生活方式指数与基因交互作用对凉山地区居民高尿酸血症影响

李婷婷; 李爽境; 田甜; 聂志超; 许望东; 刘隆健; 贾红

doi:10.16462/j.cnki.zhjbkz.2021.01.008

生活方式指数与基因交互作用对凉山地区居民高尿酸血症影响

doi: 10.16462/j.cnki.zhjbkz.2021.01.008

1.
646000 泸州，西南医科大学公共卫生学院
2.
19012 费城，宾夕法尼亚州，德雷塞尔大学公共卫生学院流行病与生物统计学系

基金项目:

达能营养中心膳食营养研究与宣教基金 DIC2013-03

详细信息

通讯作者:
贾红，E-mail：jhong_lz@163.com

中图分类号: R589;R181
计量
- 文章访问数: 563
- HTML全文浏览量: 310
- PDF下载量: 95
- 被引次数: 0
出版历程
- 收稿日期: 2020-07-23
- 修回日期: 2020-10-24
- 刊出日期: 2021-01-10

Association and interaction effect between lifestyle index and gene polymorphisms on hyperuricemia in Liangshan residents

1.
School of Public Health, Southwest Medical University, Luzhou 646000, China
2.
Department of Epidemiology and Biostatistics, School of Public Health, Drexel University, Philadelphia, PA 19012, the United States

Funds:

Danone Nutrition Center Dietary Nutrition Research and Education Fund DIC2013-03

More Information

Corresponding author: JIA Hong, E-mail: jhong_lz@163.com

摘要

摘要: 目的分析生活方式指数与基因交互作用对凉山地区居民高尿酸血症(hyperuricemia, HUA)的影响。方法以凉山地区2 646名居民为研究对象进行问卷调查、体格测量和血样采集。利用吸烟、饮酒、体育锻炼和BMI构建生活方式指数。采用非条件Logistic线性回归分析模型分析生活方式指数、基因位点与HUA的关系，运用相加、相乘模型分析二者交互作用对HUA的影响。结果调整年龄、性别和民族后，生活方式指数得分为3分与0分相比降低了63.7%的HUA患病风险(OR=0.363, 95% CI: 0.232~0.566, P < 0.001);rs2231142位点TT突变基因型患HUA的风险是GG野生型的2.993倍(OR=2.993, 95% CI: 2.015~4.445, P < 0.001)，rs11722228位点TT突变基因型会增加39.2%的HUA患病风险(OR=1.392, 95% CI: 1.029~1.884, P=0.032)。生活方式指数与rs2231142位点对HUA存在协同的相加交互作用(RERI=1.136, 95% CI:0.036~2.236; AP=0.311, 95% CI: 0.115~0.507; S=1.749, 95% CI: 1.168~2.620)，但未发现生活方式指数与rs2231142和rs11722228位点间存在相乘交互作用(均有P＞0.05)。结论凉山地区人群生活方式指数与rs2231142位点对HUA存在协同的相加交互作用，当个体携带T突变基因型时，其不健康的生活方式会增加HUA的患病风险。
- 高尿酸血症 /
- 生活方式指数 /
- 基因 /
- 交互作用
Abstract: Objective To explore the impact of lifestyle index and gene interaction on hyperuricemia of residents in Liangshan area. Methods Questionnaire survey, physical measurement and blood sample were conducted from 2 646 residents in Liangshan area. Lifestyle index was constructed by using smoking, drinking, physical activity and body mass index (BMI). Logistic regression model was used to explore the relationship among lifestyle index, SNP locus and HUA. The additive and multiplication model were used to analyze the impact of lifestyle index-gene interaction on HUA. Results Logistic regression analysis showed that after adjusting for age, gender and ethnicity, a lifestyle index score of 3 reduces the risk of HUA by 63.7% compared with score of 0 (OR=0.363, 95% CI: 0.232-0.566, P < 0.001); The TT mutation genotype at rs2231142 had HUA 2.993 times than that of GG wild type (OR=2.993, 95% CI: 2.015-4.445, P < 0.001), the TT mutation genotype at rs11722228 increased the risk of HUA by 39.2% (OR=1.392, 95% CI: 1.029-1.884, P=0.032). The interaction analysis showed that there was a synergistic additive interaction between the lifestyle index and the ABCG2 gene rs2231142 locus on HUA (RERI=1.136, 95% CI:0.036-2.236; AP=0.311, 95% CI: 0.115-0.507; S=1.749, 95% CI: 1.168-2.620), but there was no multiplicative interaction between lifestyle index and rs2231142 and rs11722228. Conclusions There is a synergistic additive interaction between the lifestyle index and the ABCG2 gene rs2231142 locus on HUA. When an individual carries the T mutation genotype, adopting an unhealthy lifestyle will increase the risk of HUA.
- Hyperuricemia /
- Lifestyle index /
- Gene /
- Interaction

HTML全文

图 1 不同基因型与HUA患病关联的森林图

Figure 1. The relationship between different genotypes and HUA

下载: 全尺寸图片幻灯片

图 2 rs2231142位点与生活方式指数相加交互作用模型图

注：U为参照，Gene & Lifestyle表示rs2231142位点与生活方式指数的相加交互作用。

Figure 2. Additive interaction between rs2231142 locus and lifestyle index

下载: 全尺寸图片幻灯片

表 1 两组研究对象基本情况比较[n (%)]

Table 1. Baseline characteristics of study participants [n(%)]

变量	HUA组	非HUA组	t/χ²值	P值
年龄(x±s, 岁)	42.70±15.06	44.62±14.09	2.948	0.003
性别			104.975	＜0.001
男	504(71.0)	941(48.6)
女	206(29.0)	995(51.4)
民族			42.412	＜0.001
汉	351(49.4)	687(35.5)
彝	359(50.6)	1249(64.5)
文化程度			84.507	＜0.001
小学及以下	276(38.9)	1142(59.0)
初中及以上	434(61.1)	794(41.0)
职业			71.841	＜0.001
农民	251(35.6)	1046(54.3)
非农民	454(64.4)	882(45.7)
吸烟			28.944	＜0.001
是	294(42.4)	590(31.1)
否	399(57.6)	1307(68.9)
饮酒			27.534	＜0.001
是	407(59.5)	894(47.8)
否	277(40.5)	977(52.2)
体育锻炼			10.444	0.001
是	95(15.7)	182(10.7)
否	511(84.3)	1518(89.3)
BMI (x±s, kg/m²)	24.22±3.60	22.44±3.11	-11.687	＜0.001
腰围(x±s, cm)	84.53±10.16	78.49±9.21	-13.857	＜0.001
SUA (x±s, mol/L)	466.42±72.77	306.27±60.61	-52.330	＜0.001
LDL-C (x±s, mol/L)	3.09±0.85	2.92±0.80	-4.538	＜0.001
HDL-C (x±s, mol/L)	1.26±0.33	1.36±0.34	6.879	＜0.001
FBG (x±s, mol/L)	5.51±1.39	5.51±1.55	0.019	0.985
TC (x±s, mol/L)	5.11±1.02	4.93±1.01	-3.991	＜0.001
TG (x±s, mol/L)	2.01±1.46	1.40±1.15	-10.092	＜0.001

下载: 导出CSV

表 2 生活方式指数对HUA影响的Logistic回归

Table 2. Logistic regression of the impact of lifestyle index on HUA

生活方式指数(分)	模型1		模型2
生活方式指数(分)	OR(95% CI)值	P值	OR(95% CI)值	P值
0	1.000		1.000
1	0.594(0.394~0.895)	0.013	0.650(0.429~0.986)	0.043
2	0.362(0.241~0.542)	＜0.001	0.485(0.319~0.737)	0.001
3	0.230(0.151~0.349)	＜0.001	0.363(0.232~0.566)	＜0.001
4	0.322(0.169~0.614)	0.001	0.428(0.219~0.839)	0.013
注：模型1表示未作任何调整; 模型2表示调整年龄、性别和民族。

下载: 导出CSV

表 3 HUA组和非HUA组各位点基因型比较[n (%)]

Table 3. Comparison of genotypes between the two groups[n (%)]

基因型/基因频率	HUA组	非HUA组	χ²值	P值
rs2231142
GG	374(52.7)	1 349(69.7)	77.959	< 0.001
GT	278(39.2)	528(27.3)
TT	58(8.2)	59(3.0)
G	1 026(72.3)	3 226(83.3)	80.525	< 0.001
T	394(27.7)	646(16.7)
rs11722228
CC	301(42.4)	904(46.7)	5.217	0.074
CT	328(46.2)	854(44.1)
TT	81(11.4)	178(9.2)
C	930(65.5)	2 662(68.8)	5.055	0.025
T	490(34.5)	1 210(31.2)

下载: 导出CSV

表 4 生活方式指数与基因位点的相加交互作用

Table 4. Additive interaction between lifestyle index and gene locus

变量	SNP位点	RERI值	AP值	S值
生活方式指数	rs2231142	1.136(0.036~2.236)	0.311(0.115~0.507)	1.749(1.168~2.620)
	rs11722228	0.267(-0.587~1.120)	0.098(-0.185~0.380)	1.183(0.719~1.944)
注：RERI表示超额相对危险度; AP表示交互作用归因比; S表示交互作用指数。

下载: 导出CSV

参考文献(27)

[1]	Multidisciplinary Expert Task Force on Hyperuricemia and Related Diseases. Chinese multidisciplinary expert consensus on the diagnosis and treatment of hyperuricemia and related diseases[J]. Chin Med J (Engl), 2017, 130(20):2473-2488. DOI: 10.4103/0366-6999.216416.
[2]	胡大一, 丁荣晶.无症状高尿酸血症合并心血管疾病诊治建议中国专家共识[J].中国全科医学, 2010, 13(11):1145-1149. Hu DY, Ding RJ. Consensus of Chinese experts on diagnosis and treatment of asymptomatic hyperuricemia combined with cardiovascular disease[J]. Chin Gen Pract, 2010, 13(11):1145-1149.
[3]	Köttgen A, Albrecht E, Teumer A, et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations[J]. Nat Genet, 2013, 45(2):145-154. DOI: 10.1038/ng.2500.
[4]	Woodward OM, Kottgen A, Goreeh J, et al. Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout[J]. Proc Natl Acad Sci USA, 2009, 106(25):10338-10342. DOI: 10.1073/pnas.0901249106.
[5]	Yang B, Mo Z, Wu C, et al. A genome-wide association study identifies common variants influencing serum uric acid concentrations in a Chinese population[J]. BMC Med Genomics, 2014, 7:10. DOI: 10.1186/1755-8794-7-10.
[6]	Barbaresko J, Rienks J, Nöthlings U. Lifestyle indices and cardiovascular disease risk: a meta-analysis[J]. Am J Prev Med, 2018, 55(4):555-564. DOI: 10.1016/j.amepre.2018.04.046.
[7]	张庆丽, 郑苇, 李泓澜, 等.健康生活方式指数与男性胃癌关系的前瞻性队列研究[J].中华预防医学杂志, 2017, 51(5):386-392. DOI: 10.3760/cma.j.issn.0253-9624.2017.05.004. Zhang QL, Zheng W, Li HL, et al. A prospective cohort study on the relationship between healthy lifestyle index and gastric cancer in men[J]. Chin J Prevent Med, 2017, 51(5):386-392. DOI: 10.3760/cma.j.issn.0253-9624.2017.05.004.
[8]	Aleksandrova K, Pischon T, Jenab M, et al. Combined impact of healthy lifestyle factors on colorectal cancer: a large european cohort study[J]. BMC Med, 2014, 12:168. DOI: 10.1186/s12916-014-0168-4.
[9]	Huang SS, Liu XR, Li H, et al. Sex difference in the association of serum uric acid with metabolic syndrome and its components: a cross sectional study in a Chinese Yi population[J]. Postgrad Med, 2017, 129(8):828-833. DOI: 10.1080/00325481.2017.1372034.
[10]	马冠生, 杜松明, 郝利楠, 等.中国成年居民过量饮酒现状的分析[J].营养学报, 2009, 31(3):213-217. Ma Gs, Du SM, Hao LN, et al. Analysis of the current situation of excessive drinking among Chinese adult residents[J]. Nutrition, 2009, 31(3):213-217.
[11]	Rothman KJ. Epidemiology: an introduction[M]. New York: Oxford University Press, 2002:168-180.
[12]	Liu R, Han C, Wu D, et al. Prevalence of hyperuricemia and gout in mainland China from 2000 to 2014: a systematic review and meta-analysis[J]. Biomed Res Int, 2015:762820. DOI: 10.1155/2015/762820.
[13]	Zhang Y, Pan XF, Chen J, et al. Combined lifestyle factors and risk of incident type 2 diabetes and prognosis among individuals with type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies[J]. Diabetologia, 2020, 63(1):21-33. DOI: 10.1007/s00125-019-04985-9.
[14]	Kessler TA, Alverson EM. Influence of lifestyle, health behavior, and health indices on the health status of underserved adults[J]. J Am Assoc Nurse Pract, 2013, 25(12):674-681. DOI: 10.1002/2327-6924.12027.
[15]	Buckland G, Travier N, Huerta JM, et al. Healthy lifestyle index and risk of gastric adenocarcinoma in the EPIC cohort study[J]. Int J Cancer, 2015, 137(3):598-606. DOI: 10.1002/ijc.29411.
[16]	Li K, Monni S, Husing A, et al. Primary preventive potential of major lifestyle risk factors for acute myocardial infarction in men: an analysis of the EPIC-Heidelberg cohort[J]. Eur J Epidemiol, 2014, 29(1):27-34. DOI: 10.1007/s10654-013-9872-1.
[17]	Hamajima N, Naito M, Okada R, et al. Significant interaction between LRP2 rs2544390 in intron 1 and alcohol drinking for serum uric acid levels among a Japanese population[J]. Gene, 2012, 503(1):131-136. DOI: 10.1016/j.gene.2012.04.064.
[18]	Berenice RP, Luis MK, Juan Carlos FL, et al. Influence of genetic and non-genetic risk factors for serum uric acid levels and hyperuricemia in Mexicans[J]. Nutrients, 2019, 11(6):1336. DOI: 10.3390/nu11061336.
[19]	Stiburkova B, Pavelcova K, Pavlikova M, et al. The impact of dysfunctional variants of ABCG2 on hyperuricemia and gout in pediatric-onset patients[J]. Arthritis Res Ther, 2019, 21(1):77. DOI: 10.1186/s13075-019-1860-8.
[20]	Matsuo H, Takada T, Ichida K, et al. Common defects of ABCG2, a high-capacity urate exporter, cause gout: a function-based genetic analysis in a Japanese population[J]. Sci Transl Med, 2009, 1(5):5-11. DOI: 10.1126/scitranslmed.3000237.
[21]	Dehghan A, Köttgen A, Yang Q, et al. Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study[J]. Lancet, 2008, 372(9654):1953-1961. DOI: 10.1016/S0140-6736(08)61343-4.
[22]	Hamajima N, Okada R, Kawai S, et al. Significant association of serum uric acid levels with SLC2A9 rs11722228 among a Japanese population[J]. Mol Genet Metab, 2011, 103(4):378-382. DOI: 10.1016/j.ymgme.2011.04.001.
[23]	Genoveva B, María Jesús OL, Enrique JR, et al. Nutrigenetics and nutrigenomics insights into diabetes etiopathogenesis[J]. Nutrients, 2014, 6(11):5338-5369. DOI: 10.3390/nu6115338.
[24]	Khera AV, Emdin CA, Drake I, et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease[J]. N Engl J Med, 2016, 375(24):2349-2358. DOI: 10.1056/NEJMoa1605086.
[25]	Nishio K, Suzuki K, Ito Y, et al. Possible interactions of the endothelial constitutive nitric oxide synthase genotype with alcohol drinking and walking time for high serum uric acid levels among Japanese[J]. Metabolism, 2005, 54(10):1302-1308. DOI: 10.1016/j.metabol.2005.04.018.
[26]	Tu HP, Ko AM, Chiang SL, et al. Joint effects of alcohol consumption and ABCG2 Q141K on chronic tophaceous gout risk[J]. J Rheumatol, 2014, 41(4):749-758. DOI: 10.3899/jrheum.130870.
[27]	Dietrich S, Jacobs S, Zheng JS, et al. Gene-lifestyle interaction on risk of type 2 diabetes: a systematic review[J]. Obes Rev, 2019, 20(11):1557-1571. DOI: 10.1111/obr.12921.