Wastewater Sampling Uncovers Community Dietary Patterns

Tracking the dietary patterns of communities helps inform policies to promote public health. However, most groups rely on individual surveys, which face limitations in recall bias, low response rates, and costs, or national import and export sales data that struggle to visualize populations specific habits.

To overcome these limitations, a team of researchers previously developed an approach to detect plant and animal DNA from individuals’ stool to study their diets.¹ Since individual sampling would not be feasible at a community level, they took inspiration from wastewater-based sampling popularized during COVID-19 to redesign their sequencing method to study population dietary patterns.

In a recent study published in the Proceedings of the National Academies of Science, researchers at Duke University joined forces with scientists at the University of North Carolina at Chapel Hill to show the utility of their platform, FoodSeqFLOW, to detect real-time population-level dietary patterns.² At a cost of less than one cent per person, the findings could help public health officials monitor, detect, and respond to nutritional changes more rapidly.

The research team collected 183 wastewater treatment samples from 21 North Carolina municipalities, in total representing 2.1 million people. They sequenced genes for plant chloroplasts and animal mitochondria to detect dietary information.

First, to test the ability of their platform to study community-level information, they compared the plant profiles of two community sampling sites in Durham, North Carolina to those of 14 individuals from the city. The researchers found highly similar dietary profiles between the community-level and individual samples. Additionally, they saw that most of the plant and animal sequences detected in the samples from wastewater treatment plants mapped to those associated with human food, reducing the likelihood of high environmental contamination.

Next, the researchers investigated whether FoodSeqFLOW could detect temporal variations in community diets. They collected samples from different treatment plants across three cities between June and December and then one additional collection in June of the following year. The team found that the food profiles shifted in response to seasonal availability of foods like vegetables and fruits.

The researchers also evaluated the ability of their method to detect geographic and socioeconomic differences in dietary patterns using principal component analyses. By studying samples from multiple treatment plants across the state at the same time, the team saw that plant-based diets were more common in affluent, urban centers. They also showed that the richness and diversity of food taxa positively correlated with socioeconomic status and the size of Asian and foreign-born populations; food insecurity negatively correlated with taxa richness and diversity. Meanwhile, samples from rural and less populated communities had a higher representation of traditional Southern American cuisine, such as potatoes, celery, onion, black-eyed peas, and black pepper.

Focusing on seafood, they saw regional differences in dietary patterns, with rural and coastal communities having greater representation of local fish, while inland and urban areas had sequences that corresponded to nationally distributed seafood species. These findings support the use of molecular techniques to study dietary patterns and to detect sociodemographic factors on food habits.

The researchers applied their technique to explore unique food habits and found evidence of plant sequences not associated with foods consumed for nutrition. Interestingly, these corresponded to craft beer ingredients and kratom, an herbal opioid alternative legal in the state; these samples correlated with high-income populations. According to the study authors, these food signatures could be used as unique biomarkers for discretionary income.

Collectively, the researchers found that FoodSeqFLOW demonstrated reliable, real-time information about the food habits across diverse communities. While the authors noted important limitations, such as the inability to sample people with septic systems, overall, they suggested that wastewater surveillance offers an affordable option to study dietary patterns to inform public health policy.