Yajuan Fan

First Affiliated Hospital of Xi'an Jiaotong University

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Xiancang Ma

First Affiliated Hospital of Xi'an Jiaotong University

Binbin Zhao

First Affiliated Hospital of Xi'an Jiaotong University

Qingyan Ma

First Affiliated Hospital of Xi'an Jiaotong University

Feng Zhu

First Affiliated Hospital of Xi'an Jiaotong University

Xiaoyan He

China International Science and Technology Cooperation

Jian Yang

First Affiliated Hospital of Xi'an Jiaotong University

Bin Yan

First Affiliated Hospital of Xi'an Jiaotong University

Fengjie Gao

Northeast Agricultural University

Ce Chen

China Pharmaceutical University

Chengge Gao

First Affiliated Hospital of Xi'an Jiaotong University

Cooperative Institutions

First Affiliated Hospital of Xi'an Jiaotong University

Xi'an Jiaotong University

Zhejiang University

First Affiliated Hospital of Zhengzhou University

Chinese Academy of Medical Sciences & Peking Union Medical College

Zhengzhou University

Shanxi Medical University

Capital Medical University

Shanghai Jiao Tong University

BGI Group (China)

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Characterizing serum amino acids in schizophrenic patients: Correlations with gut microbes

Journal of Psychiatric Research (2022)

Qingyan Ma Fengjie Gao Lina Zhou Yajuan Fan Binbin Zhao

10.1016/j.jpsychires.2022.07.006

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Citations (10)

Comparative efficacy and acceptability of first-line drugs for the acute treatment of generalized anxiety disorder in adults: A network meta-analysis

Journal of Psychiatric Research (2019)

Hairong He Yu‐Tao Xiang Fengjie Gao Ling Bai Fan Gao

Escitalopram

Tolerability

Vortioxetine

10.1016/j.jpsychires.2019.08.009

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Citations (31)

Gut microbiota dysbiosis promotes cognitive impairment via bile acid metabolism in major depressive disorder

Translational Psychiatry (2024)

Min Jia Yajuan Fan Qingyan Ma Ding Yang Yunpeng Wang

Evidence suggests that complex interactions among the gut microbiome, metabolic abnormalities, and brain have important etiological and therapeutic implications in major depressive disorder (MDD). However, the influence of microbiome-gut-brain cross-talk on cognitive impairment in MDD remains poorly characterized. We performed serum metabolomic profiling on 104 patients with MDD and 77 healthy controls (HCs), and also performed fecal metagenomic sequencing on a subset of these individuals, including 79 MDD patients and 60 HCs. The findings were validated in a separate cohort that included 40 patients with MDD and 40 HCs using serum-targeted metabolomics. Abnormal bile acid metabolism was observed in patients with MDD, which is related to cognitive dysfunction. The following gut microbiota corresponded to changes in bile acid metabolism and enzyme activities involved in the bile acid metabolic pathway, including Lachnospiraceae (Blautia_massiliensis, Anaerostipes_hadrus, Dorea_formicigenerans, and Fusicatenibacter_saccharivorans), Ruminococcaceae (Ruminococcus_bromii, Flavonifractor_plautii, and Ruthenibacterium_lactatiformans), and Escherichia_coli. Furthermore, a combinatorial marker classifier that robustly differentiated patients with MDD from HCs was identified. In conclusion, this study provides insights into the gut-brain interactions in the cognitive phenotype of MDD, indicating a potential therapeutic strategy for MDD-associated cognitive impairment by targeting the gut microbiota and bile acid metabolism.

Lachnospiraceae

Ruminococcus

Chenodeoxycholic acid

Dysbiosis

10.1038/s41398-024-03211-4

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Association of sleep timing with all-cause and cardiovascular mortality: the Sleep Heart Health Study and the Osteoporotic Fractures in Men Study

Journal of Clinical Sleep Medicine (2023)

Mingfang Ma Yajuan Fan Yuan Peng Qingyan Ma Min Jia

Previous studies have highlighted the importance of sleep patterns for human health. This study aimed to investigate the association of sleep timing with all-cause and cardiovascular disease mortality.

Bedtime

Sleep

10.5664/jcsm.10926

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Citations (1)

Sleeping Late Increases the Risk of Myocardial Infarction in the Middle-Aged and Older Populations

Frontiers in Cardiovascular Medicine (2021)

Yajuan Fan Yanhua Wu Yuan Peng Binbin Zhao Jian Yang

Objective: Sleep has a significant influence on the incidence of myocardial infarction (MI). The purpose of this study was to investigate the association between sleep timing including bedtime, wake-up time and sleep midpoint, and the incidence of MI. Methods: A total of 4,576 patients (2,065 men, 2,511 women; age 63.4 ± 11.0 years) were selected from the Sleep Heart Health Study. Sleep timings on weekdays and weekends were recorded or calculated based on the sleep habits questionnaire completed by the participants at baseline. Bedtime was divided into 10:00 PM and before, 10:01 PM−11:00 PM, 11:01 PM−12:00 AM, and later than 12:00 AM. Cox proportional hazards regression analysis was used to examine the relationship between sleep timings and MI. Results: Participants with a weekday bedtime later than 12:00 AM, between 11:01 PM−12:00 AM, and 10:00 PM or before had a higher incidence of MI than those with a bedtime between 10:01 PM and 11:00 PM (9.2% vs. 7.0% vs. 6.9% vs. 5.1%, respectively; P = 0.008). Multivariable Cox regression analysis showed that sleeping on weekdays later than 12:00 AM was associated with an increased risk of incident MI after adjusting for potential covariates (hazard ratio, 1.628; 95% confidence interval, 1.092–2.427; P = 0.017). However, there was no significant association between late bedtime on weekends and MI. In addition, no significant association of late wake-up time and delayed sleep midpoint on both weekdays and weekends with the incidence of MI was observed. Conclusion: Sleeping late on weekday (>12:00 AM) independently increased the risk of MI. This finding emphasizes the importance of a proper bedtime for the maintenance of the health of the cardiovascular system.

Bedtime

Sleep

10.3389/fcvm.2021.709468

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Citations (18)

Objective Sleep Efficiency Predicts Cardiovascular Disease in a Community Population: The Sleep Heart Health Study

Journal of the American Heart Association (2021)

Bin Yan Jian Yang Binbin Zhao Yajuan Fan Wei Wang

Background There was little evidence about the role of objective sleep efficiency (SE) in the incidence of major cardiovascular disease (CVD) events. The purpose of this study was to investigate the correlation between objective SE and CVD based on polysomnography. Methods and Results A total of 3810 participants from the SHHS (Sleep Heart Health Study) were selected in the current study. CVD was assessed during an almost 11‐year follow‐up period. The primary composite cardiovascular outcome was major adverse cardiovascular events, defined as CVD mortality, congestive heart failure, myocardial infarction, and stroke. The secondary composite cardiovascular outcome was major adverse cardiovascular event plus revascularization. Objective measured SE, including SE and wake after sleep onset, was based on in‐home polysomnography records. Cox regression analysis was used to explore the association between SE and CVD. After multivariate Cox regression analysis, poor SE (<80%) was significantly associated with primary (hazard ratio [HR], 1.338; 95% CI, 1.025–1.745; P =0.032) and secondary composite cardiovascular outcomes (HR, 1.250; 95% CI, 1.027–1.521; P =0.026); it was also found to be a predictor of CVD mortality (HR, 1.887; 95% CI, 1.224–2.909; P =0.004). Moreover, wake after sleep onset of fourth quartile (>78.0 minutes) was closely correlated with primary (HR, 1.436; 95% CI, 1.066–1.934; P =0.017), secondary composite cardiovascular outcomes (HR, 1.374; 95% CI, 1.103–1.712; P =0.005), and CVD mortality (HR, 2.240; 95% CI, 1.377–3.642; P =0.001). Conclusions Poor SE and long wake after sleep onset, objectively measured by polysomnography, were associated with the increased risk of incident CVD.

Sleep

Cardiovascular Health

10.1161/jaha.120.016201

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Citations (47)

Grey matter volume and its association with cognitive impairment and peripheral cytokines in excited individuals with schizophrenia

Brain Imaging and Behavior (2022)

Yajuan Fan Yuan Gao Qingyan Ma Binbin Zhao Xiaoyan He

Inferior temporal gyrus

Grey matter

Fusiform gyrus

Parahippocampal gyrus

10.1007/s11682-022-00717-7

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Citations (8)

Assessing the Causal Effects of Human Serum Metabolites on 5 Major Psychiatric Disorders

Schizophrenia Bulletin (2019)

Jian Yang Bin Yan Binbin Zhao Yajuan Fan Xiaoyan He

Abstract Psychiatric disorders are the leading cause of disability worldwide while the pathogenesis remains unclear. Genome-wide association studies (GWASs) have made great achievements in detecting disease-related genetic variants. However, functional information on the underlying biological processes is often lacking. Current reports propose the use of metabolic traits as functional intermediate phenotypes (the so-called genetically determined metabotypes or GDMs) to reveal the biological mechanisms of genetics in human diseases. Here we conducted a two-sample Mendelian randomization analysis that uses GDMs to assess the causal effects of 486 human serum metabolites on 5 major psychiatric disorders, which respectively were schizophrenia (SCZ), major depression (MDD), bipolar disorder (BIP), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Using genetic variants as proxies, our study has identified 137 metabolites linked to the risk of psychiatric disorders, including 2-methoxyacetaminophen sulfate, which affects SCZ (P = 1.7 × 10–5) and 1-docosahexaenoylglycerophosphocholine, which affects ADHD (P = 5.6 × 10–5). Fourteen significant metabolic pathways involved in the 5 psychiatric disorders assessed were also detected, such as glycine, serine, and threonine metabolism for SCZ (P = .0238), Aminoacyl-tRNA biosynthesis for both MDD (P = .0144) and ADHD (P = .0029). Our study provided novel insights into integrating metabolomics with genomics in order to understand the mechanisms underlying the pathogenesis of human diseases.

Mendelian Randomization

Psychiatric genetics

Genetic Association

10.1093/schbul/sbz138

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Citations (107)

Identification of serum biomarkers for chronic kidney disease using serum metabolomics

Renal Failure (2024)

Xi Gu Yindi Dong Xuemei Wang Zhigang Ren Guanhua Li

This study aimed to identify biomarkers for chronic kidney disease (CKD) by studying serum metabolomics. Serum samples were collected from 194 non-dialysis CKD patients and 317 healthy controls (HC). Using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS), untargeted metabolomics analysis was conducted. A random forest model was developed and validated in separate sets of HC and CKD patients. The serum metabolomic profiles of patients with chronic kidney disease (CKD) exhibited significant differences compared to healthy controls (HC). A total of 314 metabolites were identified as significantly different, with 179 being upregulated and 135 being downregulated in CKD patients. KEGG enrichment analysis revealed several key pathways, including arginine biosynthesis, phenylalanine metabolism, linoleic acid metabolism, and purine metabolism. The diagnostic efficacy of the classifier was high, with an area under the curve of 1 in the training and validation sets and 0.9435 in the cross-validation set. This study provides comprehensive insights into serum metabolism in non-dialysis CKD patients, highlighting the potential involvement of abnormal biological metabolism in CKD pathogenesis. Exploring metabolites may offer new possibilities for the management of CKD.

Identification

10.1080/0886022x.2024.2409346

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Increased methylation of glucocorticoid receptor gene promoter 1 F in peripheral blood of patients with generalized anxiety disorder

Journal of Psychiatric Research (2017)

Wei Wang Junqin Feng Ji Cui Xin Mu Qingyan Ma

Basal (medicine)

10.1016/j.jpsychires.2017.01.019

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Citations (43)