Search

Background Obesity is a pressing public health concern worldwide. Novel pharmacological means are urgently needed to combat the increase of obesity and accompanying type 2 diabetes (T2D). Although fully established obesity is associated with neuromolecular alterations and insulin resistance in the brain, potential obesity-promoting mechanisms in the central nervous system have remained elusive. In this triple-tracer positron emission tomography study, we investigated whether brain insulin signaling, μ-opioid receptors (MORs) and cannabinoid CB1 receptors (CB1Rs) are associated with risk for developing obesity. Methods Subjects were 41 young non-obese males with variable obesity risk profiles. Obesity risk was assessed by subjects’ physical exercise habits, body mass index and familial risk factors, including parental obesity and T2D. Brain glucose uptake was quantified with [18F]FDG during hyperinsulinemic euglycemic clamp, MORs were quantified with [11C]carfentanil and CB1Rs with [18F]FMPEP-d2. Results Subjects with higher obesity risk had globally increased insulin-stimulated brain glucose uptake (19 high-risk subjects versus 19 low-risk subjects), and familial obesity risk factors were associated with increased brain glucose uptake (38 subjects) but decreased availability of MORs (41 subjects) and CB1Rs (36 subjects). Conclusions These results suggest that the hereditary mechanisms promoting obesity may be partly mediated via insulin, opioid and endocannabinoid messaging systems in the brain.

Tylosin A is a macrolide antibiotic used in beekeeping. The aim of the study was to characterise the behaviour of tylosin A in honey after heating and during storage, and to identify its degradation products using a non-targeted approach. In addition, the possibility of a semi-quantification of tylosin B using tylosin A was assessed as a case study for the semi-quantification of degradation products using the parent compounds. The results showed significant degradation of tylosin A in aqueous solution (~96%) as well as in spiked and incurred honey dissolved in water (~50% and ~29%, respectively) after heating at 100°C for 90 min. However, at a lower heating temperature of 70°C, degradation was only observed in water (~31%). When stored at room temperature (27°C) for one year, tylosin A degraded significantly (~47%) in an incurred honey sample. Tylosin B, the only reported degradation product of tylosin A in honey so far, increased significantly in aqueous solution under all treatments, but it only increased in spiked water-honey mixture after heating at 100°C. Two new degradation products, namely 5-O-mycaminosyltylonolide (OMT) and lactenocin, were tentatively identified in water and spiked honey after heating at 100°C. The results of the present study reinforce the conclusion that relying only on the water model or spiked food matrix is not sufficient to understand the thermal degradation of antibiotics in food matrices. Finally, a semi-quantification of tylosin B with a relative error of 20% in an incurred honey sample was possible using the response factor of tylosin A, its parent compound. The results of this study prove that a semi-quantification using the parent compound to quantify its degradation compound can provide satisfactory results, but this will be analyte-dependent.

Non-targeted screening (e.g., suspected-target) is emerging as an attractive tool to investigate the occurrence of contaminants in food. The sample preparation and instrument analysis steps are known to influence the identification of analytes with non-targeted workflows, especially for complex matrices. However, for methods based on mass spectrometry, the impact of the post-analysis data treatment (e.g., feature extraction) on the capacity to correctly identify a contaminant at trace level is currently not well understood. The aim of the study was to investigate the influence of seven post-analysis data treatment parameters on the non-targeted identification of trace contaminants in honey using high-performance liquid chromatography coupled to hybrid quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS). Seven compounds reported as veterinary drugs for honeybees were applied as model compounds. Among the parameters studied, the expansion window for chromatogram extraction and the average scans included in the spectra influenced significantly the identification process results. The optimized data treatment was applied to the non-targeted screening of veterinary drugs, pesticides, and other contaminants in 55 honey samples as a proof of concept. Among the 43 compounds included in a library of honey-related compounds that was used for screening, eight compounds were tentatively identified in at least one honey sample. The tentative identity of two of these compounds (tylosin A and hydroxymethylfurfural) was further confirmed with analytical standards. Graphical Abstract.

The targeted analysis of veterinary drug residues in honey traditionally involves a series of extraction and purification steps prior to quantification with high performance liquid chromatography coupled to high resolution or tandem mass spectrometry. These steps, designed to separate the target analytes from interferences, are generally time-consuming and costly. In addition, traditional cleanup steps are likely to eliminate other compounds whose analysis could prove decisive in current or future assessment of the honey sample. Alternatively, direct injection without complex sample preparation steps has been introduced for the fast analysis of trace compounds in environmental and food matrices. The aim of this study was to develop a rapid method for the targeted analysis of 7 key veterinary drug residues in honey based on direct injection high performance liquid chromatography coupled to quadrupole time-of-flight, while simultaneously recording data-independent MS/MS (e.g. All Ions MS/MS data) for future re-examination of the data for other purposes. The new method allowed for the detection of the target residues at levels approximately 20–100 times lower than current regulatory limits, for a total analysis time of about 45 min. The recoveries (103–119%), the linearity (R ≥ 0.996) and the repeatability (RSD ≤ 7%) were satisfactory. The method was then applied to 35 honey samples from the Canadian market. Residues of tylosin A, tylosin B, sulfamethazine and sulfadimethoxine were detected in 6, 9, 6 and 23% of the samples respectively, at levels below the regulatory limits in Canada. The possibility of adding a hydrolysis step to study sulfonamides in honey was tested, which provided good results for this family of compounds but lead to degradation of some of the other analytes. Finally, the non-targeted identification of several compounds was demonstrated as a proof of concept of future re-examination of All Ions MS/MS data. This paper illustrates the capacity of this novel method to combine targeted and non-targeted screening of chemical residues in honey. Keywords: Veterinary drugs, HPLC-Q-TOF-MS, Honey, Direct injection