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Miao Z, Du W, Xiao C, Su C, Gou W, Shen L, Zhang J, Fu Y, Jiang Z, Wang Z, Jia X, Zheng JS, Wang H. Gut microbiota signatures of long-term and short-term plant-based dietary pattern and cardiometabolic health: a prospective cohort study. BMC Med 2022; 20:204. [PMID: 35701845 PMCID: PMC9199182 DOI: 10.1186/s12916-022-02402-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 05/11/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The interplay among the plant-based dietary pattern, gut microbiota, and cardiometabolic health is still unclear, and evidence from large prospective cohorts is rare. We aimed to examine the association of long-term and short-term plant-based dietary patterns with gut microbiota and to assess the prospective association of the identified microbial features with cardiometabolic biomarkers. METHODS Using a population-based prospective cohort study: the China Health and Nutrition Survey, we included 3096 participants from 15 provinces/megacities across China. We created an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI). The average PDIs were calculated using repeat food frequency questionnaires collected in 2011 and 2015 to represent a long-term dietary pattern. Short-term dietary pattern was estimated using 3-day 24-h dietary recalls collected in 2015. Fecal samples were collected in 2015 and measured using 16S rRNA sequencing. We investigated the association of long-term and short-term plant-based dietary patterns with gut microbial diversity, taxonomies, and functional pathways using linear mixed models. Furthermore, we assessed the prospective associations between the identified gut microbiome signatures and cardiometabolic biomarkers (measured in 2018) using linear regression. RESULTS We found a significant association of short-term hPDI with microbial alpha-diversity. Both long-term and short-term plant-based diet indices were correlated with microbial overall structure, whereas long-term estimates explained more variance. Long-term and short-term PDIs were differently associated with microbial taxonomic composition, yet only microbes related to long-term estimates showed association with future cardiometabolic biomarkers. Higher long-term PDI was associated with the lower relative abundance of Peptostreptococcus, while this microbe was positively correlated with the high-sensitivity C-reactive protein and inversely associated with high-density lipoprotein cholesterol. CONCLUSIONS We found shared and distinct gut microbial signatures of long-term and short-term plant-based dietary patterns. The identified microbial genera may provide insights into the protective role of long-term plant-based dietary pattern for cardiometabolic health, and replication in large independent cohorts is needed.
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Affiliation(s)
- Zelei Miao
- College of Life Sciences, Zhejiang University, Hangzhou, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China
| | - Wenwen Du
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
- Key Laboratory of Trace Element Nutrition, National Health Commission, Beijing, China
| | - Congmei Xiao
- College of Life Sciences, Zhejiang University, Hangzhou, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Chang Su
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
- Key Laboratory of Trace Element Nutrition, National Health Commission, Beijing, China
| | - Wanglong Gou
- College of Life Sciences, Zhejiang University, Hangzhou, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Luqi Shen
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jiguo Zhang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
- Key Laboratory of Trace Element Nutrition, National Health Commission, Beijing, China
| | - Yuanqing Fu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Zengliang Jiang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Zhihong Wang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
- Key Laboratory of Trace Element Nutrition, National Health Commission, Beijing, China
| | - Xiaofang Jia
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
- Key Laboratory of Trace Element Nutrition, National Health Commission, Beijing, China
| | - Ju-Sheng Zheng
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China.
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
| | - Huijun Wang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China.
- Key Laboratory of Trace Element Nutrition, National Health Commission, Beijing, China.
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Shuai M, Zhang G, Zeng F, Fu Y, Liang X, Yuan L, Xu F, Gou W, Miao Z, Jiang Z, Wang J, Zhuo L, Chen Y, Ju F, Zheng J. Human Gut Antibiotic Resistome and Progression of Diabetes. Adv Sci (Weinh) 2022; 9:e2104965. [PMID: 35142450 PMCID: PMC9008416 DOI: 10.1002/advs.202104965] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/13/2022] [Indexed: 05/02/2023]
Abstract
The antibiotic resistance crisis underlies globally increasing failures in treating deadly bacterial infections, largely due to the selection of antibiotic resistance genes (ARG) collection, known as the resistome, in human gut microbiota. So far, little is known about the relationship between gut antibiotic resistome and host metabolic disorders such as type 2 diabetes (T2D). Here, metagenomic landscape of gut antibiotic resistome is profiled in a large multiomics human cohort (n = 1210). There is a significant overall shift in gut antibiotic resistome structure among healthy, prediabetes, and T2D groups. It is found that larger ARG diversity is associated with a higher risk of T2D. The novel diabetes ARG score is positively associated with glycemic traits. Longitudinal validation analysis confirms that the ARG score is associated with T2D progression, characterized by the change of insulin resistance. Collectively, the data describe the profiles of gut antibiotic resistome and support its close relationship with T2D progression.
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Affiliation(s)
- Menglei Shuai
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
| | - Guoqing Zhang
- Key Laboratory of Coastal Environment and Resources of Zhejiang ProvinceSchool of EngineeringWestlake UniversityHangzhou310030China
- Institute of Advanced TechnologyWestlake Institute for Advanced StudyHangzhou310024China
| | - Fang‐fang Zeng
- Guangdong Provincial Key Laboratory of FoodNutrition and HealthDepartment of EpidemiologySchool of Public HealthSun Yat‐sen UniversityGuangzhou510275China
- Department of EpidemiologySchool of MedicineJinan UniversityGuangzhou510632China
| | - Yuanqing Fu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
- Institute of Basic Medical SciencesWestlake Institute for Advanced StudyHangzhou310024China
| | - Xinxiu Liang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
| | - Ling Yuan
- Key Laboratory of Coastal Environment and Resources of Zhejiang ProvinceSchool of EngineeringWestlake UniversityHangzhou310030China
- Institute of Advanced TechnologyWestlake Institute for Advanced StudyHangzhou310024China
| | - Fengzhe Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
| | - Wanglong Gou
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
| | - Zelei Miao
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
| | - Zengliang Jiang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
- Institute of Basic Medical SciencesWestlake Institute for Advanced StudyHangzhou310024China
| | - Jia‐ting Wang
- Guangdong Provincial Key Laboratory of FoodNutrition and HealthDepartment of EpidemiologySchool of Public HealthSun Yat‐sen UniversityGuangzhou510275China
| | - Lai‐bao Zhuo
- Guangdong Provincial Key Laboratory of FoodNutrition and HealthDepartment of EpidemiologySchool of Public HealthSun Yat‐sen UniversityGuangzhou510275China
| | - Yu‐ming Chen
- Guangdong Provincial Key Laboratory of FoodNutrition and HealthDepartment of EpidemiologySchool of Public HealthSun Yat‐sen UniversityGuangzhou510275China
| | - Feng Ju
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
- Key Laboratory of Coastal Environment and Resources of Zhejiang ProvinceSchool of EngineeringWestlake UniversityHangzhou310030China
- Institute of Advanced TechnologyWestlake Institute for Advanced StudyHangzhou310024China
| | - Ju‐Sheng Zheng
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou310030China
- Westlake Intelligent Biomarker Discovery LabWestlake Laboratory of Life Sciences and BiomedicineHangzhou310024China
- Institute of Basic Medical SciencesWestlake Institute for Advanced StudyHangzhou310024China
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Fu Y, Xu F, Jiang L, Miao Z, Liang X, Yang J, Larsson SC, Zheng JS. Circulating vitamin C concentration and risk of cancers: a Mendelian randomization study. BMC Med 2021; 19:171. [PMID: 34325683 PMCID: PMC8323227 DOI: 10.1186/s12916-021-02041-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/21/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Circulating vitamin C concentrations have been associated with several cancers in observational studies, but little is known about the causal direction of the associations. This study aims to explore the potential causal relationship between circulating vitamin C and risk of five most common cancers in Europe. METHODS We used summary-level data for genetic variants associated with plasma vitamin C in a large vitamin C genome-wide association study (GWAS) meta-analysis on 52,018 Europeans, and the corresponding associations with lung, breast, prostate, colon, and rectal cancer from GWAS consortia including up to 870,984 participants of European ancestry. We performed two-sample, bi-directional Mendelian randomization (MR) analyses using inverse-variance-weighted method as the primary approach, while using 6 additional methods (e.g., MR-Egger, weighted median-based, and mode-based methods) as sensitivity analysis to detect and adjust for pleiotropy. We also conducted a meta-analysis of prospective cohort studies and randomized controlled trials to examine the association of vitamin C intakes with cancer outcomes. RESULTS The MR analysis showed no evidence of a causal association of circulating vitamin C concentration with any examined cancer. Although the odds ratio (OR) per one standard deviation increase in genetically predicted circulating vitamin C concentration was 1.34 (95% confidence interval 1.14 to 1.57) for breast cancer in the UK Biobank, this association could not be replicated in the Breast Cancer Association Consortium with an OR of 1.05 (0.94 to 1.17). Smoking initiation, as a positive control for our reverse MR analysis, showed a negative association with circulating vitamin C concentration. However, there was no strong evidence of a causal association of any examined cancer with circulating vitamin C. Sensitivity analysis using 6 different analytical approaches yielded similar results. Moreover, our MR results were consistent with the null findings from the meta-analysis exploring prospective associations of dietary or supplemental vitamin C intakes with cancer risk, except that higher dietary vitamin C intake, but not vitamin C supplement, was associated with a lower risk of lung cancer (risk ratio: 0.84, 95% confidence interval 0.71 to 0.99). CONCLUSIONS These findings provide no evidence to support that physiological-level circulating vitamin C has a large effect on risk of the five most common cancers in European populations, but we cannot rule out very small effect sizes.
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Affiliation(s)
- Yuanqing Fu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Fengzhe Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Zelei Miao
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Xinxiu Liang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Jian Yang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Ju-Sheng Zheng
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
- Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
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Tian Y, Ma Y, Fu Y, Zheng JS. Application of n-of-1 Clinical Trials in Personalized Nutrition Research: A Trial Protocol for Westlake N-of-1 Trials for Macronutrient Intake (WE-MACNUTR). Curr Dev Nutr 2020; 4:nzaa143. [PMID: 32968703 PMCID: PMC7494402 DOI: 10.1093/cdn/nzaa143] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/24/2020] [Accepted: 08/24/2020] [Indexed: 12/13/2022] Open
Abstract
Personalized dietary recommendations can help with more effective disease prevention. This study aims to investigate the individual postprandial glucose response to diets with diverse macronutrient proportions at both the individual level and population level, and explore the potential of the novel single-patient (n-of-1) trial for personalization of diet. Secondary outcomes include individual phenotypic responses and the effects of dietary ingredients on the composition of gut microbiota. Westlake N-of-1 Trials for Macronutrient Intake is a multiple crossover feeding trial consisting of 3 successive 12-d dietary intervention pairs including a 6-d washout period before each 6-d isocaloric dietary intervention: a 6-d high-fat, low-carbohydrate diet, and a 6-d low-fat, high-carbohydrate diet. The results will help provide personalized dietary recommendations for macronutrients in terms of postprandial blood glucose responses. The proposed n-of-1 trial methods could help in optimizing individual health and advancing health care. This trial was registered with clinicaltrials.gov (NCT04125602).
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Affiliation(s)
- Yunyi Tian
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Yue Ma
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Yuanqing Fu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Ju-Sheng Zheng
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
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