Circulating Ceramides Predict Cardiovascular Outcomes in the Population-Based FINRISK 2002 Cohort.

OBJECTIVE: Ceramides are molecular lipids implicated in apoptosis, inflammation, obesity, and insulin resistance. An earlier study reported that ceramides were associated with fatal outcome among patients with coronary heart disease. Here, we examined whether ceramides are associated with major adverse cardiovascular events (MACEs) among apparently healthy individuals. APPROACH AND RESULTS: FINRISK 2002 is a population-based risk factor survey, which recruited men and women aged 25 to 74 years. The cohort was followed up until the end of 2014. We quantified 4 circulating ceramides, Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0), and Cer(d18:1/24:1), in 8101 serum samples by a targeted liquid chromatography-tandem mass spectrometry assay. Primary outcome of interest was incident MACE (n=813). Secondary analyses were performed for MACE death (n=116) without previous nonfatal MACE and for recurrent MACE (n=226) among survivors of a previous incident MACE. We used Cox proportional hazard models adjusted for the Framingham covariates to determine the association of ceramides with the outcomes. Of the ceramide species, Cer(d18:1/18:0) had the strongest association with incident MACE and the highest unadjusted hazard ratio of 1.31 (95% confidence interval, 1.21-1.41), which remained significant at 1.21 (95% confidence interval, 1.11-1.33) after Framingham risk factor adjustments. The hazard ratios were generally stronger for recurrent and fatal events than for first events. Clinical net reclassification improvement was 7.5% (P=6.9×10-5) for Cer(d18:1/18:0). CONCLUSIONS: Distinct serum ceramides are associated with the risk of incident MACE in apparently healthy individuals. These results should encourage more detailed analyses of ceramides in cardiovascular pathobiology and suggest new biomarkers of MACE risk.

Plasma ceramides predict cardiovascular death in patients with stable coronary artery disease and acute coronary syndromes beyond LDL-cholesterol.

AIMS: The aim was to study the prognostic value of plasma ceramides (Cer) as cardiovascular death (CV death) markers in three independent coronary artery disease (CAD) cohorts. METHODS AND RESULTS: Corogene study is a prospective Finnish cohort including stable CAD patients (n = 160). Multiple lipid biomarkers and C-reactive protein were measured in addition to plasma Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0), and Cer(d18:1/24:1). Subsequently, the association between high-risk ceramides and CV mortality was investigated in the prospective Special Program University Medicine-Inflammation in Acute Coronary Syndromes (SPUM-ACS) cohort (n = 1637), conducted in four Swiss university hospitals. Finally, the results were validated in Bergen Coronary Angiography Cohort (BECAC), a prospective Norwegian cohort study of stable CAD patients. Ceramides, especially when used in ratios, were significantly associated with CV death in all studies, independent of other lipid markers and C-reactive protein. Adjusted odds ratios per standard deviation for the Cer(d18:1/16:0)/Cer(d18:1/24:0) ratio were 4.49 (95% CI, 2.24-8.98), 1.64 (1.29-2.08), and 1.77 (1.41-2.23) in the Corogene, SPUM-ACS, and BECAC studies, respectively. The Cer(d18:1/16:0)/Cer(d18:1/24:0) ratio improved the predictive value of the GRACE score (net reclassification improvement, NRI = 0.17 and ΔAUC = 0.09) in ACS and the predictive value of the Marschner score in stable CAD (NRI = 0.15 and ΔAUC = 0.02). CONCLUSIONS: Distinct plasma ceramide ratios are significant predictors of CV death both in patients with stable CAD and ACS, over and above currently used lipid markers. This may improve the identification of high-risk patients in need of more aggressive therapeutic interventions.

Development and validation of a high-throughput LC-MS/MS assay for routine measurement of molecular ceramides.

Monitoring the levels of the ceramides (Cer) d18:1/16:0, Cer d18:1/18:0, Cer d18:1/24:0, and Cer d18:1/24:1 and ratios thereof in human plasma empowers the prediction of fatal outcome of coronary artery disease (CAD). We describe a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) methodology for clinical-scaled measurement of the four distinct ceramides. Rapid plasma precipitation was accomplished in 96-well format. Excellent extraction recoveries in the range of 98-109% were achieved for each ceramide. Addition of corresponding D7-labeled ceramide standards facilitated precise quantification of each plasma ceramide species utilizing a novel short 5-min LC-MS/MS method. Neither matrix interference nor carryover was observed. Robust intra- and inter-assay accuracy and precision <15% at five different concentrations were obtained. Linear calibration lines with regressions, R(2) > 0.99, were achieved for all analytes. Short-term bench top, long-term plasma, and extract stability demonstrated that the distinct ceramides were stable in the conditions evaluated. The validity of the methodology was demonstrated by determining the precise ceramide concentrations in a small CAD case-control study. Thus, our LC-MS/MS methodology features simple sample preparation and short analysis time for accurate quantification of Cer d18:1/16:0, Cer d18:1/18:0, Cer d18:1/24:0, and Cer d18:1/24:1, designed for routine analysis.

Statistical methods for handling unwanted variation in metabolomics data.

Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available.

Monounsaturated fatty acids in serum triacylglycerols are associated with response to neoadjuvant chemotherapy in breast cancer patients.

Changes in cellular lipid metabolism are a common feature in most solid tumors, which occur already in early stages of the tumor progression. However, it remains unclear if the tumor-specific lipid changes can be detected at the level of systemic lipid metabolism. The objective of this study was to perform comprehensive analysis of lipids in breast cancer patient serum samples. Lipidomic profiling using an established analytical platform was performed in two cohorts of breast cancer patients receiving neoadjuvant chemotherapy. The analyses were performed for 142 patients before and after neoadjuvant chemotherapy, and the results before chemotherapy were validated in an independent cohort of 194 patients. The analyses revealed that in general the tumor characteristics are not reflected in the serum samples. However, there was an association of specific triacylglycerols (TGs) in patients' response to chemotherapy. These TGs containing mainly oleic acid (C18:1) were found in lower levels in those patients showing pathologic complete response before receiving chemotherapy. Some of these TGs were also associated with estrogen receptor status and overall or disease-free survival of the patients. The results suggest that the altered serum levels of oleic acid in breast cancer patients are associated with their response to chemotherapy.

Interfacial properties of high-density lipoprotein-like lipid droplets with different lipid and apolipoprotein A-I compositions.

The surface properties of high-density lipoproteins (HDLs) are important because different enzymes bind and carry out their functions at the surface of HDL particles during metabolic processes. However, the surface properties of HDL and other lipoproteins are poorly known because they cannot be directly measured for nanoscale particles with contemporary experimental methods. In this work, we carried out coarse-grained molecular dynamics simulations to study the concentration of core lipids in the surface monolayer and the interfacial tension of droplets resembling HDL particles. We simulated lipid droplets composed of different amounts of phospholipids, cholesterol esters (CEs), triglycerides (TGs), and apolipoprotein A-Is. Our results reveal that the amount of TGs in the vicinity of water molecules in the phospholipid monolayer is 25-50% higher compared to the amount of CEs in a lipid droplet with a mixed core of an equal amount of TG and CE. In addition, the correlation time for the exchange of molecules between the core and the monolayer is significantly longer for TGs compared to CEs. This suggests that the chemical potential of TG is lower in the vicinity of aqueous phase but the free-energy barrier for the translocation between the monolayer and the core is higher compared to CEs. From the point of view of enzymatic modification, this indicates that TG molecules are more accessible from the aqueous phase. Further, our results point out that CE molecules decrease the interfacial tension of HDL-like lipid droplets whereas TG keeps it constant while the amount of phospholipids varies.

Metabolic regulation in progression to autoimmune diabetes.

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede ß-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes.

Gender-dependent progression of systemic metabolic states in early childhood.

Little is known about the human intra-individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying progression in the metabolic state. We applied the methodology to study metabolic trajectories in children between birth and 4 years of age, based on a series of samples selected from a large birth cohort study. We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance. Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids. In addition, we demonstrated the feasibility of state-based alignment of personal metabolic trajectories. We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.

Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles.

MOTIVATION: Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today. RESULTS: We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.

Effect of probiotic Lactobacillus rhamnosus GG intervention on global serum lipidomic profiles in healthy adults.

AIM: To investigate the effect of three weeks' intervention with a probiotic Lactobacillus rhamnosus GG (LGG) bacteria on global serum lipidomic profiles and evaluate whether the changes in inflammatory variables (CRP, TNF-alpha and IL-6) are reflected in the global lipidomic profiles of healthy adults. METHODS: We performed UPLC/MS-based global lipidomic platform analysis of serum samples (n = 26) in a substudy of a randomised, double-blind, placebo-controlled 3-wk clinical intervention trial investigating the immunomodulatory effects of probiotics in healthy adults. RESULTS: A total of 407 lipids were identified, corresponding to 13 different lipid classes. Serum samples showed decreases in the levels of lysophosphatidylcholines (LysoGPCho), sphingomyelins (SM) and several glycerophosphatidylcholines (GPCho), while triacylglycerols (TAG) were mainly increased in the probiotic LGG group during the intervention. Among the inflammatory variables, IL-6 was moderately associated by changes in global lipidomic profiles, with the top-ranked lipid associated with IL-6 being the proinflammatory LysoGPCho (20:4). There was a weak association between the lipidomic profiles and the two other inflammatory markers, TNF-alpha and CRP. CONCLUSION: This was the first study to investigate the effects of probiotic intervention on global lipidomic profiles in humans. There are indications that probiotic LGG intervention may lead to changes in serum global lipid profiles, as reflected in decreased GPCho, LysoGPCho and SM as well as mainly increased TAG.

Normalization method for metabolomics data using optimal selection of multiple internal standards.

BACKGROUND: Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task. RESULTS: With the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOMIS). We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized normalization methods: normalization by l2 norm and by retention time region specific standard compound profiles. The NOMIS method proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks. We also demonstrate that the method can be used to select best combinations of standard compounds for normalization. CONCLUSION: Depending on experiment design and biological matrix, the NOMIS method is applicable either as a one-step normalization method or as a two-step method where the normalization parameters, influenced by variabilities of internal standard compounds and their correlation to metabolites, are first calculated from a study conducted in repeatability conditions. The method can also be used in analytical development of metabolomics methods by helping to select best combinations of standard compounds for a particular biological matrix and analytical platform.

Gender, Contraceptives and Individual Metabolic Predisposition Shape a Healthy Plasma Lipidome.

Lipidomics of human blood plasma is an emerging biomarker discovery approach that compares lipid profiles under pathological and physiologically normal conditions, but how a healthy lipidome varies within the population is poorly understood. By quantifying 281 molecular species from 27 major lipid classes in the plasma of 71 healthy young Caucasians whose 35 clinical blood test and anthropometric indices matched the medical norm, we provided a comprehensive, expandable and clinically relevant resource of reference molar concentrations of individual lipids. We established that gender is a major lipidomic factor, whose impact is strongly enhanced by hormonal contraceptives and mediated by sex hormone-binding globulin. In lipidomics epidemiological studies should avoid mixed-gender cohorts and females taking hormonal contraceptives should be considered as a separate sub-cohort. Within a gender-restricted cohort lipidomics revealed a compositional signature that indicates the predisposition towards an early development of metabolic syndrome in ca. 25% of healthy male individuals suggesting a healthy plasma lipidome as resource for early biomarker discovery.

RNA-Seq and Mass-Spectrometry-Based Lipidomics Reveal Extensive Changes of Glycerolipid Pathways in Brown Adipose Tissue in Response to Cold.

Cold exposure greatly alters brown adipose tissue (BAT) gene expression and metabolism to increase thermogenic capacity. Here, we used RNA sequencing and mass-spectrometry-based lipidomics to provide a comprehensive resource describing the molecular signature of cold adaptation at the level of the transcriptome and lipidome. We show that short-term (3-day) cold exposure leads to a robust increase in expression of several brown adipocyte genes related to thermogenesis as well as the gene encoding the hormone irisin. However, pathway analysis shows that the most significantly induced genes are those involved in glycerophospholipid synthesis and fatty acid elongation. This is accompanied by significant changes in the acyl chain composition of triacylglycerols (TAGs) as well as subspecies-selective changes of acyl chains in glycerophospholipids. These results indicate that cold adaptation of BAT is associated with significant and highly species-selective remodeling of both TAGs and glycerophospholipids.

Plasma concentrations of molecular lipid species in relation to coronary plaque characteristics and cardiovascular outcome: Results of the ATHEROREMO-IVUS study.

BACKGROUND AND AIMS: Previous lipidomics analyses have demonstrated that several lipid molecules in plasma are associated with fatal outcome in patients with coronary artery disease (CAD). This study aims to investigate the associations of previously identified high risk lipid molecules in plasma with coronary plaque characteristics derived from intravascular ultrasound virtual histology (IVUS-VH) imaging, with coronary lipid core burden index (LCBI) on near-infrared spectroscopy (NIRS), and with one year cardiovascular outcome in patients with CAD. METHODS: Between 2008 and 2011, IVUS-VH imaging of a non-culprit coronary artery was performed in 581 patients who underwent coronary angiography for acute coronary syndrome (ACS) or stable CAD. NIRS imaging was additionally performed in 191 patients. Plasma concentrations of molecular lipids were measured with mass spectrometry. RESULTS: Several cholesteryl ester, ceramide and lactosylceramide species and ceramide ratios were associated with vulnerable plaque characteristics on IVUS-VH and NIRS imaging and with 1-year major adverse cardiac events (MACE, defined as all-cause mortality, ACS and unplanned coronary revascularization). In particular, ceramide d18:1/16:0 was consistently associated with higher necrotic core fraction on IVUS-VH (p = 0.001), higher LCBI (p = 0.024) on NIRS and higher MACE rate (adjusted HR 1.79 per standard deviation increase in log-transformed lipid concentration, 95%CI 1.24-2.59, p = 0.002). CONCLUSION: Several molecular lipid species, and particularly ceramide(d18:1/16:0), are associated with the fraction of necrotic core tissue and lipid core burden in coronary atherosclerosis, and are predictive for 1-year clinical outcome after coronary angiography. These molecular lipids may improve risk stratification in CAD and may also be interesting therapeutic targets for the treatment of atherosclerotic disease.

COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access.

Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

Searching for good strategies in adaptive minority games.

In this paper we introduce and study various adaptive minority game models in which agents try to improve their performances by modifying their strategies through genetic algorithm based crossover mechanism. One aim of this study is to find out what happens at the system as well as at the individual agent level. Adaptation is found to improve the performance of individual agents quite remarkably, to tighten the competition among the agents, and to drive the whole system towards maximum efficiency. Results from four adaptative minority games and the basic minority game are compared, and the parameter dependencies of the best performing game are discussed.

Cord serum lipidome in prediction of islet autoimmunity and type 1 diabetes.

Previous studies show that children who later progress to type 1 diabetes (T1D) have decreased preautoimmune concentrations of multiple phospholipids as compared with nonprogressors. It is still unclear whether these changes associate with development of ß-cell autoimmunity or specifically with clinical T1D. Here, we studied umbilical cord serum lipidome in infants who later developed T1D (N = 33); infants who developed three or four (N = 31) islet autoantibodies, two (N = 31) islet autoantibodies, or one (N = 48) islet autoantibody during the follow-up; and controls (N = 143) matched for sex, HLA-DQB1 genotype, city of birth, and period of birth. The analyses of serum molecular lipids were performed using the established lipidomics platform based on ultra-performance liquid chromatography coupled to mass spectrometry. We found that T1D progressors are characterized by a distinct cord blood lipidomic profile that includes reduced major choline-containing phospholipids, including sphingomyelins and phosphatidylcholines. A molecular signature was developed comprising seven lipids that predicted high risk for progression to T1D with an odds ratio of 5.94 (95% CI, 1.07-17.50). Reduction in choline-containing phospholipids in cord blood therefore is specifically associated with progression to T1D but not with development of ß-cell autoimmunity in general.

Integrated model of metabolism and autoimmune response in ß-cell death and progression to type 1 diabetes.

Progression to type 1 diabetes is characterized by complex interactions of environmental, metabolic and immune system factors, involving both degenerative pathways leading to loss of pancreatic ß-cells as well as protective pathways. The interplay between the degenerative and protective pathways may hold the key to disease outcomes, but no models have so far captured the two together. Here we propose a mathematical framework, an ordinary differential equation (ODE) model, which integrates metabolism and the immune system in early stages of disease process. We hypothesize that depending on the degree of regulation, autoimmunity may also play a protective role in the initial response to stressors. We assume that ß-cell destruction follows two paths of loss: degenerative and autoimmune-induced loss. The two paths are mutually competing, leading to termination of the degenerative loss and further to elimination of the stress signal and the autoimmune response, and ultimately stopping the ß-cell loss. The model describes well our observations from clinical and non-clinical studies and allows exploration of how the rate of ß-cell loss depends on the amplitude and duration of autoimmune response.

Metabolome in schizophrenia and other psychotic disorders: a general population-based study.

BACKGROUND: Persons with schizophrenia and other psychotic disorders have a high prevalence of obesity, impaired glucose tolerance, and lipid abnormalities, particularly hypertriglyceridemia and low high-density lipoprotein. More detailed molecular information on the metabolic abnormalities may reveal clues about the pathophysiology of these changes, as well as about disease specificity. METHODS: We applied comprehensive metabolomics in serum samples from a general population-based study in Finland. The study included all persons with DSM-IV primary psychotic disorder (schizophrenia, n = 45; other non-affective psychosis (ONAP), n = 57; affective psychosis, n = 37) and controls matched by age, sex, and region of residence. Two analytical platforms for metabolomics were applied to all serum samples: a global lipidomics platform based on ultra-performance liquid chromatography coupled to mass spectrometry, which covers molecular lipids such as phospholipids and neutral lipids; and a platform for small polar metabolites based on two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). RESULTS: Compared with their matched controls, persons with schizophrenia had significantly higher metabolite levels in six lipid clusters containing mainly saturated triglycerides, and in two small-molecule clusters containing, among other metabolites, (1) branched chain amino acids, phenylalanine and tyrosine, and (2) proline, glutamic, lactic and pyruvic acids. Among these, serum glutamic acid was elevated in all psychoses (P = 0.0020) compared to controls, while proline upregulation (P = 0.000023) was specific to schizophrenia. After adjusting for medication and metabolic comorbidity in linear mixed models, schizophrenia remained independently associated with higher levels in seven of these eight clusters (P < 0.05 in each cluster). The metabolic abnormalities were less pronounced in persons with ONAP or affective psychosis. CONCLUSIONS: Our findings suggest that specific metabolic abnormalities related to glucoregulatory processes and proline metabolism are specifically associated with schizophrenia and reflect two different disease-related pathways. Metabolomics, which is sensitive to both genetic and environmental variation, may become a powerful tool in psychiatric research to investigate disease susceptibility, clinical course, and treatment response.

Serum lipidomics meets cardiac magnetic resonance imaging: profiling of subjects at risk of dilated cardiomyopathy.

Dilated cardiomyopathy (DCM), characterized by left ventricular dilatation and systolic dysfunction, constitutes a significant cause for heart failure, sudden cardiac death or need for heart transplantation. Lamin A/C gene (LMNA) on chromosome 1p12 is the most significant disease gene causing DCM and has been reported to cause 7-9% of DCM leading to cardiac transplantation. We have previously performed cardiac magnetic resonance imaging (MRI) to LMNA carriers to describe the early phenotype. Clinically, early recognition of subjects at risk of developing DCM would be important but is often difficult. Thus we have earlier used the MRI findings of these LMNA carriers for creating a model by which LMNA carriers could be identified from the controls at an asymptomatic stage. Some LMNA mutations may cause lipodystrophy. To characterize possible effects of LMNA mutations on lipid profile, we set out to apply global serum lipidomics using Ultra Performance Liquid Chromatography coupled to mass spectrometry in the same LMNA carriers, DCM patients without LMNA mutation and controls. All DCM patients, with or without LMNA mutation, differed from controls in regard to distinct serum lipidomic profile dominated by diminished odd-chain triglycerides and lipid ratios related to desaturation. Furthermore, we introduce a novel approach to identify associations between the molecular lipids from serum and the MR images from the LMNA carriers. The association analysis using dependency network and regression approaches also helped us to obtain novel insights into how the affected lipids might relate to cardiac shape and volume changes. Our study provides a framework for linking serum derived molecular markers not only with clinical endpoints, but also with the more subtle intermediate phenotypes, as derived from medical imaging, of potential pathophysiological relevance.