Heterogeneity in Diet-related Non-communicable Disease Risks in a Chinese Population

Overview

Journal Eur J Nutr

Specialty Nutritional Sciences

Date 2024 Sep 4

PMID 39231872

Authors

Zhiyao Chang

Sander Biesbroek

Hongyi Cai

Shenggen Fan

Yuanying Ni

Xin Wen

Pieter van t Veer

Elise F Talsma

Affiliations

Soon will be listed here.

Abstract

Purpose: Sub-optimal food choices contribute to the risk of multiple non-communicable diseases (NCDs) which can be mitigated by improving diet quality. Food consumption patterns may partly account for variation of NCD risks in population subgroups in China. This study aimed to evaluate the risk of diet-related NCDs of observed Chinese diets, and to assess the potential reduction in NCD risks by adhering to certain diet recommendations.

Methods: Dose-response meta-analyses were used to derive relative risks between three diet-related NCDs and consumption of 15 food groups. 24-h dietary recall data of 12,809 adults from the 2011 China Health and Nutrition Survey were used to estimate the diet-related summed risks (SRs) of NCDs. Twelve Chinese provinces were aggregated into five regions, and stratified by age, gender, overweight status, education, income, and urbanicity. The Chinese Dietary Guideline-2016 (CDG-2016) and the EAT-Lancet diet were used as recommended diets.

Results: Associations between SRs and gender, age, educational level, income level, and urbanicity were observed. No association was found between SRs and overweight status. Both diet recommendations have lower SRs compared to observed diets among all regions. The food groups that contributed most to the variation of the SRs of diet-related NCDs in China were high consumption of red meat and refined grains, and low consumption of whole grains, fruits, and legumes.

Conclusion: To address the heterogeneity in diet-related NCD risks, focusing on region-specific dietary practical is imperative for Chinese population, in order to propose tailored guidance to adhere to diet recommendations.

Citing Articles

Low energy density, high nutrient adequacy and high nutrient density are each associated with higher diet costs in Chinese adults from Henan Province.

Zhai J, Ma B, Guo L, Wu H, Lyu Q, Khatun P BMC Public Health. 2025; 25(1):410.

PMID: 39893415 PMC: 11786565. DOI: 10.1186/s12889-024-21144-x.

References

Mertens E, Kuijsten A, Dofkova M, Mistura L, DAddezio L, Turrini A . Geographic and socioeconomic diversity of food and nutrient intakes: a comparison of four European countries. Eur J Nutr. 2018; 58(4):1475-1493. PMC: 6561990. DOI: 10.1007/s00394-018-1673-6. View

Xu X, Hall J, Byles J, Shi Z . Assessing dietary quality of older Chinese people using the Chinese Diet Balance Index (DBI). PLoS One. 2015; 10(3):e0121618. PMC: 4374685. DOI: 10.1371/journal.pone.0121618. View

Shen X, Huang Y, Zhang X, Tong X, Zheng P, Shu L . Association between dietary patterns and prediabetes risk in a middle-aged Chinese population. Nutr J. 2020; 19(1):77. PMC: 7393887. DOI: 10.1186/s12937-020-00593-1. View

Stylianou K, Fulgoni 3rd V, Jolliet O . Small targeted dietary changes can yield substantial gains for human health and the environment. Nat Food. 2023; 2(8):616-627. DOI: 10.1038/s43016-021-00343-4. View

Fadnes L, Okland J, Haaland O, Johansson K . Estimating impact of food choices on life expectancy: A modeling study. PLoS Med. 2022; 19(2):e1003889. PMC: 8824353. DOI: 10.1371/journal.pmed.1003889. View

Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209-249. DOI: 10.3322/caac.21660. View

Willett W, Rockstrom J, Loken B, Springmann M, Lang T, Vermeulen S . Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019; 393(10170):447-492. DOI: 10.1016/S0140-6736(18)31788-4. View

Clark M, Springmann M, Hill J, Tilman D . Multiple health and environmental impacts of foods. Proc Natl Acad Sci U S A. 2019; 116(46):23357-23362. PMC: 6859310. DOI: 10.1073/pnas.1906908116. View

Zhang B, Zhai F, Du S, Popkin B . The China Health and Nutrition Survey, 1989-2011. Obes Rev. 2013; 15 Suppl 1:2-7. PMC: 3869031. DOI: 10.1111/obr.12119. View

10.

Popkin B, Du S, Zhai F, Zhang B . Cohort Profile: The China Health and Nutrition Survey--monitoring and understanding socio-economic and health change in China, 1989-2011. Int J Epidemiol. 2009; 39(6):1435-40. PMC: 2992625. DOI: 10.1093/ije/dyp322. View

11.

Yuan Y, Li F, Dong R, Chen J, He G, Li S . The Development of a Chinese Healthy Eating Index and Its Application in the General Population. Nutrients. 2017; 9(9). PMC: 5622737. DOI: 10.3390/nu9090977. View

12.

Bechthold A, Boeing H, Schwedhelm C, Hoffmann G, Knuppel S, Iqbal K . Food groups and risk of coronary heart disease, stroke and heart failure: A systematic review and dose-response meta-analysis of prospective studies. Crit Rev Food Sci Nutr. 2017; 59(7):1071-1090. DOI: 10.1080/10408398.2017.1392288. View

13.

Mao L, Zhang Y, Wang W, Zhuang P, Wu F, Jiao J . Plant-sourced and animal-sourced monounsaturated fatty acid intakes in relation to mortality: a prospective nationwide cohort study. Eur J Nutr. 2019; 59(5):1989-1998. DOI: 10.1007/s00394-019-02048-8. View

14.

Schwingshackl L, Hoffmann G, Lampousi A, Knuppel S, Iqbal K, Schwedhelm C . Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol. 2017; 32(5):363-375. PMC: 5506108. DOI: 10.1007/s10654-017-0246-y. View

15.

Schwingshackl L, Schwedhelm C, Hoffmann G, Knuppel S, Laure Preterre A, Iqbal K . Food groups and risk of colorectal cancer. Int J Cancer. 2017; 142(9):1748-1758. DOI: 10.1002/ijc.31198. View

16.

Halvorsen R, Elvestad M, Molin M, Aune D . Fruit and vegetable consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of prospective studies. BMJ Nutr Prev Health. 2022; 4(2):519-531. PMC: 8718861. DOI: 10.1136/bmjnph-2020-000218. View

17.

Aune D, Keum N, Giovannucci E, Fadnes L, Boffetta P, Greenwood D . Nut consumption and risk of cardiovascular disease, total cancer, all-cause and cause-specific mortality: a systematic review and dose-response meta-analysis of prospective studies. BMC Med. 2016; 14(1):207. PMC: 5137221. DOI: 10.1186/s12916-016-0730-3. View

18.

Schwingshackl L, Schwedhelm C, Hoffmann G, Boeing H . Potatoes and risk of chronic disease: a systematic review and dose-response meta-analysis. Eur J Nutr. 2018; 58(6):2243-2251. PMC: 6689281. DOI: 10.1007/s00394-018-1774-2. View

19.

Abete I, Romaguera D, Vieira A, de Munain A, Norat T . Association between total, processed, red and white meat consumption and all-cause, CVD and IHD mortality: a meta-analysis of cohort studies. Br J Nutr. 2014; 112(5):762-75. DOI: 10.1017/S000711451400124X. View

20.

Yang X, Li Y, Wang C, Mao Z, Zhou W, Zhang L . Meat and fish intake and type 2 diabetes: Dose-response meta-analysis of prospective cohort studies. Diabetes Metab. 2020; 46(5):345-352. DOI: 10.1016/j.diabet.2020.03.004. View