2型糖尿病与钙化性主动脉瓣疾病的中介变量分析
Analysis of Mediator Variables of Type 2 Diabetes and Calcific Aortic Valve Disease
DOI: 10.12677/acm.2025.1551637, PDF, HTML, XML,   
作者: 刘晓静*：青岛大学附属医院内分泌科，山东 青岛；巩京帅：青岛大学附属医院西海岸院区心脏大血管外科，山东 青岛
关键词: 糖尿病；钙化性主动脉瓣疾病；高血压；甘油三酯葡萄糖指数；中介效应分析；Diabetes Mellitus； Calcific Aortic Valve Disease； Hypertension； Triglyceride-Glucose Index； Analysis of Mediation Effects
摘要: 本研究探讨在T2DM与CAVD的关系中体重指数、高血压、慢性肾脏病、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值的中介作用。纳入2018年1月至2022年12月青岛大学附属医院心脏大血管外科和健康体检中心的患者数据，采用因果中介效应分析方法，探讨T2DM与CAVD的中介变量。共纳入3979名参与者，CAVD组1925人，非CAVD组2054人。高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值、慢性肾脏病的中介效应分别为0.114、0.042、0.029、0.015、0.006 (P均<0.001)。高血压的中介效应最大，其次是甘油三酯葡萄糖指数。在糖尿病患者中，强化管理血压、血糖和血脂可降低CAVD风险。
Abstract: This study aims to investigate the mediating effects of body mass index, hypertension, chronic kidney disease, triglyceride-glucose index, total cholesterol/high-density lipoprotein ratio, and neutrophil-to-lymphocyte ratio in the relationship between type 2 diabetes mellitus (T2DM) and calcific aortic valve disease (CAVD). Data from patients at the Department of Cardiothoracic Surgery and the Health Examination Center of Qingdao University Affiliated Hospital between January 2018 and December 2022 were included. Causal mediation analysis was used to explore the mediating variables in the association between T2DM and CAVD. A total of 3979 participants were included, with 1925 in the CAVD group and 2054 in the non-CAVD group. The mediating effects of hypertension, triglyceride-glucose index, total cholesterol/high-density lipoprotein ratio, neutrophil-to-lymphocyte ratio, and chronic kidney disease were 0.114, 0.042, 0.029, 0.015, and 0.006, respectively (all P < 0.001). Hypertension had the largest mediating effect, followed by the triglyceride-glucose index. Based on these findings, intensive management of blood pressure, blood glucose, and blood lipids in diabetic patients may reduce the risk of CAVD.
文章引用：刘晓静, 巩京帅. 2型糖尿病与钙化性主动脉瓣疾病的中介变量分析[J]. 临床医学进展, 2025, 15(5): 2438-2453. https://doi.org/10.12677/acm.2025.1551637

1. 引言

钙化性主动脉瓣疾病(calcific aortic valve disease, CAVD)的发病率随着人群年龄的增长而增加，在60岁以上人群中的发病率为2% [1]。CAVD导致的主动脉瓣膜钙化可导致患者出现主动脉瓣狭窄，随着狭窄加重，患者可出现心力衰竭、晕厥和心绞痛等严重的并发症[2]-[5]。当CAVD患者出现症状后，如果不及时进行主动脉瓣治疗，患者的2年死亡率超过50% [6]。随着全球老龄化加重，CAVD的患病人数预计将从2000年的250万增加到2030年的450万[7]。CAVD是一种多因素疾病[4]。多项研究证实T2DM是CAVD的独立危险因素[8]-[10]。T2DM导致患者出现主动脉瓣钙化的病理机制复杂，通过炎症反应、氧化应激、内皮细胞功能障碍等影响患者的瓣膜，进而出现主动脉瓣钙化[11]-[13]。但T2DM如何诱导患者发生CAVD的机制仍有待进一步验证[10]。当前CAVD和T2DM都已成为全球老龄化的社会健康负担[14]-[16]，深入了解这两种疾病的关联，有助于临床医生监控T2DM患者CAVD的发生。目前没有研究T2DM与CAVD之间的中介变量。本研究通过回顾性地探究T2DM与CAVD之间的中介变量，为临床上T2DM患者预防和治疗CAVD提供新思路。

2. 对象与方法

2.1. 研究对象

病例组是2018年1月~2022年12月就诊于青岛大学附属医院心脏大血管外科的患者，且超声心动图的检查结果诊断为钙化性主动脉瓣疾病。对照组是就诊于青岛大学附属医院体检中心的人群，且超声心动图的检查无心脏瓣膜病变。本研究在青岛大学附属医院开展，研究方案已通过青岛大学附属医院医学伦理委员会的审批(伦理批号：QYFYWZLL28076)，同时免除患者签署知情同意书。患者入组及分组流程详见图1。

Figure 1. Flowchart of participant enrollment

图1. 研究对象入组流程图

2.2. 围手术期管理

病例组均进行冠状动脉搭桥手术，使用相同的麻醉药物及外科技术。手术后密切观察患者的生命体征。

2.3. 诊断与定义

CAVD：超声心动图符合2020年AHA/ACC和2021年ESC/EACTS瓣膜性心脏病管理指南中主动脉瓣钙化的诊断标准，主动脉瓣钙化(主动脉瓣增厚超过3 mm以上)、回声增强、瓣叶变硬、活动受限[17]-[20]。T2DM：患者的空腹血糖 ≥ 7.8 mmol/L或一天中任何时间血糖 ≥ 11.1 mmol/L或空腹血糖 < 7.8 mmol/L，但口服75%葡萄糖耐量试验二小时血糖 ≥ 11.1 mmol/L；或者患者的糖化血红蛋白 > 6.5 mmol/L；或者患者已经被诊断糖尿病，目前使用运动和饮食方案控制血糖，或接受降血糖药物的治疗[21]。高血压：当连续3次，非同日测量SBP ≥ 130 mmHg或DBP ≥ 80 mmHg，或者已接受降压治疗的患者[22]。慢性肾脏病：患者出现>3个月肾小球滤过率[estimated glomerular filtration rate (eGFR)] < 60 mL/min/1.73 m2 [23]。eGFR: Chronic Kidney Disease Epidemiology Collaboration (慢性肾脏病-EPI) equation: eGFR = α × (Scr/β)γ×0.993age, if patient is female: α = 144, β = 62, γ = −0.329 (Scr ≤ 62 umol/L) or −1.209 (Scr > 62 umol/L); if patient is male: α = 141, β = 80, γ = −0.411 (Scr ≤ 80 umol/L) or −1.209 (Scr > 80 umol/L) [24]。

甘油三酯–葡萄糖指数：Ln[Triglyceride(mg/dl) × Glucose(mg/dl) mmol/L/2] = Ln[Triglyceride(mmol/L) × 88.6 × Glucose(mmol/L) × 18/2] [25]。

2.4. 统计分析

缺失数据超过10%的变量被排除在分析之外，而缺失数据少于10%的变量则使用R语言4.2.3进行多重插补进行插补，以确保无偏估计。使用IBM SPSS 26.0进行数据正态性检验，研究对象正态分布的计量资料以均数 ± 标准差( x ¯ ±s )表示，非正态分布的计量资料以中位数(第25，75百分位数)表示。本研究使用了Student’s t-test或Mann-Whitney U-test比较组间的差异，计数资料以例(百分比)表示，并通过Pearson chi-square test或Fisher exact test进行比较。本研究进行因果中介分析时通过R语言采用Bootstrap法(T = 1000次)进行(图2为中介图示)，探究T2DM (自变量)与CAVD (因变量)的中介变量，量化中介效应的大小。采用双尾检验，P值 < 0.05被认为具有统计学显著性差异。

Figure 2. Illustration of mediation effects

图2. 中介效应图示

3. 结果

共3979名研究对象入组，根据研究对象是否患有CAVD进行分组，分为病例组(CAVD组) 1925例与对照组(non-CAVD组) 2054例。两组研究对象的基线特征见表1。

Table 1. Baseline characteristics of study participants

表1. 研究对象基线信息

研究指标

CAVD (N1 = 1925)

non-CAVD (N2 = 2054)

P-value

人口学特征

性别

<0.001

男性

1404 (72.9)

1199 (58.4)

年龄，years

63.8 (8.4)

57.4 (11.8)

<0.001

身高，cm

166.9 (7.6)

166.3 (8.7)

0.019

体重，kg

71.7 (11.9)

71.3 (13.0)

0.292

体重指数，kg/m2

25.7 (3.2)

25.7 (3.5)

0.943

收缩压，mmHg

125.6 (16.7)

130.7 (18.8)

<0.001

舒张压，mmHg

71.9 (10.6)

79.9 (11.8)

<0.001

既往史

吸烟史

846 (43.9)

434 (21.1)

<0.001

饮酒史

560 (29.1)

494 (24.1)

<0.001

用药史

2型糖尿病

961 (49.9)

305 (14.8)

<0.001

高血压

1547 (80.4)

611 (29.7)

<0.001

慢性肾脏病

181 (9.4)

63 (3.1)

<0.001

实验室检查

血肌酐，μmol/L

78.0 (24.5)

75.4 (13.9)

<0.001

甘油三酯，mmol/L

1.4 [1.1, 2.0]

1.2 [0.8, 1.8]

<0.001

总胆固醇，mmol/L

4.4 (0.9)

5.3 (0.9)

<0.001

高密度脂蛋白，mmol/L

1.2 (0.3)

1.6 (0.4)

<0.001

低密度脂蛋白，mmol/L

2.6 (0.9)

3.0 (0.8)

<0.001

空腹血糖，mmol/L

5.6 [4.9, 6.7]

5.1 [4.7, 5.6]

<0.001

尿酸，μmol/L

335.8 (90.4)

333.9 (86.4)

0.498

血红蛋白，g/L

131.8 (16.0)

146.5 (15.4)

<0.001

中性粒细胞计数，109/L

3.9 [3.1, 4.6]

3.2 [2.6, 3.9]

<0.001

淋巴细胞计数，109/L

1.9 (0.6)

2.0 (0.6)

<0.001

血小板计数，109/L

218.7 (59.7)

227.4 (51.1)

<0.001

eGFR, ml/min/1.73m2

85.0 (18.3)

89.2 (13.8)

0.001

中性粒细胞/淋巴细胞比值

2.1 [1.6, 2.5]

1.6 [1.3, 2.1]

<0.001

甘油三酯葡萄糖指数

8.9 (0.6)

8.5 (0.7)

<0.001

总胆固醇/高密度脂蛋白比值

4.0 (0.9)

3.4 (0.8)

<0.001

在未调整混杂变量时，患者的T2DM与CAVD之间存在显著的关联OR = 5.717 [95% CI (4.916~6.648), P-value < 0.001] (表2)。逐步调整患者的混杂因素(年龄、性别、吸烟史和饮酒史)后T2DM与CAVD之间一直存在显著的相关性(P < 0.001)，证明T2DM和CAVD之间的中介分析的途径c’成立。当纳入假定中介变量(高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值、慢性肾脏病和体重指数)后会影响T2DM与CAVD的OR值，证明在T2DM与CAVD之间存在中介途径。

通过ROC曲线可直观地表现假定中介变量与T2DM之间的联系，其中甘油三酯葡萄糖指数的诊断价值最好(AUC = 0.690) (图3(a))。

Table 2. Relationship between Type 2 Diabetes Mellitus (T2DM) and Calcific Aortic Valve Disease (CAVD)

表2. T2DM与CAVD关系

OR (95% CI)

P-value

Model 1

5.717 (4.916, 6.648)

<0.001

Model 2

4.975 (4.252, 5.822)

<0.001

Model 3

5.014 (4.272, 5.885)

<0.001

Model 4

3.401 (2.861, 4.043)

<0.001

Model 5

4.059 (3.440, 4.789)

<0.001

Model 6

4.618 (3.921, 5.438)

<0.001

Model 7

1.876 (1.699, 2.072)

<0.001

Model 8

4.897 (4.170, 5.751)

<0.001

Model 9

5.063 (4.310, 5.947)

<0.001

Model 10

2.826 (2.342, 3.409)

<0.001

Model 1未调整；Model 2调整age，gender；Model 3调整age，gender，smoke，alcohol；Model 4调整Model3 + HTN；Model 5调整Model3 + TyG；Model 6调整Model3 + TC/HDL；Model 7调整Model3 + NLR；Model 8调整Model3 + CKD；Model 9调整Model3 + BMI；Model 10调Model3 + HTN，TyG，TC/HDL，NLR，CKD and BMI。

Figure 3. The relationship diagram of diabetes with hypertension, triglyceride glucose index, total cholesterol/high-density lipoprotein ratio, neutrophil/lymphocyte ratio, chronic kidney disease, and body mass index

图3. 糖尿病与高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值、慢性肾脏病和体重指数的关系图

通过条形图观察到T2DM和non-T2DM两组间的高血压和慢性肾脏病发病率存在明显的差异(图3(b))，通过柱状图可观察到T2DM与甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和体重指数之间也存在明显的差异(图3(c))。进一步多元回归分析，充分调整混杂变量(年龄、性别、吸烟史和饮酒史)后显示T2DM与甘油三酯葡萄糖指数存在正相关性β = 0.307 [95% CI (0.405~0.493), P-value < 0.001]；T2DM与总胆固醇/高密度脂蛋白比值存在正相关性β = 0.264 [95% CI (0.206~0.322), P-value < 0.001]；T2DM与中性粒细胞/淋巴细胞比值存在正相关性β = 0.143 [95% CI (0.086~0.199), P-value < 0.001]；T2DM与体重指数存在正相关性β = 0.555 [95% CI (0.329~0.781), P-value < 0.001] (表3(a))，同时，在充分调整混杂变量后的多元逻辑回归显示T2DM与高血压存在显著相关性OR = 4.248 [95% CI (3.618~4.989), P-value < 0.001]；T2DM与慢性肾脏病存在显著相关性OR = 2.228 [95% CI (1.703~2.916), P-value < 0.001] (表3(b))。

Table 3. (a) Relationship between DM and TyG, TC/HDL, NLR and BMI; (b) Relationship between DM and HTN, CKD

表3. (a) 糖尿病与甘油三酯葡萄糖指数(TyG)、总胆固醇/高密度脂蛋白比值(TC/HDL)、中性粒细胞/淋巴细胞比值(NLR)和体重指数(BMI)的关系；(b) 糖尿病与高血压(HTN)和慢性肾脏病(CKD)的关系

(a)

Mediator

RR (95% CI)

P-value

TyG

0.307(0.405, 0.493)

<0.001

TC/HDL

0.264(0.206, 0.322)

<0.001

NLR

0.143(0.086, 0.199)

<0.001

BMI

0.555(0.329, 0.781)

<0.001

(b)

Mediator

OR (95% CI)

P-value

HTN

4.248 (3.618, 4.989)

<0.001

CKD

2.228 (1.703, 2.916)

<0.001

Adjusting variables: age, gender, smoke, alcohol.

通过ROC曲线展现假定中介变量对于CAVD诊断价值(图4)，高血压对于CAVD的诊断价值最好(AUC = 0.753)。通过条形状图展现出高血压/non-高血压或慢性肾脏病/non-慢性肾脏病的CAVD发病率间存在明显的差异(图4(b))。使用限制性立方样图(Restricted cubic spline, RCS)，利用平滑曲线拟合直观反映假定中介变量甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和体重指数与CAVD OR之间的关系(图4(c))，随着甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值和中性粒细胞/淋巴细胞比值升高CAVD发生的风险逐渐升高，本研究发现甘油三酯葡萄糖指数 > 8.8、总胆固醇/高密度脂蛋白比值 > 4、中性粒细胞/淋巴细胞比值 > 3时CAVD OR > 1，随着体重指数的升高CAVD发生风险先逐渐升高，再逐渐降低，呈现倒U型，然而通过RCS可以发现体重指数其结果不具有统计学意义。通过逻辑回归分析假定中介变量与CAVD的关系(表4)，在充分调节混杂变量后显示高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和慢性肾脏病与CAVD存在显著的相关性(P-value < 0.05)，但体重指数与CAVD并未出现相关性。上述分析说明只有高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和慢性肾脏病满足中介途径b。

将满足中介分析前提条件的假定中介变量纳入中介分析，进一步的中介效应统计分析后发现高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和慢性肾脏病在T2DM与

Figure 4. The relationship diagram of hypertension, triglyceride glucose index (TyG), total cholesterol/high-density lipoprotein ratio (TC/HDL), neutrophil-to-lymphocyte ratio (NLR), chronic kidney disease (CKD), and body mass index (BMI) with calcific aortic valve disease (CAVD)

图4. 高血压、甘油三酯葡萄糖指数(TyG)、总胆固醇/高密度脂蛋白比值(TC/HDL)、中性粒细胞/淋巴细胞比值(NLR)、慢性肾脏病(CKD)和体重指数(BMI)与钙化性主动脉瓣疾病(CAVD)的关系图

Table 4. Hypothetical relationships between mediator variables and Calcific Aortic Valve Disease (CAVD)

表4. 假定中介变量与CAVD的关系

OR (95% CI)

P-value

DM

5.014 (4.272, 5.885)

<0.001

HTN

8.376 (7.149, 9.813)

<0.001

TyG

2.220 (1.986, 2.483)

<0.001

TC/HDL

1.955 (1.793, 2.132)

<0.001

NLR

1.866 (1.699, 2.050)

<0.001

CKD

2.519 (1.839, 3.450)

<0.001

BMI

1.004 (0.983, 1.025)

0.741

调整age, gender, smoke, alcohol.

CAVD之间表现出显著统计学意义，中介占比的P-value < 0.001，中介占比分别为34.1%、12.7%、8.6%、4.5%和1.8% (表5)，并且甘油三酯葡萄糖指数和高血压在其中介过程中占比最高，进一步使用森林图直观地展示各变量的中介占比(图5)，最后根据上述结果绘制的中介效应图(图6)。

Table 5. Mediation analysis results of Type 2 Diabetes Mellitus (T2DM) and Calcific Aortic Valve Disease (CAVD)

表5. T2DM和CAVD中介分析结果

Mediator

Total effect

Mediation effect

Direct effect

PM (%)

P-value of PM

HTN

0.335 (0.302, 0.370)

0.114 (0.100, 0.130)

0.220 (0.186, 0.250)

34.1

<0.001

TyG

0.335 (0.302, 0.370)

0.042 (0.033, 0.050)

0.292 (0.258, 0.320)

12.7

<0.001

TC/HDL

0.335 (0.302, 0.370)

0.029 (0.022, 0.040)

0.306 (0.273, 0.340)

8.6

<0.001

NLR

0.335 (0.302, 0.370)

0.015 (0.008, 0.020)

0.319 (0.288, 0.350)

4.5

<0.001

CKD

0.335 (0.302, 0.370)

0.006 (0.003, 0.010)

0.328 (0.296, 0.360)

1.8

<0.001

BMI

0.335 (0.302, 0.370)

−0.002 (−0.005, 0.000)

0.336 (0.304, 0.370)

−0.6

0.110

调整age, gender, smoke, alcohol; PM: proportion mediate.

Figure 5. Indirect effects (with 95% CIs) from the Bootstrap samples

图5. 来自自助抽样(Bootstrap)的间接效应(附95%置信区间)

4. 讨论

本研究基于3979名研究对象进行中介效应分析，在CAVD中，首次证实了高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和慢性肾脏病为T2DM与CAVD之间的中介变量(P-value of PM < 0.05)，中介占比分别为34.1%、12.7%、8.6%、4.5%和1.8% (P-value of PM < 0.05)。

调整age, gender, smoke, alcohol; ME: Mediation effect; DE: Direct effect.

Figure 6. Mediation models of the relationship between DM and CAVD

图6. 糖尿病与钙化性主动脉瓣疾病(CAVD)关系的中介模型

CAVD已经成为心脏瓣膜疾病中最常见的疾病，随着全球老龄化加重，CAVD的发病人数会进一步增加，除了手术治疗外，尚无有效的药物可以治疗CAVD [26]-[28]，临床研究已经表明了T2DM是CAVD的独立危险因素，并且T2DM可以加速CAVD的发生和进展[9] [10] [29]-[31]。深入探究CAVD合并T2DM的发病机制以及解密这两种常见慢性疾病之间的关联，有助于我们找到有效的预防和治疗方法，降低T2DM患者发生CAVD，改善CAVD合并T2DM患者的预后[10]。但当前对于T2DM与CAVD之间的中介变量尚无研究探讨。

在2017年YAN AT等人进行了一项13年，112万人的多中心观察性研究指出高血压是CAVD的危险因素(HR: 1.71; 95% CI: 1.66~1.76) [29]。CAVD和动脉粥样硬化早期阶段的相似性，医生常采用血管紧张素转换酶抑制剂(ACEI)和他汀类药物在CAVD的治疗中使用，一项回顾性研究结果表明血ACEI对预防主动脉瓣膜钙化产生一定的治疗作用[32] [33]，印证了高血压与CAVD之间存在一定的关联。本研究证实高血压是T2DM与CAVD之间的中介变量，高血压的中介占比最高。对于T2DM和CAVD之间通过高血压介导的机制尚无直接研究，可能与T2DM激活患者的全身肾素–血管紧张素–醛固酮系统，并与血管阻力和动脉压增加相关，导致T2DM患者出现血压的升高，血管硬化和相关的心血管疾病[34] [35]，当T2DM患者出现高血压以后，血流动力学变化可直接损害VECs的结构和功能导致炎症浸润和氧化应激，导致患者的主动脉瓣瓣膜出现钙化，从而促进CAVD的发生和进展[27]。T2DM导致CAVD的过程中，高血压表现出中介作用(P-value of PM < 0.01)，建议在临床上对于T2DM患者应该及早关注患者的血压情况，早期监控患者血压，控制在合理范围内，有利于降低CAVD的发生风险。

本研究证实甘油三酯葡萄糖指数为T2DM与CAVD的中介变量，中介占比为12.7%。对于T2DM和CAVD之间通过甘油三酯葡萄糖指数介导的机制也尚无直接研究。GUERRERO-ROMERO F等人在2010年进行的一项横断面研究，明确指出了甘油三酯葡萄糖指数是评估胰岛素抵抗(IR)的最佳工具(敏感度：96.5%；特异性：85.0%) [36]，可能与T2DM导致患者出现高胰岛素血症有关，当前临床研究已将甘油三酯葡萄糖指数作为反应了T2DM患者高胰岛素血症水平的简便指标[36]-[38]，同时，研究指出甘油三酯葡萄糖指数指与心血管疾病的发生和预后相关[39]。高胰岛素血症减弱了患者的血管舒张作用，影响患者的血管系统功能，导致瓣膜内皮细胞的功能障碍，损害T2DM患者的主动脉瓣中VECs的功能而诱发主动脉瓣钙化[40] [41]，对预防CAVD的T2DM患者中同时应早期管理甘油三酯葡萄糖指数指标，将其控制在合理范围内，降低CAVD发生的风险。

本研究证实总胆固醇/高密度脂蛋白比值为T2DM与CAVD的中介变量，中介占比为8.6%。T2DM和CAVD之间通过总胆固醇/高密度脂蛋白比值介导的机制无直接研究。结合当前对于总胆固醇/高密度脂蛋白比值的研究，推测T2DM和总胆固醇/高密度脂蛋白比值与CAVD之间的联系，可能与T2DM导致患者机体出现血脂水平紊乱后，引发强烈的炎症反应有关。一项针对中国受试者的多项研究表明总胆固醇/高密度脂蛋白比值与IR存在显著相关性AUC = 0.770 [42]，结合IR机制最终导致胆固醇晶体出现在VECs、VICs和巨噬细胞衍生的泡沫细胞中，在瓣膜上形成广泛的钙化[43]。早期的研究中已经证实在心血管疾病预测方面，总胆固醇/高密度脂蛋白比值是能反应机体血脂水平的指标[44]，对预防CAVD的T2DM患者在关注甘油三酯葡萄糖指数的同时，也应关注总胆固醇/高密度脂蛋白比值指标，降低患者发生CAVD的风险。

中性粒细胞/淋巴细胞比值已成为全身炎症反应的常规标志物[45] [46]。本研究结果证实中性粒细胞/淋巴细胞比值为T2DM与CAVD的中介变量。对于T2DM和CAVD之间通过中性粒细胞/淋巴细胞比值介导的机制尚无直接研究，结合当前对于中性粒细胞/淋巴细胞比值的研究，以及T2DM和CAVD之间的联系，T2DM可导致患者的机体出现炎症反应，通过影响机体炎症水平和脂质代谢的相互作用，导致CAVD发生[7]，在预防CAVD的T2DM患者可以关注其中性粒细胞/淋巴细胞比值指标，及时进行有针对的治疗，降低发生CAVD的风险。

慢性肾脏病为T2DM与CAVD之间的中介变量。对于T2DM和CAVD之间通过慢性肾脏病介导的机制尚无直接研究。T2DM导致患者机体出现的高血糖环境会直接损伤患者的肾脏，导致患者糖尿病肾病的发生[47]。早期肾脏功能受到影响后，患者的肾小球滤过功能会受到损伤，出现肾小球的滤过率下降，内皮细胞窗孔缩小等，进而出现缩孔综合征(shrunken pore syndrome, SPS)，当出现患者发生SPS后，SPS导致患者的肾小球滤过率降低，从而血液中胱抑素C、β2-MG、RBP等这些相对分子质量为10~30,000的蛋白积聚，影响了中性粒细胞的迁移，导致局部炎症反应的出现[48]。Almen M S等人与Xhakollari L等人的研究团队通过对SPS患者的蛋白质组学研究发现，SPS患者体内MCP-3、IL-6、MCP-1、IL-18等促进动脉粥样硬化的蛋白增多，当这些蛋白在患者的血液中积聚，进一步造成机体慢性炎症状态，同时促进了动脉粥样硬化的形成[49] [50]。随着疾病进展当患者出现慢性肾脏病后，进一步加剧了患者CAVD的发生[51] [52]。在本研究结果中发现T2DM导致CAVD的过程中，慢性肾脏病扮演了中介变量角色(P-value of PM < 0.05)。

本研究发现体重指数不能满足中介分析的假定条件。在柳叶刀糖尿病内分泌杂志发表的综述中指出体重指数与心血管疾病存在显著的相关性[53]。一项全国150万人的研究指出，体重指数会影响T2DM患者的心血管事件发生率(例如：心肌梗死、缺血性卒中、心房颤动、心力衰竭) [54]。但上述研究的结局中并未明确指出CAVD，因此上述结论对于CAVD的临床实践有待进一步探究。

本研究也存在一些不足：首先，本研究为单中心回顾性研究且样本量较小，结果可能存在一定的偏倚；其次，本研究中有1.0% (42/4021)研究对象的数据由于严重缺失，无法进行统计学插补，最终被剔除了本研究，可能造成潜在的偏移。

对比既往研究中对于CAVD的探索，本研究首次证实了T2DM可通过变量高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和慢性肾脏病的中介途径，参与调节CAVD，在队列中，高血压的中介效应最大，其次为甘油三酯葡萄糖指数。基于本研究的结果，对于以后的临床工作，我们建议T2DM患者在预防或者治疗CAVD的过程中，早期可以加强对于患者血压、血糖及血脂的管理，通过监控患者的高血压、甘油三酯葡萄糖指数、总胆固醇/高密度脂蛋白比值、中性粒细胞/淋巴细胞比值和慢性肾脏病，特别是患者的高血压和甘油三酯葡萄糖指数，大大降低CAVD发生的风险。

NOTES

*通讯作者。

参考文献

[1] Goody, P.R., Hosen, M.R., Christmann, D., Niepmann, S.T., Zietzer, A., Adam, M., et al. (2020) Aortic Valve Stenosis: From Basic Mechanisms to Novel Therapeutic Targets. Arteriosclerosis, Thrombosis, and Vascular Biology, 40, 885-900. [Google Scholar] [CrossRef] [PubMed]
[2] Yadgir, S., Johnson, C.O., Aboyans, V., Adebayo, O.M., Adedoyin, R.A., Afarideh, M., et al. (2020) Global, Regional, and National Burden of Calcific Aortic Valve and Degenerative Mitral Valve Diseases, 1990-2017. Circulation, 141, 1670-1680. [Google Scholar] [CrossRef] [PubMed]
[3] DesJardin, J.T., Chikwe, J., Hahn, R.T., Hung, J.W. and Delling, F.N. (2022) Sex Differences and Similarities in Valvular Heart Disease. Circulation Research, 130, 455-473. [Google Scholar] [CrossRef] [PubMed]
[4] Moncla, L.M., Briend, M., Bossé, Y. and Mathieu, P. (2023) Calcific Aortic Valve Disease: Mechanisms, Prevention and Treatment. Nature Reviews Cardiology, 20, 546-559. [Google Scholar] [CrossRef] [PubMed]
[5] Yu, G., Gong, X., Xu, Y., Sun, H., Liu, Y., Zhai, C., et al. (2023) The Global Burden and Trends of Four Major Types of Heart Disease, 1990-2019: A Systematic Analysis for the Global Burden of Disease Study 2019. Public Health, 220, 1-9. [Google Scholar] [CrossRef] [PubMed]
[6] Otto, C.M. and Prendergast, B. (2014) Aortic-Valve Stenosis—From Patients at Risk to Severe Valve Obstruction. New England Journal of Medicine, 371, 744-756. [Google Scholar] [CrossRef] [PubMed]
[7] Rajamannan, N.M., Evans, F.J., Aikawa, E., Grande-Allen, K.J., Demer, L.L., Heistad, D.D., et al. (2011) Calcific Aortic Valve Disease: Not Simply a Degenerative Process: A Review and Agenda for Research from the National Heart and Lung and Blood Institute Aortic Stenosis Working Group: Executive Summary: Calcific Aortic Valve Disease—2011 Update. Circulation, 124, 1783-1791. [Google Scholar] [CrossRef] [PubMed]
[8] Abdul-Rahman, T., Lizano-Jubert, I., Garg, N., Talukder, S., Lopez, P.P., Awuah, W.A., et al. (2023) The Common Pathobiology between Coronary Artery Disease and Calcific Aortic Stenosis: Evidence and Clinical Implications. Progress in Cardiovascular Diseases, 79, 89-99. [Google Scholar] [CrossRef] [PubMed]
[9] Hsuan, C., Teng, S.I.F., Hsu, C., Liao, D., Chang, A.J., Lee, H., et al. (2023) Emerging Therapy for Diabetic Cardiomyopathy: From Molecular Mechanism to Clinical Practice. Biomedicines, 11, Article 662. [Google Scholar] [CrossRef] [PubMed]
[10] Chen, Y., Xiao, F. and Wang, R. (2022) Calcified Aortic Valve Disease Complicated with and without Diabetes Mellitus: The Underlying Pathogenesis. Reviews in Cardiovascular Medicine, 23, Article 7. [Google Scholar] [CrossRef] [PubMed]
[11] Kaur, R., Kaur, M. and Singh, J. (2018) Endothelial Dysfunction and Platelet Hyperactivity in Type 2 Diabetes Mellitus: Molecular Insights and Therapeutic Strategies. Cardiovascular Diabetology, 17, Article No. 121. [Google Scholar] [CrossRef] [PubMed]
[12] Kozakova, M., Morizzo, C., Goncalves, I., Natali, A., Nilsson, J. and Palombo, C. (2019) Cardiovascular Organ Damage in Type 2 Diabetes Mellitus: The Role of Lipids and Inflammation. Cardiovascular Diabetology, 18, Article No. 61. [Google Scholar] [CrossRef] [PubMed]
[13] Raddatz, M.A., Madhur, M.S. and Merryman, W.D. (2019) Adaptive Immune Cells in CALCIFIC aortic Valve Disease. American Journal of Physiology-Heart and Circulatory Physiology, 317, H141-H155. [Google Scholar] [CrossRef] [PubMed]
[14] Driscoll, K., Cruz, A.D. and Butcher, J.T. (2021) Inflammatory and Biomechanical Drivers of Endothelial-Interstitial Interactions in Calcific Aortic Valve Disease. Circulation Research, 128, 1344-1370. [Google Scholar] [CrossRef] [PubMed]
[15] Manduteanu, I., Simionescu, D., Simionescu, A. and Simionescu, M. (2021) Aortic Valve Disease in Diabetes: Molecular Mechanisms and Novel Therapies. Journal of Cellular and Molecular Medicine, 25, 9483-9495. [Google Scholar] [CrossRef] [PubMed]
[16] Thaden, J.J., Nkomo, V.T. and Enriquez-Sarano, M. (2014) The Global Burden of Aortic Stenosis. Progress in Cardiovascular Diseases, 56, 565-571. [Google Scholar] [CrossRef] [PubMed]
[17] Corrigendum to: Training, Competence, and Quality Improvement in Echocardiography: The European Association of Cardiovascular Imaging Recommendations: Update 2020. European Heart Journal-Cardiovascular Imaging, 22, 187.[CrossRef] [PubMed]
[18] Otto, C.M., Nishimura, R.A., Bonow, R.O., Carabello, B.A., Erwin, J.P., Gentile, F., et al. (2020) 2020 ACC/AHA Guideline for the Management of Patients with Valvular Heart Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 143, e35-e71. [Google Scholar] [CrossRef] [PubMed]
[19] Vahanian, A., Beyersdorf, F., Praz, F., Milojevic, M., Baldus, S., Bauersachs, J., et al. (2021) 2021 ESC/EACTS Guidelines for the Management of Valvular Heart Disease: Developed by the Task Force for the management of valvular heart disease of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS). European Heart Journal, 43, 561-632. [Google Scholar] [CrossRef] [PubMed]
[20] Chambers, J.B., Garbi, M., Nieman, K., Myerson, S., Pierard, L.A., Habib, G., et al. (2017) Appropriateness Criteria for the Use of Cardiovascular Imaging in Heart Valve Disease in Adults: A European Association of Cardiovascular Imaging Report of Literature Review and Current Practice. European Heart Journal-Cardiovascular Imaging, 18, 489-498. [Google Scholar] [CrossRef] [PubMed]
[21] American Diabetes Association Professional Practice Committee (2021) 2. Classification and Diagnosis of Diabetes:standards of Medical Care in Diabetes-2022. Diabetes Care, 45, S17-S38. [Google Scholar] [CrossRef] [PubMed]
[22] Whelton, P.K., Carey, R.M., Aronow, W.S., Casey, D.E., Collins, K.J., Dennison Himmelfarb, C., et al. (2018) 2017 Acc/AHA/AAPA/ABC/ACPM/AGS/APHA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension, 71, 1269-1324. [Google Scholar] [CrossRef] [PubMed]
[23] See, E.J. and Cullen, V. (2024) Commentary on the 2021 Update of the KDIGO Clinical Practice Guideline for Management of Blood Pressure in Chronic Kidney Disease. Nephrology, 30, e14414. [Google Scholar] [CrossRef] [PubMed]
[24] Inker, L.A., Schmid, C.H., Tighiouart, H., Eckfeldt, J.H., Feldman, H.I., Greene, T., et al. (2012) Estimating Glomerular Filtration Rate from Serum Creatinine and Cystatin C. New England Journal of Medicine, 367, 20-29. [Google Scholar] [CrossRef] [PubMed]
[25] Liu, X., Tan, Z., Huang, Y., Zhao, H., Liu, M., Yu, P., et al. (2022) Relationship between the Triglyceride-Glucose Index and Risk of Cardiovascular Diseases and Mortality in the General Population: A Systematic Review and Meta-Analysis. Cardiovascular Diabetology, 21, Article No. 124. [Google Scholar] [CrossRef] [PubMed]
[26] Sud, K., Narula, N., Aikawa, E., Arbustini, E., Pibarot, P., Merlini, G., et al. (2023) The Contribution of Amyloid Deposition in the Aortic Valve to Calcification and Aortic Stenosis. Nature Reviews Cardiology, 20, 418-428. [Google Scholar] [CrossRef] [PubMed]
[27] Shu, L., Yuan, Z., Li, F. and Cai, Z. (2023) Oxidative Stress and Valvular Endothelial Cells in Aortic Valve Calcification. Biomedicine & Pharmacotherapy, 163, Article 114775. [Google Scholar] [CrossRef] [PubMed]
[28] Liu, X., Li, T., Sun, J. and Wang, Z. (2023) The Role of Endoplasmic Reticulum Stress in Calcific Aortic Valve Disease. Canadian Journal of Cardiology, 39, 1571-1580. [Google Scholar] [CrossRef] [PubMed]
[29] Yan, A.T., Koh, M., Chan, K.K., Guo, H., Alter, D.A., Austin, P.C., et al. (2017) Association between Cardiovascular Risk Factors and Aortic Stenosis: The CANHEART Aortic Stenosis Study. Journal of the American College of Cardiology, 69, 1523-1532. [Google Scholar] [CrossRef] [PubMed]
[30] Mosch, J., Gleissner, C.A., Body, S., et al. (2017) Histopathological Assessment of Calcification and Inflammation of Calcific Aortic Valves from Patients with and without Diabetes Mellitus. Histol Histopathol, 32, 293-306.
[31] Yutzey, K.E., Demer, L.L., Body, S.C., Huggins, G.S., Towler, D.A., Giachelli, C.M., et al. (2014) Calcific Aortic Valve Disease: A Consensus Summary from the Alliance of Investigators on Calcific Aortic Valve Disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 34, 2387-2393. [Google Scholar] [CrossRef] [PubMed]
[32] Davicevic, Z., Tavciovski, D. and Matunovic, R. (2010) Medical Treatments in Aortic Stenosis: Role of Statins and Angiotensin-Converting Enzyme Inhibitors. Medicinski pregled, 63, 82-85. [Google Scholar] [CrossRef] [PubMed]
[33] O’Brien, K.D., Probstfield, J.L., Caulfield, M.T., Nasir, K., Takasu, J., Shavelle, D.M., et al. (2005) Angiotensin-Converting Enzyme Inhibitors and Change in Aortic Valve Calcium. Archives of Internal Medicine, 165, 858-862. [Google Scholar] [CrossRef] [PubMed]
[34] Jia, G. and Sowers, J.R. (2021) Hypertension in Diabetes: An Update of Basic Mechanisms and Clinical Disease. Hypertension, 78, 1197-1205. [Google Scholar] [CrossRef] [PubMed]
[35] Cote, A.T., Phillips, A.A., Harris, K.C., Sandor, G.G.S., Panagiotopoulos, C. and Devlin, A.M. (2015) Obesity and Arterial Stiffness in Children: Systematic Review and Meta-Analysis. Arteriosclerosis, Thrombosis, and Vascular Biology, 35, 1038-1044. [Google Scholar] [CrossRef] [PubMed]
[36] Guerrero-Romero, F., Simental-Mendía, L.E., González-Ortiz, M., Martínez-Abundis, E., Ramos-Zavala, M.G., Hernández-González, S.O., et al. (2010) The Product of Triglycerides and Glucose, a Simple Measure of Insulin Sensitivity. Comparison with the Euglycemic-Hyperinsulinemic Clamp. The Journal of Clinical Endocrinology & Metabolism, 95, 3347-3351. [Google Scholar] [CrossRef] [PubMed]
[37] Liang, S., Wang, C., Zhang, J., Liu, Z., Bai, Y., Chen, Z., et al. (2023) Triglyceride-Glucose Index and Coronary Artery Disease: A Systematic Review and Meta-Analysis of Risk, Severity, and Prognosis. Cardiovascular Diabetology, 22, Article No 170. [Google Scholar] [CrossRef] [PubMed]
[38] Vasques, A.C.J., Novaes, F.S., de Oliveira, M.d.S., Matos Souza, J.R., Yamanaka, A., Pareja, J.C., et al. (2011) Tyg Index Performs Better than HOMA in a Brazilian Population: A Hyperglycemic Clamp Validated Study. Diabetes Research and Clinical Practice, 93, e98-e100. [Google Scholar] [CrossRef] [PubMed]
[39] Tao, L., Xu, J., Wang, T., Hua, F. and Li, J. (2022) Triglyceride-Glucose Index as a Marker in Cardiovascular Diseases: Landscape and Limitations. Cardiovascular Diabetology, 21, Article No 68. [Google Scholar] [CrossRef] [PubMed]
[40] Selig, J.I., Ouwens, D.M., Raschke, S., Thoresen, G.H., Fischer, J.W., Lichtenberg, A., et al. (2019) Impact of Hyperinsulinemia and Hyperglycemia on Valvular Interstitial Cells—A Link between Aortic Heart Valve Degeneration and Type 2 Diabetes. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1865, 2526-2537. [Google Scholar] [CrossRef] [PubMed]
[41] Scatena, M., Jackson, M.F., Speer, M.Y., Leaf, E.M., Wallingford, M.C. and Giachelli, C.M. (2018) Increased Calcific Aortic Valve Disease in Response to a Diabetogenic, Procalcific Diet in the Ldlr-/- Apob100/100 Mouse Model. Cardiovascular Pathology, 34, 28-37. [Google Scholar] [CrossRef] [PubMed]
[42] Zhang, L., Chen, S., Deng, A., Liu, X., Liang, Y., Shao, X., et al. (2015) Association between Lipid Ratios and Insulin Resistance in a Chinese Population. PLOS ONE, 10, e0116110. [Google Scholar] [CrossRef] [PubMed]
[43] Chen, M., Meng, X., Han, Y., Yan, J., Xiao, C. and Qian, L. (2022) Profile of Crosstalk between Glucose and Lipid Metabolic Disturbance and Diabetic Cardiomyopathy: Inflammation and Oxidative Stress. Frontiers in Endocrinology, 13, Article 983713. [Google Scholar] [CrossRef] [PubMed]
[44] Criqui, M.H. and Golomb, B.A. (1998) Epidemiologic Aspects of Lipid Abnormalities. The American Journal of Medicine, 105, 48S-57S. [Google Scholar] [CrossRef] [PubMed]
[45] Imtiaz, F., Shafique, K., Mirza, S., Ayoob, Z., Vart, P. and Rao, S. (2012) Neutrophil Lymphocyte Ratio as a Measure of Systemic Inflammation in Prevalent Chronic Diseases in Asian Population. International Archives of Medicine, 5, Article No. 2. [Google Scholar] [CrossRef] [PubMed]
[46] Zahorec, R. (2021) Neutrophil-to-Lymphocyte Ratio, Past, Present and Future Perspectives. Bratislava Medical Journal, 122, 474-488. [Google Scholar] [CrossRef] [PubMed]
[47] Liu, Z., Liu, J., Wang, W., An, X., Luo, L., Yu, D., et al. (2023) Epigenetic Modification in Diabetic Kidney Disease. Frontiers in Endocrinology, 14, Article 1133970. [Google Scholar] [CrossRef] [PubMed]
[48] Einwoegerer, C.F. and Domingueti, C.P. (2018) Association between Increased Levels of Cystatin C and the Development of Cardiovascular Events or Mortality: A Systematic Review and Meta-analysis. Arquivos Brasileiros de Cardiologia, 111, 796-807. [Google Scholar] [CrossRef] [PubMed]
[49] Almén, M.S., Björk, J., Nyman, U., Lindström, V., Jonsson, M., Abrahamson, M., et al. (2019) Shrunken Pore Syndrome Is Associated with Increased Levels of Atherosclerosis-Promoting Proteins. Kidney International Reports, 4, 67-79. [Google Scholar] [CrossRef] [PubMed]
[50] Xhakollari, L., Jujic, A., Molvin, J., Nilsson, P., Holm, H., Bachus, E., et al. (2021) Proteins Linked to Atherosclerosis and Cell Proliferation Are Associated with the Shrunken Pore Syndrome in Heart Failure Patients. PROTEOMICS—Clinical Applications, 15, e2000089. [Google Scholar] [CrossRef] [PubMed]
[51] Hutcheson, J.D. and Goettsch, C. (2023) Cardiovascular Calcification Heterogeneity in Chronic Kidney Disease. Circulation Research, 132, 993-1012. [Google Scholar] [CrossRef] [PubMed]
[52] Ternacle, J., Côté, N., Krapf, L., Nguyen, A., Clavel, M. and Pibarot, P. (2019) Chronic Kidney Disease and the Pathophysiology of Valvular Heart Disease. Canadian Journal of Cardiology, 35, 1195-1207. [Google Scholar] [CrossRef] [PubMed]
[53] The Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (2014) Cardiovascular Disease, Chronic Kidney Disease, and Diabetes Mortality Burden of Cardiometabolic Risk Factors from 1980 to 2010: A Comparative Risk Assessment. The Lancet Diabetes & Endocrinology, 2, 634-647. [Google Scholar] [CrossRef] 
[54] Park, C.S., Han, K. and Kim, H. (2022) Response to Comment on Park Et Al. U-Shaped Associations between Body Weight Changes and Major Cardiovascular Events in Type 2 Diabetes Mellitus: A Longitudinal Follow-Up Study of a Nationwide Cohort of over 1.5 Million. Diabetes Care 2022; 45: 1239-1246. Diabetes Care, 45, e188-e189. [Google Scholar] [CrossRef] [PubMed]

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