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Adam Drewnowski - One of the best experts on this subject based on the ideXlab platform.
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perspective why whole grains should be incorporated into Nutrient profile models to better capture Nutrient Density
Advances in Nutrition, 2021Co-Authors: Adam Drewnowski, Jessica Smith, Nicola M Mckeown, Katrina R Kissock, Eleanor J Beck, Heddie Mejborn, Florent Vieux, Gabriel MassetAbstract:Healthy eating patterns, as described by dietary guidelines, typically favor whole grains, low-fat dairy, vegetables, fruit, legumes, and nuts and seeds. Nutrient-profiling (NP) models capture Nutrient Density of individual foods and can inform healthier food choices. Although whole grains are prominently featured in most dietary guidelines, they are not included in most NP models. Healthy foods, as identified by most NP models, are those that contain limited amounts of energy, saturated fat, total or added sugar, and sodium. As global dietary guidance turns to foods and food groups as opposed to individual Nutrients, future Nutrient-Density metrics may need to do the same. Potential methods to incorporate whole grains into the overall concept of Nutrient Density and into selected NP models are outlined in this review. Incorporating whole grains into the Nutri-Score, Health Star Rating, or the Nutrient Rich Food index will require further analyses of dietary Nutrient Density in relation to health outcomes across diverse population subgroups. We present the rationale for how the inclusion of whole grains in NP models can assist in the implementation of dietary guidance.
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the new hybrid Nutrient Density score nrfh 4 3 3 tested in relation to affordable Nutrient Density and healthy eating index 2015 analyses of nhanes data 2013 16
Nutrients, 2021Co-Authors: Adam Drewnowski, Jessica Smith, Victor L FulgoniAbstract:Background: Hybrid Nutrient Density scores are based on both Nutrients and selected food groups. Objective: To compare the new hybrid Nutrient-rich food NRFh 4:3:3 score to other Nutrient-rich food (NRF) scores, energy Density, and energy cost and to model the impact on the Healthy Eating Index (HEI-2015) of partially replacing less Nutrient-rich with more Nutrient-rich foods. Methods: Analyses were based on 5870 foods and beverages in the Food and Nutrient Database for Dietary Studies and on 24 h dietary recalls from the National Health and Nutrition Examination Survey (NHANES 2013-16). The NRFh 4:3:3 model was based on four Nutrients to encourage (protein fiber, potassium, MUFA + PUFA); three food groups to encourage (dairy, fruit, whole grains); and three Nutrients to limit (saturated fat, added sugar, sodium). Ratings generated by NRFh 4:3:3 and by other NRF models were correlated with score components, energy Density (kcal/100 g), and energy cost (USD/100 kcal). The impact on HEI-2015 of replacing foods in the lowest Nutrient Density tertile (T1) with top tertile (T3) foods at 10%, 20%, 30%, and 100% equicaloric replacement was modeled using NHANES 2013-16 dietary data by population subgroups. Results: The NRFh 4:3:3 model awarded higher scores to foods containing dairy, fruit, and whole grains and proportionately lower scores to vegetables when compared to the NRF 9.3 model. Higher NRF and NRFh Nutrient Density scores were linked to lower energy Density and higher energy cost; however, both correlations were lower for the NRFh 4:3:3. Isocaloric replacement of bottom tertile with top tertile foods as rated by both models led to significantly higher HEI-2105 values, based on complete (100%) and on partial (10-30%) replacement. Conclusion: The new NRFh 4:3:3 model provides the basis for developing new metrics of affordable Nutrient Density. The model identified "best value" food categories that were both affordable and Nutrient-rich. Total and partial replacement of low Nutrient Density with high Nutrient Density foods was associated with higher HEI-2015 scores, suggesting that even partial inclusion of more Nutrient dense foods in the diet may have an important impact on total diet quality.
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adjusting for protein quality by food source may affect Nutrient Density metrics
Nutrition Reviews, 2020Co-Authors: Adam DrewnowskiAbstract:Methods to assess Nutrient Density of foods, commonly known as Nutrient profiling (NP), typically include protein as a component. In this study, the goal was to apply a correction for protein quality by food source to selected NP algorithms. Analyses of 378 component foods of the Fred Hutch food frequency questionnaire showed that animal-source foods (ie, meat, eggs, and dairy) along with some soy products and nuts were the only foods that provided > 20% of the daily value (DV) of protein per 100 g or per 100 kcal. Most beans, pulses, legumes, grains, and vegetables provided <10% DV of protein per 100 g or per 100 kcal. Adjusting for protein quality using a simplified Protein Digestibility Corrected Amino Acid Score (PDCAAS) had consequences for point-based NP models (namely, Nutri-Score) and for continuous Nutrient Density scores (namely, Nutrient Rich Foods). Quantitative methods that use protein content to capture Nutrient Density may require a protein-quality adjustment, especially when adapted for use in low- and middle-income countries where protein quality is an issue of public health concern.
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Nutrient Density and affordability of foods in brazil by food group and degree of processing
Public Health Nutrition, 2020Co-Authors: Kennya Beatriz Siqueira, Shilpi Gupta, Cristiano Av Borges, Mirella Lima Binoti, Amanda F Pilati, Paulo Hf Da Silva, Adam DrewnowskiAbstract:Objective Affordable nutrition refers to the relation between Nutrient Density of foods and their monetary cost. There are limited data on affordable nutrition in low- and middle-income countries. The present study aimed to develop a Nutrient Density score and Nutrient affordability metrics for 377 most consumed foods in Brazil. Design The foods were aggregated into seven major food groups and four NOVA food categories. Nutrient composition data were obtained from the Brazilian Institute of Geography and Statistics. Food prices were obtained from retailer websites and were converted to prices per 100 g and 418 kJ. The Nutrient Rich Food (NRF8.2) score was based on protein, fiber, vitamins A, C and E, Ca, Fe and K. Nutrients to limit were sugar and Na. Affordability was measured as kcal/R$ and Nutrients/R$. Results Grains, fats and sweets were more energy dense and had lower NRF8.2 scores than dairy, vegetables and fruits. Grains, fats and sweets were the lowest cost sources of energy. Vegetables and fruits, beans, nuts and seeds and eggs and dairy were the lowest cost sources of multiple Nutrients. Ultra-processed foods (48 % of total) had higher energy Density and lower NRF8.2 scores than did unprocessed foods. In Brazil, fruits, vegetables and dairy products offered the most Nutrients per real. Conclusions Analysis of the relationship between Nutrient Density of foods and their cost can help identify locally available foods that are Nutrient rich, affordable and culturally acceptable. Achieving high Nutrient Density at an affordable cost should be the goal of Brazil's food systems.
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new Nutrient rich food Nutrient Density models that include Nutrients and myplate food groups
Frontiers in Nutrition, 2020Co-Authors: Adam Drewnowski, Victor L FulgoniAbstract:Background: Dietary guidelines have shifted emphasis from single Nutrients to food patterns, food groups, and dietary ingredients. Nutrient profiling models need to do the same. Methods: Dietary intake data for 23,643 persons aged >2 years came from the 2011-2016 National Health and Examination Survey (NHANES 2011-16). Healthy Eating Index HEI-2015 was the diet quality measure. The new Nutrient Rich Food hybrid score (NRFh) was based on three subscores. The subscore based on x Nutrients to encourage was defined as NRx. The subscore based on y MyPlate food groups to encourage was MPy. The negative subscore based on z Nutrients to limit was LIMz. The final algorithm was NRFh(x.y.z) = NRx + MPy - LIMz. The selection of NRFh model components from among 16 Nutrients and five food groups was based on regression analyses. Results: We conducted a total of 2,162,720 iterative regression analyses against HEI-2015 diet quality scores. NRF scores based on 16 Nutrients accounted for up to 66% of the variance, whereas scores based on 5 MP food groups accounted for 50%. The new NRFh3:4:3 score with six Nutrients and four food groups (fiber, potassium, PUFA+MUFA; whole grains, dairy, fruit, nuts and seeds; saturated fat, added sugar, sodium) explained 72%. The new NRFh4:3:3 score with seven Nutrients and three food groups (protein, fiber, potassium, PUFA+MUFA; whole grain, dairy, fruit; saturated fat, added sugar, sodium;) also explained 72%. In both NRFh models, regressions remained significant for each population subgroup examined. Conclusion: The NRFh3:4:3 and NRFh4:3:3 models correlated well with HEI-2015 scores, a measure of diet quality that tracks compliance with Dietary Guidelines. Hybrid NP models based on Nutrients and food groups could become part of dietary guidance.
Victor L Fulgoni - One of the best experts on this subject based on the ideXlab platform.
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the new hybrid Nutrient Density score nrfh 4 3 3 tested in relation to affordable Nutrient Density and healthy eating index 2015 analyses of nhanes data 2013 16
Nutrients, 2021Co-Authors: Adam Drewnowski, Jessica Smith, Victor L FulgoniAbstract:Background: Hybrid Nutrient Density scores are based on both Nutrients and selected food groups. Objective: To compare the new hybrid Nutrient-rich food NRFh 4:3:3 score to other Nutrient-rich food (NRF) scores, energy Density, and energy cost and to model the impact on the Healthy Eating Index (HEI-2015) of partially replacing less Nutrient-rich with more Nutrient-rich foods. Methods: Analyses were based on 5870 foods and beverages in the Food and Nutrient Database for Dietary Studies and on 24 h dietary recalls from the National Health and Nutrition Examination Survey (NHANES 2013-16). The NRFh 4:3:3 model was based on four Nutrients to encourage (protein fiber, potassium, MUFA + PUFA); three food groups to encourage (dairy, fruit, whole grains); and three Nutrients to limit (saturated fat, added sugar, sodium). Ratings generated by NRFh 4:3:3 and by other NRF models were correlated with score components, energy Density (kcal/100 g), and energy cost (USD/100 kcal). The impact on HEI-2015 of replacing foods in the lowest Nutrient Density tertile (T1) with top tertile (T3) foods at 10%, 20%, 30%, and 100% equicaloric replacement was modeled using NHANES 2013-16 dietary data by population subgroups. Results: The NRFh 4:3:3 model awarded higher scores to foods containing dairy, fruit, and whole grains and proportionately lower scores to vegetables when compared to the NRF 9.3 model. Higher NRF and NRFh Nutrient Density scores were linked to lower energy Density and higher energy cost; however, both correlations were lower for the NRFh 4:3:3. Isocaloric replacement of bottom tertile with top tertile foods as rated by both models led to significantly higher HEI-2105 values, based on complete (100%) and on partial (10-30%) replacement. Conclusion: The new NRFh 4:3:3 model provides the basis for developing new metrics of affordable Nutrient Density. The model identified "best value" food categories that were both affordable and Nutrient-rich. Total and partial replacement of low Nutrient Density with high Nutrient Density foods was associated with higher HEI-2015 scores, suggesting that even partial inclusion of more Nutrient dense foods in the diet may have an important impact on total diet quality.
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new Nutrient rich food Nutrient Density models that include Nutrients and myplate food groups
Frontiers in Nutrition, 2020Co-Authors: Adam Drewnowski, Victor L FulgoniAbstract:Background: Dietary guidelines have shifted emphasis from single Nutrients to food patterns, food groups, and dietary ingredients. Nutrient profiling models need to do the same. Methods: Dietary intake data for 23,643 persons aged >2 years came from the 2011-2016 National Health and Examination Survey (NHANES 2011-16). Healthy Eating Index HEI-2015 was the diet quality measure. The new Nutrient Rich Food hybrid score (NRFh) was based on three subscores. The subscore based on x Nutrients to encourage was defined as NRx. The subscore based on y MyPlate food groups to encourage was MPy. The negative subscore based on z Nutrients to limit was LIMz. The final algorithm was NRFh(x.y.z) = NRx + MPy - LIMz. The selection of NRFh model components from among 16 Nutrients and five food groups was based on regression analyses. Results: We conducted a total of 2,162,720 iterative regression analyses against HEI-2015 diet quality scores. NRF scores based on 16 Nutrients accounted for up to 66% of the variance, whereas scores based on 5 MP food groups accounted for 50%. The new NRFh3:4:3 score with six Nutrients and four food groups (fiber, potassium, PUFA+MUFA; whole grains, dairy, fruit, nuts and seeds; saturated fat, added sugar, sodium) explained 72%. The new NRFh4:3:3 score with seven Nutrients and three food groups (protein, fiber, potassium, PUFA+MUFA; whole grain, dairy, fruit; saturated fat, added sugar, sodium;) also explained 72%. In both NRFh models, regressions remained significant for each population subgroup examined. Conclusion: The NRFh3:4:3 and NRFh4:3:3 models correlated well with HEI-2015 scores, a measure of diet quality that tracks compliance with Dietary Guidelines. Hybrid NP models based on Nutrients and food groups could become part of dietary guidance.
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abstract p355 development and validation of a Nutrient Density score that includes foods groups
Circulation, 2020Co-Authors: Jessica Smith, Victor L Fulgoni, Adam DrewnowskiAbstract:Introduction: There has been considerable work performed on Nutrient profiling to assess the nutritional contribution of a food to a healthy dietary pattern. Most profiling approaches have focused ...
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Grains Contribute Shortfall Nutrients and Nutrient Density to Older US Adults: Data from the National Health and Nutrition Examination Survey, 2011–2014
MDPI AG, 2018Co-Authors: Yanni Papanikolaou, Victor L FulgoniAbstract:Previous data demonstrate grain foods contribute shortfall Nutrients to the diet of U.S. adults. The 2015–2020 Dietary Guidelines for Americans have identified several shortfall Nutrients in the U.S. population, including fiber, folate, and iron (women only). Intake of some shortfall Nutrients can be even lower in older adults. The present analyses determined the contribution of grain foods for energy and Nutrients in older U.S. adults and ranked to all other food sources in the American diet. Analyses of grain food sources were conducted using a 24-hour recall in adults (≥51 years old; n = 4522) using data from the National Health and Nutrition Examination Survey, 2011–2014. All grains provided 278 kcal/day or 14% of all energy in the total diet, ranking as the 4th largest contributor of energy compared to 15 main food groups. All grain foods ranked 1st for thiamin (33%) and niacin (23%) intake relative to 15 main food groups. The grain foods category ranked 2nd highest of 15 main food groups for daily dietary fiber (23%), iron (38%), folate (40%), and magnesium (15%) and was the 3rd largest food group contributor for daily calcium intake (13%). When considering Nutrients to limit as outlined by dietary guidance, main group of grains contributed 6% total fat, 5% saturated fat, 14% sodium and 9% added sugar. Breads, rolls and tortillas provided 150 kcal/day or 8% of all energy in the total diet, ranking as the 2nd largest contributor of energy compared to 46 food subcategories. Breads, rolls and tortillas ranked 1st of 46 foods for daily thiamin (16%) and niacin (10%) intake and 2nd for dietary fiber (12%), iron (12%), folate (13%), and magnesium (7%). Breads, rolls and tortillas ranked 3rd largest food group contributor for daily calcium (5%) intake. Ready-to-eat cereals provided 47 kcal/day or 2% of all energy in the total diet, ranking as the 20th largest contributor of energy compared to 46 food subcategories. All ready-to-eat cereals ranked 1st for daily iron (19%), 1st for folate (21%), 5th for dietary fiber (7%), 3rd for niacin (9%), 8th for magnesium (4%), and 13th for calcium (2%) intake. Given all grain foods and specific subcategories of grain foods provided a greater percentage of several underconsumed Nutrients than calories (including dietary fiber, iron, and folate), grain foods provide Nutrient Density in the American diet of the older adult
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Grain Foods Are Contributors of Nutrient Density for American Adults and Help Close Nutrient Recommendation Gaps: Data from the National Health and Nutrition Examination Survey, 2009–2012
Nutrients, 2017Co-Authors: Yanni Papanikolaou, Victor L FulgoniAbstract:The 2015–2020 Dietary Guidelines for Americans (2015-2020 DGA) maintains recommendations for increased consumption of whole grains while limiting intake of enriched/refined grains. A variety of enriched grains are sources of several shortfall Nutrients identified by 2015-2020 DGA, including dietary fiber, folate, iron, and magnesium. The purpose of this study was to determine food sources of energy and Nutrients for free-living U.S. adults using data from the National Health and Nutrition Examination Survey, 2009–2012. Analyses of grain food sources were conducted using a single 24-h recall collected in adults ≥19 years of age (n = 10,697). Sources of Nutrients contained in all grain foods were determined using United States Department of Agriculture Nutrient composition databases and the food grouping scheme for grains (excluding mixed dishes). Mean energy and Nutrient intakes from the total diet and from various grain food groups were adjusted for the sample design using appropriate weights. All grains provided 285 ± 5 kcal/day or 14 ± 0.2% kcal/day in the total diet in adult ≥19 years of age. In the total daily diet, the grain category provided 7.2 ± 0.2% (4.9 ± 0.1 g/day) total fat, 5.4 ± 0.2% (1.1 ± 0.03 g/day) saturated fat, 14.6 ± 0.3% (486 ± 9 mg/day) sodium, 7.9 ± 0.2% (7.6 ± 0.2 g/day) total sugar, 22.8 ± 0.4% (3.9 ± 0.1 g/day) dietary fiber, 13.2 ± 0.3% (122 ± 3 mg/day) calcium, 33.6 ± 0.5% (219 ± 4 mcg dietary folate equivalents (DFE)/day) folate, 29.7 ± 0.4% (5.3 ± 0.1 mg/day) iron, and 13.9 ± 0.3% (43.7 ± 1.1 mg/day) magnesium. Individual grain category analyses showed that breads, rolls and tortillas and ready-to-eat cereals provided minimal kcal/day in the total diet in men and women ≥19 years of age. Similarly, breads, rolls and tortillas, and ready-to-eat cereals supplied meaningful contributions of shortfall Nutrients, including dietary fiber, folate and iron, while concurrently providing minimal amounts of Nutrients to limit. Cumulatively, a variety of grain food groups consumed by American adults contribute to Nutrient Density in the total diet and have the potential to increase consumption of shortfall Nutrients as identified by 2015–2020 DGA, particularly dietary fiber, folate, and iron.
In Ho Kim - One of the best experts on this subject based on the ideXlab platform.
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effects of probiotic supplementation in different Nutrient Density diets on growth performance Nutrient digestibility blood profiles fecal microflora and noxious gas emission in weaning pig
Journal of the Science of Food and Agriculture, 2017Co-Authors: Ruixia Lan, Hoainam Tran, In Ho KimAbstract:BACKGROUND Probiotics can serve as alternatives to antibiotics to increase the performance of weaning pigs, and the intake of probiotics is affected by dietary Nutrient Density. The objective of this study was to evaluate the effects of a probiotic complex in different Nutrient Density diets on growth performance, digestibility, blood profiles, fecal microflora and noxious gas emission in weaning pigs. RESULTS From day 22 to day 42, both high-Nutrient-Density and probiotic complex supplementation diets increased (P < 0.05) the average daily gain. On day 42, the apparent total tract digestibility (ATTD) of dry matter, nitrogen and gross energy (GE), blood urea nitrogen concentration and NH3 and H2S emissions were increased (P < 0.05) in pigs fed high-Nutrient-Density diets. Pigs fed probiotic complex supplementation diets had higher (P < 0.05) ATTD of GE than pigs fed non-supplemented diets. Fecal Lactobacillus counts were increased whereas Escherichia coli counts and NH3 and H2S emissions were decreased (P < 0.05) in pigs fed probiotic complex supplementation diets. Interactive effects on average daily feed intake (ADFI) were observed from day 22 to day 42 and overall, where probiotic complex improved ADFI more dramatically in low-Nutrient-Density diets. CONCLUSION The beneficial effects of probiotic complex (Bacillus coagulans, Bacillus licheniformis, Bacillus subtilis and Clostridium butyricum) supplementation on ADFI is more dramatic with low-Nutrient-Density diets. © 2016 Society of Chemical Industry
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effects of essential oils supplementation in different Nutrient densities on growth performance Nutrient digestibility blood characteristics and fecal microbial shedding in weaning pigs
Animal Feed Science and Technology, 2016Co-Authors: Ruixia Lan, In Ho KimAbstract:This study was conducted to evaluate the effects of essential oils (EO) supplementation in different Nutrient Density diets on growth performance, Nutrient digestibility, blood characteristics (blood urea nitrogen and creatinine), and fecal microflora shedding in weaning pigs. Experiment 1: a total of 140 crossed [(Landrace × Yorkshire) × Duroc] weaning pigs with an initial body weight (BW) of 6.44 ± 0.93 kg were used in a 42-day trial. Pigs were randomly allocated into one of four treatment groups in a 2 × 2 factorial arrangement with two levels of Nutrient Density (normal and decreased Nutrient Density) and EO (0 and 0.04%) according to sex and BW (7 replicates each with 2 gilts and 3 barrows). Experiment 2: four weaning barrows [(Landrace × Yorkshire) × Duroc] with an average BW of 15 ± 0.58 kg were surgically equipped with T-cannulas at the distal ileum and assigned to 1 of 4 dietary treatments in a 4 × 4 Latin square design with 4 diets and 4 periods for a 60 days experiment, with two levels of Nutrient Density (normal and decrease Nutrient Density) and EO (0 and 0.04%). Pigs fed a normal Nutrient Density diet had an increased average daily gain (ADG), average daily feed intake (ADFI), and gain:feed (G:F) ratio (P < 0.05) than those fed a decreased Nutrient Density diet. Pigs fed an EO supplementation diet exhibited an increased ADG and G:F ratio (P < 0.05) compared with pigs fed a diet without EO supplementation. The apparent total tract digestibility (ATTD) of dry matter (DM) and gross energy (GE) were higher in pigs fed a normal Nutrient Density diet compared with those fed a decreased Nutrient Density diet (P < 0.05). The coefficient of apparent ileal digestibility (CAID) of DM, nitrogen, GE and amino acids were improved (P < 0.05) by EO supplementation and a normal Nutrient Density diet. The ileal digestibility of most amino acids, except threonine, valine, methionine, proline, glycine, alanine, cystine was increased (P < 0.05) in the normal Nutrient Density diet. In addition, the EO supplementation diet also showed significant positive effects on the CAID of some amino acids (P < 0.05), including isoleucine, leucine, lysine, arginine, aspartic acid, serine, and tyrosine. However, no interactive effects were observed in growth performance, Nutrient digestibility, blood profiles, and fecal microflora between Nutrient Density and EO supplementation. In conclusion, dietary supplementation with 0.04% EO has beneficial effects on growth performance and apparent ileal digestibility.
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effects of single or carbohydrases cocktail in low Nutrient Density diets on growth performance Nutrient digestibility blood characteristics and carcass traits in growing finishing pigs
Livestock Science, 2009Co-Authors: J P Wang, L Yan, J S Yoo, Hyungjee Kim, Seongmin Hong, Jaehong Lee, Hyunjun Jang, In Ho KimAbstract:Abstract This study was conducted to evaluate the effects of single or carbohydrases cocktail in low-Nutrient-Density diets on growth performance, Nutrient digestibility, blood characteristics, and carcass traits of growing–finishing pigs. A total of 96 pigs with an average initial BW of 47.50 ± 1.25 kg were used in this 8-week growth trial. Pigs were randomly allocated to 1 of the 4 treatments according to its BW and sex (4 pigs/pen, 6 replicate pens/treatment). The diets included: 1) control (CON) corn–soybean meal diet (DE, 3400 kcal/kg; CP, 17%), 2) low-Nutrient-Density (LC) diet with 6% DDGS and 5% tapioca (DE, 3280 kcal/kg; CP, 15%), 3) LC + 0.05% β-mannanase (LCS), and 4) LC + 0.05% enzyme mixture (LCM). The LC treatment had a lower ADG, and G/F compared to the other treatments in 0–4 weeks and the overall period of the feeding trial ( P P P P
Noel W Solomons - One of the best experts on this subject based on the ideXlab platform.
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Nutrient Density in complementary feeding of infants and toddlers
European Journal of Clinical Nutrition, 2013Co-Authors: Noel W Solomons, M VossenaarAbstract:The paradigm of the first 1000 days of life, the period from conception to the second birthday, has been advanced as a critical window of opportunity to save a life and a child's future. Infancy and toddler life, through the first 24 months after birth, is a unique period during which human milk is recommended as either the exclusive source of nutrition (6 months) or a variable component thereof. After the maternal delivery of milk is accounted for, the remainder of the energy and Nutrients needs come from complementary foods. There is an intrinsic gap left by the maternal milk supply in volume and microNutrient content in relation to expanding infant and toddler needs. The Nutrient Density approach provides us with a mathematical framework to manage the closing of the Nutrient gap. The intrinsic Nutrient content of the unprocessed foods appropriate for young children is limited. The most problematic Nutrients are calcium, iron and zinc. Some manner to enhance the Nutrient Density of the complementary foods is an incontestable necessity. The Nutrient Density consideration, which identifies for us the nature of the problem, offers a tool for the titrating of the fortification to an adequate--but safe--addition.
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several problem Nutrients are identified in complementary feeding of guatemalan infants with continued breastfeeding using the concept of critical Nutrient Density
European Journal of Clinical Nutrition, 2013Co-Authors: M Vossenaar, Liza Hernandez, Raquel Campos, Noel W SolomonsAbstract:The World Health Organization (WHO) recommends nutritionally adequate complementary feeding (CF) through the introduction of indigenous foodstuffs and local foods while breastfeeding for at least 2 years. To determine the adequacy of the contribution of CF to the diets of Guatemalan infants at the 7th–12th month of life receiving high-intensity continued breastfeeding. Critical Nutrient densities for CF were modelled using age- and sex-specific energy and protein requirements assuming children to be at the 50th weight percentile of local peers and 15th weight percentiles of the 2006 WHO standards. Nutrient requirements for the total diet were determined using the recommended Nutrient intakes. Breast milk was assumed to provide 75% of total energy at the 7th–9th month and 50% at the 10th–12th month. Gaps between computed critical Nutrient densities and the CF consumption of 128 Guatemalan infants based on data collected by means of three nonconsecutive 24-h quantitative intake recalls were examined. Locally consumed foods with Nutrient densities above the modelled critical densities were identified. Observed non-breast milk complementation would result in total diets providing inadequate Nutrient Density for vitamin A, niacin and vitamin C in some age groups. Major gaps for calcium, iron and zinc were ubiquitous across all groups. Few foods commonly consumed among Guatemalan infants had adequate densities of ‘problem Nutrients’. The critical Nutrient Density concept is useful to evaluate the Nutrient adequacy of the infant’s diet. Fortified foods are essential sources of the main ‘problem Nutrients’, namely calcium, iron and zinc, given that natural sources are scarce.
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the concept of critical Nutrient Density in complementary feeding the demands on the family foods for the Nutrient adequacy of young guatemalan children with continued breastfeeding
The American Journal of Clinical Nutrition, 2012Co-Authors: Marieke Vossenaar, Noel W SolomonsAbstract:BACKGROUND: The WHO recommends the introduction of nutritionally adequate safe and appropriate complementary foods at 6 mo of age with continued breastfeeding up to 2 y and beyond. Suggested strategies to support continued breastfeeding often postulate optimal use of customary "family foods" and adequate amounts of indigenous foodstuffs and local foods. OBJECTIVE: The objective was to determine the Nutrient adequacy of the diet of Guatemalan children aged 6-24 mo receiving continued breastfeeding and "family foods" rather than specially formulated "baby foods" as complementary foods. DESIGN: "Critical Nutrient densities" for complementary foods were determined by using specific energy and protein requirements assuming children to be in the 50th or 15th weight percentile of the 2006 WHO standards. Nutrient requirements for the total diet were determined by using the recommended Nutrient intakes. Breast milk was assumed to provide 75% of total energy between 7 and 9 mo 50% between 10 and 12 mo and 40% between 13 and 24 mo. Gaps between computed critical Nutrient densities and the best-scenario Guatemalan adults diet as a proxy for family foods were examined. RESULTS: Energy complementation with these diets provided adequate Nutrient Density for protein thiamine riboflavin and vitamins B-6 B-12 and C but not vitamin A niacin and folate in some groups. Major gaps for calcium iron and zinc were ubiquitous. CONCLUSIONS: The critical Nutrient Density concept is useful to evaluate the Nutrient adequacy of the young childs diet. Multiple microNutrients are likely to be limited in the diets of Guatemalan young children with continued breastfeeding and family foods.
Joanne L Slavin - One of the best experts on this subject based on the ideXlab platform.
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Healthy Snacks: Using Nutrient Profiling to Evaluate the Nutrient-Density of Common Snacks in the United States
Journal of Food Science, 2017Co-Authors: Julie Hess, Joanne L SlavinAbstract:Objective: To quantify and compare the Nutrient‐Density of commonly consumed snacks using two Nutrient‐Density measures, Nutrient Rich Foods Indices 9.3 (NRF 9.3) and 15.3 (NRF 15.3). Design: Identify commonly consumed categories of snacks and individual snack foods, calculate NRF 9.3 and 15.3 scores, rank snacks by category and by individual food based on Nutrient Density, compare and contrast scores generated by the two NRF Indices. Main Outcome Measures: NRF 9.3 and 15.3 scores. Analysis: Averages and standard deviations of Nutrient‐Density scores for each snack category. Results: Vegetables and coffee/tea received the highest category scores on both indices. Cakes/cookies/pastries and sweets had the lowest category scores. NRF 9.3 scores for individual snacks ranged from –46 (soda) to 524 (coffee). NRF 15.3 scores ranged from –45 (soda) to 736 (coffee). Conclusions and Implications: If added to food labels, NRF scores could help consumers identify more nutritious choices. The differences between NRF 9.3 and 15.3 scores generated for the same foods and the limitations of these indices highlight the need for careful consideration of which Nutrient‐Density measure to include on food labels as well as consumer education. Nutrient Density scoring, or calculating a numeric score for foods based on their Nutrient profiles, could be an important metric to include on the front of food packages to help consumers identify more healthful products. This metric could be especially useful for deciding between similar products. However, consumers’ understanding of and likelihood of using Nutrient Density scores has not yet been evaluated.
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the Nutrient Density of snacks a comparison of Nutrient profiles of popular snack foods using the Nutrient rich foods index
Global pediatric health, 2017Co-Authors: Julie M Hess, Goutham Rao, Joanne L SlavinAbstract:Background: Although Americans receive almost a quarter of their daily energy from snacks, snacking remains a poorly defined and understood eating occasion. However, there is little dietary guidance about choosing snacks. Families, clinicians, and researchers need a comprehensive approach to assessing their nutritional value. Objective: To quantify and compare the Nutrient Density of commonly consumed snacks by their overall Nutrient profiles using the Nutrient-Rich Foods (NRF) Index 10.3. Methods: NRF Index scores were calculated for the top 3 selling products (based on 2014 market research data) in different snack categories. These NRF scores were averaged to provide an overall Nutrient-Density score for each category. Results: Based on NRF scores, yogurt (55.3), milk (52.5), and fruit (30.1) emerged as the most Nutrient-dense snacks. Ice cream (-4.4), pies and cakes (-11.1), and carbonated soft drinks (-17.2) emerged as the most Nutrient-poor snacks. Conclusions: The NRF Index is a useful tool for assessing the overall nutritional value of snacks based on Nutrients to limit and Nutrients to encourage.
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the Nutrient Density of snacks a comparison of Nutrient profiles of popular snack foods using the Nutrient rich foods index
Global pediatric health, 2017Co-Authors: Julie Hess, Goutham Rao, Joanne L SlavinAbstract:Background: Although Americans receive almost a quarter of their daily energy from snacks, snacking remains a poorly defined and understood eating occasion. However, there is little dietary guidance about choosing snacks. Families, clinicians, and researchers need a comprehensive approach to assessing their nutritional value. Objective: To quantify and compare the Nutrient Density of commonly consumed snacks by their overall Nutrient profiles using the Nutrient-Rich Foods (NRF) Index 10.3. Methods: NRF Index scores were calculated for the top 3 selling products (based on 2014 market research data) in different snack categories. These NRF scores were averaged to provide an overall Nutrient-Density score for each category. Results: Based on NRF scores, yogurt (55.3), milk (52.5), and fruit (30.1) emerged as the most Nutrient-dense snacks. Ice cream (−4.4), pies and cakes (−11.1), and carbonated soft drinks (−17.2) emerged as the most Nutrient-poor snacks. Conclusions: The NRF Index is a useful tool for asse...
