Comprehensive Analysis of Demographic, Clinical, and Genetic Characteristics in Acute Myocardial Infarction Patients

Study population and design

This study included 185 patients diagnosed with and treated for ACS at the Hoan My Cuu Long General Hospital from May 2023 to October 2023. The selection criteria were patients diagnosed with ACS, including both non-ST-elevation myocardial infarction and ST-elevation myocardial infarction, according to the ACC/AHA/SCAI Guidelines for Coronary Artery Revascularization (Lawton Jennifer et al., 2022; O’Gara et al., 2013). Those who willingly participated in the study were included. Patients with terminal cancer, comatose states, or secondary ACS were excluded. This study adopted a descriptive cross-sectional approach to provide a snapshot of the patient population during a specified period to elucidate the associations between genetic variations and clinical symptoms in ACS.

Sample collection method

The study involved Blood samples were collected from 185 patients (aged 40–95 years) who were diagnosed with acute myocardial infarction. At the time of blood collection, comprehensive clinical information, including risk factors associated with acute myocardial infarction (AMI) such as sex, age, history of hypertension, triglyceride levels, and history of diabetes, was gathered (Golabgir Khademi et al., 2016). Hypertension was defined as a systolic blood pressure > 140 mmHg or diastolic blood pressure > 90 mm Hg (Golabgir Khademi et al., 2016). These risk factors were determined based on physician diagnosis, medical records, and biochemical tests (Golabgir Khademi et al., 2016). This study employed internationally standardized methods for sample collection. Blood samples were analyzed for various parameters, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). These biochemical markers were assessed to provide comprehensive insight into the biochemical profiles associated with AMI (Huang et al., 2018). Diabetes was identified through ongoing diabetes treatment or fasting blood sugar levels > 126 mg/dL. Elevated triglyceride levels were defined as fasting triglyceride levels of > 150 mg/dL (Golabgir Khademi et al., 2016).

Genotyping rs1333040 by PCR-RFLP

Human genomic DNA was extracted from the blood samples using the QiAmp DNA Mini Kit (Qiagen, Hilden, Germany). Genotyping of the investigated SNPs (rs1333040) was performed using PCR and restriction fragment length polymorphism (RFLP). The studied SNPs were 32185 nucleotides away from each other and were amplified. The oligonucleotide primers rs1333040F (5’-AAGAAGGCTGGTAGCAGGAAG-3’) and rs1333040R (5’- ATCACCACCCAAACACCAGT-3’) (Golabgir Khademi et al., 2016).

The PCR reaction was carried out in a final reaction volume of 50 μL using 3 μL genomic DNA as a template. The reaction mixture (rs1333040) contained 2 μL of each primer (10 μM), 25 μL of MyTaq Mix (2x) (Meridian Life Science Inc., USA), and 18 μL of water (ddH₂O) (Golabgir Khademi et al., 2016; Hoang Ngo et al., 2023; Le et al., 2021).

The cycling program was as follows: initial denaturation at 95 °C for 5 min followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 68 °C for 30 s, and extension at 72 °C for 60 s. The amplification was terminated after 5 min of extension at 72 °C. The size of the PCR product for the rs1333040 polymorphism is 900 bp (Golabgir Khademi et al., 2016).

The PCR products of SNP were digested with the restriction endonuclease BsmI (Thermo, Mỹ) for rs1333040 (Golabgir Khademi et al., 2016). The digested products were detected by agarose gel electrophoresis.

Statistical analysis

All data were entered and analyzed using R statistical software. Genotype and allele frequencies of the rs1333040 polymorphism were determined. Hardy-Weinberg equilibrium was tested using the chi-square test to compare the observed and expected genotype distributions (Rohlfs & Weir, 2008; Woldu et al., 2022).

Clinical characteristics and biochemical parameters between the different genotype groups were compared using the chi-square test. The association between rs1333040 genotype and disease risk was evaluated using multivariate logistic regression analysis. Statistical significance was set at P < 0.05.

Age distribution and body mass index of patients by gender

The age distribution of the patients was analyzed according to their sex. For female patients, age ranged from 54 to 89 years, with the first quartile (25th percentile) at approximately 64 years, median (50th percentile) at 67 years, mean (average) age at 68.85 years, and third quartile (75th percentile) at approximately 74 years. In contrast, male patients had ages ranging from 44 to 93 years, with the first quartile at 54 years, the median at 54.5 years, the mean age at 63.58 years, and the third quartile at approximately 70 years. This indicates a slightly higher median and mean age for female patients (67 and 68.85 years, respectively) than for male patients (54.5 and 63.58 years, respectively). The age range was broader for male patients (44–93 years) than for female patients (54–89 years), indicating greater age variability among males (Figure 1).

Figure 1. Age distribution of patients by gender.

Within the dataset, Body Mass Index (BMI) distribution highlighted the prevalence of various weight statuses. The BMI categories were thin (<18.5), normal (18.5-25), Overweight (25-30), and obese (>30), providing insights into the population’s weight classifications (Figure 2). The ‘Thinness’ category comprises 4.86% of the population (9 cases) with a BMI below 18.5. The median age of this group was 82 years, with ages ranging from 71 to 93 years, indicating a tendency of older individuals to be classified as underweight. The ‘Normal’ category, being the largest segment, includes 77.84% of the sample (144 cases) with BMIs between 18.5 and 25. The age distribution within this category ranged from 48 to 89 years, with a median age of 65 years, reflecting a diverse age range within the healthy weight category. The overweight category accounted for 15.14% of the population (28 cases), with BMIs between 25 and 30. The median age is 58 years, with ages ranging from 54 to 92 years, showing a wider age range compared to the ‘Normal’ BMI category. Finally, the ‘Obese’ category, comprising 2.16% of the cases (4 individuals), includes those with a BMI over 30. The median age of patients in this group was 64 years.

Figure 2. Age distribution within BMI categories.

Overall, the data demonstrates age and BMI distributions across the patient population, with notable differences in age distribution by gender and significant representation within the ‘Normal’ BMI category. This analysis provides a comprehensive overview of the demographic characteristics of the study population, highlighting age and BMI variations across different groups.

Medical history patterns and correlations

Cardiovascular Risk Factors - The study identified several key cardiovascular risk factors among patients with AMI. Hypertension was the most prevalent condition, affecting 83.8% of the patients. Dyslipidemia was also highly prevalent (80.0%). Diabetes mellitus was present in 33.8% of the patients, while 50.0% were smokers. Notably, 13.8% of the patients were overweight or obese, and a small percentage (2.5%) had a family history of cardiovascular disease (Table 1).

The Killip classification, which is crucial for prognosis, showed that the majority of patients (93.8%) were in Killip class I. Only 2.5% were in class II and 3.8% were in classes III-IV, indicating more severe heart failure symptoms (Table 1).

Clinical Presentation: In terms of clinical presentation, 55.0% of patients had non-ST-elevation myocardial infarction (NSTEMI) and 45.0% had ST-elevation myocardial infarction (STEMI) (Table 1).

Electrocardiogram (ECG) Findings - ECG findings revealed that 43.8% of patients had ST elevation, 45.0% had ST depression, 38.8% had T-wave abnormalities, and 36.3% had pathological Q waves. These findings are critical for diagnosing and determining AMI severity (Table 1).

Lipid profile analysis showed that 60.0% of the patients had decreased HDL-C levels, 53.8% had elevated triglycerides, 37.5% had elevated LDL-C, and 36.3% had elevated total cholesterol. These abnormalities are significant risk factors for cardiovascular diseases (Table 1).

Ejection Fraction (EF) - Echocardiographic data indicated that 78.7% of the patients had preserved EF, 11.3% had mildly reduced EF, and 10.0% had reduced EF. The preserved EF in the majority of patients suggests that the left ventricular systolic function was maintained in many cases (Table 1).

Mean hs-Troponin I Level - The mean hs-troponin I level was significantly higher in STEMI patients (16,292.54 ng/L) compared to NSTEMI patients (3,445.16 ng/L), reflecting the more extensive myocardial damage typically seen in STEMI (Table 2).

This study highlights the high prevalence of traditional cardiovascular risk factors among patients with AMI, with hypertension and dyslipidemia being the most common risk factors. The Killip classification and ECG findings underscore the severity and clinical presentation of AMI. Lipid abnormalities are prevalent and contribute to the risk profile of these patients. Ejection fraction data provide insight into the cardiac function of the patient population, while hs-troponin I levels differentiate between STEMI and NSTEMI, aiding in the stratification of patient risk and management strategies.

Genetic sequencing results for specific test samples

Three different genotypes were identified using the PCR-RFLP method and were subsequently confirmed by genetic sequencing using a Beckman Coulter CEQ8000 sequencer. The products were sequenced using an ABI 3500 sequencer and analyzed using SeqScape v2.7 software. Hardy-Weinberg equilibrium testing was applied to validate the independent segregation of the C and T alleles within the study population. The calculated chi-square value was χ² = 0.57 with p = 0.45, indicating that the AMI patient population in the study adhered to Hardy-Weinberg equilibrium.

Distribution of the rs1333040 polymorphism revealed the following genotype frequencies: Wild-type genotype TT was the most prevalent, accounting for 58.8% (n = 47) of the samples. The heterozygous genotype CT was found in 33.7% (n = 27) of the samples, whereas the homozygous CC genotype had the lowest frequency at 7.5% (n = 6) (Table 3).

When comparing allele frequencies, the T allele was more common (n = 121, 75.6%) compared to the C allele (n = 39, 24.4%). In the dominant model analysis (TT + CT), the combined frequency was 92.5%, whereas it was 41.2% in recessive model analysis (CC + CT). The results are presented in Table 3 presents the results.

These results provided a comprehensive overview of the genotype and allele distributions within the study population, reflecting the genetic diversity related to the rs1333040 polymorphism in patients with AMI (Table 3).

Association of rs1333040 polymorphism of the ANRIL gene with biochemical parameters in acute myocardial infarction patients

This study investigated the association between ANRIL rs1333040 polymorphism and various biochemical blood parameters in patients with AMI. Using weighted mean analysis and standard deviation (SD) calculations followed by ANOVA, we examined glucose, total cholesterol, serum LDL-C, HDL-C, and triglycerides. Despite the observed differences in mean values across genotypes (TT, CT, and CC).

The mean and standard deviation (mean ± SD) for each biochemical parameter across the three genotypes are presented in Table 4. For glucose, the mean values were 119.7 ± 33.4 mg/dL for the CC genotype, 118.9 ± 32.8 mg/dL for the CT genotype, and 124.1 ± 41.6 mg/dL for the TT genotype. Total cholesterol levels were 169.5 ± 74.1 mg/dL for the CC genotype, 214.7 ± 193.6 mg/dL for the CT genotype, and 186.8 ± 65.6 mg/dL for the TT genotype. HDL-C levels were 39.9 ± 13.1 mg/dL for the CC genotype, 43 ± 28.3 mg/dL for the CT genotype, and 49.5 ± 48.6 mg/dL for the TT genotype. LDL-C levels were 101.3 ± 48.5 mg/dL for the CC genotype, 118 ± 48.9 mg/dL for the CT genotype, and 122.8 ± 54.8 mg/dL for the TT genotype. Triglycerides levels were 201.2 ± 108 mg/dL for the CC genotype, 158.8 ± 119.4 mg/dL for the CT genotype, and 193.7 ± 117.7 mg/dL for the TT genotype. Although there were variations in the mean values of these biochemical parameters among the different genotypes, ANOVA indicated no significant associations (p > 0.05) for any of the parameters examined.

Our analysis did not reveal any significant association between the rs1333040 polymorphism of ANRIL and the measured biochemical parameters (Table 4). The highest mean glucose level was found in the TT genotype group (124.1 ± 41.6 mg/dL), while the CT genotype group had the highest mean total cholesterol (214.7 ± 193.6 mg/dL). The CC genotype group had the lowest mean HDL-C (39.9 ± 13.1 mg/dL). Despite these differences, none were statistically significant, highlighting the need for further studies with larger sample sizes to confirm our findings.