Altered Metabolomics in Bipolar Depression With Gastrointestinal Symptoms

Guo, Xiangjie; Xiong, Yongqian; Jia, Yuping; Cui, Xiaohong; Wu, Wen-ze; Tian, Jun-Sheng; Yang, Hong; Ren, Yan

doi:10.3389/fpsyt.2022.861285

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Hence, ketone body metabolism can be involved in the inflammation and in the oxidative stress that may be one of the mechanistic origins of neuroprogressive disorders (i.e., depressive and bipolar disorder or schizophrenia) ( Guo et al, 2022 ), hence reflecting advanced neuroanatomical and cognitive degeneration, caused by several factors such as inflammation, peripheral, brain, and oxidative stress, and mitochondrial dysfunction correlated with tryptophan metabolism disorders ( Berk et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Vernocchi,

Marangelo,

Guerrera

et al. 2023

Front. Microbiol.

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BackgroundAutism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways, and anamnestic, clinical, and nutritional patient metadata.MethodsFecal samples collected from children with ASD and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC–MS/SPME) to determine volatile organic compounds (VOCs) associated with the metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns.ResultsThe GM core volatilome for all ASD patients was characterized by a high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; and o-cymene. Patients were stratified based on age, GI symptoms, and ASD severity symptoms. Disease risk prediction allowed us to associate butanoic acid with subjects older than 5 years, indole with the absence of GI symptoms and low disease severity, propanoic acid with the ASD risk group, and p-cymene with ASD symptoms, all based on the predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid, octadecane, acetic acid, skatole, and tetradecanal features. LogisticRegression models corroborated methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole, and acetic acid as ASD predictors.ConclusionOur results will aid the development of advanced clinical decision support systems (CDSSs), assisted by ML models, for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors.

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Section: Discussionmentioning

confidence: 99%

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Vernocchi,

Marangelo,

Guerrera

et al. 2023

Front. Microbiol.

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“…Using an NMR metabolomics approach, our group has conducted several studies on the search for BD biomarkers in the early stages ( 15 – 17 ). These identified biomarkers were able to accurately distinguish BD patients from healthy controls.…”

Section: Introductionmentioning

confidence: 99%

Search for serum biomarkers in patients with bipolar disorder and major depressive disorder using metabolome analysis

Sun,

Ma,

Chen

et al. 2023

Front. Psychiatry

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ObjectiveBipolar disorder (BD) and major depressive disorder (MDD) are two common psychiatric disorders. Due to the overlapping clinical symptoms and the lack of objective diagnostic biomarkers, bipolar disorder (BD) is easily misdiagnosed as major depressive disorder (MDD), which in turn affects treatment decisions and prognosis. This study aimed to investigate biomarkers that could be used to differentiate BD from MDD.MethodsNuclear magnetic resonance (NMR) spectroscopy was performed to assess serum metabolic profiles in depressed patients with BD (n = 59), patients with MDD (n = 14), and healthy controls (n = 10). Data was analyzed using partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis and t-tests. Different metabolites (VIP > 1 and p < 0.05) were identified and further analyzed using Metabo Analyst 5.0 to identify relevant metabolic pathways.ResultsThe metabolic phenotypes of the BD and MDD groups were significantly different from those of the healthy controls, and there were different metabolite differences between them. In the BD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, β-glucose, pantothenic acid, mannose, glycerol, and lipids were significantly higher than those in the healthy control group, and the levels of lactate and acetoacetate were significantly lower than those in the healthy control group. In the MDD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, pyruvate, choline, acetoacetic acid, and lipids were significantly higher than those of healthy controls, and the levels of acetic acid and glycerol were significantly lower than those of healthy controls.ConclusionGlycerolipid metabolism is significantly involved in BD and MDD. Pyruvate metabolism is significantly involved in MDD. Pyruvate, choline, and acetate may be potential biomarkers for MDD to distinguish from BD, and pantothenic acid may be a potential biomarker for BD to distinguish from MDD.

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An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

Ribeiro,

Zandonadi,

Sussulini

2023

Expert Review of Proteomics

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Altered Metabolomics in Bipolar Depression With Gastrointestinal Symptoms

Cited by 3 publications

References 40 publications

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Search for serum biomarkers in patients with bipolar disorder and major depressive disorder using metabolome analysis

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

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