江西省居民不同肥胖指标对脑卒中风险的预测效果

周伟; 丁聪聪; 祝玲娟; 王涛; 鲍慧慧; 程晓曙

doi:10.16462/j.cnki.zhjbkz.2020.09.006

江西省居民不同肥胖指标对脑卒中风险的预测效果

doi: 10.16462/j.cnki.zhjbkz.2020.09.006

周伟¹,
丁聪聪²,
祝玲娟¹,
王涛¹,
鲍慧慧^2, ,,
程晓曙^2, ,

1.
330006 南昌, 南昌大学第二附属医院心血管病防治中心
2.
330006 南昌, 南昌大学第二附属医院心血管内科

基金项目:

国家“十二五”科技支撑项目 2011BAI11B01

详细信息

通讯作者:
鲍慧慧, E-mail:huihui_bao77@126.com

程晓曙, E-mail:xiaoshumenfan@126.com

中图分类号: R181.2
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- 被引次数: 0
出版历程
- 收稿日期: 2019-12-07
- 修回日期: 2020-07-04
- 刊出日期: 2020-09-10

Value of obesity indicators on risk prediction for stroke in Jiangxi Province

1.
Center for Prevention and Treatment of Cardiovascular Diseases, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China
2.
Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China

Funds:

National " Twelfth Five - Year Plan for Science & Technology Support 2011BAI11B01

More Information

Corresponding author: BAO Hui-hui, E-mail:huihui_bao77@126.com; CHENG Xiao-shu, E-mail:xiaoshumenfan@126.com

摘要

摘要: 目的探讨不同肥胖指标预测脑卒中风险的效果，以尽早发现高危人群。方法于2013年11月1日-2014年7月31日，采用多阶段分层随机抽样方法选取调查对象，进行问卷调查和体格测量。应用Logistic回归分析模型分析不同肥胖指标[体重指数（body mass index，BMI）、腰围（waist circumference，WC）、腰围身高比（waist-to-height ratio，WHtR）、身体圆度指数（body roundness index，BRI）、身体脂肪率（body fat rate，BFP）、内脏脂肪指数（visceral fat index，VAI）]与脑卒中的关系，并用受试者工作特征（receiver operating characteristic，ROC）曲线下面积（area under curve，AUC）分析各指标预测脑卒中的效果。结果 15 172名调查对象平均年龄为（53.00±17.91）岁；其中脑卒中患者226人。与非脑卒中人群相比，脑卒中人群的BMI、WC、WHtR、BRI、BFP、VAI均较高（均有P < 0.05）。多因素分析显示，各肥胖指标与脑卒中风险均呈正相关。各肥胖指标的AUC从大到小依次为BRI和VAI联合（0.648）、WHtR和VAI联合（0.645）、WHtR（0.631）、BRI（0.630）、WC（0.613）、VAI（0.609）、BFP（0.592）、BMI（0.572），前四个指标的AUC差异均无统计学意义（均有P>0.05）。结论肥胖指标升高可增加脑卒中风险。BRI和VAI联合、WHtR和VAI联合、WHtR、BRI均是预测脑卒中的良好指标，WHtR或BRI预测脑卒中较简便，而BRI和VAI联合、WHtR和VAI联合预测脑卒中更全面。
- 肥胖指标 /
- 脑卒中 /
- 预测 /
- 受试者工作特征曲线
Abstract: Objective To explore the effect of different obesity indexes on risk of stroke, so as to find the high-risk population. Methods From November 2013 to July 2014, a multi-stage stratified, random sampling method was used to select the subjects for questionnaire survey and physical measurement. Logistic regression was used to analyze the relationship between different obesity indexes body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), body roundness index (BRI), body fat rate (BFP), visceral fat index (VAI) and stroke, and the area under the receiver operating characteristic (ROC) curve (AUC) was used to analyze the effect of each index in predicting stroke. Results The average age of 15 172 subjects was (53.00±17.91) years old, including 226 stroke patients. Compared with non-stroke group, BMI, WC, WHtR, BRI, BFP and VAI of stroke population were higher (all P < 0.05). Multivariate analysis showed that all obesity indexes were positively correlated with stroke risk. The AUC of each obesity index from large to small was BRI and VAI combined (0.648), WHtR and VAI (0.645), WHtR (0.631), BRI (0.630), WC (0.613), VAI (0.609), BFP (0.592), and BMI (0.572). Moreover, there was no significant difference in AUC of the first four indicators (all P>0.05). Conclusions The increase of obesity index value can increase the risk of stroke. The combination of BRI and VAI, combination of WHtR and VAI, WHtR and BRI are good indicators for predicting stroke. WHtR or BRI is easier to predict stroke, while BRI and VAI, WHtR and VAI are more comprehensive in predicting stroke.
- Obesity index /
- Stroke /
- Prediction /
- Receiver operating characteristic

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图 1 肥胖指标预测脑卒中的ROC曲线

Figure 1. ROC curve of each obesity indicator in predicting strokecorn

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表 1 脑卒中人群与非脑卒中人群基本情况比较[n(%)]

Table 1. Comparison of basic conditions between stroke patients and non-stroke patients[n(%)]

变量	总人数(n=15 172)	非脑卒中人群(n=14 946)	脑卒中人群(n=226)	χ²/t值	P值
性别				10.979	< 0.001
男	6 222 (41.01)	6 105 (40.85)	117 (51.77)
女	8 950 (58.99)	8 841 (59.15)	109 (48.23)
居住地				0.830	0.362
城市	7 734 (50.98)	7 612 (50.93)	122 (53.98)
农村	7 438 (49.02)	7 334 (49.07)	104 (46.02)
吸烟				0.047	0.829
从不吸烟	12 385 (81.88)	12 202 (81.89)	183 (81.33)
吸烟	2 740 (18.12)	2 698 (18.11)	42 (18.67)
饮酒				7.247	0.007
从不饮酒	11 496 (76.05)	11 342 (76.16)	154 (68.44)
饮酒	3 621 (23.95)	3 550 (23.84)	71 (31.56)
高血压				275.775	< 0.001
无	10 771 (70.99)	10 723 (71.74)	48 (21.24)
有	4 401 (29.01)	4 223 (28.26)	178 (78.76)
年龄(岁)	53.00 ± 17.91	52.78 ± 17.91	67.76 ± 9.74	-12.543	< 0.001
BMI(kg/m ²)	22.84 ± 3.68	22.82 ± 3.66	23.85 ± 4.33	-4.075	< 0.001
WC(m)	78.95 ± 9.67	78.89 ± 9.66	82.51 ± 9.45	-5.587	< 0.001
WHtR	0.50 ± 0.06	0.50 ± 0.06	0.53 ± 0.07	-6.958	< 0.001
BRI	4.72 ± 0.98	4.72 ± 0.98	5.17 ± 1.10	-6.922	< 0.001
BFP(%)	27.39 ± 9.13	27.36 ± 9.15	29.62 ± 7.70	-3.412	< 0.001
VAI	7.24 ± 4.28	7.21 ± 4.27	8.94 ± 4.45	-5.612	< 0.001
SBP (mmHg)	125.74 ± 19.16	125.54 ± 19.03	138.95 ± 23.08	-10.381	< 0.001
DBP (mmHg)	73.99 ± 10.63	73.94 ± 10.59	77.39 ± 12.66	-2.046	< 0.001
睡眠时间(h)	7.43 ± 1.25	7.43 ± 1.25	7.38 ± 1.48	0.506	0.613

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表 2 各肥胖指标与脑卒中关系的多因素分析结果

Table 2. Multivariate analysis results of the assciation between obesity indicators and stroke

肥胖指标	模型1	P值	模型2	P值	模型3	P值
BMI	1.06 (1.03~1.10)	< 0.001	1.08 (1.05~1.11)	< 0.001	1.08 (1.05~1.11)	< 0.001
WC	1.04 (1.02~1.05)	< 0.001	1.03 (1.02~1.05)	< 0.001	1.03 (1.02~1.05)	< 0.001
WHtR	1.52 (1.35~1.71)	< 0.001	1.39 (1.22~1.58)	< 0.001	1.38 (1.21~1.57)	< 0.001
BRI	1.43 (1.29~1.57)	< 0.001	1.32 (1.18~1.48)	< 0.001	1.31 (1.17~1.48)	< 0.001
BFP	1.02 (1.01~1.03)	< 0.001	1.02 (1.00~1.03)	0.018	1.02 (1.00~1.03)	0.023
VAI	1.08 (1.05~1.10)	< 0.001	1.05 (1.02~1.08)	< 0.001	1.05 (1.02~1.08)	0.001
注：模型1：未调整；模型2：调整年龄、性别；模型3：调整年龄、性别、地区、吸烟、饮酒、高血压、睡眠时间、收缩压、舒张压。

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表 3 肥胖指标对脑卒中风险的预测结果

Table 3. Prediction results of obesity indicators on stroke

指标	最佳切点值	特异度	灵敏度	AUC(95% CI)值	标准误	P值
WHtR	0.5	0.599	0.611	0.631 (0.595~0.667)	0.021	＜0.001
BRI	4.6	0.485	0.721	0.630 (0.595~0.665)	0.021	＜0.001
WC	79.0	0.523	0.659	0.613 (0.576~0.649)	0.019	＜0.001
VAI	7.5	0.610	0.571	0.609 (0.572~0.645)	0.019	＜0.001
BFP	30.7	0.682	0.451	0.592 (0.556~0.628)	0.019	＜0.001
BMI	22.5	0.509	0.624	0.572 (0.533~0.611)	0.018	＜0.001
BRI+VAI	-	0.547	0.686	0.648 (0.614~0.681)	0.022	＜0.001
WHtR+VAI	-	0.549	0.706	0.645 (0.611~0.678)	0.022	＜0.001

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参考文献(26)

[1]	王陇德, 刘建民, 杨弋, 等.我国脑卒中防治仍面临巨大挑战-《中国脑卒中防治报告2018》概要[J].中国循环杂志, 2019, 34(2):105-119. DOI: 10.3969/j.issn.1000-3614.2019.02.001. Wang LD, Liu JM, Yang Y, et al. The prevention and treatment of stroke still face huge challenges-brief report on stroke prevention and treatment in China, 2018[J]. Chin Circul J, 2019, 34(2):105-119. DOI:10.3969/j.issn.1000-3614. 2019.02.001.
[2]	张艺宏, 王梅, 孙君志, 等. 2014年中国城乡居民超重肥胖流行现状-基于22省(市、区)国家国民体质监测点的形态数据[J].成都体育学院学报, 2016, 42(5):93-100. DOI: 10.15942/j.jcsu.2016.05.016. Zhang YH, Wang M, Sun JZ, et al. Prevalence of overweight and obesity among urban and rural residents in 2014-an analysis based on the data from the national physical fitness surveillance stations in 22 provinces[J]. Journal of Chengdu Sport University, 2016, 42(5):93-100. DOI: 10.15942/j.jcsu.2016.05.016.
[3]	Abdullah A, Stoelwinder J, Shortreed S, et al. The duration of obesity and the risk of type 2 diabetes[J]. Public Health Nutr, 2011, 14(1):119-126. DOI: 10.1017/S1368980010001813.
[4]	Parmar P, Krishnamurthi R, Ikram MA, et al. The stroke riskometer (TM) app:validation of a data collection tool and stroke risk predictor[J]. Int J Stroke, 2015, 10(2):231-244. DOI: 10.1111/ijs.12411.
[5]	胡晶晶, 王缓, 吴飞, 等.安庆地区农村人群腰高比与脑卒中发病关系的研究[J].中华疾病控制杂志, 2013, 17(4):302-305. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jbkzzz201304007 Hu JJ, Wang H, Wu F, et al. The study of waist-to-height ratio and risk of stroke in Anqing rural areas[J]. Chin J Dis Control Prev, 2013, 17(4):302-305. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jbkzzz201304007
[6]	Marie B, Kari K, Aline W, et al. Measures of abdominal adiposity and the risk of stroke:the monica risk, genetics, archiving and monograph (MORGAM) study[J]. Stroke, 2011, 42:2872-2877. DOI: 10.1161/STROKEAHA.111.614099.
[7]	Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes:0.5 could be a suitable global boundary value[J]. Nutr Res Rev, 2010, 23:247-269. DOI: 10.1017/S0954422410000144.
[8]	王佳丽, 陈祚, 张林峰, 等.新疆、西藏地区35岁及以上人群身体脂肪率、内脏脂肪指数与心脏代谢性危险因素聚集的关系[J].中华流行病学杂志, 2017, 38(6):727-731. DOI: 10.3760/cma.j.issn.0254-6450.2017.06.007. Wang JL, Chen Z, Zhang LF, et al. Association between body fat percentage, visceral fat index and cardiometabolic risk factor clustering among population aged 35 year old or over, in Tibet Autonomous Region and Xinjiang Uygur Autonomous Region[J]. Chin J Epidemiol, 2017, 38(6):727-731. DOI: 10.3760/cma.j.issn.0254-6450.2017.06.007.
[9]	郭吉平, 黄久仪, 管阳太, 等.脑血流动力学改变与脑卒中发病关系的队列研究[J].中华预防医学杂志, 2013, 47(4):323-327. DOI: 10.3760/cma.j.issn.0253-9624.2013.04.008. Guo JP, Huang JY, Guan YT, et al. A cohort study on the relationship between cerebrovascular hemodynamic changing and risk of stroke[J]. Chin J Prev Med, 2013, 47(4):323-327. DOI: 10.3760/cma.j.issn.0253-9624.2013.04.008.
[10]	中国高血压防治指南修订委员会.中国高血压防治指南2010[J].中华心血管病杂志, 2011, 39(7):579-616. DOI: 10.3760/cma.j.issn.0253-3758.2011.07.002. Writing Group of 2010 Chinese Guidelines for the Management of Hypertension.2010 Chinese guidelines for the management of hypertension[J]. Chin J Cardiol, 2011, 39(7):579-616. DOI: 10.3760/cma.j.issn.0253-3758.2011.07.002.
[11]	中国成人血脂异常防治指南修订联合委员会.中国成人血脂异常防治指南(2016年修订版)[J].中华心血管病杂志, 2016, 44(10):833-849. DOI: 10.3760/cma.j.issn.0253-3758.2016.10.005. Chinese Joint Committee on The Revision of Guidelines for the Prevention and Treatment of Dyslipidemia in Adults. 2016 Chinese guideline for the management of dyslipidemia in adults[J]. Chin J Cardiol, 2016, 44(10):833-849. DOI: 10.3760/cma.j.issn.0253-3758.2016.10.005.
[12]	Yang L, Yan J, Tang X, et al. Prevalence, awareness, treatment, control and risk factors associated with hypertension among adults in southern China, 2013[J]. PLoS One, 2016, 11(1):e0146181. DOI: 10.1371/journal.pone.0146181.
[13]	Amato MC, Giordano C. Visceral adiposity index:an indicator of adipose tissue dysfunction[J]. Int J Endocrinol, 2014, 2014:730827. DOI: 10.1155/2014/730827.
[14]	Thomas DM, Bredlau C, Bosy-Westphal, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model[J]. Obesity, 2013, 21:2264-2271. DOI: 10.1002/oby.20408.
[15]	王卫华, 崔时贝, 纪晓雯, 等.睡眠时长与脑卒中及其危险因素间关系的研究进展[J].中国全科医学, 2019, 22(31):3897-3901. DOI: 10.12114/j.iss.1007-9572.2019.00.631. Wang WH, Cui SB, Ji XW, et al. Advances in research on association between sleep duration and stroke and its risk factors[J]. Chin Genl Prac, 2019, 22(31):3897-3901. DOI: 10.12114/j.iss.1007-9572.2019.00.631.
[16]	Chen ZM, Iona A, Parish S, et al. Adiposity and risk of ischaemic and haemorrhagic stroke in 0.5 million Chinese men and women:a prospective cohort study[J]. Lancet Glob Health, 2018, 6(6):630-640. DOI:10.1016/S2214-109X (18)30216-X.
[17]	Kernan WN, Inzucchi SE, Sawan C, et al. Obesity:a stubbornly obvious target for stroke prevention[J]. Stroke, 2013, 44(1):278-286. DOI: 10.1161/STROKEAHA.111.639922.
[18]	Heymsfield SB, Scherzer R, Pietrobelli A, et al. Body mass index as a phenotypic expression of adiposity:quantitative contribution of muscularity in a population-based sample[J]. Int J Obes (Lond), 2009, 33(12):1363-1373. DOI: 10.1038/ijo.2009.184.
[19]	Muller MJ, Lagerpusch M, Enderle J, et al. Beyond the body mass index:tracking body composition in the pathogenesis of obesity and the metabolic syndrome[J]. Obes Rev, 2012, 13(Suppl 2):6-13. DOI: 10.1111/j.1467-789X.2012.01033.x.
[20]	Bazzano LA, Gu DF, Whelton MR, et al. Body mass index and risk of stroke among Chinese men and women[J]. Ann Neurol, 2010, 67(1):11-20. DOI: 10.1002/ana.21950.
[21]	Abell JE, Egan BM, Wilson PW, et al. Differences in cardiovascular disease mortality associated with body mass between Black and White persons[J]. Am J Public Health, 2008, 98(1)63-66. DOI: 10.2105/AJPH.2006.093781.
[22]	Folsom AR, Kushi LH, Anderson KE, et al. Associations of general and abdomillal obesity, with multiple health outcomes in older women:the Iowa Women's Health Study[J]. Archives of intemal medicine, 2000, 160:2117-2128. DOI:10.1001/archinte. 160.14.2117.
[23]	Maessen MF, Eijsvogels TM, Verheggen RJ, et al. Entering a new era of body indices:the feasibility of a body shape index and body roundness index to identify cardiovascular health status[J]. PLoS One, 2014, 9(9):e107212. DOI: 10.1371/journal.pone.0107212.
[24]	Tian S, Zhang X, Xu Y, et al. Feasibility of body roundness index for identifying a clustering of cardiometabolic abnormalities com-pared to BMI, waist circumference and other anthropometric indices:the China Health and Nutrition Survey, 2008 to 2009[J]. Medicine (Baltimore), 2016, 95(34):e4642. DOI: 10.1097/MD.0000000000004642
[25]	Xu Y, Yan W, Cheung YB. Body shape indices and cardiometabolic risk in adolescents[J]. Ann Hum Biol, 2015, 42(1):70-75. DOI: 10.3109/03014460.2014.903998.
[26]	Cheung YB. A body shape index in middle-age and older indonesian population:scaling exponents and association with incident hypertension[J]. PLoS One, 2014, 9(1):e85421. DOI: 10.1371/journal.pone.0085421.