Category: Exposome Perspectives Blog

A reflection on fate, free will, and health—why our genes and past exposures shape risk, not destiny, and why real change often starts only after life forces us to pay attention.

Modern science excels at measurement but struggles with emotion—so much so that we may be risking our humanity as well as our health. This essay weaves music, literature, and exposomics to question our obsession with quantification and the goal of reaching the destination more rapidly while disregarding the value of the hard work needed for a real journey. In fact experiencing the journey can be the most important part of getting to the destination.

In an era marked by misinformation and societal distrust, understanding the nuanced interplay of simplicity and complexity in public health messaging is crucial. Dr. Wright argues that while effective communication often favors simplicity, it is essential to navigate the complexities of scientific data without oversimplifying critical information.

Heritability is easy to understand, but it elevates correlation to causation. At the same time, heritability looks solely under the lamppost for genes versus environment and doesn’t consider other possibilities—like gene-environment interaction. To find gene-environment interactions, you have to look for them. If they are not measured, they will not be found, but they absolutely exist.

Most journeys go smoother when you use a map. It is more than a cliché to say life is a journey, and metaphors about life, journeys, and maps abound. We all have our own personal map of our life’s journey—which once drawn—reflects our personal exposome.

What happens when everything you thought you knew suddenly no longer fits the world?

Biological theories of molecular biology have long neglected the exposome. It’s time to integrate environment and genetics into a unified theorem.

We need a Human Exposome Project to catalyze exposome research and gene-environment interaction research—and we need it now. We need it to exponentially advance our understanding of biology. We need it to complete the human genome project—because with it, we will better understand the interactions between genes and environment.

For the last 25 years, I told myself I lived in the public health and clinical medicine worlds simultaneously, but that is incorrect. I spent time in each world sequentially in short bursts. In the morning, I’d come to my office in the Public Health School, then I would leave my research team to work a shift in the Pediatric Emergency Room. The physical proximity of the school to the ER was the only actual connection, as these worlds rarely overlapped. It was like stepping through the closet of a C.S. Lewis novel into Narnia.

Science can learn valuable lessons from Kurt Cobain and Doris Lessing, integrating knowledge from different disciplines can lead to new insights and a deeper understanding of biology. Exposomic scientists are now beginning to study autism—a field long dominated by genetics. One would think that studies combining the skills and approaches of these two vastly different fields would abound, since they are studying the same disorder.  But surprisingly, such interdisciplinary studies are extremely rare. Why?  

As we interpret exposomic data, we should consider the potential flaw of linear thinking in our pathway analyses and the role of physiology. A molecular mechanism is not the only possible story. Everything is connected, and the exposome rarely will act uniformly on all our cells and can’t predict how these disproportionate effects will carry forward to distant tissues. Time makes an effect that arises when we are young evolve to a completely different effect when we are old.

The story “Aesop, Analysis, Rigor, and Replication” centers on the theme of the importance of methodological rigor and replicability in scientific research. Through the correspondence between Dr. Donatello Tartaruga and Dr. Peter Hare, it explores the contrasting approaches to scientific inquiry and the consequences of prioritizing novelty and quantity of data over thoroughness and reliability.

Metadata—it is commonly defined as data about data, but like other “meta” examples that’s too basic and doesn’t tell the whole story. In fact, sometimes a variable can serve as “data” to predict an outcome, and in other cases the same variable is used as “metadata”.