Choosing the best method to estimate the energy density of a population using food purchase data

Association between dietary intake networks identified through a Gaussian graphical model and the risk of cancer: a prospective cohort study

PURPOSE

In this study, we aimed to investigate the association between dietary communities identified by a Gaussian graphical model (GGM) and cancer risk.

METHODS

We performed GGM to identify the dietary communities in a Korean population. GGM-derived communities were then scored and investigated for their association with cancer incidence in the entire population as well as in the 1:1 age- and sex-matched subgroup using a Cox proportional hazards model. In the sensitivity analysis, GGM-derived communities were compared to dietary patterns (DPs) that were identified by principal component analysis (PCA) and reduced rank regression (RRR).

RESULTS

During a median time to follow-up of 6.6 years, 397 cancer cases were newly diagnosed. The GGM identified 17 and 16 dietary communities for the total and matched populations, respectively. For each one-unit increase in the standard deviation of the community-specific score of the community that was composed of dairy products and bread, there was a reduced risk of cancer according to the fully adjusted model (HR: 0.80, 95% CI: 0.66-0.96). In the matched population, the third tertile of the community-specific score of the community composed of poultry, seafood, bread, cakes and sweets, and meat by-products showed a significantly reduced risk of cancer compared to that of the lowest tertile in the fully adjusted model (HR: 0.66, 95% CI: 0.50-0.86, p-trend = 0.002).

CONCLUSION

We found that the GGM-identified community composed of dairy products and bread showed a reduced risk of cancer. Further population-based prospective studies should be conducted to examine possible associations of dietary intake and specific cancer types.

Free-Living Energy Balance Behaviors Are Associated With Greater Weight Loss During a Weight Loss Program

Introduction: Free-living movement (physical activity [PA] and sedentary behavior [SB]) and eating behaviors (energy intake [EI] and food choice) affect energy balance and therefore have the potential to influence weight loss (WL). This study explored whether free-living movement and/or eating behaviors measured early (week 3) in a 14-week WL programme or their change during the intervention are associated with WL in women. Methods: In the study, 80 women (M ± SD age: 42.0 ± 12.4 years) with overweight or obesity [body mass index (BMI): 34.08 ± 3.62 kg/m²] completed a 14 week WL program focused primarily on diet (commercial or self-led). Body mass (BM) was measured at baseline, and again during week 2 and 14 along with body composition. Free-living movement (SenseWear Armband) and eating behavior (weighed food diaries) were measured for 1 week during week 3 and 12. Hierarchical multiple regression analyses examined whether early and early-late change in free-living movement and eating behavior were associated with WL. The differences in behavior between clinically significant weight losers (CWL; ≥5% WL) and non-clinically significant weight losers (NWL; ≤ 3% WL) were compared. Results: The energy density of food consumed [β = 0.45, p < 0.001] and vigorous PA [β = -0.30, p < 0.001] early in the intervention (regression model 1) and early-late change in light PA [β = -0.81 p < 0.001], moderate PA [β = -1.17 p < 0.001], vigorous PA [β = -0.49, p < 0.001], total energy expenditure (EE) [β = 1.84, p < 0.001], and energy density of food consumed [β = 0.27, p = 0.01] (regression model 2) significantly predicted percentage change in BM. Early in the intervention, CWL consumed less energy dense foods than NWL [p = 0.03]. CWL showed a small but significant increase in vigorous PA, whereas NWL showed a slight decrease in PA [p = 0.04]. Conclusion: Both early and early-late change in free-living movement and eating behaviors during a 14 week WL program are predictors of WL. These findings demonstrate that specific behaviors that contribute to greater EE (e.g., vigorous PA) and lower EI (e.g., less energy-dense foods) are related to greater WL outcomes. Interventions targeting these behaviors can be expected to increase the effectiveness of WL programs.

What can Secondary Data Tell Us about Household Food Insecurity in a High-Income Country Context?

In the absence of routinely collected household food insecurity data, this study investigated what could be determined about the nature and prevalence of household food insecurity in Scotland from secondary data. Secondary analysis of the Living Costs and Food Survey (2007–2012) was conducted to calculate weekly food expenditure and its ratio to equivalised income for households below average income (HBAI) and above average income (non-HBAI). Diet Quality Index (DQI) scores were calculated for this survey and the Scottish Health Survey (SHeS, 2008 and 2012). Secondary data provided a partial picture of food insecurity prevalence in Scotland, and a limited picture of differences in diet quality. In 2012, HBAI spent significantly less in absolute terms per week on food and non-alcoholic drinks (£53.85) compared to non-HBAI (£86.73), but proportionately more of their income (29% and 15% respectively). Poorer households were less likely to achieve recommended fruit and vegetable intakes than were more affluent households. The mean DQI score (SHeS data) of HBAI fell between 2008 and 2012, and was significantly lower than the mean score for non-HBAI in 2012. Secondary data are insufficient to generate the robust and comprehensive picture needed to monitor the incidence and prevalence of food insecurity in Scotland.

A Low Energy-Dense Diet in the Context of a Weight-Management Program Affects Appetite Control in Overweight and Obese Women

Background

Low energy-dense (LED) foods reduce energy intake (EI); whether this effect is sustained over time and during weight loss is unknown.

Objective

This trial examined the effects of LED compared with high energy-dense (HED) meals on appetite, EI, and control over eating in the laboratory and during a weight-management program that encourages unrestricted intake of LED foods [Slimming World, UK (SW)] compared with a self-led Standard Care program [NHS weight-loss plan (SC)].

Methods

Overweight and obese women [n = 96; mean ± SD age: 41.03 ± 12.61 y; mean ± SD body mass index (in kg/m2): 34.00 ± 3.61] were recruited from the SW or SC programs. Primary outcomes included appetite, food preferences (liking and wanting for LED and HED foods), cravings, and evening meal EI (LED, HED) in response to calorie-matched LED (≤0.8 kcal/g) and HED (≥2.5 kcal/g) breakfast and lunch meals. Probe-day tests were conducted at weeks 3 and 4 and repeated at weeks 12 and 13 in a within-day crossover design. Secondary outcomes, including body weight and program experience, were measured from weeks 1 to 14 in a parallel-group design. Dietary compliance was monitored with the use of weighed food diaries at weeks 3 and 12.

Results

Intention-to-treat (ITT) and completers analyses showed that the SW group lost more weight than the SC group [ITT: -5.9% (95% CI: -4.7%, -7.2%) compared with -3.5% (-2.3%, -4.8%), P < 0.05; completers: -6.2% (-4.8%, -7.6%) compared with 3.9% (-2.5%, -5.2%), P < 0.05]. The SW group reported greater control over eating and more motivation to continue the program compared with the SC group. LED meals increased sensations of fullness and reduced hunger on probe days (P < 0.001). Total-day EI was 1057 ± 73 kcal less (95% CI: 912, 1203 kcal; 36%) under LED compared with HED conditions (P < .001). Liking for LED and HED foods and wanting for HED foods were lower before lunch under LED compared with HED conditions, and liking decreased to a greater extent after the LED lunch. The SW group reported fewer cravings under LED compared with HED conditions (P < 0.05). On probe days, appetite and EI outcomes did not differ between weeks 3 and 12 or between the SW and SC groups.

Conclusion

LED meals improve appetite control in women attempting weight loss and the effect is sustainable. Consumption of LED meals likely contributed to weight loss in the SW program. This study was registered at clinicaltrials.gov as NCT02012426.

Buying less and wasting less food. Changes in household food energy purchases, energy intakes and energy density between 2007 and 2012 with and without adjustment for food waste

OBJECTIVE

Consumers in the UK responded to the rapid increases in food prices between 2007 and 2009 partly by reducing the amount of food energy bought. Household food and drink waste has also decreased since 2007. The present study explored the combined effects of reductions in food purchases and waste on estimated food energy intakes and dietary energy density.

DESIGN

The amount of food energy purchased per adult equivalent was calculated from Kantar Worldpanel household food and drink purchase data for 2007 and 2012. Food energy intakes were estimated by adjusting purchase data for food and drink waste, using waste factors specific to the two years and scaled for household size.

SETTING

Scotland.

SUBJECTS

Households in Scotland (n 2657 in 2007; n 2841 in 2012).

RESULTS

The amount of food energy purchased decreased between 2007 and 2012, from 8·6 to 8·2 MJ/adult equivalent per d (P<0·001). After accounting for the decrease in food waste, estimated food energy intake was not significantly different (7·3 and 7·2 MJ/adult equivalent per d for 2007 and 2012, respectively; P=0·186). Energy density of foods purchased increased slightly from 700 to 706 kJ/100 g (P=0·010).

CONCLUSIONS

While consumers in Scotland reduced the amount of food energy that they purchased between 2007 and 2012, this was balanced by reductions in household food and drink waste over the same time, resulting in no significant change in net estimated energy intake of foods brought into the home.

Dietary patterns by reduced rank regression are associated with obesity and hypertension in Australian adults

Evidence linking dietary patterns (DP) and obesity and hypertension prevalence is inconsistent. We aimed to identify DP derived from energy density, fibre and sugar intakes, as well as Na, K, fibre, SFA and PUFA, and investigate associations with obesity and hypertension. Adults (n 4908) were included from the cross-sectional Australian Health Survey 2011–2013. Two 24-h dietary recalls estimated food and nutrient intakes. Reduced rank regression derived DP with dietary energy density (DED), fibre density and total sugar intake as response variables for obesity and Na:K, SFA:PUFA and fibre density as variables for hypertension. Poisson regression investigated relationships between DP and prevalence ratios (PR) of overweight/obesity (BMI≥25 kg/m2) and hypertension (blood pressure≥140/90 mmHg). Obesity-DP1 was positively correlated with fibre density and sugars and inversely with DED. Obesity-DP2 was positively correlated with sugars and inversely with fibre density. Individuals in the highest tertile of Obesity-DP1 and Obesity-DP2, compared with the lowest, had lower (PR 0·88; 95 % CI 0·81, 0·95) and higher (PR 1·09; 95 % CI 1·01, 1·18) prevalence of obesity, respectively. Na:K and SFA:PUFA were positively correlated with Hypertension-DP1 and inversely correlated with Hypertension-DP2, respectively. There was a trend towards higher hypertension prevalence in the highest tertile of Hypertension-DP1 compared with the lowest (PR 1·18; 95 % CI 0·99, 1·41). Hypertension-DP2 was not associated with hypertension. Obesity prevalence was inversely associated with low-DED, high-fibre and high-sugar (natural sugars) diets and positively associated with low-fibre and high-sugar (added sugars) diets. Hypertension prevalence was higher on low-fibre and high-Na and SFA diets.

Estimating population food and nutrient exposure: a comparison of store survey data with household panel food purchases

Population exposure to food and nutrients can be estimated from household food purchases, but store surveys of foods and their composition are more available, less costly and might provide similar information. Our aim was to compare estimates of nutrient exposure from a store survey of packaged food with those from household panel food purchases. A cross-sectional store survey of all packaged foods for sale in two major supermarkets was undertaken in Auckland, New Zealand, between February and May 2012. Longitudinal household food purchase data (November 2011 to October 2012) were obtained from the nationally representative, population-weighted New Zealand Nielsen HomeScan® panel. Data on 8440 packaged food and non-alcoholic beverage products were collected in the store survey. Food purchase data were available for 1229 households and 16 812 products. Store survey data alone produced higher estimates of exposure to Na and sugar compared with estimates from household panel food purchases. The estimated mean difference in exposure to Na was 94 (95 % CI 72, 115) mg/100 g (20 % relative difference; P<0·01), to sugar 1·6 (95 % CI 0·8, 2·5) g/100 g (11 %; P<0·01), to SFA −0·3 (95 % CI −0·8, 0·3) g/100 g (6 %; P=0·3) and to energy −18 (−71, 35) kJ/100 g (2 %; P=0·51). Compared with household panel food purchases, store survey data provided a reasonable estimate of average population exposure to key nutrients from packaged foods. However, caution should be exercised in using such data to estimate population exposure to Na and sugar and in generalising these findings to other countries, as well as over time.