Gut microbiome and schizophrenia: insights from two-sample Mendelian randomization

Schizophrenia, Journal Year: 2024, Volume and Issue: 10(1)

Published: Sept. 2, 2024

Growing evidence suggests a potential link between the gut microbiome and schizophrenia. However, it is unclear whether causally associated with We performed two-sample bidirectional Mendelian randomization to detect causal relationships Summary genome-wide association study (GWAS) datasets of from MiBioGen consortium (n = 18,340) schizophrenia 130,644) were utilized in our study. Then cohort sensitive analyses was followed validate robustness MR results. identified nine taxa that exerted positive effects on (OR: 1.08–1.16) six conferred negative 0.88–0.94). On other hand, reverse analysis showed may increase abundance 1.03–1.08) reduce two 0.94). Our unveiled mutual The findings provide for treatment microbiomes

Language: Английский

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Gut microbiome and major depressive disorder: insights from two-sample Mendelian randomization

BMC Psychiatry, Journal Year: 2024, Volume and Issue: 24(1)

Published: July 8, 2024

Abstract Background Existing evidence suggests that alterations in the gut microbiome are closely associated with major depressive disorder (MDD). We aimed to reveal causal relationships between MDD and various microbial taxa gut. Methods used two-sample Mendelian randomization (TSMR) explore bidirectional effects microbiota MDD. The genome-wide association studies summary results of were obtained from two large consortia, MibioGen consortium Dutch Microbiome Project, which we analyzed separately. Results Our TSMR analysis identified 10 bacterial protective against MDD, including phylum Actinobacteria , order Clostridiales family Bifidobacteriaceae (OR: 0.96 ∼ 0.98). Ten an increased risk phyla Firmicutes Proteobacteria class genus Alistipes 1.01 1.09). On other hand, may decrease abundance 12 taxa, families Defluviitaleaceae 0.63 0.88). increase 8 Bacteroidetes genera Parabacteroides Bacteroides 1.12 1.43). Conclusions study supports there mutual certain development suggesting be targeted treatment

Language: Английский

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Association of reported sleep disturbances with objectively assessed mild cognitive impairment among adults in the United States

SAGE Open Medicine, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 1, 2025

Background: Sleep is a multifaceted phenomenon influenced by both duration and quality. Various sleep disturbances have been associated with mild cognitive impairment, but the role of specific in impairment pathophysiology remains unclear. This study investigated associations between distinct adults aged 50 older using nationally representative data. Methods: Longitudinal data from Health Retirement Study were analyzed to explore association three types disturbances: trouble falling asleep, waking up, up too early. Logistic regression models estimated unadjusted (Model 1) adjusted accounting for sex, race/ethnicity, age, social determinants health 2), general 3), depression 4), pain physical activity 5). Results: The cohort included 8877 participants ⩾50 years 2018 (baseline) who followed 2020. Overall, 15.4% reported 23.2% 12.8% early being unable fall back asleep most time. Among adults, approximately 13.1% experiencing impairment; prevalence was even higher those experienced disturbances. odds ratio (uOR) time 1.69 (95% CI: 1.42–2.03), 1.31 1.10–1.57), 1.88 1.51–2.35). However, these positive attenuated depending on covariate adjustment. Conclusions: Nearly one seven had impairment. relationship has challenging delineate. Our findings demonstrate although sensitive adjustments. These suggest pathways reducing risk

Language: Английский

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Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(6), P. 2428 - 2428

Published: March 8, 2025

Mild cognitive impairment (MCI) is a clinical condition characterized by decline in ability and progression of impairment. It often considered transitional stage between normal aging Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) traditional machine (ML) methods predicting MCI using plasma proteomic biomarkers. A total 239 adults were selected from the Disease Neuroimaging Initiative (ADNI) cohort along with pool 146 We evaluated seven ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting (GBM), extreme (XGBoost)) six variations neural network (DNN) model—the DL model H2O package. Least Absolute Shrinkage Selection Operator (LASSO) 35 biomarkers pool. Based on grid search, DNN an activation function “Rectifier With Dropout” 2 layers 32 revealed best highest accuracy 0.995 F1 Score 0.996, while among methods, XGBoost was 0.986 0.985. Several correlated APOE-ε4 genotype, polygenic hazard score (PHS), three cerebrospinal fluid (Aβ42, tTau, pTau). Bioinformatics analysis Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) several molecular functions pathways associated biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, regulation lipid localization. The results showed that may represent promising tool prediction MCI. These help early diagnosis, prognostic risk stratification, treatment interventions for individuals at

Language: Английский

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Understanding unexpectedly stable trajectories of functional mobility in people with Parkinson’s disease: A mixed methods study

Published: Aug. 25, 2024

BACKGROUND As Parkinson’s disease (PD) progresses, mobility declines. Reserves (biological, physiological, cognitive, emotional, economical or relational) may help us to understand the phenomenon of unexpectedly stable trajectories patient-reported functional mobility. OBJECTIVES To investigate reserves moderating and their daily experience by people with PD. describe characteristics individuals METHODS In this explanatory sequential mixed methods study, we combined longitudinal models qualitative interviews Specifically, first analysed associations between years since diagnosis followed a subsequent collection analysis helping meaning these quantitative findings. RESULTS While not significant after correction for multiple testing, declined slower in men 10 16 education but women. By comparing group an decreasing trajectory, trajectory showed, adjustment testing less motor- non-motor symptoms. The deductive analyses semi-structured identified transport service, i.e., driving license disponibility someone car living same household as central facilitating factor Finally, according inductive content psychosocial factors, e.g., self-efficacy, characterised despite disability (years diagnosis) challenging context (living without partner offspring rural areas). CONCLUSIONS Trajectories PD seem be multifactorial nature, little evidence general determinants. Our study highlights importance supports provision local amenities within walking distance enable active healthy ageing place. Psychosocial factors context. Further research could our generated hypotheses inform interventions promoting

Language: Английский

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