Protein quality as a complementary functional unit in life cycle assessment (LCA).

Goal and theoretical commentary: A number of recent life cycle assessment (LCA) studies have concluded that animal-sourced foods should be restricted-or even avoided-within the human diet due to their relatively high environmental impacts (particularly those from ruminants) compared with other protein-rich foods (mainly protein-rich plant foods). From a nutritional point of view, however, issues such as broad nutrient bioavailability, amino acid balances, digestibility and even non-protein nutrient density (e.g., micronutrients) need to be accounted for before making such recommendations to the global population. This is especially important given the contribution of animal sourced foods to nutrient adequacy in the global South and vulnerable populations of high-income countries (e.g., children, women of reproductive age and elderly). Often, however, LCAs simplify this reality by using 'protein' as a functional unit in their models and basing their analyses on generic nutritional requirements. Even if a 'nutritional functional unit' (nFU) is utilised, it is unlikely to consider the complexities of amino acid composition and subsequent protein accretion. The discussion herein focuses on nutritional LCA (nLCA), particularly on the usefulness of nFUs such as 'protein,' and whether protein quality should be considered when adopting the nutrient as an (n)FU. Further, a novel and informative case study is provided to demonstrate the strengths and weaknesses of protein-quality adjustment. Case study methods: To complement current discussions, we present an exploratory virtual experiment to determine how Digestible Indispensable Amino Acid Scores (DIAAS) might play a role in nLCA development by correcting for amino acid quality and digestibility. DIAAS is a scoring mechanism which considers the limiting indispensable amino acids (IAAs) within an IAA balance of a given food (or meal) and provides a percentage contribution relative to recommended daily intakes for IAA and subsequent protein anabolism; for clarity, we focus only on single food items (4 × animal-based products and 4 × plant-based products) in the current case exemplar. Further, we take beef as a sensitivity analysis example (which we particularly recommend when considering IAA complementarity at the meal-level) to elucidate how various cuts of the same intermediary product could affect the interpretation of nLCA results of the end-product(s). Recommendations: First, we provide a list of suggestions which are intended to (a) assist with deciding whether protein-quality correction is necessary for a specific research question and (b) acknowledge additional uncertainties by providing mitigating opportunities to avoid misinterpretation (or worse, dis-interpretation) of protein-focused nLCA studies. We conclude that as relevant (primary) data availability from supply chain 'gatekeepers' (e.g., international agri-food distributors and processors) becomes more prevalent, detailed consideration of IAA provision of contrasting protein sources needs to be acknowledged-ideally quantitatively with DIAAS being one example-in nLCA studies utilising protein as a nFU. We also contend that future nLCA studies should discuss the complementarity of amino acid balances at the meal-level, as a minimum, rather than the product level when assessing protein metabolic responses of consumers. Additionally, a broader set of nutrients should ideally be included when evaluating "protein-rich foods" which provide nutrients that extend beyond amino acids, which is of particular importance when exploring dietary-level nLCA.

The Moving to Health (M2H) approach to natural experiment research: A paradigm shift for studies on built environment and health.

Improving the built environment (BE) is viewed as one strategy to improve community diets and health. The present goal is to review the literature on the effects of BE on health, highlight its limitations, and explore the growing use of natural experiments in BE research, such as the advent of new supermarkets, revitalized parks, or new transportation systems. Based on recent studies on movers, a paradigm shift in built-environment health research may be imminent. Following the classic Moving to Opportunity study in the US, the present Moving to Health (M2H) strategy takes advantage of the fact that changing residential location can entail overnight changes in multiple BE variables. The necessary conditions for applying the M2H strategy to Geographic Information Systems (GIS) databases and to large longitudinal cohorts are outlined below. Also outlined are significant limitations of this approach, including the use of electronic medical records in lieu of survey data. The key research question is whether documented changes in BE exposure can be linked to changes in health outcomes in a causal manner. The use of geo-localized clinical information from regional health care systems should permit new insights into the social and environmental determinants of health.

A new method to visualize obesity prevalence in Seattle-King County at the census block level.

Objective: The aim of this study is to map obesity prevalence in Seattle King County at the census block level. Methods: Data for 1,632 adult men and women came from the Seattle Obesity Study I. Demographic, socioeconomic and anthropometric data were collected via telephone survey. Home addresses were geocoded, and tax parcel residential property values were obtained from the King County tax assessor. Multiple logistic regression tested associations between house prices and obesity rates. House prices aggregated to census blocks and split into deciles were used to generate obesity heat maps. Results: Deciles of property values for Seattle Obesity Study participants corresponded to county-wide deciles. Low residential property values were associated with high obesity rates (odds ratio, OR: 0.36; 95% confidence interval, CI [0.25, 0.51] in tertile 3 vs. tertile 1), adjusting for age, gender, race, home ownership, education, and incomes. Heat maps of obesity by census block captured differences by geographic area. Conclusion: Residential property values, an objective measure of individual and area socioeconomic status, are a useful tool for visualizing socioeconomic disparities in diet quality and health.

Optimal nutrition and the ever-changing dietary landscape: a conference report.

The field of nutrition has evolved rapidly over the past century. Nutrition scientists and policy makers in the developed world have shifted the focus of their efforts from dealing with diseases of overt nutrient deficiency to a new paradigm aimed at coping with conditions of excess-calories, sedentary lifestyles and stress. Advances in nutrition science, technology and manufacturing have largely eradicated nutrient deficiency diseases, while simultaneously facing the growing challenges of obesity, non-communicable diseases and aging. Nutrition research has gone through a necessary evolution, starting with a reductionist approach, driven by an ambition to understand the mechanisms responsible for the effects of individual nutrients at the cellular and molecular levels. This approach has appropriately expanded in recent years to become more holistic with the aim of understanding the role of nutrition in the broader context of dietary patterns. Ultimately, this approach will culminate in a full understanding of the dietary landscape-a web of interactions between nutritional, dietary, social, behavioral and environmental factors-and how it impacts health maintenance and promotion.

The use of low-calorie sweeteners is associated with self-reported prior intent to lose weight in a representative sample of US adults.

BACKGROUND: Low-calorie sweeteners (LCSs) are said to be a risk factor for obesity and diabetes. Reverse causality may be an alternative explanation. METHODS: Data on LCS use, from a single 24-h dietary recall, for a representative sample of 22 231 adults were obtained from 5 cycles of the National Health and Nutrition Examination Survey (1999-2008 NHANES). Retrospective data on intent to lose or maintain weight during the prior 12-months and 10-year weight history were obtained from the weight history questionnaire. Objectively measured heights and weights were obtained from the examination. Primary analyses evaluated the association between intent to lose/maintain weight and use of LCSs and specific LCS product types using survey-weighted generalized linear models. We further evaluated whether body mass index (BMI) may mediate the association between weight loss intent and use of LCSs. The association between 10-year weight history and current LCS use was evaluated using restricted cubic splines. RESULTS: In cross-sectional analyses, LCS use was associated with a higher prevalence of obesity and diabetes. Adults who tried to lose weight during the previous 12 months were more likely to consume LCS beverages (prevalence ratio=1.64, 95% confidence interval (CI) 1.54-1.75), tabletop LCS (prevalence ratio=1.68, 95% CI 1.47-1.91) and LCS foods (prevalence ratio=1.93, 95% CI 1.60-2.33) as compared with those who did not. In mediation analyses, BMI only partially mediated the association between weight control history and the use of LCS beverages, tabletop LCS, but not LCS foods. Current LCS use was further associated with a history of prior weight change (for example, weight loss and gain). CONCLUSIONS: LCS use was associated with self-reported intent to lose weight during the previous 12 months. This association was only partially mediated by differences in BMI. Any inference of causality between attempts at weight control and LCS use is tempered by the cross-sectional nature of these data and retrospective self-reports of prior weight loss/maintenance intent.

Validating self-reported food expenditures against food store and eating-out receipts.

BACKGROUND/OBJECTIVES: To compare objective food store and eating-out receipts with self-reported household food expenditures. SUBJECTS/METHODS: The Seattle Obesity Study II was based on a representative sample of King County adults, Washington, USA. Self-reported household food expenditures were modeled on the Flexible Consumer Behavior Survey (FCBS) Module from 2007 to 2009 National Health and Nutrition Examination Survey (NHANES). Objective food expenditure data were collected using receipts. Self-reported food expenditures for 447 participants were compared with receipts using paired t-tests, Bland-Altman plots and κ-statistics. Bias by sociodemographics was also examined. RESULTS: Self-reported expenditures closely matched with objective receipt data. Paired t-tests showed no significant differences between receipts and self-reported data on total food expenditures, expenditures at food stores or eating out. However, the highest-income strata showed weaker agreement. Bland-Altman plots confirmed no significant bias across both methods-mean difference: 6.4; agreement limits: -123.5 to 143.4 for total food expenditures, mean difference 5.7 for food stores and mean difference 1.7 for eating out. The κ-statistics showed good agreement for each (κ 0.51, 0.41 and 0.49 respectively. Households with higher education and income had significantly more number of receipts and higher food expenditures. CONCLUSIONS: Self-reported food expenditures using NHANES questions, both for food stores and eating out, serve as a decent proxy for objective household food expenditures from receipts. This method should be used with caution among high-income populations, or with high food expenditures. This is the first validation of the FCBS food expenditures question using food store and eating-out receipts.

Health Implications of Adults' Eating at and Living near Fast Food or Quick Service Restaurants.

BACKGROUND: This paper examined whether the reported health impacts of frequent eating at a fast food or quick service restaurant on health were related to having such a restaurant near home. METHODS: Logistic regressions estimated associations between frequent fast food or quick service restaurant use and health status, being overweight or obese, having a cardiovascular disease or diabetes, as binary health outcomes. In all, 2001 participants in the 2008-2009 Seattle Obesity Study survey were included in the analyses. RESULTS: Results showed eating ⩾2 times a week at a fast food or quick service restaurant was associated with perceived poor health status, overweight and obese. However, living close to such restaurants was not related to negative health outcomes. CONCLUSIONS: Frequent eating at a fast food or quick service restaurant was associated with perceived poor health status and higher body mass index, but living close to such facilities was not.

Socio-demographic correlates and trends in low-calorie sweetener use among adults in the United States from 1999 to 2008.

BACKGROUND/OBJECTIVES: Replacing added sugars in beverages and foods with low-calorie sweeteners (LCSs) is one strategy to reduce calories and manage body weight. There are few studies on LCS consumption by product category and by consumer socio-demographic status. SUBJECTS/METHODS: Data for a representative sample of 22 231 adults were obtained from five cycles of the National Health and Nutrition Examination Survey (1999-2008 NHANES). A single 24-h recall was used to identify consumers of LCS beverages, foods and tabletop sweeteners. Time-trend analyses were conducted for total LCS consumption and for LCS beverages. RESULTS: Approximately 30% of US adults consumed some type of LCS, with 19.5% consuming LCS beverages, 11.4% consuming tabletop LCS and 4.6% consuming LCS foods. LCS consumption by product category peaked at different ages, with older adults more likely to consume tabletop LCS and LCS foods. In age-adjusted analyses, LCS consumers in every product category were more likely to be women, 45-65 years old, non-Hispanic whites, US-born adults, college graduates and with higher household incomes. Predictors of LCS consumption were not altered upon adjustment for body mass index and diabetes status. LCS consumption from all sources and from LCS beverages increased between 1999 and 2008. CONCLUSIONS: LCS use was more common among populations with a lower burden of obesity and related chronic disease, specifically, non-Hispanic whites and those with more education/higher incomes. The reasons for this observed paradox are complex and merit additional research.

The spatial clustering of obesity: does the built environment matter?

BACKGROUND: Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. METHODS: The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. RESULTS: Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. CONCLUSIONS: Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes.

The relation of potassium and sodium intakes to diet cost among U.S. adults.

The 2010 Dietary Guidelines recommended that Americans increase potassium and decrease sodium intakes to reduce the burden of hypertension. One reason why so few Americans meet the recommended potassium or sodium goals may be perceived or actual food costs. This study explored the monetary costs associated with potassium and sodium intakes using national food prices and a representative sample of US adults. Dietary intake data from the 2001-2002 National Health and Nutrition Examination Survey were merged with a national food prices database. In a population of 4744 adults, the association between the energy-adjusted sodium and potassium intakes, and the sodium-to-potassium ratio (Na:K) and energy-adjusted diet cost was evaluated. Diets that were more potassium-rich or had lower Na:K ratios were associated with higher diet costs, while sodium intakes were not related to cost. The difference in diet cost between extreme quintiles of potassium intakes was $1.49 (95% confidence interval: 1.29, 1.69). A food-level analysis showed that beans, potatoes, coffee, milk, bananas, citrus juices and carrots are frequently consumed and low-cost sources of potassium. Based on existing dietary data and current American eating habits, a potassium-dense diet was associated with higher diet costs, while sodium was not. Price interventions may be an effective approach to improve potassium intakes and reduce the Na:K ratio of the diet. The present methods helped identify some alternative low-cost foods that were effective in increasing potassium intakes. The identification and promotion of lower-cost foods to help individuals meet targeted dietary recommendations could accompany future dietary guidelines.

The geographic distribution of obesity by census tract among 59 767 insured adults in King County, WA.

OBJECTIVE: To evaluate the geographic concentration of adult obesity prevalence by census tract (CT) in King County, WA, in relation to social and economic factors. METHODS AND DESIGN: Measured heights and weights from 59 767 adult men and women enrolled in the Group Health (GH) healthcare system were used to estimate obesity prevalence at the CT level. CT-level measures of socioeconomic status (SES) were median home values of owner-occupied housing units, percent of residents with a college degree and median household incomes, all drawn from the 2000 Census. Spatial regression models were used to assess the relation between CT-level obesity prevalence and socioeconomic variables. RESULTS: Smoothed CT obesity prevalence, obtained using an Empirical Bayes tool, ranged from 16.2-43.7% (a 2.7-fold difference). The spatial pattern of obesity was non-random, showing a concentration in south and southeast King County. In spatial regression models, CT-level home values and college education were more strongly associated with obesity than household incomes. For each additional $100 000 in median home values, CT obesity prevalence was 2.3% lower. The three SES factors together explained 70% of the variance in CT obesity prevalence after accounting for population density, race/ethnicity, age and spatial dependence. CONCLUSIONS: To our knowledge, this is the first report to show major social disparities in adult obesity prevalence at the CT scale that is based, moreover, on measured heights and weights. Analyses of data at sufficiently fine geographic scale are needed to guide targeted local interventions to stem the obesity epidemic.

Food environment and socioeconomic status influence obesity rates in Seattle and in Paris.

OBJECTIVE: To compare the associations between food environment at the individual level, socioeconomic status (SES) and obesity rates in two cities: Seattle and Paris. METHODS: Analyses of the SOS (Seattle Obesity Study) were based on a representative sample of 1340 adults in metropolitan Seattle and King County. The RECORD (Residential Environment and Coronary Heart Disease) cohort analyses were based on 7131 adults in central Paris and suburbs. Data on sociodemographics, health and weight were obtained from a telephone survey (SOS) and from in-person interviews (RECORD). Both studies collected data on and geocoded home addresses and food shopping locations. Both studies calculated GIS (Geographic Information System) network distances between home and the supermarket that study respondents listed as their primary food source. Supermarkets were further stratified into three categories by price. Modified Poisson regression models were used to test the associations among food environment variables, SES and obesity. RESULTS: Physical distance to supermarkets was unrelated to obesity risk. By contrast, lower education and incomes, lower surrounding property values and shopping at lower-cost stores were consistently associated with higher obesity risk. CONCLUSION: Lower SES was linked to higher obesity risk in both Paris and Seattle, despite differences in urban form, the food environments and in the respective systems of health care. Cross-country comparisons can provide new insights into the social determinants of weight and health.

Measuring diet cost at the individual level: a comparison of three methods.

BACKGROUND/OBJECTIVES: Household-level food spending data are not suitable for population-based studies of the economics of nutrition. This study compared three methods of deriving diet cost at the individual level. SUBJECTS/METHODS: Adult men and women (n=164) completed 4-day diet diaries and a food frequency questionnaire (FFQ). Food expenditures over 4 weeks and supermarket prices for 384 foods were obtained. Diet costs (US$/day) were estimated using: (1) diet diaries and expenditures; (2) diet diaries and supermarket prices; and (3) FFQs and supermarket prices. Agreement between the three methods was assessed on the basis of Pearson correlations and limits of agreement. Income-related differences in diet costs were estimated using general linear models. RESULTS: Diet diaries yielded mean (s.d.) diet costs of $10.04 (4.27) based on Method 1 and $8.28 (2.32) based on Method 2. FFQs yielded mean diet costs of $7.66 (2.72) based on Method 3. Correlations between energy intakes and costs were highest for Method 3 (r(2)=0.66), lower for Method 2 (r(2)=0.24) and lowest for Method 1 (r(2)=0.06). Cost estimates were significantly associated with household incomes. CONCLUSION: The weak association between food expenditures and food intake using Method 1 makes it least suitable for diet and health research. However, merging supermarket food prices with standard dietary assessment tools can provide estimates of individual diet cost that are more closely associated with food consumed. The derivation of individual diet cost can provide insights into some of the economic determinants of food choice, diet quality and health.

Does diet cost mediate the relation between socioeconomic position and diet quality?

BACKGROUND/OBJECTIVES: Socioeconomic disparities in diet quality are well established. This study tested the hypothesis that such disparities are mediated, in part, by diet cost. SUBJECTS/METHODS: The Seattle Obesity Study (S.O.S) was a cross-sectional study based on a representative sample of 1266 adults of King County, WA, conducted in 2008-09. Demographic and socioeconomic variables were obtained through telephone survey. Income and education were used as indicators of socioeconomic position. Dietary intake data were obtained using a food frequency questionnaire (FFQ). Diet cost was calculated based on retail prices for FFQ component foods. Energy density (KJ/g) and mean adequacy ratio (MAR) were used as two indices of overall diet quality. RESULTS: Higher income and education were each associated with lower energy density and higher MAR scores, adjusting for covariates. Higher income and education were also associated with higher energy adjusted diet cost. Higher quality diets were in turn associated with higher diet costs. All these associations were significant (P<0.0001). In formal mediation analyses, diet cost significantly mediated the pathway between income and diet quality measures, adjusting for covariates (P<0.05 each). Further, income-diet cost-diet quality pathway was found to be moderated by education level. CONCLUSIONS: The social gradient in diet quality may be explained by diet cost. Strategies to improve diet quality among lower socioeconomic strata may need to take food prices and diet cost along with nutrition education into account.

Should nutrient profiles be based on 100 g, 100 kcal or serving size?

BACKGROUND/OBJECTIVES: Nutrient profiling of foods is defined as the science of ranking or classifying foods based on their nutrient content. Nutrient profiles can be calculated based on 100 g or 100 kcal of food or on standard serving sizes. The objective of this study was to compare the performance of nutrient profiles based on 100 g, 100 kcal and government-mandated serving sizes, and to identify the optimal base of calculation. SUBJECTS/METHODS: Nutrient profiles tested were composed of positive subscores based on nutrients to encourage and negative subscores based on nutrients to limit. Alternative profiles, computed using different bases of calculation, were used to rank order 378 commonly consumed foods from a food frequency instrument. Profile performance was tested with respect to the foods' energy density. RESULTS: Serving sizes, defined by the US Food and Drug Administration as reference amounts customarily consumed (RACC), were inversely linked to energy density of foods. Positive subscores based on 100 kcal were equivalent to those calculated using RACC values. Negative subscores performed better when based on 100 g as opposed to 100 kcal. CONCLUSIONS: Models based on serving sizes and on 100 kcal were preferable for positive subscores and models based on 100 g of food were preferable for negative subscores. RACC-based profiles may represent an attractive option for the US consumer.

Testing nutrient profile models in relation to energy density and energy cost.

BACKGROUND: Nutrient profiling of foods is defined as the science of classifying foods based on their nutrient content. Food rankings generated by nutrient profile models need to be tested against objective reality as opposed to public opinion. OBJECTIVE: To test the performance of selected nutrient profile models in relation to the foods' energy density (kcal g(-1)) and energy cost (Dollar per 1000 kcal). SUBJECTS/METHODS: Analyses were based on 378 component foods of a food frequency instrument. The models tested were the French nutrient adequacy models NAS23 and NAS16 and nutrient density models NDS23 and NDS16; and a family of nutrient-rich models (NR(n), where n=5-7; 10-12, and 15). Also tested were LIM scores and a modified British Food Standards Agency model WXYfm. Profiles were calculated based on 100 g, 100 kcal and on Reference Amounts Customarily Consumed. Food rankings generated by different models were correlated with each other and with the foods' energy density and energy cost. RESULTS: Nutrient profile models based on protein, fiber, vitamins and minerals showed an inverse correlation with energy density that diminished as more micronutrients were introduced into the model. Models based on fat, sugar and sodium were highly correlated with energy density. Foods classified as healthier were generally associated with higher energy costs. CONCLUSIONS: Not all models accurately reflected the foods' content of nutrients known to be beneficial to health. High correlations with energy density meant that some models classified foods based on their energy density as opposed to nutrient content.

Nutrient-rich foods: applying nutrient navigation systems to improve public health.

The American diet is high in calories, but low in nutrients. To help consumers obtain more nutrition from the calories they consume, research is underway to develop a nutrient profiling approach that can be used to evaluate individual foods and help people build healthful diets. A nutrient profiling system that rates individual foods based on their nutrient content needs to be both science-driven and user-friendly, allowing consumers to make more healthful food choices within and across all the food groups. A recent survey, commissioned by the Nutrient Rich Food Coalition, reveals that the majority of consumers and nutrition professionals believe that better information about a food's total combined nutrient content would be effective and useful in helping them make more nutrient-rich food choices.

Intense sweeteners, energy intake and the control of body weight.

Replacing sugar with low-calorie sweeteners is a common strategy for facilitating weight control. By providing sweet taste without calories, intense sweeteners help lower energy density of beverages and some foods. Reduced dietary energy density should result in lower energy intakes--but are the energy reduction goals, in fact, achieved? The uncoupling of sweetness and energy, afforded by intense sweeteners, has been the focus of numerous studies over the past two decades. There are recurring arguments that intense sweeteners increase appetite for sweet foods, promote overeating, and may even lead to weight gain. Does reducing energy density of sweet beverages and foods have a measurable impact on appetite and energy intakes, as examined both in short-term studies and over a longer period? Can reductions in dietary energy density achieved with intense sweeteners really affect body weight control? This paper reviews evidence from laboratory, clinical and epidemiological studies in the context of current research on energy density, satiety and the control of food intake.

Low-cost diets: more energy, fewer nutrients.

Energy-dense diets offer a low-cost dietary option to the consumer. However, they are more likely to be nutrient-poor. In this study, based on the French national food consumption survey, the diet costs were estimated using retail food prices in France. Adult participants were stratified by quartiles of energy cost (in euro/10 MJ). Dietary energy density, energy and nutrient intakes were then compared across groups. Participants in the lowest quartile of energy cost had the highest energy intakes, the most energy-dense diets and the lowest daily intakes of key vitamins and micronutrients. Participants in the highest quartile of energy cost had lower energy intakes, and diets that were higher in nutrients and lower in energy density. However, their daily diet costs were 165% higher. In this observational study, the more nutrient-dense diets were associated with higher diet costs.

Liquid calories and the failure of satiety: how good is the evidence?

Increasing rates of obesity and overweight have been attributed to the growing proportion of dietary energy that is consumed in liquid form. Liquids reportedly fail to trigger physiological satiety mechanisms so that compensation for energy consumed as beverages may be imprecise and incomplete. Sweetened beverages have come under particular scrutiny, as their consumption exceeds that of milk and fruit and vegetable juices. However, the evidence that liquids have less impact on satiety than do solid foods remains inconclusive. Some published studies have reported that liquids are less satiating than solids, whereas other studies have reported that solids are less satiating than liquids. In laboratory studies, the degree of energy compensation following pre-load ingestion was influenced by subject characteristics, pre-load volume, or the time lag between the pre-load and the next meal. Such factors appear to interact with pre-load texture in their impact on hunger and satiety and later food consumption.