Rate normalization for sweat metabolomics biomarker discovery

Metabolome analyses of skin dialysate: Insights into skin interstitial fluid biomarkers

BACKGROUND

Metabolites in biofluids can serve as biomarkers for diagnosing diseases and monitoring body conditions. Among the available biofluids, interstitial fluid (ISF) in the skin has garnered considerable attention owing to its advantages, which include inability to clot, easy access to the skin, and possibility of incorporating wearable devices. However, the scientific understanding of skin ISF composition is limited.

OBJECTIVE

In this study, we aimed to compare metabolites between skin dialysate containing metabolites from the skin ISF and venous blood (plasma) samples, both collected under resting states.

METHODS

We collected forearm skin dialysate using intradermal microdialysis alongside venous blood (plasma) samples from 12 healthy young adults. We analyzed these samples using capillary electrophoresis-fourier transform mass spectrometry-based metabolomics (CE-FTMS).

RESULTS

Significant positive correlations were observed in 39 metabolites between the skin dialysate and plasma, including creatine (a mitochondrial disease biomarker), 1-methyladenosine (an early detection of cancer biomarker), and trimethylamine N-oxide (a posterior predictor of heart failure biomarker). Based on the Human Metabolome Technologies database, we identified 12 metabolites unique to forearm skin dialysate including nucleic acids, benzoate acids, fatty acids, amino acids, ascorbic acid, 3-methoxy-4-hydroxyphenylethyleneglycol (an Alzheimer's disease biomarker), and cysteic acid (an acute myocardial infarction biomarker).

CONCLUSION

We show that some venous blood biomarkers may be predicted from skin dialysate or skin ISF, and that these fluids may serve as diagnostic and monitoring tools for health and clinical conditions.

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.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Research on sweat metabolomics of athlete's fatigue induced by high intensity interval training

Objective: Sweat is an important specimen of human metabolism, which can simply and non-invasively monitor the metabolic state of the body, and its metabolites can be used as biomarkers for disease diagnosis, while the changes of sweat metabolites before and after exercise-induced fatigue are still unclear. Methods: In this experiment, high-performance chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) was used to metabolomic 28 sweat samples before and after exercise-induced fatigue of 14 long-distance runners, also IsoMS PRO and SPSS22.0 software were used to analyze the metabolite changes and differential metabolic pathways. Results: A total of 446 metabolites with high confidence were identified, and the sweat metabolome group before and after high-intensity interval exercise-induced fatigue was obvious, among which the upregulated differential metabolites mainly included hypoxanthine, pyruvate, several amino acids, etc., while the downregulated differential metabolites mainly included amino acid derivatives, vitamin B6, theophylline, etc. Conclusion: The change of hypoxanthine concentration in sweat can be used as a good biomarker for the diagnosis of exercise-induced fatigue, while the change of pyruvate content in sweat can be used as a discriminant index for the energy metabolism mode of the body before and after exercise. The main metabolic pathways involved in differential metabolites produced before and after HIIT exercise-induced fatigue are purine metabolism and amino acid metabolism.

Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

Evaluation of Sweat-Sampling Procedures for Human Stress-Biomarker Detection

Sweat is a potential biological fluid for the non-invasive analytical assessment of diverse molecules, including biomarkers. Notwithstanding, the sampling methodology is critical, and it must be assessed prior to using sweat for clinical diagnosis. In the current work, the analytical methodology was further developed taking into account the sampling step, in view of the identification and level variations of sweat components that have potential to be stress biomarkers using separation by liquid chromatography and detection by tandem mass spectrometry, in order to attain a screening profile of 26 molecules in just one stage. As such, the molecule identification was used as a test for the evaluation of the sampling procedures, including the location on the body, using patches for long-term sampling and vials for direct sampling, through a qualitative approach. From this evaluation it was possible to conclude that the sampling may be performed on the chest or back skin. Additionally, possible interference was evaluated. The long-term sampling with patches can be used under both rest and exercise conditions with variation of the detected molecule’s levels. The direct sampling, using vials, has the advantage of not having interferences but the disadvantage of only being effective after exercise in order to have enough sample for sweat analysis.

Wearable Sweat Loss Measuring Devices: From the Role of Sweat Loss to Advanced Mechanisms and Designs

Wearable sweat sensors have received significant research interest and have become popular as sweat contains considerable health information about physiological and psychological states. However, measured biomarker concentrations vary with sweat rates, which has a significant effect on the accuracy and reliability of sweat biosensors. Wearable sweat loss measuring devices (SLMDs) have recently been proposed to overcome the limitations of biomarker tracking and reduce inter- and intraindividual variability. In addition, they offer substantial potential for monitoring human body homeostasis, because sweat loss plays an indispensable role in thermoregulation and skin hydration. Previous studies have not carried out a comprehensive and systematic review of the principles, importance, and development of wearable SLMDs. This paper reviews wearable SLMDs with a new health perspective from the role of sweat loss to advanced mechanisms and designs. Two types of sweat and their measurement significance for practical applications are highlighted. Then, a comprehensive review of advances in different wearable SLMDs based on hygrometers, absorbent materials, and microfluidics is presented by describing their respective device architectures, present situations, and future directions. Finally, concluding remarks on opportunities for future application fields and challenges for future sweat sensing are presented.

Finger sweat analysis enables short interval metabolic biomonitoring in humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Touch-Based Fingertip Blood-Free Reliable Glucose Monitoring: Personalized Data Processing for Predicting Blood Glucose Concentrations

Diabetes prevalence has been rising exponentially, increasing the need for reliable noninvasive approaches for glucose monitoring. Different biofluids have been explored recently for replacing current blood finger-stick glucose strips with noninvasive painless sensing devices. While sweat has received considerable attention, there are mixed reports on correlating the sweat results with blood glucose levels. Here, we demonstrate a new rapid and reliable approach that combines a simple touch-based fingertip sweat electrochemical sensor with a new algorithm that addresses for personal variations toward the accurate estimate of blood glucose concentrations. The new painless and simple glucose self-testing protocol leverages the fast sweat rate on the fingertip for rapid assays of natural perspiration, without any sweat stimulation, along with the personalized sweat-response-to-blood concentration translation. A reliable estimate of the blood glucose sensing concentrations can thus be realized through a simple one-time personal precalibration. Such system training leads to a substantially improved accuracy with a Pearson correlation coefficient higher than 0.95, along with an overall mean absolute relative difference of 7.79%, with 100% paired points residing in the A + B region of the Clarke error grid. The speed and simplicity of the touch-based blood-free fingertip sweat assay, and the elimination of periodic blood calibrations, should lead to frequent self-testing of glucose and enhanced patient compliance toward the improved management of diabetes.

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO₂ max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.