Abberant lipid metabolism is implicated in Alzheimer’s disease (AD) pathophysiology, but the connections between AD and lipid metabolic pathways are not fully understood. To investigate plasma lipids in AD, a multiplatform screen (n = 35 by liquid chromatography–mass spectrometry and n = 35 by nuclear magnetic resonance) was developed, which enabled the comprehensive analysis of plasma from 3 groups (individuals with AD, individuals with mild cognitive impairment (MCI), and age-matched controls). This screen identified 3 phosphatidylcholine (PC) molecules that were significantly diminished in AD cases. In a subsequent validation study (n = 141), PC variation in a bigger sample set was investigated, and the same 3 PCs were found to be significantly lower in AD patients: PC 16:0/20:5 (p < 0.001), 16:0/22:6 (p < 0.05), and 18:0/22:6 (p < 0.01). A receiver operating characteristic (ROC) analysis of the PCs, combined with apolipoprotein E (ApoE) data, produced an area under the curve predictive value of 0.828. Confirmatory investigations into the background biochemistry indiciated no significant change in plasma levels of 3 additional PCs of similar structure, total choline containing compounds or total plasma omega fatty acids, adding to the evidence that specific PCs play a role in AD pathology.

In recent years, breath analysis in real time has become a noninvasive alternative for the diagnosis of diseases and for molecular fingerprinting of exhaled breath. However, the techniques used lack the capabilities for proper identification of the compounds found in the exhalome. Here, we report the use of UHPLC-HRMS as a tool for the identification of several aldehydes (2-alkenals, 4-hydroxy-2-alkenals, and 4-hydroxy-2,6-alkadienals), biomarkers of lipid peroxidation, in exhaled breath condensate of three healthy subjects (N = 3). Some of the aldehydes studied have never been identified before. Their robust identification is based on retention times, on the generation of fragmentation trees from tandem mass spectra, and on the comparison of these parameters with standards. We also show that the identified compounds can be analyzed and confirmed by MS/MS in breath in real time and, therefore, they could be used as biomarkers for the rapid and noninvasive diagnosis of related diseases.

BACKGROUND:Amino acids are frequently determined in clinical chemistry. However, current analysis methods are time-consuming, invasive, and suffer from artifacts during sampling, sample handling, and sample preparation. We hypothesized in this proof-of-principle study that plasma concentrations of amino acids can be estimated by measuring their concentrations in exhaled breath. A novel breath analysis technique described here allows such measurements to be carried out in real-time and noninvasively, which should facilitate efficient diagnostics and give insights into human physiology.

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The detection of bacterial-specific volatile metabolites may be a valuable tool to predict infection. Here we applied a real-time mass spectrometric technique to investigate differences in volatile metabolic profiles of oral bacteria that cause periodontitis. We coupled a secondary electrospray ionization (SESI) source to a commercial high-resolution mass spectrometer to interrogate the headspace from bacterial cultures and human saliva. We identified 120 potential markers characteristic for periodontal pathogens Aggregatibacter actinomycetemcomitans (n = 13), Porphyromonas gingivalis (n = 70), Tanerella forsythia (n = 30) and Treponema denticola (n = 7) in in vitro cultures. In a second proof-of-principle phase, we found 18 (P. gingivalis, T. forsythia and T. denticola) of the 120 in vitro compounds in the saliva from a periodontitis patient with confirmed infection with P. gingivalis, T. forsythia and T. denticola with enhanced ion intensity compared to two healthy controls. In conclusion, this method has the ability to identify individual metabolites of microbial pathogens in a complex medium such as saliva.

Background Therapeutic management of epilepsy remains a challenge, since optimal systemic antiseizure medication (ASM) concentrations do not always correlate with improved clinical outcome and minimal side effects. We tested the feasibility of noninvasive real-time breath metabolomics as an extension of traditional therapeutic drug monitoring for patient stratification by simultaneously monitoring drug-related and drug-modulated metabolites. Methods This proof-of-principle observational study involved 93 breath measurements of 54 paediatric patients monitored over a period of 2.5 years, along with an adult’s cohort of 37 patients measured in two different hospitals. Exhaled breath metabolome of epileptic patients was measured in real time using secondary electrospray ionisation–high-resolution mass spectrometry (SESI–HRMS). Results We show that systemic ASM concentrations could be predicted by the breath test. Total and free valproic acid (VPA, an ASM) is predicted with concordance correlation coefficient (CCC) of 0.63 and 0.66, respectively. We also find (i) high between- and within-subject heterogeneity in VPA metabolism; (ii) several amino acid metabolic pathways are significantly enriched (p < 0.01) in patients suffering from side effects; (iii) tyrosine metabolism is significantly enriched (p < 0.001), with downregulated pathway compounds in non-responders. Conclusions These results show that real-time breath analysis of epileptic patients provides reliable estimations of systemic drug concentrations along with risk estimates for drug response and side effects.

Chemical analysis of aerosols collected from electronic cigarettes (ECs) has shown that these devices produce vapors that contain harmful and potentially harmful compounds. Conventional analytical methods used for the analysis of electronic cigarettes do not reflect the actual composition of the aerosols generated because they usually neglect the changes in the chemical composition that occur during the aerosol generation process and after collection. The aim of this work was to develop and apply a method for the real-time analysis of electronic cigarette aerosols, based on the secondary electrospray ionization technique coupled to high-resolution mass spectrometry, by mimicking the "vaping" process. Electronic cigarette aerosols were successfully analyzed and quantitative differences were found between the liquids and aerosols. Thanks to the high sensitivity shown by this method, more than 250 chemical substances were detected in the aerosols, some of them showing a high correlation with the operating power of the electronic cigarettes. The method also allows proper quantification of several chemical components such as alkaloids and flavor compounds.

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