Aryeh Ganz is managing partner at Cornucopian Capital, which invests in companies developing novel foods and bioactives, therapeutic platforms, and technologies that measure and make sense of the body in real time in order to advance Medicine 3.0, a data-driven predictive, preventative, personalized, and participatory model for healthcare.
The views expressed in this article are the author’s own and do not necessarily represent those of AgFunderNews.
Wearables, continuous glucose monitors, more accessible blood testing, genetic information, and advanced diagnostics are giving ordinary people access to information that once required a clinical setting or simply wasn’t available.
The ability to measure human biology continuously and affordably is a major healthcare achievement, and as an investor in healthcare, diagnostics, and AI, I see it as essential to the transition from episodic treatment to preventative, personalized health.
However, while we’re great at measuring human biology, we’re still far from having consistent, standardized processes that tell consumers what their biomarker measurements actually mean.
A consumer might receive a panel with dozens of biomarkers, plus a continuous stream of data from a smartwatch or glucose monitor. Some results may be reassuring, while others fall outside a reference range. Still, knowing a number is high or low doesn’t necessarily tell someone whether it matters, how it relates to everything else happening in their body, or what to do next.
More data, more confusion?
We are already seeing that disconnect spill into the healthcare system
Earlier this year, two-thirds of roughly 200 physicians surveyed by Doximity said patients brought results from direct-to-consumer health tests into medical visits. Yet fewer than 10% said their institution or professional organization had well-defined guidelines for navigating those results or integrating them into routine care.
Some patients interviewed had instead turned to Google or ChatGPT to make sense of their results.
This gap will be harder to manage as biomarker data grows. A handful of unexplained lab results is one problem, but continuous streams of biological information from multiple devices and tests are another.
Patients may become overwhelmed and disengage, while clinicians inherit an ever-growing interpretation burden from tests they did not order.
At the other extreme, every abnormal result creates another opportunity for follow-up testing or treatment, whether or not acting on it ultimately improves someone’s health.
An abnormal biomarker can become the starting point for additional tests, supplements, or treatments without enough evidence that intervening will improve the patient’s health.
When precision outpaces understanding
Continuous glucose monitors are a particularly useful example. These devices have transformed diabetes care, but people without diabetes increasingly use them for wellness.
As The BMJ reported, glucose may fluctuate in response to everything from food and exercise to stress, sleep, and hormones. A continuous glucose monitor can measure those fluctuations with notable precision, but the measurement itself cannot tell a healthy consumer which fluctuations matter or whether changing them will improve their health.
That distinction between measurement precision and the certainty of its meaning will become increasingly important as more clinical technologies move into consumer health.
The challenge goes beyond giving people access to more information It also stems into creating a reliable path between a measurement and an appropriate decision.
Another part of the difficulty is that biomarkers rarely tell a complete story on their own. Genetics, diet, sleep, exercise, medications, and other biological processes influence what we measure, so context is essential to understanding whether a result warrants action.
A number outside a reference range may matter, but the number alone can’t explain why it changed, how it relates to other markers, or whether changing it will improve someone’s health.
Detection doesn’t necessarily mean better outcomes
The importance of linking measurements to outcomes becomes even clearer as biomarkers shift from monitoring health to detecting disease. Grail’s Galleri blood test, for example, analyzes patterns in cell-free DNA circulating in the blood for signals associated with cancer. In a landmark NHS trial of more than 142,000 adults, researchers tested whether adding the Galleri test to usual care could ultimately reduce late-stage cancer diagnoses.
Unfortunately, adding the test did not statistically reduce the trial’s primary endpoint of combined Stage III and IV cancers, but later screening rounds did show encouraging reductions in Stage IV disease.
These results show that detecting a biological marker, even with increasingly sophisticated technology, is only part of the challenge. A biomarker’s ultimate value depends on whether the information it delivers leads to earlier or better decisions that improve health outcomes.
In metabolic health, for example, combining glucose, insulin, lipids, and other markers with information about medications, nutrition, and activity can provide a much richer picture than a single biomarker result.
These questions will matter more as consumer-generated health data becomes more readily available via at-home diagnostics and continuous monitoring
The challenge is to build systems that can bring useful information together into a longitudinal view of the patient, rather than leaving patients and clinicians to make sense of disconnected results on their own.
That also means we should judge health data by what happens after we collect it. Does it lead to a useful recommendation? Does someone act on it? And, most importantly, do we measure again to see whether that action improved the outcome?
The investment opportunityÂ
This idea of closing the loop has become increasingly important in how I evaluate healthcare companies.
For investors, it also shifts where the next opportunity in personalized health may lie, which may be in systems that connect fragmented health information, identify what matters, put it in context, and help patients and clinicians decide what to do next.
AI will be an important part of that layer because the volume of biological information being generated already exceeds what any patient or clinician can reasonably synthesize alone, but its value will ultimately depend on whether it improves decisions rather than simply producing more sophisticated dashboards.
We have made extraordinary progress in measuring human biology, giving us the raw material to make healthcare more preventative, customized, and continuous. The opportunity now is to become equally sophisticated at turning those measurements into understanding, understanding into action, and action into assessable improvements in health.
Further reading:
From GLP-1s to biosensors: Inside Cornucopian Capital’s precision health playbook
🎥 Where are foodtech investors looking next after the boom and bust?
🎥 What is agrihealth—and can it become an investable category? The First Thirty makes the case
The post Guest article: Health data is booming. Meaningful insights? Not so much appeared first on AgFunderNews.
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