胰岛素抵抗代谢评分对原发性高血压患者并发主动脉弓钙化的预测价值

doi:10.3969/j.issn.1004-583X.2025.07.002

摘要/Abstract

摘要：

目的探究胰岛素抵抗代谢评分(metabolic score for insulin resistance, METS-IR)对原发性高血压患者并发主动脉弓钙化(aortic arch calcification, AAC)的预测价值。方法回顾性选取北京市仁和医院2022年2月-2025年2月收治的原发性高血压患者170例,依据Symeonidis法进行主动脉弓钙化评分(aortic arch calcification score, AoACS)评分测定,分为AoACS=0组(n=60)、AoACS=1组(n=46)、AoACS=2组(n=33)和AoACS=3组(n=31)。比较吸烟史、饮酒史、糖尿病史、性别、年龄、血压、体重指数(body mass index,BMI)、甘油三酯、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol, LDL-C)、高密度脂蛋白胆固醇(high-density lipoprotein cholesterol, HDL-C)、空腹血糖(fasting plasma glucose, FPG),分析各指标与原发性高血压患者并发AAC的相关性及预测价值。结果 4组饮酒史占比、性别、甘油三酯水平差异均无统计学意义(P>0.05)。4组吸烟史占比、糖尿病史占比、年龄、舒张压、收缩压、LDL-C水平、HDL-C水平、FPG水平、BMI、METS-IR差异均有统计学意义(P<0.05)。以METS-IR、吸烟史、糖尿病史、年龄、LDL-C、HDL-C、FPG、BMI、收缩压、舒张压作自变量,行有序logistic回归分析;结果表明,HDL-C为AAC的保护性因素,其余指标均为AAC的危险因素(P<0.05)。绘制受试者工作特征曲线显示,METS-IR水平预测原发性高血压并发AAC的曲线下面积为0.92,特异度96.67%,敏感度80.00%(P<0.001)。结论 METS-IR、年龄、吸烟、LDL-C、糖尿病史、FPG、BMI、收缩压、舒张压为AAC的危险因素,HDL-C为AAC的保护性因素;METS-IR预测价值更高。

关键词: 原发性高血压, 胰岛素抵抗代谢评分, 主动脉弓钙化, 回顾性分析

Abstract:

Objective To explore the predictive value of metabolic score for insulin resistance (METS-IR) in patients with essential hypertension combined with aortic arch calcification (AAC). Methods A total of 170 patients with essential hypertension admitted in the Beijing Renhe Hospital from February 2022 to February 2025 were retrospectively selected. According to the Symeonidis method, the aortic arch calcification score (AoACS) was measured and divided patients into the AoACS =0 group (n=60), AoACS =1 group (n=46), AoACS=2 group (n=33) and AoACS=3 group (n=31). Smoking history, drinking history, diabetes history, gender, age, blood pressure, body mass index (BMI), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and fasting plasma glucose (FPG) were compared. The correlation and predictive value of each index with AAC in patients with essential hypertension were analyzed. Results There were no significant differences in the proportions of drinking history, gender and TG level among the four groups (P>0.05). There were significant differences in the proportion of smoking history, the proportion of diabetes history, age, diastolic blood pressure, systolic blood pressure, LDL-C, HDL-C, FPG, BMI and METS-IR among the four groups (P<0.05). METS-IR, smoking history, diabetes history, age, LDL-C, HDL-C, FPG, BMI, systolic blood pressure and diastolic blood pressure were used as independent variables for ordinal logistic regression analysis. The results showed that HDL-C was a protective factor for AAC, and the other indicators were risk factors for AAC (P<0.05). The receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) of METS-IR in predicting primary hypertension complicated with AAC was 0.92, with a specificity of 96.67%, and a sensitivity of 80.00%(P<0.001). Conclusion METS-IR, age, smoking, LDL-C, diabetes history, FPG, BMI, and blood pressure (both systolic and diastolic) are significant risk factors for AAC in patients with essential hypertension, while HDL-C serves as a protective factor. METS-IR demonstrated superior predictive value for the presence and severity of AAC.

Key words: essential hypertension, metabolic score for insulin resistance, aortic arch calcification, retrospective analysis

中图分类号:

R544.1

刘胜华. 胰岛素抵抗代谢评分对原发性高血压患者并发主动脉弓钙化的预测价值[J]. 临床荟萃, 2025, 40(7): 589-594.

Liu Shenghua. The predictive value of the metabolic score for insulin resistance in essential hypertension patients complicated by aortic arch calcification[J]. Clinical Focus, 2025, 40(7): 589-594.

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链接本文: https://www.lchc.cn/CN/10.3969/j.issn.1004-583X.2025.07.002

https://www.lchc.cn/CN/Y2025/V40/I7/589

图/表 5

参考文献 49

[1]	范浩, 林璐璐, 马思明, 等. 针刺治疗原发性高血压病的机制研究进展[J]. 时珍国医国药, 2020, 31(4): 932-935.
[2]	周菁, 付渊博, 宋玉强, 等. 针刺参与缺血性脑卒中二级预防的研究进展[J]. 针刺研究, 2024, 49(6): 625-633.
[3]	Zeng J, Zhang T, Yang Y, et al. Association between a metabolic score for insulin resistance and hypertension: Results from National Health and Nutrition Examination Survey 2007-2016 analyses[J]. Front Endocrinol (Lausanne), 2024, 15(4):1369600.
[4]	李世杰, 张诗雨, 李雪, 等. 益气活血方防治慢性肾脏病的研究进展[J]. 中草药, 2023, 54(24): 8241-8251.
[5]	田昕彤, 周巍, 杨继, 等. 钩藤及其配伍制剂治疗高血压的研究进展[J]. 中草药, 2023, 54(13): 4395-4403.
[6]	Qian T, Sheng X, Shen P, et al. Mets-IR as a predictor of cardiovascular events in the middle-aged and elderly population and mediator role of blood lipids[J]. Front Endocrinol (Lausanne), 2023, 14(7): 1224967.
[7]	Zhang S, Wu Z, Zhuang Y, et al. The metabolic score for insulin resistance in the prediction of major adverse cardiovascular events in patients after coronary artery bypass surgery: A multicenter retrospective cohort study[J]. Diabetol Metab Syndr, 2023, 15(1): 157. doi: 10.1186/s13098-023-01133-7 pmid: 37461067
[8]	Islam MS, Wei P, Sujzauddula M, et al. The interplay of factors in metabolic syndrome: Understanding its roots and complexity[J]. Mol Med, 2024, 30(1): 279. doi: 10.1186/s10020-024-01019-y pmid: 39731011
[9]	Guo D, Zhang C, Zhang M, et al. Metabolic score for insulin resistance predicts major adverse cardiovascular event in premature coronary artery disease[J]. Aging (Albany NY), 2024, 16(7): 6364-6383.
[10]	伍晓梅, 朱玲, 吴琳娜. 原发性高血压患者胰岛素抵抗与抗心磷脂抗体的分析[J]. 河北医学, 2017, 23(07): 1064-1067.
[11]	郎博. 空腹血糖受损患者胰岛素抵抗与血压的相关性研究[D]. 重庆: 中国人民解放军陆军军医大学, 2020.
[12]	Symeonidis G, Papanas N, Giannakis I, et al. Gravity of aortic arch calcification as evaluated in adult Greek patients[J]. Int Angiol, 2002, 21(3): 233-236. pmid: 12384643
[13]	高血压精准化诊疗专家共识组成员, 老年心脑血管病教育部重点实验室. 高血压精准化诊疗中国专家共识(2024)[J]. 中华高血压杂志(中英文), 2024, 32(6): 505-519.
[14]	刘陆, 王贻琳, 肖顺丽, 等. 基于机械力离子通道Piezo1的中药生物力药理学研究进展[J]. 中国实验方剂学杂志, 2023, 29(10): 235-244.
[15]	李浩, 吴勉华, 马艳霞, 等. 中药调控铁死亡抑制肝纤维化的研究进展[J]. 南京中医药大学学报, 2023, 39(6): 587-593.
[16]	于丽, 卫靖靖, 朱明军, 等. 心力衰竭风险预警模型研究进展[J]. 中国中西医结合杂志, 2023, 43(6): 752-756.
[17]	龙英全, 钟秋安, 潘姣姣, 等. 吸烟对男性人群体质指数与血压关系的影响[J]. 中华疾病控制杂志, 2015, 19(3): 218-221.
[18]	肖谷雨, 姚姜羽, 冯群, 等. 参芪降糖颗粒治疗糖尿病肾病的临床疗效及作用机制研究进展[J]. 中草药, 2023, 54(19): 6469-6481.
[19]	Gong Y, Luo H, Li Z, et al. Metabolic profile of Alzheimer's disease: Is 10-hydroxy-2-decenoic acid a pertinent metabolic adjuster?[J]. Metabolites, 2023, 13(8):954.
[20]	Li D, Fan C, Li X, et al. The role of macrophage polarization in vascular calcification[J]. Biochem Biophys Res Commun, 2024, 710(5): 149863.
[21]	高慧, 于露. 参芪降糖颗粒对2型糖尿病模型大鼠血糖血脂的改善作用及其量效关系研究[J]. 中国药房, 2016, 27(13): 1801-1803.
[22]	北京高血压防治协会, 中国老年学和老年医学学会, 北京市社区卫生协会, 等. 成人高血压合并2型糖尿病和血脂异常基层防治中国专家共识(2024年版)[J]. 中国全科医学, 2024, 27(28):3453-3475+3482. doi: 10.12114/j.issn.1007-9572.2024.0116
[23]	中国高血压防治指南修订委员会, 高血压联盟(中国), 中国医疗保健国际交流促进会高血压病学分会, 等. 中国高血压防治指南(2024年修订版)[J]. 中华高血压杂志(中英文), 2024, 32(7):603-700.
[24]	赵承启, 邹伟. 中医药调控细胞焦亡干预缺血性脑卒中的研究进展[J]. 湖南中医药大学学报, 2023, 43(9):1742-1748.
[25]	徐思雨, 林建国, 孙梓宜, 等. 临界性高血压研究进展及思考[J]. 中国中西医结合杂志, 2024, 44(4):504-512.
[26]	张可, 蒋慕蓉, 杨文丽, 等. 黄酮类化合物改善糖尿病周围神经病变机制研究进展[J]. 中草药, 2024, 55(10):3539-3548.
[27]	Onyango AN. Cellular stresses and stress responses in the pathogenesis of insulin resistance[J]. Oxid Med Cell Longev, 2018, 2018(7): 4321714.
[28]	谢菲, 高歌, 张成敏. 血清空腹血清胰岛素、Ca2+、脂联素在妊娠期高血压中的表达及临床意义[J]. 江苏预防医学, 2020, 31(2):145-147+151.
[29]	解静, 牛美芝, 张蕊. T2DM合并高血压病人NAGL、sfrp5、ACE水平变化及与左心室重构的关系探讨[J]. 中西医结合心脑血管病杂志, 2020, 18(8):1270-1272.
[30]	王旭开, 蔡鹏. 胰岛素抵抗是高血压的原因抑或结果?[J]. 中华高血压杂志, 2020, 28(4):302-307.
[31]	衷锐, 骆晓敏, 姚娟丽, 等. 盐酸吡格列酮胶囊联合缬沙坦胶囊对代谢综合征患者血压水平及胰岛素抵抗的影响[J]. 临床医学工程, 2019, 26(12):1675-1676.
[32]	陈雪蓉, 沈冉, 靳晴, 等. 血清ANGPTL4、ANGPTL6水平与妊娠期糖尿病胰岛素抵抗的相关性研究[J]. 中国计划生育学杂志, 2019, 27(12):1686-1689.
[33]	Amaro R, Perea L, Sibila O. Future directions in bronchiectasis research[J]. Clin Chest Med, 2022, 43(1): 179-187. doi: 10.1016/j.ccm.2021.12.005 pmid: 35236557
[34]	Tamehri Zadeh SS, Cheraghloo N, Masrouri S, et al. Association between metabolic score for insulin resistance and clinical outcomes: Insights from the Tehran lipid and glucose study[J]. Nutr Metab (Lond), 2024, 21(1): 34.
[35]	Popad D, Gherasim A, Caba L, et al. Cathelicidin: Insights into Its impact on metabolic syndrome and chronic inflammation[J]. Metabolites, 2024, 14(12):672.
[36]	Foda AM, Ibrahim SS, Ibrahim SM, et al. Pterostilbene ameliorates cognitive impairment in polycystic ovary syndrome rat model through improving insulin resistance via the IRS-1/PI3K/Akt/GSK-3β pathway: A comparative study with metformin[J]. ACS Chem Neurosci, 2024, 15(16): 3064-3077. doi: 10.1021/acschemneuro.4c00352 pmid: 39119909
[37]	Gowda PC, Lobner K, Hafezi-nejad N, et al. Bibliometric analysis of interventional radiology studies in PubMed-indexed literature from 1991 to 2020[J]. Clin Imaging, 2022, 85(5): 43-47.
[38]	Radu F, Potcovaru CG, Salmen T, et al. The link between NAFLD and metabolic syndrome[J]. Diagnostics (Basel), 2023, 13(4):614.
[39]	Pacini G, Mari A. Methods for clinical assessment of insulin sensitivity and beta-cell function[J]. Best Pract Res Clin Endocrinol Metab, 2003, 17(3): 305-322. doi: 10.1016/s1521-690x(03)00042-3 pmid: 12962688
[40]	Lin D, Qi Y, Huang C, et al. Associations of lipid parameters with insulin resistance and diabetes: A population-based study[J]. Clin Nutr, 2018, 37(4): 1423-1429. doi: S0261-5614(17)30229-7 pmid: 28673690
[41]	Bello-chavolla OY, Almeda-valdes P, Gomez-velasco D, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes[J]. Eur J Endocrinol, 2018, 178(5): 533-544. doi: 10.1530/EJE-17-0883 pmid: 29535168
[42]	Zhang X, Liu F, Li W, et al. Metabolic score for insulin resistance (METS-IR) predicts adverse cardiovascular events in patients with type 2 diabetes and ischemic cardiomyopathy[J]. Diabetes Metab Syndr Obes, 2023, 16(5): 1283-1295.
[43]	Rao K, Yang J, Wu M, et al. Association between the metabolic score for insulin resistance and hypertension in adults: A meta-analysis[J]. Horm Metab Res, 2023, 55(4): 256-265.
[44]	Zhang M, Liu D, Qin P, et al. Association of metabolic score for insulin resistance and its 6-year change with incident type 2 diabetes mellitus[J]. J Diabetes, 2021, 13(9): 725-734. doi: 10.1111/1753-0407.13161 pmid: 33644990
[45]	Wu Z, CuiI H, Li W, et al. Comparison of three non-insulin-based insulin resistance indexes in predicting the presence and severity of coronary artery disease[J]. Front Cardiovasc Med, 2022, 9: 918359.
[46]	Cheng H, Yu X, Li YT, et al. Association between METS-IR and prediabetes or type 2 diabetes mellitus among elderly subjects in China: A large-scale population-based study[J]. Int J Environ Res Public Health, 2023, 20(2):1053.
[47]	Xu C, Song G, Hu D, et al. Association of METS-IR with incident hypertension in non-overweight adults based on a cohort study in Northeastern China[J]. Eur J Public Health, 2022, 32(6): 884-890.
[48]	Zhang X, Yu C, Ye R, et al. Correlation between non-insulin-based insulin resistance indexes and the risk of prehypertension: A cross-sectional study[J]. J Clin Hypertens (Greenwich), 2022, 24(5): 573-581. doi: 10.1111/jch.14449 pmid: 35411676
[49]	Hu X, Han P, Liu Y. Metabolic status and hypertension: The Impact of insulin resistance-related indices on blood pressure regulation and hypertension risk[J]. J Am Nutr Assoc, 2025: 1-11.

项目	AoACS=0组 (n=60)	AoACS=1组 (n=46)	AoACS=2组 (n=33)	AoACS=3组 (n=31)	χ²值	P值
吸烟史[例(%)]	21(35.00)	18(39.13)	20(60.61)^*	22(70.97)^*#	3.533	0.002
饮酒史[例(%)]	24(40.00)	24(52.17)	18(54.55)	22(70.97)	1.231	0.177
糖尿病史[例(%)]	19(31.67)	22(47.83)	18(54.55)^*	19(61.29)^*	2.214	0.030
男性[例(%)]	23(38.33)	22(47.83)	17(51.52)	18(58.06)	1.256	0.172
年龄(岁)	58.6±10.72	58.85±9.01	61.52±9.81	70.65±5.72^*#△	3419.919	<0.001
舒张压(mmHg)	92.52±3.34	97.46±4.51^*	102.60±4.40^*#	107.00±2.59^*#△	5002.699	<0.001
收缩压(mmHg)	146.90±4.76	156.80±5.72^*	167.20±4.88^*#	170.70±7.90^*#	15309.362	<0.001
TG(mmol/L)	1.57±0.44	1.81±0.48	1.74±0.23	1.84±0.53	1.310	0.084
LDL-C(mmol/L)	3.47±0.77	3.65±0.95	4.37±0.34^*#	4.69±0.81^*#	64.787	<0.001
HDL-C(mmol/L)	1.43±0.32	1.31±0.29	1.22±0.37^*	1.23±0.33^*	1.430	0.001
FPG(mmol/L)	6.51±1.30	6.74±1.79	7.51±1.29^*	8.21±2.17^*#△	70.123	<0.001
BMI(kg/m²)	21.07±2.22	23.69±2.09^*	25.36±4.23^*	28.68±4.02^*#△	1048.057	<0.001
METS-IR	30.57(28.73~31.62)	36.16(33.85~38.62)^*	40.38(37.03~42.08)^*#	43.93(40.31~47.31)^*#△	3934.159	<0.001

项目	AoACS=0组 (n=60)	AoACS=1组 (n=46)	AoACS=2组 (n=33)	AoACS=3组 (n=31)	χ²值	P值
吸烟史[例(%)]	21(35.00)	18(39.13)	20(60.61)^*	22(70.97)^*#	3.533	0.002
饮酒史[例(%)]	24(40.00)	24(52.17)	18(54.55)	22(70.97)	1.231	0.177
糖尿病史[例(%)]	19(31.67)	22(47.83)	18(54.55)^*	19(61.29)^*	2.214	0.030
男性[例(%)]	23(38.33)	22(47.83)	17(51.52)	18(58.06)	1.256	0.172
年龄(岁)	58.6±10.72	58.85±9.01	61.52±9.81	70.65±5.72^*#△	3419.919	<0.001
舒张压(mmHg)	92.52±3.34	97.46±4.51^*	102.60±4.40^*#	107.00±2.59^*#△	5002.699	<0.001
收缩压(mmHg)	146.90±4.76	156.80±5.72^*	167.20±4.88^*#	170.70±7.90^*#	15309.362	<0.001
TG(mmol/L)	1.57±0.44	1.81±0.48	1.74±0.23	1.84±0.53	1.310	0.084
LDL-C(mmol/L)	3.47±0.77	3.65±0.95	4.37±0.34^*#	4.69±0.81^*#	64.787	<0.001
HDL-C(mmol/L)	1.43±0.32	1.31±0.29	1.22±0.37^*	1.23±0.33^*	1.430	0.001
FPG(mmol/L)	6.51±1.30	6.74±1.79	7.51±1.29^*	8.21±2.17^*#△	70.123	<0.001
BMI(kg/m²)	21.07±2.22	23.69±2.09^*	25.36±4.23^*	28.68±4.02^*#△	1048.057	<0.001
METS-IR	30.57(28.73~31.62)	36.16(33.85~38.62)^*	40.38(37.03~42.08)^*#	43.93(40.31~47.31)^*#△	3934.159	<0.001

变量	赋值
因变量
AoACS得分	1=未见钙化影组,2=小点状或单个薄片状钙化影组,3=一个或多个区域的粗钙化影组,4=环形钙化影组
自变量
糖尿病史	1=无病史,2=有病史
吸烟史	1=无,2=有
年龄	连续性变量
LDL-C	连续性变量
HDL-C	连续性变量
FPG	连续性变量
BMI	连续性变量
收缩压	连续性变量
舒张压	连续性变量
METS-IR	连续性变量

变量	赋值
因变量
AoACS得分	1=未见钙化影组,2=小点状或单个薄片状钙化影组,3=一个或多个区域的粗钙化影组,4=环形钙化影组
自变量
糖尿病史	1=无病史,2=有病史
吸烟史	1=无,2=有
年龄	连续性变量
LDL-C	连续性变量
HDL-C	连续性变量
FPG	连续性变量
BMI	连续性变量
收缩压	连续性变量
舒张压	连续性变量
METS-IR	连续性变量

自变量	回归系数	标准误	Waldχ²值	P值	OR值	95%CI
METS-IR	0.405	0.042	93.404	<0.001	1.50	0.323~0.488
吸烟史	1.015	0.286	12.611	<0.001	2.76	0.455~1.575
糖尿病史	0.835	0.283	8.681	0.003	2.30	0.279~1.390
年龄	0.071	0.015	23.087	<0.001	1.07	0.042~0.100
LDL-C	1.723	0.216	63.492	<0.001	5.60	1.299~2.147
HDL-C	-2.026	0.518	15.267	<0.001	0.13	-3.042~-1.01
FPG	0.394	0.086	20.896	<0.001	1.48	0.225~0.564
BMI	0.480	0.057	71.710	<0.001	1.62	0.369~0.591
收缩压	0.311	0.032	94.539	<0.001	1.36	0.248~0.373
舒张压	0.411	0.042	96.812	<0.001	1.51	0.329~0.493