新型体脂指标对云南彝族人群代谢综合征的预测价值研究

郭妮; 姬燕梅; 蒙妮; 李吉胜; 贺仙钰; 刀梦瑶; 金醒昉

doi:10.16766/j.cnki.issn.1674-4152.003410

新型体脂指标对云南彝族人群代谢综合征的预测价值研究

doi: 10.16766/j.cnki.issn.1674-4152.003410

1.
昆明医科大学附属延安医院心血管内科，云南昆明 650051
2.
昆明医科大学附属延安医院心脏大血管外科

基金项目:

中央引导地方科技发展资金项目 202307AB110005

详细信息

通讯作者:
金醒昉，E-mail：jinxf177@126.com

中图分类号: R589 R446.11
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出版历程
- 收稿日期: 2023-10-19
- 网络出版日期: 2024-05-27

Study on the predictive value of innovative adipose indexes for metabolic syndrome of Yi people in Yunnan Province

1.
Department of Cardiovascular Medicine, Yan ' an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan 650051, China

摘要

摘要: 目的对比新型体脂指标与传统体脂指标对云南彝族代谢综合征(MS)患病风险的预测价值。方法 2018年11月—2023年9月期间通过随机整群抽样选取云南3个彝族聚居地居民共508名作为研究对象，收集传统体脂指标腰围、臀围、BMI、腰高比(WHtR)及相关化验指标，计算新型体脂指标中国内脏脂肪指数(CVAI)、内脏脂肪指数(VAI)、脂质蓄积指数(LAP)，进行统计分析。结果云南彝族人群代谢综合征的患病率约为20.01%(102/508)。Spearman相关性分析显示MS与各体脂指标均有相关性。ROC曲线分析显示，各体脂指标的曲线下面积均>0.7，其中CVAI的AUC最大(AUC=0.872)；CVAI、VAI、LAP最佳截断值分别为120.20、2.02、56.36；在不同性别亚组及体质量亚组中，新型体脂指标的预测价值均优于传统指标。根据ROC曲线的最佳截断值定义分组为C0组(CVAI＜120.20)、C1组(CVAI≥120.20)，V0组(VAI＜2.02)、V1组(VAI≥2.02)，L0组(LAP＜56.36)、L1组(LAP≥56.36)；多因素logistic回归分析显示，C1组发生MS的风险为C0组的8.484倍；V1组发生MS的风险为V0组的13.602倍；C1组发生MS的风险为C0组的4.916倍。结论新型体脂指标预测云南彝族人群MS具有一定的优势，但在相关血脂指标缺失时，传统指标也有一定的价值；当新型体脂指标超过最佳截断值时，MS的患病风险将显著升高。
- 新型体脂指标 /
- 代谢综合征 /
- 云南彝族 /
- 预测价值
Abstract: Objective To compare the predictive value of innovative and traditional adiposity indices for metabolic syndrome (MS) risk in the Yi people of Yunnan Province. Methods A total of 508 residents from 3 Yi settlements in Yunnan Province were selected by random cluster sampling during November 2018 to September 2023. The traditional adipose indexes of waist circumference, hip circumference, BMI, waist-to-height ratio (WHtR) and related laboratory indexes were collected, and the innovative adipose indexes of China visceral adipose index (CVAI), visceral adipose index (VAI) and lipid accumulation product (LAP) were calculated. Statistical analysis was performed. Results The prevalence of metabolic syndrome was 20.01% (102/508) in the in the Yi population of Yunnan Province. Spearman correlation analysis showed that MS was significantly correlated with all adiposity indices. ROC curve analysis showed that the area under the curve of each adipose indexes was greater than 0.7, and the AUC of CVAI was the largest (AUC=0.872). The optimal cut-off values of CVAI, VAI and LAP were 120.20, 2.02 and 56.36, respectively. In different gender and body mass subgroups, the predictive value of the innovative adipose indexes is better than that of the traditional indices. According to the optimal cut-off value of the ROC curve, the groups were defined as C0 (CVAI < 120.20), C1 (CVAI≥120.20), V0 (VAI < 2.02), V1 (VAI≥2.02), L0 (LAP < 56.36) and L1 (LAP≥56.36), Multivariate logistic regression analysis showed that the risk of MS was 8.484 times higher in group C1 than in group C0. The risk of MS was 13.602 times higher in group V1 than in group V0. The risk of MS was 4.916 times higher in group C1 than in group C0. Conclusion The innovative adipose index has certain advantages in predicting MS in the Yi population of Yunnan Province, but the traditional index is also valuable when the relevant lipid index is missing. When the innovative adipose indexes exceed the optimal cut-off value, the risk of MS is significantly increased.
- Innovative adipose indexes /
- Metabolic syndrome /
- Yunnan Yi people /
- Predictive value

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图 1 各体脂指标预测彝族居民MS发生风险的ROC曲线

Figure 1. ROC curve for adiposity index predicting MS risk in Yi people

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图 2 不同性别彝族居民各体脂指标预测MS的ROC曲线

Figure 2. ROC curve for adiposity index predicting MS in Yi people of varying genders

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图 3 不同体质量彝族居民各体脂指标预测MS的ROC曲线

Figure 3. ROC curves for adiposity index predicting MS in Yi people of varying BMI

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表 1 2组彝族居民基线资料比较

Table 1. Comparison of baseline data between two groups of Yi residents

项目	非MS组(n=406)	MS组(n=102)	统计量	P值
年龄[M(P₂₅, P₇₅)，岁]	60.50(53，68)	59(52.75，67.25)	-1.079^a	0.281
性别(男性/女性，例)	147/259	29/73	1.474^b	0.141
空腹血糖[M(P₂₅, P₇₅), mmol/L]	4.94(4.61, 5.30)	5.40(4.91, 6.19)	-6.440^a	＜0.001
TG[M(P₂₅, P₇₅), mmol/L]	1.25(0.93, 1.68)	2.49(2.02, 3.54)	-11.768^a	＜0.001
HDL-C(x±s, mmol/L)	1.71±0.43	1.31±0.34	10.170^c	＜0.001
LDL-C(x±s, mmol/L)	3.33±0.85	3.67±1.18	2.717^c	0.007
CHOL(x±s, mmol/L)	5.49±1.02	6.12±1.24	4.772^c	＜0.001
SBP(x±s, mmHg)	139.19±23.58	148.51±20.19	4.021^c	＜0.001
DBP(x±s, mmHg)	82.94±13.55	86.73±10.84	2.992^c	0.003
身高(x±s, cm)	153.25±8.65	152.67±7.90	0.615^c	0.539
体重(x±s, kg)	53.82±9.79	64.10±11.99	8.023^c	＜0.001
腰围(x±s, cm)	79.60±9.86	93.46±9.14	12.871^c	＜0.001
臀围(x±s, cm)	92.31±7.11	101.16±9.29	10.525^c	＜0.001
BMI[M(P₂₅, P₇₅)]	22.67(20.41, 24.77)	26.75(24.39, 29.85)	-9.619^a	＜0.001
WHtR(x±s)	0.52±0.07	0.61±0.06	12.265^c	＜0.001
CVAI[M(P₂₅, P₇₅)]	81.71(56.27, 104.25)	138.09(122.94, 156.52)	-12.471^a	＜0.001
VAI[M(P₂₅, P₇₅)]	1.18(0.75, 1.80)	3.48(2.29, 5.44)	-12.010^a	＜0.001
LAP[M(P₂₅, P₇₅)]	14.89(-3.36, 31.78)	76.08(41.31, 115.69)	-10.167^a	＜0.001
注：^a为Z值，^b为χ²值，^c为t值。

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表 2 各指标与代谢综合征的相关性分析

Table 2. Correlation analysis between each index and metabolic syndrome

项目	体重	腰围	臀围	BMI	WHtR	CVAI	VAI	LAP
r值	0.342	0.520	0.410	0.427	0.504	0.554	0.533	0.452
P值	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001

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表 3 各体脂指标预测彝族居民MS发生风险的ROC曲线结果

Table 3. ROC curve analysis of adipose indexes in predicting the risk of metabolic syndrome in Yi people

项目	临界值	AUC(95% CI)	灵敏度(%)	特异度(%)	最大约登指数
腰围	87.70	0.844(0.822~0.866)	80.4	79.4	0.598
臀围	97.40	0.758(0.731~0.785)	69.9	71.3	0.412
体重	57.07	0.723(0.669~0.753)	75.8	60.6	0.364
BMI	25.80	0.769(0.742~0.796)	67.1	76.2	0.433
WHtR	0.58	0.822(0.799~0.844)	78.1	74.8	0.529
CVAI	120.20	0.872(0.852~0.892)	84.7	80.3	0.650
VAI	2.02	0.857(0.836~0.877)	88.0	73.5	0.615
LAP	56.36	0.808(0.779~0.838)	69.1	88.7	0.578

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表 4 不同性别亚组彝族居民各体脂指标预测MS发生风险的ROC曲线结果

Table 4. ROC curve results of adipose indexes predicting the risk of metabolic syndrome in different genders among the Yi population

指标	男性				女性
指标	临界值	AUC(95% CI)	灵敏度(%)	特异度(%)	临界值	AUC(95% CI)	灵敏度(%)	特异度(%)
腰围	89.35	0.883(0.822~0.944)	82.8	88.4	84.75	0.874(0.829~0.919)	90.4	78.4
臀围	95.75	0.817(0.729~0.904)	82.8	72.1	94.45	0.787(0.732~0.843)	80.8	64.9
体重	62.50	0.803(0.706~0.900)	79.3	70.1	53.50	0.769(0.710~0.829)	79.5	66.0
BMI	24.89	0.826(0.740~0.910)	72.4	79.6	24.13	0.851(0.805~0.898)	80.8	68.0
WHtR	0.53	0.888(0.836~0.940)	96.6	68.0	0.57	0.797(0.741~0.853)	84.9	71.4
CVAI	102.84	0.907(0.856~0.958)	89.7	79.6	114.37	0.896(0.854~0.937)	86.3	83.8
VAI	1.90	0.878(0.809~0.948)	82.8	86.4	1.84	0.889(0.852~0.926)	93.2	71.0
LAP	4.43	0.761(0.638~0.884)	69.0	85.7	50.18	0.936(0.907~0.966)	91.8	84.6

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表 5 不同体质量亚组彝族居民各体脂指标预测MS发生风险的ROC曲线结果

Table 5. ROC curve results of adipose indexes in predicting the risk of metabolic syndrome in different BMI subgroups of Yi people

指标	BMI偏低及正常组				BMI超重及肥胖组
指标	临界值	AUC(95% CI)	灵敏度(%)	特异度(%)	临界值	AUC(95% CI)	灵敏度(%)	特异度(%)
腰围	80.65	0.789(0.688~0.889)	71.4	76.0	87.65	0.829(0.775~0.883)	87.7	68.9
臀围	94.35	0.690(0.580~0.801)	52.4	80.8	98.25	0.713(0.643~0.783)	71.6	64.4
体重	52.50	0.582(0.459~0.705)	47.6	33.2	70.50	0.619(0.540~0.698)	35.8	86.7
BMI	20.57	0.667(0.568~0.767)	90.5	39.5	28.31	0.705(0.633~0.776)	51.9	81.5
WHtR	0.53	0.793(0.710~0.875)	81.0	69.4	0.59	0.814(0.758~0.871)	74.1	80.0
CVAI	102.97	0.860(0.772~0.948)	71.4	86.7	120.20	0.837(0.784~0.890)	87.7	70.4
VAI	1.70	0.887(0.812~0.962)	95.2	74.5	2.03	0.854(0.812~0.962)	87.7	77.0
LAP	41.16	0.674(0.507~0.842)	57.1	89.7	59.06	0.819(0.755~0.884)	71.6	90.4

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表 6 各指标与MS的多因素logistic回归分析

Table 6. Multivariate logistic regression analysis of each index and MS

变量	B	SE	Waldχ²	P值	OR(95% CI)
C1	2.138	0.594	12.976	＜0.001	8.484(2.651~27.155)
V1	2.610	0.576	20.509	＜0.001	13.602(4.395~42.093)
L1	1.592	0.551	8.360	0.004	4.916(1.670~14.468)
空腹血糖	0.360	0.087	17.053	＜0.001	1.434(1.208~1.701)
TG	-0.315	0.124	6.495	0.011	0.729(0.572~0.930)
CHOL	0.783	0.276	8.048	0.005	2.189(1.274~3.761)
注：变量赋值方法如下，非MS=0，MS=1；女性=0，男性=1；C0=0，CI=1；V0=0，V1=1；L0=0，L1=1；其余连续型变量以实际值赋值。

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参考文献(18)

[1]	中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版)(上)[J]. 中国实用内科杂志, 2021, 41(8): 668-695. https://www.cnki.com.cn/Article/CJFDTOTAL-HBYX202112018.htm
[2]	李燕萍, 周裕婧, 朱士胜, 等. 中国老年人代谢综合征患病率META分析[J]. 现代预防医学, 2022, 49(5): 913-917. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202205028.htm
[3]	GIRON-ULLOA A, GONZALEZ-DOMINGUEZ E, KLIMEK R S, et al. Specific macrophage subsets accumulate in human subcutaneous and omental fat depots during obesity[J]. Immunol Cell Biol, 2020, 98(10): 868-882. doi: 10.1111/imcb.12380
[4]	POETSCH M S, STRANO A, GUAN K. Role of Leptin in cardiovascular diseases[J]. Front Endocrinol (Lausanne), 2020, 11: 354. doi: 10.3389/fendo.2020.00354
[5]	NEELAND I J, ROSS R, DESPRES J P, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement[J]. Lancet Diabetes Endocrinol, 2019, 7(9): 715-725. doi: 10.1016/S2213-8587(19)30084-1
[6]	LIM U, MONROE K R, BUCHTHAL S, et al. Propensity for intra-abdominal and hepatic adiposity varies among ethnic groups[J]. Gastroenterology, 2019, 156(4): 966-975. doi: 10.1053/j.gastro.2018.11.021
[7]	李婷欣, 刘玉萍, 但立峰, 等. 四川阿坝地区高原藏族代谢综合征患病率及危险因素调查[J]. 现代预防医学, 2020, 47(23): 4235-4240. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF202023003.htm
[8]	张竞, 阿地拉·阿里木, 郭艳英, 等. 乌鲁木齐市天山区维吾尔族女性围绝经期和绝经后期代谢综合征现况及危险因素分析[J]. 中华全科医学, 2023, 21(1): 73-76, 126. doi: 10.16766/j.cnki.issn.1674-4152.002815
[9]	ZHANG X, DING Y, SHAO Y, et al. Visceral obesity-related indices in the identification of individuals with metabolic syndrome among different ethnicities in Xinjiang, China[J]. Diabetes Metab Syndr Obes, 2021, 14: 1609-1620. doi: 10.2147/DMSO.S306908
[10]	高芸艺, 冯晓冰, 程扬帆, 等. 对比分析肥胖指标对四川地区汉族与彝族成人代谢综合征的预测价值[J]. 四川大学学报(医学版), 2019, 50(1): 77-82. https://www.cnki.com.cn/Article/CJFDTOTAL-HXYK201901015.htm
[11]	HAN M, QIE R, LI Q, et al. Chinese visceral adiposity index, a novel indicator of visceral obesity for assessing the risk of incident hypertension in a prospective cohort study[J]. Br J Nutr, 2020, 126(4): 612-620.
[12]	HUANG Y C, HUANG J C, LIN C I, et al. Comparison of innovative and traditional cardiometabolic indices in estimating atherosclerotic cardiovascular disease risk in adults[J]. Diagnostics(Basel), 2021, 11(4): 603.
[13]	PAN L, XU Q, LIU J, et al. Dose-response relationship between Chinese visceral adiposity index and type 2 diabetes mellitus among middle-aged and elderly Chinese[J]. Front Endocrinol(Lausanne), 2022, 13: 959860. DOI: 10.3389/fendo.2022.959860.
[14]	WAN H, WANG Y, XIANG Q, et al. Associations between abdominal obesity indices and diabetic complications: Chinese visceral adiposity index and neck circumference[J]. Cardiovasc Diabetol, 2020, 19(1): 118. doi: 10.1186/s12933-020-01095-4
[15]	CHEN X, SHI F, XIAO J, et al. Associations between abdominal obesity indices and nonalcoholic fatty liver disease: Chinese visceral adiposity index[J]. Front Endocrinol(Lausanne), 2022, 13: 831960. DOI: 10.3389/fendo.2022.831960.
[16]	李婧, 李小凤, 闫妮, 等. 评估肥胖相关指标对非酒精性脂肪肝的筛查价值[J]. 中华全科医学, 2023, 21(2): 195-198. doi: 10.16766/j.cnki.issn.1674-4152.002843
[17]	QIAN J D, LI X M, CHEN D S, et al. Comparative analysis of the association between traditional and lipid-related obesity indicators and isolated systolic hypertension[J]. BMC Cardiovasc Disord, 2022, 22(1): 119. doi: 10.1186/s12872-022-02564-2
[18]	WANG Y, ZHAO X, CHEN Y, et al. Visceral adiposity measures are strongly associated with cardiovascular disease among female participants in Southwest China: a population-based prospective study[J]. Front Endocrinol (Lausanne), 2022, 13: 969753. DOI: 10.3389/fendo.2022.969753.