 1: n = 906 Model 2: n = 783, Model 3: n = 777); Sample size for each model varies according to variables included; p<0.05; Abbreviations: PA, Physical Activity; MetS, Metabolic Syndrome;
- Gender (Female vs. Male), Ethnicity (Black vs. White), Family History (Yes vs. No), Education (Less than High School, High School, College/University, Graduate Degree), Smoking (Non-, Former, Current Smoker), Drinking (Non-, Moderate, Heavy Drinker), Total PA Score (Inactive, Moderately Active, Active), Currently Dieting (Yes vs. No), Fast Food Consumption (Frequency of Visit/Week); Aerobic Fitness (Unfit, Moderately Fit, Fit).
Results of Model 3 reveal that females and those with a family history of CHD were 37% and 61% more likely to be in the At-Risk group, respectively. Gender was not a significant predictor of group membership upon inclusion of modifiable risk factors.
Of the six modifiable risk factors assessed in Model 2, baseline education, PA, fast food consumption and engagement in weight reducing diets were all significantly related to the pattern of MetS development, with poorer health behaviours within the At-Risk group. Individuals with low educational attainment and who were dieting at baseline were 28% and 92% more likely to be in the At-Risk group, respectively. Regular fast food consumption was also more common in the At-Risk group (OR: 1.04, 95% CI: 1.00–1.18), an effect that was independent of current dieting practice. Lastly, higher levels of PA were associated with lower likelihood of At-Risk (0.88, 0.79–0.98) group membership; however, both PA and fast food consumption were no longer a significant predictor of group membership upon inclusion of cardiorespiratory fitness, whereas increasing aerobic fitness was inversely associated with At-Risk group status (0.55, 0.47–0.64) in Model 3.
The present study aimed to identify distinct groups of longitudinal MetS development and to develop a character profile of group membership. To our knowledge, this is the first study to use exploratory group-based modelling of longitudinal data to identify heterogeneity of long-term MetS development among a cohort of apparently healthy young adults. By way of this approach, we were able to identify intermediate groups of MetS development that may not be captured in conventional analytic approaches.
Over 20 years of follow-up, 4 distinct trajectories for MetS development were found. It is well known that adolescence and young adulthood is a critical period for the initiation of CVD
Characterization of At-Risk groups (i.e. Low, Moderate and High MetS trajectory groups) that are steadily developing MetS components has important clinical implications for therapeutic intervention. First, the overall prevalence of MetS among Non-Hispanic black individuals in the U.S. is lower than that of both Hispanics and Non-Hispanic whites
Observed ethnic differences may be further complicated by gender differences within each race/ethnicity. While extensive examination of ethnic-gender stratified models were limited due to the small sample size, preliminary analyses identified four distinct trajectories among black women, and two groups among white women. The additional trajectories observed among black females suggest a high-risk, but heterogeneous group with rapid development of MetS components. Amongst this study's cohort, black females reported a higher baseline prevalence of obesity (20%) than their white counterparts (6%) (results not presented). By contrast, both black and white males had similar trajectory plots with three distinct groups each. However, given the small sample size and exploratory nature of these analyses, interpretation of stratified models must be done with caution.
Current ATP III-R treatment guidelines recommend lifestyle modification (diet, physical activity, and weight management) as the first line of treatment for metabolic dysfunction
Key behavioural and lifestyle factors that must be considered in future analyses include diet (e.g. average caloric intake, macronutrient composition of diet, dietary restriction, and fast food consumption), PA and fitness, and weight management. In the present study, self-reported dieting was predictive of group membership. Specifically, High MetS members were 50% more likely to report current dieting. It can therefore be inferred that at-risk participants may have recognized their need to reduce their weight and manage their diet; however, from these results, it is also clear that as a group their dieting efforts were unsuccessful in light of the general increase in BMI and WC across all follow-ups. This is consistent with a study by Field et al.
Although the impact of PA on body mass, blood pressure, plasma glucose, and regulation of blood lipid profile has been widely described
There are several strengths and limitations of the current study that warrant discussion. First, PROC TRAJ is ideal for evaluating the trajectory of change in MetS over time, with a particular focus on identifying multiple distinct patterns and modelling unobserved variance in data
Although these analyses are theoretical in nature, the finding of distinct MetS trajectories provides insight into critical periods for lifestyle intervention, risk factor management, and the required aggressiveness of treatment within At-risk subgroups. Most importantly, these analyses not only capture the way in which various groups develop MetS, but also identify character profiles of individuals who are steadily developing MetS, but remain clinically unidentifiable in terms of frank diabetes, hypertension, and CVD. Additional research in large multi-ethnic samples is necessary to understand the importance of ethnic and gender differences in these patterns, and to assess the clinical implication of intermediate MetS development groups with respect to overall cardiometabolic risk.
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The Coronary Artery Disease Risk Development in Young Adults (CARDIA) study is conducted and supported by the National Heart, Lung and Blood Institute (NHLBI) in collaboration with the CARDIA Study Investigators. This Manuscript was prepared using a limited access dataset obtained from the NHLBI under a data sharing agreement and does not necessarily reflect the opinions or views of CARDIA or the NHLBI. We would like to acknowledge the NHLBI, CARDIA investigators, and all CARDIA participants and staff for their contributions to this work. Guarantor: CI Ardern.