Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics

Metabolomic biomarkers of the mediterranean diet in pregnant individuals: A prospective study

BACKGROUND AND AIMS

Metabolomic profiling is a systematic approach to identifying biomarkers for dietary patterns. Yet, metabolomic markers for dietary patterns in pregnant individuals have not been investigated. The aim of this study was to identify plasma metabolomic markers and metabolite panels that are associated with the Mediterranean diet in pregnant individuals.

METHODS

This is a prospective study of 186 pregnant individuals who had both dietary intake and metabolomic profiles measured from the Fetal Growth Studies-Singletons cohort. Dietary intakes during the peri-conception/1st trimester and the second trimester were accessed at 8-13 and 16-22 weeks of gestation, respectively. Adherence to the Mediterranean diet was measured by the alternate Mediterranean Diet (aMED) score. Fasting plasma samples were collected at 16-22 weeks and untargeted metabolomics profiling was performed using the mass spectrometry-based platforms. Metabolites individually or jointly associated with aMED scores were identified using linear regression and least absolute shrinkage and selection operator (LASSO) regression models with adjustment for potential confounders, respectively.

RESULTS

Among 459 annotated metabolites, 64 and 41 were individually associated with the aMED scores of the diet during the peri-conception/1st trimester and during the second trimester, respectively. Fourteen metabolites were associated with the Mediterranean diet in both time windows. Most Mediterranean diet-related metabolites were lipids (e.g., acylcarnitine, cholesteryl esters (CEs), linoleic acid, long-chain triglycerides (TGs), and phosphatidylcholines (PCs), amino acids, and sugar alcohols. LASSO regressions also identified a 10 metabolite-panel that were jointly associated with aMED score of the diet during the peri-conception/1st trimester (AUC: 0.74; 95% CI: 0.57, 0.91) and a 3 metabolites-panel in the 2nd trimester (AUC: 0.68; 95% CI: 0.50, 0.86).

CONCLUSION

We identified plasma metabolomic markers for the Mediterranean diet among pregnant individuals. Some of them have also been reported in previous studies among non-pregnant populations, whereas others are novel. The results from our study warrant replication in pregnant individuals by future studies.

CLINICAL TRIAL REGISTRATION NUMBER

This study was registered at ClinicalTrials.gov.

Methylxanthines Induce a Change in the AD/Neurodegeneration-Linked Lipid Profile in Neuroblastoma Cells

Alzheimer's disease (AD) is characterized by an increased plaque burden and tangle accumulation in the brain accompanied by extensive lipid alterations. Methylxanthines (MTXs) are alkaloids frequently consumed by dietary intake known to interfere with the molecular mechanisms leading to AD. Besides the fact that MTX consumption is associated with changes in triglycerides and cholesterol in serum and liver, little is known about the effect of MTXs on other lipid classes, which raises the question of whether MTX can alter lipids in a way that may be relevant in AD. Here we have analyzed naturally occurring MTXs caffeine, theobromine, theophylline, and the synthetic MTXs pentoxifylline and propentofylline also used as drugs in different neuroblastoma cell lines. Our results show that lipid alterations are not limited to triglycerides and cholesterol in the liver and serum, but also include changes in sphingomyelins, ceramides, phosphatidylcholine, and plasmalogens in neuroblastoma cells. These changes comprise alterations known to be beneficial, but also adverse effects regarding AD were observed. Our results give an additional perspective of the complex link between MTX and AD, and suggest combining MTX with a lipid-altering diet compensating the adverse effects of MTX rather than using MTX alone to prevent or treat AD.

Changes in the plasma lipidome of healthy subjects after coffee consumption reveal potential cardiovascular benefits: A randomized controlled trial

Lipid metabolism dysregulation is associated with cardiovascular disease (CVD) risk. Specific oxidized lipids are recognized CVD biomarkers involved in all stages of atherosclerosis, including foam cell formation. Moderate coffee intake is positively associated with cardiovascular health. A randomized, controlled (n = 25) clinical trial was conducted in healthy subjects to assess the changes in lipid species relevant to CVD (main inclusion criteria: coffee drinkers, nonsmokers, with no history and/or diagnosis of chronic disease and not consuming any medications). Volunteers consumed a coffee beverage (400 mL/day) containing either 787 mg (coffee A; n = 24) or 407 mg (coffee B; n = 25) of chlorogenic acids for eight weeks. We measured the total plasma levels of 46 lipids, including fatty acids, sterols, and oxysterols, at baseline and after eight weeks and assessed the effects of chlorogenic and phenolic acids, the major coffee antioxidants, in an in vitro foam cell model via targeted lipidomics. At baseline (n = 74), all participants presented oxysterols and free fatty acids (FFAs) (CVD risk markers), which are closely correlated to among them, but not with the classical clinical variables (lipid profile, waist circumference, and BMI). After eight weeks, the control group lipidome showed an increase in oxysterols (+7 ± 10%) and was strongly correlated with FFAs (e.g., arachidonic acid) and cholesteryl ester reduction (-13 ± 7%). Notably, the coffee group subjects (n = 49) had increased cholesteryl esters (+9 ± 11%), while oxysterols (-71 ± 30%) and FFAs (-29 ± 26%) decreased. No differences were found between the consumption of coffees A and B. Additionally, coffee antioxidants decreased oxysterols and regulated arachidonic acid in foam cells. Our results suggest that coffee consumption modulates the generation of oxidized and inflammatory lipids in healthy subjects, which are fundamental during CVD development. The clinical trial was registered on the International Clinical Trials Registry Platform, WHO primary registry (RPCEC00000168).

A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000-2019

Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.

Broad Metabolome Alterations Associated with the Intake of Oral Contraceptives Are Mediated by Cortisol in Premenopausal Women

The use of oral contraceptives (OCs) has been associated with elevated blood cortisol concentrations. However, metabolic downstream effects of OC intake are not well described. Here, we aimed to determine if the blood metabolome is associated with the use of OCs and to estimate if these associations might be statistically mediated by serum cortisol concentrations. Plasma metabolites measured with the Biocrates AbsoluteIDQ p180 Kit and serum cortisol concentrations measured by an immunoassay were determined in 391 premenopausal women (116 OC users) participating in two independent cohorts of the Study of Health in Pomerania (SHIP). After correction for multiple testing, 27 metabolites were significantly associated with OC intake in SHIP-TREND (discovery cohort), of which 25 replicated in SHIP-2. Inter alia, associated metabolites included 12 out of 38 phosphatidylcholines with diacyl residue, 7 out of 14 lysophosphatidylcholines and 5 out of 21 amino acids. The associations with phosphatidylcholines were statistically mediated by cortisol, whereas lysophosphatidylcholines showed no mediation effect. The results represent a step toward a better understanding of the metabolic consequences of OC intake. Connecting cortisol with metabolic consequences of OC intake could help to understand the mechanisms underlying adverse effects. The blood metabolome may serve as a biomarker for identifying users at high risk for developing such adverse effects.

Sex Affects Human Premature Neonates' Blood Metabolome According to Gestational Age, Parenteral Nutrition, and Caffeine Treatment

Prematurity is the leading cause of neonatal deaths and high economic costs; it depends on numerous biological and social factors, and is highly prevalent in males. Several factors can affect the metabolome of premature infants. Accordingly, the aim of the present study was to analyze the role played by gestational age (GA), parenteral nutrition (PN), and caffeine treatment in sex-related differences of blood metabolome of premature neonates through a MS/MS-based targeted metabolomic approach for the detection of amino acids and acylcarnitines in dried blood spots. GA affected the blood metabolome of premature neonates: male and female very premature infants (VPI) diverged in amino acids but not in acylcarnitines, whereas the opposite was observed in moderate or late preterm infants (MLPI). Moreover, an important reduction of metabolites was observed in female VPI fed with PN, suggesting that PN might not satisfy an infant's nutritional needs. Caffeine showed the highest significant impact on metabolite levels of male MLPI. This study proves the presence of a sex-dependent metabolome in premature infants, which is affected by GA and pharmacological treatment (e.g., caffeine). Furthermore, it describes an integrated relationship among several features of physiology and health.

Coffee, Black Tea, and Green Tea Consumption in Relation to Plasma Metabolites in an Asian Population

SCOPE

Coffee and tea are among the most popular beverages in the world. However, the association between habitual coffee, green tea, and black tea consumption with metabolomics profiles in Asian populations remain largely unknown.

METHODS AND RESULTS

158 metabolites (14 amino acids, 45 acylcarnitines, and 99 sphingolipids) in the blood plasma of participants are measured from the population-based Singapore Prospective Study Program cohort using mass spectrometry (MS). Linear regression models are used to obtain the estimates for the association between coffee and tea consumption with metabolite levels, adjusted for potential confounders and false discovery rate (FDR). Coffee consumption is significantly associated with higher levels of 63 sphingolipids (29 sphingomyelins, 32 ceramides, a sphingosine-1-phosphate, and a sphingosine) and lower levels of 13 acylcarnitines and alanine. Black tea consumption is significantly associated with higher levels of eight sphingolipids, and lower levels of an amino acid, whereas green tea is significantly inversely associated with four metabolites (C8:1-OH acylcarnitine, ganglioside GM3 d18:1/16:0, sphingomyelins d18:2/18:0 and d18:1/14:0).

CONCLUSIONS

Coffee, black tea, and green tea consumption are associated with plasma levels of certain classes of sphingolipids and acylcarnitines in an Asian population, particularly sphingomyelins, which may mediate the health benefits of these beverages.

Metabolomic Signatures of Long-term Coffee Consumption and Risk of Type 2 Diabetes in Women

OBJECTIVE

Coffee may protect against multiple chronic diseases, particularly type 2 diabetes, but the mechanisms remain unclear.

RESEARCH DESIGN AND METHODS

Leveraging dietary and metabolomic data in two large cohorts of women (the Nurses' Health Study [NHS] and NHSII), we identified and validated plasma metabolites associated with coffee intake in 1,595 women. We then evaluated the prospective association of coffee-related metabolites with diabetes risk and the added predictivity of these metabolites for diabetes in two nested case-control studies (n = 457 case and 1,371 control subjects).

RESULTS

Of 461 metabolites, 34 were identified and validated to be associated with total coffee intake, including 13 positive associations (primarily trigonelline, polyphenol metabolites, and caffeine metabolites) and 21 inverse associations (primarily triacylglycerols [TAGs] and diacylglycerols [DAGs]). These associations were generally consistent for caffeinated and decaffeinated coffee, except for caffeine and its metabolites that were only associated with caffeinated coffee intake. The three cholesteryl esters positively associated with coffee intake showed inverse associations with diabetes risk, whereas the 12 metabolites negatively associated with coffee (5 DAGs and 7 TAGs) showed positive associations with diabetes. Adding the 15 diabetes-associated metabolites to a classical risk factor-based prediction model increased the C-statistic from 0.79 (95% CI 0.76, 0.83) to 0.83 (95% CI 0.80, 0.86) (P < 0.001). Similar improvement was observed in the validation set.

CONCLUSIONS

Coffee consumption is associated with widespread metabolic changes, among which lipid metabolites may be critical for the antidiabetes benefit of coffee. Coffee-related metabolites might help improve prediction of diabetes, but further validation studies are needed.

Targeted proteomic response to coffee consumption

PURPOSE

Coffee is widely consumed and implicated in numerous health outcomes but the mechanisms by which coffee contributes to health is unclear. The purpose of this study was to test the effect of coffee drinking on candidate proteins involved in cardiovascular, immuno-oncological and neurological pathways.

METHODS

We examined fasting serum samples collected from a previously reported single blinded, three-stage clinical trial. Forty-seven habitual coffee consumers refrained from drinking coffee for 1 month, consumed 4 cups of coffee/day in the second month and 8 cups/day in the third month. Samples collected after each coffee stage were analyzed using three multiplex proximity extension assays that, after quality control, measured a total of 247 proteins implicated in cardiovascular, immuno-oncological and neurological pathways and of which 59 were previously linked to coffee exposure. Repeated measures ANOVA was used to test the relationship between coffee treatment and each protein.

RESULTS

Two neurology-related proteins including carboxypeptidase M (CPM) and neutral ceramidase (N-CDase or ASAH2), significantly increased after coffee intake (P < 0.05 and Q < 0.05). An additional 46 proteins were nominally associated with coffee intake (P < 0.05 and Q > 0.05); 9, 8 and 29 of these proteins related to cardiovascular, immuno-oncological and neurological pathways, respectively, and the levels of 41 increased with coffee intake.

CONCLUSIONS

CPM and N-CDase levels increased in response to coffee intake. These proteins have not previously been linked to coffee and are thus novel markers of coffee response worthy of further study. CLINICAL TRIAL REGISTRY: http://www.isrctn.com/ISRCTN12547806.

Plasma Metabolites Associated with Coffee Consumption: A Metabolomic Approach within the PREDIMED Study

Few studies have examined the association of a wide range of metabolites with total and subtypes of coffee consumption. The aim of this study was to investigate associations of plasma metabolites with total, caffeinated, and decaffeinated coffee consumption. We also assessed the ability of metabolites to discriminate between coffee consumption categories. This is a cross-sectional analysis of 1664 participants from the PREDIMED study. Metabolites were semiquantitatively profiled using a multiplatform approach. Consumption of total coffee, caffeinated coffee and decaffeinated coffee was assessed by using a validated food frequency questionnaire. We assessed associations between 387 metabolite levels with total, caffeinated, or decaffeinated coffee consumption (≥50 mL coffee/day) using elastic net regression analysis. Ten-fold cross-validation analyses were used to estimate the discriminative accuracy of metabolites for total and subtypes of coffee. We identified different sets of metabolites associated with total coffee, caffeinated and decaffeinated coffee consumption. These metabolites consisted of lipid species (e.g., sphingomyelin, phosphatidylethanolamine, and phosphatidylcholine) or were derived from glycolysis (alpha-glycerophosphate) and polyphenol metabolism (hippurate). Other metabolites included caffeine, 5-acetylamino-6-amino-3-methyluracil, cotinine, kynurenic acid, glycocholate, lactate, and allantoin. The area under the curve (AUC) was 0.60 (95% CI 0.56-0.64), 0.78 (95% CI 0.75-0.81) and 0.52 (95% CI 0.49-0.55), in the multimetabolite model, for total, caffeinated, and decaffeinated coffee consumption, respectively. Our comprehensive metabolic analysis did not result in a new, reliable potential set of metabolites for coffee consumption.

Metabolomic response to coffee consumption: application to a three-stage clinical trial

BACKGROUND

Coffee is widely consumed and contains many bioactive compounds, any of which may impact pathways related to disease development.

OBJECTIVE

To identify individual metabolite changes in response to coffee.

METHODS

We profiled the metabolome of fasting serum samples collected from a previously reported single-blinded, three-stage clinical trial. Forty-seven habitual coffee consumers refrained from drinking coffee for 1 month, consumed four cups of coffee/day in the second month and eight cups/day in the third month. Samples collected after each coffee stage were subject to nontargeted metabolomic profiling using UPLC-ESI-MS/MS. A total of 733 metabolites were included for univariate and multivariate analyses.

RESULTS

A total of 115 metabolites were significantly associated with coffee intake (P < 0.05 and Q < 0.05). Eighty-two were of known identity and mapped to one of 33 predefined biological pathways. We observed a significant enrichment of metabolite members of five pathways (P < 0.05): (i) xanthine metabolism: includes caffeine metabolites, (ii) benzoate metabolism: reflects polyphenol metabolite products of gut microbiota metabolism, (iii) steroid: novel but may reflect phytosterol content of coffee, (iv) fatty acid metabolism (acylcholine): novel link to coffee and (v) endocannabinoid: novel link to coffee.

CONCLUSIONS

The novel metabolites and candidate pathways we have identified may provide new insight into the mechanisms by which coffee may be exerting its health effects.

Untargeted Metabolomics Reveal Lipid Alterations upon 2-Deoxyglucose Treatment in Human HaCaT Keratinocytes

The glucose analogue 2-deoxyglucose (2-DG) impedes cancer progression in animal models and is currently being assessed as an anticancer therapy, yet the mode of action of this drug of high clinical significance has not been fully delineated. In an attempt to better characterize its pharmacodynamics, an integrative UPLC-Q-Exactive-based joint metabolomic and lipidomic approach was undertaken to evaluate the metabolic perturbations induced by this drug in human HaCaT keratinocyte cells. R-XCMS data processing and subsequent multivariate pattern recognition, metabolites identification, and pathway analyses identified eight metabolites that were most significantly changed upon a 3 h 2-DG exposure. Most of these dysregulated features were emphasized in the course of lipidomic profiling and could be identified as ceramide and glucosylceramide derivatives, consistently with their involvement in cell death programming. Even though metabolomic analyses did not generally afford such clear-cut dysregulations, some alterations in phosphatidylcholine and phosphatidylethanolamine derivatives could be highlighted as well. Overall, these results support the adequacy of the proposed analytical workflow and might contribute to a better understanding of the mechanisms underlying the promising effects of 2-DG.

Use of Metabolomics in Improving Assessment of Dietary Intake

BACKGROUND

Nutritional metabolomics is rapidly evolving to integrate nutrition with complex metabolomics data to discover new biomarkers of nutritional exposure and status.

CONTENT

The purpose of this review is to provide a broad overview of the measurement techniques, study designs, and statistical approaches used in nutrition metabolomics, as well as to describe the current knowledge from epidemiologic studies identifying metabolite profiles associated with the intake of individual nutrients, foods, and dietary patterns.

SUMMARY

A wide range of technologies, databases, and computational tools are available to integrate nutritional metabolomics with dietary and phenotypic information. Biomarkers identified with the use of high-throughput metabolomics techniques include amino acids, acylcarnitines, carbohydrates, bile acids, purine and pyrimidine metabolites, and lipid classes. The most extensively studied food groups include fruits, vegetables, meat, fish, bread, whole grain cereals, nuts, wine, coffee, tea, cocoa, and chocolate. We identified 16 studies that evaluated metabolite signatures associated with dietary patterns. Dietary patterns examined included vegetarian and lactovegetarian diets, omnivorous diet, Western dietary patterns, prudent dietary patterns, Nordic diet, and Mediterranean diet. Although many metabolite biomarkers of individual foods and dietary patterns have been identified, those biomarkers may not be sensitive or specific to dietary intakes. Some biomarkers represent short-term intakes rather than long-term dietary habits. Nonetheless, nutritional metabolomics holds promise for the development of a robust and unbiased strategy for measuring diet. Still, this technology is intended to be complementary, rather than a replacement, to traditional well-validated dietary assessment methods such as food frequency questionnaires that can measure usual diet, the most relevant exposure in nutritional epidemiologic studies.

Lipidomics Insights in Health and Nutritional Intervention Studies

Lipids are among the major components of food and constitute the principal structural biomolecules of human body together with proteins and carbohydrates. Lipidomics encompasses the investigation of the lipidome, defined as the entire spectrum of lipids in a biological system at a given time. Among metabolomics technologies, lipidomics has evolved due to the relevance of lipids in nutrition and their well-recognized roles in health. Mass spectrometry advances have greatly facilitated lipidomics, but owing to the complexity and diversity of the lipids, lipidome purification and analysis are still challenging. This review focuses on lipidomics strategies, applications, and achievements of studies related to nutrition and health research.

Key elements of metabolomics in the study of biomarkers of diabetes

Metabolomics is instrumental in the analysis of disease mechanisms and biomarkers of disease. The human metabolome is influenced by genetics and environmental interactions and reveals characteristic signatures of disease. Population studies with metabolomics require special study designs and care needs to be taken with pre-analytics. Gas chromatography coupled to mass spectrometry, liquid chromatography coupled to mass spectrometry or NMR are popular techniques used for metabolomic analyses in human cohorts. Metabolomics has been successfully used in the biomarker search for disease prediction and progression, for analyses of drug action and for the development of companion diagnostics. Several metabolites or metabolite classes identified by metabolomics have gained much attention in the field of diabetes research in the search for early disease detection, differentiation of progressor types and compliance with medication. This review summarises a presentation given at the 'New approaches beyond genetics' symposium at the 2015 annual meeting of the EASD. It is accompanied by another review from this symposium by Bernd Mayer (DOI: 10.1007/s00125-016-4032-2 ) and an overview by the Session Chair, Leif Groop (DOI: 10.1007/s00125-016-4014-4 ).

The effect of coffee intake on lysophosphatidylcholines: A targeted metabolomic approach

BACKGROUND & AIM

Lysophosphatidylcholines (lysoPC) are known to be a pathological component of oxidized-LDL, and several studies demonstrate its pro-inflammatory properties in vitro. Nevertheless, bioactive compounds found in coffee, such as phenolic acids might inhibit LDL oxidation. The relationship between coffee consumption and lysoPC has not been described previously in humans. The aim of the present study was to assess the association between coffee intake and plasma lysoPC levels in adults.

METHODS

Data was from the "Health Survey of Sao Paulo (ISA-Capital)", a cross-sectional population-based survey in Sao Paulo, among 169 individuals aged 20 years or older. This population was categorized into three groups: non-coffee consumers (0 mL/day-G1), low coffee consumers (≤100 mL/day-G2), and high coffee consumers (>100 mL/day-G3). Usual coffee intake was estimated by two 24HR and one FFQ, using Multiple Source Method. Quantification of the metabolites was performed by mass spectrometry (FIA-MS/MS and HPLC-MS/MS) and 14 lysoPC species were identified. The association between coffee intake and lysoPC was analyzed by multiple linear regression adjusted for age, sex, household per capita income, smoking, physical activity, body mass index, total energy intake, use of drugs, vegetables and fruit consumption and caffeine intake.

RESULTS

LysoPC levels were significantly lower in G3 than in G1, for the lysoPC a C16:1 (β = -0.56; p = 0.014), lysoPC a C18:1 (β = -2.57; p = 0.018), and lysoPC a C20:4 (β = -1.14; p = 0.037). In opposition, the ratios of C16:0/C16:1 and C18:0/18:1 was higher in G3 (β = 5.04; p = 0.025 and β = 0.28; p = 0.003, respectively).

CONCLUSION

LysoPC profile differed according to coffee intake, showing a possible beneficial health effect of this beverage on inflammatory and oxidative processes.

Metabolic biomarkers for chronic kidney disease

Chronic kidney disease (CKD) is an increasingly recognized burden for patients and health care systems with high (and growing) global incidence and prevalence, significant mortality, and disproportionately high treatment costs. Yet, the available diagnostic tools are either impractical in clinical routine or have serious shortcomings impeding a well-informed disease management although optimized treatment strategies with proven benefits for the patients have become available. Advances in bioanalytical technologies have facilitated studies that identified genomic, proteomic, and metabolic biomarker candidates, and confirmed some of them in independent cohorts. This review summarizes the CKD-related markers discovered so far, and focuses on compounds and pathways, for which there is quantitative data, substantiating evidence from translational research, and a mechanistic understanding of the processes involved. Also, multiparametric marker panels have been suggested that showed promising diagnostic and prognostic performance in initial analyses although the data basis from prospective trials is very limited. Large-scale studies, however, are underway and will provide the information for validating a set of parameters and discarding others. Finally, the path from clinical research to a routine application is discussed, focusing on potential obstacles such as the use of mass spectrometry, and the feasibility of obtaining regulatory approval for targeted metabolomics assays.

Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer

BACKGROUND

Coffee intake may be inversely associated with colorectal cancer; however, previous studies have been inconsistent. Serum coffee metabolites are integrated exposure measures that may clarify associations with cancer and elucidate underlying mechanisms.

OBJECTIVES

Our aims were 2-fold as follows: 1) to identify serum metabolites associated with coffee intake and 2) to examine these metabolites in relation to colorectal cancer.

DESIGN

In a nested case-control study of 251 colorectal cancer cases and 247 matched control subjects from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, we conducted untargeted metabolomics analyses of baseline serum by using ultrahigh-performance liquid-phase chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Usual coffee intake was self-reported in a food-frequency questionnaire. We used partial Pearson correlations and linear regression to identify serum metabolites associated with coffee intake and conditional logistic regression to evaluate associations between coffee metabolites and colorectal cancer.

RESULTS

After Bonferroni correction for multiple comparisons (P = 0.05 ÷ 657 metabolites), 29 serum metabolites were positively correlated with coffee intake (partial correlation coefficients: 0.18-0.61; P < 7.61 × 10(-5)); serum metabolites most highly correlated with coffee intake (partial correlation coefficients >0.40) included trigonelline (N'-methylnicotinate), quinate, and 7 unknown metabolites. Of 29 serum metabolites, 8 metabolites were directly related to caffeine metabolism, and 3 of these metabolites, theophylline (OR for 90th compared with 10th percentiles: 0.44; 95% CI: 0.25, 0.79; P-linear trend = 0.006), caffeine (OR for 90th compared with 10th percentiles: 0.56; 95% CI: 0.35, 0.89; P-linear trend = 0.015), and paraxanthine (OR for 90th compared with 10th percentiles: 0.58; 95% CI: 0.36, 0.94; P-linear trend = 0.027), were inversely associated with colorectal cancer.

CONCLUSIONS

Serum metabolites can distinguish coffee drinkers from nondrinkers; some caffeine-related metabolites were inversely associated with colorectal cancer and should be studied further to clarify the role of coffee in the cause of colorectal cancer. The Prostate, Lung, Colorectal, and Ovarian trial was registered at clinicaltrials.gov as NCT00002540.

Intervention Trials with the Mediterranean Diet in Cardiovascular Prevention: Understanding Potential Mechanisms through Metabolomic Profiling

Large observational epidemiologic studies and randomized trials support the benefits of a Mediterranean dietary pattern on cardiovascular disease (CVD). Mechanisms postulated to mediate these benefits include the reduction of low-grade inflammation, increased adiponectin concentrations, decreased blood coagulation, enhanced endothelial function, lower oxidative stress, lower concentrations of oxidized LDL, and improved apolipoprotein profiles. However, the metabolic pathways through which the Mediterranean diet influences CVD risk remain largely unknown. Investigating specific mechanisms in the context of a large intervention trial with the use of high-throughput metabolomic profiling will provide more solid public health messages and may help to identify key molecular targets for more effective prevention and management of CVD. Although metabolomics is not without its limitations, the techniques allow for an assessment of thousands of metabolites, providing wide-ranging profiling of small molecules related to biological status. Specific candidate plasma metabolites that may be associated with CVD include branched-chain and aromatic amino acids; the glutamine-to-glutamate ratio; some short- to medium-chain acylcarnitines; gut flora metabolites (choline, betaine, and trimethylamine N-oxide); urea cycle metabolites (citrulline and ornithine); and specific lipid subclasses. In addition to targeted metabolites, the role of a large number of untargeted metabolites should also be assessed. Large intervention trials with the use of food patterns for the prevention of CVD provide an unparalleled opportunity to examine the effects of these interventions on plasma concentrations of specific metabolites and determine whether such changes mediate the benefits of the dietary interventions on CVD risk.

Toward systems epidemiology of coffee and health

PURPOSE OF REVIEW

Coffee is one of the most widely consumed beverages in the world and has been associated with many health conditions. This review examines the limitations of the classic epidemiological approach to studies of coffee and health, and describes the progress in systems epidemiology of coffee and its correlated constituent, caffeine. Implications and applications of this growing body of knowledge are also discussed.

RECENT FINDINGS

Population-based metabolomic studies of coffee replicate coffee-metabolite correlations observed in clinical settings but have also identified novel metabolites of coffee response, such as specific sphingomyelin derivatives and acylcarnitines. Genome-wide analyses of self-reported coffee and caffeine intake and serum levels of caffeine support an overwhelming role for caffeine in modulating the coffee consumption behavior. Interindividual variation in the physiological exposure or response to any of the many chemicals present in coffee may alter the persistence and magnitude of their effects. It is thus imperative that future studies of coffee and health account for this variation.

SUMMARY

Systems epidemiological approaches promise to inform causality, parse the constituents of coffee responsible for health effects, and identify the subgroups most likely to benefit from increasing or decreasing coffee consumption.

Evaluation of various biomarkers as potential mediators of the association between coffee consumption and incident type 2 diabetes in the EPIC-Potsdam Study

BACKGROUND

The inverse association between coffee consumption and the risk of type 2 diabetes (T2D) is well established; however, little is known about potential mediators of this association.

OBJECTIVE

We aimed to investigate the association between coffee consumption and diabetes-related biomarkers and their potential role as mediators of the association between coffee consumption and T2D.

DESIGN

We analyzed a case-cohort study (subcohort: n = 1610; verified incident T2D cases: n = 417) nested within the European Prospective Investigation into Cancer and Nutrition-Potsdam study involving 27,548 middle-aged participants. Habitual coffee consumption was assessed with a validated, semiquantitative food-frequency questionnaire. We evaluated the association between coffee consumption and several T2D-related biomarkers, such as liver markers (reflected by γ-glutamyltransferase, fetuin-A, and sex hormone-binding globulin), markers of dyslipidemia (high-density lipoprotein cholesterol and triglycerides), inflammation [C-reactive protein (CRP)], an adipokine (adiponectin), and metabolites, stratified by sex.

RESULTS

Coffee consumption was inversely associated with diacyl-phosphatidylcholine C32:1 in both sexes and with phenylalanine in men, as well as positively associated with acyl-alkyl-phosphatidylcholines C34:3, C40:6, and C42:5 in women. Furthermore, coffee consumption was inversely associated with fetuin-A (P-trend = 0.06) and CRP in women and γ-glutamyltransferase and triglycerides in men. Coffee consumption tended to be inversely associated with T2D risk in both sexes, reaching significance only in men [HR (95% CI): women: ≥4 compared with >0 to <2 cups coffee/d: 0.78 (0.46, 1.33); men: ≥5 compared with >0 to <2 cups coffee/d: 0.40 (0.19, 0.81)]. The association between coffee consumption and T2D risk in men was slightly reduced after adjustment for phenylalanine or lipid markers.

CONCLUSIONS

Coffee consumption was inversely associated with a diacyl-phosphatidylcholine and liver markers in both sexes and positively associated with certain acyl-alkyl-phosphatidylcholines in women. Furthermore, coffee consumption showed an inverse trend with CRP in women and with triglycerides and phenylalanine in men. However, these markers explained only to a small extent the inverse association between long-term coffee consumption and T2D risk.

Relationship between the lipidome, inflammatory markers and insulin resistance

The objectives of the present study were to (1) examine the effects of the phenotypic factors age, gender and BMI on the lipidomic profile and (2) investigate the relationship between the lipidome, inflammatory markers and insulin resistance.

Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine

BACKGROUND

Metabolomics is a versatile unbiased method to search for biomarkers of human disease. In particular, one approach in cancer therapy is to promote apoptosis in tumour cells; this could be improved with specific biomarkers of apoptosis for monitoring treatment. We recently observed specific metabolic patterns in apoptotic cell lines; however, in that study, apoptosis was only induced with one pro-apoptotic agent, staurosporine.

OBJECTIVE

The aim of this study was to find novel biomarkers of apoptosis by verifying our previous findings using two further pro-apoptotic agents, 5-fluorouracil and etoposide, that are commonly used in anticancer treatment.

METHODS

Metabolic parameters were assessed in HepG2 and HEK293 cells using the newborn screening assay adapted for cell culture approaches, quantifying the levels of amino acids and acylcarnitines with mass spectrometry.

RESULTS

We were able to identify apoptosis-specific changes in the metabolite profile. Moreover, the amino acids alanine and glutamate were both significantly up-regulated in apoptotic HepG2 and HEK293 cells irrespective of the apoptosis inducer.

CONCLUSION

Our observations clearly indicate the potential of metabolomics in detecting metabolic biomarkers applicable in theranostics and for monitoring drug efficacy.

Systems Epidemiology: A New Direction in Nutrition and Metabolic Disease Research

Systems epidemiology applied to the field of nutrition has potential to provide new insight into underlying mechanisms and ways to study the health effects of specific foods more comprehensively. Human intervention and population-based studies have identified i) common genetic factors associated with several nutrition-related traits and ii) dietary factors altering the expression of genes and levels of proteins and metabolites related to inflammation, lipid metabolism and/or gut microbial metabolism, results of high relevance to metabolic disease. System-level tools applied type 2 diabetes and related conditions have revealed new pathways that are potentially modified by diet and thus offer additional opportunities for nutritional investigations. Moving forward, harnessing the resources of existing large prospective studies within which biological samples have been archived and diet and lifestyle have been measured repeatedly within individual will enable systems-level data to be integrated, the outcome of which will be improved personalized optimal nutrition for prevention and treatment of disease.

Lipidomics in longevity and healthy aging

The role of classical lipids in aging diseases and human longevity has been widely acknowledged. Triglyceride and cholesterol concentrations are clinically assessed to infer the risk of cardiovascular disease while larger lipoprotein particle size and low triglyceride levels have been identified as markers of human longevity. The rise of lipidomics as a branch of metabolomics has provided an additional layer of accuracy to pinpoint specific lipids and its association with aging diseases and longevity. The molecular composition and concentration of lipid species determine their cellular localization, metabolism, and consequently, their impact in disease and health. For example, low density lipoproteins are the main carriers of sphingomyelins and ceramides, while high density lipoproteins are mostly loaded with ether phosphocholines, partly explaining their opposing roles in atherogenesis. Moreover, the identification of specific lipid species in aging diseases and longevity would aid to clarify how these lipids alter health and influence longevity. For instance, ether phosphocholines PC (O-34:1) and PC (O-34:3) have been positively associated with longevity and negatively with diabetes, and hypertension, but other species of phosphocholines show no effect or an opposite association with these traits confirming the relevance of the identification of molecular lipid species to tackle our understanding of healthy aging and disease. Up-to-date, a minor fraction of the human plasma lipidome has been associated to healthy aging and longevity, further research would pinpoint toward specific lipidomic profiles as potential markers of healthy aging and metabolic diseases.

Associations between thyroid hormones and serum metabolite profiles in an euthyroid population

The aim was to characterise associations between circulating thyroid hormones-free thyroxine (FT4) and thyrotropin (TSH)-and the metabolite profiles in serum samples from participants of the German population-based KORA F4 study. Analyses were based on the metabolite profile of 1463 euthyroid subjects. In serum samples, obtained after overnight fasting (≥8), 151 different metabolites were quantified in a targeted approach including amino acids, acylcarnitines (ACs), and phosphatidylcholines (PCs). Associations between metabolites and thyroid hormone concentrations were analysed using adjusted linear regression models. To draw conclusions on thyroid hormone related pathways, intra-class metabolite ratios were additionally explored. We discovered 154 significant associations (Bonferroni p < 1.75 × 10-04) between FT4 and various metabolites and metabolite ratios belonging to AC and PC groups. Significant associations with TSH were lacking. High FT4 levels were associated with increased concentrations of many ACs and various sums of ACs of different chain length, and the ratio of C2 by C0. The inverse associations observed between FT4 and many serum PCs reflected the general decrease in PC concentrations. Similar results were found in subgroup analyses, e.g., in weight-stable subjects or in obese subjects. Further, results were independent of different parameters for liver or kidney function, or inflammation, which supports the notion of an independent FT4 effect. In fasting euthyroid adults, higher serum FT4 levels are associated with increased serum AC concentrations and an increased ratio of C2 by C0 which is indicative of an overall enhanced fatty acyl mitochondrial transport and β-oxidation of fatty acids.

Targeted metabolomics profiles are strongly correlated with nutritional patterns in women

Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeted metabolomic analyses of serum samples using the Biocrates Absolute-IDQ™ Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake and nutritional scores derived from the FFQs summarizing fruit and vegetable intake, alcohol intake, meat intake, hypo-caloric dieting and a "traditional English" diet. We studied the correlation between metabolite levels and dietary intake patterns in the larger population and identified for each trait between 14 and 20 independent monozygotic twins pairs discordant for nutritional intake and replicated results in this set. Results from both analyses were then meta-analyzed. For the metabolites associated with nutritional patterns, we calculated heritability using structural equation modelling. 42 metabolite nutrient intake associations were statistically significant in the discovery samples (Bonferroni P < 4 × 10^-5) and 11 metabolite nutrient intake associations remained significant after validation. We found the strongest associations for fruit and vegetables intake and a glycerophospholipid (Phosphatidylcholine diacyl C38:6, P = 1.39 × 10^-9) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 × 10^-13). We also found significant associations for coffee (confirming a previous association with C10 reported in an independent study), garlic intake and hypo-caloric dieting. Using the twin study design we find that two thirds the metabolites associated with nutritional patterns have a significant genetic contribution, and the remaining third are solely environmentally determined. Our data confirm the value of metabolomic studies for nutritional epidemiologic research.

Metabolomics in the identification of biomarkers of dietary intake

Traditional methods for assessing dietary exposure can be unreliable, with under reporting one of the main problems. In an attempt to overcome such problems there is increasing interest in identifying biomarkers of dietary intake to provide a more accurate measurement. Metabolomics is an analytical technique that aims to identify and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics coupled with statistical analysis for the identification of dietary biomarkers, with a number of putative biomarkers identified. This minireview focuses on metabolomics based approaches and highlights some of the key successes.

Comparative analysis of sample preparation methods to handle the complexity of the blood fluid metabolome: when less is more

Blood sample preparation before LC-MS metabolomic fingerprinting is one of the most challenging and error-prone parts of the analytical procedure. Besides proteins, phospholipids contained in blood fluids are known to cause matrix effects and ion suppression phenomena, thus masking biological variation. Nevertheless, the commonly used sample preparation techniques do not consider their removal prior to analysis. Pooled plasma and serum samples were used as biological material, partly as raw samples and partly spiked with distinct concentrations of a metabolite mix (1-5 μg/mL). Prior to LC-ESI-qToF-MS-driven metabolomic analysis, samples were subjected to different preparation methods consisting of three extractions with organic solvents (acetonitrile, methanol, and methanol/ethanol), a membrane-based solvent-free technique, and a hybrid method combining solvent extraction and SPE-mediated removal of phospholipids. The comparative analysis among sample preparation procedures was based on the capacity to detect endogenous compounds in raw samples, differentiate raw versus spiked samples, and reveal real-life metabolomic changes, following a dietary intervention. Method speed, minimum sample handling, compatibility to automation, and applicability to large-scale metabolomic studies were also considered. The combination of solvent deproteinization and the selective removal of phospholipids was revealed to be the most suitable method, in terms of improvement of nonlipid metabolite coverage, extraction reproducibility, quickness, and compatibility with automation, the minimization of matrix effects being among the most probable causes for the good extraction performance associated with the removal of phospholipid species. The main advantage of conventional solvent extraction procedures was the metabolite information coverage for lipid low-molecular-weight species, and extraction with acetonitrile was generally the second choice for sample preparation. Ultrafiltration was the least effective method for plasma and serum preparation; thus, its use without a previous solvent extraction step of the samples should be discarded. According to the presented data, there is no apparent reason to believe that sacrificing information on lipid compounds is too high of a price to pay in order to gain more information on nonlipid LMW metabolites.

Transcriptomic and metabolomic signatures of an n-3 polyunsaturated fatty acids supplementation in a normolipidemic/normocholesterolemic Caucasian population

OMIC technologies, including transcriptomics and metabolomics, may provide powerful tools for identifying the effects of nutrients on molecular functions and metabolic pathways. The objective was to investigate molecular and metabolic changes following n-3 polyunsaturated fatty acid (PUFA) supplementation in healthy subjects via traditional biomarkers as well as transcriptome and metabolome analyses. Thirteen men and 17 women followed a 2-week run-in period based on Canada's Food Guide and then underwent 6-week supplementation with n-3 PUFA (3 g/day). Traditional biochemical markers such as plasma lipids, inflammatory markers, glycemic parameters and erythrocyte fatty acid concentrations were measured. Changes in gene expression of peripheral blood mononuclear cells were assessed by microarrays, and metabolome profiles were assessed by mass spectrometry assay kit. After supplementation, plasma triglycerides decreased and erythrocyte n-3 PUFA concentrations increased to a similar extent in both genders. Further, plasma high-density lipoprotein cholesterol concentrations and fasting glucose levels increased in women after n-3 PUFA supplementation. N-3 PUFA supplementation changed the expression of 610 genes in men, whereas the expression of 250 genes was altered in women. Pathway analyses indicate changes in gene expression of the nuclear receptor peroxisome proliferator-activated receptor-alpha, nuclear transcription-factor kappaB, oxidative stress and activation of the oxidative stress response mediated by nuclear factor (erythroid-derived 2)-like 2. After n-3 PUFA supplementation, metabolomics profiles demonstrate an increase in acylcarnitines, hexose and leucine in men only and a decrease in saturation of glycerophosphatidylcholine and lysophosphatidylcholine concentrations in all subjects. Overall, traditional and novel biomarkers suggest that n-3 PUFA supplementation exerts cardioprotective effects.

Body fat free mass is associated with the serum metabolite profile in a population-based study

OBJECTIVE

To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study.

SUBJECTS AND METHODS

Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs).

RESULTS

We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism.

CONCLUSION

A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network.

Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes

Obesity is associated with multiple adverse health effects and a high risk of developing metabolic and cardiovascular diseases. Therefore, there is a great need to identify circulating parameters that link changes in body fat mass with obesity. This study combines proteomic and metabolomic approaches to identify circulating molecules that discriminate healthy lean from healthy obese individuals in an exploratory study design. To correct for variations in physical activity, study participants performed a one hour exercise bout to exhaustion. Subsequently, circulating factors differing between lean and obese individuals, independent of physical activity, were identified. The DIGE approach yielded 126 differentially abundant spots representing 39 unique proteins. Differential abundance of proteins was confirmed by ELISA for antithrombin-III, clusterin, complement C3 and complement C3b, pigment epithelium-derived factor (PEDF), retinol binding protein 4 (RBP4), serum amyloid P (SAP), and vitamin-D binding protein (VDBP). Targeted serum metabolomics of 163 metabolites identified 12 metabolites significantly related to obesity. Among those, glycine (GLY), glutamine (GLN), and glycero-phosphatidylcholine 42:0 (PCaa 42:0) serum concentrations were higher, whereas PCaa 32:0, PCaa 32:1, and PCaa 40:5 were decreased in obese compared to lean individuals. The integrated bioinformatic evaluation of proteome and metabolome data yielded an improved group separation score of 2.65 in contrast to 2.02 and 2.16 for the single-type use of proteomic or metabolomics data, respectively. The identified circulating parameters were further investigated in an extended set of 30 volunteers and in the context of two intervention studies. Those included 14 obese patients who had undergone sleeve gastrectomy and 12 patients on a hypocaloric diet. For determining the long-term adaptation process the samples were taken six months after the treatment. In multivariate regression analyses, SAP, CLU, RBP4, PEDF, GLN, and C18:2 showed the strongest correlation to changes in body fat mass. The combined serum proteomic and metabolomic profiling reveals a link between the complement system and obesity and identifies both novel (C3b, CLU, VDBP, and all metabolites) and confirms previously discovered markers (PEDF, RBP4, C3, ATIII, and SAP) of body fat mass changes.

From brain to food: analysis of phosphatidylcholins, lyso-phosphatidylcholins and phosphatidylcholin-plasmalogens derivates in Alzheimer's disease human post mortem brains and mice model via mass spectrometry

Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterized by extracellular senile plaques mainly consisting of Aβ, a 40-42 amino acid long peptide, and intracellular neurofibrillary tangles, accompanied by an excessive loss of synapses. Recently evidence accumulated that nutrition, especially polyunsaturated fatty acids, influences AD pathogenesis. Especially mid-life food habits with the consumption of specific fatty acids (FA) appear to influence the disease risk. The timely separation between food intake and disease makes a direct correlation with detailed analysis of eating habits combined with accurate food analysis nearly unattainable. A possible solution to circumvent these difficulties is to investigate the FA composition in human post mortem brain. In this study we focused on the main phospholipids phosphatidylcholin (PC), phosphatidylcholin-plasmalogen (PC-PL) and lyso-phosphatidylcholin (lyso-PC) in AD brains compared to control brains. Frontal cortices, temporal cortices and cerebellum of 30 AD (mean 78 years) and 14 control aged matched brains (mean 77.4 years) as well as APP transgenic mice compared to control mice were analyzed using an AB Sciex 4000 Qtrap mass spectrometer utilizing a FIA MS/MS method. PC, PC-PL and lyso-PC metabolites were analyzed in respect to saturation level and FA composition. As expected, the majority of the lipid species showed no significant differences, but interestingly a few species revealed a highly significant reduction in AD brains. These FAs are potential candidates for further food analysis in respect to AD pathology. Additionally, we show that the method applied with multiple reaction monitoring (MRM) used for this study is suitable for semi quantitative analysis of small amounts (10 μl) of brain tissue.

Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst

MetaboAnalyst is an integrated web-based platform for comprehensive analysis of quantitative metabolomic data. It is designed to be used by biologists (with little or no background in statistics) to perform a variety of complex metabolomic data analysis tasks. These include data processing, data normalization, statistical analysis and high-level functional interpretation. This protocol provides a step-wise description on how to format and upload data to MetaboAnalyst, how to process and normalize data, how to identify significant features and patterns through univariate and multivariate statistical methods and, finally, how to use metabolite set enrichment analysis and metabolic pathway analysis to help elucidate possible biological mechanisms. The complete protocol can be executed in approximately 45 min.

Metabolic signatures in apoptotic human cancer cell lines

Cancer cells have several specific metabolic features, which have been explored for targeted therapies. Agents that promote apoptosis in tumors are currently considered as a powerful tool for cancer therapeutics. The present study aimed to design a fast, reliable and robust system for metabolite measurements in cells lines to observe impact of apoptosis on the metabolome. For that purpose the NBS (newborn screen) mass spectrometry-based metabolomics assay was adapted for cell culture approach. In HEK 293 and in cancer cell lines HepG2, PC3, and MCF7 we searched for metabolic biomarkers of apoptosis differing from that of necrosis. Already nontreated cell lines revealed distinct concentrations of metabolites. Several metabolites indicative for apoptotic processes in cell culture including aspartate, glutamate, methionine, alanine, glycine, propionyl carnitine (C3-carnitine), and malonyl carnitine (C3DC-carnitine) were observed. In some cell lines metabolite changes were visible as early as 4 h after apoptosis induction and preceeding the detection by caspase 3/7 assay. We demonstrated for the first time that the metabolomic signatures might be used in the tests of efficacy of agents causing apoptosis in cell culture. These signatures could be obtained in fast high-throughput screening.

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting

BACKGROUND

Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.

METHODOLOGY/PRINCIPAL FINDINGS

40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).

CONCLUSIONS/SIGNIFICANCE

Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.

Lipidomics as a tool for the study of lipoprotein metabolism

Although technologies for lipidomic and proteomic investigations have developed very recently, lipidomic and proteomic studies of plasma lipoproteins have already provided several impressive examples of detailed characterization of distinct metabolic pathways potentially involved in lipoprotein metabolism in both health and disease states (obesity, insulin resistance, fatty liver disease) as well as under lifestyle and dietary modification (fish consumption, carbohydrates, probiotics) and lipid-modifying treatments (statins, low-density lipoprotein apheresis). Available lipidomic methodologies have facilitated detailed characterization of lipid classes and molecular species present in plasma as well as in lipoprotein fractions. Together with emerging proteomic techniques, lipidomics of plasma lipoproteins will soon provide molecular details of lipoprotein composition, which will ultimately be translated into integrated knowledge of the structure, metabolism, and function of lipoproteins in health and disease.

metaP-server: a web-based metabolomics data analysis tool

Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes. We developed metaP-server to facilitate data interpretation. metaP-server provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross-references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided. metaP-server is freely accessible at http://metabolomics.helmholtz-muenchen.de/metap2/.

Mouse phenotyping

Model organisms like the mouse are important tools to learn more about gene function in man. Within the last 20 years many mutant mouse lines have been generated by different methods such as ENU mutagenesis, constitutive and conditional knock-out approaches, knock-down, introduction of human genes, and knock-in techniques, thus creating models which mimic human conditions. Due to pleiotropic effects, one gene may have different functions in different organ systems or time points during development. Therefore mutant mouse lines have to be phenotyped comprehensively in a highly standardized manner to enable the detection of phenotypes which might otherwise remain hidden. The German Mouse Clinic (GMC) has been established at the Helmholtz Zentrum München as a phenotyping platform with open access to the scientific community (www.mousclinic.de; [1]). The GMC is a member of the EUMODIC consortium which created the European standard workflow EMPReSSslim for the systemic phenotyping of mouse models (http://www.eumodic.org/[2]).