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BackgroundStudies assessing links between maternal diet and pregnancy outcomes have focused predominantly on individual nutrients or foods. However, nutrients are typically consumed in combinations of foods or beverages (i.e., dietary patterns). Taking into account the diet as a whole appreciates that nutrient absorption and metabolism are influenced by other nutrients and the food matrix.ObjectiveThe aim of this study was to investigate the relationship between dietary pattern consumption in early pregnancy and pregnancy/infant outcomes, including gestational diabetes mellitus, gestational weight gain, preeclampsia, placental weight, gestational age at delivery, small-for-gestational-age, large-for-gestational-age, macrosomia, measures of infant body composition, and scores on two main indices of the Bayley Scales of Infant Development [Mental Development Index (MDI) and the Psychomotor Development Index (PDI)] at 12 months.DesignOur study included 1,437 participants from a mother-infant cohort in Chongqing, China. Maternal diet was assessed using a 96-item food frequency questionnaire at 11–14 weeks gestation. Dietary patterns were constructed using principal component analysis. Multivariate regressions were performed to assess associations between maternal dietary pattern scores and pregnancy and infant outcomes, adjusting for confounders.ResultsTwo dietary patterns were derived: a pattern high in pasta, sweetened beverages, and oils and condiments (PSO-based dietary pattern) and a pattern high in fish, poultry, and vegetables (FPV-based dietary pattern). Higher scores on the PSO-based dietary pattern were associated with lower infant standardized scores on the PDI of the Bayley Scales of Infant Development, β (95% confidence interval) = −1.276 (−2.392, −0.160); lower placental weight, β (95% CI) = −6.413 (−12.352g, −0.473); and higher infant's tricep skinfold thickness at 6 weeks of age. β (95% CI) = 0.279 (0.033, 0.526). Higher scores on the FPV-based dietary pattern were associated with higher gestational weight gain between visit 1 (11–14 week's gestation) and 3 (32–34 week's gestation). β (95% CI) = 25.612 (13.255, 37.969). No significant associations were observed between dietary pattern scores and the remaining pregnancy/infant outcomes investigated or MDI scores on the Bayley Scales of Infant Development. This was the first study to investigate the association between dietary patterns in early pregnancy and infant neurocognition in a Chinese cohort.

Abstract Background Loss of cognitive function is a significant issue as the world’s population ages. Preserving cognitive function maintains independence in older adults bringing major societal and financial benefits. Lifestyle factors such as diet are modifiable risk factors, which may help preserve cognitive function. Most nutrition research aimed at preserving cognitive function and metabolic health has focussed on individual nutrients and foods, not allowing for food combinations and interactions. A dietary pattern approach considers the entire diet including its complexity. Previous research investigating dietary patterns and cognitive function has not always considered relevant covariates such as physical activity and the Apolipoprotein E genotype, which are known to have associations with cognitive function. The aim of the REACH (Researching Eating, Activity and Cognitive Health) study is to investigate associations between dietary patterns, cognitive function and metabolic syndrome, accounting for a range of covariates. Methods This cross-sectional study design will recruit older, community-living adults (65–74 years) from Auckland, New Zealand. Dietary data will be collected via a 109-item food frequency questionnaire validated using a 4-day food record. Cognitive function will be assessed using the Montreal Cognitive Assessment (paper based) and the Computerised Mental Performance Assessment System (COMPASS) - a testing suite covering six domains. Additional data will include genetic (Apolipoprotein E ε4) and biochemical markers (fasting glucose, HbA1c, lipids profile), anthropometric measurements (weight, height, waist and hip circumference, body composition using dual X-ray absorptiometry), blood pressure, physical activity (International Physical Activity Questionnaire – short form) and health and demographics (questionnaire). Dietary patterns will be derived by principal component analysis. Associations between cognitive function and dietary patterns will be examined using multiple regression analysis. Covariates and interaction factors will include age, education, socio-economic status, physical activity, Apolipoprotein E ε4 genotype, family history of dementia or cognitive impairment, and lifestyle factors. Differences between participants with and without metabolic syndrome will also be examined. Discussion This study will bring new knowledge regarding associations between dietary patterns and cognitive function and metabolic health in older adults living in New Zealand. This is important for developing nutrition related recommendations to help older adults maintain cognitive function.

Background: Loss of cognitive function is a significant issue as the world's population ages. Preserving cognitive function maintains independence in older adults bringing major societal and financial benefits. Lifestyle factors such as diet are modifiable risk factors, which may help preserve cognitive function. Most nutrition research aimed at preserving cognitive function and metabolic health has focussed on individual nutrients and foods, not allowing for food combinations and interactions. A dietary pattern approach considers the entire diet including its complexity. Previous research investigating dietary patterns and cognitive function has not always considered relevant covariates such as physical activity and the Apolipoprotein E genotype, which are known to have associations with cognitive function. The aim of the REACH (Researching Eating, Activity and Cognitive Health) study is to investigate associations between dietary patterns, cognitive function and metabolic syndrome, accounting for a range of covariates.Methods: This cross-sectional study design will recruit older, community-living adults (65-74 years) from Auckland, New Zealand. Dietary data will be collected via a 109-item food frequency questionnaire validated using a 4-day food record. Cognitive function will be assessed using the Montreal Cognitive Assessment (paper based) and the Computerised Mental Performance Assessment System (COMPASS) - a testing suite covering six domains. Additional data will include genetic (Apolipoprotein E ε4) and biochemical markers (fasting glucose, HbA1c, lipids profile), anthropometric measurements (weight, height, waist and hip circumference, body composition using dual X-ray absorptiometry), blood pressure, physical activity (International Physical Activity Questionnaire - short form) and health and demographics (questionnaire). Dietary patterns will be derived by principal component analysis. Associations between cognitive function and dietary patterns will be examined using multiple regression analysis. Covariates and interaction factors will include age, education, socio-economic status, physical activity, Apolipoprotein E ε4 genotype, family history of dementia or cognitive impairment, and lifestyle factors. Differences between participants with and without metabolic syndrome will also be examined.Discussion: This study will bring new knowledge regarding associations between dietary patterns and cognitive function and metabolic health in older adults living in New Zealand. This is important for developing nutrition related recommendations to help older adults maintain cognitive function. [ABSTRACT FROM AUTHOR]

Examining dietary patterns provides an alternative approach to investigating dietary behaviors related to excess adiposity. The study aim was to investigate dietary patterns and body composition profiles of New Zealand European (NZE) women, participating in the women's EXPLORE (Examining the Predictors Linking Obesity Related Elements) study. Post-menarche, pre-menopausal NZE women (16-45 years) (n = 231) completed a validated 220-item, self-administrated, semi-quantitative food frequency questionnaire. Body mass index (BMI) was calculated using measured height (cm) and weight (kg); body fat percentage (BF%) was measured using air displacement plethysmography (BodPod). Dietary patterns were identified using principal component factor analysis. Associations between dietary patterns, age, BMI and BF% were investigated. Four dietary patterns were identified: snacking; energy-dense meat; fruit and vegetable; healthy, which explained 6.9%, 6.8%, 5.6% and 4.8% of food intake variation, respectively. Age (p = 0.012) and BMI (p = 0.016) were positively associated with the "energy-dense meat" pattern. BF% (p = 0.016) was positively associated with the "energy-dense meat" pattern after adjusting for energy intake. The women following the identified dietary patterns had carbohydrate intakes below and saturated fat intakes above recommended guidelines. Dietary patterns in NZE women explain only some variations in body composition. Further research should examine other potential factors including physical activity and socioeconomic status. [ABSTRACT FROM AUTHOR]

Purpose: To investigate associations between dietary patterns and suboptimal iron status in premenopausal women living in Auckland, New Zealand. Methods: Premenopausal women ( n = 375; 18-44 years) were included in this cross-sectional analysis. Suboptimal iron status was defined as serum ferritin <20 μg/L. Participants completed a 144-item iron food frequency questionnaire (FeFFQ) and a questionnaire on dietary practices to assess dietary intake over the past month. Factor analysis was used to determine dietary patterns from the FeFFQ. Logistic regression was used to determine associations between these dietary patterns and iron status. Results: Seven dietary patterns were identified: refined carbohydrate and fat; Asian; healthy snacks; meat and vegetable; high tea and coffee; bread and crackers; and milk and yoghurt. Logistic regression suggested that following a 'meat and vegetable' dietary pattern reduced the risk of suboptimal iron status by 41 % (95 % CI: 18, 58 %; P = 0.002) and following a 'milk and yoghurt' pattern increased the risk of suboptimal iron status by 50 % (95 % CI: 15, 96 %; P = 0.003). Conclusions: These results suggest that dietary patterns characterized by either a low intake of meat and vegetables or a high intake of milk and yoghurt are associated with an increased risk of suboptimal iron status. Dietary pattern analysis is a novel and potentially powerful tool for investigating the relationship between diet and iron status. [ABSTRACT FROM AUTHOR]