BMI vs Visceral

BMI vs. Visceral Fat: Why Waist-to-Hip Ratio Matters More for South Asians

 

If you’re South Asian, the answer might surprise you: probably not.


For years, doctors have used BMI (Body Mass Index) to judge if someone is healthy. But here’s the problem – BMI was never designed for South Asian bodies. And that means it might be missing something important about your health.


The good news? There’s a better way to measure your health risk. It’s called waist-to-hip ratio (WHR). And it could give you a much clearer picture of what’s really going on inside your body.

What Is BMI?

BMI is simple math. It’s calculated by dividing an individual’s weight in kilograms by their height in meters squared.


But here’s what most people don’t know: The history of BMI dates back to 1832. Adolphe Quetelet (1796-1874), a Belgian statistician, mathematician, and astronomer, was inspired by his passion for statistical analysis and bell-shaped curves to establish quantifiable characteristics of the “normal man.”


Notice anything about that? Lambert Adolphe Jacques Quetelet was a statistician (not a medical professional) who sought the specifications for the “average man” or l’homme moyen by predominantly measuring white men—and definitely no women—to find a bell curve of data where the peak was considered “normal”


That means BMI was created using data from white European men in the 1800s. It was never tested on South Asian bodies. It wasn’t meant for women. And it definitely wasn’t designed for the diverse world we live in today.


Visceral Fat: The Hidden Risk

Let’s talk about something more important than your total weight: where your body stores fat.

Not all fat is the same. There are two main types:

  • Subcutaneous fat – the fat you can pinch under your skin
  • Visceral fat – the fat that wraps around your organs inside your belly


Visceral fat is the dangerous one. Higher levels of visceral fat have been consistently linked to higher risk of metabolic diseases such as type 2 diabetes, hyperglycemia, and higher triglyceride levels, as well as cardiovascular diseases and hypertension


Here’s where it gets really important for South Asians: Asians with comparable (if not lesser) BMI measurements as Caucasians have higher body fat content, particularly visceral and abdominal fat, as well as lower skeletal muscle mass and higher rates of insulin resistance


This is called the “thin-fat” problem. You might look slim and have a “normal” BMI. But inside, you could be carrying dangerous belly fat that puts you at risk for heart disease and diabetes.


After controlling for BMI, both Asian children and adolescents exhibited higher waist circumference and greater visceral fat deposits than Caucasian children of the same age group, reflecting later patterns of increased visceral fat deposits in Asian men and women compared to Caucasian men and women

 

A Better Way: Waist-to-Hip Ratio (WHR)

WHR is much more accurate for South Asian bodies. Here’s how to measure it:

How to Measure Your Waist

Stand up straight and breathe out, then measure their waist just above the belly button with a tape measure. Make sure the tape is snug but not tight.


How to Measure Your Hips

Stand up straight and wrap a tape measure around the widest part of their hips This is usually around your buttocks.


The Formula

To calculate the WHR, divide the first measurement (waist circumference) by the second measurement (hip circumference)

Waist ÷ Hip = Your WHR


What the Numbers Mean for South Asians

The cut-off value for a healthy WHR in Asian women is 0.80 or less, compared with 0.85 or less in Caucasian women

For South Asians, here are the risk levels:

  • Women: Healthy is 0.80 or lower, risky is above 0.85
  • Men: Healthy is 0.90 or lower, risky is above 0.90

 

Example Comparison: Why BMI Can Mislead

Meet Amit and Reena (not real people, but their stories are based on real research):

Amit: 5’8″, 160 pounds, BMI 24.3 (normal)

  • Waist: 36 inches, Hips: 38 inches
  • WHR: 0.95 (high risk)


Reena
: 5’4″, 140 pounds, BMI 24.0 (normal)

  • Waist: 28 inches, Hips: 36 inches
  • WHR: 0.78 (low risk)


Both have “healthy” BMIs. But Amit carries more dangerous belly fat and has a higher risk of heart disease and diabetes. Reena’s fat is stored in safer places around her hips.

BMI would say they’re both equally healthy. WHR tells the real story.

 

What You Can Do Next

If your WHR is higher than recommended, don’t panic. Small changes can make a big difference:


Simple Steps to Reduce Visceral Fat

  • Walk more: Even 30 minutes a day helps burn belly fat
  • Cut back on sugar: Avoiding sugary beverages that are strongly associated with excessive weight gain
  • Eat more fiber: Vegetables, fruits, and whole grains help reduce visceral fat
  • Get better sleep: Poor sleep increases belly fat storage
  • Manage stress: High stress leads to more visceral fat


Talk to Your Doctor

Show your doctor your WHR number. Ask them to consider it along with your BMI. Estimation of body fat by measurement of waist circumference and waist-to-hip-ratio (WHR) have shown to be a stronger predictor of cardiometabolic dysregulation and diabetes than BMI among South Asians


Share with Family

South Asian families often face similar health risks. Share what you’ve learned. Help your loved ones understand why WHR matters more than BMI for your community.


Final Words

Measuring your waist isn’t about shame or judgment. It’s about taking control of your health.

For too long, South Asians have been judged by health standards that weren’t made for us. BMI might say you’re fine when you’re actually at risk. Or it might label you as unhealthy when you’re perfectly fine.

WHR gives you a more accurate picture. It helps you focus on the type of fat that really matters. And it empowers you to make changes that can truly protect your health.

Your waist measurement is a powerful tool. Use it wisely. Your future self will thank you.

 

Sources

This article is based on research from trusted medical sources including:

  • Harvard School of Public Health research on ethnic differences in BMI and disease risk
  • NIH studies on genetic and environmental factors in South Asian visceral fat storage
  • Lancet research on ethnicity-specific BMI cutoffs for diabetes risk
  • WHO expert consultation reports on waist circumference and waist-hip ratio
  • American College of Cardiology analysis of South Asian health metrics

Always consult with your healthcare provider before making changes to your health routine.

 

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.

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      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.

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      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.

       

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      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.

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