All diseases are shaped by genetic and environmental factors such as air pollution, climate, and socioeconomic status. However, the extent to which genetics and environment influence disease risk, and to what extent each contributes, is not well understood. Therefore, actions individuals can take to reduce their risk of disease are often unclear.
August 6, 2024
Using a large, nationally representative sample, a team led by researchers at the Pennsylvania State University College of Medicine has found a way to distinguish between genetic and environmental influences on disease risk. They found that in some cases previous assessments had overestimated the contribution of genes to disease risk, and that lifestyle and environmental factors play a larger role than previously thought. Unlike genetic factors, environmental factors, such as exposure to air pollution, are more easily modifiable, meaning there are potentially more opportunities to reduce disease risk. The researchers published their study in Nature Communications.
“We are trying to elucidate the extent to which genetics and environment influence disease development. If we can better understand how each influences, we can better predict disease risk and design more effective interventions, especially in the era of precision medicine,” said Bibo Zhang, assistant professor of public health sciences at Penn State College of Medicine and senior author of the study.
The researchers note that environmental risk factors have traditionally been difficult to quantify and measure because they encompass everything from diet and exercise to climate, but when disease risk models don’t take environmental factors into account, analyses can incorrectly attribute shared disease risk among family members to genetics.
“People who live in the same neighborhoods have the same levels of air pollution, socioeconomic status, access to health care providers, food environment, etc,” said Dajian Liu, professor emeritus, vice chair for research and director of artificial intelligence and biomedical informatics at Penn State College of Medicine and co-senior author of the study. “If we can disaggregate these shared environments, what’s left may more accurately reflect the genetic heritability of the disease.”
In this study, the team developed a spatial mixed linear effects (SMILE) model that incorporated both genetics and geolocation, with geolocation (a person’s approximate geographic location) serving as a proxy for community-level environmental risk factors.
Using data from IBM MarketScan, a health insurance claims database that contains electronic medical records for more than 50 million individuals enrolled in employer-based health insurance in the U.S., the research team extracted information for more than 257,000 nuclear families and compiled medical conditions for 1,083 diseases. They then expanded the data by adding climate and sociodemographic data, as well as publicly available environmental data, such as levels of particulate matter 2.5 (PM2.5) and nitrogen dioxide (NO2).
The research team’s analysis led to more refined estimates of disease risk contributions. For example, a previous study had concluded that genetics contributed to 37.7% of the risk of developing type 2 diabetes. When the research team re-evaluated the data, a model that took environmental effects into account reduced the estimate of genetic contribution to type 2 diabetes risk to 28.4%, suggesting that a larger portion of disease risk may be attributable to environmental factors. Similarly, the estimate of the contribution to obesity risk attributable to genetics reduced from 53.1% to 46.3% after adjusting for environmental factors.
“Previous studies have concluded that genetics play a larger role in predicting disease risk, and our study recalibrates those numbers,” Liu said. “This means that if you have a family history of type 2 diabetes, for example, you can remain hopeful because there is a lot you can do to reduce your risk.”
The research team also used the data to quantitatively assess whether two specific pollutants in the air, PM2.5 and NO2, have a causal relationship to disease risk. Previous studies have combined PM2.5 and NO2 as a comprehensive indicator of air pollution, according to the researchers. However, what the study found is that the two pollutants have distinct causal relationships with different health conditions. For example, NO2 is known to directly cause conditions such as high cholesterol, irritable bowel syndrome, and type 1 and type 2 diabetes, while PM2.5 does not. Meanwhile, PM2.5 may have a more direct causal relationship to lung function and sleep disorders.
The researchers said that ultimately the model could allow them to look more deeply into questions about why some diseases are more prevalent in certain geographic locations.
Other Penn State authors include Hubbell Marcus and Austin Montgomery, joint pre-med and doctoral students at Penn State’s College of Medicine and Fox Graduate School, Laura Carrell, professor of biochemistry and molecular biology, Arthur Berg, professor of public health sciences, and Kun-Hua Li, professor of statistics. Daniel Maguire, who was a doctoral student in the Biostatistics program at the time of the study, co-led the study. Co-authors Lina Yang and Jingyu Xu, who were doctoral students in the Biostatistics program at the time of the study, also contributed to the paper.
This research was supported in part by the National Institutes of Health and the Penn State College of Medicine Artificial Intelligence and Biomedical Informatics Pilot Funding Program. Portions of materials used in this study were provided by the Center for Applied Research in Health Economics at the Penn State College of Medicine.
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