ArticleJournal of cachexia, sarcopenia and muscle2018
Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia.
Article in Journal of cachexia, sarcopenia and muscle, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 1 of them a synthesis that pooled it.
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Who cites it
104 citing papers in PubMed, 1 synthesis or guideline pooled it, 170 citations in OpenAlex.
- Cancer- and cardiac-induced cachexia: same fate through different inflammatory mediators?Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2022Pooled it
- The therapeutic role and potential mechanism of EGCG in obesity-related precocious puberty as determined by integrated metabolomics and network pharmacology.Frontiers in endocrinology · 2023Trial
- Serum metabolomic signatures associated with frailty-related phenotypes in a cohort of older people.GeroScience · 2026Article
- Identification of Circulating Lipidomic Biomarkers of Malnutrition Risk among Oncology Patients in the Total Cancer Care (TCC) Study: A Cross-Sectional Analysis.The Journal of nutrition · 2026Article
- PEBP4 alleviates muscle wasting in lung cancer cachexia via KEAP1-NRF2-mediated redox homeostasis.Cell death & disease · 2026Article
- Cancer IDO1-Mediated Tryptophan-Kynurenine Metabolic Reprogramming to Drive Skeletal Muscle Atrophy and Cachexia Acceleration.Journal of cachexia, sarcopenia and muscle · 2026Article
- Distinct Metabolomic Alterations Are Associated With Physical Function, Weight Loss, and Muscle Mass in Men With Cancer.Journal of cachexia, sarcopenia and muscle · 2026Article
- Indirect Roles of Immunophenotypes in the Association Between Glycerophospholipid Metabolism and Endometrial Cancer Risk: A Mendelian Randomization Study.International journal of women's health · 2026Article
- The role of metabolomics in stroke: biomarkers of diagnosis and therapy.Metabolic brain disease · 2025Review
- Metabolomics and Pharmacometabolomics: Advancing Precision Medicine in Drug Discovery and Development.Metabolites · 2025Review
- Identification of serum N,N-dimethylglycine as a potential biomarker for prenatal diagnosis of congenital heart disease usingAnalytical and bioanalytical chemistry · 2025Article
- Early Markers of Cardiac and Skeletal Muscle Metabolic Derangement in the Apc(min/+) Male Mouse.Cancer reports (Hoboken, N.J.) · 2025Article
- Lactobacillus Johnsonii YH1136 alleviates schizophrenia-like behavior in mice: a gut-microbiota-brain axis hypothesis study.BMC microbiology · 2025Article
- GHSR gene knockout alleviates the liver pathological response in Echinococcus granulosus infection by reducing parasite survival.Veterinary research · 2025Article
- circ0066187 promotes pulmonary fibrogenesis through targeting STAT3-mediated metabolism signal pathway.Cellular and molecular life sciences : CMLS · 2025Article
- UPLC-Q-TOF-MS-based unbiased serum metabolomics investigation of cholangiocarcinoma.Frontiers in molecular biosciences · 2025Article
- Serum-urine metabolic integration via UPLC-QTOF/MS uncovers shared pathway biomarkers for cirrhosis diagnosis.Frontiers in medicine · 2025Article
- Microbiome and metabolomics analyses of the effect of heat-sensitive moxibustion on allergic rhinitis in rats.Frontiers in immunology · 2025Article
- Immunophenotyping identifies key immune biomarkers for coronary artery disease through machine learning.PloS one · 2025Article
- Serum metabolic profiling of patients with diabetic kidney disease based on gas chromatography-mass spectrometry.Frontiers in molecular biosciences · 2025Article
44 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCachexia 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.
methodsEighty-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.
resultsForty-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.
conclusionsThis 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.
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