New Delhi: He had no red flags. No “high-risk” tag. By standard medical calculators, he was not the kind of patient doctors worry about. Then came the heart attack – sudden and without warning. Doctors say this is no longer unusual. A major Indian study suggests it may be the norm.
In a retrospective analysis of more than 5,000 Indian patients, widely used global heart risk calculators failed to identify most people at risk, with around 80% of those who suffered a first heart attack not flagged as high-risk beforehand. Researchers led by Dr Mohit Dayal Gupta at GB Pant Hospital, Delhi, says Western models miss India-specific risk patterns, often placing patients in low or moderate categories despite underlying danger.
These models are used by doctors to decide who needs treatment, but the study found they often misclassify risk and sometimes give conflicting results. Across models, only 11%-20% of patients were labelled high-risk, even though all eventually had a heart attack.
“Indian patients and population behave totally differently. We have different risk factors, different patterns and hence Western scores may not always be appropriate,” said Dr Gupta.
At the core of the problem is a mismatch. Most of these risk calculators were designed using Western populations, where heart disease tends to occur later. In India, it strikes earlier and behaves differently – the study found the average age of patients was just 54, underscoring how premature heart disease has become.
Researchers say Indians show a distinct “South Asian phenotype”. Heart risk appears earlier, often with diabetes and insulin resistance even at normal body weight. Cholesterol patterns are misleading – low HDL and high triglycerides, while LDL may not be very high. Many also have hidden abdominal fat despite appearing lean, a risk missed by BMI-based assessments. Smoking and psychosocial stress, along with traditional risk factors like diabetes, smoking and dyslipidemia, further add to this hidden burden.
This is why Western risk scores underperform. They rely heavily on age and LDL cholesterol, leading to underestimation in younger Indians. As a result, many patients fall into an “intermediate risk” category – a grey zone that often delays preventive treatment. Crucially, these models ignore key drivers like insulin resistance, lipoprotein(a), ApoB, central obesity and chronic kidney disease. The findings have renewed calls for customised risk scores, as Indian populations remain poorly represented in global datasets.
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