Hypertension is a major independent risk factor for ischemic coronary disease and stroke. Recent cardiovascular research has pivoted from a purely hemodynamic model to an inflammatory one, recognizing atherogenesis as a chronic, low-grade inflammatory response within the arterial intima (Ross, 1993 ; Brown & Lee, 2019).

C-Reactive Protein (CRP) is a well-documented acute-phase reactant used to predict future coronary events, its elevation is often non-specific (Ridker et al., 2000; Voudris, K. V., et al.,2023). Mannose-Binding Lectin (MBL), a key component of innate immunity, activates the complement system via the lectin pathway and has been implicated in the biochemical cascade leading to plaque instability (Guo et al., 2024).

Previous studies have largely focused on either chronic hypertension or acute MI in isolation, often ignoring the comparative diagnostic weight of MBL versus CRP in a hypertensive cohort. Furthermore, traditional linear statistical methods often fail to identify specific biological thresholds where risk escalates. This study was undertaken to bridge this gap by using both comparative biochemical analysis and advanced predictive modeling to improve risk stratification in hypertensive individuals (Ogunpola et al., 2024).

Study Design: Cross sectional Case Control study     

The study protocol was approved by the Institutional Ethics Committee of Madras Medical College, Chennai.180 subjects were selected for the study. They were divided into three groups as follows

• Group A - 60 Recently diagnosed hypertensive patients of  6 months duration from the outpatients attending hypertension clinic in Rajiv Gandhi Government General Hospital, Chennai
• Group B - 60 Hypertensive patients who had myocardial infarction recently. ( < 7 days duration) from the Inpatients admitted in the cardiology department in Rajiv Gandhi Government General Hospital, Chennai.
• Group C – 60 Age and sex matched apparently healthy subjects who were staffs of Madras Medical College, their relatives and friends.

Exclusion criteria :

• Patients with Diabetes mellitus
• Patients with renal disorders.
• Patients with Liver diseases.
• Patient with acute illness / infection.
• Chronic Smokers and alcoholics.
• Other endocrinological disorders

Venous blood samples were collected. Serum MBL was estimated using a non-competitive sandwich ELISA (measurable range: 10–3000 ng/mL). BT LAB ELISA Kit (Lot No: E0335Hu) was used. The measurable range for the kit ranges from 10ng / mL – 3000 ng / mL with sensitivity of 5.16 ng/mL, inter assay CV < 10% & intra assay CV < 5%. CRP was measured via particle-enhanced immunoturbidimetry. Cobas C packs by Roche Hitachi systems was used for CRP estimation. Statistical analysis was performed using Python-based tools ( version 3.9) with scikit-learn and statmodels libraries. Continuous variables are expressed as Mean ± SD. Comparative analysis across groups was performed using ANOVA, and categorical variables were assessed via chi-square test. To evaluate the diagnostic accuracy and feature importance, a Random Forest algorithm was utilized to identify non-linear relationships and thresholds for MI prediction (Kiran et al., 2022: Mansoori et al., 2025). 

Table 1: Demographic and Anthropometric Characteristics of the Study Population

NS = Not Significant, HTN – Hypertension, MI – Myocardial Infarction

Data indicates groups were well-balanced for age, gender, and BMI.

Table 2: Inflammatory Markers among study groups

Table 3: Correlation between MBL and CRP

* Significant at 0.05 Level

Correlation analysis showed a strong positive correlation between MBL and CRP (r = 0.66, p = 0.01) only in the hypertension with MI group, suggesting a synergistic inflammatory response during acute events.

CRP and MBL for MI Prediction

Fig 1: Predictive Modeling

Table 4: Logistic Regression Coefficients

Table 5: Feature Importance for MI Prediction (Random Forest Model)

Fig 2 : ROC Curve – Logistic Regression

Table 6: ROC Curve – Logistic Regression Model Performance

Fig 3: Partial Dependence plot

Table 7: Partial Dependence Plot (PDP) – Random Forest

Hypertension is a pro-inflammatory state that promotes vascular remodeling and atherosclerosis. The elevation of MBL in hypertensive patients compared to healthy controls suggests that the lectin pathway is activated during the early, subclinical phases of vascular remodeling (Smith, 2020; Sumida et al., 2015). While hypertension provides the mechanical stress, MBL-mediated complement activation likely facilitates the progression of atherosclerotic plaques.

The most important finding of this study is the non-linear threshold behavior of MBL(Fig 1). While CRP levels rise steadily with systemic inflammation, Random Forest modeling indicates that MI risk spikes sharply once MBL reaches the 800–1000 ng/mL range (Fig 3). This threshold effect suggests that MBL may serve as a "trigger" marker for plaque rupture or acute ischemia rather than just a general indicator of inflammation.

The findings align with research by Keller et al. and Pesonen et al., which identified elevated MBL as a risk factor for future coronary events (Pesonen et al., 2009; Keller et al., 2006). The superior specificity of MBL (95.30%) compared to CRP in predicting Myocardial Infarction (MI) among hypertensive patients can be attributed to its unique role in the Lectin Complement Pathway (Ridker et al., 2000; Voudris et al., 2023). While CRP is a sensitive but non-specific marker of the general systemic inflammatory burden, MBL acts as a targeted initiator of vascular-specific immune responses.

In hypertensive individuals, chronic shear stress leads to endothelial dysfunction, exposing mannose-rich carbohydrate patterns on the damaged basement membrane (Dorflinger, G. H., et al.,2022). MBL binds specifically to these patterns, initiating a complement cascade that directly contributes to plaque instability. Unlike the linear increase observed with CRP (Table 4), the Random Forest modeling in this study revealed a sharp non-linear threshold effect at approximately 800–1000 ng/mL(Shishehbori & Awan, 2024).

This suggests that while low-level MBL elevation may be present in stable hypertension, crossing this specific concentration threshold acts as a "biological tripwire," triggering the acute complement-mediated tissue damage and plaque rupture that characterizes an MI. Consequently, MBL serves not just as a marker of inflammation, but as a specific indicator of imminent or active coronary events, offering a more precise tool for risk stratification than traditional biomarkers.

Limitations:

Longitudinal studies are required to establish a temporal causal relationship. Larger, multi-ethnic datasets are needed to account for genetic polymorphisms and ensure global applicability of the identified diagnostic thresholds.

Serum MBL is a novel and significant marker of myocardial infarction and subclinical atherosclerosis in hypertensive individuals. Its novelty lies in its non-linear threshold behaviour, which offers higher diagnostic specificity than CRP. Monitoring MBL alongside CRP can enhance early identification of high-risk patients, allowing for timely interventions to prevent the progression of cardiovascular disease.

Acknowledgments:

The authors acknowledge Mr. T. Sriram for his technical assistance in the statistical analysis and implementation of the Python-based Random Forest and ROC modeling. We also thank the Department of Biochemistry at Madras Medical College for facilitating this research.

The authors utilized Google Gemini for drafting and paraphrasing support. All AI-assisted content was critically reviewed, verified for clinical accuracy, and finalized by the authors, who accept full responsibility for the integrity of the work.

1. Arshad, R. G., & Toori, K. U. (2024). Correlation of C-reactive protein and neutrophil-lymphocyte ratio in clinical practice. Pakistan Journal of Medical Sciences, 40.
2. Brown, A., & Lee, C. (2019). Inflammatory pathways in cardiovascular risk. Journal of Heart Research.
3. Dorflinger, G. H., et al. (2022). Circulating mannose-binding lectin and risk of vascular complications: A systematic review. Journal of Clinical Endocrinology & Metabolism.
4. Firouzjahi, A., Beladi-Mousavi, S. S., & Amirkhanlou, S. (2022). Evaluation of PTX-3, hs-CRP and mannose-binding lectin in patients undergoing hemodialysis. Journal of Renal Injury Prevention, 11(2).
5. Guo, Z. Y., et al. (2024). From immune response to clinical intervention: Emerging roles of the complement system in cardiovascular pathology. Precision Clinical Medicine.
6. Keller, T. T., et al. (2006). Serum mannose-binding lectin and coronary artery disease risk. Arteriosclerosis, Thrombosis, and Vascular Biology, 26(4), 905–910.
7. Kiran, A., et al. (2022). Advanced machine learning models for patterns within heterogeneous and high-dimensional medical data. Frontiers in Artificial Intelligence.
8. Liu, H. H., et al. (2019). High-sensitivity C-reactive protein and its association with hypertension. Hypertension Research, 42(4), 540–546.
9. Mahmood, S. S., et al. (2014). The Framingham Heart Study and the epidemiology of cardiovascular diseases: A historical perspective. Lancet, 383(9921), 999–1008.
10. Mansoori, S., et al. (2025). Predicting cardiovascular risk with hybrid ensemble learning and explainable AI. PubMed Central (PMC12102235).
11. National High Blood Pressure Education Program. (2004). The seventh report of the JNC.
12. Ogunpola, A., et al. (2024). Machine learning and ensemble methods for cardiovascular disease prediction: A systematic review. MDPI Applied Sciences.
13. Pesonen, E., et al. (2009). Mannose-binding lectin as a risk factor for acute coronary syndrome. Annals of Medicine, 41(4), 284–291.
14. Ridker, P. M., et al. (2000). C-reactive protein and other markers of inflammation. New England Journal of Medicine, 342(12), 836–843.
15. Ross, R. (1993). The pathogenesis of atherosclerosis. Nature, 362(6423), 801–809.
16. Shishehbori, M., & Awan, M. S. (2024). Web-based cardiovascular disease risk prediction using machine learning. Frontiers in AI.
17. Smith, J. (2020). Hypertension and the innate immune system. Medical Immunology Today.
18. Sumida, T., et al. (2015). Complement C1q and hypertensive arterial remodelling. Nature Communications, 6(1), 1–11.
19. Voudris, K. V., et al. (2023). Novel inflammatory biomarkers in coronary artery disease. Current Medicinal Chemistry.
20. Zhang, C., et al. (2014). Antagonist of C5a receptor and cardiac remodeling. American Journal of Hypertension, 27(6), 857–864.