At Biotein, we’re increasing the accessibility of proactive diagnostics for cellular health related to aging, using a lateral flow assay. It uses similar technology to pregnancy tests, costs 10-13% of current solutions, and detects proteins involved with almost all of the hallmarks of aging, which lead to age related disease. We give a risk assessment for age related disease and recommendations for interventions to prevent damage, from supplements to exercise.
The one commonality between many of the most common chronic diseases today is that they are all age-related and stem from similar hallmarks. For example, Alzheimer's, Parkinson's, and other brain conditions are caused by misfolded proteins. Heart disease, cancer, and arthritis all involve higher inflammation levels. A lack of DNA/cellular repair capability is another common hallmark. Detecting every age-related disease before it strikes may be unfeasible, but detecting the underlying hallmarks through the levels of protein (building blocks of these cellular processes) in the body could give a risk analysis for these conditions.
Our aim is to develop an easy-to-use saliva test that is able to determine one's biological age in a low-cost way, as well as provide the ability to track progression of health due to our reliance on protein concentration measurement. As you age, your protein levels change. This shows molecular insights that genes alone cannot portray. We did months of biostatistical analysis to figure out the proteins that best encapsulate the aging process. We picked 4 proteins that are related to DNA repair mechanisms, mitochondrial stability, inflammation, senescence, nutrient sensing, protein regulation, and more. This covers almost all of the hallmarks of aging. To test one’s protein levels, we are using lateral flow assays, the same technology used in pregnancy tests. These are quick and cheap to produce, and can produce color bands based off the protein concentration.
Entering the development and testing phase for our LFA to validate our approach
Conducting machine learning analysis to correlate the levels of our proteins and biological age, as well as the effects of different types of interventions on the proteins
Launching our test
Establishing stronger links between our test and risks of different diseases, developing algorithms to predict progression of health, and add more specialized technology