Volatile organic compounds from exhaled breath in schizophrenia

A Comprehensive Review of Biomarker Sensors for a Breathalyzer Platform

Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough review of the latest advancements in sensor technologies for VOC detection, with a focus on their healthcare applications. It begins by introducing VOC detection principles, followed by a review of the rapidly evolving technologies in this area. Special emphasis is given to functionalized molecularly imprinted polymer-based biochemical sensors for detecting breath biomarkers, owing to their exceptional selectivity. The discussion examines SWaP-C considerations alongside the respective advantages and disadvantages of VOC sensing technologies. The paper also tackles the principal challenges facing the field and concludes by outlining the current status and proposing directions for future research.

Rapid detection of depression by volatile organic compounds from exhalation

Depression is a pervasive and often undetected mental health condition, which poses significant challenges for early diagnosis due to its silent and subtle nature. To evaluate exhaled volatile organic compounds (VOCs) as non-invasive biomarkers for the detection of depression using a virtual surface acoustic wave sensors array (VSAW-SA). A total of 245 participants were recruited from the Hangzhou Community Health Service Center, including 38 individuals diagnosed with depression and 207 control subjects. Breath samples were collected from all participants and subjected to analysis using VSAW-SA. Univariate and multivariate analyses were employed to assess the relationship between VOCs and depression. The findings revealed that the responses of virtual sensor ID 14, 44, 59, and 176, which corresponded respectively to ethanol, trichloroethylene or isoleucine, octanoic acid or lysine, and an unidentified compound, were sensitive to depression. Taking into account potential confounders, these sensor responses were utilized to calculate a depression detection indicator. It has a sensitivity of 81.6% and a specificity of 81.6%, with an area under the curve of 0.870 (95% CI = 0.816-0.923). Conclusions: exhaled VOCs as non-invasive biomarkers of depression could be detected by a VSAW-SA. Large-scale cohort studies should be conducted to confirm the potential ability of the VSAW-SA to diagnose depression.

Beyond Auto-Brewery: Why Dysbiosis and the Legalome Matter to Forensic and Legal Psychology

International studies have linked the consumption of ultra-processed foods with a variety of non-communicable diseases. Included in this growing body of research is evidence linking ultra-processed foods to mental disorders, aggression, and antisocial behavior. Although the idea that dietary patterns and various nutrients or additives can influence brain and behavior has a long history in criminology, in the absence of plausible mechanisms and convincing intervention trials, the topic was mostly excluded from mainstream discourse. The emergence of research across nutritional neuroscience and nutritional psychology/psychiatry, combined with mechanistic bench science, and human intervention trials, has provided support to epidemiological findings, and legitimacy to the concept of nutritional criminology. Among the emergent research, microbiome sciences have illuminated mechanistic pathways linking various socioeconomic and environmental factors, including the consumption of ultra-processed foods, with aggression and antisocial behavior. Here in this review, we examine this burgeoning research, including that related to ultra-processed food addiction, and explore its relevance across the criminal justice spectrum—from prevention to intervention—and in courtroom considerations of diminished capacity. We use auto-brewery syndrome as an example of intersecting diet and gut microbiome science that has been used to refute mens rea in criminal charges. The legalome—microbiome and omics science applied in forensic and legal psychology—appears set to emerge as an important consideration in matters of criminology, law, and justice.

Unveiling the hidden impact: How biodegradable microplastics influence CO2 and CH4 emissions and Volatile Organic Compounds (VOCs) profiles in soil ecosystems

Biodegradable plastics promise eco-friendliness, yet their transformation into microplastics (bio-MPs) raises environmental alarms. However, how those bio-MPs affect the greenhouse gases (GHGs) and volatile organic compounds (VOCs) in soil ecosystems remains largely unexplored. Here, we investigated the effects of diverse bio-MPs (PBAT, PBS, and PLA) on GHGs and VOCs emission in typical paddy or upland soils. We monitored the carbon dioxide (CO2) and methane (CH4) fluxes in-situ using the self-developed portable optical gas sensor and analyzed VOC profiles using a proton-transfer reaction mass spectrometer (PTR-MS). Our study has revealed that, despite their biodegradable nature, bio-MPs do not always promote soil GHG emissions as previously thought. Specifically, PBAT and PLA significantly increased CO2 and CH4 emissions up to 1.9-7.5 and 115.9-178.5 fold, respectively, compared to the control group. While PBS exhibited the opposite trend, causing a decrease of up to 39.9% for CO2 and up to 39.9% for CH4. In addition, different types of bio-MPs triggered distinct soil VOC emission patterns. According to the Mann-Whitney U-test and Partial Least Squares Discriminant Analysis (PLS-DA), a recognizable VOC pattern associated with different bio-MPs was revealed. This study claims the necessity of considering polymer-specific responses when assessing the environmental impact of Bio-MPs, and providing insights into their implications for climate change.

Dynamic Stability of Volatile Organic Compounds in Respiratory Air in Schizophrenic Patients and Its Potential Predicting Efficacy of TAAR Agonists

OBJECTIVES

Volatile organic compounds (VOCs) in the breathing air were found to be altered in schizophrenia patients compared to healthy participants. The aim of this study was to confirm these findings and to examine for the first time whether these VOCs are stable or change in concentration during the early treatment course. Moreover, it was investigated whether there is a correlation of the VOCs with the existing psychopathology of schizophrenia patients, i.e., whether the concentration of masses detected in the breath gas changes when the psychopathology of the participants changes.

METHODS

The breath of a total of 22 patients with schizophrenia disorder was examined regarding the concentration of VOCs using proton transfer reaction mass spectrometry. The measurements were carried out at baseline and after two weeks at three different time points, the first time immediately after waking up in the morning, after 30 min, and then after 60 min. Furthermore, 22 healthy participants were investigated once as a control group.

RESULTS

Using bootstrap mixed model analyses, significant concentration differences were found between schizophrenia patients and healthy control participants (m/z 19, 33, 42, 59, 60, 69, 74, 89, and 93). Moreover, concentration differences were detected between the sexes for masses m/z 42, 45, 57, 69, and 91. Mass m/z 67 and 95 showed significant temporal changes with decreasing concentration during awakening. Significant temporal change over two weeks of treatment could not be detected for any mass. Masses m/z 61, 71, 73, and 79 showed a significant relationship to the respective olanzapine equivalents. The length of hospital stay showed no significant relationship to the masses studied.

CONCLUSION

Breath gas analysis is an easy-to-use method to detect differences in VOCs in the breath of schizophrenia patients with high temporal stability. m/z 60 corresponding to trimethylamine might be of potential interest because of its natural affinity to TAAR receptors, currently a novel therapeutic target under investigation. Overall, breath signatures seemed to stable over time in patients with schizophrenia. In the future, the development of a biomarker could potentially have an impact on the early detection of the disease, treatment, and, thus, patient outcome.

Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations

BACKGROUND

Major depressive disorder (MDD) is one of the most common psychiatric disorders with multifactorial etiologies. Metabolomics has recently emerged as a particularly potential quantitative tool that provides a multi-parametric signature specific to several mechanisms underlying the heterogeneous pathophysiology of MDD. The main purpose of the present study was to investigate possibilities and limitations of breath-based metabolomics, breathomics patterns to discriminate MDD patients from healthy controls (HCs) and identify the altered metabolic pathways in MDD.

METHODS

Breath samples were collected in Tedlar bags at awakening, 30 and 60 min after awakening from 26 patients with MDD and 25 HCs. The non-targeted breathomics analysis was carried out by proton transfer reaction mass spectrometry. The univariate analysis was first performed by T-test to rank potential biomarkers. The metabolomic pathway analysis and hierarchical clustering analysis (HCA) were performed to group the significant metabolites involved in the same metabolic pathways or networks. Moreover, a support vector machine (SVM) predictive model was built to identify the potential metabolites in the altered pathways and clusters. The accuracy of the SVM model was evaluated by receiver operating characteristics (ROC) analysis.

RESULTS

A total of 23 differential exhaled breath metabolites were significantly altered in patients with MDD compared with HCs and mapped in five significant metabolic pathways including aminoacyl-tRNA biosynthesis (p = 0.0055), branched chain amino acids valine, leucine and isoleucine biosynthesis (p = 0.0060), glycolysis and gluconeogenesis (p = 0.0067), nicotinate and nicotinamide metabolism (p = 0.0213) and pyruvate metabolism (p = 0.0440). Moreover, the SVM predictive model showed that butylamine (p = 0.0005, p_FDR=0.0006), 3-methylpyridine (p = 0.0002, p_FDR = 0.0012), endogenous aliphatic ethanol isotope (p = 0.0073, p_FDR = 0.0174), valeric acid (p = 0.005, p_FDR = 0.0162) and isoprene (p = 0.038, p_FDR = 0.045) were potential metabolites within identified clusters with HCA and altered pathways, and discriminated between patients with MDD and non-depressed ones with high sensitivity (0.88), specificity (0.96) and area under curve of ROC (0.96).

CONCLUSION

According to the results of this study, the non-targeted breathomics analysis with high-throughput sensitive analytical technologies coupled to advanced computational tools approaches offer completely new insights into peripheral biochemical changes in MDD.