Plasma lipidome is independently associated with variability in metabolic syndrome in Mexican American families

Abstract

The full spectrum of lipid molecules in human plasma and their potential role in human health and disease are areas of intense interest ( 1-9 ). This interest owes substantially to the sophisticated technologies that make it possible to accurately capture and quantify the human lipidome ( 8, 10 ). Associative evidence gleaned from plasma lipidomic studies promises vital contributions to biomarker researchan important mainstay of the continued efforts for chronic disease prevention. The plasma lipidomic profi le of humans exhibits a wide range of diversity ( 11,12 ) and is associated with several conditions including obesity ( 13, 14 ), hypertension ( 14-16 ), disorders of glucose metabolism ( 13, 17 ), metabolic syndrome (MS) ( 10, 18 ), cardiovascular diseases ( 19 ), cystic fi brosis ( 20 ), nicotine consumption ( 21 ), and response to antilipid treatment ( 22 ).Lipidomic association studies conducted in the context of families have an important consideration that the concentrations of the plasma lipid species might be phenotypically as well as genetically correlated with each other ( 23 ) especially in related individuals. In that case, it is Abstract Plasma lipidome is now increasingly recognized as a potentially important marker of chronic diseases, but the exact extent of its contribution to the interindividual phenotypic variability in family studies is unknown. Here, we used the rich data from the ongoing San Antonio Family Heart Study (SAFHS) and developed a novel statistical approach to quantify the independent and additive value of the plasma lipidome in explaining metabolic syndrome (MS) variability in Mexican American families recruited in the SAFHS. Our analytical approach included two preprocessing steps: principal components analysis of the highresolution plasma lipidomics data and construction of a subject-subject lipidomic similarity matrix. We then used the Sequential Oligogenic Linkage Analysis Routines software to model the complex family relationships, lipidomic similarities, and other important covariates in a variance components framework. Our results suggested that even after accounting for the shared genetic infl uences, indicators of lipemic status (total serum cholesterol, TGs, and HDL cholesterol), and obesity, the plasma lipidome independently explained 22% of variability in the homeostatic model of assessment-insulin resistance trait and 16% to 22% variability in glucose, insulin, and waist circumference. Our results demonstrate that plasma lipidomic studies can additively contribute to an understanding of the interindividual variability in

Skeletal muscle and plasma lipidomic signatures of insulin resistance and overweight/obesity in humans

“…Large cohort studies, including studies from our group, have highlighted the strong potential of plasma lipidomics in explaining metabolic disease when added to traditional disease markers, including waist circumference, fasting glucose, and insulin . In the present study we refine these findings and differentiate between plasma lipids that align with insulin resistance from those aligning with excess adiposity.…”

“…Circulating lipid species likely represent a readout of tissues, particularly liver , and possibly also reflect muscle lipid composition. Our studies have revealed that the plasma lipidome signature explained much of the variability in glucose homeostasis in large cohorts , and specific lipids emerged as potential biomarkers of glucose intolerance and type 2 diabetes, including Cer(d18:1/18:0) and its dhCer precursor .…”

Hypercoagulation and elevation of blood triglycerides are characteristics of Kawasaki disease

“…In 2014, several reports were published based on a large Cohort study named San Antonio Family Heart Study covering over 1200 adult Mexican Americans. Plasma lipid composition was found to be inheritable and associated with risk of cardiovascular damages [ 35 , 36 ]. In these studies, 43 TG molecular species were identified by ESI MS, and the statistical distribution of various species was associated with age, sex and body mass index [ 37 ].…”

Mechanistic insights revealed by lipid profiling in monogenic insulin resistance syndromes

“…These results were further confirmed in the UK Twin Registry [ 3 ]. More generally, T2DM, CVD and metabolic diseases have been associated with major remodelling of lipid profiles in plasma, suggesting that either lipid classes or specific lipid species could be used to monitor disease progression and treatment efficacy [ 4 – 6 ]. In purely epidemiological studies, however, the associations do not truly probe the mechanisms responsible for the association of a given lipid species with the risk of developing T2DM or CVD.…”