The study of the informativeness and reliability of the healthy eating index for assessing of dietary peculiarity and eating behavior of Russian population

2021 ◽  
Vol 90 (5) ◽  
pp. 77-86
Author(s):  
A.N. Martinchik ◽  
◽  
N.A. Mikhaylov ◽  
E.E. Keshabyants ◽  
K.V. Kudryavtseva ◽  
...  
Keyword(s):  
Eating Behavior ◽  
Healthy Eating ◽  
Healthy Eating Index ◽  
Russian Population

scholarly journals Neural Substrates of Food Valuation and Its Relationship With BMI and Healthy Eating in Higher BMI Individuals

2020 ◽  
Author(s):  
Junaid Salim Merchant ◽  
Danielle Cosme ◽  
Elliot Berkman ◽  
Nicole Giuliani ◽  
Bryce Dirks
Keyword(s):  
Eating Behavior ◽  
Healthy Eating ◽  
Bayes Factor ◽  
Assessment Tool ◽  
Community Sample ◽  
Healthy Eating Index ◽  
Unhealthy Food ◽  
Potential Mechanism ◽  
Imaging Data ◽  
Subjective Valuation

Considerable evidence points to a link between body mass index (BMI), eating behavior, and the brain's reward system. However, much of this research focuses on food cue reactivity without examining the subjective valuation process as a potential mechanism driving individual differences in BMI and eating behavior. The current pre-registered study (https://osf.io/n4c95/) examined the relationship between BMI, healthy eating, and subjective valuation of healthy and unhealthy foods in a community sample of individuals with higher BMI who intended to eat more healthily. Particularly, we examined: (1) alterations in neurocognitive measures of subjective valuation related to BMI and healthy eating; (2) differences in the neurocognitive valuation for healthy and unhealthy foods and their relation to BMI and healthy eating; (3) and whether we could conceptually replicate prior findings demonstrating differences in neural reactivity to palatable vs. plain foods. To this end, we scanned 105 participants with BMIs ranging from 23 to 42 using fMRI during a willingness-to-pay task that quantifies trial-by-trial valuation of 30 healthy and 30 unhealthy food items. We measured out of lab eating behavior via the Automated Self-Administered 24 H Dietary Assessment Tool, which allowed us to calculate a Healthy Eating Index (HEI). We found that our sample exhibited robust, positive linear relationships between self-reported value and neural responses in regions previously implicated in studies of subjective value, suggesting an intact valuation system. However, we found no relationship between valuation and BMI nor HEI, with Bayes Factor indicating moderate evidence for a null relationship. Separating the food types revealed that healthy eating, as measured by the HEI, was inversely related to subjective valuation of unhealthy foods. Imaging data further revealed a stronger linkage between valuation of healthy (compared to unhealthy) foods and corresponding response in the ventromedial prefrontal cortex (vmPFC), and that the interaction between healthy and unhealthy food valuation in this region is related to HEI. Finally, our results did not replicate reactivity differences demonstrated in prior work, likely due to differences in the mapping between food healthiness and palatability. Together, our findings point to disruptions in the valuation of unhealthy foods in the vmPFC as a potential mechanism influencing healthy eating.

scholarly journals Neural Substrates of Food Valuation and Its Relationship With BMI and Healthy Eating in Higher BMI Individuals

2020 ◽  
Vol 14 ◽  
Author(s):  
Junaid S. Merchant ◽  
Danielle Cosme ◽  
Nicole R. Giuliani ◽  
Bryce Dirks ◽  
Elliot T. Berkman
Keyword(s):  
Eating Behavior ◽  
Healthy Eating ◽  
Bayes Factor ◽  
Assessment Tool ◽  
Community Sample ◽  
Healthy Eating Index ◽  
Unhealthy Food ◽  
Potential Mechanism ◽  
Imaging Data ◽  
Subjective Valuation

Considerable evidence points to a link between body mass index (BMI), eating behavior, and the brain's reward system. However, much of this research focuses on food cue reactivity without examining the subjective valuation process as a potential mechanism driving individual differences in BMI and eating behavior. The current pre-registered study (https://osf.io/n4c95/) examined the relationship between BMI, healthy eating, and subjective valuation of healthy and unhealthy foods in a community sample of individuals with higher BMI who intended to eat more healthily. Particularly, we examined: (1) alterations in neurocognitive measures of subjective valuation related to BMI and healthy eating; (2) differences in the neurocognitive valuation for healthy and unhealthy foods and their relation to BMI and healthy eating; (3) and whether we could conceptually replicate prior findings demonstrating differences in neural reactivity to palatable vs. plain foods. To this end, we scanned 105 participants with BMIs ranging from 23 to 42 using fMRI during a willingness-to-pay task that quantifies trial-by-trial valuation of 30 healthy and 30 unhealthy food items. We measured out of lab eating behavior via the Automated Self-Administered 24 H Dietary Assessment Tool, which allowed us to calculate a Healthy Eating Index (HEI). We found that our sample exhibited robust, positive linear relationships between self-reported value and neural responses in regions previously implicated in studies of subjective value, suggesting an intact valuation system. However, we found no relationship between valuation and BMI nor HEI, with Bayes Factor indicating moderate evidence for a null relationship. Separating the food types revealed that healthy eating, as measured by the HEI, was inversely related to subjective valuation of unhealthy foods. Imaging data further revealed a stronger linkage between valuation of healthy (compared to unhealthy) foods and corresponding response in the ventromedial prefrontal cortex (vmPFC), and that the interaction between healthy and unhealthy food valuation in this region is related to HEI. Finally, our results did not replicate reactivity differences demonstrated in prior work, likely due to differences in the mapping between food healthiness and palatability. Together, our findings point to disruptions in the valuation of unhealthy foods in the vmPFC as a potential mechanism influencing healthy eating.

Parent Food and Eating Behavior Assessments Predict Targeted Healthy Eating Index Components

2019 ◽  
Vol 51 (6) ◽  
pp. 711-718 ◽  
Author(s):  
Elizabeth H. Ruder ◽  
Barbara Lohse ◽  
Diane C. Mitchell ◽  
Leslie Cunningham-Sabo
Keyword(s):  
Eating Behavior ◽  
Healthy Eating ◽  
Healthy Eating Index ◽  
Index Components

scholarly journals Mediterranean Diet and Healthy Eating in Subjects with Prediabetes from the Mollerussa Prospective Observational Cohort Study

Nutrients ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 252
Author(s):  
Mireia Falguera ◽  
Esmeralda Castelblanco ◽  
Marina Idalia Rojo-López ◽  
Maria Belén Vilanova ◽  
Jordi Real ◽  
...  
Keyword(s):  
Glucose Tolerance ◽  
Mediterranean Diet ◽  
Healthy Eating ◽  
Dietary Patterns ◽  
Normal Glucose Tolerance ◽  
Healthy Eating Index ◽  
Study Cohort ◽  
Cross Sectional ◽  
Normal Glucose ◽  
Multivariable Analyses

We aimed to assess differences in dietary patterns (i.e., Mediterranean diet and healthy eating indexes) between participants with prediabetes and those with normal glucose tolerance. Secondarily, we analyzed factors related to prediabetes and dietary patterns. This was a cross-sectional study design. From a sample of 594 participants recruited in the Mollerussa study cohort, a total of 535 participants (216 with prediabetes and 319 with normal glucose tolerance) were included. The alternate Mediterranean Diet score (aMED) and the alternate Healthy Eating Index (aHEI) were calculated. Bivariable and multivariable analyses were performed. There was no difference in the mean aMED and aHEI scores between groups (3.2 (1.8) in the normoglycemic group and 3.4 (1.8) in the prediabetes group, p = 0.164 for the aMED and 38.6 (7.3) in the normoglycemic group and 38.7 (6.7) in the prediabetes group, p = 0.877 for the aHEI, respectively). Nevertheless, women had a higher mean of aMED and aHEI scores in the prediabetes group (3.7 (1.9), p = 0.001 and 40.5 (6.9), p < 0.001, respectively); moreover, they had a higher mean of aHEI in the group with normoglycemia (39.8 (6.6); p = 0.001). No differences were observed in daily food intake between both study groups; consistent with this finding, we did not find major differences in nutrient intake between groups. In the multivariable analyses, the aMED and aHEI were not associated with prediabetes (odds ratio (OR): 1.19, 95% confidence interval (CI): 0.75–1.87; p = 0.460 and OR: 1.32, 95% CI: 0.83–2.10; p = 0.246, respectively); however, age (OR: 1.04, 95% CI: 1.02–1.05; p < 0.001), dyslipidemia (OR: 2.02, 95% CI: 1.27–3.22; p = 0.003) and body mass index (BMI) (OR: 1.09, 95% CI: 1.05–1.14; p < 0.001) were positively associated with prediabetes. Physical activity was associated with a lower frequency of prediabetes (OR: 0.48, 95% CI: 0.31–0.72; p = 0.001). In conclusion, subjects with prediabetes did not show a different dietary pattern compared with a normal glucose tolerance group. However, further research is needed on this issue.

Genetic and Environmental Influences of Dietary Indices in a UK Female Twin Cohort

2020 ◽  
Vol 23 (6) ◽  
pp. 330-337
Author(s):  
Olatz Mompeo ◽  
Rachel Gibson ◽  
Paraskevi Christofidou ◽  
Tim D. Spector ◽  
Cristina Menni ◽  
...  
Keyword(s):  
Healthy Eating ◽  
Menopausal Status ◽  
Healthy Eating Index ◽  
Healthy Diet ◽  
Nutritional Epidemiology ◽  
Best Fitting ◽  
Heritability Estimates ◽  
Fitting Model ◽  
Dietary Indices ◽  
Health Parameters

AbstractA healthy diet is associated with the improvement or maintenance of health parameters, and several indices have been proposed to assess diet quality comprehensively. Twin studies have found that some specific foods, nutrients and food patterns have a heritable component; however, the heritability of overall dietary intake has not yet been estimated. Here, we compute heritability estimates of the nine most common dietary indices utilized in nutritional epidemiology. We analyzed 2590 female twins from TwinsUK (653 monozygotic [MZ] and 642 dizygotic [DZ] pairs) who completed a 131-item food frequency questionnaire (FFQ). Heritability estimates were computed using structural equation models (SEM) adjusting for body mass index (BMI), smoking status, Index of Multiple Deprivation (IMD), physical activity, menopausal status, energy and alcohol intake. The AE model was the best-fitting model for most of the analyzed dietary scores (seven out of nine), with heritability estimates ranging from 10.1% (95% CI [.02, .18]) for the Dietary Reference Values (DRV) to 42.7% (95% CI [.36, .49]) for the Alternative Healthy Eating Index (A-HEI). The ACE model was the best-fitting model for the Healthy Diet Indicator (HDI) and Healthy Eating Index 2010 (HEI-2010) with heritability estimates of 5.4% (95% CI [−.17, .28]) and 25.4% (95% CI [.05, .46]), respectively. Here, we find that all analyzed dietary indices have a heritable component, suggesting that there is a genetic predisposition regulating what you eat. Future studies should explore genes underlying dietary indices to further understand the genetic disposition toward diet-related health parameters.

scholarly journals Eating behavior among persons with type 2 diabetes mellitus in North Ethiopia: a cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hagos Amare Gebreyesus ◽  
Girmatsion Fisseha Abreha ◽  
Sintayehu Degu Besherae ◽  
Merhawit Atsbha Abera ◽  
Abraha Hailu Weldegerima ◽  
...  
Keyword(s):  
Nutrition Education ◽  
Eating Behavior ◽  
Healthy Eating ◽  
Food Selection ◽  
Cross Sectional Study ◽  
Three Dimensions ◽  
Cross Sectional ◽  
Healthy Eating Behavior ◽  
Meal Planning

Abstract Background Diet is central to the management of type 2 diabetes mellitus (T2DM). Depending on the stage of the disease at which the recommended diet is initiated, optimal adherence can reduce HbA1c by about 1 to 2%. However, evidence on eating behavior is generally scarce including in Ethiopia. The present study aimed to assess the eating behavior of adults with T2DM in North Ethiopia. Methods This cross-sectional study was conducted among 421 adults with T2DM from September to November 2019. Socio-demographic variables were collected using structured questionnaires; an asset-based wealth index was used to determine socioeconomic status. Three dimensions of eating behavior were assessed using Likert-type items: food selection, meal planning and calorie recognition. Raw Likert scores in each dimension were transformed to percent scales to maximum (%SM). Participants’ behavior in each dimension was categorized into healthy and unhealthy taking 66.7% SM score as a cutoff. Overall eating behavior was determined by aggregating ranks scored in the three dimensions. Correlates of overall eating behavior were identified using Chi-square test and multinomial logistic regression with statistical significance set at P-value < 0.05. Result Only 1% of the participants had overall healthy eating behavior. Yet, overall unhealthy eating was apparent in 54.4%. By dimensions, healthy eating behaviors in food selection, meal planning and calorie recognition were seen in 43.5, 7.4 and 2.9% participants, respectively. Factors that were positively associated with having healthy eating behavior in one dimension relative to unhealthy in all were: receiving nutrition education [AOR 1.73; CI 1.09, 2.74], female gender [AOR 1.78; CI 1.03, 3.08] & being in 26–44 age category [AOR 3.7; CI 1.56, 8.85]. But, being in the poor [AOR 0.42; CI 0.16, 1.32] or average [AOR 0.54; CI 0.19, 1.55] socioeconomic strata were negatively associated. However, only receiving nutrition education [AOR 3.65; CI 1.31, 10.18] was significantly associated with having healthy behavior in two eating dimensions over unhealthy in all. Conclusion In North Ethiopia, the overall eating behavior of adults with T2DM is extremely poor. Diverse and integrated approaches including nutrition education during consultation should be implemented to address the gap.

Higher diet quality is associated with higher diet costs when eating at home and away from home: National Health and Nutrition Examination Survey, 2005-2016

2021 ◽  
pp. 1-29
Author(s):  
Zach Conrad ◽  
Sarah Reinhardt ◽  
Rebecca Boehm ◽  
Acree McDowell
Keyword(s):  
Healthy Eating ◽  
National Health ◽  
Diet Quality ◽  
Healthy Eating Index ◽  
Food Prices ◽  
Nutrition Examination Survey ◽  
Food Away From Home ◽  
Health And Nutrition ◽  
Food Consumed ◽  
At Home

Abstract Objectives: To evaluate the association between diet quality and cost for foods purchased for consumption at home and away from home. Design: Cross-sectional analysis. Multivariable linear regression models evaluated the association between diet quality and cost for all food, food at home, and food away from home. Setting: Daily food intake data from the National Health and Nutrition Examination Survey (2005-2016). Food prices were derived using data from multiple, publicly available databases. Diet quality was assessed using the Healthy Eating Index-2015 and the Alternative Healthy Eating Index-2010. Participants: 30,564 individuals ≥20 y with complete and reliable dietary data. Results: Mean per capita daily diet cost was $14.19 (95% CI: $13.91-14.48), including $6.92 ($6.73-7.10) for food consumed at home and $7.28 ($7.05-7.50) for food consumed away from home. Diet quality was higher for food at home compared to food away from home (P<0.001). Higher diet quality was associated with higher food costs overall, at home, and away from home (P<0.001 for all comparisons). Conclusions: These findings demonstrate that higher diet quality is associated with higher costs for all food, food consumed at home, and food consumed away from home. This research provides policymakers, public health professionals, and clinicians with information needed to support healthy eating habits. These findings are particularly relevant to contemporary health and economic concerns that have worsened because of the COVID-19 pandemic.

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