Walk through most preventive health protocols today, the panels, the risk scores, the reference ranges used to flag what counts as normal, and a lot of it traces back to studies built predominantly on male cohorts. Add midlife women into that picture and the gaps widen. A woman in her forties reporting fatigue, brain fog, joint pain or disrupted sleep often finds that standard panels do not fully capture what she is experiencing, and the hormonal driver behind it can go unexamined. She sits in an uncomfortable middle: too complex for general wellness, not quite a fit for gynaecology, which is usually built around fertility and reproductive years rather than the decade of hormonal transition that follows.
That is a real gap, and worth naming clearly. But the more useful way to think about it, especially for anyone building or funding in this space, is not only as a gap in empathy. It is a gap in market design. The longevity industry has largely been built on reference data and risk models drawn from male and western populations. That means a meaningful share of the addressable population is being served by protocols that were never designed around their biology. In most industries, building a product on data that does not represent half your users would be called a design flaw, and design flaws are usually where the returns are, once someone fixes them.
It helps to be precise about what kind of gap this is. Femtech has already proven that women's health can be built as a serious, fundable category, but capital and product innovation so far have concentrated almost entirely on fertility and reproductive years. The midlife-to-longevity window, arguably the highest lifetime-value phase for a woman engaging with her own health, has been comparatively untouched. That is less a story about an underserved group and more a story about an underbuilt category sitting in plain sight. Perimenopause is the clearest example, and it is worth stating in economic terms. It falls between two established categories, gynaecology and general wellness, which means it currently has no clear clinical owner and almost no longitudinal data infrastructure tracking it over time.
The way to capture it is not by adding a women's wellness tab to an existing offering. Generic protocols are already a commodity. The more durable opportunity lies in building around a woman's hormonal life stages and risk factors from the outset, not layering them on later. That requires something health care has historically struggled to maintain: contextual memory. One woman's health story today might sit across a gynaecologist's notes, a primary care record, a lab portal and a wearable app, each holding a fragment, none holding the whole trajectory. The next generation of longevity care will likely move toward a continuously updated model of the patient, a living profile that interprets each new signal against everything that came before, rather than reconstructing the story from scratch at every visit. That kind of longitudinal intelligence compounds in a way a snapshot never can, and it is what begins to look like a genuine data moat rather than a feature checklist.
{{/usCountry}}The way to capture it is not by adding a women's wellness tab to an existing offering. Generic protocols are already a commodity. The more durable opportunity lies in building around a woman's hormonal life stages and risk factors from the outset, not layering them on later. That requires something health care has historically struggled to maintain: contextual memory. One woman's health story today might sit across a gynaecologist's notes, a primary care record, a lab portal and a wearable app, each holding a fragment, none holding the whole trajectory. The next generation of longevity care will likely move toward a continuously updated model of the patient, a living profile that interprets each new signal against everything that came before, rather than reconstructing the story from scratch at every visit. That kind of longitudinal intelligence compounds in a way a snapshot never can, and it is what begins to look like a genuine data moat rather than a feature checklist.
{{/usCountry}}There is a substantial economic case underneath this too. Research from the McKinsey Health Institute and World Economic Forum estimates that narrowing the women's health gap could add close to a trillion dollars annually to the global economy by 2040. Women aged 50 and above already made up 26% of women and girls globally in 2021, up from 22% a decade earlier, and that share is rising.
India is well placed to build this correctly rather than retrofit it later. Its longevity and preventive health sector is still taking shape, which means the decisions being made now, what data gets collected, whose biology the default protocols are built around, will define the category for years. The platforms that treat female biology as core infrastructure from day one are likely to end up with better data, better outcomes, and a harder-to-replicate offering. In a category still being defined, that is usually how a leader gets built.
(The views expressed are personal)
This article is authored by Adil Khan, founder and CEO, Tulu Health.