Massey Documents by Type

Permanent URI for this communityhttps://mro.massey.ac.nz/handle/10179/294

Browse

Subject: search.filters.subject.dietary intake

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    The fecal microbiotas of women of Pacific and New Zealand European ethnicities are characterized by distinctive enterotypes that reflect dietary intakes and fecal water content.
    (Taylor and Francis Groups, 2023-02-17) Renall N; Lawley B; Vatanen T; Merz B; Douwes J; Corbin M; Te Morenga L; Kruger R; Breier BH; Tannock GW
    Obesity is a complex, multifactorial condition that is an important risk factor for noncommunicable diseases including cardiovascular disease and type 2 diabetes. While prevention and management require a healthy and energy balanced diet and adequate physical activity, the taxonomic composition and functional attributes of the colonic microbiota may have a supplementary role in the development of obesity. The taxonomic composition and metabolic capacity of the fecal microbiota of 286 women, resident in Auckland New Zealand, was determined by metagenomic analysis. Associations with BMI (obese, nonobese), body fat composition, and ethnicity (Pacific, n = 125; NZ European women [NZE], n = 161) were assessed using regression analyses. The fecal microbiotas were characterized by the presence of three distinctive enterotypes, with enterotype 1 represented in both Pacific and NZE women (39 and 61%, respectively), enterotype 2 mainly in Pacific women (84 and 16%) and enterotype 3 mainly in NZE women (13 and 87%). Enterotype 1 was characterized mainly by the relative abundances of butyrate producing species, Eubacterium rectale and Faecalibacterium prausnitzii, enterotype 2 by the relative abundances of lactic acid producing species, Bifidobacterium adolescentis, Bifidobacterium bifidum, and Lactobacillus ruminis, and enterotype 3 by the relative abundances of Subdoligranulum sp., Akkermansia muciniphila, Ruminococcus bromii, and Methanobrevibacter smithii. Enterotypes were also associated with BMI, visceral fat %, and blood cholesterol. Habitual food group intake was estimated using a 5 day nonconsecutive estimated food record and a 30 day, 220 item semi-quantitative Food Frequency Questionnaire. Higher intake of 'egg' and 'dairy' products was associated with enterotype 3, whereas 'non-starchy vegetables', 'nuts and seeds' and 'plant-based fats' were positively associated with enterotype 1. In contrast, these same food groups were inversely associated with enterotype 2. Fecal water content, as a proxy for stool consistency/colonic transit time, was associated with microbiota taxonomic composition and gene pools reflective of particular bacterial biochemical pathways. The fecal microbiotas of women of Pacific and New Zealand European ethnicities are characterized by distinctive enterotypes, most likely due to differential dietary intake and fecal consistency/colonic transit time. These parameters need to be considered in future analyses of human fecal microbiotas.
  • Thumbnail Image
    Item
    The effect of mild sleep deprivation on diet and eating behaviour in children: protocol for the Daily Rest, Eating, and Activity Monitoring (DREAM) randomized cross-over trial
    (BioMed Central Ltd, 2019-10-22) Ward AL; Galland BC; Haszard JJ; Meredith-Jones K; Morrison S; McIntosh DR; Jackson R; Beebe DW; Fangupo L; Richards R; Te Morenga L; Smith C; Elder DE; Taylor RW
    BACKGROUND: Although insufficient sleep has emerged as a strong, independent risk factor for obesity in children, the mechanisms by which insufficient sleep leads to weight gain are uncertain. Observational research suggests that being tired influences what children eat more than how active they are, but only experimental research can determine causality. Few experimental studies have been undertaken to determine how reductions in sleep duration might affect indices of energy balance in children including food choice, appetite regulation, and sedentary time. The primary aim of this study is to objectively determine whether mild sleep deprivation increases energy intake in the absence of hunger. METHODS: The Daily, Rest, Eating, and Activity Monitoring (DREAM) study is a randomized controlled trial investigating how mild sleep deprivation influences eating behaviour and activity patterns in children using a counterbalanced, cross-over design. One hundred and ten children aged 8-12 years, with normal reported sleep duration of 8-11 h per night will undergo 2 weeks of sleep manipulation; seven nights of sleep restriction by going to bed 1 hr later than usual, and seven nights of sleep extension going to bed 1 hr earlier than usual, separated by a washout week. During each experimental week, 24-h movement behaviours (sleep, physical activity, sedentary behaviour) will be measured via actigraphy; dietary intake and context of eating by multiple 24-h recalls and wearable camera images; and eating behaviours via objective and subjective methods. At the end of each experimental week a feeding experiment will determine energy intake from eating in the absence of hunger. Differences between sleep conditions will be determined to estimate the effects of reducing sleep duration by 1-2 h per night. DISCUSSION: Determining how insufficient sleep predisposes children to weight gain should provide much-needed information for improving interventions for the effective prevention of obesity, thereby decreasing long-term morbidity and healthcare burden. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618001671257 . Registered 10 October 2018.
  • Thumbnail Image
    Item
    Sugar intake of young children in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Nutrition and Dietetics, Massey University, Albany, New Zealand
    (Massey University, 2023) Bhula, Jayshree
    Background: Sugar intake has been linked to noncommunicable diseases such as overweight, obesity, type 2 diabetes, and dental caries in children. In New Zealand, the prevalence of child obesity is increasing, especially amongst Māori and Pasifika, and dental caries are reported in young children. Demographic characteristics and feeding practices have been linked to poor nutrition choices, including a high intake of sugar. There is currently a gap in our knowledge about the dietary intake of sugar in young New Zealand children. Objectives: To describe total sugar intake and describe differences in total sugar intake based on anthropometry, demographic characteristics and feeding practices in New Zealand 1-3.9-year-olds. Methods: Data from the Young Foods New Zealand study (YFNZ) were used. The current study was a cross-sectional study of young New Zealand children (n=289) aged 1-3.9 years living in Auckland, Wellington, and Dunedin. Two non-consecutive 24-hour diet recalls were administered to parents/caregivers using the triple pass method, and analysed for intake of total sugar, energy, carbohydrate, protein, fat, and fibre. Length, height was measured using a portable stadiometer for children and weight using adult weight scales. BMI z-score was calculated using World Health Organization reference data. Demographic (age, sex, ethnicity, deprivation, parental education), feeding method and food pouch use were measured using a questionnaire. Independent sample t-tests determined the differences in total sugar intake between age and sex. Analysis of Covariance Tests (ANCOVA) tests determined the differences in total sugar intake between BMI z-score classification, feeding method, and demographics. Results: Mean (SD) total sugar intake was 67.3 (15.1) g/day which contributed 22.9 (4.4) % of total energy. Toddlers (n=108) consumed a mean total sugar intake of 65.2 (16.9) g/day which contributed 23.7 (5.1) % of total energy and pre-schoolers (n=181) consumed 68.5 (13.9) g/day which contributed 22.4 (3.8) % of total energy. Boys (n=142) consumed a mean total sugar intake of 67.8 (15.3) g/day which contributed 22.7 (4.4) % of total energy and girls (n=147) consumed 66.8 (15.0) g/day which contributed 23.1 (4.4) % of total energy. Total sugar intake (g/day) varied by BMI z-score category (p=0.004), but not % of total energy (p=0.14). Children who were obese (n=31) had a higher intake of total sugar (g/day) than normal or underweight children (n=148), 75.4 (17.3) vs 65.6 (14.9) respectively, (p=0.006), and overweight children (n=70), 69.4 (14.8), but not significantly (p=0.19). 4 European children (n=125) consumed 66.3 (12.7) g which contributed 22.7 (3.7) % of total energy per day. Māori children (n=75) consumed 69.7 (17.7) g/day which contributed 24.1 (5.2) % of total energy. Pasifika children (n=47) consumed 67.8 (16.0) g/day which contributed 22.4 (4.7) % of total energy. Asian/other children (n=42) consumed 65.3 (15.8) g/day which contributed 21.9 (4.0) % of total energy. Total sugar intake in g/day and % of total energy did not vary by ethnicity (p=0.25) and (p=0.07), deprivation score (p=0.57) and (p=0.25), parental education (p=0.94) and (p=0.55) or feeding method (p=0.63) and (p=0.9), respectively. Total sugar intake (g/day) varies by the number of pouches consumed (p=0.01). Children who consumed 7-14 pouches per week (n=25) had a higher total sugar intake (g/day) compared to children who consumed 1-6 pouches (n=112), 74.7 (19.6) vs 65.3 (14.3) g/day, respectively (p=0.011), but not significantly higher than children who consumed none (n=150) 67.3 (14.2) g/day, (p=0.052). % of total energy from total sugar intake varied by the number of pouches consumed (p<0.001). Children who consumed 7-14 pouches had a higher total sugar intake (% E) than children who consumed none 26.1 (5.2) vs 22.1 (4.1), respectively (p<0.001), and children who consumed 1-6 pouches 23.1 (4.1), (p=0.004). Conclusion: In this cohort of young New Zealand children, total sugar intake contributed 22.9% to daily energy intake. Total sugar intake was higher in children classified as obese compared with children who were normal or underweight. Total sugar intake in g/day and % of total energy from total sugar was higher in children who consumed seven or more pouches per week compared with children who consumed less than seven. There were no differences in total sugar intake according to ethnicity, deprivation, parental education or feeding method.
  • Thumbnail Image
    Item
    Associations between physical activity, body composition, nutrient intake, and bone mineral density in pre-menopausal Pacific Island women living in New Zealand : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Nutrition and Dietetics at Massey University, Albany, New Zealand
    (Massey University, 2015) Casale, Maria
    Background/Aim: Anecdotally it is suggested that Pacific Island women have good bone mineral density (BMD); however little evidence for this or for associated factors exists. The aim of this study is to explore associations between several key predictors of bone health with bone mineral density, as measured by BMD (g/cm2), in pre-menopausal Pacific Island women. Methods: Healthy pre-menopausal Pacific Island women (n=91; age 16-45y) were recruited. Participants’ body composition and total body BMD were assessed using DXA and air-displacement plethysmography (BodPod). A food frequency questionnaire (FFQ) and current bone-specific physical activity questionnaire (cBPAQ) were completed. Variables that significantly correlated with BMD were applied to a hierarchical multiple regression analysis. Results: The mean BMD was 1.1 g/cm2 ± 0.08. Bone-free, fat-free lean mass only (LMO, 52.4kg ± 6.9) and total mass (90.4kg ± 19) were the only factors to show a significant correlation with BMD. Body-fat (38.4% ± 7.6), cBPAQ score (1.7 (0.4,5.2)), and dietary calcium (1016mg ± 442), protein (18% ± 3.8) and vitamin C (125mg (94, 216)) showed no correlation with BMD. The regression analysis suggests that LMO is the most important predictor of BMD, explaining 13.4% of the variance, while total mass accounts for a further 2.5% of the variance. Together, these factors explain a total of 15.9% of the variability. Conclusions: LMO is the strongest predictor of BMD, while many established contributors to bone health (calcium, physical activity, protein, and vitamin C) do not appear to be associated with BMD in this population. As just 15.9% of the variability can be explained, further research is needed in this area.