As part of the 12-month follow-up of the longitudinal cohort study, Life and Living in Advanced Age: A Cohort Study in New Zealand, dietary intake was assessed in 216 Māori and 362 non-Māori octogenarians using repeat 24-h multiple pass recalls. Energy and macronutrient intakes were calculated, and food items reported were allocated to food groups used in the New Zealand Adult Nutrition Survey (NZANS). Intakes were compared with the nutrient reference values (NRV) for Australia and New Zealand. The median BMI was higher for Māori (28·3 kg/m2) than for non-Māori (26·2 kg/m2) P=0·007. For Māori, median energy intake was 7·44 MJ/d for men and 6·06 MJ/d for women with 16·3 % energy derived from protein, 43·3 % from carbohydrate and 38·5 % from fat. Median energy intake was 7·91 and 6·26 MJ/d for non-Māori men and women, respectively, with 15·4 % of energy derived from protein, 45 % from carbohydrate and 36·7 % from fat. For both ethnic groups, bread was the top contributor to energy and carbohydrate intakes. Protein came from beef and veal, fish and seafood, bread, milk and poultry with the order differing by ethnic groups and sex. Fat came mainly from butter and margarine. Energy-adjusted protein was higher for Māori than non-Māori (P=0·049). For both ethnic groups, the median energy levels were similar, percent carbohydrate tended to be lower and percent fat higher compared with adults aged >70 years in NZANS. These unique cross-sectional data address an important gap in our understanding of dietary intake in this growing section of our population and highlight lack of age-appropriate NRV.

This analysis aimed to examine the association between intake of sugars (total or added) and nutrient intake with data from a recent Australian national nutrition survey, the 2007 Australian National Children’s Nutrition and Physical Activity Survey (2007ANCNPAS). Data from participants (n 4140; 51 % male) who provided 2×plausible 24-h recalls were included in the analysis. The values on added sugars for foods were estimated using a previously published ten-step systematic methodology. Reported intakes of nutrients and foods defined in the 2007ANCNPAS were analysed by age- and sex-specific quintiles of %energy from added sugars (%EAS) or %energy from total sugars (%ETS) using ANCOVA. Linear trends across the quintiles were examined using multiple linear regression. Logistic regression analysis was used to calculate the OR of not meeting a specified nutrient reference values for Australia and New Zealand per unit in %EAS or %ETS. Analyses were adjusted for age, sex, BMI z-score and total energy intake. Small but significant negative associations were seen between %EAS and the intakes of most nutrient intakes (all P<0·001). For %ETS the associations with nutrient intakes were inconsistent; even then they were smaller than that for %EAS. In general, higher intakes of added sugars were associated with lower intakes of most nutrient-rich, ‘core’ food groups and higher intakes of energy-dense, nutrient-poor ‘extra’ foods. In conclusion, assessing intakes of added sugars may be a better approach for addressing issues of diet quality compared with intakes of total sugars.

Previous research shows that older men tend to have lower nutritional intakes and higher risk of under-nutrition compared with younger men. The objectives of this study were to describe energy and nutrient intakes, assess nutritional risk and investigate factors associated with poor intake of energy and key nutrients in community-dwelling men aged ≥75 years participating in the Concord Health and Ageing in Men Project – a longitudinal cohort study on older men in Sydney, Australia. A total of 794 men (mean age 81·4 years) had a detailed diet history interview, which was carried out by a dietitian. Dietary adequacy was assessed by comparing median intakes with nutrient reference values (NRV): estimated average requirement, adequate intake or upper level of intake. Attainment of NRV of total energy and key nutrients in older age (protein, Fe, Zn, riboflavin, Ca and vitamin D) was incorporated into a ‘key nutrients’ variable dichotomised as ‘good’ (≥5) or ‘poor’ (≤4). Using logistic regression modelling, we examined associations between key nutrients with factors known to affect food intake. Median energy intake was 8728 kJ (P5=5762 kJ, P95=12 303 kJ), and mean BMI was 27·7 (sd 4·0) kg/m2. Men met their NRV for most nutrients. However, only 1 % of men met their NRV for vitamin D, only 19 % for Ca, only 30 % for K and only 33 % for dietary fibre. Multivariate logistic regression analysis showed that only country of birth was significantly associated with poor nutritional intake. Dietary intakes were adequate for most nutrients; however, only half of the participants met the NRV of ≥5 key nutrients.