PURPOSE: Several studies have reported seasonal variation in intake of food groups and certain nutrients. However, whether this could lead to a seasonal pattern of diet quality has not been addressed. We aimed to describe the seasonality of diet quality, and to examine the contribution of the food groups included in the dietary guidelines to this seasonality.METHODS: Among 9701 middle-aged and elderly participants of the Rotterdam Study, a prospective population-based cohort, diet was assessed using food-frequency questionnaires (FFQ). Diet quality was measured as adherence to the Dutch dietary guidelines, and expressed in a diet quality score ranging from 0 to 14 points. The seasonality of diet quality and of the food group intake was examined using cosinor linear mixed models. Models were adjusted for sex, age, cohort, energy intake, physical activity, body mass index, comorbidities, and education.RESULTS: Diet quality had a seasonal pattern with a winter-peak (seasonal variation = 0.10 points, December-peak) especially among participants who were men, obese and of high socio-economic level. This pattern was mostly explained by the seasonal variation in the intake of legumes (seasonal variation = 3.52 g/day, December-peak), nuts (seasonal variation = 0.78 g/day, January-peak), sugar-containing beverages (seasonal variation = 12.96 milliliters/day, June-peak), and dairy (seasonal variation = 17.52 g/day, June-peak).CONCLUSIONS: Diet quality varies seasonally with heterogeneous seasonality of food groups counteractively contributing to the seasonal pattern in diet quality. This seasonality should be considered in future research on dietary behavior. Also, season-specific recommendations and policies are required to improve diet quality throughout the year.
BACKGROUND & AIMS: Diagnosed prevalence of malnutrition and dietary intake are currently unknown in patients with severe aortic stenosis planned to undergo Transcatheter Aortic Valve Implantation (TAVI). This study describes the preprocedural nutritional status, protein intake and diet quality.METHODS: Consecutive preprocedural TAVI patients were asked to participate in this explorative study. Nutritional status was diagnosed with the global leadership initiative on malnutrition (GLIM) criteria. Preprocedural protein intake and diet quality were assessed with a three-day dietary record. To increase the record's validity, a researcher visited the participants at their homes to confirm the record. Protein intake was reported as an average intake of three days and diet quality was assessed using the Dutch dietary guidelines (score range 0-14, 1 point for adherence to each guideline).RESULTS: Of the included patients (n = 50, median age 80 ± 5, 56% male) 32% (n = 16) were diagnosed with malnutrition. Patients diagnosed with malnutrition had a lower protein intake (1.02 ± 0.28 g/kg/day vs 0.87 ± 0.21 g/kg/day, p = 0.04). The difference in protein intake mainly took place during lunch (20 ± 13 g/kg vs 13 ± 7 g/kg, p = 0.03). Patients adhered to 6.4 ± 2.2 out of 14 dietary guidelines. Adherence to the guideline of whole grains and ratio of whole grains was lower in the group of patients with malnutrition than in patients with normal nutritional status (both 62% vs 19%, p = 0.01). In a multivariate analysis diabetes mellitus was found as an independent predictor of malnutrition.CONCLUSION: Prevalence of malnutrition among TAVI patients is very high up to 32%. Patients with malnutrition had lower protein and whole grain intake than patients with normal nutritional status. Furthermore, we found diabetes mellitus as independent predictor of malnutrition. Nutrition interventions in this older patient group are highly warranted.
Objective We examined whether the role of maternal education in children's unhealthy snacking diet is moderated by other socio-economic indicators. Methods Participants were selected from the Amsterdam Born Children and their Development cohort, a large ongoing community-based birth cohort. Validated Food Frequency Questionnaires (FFQ) (n = 2782) were filled in by mothers of children aged 5.7±0.5yrs. Based on these FFQs, a snacking dietary pattern was derived using Principal Component Analysis. Socio-economic indicators were: maternal and paternal education (low, middle, high; based on the highest education completed) household finance (low, high; based on ability to save money) and neighbourhood SES (composite score including educational level, household income and employment status of residents per postal code). Cross-sectional multivariable linear regression analysis was used to assess the association and possible moderation of maternal education and other socio-economic indicators on the snacking pattern score. Analyses were adjusted for children's age, sex and ethnicity. Results Low maternal education (B 0.95, 95% CI 0.83;1.06), low paternal education (B 0.36, 95% CI 0.20;0.52), lower household finance (B 0.18, 95% CI 0.11;0.26) and neighbourhood SES (B -0.09, 95% CI -0.11;-0.06) were independently associated with higher snacking pattern scores (p<0.001). The association between maternal education and the snacking pattern score was somewhat moderated by household finance (p = 0.089) but remained strong. Children from middle-high educated mothers (B 0.44, 95% CI 0.35;0.52) had higher snacking pattern scores when household finance was low (B 0.49, 95% CI 0.33;0.65). Conclusions All socio-economic indicators were associated with increased risk of unhealthy dietary patterns in young children, with low maternal education conferring the highest risk. Yet, within the group of middle-high educated mothers, lower household finance was an extra risk factor for unhealthy dietary patterns. Intervention strategies should therefore focus on lower educated mothers and middle-high educated mothers with insufficient levels of household finance.
