10 results on '"Chen, Yu-Ming"'
Search Results
2. Gut microbiome, cognitive function and brain structure: a multi-omics integration analysis
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Liang, Xinxiu, Fu, Yuanqing, Cao, Wen-ting, Wang, Zhihong, Zhang, Ke, Jiang, Zengliang, Jia, Xiaofang, Liu, Chun-ying, Lin, Hong-rou, Zhong, Haili, Miao, Zelei, Gou, Wanglong, Shuai, Menglei, Huang, Yujing, Chen, Shengdi, Zhang, Bing, Chen, Yu-ming, and Zheng, Ju-Sheng
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- 2022
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3. Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults.
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Zeng, Fangfang, Su, Xin, Liang, Xinxiu, Liao, Minqi, Zhong, Haili, Xu, Jinjian, Gou, Wanglong, Zhang, Xiangzhou, Shen, Luqi, Zheng, Ju-Sheng, and Chen, Yu-ming
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NON-alcoholic fatty liver disease ,MIDDLE-aged persons ,GUT microbiome ,MACHINE learning ,DISEASE risk factors - Abstract
Background: The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD. Methods: The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018. We collected 272 metadata points from 1546 individuals. The metadata were input into four interpretable machine learning models to identify important gut microbiota associated with NAFLD. These models were subsequently applied to two validation cohorts [the internal validation cohort (n = 377), and the prospective validation cohort (n = 749)] to assess generalizability. We constructed an individual microbiome risk score (MRS) based on the identified gut microbiota and conducted animal faecal microbiome transplantation experiment using faecal samples from individuals with different levels of MRS to determine the relationship between MRS and NAFLD. Additionally, we conducted targeted metabolomic sequencing of faecal samples to analyse potential metabolites. Results: Among the four machine learning models used, the lightGBM algorithm achieved the best performance. A total of 12 taxa-related features of the microbiota were selected by the lightGBM algorithm and further used to calculate the MRS. Increased MRS was positively associated with the presence of NAFLD, with odds ratio (OR) of 1.86 (1.72, 2.02) per 1-unit increase in MRS. An elevated abundance of the faecal microbiota (f__veillonellaceae) was associated with increased NAFLD risk, whereas f__rikenellaceae, f__barnesiellaceae, and s__adolescentis were associated with a decreased presence of NAFLD. Higher levels of specific gut microbiota-derived metabolites of bile acids (taurocholic acid) might be positively associated with both a higher MRS and NAFLD risk. FMT in mice further confirmed a causal association between a higher MRS and the development of NAFLD. Conclusions: We confirmed that an alteration in the composition of the core gut microbiota might be biologically relevant to NAFLD development. Our work demonstrated the role of the microbiota in the development of NAFLD. [ABSTRACT FROM AUTHOR]
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- 2024
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4. The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for complex human diseases
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Xu, Fengzhe, Fu, Yuanqing, Sun, Ting-yu, Jiang, Zengliang, Miao, Zelei, Shuai, Menglei, Gou, Wanglong, Ling, Chu-wen, Yang, Jian, Wang, Jun, Chen, Yu-ming, and Zheng, Ju-Sheng
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- 2020
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5. Gut microbiota and acylcarnitine metabolites connect the beneficial association between equol and adiposity in adults: a prospective cohort study.
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Wu, Yan-yan, Gou, Wanglong, Yan, Yan, Liu, Chun-ying, Yang, Yingdi, Chen, Danyu, Xie, Keliang, Jiang, Zengliang, Fu, Yuanqing, Zhu, Hui-lian, Zheng, Ju-Sheng, and Chen, Yu-ming
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OBESITY risk factors ,FECAL analysis ,CARNITINE ,HIGH performance liquid chromatography ,GUT microbiome ,ISOFLAVONES ,DESCRIPTIVE statistics ,MASS spectrometry ,BODY mass index ,ODDS ratio ,PATH analysis (Statistics) ,METABOLITES ,ADIPOSE tissues ,LONGITUDINAL method - Abstract
Background Many studies have investigated the effects of soy isoflavones on weight control, but few have focused on the role of equol, a gut-derived metabolite of daidzein with greater bioavailability than other soy isoflavones. Objectives This study examined the association of equol production with obesity and explored the mediating roles of equol-related gut microbiota and microbial carnitine metabolites. Methods This 6.6-y prospective study included 2958 Chinese adults (2011 females and 947 males) aged 60.6 ± 6.0 y (mean ± SD) at baseline. Urinary equol and isoflavones were measured using HPLC–tandem MS. BMI, percentage fat mass (%FM), and serum triglycerides (TGs) were assessed every 3 y. Metagenomics sequencing and assessment of carnitine metabolites in feces were performed in a subsample of 897 participants. Results Urinary equol, but not daidzein and genistein, was independently and inversely associated with the obesity-related indicators of BMI, %FM, and a biomarker (TGs). Equol producers (EPs) had lower odds of adiposity conditions and a reduced risk of 6.6-y obesity progression than non-EPs among total participants. Gut microbial analyses indicated that EPs had higher microbiome species richness (P = 3.42 × 10
−5 ) and significantly different β-diversity of gut microbiota compared with the non-EP group (P = 0.001), with 20 of 162 species differing significantly. EPs (compared with non-EPs) had higher abundances of Alistipes senegalensis and Coprococcus catus but lower abundances of Ruminococcus gnavus (false discovery rate <0.05). Among the 7 determined fecal acylcarnitine metabolites, palmitoylcarnitine, oleylcarnitine 18:1, and stearylcarnitine were inversely associated with EPs but positively correlated with obesity conditions and progression. Path analyses indicated that the beneficial association between equol and obesity might be mediated by gut microbiota and decreased production of 3 acylcarnitines in feces. Conclusions This study suggests a beneficial association between equol and obesity, mediated by the gut microbiome and acylcarnitines, in adults. This trial was registered at clinicaltrials.gov as NCT03179657. [ABSTRACT FROM AUTHOR]- Published
- 2022
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6. The gut microbiota-bile acid axis links the positive association between chronic insomnia and cardiometabolic diseases.
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Jiang, Zengliang, Zhuo, Lai-bao, He, Yan, Fu, Yuanqing, Shen, Luqi, Xu, Fengzhe, Gou, Wanglong, Miao, Zelei, Shuai, Menglei, Liang, Yuhui, Xiao, Congmei, Liang, Xinxiu, Tian, Yunyi, Wang, Jiali, Tang, Jun, Deng, Kui, Zhou, Hongwei, Chen, Yu-ming, and Zheng, Ju-Sheng
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HEART metabolism disorders ,INSOMNIA ,CHOLIC acid ,DEOXYCHOLIC acid ,GUT microbiome ,BILE acids ,ENTEROHEPATIC circulation - Abstract
Evidence from human cohorts indicates that chronic insomnia is associated with higher risk of cardiometabolic diseases (CMD), yet whether gut microbiota plays a role is unclear. Here, in a longitudinal cohort (n = 1809), we find that the gut microbiota-bile acid axis may link the positive association between chronic insomnia and CMD. Ruminococcaceae UCG-002 and Ruminococcaceae UCG-003 are the main genera mediating the positive association between chronic insomnia and CMD. These results are also observed in an independent cross-sectional cohort (n = 6122). The inverse associations between those gut microbial biomarkers and CMD are mediated by certain bile acids (isolithocholic acid, muro cholic acid and nor cholic acid). Habitual tea consumption is prospectively associated with the identified gut microbiota and bile acids in an opposite direction compared with chronic insomnia. Our work suggests that microbiota-bile acid axis may be a potential intervention target for reducing the impact of chronic insomnia on cardiometabolic health. Chronic insomnia is associated with cardiometabolic diseases. Here, in two clinical cohorts (n = 7,931), authors show that gut microbiota-bile acid axis may be an intervention target to attenuate the impact of chronic insomnia on cardiometabolic health. [ABSTRACT FROM AUTHOR]
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- 2022
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7. Temporal relationship among adiposity, gut microbiota, and insulin resistance in a longitudinal human cohort.
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Deng, Kui, Shuai, Menglei, Zhang, Zheqing, Jiang, Zengliang, Fu, Yuanqing, Shen, Luqi, Zheng, Ju-Sheng, and Chen, Yu-ming
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GUT microbiome ,INSULIN resistance ,OBESITY ,CHINESE people ,PATH analysis (Statistics) - Abstract
Background: The temporal relationship between adiposity and gut microbiota was unexplored. Whether some gut microbes lie in the pathways from adiposity to insulin resistance is less clear. Our study aims to reveal the temporal relationship between adiposity and gut microbiota and investigate whether gut microbiota may mediate the association of adiposity with insulin resistance in a longitudinal human cohort study.Methods: We obtained repeated-measured gut shotgun metagenomic and anthropometric data from 426 Chinese participants over ~3 years of follow-up. Cross-lagged path analysis was used to examine the temporal relationship between BMI and gut microbial features. The associations between the gut microbes and insulin resistance-related phenotypes were examined using a linear mixed-effect model. We examined the mediation effect of gut microbes on the association between adiposity and insulin resistance-related phenotypes. Replication was performed in the HMP cohort.Results: Baseline BMI was prospectively associated with levels of ten gut microbial species. Among them, results of four species (Adlercreutzia equolifaciens, Parabacteroides unclassified, Lachnospiraceae bacterium 3 1 57FAA CT1, Lachnospiraceae bacterium 7 1 58FAA) were replicated in the independent HMP cohort. Lachnospiraceae bacterium 3 1 57FAA CT1 was inversely associated with HOMA-IR and fasting insulin. Lachnospiraceae bacterium 3 1 57FAA CT1 mediated the association of overweight/obesity with HOMA-IR (FDR<0.05). Furthermore, Lachnospiraceae bacterium 3 1 57FAA CT1 was positively associated with the butyrate-producing pathway PWY-5022 (p < 0.001).Conclusions: Our study identified one potentially beneficial microbe Lachnospiraceae bacterium 3 1 57FAA CT1, which might mediate the effect of adiposity on insulin resistance. The identified microbes are helpful for the discovery of novel therapeutic targets, as to mitigate the impact of adiposity on insulin resistance. [ABSTRACT FROM AUTHOR]- Published
- 2022
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8. Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes.
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Gou, Wanglong, Ling, Chu-wen, He, Yan, Jiang, Zengliang, Fu, Yuanqing, Xu, Fengzhe, Miao, Zelei, Sun, Ting-yu, Lin, Jie-sheng, Zhu, Hui-lian, Zhou, Hongwei, Chen, Yu-ming, and Zheng, Ju-Sheng
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GUT microbiome ,MACHINE learning ,TYPE 2 diabetes ,BODY composition - Abstract
Objective: To identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features.Research Design and Methods: We used an interpretable machine learning framework to identify the type 2 diabetes-related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS-type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832).Results: The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23-1.33), 1.23 (1.13-1.34), and 1.12 (1.06-1.18) across three cohorts. The MRS was positively associated with future glucose increment (P < 0.05) and was correlated with a variety of gut microbiota-derived blood metabolites. Animal study further confirmed the MRS-type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome-type 2 diabetes relationship.Conclusions: Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment. [ABSTRACT FROM AUTHOR]- Published
- 2021
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9. Erythrocyte n-6 Polyunsaturated Fatty Acids, Gut Microbiota, and Incident Type 2 Diabetes: A Prospective Cohort Study.
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Miao, Zelei, Lin, Jie-sheng, Mao, Yingying, Chen, Geng-dong, Zeng, Fang-fang, Dong, Hong-li, Jiang, Zengliang, Wang, Jiali, Xiao, Congmei, Shuai, Menglei, Gou, Wanglong, Fu, Yuanqing, Imamura, Fumiaki, Chen, Yu-ming, and Zheng, Ju-Sheng
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OMEGA-6 fatty acids ,TYPE 2 diabetes ,UNSATURATED fatty acids ,GUT microbiome ,LINOLEIC acid ,FUNCTIONAL colonic diseases ,ERYTHROCYTE metabolism ,RESEARCH ,RESEARCH methodology ,DISEASE incidence ,MEDICAL cooperation ,EVALUATION research ,COMPARATIVE studies ,RESEARCH funding ,ARACHIDONIC acid ,ERYTHROCYTES ,LONGITUDINAL method - Abstract
Objective: To examine the association of erythrocyte n-6 polyunsaturated fatty acid (PUFA) biomarkers with incident type 2 diabetes and explore the potential role of gut microbiota in the association.Research Design and Methods: We evaluated 2,731 participants without type 2 diabetes recruited between 2008 and 2013 in the Guangzhou Nutrition and Health Study (Guangzhou, China). Case subjects with type 2 diabetes were identified with clinical and biochemical information collected at follow-up visits. Using stool samples collected during the follow-up in the subset (n = 1,591), 16S rRNA profiling was conducted. Using multivariable-adjusted Poisson or linear regression, we examined associations of erythrocyte n-6 PUFA biomarkers with incident type 2 diabetes and diversity and composition of gut microbiota.Results: Over 6.2 years of follow-up, 276 case subjects with type 2 diabetes were identified (risk 0.10). Higher levels of erythrocyte γ-linolenic acid (GLA), but not linoleic or arachidonic acid, were associated with higher type 2 diabetes incidence. Comparing the top to the bottom quartile groups of GLA levels, relative risk was 1.72 (95% CI 1.21, 2.44) adjusted for potential confounders. Baseline GLA was inversely associated with gut microbial richness and diversity (α-diversity, both P < 0.05) during follow-up and significantly associated with microbiota β-diversity (P = 0.002). α-Diversity acted as a potential mediator in the association between GLA and type 2 diabetes (P < 0.05). Seven genera (Butyrivibrio, Blautia, Oscillospira, Odoribacter, S24-7 other, Rikenellaceae other, and Clostridiales other) were enriched in quartile 1 of GLA and in participants without type 2 diabetes.Conclusions: Relative concentrations of erythrocyte GLA were positively associated with incident type 2 diabetes in a Chinese population and also with gut microbial profiles. These results highlight that gut microbiota may play an important role linking n-6 PUFA metabolism and type 2 diabetes etiology. [ABSTRACT FROM AUTHOR]- Published
- 2020
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10. Dietary fruit and vegetable intake, gut microbiota, and type 2 diabetes: results from two large human cohort studies.
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Jiang, Zengliang, Sun, Ting-yu, He, Yan, Gou, Wanglong, Zuo, Luo-shi-yuan, Fu, Yuanqing, Miao, Zelei, Shuai, Menglei, Xu, Fengzhe, Xiao, Congmei, Liang, Yuhui, Wang, Jiali, Xu, Yisong, Jing, Li-peng, Ling, Wenhua, Zhou, Hongwei, Chen, Yu-ming, and Zheng, Ju-Sheng
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TYPE 2 diabetes ,GUT microbiome ,NONNUTRITIVE sweeteners ,FRUIT ,HUMAN microbiota ,GLYCOSYLATED hemoglobin - Abstract
Background: Little is known about the inter-relationship among fruit and vegetable intake, gut microbiota and metabolites, and type 2 diabetes (T2D) in human prospective cohort study. The aim of the present study was to investigate the prospective association of fruit and vegetable intake with human gut microbiota and to examine the relationship between fruit and vegetable-related gut microbiota and their related metabolites with type 2 diabetes (T2D) risk.Methods: This study included 1879 middle-age elderly Chinese adults from Guangzhou Nutrition and Health Study (GNHS). Baseline dietary information was collected using a validated food frequency questionnaire (2008-2013). Fecal samples were collected at follow-up (2015-2019) and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Blood samples were collected and analyzed for glucose, insulin, and glycated hemoglobin. We used multivariable linear regression and logistic regression models to investigate the prospective associations of fruit and vegetable intake with gut microbiota and the association of the identified gut microbiota (fruit/vegetable-microbiota index) and their related fecal metabolites with T2D risk, respectively. Replications were performed in an independent cohort involving 6626 participants.Results: In the GNHS, dietary fruit intake, but not vegetable, was prospectively associated with gut microbiota diversity and composition. The fruit-microbiota index (FMI, created from 31 identified microbial features) was positively associated with fruit intake (p < 0.001) and inversely associated with T2D risk (odds ratio (OR) 0.83, 95%CI 0.71-0.97). The FMI-fruit association (p = 0.003) and the FMI-T2D association (OR 0.90, 95%CI 0.84-0.97) were both successfully replicated in the independent cohort. The FMI-positive associated metabolite sebacic acid was inversely associated with T2D risk (OR 0.67, 95%CI 0.51-0.86). The FMI-negative associated metabolites cholic acid (OR 1.35, 95%CI 1.13-1.62), 3-dehydrocholic acid (OR 1.30, 95%CI 1.09-1.54), oleylcarnitine (OR 1.77, 95%CI 1.45-2.20), linoleylcarnitine (OR 1.66, 95%CI 1.37-2.05), palmitoylcarnitine (OR 1.62, 95%CI 1.33-2.02), and 2-hydroglutaric acid (OR 1.47, 95%CI 1.25-1.72) were positively associated with T2D risk.Conclusions: Higher fruit intake-associated gut microbiota and metabolic alteration were associated with a lower risk of T2D, supporting the public dietary recommendation of adopting high fruit intake for the T2D prevention. [ABSTRACT FROM AUTHOR]- Published
- 2020
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