ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2019
Urinary biomarkers for diagnosing poststroke depression in patients with type 2 diabetes mellitus.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The Role of Gut Bacteria and Plasma Metabolites in Predicting Post-Stroke Depression in Patients with Acute Ischemic Stroke.Current neuropharmacology · 2026Article
- Impact of insulin resistance on post-stroke depression and outcomes in diabetes.Journal of diabetes investigation · 2025Article
- Biological links between psychological factors and adolescent depression: childhood trauma, rumination, and resilience.BMC psychiatry · 2024Article
- Effect of hyperbaric oxygen on post-stroke depression.World journal of psychiatry · 2023Article
- Effects of Electroacupuncture on Gut Microbiota and Fecal Metabolites in Rats with Poststroke Depression.Neuropsychiatric disease and treatment · 2023Article
- Epitranscriptomic Modifications Modulate Normal and Pathological Functions in CNS.Translational stroke research · 2022Review
- Does urinary metabolite signature act as a biomarker of post-stroke depression?Frontiers in psychiatry · 2022Review
- Urinary Metabolite Signatures for Predicting Elderly Stroke Survivors with Depression.Neuropsychiatric disease and treatment · 2021Article
- Identification of Potential Metabolite Markers for Middle-Aged Patients with Post-Stroke Depression Using Urine Metabolomics.Neuropsychiatric disease and treatment · 2020Article
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Authors and funding
9 authors.
Funding
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Abstract
backgroundDepression can seriously affect the quality of life of type 2 diabetes mellitus (T2DM) patients after stroke. However, there were still no objective methods to diagnose T2DM patients with poststroke depression (PSD). Therefore, we conducted this study to deal with this problem.
methodsGas chromatography-mass spectroscopy (GC-MS)-based metabolomics profiling method was used to profile the urinary metabolites from 83 nondepressed T2DM patients after stroke and 101 T2DM patients with PSD. The orthogonal partial least-squares discriminant analysis was conducted to explore the metabolic differences in T2DM patients with PSD. The logistic regression analysis was performed to identify the optimal and simplified biomarker panel for diagnosing T2DM patients with PSD. The receiver operating characteristic curve analysis was used to assess the diagnostic performance of this biomarker panel.
resultsIn total, 23 differential metabolites (7 decreased and 16 increased in T2DM patients with PSD) were found. A panel consisting of pseudouridine, malic acid, hypoxanthine, 3,4-dihydroxybutyric acid, fructose and inositol was identified. This panel could effectively separate T2DM patients with PSD from nondepressed T2DM patients after stroke. The area under the curve was 0.965 in the training set and 0.909 in the validation set. Meanwhile, we found that the galactose metabolism was significantly affected in T2DM patients with PSD.
conclusionOur results could be helpful for future development of an objective method to diagnose T2DM patients with PSD and provide novel ideas to study the pathogenesis of depression.
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