Association of the triglyceride-glucose index with blood pressure and hypertension in a large adult population: A cross-sectional study

Abstract

Background

The triglyceride-glucose index (TyG), a marker of insulin resistance, has been linked to various cardiometabolic disorders. However, its association with blood pressure (BP) and hypertension remains inconsistent. This study aimed to investigate the association between the TyG index and BP, hypertension, and prehypertension in a large sample of Iranians.

Methods

This cross-sectional analysis utilized baseline data from 7,996 participants in the PERSIAN Kharameh cohort study. Multivariable-adjusted linear and logistic regression models were employed to assess the relationship between the TyG index and BP components (systolic BP, diastolic BP, pulse rate), as well as hypertension and prehypertension. The predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

A positive relationship was observed between the TyG index and systolic BP, diastolic BP, and pulse rate, even after adjusting for confounding variables. Furthermore, each one-unit increase in the TyG index was associated with a 21% increase in the odds of hypertension (OR = 1.21, 95% CI: 1.05–1.40) and a 34% increase in the odds of prehypertension (OR = 1.34, 95% CI: 1.17–1.54). However, the discriminative ability of the TyG index for diagnosing hypertension and prehypertension was modest, with area under the curve (AUC) values of 0.571 and 0.574, respectively.

Conclusion

The TyG index is significantly and independently associated with higher BP levels and increased odds of hypertension and prehypertension. However, its utility as a standalone diagnostic tool for hypertension is limited.

Keywords

hypertension prehypertension insulin resistance dyslipidemias heart rate

1. Introduction

Hypertension, often referred to as the “silent killer,” is a leading cause of cardiovascular diseases and premature mortality. According to the World Health Organization, the prevalence of hypertension has increased from 594 million adults in 1975 to 1.13 billion in 2015, with the highest rates of increase observed in low- and middle-income countries.^1,2 It is estimated that only 44% of adult patients with hypertension are diagnosed and treated,² highlighting the importance of identifying strategies for more timely diagnosis and improved management of this public health issue.

Dyslipidemia and insulin resistance have been linked to an increased risk of hypertension, partly by impairing vasodilation mechanisms and endothelial function, as well as by increasing arterial stiffness and inflammation.^3,4 The triglyceride-glucose index (TyG) is a newly established marker calculated from fasting plasma glucose (FPG) and triglyceride (TG) levels, reflecting both glucose and lipid metabolism. It has been proposed as a clinically useful and cost-effective marker for assessing insulin resistance.⁵ Previous studies have reported the predictive value of this index in relation to diabetes,⁶ non-alcoholic fatty liver disease,⁷ metabolic syndrome,⁸ cognitive dysfunction,⁹ and cardiovascular diseases.^10,11 Several studies have also indicated an association between TyG levels and an increased risk of hypertension; however, the existing literature is conflicting.¹² Therefore, we aimed to investigate the relationship between TyG levels and blood pressure (BP), as well as hypertension, among Iranians using the Prospective Epidemiological Research Studies in Iran (PERSIAN) Kharameh cohort study (KHCS). In addition to systolic and diastolic blood pressure (SBP and DBP), pulse rate was also evaluated because resting pulse rate reflects autonomic cardiovascular regulation and sympathetic nervous system activity, which may be altered in insulin resistance states.^13,14

2. Methods

2.1. Study design and population

In this cross-sectional study, we utilized baseline data from the KHCS, a branch of the PERSIAN study supported by the Iranian Ministry of Health. These data were collected during the recruitment phase, which spanned from April 2015 to March 2017. The protocol for the KHCS has been previously published.¹⁵ In summary, the KHCS included 10,663 participants (44.3% men) who were urban and rural residents of Kharameh, a city located in southwestern Iran, near Shiraz. The study aimed to investigate the incidence and risk factors associated with non-communicable diseases. All participants provided written informed consent to take part in the study.

For the current analysis, subjects were excluded if they had any of the following conditions: pregnancy (n = 113), diabetes (n = 1,600), cancer (n = 62), hepatitis (n = 26), renal failure (n = 38), multiple sclerosis (n = 8), or if they were taking lipid-lowering medications (n = 1,577). These subjects were excluded to avoid confounding effects of insulin resistance, lipid metabolism, and inflammation on BP and the TyG index. Additionally, subjects with missing data for FPG and TG were also excluded from the analysis (n = 57). This study was approved by the Ethics Committee of Shiraz University of Medical Sciences (Code: IR. SUMS.REC.1404.246).

2.2. Data collection

After the enrollment of eligible subjects, general and medical questionnaires were completed, and physical examinations were conducted by a physician and trained staff. Socioeconomic status was assessed using the wealth score index (WSI), which was derived from multiple correspondence analysis of household assets and facilities, as previously described¹⁶; lower scores indicated higher socioeconomic status. Physical activity was measured using the validated Persian version of the questionnaire and the total physical score was calculated as metabolic equivalent (MET)-hours per week.^16–19 Blood samples were collected after an 8-12 hour fasting period. Height and weight were measured using a wall-mounted measuring device (Seca, model 206) and a scale (Seca, model 755), respectively. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Blood pressure was measured in a quiet office setting after at least five minutes of seated rest. Two measurements were taken from both arms at a 15-minute interval using a calibrated Reister sphygmomanometer (Germany). The average of the second measurements from both arms was used for all analyses. All measurements were performed according to a standardized written protocol, and the device was calibrated monthly. The lipid profile and FPG levels were assessed using Pars Azmoon enzymatic kits (Iran).

2.3. Definitions

The TyG index was calculated using the following formula: ln [TG (mg/dl) × FPG (mg/dl)/2].⁵ The thresholds for the quartiles were determined by calculating the 25th, 50th (median), and 75th percentiles of the TyG index values within our study cohort. The quartile cutoffs were: Q1 ≤ 8.13, Q2 = 8.13–8.48, Q3 = 8.49–8.84, Q4 ≥ 8.84. Subjects were classified as hypertensive if they met at least one of the following criteria: SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, or current use of antihypertensive medication. Prehypertension was defined as having 120 ≤ SBP < 140 mmHg and/or 80 ≤ DBP < 90 mmHg.^12,20 Alcohol consumption was defined as the intake of approximately 200 ml of beer or 45 ml of liquor, consumed once per week for a minimum of six months.

2.4. Statistical analyses

The normality of the continuous data was assessed using skewness, kurtosis, mean, and standard deviation. Accordingly, parametric data were analyzed using analysis of variance (ANOVA), while non-parametric data were analyzed using the Kruskal–Wallis test. Categorical data were analyzed using the chi-square test. Participants were divided into four groups according to the quartiles of the TyG index, with the lowest quartile serving as the reference group. Linear models were employed to assess the association between the TyG index and BP and pulse rate, reporting data as β and 95% confidence intervals (CI). Multicollinearity between covariates was assessed using variance inflation factor (VIF). Additionally, multivariable logistic regression was conducted to evaluate the association between the TyG index and hypertension as well as prehypertension, with results reported as odds ratios (OR) and 95% confidence intervals (CI). The fully adjusted multivariable model included the following covariates: age (continuous, years), sex (male/female), BMI (continuous, kg/m²), alcohol consumption (yes/no), physical activity (continuous, MET-h/week), socioeconomic status (WSI, tertiles), family history of hypertension (yes/no), high-density lipoprotein cholesterol (HDL-C, continuous, mg/dl), and non-HDL-C (total cholesterol minus HDL-C, continuous, mg/dl). Antihypertensive medication use was included in the definition of hypertension; however, it was not entered as a covariate in multivariable models to avoid overadjustment bias, as it may act as a mediator or collider in the relationship between metabolic factors and BP. However, a sensitivity analysis was conducted excluding participants who were using antihypertensive medications or who had hypertension. A receiver operating characteristic (ROC) curve was utilized to assess the effectiveness of the TyG index in predicting hypertension and prehypertension, reporting both sensitivity and specificity. The predictive power and threshold value were evaluated using the area under the curve (AUC) and Youden’s index, respectively. Statistical analyses were conducted using Stata 17 and MedCalc, with a P-value of <0.05 considered statistically significant.

3. Results

After excluding ineligible subjects, a total of 7,996 participants (48.7% male, with a mean age of 50.89 ± 8.03 years) were included in the final analysis (Figure 1), and their characteristics are reported in Table 1. Among the included subjects, 1,493 (18.7%) had hypertension, and 1,654 (20.7%) had prehypertension, with a significant increase in their proportions across different quartiles of the TyG index. Furthermore, subjects in the higher quartiles were more likely to be drinkers (P-value = 0.015), have a better socioeconomic status (P-value < 0.001), have family history of hypertension (P-value = 0.015), exhibit lower physical activity levels, and present higher anthropometric indices, as well as poorer lipid profiles, FPG, BP, and pulse rate (P-value < 0.001 for all) compared to those in the lower quartiles. The TyG index ranged from 6.79 to 11.74, with a mean of 8.51 (standard deviation [SD]= 0.54) and median of 8.48 (interquartile range [IQR]: 8.13–8.84).

Figure 1.

Flowchart of participant selection process.

Table 1.

Characteristics of the participants according to the quartiles of triglyceride-glucose index.

Characteristics	All	Q1 (≤8.13)	Q2 (8.13-8.48)	Q3 (8.49-8.84)	Q4 (>8.84)	P-value
n	7996	2003	1995	1999	1999	-
Sex (male), n (%)	3896 (48.7)	996 (49.7)	953 (47.8)	949 (47.5)	998 (49.9)	0.268
Current smoker, n (%)	1621 (20.3)	415 (20.7)	415 (20.8)	421 (21.1)	370 (18.5)	0.157
Drinker, n (%)	341 (4.3)	67 (3.3)	76 (3.8)	104 (5.2)	94 (4.7)	0.015
Wealth score index, n (%)
1^st tertile	29.3 (36.3)	834 (41.6)	734 (36.8)	689 (34.5)	646 (32.3)	<0.001
2^nd tertile	2350 (29.4)	584 (29.2)	578 (29.0)	560 (28.0)	628 (31.4)
3^rd tertile	2743 (34.3)	585 (29.2)	683 (34.2)	750 (37.5)	725 (36.3)
Night shift work, n (%)	1055 (13.2)	247 (12.3)	264 (13.2)	273 (13.7)	271 (13.6)	0.592
Family history of hypertension (first grade), n (%)	4050 (50.7)	975 (48.7)	991 (49.7)	1014 (50.7)	1070 (53.5)	0.015
Antihypertensive drugs, n (%)	953 (11.9)	175 (8.7)	233 (11.7)	260 (13.0)	285 (14.3)	<0.001
Hypertension, n (%)	1493 (18.7)	277 (13.8)	344 (17.2)	408 (20.4)	464 (23.2)	<0.001
Prehypertension, n (%)	1654 (25.4)	336 (19.5)	393 (23.8)	426 (26.8)	499 (32.5)	<0.001
Age (years), mean±SD	50.89±8.03	50.65±8.15	50.96±8.24	50.93±7.91	51.01±7.80	0.489
BMI (kg/m²), mean±SD	25.70±4.42	23.97±4.31	25.21±4.41	26.28±4.30	27.34±3.94	<0.001
Waist circumference (cm), mean±SD	94.30±12.05	88.94±12.13	92.82±11.81	96.03±11.39	99.40±10.22	<0.001
Serum TC (mg/dl), mean±SD	188.95±40.39	165.88±31.63	183.15±34.63	195.63±37.08	211.19±42.98	<0.001
Serum HDL-C (mg/dl), mean±SD	47.81±12.53	52.42±13.05	49.54±12.67	46.36±11.23	42.92±11.01	<0.001
Serum TG (mg/dl), median (IQR)	107.00 (78.00-151.00)	63.00 (54.00-72.00)	93.00 (84.00-102.00)	126.00 (113.00-141.00)	193.00 (164.00-239.00)	<0.001
FPG (mg/dl), median (IQR)	90.00 (83.00-97.00)	85.00 (80.00-91.00)	88.00 (82.00-95.00)	90.00 (84.00-97.00)	94.00 (88.00-104.00)	<0.001
SBP (mmHg), mean±SD	113.75±17.31	110.41±16.44	112.71±17.31	114.78±17.58	117.12±17.19	<0.001
DBP (mmHg), mean±SD	71.80±10.25	69.59±9.66	71.11±10.14	72.35±10.31	74.18±10.31	<0.001
Pulse rate, mean±SD	73.14±10.23	71.25±9.90	72.81±10.14	73.67±10.16	74.84±10.37	<0.001
Activity level (MET-h/week), mean±SD	39.04±6.42	40.09±6.75	39.19±6.56	38.69±6.00	38.18±6.17	<0.001

Abbreviations: BMI: Body mass index, DBP: Diastolic blood pressure, FPG: Fasting plasma glucose, HDL-C: High-density lipoprotein cholesterol, IQR: interquartile range, SBP: Systolic blood pressure, TC: total cholesterol, TG: triglyceride. Parametric, non-parametric, and categorical data are expressed as mean ± standard deviation (SD), median (IQR), or frequency (percentages), respectively.

Between-group differences in variables were assessed using the analysis of variance (ANOVA) test for parametric variables, the Kruskal–Wallis test for non-parametric parameters, and the Chi-square test for categorical variables. Bold values indicate significant differences between quartiles (P < 0.05).

There was a dose-response linear relationship between the TyG index and both BP and pulse rate, even after adjusting for potential confounders. Specifically, a one-unit increase in the TyG index was associated with a 2.44 mmHg (95% CI: 1.58-3.30) increase in SBP, a 1.58 mmHg (95% CI: 1.06-2.10) increase in DBP, and a 2.13 beats per minute (95% CI: 1.63-2.63) increase in pulse rate in the multivariable model (Table 2).

Table 2.

Association between triglyceride-glucose index and blood pressure and pulse rate.

	β (95% CI)
	Model 1	Model 2
SBP
Continuous	4.46 (3.80-5.13)	2.44 (1.58-3.30)
Q1	Ref.	Ref.
Q2	2.10 (1.09-3.10)	1.06 (0.02-2.09)
Q3	4.19 (3.18-5.20)	2.26 (1.15-3.37)
Q4	6.50 (5.50-7.50)	3.51 (2.27-4.75)
P for trend	<0.001	<0.001
DBP
Continuous	3.08 (2.68-3.48)	1.58 (1.06-2.10)
Q1	Ref.	Ref.
Q2	1.45 (0.84-2.05)	0.64 (0.02-1.27)
Q3	2.70 (2.09-3.31)	1.24 (0.57-1.91)
Q4	4.51 (3.90-5.12)	2.30 (1.55-3.05)
P for trend	<0.001	<0.001
PR
Continuous	2.65 (2.27-3.04)	2.13 (1.63-2.63)
Q1	Ref.	Ref.
Q2	1.42 (0.84-2.00)	1.08 (0.47-1.68)
Q3	2.26 (1.68-2.84)	1.59 (0.94-2.24)
Q4	3.61 (3.01-4.20)	2.54 (1.82-3.26)
P for trend	<0.001	<0.001

Abbreviations: DBP: diastolic blood pressure, PR: pulse rate, SBP: systolic blood pressure.

Model 1: adjusted for age and sex.

Model 2: adjusted for age, sex, socioeconomic status, body mass index, family history of hypertension, drinking, physical activity, high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.

In a separate analysis, we excluded all participants with hypertension to examine the association between the TyG index and BP in a normotensive population only (n = 6,503). As shown in Supplementary Table 1, after multivariable adjustment, the TyG index remained significantly and positively associated with SBP (β = 2.27 mmHg, 95% CI: 1.60–2.94), DBP (β = 1.29 mmHg, 95% CI: 0.84–1.75), and pulse rate (β = 2.23 beats per minute, 95% CI: 1.70–2.76). When analyzed by TyG quartiles, a clear dose-response relationship was observed: compared to the lowest quartile (Q1), participants in the highest quartile (Q4) had significantly higher BP and pulse rate (all P-values for trend < 0.001).

The association between the TyG index and the odds of hypertension and prehypertension is presented in Table 3. Both continuous and categorical analyses indicated a significant positive association between the TyG index and hypertension as well as prehypertension. In the continuous model, the multivariable-adjusted ORs for hypertension and prehypertension with each one-unit increment of the TyG index were 1.21 (95% CI: 1.05-1.40) and 1.34 (95% CI: 1.17-1.54), respectively. In the categorical analysis, significant positive associations were observed between quartiles Q3 and Q4 of the TyG index and the odds of hypertension and prehypertension; however, no significant association was found for quartile Q2.

Table 3.

Association between triglyceride-glucose index and hypertension and prehypertension.

	OR (95% CI)
	Model 1	Model 2
Hypertension
Continuous	1.54 (1.38-1.71)	1.21 (1.05-1.40)
Q1	Ref.	Ref.
Q2	1.25 (1.04-1.49)	1.12 (0.93-1.32)
Q3	1.56 (1.32-1.86)	1.25 (1.03-1.52)
Q4	1.88 (1.59-2.23)	1.34 (1.08-1.65)
P for trend	<0.001	<0.001
Prehypertension
Continuous	1.64 (1.48-1.82)	1.34 (1.17-1.54)
Q1	Ref.	Ref.
Q2	1.30 (1.10-1.53)	1.17 (0.98-1.39)
Q3	1.53 (1.30-1.80)	1.25 (1.04-1.51)
Q4	2.04 (1.73-2.39)	1.48 (1.21-1.82)
P for trend	<0.001	<0.001

Abbreviations: OR: odds ratio.

Model 1: adjusted for age and sex.

Model 2: adjusted for age, sex, socioeconomic status, body mass index, family history of hypertension, drinking, physical activity, high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.

The diagnostic performance of the TyG index for hypertension and prehypertension is depicted in Figure 2. The AUC values for hypertension and prehypertension were 0.571 and 0.574, respectively, with optimal cutoff points of 8.35 and 8.58. The sensitivity and specificity for hypertension were 69.86% and 42.00%, respectively, while for prehypertension they were 49.03% and 61.83%.

Figure 2.

ROC curve of the association between triglyceride-glucose index and hypertension and prehypertension.

4. Discussion

The findings of the current large-sample population-based cross-sectional study demonstrate a positive independent linear association between the TyG index and SBP and DBP. Furthermore, we found that with each one-unit increment in the TyG index, the odds of hypertension and prehypertension increased by 21% and 34%, respectively, indicating small effect sizes.²¹ Although the direction of association was consistent, the magnitude of the effect in our study was smaller than that reported in a prior meta-analysis, which found a 57% increase in hypertension risk in cohort studies and approximately a twofold increase in cross-sectional studies.¹² However, further prospective studies are warranted to explore this relationship. Additionally, we assessed the diagnostic performance of the TyG index for hypertension and prehypertension and found its limited discriminative ability in diagnosing these conditions (AUC = 0.57), consistent with cutoff points established in a previous study.²² Similarly, a cohort study involving 17,977 Chinese individuals reported an AUC of 0.58 (95% CI: 0.570 to 0.594), with sensitivity and specificity values of 57.85% and 55.40%, respectively.²³ Notably, the TyG index, as a convenient surrogate marker of insulin resistance, has been reported to demonstrate better discriminative performance for hypertension compared with some individual lipid and glycemic parameters.²⁴ However, in our study, its overall predictive performance remained modest. Therefore, the TyG index should not be used as a standalone diagnostic or screening tool for hypertension. Instead, its potential utility may lie in combination with other simple clinical variables (e.g., age, BMI, and family history of hypertension), particularly in resource-limited settings. In this regard, previous evidence suggests that integrating the TyG index with anthropometric parameters yielded better predictive performance for incident hypertension than the TyG index alone.²⁵

In the present study, we found a positive independent association between the TyG index and BP within our normotensive population. These results are consistent with a previous study that reported a dose–response relationship between the TyG index and BP levels in normotensive individuals.²⁶ It is important to note that although the observed associations were statistically significant, the magnitude of these changes (approximately 2–3 mmHg for SBP and 1–2 mmHg for DBP per unit increase in TyG) is relatively modest and may have limited clinical relevance. Another finding of our study was the positive relationship between the TyG index and pulse rate, which may provide further insight into the cardiovascular effects of insulin resistance. Insulin resistance is known to promote sympathetic activity and impair autonomic regulation, which may lead to increased heart rate even before overt cardiovascular disease develops.^13,14

Insulin resistance is characterized by impaired responsiveness of tissues—primarily the liver, skeletal muscle, and adipose tissue—to insulin for glucose disposal, leading to hyperinsulinemia. Insulin resistance has been implicated in the development of inflammation, vascular complications, non-alcoholic fatty liver disease, hyperlipidemia, and hypertension.²³ The hyperinsulinemic-euglycemic clamp is considered the gold standard for evaluating insulin resistance; however, it is an expensive and inconvenient method.²⁷ Consequently, several alternative indices have been developed, including the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), which is the most widely used index calculated using fasting plasma insulin and FPG. Nonetheless, plasma insulin is not routinely measured in clinical practice, highlighting the need for more accessible indices. The TyG index is recognized as a cost-effective and clinically available method that demonstrates superior performance in predicting insulin resistance and metabolic syndrome compared to HOMA-IR.^28,29

The observed association between the TyG index and BP underscores the potential link between insulin resistance and hypertension, both of which are components of metabolic syndrome. Insulin resistance, along with subsequent hyperinsulinemia, has been reported to increase renal sodium reabsorption, activate the sympathetic nervous system, and dysregulate the renin-angiotensin-aldosterone system as well as endothelium-dependent vasodilation, thereby promoting hypertension.³⁰ Furthermore, both components of the TyG index—triglycerides and fasting plasma glucose—are also associated with an increased risk of cardiovascular diseases.^31,32

Our study has several limitations. First, this analysis was restricted to baseline data from the KHCS because comprehensive laboratory tests required for TyG index calculation were not available in the follow-up assessments (the 5-year follow-up included laboratory tests for a random subsample, and other follow-ups were conducted by telephone interview). Consequently, the cross-sectional design precludes establishing causal relationships, and longitudinal cohort studies are necessary to determine causality. Second, the possibility of residual confounding by unmeasured metabolic factors and some degree of collinearity among cardiometabolic variables cannot be fully ruled out. Third, we were unable to assess insulin resistance using the hyperinsulinemic-euglycemic clamp or HOMA-IR to compare our results related to the TyG index. Fourth, our findings were derived from adults without severe metabolic disturbances, which limits their generalizability to real-world clinical populations. Furthermore, a formal sample size calculation was not performed for the present analysis, as it was based on all eligible participants from the baseline data of the KHCS cohort. This may limit the ability to define a priori statistical power; however, the large sample size likely provided adequate analytical power.

5. Conclusion

In conclusion, we found a positive association between the TyG index and elevated BP and hypertension. However, due to the magnitude of effect sizes and its limited discriminative ability in diagnosing hypertension and prehypertension, future large-sample prospective studies—particularly those focusing on its relationship with other biomarkers—are warranted to better assess the clinical applicability of this index. Furthermore, future studies should consider stratifying hypertensive individuals according to hypertension duration and BP control status, as this could provide deeper insight into the relationship between the TyG index and hypertension.

Supplemental material

Supplemental material - Association of the triglyceride-glucose index with blood pressure and hypertension in a large adult population: A cross-sectional study

Supplemental material for Association of the triglyceride-glucose index with blood pressure and hypertension in a large adult population: A cross-sectional study by Abbas Rezaianzadeh, Ahmed Mohammed Ali Hussein Alhurry, Fahimeh Hajihosseini, Masoumeh Ghoddusi Johari, Sara Shojaei-Zarghani and Seyed Vahid Hosseini by Sage Open Medicine.

Footnotes

Acknowledgments

The Iranian Ministry of Health and Medical Education has contributed to the funding used in the PERSIAN cohort through Grant no 700/534.

ORCID iDs

Abbas Rezaianzadeh

Masoumeh Ghoddusi Johari

Sara Shojaei-Zarghani

Seyed Vahid Hosseini

Ethical considerations

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shiraz University of medical sciences, Shiraz, Iran (Code: IR. SUMS.REC.1404.246).

Consent to participate

Written informed consent was obtained from all participants included in the study.

Author contributions

Conceptualization: SSZ; Methodology: SSZ, AMAHA, FH, MGJ, AR, SVH; Data curation, Formal analysis, Software: S.S.Z., AR; Project administration, Validation, and Supervision: SSZ, AR, SVH; Funding acquisition: SSZ; Writing – original draft: S.S.Z.; Writing – review & editing: SSZ, AMAHA, FH, MGJ, AR, SVH; All authors have read and approved the manuscript

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Vice-Chancellor for Research and Technology of Shiraz University of Medical Sciences (Code: 32957). The funder had no role in the study design, analysis, decision to publish, or preparation of the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data supporting the results of this study is available from the corresponding author, upon reasonable request.*

Declaration of generative AI in scientific writing

We utilized AI to assist in correcting the English writing of the manuscript, which was subsequently reviewed and controlled by the authors.

Supplemental material

Supplemental material for this article is available online.

Appendix

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