UniqPy: A tool for estimation of short-chain fatty acids composition by gas-chromatography/mass-spectrometry with headspace extraction.

Short-chain fatty acids are metabolites widely presented in many natural sources, including human feces and blood. Estimation of their composition is a common procedure, usually performed using nuclear magnetic resonance or gas chromatography with a flame ionization detector. However, the commonly used methods often depend on specific sample preparation, such as filtration and homogenization. The gas-chromatography/mass-spectrometry (GC/MS) method with headspace extraction allows sample preparation to be kept to a minimum regardless of the physical state of the sample, which can be potentially useful in metabolomics research of complex natural samples such as blood or feces. In this work, we have demonstrated the applicability of Headspace GC-MS for estimating short chain fatty acid (SCFA) composition. The main problem here is the complex, non-linear dependence between the composition of the compounds in the source phase and the relative pressures in the vapor phase, which are directly measured by this method. We have implemented a thermodynamic model that performs the reverse transformation of relative abundances in the vapor phase to relative concentrations in the liquid phase, and have tested it on some synthetic SCFA mixtures. The developed method is available as a pip package called UniqPy and can be used to describe liquid-vapor equilibrium for any multicomponent system if a sufficient amount of training data is provided. The gas chromatography method with headspace extraction in conjunction with the UniqPy data transformation showed satisfactory quantification accuracy for propionic acid, butyric acid, isobutyric acid, and valeric acid (R-squared > 0.96). The applicability of the method was additionally demonstrated on a series of fecal samples.

Estimating population-level of alcohol, tobacco and morphine use in a small Russian region using wastewater-based epidemiology.

INTRODUCTION: Alcohol, tobacco and illicit drug use combined are the largest modifiable health risk factors. Wastewater-based epidemiology (WBE) is a complementary approach for monitoring substance use in the population. In this study we applied WBE technique to a community in the Moscow region to estimate population-level consumption of alcohol, tobacco and morphine. METHODS: Wastewater sampling was carried out over 47 days, in 2018 and 2019, including the New Year period. Analysis of the samples for consumption biomarkers (ethyl sulphate, cotinine and morphine) were undertaken using liquid chromatography tandem mass spectrometry (LC-MS/MS). Daily consumption estimates were then compared with sales/production/prescription data and between different days of the week using Mann-Whitney U test. RESULTS: Alcohol consumption was significantly higher on Sundays and during the New Year and Russian Christmas period compared to weekdays and Saturdays. Tobacco consumption estimates were largely consistent throughout the week. Morphine was detected by WBE during the monitoring period but was inconsistent with prescription record data. DISCUSSION AND CONCLUSIONS: This study provides evidence for the feasibility of conducting WBE in Russia. Estimates of alcohol consumption derived from WBE were higher than average alcohol sales data for the country. The estimated consumption of nicotine is generally consistent with the production data, with estimates higher than in most other countries. Our results also suggest potential illegal use of opioids (morphine-based) in the population. Given the considerable health and economic costs of substance use in Russia, more extensive WBE testing is recommended to inform and evaluate public health policies.