Longitudinal Association between Urbanicity and Total Dietary Fat Intake in Adults in Urbanizing China from 1991 to 2015: Findings from the CHNS

Su, Chang; Song, Xiaoyun; Hu, Haojie; Du, Wenwen; Wang, Huijun; Zhang, Bing

doi:10.3390/nu12061597

Cited by 16 publications

(14 citation statements)

References 42 publications

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“…Two household-level covariates are considered: Per capita household income in 2015 was calculated and categorized into low, medium, and high. Community urbanicity index was calculated based on 12 multidimensional components including physical, social, cultural, and economic environment of the community [22]. Moreover, BMI, WC, SBP, DBP, TG, TC, HDL_C, LDL_C were also considered as potential confounders.…”

Section: Covariatesmentioning

confidence: 99%

Association of Time-of-Day Energy Intake Patterns with Nutrient Intakes, Diet Quality, and Insulin Resistance

Song

Wang

et al. 2021

Nutrients

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Evidence shows time-of-day of energy intake are associated with health outcomes; however, studies of time-of-day energy patterns and their health implication are still lacking in the Asian population. This study aims to examine the time-of-day energy intake pattern of Chinese adults and to examine its associations with nutrient intakes, diet quality, and insulin resistance. Dietary data from three 24-h recalls collected during the 2015 China Health and Nutrition Survey (CHNS) were analyzed (n = 8726, aged ≥ 18 years). Time-of-day energy intake patterns were determined by latent class analysis (LCA). General Linear Models and Multilevel Mixed-effects Logistic Regression Models were applied to investigate the associations between latent time-of-day energy intake patterns, energy-adjusted nutrient intakes, diet quality score, and insulin resistance. Three time-of-day energy intake patterns were identified. Participants in the “Evening dominant pattern” were younger, had higher proportions of alcohol drinkers and current smokers. The “Evening dominant pattern” was associated with higher daily energy intake and a higher percentage of energy from fat (%) (p < 0.001), as well as higher insulin resistance risk (OR = 1.21; 95% CI: 1.05, 1.40), after adjusting for multivariate covariates. The highest diet quality score was observed in participants with “Noon dominant pattern” (p < 0.001). A higher proportion of energy in the later of the day was associated with insulin resistance in free-living individuals.

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Section: Covariatesmentioning

confidence: 99%

Association of Time-of-Day Energy Intake Patterns with Nutrient Intakes, Diet Quality, and Insulin Resistance

Song

Wang

et al. 2021

Nutrients

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“…1 Estimated trajectory groups of energy intake distribution among Chinese adults. Lines with a diamond represent Group 1, labeled "Energy evenly distributed with steady trend group"; Lines with a square represent Group 2, labeled "Dinner and lunch energy dominant with relatively steady trend group"; Lines with a triangle represent Group 3, labeled "Dinner energy dominant with increasing trend and breakfast energy with declining trend group"; Lines with a point represent Group 4, labeled "breakfast and dinner energy dominant with increasing trend group" Table 1 Baseline characteristics by the four estimated latent trajectory groups Total physical activity [high (i.e., total physical activity days ≥ 7 days/week and total physical activity level ≥ 3000 METs-min/week), medium (i.e., total physical activity days ≥ 5 days/week and total physical activity level ≥ 600 METs-min/week), and low (i.e., physical activity not meeting the above medium and high grouping criteria) [17]]; community urbanicity index, calculated based on 12 multi-dimensional components including physical, social, cultural and economic environment of the community [18]; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; CDGI (2019)-A score, calculated based on 13 food-related components and 1 nutrient-related component reflecting compliance for meeting the Chinese Dietary Guidelines 2016 [19]; EI%, percentage of total energy intake setting by latent class analysis in a previous study [24]. However, none of these studies investigated secular trend of energy intake contribution from meals.…”

Section: Discussionmentioning

confidence: 99%

Trajectories of energy intake distribution and subsequent risk of hyperglycemia among Chinese adults: findings from the China Health and Nutrition Survey (1997–2018)

Song

Wang

et al. 2021

Eur J Nutr

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Aims Few studies have examined the secular trend of the energy intake distribution, and its effect on future risk of hyperglycemia. This study aims to describe trajectories of energy intake distribution over 12 years and relate them to subsequent risk of hyperglycemia over 9 years of follow-up. Methods Our study used ten waves of data from the CHNS survey, a population-based longitudinal survey in China, ongoing since 1989. We examined a cohort of adult participants who were free from diabetes but had at least three waves of dietary data from 1997 to 2009. We assessed energy intake using three consecutive 24 h recalls. We used these data to identify trajectory groups of energy intake distribution by multi-trajectory model based on energy intake proportions of breakfast, lunch, and dinner. We followed up participants for hyperglycemia, diabetes, and impaired fasting glucose for 9 years from 2009 to 2018. Outcomes were ascertained with fasting glucose, serum HbA1c, and self-report of diabetes and/or glucose-lowering medication. We estimated relative risk (RR) for hyperglycemia, diabetes, and impaired fasting glucose by identified trajectory groups using multilevel mixed-effects modified Poisson regression with robust (sandwich) estimation of variance. Gender difference was additionally examined. Results A total of 4417 participants were included. Four trajectory groups were identified, characterized and labeled by “Energy evenly distributed with steady trend group” (Group 1), “Dinner and lunch energy dominant with relatively steady trend group” (Group 2), “Dinner energy dominant with increasing trend and breakfast energy with declining trend group” (Group 3), and “breakfast and dinner energy dominant with increasing trend group” (Group 4). During 48,091 person-years, 1053 cases of incident hyperglycemia occurred, 537 cases of incident diabetes occurred, and 516 cases of impaired fasting glucose occurred. Compared with Group 1, Group 3 was associated with higher subsequent risk of incident hyperglycemia in 9 years of follow-up (RR = 1.28, 95% CI = 1.02, 1.61). No association was found for incident diabetes and impaired fasting glucose. Among males, Group 3 was associated with higher risk of incident hyperglycemia in 9 years of follow-up (RR = 1.44, 95% CI = 1.07, 1.94). No relationship was found in females. Conclusions Energy intake distribution characterized by over 40% of energy intake from dinner with a rising trend over years was associated with higher long-term risk of hyperglycemia in Chinese adults.

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“…Urbanicity outperforms the urban/rural dichotomy to quantify the urban environment or features (Vlahov and Galea, 2002;Dahly and Adair, 2007;Jones-Smith and Popkin, 2010). A recent study indicates that adults living in the highest urbanicity have more dietary fat intake, and a greater proportion of energy from total fat, compared to the lowest urbanicity (Su et al, 2020).…”

Section: Caer 133mentioning

confidence: 99%

Urbanicity and nutrition: evidence from rural–urban migrants in China

Sun

Rahut

2021

CAER

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PurposeThe purpose of this paper is to examine the impact of urbanicity on rural–urban migrants' dietary diversity and nutrition intake and whether its effect differs across various urban environments of migrants.Design/methodology/approachUsing the individual- and time-invariant fixed effects (two-way FE) model and five-year panel data from the China Health and Nutrition Survey (CHNS), this paper estimates a linear and nonlinear relationship between urbanicity and nutrition. The paper also explores the spatial heterogeneity between rural–urban migrants and rural–suburban migrants. Dietary diversity, total energy intake and the shares of energy obtained from protein and fat, respectively, are used to measure rural–urban migrants' nutrition on both quality and quantity aspects.FindingsThe study shows that rural–urban migrants have experienced access to more diverse, convenient and prepared foods, and the food variety consumed is positively associated with community urbanicity. Energy intake is positively and significantly affected by community urbanicity, and it also varies with per capita household income. The obvious inverse U-shaped relationship reveals that improving community urbanicity promotes an increase in the shares of energy obtained from protein and fat at a decreasing rate, until reaching the urbanicity index threshold of 66.69 and 54.26, respectively.Originality/valueThis paper focuses on the nutritional status of rural–urban migrants, an important pillar for China's development, which is often neglected in the research. It examines the urbanicity and the nutrition of migrants in China, which provides a new perspective to understand the dietary and nutritional intake among migrants in the economic and social development. Moreover, the urbanicity index performs better at measuring urban feathers rather than the traditional rural/urban dichotomous classification.

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Longitudinal Association between Urbanicity and Total Dietary Fat Intake in Adults in Urbanizing China from 1991 to 2015: Findings from the CHNS

Cited by 16 publications

References 42 publications

Association of Time-of-Day Energy Intake Patterns with Nutrient Intakes, Diet Quality, and Insulin Resistance

Association of Time-of-Day Energy Intake Patterns with Nutrient Intakes, Diet Quality, and Insulin Resistance

Trajectories of energy intake distribution and subsequent risk of hyperglycemia among Chinese adults: findings from the China Health and Nutrition Survey (1997–2018)

Urbanicity and nutrition: evidence from rural–urban migrants in China

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