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.
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Background. In an ageing society cognitive decline is expected to become an important health problem. Previous studies showed that a healthy lifestyle, i.e. sufficient physical activity and a healthy diet,can benefit cognitive function. In this study, we aimed to assess the (synergistic) association of physical activity and a healthy diet with cognitive functioning in 1,741 Dutch men and women aged 57-97 years.
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ObjectivesAdherence to lifestyle interventions is crucial for the treatment of obesity. However, there is little research about adherence to lifestyle interventions in persons around retirement age. The objectives of this study are (1) to identify factors associated with the adherence to resistance training and a hypocaloric diet and (2) to describe the association between adherence and changes in body composition outcome parameters.DesignThis secondary data analysis included three randomized controlled trials.Setting & participantsThe inclusion criteria of the participants were an age of 55–75 years, a BMI ≥ 25 kg/m2 and receiving both a hypocaloric diet and resistance training. All participants were residing in the community.MeasurementsAdherence to hypocaloric diet was measured through the mean dietary intake on the basis of a 3-day dietary record. If the participant consumed at least 600 kcal less than the individual caloric requirements, they were considered adherent. Adherence to resistance training was achieved if ≥67% of the recommended training sessions were attended over the course of the study periods.Results232 participants were included, 47.0% female, mean age 64.0 (±5.5) years. 80.2% adhered to resistance training and 51.3% adhered to a hypocaloric diet. Older age (Beta 0.41; 95% CI 0.05, 0.78; p = 0.028) and male sex (Beta 7.7; 95% CI 3.6, 11; p < 0.001) were associated with higher resistance training adherence. A higher BMI at baseline (Beta 6.4; 95% CI 3.6, 9.2; p < 0.001) and male sex (Beta 65; 95% CI 41, 88; p < 0.001) were associated with higher adherence to hypocaloric diet.ConclusionWe identified several associated factors (sex, age and BMI at baseline) that should be considered to promote adherence in future lifestyle intervention studies in persons around retirement age. We recommend including behavior change techniques in lifestyle interventions and consider sex-specific interventions to improve the adherence of women.
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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.
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The primary aim was to investigate feasibility of a web-based cross-over Paleolithic diet intervention in the general population. The secondary aim was to calculate the sample size needed to reach a statistically significant difference in effect of a Paleolithic-like diet on psychological and somatic symptoms compared with the Dutch consensus diet.
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Objectives: Previous research has demonstrated that being both physically active and adhering a healthy diet is associated with improved cognitive functioning; however, it remains unclear whether these factors act synergistically. We investigated the synergistic association of a healthy diet and being physically active with cognitive functioning. Design: Cross-sectional study. Setting and participants: Data from the Longitudinal Aging Study Amsterdam (LASA) were used. We analyzed data from 2,165 community dwelling adults who were aged 55-85 years, 56% of whom were female. Cognitive functioning was assessed by the Mini-Mental State Examination (MMSE), an MMSE score of >26 indicates good cognitive functioning. Physical activity was assessed by the LASA Physical Activity Questionnaire and was considered sufficient if the person engaged in moderately intense physical activity ≥ 20 min/day. A healthy diet score was based on the intake of fruit, vegetables and fish. Each of the food groups was assigned a score that ranged from 1 (well below the Dutch guideline for a healthy diet) to 4 (well above the Dutch guideline for a healthy diet), and the scores were aggregated to determine a healthy diet (healthy ≥ 9 points). Multiple logistic and linear regression analyses were used to examine the (synergistic) association among physical activity, a healthy diet and cognitive functioning. All analyses were adjusted for potential chronic diseases and lifestyle confounders. Results: Of all of the participants, 25% were diagnosed with a cognitive impairment (MMSE ≤26), 80% were physically active and 41% had a healthy diet. Sixty three percent of the participants both adhered to a healthy diet and were physically active. Sufficient daily physical activity (OR=2.545 p<.001) and adherence to a healthy diet (OR=1.766 p=.002) were associated with good cognitive functioning. After adjusting for confounding factors, sufficient physical activity was not significantly related to cognitive functioning (p=.163); however adherence to a healthy diet remained significantly associated with good cognitive functioning (p=.017). No interaction among sufficient physical activity, healthy diet adherence and good cognitive functioning was observed (crude: p=.401, adjusted: p=.216).Conclusion: The results of this cross-sectional study indicate that adherence to a healthy diet is inde-pendently related to cognitive functioning. Being physically active does not modify this association. Furthermore, these two lifestyle factors do not synergistically relate to cognitive functioning.
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RATIONALE: Disturbed protein metabolism may result in malnutrition. A non-invasive low cost clinical tool to measure protein metabolism is lacking. Explorative research (n=1) with a newly developed non-invasive 13C-protein breath test suggested a decrease in protein oxidation after a protein restricted diet. Now, we aimed to test the effect of protein restriction in more subjects, to assess sensitivity of the test.METHODS: In this exploratory study, 14 healthy male subjects (23±3 y) participated. Habitual intake was assessed by a 4-day food diary. Next, subjects were instructed to use a 4-day isocaloric protein restricted diet (0.25 g protein/kg bw/day). After an overnight fast, a 30 g naturally enriched 13C-milk protein test drink was consumed, followed by collection of breath samples up to 330 min. Protein oxidation was analyzed by Isotope Ratio Mass Spectrometry. 24-h urine was collected on day 4 of the habitual diet, and on every day of the 4-day protein restricted diet, to assess actual change in protein intake.RESULTS: After the protein restricted diet, 30.2%±7.7 of the 30 g 13C-milk protein was oxidized over 330 min, compared to 30.6%±6.2 (NS) after the subject’s habitual diet (1.4±0.3 g protein/kg bw/day). Within subjects, both increase and decrease in oxidation was found. During the 4-day protein restricted diet, urinary urea:creatinine ratio decreased by 56%±10, consistent with a reduction in protein intake of 44%±15 (g/day) and 53%±12 (g/kg bw/day), based on urea and food diary, respectively.CONCLUSIONS: The breath test shows variation within subjects and between diets, which could be related to the sensitivity of the test. We cannot explain the variation by the measured variables. Alternatively, our results may implicate that in some of our subjects, protein intake did not sufficiently decrease to levels that could alter protein metabolism.
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RATIONALE: Disturbed protein metabolism may result in malnutrition. A non-invasive low cost clinical tool to measure protein metabolism is lacking. Explorative research (n=1) with a newly developed non-invasive 13C-protein breath test suggested a decrease in protein oxidation after a protein restricted diet. Now, we aimed to test the effect of protein restriction in more subjects, to assess sensitivity of the test.METHODS: In this exploratory study, 14 healthy male subjects (23±3 y) participated. Habitual intake was assessed by a 4-day food diary. Next, subjects were instructed to use a 4-day isocaloric protein restricted diet (0.25 g protein/kg bw/day). After an overnight fast, a 30 g naturally enriched 13C-milk protein test drink was consumed, followed by collection of breath samples up to 330 min. Protein oxidation was analyzed by Isotope Ratio Mass Spectrometry. 24-h urine was collected on day 4 of the habitual diet, and on every day of the 4-day protein restricted diet, to assess actual change in protein intake.RESULTS: After the protein restricted diet, 30.2%±7.7 of the 30 g 13C-milk protein was oxidized over 330 min, compared to 30.6%±6.2 (NS) after the subject’s habitual diet (1.4±0.3 g protein/kg bw/day). Within subjects, both increase and decrease in oxidation was found. During the 4-day protein restricted diet, urinary urea:creatinine ratio decreased by 56%±10, consistent with a reduction in protein intake of 44%±15 (g/day) and 53%±12 (g/kg bw/day), based on urea and food diary, respectively.CONCLUSIONS: The breath test shows variation within subjects and between diets, which could be related to the sensitivity of the test. We cannot explain the variation by the measured variables. Alternatively, our results may implicate that in some of our subjects, protein intake did not sufficiently decrease to levels that could alter protein metabolism.
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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.
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Front-of-pack nutrition labels can help consumers to make healthier choices and stimulate healthier product development. This is the first modeling study to investigate the potential impact on cholesterol levels of consuming a diet consisting of products that comply with the criteria for a ‘healthier choice logo’.
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