Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Determinants of accelerated metabolomic and epigenetic aging in a UK cohort.

Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.

Using ANOVA-PCA for discriminant analysis: application to the study of mid-infrared spectra of carrageenan gels as a function of concentration and temperature.

In this work the ANOVA-PCA method is applied to a MIR spectroscopy dataset of carrageenan in order to evaluate which of the factors within its fixed effects experimental design are significant in relation to the residual error. The factors defined in the experimental design are concentration (1% and 2%), temperature (30, 40, 45, 50, and 60 degrees C), day (1 and 2) and sample (20 samples, 3 repetitions). The two factors, concentration and temperature, were considered as significant and the main features related with its physico-chemical properties were identified. It is also of interest to acquire a better understanding of the interaction between concentration and temperature and its effect on the adhesion of gels onto the surface of contact. In fact, no significant interaction was found between the two factors, but it was shown that the factor temperature behaves in a non-linear way. As classification using the ANOVA-PCA procedure has not been developed until now, a new method is proposed for the classification of new samples in respect to the levels of each significant factor.