Association between diet quality, dietary patterns and cardiometabolic health in Australian adults: a cross-sectional study

BackgroundDiet quality indices score dietary intakes against recommendations, whereas dietary patterns consider the pattern and combination of dietary intakes. Studies evaluating both methodologies in relation to cardiometabolic health in a nationally representative sample are limited. The aim of the present study was to investigate the relationship between diet quality, dietary patterns and markers of cardiometabolic health in Australian adults.MethodsDietary data, using two 24-h dietary recalls, were collected from adults in the cross-sectional Australian Health Survey 2011&ndash;2013 (n&thinsp;=&thinsp;2121; 46.4 (SE 0.48) years). Diet quality was estimated using the Dietary Guideline Index (DGI). Dietary patterns (DPs), derived using reduced rank regression, were estimated using fiber density, SFA: PUFA and total sugars intake as intermediate markers. Multi-variable adjusted linear regression analyses were used to examine associations between diet quality and DPs and blood biomarkers, body mass index, waist circumference, diastolic and systolic blood pressure and an overall cardiometabolic risk score.ResultsDGI was associated with lower glucose (coef &minus;&thinsp;0.009, SE 0.004; P-trend&thinsp;=&thinsp;0.033), body mass index (coef &minus;&thinsp;0.017, SE 0.007; P-trend&thinsp;=&thinsp;0.019) and waist circumference (coef &minus;&thinsp;0.014, SE 0.005; P-trend&thinsp;=&thinsp;0.008). Two dietary patterns were derived: dietary pattern-1 was characterized by higher intakes of pome fruit and wholegrain bread, while dietary pattern-2 was characterized by higher intakes of added sugars and tropical fruit. Dietary pattern-1 was associated with lower body mass index (coef &minus;&thinsp;0.028, SE 0.007; P-trend&lt;&thinsp;0.001) and waist circumference (coef &minus;&thinsp;0.017, SE 0.005; P-trend&thinsp;=&thinsp;0.001). There was a trend towards lower cardiometabolic risk score. Dietary pattern-2 was associated with lower HDL-cholesterol (coef &minus;&thinsp;0.026, SE 0.012; P-trend&thinsp;=&thinsp;0.028). There was a trend towards lower diastolic blood pressure. No associations with other markers were observed.ConclusionsBetter diet quality and healthier dietary patterns were primarily associated with favorable anthropometric markers of cardiometabolic health. Findings support the need for comparison of whole-diet based methodologies that take into consideration the interactions between foods and nutrients. Longitudinal studies are warranted to better understand causal relationships between diet and cardiometabolic health.<br /

A comparison of diet quality indices in a nationally representative cross-sectional study of Iranian households

Background: Iranian diet quality has been evaluated using indices that have not been created based on Iranian dietary guidelines. This study aimed to examine the applicability of two diet quality indices by examining their associations with nutrient adequacy, nutrient intakes and sociodemographics. Methods: Dietary data were collected using three 24-h dietary recalls from Iranian households. Nutrient adequacy was assessed using World Health Organization/Food and Agriculture Organization 2002 (WHO/FAO) cut points. Household diet quality was calculated using the Healthy Eating Index (HEI) and Diet Quality Index-International (DQI-I). Sociodemographics of the household members were assessed. Regression analyses were used to examine associations between diet quality and nutrient adequacy, and between sociodemographics and diet quality. Results: A total of 6935 households were included in the analysis. Higher household diet quality was associated with adequate intake of calcium (HEI: OR 1.11, 95% CI: 1.10, 1.13; DQI-I: OR 1.14, 95% CI: 1.13, 1.16), vitamin C (HEI: OR 1.19, 95% CI: 1.17, 1.20; DQI-I: OR 1.12, 95% CI: 1.11, 1.12) and protein (HEI: OR 1.01, 95% CI: 1.00, 1.02; DQI-I: OR 1.09, 95% CI: 1.08, 1.09). Higher household diet quality was associated with household heads who were older (&gt;&thinsp;56&thinsp;years old) (HEI: &beta; 2.06, 95% CI: 1.63, 2.50; DQI-I &beta; 2.90, 95% CI: 2.34, 3.45), higher educated (college/university completed) (HEI: &beta; 4.54, 95% CI: 4.02, 5.06; DQI-I: &beta; 2.11, 95% CI: 1.45, 2.77) and living in urban areas (HEI: &beta; 2.85, 95% CI: 2.54, 3.16; DQI-I: &beta; 0.72, 95% CI: 0.32, 1.12). Conclusions: Based on associations with nutrient adequacy and sociodemographics, the applicability of two diet quality indices for assessing the diet quality of Iranian households was demonstrated. Results also indicated DQI-I may be more applicable than HEI for evaluating Iranian nutrient adequacy. Findings have implications for the design and assessment of diet quality in Iranian populations. Future research should examine the link between these diet quality indices and health outcomes

Can personalized nutrition improve people’s diets?

Each person differs in physical characteristics such as eye color, but also in likes and dislikes. These differences are due to our genes and our environments, including what we eat. What we eat affects our health, and each of us has individual nutritional needs. This is the basis for the idea of personalized nutrition. In our research study, called the Food4Me Study, we tested whether personalized nutrition advice helped over 1,600 people to eat healthier diets. We collected information about each person, including what they ate, and we collected samples of saliva to examine their genes. We gave each person either the usual advice about healthy eating (such as “eat more vegetables”) or advice that was personalized based on the individual’s characteristics. After 6 months, we discovered that people who received personalized nutrition advice improved their diets more than people who received the typical healthy eating advice

Unhealthy lifestyle, genetics and risk of cardiovascular disease and mortality in 76,958 individuals from the UK Biobank cohort study

To examine associations of unhealthy lifestyle and genetics with risk of all-cause mortality, cardiovascular disease (CVD) mortality, myocardial infarction (MI) and stroke. We used data on 76,958 adults from the UK Biobank prospective cohort study. Favourable lifestyle included no overweight/obesity, not smoking, physical activity, not sedentary, healthy diet and adequate sleep. A Polygenic Risk Score (PRS) was derived using 300 CVD-related single nucleotide polymorphisms. Cox proportional hazard ratios (HR) were used to model effects of lifestyle and PRS on risk of CVD and all-cause mortality, stroke and MI. New CVD (n = 364) and all-cause (n = 2408) deaths, and stroke (n = 748) and MI (n = 1140) events were observed during a 7.8 year mean follow-up. An unfavourable lifestyle (0−1 healthy behaviours) was associated with higher risk of all-cause mortality (HR: 2.06; 95% CI: 1.73, 2.45), CVD mortality (HR: 2.48; 95% CI: 1.64, 3.76), MI (HR: 2.12; 95% CI: 1.65, 2.72) and stroke (HR:1.74; 95% CI: 1.25, 2.43) compared to a favourable lifestyle (≥4 healthy behaviours). PRS was associated with MI (HR: 1.35; 95% CI: 1.27, 1.43). There was evidence of a lifestyle-genetics interaction for stroke (p = 0.017). Unfavourable lifestyle behaviours predicted higher risk of all-cause mortality, CVD mortality, MI and stroke, independent of genetic risk

Correlates of meal skipping in young adults: a systematic review

BACKGROUND: Meal skipping rates may be highest during young adulthood, a period of transition and development. Although these dietary behaviours may increase future risk of chronic disease, limited research has investigated correlates of meal skipping in young adults. METHODS: A systematic literature search was conducted to identify studies that investigated correlates of meal skipping behaviours in young adults (aged 18-30 years). EBSCO host, MEDLINE Complete, Global Health, Scopus, EMBASE, Web of Science and Informit platforms were searched for eligible articles. Correlates were defined as any factor that was either associated with meal skipping or was self-reported by the participant to have an influence on meal skipping. Randomised controlled trials, prospective cohort studies, case-control studies, nested case-control studies, cross-sectional studies, and longitudinal studies were eligible for inclusion. RESULTS: Three-hundred and thirty-one articles were identified, 141 full-text articles assessed for eligibility, resulting in 35 included studies. Multiple methodological and reporting weaknesses were apparent in the reviewed studies with 28 of the 35 studies scoring a negative rating in the risk of bias assessment. Meal skipping (any meal), defined as the skipping of any meal throughout the day, was reported in 12 studies with prevalence ranging between 5 and 83%. The remaining 25 studies identified specific meals and their skipping rates, with breakfast the most frequently skipped meal 14-88% compared to lunch 8-57% and dinner 4-57%. Lack of time was consistently reported as an important correlate of meal skipping, compared with correlates such as cost and weight control, while sex was the most commonly reported associated correlate. Breakfast skipping was more common among men while lunch or dinner skipping being more common among women. CONCLUSIONS: This review is the first to examine potential correlates of meal skipping in young adults. Future research would benefit from stronger design and reporting strategies, using a standardised approach for measuring and defining meal skipping

Are dietary inequalities among Australian adults changing? a nationally representative analysis of dietary change according to socioeconomic position between 1995 and 2011-13

Abstract Background Increasing inequalities in rates of obesity and chronic disease may be partly fuelled by increasing dietary inequalities, however very few nationally representative analyses of socioeconomic trends in dietary inequalities exist. The release of the 2011–13 Australian National Nutrition and Physical Activity Survey data allows investigation of change in dietary intake according to socioeconomic position (SEP) in Australia using a large, nationally representative sample, compared to the previous national survey in 1995. This study examined change in dietary intakes of energy, macronutrients, fiber, fruits and vegetables among Australian adults between 1995 and 2011–13, according to SEP. Methods Cross-sectional data were obtained from the 1995 National Nutrition Survey, and the 2011–13 National Nutrition and Physical Activity Survey. Dietary intake data were collected via a 24-h dietary recall (n = 17,484 adults) and a dietary questionnaire (n = 15,287 adults). SEP was assessed according to educational level, equivalized household income, and area-level disadvantage. Survey-weighted linear and logistic regression models, adjusted for age, sex/gender and smoking status, examined change in dietary intakes over time. Results Dietary intakes remained poor across the SEP spectrum in both surveys, as evidenced by high consumption of saturated fat and total sugars, and low fiber, fruit and vegetable intakes. There was consistent evidence (i.e. according to ≥2 SEP measures) of more favorable changes in dietary intakes of carbohydrate, polyunsaturated and monounsaturated fat in higher, relative to lower SEP groups, particularly in women. Intakes of energy, total fat, saturated fat and fruit differed over time according to a single SEP measure (i.e. educational level, household income, or area-level disadvantage). There were no changes in intake of total sugars, protein, fiber or vegetables according to any SEP measures. Conclusions There were few changes in dietary intakes of energy, most macronutrients, fiber, fruits and vegetables in Australian adults between 1995 and 2011–13 according to SEP. For carbohydrate, polyunsaturated and monounsaturated fat, more favorable changes in intakes occurred in higher SEP groups. Despite the persistence of suboptimal dietary intakes, limited evidence of widening dietary inequalities is positive from a public health perspective. Trial registration Clinical trials registration: ACTRN12617001045303

Diet quality indices, genetic risk and risk of cardiovascular disease and mortality: a longitudinal analysis of 77 004 UK Biobank participants

Objectives: To examine associations of three diet quality indices and a polygenic risk score with incidence of all-cause mortality, cardiovascular disease (CVD) mortality, myocardial infarction (MI) and stroke. Design: Prospective cohort study. Setting: UK Biobank, UK. Participants: 77 004 men and women (40–70 years) recruited between 2006 and 2010. Main outcome measures: A polygenic risk score was created from 300 single nucleotide polymorphisms associated with CVD. Cox proportional HRs were used to estimate independent effects of diet quality and genetic risk on all-cause mortality, CVD mortality, MI and stroke risk. Dietary intake (Oxford WebQ) was used to calculate Recommended Food Score (RFS), Healthy Diet Indicator (HDI) and Mediterranean Diet Score (MDS). Results: New all-cause (n=2409) and CVD (n=364) deaths and MI (n=1141) and stroke (n=748) events were identified during mean follow-ups of 7.9 and 7.8 years, respectively. The adjusted HR associated with one-point higher RFS for all-cause mortality was 0.96 (95% CI: 0.94 to 0.98), CVD mortality was 0.94 (95% CI: 0.90 to 0.98), MI was 0.97 (95% CI: 0.95 to 1.00) and stroke was 0.94 (95% CI: 0.91 to 0.98). The adjusted HR for all-cause mortality associated with one-point higher HDI and MDS was 0.97 (95% CI: 0.93 to 0.99) and 0.95 (95% CI: 0.91 to 0.98), respectively. The adjusted HR associated with one-point higher MDS for stroke was 0.93 (95% CI: 0.87 to 1.00). There was little evidence of associations between HDI and risk of CVD mortality, MI or stroke. There was evidence of an interaction between diet quality and genetic risk score for MI. Conclusion: Higher diet quality predicted lower risk of all-cause mortality, independent of genetic risk. Higher RFS was also associated with lower risk of CVD mortality and MI. These findings demonstrate the benefit of following a healthy diet, regardless of genetic risk

Dietary patterns derived using principal component analysis and associations with sociodemographic characteristics and overweight and obesity: A cross-sectional analysis of Iranian adults

IntroductionThis study examined the cross-sectional association between household dietary patterns and sociodemographic characteristics and BMI in a nationally representative sample of Iranian adults.MethodsData on 6,833 households (n = 17,824 adults) from the National Comprehensive Study on Household Food Consumption Pattern and Nutritional Status 2001–2003 were used. Principal component analysis (PCA) was used to extract dietary patterns from three household 24-h dietary recalls. Linear regression analyses were used to examine associations between dietary patterns and sociodemographic characteristics and BMI.ResultsThree dietary patterns were identified: the first was characterized by high citrus fruit intake, the second by high hydrogenated fats intake and the third by high non-leafy vegetables intake. The first and third patterns were associated with household heads with higher education and living in urban areas, while the second was associated with household heads with lower education and living in rural areas. All dietary patterns were positively associated with BMI. The strongest association was found with the first dietary pattern (β: 0.49, 95% CI: 0.43, 0.55).DiscussionWhile all three dietary patterns were positively associated with BMI, the sociodemographic characteristics of Iranian adults who consumed them differed. These findings inform the design of population-level dietary interventions to address rising obesity rates in Iran

Capturing health and eating status through a nutritional perception screening questionnaire (NPSQ9) in a randomised internet-based personalised nutrition intervention : the Food4Me study

BACKGROUND: National guidelines emphasize healthy eating to promote wellbeing and prevention of non-communicable diseases. The perceived healthiness of food is determined by many factors affecting food intake. A positive perception of healthy eating has been shown to be associated with greater diet quality. Internet-based methodologies allow contact with large populations. Our present study aims to design and evaluate a short nutritional perception questionnaire, to be used as a screening tool for assessing nutritional status, and to predict an optimal level of personalisation in nutritional advice delivered via the Internet. METHODS: Data from all participants who were screened and then enrolled into the Food4Me proof-of-principle study (n = 2369) were used to determine the optimal items for inclusion in a novel screening tool, the Nutritional Perception Screening Questionnaire-9 (NPSQ9). Exploratory and confirmatory factor analyses were performed on anthropometric and biochemical data and on dietary indices acquired from participants who had completed the Food4Me dietary intervention (n = 1153). Baseline and intervention data were analysed using linear regression and linear mixed regression, respectively. RESULTS: A final model with 9 NPSQ items was validated against the dietary intervention data. NPSQ9 scores were inversely associated with BMI (β = -0.181, p < 0.001) and waist circumference (Β = -0.155, p < 0.001), and positively associated with total carotenoids (β = 0.198, p < 0.001), omega-3 fatty acid index (β = 0.155, p < 0.001), Healthy Eating Index (HEI) (β = 0.299, p < 0.001) and Mediterranean Diet Score (MDS) (β = 0. 279, p < 0.001). Findings from the longitudinal intervention study showed a greater reduction in BMI and improved dietary indices among participants with lower NPSQ9 scores. CONCLUSIONS: Healthy eating perceptions and dietary habits captured by the NPSQ9 score, based on nine questionnaire items, were associated with reduced body weight and improved diet quality. Likewise, participants with a lower score achieved greater health improvements than those with higher scores, in response to personalised advice, suggesting that NPSQ9 may be used for early evaluation of nutritional status and to tailor nutritional advice. TRIAL REGISTRATION: NCT01530139 .Peer reviewedFinal Published versio