Evolutionary medicine: Studying disease in a Darwinian context

What do Charles Darwin, wisdom teeth, and cancer have in common? They are all related to an emerging field called evolutionary medicine, the application of evolutionary principles to understanding why and how organisms get sick. Scientists in the field believe that an evolutionary perspective can help improve our diagnosis and treatment of disease.

In his keynote address at the inaugural meeting of the International Society for Evolution, Medicine, and Public Health (held March 19-21 in Tempe, AZ), Dr. Harvey Fineberg stated that an understanding of evolution is central to health. Fineberg, the former Dean of the Harvard School of Public Health, argued that an evolutionary viewpoint is necessary to explain structures and functions of the human body (like the fact that wisdom teeth were helpful in some way to our ancestors but serve no purpose now) and evolution can provide insight into diseases that develop and spread under evolutionary mechanisms, like infectious disease and cancer.

Antimicrobial resistance occurs when bacteria, viruses, and other infection-causing microorganisms evolve and develop mutations that enable them to resist drug therapies. Drug-resistant bacteria alone affect over two million Americans each year, according to the CDC. The process of microbial evolution follows the guiding principles of natural selection, so scientists can use their knowledge of evolution to understand how microbes attain resistance and perhaps even prevent it. For example, the current methods of treating bacterial infections target a mechanism of mutation called de novo mutation, but scientists have learned that antibiotic resistance mostly develops from a different method called horizontal gene transfer (Sterns, 2012), which suggests that we may need new therapies for bacterial infection.

Bacteria, like the mycobacteria above that cause tuberculosis, develop drug resistances by evolving and mutating under the influence of natural selection, just like all other organisms. Image source: CDC

Evolutionary medicine has also started to play a role in cancer research. Some scientists are using an evolutionary background to understand how cancers develop, spread, and metastasize as well as to find effective treatments. For instance, a group of scientists is trying to understand how large animals with long lifespans, like the blue whale, have evolved and developed cancer suppression techniques that are reportedly 1000 times better than those of humans. Many hypotheses attempting to explain this phenomenon exist: the lower metabolic rate of large animals might lead to a lower mutation rate, or perhaps tumors are so much bigger in large animals that they are actually less likely to become malignant than smaller tumors (Nagy et. al., 2007). Whatever the explanation, understanding why and how large animals evolved to gain such effective tumor suppressor mechanisms could provide new therapies for cancer in humans. (Caulin and Maley, 2012.)

Additionally, studying deviations from physiological homeostasis in an evolutionary light may suggest that we need to make changes in how we treat some conditions. As Dr. Joe Alcock of the University of New Mexico commented at the ISEMPH meeting, what is defined as “normal” for the human body may be different depending on the conditions. For instance, doctors typically test patients for normal levels of hemoglobin (the molecule that transports oxygen in the blood) and glucose. But evolution has led some populations to adapt to unique environments and develop abnormal levels of these molecules; those living at higher altitudes are found to have a higher base level of hemoglobin than normal, pregnant women exhibit lower concentrations of hemoglobin as an adaptation to pregnancy, and patients with sepsis, a severe complication of infection, have elevated glucose levels, which may be an adaptive survival response. When doctors detect these abnormal glucose and hemoglobin levels, they will often treat the patients to return them to normal; however, Alcock argues that trying to restore every patient to one standard level may in fact do more harm than good if deviation from normalcy has an adaptive purpose.

The growth in the field of evolutionary biology, along with the sharp decline in genome-sequencing costs, has led to a new discipline of treating and diagnosing diseases called phylomedicine (Kumar et. al., 2011). Studying the differences between genomic information of healthy and diseased people, scientists have discovered many genetic diseases and the DNA variations associated with them. For example, mutations in the ALDH1L1 gene are associated with an increased risk of stroke (Williams et. al., 2014). However, simply analyzing individual genomes to discover the variations linked with certain diseases is inefficient and produces an extremely high volume of data, not all of which are significant. Instead, scientists can combine this analysis with a multi-species evolutionary perspective to narrow down the list and determine which genetic markers are associated with disease. Once these markers, like the ALDH1L1 gene, are identified, we can use them for diagnosis and as potential therapeutic targets.

Evolutionary principles can give insight into a wide range of medical topics: besides cancer and infectious disease, evolutionary thinking has shed light on other diseases like jaundice, influenza, and mental disorders (Nesse and Stearns, 2008). Also, studying the timeline of animal evolution and trait development can tell us which animals are most accurate models of human physiology for drug and device preclinical testing.

Members of the discipline see evolutionary medicine as having the potential to revolutionize the way we think about medicine. Adopting a new, evolutionary viewpoint on some of our most complex diseases could greatly benefit patients.

In our next post, we’ll go into more detail about a specific clinical application of evolutionary medicine. Is there a topic you’d like to hear more about? Let us know in the comments section.

This series is supported by NSF Grant #DBI-1356548 to RA Cartwright.