Assessment of Hematological and Biochemical Markers in Saudi Patients with Diabetes and Non-Diabetic Acute Myocardial Infarction

INTRODUCTION

The leading cause of death on a global scale is Cardiovascular Diseases (CVDs). One of the most risky and potentially fatal types of CVD that result in a considerable percentage of such deaths is Acute Myocardial Infarction (AMI) (Nesbitt et al., 2025). An acute coronary occlusion leads to AMI, ultimately resulting in an ischemic injury and myocardial necrosis. ACS is a serious risk factor and predictor influenced by the severity of Diabetes Mellitus (DM) (Stampouloglou et al., 2023; Smith et al., 1984). It is a chronic metabolic illness, which is defined by chronic hyperglycemia due to insulin resistance or insulin deficiency and predisposes diabetic patients to AMI two to four times (Ormazabal et al., 2018). This leads to increased myocardial injury, recovery, and poor clinical outcome of such patients as compared to patients who are non-diabetic.

In India, much like in most of the developing countries, diabetes and AMI are on the rise rather fast due to urbanization, sedentary lifestyle, and dietary modification (Kumar et al., 2020). This dual epidemic has imposed a major public health burden (Mohan et al., 2010). The current research was carried out in the Darbhanga area, which has been characterized by a significant increase in heart-related complications brought about by diabetes. Knowledge of the biochemical differences between diabetic and non-diabetic patients with ACS in these regional populations is important for the design of specific prevention and management measures (Sabitha et al., 2024; Ali et al., 2016).

Biochemical and hematological markers are essential tools for identifying myocardial injury and metabolic alterations (Tidy et al., 2019). Cardiac Troponin I and Creatine Kinase-MB (CK-MB) are specific indicators of myocardial cell damage (Daubert et al., 2010; Wega et al., 2021). Lipid profile parameters such as total cholesterol, triglycerides, LDL, and HDL reflect the extent of dyslipidemia, a major contributor to atherosclerosis and coronary artery disease (Puri et al., 2023). Serum levels of inflammatory markers, such as C-Reactive Protein (CRP), and oxidative stress markers, such as Malondialdehyde (MDA), Superoxide Dismutase (SOD), and Glutathione (GSH), provide additional insight into the underlying oxidative and inflammatory stress that promotes myocardial injury, at least in diabetic individuals (Narres et al., 2022; Cecerska-Heryć et al., 2025).

This research is intended to evaluate and compare these hematological and biochemical parameters within the Normal, Non-Diabetic AMI (ND-AMI) and Diabetic AMI (D-AMI) samples. The study aims to determine the effects of diabetes on myocardial damage and metabolic imbalances by evaluating cardiac enzymes, lipid levels, and oxidative stress markers. It is hoped that the results of the research will contribute to improved diagnostic accuracy, earlier intervention, and the clinician's orientation toward personalized therapeutic interventions for AMI patients with and without diabetes.

MATERIALS AND METHODS

Study Design and Population

The current research is a hospital-based comparative study conducted in the Department of Biochemistry at Darbhanga Medical College and Hospital, Bihar. A group of 600 subjects was selected and then stratified into three groups, each of which had 200 subjects: (i) healthy controls; (ii) acute myocardial infarction in the Non-Diabetic (ND-AMI) group; (iii) acute myocardial infarction in the Diabetic (D-AMI) group. Diagnostic accuracy was achieved by combining clinical symptomatology, electrocardiographic findings, and elevated cardiac biomarker levels to diagnose acute myocardial infarction. Diabetic status was determined by fasting plasma glucose ≥126 mg/dL or HbA_1C≥6.5%, according to American Diabetes Association (ADA) criteria (ADA, 2023).

Inclusion Criteria

The study included male and female participants aged 35-80 years. Healthy individuals without cardiovascular, diabetic, renal, or hepatic disorders were taken as controls. ND-AMI patients had acute myocardial infarction without diabetes (FPG<126 mg/dL, HbA_1C<6.5%), whereas D-AMI patients had AMI with diabetes (FPG≥126 mg/dL or HbA_1C≥6.5%) according to the ADA 2023 criteria (Cui et al., 2012). All participants gave written informed consent before sample collection.

Exclusion Criteria

Patients with chronic liver or renal disease, thyroid disorders, and chronic inflammatory conditions were barred. No one taking antioxidant supplements, lipid-lowering medications, or anti-inflammatory medications was included, either. The study excluded subjects who were recently infected, had cancer lesions, autoimmune diseases, were pregnant women, lactating women, or had uncertain records or lacked the willingness to take part in the study (Ceriello et al., 2009).

Sample Collection

Under aseptic precautions, 5 mL of venous blood was drawn from each participant after overnight fasting. The blood samples were divided into two parts:

• Serum was separated by centrifugation at 3000 rpm for 10 min and used for biochemical assays.
• EDTA-treated plasma was used for hematological parameters.

All analyses were performed within 2 hr of collection to maintain sample integrity.

Biochemical Analysis

The levels of creatine kinase-MB and serum cardiac troponin I were tested using an automated chemiluminescent immunoassay analyzer. The level of C-reactive protein was measured using a highly sensitive immunoturbidimetric method. The lipid profile parameters (total cholesterol, triglycerides, High-Density Lipoprotein (HDL), and Low-Density Lipoprotein (LDL)) were measured using enzymatic colorimetric kits (Erba Diagnostics, India).

Oxidative Stress Markers

The Thiobarbituric Acid Reactive Substances (TBARS) method, which assesses lipid peroxidation, was used to measure Malondialdehyde (MDA) levels. The pyrogallol autoxidation method, developed by Marklund and Marklund (1974), was used to assess Superoxide Dismutase (SOD) activity (Yao et al., 2021). Ellman's reagent was used to measure Glutathione (GSH) concentration, and the Aebi method (1984) was used to assess Catalase (CAT) activity (De Leon et al., 2020). A UV-visible spectrophotometer was used to take all absorbance measurements.

Hematological and Blood Pressure Measurement

An automated hematology analyzer was used to determine the Hemoglobin (Hb) concentration. The systolic and diastolic blood pressures (SBP, DBP) after 10 min of rest in a sitting position were measured using a routine mercury sphygmomanometer.

Statistical Analysis

The data was analyzed with SPSS version 24.0. The qualitative data were summarized in the form of frequencies or percentages, and the quantitative variables in the form of mean±SD. We have used the Shapiro-Wilk test to test the normality of the continuous distributions. Intergroup comparison among the three research groups was conducted using one-way ANOVA, with the assumption of homogeneity of variances addressed by a Kruskal-Wallis test, followed by an independent-samples t-test between the study groups ND-AMI and D-AMI at the pairwise level. Pearson correlation coefficients were used to assess the relationships between Troponin I concentrations and biochemical markers, and multiple linear regression was performed to identify independent predictors of Troponin I levels. The statistical significance of p-values less than 0.05 (two-tailed) was considered significant.

RESULTS

The current research compared demographic and biochemical factors, as well as oxidative stress indicators, among the Normal, ND-AMI, and D-AMI groups. Continuous variables are presented as means with standard deviations, and categorical variables as frequencies and percentages. Depending on the characteristics of the variable distributions, one-way analysis of variance, the Kruskal-Wallis test, and independent-samples t-tests were used to test differences between the groups. In addition, the correlation and regression analyses were conducted to both test the relationship and to identify predictors of cardiac biomarkers. The most important findings have been summarized below.

Demographic and Clinical Characteristics

Table 1 shows that there was no statistically significant difference between the mean age and sex distribution between the three cohorts, and thus, the demographic profile was consistent. This homogeneity in age affirms that later biochemical differences were driven by disease rather than senescence. Although there was no significant difference between baseline hematological parameters like leukocyte and platelet counts, there was a significant, though mild, decrease in hemoglobin in the acute myocardial infarction arms. It is interesting to note that systolic and diastolic pressures increased steadily from the healthy control group to the non-diabetic AMI group and culminated in the diabetic AMI group, clearly indicating that hypertension is aggressive when combined with glycemic derangement in infarction (Figure 1A).

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Biochemical, Lipid, and Oxidative Stress Parameters

Table 2 shows that there is a definite pathological gradient of the groups, and the AMI diabetic patients have the greatest biochemical load. The indices of cardiac injury- Troponin I, CK-MB, and C-reactive protein were significantly increased in diabetic patients in comparison to non-diabetic AMI patients and healthy controls. The lipid panel reflected this degree of severity, with increased total cholesterol, triglycerides, and low-density lipoprotein, and reduced high-density lipoprotein, in the diabetic group. Moreover, antioxidant defenses such as superoxide dismutase, catalase, and reduced glutathione were significantly reduced in diabetic AMI, and malondialdehyde levels were elevated, all of which indicate that diabetes enhances oxidative injury and suppresses protective enzyme activity during myocardial infarction (Figure 1B).

Intergroup Statistical Comparisons and Normality Assessment

Table 3 results revealed that most continuous variables did not meet the Gaussian assumption, necessitating the use of non-parametric tests for several comparisons. The across-group analysis revealed significant differences in almost all cardiac, inflammatory, lipid, and oxidative stress parameters, whereas age, white blood cell count, and platelet count were not statistically different. These differences were further highlighted by pairwise comparisons between diabetic and non-diabetic ACS patients, which revealed statistically significant disparities in Troponin I, CRP, LDL, HDL, malondialdehyde, and superoxide dismutase. These results confirm that diabetes by itself exacerbates the metabolic and inflammatory disturbances in acute coronary syndromes (Figure 1C).

Correlation and Multiple Linear Regression Analysis

Bivariate analysis, as shown in Table 4, showed that an increase in Troponin I was correlated with increases in CK-MB, CRP, LDL, and malondialdehyde, but was anti-correlated with superoxide dismutase activity. Multivariate models further reduced these relations: CRP and malondialdehyde became strong positive determinants of myocardial injury, whereas HDL, glutathione, and superoxide dismutase had strong negative impacts, suggesting cardioprotective effects. After controlling for other biomarkers, low-density lipoprotein no longer had independent predictive value. Inflammation and oxidative stress seem to dominate the mechanistic environment of diabetic myocardial damage, with about 47% of the variance in Troponin I explained by the model (Figure 2).

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The study showed clear biochemical and physiological differences among the three groups. Both ND-AMI and D-AMI patients had higher cardiac and inflammatory markers compared to the normal group. Diabetic AMI patients, in particular, showed the most significant changes, with much higher levels of Troponin I, CK-MB, CRP, LDL, and MDA, along with much lower activity of antioxidant enzymes (SOD, GSH, and Catalase). Blood pressure readings were also significantly higher in the D-AMI group, indicating a greater prevalence of hypertension. These findings suggest that diabetes worsens heart damage, inflammation, and oxidative stress (Figure 3).

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DISCUSSION

The age structure of the Normal, ND-AMI, and D-AMI samples was examined to determine whether age differences might affect biochemical or oxidative measurements. The mean age of the three groups did not differ significantly according to our results. This indicates that the study groups were age-matched, and the observed differences in biochemicals were not due to age but rather to disease status.

We find that the previous clinical research is consistent with ours. According to the FINAMI Study, diabetic and non-diabetic individuals were not significantly different in the mean age, which was the time at which the myocardium was first struck, and this suggests that diabetes changes the severity and outcome of AMI and not its age (Pajunen et al., 2005; Tipparapu et al., 2025). A recent systematic review also found no statistically significant age difference between diabetic and non-diabetic AMI cohorts across multiple global studies (Alawaji et al., 2024). Both studies support our observation that metabolic disturbances, not age, primarily influence the biochemical and oxidative patterns associated with AMI.

We compared the general demographic and clinical characteristics among the Normal, ND-AMI, and D-AMI groups to establish baseline comparability. Our data showed that gender distribution was nearly identical across groups, with male-to-female ratios of 106/94 in Normal, 106/94 in ND-AMI, and 109/91 in D-AMI (p=0.941). The mean age was also comparable, with values of 70.52±8.91 years in Normal, 69.45±9.19 years in ND-AMI, and 68.97±9.38 years in D-AMI (p=0.2258), indicating that age and gender did not differ significantly. However, systolic and diastolic blood pressures were significantly higher in the AMI groups. The average systolic blood pressure rose from 130.49±15.03 mmHg in normal subjects to 145.55±18.88 mmHg in ND-AMI and then increased to 149.26±17.11 mmHg in D-AMI. Similarly, diastolic blood pressure rose from 80.10±10.39 mmHg in Normal to 90.90±10.77 mmHg in ND-AMI and 96.15±58.36 mmHg in D-AMI (both p<0.001). These findings show that the severity of blood pressure among AMI patients, especially diabetics, is progressively increasing.

Similar trends were described in recent research. Another study found the level of hypertension in myocardial infarction in type 2 diabetic patients to be significantly higher than that of people without diabetes, which substantiates our results that diabetes intensifies the hypertensive impact during myocardial infarction (Yen et al., 2022). Likewise, Karpay et al. (2022) reported elevated systolic and diastolic pressures in diabetic AMI subjects, linking this rise to endothelial dysfunction and metabolic stress (Karpay et al., 2022). Together with our past data, our results confirm the fact that both age and gender were well balanced, but hypertension is a determining clinical feature in AMI, especially in diabetic patients.

We compared the parameters of biochemical stress, oxidative stress, and ND-AMI and D-AMI groups. We found increases in Cardiac Troponin I, CK-MB, CRP, Total Cholesterol, Triglycerides, LDL, and MDA, and significant decreases in HDL, SOD, Catalase, and GSH. These tendencies suggest that the level of myocardial damage, inflammation, and oxidative stress rises dramatically in the case of AMI, particularly in diabetic patients.

Another study reported similar levels of Troponin I, CK-MB, and CRP in AMI patients, supporting our evidence of elevated cardiac and inflammatory markers (Karpay et al., 2022; Geeshma et al., 2025). It also observed increased MDA levels and reduced antioxidant enzyme activities in AMI, consistent with our oxidative stress data (Zhao et al., 2023). Likewise, another study found higher LDL and triglyceride levels and lower HDL levels in diabetic AMI cases, consistent with our lipid profile results.

To assess the distribution of all biochemical and oxidative parameters, we used the Shapiro-Wilk test to determine which statistical test to use to compare the groups. The majority of variables, such as Troponin I, CRP, Triglycerides, and MDA, were not normally distributed (p<0.05), whereas the Age, SBP, and DBP parameters were normally distributed (p>0.05). These results were used to apply non-parametric (Kruskal-Wallis) tests to skewed data and parametric (ANOVA, t-test) tests to normally distributed data.

We find the same results as in the study, which observed that lipid peroxidation and inflammatory biomarkers are not normally distributed in patients with AMI (Jovanovic et al., 2024). Similarly, observed that oxidative and lipid variables in AMI cohorts exhibited substantial variability due to disease severity and metabolic differences. These studies confirm that non-normality is common in clinical datasets involving AMI and diabetes because of biological heterogeneity and differential oxidative responses.

One-way ANOVA was used to test whether the variables were normally distributed across the three groups, namely Normal, ND-AMI, and D-AMI. The analysis found significant differences (p<0.05) in most cardiac, lipid, and oxidative stress markers. The levels of cardiac Troponin I, CK-MB, and CRP were significantly higher in both ACS groups, with the highest mean values observed in D-AMI, indicating greater myocardial injury and inflammation in diabetic patients. The lipid parameters were no different, with AMI patients having considerably higher Total Cholesterol, Triglycerides, and LDL, and lower HDL compared to the Normal group.

Our results are consistent with reports of higher Troponin I and CK-MB levels in AMI subjects compared with healthy controls (Karpay et al., 2022). Similarly, Sabharwal et al. (2019) documented elevated CRP and dyslipidemia in diabetic AMI cases, highlighting a strong metabolic link between inflammation and lipid disturbance. Moreover, Hussien et al. (2023) demonstrated significantly higher LDL and lower HDL in patients with diabetic cardiac disease, further supporting our findings.

The factors that did not follow a normal distribution, such as SBP, DBP, Hemoglobin, Age, and WBC count, were analyzed using the Kruskal-Walli’s test. The findings showed statistically significant differences (p<0.001) in SBP, DBP, and Haemoglobin among the three groups, but no significant differences in Age and WBC. The mean ranks of systolic and diastolic blood pressure were the highest in the D-AMI group and then in ND-AMI and normal, thus far confirming the severity of hypertension in diabetic AMI patients. There was also variation in hemoglobin levels, indicating mild anemia in AMI cases compared with normal individuals.

Our findings are consistent with those of those who discovered that diabetic patients with hypertension had much higher blood pressure and cardiovascular load during myocardial infarction. Similarly, it has been demonstrated that AMI patients tend to have reduced hemoglobin and elevated blood pressure due to oxidative damage and vascular inflammation. These findings strengthen our conclusion that non-normally distributed clinical parameters, such as BP and Hemoglobin, show clear pathological escalation in diabetic AMI subjects (Karpay et al., 2022).

We used an independent t-test to compare the biochemical parameters between D-AMI and ND-AMI groups. The findings showed statistically significant differences across most parameters (p<0.05). Cardiac Troponin I, CRP, LDL, and MDA were significantly greater in D-AMI, but HDL and SOD were significantly less. These results show that diabetic AMI patients experience more heart damage, inflammation, lipid imbalance, and oxidative stress than non-diabetic AMI patients.

Several recent studies back up our findings. found that LDL and triglyceride levels were significantly higher, while HDL levels were significantly lower in patients with diabetic AMI. This explains how diabetes worsens lipid metabolism. (Hussien et al., 2023). Similarly, elevated Troponin I and CRP levels were reported in AMI subjects, confirming enhanced cardiac injury and inflammation. They also found that oxidative stress markers, such as MDA, were elevated in diabetic AMI cases. In contrast, antioxidant enzymes, such as SOD, were lower than in non-diabetics.

We conducted a correlation analysis to look at the relationship between Cardiac Troponin I and key biochemical parameters: CK-MB, CRP, LDL, MDA, and SOD. Our results showed strong, statistically significant correlations (p<0.001). Troponin I showed positive correlations with CK-MB (r=0.556), CRP (r=0.539), LDL (r=0.446), and MDA (r=0.529), while a significant negative correlation was found with SOD (r=-0.531). These results indicate that inflammation, dyslipidemia, and oxidative stress are directly correlated with the extent of myocardial injury and the activities of antioxidant enzymes, respectively.

Similar relationships are reported in the recent literature. A significant positive correlation between Troponin I and CRP has also been observed in ACS patients by another study, suggesting concomitant elevation of cardiac infarction and systemic inflammation.^{23, 29} Similarly, Hussien and Abdinibi (2023) observed strong positive correlations between Troponin I and LDL, linking lipid abnormalities to myocardial damage in subjects with diabetic AMI. Jovanović et al. (2024) also reported that Troponin I positively correlates with MDA and inversely correlates with antioxidant enzymes such as SOD, reinforcing our results.

Collectively, these results indicate that inflammation, lipid imbalance, and oxidative stress are strongly associated with myocardial injury in AMI, and the extent of the rise in Troponin I indicates the cumulative biochemical stress on the myocardium.

A multiple linear regression was conducted to identify the biochemical variables that affect Cardiac Troponin I values. LDL, HDL, CRP, MDA, SOD, and GSH were used as independent variables. The model was significant (F=87.76, p<0.001), with an R² of 0.470 and an adjusted R² of 0.460, explaining nearly 47% of the variation in Troponin I.

CRP (B=0.0489, p<0.001) and MDA (B=0.2178, p<0.001) were strong positive predictors, while SOD (B=-0.4576, p<0.001), GSH (B=-0.0909, p<0.001), and HDL (B=-0.0096, p=0.011) showed negative associations, suggesting that inflammation and oxidative stress elevate Troponin I, whereas antioxidants and HDL exert a protective effect. LDL (p=0.114) was not significant.

Our findings align with those of others who reported that CRP and MDA strongly correlate with myocardial injury, while antioxidant enzymes mitigate damage (Karpay et al., 2022; Hussien, 2023). Also found that low HDL aggravates cardiac injury in diabetic AMI (Polyakova et al., 2020; Srivastava et al., 2018). Together, these studies confirm that inflammation and oxidative stress are key predictors of Troponin I elevation.

CONCLUSION

This paper has shown that diabetes is a serious aggravator of myocardial injury, lipid imbalance, and oxidative stress among patients with acute myocardial infarction. The AMI subjects with and without diabetes exhibited high cardiac biomarkers and inflammatory indices, although the levels were high in diabetic subjects. High levels of Troponin I, CK-MB, CRP, LDL, and MDA, and decreased HDL, SOD, and GSH concentrations indicate that diabetes amplifies the effects of oxidative and inflammatory stressors on cardiac tissue.

We have highlighted in our findings that oxidative stress and inflammation play a significant role in maintaining the intensity of myocardial infarction, particularly in diabetic subjects. The use of oxidative stress and lipid parameters, along with conventional cardiac parameters, would enable the timely diagnosis of high-risk patients and the prescription of more effective interventions. The work supports the use of drug therapies involving antioxidants and lipid management in the management of diabetic AMI to enhance the prognosis and lower the complications.

To conclude, diabetes is an important condition that often occurs alongside myocardial infarction. Oxidative stress and metabolic imbalance are critical issues to tackle in order to improve heart outcomes for these patients.

ACKNOWLEDGEMENT

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University, KSA, for its support.

ABBREVIATIONS

DM

Diabetes mellitus

AMI

Acute Myocardial Infarction

MDA

Malondialdehyde

SOD

Superoxide Dismutase

GSH

Reduced Glutathione

LDL

Low-Density Lipoprotein

HDL

High-Density Lipoprotein

CK-MB

Creatine Kinase Myocardial Band

CRP

C-Reactive Protein

ANOVA

Analysis of Variance

CVDs

Cardiovascular diseases

ADA

American Diabetes Association

TBARS

Thiobarbituric acid reactive substances

CAT

Catalase

Hb

Hemoglobin

ND-AMI

Non-Diabetic Acute Myocardial Infarction

D-AMI

Diabetic Acute Myocardial Infarction

ACS

Acute Coronary Syndrome

FPG

Fasting Plasma Glucose

HbA_1C

Glycated Hemoglobin

EDTA

Ethylenediaminetetraacetic Acid

SBP

Systolic Blood Pressure

DBP

Diastolic Blood Pressure

WBC

White Blood Cell

UV

Ultraviolet

SPSS

Statistical Package for the Social Sciences.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

FUNDING

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP2/361/46.

ETHICAL APPROVAL

This work follows ethical principles of honesty, respect, and responsibility. We have ethical approval with REC-43/02/017.

GENERATIVE AI STATEMENT

The author declares that generative AI (Artificial Intelligence) tools, including Grammarly and QuillBot, were used to enhance the language and clarity of this work. The author takes full responsibility for the accuracy and integrity of the content.