Transitions in Metabolic Risk and Long‐Term Cardiovascular Health: Coronary Artery Risk Development in Young Adults (CARDIA) Study

Abstract

BackgroundDespite evidence suggesting that early metabolic dysfunction impacts cardiovascular disease risk, current guidelines focus on risk assessments later in life, missing early transitions in metabolic risk that may represent opportunities for averting the development of cardiovascular disease.Methods and ResultsIn 4420 young adults in the Coronary Artery Risk Development in Young Adults (CARDIA) study, we defined a “metabolic” risk score based on components of the Third Report of the Adult Treatment Panel's definition of metabolic syndrome. Using latent class trajectory analysis adjusted for sex, race, and time‐dependent body mass index, we identified 6 distinct metabolic trajectories over time, specified by initial and final risk: low‐stable, low‐worsening, high‐stable, intermediate‐worsening, intermediate‐stable, and high‐worsening. Overall, individuals gained weight over time in CARDIA with statistically but not clinically different body mass index trend over time. Dysglycemia and dyslipidemia over time were highest in initially high or worsening trajectory groups. Divergence in metabolic trajectories occurred in early adulthood (before age 40), with 2 of 3 individuals experiencing an increase in metabolic risk over time. Membership in a higher‐risk trajectory (defined as initially high or worsening over time) was associated with greater prevalence and extent of coronary artery calcification, left ventricular mass, and decreased left ventricular strain at year 25. Importantly, despite similar rise in body mass index across trajectories over 25 years, coronary artery calcification and left ventricular structure and function more closely tracked risk factor trajectories.ConclusionsTransitions in metabolic risk occur early in life. Obesity‐related metabolic dysfunction is related to subclinical cardiovascular phenotypes independent of evolution in body mass index, including coronary artery calcification and myocardial hypertrophy and dysfunction.

Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe

“…Severe obesity was linked to substantially greater disease risk, the odds ratios being almost 19-fold for diabetes alone and 30-fold for diabetes followed by vascular disease. The contribution of high BMI to diabetes development is marked and clearly exceeds its effect on vascular disease 47, 48…”

Triglyceride glucose index is an independent predictor for the progression of coronary artery calcification in the absence of heavy coronary artery calcification at baseline

“…However, previous studies revealed the pivotal role of IR in atherosclerosis progression via promoting apoptosis of macrophages, endothelial cells, and vascular smooth muscle cells [36][37][38]. Recently, data from Coronary Artery Risk Development in Young Adults (CARDIA) study revealed that transitions in metabolic risk occurred early in life and metabolic dysfunction is related to subclinical cardiovascular phenotypes including CAC and myocardial hypertrophy and dysfunction [39]. Park et al suggested that TyG index was an effective marker for early detecting subclinical coronary atherosclerosis even in the absence of traditional CV risk factors [40].…”

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults

“…Metabolic syndrome is common even in young adulthood, doubling the risk for cardiovascular disease and increasing risk for diabetes five-fold [75]. Young adulthood further appears to be a key transition time for health consequences of metabolic risk, as a high metabolic risk score at the age of 25, or a worsening score from ages 25-40, predicted two-three times the likelihood of coronary artery calcification at year 25 [76]. In this study, we generated a metabolic risk score that was comprised of fasting insulin, glucose, HDL cholesterol, triglycerides, BMI, and waist-to-hip ratio to examine the relationships with DQIs.…”