Severity of Metabolic Syndrome as a Predictor of Cardiovascular Disease Between Childhood and Adulthood: The Princeton Lipid Research Cohort Study

Research Letter

The long-term ability of the metabolic syndrome (MetS) to predict cardiovascular disease (CVD) has been limited by the binary nature of traditional MetS criteria and by discrepancies among African Americans, who have low rates of MetS classification despite higher rates of death from CVD(1). We previously used confirmatory factor analysis to formulate MetS-severity-z-scores for adolescents(2) and adults(3) that place differential weights on the individual MetS components to account for variation in how MetS is manifest by sex- and racial/ethnic group. Our goal was to assess for the ability of these scores to determine long-term risk for CVD.

The Princeton Lipid Research Cohort Study followed white and black (30.5%) individuals (55.5% female) over three phases: 1. the Lipid Research Clinic (LRC, 1973–1976) evaluated MetS measures on students in grades 1–12(4); 2. the Princeton Follow-Up Study (PFS, 1998–2003) evaluated complete MetS-measures and reported CVD status on 629 LRC participants(4); and 3. the Princeton Health Update (PHU, 2010–2014) assessed CVD outcomes via phone interviews and National Death Index query on 354 cohort members. CVD was classified as self-reported myocardial infarction, coronary artery bypass, other heart surgery, coronary revascularization procedure (angioplasty, stent placement) or stroke. MetS-severity-z-scores were calculated from each individual’s measures of BMI-z-score (children/adolescents) or waist circumference (adults), systolic blood pressure, fasting triglycerides, and fasting glucose, based on equations specific to sex and racial/ethnic sub-group (http://publichealth.hsc.wvu.edu/biostatistics/metabolic-syndrome-severity-calculator/) from LRC and PFS visits. Mean MetS-z-scores were compared based on participants’ CVD diagnosis by PFS or PHU. Logistic regression and ROC curves were used to evaluate the ability of MetS severity scores to predict future CVD.

MetS-severity-z-scores during childhood (LRC, mean age 12.9 years) were lowest among those who never developed CVD, highest among those with early CVD (PFS, mean age 38.4) and intermediate among those with later CVD (PHU, mean age 49.6)(Figure 1). In predicting future CVD, ROC curves revealed that childhood MetS-severity-z-scores had areas-under-the-curve (AUC) of 0.91 and 0.65 by PFS and PHU, respectively, while MetS-z-scores at PFS had AUC of 0.84 for subsequent CVD by PHU.

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Figure 1

Mean MetS severity scores (mean, 95-percent CI) by adult CVD status. Childhood (LRC) and adult (PFS) scores for three groups: 1) disease-free throughout, 2) early CVD (between LRC and PFS), and 3) later incident CVD (between PFS and PHU). Comparison with disease-free group: * p<0.001. Comparison with incident disease between PFS and PHU: # p<0.05.

Using logistic regression, each 1.0 increase in childhood z-scores carried elevated odds ratios (OR) of 9.8 and 2.4 for incident CVD by PFS and PHU, respectively (p<0.001 and p<0.05). When change in MetS-z-score from LRC to PFS was added to baseline LRC z-score in the model, this carried a further elevated OR of 3.4 for incident CVD between PFS and PHU (p<0.01).

The long-term health consequences of obesity—including CVD—underscore the need for clinical tools to assist in risk prediction to target at-risk individuals for preventive therapy. We found that a sex- and race/ethnicity-specific MetS-severity-z-score may serve as such a tool in assisting disease prediction in two ways: 1) baseline MetS-severity scores in childhood and in mid-adulthood predicted later CVD diagnosis and 2) the change in score during the interval from childhood to adulthood was associated with future disease, even after adjustment for baseline scores. In this sense, this score overcomes limitations of traditional MetS criteria, which are based on individuals having abnormalities in ≥3 of the individual MetS components, and are thus unable to assess for changes in MetS over time within an individual (besides its presence/absence)—and are unable to assess risk related to component values just below the population-based cut-off.

This score is associated with risk for CVD and may serve as a marker of the degree of the severity of metabolic derangements behind MetS. Such a score—potentially calculated automatically in an electronic health record system—could enable tracking changes in a given individual’s MetS severity, both to assess response to specific therapies and to identify ominous increases in MetS severity as a marker of risk and a trigger for further intervention. Future research is needed to determine clinically-useful cut-offs of particularly-elevated risk and whether this score improves CVD risk prediction above traditional criteria on a sex- and race/ethnic basis.

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