Association of Triglyceride-Glucose Index with the Progression of Atherosclerosis and Impairment of Arterial Endothelial Function

doi:10.21203/rs.3.rs-4977698/v1

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Research Article

Association of Triglyceride-Glucose Index with the Progression of Atherosclerosis and Impairment of Arterial Endothelial Function

https://doi.org/10.21203/rs.3.rs-4977698/v1

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Aims

This study aims to investigate the correlation between the (TyG) index and the progression of atherosclerosis and endothelial dysfunction.

Methods

We retrospectively collected data from 150 adult inpatients with atherosclerosis who received consistent medical care at the Cardiovascular Department of Shuguang Hospital, affiliated with Shanghai University of Traditional Chinese Medicine, from January 2018 to December 2023. The TyG index was calculated by using the formula TyG = ln[triglyceride (TG) [mg/dL] × fasting blood glucose (FBG) [mg/dL]/2]. The relationship between TyG and the progression of atherosclerosis, Flow-Mediated Dilation (FMD), Brachial-Ankle Pulse Wave Velocity (baPWV), and Ankle-Brachial Index (ABI) was explored using SPSS 27.0 software and R (version 4.3.1, Austria) software.

Results

There was a significant correlation between the patient’s TyG index and the intima-media thickness (IMT) of both carotid and lower limb arteries (P < 0.05). There was also a noteworthy correlation between the TyG index and the peak systolic velocity (PSV) of the left femoral artery (P = 0.019), flow-mediated dilation (FMD), and brachial-ankle pulse wave velocity (baPWV) (P < 0.001). After adjusting for confounding factors, logistic regression analysis still showed that TyG is correlated with the progression of atherosclerosis (P < 0.0001). ROC curve analysis demonstrated that the TyG index possessed predictive value for the progression of atherosclerosis in carotid arteries (AUC = 0.774, P < 0.001) and lower limb arteries (AUC = 0.8, P < 0.001) comparable to the Framingham Risk Score.

Conclusion

The TyG index is closely correlated with the progression of atherosclerosis and arterial stiffness.

TyG

FMD

baPWV

atherosclerosis

arterial plaque

Atherosclerotic cardiovascular diseases (ASCVD), comprising ischemic heart disease and ischemic stroke, stand as the primary causes of death of both urban rural residents in China, constituting over 40% of the total mortality composition[1]. In recent years, the disease burden of atherosclerotic cardiovascular diseases (ASCVD) in China has continued to increase, presenting a significant challenge for prevention and control [2]. Dyslipidemia, which contributes to atherosclerosis, continues to be a fundamental pathological factor in ASCVD and has consistently been a focal point for clinical intervention. However, according to surveys, only 16.1% of adults aged 35 and above in China are aware of dyslipidemia[3]. It is evident that there is an urgent need to enhance the management of atherosclerosis in the Chinese population.

Triglyceride-glucose (TyG) serves as a pathophysiological marker for obesity, metabolic syndrome, and insulin resistance in diabetes[4]. The Triglyceride-Glucose Index (TyG) is calculated as ln[triglyceride (TG) [mg/dL] × fasting blood glucose (FBG) [mg/dL]/2][5]. This index has recently shown utility in the clinical prognosis and prediction of cardiovascular disease occurrence and mortality risk[6, 7, 8]. Flow-Mediated Dilation (FMD), Brachial-Ankle Pulse Wave Velocity (baPWV), and Ankle-Brachial Index (ABI) are non-invasive examination methods employed in clinical assessments of arterial stiffness. These methods aid in the early diagnosis of atherosclerosis and atherosclerotic cardiovascular diseases[9, 10, 11]. Recent studies suggest that TyG is beneficial in the early prediction of the progression of atherosclerosis[12]. However, there are few research reports on the impact of TyG on different arterial sites. Therefore, this study will evaluate arterial function using FMD, baPWV, and ABI, and observe changes in carotid and lower limb arterial plaques. The analysis will be conducted from both atherosclerotic and non-atherosclerotic perspectives to clarify the relationship between the TyG index, arterial endothelial function damage, and the progression of atherosclerosis.

1.1 Participant Definition

The participants of this study were drawn from adult inpatients with atherosclerosis, aged 18 and above, who sought treatment in the Cardiovascular Department of Shuguang Hospital, Affiliated to Shanghai University of Traditional Chinese Medicine, from January 2018 to December 2023. A total of 150 cases were included. All participants signed informed consent forms before the commencement of the investigation. Exclusion criteria: (1) Limb skin lesions, infections, fractures, or other severe injuries; (2) Pregnant or lactating women; (3) New York Heart Association (NYHA) Functional Class IV or end-stage renal disease; (4) Those who did not complete the prescribed treatment during hospitalization or were transferred to other departments for treatment due to other illnesses; (5) Long-term alcohol abuse or individuals with mental disorders; (6) Those who have not undergone carotid or lower limb ultrasound examination in the past five years or have incomplete medical history data; (7) Individuals who have not signed the informed consent form. This study was approved by the ethics committee of ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (approval no. 2023-1406-173-01). We certify that the study was performed in accordance with the 1964 declaration of HELSINKI and later amendments.

1.2 Observational Measures

1.2.1 Baseline Characteristics

The study subjects were assessed using standardized medical history data collection, including (1) demographic information; (2) Medical and personal history, including hypertension, hyperlipidemia, type 2 diabetes, coronary heart disease, atrial fibrillation, stroke, renal insufficiency, smoking history, and alcohol consumption history; (3) prescribed medications; (4) laboratory test indicators, such as complete blood count, liver and kidney function, lipid metabolism, and blood glucose; (5) measurement of patient height and weight to calculate the Body Mass Index (BMI) = weight (kg) / height (m)².

1.2.2 Supplementary Examination Data

Supplementary examination data were collected by specialized physicians, including (1) Vascular Color Doppler ultrasound examination: Intima Media Thickness (IMT), inner diameter, peak systolic velocity (PSV), and Resistance Index (RI) at different locations of carotid and lower limb arteries; (2) Non-invasive imaging techniques of coronary arteries, such as Computed Tomography Angiography (CTA); (3) Flow-Mediated Dilation (FMD) for assessing endothelial-dependent vasodilation; (4) Brachial-Ankle Pulse Wave Velocity (baPWV); (5) Ankle-Brachial Index (ABI). The data collection involved five years of carotid and lower limb artery ultrasound reports for the study subjects, focusing on plaque size, IMT, and other relevant indicators at various locations of carotid and lower limb arteries.

1.2.3 Primary outcome

The study will compare the carotid and lower limb arterial plaque volume and IMT changes of the subjects over the five years prior to admission. If the plaque volume increases or IMT significantly thickens, it will be defined as atherosclerosis progression; otherwise, it will be defined as no progression.

1.3 Examination Methods

1.3.1 Measurement of baPWV and ABI

The BP-203RPEⅢ fully automatic arterial stiffness detector from Omron Corporation, Japan, was utilized for the measurement. The subjects were placed in a supine position and instructed to remain quiet for 5 to 10 minutes. The upper arm cuff was placed 2 to 3 cm above the elbow with the cuff marker aligned with the brachial artery. The lower limb cuff was applied to the ankle with the cuff marker positioned on the inner side of the ankle. The heart sound collection device was placed in the precordial area. The instrument automatically analyzed and outputted the values of baPWV and ABI. The higher value of baPWV from the left and right sides was recorded, and the lower value of ABI was taken.

1.3.2 FMD

The ultrasound examination was conducted by the same ultrasound specialist using the UNEX Endo-PAT FMD measurement system (UNEXEF 18VG color Doppler ultrasound instrument) from Japan. The subjects were positioned in a supine posture, and the right brachial artery was selected for examination. The arm was extended 90°, and a pressure cuff was wrapped around the forearm, with the upper edge of the cuff slightly above the elbow crease. The scanning position of the probe was set 3–5 cm above the elbow crease. Initially, the baseline brachial artery diameter was recorded. The cuff was then inflated to a pressure 50 mmHg higher than the systolic blood pressure, completely occluding blood flow for 5 minutes. During this period, the pressure inside the cuff was monitored to ensure fluctuations did not exceed 10 mmHg. After 5 minutes, the pressure was rapidly released, and two-dimensional images of the brachial artery diameter and Doppler blood flow signal images were recorded within 2 minutes of cuff deflation. Flow-Mediated Dilation (FMD) was calculated using the formula: FMD = (post-hyperemic diameter - baseline diameter) / baseline diameter × 100%.

1.4 Statistical analysis

In this study, SPSS 27.0 software was used for data analysis. Measurement data were expressed as mean ± standard deviation (\(\:\stackrel{-}{\text{x}}\)±s), and t-tests were used for comparisons between groups. Categorical data were analyzed using the χ² test. Correlation analysis was performed with Spearman correlation. Multivariate logistic regression was employed to investigate the relationship between TyG and the progression of atherosclerosis. The diagnostic performance of TyG for atherosclerosis progression was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity. Finally, stratified analysis and interaction tests were conducted to further explore the impact of TyG on the progression of atherosclerosis in different subgroups. A significance level of P < 0.05 was considered statistically significant.

2.1 Baseline data

This study comprised a total of 150 participants, including 61 males (40.67%). The average age was 66.73 ± 13.73 years, and the mean body mass index was 24.44 ± 3.06 kg/m². The proportions of patients with hypertension, diabetes, hyperlipidemia, coronary heart disease, atrial fibrillation, stroke, and renal insufficiency were 75.33%, 27.33%, 21.33%, 23.33%, 6.67%, 18.67%, and 4.67%, respectively. Smoking and alcohol consumption were reported by 18% and 21.33% of participants, respectively. The baseline mean levels were as follows: total cholesterol 4.67 ± 1.24 mmol/L, triglycerides 1.39 ± 0.58 mmol/L, low-density lipoprotein cholesterol 2.89 ± 1.11 mmol/L, fasting blood glucose 6 ± 2.07 mmol/L, creatinine 74.15 ± 28 µmol/L, and uric acid 340.75 ± 86.73 µmol/L. The mean Triglyceride-Glucose Index for all patients was 8.67 ± 0.56. (Table 1.)

Table 1

Baseline characteristics of study participants according to atherosclerosis in different places
	level	Overall	Q1	Q2	Q3	Q4	p
n		150	37	37	37	38
gender (%)	female	89 (59.3)	24 (64.9)	19 (51.4)	23 (62.2)	22 (57.9)	0.661
	male	61 (40.7)	13 (35.1)	18 (48.6)	14 (37.8)	16 (42.1)
age (mean (SD))		66.7333 (13.7307)	63.4324 (15.7983)	66.1081 (15.7265)	67.4865 (13.3804)	69.4474 (8.7540)	0.285
height (mean (SD))		163.5533 (8.0590)	162.9189 (7.1000)	164.7027 (9.3864)	162.4595 (7.2403)	164.5263 (8.0831)	0.529
weight (mean (SD))		65.5620 (10.5206)	64.5135 (10.6527)	65.3324 (11.6287)	64.1351 (9.4264)	68.6053 (9.9017)	0.235
BMI (mean (SD))		24.4400 (3.0620)	24.2946 (3.6559)	23.9541 (2.9423)	24.2108 (2.4235)	25.3211 (3.0719)	0.227
HT (%)	0	37 (24.7)	14 (37.8)	12 (32.4)	6 (16.2)	5 (13.2)	0.033
	1	113 (75.3)	23 (62.2)	25 (67.6)	31 (83.8)	33 (86.8)
DM (%)	0	109 (72.7)	32 (86.5)	30 (81.1)	27 (73.0)	19 (50.0)	0.002
	1	41 (27.3)	5 (13.5)	7 (18.9)	10 (27.0)	19 (50.0)
Hyperlipidemia (%)	0	118 (78.7)	30 (81.1)	30 (81.1)	24 (64.9)	33 (86.8)	0.115
	1	32 (21.3)	7 (18.9)	7 (18.9)	13 (35.1)	5 (13.2)
CHD (%)	0	115 (76.7)	29 (78.4)	29 (78.4)	27 (73.0)	29 (76.3)	0.94
	1	35 (23.3)	8 (21.6)	8 (21.6)	10 (27.0)	9 (23.7)
AF (%)	0	140 (93.3)	35 (94.6)	33 (89.2)	35 (94.6)	36 (94.7)	0.724
	1	10 ( 6.7)	2 ( 5.4)	4 (10.8)	2 ( 5.4)	2 ( 5.3)
stroke (%)	0	122 (81.3)	31 (83.8)	32 (86.5)	29 (78.4)	29 (76.3)	0.653
	1	28 (18.7)	6 (16.2)	5 (13.5)	8 (21.6)	9 (23.7)
smokeing (%)	0	123 (82.0)	33 (89.2)	33 (89.2)	30 (81.1)	26 (68.4)	0.062
	1	27 (18.0)	4 (10.8)	4 (10.8)	7 (18.9)	12 (31.6)
drinking (%)	0	118 (78.7)	31 (83.8)	31 (83.8)	28 (75.7)	27 (71.1)	0.449
	1	32 (21.3)	6 (16.2)	6 (16.2)	9 (24.3)	11 (28.9)
TC (mean (SD))		4.6727 (1.2411)	4.5162 (0.8129)	4.1359 (0.9939)	4.7400 (1.3812)	5.3087 (1.4081)	< 0.001
TG (mean (SD))		1.3908 (0.5844)	0.7608 (0.1253)	1.1311 (0.2210)	1.6373 (0.2818)	2.0429 (0.4975)	< 0.001
sdLDL (mean (SD))		30.7317 (21.4027)	21.9054 (7.0703)	30.8703 (31.3746)	33.7270 (19.2521)	36.9121 (18.3213)	0.016
B (mean (SD))		0.8697 (0.2937)	0.7624 (0.1458)	0.7559 (0.2275)	0.9614 (0.3622)	1.0082 (0.3002)	< 0.001
GLU (mean (SD))		6.0074 (2.0715)	4.9943 (0.7258)	5.3008 (0.8162)	5.8276 (0.9442)	7.8861 (3.1680)	< 0.001
Cr (mean (SD))		74.1487 (28.0227)	69.7838 (17.0394)	77.5135 (34.4485)	72.5486 (29.8174)	76.8421 (28.6135)	0.6
UA (mean (SD))		340.7533 (86.7281)	302.2432 (83.7504)	349.2162 (83.8276)	346.4865 (87.8340)	363.5526 (83.4929)	0.014
WBC (mean (SD))		6.2085 (1.6743)	6.0703 (1.6731)	5.8749 (1.7139)	6.4692 (1.7860)	6.4658 (1.4869)	0.333

BMI, body mass index; CHD, coronary heart disease; AF, atrial fibrillation; TC, cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein; FBG, fasting blood glucose; TyG, triglyceride-glucose index

2.2 Relationship between TyG and IMT

The results of this study indicate a significant correlation (P < 0.05) between the TyG index of patients and the intima-media thickness (IMT) in both carotid and lower limb arteries. Furthermore, the scatter plots reveal a positive correlation (Table 2 and Fig. 1).

Table 2

Relationship between TyG and IMT
		ρ	P value
Common carotid artery	Left	0.253	0.002**
Common carotid artery	Right	0.22	0.007**
Internal carotid artery	Left	0.231	0.004**
Internal carotid artery	Right	0.234	0.004**
Common femoral artery	Left	0.195	0.017*
Common femoral artery	Right	0.201	0.014*
Superficial femoral artery	Left	0.237	0.003**
Superficial femoral artery	Right	0.208	0.011*
Popliteal artery	Left	0.226	0.005**
Popliteal artery	Right	0.265	0.001**

2.3 Relationship between TyG and PSV

Although, the results exhibit no significant correlation between the TyG index of patients and the Peak Systolic Velocity (PSV) in the bilateral common carotid arteries and internal carotid arteries (P > 0.05), a negative correlation trend can be discerned in the scatter plot. Additionally, a significant correlation is observed between the TyG index and the PSV of the left femoral artery (P = 0.019), which is not mirrored in the right femoral artery, and a positive correlation trend is noted between the TyG index and femoral artery PSV, in contrast to the observed trend in the carotid arteries. (Table 3 and Fig. 2)

Table 3

Relationship between TyG and PSV
		ρ	P value
Common carotid artery	Left	-0.12	0.146
Common carotid artery	Right	-0.152	0.064
Internal carotid artery	Left	-0.085	0.302
Internal carotid artery	Right	-0.067	0.415
Common femoral artery	Left	0.193	0.019*
Common femoral artery	Right	0.143	0.083

The TyG index of patients demonstrates a significant correlation with the Resistance Index (RI) in the right internal carotid artery (P = 0.002). Despite that there is no significant correlation observed between the TyG index of patients and the RI in the bilateral common carotid arteries and the left internal carotid artery (P > 0.05), a positive correlation trend can be discerned in the scatter plot (Table 4 and Fig. 3).

Table 4

Relationship between TyG and RI
		ρ	P value
Common carotid artery	Left	0.079	0.339
Common carotid artery	Right	0.069	0.402
Internal carotid artery	Left	0.04	0.624
Internal carotid artery	Right	0.25	0.002**

2.4 The relationship between TyG and baPWV, and FMD

The results of this study reveal a negative correlation between the TyG index of patients and brachial artery Flow-Mediated Dilation (FMD) with statistical significance (P < 0.001). Similarly, both left and right Brachial-Ankle Pulse Wave Velocity (baPWV) demonstrated a close association with the TyG index, exhibiting a positive correlation with statistical significance (P < 0.001). (Table 5, Fig. 4, and Fig. 5 )

Furthermore, a correlation analysis was performed between the bilateral Ankle-Brachial Index (ABI) and the TyG index of patients. In contrast to the earlier findings for brachial-ankle pulse wave velocity (baPWV) and flow-mediated dilation (FMD), no significant correlation was observed (P > 0.05). However, a negative correlation trend can be discerned in the corresponding scatter plots (Table 5, Fig. 4 and Fig. 5 ).

Table 5

Relationship between TyG and FMD、baPWV、ABI
		ρ	P value
FMD		-0.414	< 0.001***
baPWV	Left	0.314	< 0.001***
baPWV	Right	0.353	< 0.001***
ABI	Left	-0.047	0.566
ABI	Right	0.065	0.428

2.5 The correlation between TyG and plaque progression

To investigate the independent effect of TyG on plaque progression, we employed three multiple logistic regression models (Table 2). In Model 1, no covariates were adjusted. Model 2 adjusted for age and gender, while Model 3 further adjusted for BMI, hypertension, diabetes mellitus, hyperlipidemia, coronary heart disease, atrial fibrillation, stroke, renal insufficiency, smoking, drinking, total cholesterol, triglycerides, LDL cholesterol, HDL cholesterol, sdLDL, apolipoprotein a.1, apolipoprotein a.2, apolipoprotein B, apolipoprotein C.II, apolipoprotein C.III, glucose, creatinine, uric acid, and white blood cells.

The results show that, in Model 1, TyG is significantly associated with atherosclerosis progression in both carotid and lower limb arteries (P < 0.0001), with HR and 95% CI is 7.54 (3.47–16.38) and 10.81 (4.63–25.22), respectively. These findings are consistent with those of Models 2 and 3. In Model 2, the HR and 95% CI are 7.53 (3.33–17.04) and 13.17 (4.99–34.74), respectively. In Model 3, the HR and 95% CI are 0.03 (0.01–0.09) and 431.92 (139.13-1340.81), respectively. Table 6

Table 6

Multiple logistic regression models for TyG
Variable	Model-I HR	95%CI	P-value	Model-II HR	95%CI	P-value	Model-III HR	95%CI	P-value
Carotid artery
TyG	7.54	3.47–16.38	< 0.0001	7.53	3.33–17.04	< 0.0001	0.03	0.01–0.09	< 0.0001
Q1	1			1			1
Q2	2.19	0.71–6.72	0.172	1.950	0.61–6.27	0.263	0.690	0.12–4.11	0.682
Q3	5.45	1.84–16.16	0.002	5.480	1.75–17.15	0.003	0.220	0.01–3.93	0.301
Q4	19.37	6.00-62.51	< 0.0001	19.06	5.62–64.65	< 0.0001	0.12	0.002–6.72	0.306
Lower limb artery
TyG	10.81	4.63–25.22	< 0.0001	13.17	4.99–34.74	< 0.0001	431.92	139.13-1340.81	< 0.0001
Q1	1			1			1
Q2	1.61	0.61–4.22	0.332	1.36	0.47–4.19	0.549	0.94	0.12–7.27	0.953
Q3	8.57	2.99–24.56	< 0.0001	13.61	3.67–50.53	< 0.0001	2.66	0.11–65.45	0.549
Q4	15.6	4.82–50.53	< 0.0001	18.06	4.87–66.93	< 0.0001	0.72	0.01–71.56	0.887

2.6 The predictive value of TyG for plaque progression

The ROC curve analysis in this study revealed a statistically significant predictive value of the Triglyceride-Glucose Index (TyG) for the progression of plaques in the carotid arteries (AUC = 0.774, Sensitivity = 0.687, Specificity = 0.807, P < 0.001, 95% CI 0.696–0.852), lower limb arteries (AUC = 0.8, Sensitivity = 0.807, Specificity = 0.71, P < 0.001, 95% CI 0.728–0.871). All AUC values were greater than 0.7, suggesting that the TyG index can effectively predict the progression of plaques in the carotid and lower limb arteries within five years. (Table 7,Figs. 6, and Fig. 7)

TyG was then compared with the Framingham Risk Score. The AUC for predicting carotid artery atherosclerosis using the Framingham Risk Score was 0.7806, and the AUC for predicting lower extremity artery atherosclerosis was 0.7532, showing that TyG and the Framingham Risk Score are comparable in predicting the progression of atherosclerosis in the carotid and lower extremity.

Table 7

The ROC analysis of the progression of atherosclerosis with TyG
	AUC	Sensitivities	Specificities	P value	95%CI
CAS progression	0.774	0.687	0.807	< 0.001***	0.696–0.852
LEAS progression	0.8	0.807	0.71	< 0.001***	0.728–0.871
Both CAS and LEAS progression	0.822	0.778	0.792	< 0.001***	0.75–0.894

*Red: TyG, Yellow: Framingham cardiovascular risk scores

2.7 Subgroup

This study also performed stratified analysis and interaction tests based on age (< 65 years or ≥ 65 years), gender, hypertension, type 2 diabetes mellitus, coronary heart disease, hyperlipidemia, atrial fibrillation, stroke, renal insufficiency, smoking, and alcohol consumption (Figure. 8). The results showed that TyG was significantly associated with higher risks of carotid atherosclerosis progression in the non-atrial fibrillation subgroup (P = 0.043), with an HR (95% CI) of 4.26 (2.67–6.80) for this subgroup compared to an HR (95% CI) of 0.52 (0.07–3.86) for the atrial fibrillation subgroup.

TyG was significantly associated with a higher risk of lower limb atherosclerosis progression in the < 65 years subgroup (P = 0.027), with an HR (95% CI) of 11.2 (2.86–43.89) for this subgroup, compared to an HR (95% CI) of 2.51 (1.68–3.76) for the ≥ 65 years subgroup. TyG was also significantly associated with higher risks of lower limb atherosclerosis progression in the non-smoking subgroup (P = 0.029), with an HR (95% CI) of 3.91 (2.42–6.32) for this subgroup compared to an HR (95% CI) of 1.68 (0.87–3.24) for the smoking subgroup.

This study explored the correlation and predictive diagnostic value of the Triglyceride-Glucose Index (TyG) in relation to atherosclerosis. The findings demonstrated a notable association between the TyG index and the progression of atherosclerosis. Originally introduced as a marker for predicting cardiovascular diseases, the TyG index has gained the attention for its role in assessing insulin resistance[13]. It is crucial to highlight that insulin resistance constitutes a fundamental pathological link in metabolic disorders, acting as the shared pathophysiological basis for conditions like diabetes, obesity, and cardiovascular diseases[14]. The mentioned diseases and the dysfunction of arterial endothelial function mutually cause and reinforce each other. Insulin resistance can result in reduced activity of fatty acid decomposition, an elevation in plasma-free fatty acids, and subsequent activation of protein kinase C (PKC). It also induces serine phosphorylation of insulin receptor substrate-1 (IRS-1) instead of the normal tyrosine phosphorylation, exacerbating insulin resistance and contributing to a vicious cycle of abnormal insulin signal transduction[15]. In this process, the pathologically elevated plasma-free fatty acids cause an increase in the production of reactive oxygen species (ROS) in monocytes and an upregulation of NF-κB expression. Concurrently, the macrophage migration inhibitory factor (MIF) also increases, thus intensifying the inflammatory response[16]. Moreover, the abnormal activation of protein kinase C (PKC) hampers the activation of protein kinase B (PKB) and endothelial nitric oxide synthase (eNOS) activity, thus influencing the production of nitric oxide (NO). When coupled with the high-glucose microenvironment arising from insulin resistance, these factors collectively contribute to endothelial dysfunction[17]. Furthermore, elevated serum-free fatty acids can expedite the transfer of lipids, such as triglycerides, to the dysfunctional endothelial interstitium[18]. From this, it can be observed that serum triglycerides and glucose play a pivotal role in the pathological processes leading to endothelial dysfunction and atherosclerosis. This observation may also elucidate why the TyG index serves as an assessment criterion for the prognosis of cardiovascular events.

This study concurrently examined the correlation between the TyG index and intima-media thickness (IMT) at various arterial locations. The findings revealed a close association between the TyG index and IMT, exhibiting a positive correlation, irrespective of whether it was in the carotid or lower limb arteries. Under normal circumstances, endothelial cells release endothelium-derived relaxing and contracting factors, maintaining a balance to preserve the normal physiological and elastic function of blood vessels[19]. However, an environment with elevated levels of sugar and fat in the blood can disrupt the normal function of arterial endothelium, triggering a series of pathological changes such as endothelial cell apoptosis, lipid deposition, and inflammatory reactions. Vascular smooth muscle cells migrate from the media to the sub-intimal space, undergo abnormal proliferation, contributing to the thickening of the intima and media. In this process, the production and activity of endothelium-derived relaxing factors decrease, disrupting the delicate balance between them. The vascular contraction function becomes stronger than the relaxation function, resulting in a state known as endothelial dysfunction [19]. Arteries in this condition frequently manifest impaired dilation function. This not only clarifies the correlation between the thickening of intima-media thickness (IMT) and elevated levels of triglycerides and glucose in the serum, but also provides further explanation for the higher brachial-ankle pulse wave velocity (baPWV) and lower flow-mediated dilation (FMD) observed in the population with a high TyG index in this study.

As the disease progresses, the macrophages and lipids continuously accumulate beneath the endothelium, thickening the vessel wall and reducing the internal diameter[20]. In clinical practice, vascular ultrasound is widely used to assess arterial stenosis. It is important to note that as the arterial diameter decreases, the peak systolic velocity (PSV) typically increases[21]. There exists a strong correlation between flow velocity, blood flow volume, and the narrowed inner diameter of the vessel. This correlation was initially characterized by Spencer and Reid as the 'Spencer curve' [22, 23]. Based on this principle, the peak systolic velocity (PSV) in patients was monitored, and a close and positive correlation between the relatively increased PSV in the femoral artery and TyG was identified. In contrast, a negative correlation was observed between the carotid arteries and TyG. The discrepancy in these findings may be attributed to the probe's location. When the monitoring site is situated at the proximal and distal of plaque, blood flow at this point may create a vortex, thus influencing the blood flow velocity and direction[24]. As the size of the plaque or local intima-media thickness (IMT) increases with TyG, the impact on local hemodynamics also intensifies. Additionally, some studies have reported that the flow velocity at the bifurcation of the carotid artery is notably lower than in other areas, and the blood flow at this location varies significantly at different times[25]. This observation may be associated with blood diversion. Finally, an analysis of blood flow resistance in the carotid arteries was conducted. A significant correlation between the increase in blood flow resistance in the right carotid artery and TyG was observed. Although no statistical difference was observed in other segments of the carotid arteries, the scatter plots all depicted a positive correlation trend. This correlation can be attributed to the promotion of wall thickening and plaque formation by elevated blood sugar and high fat levels in the bloodstream. These findings further affirm that individuals with a high TyG index not only induce changes in vascular structure but also subsequently influence hemodynamic phenomena through these structural alterations.

Following the aforementioned observations, we investigated whether the TyG index could function as a predictive indicator for the five-year plaque trend in patients. An ROC curve for verification was applied, and we discovered that the TyG index exhibited predictive capability for progression. The area under the ROC curve (AUC) for carotid plaque was 0.774, with sensitivities of 0.687, specificities of 0.807, and a significance level of P < 0.001. Similarly, the AUC for lower limb arterial plaque was 0.8, with sensitivities of 0.807, specificities of 0.71, and a significance level of P < 0.001. Furthermore, the AUC for both carotid and lower limb arterial plaque was 0.822, with sensitivities of 0.778, specificities of 0.792, and a significance level of P < 0.001. These findings suggest that utilizing the TyG index may enhance clinicians' ability to identify patients with subclinical atherosclerosis early, thus facilitating better control of atherosclerosis progression and the associated risk of ASCVD.

This study, which explores the relationship between TyG and FMD, baPWV, ABI, and the progression of plaques in carotid and lower limb arteries, has elucidated that the TyG index, through its impact on arterial structure and hemodynamics, adversely affects endothelial function and the advancement of atherosclerotic plaques. However, there are several limitations to this study. Firstly, as a retrospective study, various types and degrees of interventions the participants received during the disease course and differences in blood tests and ultrasound follow-ups may introduce biases to the study results. Secondly, the sample size of this study is relatively small, indicating only a trend of positive or negative correlation without reaching statistical significance in several of the results. Moreover, the participants in this study are relatively homogeneous. Future research with larger sample sizes and diverse populations is necessary to further investigate these relationships. Finally, the primary aim of this study is to clarify the evaluative value of TyG in endothelial function and atherosclerosis progression. We have solely conducted an analysis of the correlation between TyG and relevant indicators, but have not established or validated a predictive model. Therefore, we anticipate the near future when diagnostic and predictive models for subclinical atherosclerosis are developed.

In summary, this study suggests that the TyG index is closely related to the progression of atherosclerosis and arterial stiffness. Given the limitations of this study, we look forward to larger-scale prospective clinical studies in the near future to develop diagnostic and predictive models for subclinical atherosclerosis.

Contributors

Hu Y.Q. designed the study, wrote the first draft of the manuscript and verified the underlying data. Hu Y.Q., Xu Z.H., Yang J.H. and Shao Q. conducted the statistical analysis, Xu Z.H., Lu C. and Shi T.Y. played a role in the acquisition of the data and analyses, and participated in data interpretation. Wan Q.Q edited the figures and tables. Wang X.L. and Liu Y.M. directed the study design and funded the study. All authors revised and approved the final manuscript. The guarantor (Liu Y.M.) confirms that all listed authors meet the authorship criteria and that no others meeting the criteria have been omitted.

Data Sharing Statement

Data available on request from the authors

Declaration of Competing Interests

All authors declare the following: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.

Funding declaration

This study was supported by the National Natural Science Foundation of China (No. 81973611) and the Traditional Chinese Medicine Advantage Disease Diagnosis and Treatment Capacity Construction Project of Administration of Traditional Chinese Medicine of Anhui Province (No. 340000232428000100010), and Medical Disciplinary Development Project of Pudong New Area Health System (No. PWYgy2021-10).

Clinical trial number

Not applicable.

Human Ethics and Consent to Participate declarations

This study was approved by the ethics committee of ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (approval no. 2023-1406-173-01).

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Association of Triglyceride-Glucose Index with the Progression of Atherosclerosis and Impairment of Arterial Endothelial Function

Status:

Version 1

Abstract

Aims

Methods

Results

Conclusion

Figures

Introduction

Methods

1.1 Participant Definition

1.2 Observational Measures

1.2.1 Baseline Characteristics

1.2.2 Supplementary Examination Data

1.2.3 Primary outcome

1.3 Examination Methods

1.3.1 Measurement of baPWV and ABI

1.3.2 FMD

1.4 Statistical analysis

Results

2.1 Baseline data

2.2 Relationship between TyG and IMT

2.3 Relationship between TyG and PSV

2.5 The correlation between TyG and plaque progression

2.6 The predictive value of TyG for plaque progression

2.7 Subgroup

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1