Whole-body intelligence company brings in liver disease expert to turn early diagnosis into real clinical action.
Crooked BiologiesThe “whole body intelligence” company, which aims to detect organ dysfunction before symptoms appear, continues its transition from platform promise to clinical execution with the appointment of Dr. Amit Singhal as chief medical officer. After Last year, Curve raised $40 millionThe move shows a clear goal of incorporating blood tests into real-world care pathways, starting with liver disease—a critical area where early intervention can dramatically change outcomes.
Dr. Singhal has deep expertise in hepatology, cancer screening, and public health, with a career focused on improving the early diagnosis and management of chronic liver disease. His appointment comes as Curve advances an Atlas-based whole-body approach that returns circulating DNA to the tissue of origin so that disease can be detected at an earlier, more actionable stage – and most importantly, doing so in a way that fits into existing clinical workflows.
As Curve seeks to translate its technology into practice, the focus is on what early diagnosis actually enables in the clinic—not just identifying risk, but changing decisions, pathways, and ultimately, patient outcomes.
Longevity.Technology: Early detection has become a mantra in both precision medicine and longevity circles – used more often and less often. The challenge is not only to identify biological changes before symptoms appear, but also to decide what to do after this information is available. Curve Biosciences’ approach, now bolstered by the appointment of liver disease specialist Dr. Amit Singhal, builds directly on this tension; it’s not just a platform designed to find earlier signals, it’s a platform built around the more challenging task of making those signals clinically relevant. By mapping circulating DNA to the tissue of its origin and filtering out the significant background noise inherent in blood tests, the company is trying to draw a clearer line between normal aging and actual pathology—a distinction that remains one of the most blind spots in the field. Just as important, its emphasis on simplicity—a single blood draw that replaces fragmented monitoring methods—warns that adoption will depend on workflow and accuracy. In this sense, the Curve is not simply defining the gray area between health and disease; tries to define it, construct it and finally apply it. To learn more about this clinical translation and real-world integration, we sat down with Dr. Amit Singhal, Chief Medical Officer of Curve Biosciences.
From signal to clinic
For Dr. Amit Singhal, the appeal of Curve Biosciences lies less in the beauty of the technology than in its potential to fill a practical gap — one that doctors working in liver disease know all too well. Patients rarely present quickly; by the time the disease is detected, intervention is often limited, piecemeal, or too late to change the trajectory.
“Doing” is a term often used in diagnosis, but Singal is careful to link it to everyday clinical reality. “Treatment means identifying organ failure early enough to change the way the patient is managed and reduce the risk of long-term complications,” he says. In liver disease, this can turn into something very specific – more control, early imaging, appropriate treatment – small changes, perhaps, but those that change the arc of the disease before it goes into decompensation or cancer.
This pragmatism permeates his thinking. Today’s standard of maintenance, he notes, does not reduce the compatibility of the tools; multiple inspections, intermittent monitoring, inconsistent compliance. Curve’s offering—a single blood-based signal with higher sensitivity than earlier stages—was designed not to reinvent the system, but to fit within it. “We allow clinicians to act earlier and improve outcomes without requiring new treatment paradigms.”
Crossing the line between aging and disease
If early diagnosis is operationally complex, defining what counts as “early disease” is conceptually more difficult. Longevity medicine has long grappled with the question of where normal aging ends and pathology begins—the boundary that is not normal.
Curve’s approach, Singal explains, is to move away from generic biomarkers and instead focus on organ-level biology. “Aging follows broad trends, but disease produces specific deviations at the organ level,” he says. By mapping circulating DNA to the tissue of its origin, the company aims to isolate these deviations—filtering the signal from the noise—and in doing so, narrowing the gray area between expected reduction and actual risk.
This is an interesting idea; a kind of biological cartography that trades averages for specifics. But it also raises a quieter question—not just whether we can detect a deficit earlier, but whether we’re willing to interpret it with enough precision to act.
Making the problem workable
Of course, even the most accurate signal is of limited value if it is not absorbed into an already stretched clinical workflow. Here, Sinhalese returns to a familiar point of friction: fragmentation. Chronic liver disease monitoring today often requires imaging, laboratory tests, and follow-up visits—a system that effectively excludes patients at every stage.
“We designed the system to simplify workflows, not complexity,” he says. The goal is not to deliver more data, but to deliver clearer data—something that can be read, interpreted, and acted upon just like existing lab results. Single blood collection, fewer moving parts, more compliance.
There is a certain limitation in this framework. No claims of great conversion; but an attempt at incremental improvement, accumulated over time and among the population, may prove trivial.
Economics, facts and entry points
Acceptance will inevitably follow the contours of economics like science. The transition to liver disease begins not only because of clinical needs, but also because the dynamics of costs are already well understood – late-stage care is expensive, early intervention is less. This creates an immediate value proposition for clinicians and health systems, even before full reimbursement structures.
“We’re starting with use cases like therapy monitoring and high-risk patients that are of immediate benefit,” Singal explains, with the goal of expanding to broader risk stratification over time. This is a step-by-step approach – pragmatic, perhaps even cautious – but reflects the realities of diagnostic translation in practice.
Beside the definition
What emerges, then, is not just a story about better detection, but about redefining what detection is for. Earlier signals, cleaner signals, more accurate signals—all are valuable, but only if they change decisions, change paths, change outcomes.
Which leaves an even more pressing question: As our ability to spot the early signs of disorder improves, will medicine evolve quickly enough to act on what it discovers — or will we find ourselves with faster understanding and the same old limitations?
A line is drawn.
