Chronic diseases like heart disease, diabetes, and obesity are the leading cause of death and disability in the USA.
Every year, more than 1.7 million Americans die because of these diseases.
Preventable chronic diseases are chronic conditions caused by lifestyle, so we can cure them by living healthy, but so many of us fail to do these basics everyday. And we fail to do these basics because we don’t have any idea what we should be personally doing for our health. What might be good for one person isn’t good for another. Like in some cases, eating that cookie is good for you because it boosts your blood sugar levels and provides you energy. In order to overcome all preventable chronic diseases, we must be able to create personal health plans for patients so that they know exactly what they need to do in order to prevent or recover from these chronic conditions.
With Topi, we can find the right path to a healthy life.
Topi utilizes causal modeling of complex genomic, epigenetic, and lifestyle data to generate personalized health plans for you. Topi will help you envision the future of your health and guide that future to where you want it to be.
Topi is a physical AI agent that possesses powerful computer vision, natural language processing, and reasoning abilities. These capabilities allow it to engage with you and build a meaningful relationship. With this understanding and personal relationship, Topi is always able to nudge you towards the right direction
How does Topi Work?
Understanding Chronic Disease Mechanisms
Chronic conditions are very complex, influenced by genetic, epigenetic, and lifestyle factors which are far from being fully understood.
Epigenetic factors are a combination of genetic factors and lifestyle factors where the exposures you receive (such as alcohol or smoke) affect which genes are expressed.
In genomics and epigenomics, there have been associative links found between certain genetic and epigenetic patterns and disease, but a complete picture of the causal role genes and epigenetic patterns play has not been discovered. For example, there are 120 gene variants associated with T2D, but the functions of all these variants have not been mapped out. Similarly, the epigenome is present in the genome as DNA methylation or histone modifications, and while certain patterns of DNA methylation have been associated with T2D, no causal link has been found.
Once individual causal factors are understood, Topi is able to create a causal model of:
- Genomic Factors
- Epigenomic Factors
- Lifestyle Factors
- Disease Risk
With this causal model, we can understand how these factors affect disease risk. This is key to recommendation generation. By understanding how a patient’s risk for a chronic disease change over time, we can calculate what factors must change and by how much in order to decrease that risk. We can then turn these factor changes into meaningful health plans for patients. I created a proof-of-concept that utilizes causal modeling to generate personalzied health plans for people with hypertension. Check it out here.
In order to make these health plans personal we need to access personal data about a patient’s genome, epigenome, and lifestyle. Genome sequencing and epigenome mapping are both technologies that are relatively mature, but in order to gain access to the personal lifestyle information such as:
We need a multipurpose biosensor that is able to track these factors. Such a multipurpose minimally-invasive biosensor is still many years away. Current approaches either need blood samples in order to measure biomarkers or they use light diffraction approaches that aren’t always accurate. With the personal lifestyle data from the biosensor and other data from genome and epigenome mappings, we can create true personalized health plans for patients.