Association between blood lipids-related dietary patterns derived by reduced rank regression and diabetes

QIAO Ting-ting; ZHANG Huan-wen; LUO Tao; ZHAO Hui; ZHANG Guo-zhen; DAI Jiang-hong

doi:10.16462/j.cnki.zhjbkz.2023.01.006

Volume 27 Issue 1

Jan. 2023

Turn off MathJax

Article Contents

Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(1): 29-35

QIAO Ting-ting, ZHANG Huan-wen, LUO Tao, ZHAO Hui, ZHANG Guo-zhen, DAI Jiang-hong. Association between blood lipids-related dietary patterns derived by reduced rank regression and diabetes[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(1): 29-35. doi: 10.16462/j.cnki.zhjbkz.2023.01.006

Citation:

QIAO Ting-ting, ZHANG Huan-wen, LUO Tao, ZHAO Hui, ZHANG Guo-zhen, DAI Jiang-hong. Association between blood lipids-related dietary patterns derived by reduced rank regression and diabetes[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(1): 29-35. doi: 10.16462/j.cnki.zhjbkz.2023.01.006

Citation:

QIAO Ting-ting, ZHANG Huan-wen, LUO Tao, ZHAO Hui, ZHANG Guo-zhen, DAI Jiang-hong. Association between blood lipids-related dietary patterns derived by reduced rank regression and diabetes[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(1): 29-35. doi: 10.16462/j.cnki.zhjbkz.2023.01.006

PDF( 840 KB)

Association between blood lipids-related dietary patterns derived by reduced rank regression and diabetes

doi: 10.16462/j.cnki.zhjbkz.2023.01.006

Department of Epidemiology and Biostatistics, School of Public Health, Xinjiang Medical University, Urumqi 830000, China

Funds:

National Natural Science Foundation of China 82160640

National Key Research and Development Program of China 2017YFC0907203

More Information

Corresponding author: DAI Jiang-hong, E-mail: epi102@sina.com
Received Date: 2022-05-19
Rev Recd Date: 2022-07-29

Available Online: 2023-02-09

Publish Date: 2023-01-10

Abstract

Abstract

Objective To explore the association between lipid-related dietary patterns and diabetes, thus providing a scientific basis for dietary intervention in diabetes. Methods A total of 4 759 rural residents from Huocheng County in Yili were selected as the study subjects, and relevant data were collected through questionnaires, physical examinations and laboratory tests. Lipid-related dietary patterns were extracted by reduced-rank regression. The correlations between lipid-related dietary pattern scores and intake of each food group and lipid were assessed by Pearson correlation analysis. The relationship between lipid-related dietary pattern and diabetes risk was analyzed by logistic regression model. Results Lipid-related dietary pattern was characterized by higher intakes of wheat, meat and fried pasta, and lower intake of mixed grains, vegetables and fruits. Lipid-related dietary pattern was positively correlated with total cholesterol (r=0.296, P < 0.001), low density lipoprotein cholesterol (r=0.225, P < 0.001) and triglyceride (r=0.332, P < 0.001), and negatively correlated with high density lipoprotein cholesterol (r=-0.237, P < 0.001). Logistic regression analysis showed that after adjusting for confounding factors, the risk of diabetes increased by 99% (OR=1.99; 95% CI: 1.47-2.68; P < 0.001) in the lipid-related dietary pattern score Q4 group compared with Q1 group. And there was a linear trend of significantly higher risk of diabetes with increasing dietary pattern scores (P_trend < 0.001). Conclusions Lipid-related dietary pattern scores are positively associated with diabetes risk, and optimizing lipid levels through diet may contribute to early prevention of diabetes.
- Reduced rank regression,
- Blood lipids,
- Dietary patterns,
- Diabetes mellitus

FullText(HTML)

References(21)

References

[1]	International Diabetes Federation. IDF diabetes atlas 10th edition[EB/OL]. (2021-11-08)[2022-05-06]. https://diabetesatlas.org/data/en/world/.
[2]	Lorzadeh E, Akhondi-Meybodi M, Mozaffari-Khosravi H, et al. Association between empirically derived dietary patterns and liver function tests in adults: shahedieh cohort study[J]. Nutrition, 2021, 81: 110897. DOI: 10.1016/j.nut.2020.110897.
[3]	Sun Q, Wen Q, Lyu J, et al. Dietary pattern derived by reduced-rank regression and cardiovascular disease: a cross-sectional study[J]. Nutr Metab Cardiovasc Dis, 2022, 32(2): 337-345. DOI: 10.1016/j.numecd.2021.10.008.
[4]	白朝, 张德生, 张瑞, 等. 传统及联合脂质代谢指标与糖尿病发病关系的巢式病例对照研究[J]. 中华流行病学杂志, 2021, 42(4): 656-661. DOI: 10.3760/cma.j.cn112338-20200401-00490. Bai C, Zhang DS, Zhang R, et al. A nested case-control study on relationship of traditional and combined lipid metabolism indexes with incidence of diabetes[J]. Chin J Epidemiol, 2021, 42(4): 656-661. DOI: 10.3760/cma.j.cn112338-20200401-00490.
[5]	Schmidt MI, Duncan BB, Bang H, et al. Identifying individuals at high risk for diabetes: the atherosclerosis risk in communities study[J]. Diabetes Care, 2005, 28(8): 2013-2018. DOI: 10.2337/diacare.28.8.2013.
[6]	Sattar N, Preiss D, Murray HM, et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials[J]. Lancet, 2010, 375 (9716): 735-742. DOI: 10.1016/S0140-6736(09)61965-6.
[7]	Pasta A, Formisano E, Cremonini AL, et al. Diet and nutraceutical supplemetation in dyslipidemic patients: first results of an Italian single center real-world retrospective analysis[J]. Nutrients, 2020, 12(7): 2056. DOI: 10.3390/nu12072056.
[8]	Tiong XT, Nursara Shahirah A, Pun VC, et al. The association of the dietary approach to stop hypertension (DASH) diet with blood pressure, glucose and lipid profiles in Malaysian and Philippines populations[J]. Nutr Metab Cardiovasc Dis, 2018, 28(8): 856-863. DOI: 10.1016/j.numecd.2018.04.014.
[9]	Tao L, Tian T, Liu L, et al. Cohort profile: the Xinjiang multiethnic cohort (XMC) study[J]. BMJ Open, 2022, 12(5): e048242. DOI: 10.1136/bmjopen-2020-048242.
[10]	Shi Z, Ganji V. Dietary patterns and cardiovascular disease risk among Chinese adults: a prospective cohort study[J]. Eur J Clin Nutr, 2020, 74(12): 1725-1735. DOI: 10.1038/s41430-020-0668-6.
[11]	杨月欣, 王光亚, 潘兴. 中国食物成分表[M]. 北京: 北京大学医学出版社, 2009: 2-4. Yang YX, Wang GY, Pan X, et al. China food composition[M]. Beijing: Peking University Medical Press, 2009: 2-4.
[12]	Choi S, Chon J, Lee SA, et al. Central obesity is associated with lower prevalence of sarcopenia in older women, but not in men: a cross-sectional study[J]. BMC Geriatr, 2022, 22(1): 406. DOI: 10.1186/s12877-022-03102-7.
[13]	Sun Q, Wen Q, Lyu J, et al. Dietary pattern derived by reduced-rank regression and cardiovascular disease: a cross-sectional study[J]. Nutr Metab Cardiovasc Dis, 2022, 32(2): 337-345. DOI: 10.1016/j.numecd.2021.10.008.
[14]	Maddock J, Ambrosini GL, Griffin JL, et al. A dietary pattern derived using B-vitamins and its relationship with vascular markers over the life course[J]. Clin Nutr, 2019, 38(3): 1464-1473. DOI: 10.1016/j.clnu.2018.06.969.
[15]	Pastorino S, Richards M, Pierce M, et al. A high-fat, high-glycaemic index, low-fibre dietary pattern is prospectively associated with type 2 diabetes in a British birth cohort[J]. Br J Nutr, 2016, 115(9): 1632-1642. DOI: 10.1017/S0007114516000672.
[16]	Wang Y, Xu L, Wang N, et al. Associations of dietary patterns and incident type 2 diabetes in a community population cohort from southwest China[J]. Front Public Health, 2022, 10: 773172. DOI: 10.3389/fpubh.2022.773172.
[17]	Hassannejad R, Moosavian SP, Mohammadifard N, et al. Long-term association of red meat consumption and lipid profile: a 13-year prospective population- based cohort study[J]. Nutrition, 2021, 86: 111144. DOI: 10.1016/j.nut.2021.111144.
[18]	Adebawo O, Salau B, Ezima E, et al. Fruits and vegetables moderate lipid cardiovascular risk factor in hypertensive patients[J]. Lipids Health Dis, 2006, 5: 14. DOI: 10.1186/1476-511X-5-14.
[19]	Guasch-Ferré M, Satija A, Blondin SA, et al. Meta-analysis of randomized controlled trials of red meat consumption in comparison with various comparison diets on cardiovascular risk factors[J]. Circulation, 2019, 139(15): 1828-1845. DOI: 10.1161/CIRCULATIONAHA.118.035225.
[20]	Kim SY, Woo HW, Lee YH, et al. Association of dietary glycaemic index, glycaemic load, and total carbohydrates with incidence of type-2 diabetes in adults aged≥40 years: the multi-rural communities cohort (MRCohort)[J]. Diabetes Res Clin Pract, 2020, 160: 108007. DOI: 10.1016/j.diabres.2020.108007.
[21]	宋孟娜, 程潇, 孔静霞, 等. 我国中老年人超重、肥胖变化情况及影响因素分析[J]. 中华疾病控制杂志, 2018, 22(8): 804-808. DOI: 10.16462/j.cnki.zhjbkz.2018.08.010. Song MN, Cheng X, Kong JX, et al. Prevalence and influencing factors of overweight and obesity among middle-aged[J]. Chin J Dis Control Prev, 2018, 22(8): 804-808. DOI: 10.16462/j.cnki.zhjbkz.2018.08.010.