Comparison of Environmental Impact and Nutritional Quality among a European Sample Population – findings from the Food4Me study

This study evaluates the relationship between environmental impacts and diet quality through several environmental and nutritional indicators, using data from over 1400 participants across seven European countries in the Food4Me study. Comparisons of environmental impacts and dietary quality were evaluated across country, gender groups, and dietary patterns. While there was clear variability within the different subsets, there were large differences observed in both dietary quality and environmental impacts between cultures, genders, and dietary patterns. Individuals abstaining from red meat consistently had lower impacts in combination with lower consumption of harmful nutrients (saturated fats, sodium, and sugars) while maintaining average intake of beneficial nutrients. A ‘best practice’ diet with low impacts, adequate nutrient intake, and low saturated fats, sodium, and sugars, was constructed from the sample and used as a benchmark. Recorded eating patterns were compared to this recommended diet. On average, intakes of sweets, meats, and drinks should be decreased and intakes of vegetables and cereals increased, at varying rates depending on country and gender. However, the study shows a large spread of eating patterns and recommendations for lowering environmental impacts and increasing nutritional quality vary greatly among individuals.

. Daily average impacts for each category and population subset. Ranges represent the standard 17 deviation of the subset. Severity of the impact in relation to other subsets is represented through shading.

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A darker shade indicates relatively higher impacts while a lighter shade indicates relatively lower 19 impacts. Italicized numbers indicate a significant difference between the male and female subsets of the 20 specific country, or in the case of no meat and fish, no red meat, no dairy, or diet quality indicators, 21 diets with significant differences from the TOTAL population average. Statistical significance between 22 subsets was verified using an unpaired two sided t-test under the assumption that p-values lower than 23 0.05 indicated statistically significant differences between the means of subsets. 24  with a circle or triangle represent the female or male subset, respectively, and no marker indicates both 33 males and females were considered for the average. Length of the error bars represent the 95% 34 confidence interval for the standard error of the mean. See Table S1 for sample size numbers. indicates both males and females were considered for the average. Length of the error bars represent 42 the 95% confidence interval for the standard error of the mean. See Table S1  We investigated what type of eating patterns (Fig 14) were associated with both good and poor 94 quality diets (and the impacts associated with these diets (Table S1)), the eating patterns for low and 95 high impact diets in each impact category, and the eating patterns that fell at the intersection of low 96 impact and good quality diets. includes the water content of the evaluated beverages. *To improve visualization of the graphs, the 106 grams of drinks consumed was divided by 10. 107 Table S3. Recommended percent changes in typical subset eating patterns to achieve a diet that is both high quality (as quantified by high MAR, low MER, 108 and high NRF9.3) and low impacts (as quantified by low climate change, low biodiversity loss, and low water scarcity footprint). Green shading represents 109 that increases in the food group consumption are required, red shading represents that decreases in the food group consumption are required. 110 frequency and portion size for various food and drink items (9)(10). The FFQ contained 162 food items 132

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(both single items and composite dishes), aggregated into 12 food groups, from which participants could 133 choose. The study included over 1'400 men and women from seven European countries (Germany, 134 Greece, Ireland, Netherlands, Spain, Poland, and the UK) between the ages of 18 and 79, with full details 135 regarding age, gender, weight, health, physical activity levels, and reasons for participating in the study, 136 published elsewhere (11). As the Food4Me study was intended to alter an individual's eating patterns 137 based on personalized diet and nutrition advice, food consumption data from the baseline month, prior 138 to recommendations for changing one's diet, was used. 139

Diet Quality Indicator 140
Daily nutrient intake values were based on the European Food Safety Authority's (EFSA) 141 adequate intake (AI) dietary reference values (shown in Table S4) (12). AI values are based on 142 experimental data and are the recommended average daily nutrient intake level to meet or exceed the 143 needs of most healthy individuals (13). Because the population subset studied here is located in various 144 countries throughout Europe, the AI values from EFSA (and not from an individual country) were used 145 in calculating the nutrition indicators. However, gender and age specific RDA values published by the 146 US National Institute of Medicine (13) were also considered. The sensitivities of the rate of consuming 147 less than the recommended intake to the choice of dietary reference value (AI or RDA) is included in 148 the supporting information (Table S5). 149 1.97%** * values in parentheses indicate the age range for a given intake ** DALYs associated with dietary risk for under consumed protein, fiber, calcium, iron, and iodine or overconsumed sodium, respectively, for western Europe for 2015. *** includes DALYs due to high intake of processed meats, trans fat, red meat, and sugar sweetened beverages.

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where the nutrient ratio (NR) is the ratio of the intakeen (the daily consumed mass of a specific 159 encouraged nutrient (en)) to the AIen. Nutrients 1 through 19 in Table S4 were considered in this 160 calculation. The NR for each nutrient i was capped at one to avoid that overconsumption of one nutrient 161 compensate for under supply of others in an individual's average MAR value. In Vieux's study (17), 162 Vitamin D was also included as a nutrient in the calculation, however because it is also synthesized by 163 the body upon skin exposure to sunlight, we have decided it should not be included in the calculation 164 for a diet based indicator. 165 Because MAR does not capture consumption of nutrients that should be consumed in limited 166 quantities, the Mean Excess Ratio (MER), as developed by (17)

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MRV limits for saturated fats were set to 10% of the total required daily energy consumption and for 174 sugars were set to 25% of an individual's total required daily energy consumption (14). Sodium MRV 175 was set to 2.3g per day (15)(14).

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The Nutrient Rich Food Index 9.3 (NRF9.3) was used as an efficiency indicator to measure the 177 nutritional quality of each diet and includes the combination of both beneficial and harmful nutrients as 178 well as energy intake. It was found that NRF9.3 was a good indicator for identifying poor quality diets, 179 as it correlated well with MER, but was not a good indicator to identify people who consumed less than 180 recommended levels of beneficial nutrients. This was developed as a method of ranking the nutritional 181 quality of foods and was found to be highly correlated to diet quality as measured through the Healthy 182 Eating Index (HEI) (18). The nutrients included in the NRF9.3 were chosen by (18) because they showed 183 the best correlation to the HEI when compared to other sets of nutrient combinations. For this indicator, 184 the NR was set to a maximum of one for encouraged nutrients and set to a minimum of one for limiting 185 nutrients, as in the MAR and MER calculations. Because the NRF value is not an average as the MAR 186 and MER, it will change depending on the number of nutrients considered in the calculation and is 187 relative to calorie intake. This indicator utilizes nine encouraged nutrients (Table S4 items 1 to 9) and 188 three nutrients to limit (Table S4 items  Composite foods were broken down into their three main ingredients by mass using a generic recipe or 194 product label. Impacts were calculated for the mass of each ingredient and summed for a total impact 195 per gram of each composite food. In many cases, impacts were available per crop type or ingredient 196 (e.g. tomatoes) but not for a product (e.g. ketchup) derived from that crop. In this case, the impact 197 associated with the root product (tomatoes) was determined and conversion factors, as provided in (