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For: Xu J, Wu L, Deng J, Yang L, Wang Y, Tian H, Ji Y, Xia Q, Han Z, Cheng Y, Lu X. Population attributable fractions of fatty liver disease for type 2 diabetes Mellitus. BMC Endocr Disord 2023;23:201. [PMID: 37726718 PMCID: PMC10507899 DOI: 10.1186/s12902-023-01433-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 08/16/2023] [Indexed: 09/21/2023] Open

For:	Xu J, Wu L, Deng J, Yang L, Wang Y, Tian H, Ji Y, Xia Q, Han Z, Cheng Y, Lu X. Population attributable fractions of fatty liver disease for type 2 diabetes Mellitus. BMC Endocr Disord 2023;23:201. [PMID: 37726718 PMCID: PMC10507899 DOI: 10.1186/s12902-023-01433-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 08/16/2023] [Indexed: 09/21/2023] Open

Number

Cited by Other Article(s)

Dong W, Li Y, Man Q, Zhang Y, Yu L, Zhao R, Zhang J, Song P, Ding G. Geographical Distribution of Dietary Patterns and Their Association with T2DM in Chinese Adults Aged 45 y and Above: A Nationwide Cross-Sectional Study. Nutrients 2023;16:107. [PMID: 38201937 PMCID: PMC10780680 DOI: 10.3390/nu16010107] [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: 11/13/2023] [Revised: 12/08/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open

Abstract

BACKGROUND

This study aimed to investigate the geographical distribution of dietary patterns and their association with T2DM among Chinese adults aged 45 years and above.

METHODS

Data was from the China Adults Chronic Diseases and Nutrition Surveillance (2015). Dietary intake for each participant was determined through a combination of 3-day 24-h dietary recall interviews and food frequency questionnaires. Principal component analysis was used to extract dietary patterns and spatial analysis was employed to investigate the geographic distribution of them. T2DM was diagnosed using criteria of ADA 2018, and binary logistic regression was employed to examine the relationship between dietary patterns and T2DM.

RESULTS

A total of 36,648 participants were included in the study; 10.9% of them were diagnosed as T2DM. Three dietary patterns were identified with the name of plant-based pattern, animal-based pattern, and oriental traditional pattern, which were represented located in northern, northwest, and southern regions, respectively. After adjusting for potential confounders, participants in the highest quartile of the plant-based pattern were associated with lower T2DM odds (OR = 0.82, 95% CI: 0.74, 0.90) when comparing with the lowest quartile. However, participants inclined to higher quartiles of animal-based pattern had a higher risk of T2DM (OR = 1.15, 95% CI: 1.04, 1.27) compared with those in the lower quartiles. No significant association was found between the oriental traditional pattern and T2DM (OR = 1.03, 95% CI: 0.93, 1.14).

CONCLUSION

Dietary patterns of Chinese population revealed geographical disparities, with plant-based dietary pattern showing protective effects and animal-based pattern carrying high risks for T2DM. Regional dietary variations and food environment are paramount in T2DM prevention and management.

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Affiliation(s)

Weihua Dong National Institute for Nutrition and Health, Department of Geriatric and Clinical Nutrition, Chinese Center for Diseases Control and Prevention, Beijing 100050, China; (W.D.); (Y.L.); (Q.M.); (J.Z.)
Yuqian Li National Institute for Nutrition and Health, Department of Geriatric and Clinical Nutrition, Chinese Center for Diseases Control and Prevention, Beijing 100050, China; (W.D.); (Y.L.); (Q.M.); (J.Z.)
Qingqing Man National Institute for Nutrition and Health, Department of Geriatric and Clinical Nutrition, Chinese Center for Diseases Control and Prevention, Beijing 100050, China; (W.D.); (Y.L.); (Q.M.); (J.Z.) Key Laboratory of Trace Elements and Nutrition of National Health Commission, Beijing 100050, China
Yu Zhang Chinese Center for Diseases Control and Prevention, Beijing 100050, China;
Lianlong Yu Shandong Center for Disease Control and Prevention, Jinan 250014, China;
Rongping Zhao Department of Clinical Nutrition, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610056, China;
Jian Zhang National Institute for Nutrition and Health, Department of Geriatric and Clinical Nutrition, Chinese Center for Diseases Control and Prevention, Beijing 100050, China; (W.D.); (Y.L.); (Q.M.); (J.Z.) Key Laboratory of Trace Elements and Nutrition of National Health Commission, Beijing 100050, China
Pengkun Song National Institute for Nutrition and Health, Department of Geriatric and Clinical Nutrition, Chinese Center for Diseases Control and Prevention, Beijing 100050, China; (W.D.); (Y.L.); (Q.M.); (J.Z.) Key Laboratory of Trace Elements and Nutrition of National Health Commission, Beijing 100050, China
Gangqiang Ding National Institute for Nutrition and Health, Department of Geriatric and Clinical Nutrition, Chinese Center for Diseases Control and Prevention, Beijing 100050, China; (W.D.); (Y.L.); (Q.M.); (J.Z.)

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