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Cardiovascular diseases caused by atherosclerosis do not typically manifest before middle age; however, the disease process begins early in life. Preclinical atherosclerosis can be quantified with imaging methods in healthy populations long before clinical manifestations present. Cohort studies have shown that childhood exposure to risk factors, such as dyslipidaemia, elevated blood pressure and tobacco smoking, are associated with adult preclinical atherosclerotic phenotypes. Importantly, these long-term effects are substantially reduced if the individual becomes free from the risk factor by adulthood. As participants in the cohorts continue to age and clinical end points accrue, the strongest evidence linking exposure to risk factors in early life with cardiovascular outcomes has begun to emerge. Although science has deciphered the natural course of atherosclerosis, discovered its causal risk factors and developed effective means to intervene, we are still faced with an ongoing global pandemic of atherosclerotic diseases. In general, atherosclerosis goes undetected for too long, and preventive measures, if initiated at all, are inadequate and/or come too late. In this Review, we give an overview of the available literature suggesting the importance of initiating the prevention of atherosclerosis in early life and provide a summary of the major paediatric programmes for the prevention of atherosclerotic disease. We also highlight the limitations of current knowledge and indicate areas for future research.In this Review, Raitakari and colleagues discuss the importance of initiating the prevention of atherosclerosis in early life and summarize the major paediatric programmes for the prevention of atherosclerotic cardiovascular disease.

In this study, childhood cardiovascular risk factors including BMI, systolic blood pressure, lipid levels, and smoking were correlated with cardiovascular events in adulthood after a mean follow-up of 35 years. Childhood risk factors and the change in risk score between childhood and adulthood were associated with midlife cardiovascular events.

This study examined the relationship between income and physical activity by using three measures to illustrate daily physical activity: the self-reported physical activity index for leisure-time physical activity, pedometer-based total steps for overall daily physical activity, and pedometer-based aerobic steps that reflect continuous steps for more than 10 min at a time. The study population consisted of 753 adults from Finland (mean age 41.7 years; 64% women) who participated in 2011 in the follow-up of the ongoing Young Finns study. Ordinary least squares models were used to evaluate the associations between income and physical activity. The consistency of the results was explored by using register-based income information from Statistics Finland, employing the instrumental variable approach, and dividing the pedometer-based physical activity according to weekdays and weekend days. The results indicated that higher income was associated with higher self-reported physical activity for both genders. The results were robust to the inclusion of the control variables and the use of register-based income information. However, the pedometer-based results were gender-specific and depended on the measurement day (weekday vs. weekend day). In more detail, the association was positive for women and negative or non-existing for men. According to the measurement day, among women, income was positively associated with aerobic steps despite the measurement day and with totals steps measured on the weekend. Among men, income was negatively associated with aerobic steps measured on weekdays. The results indicate that there is an association between income and physical activity, but the association is gender-specific and depends on the measurement type of physical activity.

Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood. We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16-39 y; 51% women; mean ± standard deviation BMI 24 ± 4 kg/m(2)). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%-183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87% ± 3%; R(2)= 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160% ± 2%; R(2) = 0.92). Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood. Please see later in the article for the Editors' Summary.

Branched-chain and aromatic amino acids are associated with the risk for future type 2 diabetes; however, the underlying mechanisms remain elusive. We tested whether amino acids predict insulin resistance index in healthy young adults. Circulating isoleucine, leucine, valine, phenylalanine, tyrosine, and six additional amino acids were quantified in 1,680 individuals from the population-based Cardiovascular Risk in Young Finns Study (baseline age 32 ± 5 years; 54% women). Insulin resistance was estimated by homeostasis model assessment (HOMA) at baseline and 6-year follow-up. Amino acid associations with HOMA of insulin resistance (HOMA-IR) and glucose were assessed using regression models adjusted for established risk factors. We further examined whether amino acid profiling could augment risk assessment of insulin resistance (defined as 6-year HOMA-IR >90th percentile) in early adulthood. Isoleucine, leucine, valine, phenylalanine, and tyrosine were associated with HOMA-IR at baseline and for men at 6-year follow-up, while for women only leucine, valine, and phenylalanine predicted 6-year HOMA-IR (P < 0.05). None of the other amino acids were prospectively associated with HOMA-IR. The sum of branched-chain and aromatic amino acid concentrations was associated with 6-year insulin resistance for men (odds ratio 2.09 [95% CI 1.38-3.17]; P = 0.0005); however, including the amino acid score in prediction models did not improve risk discrimination. Branched-chain and aromatic amino acids are markers of the development of insulin resistance in young, normoglycemic adults, with most pronounced associations for men. These findings suggest that the association of branched-chain and aromatic amino acids with the risk for future diabetes is at least partly mediated through insulin resistance.

In four prospective cohort studies, obese adults who were overweight or obese in childhood had increased rates of cardiovascular risk factors. Those who were overweight or obese as children but not as adults had risks similar to the risks among those who were never obese. During the past three decades, the prevalences of overweight and obesity in the pediatric population have increased substantially. 1 Childhood obesity is a predictor of an increased rate of death, owing primarily to an increased risk of cardiovascular disease. 2 – 4 Forecasts suggest that the “obesity epidemic” may reverse the current trend of the declining rate of death from cardiovascular causes, 5 leading to a shorter lifespan for today's children. 6 There is substantial evidence that the association between obesity and cardiovascular disease is explained by the adverse cardiovascular risk-factor profile that is frequently seen in obese adults. This profile includes increased rates of . . .

Depressive mood is often preceded by sleep problems, suggesting that they increase the risk of depression. Sleep problems can also reflect prodromal symptom of depression, thus temporal precedence alone is insufficient to confirm causality. The authors applied recently introduced statistical causal-discovery algorithms that can estimate causality from cross-sectional samples in order to infer the direction of causality between the two sets of symptoms from a novel perspective. Two common-population samples were used; one from the Young Finns study (690 men and 997 women, average age 37.7 years, range 30-45), and another from the Wisconsin Longitudinal study (3101 men and 3539 women, average age 53.1 years, range 52-55). These included three depression questionnaires (two in Young Finns data) and two sleep problem questionnaires. Three different causality estimates were constructed for each data set, tested in a benchmark data with a (practically) known causality, and tested for assumption violations using simulated data. Causality algorithms performed well in the benchmark data and simulations, and a prediction was drawn for future empirical studies to confirm: for minor depression/dysphoria, sleep problems cause significantly more dysphoria than dysphoria causes sleep problems. The situation may change as depression becomes more severe, or more severe levels of symptoms are evaluated; also, artefacts due to severe depression being less well presented in the population data than minor depression may intervene the estimation for depression scales that emphasize severe symptoms. The findings are consistent with other emerging epidemiological and biological evidence.

Alexander Gusev, Bogdan Pasaniuc and colleagues present a strategy that integrates gene expression measurements with summary statistics from large-scale genome-wide association studies to identify genes whose cis -regulated expression is associated with complex traits. They identify 69 new genes significantly associated with obesity-related traits and illustrate how this approach can provide insights into the genetic basis of complex traits. Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis -regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Metabolite associations with insulin resistance were studied in 7,098 young Finns (age 31 ± 3 years; 52% women) to elucidate underlying metabolic pathways. Insulin resistance was assessed by the homeostasis model (HOMA-IR) and circulating metabolites quantified by high-throughput nuclear magnetic resonance spectroscopy in two population-based cohorts. Associations were analyzed using regression models adjusted for age, waist, and standard lipids. Branched-chain and aromatic amino acids, gluconeogenesis intermediates, ketone bodies, and fatty acid composition and saturation were associated with HOMA-IR (P < 0.0005 for 20 metabolite measures). Leu, Ile, Val, and Tyr displayed sex- and obesity-dependent interactions, with associations being significant for women only if they were abdominally obese. Origins of fasting metabolite levels were studied with dietary and physical activity data. Here, protein energy intake was associated with Val, Phe, Tyr, and Gln but not insulin resistance index. We further tested if 12 genetic variants regulating the metabolites also contributed to insulin resistance. The genetic determinants of metabolite levels were not associated with HOMA-IR, with the exception of a variant in GCKR associated with 12 metabolites, including amino acids (P < 0.0005). Nonetheless, metabolic signatures extending beyond obesity and lipid abnormalities reflected the degree of insulin resistance evidenced in young, normoglycemic adults with sex-specific fingerprints.

Samuli Ripatti and colleagues report a genome-wide association study for human serum metabolites using NMR of serum samples from over 8,000 Finnish individuals. They identify 31 loci associated with at least one of 216 serum metabolic measures. Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations ( P < 2.31 × 10 −10 ) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.