Background: Despite well‐recognized differences in the atherosclerotic cardiovascular disease risk between men and women, sex differences in risk factors and sex‐specific mechanisms in the pathophysiology of atherosclerotic cardiovascular disease remain poorly understood. Lipid metabolism plays a central role in the development of atherosclerotic cardiovascular disease. Understanding sex differences in lipids and their genetic determinants could provide mechanistic insights into sex differences in atherosclerotic cardiovascular disease and aid in precise risk assessment. Herein, we examined sex differences in plasma lipidome and heterogeneity in genetic influences on lipidome in men and women through sex‐stratified genome‐wide association analyses. Methods and Results: We used data consisting of 179 lipid species measured by shotgun lipidomics in 7266 individuals from the Finnish GeneRISK cohort and sought for replication using independent data from 2045 participants. Significant sex differences in the levels of 141 lipid species were observed ( P <7.0×10 ⁻⁴ ). Interestingly, 121 lipid species showed significant age‐sex interactions, with opposite age‐related changes in 39 lipid species. In general, most of the cholesteryl esters, ceramides, lysophospholipids, and glycerides were higher in 45‐ to 50‐year‐old men compared with women of same age, but the sex differences narrowed down or reversed with age. We did not observe any major differences in genetic effect in the sex‐stratified genome‐wide association analyses, which suggests that common genetic variants do not have a major role in sex differences in lipidome. Conclusions: Our study provides a comprehensive view of sex differences in circulatory lipids pointing to potential sex differences in lipid metabolism and highlights the need for sex‐ and age‐specific prevention strategies.

Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.

Cardiovascular diseases (CVD) are the leading cause of death worldwide for both men and women, but their prevalence and burden show marked sex differences. The existing knowledge gaps in research, prevention, and treatment for women emphasize the need for understanding the biological mechanisms contributing to the sex differences in CVD. Sex differences in the plasma lipids that are well-known risk factors and predictors of CVD events have been recognized and are believed to contribute to the known disparities in CVD manifestations in men and women. However, the current understanding of sex differences in lipids has mainly come from the studies on routinely measured standard lipids- low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total triglycerides, and total cholesterol, which have been the mainstay of the lipid profiling. Sex differences in individual lipid species, collectively called the lipidome, have until recently been less explored due to the technological challenges and analytic costs. With the technological advancements in the last decade and growing interest in understanding mechanisms of sexual dimorphism in metabolic disorders, many investigators utilized metabolomics and lipidomics based platforms to examine the effect of biological sex on detailed lipidomic profiles and individual lipid species. This review presents an overview of the research on sex differences in the concentrations of circulating lipid species, focusing on findings from the metabolome- and lipidome-wide studies. We also discuss the potential contribution of genetic factors including sex chromosomes and sex-specific physiological factors such as menopause and sex hormones to the sex differences in lipidomic profiles.

Diabetes Mellitus, which affects 366 million people worldwide, is a leading cause of mortality, morbidity, and loss of quality of life. South Asians, comprising 24% of the world’s population, suffer a large burden of type 2 diabetes. With intriguing risk phenotypes, unique environmental triggers, and potential genetic predisposition, South Asians offer a valuable resource for investigating the pathophysiology of type 2 diabetes. Genomics has proven its potential to underpin some of the etiology of type 2 diabetes by identifying a number of susceptibility genes, but such data are scarce and unclear in South Asians. We present a systematic review of studies on the genetic basis of type 2 diabetes or its complications in South Asians published between 1987-2012, and discuss the findings and limitations of the available data. Of the 91 eligible studies meeting our inclusion criteria, a vast majority included Indian populations, followed by a few in those of Pakistani origin, while other South Asian countries were generally under-represented. Though a large number of studies focused on the replication of findings from genome-wide association studies (GWAS) in European populations, a few studies explored new genes and pathways along with GWAS in South Asians and suggested the potential to unravel population- specific susceptibility genes in this population. We find encouraging improvements in study designs, sample sizes and the numbers of genetic variants investigated over the last five years, which reflect the existing capacity and scope for large-scale genetic studies in South Asians.

Personalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes. Whole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness. Longitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression. People express an “omic personality” consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions.

The Center for Health Discovery and Wellbeing (CHDWB) is an academic program designed to evaluate the efficacy of clinical self-knowledge and health partner counseling for development and maintenance of healthy behaviors. This paper reports on the change in health profiles for over 90 traits, measured in 382 participants over three visits in the 12 months following enrolment. Significant changes in the desired direction of improved health are observed for many traits related to cardiovascular health, including BMI, blood pressure, cholesterol, and arterial stiffness, as well as for summary measures of physical and mental health. The changes are most notable for individuals in the upper quartile of baseline risk, many of whom showed a positive correlated response across clinical categories. By contrast, individuals who start with more healthy profiles do not generally show significant improvements and only a modest impact of targeting specific health attributes was observed. Overall, the CHDWB model shows promise as an effective intervention particularly for individuals at high risk for cardiovascular disease.