Study protocol: associations between dietary patterns, cognitive function and metabolic syndrome in older adults – a cross-sectional study

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

Relative Validity and Reproducibility of a Semi-Quantitative Food Frequency Questionnaire for Determining Nutrient Intake in Older Adults in New Zealand: The REACH Study

Dietary intake is an important predictor of health and disease outcomes. This cross-sectional study evaluated the relative validity and reproducibility of a semi-quantitative food frequency questionnaire (FFQ) for assessing energy and nutrient intake in older adults. Dietary data were collected 2018–2019 in Auckland, New Zealand from a convenience sample of community-dwelling adults (65–74 years, n = 294, 37% male) using a 109-item self-administered FFQ at baseline (FFQ1) and four weeks later to assess reproducibility. FFQ1 was compared to a four-day food record to determine relative validity. Agreement between dietary assessment tools was assessed for both raw and energy-adjusted nutrient intakes using paired t-tests, correlation coefficients, weighted kappa statistic, Bland–Altman plots, and linear regression analysis. Energy adjustments moderately improved the relative validity and reproducibility for most nutrients. For energy and energy-adjusted nutrient intakes, the mean correlation coefficients were 0.38 (validity) and 0.65 (reproducibility); the mean weighted kappa statistics were 0.27 (validity) and 0.51 (reproducibility). A significant slope of bias was present in 54% (validity) and 25% (reproducibility) of Bland–Altman plots. The Researching Eating, Activity, and Cognitive Health (REACH) FFQ has acceptable relative validity and good reproducibility for ranking nutrient intakes in older New Zealand adults, but is less suitable for assessing absolute nutrient intakes

Plasma nervonic acid levels were negatively associated with attention levels in community-living older adults in New Zealand

The global population is aging. Preserving function and independence of our aging population is paramount. A key component to maintaining independence is the preservation of cognitive function. Metabolomics can be used to identify biomarkers of cognition before noticeable deterioration. Our study investigated the plasma metabolome of 332 community-living New Zealanders between 65 and 74 years of age, using gas chromatography-mass spectrometry. Six cognitive domains were assessed. Of the 123 metabolites identified using an in-house mass spectral libraries of standards, nervonic acid had a significant, inverse association with the attention domain (P-value = 1.52E− 4; FDR = 0.019), after adjusting for covariates (apolipoprotein E -ε4 genotype, sex, body fat percentage (standardised by sex), age, education, deprivation index, physical activity, metabolic syndrome, polypharmacy, smoking status, and alcohol intake) and multiple testing. Attention is defined as the ability to concentrate on selected aspects of the environment while ignoring other stimuli. This is the first study to identify nervonic acid as a potential biomarker of attention in older adults. Future research should confirm this association in a longitudinal study

Associations between dietary patterns and metabolic syndrome in older adults in New Zealand: the REACH study

Metabolic syndrome is common in older adults and may be modified by the diet. The aim of this study was to examine associations between a posteriori dietary patterns and metabolic syndrome in an older New Zealand population. The REACH study (Researching Eating, Activity, and Cognitive Health) included 366 participants (65-74 years, 36% male) living independently in Auckland, New Zealand. Dietary data were collected using a 109-item food frequency questionnaire with demonstrated validity and reproducibility for assessing dietary patterns using principal component analysis. Metabolic syndrome was defined by the National Cholesterol Education Program Adult Treatment Panel III. Associations between dietary patterns and metabolic syndrome, adjusted for age, sex, index of multiple deprivation, physical activity, and energy intake were analysed using logistic regression analysis. Three dietary patterns explained 18% of dietary intake variation - 'Mediterranean style' (salad/leafy cruciferous/other vegetables, avocados/olives, alliums, nuts/seeds, shellfish and white/oily fish, berries), 'prudent' (dried/fresh/frozen legumes, soy-based foods, whole grains, carrots), and 'Western' (processed meat/fish, sauces/condiments, cakes/biscuits/puddings, meat pies/hot chips). No associations were seen between 'Mediterranean style' [OR=0.75 (95% CI 0.53, 1.06), P=0.11] or 'prudent' [OR=1.17 (95% CI 0.83, 1.59), P=0.35] patterns and metabolic syndrome after co-variate adjustment. The 'Western' pattern was positively associated with metabolic syndrome [OR=1.67 (95% CI 1.08, 2.63), P=0.02]. There was also a small association between an index of multiple deprivation [OR=1.04 (95% CI 1.02, 1.06), P<0.001] and metabolic syndrome. This cross-sectional study provides further support for a Western dietary pattern being a risk factor for metabolic syndrome in an older population

Automated contouring and statistical process control for plan quality in a breast clinical trial

Background and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods: A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients’ computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data’s mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results: Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions: An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial