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28 results on '"Mei, Zhendong"'

1. Dietary factors, gut microbiota, and serum trimethylamine-N-oxide associated with cardiovascular disease in the Hispanic Community Health Study/Study of Latinos

Author

Mei, Zhendong, Chen, Guo-Chong, Wang, Zheng, Usyk, Mykhaylo, Yu, Bing, Baeza, Yoshiki Vazquez, Humphrey, Greg, Benitez, Rodolfo Salido, Li, Jun, Williams-Nguyen, Jessica S, Daviglus, Martha L, Hou, Lifang, Cai, Jianwen, Zheng, Yan, Knight, Rob, Burk, Robert D, Boerwinkle, Eric, Kaplan, Robert C, and Qi, Qibin

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Clinical Research, Cardiovascular, Prevention, Nutrition, Good Health and Well Being, Adult, Aged, Biomarkers, Cardiovascular Diseases, Cross-Sectional Studies, Diet, Female, Gastrointestinal Microbiome, Hispanic or Latino, Humans, Male, Methylamines, Middle Aged, Public Health, cardiovascular disease, diet, gut microbiota, trimethylamine-N-oxide, Hispanic Americans, Engineering, Medical and Health Sciences, Nutrition & Dietetics, Clinical sciences, Nutrition and dietetics
Abstract

BackgroundTrimethylamine-N-oxide (TMAO), a diet-derived and gut microbiota-related metabolite, is associated with cardiovascular disease (CVD). However, major dietary determinants and specific gut bacterial taxa related to TMAO remain to be identified in humans.ObjectivesWe aimed to identify dietary and gut microbial factors associated with circulating TMAO.MethodsThis cross-sectional study included 3972 participants (57.3% women) aged 18-74 y from the Hispanic Community Health Study/Study of Latinos in the United States. Dietary information was collected by 24-h dietary recalls at baseline interview (2008-2011), and baseline serum TMAO and its precursors were measured by an untargeted approach. Gut microbiome was profiled by shotgun metagenomic sequencing in a subset of participants (n = 626) during a follow-up visit (2016-2018). Logistic and linear regression were used to examine associations of inverse-normalized metabolites with prevalent CVD, dietary intake, and bacterial species, respectively, after adjustment for sociodemographic, behavioral, and clinical factors.ResultsTMAO was positively associated with prevalent CVD (case number = 279; OR = 1.34; 95% CI: 1.17, 1.54, per 1-SD). Fish (P = 1.26 × 10-17), red meat (P = 3.33 × 10-16), and egg (P = 3.89 × 10-5) intakes were top dietary factors positively associated with TMAO. We identified 9 gut bacterial species significantly associated with TMAO (false discovery rate

Published
2021

6. Exposure to Multiple Air Pollutants and the Risk of Fractures: A Large Prospective Population‐Based Study.

Author

Qi, Wenhao, Mei, Zhendong, Sun, Zhonghan, Lin, Chenhao, Lin, Jinran, Li, Jialin, Ji, John S., and Zheng, Yan

Abstract

Atmospheric chemistry studies suggest air pollution impedes ultraviolet B photons and thus reduces cutaneous vitamin D3 synthesis. Biological evidence shows that inhaled pollutants disrupt circulating 25‐hydroxyvitamin D (25[OH]D) metabolism and ultimately impact bone health. The hypothesis is that higher air pollution concentrations are associated with a higher risk of fractures, mediated by lower circulating 25(OH)D. The study included participants of the UK Biobank who were free of fracture history at enrollment (2006 to 2010) and analyzed their environmental exposure data (2007 to 2010). Air pollution measurements included the annual averages of air particulate matter (PM2.5, PM2.5–10, and PM10), nitrogen oxides (NO2 and NOx), and a composite air pollution score. Multivariable Cox proportional hazard models were used to assess the associations of the individual pollutants and the score with fracture risks. Mediation analyses were conducted to assess the underlying role of serum 25(OH)D in such associations. Among 446,395 participants with a median of 8‐year follow‐up, 12,288 incident fractures were documented. Participants living in places with the highest quintile of air pollution score had a 15.3% increased risk of fractures (hazard ratio [95%CI]: 1.15[1.09,1.22]) compared to those in the lowest, and 5.49% of this association was mediated through serum 25(OH)D (pmediation < 0.05). Pollutant‐specific hazard of top‐to‐bottom quintiles was 16% for PM2.5, 4% for PM2.5–10, 5% for PM10, 20% for NO2, and 17% for NOx, with a 4% to 6% mediation effect of serum 25(OH)D concentrations. The associations of the air pollution score with fracture risks were weaker among female participants, those who drank less alcohol, and consumed more fresh fruit than their counterparts (pinteraction < 0.05). © 2023 American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]

Published
2023
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9. Gut Microbiome Alterations in Pulmonary Hypertension in Highlanders and Lowlanders

Author

Dong, Wenxue, primary, Ma, Lifeng, additional, Huang, Qiumin, additional, Yang, Xu, additional, Mei, Zhendong, additional, Kong, Mengmeng, additional, Sun, Zhonghan, additional, Zhang, Zhiying, additional, Li, Jing, additional, Zou, Jiaojiao, additional, Chen, Dandan, additional, Dai, Yuxiang, additional, Zhou, Daxin, additional, Zheng, Yan, additional, and Kang, Longli, additional

Published
2023
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10. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

Author

Mei, Zhendong, Wang, Fenglei, Bhosle, Amrisha, Dong, Danyue, Mehta, Raaj, Ghazi, Andrew, Zhang, Yancong, Liu, Yuxi, Rinott, Ehud, Ma, Siyuan, Rimm, Eric B., Daviglus, Martha, Willett, Walter C., Knight, Rob, Hu, Frank B., Qi, Qibin, Chan, Andrew T., Burk, Robert D., Stampfer, Meir J., Shai, Iris, Kaplan, Robert C., Huttenhower, Curtis, and Wang, Dong D.

Abstract

The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteaeand depleted Butyrivibrio crossotus. These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale. In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D.

Published
2024
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12. Gut microbiota and plasma metabolites associated with bone mineral density in women with or at risk of HIV infection

Author

Mei, Zhendong, primary, Yin, Michael T., additional, Sharma, Anjali, additional, Wang, Zheng, additional, Peters, Brandilyn A., additional, Chandran, Aruna, additional, Weber, Kathleen M., additional, Ross, Ryan D., additional, Gustafson, Deborah, additional, Zheng, Yan, additional, Kaplan, Robert C., additional, Burk, Robert D., additional, and Qi, Qibin, additional

Published
2022
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14. Additional file 1 of Gut microbiome alterations and gut barrier dysfunction are associated with host immune homeostasis in COVID-19 patients

Author

Sun, Zhonghan, Song, Zhi-Gang, Liu, Chenglin, Tan, Shishang, Lin, Shuchun, Zhu, Jiajun, Dai, Fa-Hui, Gao, Jian, She, Jia-Lei, Mei, Zhendong, Lou, Tao, Zheng, Jiao-Jiao, Liu, Yi, He, Jiang, Zheng, Yuanting, Ding, Chen, Qian, Feng, Zheng, Yan, and Chen, Yan-Mei

Abstract

Additional file 1: Figure S1. The COVID-19 related inflammatory biomarkers in patients at different disease stages. Figure S2. Timeline of disease progression and sample collection for COVID-19 patients with multiple samples. Figure S3. The ��-diversity of the gut microbiome among all the participants. Figure S4. The relative abundance of Bacteroides nordii throughout the hospitalization of COVID-19 patients. Figure S5. The relative abundance of Blautia sp. CAF 257 throughout the hospitalization of COVID-19 patients. Figure S6. The relative abundance of Burkholderia contaminans throughout the hospitalization of COVID-19 patients. Figure S7. The relative abundance of Bifidobacterium longum throughout the hospitalization of COVID-19 patients. Figure S8. The associations between COVID-19 related microbial features and RNA modules indicating T cell response. Figure S9. The influence of oral antibiotics uses on the gut microbiome of severe COVID-19 patients. Figure S10. The detail of samples for multi-omics measurement. Figure S11. The profiles of the gut microbiome annotated by the metaproteomics approach. Figure S12. The significantly differential abundant human proteins in fecal samples from COVID-19 patients and controls. Figure S13. The circulating levels of LPS-binding protein in COVID-19 patients. Figure S14. The microbial taxa identified in plasma samples from COVID-19 patients and controls through the proteomic approach.

Published
2022
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16. Additional file 2 of Associations of visceral adipose tissue with bone mineral density and fracture: observational and Mendelian randomization studies

Author

Hu, Jianying, Zhao, Manying, Lin, Chenhao, Sun, Zhonghan, Chen, Guo-Chong, Mei, Zhendong, and Zheng, Yan

Subjects
body regions, technology, industry, and agriculture, nutritional and metabolic diseases, human activities, tissues
Abstract

Additional file 2: Fig. 1. The selection of participants. Figure 2. Association of VAT^ with hip fractures risk using restricted cubic splines. Figure 3. Association of VAT^ with vertebrae fractures risk using restricted cubic splines. Figure 4. Association of VAT^ with other fractures risk using restricted cubic splines.

Published
2022
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17. Additional file 3 of The causal associations of circulating amino acids with blood pressure: a Mendelian randomization study

Author

Lin, Chenhao, Sun, Zhonghan, Mei, Zhendong, Zeng, Hailuan, Zhao, Manying, Hu, Jianying, Xia, Mingfeng, Huang, Tao, Wang, Chaolong, Gao, Xin, and Zheng, Yan

Abstract

Additional file 3: Figure S1. Heatmap of Pearson correlation coefficients between circulating levels of amino acids in UK Biobank. Figure S2. Distribution of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in 286 390 European participants without metabolomic data from UK Biobank. Figure S3. Distribution of circulating levels of amino acids in 98 317 European participants with metabolomic data from UK Biobank. Figure S4. Estimated causal effects of circulating levels of amino acids on blood pressure and risk of hypertension. Figure S5. Scatter plots of SNPs used as instrumental variables for the MR analyses of circulating amino acids with (A) systolic blood pressure, (B) diastolic blood pressure and (C)hypertension. Figure S6. Leave-one-out plots to assess if a single SNP is driving the causal effects of circulating amino acids on systolic blood pressure (SBP). Figure S7. Leave-one-out plots to assess if a single SNP is driving the causal effects of circulating amino acids on diastolic blood pressure (DBP). Figure S8. Leave-one-out plots to assess if a single SNP is driving the causal effects of circulating amino acids on hypertension. Figure S9. Scatter plots of SNPs used as IVs for the reverse MR analyses of (A) systolic blood pressure, (B) diastolic blood pressure and (C) hypertension with circulating levels of amino acids.

Published
2022
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19. A Predictive Model Based on a New CI-AKI Definition to Predict Contrast Induced Nephropathy in Patients With Coronary Artery Disease With Relatively Normal Renal Function

Author

Mo, Hanjun, primary, Ye, Fang, additional, Chen, Danxia, additional, Wang, Qizhe, additional, Liu, Ru, additional, Zhang, Panpan, additional, Xu, Yaxin, additional, Cheng, Xuelin, additional, Mei, Zhendong, additional, Zheng, Yan, additional, Dai, Yuxiang, additional, Jiang, Sunfang, additional, and Ge, Junbo, additional

Published
2021
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25. Myocardial injury and COVID-19: Serum hs-cTnI level in risk stratification and the prediction of 30-day fatality in COVID-19 patients with no prior cardiovascular disease

Author

Cao, Jiatian, primary, Zheng, Yan, additional, Luo, Zhe, additional, Mei, Zhendong, additional, Yao, Yumeng, additional, Liu, Zilong, additional, Liang, Chao, additional, Yang, Hongbo, additional, Song, Yanan, additional, Yu, Kaihuan, additional, Gao, Yan, additional, Zhu, Chouwen, additional, Huang, Zheyong, additional, Qian, Juying, additional, and Ge, Junbo, additional

Published
2020
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27. Elevated serum urate is a potential factor in reduction of total bilirubin: a Mendelian randomization study

Author

Zhang, Hui, primary, Liu, Jing, additional, Dong, Zheng, additional, Ding, Yue, additional, Qian, Qiaoxia, additional, Zhou, Jingru, additional, Ma, Yanyun, additional, Mei, Zhendong, additional, Chen, Xiangxiang, additional, Li, Yuan, additional, Yuan, Ziyu, additional, Zhang, Juan, additional, Yang, Yajun, additional, Chen, Xingdong, additional, Jin, Li, additional, Zou, Hejian, additional, Wang, Xiaofeng, additional, and Wang, Jiucun, additional

Published
2017
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28. Mendelian randomization analysis does not support causal associations of birth weight with hypertension risk and blood pressure in adulthood

Author

George Dedoussis, Frédéric Fumeron, Zhonghan Sun, E-Shyong Tai, Bruce M. Psaty, Paul M. Ridker, Kim Overvad, Marcus E. Kleber, Wolfgang Lieb, Marju Orho-Melander, Xueling Sim, Philippe Froguel, Mark I. McCarthy, Tiange Wang, Diana van Heemst, Emily Sonestedt, Veit Grote, Karen L. Mohlke, Inke R. König, Tibor V. Varga, Chris Power, Matthias Munz, Ulrika Ericson, Luc Djoussé, Dariush Mozaffarian, Tuomas O. Kilpeläinen, Jorma Viikari, Ralph Burkhardt, Chao-Qiang Lai, Klaus Bønnelykke, Mariaelisa Graff, Woon-Puay Koh, Jin-Fang Chai, Nanette R. Lee, Harri Niinikoski, Torben Hansen, Christina-Alexandra Schulz, Caizheng Yu, Renée de Mutsert, Colin Boreham, Jean Dallongeville, Anne Tjønneland, Philippe Amouyel, Tao Zhang, Markus Scholz, Elina Hyppönen, Tarunveer S. Ahluwalia, Niina Pitkänen, Fernando Rivadeneira, Rebecca K. Vinding, Donna K. Arnett, Craig E. Pennell, Joachim Thiery, Jose M. Ordovas, Thorkild I. A. Sørensen, Lu Qi, Oscar Coltell, Daniel I. Chasman, Olli T. Raitakari, Preeti Gupta, Dariusz Gruszfeld, Rajkumar Dorajoo, Berthold Koletzko, Seang-Mei Saw, Tangchun Wu, Charumathi Sabanayagam, Rozenn N. Lemaitre, Meian He, Tao Huang, Carol A. Wang, Graciela E. Delgado, Rob M. van Dam, Luis A. Moreno, Peter Rossing, Mika Kähönen, Aline Meirhaeghe, Christina Ellervik, Frédéric Gottrand, Yan Zheng, Katherine L. Tucker, Ying Wu, Lavinia Paternoster, Lingyao Zeng, Joaquin Escribano, David A. Mackey, Raymond Noordam, Masato Akiyama, Dolores Corella, Thorsten Kessler, Frank J. A. van Rooij, Terho Lehtimäki, Jeanette Erdmann, Ang Zhou, Michiaki Kubo, Winfried März, Frits R. Rosendaal, Kari E. North, Cécile Lecoeur, Cornelia Enzenbach, Albert Hofman, Elvira Verduci, Tien Yin Wong, Hans Bisgaard, Zhendong Mei, Trudy Voortman, Yujie Wang, Heribert Schunkert, Beverley Balkau, Annette Peters, Dennis O. Mook-Kanamori, Vincent W. V. Jaddoe, Peter Rzehak, Christina Willenborg, Katja Pahkala, Jean-Paul Langhendries, Janine F. Felix, Angela C. Estampador, Ramon Estruch, Centre de recherche en épidémiologie et santé des populations (CESP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 (RID-AGE), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institute for Translational Research in Inflammation - U 1286 (INFINITE (Ex-Liric)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning National Basic Research Program of China (973 Program): 2016YFC1304801 2017SHZDZX01, This work was supported by Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01) and the National Key Research and Development Program of China (Grant No. 2016YFC1304801). YZ was supported by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning. For the funding information of each study within CHARGE-BIG consortium, please see Supplemental Table 8., CHU Lille, CNRS, Inserm, Université de Lille, Génomique Intégrative et Modélisation des Maladies Métaboliques (EGID) - UMR 8199, Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 [EGENODIA (GI3M)], Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement (RID-AGE) - U1167, Lille Inflammation Research International Center (LIRIC) - U995, Epidemiology, Erasmus MC other, Pediatrics, Internal Medicine, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Zheng, Yan, Huang, Tao, Wang, Tiange, Mei, Zhendong, Zhou, A, Hypponen, E, and Qi, L

Subjects
Adult, Male, medicine.medical_specialty, Epidemiology, Birth weight, [SDV]Life Sciences [q-bio], Birthweight, 030204 cardiovascular system & hematology, Polymorphism, Single Nucleotide, Article, Body Mass Index, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Mendelian randomization, medicine, Birth Weight, Humans, Hypertension, Blood pressure, Causal association, Genetic Predisposition to Disease, 030212 general & internal medicine, business.industry, Infant, Newborn, Mendelian Randomization Analysis, Odds ratio, Infant, Low Birth Weight, 3. Good health, Pulse pressure, Cohort, Female, business, Genome-Wide Association Study
Abstract

International audience; Epidemiology studies suggested that low birthweight was associated with a higher risk of hypertension in later life. However, little is known about the causality of such associations. In our study, we evaluated the causal association of low birthweight with adulthood hypertension following a standard analytic protocol using the study-level data of 183,433 participants from 60 studies (CHARGE-BIG consortium), as well as that with blood pressure using publicly available summary-level genome-wide association data from EGG consortium of 153,781 participants, ICBP consortium and UK Biobank cohort together of 757,601 participants. We used seven SNPs as the instrumental variable in the study-level analysis and 47 SNPs in the summary-level analysis. In the study-level analyses, decreased birthweight was associated with a higher risk of hypertension in adults (the odds ratio per 1 standard deviation (SD) lower birthweight, 1.22; 95% CI 1.16 to 1.28), while no association was found between genetically instrumented birthweight and hypertension risk (instrumental odds ratio for causal effect per 1 SD lower birthweight, 0.97; 95% CI 0.68 to 1.41). Such results were consistent with that from the summary-level analyses, where the genetically determined low birthweight was not associated with blood pressure measurements either. One SD lower genetically determined birthweight was not associated with systolic blood pressure (β = − 0.76, 95% CI − 2.45 to 1.08 mmHg), 0.06 mmHg lower diastolic blood pressure (β = − 0.06, 95% CI − 0.93 to 0.87 mmHg), or pulse pressure (β = − 0.65, 95% CI − 1.38 to 0.69 mmHg, all p > 0.05). Our findings suggest that the inverse association of birthweight with hypertension risk from observational studies was not supported by large Mendelian randomization analyses.

Published
2020

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