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Ling CW, Zhong H, Zeng FF, Chen G, Fu Y, Wang C, Zhang ZQ, Cao WT, Sun TY, Ding D, Liu YH, Dong HL, Jing LP, Ling W, Zheng JS, Chen YM. Cohort Profile: Guangzhou Nutrition and Health Study (GNHS): A Population-based Multi-omics Study. J Epidemiol 2024; 34:301-306. [PMID: 37813622 PMCID: PMC11078596 DOI: 10.2188/jea.je20230108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND The Guangzhou Nutrition and Health Study (GNHS) aims to assess the determinants of metabolic disease in nutritional aspects, as well as other environmental and genetic factors, and explore possible biomarkers and mechanisms with multi-omics integration. METHODS The population-based sample of adults in Guangzhou, China (baseline: 40-83 years old; n = 5,118) was followed up about every 3 years. All are tracked via on-site follow-up and health information systems. We assessed detailed information on lifestyle factors, physical activities, dietary assessments, psychological health, cognitive function, body measurements, and muscle function. Instrument tests included dual-energy X-ray absorptiometry scanning, carotid artery and liver ultrasonography evaluations, vascular endothelial function evaluation, upper-abdomen and brain magnetic resonance imaging, and 14-day real-time continuous glucose monitoring tests. We also measured multi-omics, including host genome-wide genotyping, serum metabolome and proteome, gut microbiome (16S rRNA sequencing, metagenome, and internal transcribed spacer 2 sequencing), and fecal metabolome and proteome. RESULTS The baseline surveys were conducted from 2008 to 2015. Now, we have completed 3 waves. The 3rd and 4th follow-ups have started but have yet to end. A total of 5,118 participants aged 40-83 took part in the study. The median age at baseline was approximately 59.0 years and the proportion of female participants was about 69.4%. Among all the participants, 3,628 (71%) completed at least one on-site follow-up, with a median duration of 9.48 years. CONCLUSION The cohort will provide data that will be influential in establishing the role of nutrition in metabolic diseases with multi-omics.
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Affiliation(s)
- Chu-Wen Ling
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University
| | - Haili Zhong
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University
| | - Fang-Fang Zeng
- Department of Epidemiology, School of Medicine, Jinan University
| | - Gengdong Chen
- Department of Obstetrics, Foshan Institute of Fetal Medicine, Southern Medical University Affiliated Maternal & Child Health Hospital of Foshan
| | - Yuanqing Fu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University
| | - Cheng Wang
- Department of Clinical Nutrition, Sun Yat-sen Memorial Hospital
| | - Zhe-Qing Zhang
- Department of Nutrition and Food Hygiene, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University
| | - Wen-Ting Cao
- International School of Public Health and One Health, Hainan Medical University
| | - Ting-Yu Sun
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University
| | - Ding Ding
- Global Health Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences
| | - Yan-Hua Liu
- Department of Nutrition, the First Affiliated Hospital of Zhengzhou University
| | - Hong-Li Dong
- Scientific Education Section and Department of Child Healthcare, Affiliated Maternity & Child Health Care Hospital of Nantong University
| | - Li-Peng Jing
- Department of Epidemiology, School of Public Health, Lanzhou University
| | - Wenhua Ling
- Department of Nutrition, School of Public Health, Sun Yat-sen University
| | - Ju-Sheng Zheng
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University
| | - Yu-Ming Chen
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University
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Gao X, Tian Z, Zhao D, Li K, Zhao Y, Xu L, Wang X, Fan D, Ma X, Ling W, Meng H, Yang Y. Associations between Adherence to Four A Priori Dietary Indexes and Cardiometabolic Risk Factors among Hyperlipidemic Patients. Nutrients 2021; 13:2179. [PMID: 34202823 PMCID: PMC8308401 DOI: 10.3390/nu13072179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/17/2021] [Accepted: 06/22/2021] [Indexed: 01/25/2023] Open
Abstract
Little is known about which currently available a priori dietary indexes provide best guidance for reducing cardiometabolic risk factors (CMRF) among hyperlipidemic patients. This study was designed to compare the associations between four a priori dietary indexes, including Diet Balance Index (DBI-16), Chinese Healthy Eating Index (CHEI), Mediterranean Diet Score (MDS) and Dietary Approaches to Stop Hypertension (DASH) and CMRF among hyperlipidemic patients. A total of 269 participants were enrolled into the cross-sectional study. DBI-16, CHEI, MDS, and DASH scores were calculated using established methods. CMRF was measured using standard methods. DBI-total scores (DBI-TS) were inversely associated with triglyceride concentrations and TC:HDL-C ratio, and positively associated with HDL-C and ApoA1 concentrations (all p < 0.05), while the results for DBI-low bound scores (DBI-LBS) were opposite. DBI-high bound scores (DBI-HBS) and DASH scores were positively and inversely associated with glucose concentrations, respectively (both p < 0.05). Higher diet quality distance (DQD) was positively associated with higher TC, LDL-C and ApoB concentrations, and TC:HDL-C and LDL-C:HDL-C ratios, and lower HDL-C and ApoA1 concentrations and ApoA1:ApoB ratio (all p < 0.05). CHEI scores were inversely associated with triglyceride concentrations (p = 0.036). None of the dietary indexes was associated with blood pressures. DBI-16 provided most comprehensive evaluations of the overall diet quality and balance for optimizing cardiometabolic health among hyperlipidemic individuals.
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Affiliation(s)
- Xiaoli Gao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Zezhong Tian
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Dan Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Kongyao Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Yimin Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Lin Xu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Xu Wang
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; (X.W.); (D.F.)
| | - Die Fan
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; (X.W.); (D.F.)
| | - Xilin Ma
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Wenhua Ling
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; (X.W.); (D.F.)
| | - Huicui Meng
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
| | - Yan Yang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518106, China; (X.G.); (Z.T.); (D.Z.); (K.L.); (Y.Z.); (L.X.); (X.M.); (H.M.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China;
- Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, China
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Sun L, Zong G, Li H, Lin X. Fatty acids and cardiometabolic health: a review of studies in Chinese populations. Eur J Clin Nutr 2020; 75:253-266. [PMID: 32801302 DOI: 10.1038/s41430-020-00709-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 06/19/2020] [Accepted: 08/04/2020] [Indexed: 11/09/2022]
Abstract
Rapid nutrition transition from plant-based traditional diet to westernized diet has led to dramatically heightening burdens of cardiometabolic diseases in China in past decades. Recently, national surveys reported that poor dietary quality including low marine n-3 fatty acids and high intakes of red meat and processed meat was associated with considerably elevated cardiometabolic deaths. Previous studies mainly from Western population-based cohorts have indicated that not only fat quantity but also quality linked with different cardiometabolic outcomes. Compared with Western peoples, Asian peoples, including Chinese, are known to have different dietary patterns and lifestyle, as well as genetic heterogeneities, which may modify fatty acid metabolism and disease susceptibility in certain degree. To date, there were limited prospective studies investigating the relationships between fatty acids and cardiometabolic disease outcomes in Chinese, and most existing studies were cross-sectional nature and within one or two region(s). Notably, shifting dietary patterns could change not only amount, types, and ratio of fatty acids accounting for overall energy intake, but also their food sources and ratio to other macronutrients. Moreover, large geographic and urban-rural variations in prevalence of cardiometabolic diseases among Chinese may also reflect the effects of socioeconomic development and local diets on health status. Therefore, current review will summarize available literatures with more focus on the Chinese-based studies which may extend current knowledge about the roles of fatty acids in pathogenesis of cardiometabolic diseases for Asian populations and also provide useful information for trans-ethnic comparisons with other populations.
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Affiliation(s)
- Liang Sun
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Geng Zong
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Huaixing Li
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xu Lin
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China. .,Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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