The Geometric Framework and Nutrition in Older Age

Aims: There were three main aims: To validate a diet history questionnaire (DHQ) used to collect dietary data of a group of older men; 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; and to investigate the association between macronutrient intake and health outcomes of a group of older men living in Sydney, Australia. Methods: This thesis analyses data from 761 community-dwelling men aged 75 years and older who participated in the five-year follow-up phase of the Concord Health and Ageing in Men project (CHAMP). The diet history questionnaire used to collect dietary data validated against a four-day weighed food record in 56 men aged 75 to 86 years (mean 79 years, SD 2.96). Dietary adequacy was assessed by comparing (unadjusted) median intakes to Nutrient Reference Values (NRVs). Attainment of NRVs of (unadjusted) total energy and key nutrients in older age (protein, iron, zinc, riboflavin, calcium and vitamin D) was incorporated into a “key nutrients” variable dichotomised as “good” (≥5) or “poor” (≤4). Using logistic regression modelling the associations between key nutrients with factors (sociodemographic, economic health and lifestyle factors) known to affect food intake were examined. The geometric framework, generalised additive models and multiple regression models were used to assess the association between macronutrient intake (protein, fat and carbohydrate) and the following health outcomes: total energy intake, body mass index (BMI), percentage body fat, waist-to-hip ratio, insulin, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, homeostatic model assessment for insulin resistance (HOMA-IR), number of medical conditions, SF12 (MCS and PCF), GDS and frailty score. Results: In the validation study involving 56 men, DHQ estimates of intakes tended to be higher than estimates from weighed food records. Differences between the two methods were generally less than 20% with the exception of β-carotene (37%), vitamin E (25%) and vitamin A (24%). Both fixed and proportional biases were only present for retinol, β-carotene, magnesium, phosphorus and percentage of energy from protein. Most of the 761 men in CHAMP met their NRVs for most nutrients. However, only 1% of men met their NRV for vitamin D, only 19% for calcium, only 30% for potassium, and only 33% for dietary fibre. Multivariate logistic regression analysis showed that only country of birth was significantly associated with poor nutritional intake where Italian/Greek born men had poorer intakes of key nutrients. In adjusted analyses investigating the association between macronutrient intake and health outcomes, protein intake stood out. After adjustment for age, physical activity level, number of morbidities, marital status, income, education, frailty status and alcohol intake (for triglycerides only), low protein intake (adjusted by body weight) was associated with higher total energy intake, higher BMI, higher percentage body fat, higher waist-to-hip ratios, higher insulin levels, and higher HOMA-IR. High protein intake (adjusted by body weight) was associated with higher HDLc and triglycerides levels. Low carbohydrate intake (adjusted by body weight) was associated with poor body composition, whereas high carbohydrate intake was associated with better physical performance. Fat intake (adjusted by body weight) was higher when protein intake was low; however, fat intake had very little influence on any of the health outcomes investigated. Conclusion: The DHQ used in CHAMP to measure the nutritional intake of its participants is appropriate to this age group and provides reasonably similar results to the 4dWFR for the majority of nutrients analysed. Dietary intakes of community-dwelling older Australian men were adequate for most nutrients. However only half of the participants met NRVs of ≥5 key nutrients and being born in Italy or Greece was associated with poor nutritional intake of key nutrients. Lower protein intake was associated with higher levels of the majority of the health outcomes investigated