Skip to main content
×
×
Home

Dietary patterns and the incidence of hyperglyacemia in China

  • Xin Hong (a1), Fei Xu (a1) (a2), Zhiyong Wang (a1), Yaqiong Liang (a1) and Jiequan Li (a1)...
Abstract
Abstract Objective

Epidemiological studies have examined associations between dietary patterns and the risk of type 2 diabetes. However, information on dietary patterns and the risk of type 2 diabetes in Chinese populations is scarce. The aim of the present study was to identify dietary patterns and examine their association with incident hyperglycaemia in Nanjing, China.

Design

A community-based prospective cohort study. Dietary assessment was carried out using a validated eighty-seven-item FFQ. Dietary patterns were identified by exploratory factor analysis. Participants were categorized into tertiles of dietary factor score for each dietary pattern. The relationship between dietary patterns and hyperglycaemia risk was analysed using multivariable linear and Cox regression.

Setting

Seven communities from two urban districts in Nanjing, China.

Subjects

A total of 2900 of Chinese local residents aged 30 years or above, free of hyperglycaemia and other serious diseases, who participated in the baseline survey from June to September 2007 were followed up 3 years later from June to September 2010 for the development of hyperglycaemia. Fasting blood samples were collected at both baseline and 3-year follow-up surveys. Hyperglycaemia was defined as fasting plasma glucose concentration of ≥6·1 mmol/l or already taking oral hyperglycaemia agents for treatment of type 2 diabetes.

Results

Five major dietary patterns were identified: (i) the ‘condiments’ pattern; (ii) the ‘animal and plant protein’ pattern; (iii) the ‘healthy traditional’ pattern; (iv) the ‘fruits, eggs and juice’ pattern; and (v) the ‘alcohol, milk and tea’ pattern. A total of 2093 (72·2 %) individuals completed the follow-up survey and the 3-year cumulative incidence of hyperglycaemia was 7·5 % (158/2093). A 1-unit increase in the score for the ‘healthy traditional’ pattern was associated with a decrease of 0·054 mmol/l in fasting plasma glucose (P=0·017), while a 1-unit increase in the ‘fruits, eggs and juice’ pattern score was associated with an increase of 0·050 mmol/l in fasting plasma glucose (P=0·023) by multivariable linear regression. For men, tertile 3 of the ‘fruits, eggs and juice’ pattern was associated with an 88 % greater risk (hazard ratio=1·88; 95 % CI 1·04, 3·54) of hyperglycaemia than tertile 1 of this pattern. Being in tertile 3 of the ‘alcohol, milk and tea’ pattern was associated with a 35 % greater risk (hazard ratio=1·35; 95 % CI 1·04, 2·16) relative to tertile 1 in women, while for the ‘'healthy traditional’ pattern tertile 3 was associated with a 41 % lower risk (hazard ratio=0·59; 95 % CI 0·35, 0·99) compared with tertile 1. The ‘condiments’ and the ‘animal and plant protein’ patterns were not independently associated with hyperglycaemia.

Conclusions

Our findings suggest that modifying dietary patterns could reduce hyperglycaemia incidence in the mainland Chinese adult population.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Dietary patterns and the incidence of hyperglyacemia in China
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Dietary patterns and the incidence of hyperglyacemia in China
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Dietary patterns and the incidence of hyperglyacemia in China
      Available formats
      ×
Copyright
Corresponding author
* Corresponding author: Email f.xufei@gmail.com
References
Hide All
1. Rathmann W & Giani G (2004) Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27, 25682569.
2. Yang G, Kong L, Zhao W et al. (2008) Emergence of chronic non-communicable diseases in China. Lancet 372, 16971705.
3. Whiting DR, Guariguata L, Weil C et al. (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 94, 311321.
4. National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention (2010) Report on Chronic Disease Risk Factor Surveillance in China . Beijing: Military Medical Science Press (in Chinese).
5. Li MZ, Su L, Liang BY et al. (2013) Trends in prevalence, awareness, treatment, and control of diabetes mellitus in mainland china from 1979 to 2012. Int J Endocrinol 2013, 753150.
6. Zuo H, Shi Z & Hussain A (2014) Prevalence, trends and risk factors for the diabetes epidemic in China: a systematic review and meta-analysis. Diabetes Res Clin Pract 104, 6372.
7. Virtanen SM & Aro A (1994) Dietary factors in the aetiology of diabetes. Ann Med 26, 469478.
8. Hu FB (2002) Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 13, 39.
9. DiBello JR, Kraft P, McGarvey ST et al. (2008) Comparison of 3 methods for identifying dietary patterns associated with risk of disease. Am J Epidemiol 168, 14331443.
10. Reedy J, Wirfält E, Flood A et al. (2010) Comparing 3 dietary pattern methods – cluster analysis, factor analysis, and index analysis – with colorectal cancer risk: the NIH-AARP Diet and Health Study. Am J Epidemiol 171, 479487.
11. Odegaard AO, Koh WP, Butler LM et al. (2011) Dietary patterns and incident type 2 diabetes in Chinese men and women: The Singapore Chinese Health Study. Diabetes Care 34, 880885.
12. Erber E, Hopping BN, Grandinetti A et al. (2010) Dietary patterns and risk for diabetes: the multiethnic cohort. Diabetes Care 33, 532538.
13. Montonen J, Knekt P, Härkänen T et al. (2005) Dietary patterns and the incidence of type 2 diabetes. Am J Epidemiol 161, 219227.
14. Malik VS, Fung TT, van Dam RM et al. (2012) Dietary patterns during adolescence and risk of type 2 diabetes in middle-aged women. Diabetes Care 35, 1218.
15. Yu R, Woo J, Chan R et al. (2011) Relationship between dietary intake and the development of type 2 diabetes in a Chinese population: the Hong Kong Dietary Survey. Public Health Nutr 14, 11331141.
16. Villegas R, Yang G, Gao YT et al. (2010) Dietary patterns are associated with lower incidence of type 2 diabetes in middle-aged women: the Shanghai Women’s Health Study. Int J Epidemiol 39, 889899.
17. Fung TT, Schulze M, Manson JE et al. (2004) Dietary patterns, meat intake, and the risk of type 2 diabetes in women. Arch Intern Med 164, 22352240.
18. van Dam RM, Rimm EB, Willett WC et al. (2002) Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med 136, 201209.
19. McNaughton SA, Mishra GD & Brunner EJ (2008) Dietary patterns, insulin resistance, and incidence of type 2 diabetes in the Whitehall II Study. Diabetes Care 31, 13431348.
20. Hodge AM, English DR, O’Dea K et al. (2007) Dietary patterns and diabetes incidence in the Melbourne Collaborative Cohort Study. Am J Epidemiol 165, 603610.
21. Nettleton JA, Steffen LM, Ni H et al. (2008) Dietary patterns and risk of incident type 2 diabetes in the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care 31, 17771782.
22. World Health Organization (1999) Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Report of a WHO Consultation. Part I: Diagnosis and Classification of Diabetes Mellitus. Geneva: WHO.
23. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (2003) Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26, 31603167.
24. Zhou BF (2002) Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults: study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 15, 8396.
25. Xu F, Yin XM & Wang Y (2007) The association between amount of cigarettes smoked and overweight, central obesity among Chinese adults in Nanjing, China. Asia Pac J Clin Nutr 16, 240247.
26. Xu F, Yin XM, Zhang M et al. (2005) Family average income and body mass index above the healthy weight range among urban and rural residents in regional mainland China. Public Health Nutr 8, 4751.
27. Yang G, Fan L, Tan J et al. (1999) Smoking in China: findings of the 1996 National Prevalence Survey. JAMA 282, 12471253.
28. Zhuang M, Yuan Z, Lin L et al. (2012) Reproducibility and relative validity of a food frequency questionnaire developed for adults in Taizhou, China. PLoS One 7, e48341.
29. Qu NN & Li KJ (2004) Study on the reliability and validity of international physical activity questionnaire. Chin J Epidemiol 25, 265268 (in Chinese).
30. Xu L, J Dibley M & D’Este C (2004) Reliability and validity of a food-frequency questionnaire for Chinese postmenopausal women. Public Health Nutr 7, 9198.
31. Hu FB, Rimm E, Smith-Warner SA et al. (1999) Reproducibility and validity of dietary patterns assessed by a food frequency questionnaire. Am J Clin Nutr 69, 243249.
32. Qin Y, Melse-Boonstra A, Yuan B et al. (2012) Zinc biofortification of rice in China: a simulation of zinc intake with different dietary patterns. Nutrients 4, 517528.
33. Shi Z, Hu X, Yuan B et al. (2008) Vegetable-rich food pattern is related to obesity in China. Int J Obes (Lond) 32, 975984.
34. Nanri A, Shimazu T, Ishihara J et al. (2012) Reproducibility and validity of dietary patterns assessed by a food frequency questionnaire used in the 5-year follow-up survey of the Japan Public Health Center-Based Prospective Study. J Epidemiol 22, 205215.
35. Hatcher LA (1994) Step-By-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling. Cary, NC: SAS Institute.
36. Dai X, He P, Zhang YF et al. (2010) Dietary pattern of Shanghai community-based middle and aged women. J Hyg Res 39, 472477 (in Chinese).
37. Luo YZ, Chen XW, Miu GZ et al. (2009) Association between hypertension and dietary patterns in residents of Jiangyin city. Chin Public Health 25, 314316 (in Chinese).
38. Esposito K, Kastorini CM, Panagiotakos DB et al. (2010) Prevention of type 2 diabetes by dietary patterns: a systematic review of prospective studies and meta-analysis. Metab Syndr Relat Disord 8, 471476.
39. Zhai F, Wang H, Du S et al. (2009) Prospective study on nutrition transition in China. Nutr Rev 67, Suppl. 1, S56S61.
40. Denova-Gutiérrez E, Castañón S, Talavera JO et al. (2010) Dietary patterns are associated with metabolic syndrome in an urban Mexican population. J Nutr 140, 18551863.
41. Naja F, Nasreddine L, Itani L et al. (2013) Association between dietary patterns and the risk of metabolic syndrome among Lebanese adults. Eur J Nutr 52, 97105.
42. Wirfält E, Hedblad B, Gullberg B et al. (2001) Food patterns and components of the metabolic syndrome in men and women: a cross-sectional study within the Malmö Diet and Cancer cohort. Am J Epidemiol 154, 11501159.
43. Villegas R, Shu XO, Gao YT et al. (2008) Vegetable but not fruit consumption reduces the risk of type 2 diabetes in Chinese women. J Nutr 138, 574580.
44. Choi HK, Willett WC, Stampfer MJ et al. (2005) Dairyconsumption and risk of type2 diabetes mellitus in men: a prospective study. Arch Intern Med 165, 9971003.
45. Wakabayashi I (2014) Frequency of heavy alcohol drinking and risk of metabolic syndrome in middle-aged men. Alcohol Clin Exp Res 38, 16891696.
46. Rasouli B, Andersson T, Carlsson PO et al. (2014) Alcohol and the risk for latent autoimmune diabetes in adults: results based on Swedish ESTRID study. Eur J Endocrinol 171, 535543.
47. Sun J, Buys N & Shen S (2013) Dietary patterns and cardiovascular disease-related risks in Chinese older adults. Front Public Health 1, 4856.
48. Mullie P, Clarys P, Hulens M et al. (2010) Dietary patterns and socioeconomic position. Eur J Clin Nutr 64, 231238.
49. Khani BR, Ye W, Terry P et al. (2004) Reproducibility and validity of major dietary patterns among Swedish women assessed with a food-frequency questionnaire. J Nutr 134, 15411545.
50. Martinez ME, Marshall JR & Sechrest L (1998) Invited commentary: factor analysis and the search for objectivity. Am J Epidemiol 148, 1719.
51. Ning F, Wang S, Wang Y et al. (2014) Weight change in association with the incidence of type 2 diabetes in adults from Qingdao, China. Zhong Hua Liu Xing Bing Xue Za Zhi 35, 764768 (in Chinese).
52. Newby PK & Tucker KL (2004) Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 62, 177203.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Public Health Nutrition
  • ISSN: 1368-9800
  • EISSN: 1475-2727
  • URL: /core/journals/public-health-nutrition
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 20
Total number of PDF views: 155 *
Loading metrics...

Abstract views

Total abstract views: 317 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 18th February 2018. This data will be updated every 24 hours.