Q&A: New Alzheimer’s dataset allows ‘whispers from the brain’ to assist in early detection

Key takeaways:

A large, real-world dataset allows clinicians to track early warning signs of Alzheimer’s disease.

The dataset leverages medical claims, EHRs and lab and imaging data to foster health care collaboration.

The future of Alzheimer’s disease detection and treatment may lie in AI-based datasets encompassing an abundance of real-world evidence from a large number of patients, an expert said. Healio spoke to Carl D. Marci, MD, chief clinical officer at Boston-based OM1, to gain his insights into how his company’s new dataset can move the process forward.

Healio: With data from 1 million patients and deeper insights from more than 17,000 individuals, what do you expect this dataset to yield in terms of assistance for patients with AD?

Marci: The power of a dataset like this is in the stories it can tell about patients were previously unheard. We're talking about real people, with real lives in the real world, not the world of pristine controlled clinical trials required by FDA.

What excites me most is how this vast amount of data from over a million patients can help us make progress against Alzheimer's disease. By analyzing information, we’re looking to uncover the subtle early warning signs — think of them as little whispers from the brain long before the shouting starts.

This isn’t just about slowing down; it’s about improving lives, helping people stay connected with their loved ones for longer and finding the right to do so.

Healio: How can the new dataset assist clinicians and other health care professionals define where an early Alzheimer’s diagnosis may be made?

Marci: Right now, detecting Alzheimer’s early is challenging. The signs are subtle, and each individual is unique. But this dataset, when combined with our advanced AI and machine learning, allows us to create a new roadmap for early detection.

Our PhenOM AI platform can process this data and go beyond traditional signs and symptoms by creating digital biomarkers of early disease that pick up on the hidden patterns that clinicians just can't see.

The goal is to shift the timeline from reacting to symptoms to preventing them from progressing in the first place.

Healio: How can this knowledge be applied to gaps in knowledge for treatment and patient care for those with suspected or diagnosed Alzheimer’s?

Marci: For too long, we lacked treatments that could alter the course of Alzheimer’s disease. With the new generation of disease modifying medicines, clinicians and patients have hope for the first time. But many questions remain about not only how to identify patients early in their course of disease, but who is most likely to benefit from these new treatments and who is most likely to suffer some of the more devastating side effects. This dataset can help fill those gaps by providing real-world evidence on how patients actually respond to different treatments over longer periods of time than clinical trials afford. e can see which therapies are working, for whom, and why — allowing us to tailor care more precisely to individual needs. It’s about moving from a one-size-fits-all approach to one where every patient gets the care that’s right for them based on their unique profile.

Healio: How do you anticipate this dataset would bring together mental and physical health care professionals?

Marci: Alzheimer’s affects the whole person, not just the brain. Our dataset brings together data from mental health, neurology, primary care and beyond, breaking down the silos that have traditionally separated these fields. A complex disease like Alzheimer’s requires data from many sources. This is why the OM1 approach to multi-source data is critical. By leveraging medical claims, electronic health records, lab data, imaging data, we can create a shared understanding of each patient’s condition and foster collaboration among all types of health care professionals.

Healio: What might the advances that arise from this dataset in detection, treatment and care for those with Alzheimer’s mean for future datasets?

Marci: This dataset isn’t just a one-off; it’s setting a new for how we tackle Alzheimer’s and other complex diseases. Similar to advances in oncology for cancer treatment, think of this as the start of a new wave of understanding of this complex illness. The breakthroughs we make today — in early detection, understanding patient responses and adverse events, and the long-term impact of treatment — will shape how we treat Alzheimer’s in the future.

We’re talking about creating a new standard where real-world data isn’t just a support tool but a driving force in patient care, continuously evolving and getting smarter as more data is collected. These knowledge networks will get even better over time, helping us move faster from insights to action and, ultimately, bringing us closer to a world where Alzheimer’s is a manageable chronic condition. 

For more information:

Carl D. Marci also serves as part-time staff in the department of Psychiatry at Massachusetts General Hospital and as an assistant professor at Harvard Medical School. He can be contacted via LinkedIn: https://www.linkedin.com/in/carl-d-marci-m-d-05b84a1/ and on Twitter: @CMBiometrics.

Reference:

OM1 contributes to Alzheimer’s disease research with new PremiOM dataset of over 1 million patients. https://www.om1.com/resource/om1-contributes-to-alzheimers-disease-research-with-new-premiom-dataset-of-over-1-million-patients. Published July 30, 2024. Accessed Sept. 18, 2024.