Association between blood lipids-related dietary patterns derived by reduced rank regression and diabetes
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摘要:
目的 探寻血脂相关膳食模式与糖尿病的关联,为糖尿病的膳食干预提供科学依据。 方法 选取4 759名来自伊犁州霍城县的农村居民作为研究对象,通过问卷调查、体格检查及实验室检测收集研究对象的相关资料。通过降秩回归法提取血脂相关膳食模式;利用Pearson相关分析评估血脂相关膳食模式评分及各食物组摄入量与血脂的相关性;并通过logistic回归分析模型探寻血脂相关膳食模式与糖尿病患病风险之间的关联。 结果 血脂相关膳食模式主要以小麦、畜肉和油炸面食摄入较多,而杂粮、蔬菜和水果等摄入较少为特征。血脂相关膳食模式与TC (r=0.296, P < 0.001)、LDL-C(r=0.225, P < 0.001)及TG(r=0.332, P < 0.001)呈正相关,而与HDL-C(r=-0.237, P < 0.001)呈负相关。logistic回归分析模型分析结果显示,调整混杂因素后,血脂相关膳食模式评分Q4组比Q1组的糖尿病患病风险增加99%(OR=1.99; 95% CI: 1.47~2.68; P < 0.001),且随着膳食模式评分增高,糖尿病患病风险具有升高的线性趋势(P趋势 < 0.001)。 结论 血脂相关膳食模式评分与糖尿病患病风险呈正相关,通过膳食来优化血脂水平可能有助于糖尿病的早期预防。 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 (Ptrend < 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. -
Key words:
- Reduced rank regression /
- Blood lipids /
- Dietary patterns /
- Diabetes mellitus
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表 1 糖尿病患者与对照组的基本特征比较[n(%)]
Table 1. Comparison of basic characteristics between diabetic patients and controls [n(%)]
变量 总计(N=4 759) 对照组(n=4 316) 糖尿病(n=443) χ2/Z值 P值 年龄(岁) 68.093 < 0.001 35~<60 3 746(78.71) 3 465(80.28) 281(63.43) 60~<75 1 013(21.29) 852(19.72) 162(36.57) 性别 0.712 0.399 男 2 153(45.24) 1 961(45.44) 192(43.34) 女 2 606(54.76) 2 355(54.56) 251(56.66) 民族 48.883 < 0.001 汉族 616(12.94) 552(12.79) 64(14.45) 回族 1 588(33.37) 1 414(32.76) 174(39.28) 维吾尔族 1 325(27.84) 1 177(27.27) 148(33.41) 哈萨克族 1 146(24.08) 1 099(25.46) 47(10.61) 其他 84(1.77) 74(1.71) 10(2.26) BMI(kg/m2) -6.514 < 0.001 a < 24.0 1 417(29.77) 1 333(30.89) 84(18.96) 24.0~<28.0 1 795(37.72) 1 635(37.88) 160(36.12) ≥28.0 1 547(32.51) 1 348(31.23) 199(44.92) 中心性肥胖 49.275 < 0.001 是 2 722(57.20) 2 399(55.58) 323(72.91) 否 2 037(42.80) 1 917(44.42) 120(27.09) 文化程度 -0.878 0.380 a 小学及以下 3 428(72.03) 3 101(71.85) 327(73.81) 初中及高中 1 018(21.39) 929(21.52) 89(20.09) 大专及以上 313(6.58) 286(6.63) 27(6.10) 家庭年收入(万元) 3.438 0.064 < 2.0 4 177(87.77) 3 776(87.49) 401(90.52) ≥2.0 582(12.23) 540(12.51) 42(9.48) 体力活动 -0.618 0.537 a 从不锻炼 437(9.18) 403(9.34) 34(7.67) 偶尔锻炼 4 102(86.19) 3 716(86.10) 386(87.13) 经常锻炼 220(4.62) 197(4.56) 23(5.19) 高血压 113.666 < 0.001 是 1 856(39.00) 1 579(36.58) 277(62.53) 否 2 903(61.00) 2 737(63.42) 166(37.47) 吸烟 0.932 0.334 是 1 042(21.90) 937(21.71) 105(23.70) 否 3 717(78.10) 3 379(78.29) 338(76.30) 饮酒 0.987 0.320 是 299(6.28) 276(6.39) 23(5.19) 否 4 460(93.72) 4 040(93.61) 420(94.81) 注:a采用Wilcoxon检验进行组间比较。 表 2 血脂相关膳食模式中各食物组的因子负荷
Table 2. Factor loadings for each food group in lipid-related dietary pattern
食物组 因子负荷 食物组 因子负荷 大米 0.064 蔬菜 -0.475 a 小麦 0.491 a 腌制菜 -0.015 杂粮 -0.270 a 水果 -0.315 a 薯类 -0.064 菌藻类 -0.070 畜肉 0.344 a 豆类及豆制品 -0.199 禽肉 0.132 坚果 -0.157 内脏 0.141 油炸面食 0.215 a 水产品 -0.098 甜食 0.101 奶制品 -0.041 果汁 0.031 奶类 -0.155 饮料类 0.110 蛋类 0.037 茶水 -0.147 注:a因子负荷绝对值≥0.20。 表 3 血脂相关膳食模式评分及各食物组摄入量与血脂的相关性
Table 3. Correlation of lipid-related dietary pattern scores and intake of each food group with blood lipids
血脂相关膳食模式评分/食物组 血脂(r值) 血脂相关膳食模式评分/食物组 血脂(r值) TC HDL-C LDL-C TG TC HDL-C LDL-C TG 血脂相关膳食模式评分 0.296 a -0.237 a 0.225 a 0.332 a 蔬菜 -0.220 a 0.196 a -0.143 a -0.172 a 大米 0.016 0.004 0.023 0.048 a 腌制菜 -0.025 -0.011 -0.008 0.002 小麦 0.212 a -0.150 a 0.144 a 0.228 a 水果 -0.087 a 0.116 a -0.093 a -0.178 a 杂粮 -0.114 a 0.099 a -0.061 a -0.130 a 菌藻类 -0.023 0.007 -0.040 a -0.033 b 薯类 -0.016 -0.019 -0.023 -0.066 a 豆类及豆制品 -0.085 a 0.056 a -0.072 a -0.089 a 畜肉 0.126 a -0.113 a 0.114 a 0.170 a 坚果 -0.068 a 0.068 a -0.050 a -0.057 a 禽肉 0.049 a -0.008 0.065 a 0.077 a 油炸面食 0.080 a -0.051 a 0.067 a 0.120 a 内脏 0.062 a -0.044 a 0.056 a 0.055 a 甜食 0.055 b -0.016 0.043 a 0.039 a 水产品 -0.078 a 0.027 -0.040 a -0.008 果汁 0.022 -0.010 0.002 0.014 奶制品 0.002 0.078 a 0.014 -0.005 含糖饮料 0.051 a -0.022 0.052 a 0.045 b 奶类 -0.037 a 0.052 a -0.054 a -0.088 a 茶 -0.075 a 0.031 b -0.042 a -0.070 a 蛋类 0.009 0.048 a 0.030 b 0.053 a 注:aP < 0.01; b P < 0.05。 表 4 血脂相关膳食模式与糖尿病关系的多因素logistic回归分析
Table 4. Multivariate logistic regression analysis of the relationship between lipid-related dietary patterns and diabetes mellitus
血脂相关膳食模式评分的四分位数 模型1 模型2 模型3 OR(95% CI)值 P值 OR(95% CI)值 P值 OR(95% CI)值 P值 Q1 1.00 1.00 1.00 Q2 1.39(1.03~1.89) 0.033 1.38(1.01~1.89) 0.041 1.33(0.97~1.82) 0.075 Q3 1.51(1.12~2.04) 0.004 1.54(1.13~2.09) 0.006 1.45(1.06~1.98) 0.019 Q4 1.98(1.48~2.64) < 0.001 2.20(1.63~2.95) < 0.001 1.99(1.47~2.68) < 0.001 P趋势值 < 0.001 < 0.001 < 0.001 注:模型1未调整;模型2调整了年龄、性别、民族、文化程度和家庭年收入;模型3在模型2的基础上调整了BMI、中心性肥胖、体力活动、高血压、吸烟、饮酒和能量摄入。 表 5 血脂相关膳食模式与糖尿病患病风险的亚组分析
Table 5. Subgroup analysis of lipid-related dietary patterns and risk of diabetes mellitus
变量 血脂相关膳食模式评分[OR(95% CI)值] P趋势值 Q1组 Q2组 Q3组 Q4组 年龄(岁) 35~ < 60 1.00 1.39(0.95~2.02) 1.43(0.99~2.07) 1.76(1.23~2.53) < 0.001 60~ < 75 1.00 1.36(0.76~2.42) 1.50(0.85~2.64) 2.51(1.45~4.37) < 0.001 民族 汉族 1.00 1.36(0.59~3.11) 1.82(0.78~4.24) 1.93(0.78~4.78) 0.072 回族 1.00 1.61(0.95~2.74) 1.43(0.84~2.45) 1.91(1.14~3.18) 0.011 维吾尔族 1.00 1.09(0.64~1.85) 1.22(0.74~2.02) 1.78(1.09~2.91) 0.001 哈萨克族及其他 1.00 1.89(0.58~6.15) 2.89(0.95~8.84) 3.67(1.26~10.69) 0.004 BMI(kg/m2) < 24.0 1.00 1.13(0.59~2.15) 0.89(0.44~1.79) 1.79(0.95~3.37) 0.105 24.0~ < 28.0 1.00 1.54(0.88~2.68) 2.04(1.19~3.48) 2.20(1.28~3.77) 0.001 ≥28.0 1.00 1.33(0.82~2.15) 1.29(0.80~2.07) 1.91(1.22~3.00) < 0.001 中心性肥胖 是 1.00 1.30(0.90~1.89) 1.47(1.03~2.11) 1.76(1.23~2.50) < 0.001 否 1.00 1.46(0.81~2.65) 1.30(0.69~2.42) 2.63(1.48~4.65) < 0.001 高血压 是 1.00 1.19(0.79~1.77) 1.22(0.82~1.81) 1.72(1.18~2.51) < 0.001 否 1.00 1.65(0.98~2.77) 1.90(1.14~3.16) 2.55(1.54~4.21) < 0.001 注:模型调整了年龄、性别、民族、文化程度、家庭年收入、BMI、中心性肥胖、体力活动、高血压、吸烟、饮酒和能量摄入,但不包括分层变量。 -
[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.