Real-time health monitoring through urine metabolomics

Urine metabolite profiling in Indian males exposed to high-altitude: a longitudinal pilot study

People who visit high-altitude for research and development work, pilgrimage, recreational purposes and deployments are exposed to different environmental conditions such as low temperature and atmospheric pressure, leading to hypoxia, high radiation, dry air, and non-availability of fresh food and vegetables. These environmental stressors have significant physiological effects on the human body. Among these challenges, hypobaric hypoxia at high-altitude affects aerobic metabolism and thereby reduces the supply of metabolic energy. Metabolic alterations may further lead to extreme environment related maladaptation as evidenced by alterations in the levels of metabolites and metabolic pathways. To investigate the variation in the metabolite profile, urine samples were collected from 16 individuals at baseline (BL, 210 m) and high-altitude (HA, 4200 m). Untargeted urinary metabolic profiling was performed by liquid chromatography-mass spectrometry (LC-MS) in conjunction with statistical analysis. Univariate and multivariate statistical analyses revealed 33 differentially abundant metabolites based on fold change, VIP score and p value. These distinct metabolites were primarily associated with pathways related to phenylalanine, tyrosine and tryptophan biosynthesis; metabolism of phenylalanine, biotin, tyrosine, cysteine and methionine along with alanine, aspartate and glutamate metabolism. Thes pathways are also linked with pentose and glucuronate interconversions, citrate cycle, vitamin B6 and porphyrin metabolism. Furthermore, receiver operating characteristic curve analysis detected five metabolites namely, 2-Tetrahydrothiopheneacetic acid, 1-Benzyl-7,8-dimethoxy-3-phenyl-3H-pyrazolo [3,4-c] isoquinoline, Abietin, 4,4'-Thiobis-2-butanone, and Hydroxyisovaleroyl carnitine with high range of sensitivity and specificity. In summary, this longitudinal study demonstrated novel metabolic variations in humans exposed to high-altitude, utilising the potential of LC-MS based metabolomics. Thus, the present findings shed light on the impact of hypoxic exposure on metabolic adaptation and provide a better understanding about the pathophysiological mechanisms underlying high-altitude illnesses correlated to tissue hypoxia.

Rapid quantitative analysis of urinary nicotine metabolites via a high-performance nanoconcentrator

Tobacco use is a widespread public health issue. The detection of smoke metabolites in body fluids using in vitro assays can offer a rapid, efficient and non-invasive approach to measure human exposure to smoke and assess its associated health risks, enabling the development of more impactful follow-up strategies. In this study, we prepared a high-performance nanoconcentrator to construct a rapid magnetic response-based integrated enrichment and detection platform, ELDI-TOF-MS, for the purpose of enriching nicotine metabolites in conjunction with successive LDI-TOF-MS analysis. Concurrently functioning as both a pre-enrichment unit and an LDI-TOF-MS matrix, the nanoconcentrator possesses a number of advantageous properties, including a rapid magnetic response within 1 s, a substantial specific surface area as high as 862 m2 g-1, high UV-vis absorption capacity, and high reproducibility in MS analysis. With these advantages, the nanoconcentrator-based ELDI-TOF-MS platform demonstrates high sensitivity and linearity for low concentration samples, rendering it suitable for both qualitative and quantitative analysis. Also, ELDI-TOF-MS responds to samples at low concentrations down to 2.5 ng mL-1. For the analysis of two metabolites of nicotine, cotinine and norcotinine, the concentrations can be enriched by 2700 and 260 times. Furthermore, the rapid and efficient process facilitates batch analysis of up to 20 samples, with each batch requiring only 40 min. It is anticipated that the platform will enhance clinical efforts to detect nicotine use and augment the depth and breadth of testing.

Headspace Injection Method for Intermittent Sampling and Profiling Analyses of Volatile Organic Compounds Using Dielectric Barrier Discharge Ionization (DBDI)

A direct headspace injection method is presented and optimized for the analysis of volatile organic compounds (VOCs) using dielectric barrier discharge ionization-mass spectrometry (DBDI-MS), incorporating an intermediate vial in which the sample headspace is injected. The setup is built of commonly available, cheap consumable parts and easily enables the incorporation of different gases for generating different ionization atmospheres. The method can be fully automated by using standard GC autosamplers, and its rapid analysis time is suitable for high-throughput applications. We show that this method is suitable for both profiling analysis of complex samples such as biofluids and quantitative measurements for real-time reaction monitoring. Our optimized method demonstrated improved reproducibility and sensitivity, with detection limits for compounds tested in the high nanomolar to the low micromolar range, depending on the compound. Key parameters for method optimization were identified such as sample vial volume, headspace-to-liquid ratio, incubation temperature, and equilibration time. These settings were systematically evaluated to maximize the signal intensity and improve repeatability between measurements. Two use cases are demonstrated: (i) quantitative measurement of ethanol production by a metal-organic framework from CO2 and (ii) profiling of biofluids following the consumption of asparagus.

Investigation of pH-dependent 1H NMR urine metabolite profiles for diagnosis of obesity-related disordering

BACKGROUND

Human urine is highly favorable for 1H NMR metabolomics analyses of obesity-related diseases, such as non-alcoholic fatty liver, type 2 diabetes, and hyperlipidemia (HL), due to its non-invasiveness and ease of large-scale collection. However, the wide range of intrinsic urine pH (5.5-8.5) results in inevitably chemical shift and signal intensity modulations in the 1H NMR spectra. For patients where acidic urine pH is closely linked to obesity-related disease phenotypes, the pH-dependent modulations complicate the spectral analysis and deteriorate quantifications of urine metabolites.

METHODS

We characterized human urine metabolites by NMR at intrinsic urine pH, across urine pH 4.5 to 9.5, to account for pH-dependent modulations. A pH-dependent chemical shift database for quantifiable urine metabolites was generated and integrated into a "pH intelligence" program developed for quantifications of pH-dependent modulations at various pH. The 1H NMR spectra of urines collected from patients with Ob-HL and healthy controls were compared to uncover potential metabolic biomarkers of Ob-HL disease.

RESULTS

Three urine metabolites were unveiled by pH-dependent NMR approach, i.e., TMAO, glycine, and pyruvic acid, with VIP score >1.0 and significant q-value < 0.05, that represent as potential biomarkers for discriminating Ob-HL from healthy controls. Further ROC-AUC analyses revealed that TMAO alone achieved the highest diagnostic accuracy (AUC 0.902), surpassed to that obtained by neutralizing pH approach (AUC 0.549) and enabled better recovering potential urine metabolites from the Ob-HL disease phenotypes.

CONCLUSIONS

We concluded that 1H NMR-derived urine metabolite profile represents a snapshot that can reveal the physiological condition of humans in either a healthy or diseased state under intrinsic urine pH. We demonstrated a systematic analysis of pH-dependent modulations on the human urine metabolite signals and further developed software for quantification of urine metabolite profiles with high accuracy, enabling the uncovering of potential metabolite biomarkers in clinical diagnosis applications.

Urinary biochemical ecology reveals microbiome-metabolite interactions and metabolic markers of recurrent urinary tract infection

Recurrent urinary tract infections (rUTIs) are a major clinical challenge and their increasing prevalence underscores the need to define host-microbiome interactions underlying susceptibility. How the urinary microbiota engages with the biochemical environment of the urogenital tract is yet to be fully defined. Here, we leverage paired metagenomic and quantitative metabolomic data to establish a microbe-metabolite association network of the female urinary microbiome and define metabolic signatures of rUTI. We observe unique metabolic networks of uropathogens and uroprotective species, highlighting potential metabolite-driven ecological shifts influencing rUTI susceptibility. We find distinct metabolites are associated with urinary microbiome diversity and identify a lipid signature of active rUTI that accurately distinguishes cases from controls. Finally, we identify deoxycholic acid as a prognostic indicator for UTI recurrence. Together these findings provide insight into microbiome-metabolite interactions within the female urinary tract and highlight new biomarkers for the development of new diagnostic tools to improve patient outcomes.

Relationship between the level of mixed chemicals in male urine and the prevalence of male cancers, especially prostate cancer

Objectives

The aim of this study was to investigate the relationship between mixed chemicals in urine and the prevalence of cancers in men.

Methods

A total of 1,068 male subjects were included in this study. Analyses were performed by several analytical methods to ensure the stability of the results: one-way analysis, WQS analysis, Qgcomp analysis, BKMR analysis, and Restricted Cubic Spline (RCS).

Results

In the final adjusted model, each 1 increase in ln-transformed BPS increased the risk of developing cancerous prostate by 49% (95% CI: 1.00-2.20). The results of multiple sensitivity analyses by WQS and Qgcomp showed that the mixed chemicals was positively correlated with the prevalence of cancers and prostate cancer in men. In the final adjusted model, each quartile increase in the WQS index was associated with a 78% (OR: 1.78, 95% CI: 1.10-2.87) increase in the risk of cancers and a 148% (OR: 2.48, 95% CI: 1.07-5.71) increase in the risk of prostate cancer. Each quartile increase in the Qgcomp index was associated with a 59% (OR: 1.59, 95% CI: 1.09-2.33) increase in the risk of cancers, and a 105% (OR: 2.05, 95% CI: 1.04-4.06) increase in the risk of prostate cancer.

Conclusion

In conclusion, this study showed a positive correlation between the concentrations of the three groups of mixed chemicals in urine and the prevalence of cancers in men, as well as a positive correlation with the prevalence of prostate cancer.

Molecular engineering of functional DNA molecules toward point-of-care diagnostic devices

The pursuit of rapid, sensitive, and specific diagnostic methodologies is imperative across diverse applications, including the detection of pathogens and disease biomarkers, food safety testing and environmental monitoring. Point-of-care testing (POCT) is characterized by its portability, ease of use, rapidity, and affordability, emerging as an attractive alternative for traditional diagnostics. Over recent years, the incorporation of functional DNA (fDNA) into POC diagnostic devices has emerged as a groundbreaking advancement, significantly enhancing sensitivity, specificity, and user-friendliness. In this review, we explore the innovative applications of fDNA in POC devices, highlighting its potential to revolutionize diagnostics by providing rapid, portable, and precise solutions. We discuss the unique advantages of fDNA, including its stability in complex biological matrices and its ability to recognize a wide range of targets. Furthermore, we explore the potential synergy between fDNA and cutting-edge technologies, such as nanotechnology and artificial intelligence (AI), to forge a path toward more personalized and accessible healthcare solutions. Despite significant progress, challenges remain in translating these innovations from the bench to the clinic. This review aims to provide a comprehensive overview of the current status of fDNA-based POCT devices and future directions for their development, emphasizing their critical role in meeting the global demand for accessible, efficient, and precise diagnostic solutions.

Mass spectrometry (MS)-based metabolomics of plasma and urine in dry eye disease (DED)-induced rat model

Dry eye disease (DED) is an ophthalmic disease associated with poor quality and quantity of tears, and the number of patients is steadily increasing. The aim of this study was to determine plasma and urine metabolites obtained from DED scopolamine animal model where dry eye conditions (DRY) are induced. It was also of interest to examine whether DED (scopolamine) rat model was exacerbated by treatment with benzalkonium chloride (BAC). Subsequently, plasma and urine metabolites were analyzed using liquid chromatography (LC) and gas chromatography (GC)-mass spectrometry (MS), respectively. Data demonstrated that DED indicators such as tear volume, tear breakup time (TBUT), and corneal damage in the DED groups (DRY and BAC group) differed from those of control (CON). Similar results were noted in inflammatory factors such as interleukin (IL-1β), IL-6, and tumor necrosis factor (TNF)-α. In the partial least squares-discriminant analysis (PLS-DA) score plots, the three groups were distinctly separated from each other. In addition, the related metabolites were also associated with these distinct separations as evidenced by 9 and 14 in plasma and urine, respectively. Almost all of the selected metabolites were decreased in the DRY group compared to CON, and the BAC group was lower than the DRY. In plasma and urine, lysophosphatidylcholine/lysophosphatidylethanolamine, organic acids, amino acids, and sugars varied between three groups, and these metabolites were related to inflammation and oxidative stress. Data suggest that treatment with scopolamine with/without BAC-induced DED and affected the level of systemic metabolites involved in inflammation and oxidative stress.

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

Engineering a Universal and Fully Automatic Analyzer for Multichannel and On-Site Biochemical Detection in Body Fluids

OBJECTIVE

Rising concerns over wellness and aging have heightened the demand for convenient and efficient on-site health monitoring and disease screening. Current research, focused on specific biomarker detection, often neglects the complexities of sample matrix interference and the absence of a comprehensive, automated platform. To address these issues, we have developed a universal, fully automated analyzer for multifaceted, on-site biochemical analysis of body fluids.

METHODS

This analyzer integrates automated sample pretreatment, automatic dilution, detection, and self-cleaning functionalities seamlessly. It is designed to detect a wide range of analytes, from small molecules to macromolecules, including ions and proteins, utilizing spectrophotometric sensing. After optimization, the analyzer achieves performance comparable to traditional Enzyme-Linked Immunosorbent Assay (ELISA), while significantly expanding its detection range through automated dilution.

RESULTS

Demonstrations of small molecule detection include the simultaneous assessment of citric acid (CA) and oxalic acid (OA) in urine, achieving recovery rates between 96.65%-106.42% and 93.13%-112.50%, respectively. For protein detection, the analyzer successfully identified Cyfra21-1 in saliva with a recovery rate of 104.93%-111.31%. The pre-treatment process requires only 8.8 minutes, showing enhanced recovery rates for CA and OA at 97.8% and 97.6% respectively, which are superior and more rapid than manual methods.

CONCLUSION

The exemplary pretreatment and detection performance of the analyzer underlines its effectiveness in multifaceted, on-site biomarker detection, establishing it as a promising and versatile tool for disease screening and health monitoring.

SIGNIFICANCE

This analyzer offers a powerful technological solution for on-site fluid testing, advancing community health care by facilitating more reliable and rapid diagnostics.

Matched serum- and urine-derived biomarkers of interstitial cystitis/bladder pain syndrome

Setting up the correct diagnosis of interstitial cystitis/bladder pain syndrome (IC/BPS), a chronic inflammatory disease of the bladder, is a challenge, as there are neither diagnostic criteria nor reliable and non-invasive disease biomarkers available. The aim of the present study was to simultaneously determine matched serum- and urine-derived biomarkers of IC/BPS, which would provide additional insights into disease mechanisms and set the basis for further biomarker validation. Our study included 12 female patients with IC/BPS and 12 healthy controls. A total of 33 different biomarkers were measured, including cytokines and chemokines, proteins involved in extracellular matrix remodeling, adhesion molecules, growth factors, and markers of oxidative stress using enzyme linked immunoassays and multiplex technology. Heatmaps and principal component analysis based on significantly altered biomarkers, revealed urine- and serum-associated IC/BPS signatures that clearly differentiated IC/BPS patients from controls. Four biomarkers, including CCL11, BAFF, HGF and MMP9, were significantly upregulated in both serum and urine of patients with IC/BPS compared to controls. Serum levels of MMP9 were associated with disease severity and could distinguish well between IC/BPS patients with and without Hunner's lesions. Systemic levels of MMP9 can therefore mirror the local pathology within the bladders of IC/BPS patients, and MMP9 may prove to be a useful target for the development of novel therapeutic interventions. Utilizing a comprehensive panel of both urine and serum biomarkers, identified here, holds promise for disease detection in IC/BPS patients.

Machine Learning and Metabolomics Predict Mesenchymal Stem Cell Osteogenic Differentiation in 2D and 3D Cultures

Stem cells have been widely used to produce artificial bone grafts. Nonetheless, the variability in the degree of stem cell differentiation is an inherent drawback of artificial graft development and requires robust evaluation tools that can certify the quality of stem cell-based products and avoid source-tissue-related and patient-specific variability in outcomes. Omics analyses have been utilised for the evaluation of stem cell attributes in all stages of stem cell biomanufacturing. Herein, metabolomics in combination with machine learning was utilised for the benchmarking of osteogenic differentiation quality in 2D and 3D cultures. Metabolomics analysis was performed with the use of gas chromatography-mass spectrometry (GC-MS). A set of 11 metabolites was used to train an XGboost model which achieved excellent performance in distinguishing between differentiated and undifferentiated umbilical cord blood mesenchymal stem cells (UCB MSCs). The model was benchmarked against samples not present in the training set, being able to efficiently capture osteogenesis in 3D UCB MSC cultures with an area under the curve (AUC) of 82.6%. On the contrary, the model did not capture any differentiation in Wharton's Jelly MSC samples, which are well-known underperformers in osteogenic differentiation (AUC of 56.2%). Mineralisation was significantly correlated with the levels of fumarate, glycerol, and myo-inositol, the four metabolites found most important for model performance (R2 = 0.89, R2 = 0.94, and R2 = 0.96, and p = 0.016, p = 0.0059, and p = 0.0022, respectively). In conclusion, our results indicate that metabolomics in combination with machine learning can be used for the development of reliable potency assays for the evaluation of Advanced Therapy Medicinal Products.

Biochemical Characteristics of Urine Metabolomics in Female Giant Pandas at Different Estrous Stages

The composition of urinary metabolites can reflect the physiological state of animals. Investigating the alterations in urine metabolomics during the estrus stage can provide valuable insights for enhancing the efficacy of estrus monitoring. This study aimed to perform an analysis of urinary metabolomics in female giant pandas, specifically examining the variations in specific metabolites across different estrous stages, namely, diestrus, proestrus, estrus, and metestrus. A total of 1234 metabolites were identified in positive ion mode from 76 samples of 19 individuals, with 643 metabolites identified in negative ion mode. The content of urine metabolites exhibited significant variation throughout different stages of estrus. During the peak of estrus, the metabolic pathways primarily enriched by significantly differential metabolites were the AMPK signaling pathway, vitamin digestion and absorption, galactose metabolism, and cysteine and methionine metabolism, as well as taurine and hypotaurine metabolism. By comparing the content of specific metabolites in distinct pathways across the four distinct estrous stages, higher levels of acetylcholine, D-fructose1,6-bisphosphate, L-homocystine, dulcitol, inositol, and S-sulfo-L-cysteine and lower levels of phosphoethanolamine, vitamin A, vitamin B12, and maleic acid were detected at estrus. This study offers a novel comparative analysis of urine metabolomics across different estrus stages in female giant pandas, identifying several potential perspectives for estrus monitoring and contributing to the breeding management of captive giant panda populations.

Diagnostic accuracy of urinary cytokeratin fragment-19 (CYFRA21-1) for bladder cancer

Bladder cancer (BC) is known for its high recurrence rate and requires constant patient monitoring. To confirm the diagnosis, a tissue sample from a cystoscopy is required, which the patient often avoids. Urine has the potential to be utilized as a diagnostic fluid because of its non-invasive nature and various biomarker contents. The aim of this study was to determine the diagnostic value of cytokeratin fragment-19 (CYFRA21-1) levels in urine for diagnosing BC. This single-center cross-sectional study included adults aged ≥18 years who presented with hematuria and had suspected BC based on imaging findings. Patients with a history of intravesical chemotherapy, radiotherapy and immunotherapy were excluded. Urine samples were collected prior to the cystoscopy. Detection of urinary CYFRA21-1 was carried out using the enzyme-linked immunosorbent assay (ELISA) method. Of 154 patients included in the study, the diagnosis of BC was confirmed in 92 patients. Patients with BC had significantly higher urinary CYFRA21-1 levels compared to the non-bladder cancer group. The sensitivity, specificity, positive and negative predictive value, and positive likelihood ratio of the CYFRA21-1 were 80.4%, 43.5%, 67.9%, 60% and 1.425, respectively. The area under the curve (AUC) for CYFRA21-1 was 0.608, computed from a receiver operating curve (ROC) with a cut-off value of 13.3 ng/mL. In conclusion, urinary CYFRA21-1 levels have moderate diagnostic accuracy in determining BC among suspected individuals. Due to its high sensitivity, this biomarker could potentially be used alongside other screening tools for BC detection.

Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients

The assessment of kidney function within the first year following transplantation is crucial for predicting long-term graft survival. This study aimed to develop a robust and accurate model using metabolite profiles to predict early long-term outcomes in patient groups at the highest risk of early graft loss. A group of 61 kidney transplant recipients underwent thorough monitoring during a one-year follow-up period, which included a one-week hospital stay and follow-up assessments at three and six months. Based on their 12-month follow-up serum creatinine levels: Group 2 had levels exceeding 1.5 mg/dl, while Group 1 had levels below 1.5 mg/dl. Metabolites were detected by mass spectrometer and first pre-processed. Univariate and multivariate statistical analyses were employed to identify significant differences between the two groups. Nineteen metabolites were found to differ significantly in the 1st week, and seventeen metabolites in the 3rd month (adjusted p-value < 0.05, quality control (QC) < 30, a fold change (FC) > 1.1 or a FC < 0.91, Variable Influence on Projection (VIP) > 1). However, no significant differences were observed in the 6th month. These distinctive metabolites mainly belonged to lipid, fatty acid, and amino acid categories. Ten models were constructed using a backward conditional approach, with the best performance seen in model 5 for Group 2 at the 1st-week mark (AUC 0.900) and model 3 at the 3rd-month mark (AUC 0.924). In conclusion, the models developed in the early stages may offer potential benefits in the management of kidney transplant patients.

The clinical efficacy of cGMP-specific sildenafil on mitochondrial biogenesis induction and renal damage in cats with acute on chronic kidney disease

BACKGROUND

Mitochondrial biogenesis (MB) induction has recently emerged as potential therapeutic approaches in kidney pathology and the mitochondria-targeted therapies should be investigated to improve treatment of animals with kidney diseases. This study aimed to investigate the effects of MB induction with sildenafil citrate on the cGMP/NO pathway, glomerular filtration, and reduction of kidney damage and fibrosis (TGF-β/SMAD pathway) in cats with acute on chronic kidney disease (ACKD). Thirty-three cats were divided into the non-azotemic (healthy) group (n:8) and the ACKD group (n:25), comprising different breeds, sexes, and ages. Sildenafil citrate was administered to the non-azotemic and ACKD groups (2.5 mg/kg, PO, q12 hours) for 30 days. Serum and urine NO, MDA, NGAL, KIM-1, TGF-β1, IL-18, FGF 23, PGC-1α and cGMP concentrations were measured.

RESULTS

Serum cGMP concentrations increased (P < 0.05) in the non-azotemic group during the 2nd (median 475.99 pmol/mL) and 3rd (median 405.01 pmol/mL) weeks of the study, whereas serum cGMP concentrations decreased in the ACKD group during the 4th(median 188.52 pmol/mL) week compared to the non-azotemic group (P < 0.05). No difference was observed in serum biomarker concentrations except NO, which increased in the 4th week (P < 0.05). The urinary concentrations of NO, MDA, PGC-1α, TGF-β1, NGAL, KIM-1, IL-18, and FGF 23 in the ACKD group were found to be higher compared to those in the non-azotemic group from the 1st to the 4th week (P  < 0.05). In the ACKD group, the urine PGC-1α concentration in the 2nd (median 6.10 ng/mL) week was lower compared to that in the 0 and 1st (median 7.65 and 7.21 ng/mL, respectively) week, and the NO concentration in the 3rd (median 28.94 µmol/mL) week was lower than that in the 0th (median 37.43 µmol/mL) week (P < 0.05).

CONCLUSIONS

While sildenafil citrate has been determined to induce a low level of MB and to have a beneficial effect on glomerular filtration, it is observed to be ineffective in mitigating renal damage and fibrosis via the TGF-β/SMAD pathway in cats with ACKD.

Healthy Lifestyle, Metabolic Signature, and Risk of Cardiovascular Diseases: A Population-Based Study

BACKGROUND

Although healthy lifestyle has been linked with a reduced risk of cardiovascular diseases (CVDs), the potential metabolic mechanism underlying this association remains unknown.

METHODS

We included 161,018 CVD-free participants from the UK Biobank. Elastic net regression was utilized to generate a healthy lifestyle-related metabolic signature. The Cox proportional hazards model was applied to investigate associations of lifestyle-related metabolic signature with incident CVDs, and mediation analysis was conducted to evaluate the potential mediating role of metabolic profile on the healthy lifestyle-CVD association. Mendelian randomization (MR) analysis was conducted to detect the causality.

RESULTS

During 13 years of follow-up, 17,030 participants developed incident CVDs. A healthy lifestyle-related metabolic signature comprising 123 metabolites was established, and it was inversely associated with CVDs. The hazard ratio (HR) was 0.83 (95% confidence interval [CI]: 0.81, 0.84) for CVD, 0.83 (95% CI: 0.81, 0.84) for ischemic heart disease (IHD), 0.86 (95% CI: 0.83, 0.90) for stroke, 0.86 (95% CI: 0.82, 0.89) for myocardial infarction (MI), and 0.75 (95% CI: 0.72, 0.77) for heart failure (HF) per standard deviation increase in the metabolic signature. The metabolic signature accounted for 20% of the association between healthy lifestyle score and CVD. Moreover, MR showed a potential causal association between the metabolic signature and stroke.

CONCLUSIONS

Our study revealed a potential link between a healthy lifestyle, metabolic signatures, and CVD. This connection suggests that identifying an individual's metabolic status and implementing lifestyle modifications may provide novel insights into the prevention of CVD.

Simultaneous Detection of Biomarkers in Urine Using a Multicalibration Potentiometric Sensing Array Combined with a Portable Analyzer

Domestic monitoring devices make real-time and long-term health monitoring possible, allowing people to track their health status regularly. Uric acid (UA), creatinine, and urea in urine are three important biomarkers for various diseases, especially kidney diseases. This work proposed a 10-channel potentiometric sensing array containing a UA electrode group, a creatinine electrode group, a urea electrode group, a pH electrode group, and one pair of reference channels, which could be connected with a portable potentiometric analyzer, realizing the simultaneous detection of UA, creatinine, urea, and pH in urine. The prepared Pt/carbon nanotubes (CNTs)-uricase, creatinine deiminase, Au@urease, and polyaniline were employed as the sensing materials, showing responses to four targets with high sensitivity and selectivity. To improve the accuracy of domestic monitoring, a calibration channel was integrated into each electrode group to calibrate the basic potential of the sensing channels, and the influences of pH and temperature on the responses were investigated through the pH electrode group and an external temperature probe to calibrate the slope and intercept. With the preset of the deduced calibration parameters and computational formula for the four targets in the analyzer in Lab Mode, the concentrations of UA, creatinine, and urea and the pH of the human urine samples were directly displayed on the screen of the analyzer in Practical Mode. The agreement of these results with those obtained from commercial kits and pH meters reveals the high potential of these methods for developing domestic devices to facilitate health monitoring.

The association of infant urinary adrenal steroids with the risk of childhood asthma development

BACKGROUND

Adrenal steroids play important roles in early-life development. However, our understanding of the effects of perinatal adrenal steroids on the development of childhood asthma is incomplete.

OBJECTIVE

To evaluate the associations between early-life adrenal steroid levels and childhood asthma.

METHODS

Participants included the Infant Susceptibility to Pulmonary Infections and Asthma following Respiratory Syncytial Virus Exposure birth cohort children with untargeted urinary metabolomics data measured during early infancy (n = 264) and nasal immune mediator data measured concurrently at age 2 to 6 months (n = 76). A total of 11 adrenal steroid compounds were identified using untargeted metabolomics and 6 asthma-relevant nasal immune mediators from multiplex assays were a priori selected. Current asthma at ages 5 and 6 years was ascertained using validated questionnaires. Associations were tested using logistic and linear regression with confounders adjustment.

RESULTS

Pregnenetriol disulfate (adjusted odds ratio [aOR] = 0.20, 95% CI = 0.06-0.68) and 3a,21-dihydroxy-5b-pregnane-11,20-dione-21-glucuronide (aOR = 0.34, 95% CI = 0.14-0.75) were inversely associated with childhood asthma at 5 and 6 years after multiple testing adjustment. There was a significant interaction effect of pregnanediol-3-glucuronide by biological sex assigned at birth (aOR = 0.11, 95% CI = 0.02-0.51, for those with female sex) on childhood asthma. Pregnenetriol disulfate was inversely associated with granulocyte-macrophage colony-stimulating factor (β = -0.45, q-value = 0.05). 3a,21-dihydroxy-5b-pregnane-11,20-dione 21-glucuronide was inversely associated with interleukin [IL]-4 (β = -0.29, q-value = 0.02), IL-5 (β = -0.35, q-value = 0.006), IL-13 (β = -0.26, q-value = 0.02), granulocyte-macrophage colony-stimulating factor (β = -0.35, q-value = 0.006), and fibroblast growth factor-β (β = -0.24, q-value = 0.01) after multiple testing adjustment.

CONCLUSION

The inverse association between adrenal steroids downstream of progesterone and 17-hydroxypregnenolone and the odds of childhood asthma and nasopharyngeal type 2 immune biomarkers suggest that increased early-life adrenal steroids may suppress type 2 inflammation and protect against the development of childhood asthma.

Repurposing Sewage and Toilet Systems: Environmental, Public Health, and Person-Centered Healthcare Applications

Global terrestrial water supplies are rapidly depleting due to the consequences of climate change. Water scarcity results in an inevitable compromise of safe hygiene and sanitation practices, leading to the transmission of water-borne infectious diseases, and the preventable deaths of over 800.000 people each year. Moreover, almost 500 million people lack access to toilets and sanitation systems. Ecosystems are estimated to be contaminated by 6.2 million tons of nitrogenous products from human wastewater management practices. It is therefore imperative to transform toilet and sewage systems to promote equitable access to water and sanitation, improve public health, conserve water, and protect ecosystems. Here, the integration of emerging technologies in toilet and sewage networks to repurpose toilet and wastewater systems is reviewed. Potential applications of these systems to develop sustainable solutions to environmental challenges, promote public health, and advance person-centered healthcare are discussed.

NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples

Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.

Psychobiotics and the Microbiota-Gut-Brain Axis: Where Do We Go from Here?

The bidirectional relationship between the gut microbiota and the nervous system is known as the microbiota-gut-brain axis (MGBA). The MGBA controls the complex interactions between the brain, the enteric nervous system, the gut-associated immune system, and the enteric neuroendocrine systems, regulating key physiological functions such as the immune response, sleep, emotions and mood, food intake, and intestinal functions. Psychobiotics are considered tools with the potential to modulate the MGBA through preventive, adjunctive, or curative approaches, but their specific mechanisms of action on many aspects of health are yet to be characterized. This narrative review and perspectives article highlights the key paradigms needing attention as the scope of potential probiotics applications in human health increases, with a growing body of evidence supporting their systemic beneficial effects. However, there are many limitations to overcome before establishing the extent to which we can incorporate probiotics in the management of neuropsychiatric disorders. Although this article uses the term probiotics in a general manner, it remains important to study probiotics at the strain level in most cases.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Urinary Metabolomic Differentiation of Infants Fed on Human Breastmilk and Formulated Milk

Human breastmilk is an invaluable nutritional and pharmacological resource with a highly diverse metabolite profile, which can directly affect the metabolism of infants. Application of metabolomics can discriminate the complex relationship between such nutrients and infant health. As the most common biological fluid in metabolomic study, infant urinary metabolomics may provide the physiological impacts of different nutritional resources, namely human breastmilk and formulated milk. In this study, we aimed to identify possible differences in the urine metabolome of 30 infants (1-14 days after birth) fed with breast milk (n = 15) or formulated milk (n = 15). From metabolomic analysis with gas chromatography-mass spectrometry, 163 metabolites from single mass spectrometry (GC-MS), and 383 metabolites from tandem mass spectrometry (GC-MS/MS) were confirmed in urinary samples. Various multivariate statistical analysis were performed to discriminate the differences originating from physiological/nutritional variables, including human breastmilk/formulate milk feeding, sex, and duration of feeding. Both unsupervised and supervised discriminant analyses indicated that feeding resources (human breastmilk/formulated milk) gave marginal but significant differences in urinary metabolomes, while other factors (sex, duration of feeding) did not show notable discrimination between groups. According to the biomarker analyses, several organic acid and amino acids showed statistically significant differences between different feeding resources, such as 2-hydroxyhippurate.

Multi-omics microsampling for the profiling of lifestyle-associated changes in health

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Study on the acute toxicity of trichlorfon and its breakdown product dichlorvos to goldfish (Carassius auratus) based on 1H NMR metabonomics

Trichlorfon, one of the most widely used organophosphate insecticides, is commonly employed in aquaculture and agriculture to combat parasitic infestations. However, its inherent instability leads to rapid decomposition into dichlorvos (DDVP), increasing its toxicity by eightfold. Therefore, the environmental effects of trichlorfon in real-world scenarios involve the combined effects of trichlorfon and its degradation product, DDVP. In this study, we systematically investigated the degradation of trichlorfon in tap water over time using HPLC and LC-MS/MS analysis. Subsequently, an experiment was conducted to assess the acute toxicity of trichlorfon and DDVP on goldfish (Carassius auratus), employing a 1H NMR-based metabolic approach in conjunction with serum biochemistry, histopathological inspection, and correlation network analysis. Exposure to trichlorfon and its degradation product DDVP leads to increased lipid peroxidation, reduced antioxidant activity, and severe hepatotoxicity and nephrotoxicity in goldfish. Based on the observed pathological changes and metabolite alterations, short-term exposure to trichlorfon significantly affected the liver and kidney functions of goldfish, while exerting minimal influence on the brain, potentially due to the presence of the blood-brain barrier. The changes in the metabolic profile indicated that trichlorfon and DDVP influenced several pathways, including oxidative stress, protein synthesis, energy metabolism, and nucleic acid metabolism. This study demonstrated the applicability and potential of 1H NMR-based metabonomics in pesticide environmental risk assessment, providing a feasible method for the comprehensive study of pesticide toxicity in water environments.

NMR-Based Mitochondria Metabolomic Profiling: A New Approach To Reveal Cancer-Associated Alterations

Studying metabolism may assist in understanding the relationship between normal and dysfunctional mitochondrial activity and various diseases, such as neurodegenerative, cardiovascular, autoimmune, psychiatric, and cancer. Nuclear magnetic resonance-based metabolomics represents a powerful method to characterize the chemical content of complex samples and has been successfully applied to studying a range of conditions. However, an optimized methodology is lacking for analyzing isolated organelles, such as mitochondria. In this study, we report the development of a protocol to metabolically profile mitochondria from healthy, tumoral, and metastatic tissues. Encouragingly, this approach provided quantitative information about up to 45 metabolites in one comprehensive and robust analysis. Our results revealed significant differences between whole-cell and mitochondrial metabolites, which supports a more refined approach to metabolic analysis. We applied our optimized methodology to investigate aggressive and metastatic breast cancer in mouse tissues, discovering that lung mitochondria exhibit an altered metabolic fingerprint. Specific amino acids, organic acids, and lipids showed significant increases in levels when compared with mitochondria from healthy tissues. Our optimized methodology could promote a better understanding of the molecular mechanisms underlying breast cancer aggressiveness and mitochondrial-related diseases and support the optimization of new advanced therapies.

Designing self-tracking experiences: A qualitative study of the perceptions of barriers and facilitators to adopting digital health technology for automatic urine analysis at home

Self-tracking technologies open new doors to previously unimaginable scenarios. The diagnosis of diseases years in advance, or supporting the health of astronauts on missions to Mars are just some of many example applications. During the COVID-19 pandemic, a wide range of self-monitoring protocols emerged, revealing opportunities but also challenges including difficulties in understanding how to self-use monitoring systems, struggling to recognize the benefit of such systems and a high likelihood of abandonment. In this paper, we explore the role that design plays in the creation of a user experience of self-tracking, with a focus on urine analysis at home. We investigate adoption factors and forms of data expression to overcome the presented challenges. By combining insights from related work, semi-structured interviews and indicative user-tests, we show the potential of pairing a traditional numerical data representation (data quantification) with a qualitative expression of the data (data qualification). Indeed, qualitative expressions have the potential to convey the complexity of the phenomena tracked, enabling deep meaning-making and emotional connection to personal data. At the same time, we also identify issues with this approach, which can require a longer learning curve and lead to rejection by users more accustomed to traditional, numerical approaches. Based on the results, several recommendations have been converted into an experimental proposition, which also presents future plans for the continuation of the project. This article presents the first fundamental step in creating a meaningful experience of self-tracking, taking into consideration the needs and expectations of future users.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A Review

Biosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain. As such, this review aims to provide an overview of biomarkers that are or could be used to diagnose and monitor diseases, including cancer, cardiovascular diseases, kidney diseases, and neurodegenerative disorders, such as Alzheimer’s disease. Additionally, the different materials and techniques for the fabrication of microfluidic structures along with the biosensing technologies often used to detect and quantify biological molecules and organisms are reviewed. Ultimately, this review discusses the current state of point-of-care urinalysis devices and highlights the potential of these technologies to improve patient outcomes. Traditional point-of-care urinalysis devices require the manual collection of urine, which may be unpleasant, cumbersome, or prone to errors. To overcome this issue, the toilet itself can be used as an alternative specimen collection and urinalysis device. This review then presents several smart toilet systems and incorporated sanitary devices for this purpose.

Passive monitoring by smart toilets for precision health

Smart toilets are a key tool for enabling precision health monitoring in the home, but such passive monitoring has ethical considerations.

Metabolomics in asthma: A platform for discovery

Asthma, characterized by airway hyperresponsiveness, inflammation and remodeling, is a chronic airway disease with complex etiology. Severe asthma is characterized by frequent exacerbations and poor therapeutic response to conventional asthma therapy. A clear understanding of cellular and molecular mechanisms of asthma is critical for the discovery of novel targets for optimal therapeutic control of asthma. Metabolomics is emerging as a powerful tool to elucidate novel disease mechanisms in a variety of diseases. In this review, we summarize the current status of knowledge in asthma metabolomics at systemic and cellular levels. The findings demonstrate that various metabolic pathways, related to energy metabolism, macromolecular biosynthesis and redox signaling, are differentially modulated in asthma. Airway smooth muscle cell plays pivotal roles in asthma by contributing to airway hyperreactivity, inflammatory mediator release and remodeling. We posit that metabolomic profiling of airway structural cells, including airway smooth muscle cells, will shed light on molecular mechanisms of asthma and airway hyperresponsiveness and help identify novel therapeutic targets.

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7–12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The monozygotic (MZ) and dizygotic (DZ) correlations for the urinary metabolites indicated a role for non-additive genetic factors for 50 amines, 13 organic acids, and 6 steroids. The average broad-sense heritability for these amines, organic acids, and steroids was 0.49 (range: 0.25–0.64), 0.50 (range: 0.33–0.62), and 0.64 (range: 0.43–0.81), respectively. For 6 amines, 7 organic acids, and 4 steroids the twin correlations indicated a role for shared environmental factors and the average narrow-sense heritability was 0.50 (range: 0.37–0.68), 0.50 (range; 0.23–0.61), and 0.47 (range: 0.32–0.70) for these amines, organic acids, and steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

Volatile Organic Compounds Analysis as a Potential Novel Screening Tool for Breast Cancer: A Systematic Review

Introduction

An early diagnosis is crucial in reducing mortality among people who have breast cancer (BC). There is a shortfall of characteristic early clinical symptoms in BC patients, highlighting the importance of investigating new methods for its early detection. A promising novel approach is the analysis of volatile organic compounds (VOCs) produced and emitted through the metabolism of cancer cells.

Methods

The purpose of this systematic review is to outline the published research regarding BC-associated VOCs. For this, headspace analysis of VOCs was explored in patient-derived body fluids, animal model-derived fluids, and BC cell lines to identify BC-specific VOCs. A systematic search in PubMed and Web of Science databases was conducted according to the PRISMA guidelines.

Results

Thirty-two studies met the criteria for inclusion in this review. Results highlight that VOC analysis can be promising as a potential novel screening tool. However, results of in vivo, in vitro and case-control studies have delivered inconsistent results leading to a lack of inter-matrix consensus between different VOC sampling methods.

Discussion

Discrepant VOC results among BC studies have been obtained, highly due to methodological discrepancies. Therefore, methodological issues leading to disparities have been reviewed and recommendations have been made on the standardisation of VOC collection and analysis methods for BC screening, thereby improving future VOC clinical validation studies.

The Need to Pair Molecular Monitoring Devices with Molecular Imaging to Personalize Health

By enabling the non-invasive monitoring and quantification of biomolecular processes, molecular imaging has dramatically improved our understanding of disease. In recent years, non-invasive access to the molecular drivers of health versus disease has emboldened the goal of precision health, which draws on concepts borrowed from process monitoring in engineering, wherein hundreds of sensors can be employed to develop a model which can be used to preventatively detect and diagnose problems. In translating this monitoring regime from inanimate machines to human beings, precision health posits that continual and on-the-spot monitoring are the next frontiers in molecular medicine. Early biomarker detection and clinical intervention improves individual outcomes and reduces the societal cost of treating chronic and late-stage diseases. However, in current clinical settings, methods of disease diagnoses and monitoring are typically intermittent, based on imprecise risk factors, or self-administered, making optimization of individual patient outcomes an ongoing challenge. Low-cost molecular monitoring devices capable of on-the-spot biomarker analysis at high frequencies, and even continuously, could alter this paradigm of therapy and disease prevention. When these devices are coupled with molecular imaging, they could work together to enable a complete picture of pathogenesis. To meet this need, an active area of research is the development of sensors capable of point-of-care diagnostic monitoring with an emphasis on clinical utility. However, a myriad of challenges must be met, foremost, an integration of the highly specialized molecular tools developed to understand and monitor the molecular causes of disease with clinically accessible techniques. Functioning on the principle of probe-analyte interactions yielding a transducible signal, probes enabling sensing and imaging significantly overlap in design considerations and targeting moieties, however differing in signal interpretation and readout. Integrating molecular sensors with molecular imaging can provide improved data on the personal biomarkers governing disease progression, furthering our understanding of pathogenesis, and providing a positive feedback loop toward identifying additional biomarkers and therapeutics. Coupling molecular imaging with molecular monitoring devices into the clinical paradigm is a key step toward achieving precision health.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

Noninvasive urine metabolomics of prostate cancer and its therapeutic approaches: a current scenario and future perspective

INTRODUCTION

The sensitive, specific, fast, robust and noninvasive biomarkers for the evaluation of prostate cancer (PC) remain elusive in medical research. However, efforts are in full sway to investigate and resolve these puzzles for clinical practice. Advances in modern analytical techniques, sample processing, and the emergence of multiple omics approaches have created a great hope for the development of better detection modalities for PC. The objective of the present review is to provide a concise overview of the PC metabolomics-based potential discriminating molecules in urine samples using nuclear magnetic resonance spectroscopy and mass spectrometry.

AREA COVERED

A literature search was executed to find the studies reporting the noninvasive urine-based biomarkers for the diagnosis and prognosis of underlying disease. Most studies have extensivelyreported PC discriminating molecules with their respective controls. Additionally, pathophysiology and the treatment paradigm of PC are summarized and related to the insights underpinning the therapeutic intervention of PC.

EXPERT OPINION

With multi-centric, global, comprehensive omics approaches via either a non- or least-invasive bio-matrix may open new avenues of research for PC biomarker discovery, backed by a molecular mechanistic outline.

Metabolomic Profiling of Human Urine Samples Using LC-TIMS-QTOF Mass Spectrometry

The identification of metabolites in biological samples is challenging due to their chemical and structural diversity. Ion mobility spectrometry (IMS) separates ionized molecules based on their mobility in a carrier buffer gas giving information about the ionic shape by measuring the rotationally averaged collision cross-section (CCS) value. This orthogonal descriptor, in combination with the m/z, isotopic pattern distribution, and MS/MS spectrum, has the potential to improve the identification of molecular molecules in complex mixtures. Urine metabolomics can reveal metabolic differences, which arise as a result of a specific disease or in response to therapeutic intervention. It is, however, complicated by the presence of metabolic breakdown products derived from a wide range of lifestyle and diet-related byproducts, many of which are poorly characterized. In this study, we explore the use of trapped ion mobility spectrometry (TIMS) via LC parallel accumulation with serial fragmentation (PASEF) for urine metabolomics. A total of 362 urine metabolites were characterized from 80 urine samples collected from healthy volunteers using untargeted metabolomics employing HILIC and RP chromatography. Additionally, three analytes (Trp, Phe, and Tyr) were selected for targeted quantification. Both the untargeted and targeted data was highly reproducible and reported CCS measurements for identified metabolites were robust in the presence of the urine matrix. A comparison of CCS values among different laboratories was also conducted, showing less than 1.3% ΔCCS values across different platforms. This is the first report of a human urine metabolite database compiled with CCS values experimentally acquired using an LC-PASEF TIMS-qTOF platform.

The snapshot of metabolic health in evaluating micronutrient status, the risk of infection and clinical outcome of COVID-19

COVID-19 has re-established the significance of analyzing the organism through a metabolic perspective to uncover the dynamic interconnections within the biological systems. The role of micronutrient status and metabolic health emerge as pivotal in COVID-19 pathogenesis and the immune system's response. Metabolic disruption, proceeding from modifiable factors, has been proposed as a significant risk factor accounting for infection susceptibility, disease severity and risk for post-COVID complications. Metabolomics, the comprehensive study and quantification of intermediates and products of metabolism, is a rapidly evolving field and a novel tool in biomarker discovery. In this article, we propose that leveraging insulin resistance biomarkers along with biomarkers of micronutrient deficiencies, will allow for a diagnostic window and provide functional therapeutic targets. Specifically, metabolomics can be applied as: a. At-home test to assess the risk of infection and propose nutritional support, b. A screening tool for high-risk COVID-19 patients to develop serious illness during hospital admission and prioritize medical support, c(i). A tool to match nutritional support with specific nutrient requirements for mildly ill patients to reduce the risk for hospitalization, and c(ii). for critically ill patients to reduce recovery time and risk of post-COVID complications, d. At-home test to monitor metabolic health and reduce post-COVID symptomatology. Metabolic rewiring offers potential virtues towards disease prevention, dissection of high-risk patients, taking actionable therapeutic measures, as well as shielding against post-COVID syndrome.

Association between urinary per- and poly-fluoroalkyl substances and COVID-19 susceptibility

BACKGROUND AND OBJECTIVE

The growing impact of the COVID-19 pandemic has heightened the urgency of identifying individuals most at risk of infection. Per- and poly-fluoroalkyl substances (PFASs) are manufactured fluorinated chemicals widely used in many industrial and household products. The objective of this case-control study was to assess the association between PFASs exposure and COVID-19 susceptibility and to elucidate the metabolic dysregulation associated with PFASs exposure in COVID-19 patients.

METHODS

Total 160 subjects (80 COVID-19 patients and 80 symptom-free controls) were recruited from Shanxi and Shandong provinces, two regions heavily polluted by PFASs in China. Twelve common PFASs were quantified in both urine and serum. Urine metabolome profiling was performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).

RESULTS

In unadjusted models, the risk of COVID-19 infection was positively associated with urinary levels of perfluorooctanesulfonic acid (PFOS) (Odds ratio: 2.29 [95% CI: 1.52-3.22]), perfluorooctanoic acid (PFOA) (2.91, [1.95-4.83], and total PFASs (∑ (12) PFASs) (3.31, [2.05-4.65]). After controlling for age, sex, body mass index (BMI), comorbidities, and urine albumin-to-creatinine ratio (UACR), the associations remained statistically significant (Adjusted odds ratio of 1.94 [95% CI: 1.39-2.96] for PFOS, 2.73 [1.71-4.55] for PFOA, and 2.82 [1.97-3.51] for ∑ (12) PFASs). Urine metabolome-PFASs association analysis revealed that 59% of PFASs-associated urinary endogenous metabolites in COVID-19 patients were identified to be produced or largely regulated by mitochondrial function. In addition, the increase of PFASs exposure was associated with the accumulation of key metabolites in kynurenine metabolism, which are involved in immune responses (Combined β coefficient of 0.60 [95% CI: 0.25-0.95, P = 0.001]). Moreover, alternations in PFASs-associated metabolites in mitochondrial and kynurenine metabolism were also correlated with clinical lab biomarkers for mitochondrial function (serum growth/differentiation factor-15) and immune activity (lymphocyte percentage), respectively.

CONCLUSION

Elevated exposure to PFASs was independently associated with an increased risk of COVID-19 infection. PFASs-associated metabolites were implicated in mitochondrial function and immune activity. Larger studies are needed to confirm our findings and further understand the underlying mechanisms of PFASs exposure in the pathogenesis of SARS-CoV2 infection.

MetFish: a Metabolomics Pipeline for Studying Microbial Communities in Chemically Extreme Environments

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well.

IMPORTANCE The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples. To address this gap, we developed and demonstrated a simple yet sensitive and accurate method—MetFish—using chemical derivatization to enable mass spectrometry-based metabolomics in a variety of hypersaline samples from varied ecosystems and containing up to 2 M dissolved salts.

Open, High-Resolution EI+ Spectral Library of Anthropogenic Compounds

To address the lack of high-resolution electron ionisation mass spectral libraries (HR-[EI+]-MS) for environmental chemicals, a retention-indexed HR-[EI+]-MS library has been constructed following analysis of authentic compounds via GC-Orbitrap MS. The library is freely provided alongside a compound database of predicted physicochemical properties. Currently, the library contains over 350 compounds from 56 compound classes and includes a range of legacy and emerging contaminants. The RECETOX Exposome HR-[EI+]-MS library expands the number of freely available resources for use in full-scan chemical exposure studies and is available at: https://doi.org/10.5281/zenodo.4471217.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet

OBJECTIVES

The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss.

METHODS

The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight.

RESULTS

Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research.

CONCLUSIONS

Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.

Metabolomics Profiles of Smokers from Two Ethnic Groups with Differing Lung Cancer Risk

African American (AA) smokers are at a higher risk of developing lung cancer compared to whites. The variations in the metabolism of nicotine and tobacco-derived carcinogens in these groups were reported previously with the levels of nicotine metabolites and carcinogen-derived metabolites measured using targeted approaches. While useful, these targeted strategies are not able to detect global metabolic changes for use in predicting the detrimental effects of tobacco use and ultimately lung cancer susceptibility among smokers. To address this limitation, we have performed global untargeted metabolomics profiling in urine of AA and white smokers to characterize the pattern of metabolites, identify differentially regulated pathways, and correlate these profiles with the observed variations in lung cancer risk between these two populations. Urine samples from AA (n = 30) and white (n = 30) smokers were used for metabolomics analysis acquired in both positive and negative electrospray ionization modes. LC-MS data were uploaded onto the cloud-based XCMS online (http://xcmsonline.scripps.edu) platform for retention time correction, alignment, feature detection, annotation, statistical analysis, data visualization, and automated systems biology pathway analysis. The latter identified global differences in the metabolic pathways in the two groups including the metabolism of carbohydrates, amino acids, nucleotides, fatty acids, and nicotine. Significant differences in the nicotine degradation pathway (cotinine glucuronidation) in the two groups were observed and confirmed using a targeted LC-MS/MS approach. These results are consistent with previous studies demonstrating AA smokers with lower glucuronidation capacity compared to whites. Furthermore, the d-glucuronate degradation pathway was found to be significantly different between the two populations, with lower amounts of the putative metabolites detected in AA compared to whites. We hypothesize that the differential regulation of the d-glucuronate degradation pathway is a consequence of the variations in the glucuronidation capacity observed in the two groups. Other pathways including the metabolism of amino acids, nucleic acids, and fatty acids were also identified, however, the biological relevance and implications of these differences across ethnic groups need further investigation. Overall, the applied metabolomics approach revealed global differences in the metabolic networks and endogenous metabolites in AA and whites, which could be used and validated as a new potential panel of biomarkers that could be used to predict lung cancer susceptibility among smokers in population-based studies.