Objective The authors hypothesize that an untargeted metabolomics study will identify novel mechanisms underlying smoking‐associated weight loss. Methods This study performed cross‐sectional analyses among 1,252 participants in the Bogalusa Heart Study and assessed 1,202 plasma metabolites for mediation effects on smoking‐BMI associations. Significant metabolites were tested for associations with smoking genetic risk scores among a subset of participants (n = 654) with available genomic data, followed by direction dependence analysis to investigate causal relationships between the metabolites and smoking and BMI. All analyses controlled for age, sex, race, education, alcohol drinking, and physical activity. Results Compared with never smokers, current and former smokers had a 3.31‐kg/m2 and 1.77‐kg/m2 lower BMI after adjusting for all covariables, respectively. A total of 22 xenobiotics and 94 endogenous metabolites were significantly associated with current smoking. Eight xenobiotics were also associated with former smoking. Forty metabolites mediated the smoking‐BMI associations, and five showed causal relationships with both smoking and BMI. These metabolites, including 1‐oleoyl‐GPE (18:1), 1‐linoleoyl‐GPE (18:2), 1‐stearoyl‐2‐arachidonoyl‐GPE (18:0/20:4), α‐ketobutyrate, and 1‐palmitoyl‐GPE (16:0), mediated 26.0% of the association between current smoking and BMI. Conclusions This study cataloged plasma metabolites altered by cigarette smoking and identified five metabolites that partially mediated the association between current smoking and BMI.

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

Objective The aim of this study was to explore the association of lifelong smoking status with risk of major adverse cardiovascular events (MACE) accounting for weight change in a Chinese cohort. Methods The cohort of the People’s Republic of China‐United States of America (PRC‐USA) Collaborative Study of Cardiovascular and Cardiopulmonary Epidemiology was established in 1983 to 1984, resurveyed during 1987 to 1988 and 1993 to 1994, and followed up to 2005. A total of 5,849 participants who survived in 1993 to 1994 were classified into never smokers, long‐term quitters, short‐term quitters, short‐term relapsers and new smokers, long‐term relapsers and new smokers, and persistent smokers according to the information on lifelong smoking status collected in all three surveys. The associations of lifelong smoking status with MACE in the subsequent 10 years were explored with Cox proportional hazards models. Results During a median follow‐up of 10.2 years, 694 participants had MACE. Compared with persistent smokers, the multivariable‐adjusted hazard ratio of developing MACE was 0.83 (95% CI: 0.61‐1.12) for short‐term quitters, 0.75 (95% CI: 0.54‐1.02) for long‐term quitters, and 0.68 (95% CI: 0.54‐0.85) for never smokers (ptrend = 0.001). In comparison, the hazard ratio was 1.03 (95% CI: 0.77‐1.35) for long‐term relapsers and new smokers and 0.78 (95% CI: 0.46‐1.22) for short‐term relapsers and new smokers (ptrend = 0.018). These associations were not significantly altered by further adjusting for weight change in the past 10 years. Conclusions Lifelong smoking status is significantly associated with risk of MACE. As time duration increased, health benefit to quitters would become close to that of never smokers, and harms to relapsers and new smokers would become close to that of persistent smokers.