Boiled, Fried, or Omelet? Why Cooking Method May Matter More Than the Egg Itself

Picture this: It’s a Sunday morning, and you’re enjoying a perfectly spiced masala omelet alongside a buttery paratha. The combination is delicious—crispy, rich, and satisfying. But as you take another bite, a small voice in your head whispers: “Is this good for my heart?”


If you’ve worried about eggs and heart health, you’re not alone. For decades, eggs have been caught in a tug-of-war between nutrition and concern. But recent science suggests we may have been asking the wrong question all along. It’s not just about whether you eat eggs—but
how you prepare them.


What Science Really Says About Eggs and Heart Health

A landmark 2013 meta-analysis published in the British Medical Journal (BMJ) examined data from eight studies including over 3 million person-years of follow-up and thousands of heart disease cases (Rong et al., 2013). Their conclusion? Eating up to one egg per day was not associated with increased risk of coronary heart disease or stroke in the general population.


This finding contradicted decades of warnings about eggs. But there’s an important detail the researchers acknowledged: most studies didn’t account for how the eggs were cooked or what they were eaten with. And this might be where the real heart health story begins.


The Missing Piece: It’s Not Just the Egg, But Everything Around It

When we eat eggs, we rarely eat them plain. In South Asian cooking especially, eggs are transformed with:

  • Oils and ghee for frying
  • Spices and salt
  • Accompaniments like parathas, white bread, or processed meats
  • Cooking methods that may oxidize cholesterol


Dr. Frank Hu, a Harvard researcher cited in the BMJ study, noted that in many studies showing harm from eggs, the eggs were often consumed with bacon, sausage, and white toast with butter—all foods independently linked to heart disease (Hu et al., 1999).


How Cooking Methods Change Your Egg’s Heart Impact

The way you cook your egg can dramatically alter its nutritional profile and health effects:


Oxidized Cholesterol: The Hidden Risk

When eggs are cooked at high temperatures, especially when the yolk is exposed to air (like in sunny-side-up or scrambled eggs), cholesterol can become oxidized. Oxidized cholesterol is more likely to build up in artery walls than regular cholesterol (Jiang et al., 2019).


Added Fats Matter

While eggs themselves contain healthy fats, what we add during cooking can tip the balance:

  • Butter and ghee add saturated fat, which can raise LDL cholesterol
  • Refined vegetable oils may create inflammatory compounds when heated
  • Olive oil adds heart-healthy monounsaturated fats when used in moderate amounts


Side Note:
Eggs themselves don’t significantly raise blood cholesterol in most people. Your liver actually produces most of your body’s cholesterol, and when you eat more cholesterol, it often makes less to compensate (McNamara, 2000).


How South Asian Egg Preparations Stack Up

South Asian cuisines feature many beloved egg preparations—some healthier than others:

Egg Dish Ingredients Heart Health Score Why?
Boiled Egg Egg only ★★★★★ No added fats, minimal oxidation
Egg Bhurji (light oil) Eggs, vegetables, spices, small amount of oil ★★★★☆ Vegetables add fiber, limited oil
Poached Egg with Spinach Eggs, leafy greens ★★★★☆ No added fat, vegetables add nutrients
Akuri (Parsi-style eggs) Eggs, vegetables, moderate oil/ghee ★★★☆☆ Mixed – vegetables good, but may have significant added fat
Omelet with Cheese Eggs, cheese, oil ★★☆☆☆ Added saturated fat from cheese and cooking oil
Fried Egg with Paratha Egg, white flour paratha with ghee ★☆☆☆☆ Refined carbs, significant added saturated fat

Making Traditional Favorites Heart-Healthier

You don’t have to give up your favorite egg dishes—just consider these modifications:


For Egg Bhurji (Scrambled Eggs):

  • Use 1 tsp oil instead of 1 Tbsp
  • Add more vegetables like spinach, tomatoes, and onions
  • Serve with whole grain roti instead of white bread


For Anda Curry:

  • Use yogurt to create creaminess instead of heavy cream
  • Reduce oil by half
  • Include plenty of vegetables in the curry


For Breakfast Parathas:

  • Make the paratha with whole wheat flour
  • Use minimal oil for cooking
  • Fill with vegetables along with egg


Healthy Egg Hacks

  • Hard-boil eggs in batches for quick protein-rich snacks
  • Try egg whites only for some meals if watching cholesterol
  • Add vegetables to every egg dish to increase fiber and nutrients
  • Use non-stick pans to reduce needed cooking oil
  • Try steamed eggs (like South Indian egg podimas) for a gentler cooking method


Heart-Healthy Egg Cooking Methods: Best to Worst

  1. Boiled: No added fat, minimal cholesterol oxidation
  2. Poached: No added fat, gentle cooking
  3. Steamed: Traditional in some South Asian dishes, gentle heat
  4. Scrambled with water/low-fat milk: Minimal added fat
  5. Baked (like frittata): Moderate heat, can control added fats
  6. Omelet: Can be healthy with minimal oil, but easy to overdo
  7. Pan-fried: Significant added fat, moderate oxidation
  8. Deep-fried (like egg pakoras): Highest added fat, maximum oxidation


The Whole Picture: Beyond Cooking Methods

Remember that heart health isn’t determined by single foods but by your overall eating pattern. Even the healthiest boiled egg won’t offset an otherwise unhealthy diet.

Here’s what matters most for heart health:

  • Regular physical activity
  • Plant-forward eating with plenty of vegetables
  • Limiting processed foods
  • Managing stress
  • Avoiding smoking


Conclusion: Enjoy Eggs Mindfully

The science suggests that for most people, enjoying up to one egg daily is perfectly compatible with heart health. By choosing heart-friendly cooking methods and healthy accompaniments, you can continue to enjoy this nutritious food as part of your South Asian diet.

Next time you’re preparing eggs, remember: it’s not just what you eat, but how you prepare it that counts.


References:

  1. Rong Y, Chen L, Zhu T, et al. Egg consumption and risk of coronary heart disease and stroke: dose-response meta-analysis of prospective cohort studies. BMJ. 2013;346:e8539.
  2. Hu FB, Stampfer MJ, Rimm EB, et al. A prospective study of egg consumption and risk of cardiovascular disease in men and women. JAMA. 1999;281:1387–1394.
  3. McNamara DJ. The impact of egg limitations on coronary heart disease risk: do the numbers add up? J Am Coll Nutr. 2000;19:540S–548S.
  4. Jiang Z, Ahn DU, Ladner L, et al. Influence of feeding full-fat flax and sunflower seeds on internal and sensory qualities of eggs. Poult Sci. 2019;71:378–382.

 

About the Author

Southasianheart Staff

We are a group of healthcare professionals, public health experts, and community advocates dedicated to raising awareness about heart disease in the South Asian community.

Leave a Reply

Your email address will not be published. Required fields are marked *

You may also like these

5 Life Saving Tests Every South Asian Should Consider.

Understand and reduce your heart disease risk with these important tests.

  • Learn which tests can detect heart disease early
  • Fight genetics with actionable steps
  • Be prepared by advocating for your health



    *We respect your privacy, means no spam mails ever

    This will close in 0 seconds

    7-Day Meal Plan for South Asians.

    Follow a traditional heart healthy diet with simple and satisfying dishes

    • Get a detailed meal plan for every day of the week
    • Enjoy familiar flavors with a healthier twist
    • Support your heart without difficult restrictions



      *We respect your privacy, means no spam mails ever

      This will close in 0 seconds

      logo image

      What is a Cardiovascular Risk Calculator?

      Understanding Your Heart Attack Risk

      A cardiovascular risk calculator is a medical tool that estimates your chance of having a heart attack or stroke in the next 10 years.
      Think of it as a personalized weather forecast for your heart health—it combines multiple factors about your health to predict future risk.

      How Risk Calculators Work

      The Science Behind Prediction

      Risk calculators are built using data from large medical studies that follow thousands of people over many years.
      Researchers track who develops heart disease and identify the common factors that increase risk.
      These patterns are then turned into mathematical formulas that can predict individual risk.

      Key Components:

      • Population Data: Studies of 10,000+ people followed for 10–30 years
      • Risk Factors: Medical conditions and lifestyle factors that increase heart disease risk
      • Statistical Models: Mathematical equations that combine all factors into a single risk percentage

      What Risk Calculators Measure

      Most calculators evaluate these core factors:

      • Age and Gender: Risk increases with age; men typically have higher risk earlier
      • Blood Pressure: Both systolic (top number) and diastolic (bottom number)
      • Cholesterol Levels: Including "good" (HDL) and "bad" (LDL) cholesterol
      • Diabetes Status: Blood sugar control significantly impacts heart risk
      • Smoking History: One of the most controllable risk factors
      • Family History: Genetic predisposition to heart disease

      Reading Your Results

      Risk Categories:

      • Low Risk: Less than 5% chance in 10 years
      • Moderate Risk: 5–20% chance in 10 years
      • High Risk: More than 20% chance in 10 years

      What Your Number Means: A 10% risk means that out of 100 people exactly like you, about 10 will have a heart attack in the next 10 years. It's a probability, not a certainty.

      Why Traditional Calculators Fall Short for South Asians

      The Problem with "One Size Fits All"

      Most widely-used risk calculators were developed using predominantly white populations.
      This creates significant problems for South Asians:

      • Systematic Underestimation: Traditional calculators can underestimate South Asian heart disease risk by up to 50%
      • Different Risk Patterns:
        • About 10 years earlier than other populations
        • At lower body weights and smaller waist sizes
        • With different cholesterol patterns
        • With higher rates of diabetes and metabolic problems

      The Solution: Population-Specific Assessment

      Why Specialized Calculators Matter

      Just as weather forecasts are more accurate when they account for local geography and climate patterns,
      heart disease risk assessment is more accurate when it accounts for population-specific health patterns.

      • Improved Accuracy: Better identifies who is truly at high risk
      • Earlier Detection: Catches problems before they become severe
      • Targeted Prevention: Focuses on risk factors most relevant to your population
      • Better Outcomes: More accurate assessment leads to more effective treatment

      Making Risk Assessment Actionable

      Understanding Your Results

      A good risk calculator doesn't just give you a number—it helps you understand:

      • Which factors contribute most to your risk
      • What you can change (lifestyle factors)
      • What you can't change (age, genetics) but should monitor
      • When to seek medical attention

      Using Results for Prevention

      Risk assessment is most valuable when it guides action:

      • Lifestyle Changes: Diet, exercise, stress management, smoking cessation
      • Medical Management: Blood pressure control, cholesterol treatment, diabetes management
      • Monitoring Schedule: How often to check risk factors and repeat assessments
      • Family Planning: Understanding genetic risks for family members

      The Future of Risk Assessment

      Advancing Technology

      Modern risk calculators are becoming more sophisticated:

      • Machine Learning: AI algorithms that can detect complex patterns in health data
      • Advanced Biomarkers: New blood tests that provide more precise risk information
      • Imaging Integration: Heart scans that directly visualize artery health
      • Continuous Monitoring: Wearable devices that track risk factors in real-time

      Personalized Medicine

      The future of cardiovascular risk assessment is moving toward truly personalized predictions that account for:

      • Genetic Testing: DNA analysis for inherited risk factors
      • Environmental Factors: Air quality, stress levels, social determinants
      • Lifestyle Tracking: Detailed diet, exercise, and sleep patterns
      • Cultural Factors: Population-specific risk patterns and cultural practices

      Key Takeaways

      Remember These Important Points:

      • Risk calculators provide estimates, not certainties
      • Population-specific tools are more accurate than general calculator
      • Risk assessment is most valuable when it guides prevention and treatment
      • Regular reassessment is important as risk factors change over time
      • No calculator replaces professional medical evaluation and care

      Bottom Line: A good cardiovascular risk calculator is a powerful tool for understanding and preventing heart disease,
      but it works best when designed for your specific population and used alongside professional medical care.

      This information is for educational purposes only and should not replace professional medical advice.
      Always consult with your healthcare provider for proper cardiovascular risk assessment and treatment decisions.

      This will close in 0 seconds

      logo

      SACRA Calculator Scientific References

      Primary Foundation Studies

      2025 Core Research (Primary Foundation)

      1. Rejeleene R, Chidambaram V, Chatrathi M, et al. Addressing myocardial infarction in South-Asian populations: risk factors and machine learning approaches. npj Cardiovascular Health. 2025;2:4. doi:10.1038/s44325-024-00040-8

      INTERHEART Study (Global Foundation)

      1. Yusuf S, Hawken S, Ôunpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The Lancet. 2004;364(9438):937-952. doi:10.1016/S0140-6736(04)17018-9
      2. Rosengren A, Hawken S, Ôunpuu S, et al. Association of psychosocial risk factors with risk of acute myocardial infarction in 11,119 cases and 13,648 controls from 52 countries (the INTERHEART study): case-control study. The Lancet. 2004;364(9438):953-962. doi:10.1016/S0140-6736(04)17019-0
      3. Joshi P, Islam S, Pais P, et al. Risk factors for early myocardial infarction in South Asians compared with individuals in other countries. JAMA. 2007;297(3):286-294. doi:10.1001/jama.297.3.286

      PREVENT Study (AHA 2023 Guidelines)

      1. Khan SS, Matsushita K, Sang Y, et al. Development and Validation of the American Heart Association's PREVENT Equations. Circulation. 2024;149(6):430-449. doi:10.1161/CIRCULATIONAHA.123.067626
      2. Lloyd-Jones DM, Braun LT, Ndumele CE, et al. Use of Risk Assessment Tools to Guide Decision-Making in the Primary Prevention of Atherosclerotic Cardiovascular Disease: A Special Report From the American Heart Association and American College of Cardiology. Circulation. 2019;139(25):e1162-e1177.

      Machine Learning Studies for MI Detection & Prediction

      High-Performance ML Algorithms (93.53%-99.99% Accuracy)

      1. Xiong P, Lee SM-Y, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Frontiers in Cardiovascular Medicine. 2022;9:860032. doi:10.3389/fcvm.2022.860032
      2. Than MP, Pickering JW, Sandoval Y, et al. Machine Learning to Predict the Likelihood of Acute Myocardial Infarction. Circulation. 2019;140(11):899-909. doi:10.1161/CIRCULATIONAHA.119.041980
      3. Doudesis D, Adamson PD, Perera D, et al. Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogeneous population. The Lancet Digital Health. 2022;4(5):e300-e308. doi:10.1016/S2589-7500(22)00033-9
      4. Chen P, Huang Y, Wang F, et al. Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus. BMC Cardiovascular Disorders. 2023;23:585. doi:10.1186/s12872-023-03626-9
      5. Aziz F, Tk N, Tk A, et al. Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach. PLoS One. 2021;16(8):e0254894. doi:10.1371/journal.pone.0254894
      6. Kasim S, Ibrahim S, Anaraki JR, et al. Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI). Scientific Reports. 2024;14:12378. doi:10.1038/s41598-024-61151-x
      7. Zhu X, Xie B, Chen Y, et al. Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction. Clinica Chimica Acta. 2024;554:117776. doi:10.1016/j.cca.2024.117776

      Advanced AI and Transformer Models

      1. Vaid A, Johnson KW, Badgeley MA, et al. A foundational vision transformer improves diagnostic performance for electrocardiograms. NPJ Digital Medicine. 2023;6:108. doi:10.1038/s41746-023-00840-9
      2. Selivanov A, Kozłowski M, Cielecki L, et al. Medical image captioning via generative pretrained transformers. Scientific Reports. 2023;13:4171. doi:10.1038/s41598-023-31251-2

      MASALA Study (South Asian Specific)

      1. Kanaya AM, Kandula N, Herrington D, et al. MASALA study: objectives, methods, and cohort description. Clinical Cardiology. 2013;36(12):713-720. doi:10.1002/clc.22219
      2. Kanaya AM, Vittinghoff E, Kandula NR, et al. Incidence and progression of coronary artery calcium in South Asians. Journal of the American Heart Association. 2019;8(5):e011053. doi:10.1161/JAHA.118.011053
      3. Reddy NK, Kanaya AM, Kandula NR, et al. Cardiovascular risk factor profiles in Indian and Pakistani Americans: The MASALA Study. American Heart Journal. 2022;244:14-18. doi:10.1016/j.ahj.2021.11.021

      South Asian Cardiovascular Research

      Population-Specific Risk Studies

      1. Patel AP, Wang M, Kartoun U, et al. Quantifying and Understanding the Higher Risk of Atherosclerotic Cardiovascular Disease Among South Asian Individuals. Circulation. 2021;144(6):410-422. doi:10.1161/CIRCULATIONAHA.121.012813
      2. Nammi JY, Pasupuleti V, Matcha N, et al. Cardiovascular Disease Prevalence in Asians Versus Americans: A Review. Cureus. 2024;16(4):e58361. doi:10.7759/cureus.58361
      3. Satish P, Sadiq A, Prabhu S, et al. Cardiovascular burden in five Asian groups. European Journal of Preventive Cardiology. 2022;29(6):916-924. doi:10.1093/eurjpc/zwab070
      4. Agarwala A, Satish P, Mehta A, et al. Managing ASCVD risk in South Asians in the U.S. JACC: Advances. 2023;2(3):100258. doi:10.1016/j.jacadv.2023.100258

      Risk Calculator Validation Studies

      1. Rabanal KS, Selmer RM, Igland J, et al. Validation of the NORRISK 2 model in South Asians. Scandinavian Cardiovascular Journal. 2021;55(1):56-62. doi:10.1080/14017431.2020.1821407
      2. Kaptoge S, Pennells L, De Bacquer D, et al. WHO cardiovascular disease risk charts for global regions. The Lancet Global Health. 2019;7(10):e1332-e1345. doi:10.1016/S2214-109X(19)30318-3

      Biomarkers and Advanced Testing

      ApoB/ApoA1 and Lipid Research

      1. Walldius G, Jungner I, Holme I, et al. High ApoB, low ApoA-I in MI prediction: AMORIS. The Lancet. 2001;358(9298):2026-2033. doi:10.1016/S0140-6736(01)07098-2
      2. Enas EA, Varkey B, Dharmarajan TS, et al. Lipoprotein(a): genetic factor for MI. Indian Heart Journal. 2019;71(2):99-112. doi:10.1016/j.ihj.2019.03.004
      3. Tsimikas S, Fazio S, Ferdinand KC, et al. Reducing Lp(a)-mediated risk: NHLBI guidelines. JACC. 2018;71(2):177-192. doi:10.1016/j.jacc.2017.11.014

      Coronary Artery Calcium and Advanced Imaging

      1. Greenland P, Blaha MJ, Budoff MJ, et al. Coronary Artery Calcium Score and Cardiovascular Risk. JACC. 2018;72(4):434-447. doi:10.1016/j.jacc.2018.05.027

      Dietary and Lifestyle Factors

      South Asian Dietary Patterns

      1. Radhika G, Van Dam RM, Sudha V, et al. Refined grain consumption and metabolic syndrome. Metabolism. 2009;58(5):675-681. doi:10.1016/j.metabol.2009.01.008
      2. Gadgil MD, Anderson CAM, Kandula NR, Kanaya AM. Dietary patterns and metabolic risk factors. Journal of Nutrition. 2015;145(6):1211-1217. doi:10.3945/jn.114.207753

      Metabolic Syndrome and Obesity

      1. Gujral UP, Pradeepa R, Weber MB, Narayan KMV, Mohan V. Type 2 diabetes in South Asians: similarities and differences with white Caucasian and other populations. Annals of the New York Academy of Sciences. 2013;1281(1):51-63. doi:10.1111/j.1749-6632.2012.06838.x
      2. McKeigue PM, Shah B, Marmot MG. Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. The Lancet. 1991;337(8738):382-386. doi:10.1016/0140-6736(91)91164-P

      Psychosocial Risk Factors

      1. Anand SS, Islam S, Rosengren A, et al. Risk factors for myocardial infarction in women and men: insights from the INTERHEART study. European Heart Journal. 2008;29(7):932-940. doi:10.1093/eurheartj/ehn018
      2. Prabhakaran D, Jeemon P, Roy A. Cardiovascular Diseases in India: Current Epidemiology and Future Directions. Circulation. 2016;133(16):1605-1620. doi:10.1161/CIRCULATIONAHA.114.008729

      Key Historical Context

      1. Ajay VS, Prabhakaran D. Coronary heart disease in Indians: Implications of the INTERHEART study. Indian Journal of Medical Research. 2010;132(5):561-566.

       

      Note: This comprehensive reference list includes 35 peer-reviewed studies that form the scientific foundation for the SACRA Calculator, with emphasis on the latest 2025 machine learning research, South Asian-specific cardiovascular risk factors, and validated global studies like INTERHEART and MASALA. The calculator algorithm incorporates findings from all these studies to provide evidence-based risk assessment tailored specifically for South Asian populations.

       

      This will close in 0 seconds

      logo

       

      Scientific Basis of SACRA

      Evidence-Based Risk Assessment for South Asians

      The Crisis: South Asian Cardiovascular Disease Burden

      • 17.9 million annual heart attack deaths globally among South Asians

      • Heart attacks occur about a decade earlier compared to other populations

      • 40% higher mortality risk from cardiovascular disease

      • 2–4 times higher baseline risk for heart disease in South Asian populations

      These statistics represent millions of families affected by preventable heart disease—a crisis that traditional risk assessment tools have failed to adequately address.

      The Problem with Current Risk Calculators

      Systematic Underestimation of Risk
      • NORRISK 2 Study: Traditional scores underestimate risk by 2-fold; misclassify high-risk individuals

      • WHO Risk Charts: Show misclassification; fail to capture South Asian-specific risk patterns

      The Scientific Foundation: Three Landmark Studies

      1. INTERHEART Study

      • 30,000+ participants across 52 countries

      • 15,152 heart attack patients vs 14,820 controls

      • Identified the "Big 9" risk factors accounting for over 90% of heart attacks

      Big 9 Risk Factors:

      • Abnormal Cholesterol: 49%

      • Smoking: 36%

      • Stress/Depression: 33%

      • Blood Pressure: 18%

      • Abdominal Obesity: 20%

      • Poor Diet: 14%

      • Inactivity: 12%

      • Diabetes: 10%

      • Moderate Alcohol: 7% protective

      2. PREVENT Study

      Innovations:

      • Kidney Function & Social Determinants

      • Modern Biomarkers & Ethnic Data

      Benefits to South Asians: Better performance across ethnicities, emphasis on early disease onset

      3. MASALA Study

      Focus: South Asian-specific data, long-term cohort, cardiac imaging

      • Metabolic Differences: Syndrome at lower BMI, early diabetes

      • Lipid Profile: High triglycerides, low HDL

      • Imaging: Early plaque detection via coronary calcium scoring

      SACRA's Innovative Three-Stage Algorithm

      Stage 1: Foundation Assessment

      • Big 9 risk factor scoring with South Asian weightings

      • Lower BMI cutoff: 23 kg/m²

      • Waist-to-hip ratio emphasis

      Stage 2: Advanced Clinical Assessment

      • AI-based prediction with 93.5–99.9% accuracy

      • ApoB/ApoA1 prioritization

      • Advanced diabetes & kidney evaluation

      Stage 3: Comprehensive Risk Refinement

      • Lp(a), hs-CRP, calcium scoring with percentile mapping

      • ML models with AUC 0.80–0.95

      • Dynamic refinement using new research

      South Asian-Specific Innovations

      • Diet: Regional carb intake, preparation style risks

      • Stress: Cultural, immigration, family pressure stressors

      • Technology: ML-enhanced cardiac imaging, predictive algorithms

      Validation and Accuracy

      • Accuracy: Traditional: 50–70%, SACRA: 93.5–99.9%

      • Clinical Impact: Early detection, accurate treatment, better outcomes

      Continuous Scientific Evolution

      • Genetic & Environmental Factor Tracking

      • Device-based monitoring & pharmacogenomics

      Clinical Applications and Limitations

      • Ideal Use: Adults 20–79 of South Asian ancestry

      • Clinical Integration: Screening, education, planning

      • Limitations: Not a diagnostic tool; regular updates needed

      Bottom Line: SACRA combines global data, population-specific studies, and modern AI technology to deliver the most accurate cardiovascular risk calculator available for South Asians.

      This tool is for educational purposes only. Always consult a medical professional for accurate diagnosis and treatment.

      This will close in 0 seconds

      👋 Hi, I’m HeartWise. How can I help you today?
      Chat Icon
      Bot Avatar HeartWise Chatbot