Built for the Wrong Biology
What the MASALA study reveals about South Asian cardiovascular risk and why it matters for everyone
There is a population of people in the United States developing heart disease earlier than almost any other group. Their cholesterol numbers often look acceptable. Their BMI frequently falls within normal range. By every standard metric medicine uses to assess cardiovascular risk, many of them appear fine.
They are not fine.
The MASALA study, the first long term investigation of cardiovascular disease in South Asians living in America, has been quietly documenting something that clinicians who treat this population already suspect. Standard risk calculators were not built for South Asian biology. And the consequences of that mismatch are showing up in cardiology units and liver clinics across the country.
As someone of South Asian descent, this is not an abstract problem for me. It is the pattern I watched move through my own family before I had the vocabulary to name it.
Here is what the data shows.
South Asians develop cardiovascular disease earlier than other ethnic groups, often despite lower body weight. Fat in this population tends to accumulate not under the skin where it is visible, but around abdominal organs and inside the liver. This is the kind of fat that drives insulin resistance, metabolic dysfunction, and cardiovascular risk. It does not show up on a standard scale. It does not move a BMI number. But it is doing damage.
The MASALA study also found rates of diabetes and prediabetes in this population that should be alarming to anyone working in preventive medicine. One in four participants had diabetes. One in three had prediabetes. These are not people who look sick by conventional measures. They are people whose biology is responding to a modern environment in ways that inherited metabolic patterns make particularly dangerous.
This is precisely the argument at the center of Inherited Risk.
Different populations carry different biological vulnerabilities shaped by generations of environmental adaptation. When those populations encounter modern food systems, sedentary environments, and chronic stress, the interaction is not random. It is predictable. And it follows the patterns of the three signals that govern metabolic health: glucose stability, muscle preservation, and inflammatory balance.
The tools medicine currently uses to assess risk were largely built on data from other populations. That means millions of South Asians are being evaluated against a standard that was never designed for their biology. Their risk is being systematically underestimated. And the window to act is closing earlier than anyone is telling them.
This is not only a South Asian story. It is a story about what happens when inherited biology meets an environment it was never designed for. That story is playing out across every population navigating the modern world.
But for South Asians specifically, the data is now clear. The risk is real. It is measurable. And the conversation needs to start earlier than it currently does.
This is an important and overdue callout.
What stands out most is the gap between what we *measure* and what actually *matters*. When people who look “healthy” by conventional standards are still developing early cardiovascular disease, that’s not an individual failure—it’s a systems failure. Risk models built on incomplete or non-representative data will inevitably miss entire populations, and South Asians are a clear example of that blind spot.
The point about fat distribution is especially critical. Visceral and hepatic fat don’t announce themselves the way subcutaneous fat does, yet they are far more metabolically active and dangerous. If clinicians (and patients) are relying heavily on BMI or even basic lipid panels, they’re often seeing a reassuring picture that isn’t telling the full story.
It also reinforces something that doesn’t get enough attention in preventive care: *early and tailored screening matters*. For South Asians, that likely means looking beyond standard panels—things like insulin resistance markers, triglyceride-to-HDL ratio, liver fat indicators, and earlier glucose monitoring. Not because this population is “unhealthy,” but because the baseline risk profile is different.
I also appreciate how you broadened the frame beyond one group. The larger issue is that medicine has historically treated “average” as universal, when in reality it’s often just “most studied.” As more diverse longitudinal data emerges (like from the MASALA study), it should push a shift toward more personalized, population-aware prevention strategies.
If anything, posts like this highlight a key takeaway:
we need to move from reactive care based on late signals to proactive care based on *true risk*, even when it’s not immediately visible.
Curious how you think this should translate at the primary care level—what would you want to see change first: screening guidelines, physician education, or patient awareness?