For decades, scientists have relied on model organisms like mice, fruit flies, and even roundworms to understand the complexities of human biology. These relatively simple species have provided valuable clues and helped researchers make important discoveries about human biology and how diseases develop. But as helpful as these models are, they have limitations because their biology is different from ours.
Instead of relying on proxies, Calico is analyzing data from humans. “It makes sense that studying human biology directly would efficiently and rapidly translate discoveries into treatments,” says Bob Cohen, Calico Fellow. However, this approach is not as straightforward as it sounds.
“One challenge with human data is that it’s often collected at different institutions and analyzed using differing methods, limiting the scale and making it hard to interpret,” says Bob. “Human biology is complex. A person’s well-being depends on a mix of genetics, environment, and lifestyle factors. To understand aging and disease better, large datasets are needed to reliably capture this complexity.”
That’s where initiatives like UK Biobank come in. The large-scale biobank has amassed a collection of de-identified health data from over half a million volunteers, enabling researchers to chip away at understanding some of the underlying causes of disease and explore questions about human aging in unprecedented detail. Recognizing the power of this approach, Calico has partnered directly with UK Biobank. “Coupling this partnership, and the data it provides, with our internal computing power has allowed us to gain insights from this vast biomedical resource,” says Nick van Bruggen, Fellow at Calico.
Using advanced bioinformatics to analyze UK Biobank data, Calico scientists have identified blood biomarkers of the aging process and, in collaboration with researchers at the University of Westminster and the University of Lincoln, discovered genetic variants linked to liver fat accumulation and assessed their impact on health risks. Using advanced machine learning algorithms, the team also found genes connected to a condition where abnormal bone growths form on the spine and other bones during aging. Calico is currently investigating the importance of this observation for a variety of bone diseases, including diseases of aging.
But Calico’s mission goes beyond simply identifying risk factors and predicting disease. We want to use the information gained to tackle the fundamental mechanisms of aging, and devise interventions that might help increase healthspan.
“By combining large scale profiling with long follow-up, these datasets open up exciting new possibilities for discovery and drug development,” says Madeleine Cule, Director of Data Science at Calico. “We are able to extract and explore new traits in an unbiased way and move towards a more comprehensive description of aging, beyond viewing it as just the sum of clinically-defined diseases.”
By joining forces with UK Biobank to build the world’s largest longitudinal imaging dataset – comprising repeat imaging scans of the brain, heart, and other organs of 60,000 participants taken years apart – Calico anticipates learning more about how organs change with age, giving the team an even deeper understanding of the aging process and one that we believe can be efficiently applied to drug development.
Image credit UK Biobank