Discovery of Volatile Biomarkers of Parkinson's Disease from Sebum

Sebaceous origins of human odor

The compounds that make up human body odor have been catalogued by researchers in many fields. Yet few are aware of exactly where these molecules come from. Volatile body-odor compounds are often cited as being produced primarily via microbial activity from precursors in sweat. While this is a source of many human volatiles, here we synthesize data showing that some of the most distinctive and abundant components of human odor instead originate from precursors in sebum, via reactions that do not involve the skin microbiome. We also review the unique biochemistry of human sebaceous glands and discuss evolutionary hypotheses that may partly explain why human sebum is so unique. Finally, we discuss how sebum-derived volatiles intersect with human health and disease, for example, via attraction of disease-vector mosquitoes or use in medical diagnostics. Our review draws insights from multiple fields, which together provide surprising clarity on some of the proximate and ultimate mechanisms underlying the distinctive composition of human odor.

Study of sampling phases for body odor sampling prior to analysis by TD-GC×GC/ToFMS

Body odor consists of a complex matrix of volatile organic compounds (VOCs), which has garnered increasing interest in fields like medicine for its potential in disease diagnosis. However, the field of body odor analysis is advancing slowly, partly due to a lack of standardized methodologies. Although gas chromatography-mass spectrometry (GC-MS) is widely used for VOC analysis, there is a broad range of sampling and extraction methods, leading to different or even sometimes contradictory results. To move toward standardized procedures, this study compares five sampling phases for direct body odor sampling in terms of analytical cleanliness and VOC trapping/release efficiency: gauze, glass beads, PowerSorb®, Getxent® microtubes, and passive sampling pillows (PSP). Thermodesorption was employed to simplify the protocol and minimize contamination or sample loss, which often occurs during multistep processes. Given the matrix's complexity and the need to detect trace-level compounds, comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/ToFMS) was used to achieve high sensitivity and peak capacity. PSP and PowerSorb® demonstrated the best performance, with mean recovery yields of 95% and 71%, respectively, and 22% and 10% variability, ensuring good repeatability. These findings, initially obtained under simulated conditions with a synthetic mixture, were validated with real body odor samples, with an optimal sampling duration estimated between 30 min and 1 h. This study not only highlights these effective sampling solutions but also emphasizes the risks associated with using sorbent phases that lack adequate analytical cleanliness (i.e., clean blank) such as gauze.

MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics

We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23,838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1,836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices.

Epidermal secretion-purified biosensing patch with hydrogel sebum filtering membrane and unidirectional flow microfluidic channels

The development of biosensing electronics for real-time sweat analysis has attracted increasing research interest due to their promising applications for non-invasive health monitoring. However, one of the critical challenges lies in the sebum interference that largely limits the sensing reliability in practical scenarios. Herein, we report a flexible epidermal secretion-purified biosensing patch with a hydrogel filtering membrane that can effectively eliminate the impact of sebum and sebum-soluble substances. The as-prepared sebum filtering membranes feature a dual-layer sebum-resistant structure based on the poly(hydroxyethyl methacrylate) hydrogel functionalized with nano-brush structured poly(sulfobetaine) to eliminate interferences and provide self-cleaning capability. Furthermore, the unidirectional flow microfluidic channels design based on the Tesla valve was incorporated into the biosensing patch to prevent external sebum contamination and allow effective sweat refreshing for reliable sensing. By seamlessly combining these components, the epidermal secretion-purified biosensing patch enables continuous monitoring of sweat uric acid, pH, and sodium ions with significantly improved accuracy of up to 12 %. The proposed strategy for enhanced sweat sensing reliability without sebum interference shows desirable compatibility for different types of biosensors and would inspire the advances of flexible and wearable devices for non-invasive healthcare.

From Complexity to Clarity: Expanding Metabolome Coverage With Innovative Analytical Strategies

Metabolomics, a powerful discipline within systems biology, aims at comprehensive profiling of small molecules in biological samples. The challenges of biological sample complexity are addressed through innovative sample preparation methods, including solid-phase extraction and microextraction techniques, enhancing the detection and quantification of low-abundance metabolites. Advances in chromatographic separation, particularly liquid chromatography (LC) and gas chromatography (GC), coupled with high-resolution (HR) mass spectrometry (MS), have significantly improved the sensitivity, selectivity, and throughput of metabolomic studies. Cutting-edge techniques, such as ion-mobility mass spectrometry (IM-MS) and tandem MS (MS/MS), further expand the capacity for comprehensive metabolite profiling. These advanced analytical platforms each offer unique advantages for metabolomics, with continued technological improvements driving deeper insights into metabolic pathways and biomarker discovery. By providing a detailed overview of current trends and techniques, this review aims to offer valuable insights into the future of metabolomics in human health research and its translational potential in clinical settings. Toward the end, this review also highlights the biomedical applications of metabolomics, emphasizing its role in biomarker discovery, disease diagnostics, personalized medicine, and drug development.

Dermatological Manifestations and Sebum Composition in Parkinson's Disease

INTRODUCTION

Parkinson disease (PD) is a multifaceted neurodegenerative disorder known for its hallmark motor symptoms. However, nonmotor manifestations, specifically dermatological changes, precede motor symptoms and may thus serve as vital early indicators of PD.

OBJECTIVES

This article explores the skin-related changes associated with PD, focusing on alterations in sebum composition, microbial dysbiosis, and the potential for leveraging dermatological assessments as early, noninvasive diagnostic markers for PD.

METHODS

A comprehensive literature review was conducted to investigate dermatological manifestations of PD, focusing on sebum changes in affected individuals. Research explored the clinical relevance of altered lipid profiles, volatile organic compound (VOC) contributions, and microbiome dysbiosis in those with PD.

RESULTS

Individuals with PD exhibit excess sebum production characterized by altered lipid profiles, including elevated short-chain fatty acids (SCFAs) and disruptions in sphingolipid metabolism. The lipid-rich environment also promotes overgrowth of Malessezia yeast, contributing to varied dermatological symptoms in those with PD. VOCs identified in sebum have been linked to unique odors and serve as biomarkers for diagnostic potential. These findings support the potential for early PD diagnosis through dermatologic assessment and sebum analysis.

CONCLUSION

Dermatological manifestations in PD offer promising noninvasive biomarkers for early diagnosis. Future research should aim to further elucidate the mechanisms underlying sebum dysregulation in PD and validate the clinical relevance of these biomarkers in larger populations.

Dairy-based multi-strain probiotic community successfully mitigated obesity-related gut microbiota dysbiosis in vitro (CoMiniGut)

Introduction. Obesity is a global health concern, affecting individuals of all ages and genders. One promising strategy to combat obesity is by addressing gut microbiota dysbiosis, with probiotics being a reliable intervention. However, single-strain probiotics may not effectively modulate the complex microbial communities in the gut, suggesting the need for multi-strain approaches.Gap Statement. Probiotics are known to benefit gut health; however, the efficacy of single-strain probiotics in modulating gut microbiota is limited. Multi-strain probiotic community (MSPC) may offer a more effective approach for addressing obesity-related gut dysbiosis, but its specific effects on individuals and microbial diversity require further investigation.Aim. This study aimed to evaluate the potential of a dairy-origin MSPC in modulating obesity-related gut microbiota from lean and obese Pakistani volunteers using a simulated CoMiniGut model.Methodology. Gut microbiota from lean and obese volunteers were treated with MSPC in a simulated CoMiniGut system. Bacterial counts, microbial diversity (α- and β-diversity) and microbial community composition were analysed pre- and post-treatment. The impact of MSPC on specific bacterial genera and microbial metabolites was assessed, with statistical significance determined (P≤0.05).Results. The effect of MSPC was individualized, reducing bacterial counts in lean 1 and lean 2 samples, while significantly increasing bacterial counts in obese 2 and obese 3 samples (P≤0.05). MSPC significantly improved α-diversity in lean 2, lean 3, obese 2 and obese 3 samples (P≤0.05). Proteobacteria decreased in the lean group and increased in the obese group post-MSPC treatment. In the lean group, pathogenic bacteria such as Klebsiella, Escherichia and Enterobacter were significantly reduced (P≤0.05), whereas beneficial bacteria like Bifidobacterium and Lactobacillus increased significantly in the obese group (P≤0.05). Among the selected metabolites, only butanoic acid was detected in all tested samples, with MSPC affecting metabolite concentrations and types.Conclusion. MSPC demonstrated a potential for modulating gut microbiota dysbiosis in both lean and obese individuals, with effects on bacterial counts, microbial diversity and metabolite concentrations. MSPC could serve as a promising option for personalized the modulation of gut microbiota in obesity management.

From small to tall: breed-varied household pet dogs can be trained to detect Parkinson's Disease

Parkinson's Disease (PD) is a clinically diagnosed disease that carries a reported misdiagnosis rate of 10-20%. Recent scientific discoveries have provided evidence of volatile organic compounds in sebum that are unique to patients with PD. The primary objective of this study was to determine if companion dogs could be trained to distinguish between sebum samples provided by PD-positive patients and PD-negative human controls. This was a randomized, handler-blind, controlled study. Twenty-three canines of varying breeds, ages, and environmental backgrounds were included. The study period encompassed 200 total working days from 2021 to 2022. Factors investigated included donor gender and levodopa drug affectivity, as well as canine breed, age, and duration of training time. The findings in this study were compiled from data collected during the final two years of a seven-year research program. For this two-year reporting period, when averaged as a group, the 23 dogs were 89% sensitive and 87% specific to olfactory distinction between PD-positive and PD-negative human donor samples. Ten of the twenty-three dogs averaged 90% or higher in both sensitivity and specificity. In 161 separate trials, a dog was presented with both novel PD-positive and PD-negative samples. For these novel exposures, the dogs collectively averaged 86% sensitivity and 89% specificity. PD medication was also investigated and was found to have no discernible impact on canine sensitivity or specificity results. Study findings support the application of companion dogs, trained with force-free, reward-based methodologies, for the detection of PD-positive and PD-negative samples under controlled conditions.

Human perception of Parkinson's disease body odor in comparison to the volatile organic compounds of Parkinson's disease

INTRODUCTION

Patients with Parkinson's Disease (PD) have a distinctive body odor, which was first described by a patient's wife as musky and strong. Later analysis of sebum of patients with PD revealed four volatile organic compounds (VOC) (perillic aldehyde, hippuric acid, eicosane, octadecanal), that differed from healthy subjects, and the patient's wife confirmed that three of them smelled like patients with PD. However, it is unclear whether other people can also perceive this PD body odor and whether it can be artificially recreated. Hence, we aimed to systematically assess whether young women can perceive the PD body odor and whether they can discriminate between the PD body odor and the "artificial PD odor" composed of the four VOCs mentioned above.

METHODS

T-shirts were collected from 19 people with idiopathic PD and 15 age- and gender-matched healthy participants to represent the PD body odor and the healthy body odor, respectively. The four VOCs were diluted in 1,2-propanediol to prepare the artificial PD body odor. Body odors were rated by 26 young women.

RESULTS

PD body odor was perceived as more musty, strong, smelly, and unpleasant compared to healthy and artificial PD body odor. Furthermore, around 80 % of women were able to discriminate PD body odor from artificial PD body odor.

CONCLUSION

Overall, this study confirmed a distinctive body odor quality of patients with PD, which can be perceived by young women. However, the four VOCs, composing the artificial PD body odor, were insufficient to reproduce the body odor from PD patients.

How storage post sampling influences the stability of sebum when used for mass spectrometry metabolomics analysis?

Sebum is a biofluid excreted by sebaceous glands in the skin. In recent years sebum has been shown to contain endogenous metabolites diagnostic of disease, with remarkable results for Parkinson's Disease. Given that sebum sampling is facile and non-invasive, its potential for use in clinical biochemistry diagnostic assays should be explored including the parameters for standard operating procedures around collection, transport, and storage. To this aim we have here investigated the reproducibility of mass spectrometry data from sebum in relation to both storage temperature and length of storage. Sebum samples were collected from volunteers and stored for up to four weeks at a range of temperatures: ambient (circa 17 °C), -20 °C and -80 °C. Established extraction protocols were employed and samples were analysed by two chromatographic mass spectrometry techniques and data investigated using PCA, PLS-DA and ANOVA. We cannot discriminate samples as a function of storage temperature or time stored in unsupervised analysis using data acquired via TD-GC-MS and LC-IM-MS, although the sampling of volatiles was susceptible to batch effects. This study indicates that the requirements for storage and transport of sebum samples that may be used in clinical assays are less stringent than for liquid samples and indicate that sebum is suitable for remote and at home sampling prior to analysis.

Strategies to Improve Bladder Control: A Preliminary Case Study

Background: Lower urinary tract symptoms (LUTSs) are a common complaint in adult and elderly men with bladder outlet obstruction, and have a considerable impact on their quality of life. Symptoms affect storage, voiding and post micturition stages. Among the latter, a feeling of incomplete emptying is one of the most bothersome for the patients; a condition that in turn contributes to affect urinary urgency, nocturia and frequency. Common recommendations include self-management practices (e.g., control of fluid intake, double-voiding and distraction techniques) to relieve patients' symptoms, whose effectiveness, however, is under debate. Methods: In this report we describe two pioneering procedures to favor bladder residual content voiding in people complaining of LUTS disorders. The first is based on motor imagery and the second on the use of odors. The beneficial effects of Mental imagery techniques on various tasks (e.g., in the treatment of several pathological conditions or as valid mnemonics aids have a long tradition and have received consistently experimental support. Thus, a patient (a 68-year-old Caucasian man) complaining of LUTS was trained to use a motor imagery technique (building up a visual image comprising the bladder, the detrusor muscle and the urethra, and to imagine the detrusor muscle contracting and the flow of urine expelled) for 90 days and two odors (coffee and a lavender scented cleanser) for 10 days, as a trigger for micturition. He was asked to record-immediately after the first morning micturition-the time interval between the first (free) and the second (cued) micturition. Results: Reported data suggest the efficacy of motor imagery in favoring the bladder residual urine voiding in a few minutes (M = 4.75 min.) compared to the control condition, i.e., the baseline of the patient (M = 79.5 min.), while no differences between the odor-based procedures (M 1st odorant = 70.6 min.; M 2nd odorant = 71.1 min) and the latter were observed. Conclusions: A procedure based on an imagery technique may, therefore, be of general value-as a suggested protocol-and accordingly can be applicable to clinical settings. An olfactory bladder control hypothesis cannot, however, be ruled out and is discussed as a promising future line of research.

Pesticides and the Gut Microbiota: Implications for Parkinson's Disease

Parkinson's disease (PD) affects more people worldwide than just aging alone can explain. This is likely due to environmental influences, genetic makeup, and changes in daily habits. The disease develops in a complex way, with movement problems caused by Lewy bodies and the loss of dopamine-producing neurons. Some research suggests Lewy bodies might start in the gut, hinting at a connection between these structures and gut health in PD patients. These patients often have different gut bacteria and metabolites. Pesticides are known to increase the risk of PD, with evidence showing they harm more than just dopamine neurons. Long-term exposure to pesticides in food might affect the gut barrier, gut bacteria, and the blood-brain barrier, but the exact link is still unknown. This review looks at how pesticides and gut bacteria separately influence PD development and progression, highlighting the harmful effects of pesticides and changes in gut bacteria. We have examined the interaction between pesticides and gut bacteria in PD patients, summarizing how pesticides cause imbalances in gut bacteria, the resulting changes, and their overall effects on the PD prognosis.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI-the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

Long-wave infrared pulsed external-cavity QCL spectrometer using a hollow waveguide gas cell

A spectrometer built using an external cavity pulsed quantum cascade laser is described. The spectrometer has a tuning range from 10 - 13 µm (1,000 - 769 cm-1) and is designed to target volatile organic compounds (VOCs) which often exhibit water-free molecular absorption within the region. The spectrometer utilizes a hollow silica waveguide gas cell which has an internal volume of a few millilitres, a fast response time (∼1 s), and is advantageous when only low sample volumes, similar to the cell volume, are available. Propane is used as a test gas because it is easy to handle, and its spectral profile is comparable to VOCs of interest. Its absorption in the region is primarily within the ν21 band which spans from 10.55 - 11.16 µm (948 - 896 cm-1). Spectral measurements at a range of concentrations show good linearity and an Allan deviation of absorbance values recorded over a 100-minute period indicates a minimum detectable absorbance of 3.5×10-5 at an integration time of 75 s.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease

BACKGROUNDS

Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context.

OBJECTIVES

Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD.

METHODS

This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter.

FINDINGS

This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD.

CONCLUSION

The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

Neuroprotective and anti-inflammatory effects of eicosane on glutamate and NMDA-induced retinal ganglion cell injury

AIM

To investigate the protective effects, antioxidant potential, and anti-inflammatory mechanisms of eicosane on glutamate-induced cell damage and on N-methyl-D-aspartate (NMDA)-induced retinal ganglion cell (RGC) injury in a mouse model of glaucoma.

METHODS

The protective effects of eicosane on the rat R28 retinal precursor cell line were assessed using cell counting kit-8 assays and Hoechst-propidium iodide staining. Intracellular reactive oxygen species (ROS) production was measured using the fluorescent probe 2'-7'-dichlorofluorescin diacetate and flow cytometry. The protective role of eicosane on NMDA-induced RGC injury in a mouse glaucoma model was determined by immunostaining of frozen sections of retina. The effects of eicosane on the metabolome of the retina in mice with NMDA-induced RGC damage were evaluated by liquid chromatography-mass spectroscopy (LC-MS) and untargeted metabolomics analyses.

RESULTS

Eicosane treatment significantly attenuated glutamate-induced damage to R28 cells in vitro. Eicosane also protected RGCs against NMDA-induced injury in a mouse glaucoma model. Untargeted metabolomics analyses showed that eicosane increased multiple metabolites, including L-arginine and L-carnitine, in the retina.

CONCLUSION

Eicosane has protective effects, antioxidant potential, and anti-inflammatory properties in an in vitro model of glutamate-induced cell damage and in an in vivo model of NMDA-induced RGC injury in mouse glaucoma through modulation of L-arginine and/or L-carnitine metabolism.

Skin emitted volatiles analysis for noninvasive diagnosis: the current advances in sample preparation techniques for biomedical application

Human skin emits a series of volatile compounds from the skin due to various metabolic processes, microbial activity, and several external factors. Changes in the concentration of skin volatile metabolites indicate many diseases, including diabetes, cancer, and infectious diseases. Researchers focused on skin-emitted compounds to gain insight into the pathophysiology of various diseases. In the case of skin volatolomics research, it is noteworthy that sample preparation, sampling protocol, analytical techniques, and comprehensive validation are important for the successful integration of skin metabolic profiles into regular clinical settings. Solid-phase microextraction techniques and polymer-based active sorbent traps were developed to capture the skin-emitted volatile compounds. The primary advantage of these sample preparation techniques is the ability to efficiently and targetedly capture skin metabolites, thus improving the detection of the biomarkers associated with various diseases. In further research, polydimethyl-based patches were utilized for skin research due to their biocompatibility and thermal stability properties. The microextraction sampling tools coupled with high sensitive Gas Chromatography-Mass Spectrometer provided a potential platform for skin volatolomes, thus emerging as a state-of-the-art analytical technique. Later, technological advancements, including the design of wearable sensors, have enriched skin-based research as it can integrate the information from skin-emitted volatile profiles into a portable platform. However, individual-specific hydration, temperature, and skin conditions can influence variations in skin volatile concentration. Considering the subject-specific skin depth, sampling time standardization, and suitable techniques may improve the skin sampling techniques for the potential discovery of various skin-based marker compounds associated with diseases. Here, we have summarised the current research progress, limitations, and technological advances in skin-based sample preparation techniques for disease diagnosis, monitoring, and personalized healthcare applications.

Shotgun Lipidomic Profiling of Sebum Lipids via Photocatalyzed Paternò-Büchi Reaction and Ion Mobility-Mass Spectrometry

Sebum lipids are composed of nonpolar lipids, and they pose challenges for mass spectrometry-based analysis due to low ionization efficiency and the existence of numerous isomers and isobars. To address these challenges, we have developed ethyl 2-oxo-2-(pyridine-3-yacetate as a charge-tagging Paternò-Büchi reagent and Michler's ketone as a highly efficient photocatalyst, achieving ∼90% conversion for C═C derivatization under 440 nm LED irradiation. This derivatization, when coupled with electrospray ionization-tandem mass spectrometry, boosts the detection of sebum lipids and pinpoints C═C location in a chain-specific fashion. Identification and quantitation of isomers are readily achieved for wax esters, a class of underexplored sebum lipids, which have C═C bonds distributed in fatty alcohol and fatty acyl chains. A shotgun analysis workflow has been developed by pairing the offline PB derivatization with cyclic ion mobility spectrometry-mass spectrometry. Besides the dominant n-10 C═C location in unsaturated wax esters, profiling of low abundance isomers, including the rarely reported n-7 and n-13 locations, is greatly enhanced due to separations of C═C diagnostic ions by ion mobility. Over 900 distinct lipid structures from human sebum lipid extract have been profiled at the chain-specific C═C level, including wax esters (500), glycerolipids (393), and cholesterol esters (22), far more exceeding previous reports. Overall, we have developed a fast and comprehensive lipidomic profiling tool for sebum samples, a type of noninvasive biofluids holding potential for the discovery of disease markers in distal organs.

Predicting Chronological Age via the Skin Volatile Profile

Skin volatile emissions offer a noninvasive insight into metabolic activity within the body as well as the skin microbiome and specific volatile compounds have been shown to correlate with age, albeit only in a few small studies. Building on this, here skin volatiles were collected and analyzed in a healthy participant study (n = 60) using a robust headspace-solid phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS) workflow. Following processing, 18 identified compounds were deemed suitable for this study. These were classified according to gender influences and their correlations with age were investigated. Finally, 6 volatiles (of both endogenous and exogenous origin) were identified as significantly changing in abundance with participant age (p < 0.1). The potential origins of these dysregulations are discussed. Multiple linear regression (MLR) analysis was employed to model age based on these significant volatiles as independent variables, along with gender. Our analysis shows that skin volatiles show a strong predictive ability for age (explained variance of 68%), stronger than other biochemical measures collected in this study (skin surface pH, water content) which are understood to vary with chronological age. Overall, this work provides new insights into the impact of aging on the skin volatile profiles which comprises both endogenously and exogenously derived volatile compounds. It goes toward demonstrating the biological significance of skin volatiles and will help pave the way for more rigorous consideration of the healthy "baseline" skin volatile profile in volatilomics-based health diagnostics development going forward.

A fast and non-invasive artificial intelligence olfactory-like system that aids diagnosis of Parkinson's disease

BACKGROUND AND PURPOSE

Several previous studies have shown that skin sebum analysis can be used to diagnose Parkinson's disease (PD). The aim of this study was to develop a portable artificial intelligence olfactory-like (AIO) system based on gas chromatographic analysis of the volatile organic compounds (VOCs) in patient sebum and explore its application value in the diagnosis of PD.

METHODS

The skin VOCs from 121 PD patients and 129 healthy controls were analyzed using the AIO system and three classic machine learning models were established, including the gradient boosting decision tree (GBDT), random forest and extreme gradient boosting, to assist the diagnosis of PD and predict its severity.

RESULTS

A 20-s time series of AIO system data were collected from each participant. The VOC peaks at a large number of time points roughly concentrated around 5-12 s were significantly higher in PD subjects. The gradient boosting decision tree model showed the best ability to differentiate PD from healthy controls, yielding a sensitivity of 83.33% and a specificity of 84.00%. However, the system failed to predict PD progression scored by Hoehn-Yahr stage.

CONCLUSIONS

This study provides a fast, low-cost and non-invasive method to distinguish PD patients from healthy controls. Furthermore, our study also indicates abnormal sebaceous gland secretion in PD patients, providing new evidence for exploring the pathogenesis of PD.

Fungal-Bacterial Combinations in Plant Health under Stress: Physiological and Biochemical Characteristics of the Filamentous Fungus Serendipita indica and the Actinobacterium Zhihengliuella sp. ISTPL4 under In Vitro Arsenic Stress

Fungal-bacterial combinations have a significant role in increasing and improving plant health under various stress conditions. Metabolites secreted by fungi and bacteria play an important role in this process. Our study emphasizes the significance of secondary metabolites secreted by the fungus Serendipita indica alone and by an actinobacterium Zhihengliuella sp. ISTPL4 under normal growth conditions and arsenic (As) stress condition. Here, we evaluated the arsenic tolerance ability of S. indica alone and in combination with Z. sp. ISTPL4 under in vitro conditions. The growth of S. indica and Z. sp. ISTPL4 was measured in varying concentrations of arsenic and the effect of arsenic on spore size and morphology of S. indica was determined using confocal microscopy and scanning electron microscopy. The metabolomics study indicated that S. indica alone in normal growth conditions and under As stress released pentadecanoic acid, glycerol tricaprylate, L-proline and cyclo(L-prolyl-L-valine). Similarly, d-Ribose, 2-deoxy-bis(thioheptyl)-dithioacetal were secreted by a combination of S. indica and Z. sp. ISTPL4. Confocal studies revealed that spore size of S. indica decreased by 18% at 1.9 mM and by 15% when in combination with Z. sp. ISTPL4 at a 2.4 mM concentration of As. Arsenic above this concentration resulted in spore degeneration and hyphae fragmentation. Scanning electron microscopy (SEM) results indicated an increased spore size of S. indica in the presence of Z. sp. ISTPL4 (18 ± 0.75 µm) compared to S. indica alone (14 ± 0.24 µm) under normal growth conditions. Our study concluded that the suggested combination of microbial consortium can be used to increase sustainable agriculture by combating biotic as well as abiotic stress. This is because the metabolites released by the microbial combination display antifungal and antibacterial properties. The metabolites, besides evading stress, also confer other survival strategies. Therefore, the choice of consortia and combination partners is important and can help in developing strategies for coping with As stress.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

The Skin and Lewy Body Disease

This manuscript reviews the significant skin manifestations of Lewy body disease, including Parkinson's disease and dementia with Lewy bodies, and the diagnostic utility of skin biopsy. Besides classic motor and cognitive symptoms, non-motor manifestations, particularly dermatologic disorders, can play a crucial role in disease presentation and diagnosis. This review explores the intricate relationship between the skin and Lewy body disease. Seborrheic dermatitis, autoimmune blistering diseases (bullous pemphigoid and pemphigus), rosacea, and melanoma are scrutinized for their unique associations with Parkinson's disease, revealing potential links through shared pathophysiological mechanisms. Advances in diagnostic techniques allow the identification of promising biomarkers such as α-synuclein in samples obtained by skin punch biopsy. Understanding the dermatologic aspects of Lewy body disease not only contributes to its holistic characterization but also holds implications for innovative diagnostic approaches.

Volatile organic compounds analysis as promising biomarkers for Parkinson's disease diagnosis: A systematic review and meta-analysis

OBJECTIVE

Researchers are investigating the potential of volatile organic compounds (VOCs) obtained from exhaled breath and sebum as non-invasive tools for early Parkinson's disease (PD) diagnosis. The present study aims to assess the feasibility of using VOC analysis for PD diagnosis and determine the overall diagnostic accuracy of the proposed tests.

METHODS

We performed systematic searches based on the PRISMA guidelines to identify relevant studies on VOCs in PD diagnosis using exhaled breath or sebum samples. The selected articles were described, and meta-analysis was conducted on those that provided the sensitivity and specificity data.

RESULTS

Out of 1268 articles initially identified, 8 met the inclusion criteria and provided specific sensitivity and specificity data for PD, which were included in the current meta-analysis. The pooled analysis of these findings showed a mean area under the receiver operating characteristic curve of 0.85, a sensitivity of 0.81 (95% confidence interval (CI): 0.72, 0.88), and a specificity of 0.76 (95% CI: 0.66, 0.84).

CONCLUSION

The analysis of VOCs in exhaled breath and sebum has shown promise as a new avenue for non-invasive diagnosis of PD. VOCs' ability to distinguish PD from healthy controls suggests their potential clinical application in screening for the disease. Consequently, VOCs hold significant potential as biomarkers for PD diagnosis and offer a promising novel approach to identifying and diagnosing the condition.

Mass Specthoscope: A Hand-held Extendable Probe for Localized Noninvasive Sampling of Skin Volatome for Online Analysis

Human skin emits a unique set of volatile organic compounds (VOCs). These VOCs can be probed in order to obtain physiological information about the individuals. However, extracting the VOCs that emanate from human skin for analysis is troublesome and time-consuming. Therefore, we have developed "Mass Specthoscope"─a convenient tool for rapid sampling and detecting VOCs emitted by human skin. The hand-held probe with a pressurized tip and wireless button enables sampling VOCs from surfaces and their transfer to the atmospheric pressure chemical ionization source of quadrupole time-of-flight mass spectrometer. The system was characterized using chemical standards (acetone, benzaldehyde, sulcatone, α-pinene, and decanal). The limits of detection are in the range from 2.25 × 10-5 to 3.79 × 10-5 mol m-2. The system was initially tested by detecting VOCs emanating from porcine skin spiked with VOCs as well as unspiked fresh and spoiled ham. In the main test, the skin of nine healthy participants was probed with the Mass Specthoscope. The sampling regions included the armpit, forearm, and forehead. Numerous skin-related VOC signals were detected. In the final test, one participant ingested a fenugreek drink, and the participant's skin surface was probed using the Mass Specthoscope hourly during the 8 h period. The result revealed a gradual release of fenugreek-related VOCs from the skin. We believe that this analytical approach has the potential to be used in metabolomic studies and following further identification of disease biomarkers─also in noninvasive diagnostics.

Unraveling the potential of breath and sweat VOC capture devices for human disease detection: a systematic-like review of canine olfaction and GC-MS analysis

The development of disease screening methods using biomedical detection dogs relies on the collection and analysis of body odors, particularly volatile organic compounds (VOCs) present in body fluids. To capture and analyze odors produced by the human body, numerous protocols and materials are used in forensics or medical studies. This paper provides an overview of sampling devices used to collect VOCs from sweat and exhaled air, for medical diagnostic purposes using canine olfaction and/or Gas Chromatography-Mass spectrometry (GC-MS). Canine olfaction and GC-MS are regarded as complementary tools, holding immense promise for detecting cancers and infectious diseases. However, existing literature lacks guidelines for selecting materials suitable for both canine olfaction and GC-MS. Hence, this review aims to address this gap and pave the way for efficient body odor sampling materials. The first section of the paper describes the materials utilized in training sniffing dogs, while the second section delves into the details of sampling devices and extraction techniques employed for exhaled air and sweat analysis using GC-MS. Finally, the paper proposes the development of an ideal sampling device tailored for detection purposes in the field of odorology. By bridging the knowledge gap, this study seeks to advance disease detection methodologies, harnessing the unique abilities of both dogs and GC-MS analysis in biomedical research.

Transforming Parkinson's Care in Africa (TraPCAf): protocol for a multimethodology National Institute for Health and Care Research Global Health Research Group project

BACKGROUND

Parkinson's disease (PD) is the second most common neurodegenerative disorder and, according to the Global Burden of Disease estimates in 2015, was the fastest growing neurological disorder globally with respect to associated prevalence, disability, and deaths. Information regarding the awareness, diagnosis, phenotypic characteristics, epidemiology, prevalence, risk factors, treatment, economic impact and lived experiences of people with PD from the African perspective is relatively sparse in contrast to the developed world, and much remains to be learned from, and about, the continent.

METHODS

Transforming Parkinson's Care in Africa (TraPCAf) is a multi-faceted, mixed-methods, multi-national research grant. The study design includes multiple sub-studies, combining observational (qualitative and quantitative) approaches for the epidemiological, clinical, risk factor and lived experience components, as appropriate, and interventional methods (clinical trial component). The aim of TraPCAf is to describe and gain a better understanding of the current situation of PD in Africa. The countries included in this National Institute for Health and Care Research (NIHR) Global Health Research Group (Egypt, Ethiopia, Ghana, Kenya, Nigeria, South Africa and Tanzania) represent diverse African geographies and genetic profiles, with differing resources, healthcare systems, health and social protection schemes, and policies. The research team is composed of experts in the field with vast experience in PD, jointly led by a UK-based and Africa-based investigator.

DISCUSSION

Despite the increasing prevalence of PD globally, robust data on the disease from Africa are lacking. Existing data point towards the poor awareness of PD and other neurological disorders on the continent and subsequent challenges with stigma, and limited access to affordable services and medication. This multi-site study will be the first of its kind in Africa. The data collected across the proposed sub-studies will provide novel and conclusive insights into the situation of PD. The selected country sites will allow for useful comparisons and make results relevant to other low- and middle-income countries. This grant is timely, as global recognition of PD and the public health challenge it poses builds. The work will contribute to broader initiatives, including the World Health Organization's Intersectoral global action plan on epilepsy and other neurological disorders.

TRIAL REGISTRATION

https://doi.org/10.1186/ISRCTN77014546 .

Increased diversity of Malassezia species on the skin of Parkinson's disease patients

Background

Parkinson's disease (PD) is characterized by motor disorders and the composition of Lewy bodies (LBs) in the substantia nigra. Due to the lack of a definitive biomarker, the current treatments do not modify the progression of PD. Recently, researchers revealed lipid dysregulation and some potential volatile biomarkers of PD related to a unique odor from PD patients by metabolomics of sebum, which is supposed to cause a potential change for skin microflora. In this study, we identified the 4 Malassezia species in PD patients and compared them with healthy controls.

Methods

We collected 95 sebum samples (47 PDs and 48 Controls) by cotton swabs and extracted the DNA. The identification of Malassezia species was performed by Nested PCR. Specific primers for each species were used to amplify corresponding yeasts in each sample.

Results

M. restricta and M. globosa are the most common species for both groups. The prevalence of M. slooffiae and M. sympodialis were significantly higher in the PD group compared with controls (63.8% vs. 29.1 and 74.5% vs. 54.2% respectively), the binary logistic regression model further indicated that M. slooffiae (OR = 9.358, p < 0.001) was associated with PD. Moreover, the diversity of Malassezia species was significantly greater (3.5 vs. 2.9 species per individual, p = 0.002) in the PD group.

Conclusion

Based on our results, we preliminarily observed a change in Malassezia species incidence and diversity on the skin of PD patients, which could be associated with lipid dysregulation; meanwhile, it might also be a noninvasive biomarker for PD.

Melanin: a unifying theory of disease as exemplified by Parkinson's, Alzheimer's, and Lewy body dementia

Melanin, a ubiquitous dark pigment, plays important roles in the immune system, including scavenging reactive oxygen species formed in response to ultraviolet radiation absorption, absorbing metals, thermal regulation, drug uptake, innate immune system functions, redox, and energy transduction. Many tissue types, including brain, heart, arteries, ovaries, and others, contain melanin. Almost all cells contain precursors to melanin. A growing number of diseases in which there is a loss of melanin and/or neuromelanin are increasingly thought to have infectious etiologies, for example, Alzheimer's disease (AD), Parkinson's disease (PD), Lewy Body Dementia (LBD), and vitiligo. AD, PD, LBD, and vitiligo have been linked with herpesvirus, which enters melanosomes and causes apoptosis, and with gut dysbiosis and inflammation. Herpesvirus is also linked with gut dysbiosis and inflammation. We theorize that under normal healthy states, melanin retains some of the energy it absorbs from electromagnetic radiation, which is then used to fuel cells, and energy from ATP is used to compliment that energy supply. We further theorize that loss of melanin reduces the energy supply of cells, which in the case of AD, PD, and LBD results in an inability to sustain immune system defenses and remove the plaques associated with the disease, which appear to be part of the immune system's attempt to eradicate the pathogens seen in these neurodegenerative diseases. In addition, in an attempt to explain why removing these plaques does not result in improvements in cognition and mood and why cognitions and moods in these individuals have ebbs and flows, we postulate that it is not the plaques that cause the cognitive symptoms but, rather, inflammation in the brain resulting from the immune system's response to pathogens. Our theory that energy retained in melanin fuels cells in an inverse relationship with ATP is supported by studies showing alterations in ATP production in relationship to melanin levels in melanomas, vitiligo, and healthy cells. Therefore, alteration of melanin levels may be at the core of many diseases. We propose regulating melanin levels may offer new avenues for treatment development.

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring.

Neuromorphic applications in medicine

In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson's Disease

The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy of ML and extent of information obtained from metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analyzing many chemical features with abundances that are correlated and "noisy". Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics data sets without a priori feature selection. The performance of the NN approach for predicting Parkinson's disease (PD) from blood plasma metabolomics data is significantly higher than other ML methods with a mean area under the curve of >0.995. PD-specific markers that predate clinical PD diagnosis and contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many diseases using metabolomics and other untargeted 'omics methods.

Rapid skin biomarker discovery using hydrogel-phase sampling followed by semi-automated liquid-phase re-extraction high-resolution mass spectrometry

A facile and rapid skin metabolomics protocol is proposed. The liquid microjunction-surface sampling probe system has been partly automated, and used in conjunction with hydrogel probes for skin metabolite analysis. A control device was built to precisely control the segmented solvent flow and analyte re-extraction into the liquid microjunction. This mode provides rapid online re-extraction of the analytes from hydrogel probes. Humectant was added to the hydrogel, and moist heat treatment was used to make the hydrogel probes rugged for sampling in the clinical setting. The developed method was validated for the analysis of choline - a putative biomarker of psoriasis. A linear relationship over six calibration levels from 3.18 × 10^-5 to 3.18 × 10^-4 mol m^-2 has been obtained. The limit of detection was 6.6 × 10^-6 mol m^-2, while the recoveries range from 92 to 109%. The within-run and between-run precision were evaluated and the coefficients of variation range from 1.84 to 7.13%. Furthermore, the developed method has been used to screen patients (n = 10) and healthy participants (control group; n = 10) for psoriasis-related skin metabolites. Metabolomic profiling of the skin excretion-related signals identified potential biomarkers of psoriasis: choline, pipecolic acid, ornithine, urocanic acid, and methionine.

No skin off your back: the sampling and extraction of sebum for metabolomics

INTRODUCTION

Sebum-based metabolomics (a subset of "sebomics") is a developing field that involves the sampling, identification, and quantification of metabolites found in human sebum. Sebum is a lipid-rich oily substance secreted by the sebaceous glands onto the skin surface for skin homeostasis, lubrication, thermoregulation, and environmental protection. Interest in sebomics has grown over the last decade due to its potential for rapid analysis following non-invasive sampling for a range of clinical and environmental applications.

OBJECTIVES

To provide an overview of various sebum sampling techniques with their associated challenges. To evaluate applications of sebum for clinical research, drug monitoring, and human biomonitoring. To provide a commentary of the opportunities of using sebum as a diagnostic biofluid in the future.

METHODS

Bibliometric analyses of selected keywords regarding skin surface analysis using the Scopus search engine from 1960 to 2022 was performed on 12th January 2023. The published literature was compartmentalised based on what the work contributed to in the following areas: the understanding about sebum, its composition, the analytical technologies used, or the purpose of use of sebum. The findings were summarised in this review.

RESULTS

Historically, about 15 methods of sampling have been used for sebum collection. The sample preparation approaches vary depending on the analytes of interest and are summarised. The use of sebum is not limited to just skin diseases or drug monitoring but also demonstrated for other systemic disease. Most of the work carried out for untargeted analysis of metabolites associated with sebum has been in the recent two decades.

CONCLUSION

Sebum has a huge potential beyond skin research and understanding how one's physiological state affects or reflects on the skin metabolome via the sebaceous glands itself or by interactions with sebaceous secretion, will open doors for simpler biomonitoring. Sebum acts as a sink to environmental metabolites and has applications awaiting to be explored, such as biosecurity, cross-border migration, localised exposure to harmful substances, and high-throughput population screening. These applications will be possible with rapid advances in volatile headspace and lipidomics method development as well as the ability of the metabolomics community to annotate unknown species better. A key issue with skin surface analysis that remains unsolved is attributing the source of the metabolites found on the skin surface before meaningful biological interpretation.

Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section

The investigation of macromolecular biomolecules with ion mobility mass spectrometry (IM-MS) techniques has provided substantial insights into the field of structural biology over the past two decades. An IM-MS workflow applied to a given target analyte provides mass, charge, and conformation, and all three of these can be used to discern structural information. While mass and charge are determined in mass spectrometry (MS), it is the addition of ion mobility that enables the separation of isomeric and isobaric ions and the direct elucidation of conformation, which has reaped huge benefits for structural biology. In this review, where we focus on the analysis of proteins and their complexes, we outline the typical features of an IM-MS experiment from the preparation of samples, the creation of ions, and their separation in different mobility and mass spectrometers. We describe the interpretation of ion mobility data in terms of protein conformation and how the data can be compared with data from other sources with the use of computational tools. The benefit of coupling mobility analysis to activation via collisions with gas or surfaces or photons photoactivation is detailed with reference to recent examples. And finally, we focus on insights afforded by IM-MS experiments when applied to the study of conformationally dynamic and intrinsically disordered proteins.

Why Do These Yeasts Smell So Good? Volatile Organic Compounds (VOCs) Produced by Malassezia Species in the Exponential and Stationary Growth Phases

Malassezia synthesizes and releases volatile organic compounds (VOCs), small molecules that allow them to carry out interaction processes. These lipid-dependent yeasts belong to the human skin mycobiota and are related to dermatological diseases. However, knowledge about VOC production and its function is lacking. This study aimed to determine the volatile profiles of Malassezia globosa, Malassezia restricta, and Malassezia sympodialis in the exponential and stationary growth phases. The compounds were separated and characterized in each growth phase through headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS). We found a total of 54 compounds, 40 annotated. Most of the compounds identified belong to alcohols and polyols, fatty alcohols, alkanes, and unsaturated aliphatic hydrocarbons. Unsupervised and supervised statistical multivariate analyses demonstrated that the volatile profiles of Malassezia differed between species and growth phases, with M. globosa being the species with the highest quantity of VOCs. Some Malassezia volatiles, such as butan-1-ol, 2-methylbutan-1-ol, 3-methylbutan-1-ol, and 2-methylpropan-1-ol, associated with biological interactions were also detected. All three species show at least one unique compound, suggesting a unique metabolism. The ecological functions of the compounds detected in each species and growth phase remain to be studied. They could interact with other microorganisms or be an important clue in understanding the pathogenic role of these yeasts.

Epidermal Wearable Biosensors for Monitoring Biomarkers of Chronic Disease in Sweat

Biological information detection technology is mainly used for the detection of physiological and biochemical parameters closely related to human tissues and organ lesions, such as biomarkers. This technology has important value in the clinical diagnosis and treatment of chronic diseases in their early stages. Wearable biosensors can be integrated with the Internet of Things and Big Data to realize the detection, transmission, storage, and comprehensive analysis of human physiological and biochemical information. This technology has extremely wide applications and considerable market prospects in frontier fields including personal health monitoring, chronic disease diagnosis and management, and home medical care. In this review, we systematically summarized the sweat biomarkers, introduced the sweat extraction and collection methods, and discussed the application and development of epidermal wearable biosensors for monitoring biomarkers in sweat in preclinical research in recent years. In addition, the current challenges and development prospects in this field were discussed.

Widening the Spectrum of Risk Factors, Comorbidities, and Prodromal Features of Parkinson Disease

Importance

The prodromal phase of Parkinson disease (PD) may last for more than 10 years. Recognition of the spectrum and occurrence of risk factors, comorbidities, and prodromal features of PD can increase understanding of the causes and development of the disease and help identify individuals at risk.

Objective

To identify the association of a subsequent diagnosis of PD with a range of risk factors and prodromal features, including lifestyle factors, comorbidities, and potential extracerebral manifestations of PD.

Design, Setting, and Participants

This was a case-control study using insurance claims of outpatient consultations of patients with German statutory health insurance between January 1, 2011, and December 31, 2020. Included were patients with incident diagnosis of PD without a previous diagnosis of parkinsonism or dementia and controls matched 1:2 for age, sex, region, and earliest year of outpatient encounter.

Exposures

Exposures were selected based on previous systematic reviews, case-control and cohort studies reporting on risk factors, comorbidities, and prodromal features of PD.

Main Outcomes and Measures

Previously postulated risk factors and prodromal features of PD, using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) coding.

Results

A total of 138 345 patients with incident PD (mean [SD] age, 75.1 [9.8] years; 73 720 male [53.3%]) and 276 690 matched controls (mean [SD] age, 75.1 (9.8) years; 147 440 male [53.3%]) were identified. Study participants were followed up for a mean (SD) of 6.0 (2.0) years. Consistent with previous reports, risk factors and prodromal features associated with PD included traumatic brain injury, odds ratio (OR), 1.62; 95% CI, 1.36-1.92; alcohol misuse, OR, 1.32; 95% CI, 1.21-1.44; hypertension, OR, 1.29; 95% CI, 1.26-1.31; anosmia, OR, 2.16; 95% CI, 1.59-2.93; and parasomnias (including RBD), OR, 1.62; 95% CI, 1.42-1.84. In addition, there were associations with restless legs syndrome (OR, 4.19; 95% CI, 3.91-4.50), sleep apnea (OR, 1.45; 95% CI, 1.37-1.54), epilepsy (OR, 2.26; 95% CI, 2.07-2.46), migraine (OR, 1.21; 95% CI, 1.12-1.29), bipolar disorder (OR, 3.81; 95% CI, 3.11-4.67), and schizophrenia (OR, 4.48; 95% CI, 3.82-5.25). The following diagnoses were also found to be associated with PD: sensory impairments beyond anosmia, such as hearing loss (OR, 1.14; 95% CI, 1.09-1.20) and changes of skin sensation (OR, 1.31; 95% CI, 1.21-1.43). There were also positive associations with skin disorders (eg, seborrheic dermatitis, OR, 1.30; 95% CI, 1.15-1.46; psoriasis, OR, 1.13; 95% CI, 1.05-1.21), gastrointestinal disorders (eg, gastroesophageal reflux, OR, 1.29; 95% CI, 1.25-1.33; gastritis, OR, 1.28; 95% CI, 1.24-1.33), conditions with a potential inflammatory component (eg, seronegative osteoarthritis, OR, 1.21; 95% CI, 1.03-1.43), and diabetes types 1 (OR, 1.32; 95% CI, 1.21-1.43) and 2 (OR, 1.24; 95% CI, 1.20-1.27). Associations even 5 to 10 years before diagnosis included tremor (odds ratio [OR], 4.49; 95% CI, 3.98-5.06), restless legs syndrome (OR, 3.73; 95% CI, 3.39-4.09), bipolar disorder (OR, 3.80; 95% CI, 2.82-5.14), and schizophrenia (OR, 4.00; 95% CI, 3.31-4.85).

Conclusions and Relevance

Results of this case-control study suggest that the associations found between PD and certain risk factors, comorbidities, and prodromal symptoms in a representative population may reflect possible early extrastriatal and extracerebral pathology of PD. This may be due to shared genetic risk with PD, medication exposure, or direct causation, or represent pathophysiologically relevant factors contributing to the pathogenesis of PD.

Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study

Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools. Cutibacterium, Staphylococcus, Micrococcus, Streptococcus, Lawsonella, Anaerococcus, and Corynebacterium species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.

The neglected potential of invertebrates in detecting disease via olfaction

Agents that cause disease alter the cell metabolism of their hosts. Cells with an altered metabolism produce particular profiles of biomolecules, which are different from those of healthy cells. Such differences may be detected by olfaction. Historically, physicians used olfactory cues to diagnose sickness by smelling the breath or the urine of patients. However, other species have been shown to possess excellent olfactory abilities. Dogs, for instance, have been frequently used as biodetectors of human diseases, including cancer, viral and bacterial infections. Other mammalian species, such as rats, have been trained to perform similar tasks, but their disease detection abilities remain poorly explored. Here, we focus on the overlooked potential of invertebrate species and we review the current literature on olfactory detection of diseases by these animals. We discuss the possible advantages of exploring further the abilities of invertebrates as detection tools for human disease.

The Versatility and Diagnostic Potential of VOC Profiling for Noninfectious Diseases

A variety of volatile organic compounds (VOCs) are produced and emitted by the human body every day. The identity and concentration of these VOCs reflect an individual's metabolic condition. Information regarding the production and origin of VOCs, however, has yet to be congruent among the scientific community. This review article focuses on the recent investigations of the source and detection of biological VOCs as a potential for noninvasive discrimination between healthy and diseased individuals. Analyzing the changes in the components of VOC profiles could provide information regarding the molecular mechanisms behind disease as well as presenting new approaches for personalized screening and diagnosis. VOC research has prioritized the study of cancer, resulting in many research articles and reviews being written on the topic. This review summarizes the information gained about VOC cancer studies over the past 10 years and looks at how this knowledge correlates with and can be expanded to new and upcoming fields of VOC research, including neurodegenerative and other noninfectious diseases. Recent advances in analytical techniques have allowed for the analysis of VOCs measured in breath, urine, blood, feces, and skin. New diagnostic approaches founded on sensor-based techniques allow for cheaper and quicker results, and we compare their diagnostic dependability with gas chromatography- and mass spectrometry-based techniques. The future of VOC analysis as a clinical practice and the challenges associated with this transition are also discussed and future research priorities are summarized.

A Complete Pipeline for Untargeted Urinary Volatolomic Profiling with Sorptive Extraction and Dual Polar and Nonpolar Column Methodologies Coupled with Gas Chromatography Time-of-Flight Mass Spectrometry

Volatolomics offers an opportunity for noninvasive detection and monitoring of human disease. While gas chromatography-mass spectrometry (GC-MS) remains the technique of choice for analyzing volatile organic compounds (VOCs), barriers to wider adoption in clinical practice still exist, including: sample preparation and introduction techniques, VOC extraction, throughput, volatolome coverage, biological interpretation, and quality control (QC). Therefore, we developed a complete pipeline for untargeted urinary volatolomic profiling. We optimized a novel extraction technique using HiSorb sorptive extraction, which exhibited high analytical performance and throughput. We achieved a broader VOC coverage by using HiSorb coupled with a set of complementary chromatographic methods and time-of-flight mass spectrometry. Furthermore, we developed a data preprocessing strategy by evaluating internal standard normalization, batch correction, and we adopted strict QC measures including removal of nonlinearly responding, irreproducible, or contaminated metabolic features, ensuring the acquisition of high-quality data. The applicability of this pipeline was evaluated in a clinical cohort consisting of pancreatic ductal adenocarcinoma (PDAC) patients (n = 28) and controls (n = 33), identifying four urinary candidate biomarkers (2-pentanone, hexanal, 3-hexanone, and p-cymene), which can successfully discriminate the cancer and noncancer subjects. This study presents an optimized, high-throughput, and quality-controlled pipeline for untargeted urinary volatolomic profiling. Use of the pipeline to discriminate PDAC from control subjects provides proof of principal of its clinical utility and potential for application in future biomarker discovery studies.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Plasma Metabolic Disturbances in Parkinson's Disease Patients

Plasma from patients with Parkinson's disease (PD) is a valuable source of information indicating altered metabolites associated with the risk or progression of the disease. Neurotoxicity of dopaminergic neurons, which is triggered by aggregation of α-synuclein, is the main pathogenic feature of PD. However, a growing body of scientific reports indicates that metabolic changes may precede and directly contribute to neurodegeneration. Identification and characterization of the abnormal metabolic pattern in patients' plasma are therefore crucial for the search for potential PD biomarkers. The aims of the present study were (1) to identify metabolic alterations in plasma metabolome in subjects with PD as compared with the controls; (2) to find new potential markers, some correlations among them; (3) to identify metabolic pathways relevant to the pathophysiology of PD. Plasma samples from patients with PD (n = 25) and control group (n = 12) were collected and the gas chromatography-time-of-flight-mass spectrometry GC-TOFMS-based metabolomics approach was used to evaluate the metabolic changes based on the identified 14 metabolites with significantly altered levels using univariate and multivariate statistical analysis. The panel, including 6 metabolites (L-3-methoxytyrosine, aconitic acid, L-methionine, 13-docosenamide, hippuric acid, 9,12-octadecadienoic acid), was identified to discriminate PD from controls with the area under the curve (AUC) of 0.975, with an accuracy of 92%. We also used statistical criteria to identify the significantly altered level of metabolites. The metabolic pathways involved were associated with linoleic acid metabolism, mitochondrial electron transport chain, glycerolipid metabolism, and bile acid biosynthesis. These abnormal metabolic changes in the plasma of patients with PD were mainly related to the amino acid metabolism, TCA cycle metabolism, and mitochondrial function.

A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath

Liver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds. We utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis. A 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted. CNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.

Paper Spray Ionization Ion Mobility Mass Spectrometry of Sebum Classifies Biomarker Classes for the Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and identification of robust biomarkers to complement clinical diagnosis will accelerate treatment options. Here, we demonstrate the use of direct infusion of sebum from skin swabs using paper spray ionization coupled with ion mobility mass spectrometry (PS-IM-MS) to determine the regulation of molecular classes of lipids in sebum that are diagnostic of PD. A PS-IM-MS method for sebum samples that takes 3 min per swab was developed and optimized. The method was applied to skin swabs collected from 150 people and elucidates ∼4200 features from each subject, which were independently analyzed. The data included high molecular weight lipids (>600 Da) that differ significantly in the sebum of people with PD. Putative metabolite annotations of several lipid classes, predominantly triglycerides and larger acyl glycerides, were obtained using accurate mass, tandem mass spectrometry, and collision cross section measurements.

The Human Skin Volatolome: A Systematic Review of Untargeted Mass Spectrometry Analysis

The analysis of volatile organic compounds (VOCs) can provide important clinical information (entirely non-invasively); however, the exact extent to which VOCs from human skin can be signatures of health and disease is unknown. This systematic review summarises the published literature concerning the methodology, application, and volatile profiles of skin VOC studies. An online literature search was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis, to identify human skin VOC studies using untargeted mass spectrometry (MS) methods. The principal outcome was chemically verified VOCs detected from the skin. Each VOC was cross-referenced using the CAS number against the Human Metabolome and KEGG databases to evaluate biological origins. A total of 29 studies identified 822 skin VOCs from 935 participants. Skin VOCs were commonly sampled from the hand (n = 9) or forearm (n = 7) using an absorbent patch (n = 15) with analysis by gas chromatography MS (n = 23). Twenty-two studies profiled the skin VOCs of healthy subjects, demonstrating a volatolome consisting of aldehydes (18%), carboxylic acids (12%), alkanes (12%), fatty alcohols (9%), ketones (7%), benzenes and derivatives (6%), alkenes (2%), and menthane monoterpenoids (2%). Of the VOCs identified, 13% had putative endogenous origins, 46% had tentative exogenous origins, and 40% were metabolites from mixed metabolic pathways. This review has comprehensively profiled the human skin volatolome, demonstrating the presence of a distinct VOC signature of healthy skin, which can be used as a reference for future researchers seeking to unlock the clinical potential of skin volatolomics. As significant proportions of identified VOCs have putative exogenous origins, strategies to minimise their presence through methodological refinements and identifying confounding compounds are discussed.