Validated and predictive processing of gas chromatography-mass spectrometry based metabolomics data for large scale screening studies, diagnostics and metabolite pattern verification

Plasma metabolomic patterns in patients with exhaustion disorder

Exhaustion disorder (ED) is a stress-related disorder that often implies a great burden on the individual patient as well as on society. Previous studies have shown that ED is associated with metabolic deviations, such as lowered fasting glucose. Several mechanisms have been discussed as a plausible explanation of the lack of energy described by these patients. Metabolic processes and reduced ability to mobilize energy have been suggested as important factors. This study investigated metabolomics in 20 patients diagnosed with ED and compared them with 21 healthy controls. Plasma metabolic profiles were examined in both fasting and nonfasting (postprandial) conditions. Blood plasma samples were analyzed for metabolite content using gas chromatography mass spectrometry. A total of 62 different metabolites were simultaneously detected in each of the samples. Multivariate models indicated systematic differences between patients with ED and healthy controls in both their fasting and nonfasting plasma metabolite levels. Lysine and octadecenoic acid were more abundant and glutamine, glycine, serine and gluconic acid were less abundant in the patients across both conditions. In the present study, we comprehensively and simultaneously screen for changes in a large number of metabolites. Our results show a difference in systemic metabolites between patients with exhaustion disorder and healthy controls both in the fasting and in the postprandial states. Here, we present new potential biomarkers mirroring exhaustion disorder metabolism. Lay summary Exhaustion disorder (ED) patients suffer from stress-related symptoms including a reduced energy level. This study investigates the body's metabolism in patients with ED, both fasting and after a meal. New potential markers that may help future investigations on ED were identified.

Decreased lipogenesis-promoting factors in adipose tissue in postmenopausal women with overweight on a Paleolithic-type diet

Purpose

We studied effects of diet-induced postmenopausal weight loss on gene expression and activity of proteins involved in lipogenesis and lipolysis in adipose tissue.

Methods

Fifty-eight postmenopausal women with overweight (BMI 32.5 ± 5.5) were randomized to eat an ad libitum Paleolithic-type diet (PD) aiming for a high intake of protein and unsaturated fatty acids or a prudent control diet (CD) for 24 months. Anthropometry, plasma adipokines, gene expression of proteins involved in fat metabolism in subcutaneous adipose tissue (SAT) and lipoprotein lipase (LPL) activity and mass in SAT were measured at baseline and after 6 months. LPL mass and activity were also measured after 24 months.

Results

The PD led to improved insulin sensitivity (P < 0.01) and decreased circulating triglycerides (P < 0.001), lipogenesis-related factors, including LPL mRNA (P < 0.05), mass (P < 0.01), and activity (P < 0.001); as well as gene expressions of CD36 (P < 0.05), fatty acid synthase, FAS (P < 0.001) and diglyceride acyltransferase 2, DGAT2 (P < 0.001). The LPL activity (P < 0.05) and gene expression of DGAT2 (P < 0.05) and FAS (P < 0.05) were significantly lowered in the PD group versus the CD group at 6 months and the LPL activity (P < 0.05) remained significantly lowered in the PD group compared to the CD group at 24 months.

Conclusions

Compared to the CD, the PD led to a more pronounced reduction of lipogenesis-promoting factors in SAT among postmenopausal women with overweight. This could have mediated the favorable metabolic effects of the PD on triglyceride levels and insulin sensitivity.

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

INTRODUCTION

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

OBJECTIVES

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

METHODS

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

RESULTS

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

CONCLUSION

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

Obesity-related metabolite profiles of black women spanning the epidemiologic transition

In developed countries, specific metabolites have been associated with obesity and metabolic diseases, e.g. type 2 diabetes. It is unknown whether a similar profile persists across populations of African-origin, at increased risk for obesity and related diseases. In a cross-sectional study of normal-weight and obese black women (33.3 ± 6.3 years) from the US (N = 69, 65 % obese), South Africa (SA, N = 97, 49 % obese) and Ghana (N = 82, 33 % obese) serum metabolite profiles were characterized via gas chromatography-time of flight/mass spectrometry. In US and SA women, BMI correlated with branched-chain and aromatic amino acids, as well as dopamine and aminoadipic acid. The relationship between BMI and lipid metabolites differed by site; BMI correlated positively with palmitoleic acid (16:1) in the US; negatively with stearic acid (18:0) in SA, and positively with arachidonic acid (20:4) in Ghana. BMI was also positively associated with sugar-related metabolites in the US; i.e. uric acid, and mannitol, and with glucosamine, glucoronic acid and mannitol in SA. While we identified a common amino acid metabolite profile associated with obesity in black women from the US and SA, we also found site-specific obesity-related metabolites suggesting that the local environment is a key moderator of obesity.

Systemic differences in serum metabolome: a cross sectional comparison of women with localised and widespread pain and controls

Chronic musculoskeletal pain exists either as localised to a single region or as widespread to multiple sites in several quadrants of the body. Prospective studies indicate that widespread pain could act as a far end of a continuum of musculoskeletal pain that started with chronic localised pain. The mechanism by which the transition from localised pain to widespread occurs is not clear, although many studies suggest it to be an altered metabolism. In this study, systemic metabolic differences between women with chronic localised neck-shoulder pain (NP), women with chronic widespread pain (CWP) and women who were healthy (CON) were assessed. Blood samples were analysed taking a metabolomics approach using gas chromatography mass spectrometry (GC-MS) and orthogonal partial least square discriminant analysis (OPLS-DA). The metabolomics analysis showed a clear systematic difference in the metabolic profiles between the subjects with NP and the CON but only a weak systematic difference between the subjects with CWP and the CON. This most likely reflects a difference in the portion of the metabolome influenced by the two pain conditions. In the NP group, the overall metabolic profile suggests that processes related to energy utilisation and lipid metabolism could be central aspects of mechanisms maintaining disorder.

Serum metabolomic biomarkers of dementia

Aims: This study compared serum metabolites of demented patients (Alzheimer's disease and vascular dementia) and controls, and explored serum metabolite profiles of nondemented individuals 5 years preceding the diagnosis. Methods: Cognitively healthy participants were followed up for 5-20 years. Cognitive assessment, serum sampling, and diagnosis were completed every 5 years. Multivariate analyses were conducted on the metabolite profiles generated by gas chromatography/time-of-flight mass spectrometry. Results: A significant group separation was found between demented patients and controls, and between incident cases and controls. Metabolites that contributed in both analyses were 3,4-dihydroxybutanoic acid, docosapentaenoic acid, and uric acid. Conclusions: Serum metabolite profiles are altered in demented patients, and detectable up to 5 years preceding the diagnosis. Blood sampling can make an important contribution to the early prediction of conversion to dementia.

Comparative metabolomics of muscle interstitium fluid in human trapezius myalgia: an in vivo microdialysis study

Purpose

The mechanisms behind trapezius myalgia are unclear. Many hypotheses have been presented suggesting an altered metabolism in the muscle. Here, muscle microdialysate from healthy and myalgic muscle is analysed using metabolomics. Metabolomics analyse a vast number of metabolites, enabling a comprehensive explorative screening of the cellular processes in the muscle.

Methods

Microdialysate samples were obtained from the shoulder muscle of healthy and myalgic subjects that performed a work and stress test. Samples from the baseline period and from the recovery period were analysed using gas chromatography—mass spectrometry (GC–MS) together with multivariate analysis to detect differences in extracellular content of metabolites between groups. Systematic differences in metabolites between groups were identified using multivariate analysis and orthogonal partial least square discriminate analysis (OPLS-DA). A complementary Mann–Whitney U test of group difference in individual metabolites was also performed.

Results

A large number of metabolites were detected and identified in this screening study. At baseline, no systematic differences between groups were observed according to the OPLS-DA. However, two metabolites, l-leucine and pyroglutamic acid, were significantly more abundant in the myalgic muscle compared to the healthy muscle. In the recovery period, systematic difference in metabolites between the groups was observed according to the OPLS-DA. The groups differed in amino acids, fatty acids and carbohydrates. Myristic acid and putrescine were significantly more abundant and beta-d-glucopyranose was significantly less abundant in the myalgic muscle.

Conclusion

This study provides important information regarding the metabolite content, thereby presenting new clues regarding the pathophysiology of the myalgic muscle.