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Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia. Yang Quan-Jun,Zhao Jiang-Rong,Hao Juan,Li Bin,Huo Yan,Han Yong-Long,Wan Li-Li,Li Jie,Huang Jinlu,Lu Jin,Yang Gen-Jin,Guo Cheng Journal of cachexia, sarcopenia and muscle BACKGROUND:Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance-based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model. METHODS:Eighty-four cancer cachexia patients, 33 pre-cachectic patients, 105 weight-stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model. RESULTS:Forty-six cancer cachexia patients, 22 pre-cachectic patients, 68 weight-stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre-cachectic patients, 37 weight-stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age-matched and sex-matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = -400.53 - 481.88 × log(Carnosine) -239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set. CONCLUSIONS:This metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at-risk populations through the detection of serum metabolites. 10.1002/jcsm.12246
A pilot study of metabolomic pathways associated with fatigue in patients with colorectal cancer receiving chemotherapy. Chou Yun-Jen,Kober Kord M,Yeh Kun-Huei,Cooper Bruce A,Kuo Ching-Hua,Lin Been-Ren,Kuo Tien-Chueh,Tseng Yufeng J,Miaskowski Christine,Shun Shiow-Ching European journal of oncology nursing : the official journal of European Oncology Nursing Society PURPOSE:The aim of this pilot study was to evaluate for differences in metabolomic profiles between fatigued and non-fatigued patients with colorectal cancer (CRC) during chemotherapy (CTX). METHOD:Patients were recruited from the department of surgery in a large medical center in Taiwan. In this longitudinal pilot study, the Fatigue Symptom Inventory and fasting blood samples were collected at three assessments (i.e., prior to surgery (T0), three months (T1) and six months (T2) after surgery). Metabolomic profile analysis was used. Multilevel regression and pathway analyses were performed to identify differences in metabolomic profiles between the fatigued and non-fatigued groups. RESULTS:Of the 49 patients, 55.1% (n = 27) were in the fatigue group. All of the 15 metabolites that had statistically significant group × time interactions in the differential metabolite analysis were entered into the pathway analysis. Two pathways were enriched for these metabolites, namely galactose metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis. CONCLUSIONS:The results from this pilot study suggest that pathways involved in galactose metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis are associated with cancer-related fatigue (CRF) in patients with CRC during CTX. These findings are consistent with the hypotheses that alterations in energy metabolism and increases in inflammation are associated with the development and maintenance of CRF. 10.1016/j.ejon.2022.102096
A Pilot Study of Metabolomic Pathways Associated With Fatigue in Survivors of Colorectal Cancer. Chou Yun-Jen,Kober Kord M,Kuo Ching-Hua,Yeh Kun-Huei,Kuo Tien-Chueh,Tseng Yufeng J,Miaskowski Christine,Liang Jin-Tung,Shun Shiow-Ching Biological research for nursing BACKGROUND:Over 30% of cancer survivors experience chronic fatigue. An alteration in energy metabolism is one of the hypothesized mechanisms for cancer-related fatigue (CRF). No studies have evaluated for changes in metabolic profiles in cancer survivors with CRF. The purpose of this pilot study was to evaluate for differences in metabolic profiles between fatigued and non-fatigued survivors of colorectal cancer (CRC). METHODS:Survivors were recruited from the surgical outpatient department and the oncology clinic of a medical center in northern Taiwan. Fatigue was assessed using the Fatigue Symptom Inventory. Fasting blood samples were collected on the day the fatigue questionnaire was completed. Metabolomic profile analysis was performed using non-targeted, liquid chromatography/time-of-flight mass spectrometry. Fold change analyses, t-tests, and pathway analyses were performed to identify differences in metabolomic profiles between the fatigued and non-fatigued survivors. RESULTS:Of the 56 CRC survivors in this study, 28.6% (n = 16) were in the fatigue group. Statistically significant differences in carnitine, L-norleucine, pyroglutamic acid, pyrrolidonecarboxylic acid, spermine, hydroxyoctanoic acid, and paraxanthine were found between the two fatigue groups. In addition, two pathways were enriched for these metabolites (i.e., glutathione metabolism, D-glutamine and D-glutamate metabolism). CONCLUSIONS:Findings from this pilot study provide preliminary evidence that two pathways that are involved with the regulation of ATP production and cellular energy (i.e., glutathione metabolism, D-glutamine and D-glutamate metabolism) are associated with fatigue in CRC survivors. If these findings are confirmed, they may provide new therapeutic targets to decrease fatigue in cancer survivors. 10.1177/1099800420942586
Metabolomics analysis of blood identifies potential biomarkers and possible treatment targets for nocturia. Therapeutic advances in urology BACKGROUND:Our aim was to investigate the association between serum metabolites and nocturia. METHODS:A total of 66 males aged 65-80 years were enrolled in this study and stratified according to micturition behavior, which was characterized in terms of the 24 h frequency volume chart (FVC) for 3 consecutive days, the International Prostate Symptom Score (IPSS), and quality-of-life score. The nocturia group included participants with any total IPSS and ⩾1.5 micturitions/night as the mean of 3 nights, while the control group included participants with total IPSS < 8 and <1.5 micturitions/night. We conducted a comprehensive capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) study of plasma metabolites. Between-group comparisons of metabolite levels employed the Welch test. The relationship between nocturia and metabolite profiles was determined using multivariable logistic regression analysis. RESULTS:Of 66 participants, 45 were included in the nocturia group and 21 in the control group. There were no differences in background factors between the two groups. FVC analysis revealed that urine production during night-time, as well as micturition frequency during daytime and night-time were significantly higher in the nocturia group. CE-TOFMS identified eight metabolites whose plasma levels differed between the two groups. Multivariate analysis indicated that increased levels of lauric acid and imidazolelactic acid, as well as decreased levels of thiaproline and glycerol, contribute to the etiology of nocturia in aged men. CONCLUSIONS:Our findings suggest that abnormal serum levels of metabolites in several pathways play a role in the pathogenesis of nocturia in aged men. 10.1177/1756287219850087
Metabolomics and Cytokine Analysis for Identification of Schizophrenia with Auditory Hallucination. Clinical and investigative medicine. Medecine clinique et experimentale PURPOSE:To investigate the metabolic profile and biomarkers of schizophrenia with auditory hallucinations (AHs). METHODS:A total of 18 schizophrenic patients with the symptom of pure AHs (pAHs), 28 without AH (nAHs) and 43 age-matched healthy persons (Con) were enrolled in this study. Participants in pAHs and nAHs groups had relapsed into exacerbations of psychosis after self-discontinuing antipsychotics for at least one month; blood samples were drawn prior to restarting anti-psychotic treatment. Participants with history of recreational substance use were excluded. Positive and Negative Syndrome Scale (PANSS) and Auditory Hallucinations Rating Scale (AHRS) were used to assess the clinical mental state of all samples. Enzyme-linked immunosorbent assay (ELISA) was used to estimate the level of cytokines, and metabolomics analysis to identify potential biomarkers and pathways in the three groups. Graphpad 8.0 software was used to calculate the area under the receiver operating characteristic (ROC) curve. The relationship between metabolites and cytokines were determined using correlation analysis. RESULTS:Questionnaire scores showed significant differences in the positive symptom scale and PANSS total between nAHs and pAHs groups. Four cytokines (BDNF, IL-2, NGF-β and TNF-α) differed significantly among the three groups. Six molecules in the nAHs group (phenylalanine, hippurate, serine, glutamate, valine and cystine) and four in the pAHs group (phenylalanine, serine, glutamate and cystine) were identified as potential biomarkers. In addition, phenylalanine was shown as a potential independent diagnostic biomarker for pAHs. Correlation analysis revealed that cystine and serine were significantly negatively correlated with IL-2 in the pAHs group. CONCLUSIONS:This study revealed the metabolic profile of patients with schizophrenia with AHs and provided new information to support the diagnosis. The identification of unique biomarkers would contribute to objective and reliable diagnoses of patients with schizophrenia with AH. 10.25011/cim.v45i2.38096
Serum Metabolomics Coupling With Clinical Laboratory Indicators Reveal Taxonomic Features of Leukemia. Frontiers in pharmacology Metabolic abnormality has been considered to be the seventh characteristic in cancer cells. The potential prospect of using serum biomarkers metabolites to differentiate ALL from AML remains unclear. The purpose of our study is to probe whether the differences in metabolomics are related to clinical laboratory-related indicators. We used LC-MS-based metabolomics analysis to study 50 peripheral blood samples of leukemia patients from a single center. Then Chi-square test and T test were used to analyze the clinical characteristics, laboratory indicators and cytokines of 50 patients with leukemia. Correlation analysis was used to explore the relationship between them and the differential metabolites of different types of leukemia. Our study shows that it is feasible to better identify serum metabolic differences in different types and states of leukemia by metabolomic analysis on existing clinical diagnostic techniques. The metabolism of choline and betaine may also be significantly related to the patient's blood lipid profile. The main enrichment pathways for distinguishing differential metabolites in different types of leukemia are amino acid metabolism and fatty acid metabolism. All these findings suggested that differential metabolites and lipid profiles might identify different types of leukemia based on existing clinical diagnostic techniques, and their rich metabolic pathways help us to better understand the physiological characteristics of leukemia. 10.3389/fphar.2022.794042