Salivary metabolomic biomarkers for non-invasive lung cancer detection.

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics.

PURPOSE: Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach. METHODS: Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles. RESULTS: Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points. CONCLUSION: The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

Mechanobiological stimulation in organ-on-a-chip systems reduces hepatic drug metabolic capacity in favor of regenerative specialization.

Hepatic physiology depends on the liver's complex structural composition which among others, provides high oxygen supply rates, locally differential oxygen tension, endothelial paracrine signaling, as well as residual hemodynamic shear stress to resident hepatocytes. While functional improvements were shown by implementing these factors into hepatic culture systems, direct cause-effect relationships are often not well characterized-obfuscating their individual contribution in more complex microphysiological systems. By comparing increasingly complex hepatic in vitro culture systems that gradually implement these parameters, we investigate the influence of the cellular microenvironment to overall hepatic functionality in pharmacological applications. Here, hepatocytes were modulated in terms of oxygen tension and supplementation, endothelial coculture, and exposure to fluid shear stress delineated from oxygen influx. Results from transcriptomic and metabolomic evaluation indicate that particularly oxygen supply rates are critical to enhance cellular functionality-with cellular drug metabolism remaining comparable to physiological conditions after prolonged static culture. Endothelial signaling was found to be a major contributor to differential phenotype formation known as metabolic zonation, indicated by WNT pathway activity. Lastly, oxygen-delineated shear stress was identified to direct cellular fate towards increased hepatic plasticity and regenerative phenotypes at the cost of drug metabolic functionality - in line with regenerative effects observed in vivo. With these results, we provide a systematic evaluation of critical parameters and their impact in hepatic systems. Given their adherence to physiological effects in vivo, this highlights the importance of their implementation in biomimetic devices, such as organ-on-a-chip systems. Considering recent advances in basic liver biology, direct translation of physiological structures into in vitro models is a promising strategy to expand the capabilities of pharmacological models.

Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS).

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study used an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability, functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

Salivary metabolites as potential predictive biomarkers for lung surgery complications: a retrospective cross-sectional study.

PURPOSE: Saliva is often used as a tool for identifying systemic diseases because of the noninvasive nature of its collection. Moreover, salivary metabolites can be potential predictive factors for postoperative survival. We conducted the present study to establish whether salivary metabolites can function as predictive biomarkers for lung surgery complications. METHODS: Unstimulated salivary samples were collected from 412 patients before lung surgery. Salivary metabolites were analyzed comprehensively by capillary electrophoresis mass spectrometry. Clinical data with the discriminatory ability of biomarkers were assessed to predict lung surgery complications using multivariate logistic regression analysis. The primary endpoint was the risk factors for postoperative complications of Clavien-Dindo grade ≥ III. RESULTS: Postoperative complications of Clavien-Dindo grade ≥ III developed in 36 patients (8.7%). There was no postoperative 30-day mortality. Male sex (odds ratio [OR], 3.852; 95% confidence interval CI 1.455-10.199; p = 0.007) and salivary gamma-butyrobetaine (OR, 0.809; 95% CI 0.694-0.943; p = 0.007) were identified as significant risk factors for postoperative complications of Clavien-Dindo grade ≥ III. CONCLUSION: Salivary metabolites are potential noninvasive biomarkers for predicting postoperative complications of lung surgery.

Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics.

Acute coronary syndrome (ACS) is a life-threatening condition that requires a prompt diagnosis and therapeutic intervention. Although serum troponin I and creatinine kinase-MB (CK-MB) are established biomarkers for ACS, reaching diagnostic values for ACS may take several hours. In this study, we attempted to explore novel biomarkers for ACS with higher sensitivity than that of troponin I and CK-MB. The metabolomic profiles of 18 patients with ACS upon hospital arrival and those of the age-matched control (HC) group of 24 healthy volunteers were analyzed using liquid chromatography time-of-flight mass spectrometry. Volcano plots showed 24 metabolites whose concentrations differed significantly between the ACS and HC groups. Using these data, we developed a multiple logistic regression model for the ACS diagnosis, in which lysine, isocitrate, and tryptophan were selected as minimum-independent metabolites. The area under the receiver operating characteristic curve value for discriminating ACS from HC was 1.00 (95% confidence interval [CI]: 1.00-1.00). In contrast, those for troponin I and CK-MB were 0.917 (95% confidence interval [CI]: 0.812-1.00) and 0.988 (95% CI: 0.966-1.00), respectively. This study showed the potential for combining three plasma metabolites to discriminate ACS from HC with a higher sensitivity than troponin I and CK-MB.

Identification of exhaled volatile organic compounds that characterize asthma phenotypes: A J-VOCSA study.

BACKGROUND: Asthma is characterized by phenotypes of different clinical, demographic, and pathological characteristics. Identifying the profile of exhaled volatile organic compounds (VOCs) in asthma phenotypes may facilitate establishing biomarkers and understanding asthma background pathogenesis. This study aimed to identify exhaled VOCs that characterize severe asthma phenotypes among patients with asthma. METHODS: This was a multicenter cross-sectional study of patients with severe asthma in Japan. Clinical data were obtained from medical records, and questionnaires were collected. Exhaled breath was sampled and subjected to thermal desorption gas chromatography-mass spectrometry (GC/MS). RESULTS: Using the decision tree established in the previous nationwide asthma cohort study, 245 patients with asthma were divided into five phenotypes and subjected to exhaled VOC analysis with 50 healthy controls (HCs). GC/MS detected 243 VOCs in exhaled breath samples, and 142 frequently detected VOCs (50% of all samples) were used for statistical analyses. Cluster analysis assigning the groups with similar VOC profile patterns showed the highest similarities between phenotypes 3 and 4 (early-onset asthma phenotypes), followed by the similarities between phenotypes 1 and 2 (late-onset asthma phenotypes). Comparisons between phenotypes 1-5 and HC revealed 19 VOCs, in which only methanesulfonic anhydride showed p < 0.05 adjusted by false discovery rate (FDR). Comparison of these phenotypes yielded several VOCs showing different trends (p < 0.05); however, no VOCs showed p < 0.05 adjusted by FDR. CONCLUSIONS: Exhaled VOC profiles may be useful for distinguishing asthma and asthma phenotypes; however, these findings need to be validated, and their pathological roles should be clarified.

A population-based urinary and plasma metabolomics study of environmental exposure to cadmium.

BACKGROUND: The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies. METHOD: We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure. RESULT: Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle). CONCLUSION: These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

Effect of aromatic massage on brain waves and physiological indices of older adults.

BACKGROUND: Massage and aromatherapy are frequently used by older adults as alternative interventions to enhance immunity and induce relaxation. This pilot study evaluated the effect of massage therapy with oil and aromatherapy alone and in combination using objective biological indices. METHODS: Twenty-eight participants recruited by convenience sampling included adults aged between 25 and 65 years (Group 1), elderly individuals over 65 years without nursing care (Group 2), and older adults over 65 needing long-term nursing support (Group 3). A multiple-group pretest-post-test design was employed, and the effect among the three groups was compared. Interventions included: (i) oil massage therapy; (ii) aromatherapy; and (iii) aroma oil massage therapy. Each therapy session lasted 5 min, with 3 min of observation before and after the session and 10 min interval between sessions. Group 3 omitted one therapy (2: aromatherapy) to reduce their physical burden. An electroencephalogram (EEG) was recorded for α, ß, and θ activities of brain waves. EEG data were collected at three points: before, during, and after each treatment. Salivary secretory immunoglobulin A (s-IgA) concentration, oxygen saturation (SPO2), and pulse rate were measured before and after each session. RESULTS: Across all therapy modalities, there was a noticeable increase in the α wave, indicative of relaxation, during the treatment. Significant differences were observed before and during the oil massage in both Group 1 and Group 2. Aromatherapy demonstrated a significant difference before and during treatment in Group 1. Among the biological parameters, s-IgA levels indicated no significant changes. The pulse rate decreased with oil massage. Significant differences were noted before and after therapy in all cases for SPO2 and in Group 2 for pulse rate. CONCLUSIONS: Three therapies induced EEG and physiological changes in the adult group and older adults without nursing care. However, these effects are limited in older adults requiring nursing care.

Discovery of lipid profiles in plasma-derived extracellular vesicles as biomarkers for breast cancer diagnosis.

Lipids are a major component of extracellular vesicles; however, their significance in tumorigenesis and progression has not been well elucidated. As we previously found that lipid profiles drastically changed in breast tumors upon progression, we hypothesized that lipid profiles of plasma-derived extracellular vesicles could be utilized as breast cancer biomarkers. Here, we adopted modified sucrose cushion ultracentrifugation to isolate plasma-derived extracellular vesicles from breast cancer (n = 105), benign (n = 11), and healthy individuals (n = 43) in two independent cohorts (n = 126 and n = 33) and conducted targeted lipidomic analysis. We established a breast cancer diagnostic model comprising three lipids that showed favorable performance with the area under the receiver operating characteristic curve of 0.759, 0.743, and 0.804 in the training, internal validation, and external test sets, respectively. Moreover, we identified several lipids that could effectively discriminate breast cancer progression and subtypes: phosphatidylethanolamines and phosphatidylserines were relatively higher in Stage III, whereas phosphatidylcholines and sphingomyelins were higher in Stage IV; phosphatidylcholines and ceramides were correspondingly concentrated in HER2-positive patients, while lysophosphatidylcholines and polyunsaturated triglycerides were concentrated in the triple-negative breast cancer subtype. Lipid profiling of plasma-derived extracellular vesicles is a non-invasive and promising approach for diagnosing, staging, and subtyping breast cancer.

Metabolomics profiles alterations in cigarette smokers and heated tobacco product users.

BACKGROUND: Heated tobacco products (HTPs) have gained global popularity, but their health risks remain unclear. Therefore, the current study aimed to identify plasma metabolites associated with smoking and HTP use in a large Japanese population to improve health risk assessment. METHODS: Metabolomics data from 9,922 baseline participants of the Tsuruoka Metabolomics Cohort Study (TMCS) were analyzed to determine the association between smoking habits and plasma metabolites. Moreover, alterations in smoking-related metabolites among HTP users were examined based on data obtained from 3,334 participants involved from April 2018 to June 2019 in a follow-up survey. RESULTS: Our study revealed that cigarette smokers had metabolomics profiles distinct from never smokers, with 22 polar metabolites identified as candidate biomarkers for smoking. These biomarker profiles of HTP users were closer to those of cigarette smokers than those of never smokers. The concentration of glutamate was higher in cigarette smokers, and biomarkers involved in glutamate metabolism were also associated with cigarette smoking and HTP use. Network pathway analysis showed that smoking was associated with the glutamate pathway, which could lead to endothelial dysfunction and atherosclerosis of the vessels. CONCLUSIONS: Our study showed that the glutamate pathway is affected by habitual smoking. These changes in the glutamate pathway may partly explain the mechanism by which cigarette smoking causes cardiovascular disease. HTP use was also associated with glutamate metabolism, indicating that HTP use may contribute to the development of cardiovascular disease through mechanisms similar to those in cigarette use.

Targeted Metabolomic Profiling of Plasma Samples in Gastric Cancer by Liquid Chromatography-Mass Spectrometry.

INTRODUCTION: As the high mortality rate of gastric cancer (GC) is due to delayed diagnosis, early detection is vital for improved patient outcomes. Metabolic deregulation plays an important role in GC. Although various metabolite-level biomarkers for early detection have been assessed, there is still no unified early detection method. We conducted a plasma metabolome study to assess metabolites that may distinguish GC samples from non-GC samples. METHODS: Blood samples were collected from 72 GC patients and 29 control participants (non-GC group) at the Tokyo Medical University Hospital between March 2020 and November 2020. Hydrophilic metabolites were identified and quantified using liquid chromatography-time-of-flight mass spectrometry. Differences in metabolite concentrations between the GC and non-GC groups were evaluated using the Mann-Whitney test. The discrimination ability of each metabolite was evaluated by the area under the receiver operating characteristic curve. A radial basis function (RBF) kernel-based support vector machine (SVM) model was developed to assess the discrimination ability of multiple metabolites. The selection of variables used for the SVM utilized a step-wise regression method. RESULTS: Of the 96 quantified metabolites, 8 were significantly different between the GC and non-GC groups. Of these, N1-acetylspermine, succinate, and histidine were used in the RBF-SVM model to discriminate GC samples from non-GC samples. The area under the curve (AUC) of the RBF-SVM model was higher (0.915; 95% CI: 0.865-0.965, p < 0.0001), indicating good performance of the RBF-SVM model. The application of this RBF-SVM to the validation dataset resulted from the AUC of the RBF-SVM model was (0.885; 95% CI: 0.797-0.973, p < 0.0001), indicating the good performance of the RBF-SVM model. The sensitivity of the RBF-SVM model was better (69.0%) than those of the common tumor markers carcinoembryonic antigen (CEA) (10.5%) and carbohydrate antigen 19-9 (CA19-9) (2.86%). The RBF-SVM showed a low correlation with CEA and CA19-9, indicating its independence. CONCLUSION: We analyzed plasma metabolomics, and a combination of the quantified metabolites showed high sensitivity for the detection of GC. The independence of the RBF-SVM from tumor markers suggested that their complementary use would be helpful for GC screening.

Salivary Polyamines Help Detect High-Risk Patients with Pancreatic Cancer: A Prospective Validation Study.

Pancreatic cancer is one of the most malignant cancer types and has a poor prognosis. It is often diagnosed at an advanced stage because of the absence of typical symptoms. Therefore, it is necessary to establish a screening method for the early detection of pancreatic cancer in high-risk individuals. This is a prospective validation study conducted in a cohort of 1033 Japanese individuals (male, n = 467, age = 63.3 ± 11.5 years; female, n = 566, age = 64.2 ± 10.6 years) to evaluate the use of salivary polyamines for screening pancreatic diseases and cancers. Patients with pancreatic cancer were not included; however, other pancreatic diseases were treated as positive cases for accuracy verification. Of the 135 individuals with elevated salivary polyamine markers, 66 had pancreatic diseases, such as chronic pancreatitis and pancreatic cysts, and 1 had gallbladder cancer. These results suggest that the salivary polyamine panel is a useful noninvasive pancreatic disease screening tool.

Relationship between Urinary Metabolomic Profiles and Depressive Episode in Antarctica.

Antarctic expeditions have a high risk of participant depression owing to long stays and isolated environments. By quantifying the stress state and changes in biomolecules over time before the onset of depressive symptoms, predictive markers of depression can be explored. Here, we evaluated the psychological changes in 30 participants in the Japanese Antarctic Research Expedition using the Patient Health Questionnaire-9 (PHQ-9). Urinary samples were collected every three months for a year, and comprehensive urinary metabolomic profiles were quantified using liquid chromatography time-of-flight mass spectrometry. Five participants showed major depressive episodes (PHQ-9 ≥ 10) at 12 months. The urinary metabolites between these participants and the 25 unaffected participants were compared at individual metabolite and pathway levels. The individual comparisons showed the most significant differences at 12 months in 14 metabolites, including ornithine and beta-alanine. Data from shorter stays showed less significant differences. In contrast, pathway and enrichment analyses showed the most significant difference at three months and a less significant difference at longer stays. These time transitions of urinary metabolites could help in the development of urinary biomarkers to detect subjects with depressive episodes at an early stage.

Effects of Denture Treatment on Salivary Metabolites: A Pilot Study.

Symptoms of oral discomfort such as dry mouth are common in older people wearing dentures. Such symptoms are mainly treated symptomatically. Many of these symptoms are related to saliva, and associations with salivary volume have been reported. Although denture treatment improves symptoms by increasing the amount of saliva, the effects on salivary components remain unclear. This study aimed to investigate the effects of denture treatment on salivary metabolite changes based on salivary metabolome analyses. We enrolled 21 patients requiring denture treatment. At the first visit, and after completion of denture treatment, saliva outflow was measured under resting and stimulated conditions, samples for salivary metabolite analysis were collected, and masticatory efficiency was tested. In all participants, masticatory efficiency increased after denture treatment. Moreover, the amounts of resting and stimulated saliva were increased. Using salivary metabolome analysis, 61 salivary metabolites were detected. Substantial concentration changes were observed for 4 and 21 metabolites in resting and stimulated saliva, respectively. The four metabolites common to both saliva tests had significantly lower concentrations after treatment. These results suggest that the improvement in masticatory function by dentures is related not only to salivary secretion volume, but also to salivary metabolite composition.

Perturbation of monoamine metabolism and enhanced fear responses in mice defective in the regeneration of tetrahydrobiopterin.

Increasing evidence suggests the involvement of peripheral amino acid metabolism in the pathophysiology of neuropsychiatric disorders, whereas the molecular mechanisms are largely unknown. Tetrahydrobiopterin (BH4) is a cofactor for enzymes that catalyze phenylalanine metabolism, monoamine synthesis, nitric oxide production, and lipid metabolism. BH4 is synthesized from guanosine triphosphate and regenerated by quinonoid dihydropteridine reductase (QDPR), which catalyzes the reduction of quinonoid dihydrobiopterin. We analyzed Qdpr-/- mice to elucidate the physiological significance of the regeneration of BH4. We found that the Qdpr-/- mice exhibited mild hyperphenylalaninemia and monoamine deficiency in the brain, despite the presence of substantial amounts of BH4 in the liver and brain. Hyperphenylalaninemia was ameliorated by exogenously administered BH4, and dietary phenylalanine restriction was effective for restoring the decreased monoamine contents in the brain of the Qdpr-/- mice, suggesting that monoamine deficiency was caused by the secondary effect of hyperphenylalaninemia. Immunohistochemical analysis showed that QDPR was primarily distributed in oligodendrocytes but hardly detectable in monoaminergic neurons in the brain. Finally, we performed a behavioral assessment using a test battery. The Qdpr-/- mice exhibited enhanced fear responses after electrical foot shock. Taken together, our data suggest that the perturbation of BH4 metabolism should affect brain monoamine levels through alterations in peripheral amino acid metabolism, and might contribute to the development of anxiety-related psychiatric disorders. Cover Image for this issue: https://doi.org/10.1111/jnc.15398.

Salivary metabolomics with machine learning for colorectal cancer detection.

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva-based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry. An alternative decision tree (ADTree)-based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross-validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver-operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828-0.891) for CV and 0.870 (95% CI: 0.837-0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851-0.907) and 0.870 (95% CI: 0.838-0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.

Tumor metabolic alterations after neoadjuvant chemoradiotherapy predict postoperative recurrence in patients with pancreatic cancer.

OBJECTIVE: We investigated the metabolic changes in pancreatic ductal adenocarcinoma to identify the mechanisms of treatment response of neoadjuvant chemoradiation therapy. METHODS: Frozen tumor and non-neoplastic pancreas tissues were prospectively obtained from 88 patients with pancreatic ductal adenocarcinoma who underwent curative-intent surgery. Sixty-two patients received neoadjuvant chemoradiation therapy and 26 patients did not receive neoadjuvant therapy (control group). Comprehensive analysis of metabolites in tumor and non-neoplastic pancreatic tissue was performed by capillary electrophoresis-mass spectrometry. RESULTS: Capillary electrophoresis-mass spectrometry detected 90 metabolites for analysis among more than 500 ionic metabolites quantified. There were significant differences in 27 tumor metabolites between the neoadjuvant chemoradiation therapy and control groups. There were significant differences in eight metabolites [1-MethylnNicotinamide, Carnitine, Glucose, Glutathione (red), N-acetylglucosamine 6-phosphate, N-acetylglucosamine 1-phosphate, UMP, Phosphocholine] between good responder and poor responder for neoadjuvant chemoradiation therapy. Among these metabolites, phosphocholine, Carnitine and Glutathione were associated with recurrence-free survival only in the neoadjuvant chemoradiation therapy group. Microarray confirmed marked gene suppression of choline transporters [CTL1-4 (SLC44A1-44A4)] in pancreatic ductal adenocarcinoma tissue of neoadjuvant chemoradiation therapy group. CONCLUSION: The present study identifies several important metabolic consequences and potential neoadjuvant chemoradiation therapy targets in pancreatic ductal adenocarcinoma. Choline metabolism is one of the key pathways involved in recurrence of the patients with pancreatic ductal adenocarcinoma who received neoadjuvant chemoradiation therapy.

Biomarkers for Alzheimer's Disease in the Current State: A Narrative Review.

Alzheimer's disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

Metabolomic Profiling of Plasma, Urine, and Saliva of Kidney Transplantation Recipients.

Kidney biopsy is commonly used to diagnose kidney transplant dysfunction after transplantation. Therefore, the development of minimally invasive and quantitative methods to evaluate kidney function in transplant recipients is necessary. Here, we used capillary electrophoresis-mass spectrometry to analyze the biofluids collected from transplant recipients with impaired (Group I, n = 31) and stable (Group S, n = 19) kidney function and from donors (Group D, n = 9). Metabolomics analyses identified and quantified 97 metabolites in plasma, 133 metabolites in urine, and 108 metabolites in saliva. Multivariate analyses revealed apparent differences in the metabolomic profiles of the three groups. In plasma samples, arginine biosynthesis and purine metabolism between the I and S Groups differed. In addition, considerable differences in metabolomic profiles were observed between samples collected from participants with T cell-mediated rejection (TCR), antibody-mediated rejection, and other kidney disorders (KD). The metabolomic profiles in the three types of biofluids showed different patterns between TCR and KD, wherein 3-indoxyl sulfate showed a significant increase in TCR consistently in both plasma and urine samples. These results suggest that each biofluid has different metabolite features to evaluate kidney function after transplantation and that 3-indoxyl sulfate could predict acute rejection.