Correlation between triglyceride glucose-body mass index and hypertension in patients with acute coronary syndrome

doi:10.21203/rs.3.rs-4206522/v1

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Correlation between triglyceride glucose-body mass index and hypertension in patients with acute coronary syndrome

https://doi.org/10.21203/rs.3.rs-4206522/v1

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The triglyceride glucose-body mass index (TyG-BMI index) is a simple, cost-effective, practical alternative marker for early detection of insulin resistance. However, studies on the relationship between TyG-BMI index and hypertension in patients with acute coronary syndrome (ACS) are limited. We investigated the relationship between TyG-BMI index and hypertension in patients with ACS. We included 2,635 patients with ACS. TyG-BMI index was grouped into tertiles. In the overall ACS population, after adjusting for covariates, multivariate logistic regression analyses showed that TyG-BMI index was an independent risk factor for hypertension in patients with ACS. In subgroup analyses, the TyG-BMI index was found to be an independent risk factor for hypertension in older, nondiabetic, and male ACS patients. Restricted cubic spline analysis revealed a nonlinear correlation between TyG-BMI index and hypertension in patients with ACS. The baseline risk model’s predictive power was not improved by the inclusion of TyG-BMI index in patients with ACS. TyG-BMI index was nonlinearly correlated with hypertension in patients with ACS, and the inclusion of TyG-BMI index did not enhance the predictive power of the baseline risk model in patients with ACS.

Health sciences/Cardiology

Health sciences/Cardiology/Cardiovascular biology

Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases

Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases/Hypertension

acute coronary syndrome

triglyceride glucose-body mass index

insulin resistance

hypertension

type 2 diabetes mellitus

Hypertension is one of the most important risk factors for ischaemic heart disease, and its prevalence in East Asian populations has gradually increased in recent years ¹. Acute coronary syndromes (ACSs) are divided into three categories: acute non-ST-segment elevation myocardial infarction, acute ST-segment elevation myocardial infarction, and unstable angina ². Studies have demonstrated that patients with ACS and comorbid hypertension are at a higher risk of experiencing adverse cardiovascular events. In a case-control study, Fresco et al ³ examined 10,712 patients with acute myocardial infarction (AMI) treated with thrombolysis and found that patients with AMI and comorbid hypertension had a higher mortality rate both in hospital and over the next 6 months. In addition, other studies have shown that hypertension is strongly associated with short- and long-term adverse cardiac outcomes in patients with ACS ^4,5. Current research suggests that aggressive and effective blood pressure (BP) control reduces the incidence of adverse cardiovascular and cerebrovascular events ⁶. Moreover, existing studies have shown that patients with ACS and high BP have a significantly increased risk of future cardiovascular events ⁷. Therefore, early identification of potential patients with hypertension and ACS and strict control of BP are clinically important to improve the prognosis of patients with ACS. However, epidemiological data regarding the prognosis of patients with ACS and hypertension are scarce [3].

Insulin resistance (IR) is defined as the body’s reduced sensitivity to insulin metabolism ⁸. Studies have shown that IR has an important role in the development of hypertension ⁹ and that hypertension typically coexists with type 2 diabetes mellitus (DM), obesity, and dyslipidaemia ¹⁰. Currently, the triglyceride glucose-body mass index (TyG-BMI index) is considered an easily accessible and practical alternative marker of IR ¹¹. TyG-BMI index is a fusion of three indicators that are closely related to hypertension—lipids, glucose, and obesity—and shows potential predictive value for the identification of patients with hypertension. Several studies have shown that TyG-BMI index is independently associated with hypertension in East Asian and non-DM populations ^12,13. However, it remains unclear whether there is a relationship between TyG-BMI index and hypertension in patients with ACS. Thus, this study aimed to examine the association between TyG-BMI index and hypertension and assess the predictive value of hypertension in patients with ACS. TyG-BMI index is expected to be used to identify patients with hypertension and ACS early, providing a reference for BP control strategies in such patients and thus improving patient prognosis.

Baseline characteristics

This study included 2,635 patients with ACS. The patients had a mean age of 62 years (54, 70). Among the patients, 1,892 (71.8%) were males and 1,490 (56.5%) had DM (Table 1). There were differences in glycaemia; age; sex; body mass index (BMI); systolic blood pressure (SBP); diastolic blood pressure (DBP); creatinine (Cr), haemoglobin (Hb), glycated haemoglobin (HbA1c), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG), and uric acid levels; smoking history, previous stroke, and DM among the three groups (all P < 0.05). The T3 group had significantly higher levels of glycaemia, TC, TG, BMI, LDL-C, and insulin and higher oral hypoglycaemic drug use than the other groups did.

Table 1

Baseline characteristics according to tertiles of the TyG-BMI index
	Total (N = 2,635)	T1 (N = 878)	T2 (N = 878)	T3 (N = 879)	P
Glycemia (mmol/L)	7.57 (5.74, 11.03)	6.21 (5.07, 8.17)	7.57(5.88, 10.77)	9.60 (7.04, 13.76)	< 0.001
Age (years)	62 (54,70)	65 (56,73)	62 (55,70)	58 (52,68)	< 0.001
BMI (kg/m²)	23.88(21.88,26.16)	20.96(19.59,22.41)	23.88(22.84,24.97)	27.34(25.59,29.39)	< 0.001
Creatinine(µmol/L)	78 (66, 96)	77 (65, 95)	80 (67, 95)	79 (67, 96)	0.024
hs-CRP (mg/L)	3.67 (1.26, 13.42)	3.71 (1.21, 13.13)	3.42 (1.20, 13.91)	3.82 (1.40, 12.85)	0.652
DBP (mmHg)	76 (68, 85)	74 (67, 84)	76 (68, 86)	76 (68, 85)	0.003
LVEF (%)	55 (46, 60)	55 (45, 60)	55 (45, 60)	56 (48, 61)	0.074
Egfr (mL/min/1.73m²)	86.73(68.50,103.79)	87.16(67.46,104.16)	85.70(68.03,101.61)	87.42(70.55,105.42)	0.284
Hb (g/L)	138 (125, 151)	133 (121, 145)	138 (125, 150)	144 (131, 156)	< 0.001
HbA1c (%)	6.5 (5.7, 7.9)	6.0 (5.6, 6.9)	6.6 (5.8, 8.1)	7.1 (6.1, 8.9)	< 0.001
HDL-C (mmol/L)	1.08 (0.93, 1.25)	1.12 (0.97, 1.32)	1.08 (0.93, 1.24)	1.03 (0.88, 1.21)	< 0.001
LDL-C (mmol/L)	2.87 (2.27, 3.55)	2.74 (2.15, 3.41)	2.85 (2.27, 3.53)	3.03 (2.40, 3.69)	< 0.001
SBP (mmHg)	124 (112, 138)	122 (110, 135)	125 (112, 138)	124 (112, 138)	0.035
TC (mmol/L)	4.76 (3.86, 5.69)	4.52 (3.71, 5.42)	4.74 (3.83, 5.63)	5.06 (4.05, 6.02)	< 0.001
TG (mmol/L)	1.82 (1.24, 2.83)	1.31 (0.97, 1.82)	1.87 (1.33, 2.66)	2.64 (1.77, 4.18)	< 0.001
Uric acid (µmol/L)	360 (298, 437)	343 (284, 423)	365 (303, 439)	368 (308, 446)	< 0.001
Male sex	1,892 (71.8)	575 (65.5)	634 (72.2)	683 (77.7)	< 0.001
Smoking status					< 0.001
Current	1,089 (41.3)	308 (35.1)	372 (42.4)	409 (46.5)
Former	366 (13.9)	132 (15.0)	120 (13.7)	114 (13.0)
Never	1,180 (44.8)	438 (49.9)	386 (44.0)	356 (40.5)
Hypertension	1,628 (61.8)	510 (58.1)	557 (63.4)	561 (63.8)	0.022
Previous stroke	302 (11.5)	90 (10.3)	121 (13.8)	91 (10.4)	0.030
DM	1,490 (56.5)	362 (41.2)	507 (57.7)	621 (70.6)	< 0.001
Insulin	637 (24.2)	135 (15.4)	219 (24.9)	283 (32.2)	< 0.001
Oral hypoglycaemic drugs	1,139 (43.2)	267 (30.4)	398 (45.3)	474 (53.9)	< 0.001
Data are presented as means ± SDs, medians (interquartile ranges), or n (%).

Clinical outcomes

Table 2 shows the relationship between TyG-BMI index and hypertension in patients with ACS. Univariate logistic regression analyses showed that TyG-BMI index was associated with hypertension in patients with ACS (odds ratio [OR] = 1.0026; 95% confidence interval [CI] = 1.0006–1.0045; P = 0.0090). After adjusting for covariates, multivariate logistic regression analyses revealed that TyG-BMI index in patients with ACS was an independent risk factor for hypertension (OR = 1.0038; 95% CI = 1.0014–1.0061; P = 0.0020); the risk of hypertension in the T2 group (OR = 1.2500; 95% CI = 1.0100–1.5400; P = 0.0360) was 1.25 times higher than that in the T1 group, and the risk of hypertension in the T3 group (OR = 1.3700; 95% CI = 1.0900–1.7200; P = 0.0060) was 1.37 times higher than that in the T1 group. Therefore, we built a restricted cubic spline model to define the relationship between TyG-BMI index and hypertension, and the model showed a nonlinear correlation (nonlinear P = 0.043) (Fig. 1). The OR per standard deviation (SD) for predicting hypertension was 1.2000 (95% CI = 1.0700–1.3400) when TyG-BMI index was < 240.656 and 1.0300 (95% CI = 0.8930–1.1900) when TyG-BMI index was ≥ 240.656 (Table 3).

Table 2

Associations between TyG-BMI index and hypertension
	Events/N	Model 1			Model 2
		OR	95% CI	P	OR	95% CI	P
TyG-BMI index	1,628/2,635	1.0026	(1.0006–1.0045)	0.0090	1.0038	(1.0014–1.0061)	0.0020
T1	510/878	Reference			Reference
T2	557/878	1.2500	(1.0300–1.5200)	0.0220	1.2500	(1.0100–1.5400)	0.0360
T3	561/879	1.2700	(1.0500–1.5400)	0.0140	1.3700	(1.0900–1.7200)	0.0060
P for trend				0.0134			0.0060

Table 3

Relationship between TyG-BMI index (per SD) and hypertension in patients with ACS
	OR per SD	95% CI
TyG-BMI index ≥ 240.656	1.0300	0.8930–1.1900
TyG-BMI index < 240.656	1.2000	1.0700–1.3400

Subgroup analyses

Subgroup analyses were conducted to investigate the correlation between TyG-BMI index and hypertension in different populations based on DM status, sex, and age. Table 4 shows the relationship between TyG-BMI index and hypertension in patients with ACS with different DM statuses. There was a significant interaction between the DM subgroups and the association of TyG-BMI index with hypertension (interaction P-value = 0.0180). After adjusting for covariates, multivariate logistic regression analyses revealed that TyG-BMI index was an independent risk factor for hypertension in patients without DM (OR = 1.0059; 95% CI = 1.0023–1.0095; P = 0.0010), and the risk of hypertension in the T3 group (OR = 1.4600; 95% CI = 1.0400–2.0400; P = 0.0300) was 1.46 times higher than that in the T1 group.

Table 4

Associations between TyG-BMI index and hypertension according to patients’ diabetes statuses
	Events/N	Model 1			Model 2
		OR	95% CI	P	OR	95% CI	P	P for interaction
Glucose metabolism state								0.0180
DM	985/1,490	0.9985	(0.9959–1.0011)	0.2580	1.0015	(0.9984–1.0047)	0.3370
T1	240/362	Reference			Reference
T2	342/507	1.0500	(0.7900–1.400)	0.7210	1.1200	(0.8200–1.5200)	0.4880
T3	403/621	0.9400	(0.7200–1.2300)	0.6560	1.2100	(0.8800–1.6600)	0.2410
Non-DM	643/1,145	1.0050	(1.0018–1.0081)	0.0020	1.0059	(1.0023–1.0095)	0.0010
T1	270/516	Reference			Reference
T2	215/371	1.2600	(0.9600–1.6400)	0.0970	1.3200	(0.9900–1.7600)	0.0620
T3	158/258	1.4400	(1.0600–1.9500)	0.0190	1.4600	(1.0400–2.0400)	0.0300

Table 5 shows the relationship between TyG-BMI index and hypertension in older and younger patients with ACS. There was no significant interaction between the age subgroups and the association of TyG-BMI index with hypertension (interaction P-value = 0.8360). After adjusting for covariates, multivariate logistic regression analyses showed that TyG-BMI index was an independent risk factor for hypertension in patients aged < 60 years (OR = 1.0048; 95% CI = 1.0014–1.0082; P = 0.0050), and the risk of hypertension in the T3 group (OR = 1.4400; 95% CI = 1.0400–2.0000; P = 0.0300) was 1.44 times higher than that in the T1 group.

Table 5

Associations between TyG-BMI index and hypertension according to patients’ ages
	Events/N	Model 1			Model 2
		OR	95% CI	P	OR	95% CI	P	P for interaction
Age, years								0.8360
Age ≥ 60 years	970/1,450	1.0047	(1.0019–1.0075)	< 0.001	1.0029	(0.9996–1.0062)	0.0850
T1	350/568	Reference			Reference
T2	339/482	1.4800	(1.1400–1.9100)	0.0030	1.3600	(1.0300–1.7900)	0.0320
T3	281/400	1.4700	(1.1200–1.9300)	0.0060	1.2600	(0.9200–1.7300)	0.1470
Age < 60 years	658/1,185	1.0030	(1.0001–1.0059)	0.0410	1.0048	(1.0014–1.0082)	0.0050
T1	160/310	Reference			Reference
T2	218/396	1.1500	(0.8500–1.5500)	0.3640	1.0500	(0.7600–1.4500)	0.7700
T3	280/479	1.3200	(0.9900–1.7600)	0.0590	1.4400	(1.0400–2.0000)	0.0300

Supplementary Table S1 shows the relationship between TyG-BMI index and hypertension in patients with ACS of different sexes. There was no significant interaction between the sex subgroups and the association of TyG-BMI index with hypertension (interaction P-value = 0.8639). After adjusting for covariates, multivariate logistic regression analyses showed that TyG-BMI index was an independent risk factor for hypertension in men with ACS (OR = 1.0042; 95% CI = 1.0014–1.0070; P = 0.0030), and the risk of hypertension in the T3 group (OR = 1.4800; 95% CI = 1.1300–1.9300; P = 0.0040) was 1.48 times higher than that in the T1 group.

Incremental effect of TyG-BMI index in predicting hypertension in patients with ACS

In the analysis of the data of patients with ACS, receiver operating characteristic (ROC) curves were constructed to assess the predictive ability of the baseline risk model and the baseline risk model plus TG, fasting blood glucose (FBG), BMI, and TyG-BMI index for the risk of developing hypertension (Fig. 2). There was no significant difference between the baseline risk model (area under the curve [AUC]: 0.6771 [0.6563–0.6978]) and the TyG-BMI index model (AUC: 0.6786 [0.6579–0.6993]) (P = 0.5049). Supplementary Table S2 lists the C-statistics, net reclassification index (NRI), and integrated discrimination index (IDI). The results indicated that the inclusion of TyG-BMI index did not improve the predictive ability of the baseline risk model (NRI: 0.0065 [-0.0059–0.0188], P = 0.3038; IDI: 0.0016 [0.0001–0.0032], P = 0.0398).

The prognosis of patients with ACS can be improved by early identification of hypertensive individuals and effective BP control. This study examined the possible correlation between TyG-BMI index and hypertension in patients with ACS. The main findings were as follows: (1) TyG-BMI index is an independent risk factor for hypertension in patients with ACS, and consistent results were observed in the elderly population, the nondiabetic population, and the male population, and (2) the addition of TyG-BMI index did not enhance the predictive ability of the baseline risk model (this model included age, HDL-C, estimated glomerular filtration rate [eGFR], Cr, uric acid, glycemia, Hb, high-sensitivity C-reactive protein [hs-CRP], HbA1c, left ventricular ejection fraction (LVEF), sex, smoking status, previous stroke, and DM) for patients with ACS.

IR plays a crucial role in the development of hypertension, and patients with this condition typically have higher BP than those with normal insulin levels ⁹. In addition, inappropriate activation of the renin-angiotensin-aldosterone and sympathetic nervous system, mitochondrial dysfunction, and oxidative stress are potential mechanisms through which IR causes hypertension ¹⁴. Simple measures of IR have been developed, including the TyG index and several combinations of the TyG index and obesity indicators. These indicators of obesity include BMI, waist circumference, and waist-to-height ratio, and the combination of TyG and BMI (TyG-BMI index) was found to be the most valuable surrogate marker of IR ^15,16. TyG-BMI index is a simple, cost-effective, and practical alternative marker for the early detection of IR ¹¹. Therefore, this study investigated the correlation between TyG-BMI index and hypertension in patients with ACS and analysed the potential predictive value of TyG-BMI index for hypertension in this population. To the best of our knowledge, this is the first study to investigate the relationship between TyG-BMI index and hypertension in patients with ACS.

In our study, adjustments were made for possible risk factors. In the entire study population, TyG-BMI index was an independent risk factor for hypertension in patients with ACS and the risk of hypertension in patients with ACS increased with increasing TyG-BMI index. Previous studies have found that most patients with IR also exhibit other metabolic abnormalities, including elevated serum TG levels, low HDL-C levels, and high LDL-C levels. This metabolic syndrome is a hallmark feature of hypertension ⁹. The baseline information table for this study showed that the tertile groupings of TyG-BMI index were consistent with this trend profile. Although the result suggests that TyG-BMI index is a reliable and objective predictor of hypertension, we did not observe an incremental effect of the inclusion of TyG-BMI index in the baseline risk model on the predictive value of hypertension in patients with ACS. Subgroup analyses showed that TyG-BMI index remained an independent risk factor for hypertension in older, nondiabetic, and male ACS patients. Previous studies have also reported a correlation between TyG-BMI index and hypertension in patients without DM. For example, Peng et al ¹² conducted a cross-sectional study that included 15,464 individuals without DM and showed that TyG-BMI index was significantly and positively correlated with hypertension; this correlation was stronger in the younger population. This finding is consistent with our study’s findings. Previous studies have also shown that IR is strongly associated with elevated BP in younger populations ^17,18, which is consistent with the results of the present study. In addition, IR plays a crucial role in the development of type 2 DM ¹⁹. However, no significant correlation between TyG-BMI index and hypertension was observed in patients with ACS and DM. This overturns some of our previous perceptions that patients with IR are at a greater risk of hypertension ¹⁴. We hypothesised that patients with DM may have improved IR due to the use of oral hypoglycaemic agents ^20,21.

Furthermore, no significant correlation was observed between TyG-BMI index and hypertension in women with ACS. We speculate that this may be due to an insufficient sample size. No significant correlation between TyG-BMI index and hypertension was observed in older patients with ACS, which can be partially explained as follows: 1) the association between insulin sensitivity and the development of hypertension may be more confounded and difficult to discern because of the existence of multiple risk factors for hypertension in older adults ²² and 2) the T3 and T2 groups included more young patients than the T1 group did. To determine the threshold of TyG-BMI index and explore its predictive value for cardiovascular outcomes in patients with ACS, it is recommended that larger prospective cohort studies be conducted.

This is the first study to propose a nonlinear correlation between TyG-BMI index and hypertension in patients with ACS using a restricted cubic spline analysis. However, this study had some limitations. It was conducted at a single centre and only included Asian patients; therefore, the results should be interpreted with caution. Second, the study’s retrospective design limits the ability to infer causality. Thus, further prospective multicentre studies are required to validate these results.

In conclusion, TyG-BMI index was nonlinearly associated with hypertension in patients with ACS, and the predictive ability of baseline risk models for patients with ACS did not improve with the addition of TyG-BMI index. The findings of this study will contribute to the development of BP control strategies and improve the early identification and prognosis of patients with hypertension and ACS. To assess the predictive value of TyG-BMI index in patients with ACS and the underlying mechanisms of the nonlinear correlations, it is recommended that further prospective, large-scale, multicentre studies be conducted.

Study design and population

This cross-sectional study was conducted at the Affiliated Hospital of Zunyi Medical College. This study adheres to the Declaration of Helsinki. Due to the retrospective nature of this study. Research using human data with identifiable information ,the subject could no longer be located ,and the research project does not involve personal privacy or commercial interests. Therefore, in our study, we submitted an "Application for Waiver of Informed Consent Form" to the Biomedical Research Ethics Committee of Zunyi Medical University Affiliated Hospital. The study was approved by the Biomedical Research Ethics Committee of Zunyi Medical University Affiliated Hospital. This study included patients with ACS who underwent percutaneous coronary intervention (PCI) at the Affiliated Hospital of Zunyi Medical University between 1 May 2019 and 1 November 2023. Patients with ACS who were treated with PCI were included. Patients who met the following criteria were excluded: 1) history of malignancy and 2) missing data (Fig. 3). Ultimately, 2,635 patients with ACS were included in the study. The patients were divided into three groups based on TyG-BMI index: T1, TyG-BMI index <208.71; T2, 208.71 ≤ TyG-BMI index <243.26; and T3, 243.26 ≤ TyG-BMI index. The study endpoint was the prevalence of hypertension in patients with ACS.

Data measurement and definitions

The baseline demographic and clinical data of all patients were collected retrospectively from medical records of the Affiliated Hospital of Zunyi Medical University. Demographic data included age, sex, BMI, smoking status, and comorbidities (DM and previous stroke). Clinical data included the patients’ SBP and DBP on admission, imaging data, laboratory test findings (LVEF, TGs, TC, HDL-C, LDL-C, FBG, HbA1c, Hb, Cr, uric acid, hs-CRP), and medication regimen during hospitalisation for DM (insulin and oral hypoglycaemic drugs). Blood samples were collected from all patients in the fasting state. FBG and TG levels were determined using an AU5800 system (Beckman Coulter, California, USA). BMI was calculated as weight (kg) divided by height (m) squared, and the eGFR was calculated according to the Modification of Diet in Renal Disease equation ²³: men: eGFR = 186 × Cr^-1.154× age^-0.203 and women: eGFR = 186 × Cr^-1.154× age^-0.203× 0.742. TyG-BMI index was defined as ln [TG (mg/dL) × FBG (mg/dL)/2] × BMI ¹¹. DM represents type 2 DM, defined as a history of type 2 DM or HbA1c ≥6.5% ²⁴. Older patients were defined as those aged ≥60 years ²⁵. The criteria described in the 2018 Chinese guidelines were used to define hypertension ⁶.

Statistical analyses

Statistical analyses were performed using R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables with a normal distribution are presented as means±standard deviations, and continuous variables not normally distributed are presented as medians (interquartile ranges). Categorical variables are presented as numbers (percentages). Comparisons among the three groups of continuous variables that conformed to a normal distribution with chi-square variance were performed using an analysis of variance, and comparisons among the three groups of continuous variables that did not conform to a normal distribution or chi-square variance were performed using the Kruskal–Wallis rank-sum test. Comparisons of categorical values among the three groups were performed using chi-square analysis.

To analyse the relationship between TyG-BMI index and hypertension in patients with ACS, we used logistic regression analysis to calculate the ORs and 95% CIs. In this study, Model 1 was not adjusted. Model 2 was a multivariate model. Before constructing the multivariate model, we examined the collinearity between TyG-BMI index and other covariates by calculating the generalised variance inflation factor (GVIF). Covariates were considered significant for collinearity if GVIF^(1/2Df) was ≥2 (Df, degrees of freedom). The least absolute shrinkage and selection operator regression was used to filter covariates from the pool of variables. The following covariates were adjusted in the multivariate logistic regression analyses for the overall population: age, HDL-C, eGFR, creatinine, uric acid, glycaemia, Hb, hs-CRP, HbA1c, LVEF, sex, smoking status, previous stroke, and DM. Restricted cubic spline analyses were used to explore the relationship between TyG-BMI index and hypertension. The subgroups were analysed according to DM status, age, and sex, and univariate and multivariate logistic regression analyses were performed. Diagnostic value analyses were performed using ROC curves, and the AUC, measured using the C-statistic, was calculated to quantify the predictive power of the logistic model for hypertension. AUC-based comparisons among models were assessed using the DeLong test. In addition, the NRI and IDI were calculated to further assess the additional predictive value of TyG-BMI index over and above the identified risk factors for hypertension. P values <0.05 were considered statistically significant, and all analyses were two tailed.

Acknowledgements

We thank all the members who contributed to this study.

Author contributions

XYY and KL designed the study, analysed the data, and wrote the manuscript; XYY, KL, LC, HX, CLY, YXC, YHL, and JJW collected and interpreted the data; JL supervised the analysis. YM and GXX critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Additional information

Competing interests

All authors declare no competing interests.

Funding

This study was supported by the Biomedical Joint Funding of Guizhou Provincial Science and Technology Department (No. Qiankehe LH Zi [2017] 7109).

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Correlation between triglyceride glucose-body mass index and hypertension in patients with acute coronary syndrome

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Abstract

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Introduction

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Baseline characteristics

Clinical outcomes

Subgroup analyses

Incremental effect of TyG-BMI index in predicting hypertension in patients with ACS

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