A three-component Breakfast Quality Score (BQS) to evaluate the nutrient density of breakfast meals

BackgroundNutrient profiling methods can be applied to individual foods or to composite meals. This article introduces a new method to assess the nutrient density of breakfast meals.ObjectiveThis study aimed to develop a new breakfast quality score (BQS), based on the nutrient standards previously published by the International Breakfast Research Initiative (IBRI) consortium.MethodsBQS was composed of three sub-scores derived from the weighted arithmetic mean of corresponding nutrient adequacy: an eLIMf sub-score (energy, saturated fat, free sugars, and sodium), a PF (protein and fiber) sub-score, and a VMn1 − 14 micronutrient sub-score, where n varied from 0 to 14. The effects of assigning different weights to the eLIMf, PF, and VMn were explored in four alternative models. The micronutrients were calcium, iron, potassium, magnesium, zinc, vitamin A, thiamin, riboflavin, niacin, vitamin B5, vitamin B6, vitamin B12, vitamin C, and vitamin D. Micronutrient permutations were used to develop alternate VMn1 − 14 sub-scores. The breakfast database used in this study came from all breakfasts declared as consumed by adults (>18 years old) in the French dietary survey INCA3. All models were tested with respect to the Nutrient Rich Food Index (NRF9.3). BQS sensitivity was tested using three prototype French breakfasts, for which improvements were made.ResultsThe correlations of the models with NRF9.3 improved when the VMn>3 sub-score (n > 3) was included alongside the PF and eLIMf sub-scores. The model with (PF+VMn) and eLIMf each accounting for 50% of the total score showed the highest correlations with NRF9.3 and was the preferred final score (i.e., BQS). BQS was sensitive to the changing quality of three prototype breakfasts defined as tartine, sandwich, and cereal.ConclusionThe proposed BQS was shown to valuably rank the nutritional density of breakfast meals against a set of nutrient recommendations. It includes nutrients to limit along with protein, fiber, and a variable number of micronutrients to encourage. The flexible VMn sub-score allows for the evaluation of breakfast quality even when nutrient composition data are limited

Can nutrient profiling help to identify foods which diet variety should be encouraged? Results from the Whitehall II cohort

Higher variety of recommended foods, identified arbitrarily based on dietary guidelines, has been associated with better health status. Nutrient profiling is designed to identify objectively, based on nutrient content, healthier foods whose consumption should be encouraged. The objective was to assess the prospective associations between total food variety (food variety score, FVS) and variety from selected recommended and non-recommended foods (RFV and NRFV, respectively) and risk of chronic disease and mortality. In 1991–3, 7251 participants of the Whitehall II study completed a 127-item FFQ. The FVS was defined as the number of foods consumed more than once a week. (N)RFV(Ofcom) and (N)RFV(SAIN,LIM) were similarly derived selecting healthier (or less healthier) foods as defined by the UK Ofcom and French SAIN,LIM nutrient profile models, respectively. Multi-adjusted Cox regressions were fitted with incident CHD, diabetes, CVD, cancer and all-cause mortality (318, 754, 137, 251 and 524 events, respectively – median follow-up time 17 years). RFV and NRFV scores were mutually adjusted. The FVS (fourth v. first quartile) was associated with a 39 and 26 % reduction of prospective CHD and all-cause mortality risk, respectively. The RFV(Ofcom) (third v. first quartile) was associated with a 27 and 35 % reduction of all-cause mortality and cancer mortality risk, respectively; similar associations were suggested, but not significant for the RFV(SAIN,LIM). No prospective associations were observed with NRFV scores. The results strengthen the rationale to promote total food variety and variety from healthy foods. Nutrient profiling can help in identifying those foods whose consumption should be encouraged

Which functional unit to identify sustainable foods?

International audienceObjective: In life-cycle assessment, the functional unit defines the unit for calculation of environmental indicators. The objective of the present study was to assess the influence of two functional units, 100 g and 100 kcal (420 kJ), on the associations between three dimensions for identifying sustainable foods, namely environmental impact (via greenhouse gas emissions (GHGE)), nutritional quality (using two distinct nutrient profiling systems) and price. Design: GHGE and price data were collected for individual foods, and were each expressed per 100 g and per 100 kcal. Two nutrient profiling models, SAIN, LIM and UK Ofcom, were used to assess foods' nutritional quality. Spearman correlations were used to assess associations between variables. Sustainable foods were identified as those having more favourable values for all three dimensions. Setting: The French Individual and National Dietary Survey (INCA2), 2006-2007. Subjects: Three hundred and seventy-three foods highly consumed in INCA2, covering 65 % of total energy intake of adult participants. Results: When GHGE and price were expressed per 100 g, low-GHGE foods had a lower price and higher SAIN, LIM and Ofcom scores (r=0.59, -0.34 and -0.43, respectively), suggesting a compatibility between the three dimensions; 101 and 100 sustainable foods were identified with SAIN, LIM and Ofcom, respectively. When GHGE and price were expressed per 100 kcal, low-GHGE foods had a lower price but also lower SAIN, LIM and Ofcom scores (r=0.67, 0.51 and 0.47, respectively), suggesting that more environment-friendly foods were less expensive but also less healthy; thirty-four sustainable foods were identified with both SAIN, LIM and Ofcom. Conclusions: The choice of functional unit strongly influenced the compatibility between the sustainability dimensions and the identification of sustainable foods

Nutrition-Oriented Reformulation of Extruded Cereals and Associated Environmental Footprint: A Case Study

The global food system faces a dual challenge for the decades ahead: to (re)formulate foods capable to feed a growing population while reducing their environmental footprint. In this analysis, nutritional composition, recipe, and sourcing data were analyzed alongside five environmental indicators: climate change (CC), freshwater consumption scarcity (FWCS), abiotic resource depletion (ARD), land use impacts on biodiversity (LUIB), and impacts on ecosphere/ecosystems quality (IEEQ) to assess improvement after three reformulation cycles (2003, 2010, 2018) in three extruded breakfast cereals. A life cycle assessment (LCA) was performed using life cycle inventory (LCI) composed by both primary data from the manufacturer and secondary data from usual third-party LCI datasets. Reformulation led to improved nutritional quality for all three products. In terms of environmental impact, improvements were observed for the CC, ARD, and IEEQ indicators, with average reductions of 12%, 14%, and 2% between 2003 and 2018, respectively. Conversely, the FWCS and LUIB indicators were increased by 57% and 70%, respectively. For all indicators but ARD, ingredients contributed most to the environmental impact. This study highlights the need for further focus on the selection of less demanding ingredients and improvements in agricultural practices in order to achieve environmental and nutritional improvements

To what extent are dietary shifts a potential lever to reduce greenhouse gas emissions? A simulation study

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Identifying Sustainable Foods: The Relationship between Environmental Impact, Nutritional Quality, and Prices of Foods Representative of the French Diet

International audienceBackground Sustainable diets, as defined by the Food and Agriculture Organization, need to combine environment, nutrition, and affordability dimensions. However, it is unknown whether these dimensions are compatible, and no guidance is available in the official recommendations. Objective To identify foods with compatible sustainability dimensions. Methods For 363 of the most commonly consumed foods in the Second French Individual and National Study on Food Consumption, environmental impact indicators (ie, greenhouse gas [GHG] emissions, acidification, and eutrophication), and prices were collected. The nutritional quality of the foods was assessed by calculating the score for the nutritional adequacy of individual foods (SAIN) to score for disqualifying nutrients (LIM) ratio. A sustainability score based on the median GHG emissions, price, and SAIN:LIM was calculated for each food; the foods with the best values for all three variables received the highest score. Results The environmental indicators were strongly and positively correlated. Meat, fish, and eggs and dairy products had the strongest influence on the environment; starchy foods, legumes, and fruits and vegetables had the least influence. GHG emissions were inversely correlated with SAIN:LIM (r=-0.37) and positively correlated with price per kilogram (r=0.59); the correlation with price per kilocalorie was null. This showed that foods with a heavy environmental impact tend to have lower nutritional quality and a higher price per kilogram but not a lower price per kilocalorie. Using price per kilogram, 94 foods had a maximum sustainability score, including most plant-based foods and excluding all foods with animal ingredients except milk, yogurt, and soups. Using price per kilocalorie restricted the list to 42 foods, including 52% of all starchy foods and legumes but only 11% of fruits and vegetables (mainly 100% fruit juices). Conclusions Overall, the sustainability dimensions seemed to be compatible when considering price per kilogram of food. However, this conclusion is too simplistic when considering price per kilocalorie, which highlights the need to integrate the data at the diet level

Diet optimization methods can help translate dietary guidelines into a cancer prevention food plan

International audienceMathematical diet optimization models are used to create food plans that best resemble current eating habits while meeting prespecified nutrition and cost constraints. This study used linear programming to generate food plans meeting the key 2007 dietary recommendations issued by the World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR). The models were constructed to minimize deviations in food intake between the observed and the WCRF/AICR-recommended diets. Consumption constraints were imposed to prevent food plans from including unreasonable amounts of food from a single group. Consumption norms for nutrients and food groups were taken from dietary intake data for a sample of adult men and women (n = 161) in the Pacific Northwest. Food plans meeting the WCRF/AICR dietary guidelines numbers 3–5 and 7 were lower in refined grains and higher in vegetables and fruits than the existing diets. For this group, achieving cancer prevention goals required little modification of existing diets and had minimal impact on diet quality and cost. By contrast, the need to meet all nutritional needs through diet alone (guideline no. 8) required a large food volume increase and dramatic shifts from the observed food intake patterns. Putting dietary guidelines into practice may require the creation of detailed food plans that are sensitive to existing consumption patterns and food costs. Optimization models provide an elegant mathematical solution that can help determine whether sets of dietary guidelines are achievable by diverse U.S. population subgroup