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Yang J, Luo S, Liu Y, Hong M, Qiu X, Lin Y, Zhang W, Gao P, Li Z, Hu Z, Xia M. Cohort Profile: South China Cohort. Int J Epidemiol 2024; 53:dyae028. [PMID: 38412541 DOI: 10.1093/ije/dyae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 02/06/2024] [Indexed: 02/29/2024] Open
Affiliation(s)
- Jialu Yang
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Shiyun Luo
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yan Liu
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Minghuang Hong
- Department of Clinical Trial Centre, Sun Yat-sen University Cancer Centre, Guangzhou, China
| | - Xiaoqiang Qiu
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, China
| | - Yingzi Lin
- School of Tropical Medicine, Hainan Medical University, Haikou, China
| | - Weisen Zhang
- Molecular Epidemiology Research Centre, Guangzhou Twelfth People's Hospital, Guangzhou, China
| | - Peisong Gao
- Luohu People's Hospital, Shen Zhen Luohu Hospital Group, Shenzhen, China
| | - Zhibin Li
- First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Zhijian Hu
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Min Xia
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
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Jing H, Teng Y, Chacha S, Wang Z, Shi G, Mi B, Zhang B, Cai J, Liu Y, Li Q, Shen Y, Yang J, Kang Y, Li S, Liu D, Wang D, Yan H, Dang S. Is Increasing Diet Diversity of Animal-Source Foods Related to Better Health-Related Quality of Life among Chinese Men and Women? Nutrients 2023; 15:4183. [PMID: 37836467 PMCID: PMC10574670 DOI: 10.3390/nu15194183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Diet plays a crucial role in regulating individuals' lifestyles and is closely related to health. The intake of animal-sourced foods (ASF) provides the human body with high-quality protein and various micronutrients. This study aimed to investigate whether the diversity of animal foods has a positive impact on the health-related quality of life (HRQoL) among residents. The data came from the Shaanxi baseline survey of the Northwest Chinese Regional Ethnic Cohort Study, which recruited more than 100 thousand participants aged 35 to 74 from five provinces between June 2018 and May 2019. A total of 39,997 participants in Shaanxi (mean age: 50 years; 64% women) were finally included in this current study. The animal source food diet diversity score (ASFDDS) was established based on the frequency of consuming pork, mutton, beef, poultry, seafood, eggs, pure milk, and yogurt. The physical component score (PCS) and mental component score (MCS), ranging from 0 to 100 on the 12-Item Short Form Survey (SF-12), were used to assess participants' HRQoL. Better PCS/MCS was defined as scores higher than the 90th percentile. The results showed that men had a higher intake of ASF and ASFDDS than women. After adjusting for potential confounders, compared with those who never or rarely consumed animal foods, the likelihood of having better PCS and MCS increased by 16% (OR = 1.16, 95%CI: 1.01-1.34) and 24% (OR = 1.24, 95%CI: 1.03-1.448), respectively, in men with an ASFDDS ≥ 2. In women, a 34% increase (OR = l.34, 95%CI: 116-l.54) likelihood for better PCS was observed for an ASFDDS ≥ 2, but no association was observed for MCS. Increasing each specific animal source's food intake was associated with better PCS after adjusting for all covariates. However, for MCS, positive associations were only observed in seafood consumption among men and eggs among women. Restricted cubic splines showed a substantial dose-response association between intake frequency of animal-source foods and PCS, both in men and women. The study suggests that a diverse intake of animal-sourced foods can potentially improve the HRQoL of Chinese adults.
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Affiliation(s)
- Hui Jing
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Yuxin Teng
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Samuel Chacha
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Ziping Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Guoshuai Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Baibing Mi
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Binyan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Jiaxin Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Yezhou Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Qiang Li
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Yuan Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Jiaomei Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Yijun Kang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Shanshan Li
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Danmeng Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Duolao Wang
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool L7 8XZ, UK;
| | - Hong Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
| | - Shaonong Dang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; (H.J.); (Y.T.); (S.C.); (Z.W.); (G.S.); (B.M.); (B.Z.); (J.C.); (Y.L.); (Q.L.); (Y.S.); (J.Y.); (Y.K.); (S.L.); (D.L.)
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Liu J, Cao S, Huo Y, Liu H, Wang Y, Zhang B, Xu K, Yang P, Zeng L, Dang S, Yan H, Mi B. Association of sleep behavior with depression: a cross-sectional study in northwestern China. Front Psychiatry 2023; 14:1171310. [PMID: 37426097 PMCID: PMC10327479 DOI: 10.3389/fpsyt.2023.1171310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023] Open
Abstract
Background This study aimed to examine the association between sleep duration, sleep problems, and depression in Northwest China. Method Depression was diagnosed at the hospital and self-reported by the participants in the baseline survey. Sleep duration and problems, including difficulty initiating and maintaining sleep, early morning awakening, daytime dysfunction, use of sleeping pills or drugs, and any sleep problems, were obtained by a self-reported questionnaire. Logistic regression was used to estimate odds ratios (ORs) with corresponding 95% confidence intervals (CIs) for exploring the association between sleep duration, sleep problems, and depression, adjusting for demographic and socioeconomic characteristics and health behaviors. The association between depression and sleep duration was also evaluated continuously with restricted cubic spline curves based on logistic models. Results 36,515 adults from Regional Ethnic Cohort Study in Northwest China were included. About 24.04% of participants reported short sleep duration (<7 h), and 15.64% reported long sleep duration (≥9 h). Compared with standard sleep duration (7-9 h), short sleep duration was associated with a higher risk of depression (OR: 1.69, 95%CI: 1.26-2.27, p = 0.001). Self-reported sleep problems were also related to four times depression risk increased (OR: 4.02, 95%CI: 3.03-5.35, p < 0.001) compared with no sleep problems. In addition, a nonlinear relationship was found between sleep duration and depression after adjusting covariates (p = 0.043). Conclusion Sleep duration and sleep problems are associated with depression. Enough sleep time and healthy sleep habits in life course might be a practical health promotion approach to reduce depression risk in Northwest Chinese adults. A further study from cohort study is needed to verify the temporal association.
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Affiliation(s)
- Jingchun Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Suixia Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Yating Huo
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Huimeng Liu
- Department of Occupational and Environmental Health, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Binyan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Kun Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Peiying Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Lingxia Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Shaonong Dang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Hong Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Baibing Mi
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
- Shaanxi Open Sharing Platform of Critical Disease Prevention and Big Health Data Science, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
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Yan T, Shi L, Xu K, Bai J, Wen R, Liao X, Dai X, Wu Q, Zeng L, Peng W, Wang Y, Yan H, Dang S, Liu X. Habitual intakes of sugar-sweetened beverages associated with gut microbiota-related metabolites and metabolic health outcomes in young Chinese adults. Nutr Metab Cardiovasc Dis 2023; 33:359-368. [PMID: 36577637 DOI: 10.1016/j.numecd.2022.10.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 10/05/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND AND AIMS Reducing consumption of sugar-sweetened beverages (SSBs) is a global public health priority because of their limited nutritional value and associations with increased risk of obesity and metabolic diseases. Gut microbiota-related metabolites emerged as quintessential effectors that may mediate impacts of dietary exposures on the modulation of host commensal microbiome and physiological status. METHODS AND RESULTS This study assessed the associations among SSBs, circulating microbial metabolites, and gut microbiota-host co-metabolites, as well as metabolic health outcomes in young Chinese adults (n = 86), from the Carbohydrate Alternatives and Metabolic Phenotypes study in Shaanxi Province. Five principal component analysis-derived beverage drinking patterns were determined on self-reported SSB intakes, which were to a varying degree associated with 143 plasma levels of gut microbiota-related metabolites profiled by untargeted metabolomics. Moreover, carbonated beverages, fruit juice, energy drinks, and bubble tea exhibited positive associations with obesity-related markers and blood lipids, which were further validated in an independent cohort of 16,851 participants from the Regional Ethnic Cohort Study in Northwest China in Shaanxi Province. In contrast, presweetened coffee was negatively associated with the obesity-related traits. A total of 79 metabolites were associated with both SSBs and metabolic markers, particularly obesity markers. Pathway enrichment analysis identified the branched-chain amino acid catabolism and aminoacyl-tRNA biosynthesis as linking SSB intake with metabolic health outcomes. CONCLUSION Our findings demonstrate the associations between habitual intakes of SSBs and several metabolic markers relevant to noncommunicable diseases, and highlight the critical involvement of gut microbiota-related metabolites in mediating such associations.
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Affiliation(s)
- Tao Yan
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi' an, Shaanxi, China
| | - Lin Shi
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi' an, Shaanxi, China; Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Kun Xu
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Jinyu Bai
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi' an, Shaanxi, China
| | - Ruixue Wen
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi' an, Shaanxi, China
| | - Xia Liao
- Department of Nutrition, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaoshuang Dai
- BGI Institute of Applied Agriculture, BGI-Agro, Shenzhen, China
| | - Qian Wu
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Lingxia Zeng
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Wen Peng
- Nutrition and Health Promotion Center, Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, China
| | - Youfa Wang
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China; School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Hong Yan
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Shaonong Dang
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
| | - Xin Liu
- Global Health Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
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Staple Food Preference and Obesity Phenotypes: The Regional Ethnic Cohort Study in Northwest China. Nutrients 2022; 14:nu14245243. [PMID: 36558402 PMCID: PMC9784345 DOI: 10.3390/nu14245243] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Staple food preference vary in populations, but evidence of its associations with obesity phenotypes are limited. Using baseline data (n = 105,840) of the Regional Ethnic Cohort Study in Northwest China, staple food preference was defined according to the intake frequency of rice and wheat. Overall and specifically abdominal fat accumulation were determined by excessive body fat percentage and waist circumference. Logistic regression and equal frequency substitution methods were used to evaluate the associations. We observed rice preference (consuming rice more frequently than wheat; 7.84% for men and 8.28% for women) was associated with a lower risk of excessive body fat (OR, 0.743; 95%CI, 0.669-0.826) and central obesity (OR, 0.886; 95%CI, 0.807-0.971) in men; and with lower risk of central obesity (OR, 0.898; 95%CI, 0.836-0.964) in women, compared with their wheat preference counterparties. Furthermore, similar but stronger inverse associations were observed in participants with normal body mass index. Wheat-to-rice (5 times/week) reallocations were associated with a 36.5% lower risk of normal-weight obesity in men and a 20.5% lower risk of normal-weight central obesity in women. Our data suggest that, compared with wheat, rice preference could be associated with lower odds ratios of certain obesity phenotypes in the Northwest Chinese population.
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Preference for Stronger Taste Associated with a Higher Risk of Hypertension: Evidence from a Cross-Sectional Study in Northwest China. Int J Hypertens 2022; 2022:6055940. [DOI: 10.1155/2022/6055940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/18/2022] [Accepted: 10/29/2022] [Indexed: 11/24/2022] Open
Abstract
Background. Dietary modulation is a primary lifestyle approach for reducing the risk of hypertension. However, evidence of the potential role that a dietary taste preference plays in the risk of hypertension remains limited. Methods. A cross-sectional analysis was conducted based on the Shaanxi baseline survey of the Regional Ethnic Cohort Study. We used self-reported salt consumption and intensity preferences for sourness and spiciness to calculate the taste preference score, which was categorized into bland, moderate, and strong. A generalized linear mixed model and quantile regression were performed to estimate associations between taste preferences and hypertension/blood pressure. Results. Among 27,233 adults, 72.2% preferred a moderate taste and 21.4% preferred a strong taste. Compared with a bland taste, a stronger taste preference might be associated with a higher risk of hypertension (adjusted OR for a moderate taste = 1.25, 95% CI: 1.06, 1.49; adjusted OR for a strong taste = 1.41, 95% CI: 1.15, 1.71; Ptrend = 0.002), especially in females (adjusted OR for a moderate taste = 1.43, 95% CI: 1.24, 1.66; adjusted OR for a strong taste = 1.55, 95% CI: 1.32, 1.83;
). Quantile regression showed that the taste preference was positively associated with diastolic blood pressure (DBP) (P5-P80) in females, with an average increase of 3.31 mmHg for a strong taste (β = 3.31,
) and 1.77 mmHg for a moderate taste (β = 1.77, P = 0.008). Conclusions. A preference for stronger multitastes of salty, sour, and spicy might be associated with a higher risk of hypertension, especially in females. This relationship possibly occurs through increasing DBP. Dietary modulation with the promotion of a bland taste is encouraged.
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Wang Y, Liu H, He D, Zhang B, Liu Y, Xu K, Cao S, Huo Y, Liu J, Zeng L, Yan H, Dang S, Mi B. Association between physical activity and major adverse cardiovascular events in northwest China: A cross-sectional analysis from the Regional Ethnic Cohort Study. Front Public Health 2022; 10:1025670. [PMID: 36466532 PMCID: PMC9713839 DOI: 10.3389/fpubh.2022.1025670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022] Open
Abstract
Background To examine the association between daily physical activity (PA) and major adverse cardiovascular events (MACEs) in northwest China. Methods The data in this analysis were part of the baseline survey of the Regional Ethnic Cohort Study in Northwest China from June 2018 to May 2019 in Shaanxi Province. This study used standardized self-reported total physical activity (continuous and categorical variables) and self-reported outcomes of MACEs. All analyses were conducted using the logistic regression model and stratified by age, sex, body mass index (BMI), and region. The dose-response relationships were assessed with a restricted cubic spline. Results The average level of total PA was 17.60 MET hours per day (MET-h/d). Every increase of four MET-h/d of total PA was associated with a lower risk of MACEs [adjusted OR = 0.95 (95% CI, 0.93~0.98)]. Compared with participants in the bottom quartile of total PA, a lower risk of MACEs was observed in the top quartile group [≥23.3 MET-h/d, 0.68 (0.55~0.83)]. Stratified analyses showed similar results in males, females, participants over 45 years old, participants in the rural region, and normal weight range participants (BMI < 24 kg/m2). Total participants also observed a dose-response relationship after adjusting for socioeconomic and lifestyle factors. Conclusions A higher level of PA was associated with a lower MACE risk. Future research should examine the longitudinal association of prospectively measured PA and the risk of MACEs.
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Chang Q, Wu Q, Xia Y, Zhang H, Gao S, Zhang Y, Yuan Y, Jiang J, Qiu H, Li J, Lu C, Ji C, Xu X, Huang D, Dai H, Zhao Z, Li H, Li X, Qin X, Liu C, Ma X, Xu X, Yao D, Zhao Y. Cohort Profile: The Northeast China Biobank (NEC-Biobank). Int J Epidemiol 2022; 52:e125-e136. [PMID: 36018264 DOI: 10.1093/ije/dyac172] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Qing Chang
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qijun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Xia
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hehua Zhang
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Shanyan Gao
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yixiao Zhang
- Department of Urology Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yuan Yuan
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, First Hospital of China Medical University, Shenyang, China
| | - Jing Jiang
- Division of Clinical Research, First Hospital of Jilin University, Changchun, China
| | - Hongbin Qiu
- Department of Epidemiology and Biostatistics, School of Public Health, Jiamusi University, Heilongjiang, China
| | - Jing Li
- Department of Endocrinology and Metabolism and Institute of Endocrinology, NHC Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, First Hospital of China Medical University, Shenyang, China
| | - Chunming Lu
- Liaoning Provincial Center for Disease Control and Prevention, Shenyang, China
| | - Chao Ji
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xin Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Donghui Huang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Huixu Dai
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zhiying Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hang Li
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoying Li
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaosong Qin
- Department of Clinical Laboratory, Shengjing Hospital of China Medical University, Shenyang, China
| | - Caigang Liu
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoyu Ma
- Ethics Committee, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xinrui Xu
- Department of Human Resource, Shengjing Hospital of China Medical University, Shenyang, China
| | - Da Yao
- Department of Post-Graduation Training, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yuhong Zhao
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China.,Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
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