Evaluation of the relationship between coronary artery disease complexity and metabolic and hormonal parameters in non-diabetic patients with acute coronary syndrome

Abstract

Introduction

Despite long-standing research on the relationship between coronary artery disease complexity and metabolic and hormonal parameters, the number of studies focusing on non-diabetic patients, a key subgroup of this disease, has remained limited. This study aims to investigate the relationship between specific metabolic and hormonal markers and disease complexity in non-diabetic patients with coronary artery disease. This work is intended to contribute to a growing body of literature, supporting the accelerating trend in research rather than the previously observed plateau.

Materials and methods

In this single-center observational study, between January 2021 and December 2024, patients hospitalized with acute coronary syndrome and undergoing coronary angiography were retrospectively screened based on their medical records and laboratory data (blood tests obtained after fasting for at least 12 hours). Patients were categorized into two groups: those with a diagnosis of diabetes and those without. A relationship analysis was performed between the two groups based on HbA1c levels (HbA1c ≥ 6.5% and HbA1c < 6.5%) and hospital Information Management System data, information on whether or not the patient has been previously diagnosed with diabetes.Additionally, an analysis of the SYNTAX score and its components was conducted to investigate the association between coronary artery disease complexity and various metabolic and hormonal parameters in non-diabetic patients with acute coronary syndrome by identifying correlations differing from those observed in the diabetic group.

Results

Among the 636 study participants, the mean HbA1c level was 6.34 ± 1.38. Of these patients, 65.4% (n = 416) had HbA1c levels of 6.4% or below, while 34.6% (n = 220) had levels of 6.5% or above. Spearman correlation analysis was used to evaluate the relationships between non-normal distributions of the SYNTAX score and clinical and biochemical variables. The SYNTAX score demonstrated a positive and statistically significant correlation with HbA1c (r = 0.242, p < 0.05), age (ρ = 0.215, p < 0.05), and serum uric acid (SUA) (ρ = 0.139, p < 0.05). In the group with HbA1c levels < 6.5%, there was a positive correlation between the SYNTAX score and HbA1c, age, and serum uric acid. In contrast, in the group with HbA1c levels ≥ 6.5%, the SYNTAX score showed a positive correlation only with age (ρ = 0.164, p < 0.05), and negative correlation with GGT (ρ = -0.152, p < 0.05) and BMI (ρ = -0.144, p < 0.05).

Conclusion

TSH and serum uric acid (SUA) levels were independently associated with the severity of coronary artery disease, particularly in the non-diabetic subgroup of patients with obstructive coronary artery disease. These findings may support more accurate risk stratification in clinical practice and facilitate the development of tailored approaches for each coronary artery disease subgroup by identifying potential new target parameters.

Introduction

According to paleopathological findings, coronary artery disease has existed since ancient times, indicating a long historical presence. It remains one of the leading causes of morbidity and mortality worldwide [1]. The development of diagnostic, treatment, and follow-up algorithms for this prevalent condition has also been a continuous focus of scientific research.

The complexity of coronary artery disease (CAD) is directly related to its severity, with adverse clinical outcomes highlighting the need for a bidirectional analysis of cause and effect [2]. The SYNTAX score is a widely used method of angiographic analysis for grading CAD complexity. It plays a decisive role in determining the revascularization strategy between coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) in patients with critical coronary artery disease [3], while its value in predicting major adverse cardiovascular events (MACE) has also recently been demonstrated in patients undergoing PCI for multivessel CAD [4, 5] and/or left main CAD [6]. More recent data has confirmed its ability to predict peri-procedural myocardial infarction (MI) in patients undergoing elective PCI [7].

Lesion complexity is critical for determining treatment strategies and predicting prognos is in patients with coronary artery disease, particularly those with acute coronary syndrome(ACS). Current data indicates that approximately 5% of ACS patients die before hospital discharge [8, 9]. Studies have shown that diabetic patients, a key subgroup within ACS populations, are at increased risk of major adverse cardiac and cerebrovascular events (MACCE) during hospitalization [10, 11]. Additionally, diabetic patients have been shown to have a higher burden of high-risk (or vulnerable) plaques in both culprit and non-culprit lesions, which are associated with worse long-term prognosis [12, 13]. The current plateau in the literature focusing on diabetic ACS patients makes the analysis of non-diabetic subgroups an important and inevitable next step [14, 15].

Hyperuricemia is implicated in the pathogenesis of elevated serum uric acid (SUA) levels and is closely associated with an increased risk of adverse cardiovascular outcomes in CAD patients [16]. As SUA is associated with several established cardiovascular risk factors, including hypertension, dyslipidemia, obesity, and metabolic syndrome, its role as an independent CAD risk factor remains a topic of significant debate [17–19]. Given its close relationship with these risk factors, groups that should be examined to determine the isolated effect of elevated uric acid on coronary artery disease complexity include primarily diabetic patients, who represent the largest clinical subgroup, as well as non-diabetic patients.

Hypothyroidism, hyperthyroidism, and subclinical thyroid disorders, along with other hypothalamic–pituitary–adrenal axis abnormalities, have been associated with major adverse cardiovascular events (MACE) in patients with cardiovascular disease [20–22]. Possible mechanisms include cellular inflammatory responses, vascular endothelial damage exacerbated by dyslipidemia, atherosclerosis, and cardiac dysfunction [23, 24]. These mechanisms have been described in studies involving patient groups with coronary artery disease and diabetes. However, there is insufficient research examining the relationship between hypothalamic–pituitary–adrenal axis dysfunction and coronary artery disease in non-diabetic patient groups.

The complexity of coronary artery disease (CAD) is closely linked to the plateau observed in studies primarily focused on diabetes-centered clinical presentations. Therefore, the investigation of metabolic and hormonal parameters demonstrating a significant correlation with coronary lesion complexity (SYNTAX score) in non-diabetic patient populations is necessary for advancing scientific progress with an innovative approach.

Materials and methods

At the Cardiology Clinic of Fırat University Hospital, patients hospitalized with acute coronary syndrome (ACS) between January 2021 and December 2024 who underwent coronary angiography were retrospectively screened based on their medical records and laboratory data. Patients were categorized as either having or not having a diagnosis of diabetes.Patients were divided into two groups: those without a prior diagnosis of diabetes mellitus (therefore not receiving antidiabetic treatment) and with an HbA1c level < 6.5%, and those with or without a prior diagnosis but with an HbA1c level ≥ 6.5%.

The study was conducted in accordance with the Declaration of Helsinki to ensure patient confidentiality. No additional procedures, interventions, or direct patient contact were required. Patients with acute infection, active malignancy, severe liver or kidney failure (glomerular filtration rate < 30 mL/min), or missing laboratory, imaging, or angiographic data were excluded. Patients were also excluded if their data was incomplete or inconsistent, if their clinical diagnosis and records were contradictory, or if their angiographic images could not be evaluated using the SYNTAX scoring system.

The study was approved by the Ethics Committee of Fırat University Hospital. It was conducted in accordance with national legislation and institutional requirements. According to national and institutional regulations, written informed consent from participants or their legal guardians/relatives was not required.No clinical trial registration number is available for this study; therefore, clinical trial registration is not applicable.

Laboratory test analyses

All laboratory parameters evaluated in this study are routinely requested during the clinical assessment of patients at our hospital and entered into the electronic medical records system. These include fasting blood glucose, HbA1c, lipid profile (total cholesterol, LDL, HDL, and triglycerides), uric acid, TSH, GGT, free T3 (fT3), and free T4 (fT4). All tests were performed in the hospital’s biochemistry laboratory using blood samples obtained at the time of referral or hospital admission and were not specifically repeated for this study.

Angiographic analysis

Two cardiologists who were blinded to the clinical data analyzed the coronary angiograms. Minimal lumen diameter, reference diameter, and percent diameter stenosis were evaluated using quantitative coronary angiography. Each coronary lesion causing ≥ 50% diameter stenosis in vessels ≥ 1.5 mm was scored separately from the initial diagnostic angiogram, and the scores were summed to obtain the total SYNTAX score. The score was calculated using the SYNTAX scoring algorithm. All scoring process was blinded to laboratory results.

The SYNTAX score was calculated using the original SYNTAX scoring algorithm, which incorporates lesion characteristics such as total occlusion, bifurcation or trifurcation involvement, severe tortuosity, heavy calcification, thrombus presence, and lesion length, as previously described by Sianos et al. [3].

Statistical analysis

All statistical analyses were performed using SPSS version 22.0 (IBM Corp., Armonk, NY, USA) and Jamovi version 2.6.22. Descriptive statistics for continuous variables were reported as mean ± standard deviation (SD), while median and interquartile range (IQR) were reported for non-normally distributed data. Categorical variables were expressed as counts (percentages).

The Shapiro–Wilk test was used to assess the distribution characteristics of the data. The Mann–Whitney U test was used to compare two groups classified based on HbA1c levels (HbA1c ≤ 6.4% and HbA1c ≥ 6.5%), while a chi-square test was used to compare categorical variables. Linear regression analysis was performed to identify independent variables associated with HbA1c. Additionally, bivariate correlation analysis was conducted to evaluate the relationship between the SYNTAX score and basic metabolic parameters (e.g., HbA1c, age, and serum uric acid). Spearman correlation coefficients were used in these analyses. Multiple linear regression models were developed to model the relationship between the dependent variable, SYNTAX score, and the independent variables. Model assumptions were evaluated based on standard linear regression assumptions, including homoscedasticity, normality of residuals, absence of multicollinearity, and lack of influential outliers.A p-value < 0.05 was considered statistically significant.

Results

A total of 636 individuals were included in the study. The mean age of the participants was 61.3 ± 11.12 years, with a median age of 62 years(20–88).The mean body mass index (BMI) was 29.2 ± 4.45 kg/m², with a median of 29.2 kg/m² (19.4–49.2).

Regarding gender distribution, 58.3% of participants were female (n = 371), with respect to smoking status, 33.6% of participants were smokers (n = 214) (Table 1).

Table 1.

Baseline demographics

	N	Mean	Median	SD	IQR	Minimum	Maximum
Age(years)	636	61.3	62.0	11.12	16.00	20.0	88.0
BodyMassIndex(kg/m²)	636	29.2	29.2	4.45	5.90	19.4	49.2
Gender	Count	% of Total
Female	371	58.3%
Male	265	41.7%
Smokingstatus	Count	% of Total
Non-smoker	422	66.4%
Smoker	214	33.6%

SD StandardDeviation, IQR InterQuartileRange(Q1-Q3)

Examining the distribution of HbA1c levels among the 636 participants revealed a mean value of 6.34 ± 1.38. Of these individuals, 65% (n = 416) had HbA1c levels of 6.4% or below, while 35% (n = 220) had levels of 6.5% or above, classifying them as diabetic patients (Fig. 1).

Fig. 1.

Distribution of HbA1c (n = 636). (a) The histogram illustrate she overall distribution, (b) the table summarizes descriptive statistics and (c) the piechart shows the categorical distribution. IQR: Inter Quartile Range (Q1-Q3)

Table 2 shows the distribution of SYNTAX scores and HbA1c values. SYNTAX was found to be 8.83 ± 12.06 (range0–69.5) and HbA1c was 6.33 ± 1.38 (range4.01–12.80).

Table 2.

Distribution of HbA1c and SYNTAX

		SYNTAX	HbA1c
Mean		8.83	6.34
95%CI	LowerBound	7.89	6,23
	UpperBound	9.77	6,44
5%TrimmedMean		7.58	6.22
Median		2.00	5.97
Variance		145.46	1.91
Std.Deviation		12.06	1.38
Minimum		0.0	4.01
Maximum		69.50	12.80
Range		69.50	8.79
InterquartileRange		14.90	1.68
Skewness		1.43	1.33
Kurtosis		1.44	1.95

Spearman correlation analysis was used to evaluate the relationships between SYNTAX scores and clinical and biochemical variables (Table 3; Fig. 2). In the overall sample, the SYNTAX score demonstrated positive and statistically significant correlations with HbA1c (ρ = 0.242, p < 0.05), age (ρ = 0.215, p < 0.05), and uric acid (ρ = 0.139, p < 0.05). Additionally SYNTAX score demonstrated negative and statistically significant correlations with TSH (ρ = -0.092, p < 0.05).

Table 3.

Correlation analysis between SYNTAX score and clinical/biochemical variables

		SYNTAX
		Total	HbA1c < 6.5% (n = 416)	HbA1c ≥ 6.5% ( n  = 220)
SYNTAX	ρ	1	1	1
	n	636	416	220
HbA1c(%)	ρ	0.242* (0.165–0.316)	0.138* (0,039-0.234)	0.018 (-0.118-0.154)
Age(years)	ρ	0.215* (0.138–0.290)	0.207* (0.110–0.300)	0.200* (0.066–0.327)
Totalcholesterol(mg/dL)	ρ	-0.056 (-0.135-0.024)	-0.057 (-0.155-0.042)	-0.014 (-0.149-0.123)
Uricacid(mg/dL)	ρ	0.139* (0.060–0.217)	0.166* (0.068–0.260)	0.074 (-0.063-0.208)
Gamma-glutamyltransferase(U/L)	ρ	-0.013 (-0.093-0.067)	0.031 (-0.068-0.130)	-0.152* (-0.282- -0.016)
Thyroid-stimulatinghormone(mIU/L)	ρ	-0.092* (-0.171-0.012)	-0.084 (-0.181-0.015)	-0.056 (-0.191-0.080)
BodyMassIndex(kg/m²)	ρ	-0.060 (-0.086-0.074)	0.019 (-0.080-0.118)	-0.144* (-0.275- -0.009)

ρ Spearman Correlation

Fig. 2.

Graphical representation of the correlation between SYNTAX score and clinical variables (n = 636): (a) all participants, (b) participants with HbA1c < 6.5%, and (c) participants with HbA1c ≥ 6.5%

In the subgroup with HbA1c levels < 6.5%, the SYNTAX score showed a positive and significant correlation with HbA1c (ρ = 0.138, p < 0.05), age (ρ = 0.207, p < 0.05), and uric acid (ρ = 0.166, p < 0.05). In contrast, among individuals with HbA1c levels ≥ 6.5%, the SYNTAX score was positively correlated only with age (ρ = 0.200, p < 0.05), and negatively correlated with GGT (ρ = -0.152, p < 0.05) and BMI (ρ = -0.144, p < 0.05) with no significant associations identified for other parameters.

In the subgroup of patients with HbA1c < 6.5%, additional analyses were performed using SYNTAX score stratification. In individuals with low HbA1c levels (≤ 5.60%, n = 239), the SYNTAX score showed positive correlations with age (ρ = 0.253, p < 0.05) and uric acid (ρ = 0.233, p < 0.05). These findings suggest that age and uric acid levels may be associated with the severity of coronary artery disease, particularly in individuals with lower HbA1c values. In contrast, in the subgroup with HbA1c levels between 5.70% and 6.40%, no significant relationships were observed between the SYNTAX score and age, total cholesterol, uric acid, GGT, TSH, or BMI (Table 4).

Table 4.

Subgroup analys is of correlations between SYNTAX score and clinical variables based on HbA1c levels

		SYNTAX
		HbA1cLow (≤ 5.60%) ( n  = 239)	HbA1cHigh (5.70%≤HbA1c ≤ 6.40%) ( n  = 169)
HbA1c(%)	ρ	0.110 (-0.021-0.237)	0.060 (-0.096-0.213)
Age(years)	ρ	0.253* (0.126–0.371)	0.121 (-0.035-0.271)
Totalcholesterol(mg/dL)	ρ	-0.013 (-0.143-0.118)	-0.124 (-0.274-0.032)
Uricacid(mg/dL)	ρ	0.233* (0.105–0.353)	0.037 (-0.119-0.191)
Gamma-glutamyltransferase(U/L)	ρ	0.014 (-0.117-0.144)	0.050 (-0.107-0.203)
Thyroid-stimulatinghormone(mIU/L)	ρ	-0.091 (-0.219-0.040)	-0.126 (-0.276-0.030)
BodyMassIndex(kg/m²)	ρ	0.018 (-0.148-0.113)	0.042 (-0.114-0.196)

r Spearman Correlation

Spearman correlation analysis demonstrated a positive correlation between SYNTAX scores and clinical and biochemical variables, including uric acid parameters. In the subgroup with HbA1c levels < 6.5%, the SYNTAX score showed a positive and statistically significant correlation with uric acid levels (ρ = 0.166, p < 0.05). Conversely, no significant associations were identified between the SYNTAX score and the evaluated parameters in the subgroup with HbA1c levels ≥ 6.5% (Table 3; Fig. 3).

Fig. 3.

Graphical representation of the correlation between SYNTAX score and uric acid: (a) all participants, (b) participants with HbA1c < 6.5%, and (c) participants with HbA1c ≥ 6.5%

Significant differences in uric acid levels were observed between the groups. In the subgroup with HbA1c levels < 6.5%, 91.6% of individuals had uric acid levels within the normal reference range (3.1–7.8 mg/dL). In contrast, in the subgroup with HbA1c levels ≥ 6.5%, this proportion was 86.4%. The respective proportions of individuals with elevated uric acid levels (> 7.8 mg/dL) were 8.4% and 13.6%, demonstrating a statistically significant difference (p = 0.039) (Table 5).

Table 5.

Comparison of uric acid levels between participants with HbA1c < 6.5% and HbA1c ≥ 6.5%

		Group
		HbA1c < 6.5% (n = 416)		HbA1c ≥ 6.5% (n = 220)		Total		p
		Count	N%	Count	N%	Count	N%
UricAcid	≤ 7.7	381	91.6	190	86.4	571	89.8	0.039*
	7.8≥	35	8.4	30	13.6	65	10.2

Spearman correlation analysis demonstrated a negative and statistically significant correlation between the SYNTAX score and clinical and biochemical variables and TSH levels. In the subgroup with HbA1c levels < 6.5%, correlation was not observed between the SYNTAX score and TSH (ρ = −0.084, p = 0.416). Similarly, no statistically significant relationship was identified between these parameters in the subgroup with HbA1c levels ≥ 6.5% (Table 3; Fig. 4).

Fig. 4.

Graphical representation of the correlation between SYNTAX score and TSH: (a) all participants, (b) participants with HbA1c < 6.5%, and (c) participants with HbA1c ≥ 6.5%

In the subgroup with HbA1c < 6.5%,TSH demonstrated failed to show a statistically significant correlation with the SYNTAX score. Further analyses were performed in two subgroups within this cohort; however, no statistically significant relationship was observed between the SYNTAX score and TSH in either subgroup (Table 4).

TSH values were also predominantly within the normal range in both groups, and no statistically significant difference was observed between them (p = 0.603) (Table 6).

Table 6.

Comparison of TSH levels between participants with HbA1c < 6.5% andHbA1c ≥ 6.5%

		Group
		HbA1c < 6.5% (n = 416)		HbA1c ≥ 6.5% (n = 220)		Total		p
		Count	N%	Count	N%	Count	N%
TSH	≤ 5.4	409	98.3	215	97.7	624	98.1	0.603
	5.5≥	7	1.7	5	2.3	12	1.9

Multivariable linear regression was used to find the independent variables’ effect on SYNTAX score. Model was significant (F(8,627) = 6.91, p < 0.05). Age, uric acid and HbA1C levels were independently associated with SYNTAX score for all patients. 1 unit increase of age increased SYNTAX score by 0.15 (β = 0.15, %95 CI 0.06–0.23, p < 0.05), 1 unit increase in uric acid level increased SYNTAX score by 0.90 (β = 0.91, %95 CI 0.30–1.51, p < 0.05). 1 unit increase in HbA1C level increased SYNTAX score by 1.65 (β = 1.65, %95 CI 0.97–2.33, p < 0.05). Other variables were insignificant. Overall, model explained 6.9% of variability of SYNTAX score (Table 7).

Table 7.

Multivariable linear regression for predicting SYNTAX score for all patients

	Coefficient	%95 Confidence Interval	p
Interception	-12.04	-21.79 - -2.28	< 0.05*
Age	0.15	0.06–0.23	< 0.05*
Total Cholesterol	0.001	-0.02–0.02	0.869
Uric Acid	0.91	0.30–1.51	< 0.05*
Gamma-glutamyl transferase (U/L)	0.01	-0.01–0.03	0.156
Thyroid-stimulating hormone (mIU/L)	-0.25	-0.57–0.06	0.116
HbA1C	1.65	0.97–2.33	< 0.05*
Body Mass Index (kg/m²)	-0.16	-0.37–0.05	0.140
Smoker	0.14	-1.80–2.09	0.884

For patients with HbA1C level < 6.5%, another multivariable linear regression was used. Model was significant (F(8,407) = 3.53, p < 0.05). Age, uric acid and TSH levels were independently associated with SYNTAX score.1 unit increase of age increased SYNTAX score by 0.12 (β = 0.12, %95 CI 0.03–0.21, p < 0.05), 1 unit increase in uric acid level increased SYNTAX score by 0.92 (β = 0.92, %95 CI 0.19–1.65, p < 0.05). 1 unit increase in TSH level decreased SYNTAX score by 0.56 (β = -0.56, %95 CI -1.05- -0.06, p < 0.05). Other variables were insignificant. Overall, model explained 4.6% of variability of SYNTAX score (Table 8).

Table 8.

Multivariable linear regression for predicting SYNTAX score for patients with HbA1C level < 6.5%

	Coefficient	%95 Confidence Interval	p
Interception	-14.90	-29.57–0.23	< 0.05*
Age	0.12	0.03–0.21	< 0.05*
Total Cholesterol	-0.01	-0.03–0.02	0.555
Uric Acid	0.92	0.19–1.65	< 0.05*
Gamma-glutamyl transferase (U/L)	0.02	-0.003–0.04	0.087
Thyroid-stimulating hormone (mIU/L)	-0.56	-1.05 – -0.06	< 0.05*
HbA1C	1.80	-0.23–3.83	0.082
Body Mass Index (kg/m²)	0.02	-0.23–0.28	0.868
Smoker	0.15	− 0.207–2.37	0.897

Finally, for patients with HbA1C level > 6.5%, another multivariable linear regression was used. Model was significant (F(8, 211) = 1.56, p < 0.05). Only BMIwasindependently associated with SYNTAX score.1 unit increase of BMI decreased SYNTAX score by 0.40 (β = -0.40, %95 CI -0.78- -0.01, p < 0.05). Other variables were insignificant. Overall, model explained 2.0% of variability of SYNTAX score (Table 9).

Table 9.

Multivariable linear regression for predicting SYNTAX score for patients with HbA1C level > 6.5%

	Coefficient	%95 Confidence Interval	p
Interception	-1.22	-24.01–21.57	0.916
Age	0.18	-0.01–0.37	0.068
Total Cholesterol	0.03	-0.01–0.07	0.195
Uric Acid	0.72	-0.39–1.84	0.204
Gamma-glutamyl transferase (U/L)	0.0004	-0.04–0.04	0.983
Thyroid-stimulating hormone (mIU/L)	-0.14	-0.60–0.31	0.545
HbA1C	0.54	-1.02–2.10	0.498
Body Mass Index (kg/m²)	-0.40	-0.78–0.01	< 0.05*
Smoker	0.69	-3.05–4.44	0.716

Discussion

Numerous studies have evaluated the association between glycemic parameters, such as stress hyperglycemia ratio and HbA1c, and coronary artery disease complexity in patients with acute coronary syndrome [25, 26]. However, evidence specifically addressing non-diabetic subgroups remains comparatively limited.In this study, designed to identify significant variables specific to non-diabetic patients without the metabolic influence of diabetes, coronary artery disease (CAD) complexity was positively correlated with elevated serum uric acid and negatively correlated with TSH levels. SUA and TSH levels suggest a significant correlation with CAD complexity based on SYNTAX scoring among non-diabetic patients with obstructive CAD.

A key finding of our study is that uric acid demonstrated the strongest positive correlation with SYNTAX score in the non-diabetic subgroup, whereas no significant correlation was observed in patients with HbA1c ≥ 6.5%. This highlights the importance of uric acid elevation in CAD complexity and the clinical course of diabetes mellitus. Epidemiological studies have similarly shown high SUA levels to be an independent risk factor for cardiovascular events, particularly CAD, cardiovascular mortality, and all-cause mortality [16, 27]. A meta-analysis of such studies with samples from all patient groups confirmed an association between hyperuricemia and increased coronary artery disease (CAD) incidence and CAD-related mortality [28]. Despite the strong associations between high SUA levels and coronary artery disease (CAD), the absence of specific analyses of genetic risk factors related to obstructive coronary artery disease has inevitably led to variable findings [29, 30].

Based on the premise that the role of SUA as an independent risk factor for cardiovascular events can only be clarified through the analysis of specific patient groups, research has first examined acute coronary syndrome, a critical subset of coronary artery disease. A significant association between SUA levels and CAD complexity was previously demonstrated in ACS patients regardless of diabetes status after 75 months of follow-up [31]. In this study, the SYNTAX–SUA correlation is presented with analyses specific to the non-diabetic population, aiming to enhance subgroup specificity by isolating the acute coronary syndrome pattern of coronary artery disease.

Previous work examining the relationship between SUA and blood glucose levels in healthy, prediabetic, and diabetic groups also demonstrated a significant association between SUA and fasting blood glucose (FBG) according to all regression analysis models [32]. This strong relationship between SUA and diabetes suggests that the association between elevated uric acid levels and SYNTAX score may involve two components. The first reflects the contribution of diabetes-related clinical presentations, as demonstrated by the significant SYNTAX–diabetes correlation (Table 3; Fig. 2).The second component is the independent relationship between coronary artery disease (CAD) complexity and SUA levels in patients with acute coronary syndrome (ACS), a subgroup of obstructive CAD.Another distinguishing factor in establishing an independent relationship between serum uric acid (SUA) levels and the complexity of coronary artery disease in non-diabetic patients with obstructive coronary artery disease is that blood tests are taken before the acute phase reactant uric acid increase occurs.Regarding differentiation from high uric acid levels as an acute phase marker: Since it is a late-stage reactant that appears after the first 12 h, the value obtained at the patient’s hospitalization (within the first 12 h) indicates the basal uric acid level.Considering the cumulative effects on coronary artery disease complexity and the associations examined in this study, the SYNTAX–SUA correlation was most prominent in the non-diabetic subgroup, based on comparative analyses between non-diabetic and diabetic patients.

The identification of an independent relationship between SUA levels and CAD complexity in non-diabetic patients with obstructive coronary artery disease may support the development of a more individualized risk-stratification approach to each subgroup.With data demonstrating a direct impact of elevated uric acid levels (after excluding the effects of clinical diabetes) as the primary component of the SYNTAX-SUA correlation, the development of such an individualized approach supports the hypothesis that it could be effective in the management of coronary artery disease.

Another principal finding of this study is the negative correlation between SYNTAX score and TSH in non-diabetic patients, with no significant association observed in those with HbA1c ≥ 6.5%, along with an emphasis on the importance of thyroid system functions in non-diabetic patients with obstructive coronary artery disease (CAD). Recent studies have examined thyroid hormone sensitivity indices in relation to hypertension, cardiovascular disease, prediabetes, diabetes, metabolic syndrome, frailty, renal failure, and residual cholesterol levels in euthyroid individuals [33–35].

Hyperthyroidism influences glucose metabolism through multiple mechanisms, including changes in beta cell mass, reduced insulin secretion, increased intestinal glucose absorption, elevated glucagon secretion, enhanced insulin degradation, insulin resistance, and increased catecholamine levels. These mechanisms contribute to the well-known association between thyroid system dysfunction and diabetes mellitus, which remain closely linked within the spectrum of coronary artery disease analysis [36]. In addition, various indices used to evaluate thyroid function, particularly the free triiodothyronine/free thyroxine (fT3/fT4) ratio, have been associated with the risk of diabetes and cardiovascular disease [37].

Unlike prior studies where the thyroid–cardiovascular relationship was confounded by diabetes mellitus, our results demonstrate that a close relationship remains between obstructive coronary artery disease and thyroid disorders in non-diabetic patients. The weak correlation observed for TSH in the subgroup analysis suggests that larger, adequately powered studies are needed to clarify the association. Accordingly, the present results should be considered exploratory rather than directly clinically applicable. Nevertheless, the findings support further investigation into the roles of these parameters in determining coronary artery disease complexity in non-diabetic patients with acute coronary syndrome.

Multivariate linear regression was used to assess the effects of independent variables on the SYNTAX score, and the overall model was statistically significant (Tables 7, 8 and 9). In the non-diabetic group, both SUA and TSH were independently associated with the SYNTAX score (Table 8). However, despite statistical significance, the models showed low explanatory power (2.0%–6.9%), which limits clinical applicability. These results indicate that more definitive clinical inference will likely require studies with larger populations.

The study’s effect size was limited because the subgroup evaluated for coronary artery disease complexity (non-diabetic patients) constituted a minority of the overall CAD population. The study contributes to the literature through its observational design, its focus on a specific CAD subgroup, and its hypothesis-generating aim. The SYNTAX score showed only a limited association with CAD complexity in this analysis, as reflected by the low adjusted R² values. Additional covariates both established and currently unmeasured may improve model performance and should be examined in future studies. Another limitation is the decision not to apply a Bonferroni correction, based on the assumption that each bivariate correlation represented an independent test. Not applying this correction increases the risk of type I error, which should be addressed in future work.

This study has several limitations that should be acknowledged, including its retrospective design and the use of single-center data. Patients are not randomly selected and prone to selection bias and sampling bias. Efforts were made to exclude missing, inaccurate, or inconsistent data in order to minimize measurement bias. Also by including multivariate linear regression analysis to find which variables affect SYNTAX score, we tried to reduce confounding factors. The relatively low R-squared values observed in the multivariable linear regression models suggest that additional unmeasured factors may influence the SYNTAX score and underscore the need for further investigation.Also future studies can analyze in three different groups, HbA1C levels < 5.7% vs. 5.7–6.4% vs. ≥6.5% and add more biochemical values such as creatinine and CRP. Absance of longitudinal outcomes is another limitation of this study.

Conclusion

In this study, no significant correlation was observed between SYNTAX score and clinical or biochemical variables such as uric acid and TSH levels in patients with diabetes mellitus; however, significant associations were identified in the non-diabetic subgroup. These findings, the product of an observational study, confirm the hypothesis that they generate predictions for diagnostic, treatment, and follow-up strategies for coronary artery disease, a leading cause of morbidity and mortality worldwide, and specifically predict the relevant clinical prognosis in the patient group studied.Evaluation of subgroup-specific analyses highlights the potential prognostic value of these parameters in assessing coronary artery disease and its management across all stages.

Abbreviations

ACS

Acute Coronary Syndrome

BMI

Body Mass Index

CABG

Coronary Artery Bypass Grafting

CAD

Coronary Artery Disease

CI

Confidence Interval

FBG

Fasting Blood Glucose

fT3

Free Triiodothyronine

fT4

Free Thyroxine

GFR

Glomerular Filtration Rate

GGT

Gamma-Glutamyl Transferase

GRACE

Global Registry of Acute Coronary Events

HbA1c

Hemoglobin A1c

HDL-C

High-Density Lipoprotein Cholesterol

IQR

Interquartile Range

LDL-C

Low-Density Lipoprotein Cholesterol

MACE

Major Adverse Cardiac Events

MACCE

Major Adverse Cardiac and Cerebrovascular Events

MI

Myocardial Infarction

PCI

Percutaneous Coronary Intervention

SD

Standard Deviation

SHR

Stress Hyperglycemia Ratio

SUA

Serum Uric Acid

SYNTAX

“Synergy between PCI with TAXUS and Cardiac Surgery” score

TC

Total Cholesterol

TG

Triglycerides

TSH

Thyroid-Stimulating Hormone

Units

mg/dL

Milligrams per deciliter

mIU/L

Milli-international units per liter

U/L

Units per liter

kg/m²

Kilograms per square meter

Authors’ contributions

M.Ş. and Ö.I. conceived and designed the study, collected and analyzed the data, and wrote the main manuscript text. E.P. contributed to data interpretation, literature review, and manuscript editing. C.Ş. and M.N.D. provided critical revision, language editing, and overall supervision of the study. All authors reviewed and approved the final version of the manuscript.

Funding

No funding was received for this study.

Data availability

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

Declarations

Ethics approval and consent to participate

This study was approved by the Non-Interventional Research Ethics Committee of Fırat University (Session No: 2025/12–12, Date: 04.09.2025, Document No: 38804). The study was conducted in accordance with the Declaration of Helsinki. Due to the retrospective design of the study, the requirement for informed consent was waived. No clinical trial registration number is available for this study; therefore, clinical trial registration is not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.