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Elevated Nuclear Factor Kappa B1 Gene expression and Dysregulated Metabolic Profiles in Coronary Artery Disease: A Case - Control Study
*Corresponding author: Dr. Jaideep Mahendra, Department of Periodontology, Meenakshi Ammal Dental College and Hospital, Meenakshi Academy of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, India. drjaideep.perio@madch.edu.in
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Received: ,
Accepted: ,
How to cite this article: Neroth Kandy L, Mahendra J, Jayaraman S, Retnakaran R, Paruthiparayil Jose J, Ramachandran R, et al. Elevated Nuclear Factor Kappa B1 Gene expression and Dysregulated Metabolic Profiles in Coronary Artery Disease: A Case - Control Study. Indian J Cardiovasc Dis Women. doi: 10.25259/IJCDW_56_2025
Abstract
Objectives:
Coronary artery disease (CAD) remains a significant public health challenge globally, with South Asians, particularly in regions like Kerala, India, experiencing disproportionately high incidence and severity. While epidemiological data from Kerala are available, there is limited insight into this population’s molecular and metabolic alterations underpinning CAD. This study aimed to evaluate the expression of a genetic marker, specifically nuclear factor kappa-light-chain-enhancer of activated B-cells 1 (NF-kB1), and its association with metabolic dysregulation in CAD subjects.
Materials and Methods:
A case–control study involving a total of 300 participants-CAD subjects (n = 150) and healthy controls (n = 150) was conducted. NF-κB1 gene expression was quantified through real-time polymerase chain reaction, and serum tumor necrosis factor-alpha (TNF-a) levels were assessed using enzyme-linked immunosorbent assay. Fasting glucose and lipid profiles were also measured. Statistical analyses included group comparisons, correlation studies, and receiver operating characteristic (ROC) analysis.
Results:
CAD patients exhibited significantly elevated NF-κB1 gene expression (1.6 ± 0.6 vs. 1.0 ± 0.2; P < 0.001), TNF-a levels, fasting glucose, triglycerides, and low-density lipoprotein-cholesterol (LDL-C), alongside significantly lower high-density lipoprotein-cholesterol (HDL-C). NF-kB1 expression showed positive correlations with glucose, triglycerides, LDL-C, and TNF-a and a negative correlation with HDL-C. ROC analysis revealed that NF-kB1 had the highest diagnostic accuracy among all markers studied.
Conclusion:
Our findings reveal a strong link between NF-kB1-mediated inflammation and metabolic dysregulation in CAD. NF-kB1 may be a promising biomarker for early detection and risk stratification, particularly in high-risk South Asian populations. This study provides region-specific molecular insights that could inform targeted prevention and management strategies for CAD in Kerala.
Keywords
Coronary artery disease
Gene expression
Inflammation
Metabolic dysregulation
ABSTRACT IMAGE
INTRODUCTION
Coronary artery disease (CAD) remains the leading cause of global morbidity and mortality, with developing countries like India disproportionately affected due to rapid urbanization, lifestyle transitions, and underlying genetic susceptibility. According to the Global Burden of Disease Study 2023, ischemic heart disease continues to be the foremost cause of death globally, with India contributing significantly to this burden.[1] In the Indian state of Kerala – despite having one of the highest literacy rates and better healthcare access – the rising prevalence of non-communicable diseases, including CAD, is a growing concern. This paradoxical trend has been attributed to increasing obesity rates, dietary shifts, and sedentary lifestyles.[2]
CAD is primarily driven by atherosclerosis – a chronic, low-grade inflammatory condition affecting the arterial wall. Inflammation is central to all stages of atherosclerosis, from initial endothelial dysfunction to eventual plaque rupture.[3] At the molecular level, this inflammatory process is tightly regulated by various transcription factors, with the nuclear factor kappa-light-chain-enhancer of activated B-cells 1 (NF-kB1) playing a key role. The NF-κB1 gene encodes the p105 precursor protein, which is processed into the p50 subunit. This subunit forms dimers that regulate the expression of numerous pro-inflammatory genes.[4] Activation of NF-kB1 is closely linked to the initiation and progression of atherosclerotic plaques. It is notably upregulated in vascular endothelial cells and monocytes in response to oxidized low-density lipoproteins (ox-LDL), hyperglycemia, and reactive oxygen species (ROS) – factors frequently observed in individuals with metabolic syndrome and CAD.[5] Recent studies have shown that elevated expression of NF-kB1 is associated with increased levels of inflammatory markers such as TNF-a, which drive atherosclerotic progression and correlate with adverse cardiac events.[6]
In parallel, metabolic dysregulation encompassing central obesity, insulin resistance, hyperlipidemia, and elevated triglyceride levels – has long been recognized as a significant risk factor for CAD. Recent evidence, however, indicates that these metabolic abnormalities do not act in isolation. Instead, they are closely intertwined with chronic inflammation and altered gene expression. Notably, NF-kB1 activation in peripheral mononuclear cells has been reported to correlate positively with elevated LDL-C, triglycerides, and fasting glucose in CAD patients. This supports the idea that chronic inflammation driven by metabolic disturbances contributes significantly to atherogenesis and disease progression.[7]
Despite advancements in understanding CAD, region-specific data remain limited, especially from South India and Kerala. Kerala’s unique dietary, genetic, and environmental factors may influence disease patterns, highlighting the need for localized research. This study examines NF-κB1 gene expression in CAD patients from Kerala and explores its correlation with metabolic parameters such as lipid profile and fasting blood glucose. The study aims to identify potential biomarkers for early diagnosis and personalized therapies by linking gene expression with metabolic dysfunction.
MATERIALS AND METHODS
Study design and participants
Between 2022 and 2024, this case–control study was in accordance with the ethical principles outlined in the Declaration of Helsinki (1964). Ethical approval was obtained from the Institutional Ethics Committee (Ref No: 06/2022/IECG). All participants provided informed consent before participation. A structured questionnaire gathered relevant demographic, lifestyle, and clinical data. The study cohort comprised 300 participants aged 30–60, including 150 patients diagnosed with CAD and 150 age- and gender-matched healthy controls. Participants were enrolled through the Medical Camp and referrals from multiple blood collection centers.
Inclusion exclusion criteria
Inclusion criteria
Age 30–60 years
Clinically confirmed CAD cases
Healthy controls without CAD
Both sexes included
Exclusion criteria
Acute/chronic illness
Cancer
Prolonged medication
Exposure to radiation/chemotherapy
Age <30 or >60
Laboratory investigations
Fasting venous blood samples (6–8 mL) were collected in plain and ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes. The collected blood was processed for biochemical analysis following standardized laboratory protocols. Tumor necrosis factor-alpha (TNF-a) was quantified using an enzyme-linked immunosorbent assay. Ribonucleic acid (RNA) isolation, complementary DNA (cDNA) synthesis, and real-time polymerase chain reaction (PCR) assessed NFκB1 gene expression using blood from EDTA tubes.
Metabolic profiling
Metabolic profiling – including lipid parameters and fasting blood glucose (FBG) – was carried out using standard clinical chemistry methods. Serum FBG, LDL-C, HDL-C, and triglycerides were measured using a semi-automated biochemistry analyzer (Mispa-Neo). In addition, body mass index (BMI) was calculated by dividing the weight (kg) by the square of the height (m2).
Genetic analysis
Gene expression was measured from peripheral blood. The study did not isolate B-cells, T-cells, or monocytes separately, which is acknowledged as a methodological limitation. Total RNA was extracted from all blood samples using total RNA extraction and first-strand cDNA generation using kits (Origin Diagnostics and Research, Kerala, India). A spectrophotometer was applied to determine RNA concentration, which was adjusted to 1000 ng/μL. A cDNA synthesis kit was used to synthesize cDNA on the same day.
Real-time polymerase chain reaction (PCR) was used to evaluate NF-κB1 levels using specific primers [Table 1]. The internal control was the glyceraldehyde-3-phosphate dehydrogenase gene. Fresh blood was mixed with Buffer RZ in a 3:1 ratio and incubated at 15–30°C for 5 min. This was followed by chloroform extraction and centrifugation at 12,000 rpm for 10 min at 4°C. The aqueous phase was carefully transferred, mixed with ethanol, and processed through a spin column system. After a series of sequential washes, RNA was eluted using RNase-free water.
| Gene | Primer sequence | Purification |
|---|---|---|
| NF-κB1 | Forward: 5’-GCAGCACTACTTCTTGACCACC-3’ Reverse: 5’-TCTGCTCCTGAGCATTGACGTC-3’ |
HPSF |
| GAPDH | Forward: 5'-CCATGGAGAAGGCTGGGG-3' Reverse: 5'-CAAAGTTGTCATGGATGACC-3' |
HPSF |
HPSF: High purity salt-free, NF-κB1: Nuclear factor kappa-light-chain-enhancer of activated B-cells 1, GAPDH: Glyceraldehyde-3-phosphate dehydrogenase
For cDNA synthesis, 50 ng of RNA was combined with Oligo (dT)18, random hexamer primers, deoxyribonucleotide triphosphates (dNTPs), reverse transcriptase (RT) Buffer, and reverse transcription (RT). The reaction was incubated at 25°C for 5 min, followed by 50°C for 60 min, then heated to 95°C for 5 min to inactivate the enzyme.
Real-time PCR was performed in a 20 μL reaction volume containing ×2 real-time PCR Master Mix, specific primers for the NF-κB1 gene, cDNA, and nuclease-free water. The thermal cycling conditions included an initial denaturation at 94°C, followed by 32 cycles of denaturation, annealing at 58°C, and extension at 72°C, each lasting 1 min. A final extension was performed at 72°C for 10 min to ensure complete amplification. Melt curve analysis was conducted to confirm the specificity of the amplified products. Relative expression of the NF-κB1 gene was analyzed using the 2−ΔΔCt method, and the results were graphically represented. The present study evaluated NF-κB1 gene expression only. No polymorphism analysis (e.g., rs28362491 or −94 ins/del) was undertaken.
Sample size calculation
The minimum required sample size was calculated using the formula n = Z2pq/d2n, assuming a 95% confidence level (Z = 1.96), a CAD prevalence of 12% in Kerala (P = 0.12), q = 0.88, and a 5% allowable error (d = 0.05). The calculated sample size was 162/group. For feasibility, 150 CAD cases and 150 controls (total n = 300) were recruited, providing adequate statistical power.
Statistical analysis
The statistical analysis began with basic descriptive statistics to examine the overall distribution of data. Group comparisons between CAD cases and controls were performed using Chi-square tests for categorical data and t-tests for continuous variables. ROC curve analysis was used to assess the diagnostic value of biochemical markers, and key indicators such as area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were calculated. All analyses were performed using Stata version 17.0, with statistical significance set at P < 0.05.
RESULTS
Baseline characteristics
This study included 300 participants – 150 clinically confirmed CAD cases and 150 age- and gender-matched healthy controls. Demographic and anthropometric details are presented in Table 2. Sex distribution was similar between groups (P = 0.907), while socioeconomic status differed significantly (P = 0.005), with more controls in the high socioeconomic status (SES) category. Obesity was more common among cases (31.3% vs. 12.0%; P < 0.001). Although age and height did not differ significantly (P = 0.291 and P = 0.584), both weight and BMI were markedly higher in the CAD group (P < 0.001), reflecting greater adiposity among cases.
| Variable | Category | Cases (n=150) | Controls (n=150) | Total (n=300) | P-value |
|---|---|---|---|---|---|
| Sex | Female | 84 (56.0%) | 83 (55.3%) | 167 (55.7%) | 0.907 |
| Male | 66 (44.0%) | 67 (44.7%) | 133 (44.3%) | ||
| Socioeconomic status | Low | 28 (18.7%) | 18 (12.0%) | 46 (15.3%) | 0.005 |
| Average | 107 (71.3%) | 97 (64.7%) | 204 (68.0%) | ||
| High | 15 (10.0%) | 35 (23.3%) | 50 (16.7%) | ||
| Obesity | Yes | 47 (31.3%) | 18 (12.0%) | 65 (21.7%) | P<0.001 |
| No | 103 (68.7%) | 132 (88.0%) | 235 (78.3%) | ||
| Age (years) | Mean±SD | 47.5±7.4 | 46.6±7.5 | 47.0±7.5 | 0.291 |
| Height (cm) | Mean±SD | 159.1±10.7 | 158.2±9.0 | 158.6±9.9 | 0.584 |
| Weight (kg) | Mean±SD | 68.6±15.2 | 60.0±10.7 | 64.3±13.8 | P<0.001 |
| BMI (kg/m2) | Mean±SD | 27.0±5.1 | 23.9±3.8 | 25.5±4.8 | P<0.001 |
SD: Standard deviation, P value: Probability value indicating statistical significance. P< 0.05
Descriptive statistics of the biochemical parameters for cases and controls
Fasting glucose levels were significantly higher in CAD cases (137.1 ± 43.7 mg/dL) than in controls (93.5 ± 16.3 mg/dL; P < 0.001). Triglycerides were also elevated in cases (153.0 ± 42.2 mg/dL vs. 115.3 ± 25.2 mg/dL; P < 0.001), while HDL-C was lower (43.7 ± 7.9 mg/dL vs. 50.9 ± 7.3 mg/dL; P < 0.001). LDL-C was markedly higher in cases (153.1 ± 40.9 mg/dL) compared to controls (104.5 ± 24.5 mg/dL; P < 0.001). TNF-a levels were elevated in cases (25.7 ± 9.1 pg/mL vs. 16.3 ± 7.2 pg/mL; P < 0.001). Consistently, NF-κB1 gene expression was significantly higher in cases (1.6 ± 0.6) than in controls (1.0 ± 0.2; P < 0.001) [Table 3].
| Variable name | Cases | Controls | P-value | ||
|---|---|---|---|---|---|
| Min–max | Mean±SD | Min–max | Mean±SD | ||
| Fasting blood glucose (mg/dL) | 72.5–254 | 137.1±43.7 | 70.7–138.6 | 93.5±16.3 | P<0.001 |
| Serum triglycerides (mg/dL) | 67.5–300 | 153.0±42.2 | 78.0–188.0 | 115.3±25.2 | P<0.001 |
| Serum HDL cholesterol (mg/dL) | 27–63 | 43.7±7.9 | 35–69 | 50.9±7.3 | P<0.001 |
| Serum LDL cholesterol (mg/dL) | 49.8–274.4 | 153.1±40.9 | 57.1–159.4 | 104.5±24.5 | P<0.001 |
| TNF-α (pg/mL) | 7.7–48.7 | 25.7±9.1 | 4.6–38.6 | 16.3±7.2 | P<0.001 |
| NF-κB1 gene expression (2−ΔΔCT) | 0.6–3.9 | 1.6±0.6 | 0.5–1.6 | 1.0±0.2 | P<0.001 |
P< 0.05. SD: Standard deviation, TNF: Tumor necrosis factor, NF-κB1: Nuclear factor kappa B1
ROC curve for various clinical parameters
The ROC analysis [Figure 1] highlights the diagnostic effectiveness of key biochemical and gene expression markers in distinguishing cases from controls. Among the parameters evaluated, NF-kB1 gene expression showed the most significant deviation from the diagonal line, indicating excellent discriminatory power. These findings suggest that inflammation-related biomarkers effectively identify early metabolic and cardiovascular disturbances.
- Receiver operating characteristic (ROC) curve for various clinical parameters was depicted.
Serum LDL cholesterol also demonstrated substantial predictive accuracy, confirming its relevance in disease classification. Its elevated levels in the case group reflect its known contribution to cardiovascular risk. On the other hand, serum HDL cholesterol displayed poor diagnostic performance, with its ROC curve closely following the diagonal, suggesting limited utility when used alone.
Fasting blood glucose (FBS) and serum triglycerides showed moderate diagnostic accuracy, indicating that while they are relevant in metabolic health, their standalone value in distinguishing cases from controls is somewhat limited. Their effectiveness may improve when considered alongside other markers.
Overall, NF-κB1 gene expression is the most promising marker in this study, followed by LDL cholesterol. In contrast, HDL cholesterol, FBS, and triglycerides may require integration into a composite panel for more accurate disease prediction.
DISCUSSION
CAD remains a leading global health concern with substantial clinical and socioeconomic implications. Although race and ethnicity have been widely studied in CAD risk, most existing literature focuses on populations from developed countries, particularly among European, African, Latino, and selected Asian groups.[8-12] Several studies have emphasized that individuals of Asian descent, especially South Asians, exhibit a heightened risk for CAD, often manifesting at a younger age and with more severe clinical presentations.[13-15] However, within the Indian subcontinent, particularly in regions such as Kerala, there is a lack of comprehensive data addressing the pathophysiological underpinnings of CAD. Previous studies from Kerala have primarily focused on the prevalence and epidemiological trends of the disease[16,17] with limited investigation into molecular or metabolic factors. This study bridges this knowledge gap by exploring gene expression and metabolic dysregulation in a Kerala-based cohort. It provides region-specific insights that could inform more tailored strategies for early diagnosis, risk stratification, and therapeutic interventions.
One of the most notable findings of this study is the significant upregulation of NF-κB1 gene expression in CAD patients compared to healthy controls (1.6 ± 0.6 vs. 1.0 ± 0.2; P < 0.001) [Table 3]. Earlier studies focused on NF-kB1 polymorphisms, showing that the rs28362491 D allele increases CAD risk. Our study adds new insight by evaluating NF-κB1 gene expression instead.[18] Similarly, the −94 ins/del adenine–thymine–thymine–guanine (ATTG) polymorphism in the NF-kB1 promoter has been linked to CAD susceptibility in the Chinese Han population.[19] However, investigations into the expression levels of NF-kB1 as opposed to its genetic variants remain limited. To the best of our knowledge, no previous studies have directly examined NF-κB1 gene expression in dysregulated metabolic profiles among individuals with CAD.
Elevated TNF-a levels observed in our CAD patients [Table 3] also align with findings by Zhang and Dhalla,[20] who reported that TNF-a and other pro-inflammatory cytokines were elevated in patients with cardiovascular events, reinforcing its role in plaque instability and thrombosis. Similarly, Popa et al.[21] noted that high circulating TNF-a levels correlate with the extent and severity of coronary lesions. Thus, our study supports these inflammatory associations and demonstrates that both gene-level and protein-level inflammatory markers are significantly elevated in CAD. These results support the inflammatory basis of CAD and highlight key biomarkers that may aid early detection and risk stratification, especially in high-risk groups.
In our study, fasting blood glucose levels were significantly higher in CAD patients than in controls [Table 3]. This finding supports the well-established association between impaired glycemic control and CAD. Similar elevations in fasting glucose among CAD patients were reported by Nielson et al.,[22] who found a positive association between elevated fasting glucose and increased risk of vascular events, including CAD. Likewise, Soma and Rheeder[23] highlighted that undiagnosed glucose abnormalities were common in CAD patients, even in those without a prior diabetes diagnosis. Our results align with these findings, reinforcing the role of glucose dysregulation as a key metabolic risk factor for CAD.
Our findings showed significantly higher serum triglyceride levels in CAD patients compared to controls (153.0 ± 42.2 mg/dL vs. 115.3 ± 25.2 mg/dL; P < 0.001) [Table 3]. This observation reinforces the well-established role of triglycerides as an essential contributor to atherogenesis and cardiovascular risk. Elevated triglyceride levels have been shown to promote the formation of small, dense LDL particles, and impair endothelial function – both of which are key mechanisms in the development of atherosclerosis.[24] In line with our results, Nordestgaard et al.[25] demonstrated a strong association between elevated fasting triglyceride concentrations and increased risk of myocardial infarction. Furthermore, Chen et al.[26] reported that elevated triglyceride levels strongly correlate with both the severity and extent of CAD, underscoring their value as a useful clinical marker[27] and also emphasized the impact of hypertriglyceridemia on cardiovascular outcomes, showing that a rise of 88 mg/dL (1.0 mmoL/L) in plasma triglyceride levels was associated with a ~30% increased risk of cardiovascular disease in men and a striking ~75% increased risk in women.[27] These findings, collectively with our data, underscore the significance of triglyceride regulation in preventing and managing CAD.
In addition to elevated triglycerides, our study found significantly higher LDL cholesterol levels in CAD patients than in healthy controls [Table 3]. This observation is consistent with a substantial body of evidence linking elevated LDL cholesterol to increased cardiovascular risk. Previous studies have shown that CAD patients’ LDL-C and VLDL-C levels tend to be significantly elevated.[28] Similarly, Haddad et al.[29] reported that Jordanian patients with CAD exhibited substantially higher total cholesterol and LDL-C concentrations compared to controls. These findings align with our results, reinforcing the pivotal role of lipid disturbances – particularly elevated LDL-C – in CAD pathogenesis. As shown in Table 3, HDL-C was significantly lower in CAD patients (43.7 ± 7.9 mg/dL), consistent with earlier studies such as,[30] which identified reduced HDL as an important risk factor for atherosclerosis. However, our ROC analysis showed that HDL had poor discriminatory power for predicting CAD in this cohort [Figure 1], which aligns with newer perspectives that HDL function, rather than quantity, may be more relevant. Thus, while our findings support traditional associations, they also reflect recent shifts in HDL research.
Our ROC analysis showed that NF-κB1 gene expression had the highest AUC, surpassing traditional markers such as fasting glucose and lipid levels [Figure 1]. This suggests that NF-κB1 may serve as a more sensitive and specific biomarker for CAD in this population. This finding supports studies by Hong et al.,[31] who emphasized the utility of inflammation-related gene markers over conventional lipid measures in predicting subclinical atherosclerosis. Furthermore, our study’s high predictive value of LDL cholesterol aligns with numerous global studies (e.g., the Framingham Heart Study), reinforcing its continued relevance in cardiovascular risk assessment. In contrast, when used in isolation, the limited discriminatory value of HDL cholesterol and triglycerides suggests that a multi-marker panel that includes NF-κB1 expression may provide better diagnostic accuracy than lipid markers alone. This supports the emerging shift in cardiovascular research toward integrated biomarker strategies rather than reliance on singular metabolic indicators.
Our study found that individuals with CAD had significantly elevated levels of NF-kB1 gene expression and TNF-a, alongside clear signs of metabolic dysregulation, including increased fasting blood glucose, serum triglycerides, LDL cholesterol, and decreased HDL cholesterol, when compared to age- and sex-matched healthy controls. Scatter plot analyses [Figures 2a-f] revealed that NF-kB1 expression was positively associated with fasting glucose, triglycerides, total cholesterol, LDL cholesterol, and TNF-a, while showing a negative association with HDL cholesterol. These associations were more pronounced in CAD cases, supporting the hypothesis that metabolic stress is closely linked with inflammatory gene activation in the disease process. Only 12% of CAD patients showed elevated NF-kB1 expression without marked metabolic changes, suggesting that inflammation may precede or operate independently of metabolic abnormalities in a subset of patients.
- (a) Association between fasting blood glucose and nuclear factor kappa-light-chain-enhancer of activated B-cells 1 (NF-κB1) gene expression in cases and controls was depicted. (b) Association between serum triglyceride and NF-κB1 gene expression in cases and controls was depicted. (c) Association between serum total cholesterol and NF-κB1 gene expression in cases and controls was depicted. (d) Association between serum HDL cholesterol and NF-κB1 gene expression in cases and controls was depicted. (e) Association between serum LDL cholesterol and NF-κB1 gene expression in cases and controls was depicted. (f) Association between TNF-α and NF-κB1 gene expression in cases and controls was depicted. HDL: High-density lipoprotein, LDL: Low-density lipoprotein.
Kerala shows a higher burden of diabetes and dyslipidemia, which may amplify NF-kB1 activation. Our findings indicate that NF-kB1 expression in this South Indian cohort is higher than values reported in East Asian and European datasets, suggesting possible regional metabolic-inflammatory interactions. Studies from Chinese, Turkish, and European cohorts report modest NF-kB1 elevation,[32] whereas our population demonstrates stronger upregulation, possibly due to higher metabolic stress, genetic background, and lifestyle patterns unique to Kerala. This region-specific inflammatory signature supports the idea that chronic metabolic stress and high diabetes prevalence in Kerala may accelerate NF-kB1–mediated pathways, contributing to earlier and more aggressive CAD presentations.
Female subpopulation in this study showed that women constituted a slightly higher proportion of both CAD cases (56%) and controls (55.3%). Although the study was not powered for sex-stratified statistical analysis, the biochemical and molecular trends observed in the overall population were also evident among female participants. Women with CAD demonstrated higher fasting glucose, triglycerides, LDL cholesterol, TNF-a levels, and NF-κB1 gene expression, along with lower HDL cholesterol, compared with female controls. These patterns suggest that the inflammatory– metabolic dysregulation identified in the total cohort is similarly reflected in women, indicating consistency of the disease profile across sexes within this study population.
While our study provides novel insights into the gene expression profiles in CAD, several limitations must be acknowledged. Unlike prospective cohort studies, case– control studies do not allow us to determine whether increased NF-kB1 precedes CAD development or is a consequence of established disease. Longitudinal studies are needed to better understand the causality of NF-κB1 upregulation regarding CAD progression. Gender-stratified gene expression analysis was not performed due to sample size constraints, although future studies may explore sex-specific inflammatory signaling differences.
Second, our gene expression analysis was limited to NFκB1 and TNF-a. Future studies should incorporate a broader range of inflammatory markers, such as CRP and other transcription factors, such as STAT3 or AP-1, to better understand the molecular pathways involved in CAD. Expanding this analysis could also help clarify the intricate relationship between inflammation and metabolic dysregulation. Furthermore, integrating proteomic or epigenetic data could offer deeper insights into regulating inflammation-related genes in CAD. hs-CRP was not measured in this study. Future studies may incorporate hsCRP for a broader inflammatory profiling.
Finally, given Kerala’s unique dietary, lifestyle, and genetic factors and the region’s high prevalence of diabetes and dyslipidemia, the findings from our case–control study are highly relevant to local risk profiles. However, to generalize these results, validating the findings in larger, multi-center, and more diverse populations is essential. This would help determine whether NF-κB1 expression can be reliably used as a diagnostic or prognostic biomarker for CAD across different ethnic and geographical groups.
CONCLUSION
NF-kB1 gene expression and TNF-a levels were markedly elevated in CAD subjects, accompanied by higher fasting glucose, triglycerides, LDL, and lower HDL. Together, these findings highlight the combined impact of inflammation and metabolic imbalance in CAD. Notably, NF-kB1 showed superior diagnostic potential than traditional lipid markers, suggesting its promise as a sensitive biomarker for early detection and risk assessment. By focusing on a Kerala-based population, this study addresses a regional research gap and provides valuable insights into CAD-specific molecular profiles. Further longitudinal and multi-center studies are needed to validate these findings and explore broader inflammatory pathways in diverse populations. If validated in larger cohorts, NF-kB1 expression could serve as a population-specific biomarker for CAD screening in Kerala, enabling targeted prevention, early detection, and personalized anti-inflammatory therapeutic strategies.
Acknowledgments:
The authors gratefully acknowledge the guidance and valuable suggestions received during the conception, execution, and analysis of this study. The support provided by colleagues and peers in refining the methodology and interpretation of the results is sincerely appreciated. Their contributions have been instrumental in enhancing the quality of this work.
Ethical approval:
The research/study was approved by the Institutional Review Board at the Institutional Ethics Committee of Genetika, number No.06/2022/IECG, dated November 27, 2022.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patients have given their consent for their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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