Semi-automated non-target processing in GC × GC–MS metabolomics analysis: applicability for biomedical studies

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC–MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC–MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC–MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC–MS and GC–MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC–MS and GC × GC–MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC–MS processing compared to targeted GC–MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC–MS were somewhat higher than with GC–MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC–MS was demonstrated; many additional candidate biomarkers were found with GC × GC–MS compared to GC–MS

Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies

Reconstructing El Niño Southern Oscillation using data from ships' logbooks, 1815- 1854. Part I: Methodology and Evaluation

The meteorological information found within ships’ logbooks is a unique and fascinating source of data for historical climatology. This study uses wind observations from logbooks covering the period 1815 to 1854 to reconstruct an index of El Niño Southern Oscillation (ENSO) for boreal winter (DJF). Statistically-based reconstructions of the Southern Oscillation Index (SOI) are obtained using two methods: principal component regression (PCR) and composite-plus-scale (CPS). Calibration and validation are carried out over the modern period 1979–2014, assessing the relationship between re-gridded seasonal ERA-Interim reanalysis wind data and the instrumental SOI. The reconstruction skill of both the PCR and CPS methods is found to be high with reduction of error skill scores of 0.80 and 0.75, respectively. The relationships derived during the fitting period are then applied to the logbook wind data to reconstruct the historical SOI. We develop a new method to assess the sensitivity of the reconstructions to using a limited number of observations per season and find that the CPS method performs better than PCR with a limited number of observations. A difference in the distribution of wind force terms used by British and Dutch ships is found, and its impact on the reconstruction assessed. The logbook reconstructions agree well with a previous SOI reconstructed from Jakarta rain day counts, 1830–1850, adding robustness to our reconstructions. Comparisons to additional documentary and proxy data sources are provided in a companion paper

Ginkgo biloba for the treatment of vitilgo vulgaris: an open label pilot clinical trial

<p>Abstract</p> <p>Background</p> <p>Vitiligo is a common hypopigmentation disorder with significant psychological impact if occurring before adulthood. A pilot clinical trial to determine the feasibility of an RCT was conducted and is reported here.</p> <p>Methods</p> <p>12 participants 12 to 35 years old were recruited to a prospective open-label pilot trial and treated with 60 mg of standardized <it>G. biloba </it>two times per day for 12 weeks. The criteria for feasibility included successful recruitment, 75% or greater retention, effectiveness and lack of serious adverse reactions. Effectiveness was assessed using the Vitiligo Area Scoring Index (VASI) and the Vitiligo European Task Force (VETF), which are validated outcome measures evaluating the area and intensity of depigmentation of vitiligo lesions. Other outcomes included photographs and adverse reactions. Safety was assessed by serum coagulation factors (platelets, PTT, INR) at baseline and week 12.</p> <p>Results</p> <p>After 2 months of recruitment, the eligible upper age limit was raised from 18 to 35 years of age in order to facilitate recruitment of the required sample size. Eleven participants completed the trial with 85% or greater adherence to the protocol. The total VASI score improved by 0.5 (P = 0.021) from 5.0 to 4.5, range of scale 0 (no depigmentation) to 100 (completely depigmented). The progression of vitiligo stopped in all participants; the total VASI indicated an average repigmentation of vitiligo lesions of 15%. VETF total vitiligo lesion area decreased 0.4% (P = 0.102) from 5.9 to 5.6 from baseline to week 12. VETF staging score improved by 0.7 (P = 0.101) from 6.6 to 5.8, and the VETF spreading score improved by 3.9 (P < 0.001)) from 2.7 to -1.2. There were no statistically significant changes in platelet count, PTT, or INR.</p> <p>Conclusions</p> <p>The criteria for feasibility were met after increasing the maximum age limit of the successful recruitment criterion; participant retention, safety and effectiveness criteria were also met. Ingestion of 60 mg of <it>Ginkgo biloba </it>BID was associated with a significant improvement in total VASI vitiligo measures and VETF spread, and a trend towards improvement on VETF measures of vitiligo lesion area and staging. Larger, randomized double-blind clinical studies are warranted and appear feasible.</p> <p>Trial Registration</p> <p>Clinical trials.gov registration number <a href="http://www.clinicaltrials.gov/ct2/show/NCT00907062">NCT00907062</a></p

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided