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      Characteristics of spicy food consumption and its relation to lifestyle behaviours: results from 0.5 million adults

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          Abstract

          <p id="d8488928e377">This study aimed to describe the characteristics and lifestyle differences of spicy food consumption in 0.5 million adults. Participants were recruited from 2004 to 2008 in the baseline research of the CKB study. Higher frequency and stronger pungency degree in spicy food positively correlated with preference for salty taste, eating snacks/deep-fried foods, tea/alcohol drinking and tobacco smoking. Among weekly tea/alcohol drinkers and current regular smokers, participants with a higher frequency of spicy food consumption or preference for stronger pungency degree were more likely to prefer strong tea, drink alcohol exceed the healthy amount, drink alcohol in the morning every day, smoke ≥ 40 cigarettes per day, consume a larger amount of tea leaves, alcohol and cigarettes each day, and start habitual tea/alcohol drinking or smoking at an earlier age. Differences existed in lifestyle factors related to major chronic diseases according to spicy food consumption frequency and pungency degree among the Chinese population. </p>

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          Most cited references26

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          China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up.

          Large blood-based prospective studies can provide reliable assessment of the complex interplay of lifestyle, environmental and genetic factors as determinants of chronic disease. The baseline survey of the China Kadoorie Biobank took place during 2004-08 in 10 geographically defined regions, with collection of questionnaire data, physical measurements and blood samples. Subsequently, a re-survey of 25,000 randomly selected participants was done (80% responded) using the same methods as in the baseline. All participants are being followed for cause-specific mortality and morbidity, and for any hospital admission through linkages with registries and health insurance (HI) databases. Overall, 512,891 adults aged 30-79 years were recruited, including 41% men, 56% from rural areas and mean age was 52 years. The prevalence of ever-regular smoking was 74% in men and 3% in women. The mean blood pressure was 132/79 mmHg in men and 130/77 mmHg in women. The mean body mass index (BMI) was 23.4 kg/m(2) in men and 23.8 kg/m(2) in women, with only 4% being obese (>30 kg/m(2)), and 3.2% being diabetic. Blood collection was successful in 99.98% and the mean delay from sample collection to processing was 10.6 h. For each of the main baseline variables, there is good reproducibility but large heterogeneity by age, sex and study area. By 1 January 2011, over 10,000 deaths had been recorded, with 91% of surviving participants already linked to HI databases. This established large biobank will be a rich and powerful resource for investigating genetic and non-genetic causes of many common chronic diseases in the Chinese population.
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            The China Health and Nutrition Survey, 1989-2011.

            The China Health and Nutrition Survey (CHNS) began in 1989 with the goal of creating a multilevel method of data collection from individuals and households and their communities to understand how the wide-ranging social and economic changes in China affect a wide array of nutrition and health-related outcomes. Initiated with a partial sample in 1989, the full survey runs from 1991 to 2011, and this issue documents the CHNS history. The CHNS cohort includes new household formation and replacement communities and households; all household members are studied. Furthermore, in-depth community data are collected. The sample began with eight provinces and added a ninth, Heilongjiang, in 1997 and three autonomous cities, Beijing, Shanghai, and Chongqing, in 2011. The in-depth community contextual measures have allowed us to create a unique measure of urbanicity that captures major dimensions of modernization across all 288 communities currently in the CHNS sample. The standardized, validated urbanicity measure captures the changes in 12 dimensions: population density; economic activity; traditional markets; modern markets; transportation infrastructure; sanitation; communications; housing; education; diversity; health infrastructure; and social services. Each is based on numerous measures applicable to each dimension. They are used jointly and separately in hundreds of studies.
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              Physical activity and sedentary leisure time and their associations with BMI, waist circumference, and percentage body fat in 0.5 million adults: the China Kadoorie Biobank study.

              Few large studies in China have investigated total physical activity and sedentary leisure time and their associations with adiposity. We investigated determinants of physical activity and sedentary leisure time and their associations with adiposity in China. A total of 466,605 generally healthy participants (age: 30-79 y, 60% female) in the China Kadoorie Biobank were included in this cross-sectional analysis. Self-reported information on a range of activities was collected by interviewer-administered questionnaire. Physical activity was calculated as metabolic equivalent task hours per day (MET-h/d) spent on work, transportation, housework, and nonsedentary recreation. Sedentary leisure time was quantified as hours per day. Adiposity measures included BMI, waist circumference, and percentage body fat (by bioimpedance analysis). Associations were estimated by linear and logistic regression. The mean physical activity was 22 MET-h/d, and the mean sedentary leisure time was 3.0 h/d. For each sex, physical activity was about one-third lower among professionals/administrators than among factory workers, with intermediate levels for other occupational categories. A 1-SD (14 MET-h/d) greater physical activity was associated with a 0.15-unit (95% CI: 0.14, 0.16) lower BMI (in kg/m(2)), a 0.58-cm (95% CI: 0.55, 0.61) smaller waist circumference, and 0.48 (95% CI: 0.45, 0.50) percentage points less body fat. In contrast, a 1-SD (1.5 h/d) greater sedentary leisure time was associated with a 0.19-unit higher BMI (95% CI: 0.18, 0.20), a 0.57-cm larger waist circumference (95% CI: 0.54, 0.59), and 0.44 (95% CI: 0.42, 0.46) percentage points more body fat. For any given physical activity level, greater sedentary leisure time was associated with a greater prevalence of increased BMI, as was lower physical activity for any given sedentary leisure time. In adult Chinese, physical activity varies substantially by occupation, and lack of physical activity and excess sedentary leisure time are independently and jointly associated with greater adiposity.
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                Author and article information

                Journal
                International Journal of Food Sciences and Nutrition
                International Journal of Food Sciences and Nutrition
                Informa UK Limited
                0963-7486
                1465-3478
                May 19 2021
                November 18 2020
                May 19 2021
                : 72
                : 4
                : 569-576
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China;
                [2 ]Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK;
                [3 ]Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom;
                [4 ]Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China;
                [5 ]Peking University Institute of Environmental Medicine, Beijing, China;
                [6 ]Chinese Academy of Medical Sciences, Beijing, China;
                [7 ]Hainan Center for Disease Control & Prevention, Hainan, China;
                [8 ]The First Affiliated Hospital of Hainan Medical University, Hainan, China;
                [9 ]China National Center for Food Safety Risk Assessment, Beijing, China
                Article
                10.1080/09637486.2020.1849038
                19b801ea-ccf0-4499-b296-8646a407d3d6
                © 2021

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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