Foodprint 2.0: A computational simulation model that estimates the agricultural resource requirements of diet patterns.

Reducing the environmental pressures stemming from food production is central to meeting global sustainability targets. Shifting diets represents one lever for improving food system sustainability, and identifying sustainable diet opportunities requires computational models to represent complex systems and allow users to evaluate counterfactual scenarios. Despite an increase in the number of food system sustainability models, there remains a lack of transparency of data inputs and mathematical formulas to facilitate replication by researchers and application by diverse stakeholders. Further, many models lack the ability to model multiple geographic scales. The present study introduces Foodprint 2.0, which fills both gaps. Foodprint 2.0 is an updated biophysical simulation model that estimates the agricultural resource requirements of diet patterns and can be adapted to suit a variety of research purposes. The objectives of this study are to: 1) describe the new features of Foodprint 2.0, and 2) demonstrate model performance by estimating the agricultural resource requirements of food demand in the United States (US) using nationally representative dietary data from the National Health and Nutrition Examination Survey from 2009-2018. New features of the model include embedded functions to integrate individual-level dietary data that allow for variance estimation; new data and calculations to account for the resource requirements of food trade and farmed aquatic food; updated user interface; expanded output data for over 200 foods that include the use of fertilizer nutrients, pesticides, and irrigation water; supplementary files that include input data for all parameters on an annual basis from 1999-2018; sample programming code; and step-by-step instructions for users. This study demonstrates that animal-sourced foods consumed in the US accounted for the greatest share of total land use, fertilizer nutrient use, pesticide use, and irrigation water use, followed by grains, fruits, and vegetables. Greater adherence to the Dietary Guidelines for Americans was associated with lower use of land and fertilizer nutrients, and greater use of pesticides and irrigation water. Foodprint 2.0 is a highly modifiable model that can be a useful resource for informing sustainable diet policy discussions.

Dietary fish oil enriched in very-long-chain polyunsaturated fatty acid reduces cardiometabolic risk factors and improves retinal function.

Very-long-chain polyunsaturated fatty acids (VLCPUFAs; C24-38) constitute a unique class of PUFA that have important biological roles, but the lack of a suitable dietary source has limited research in this field. We produced an n-3 C24-28-rich VLCPUFA-oil concentrated from fish oil to study its bioavailability and physiological functions in C57BL/6J mice. The serum and retinal C24:5 levels increased significantly compared to control after a single-dose gavage, and VLCPUFAs were incorporated into the liver, brain, and eyes after 8-week supplementation. Dietary VLCPUFAs resulted in favorable cardiometabolic changes, and improved electroretinography responses and visual performance. VLCPUFA supplementation changed the expression of genes involved in PPAR signaling pathways. Further in vitro studies demonstrated that the VLCPUFA-oil and chemically synthesized C24:5 are potent agonists for PPARs. The multiple potential beneficial effects of fish oil-derived VLCPUFAs on cardiometabolic risk and eye health in mice support future efforts to develop VLCPUFA-oil into a supplemental therapy.

Signaling mechanisms in renal compensatory hypertrophy revealed by multi-omics.

Loss of a kidney results in compensatory growth of the remaining kidney, a phenomenon of considerable clinical importance. However, the mechanisms involved are largely unknown. Here, we use a multi-omic approach in a unilateral nephrectomy model in male mice to identify signaling processes associated with renal compensatory hypertrophy, demonstrating that the lipid-activated transcription factor peroxisome proliferator-activated receptor alpha (PPARα) is an important determinant of proximal tubule cell size and is a likely mediator of compensatory proximal tubule hypertrophy.