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Using exploratory factor analysis of FFQ data to identify dietary patterns among Yup'ik people.

ABSTRACT: OBJECTIVE:An FFQ developed by the Center for Alaska Native Health Research for studies in Yup'ik people includes market foods and subsistence foods such as moose, seal, waterfowl and salmon that may be related to disease risk. Because the FFQ contains >100 food items, we sought to characterize dietary patterns more simply for use in ongoing pharmacogenomics studies. DESIGN:Exploratory factor analysis was used to derive a small number of 'factors' that explain a substantial amount of the variation in the Yup'ik diet. We estimated factor scores and measured associations with demographic characteristics and biomarkers. SETTING:South-west Alaska, USA. SUBJECTS:Yup'ik people (n 358) aged ?18 years. RESULTS:We identified three factors that each accounted for ?10 % of the common variance: the first characterized by 'processed foods' (e.g. salty snacks, sweetened cereals); the second by 'fruits and vegetables' (e.g. fresh citrus, potato salad); and the third by 'subsistence foods' (seal or walrus soup, non-oily fish). Participants from coastal communities had higher values for the 'subsistence' factor, whereas participants from inland communities had higher values for the 'fruits and vegetables' factor. A biomarker of marine intake, ? 15N, was correlated with the 'subsistence' factor, whereas a biomarker of corn- and sugarcane-based market food intake, ? 13C, was correlated with 'processed foods'. CONCLUSIONS:The exploratory factor analysis identified three factors that appeared to reflect dietary patterns among Yup'ik based on associations with participant characteristics and biomarkers. These factors will be useful for chronic disease studies in this population.

SUBMITTER: Ryman TK

PROVIDER: S-EPMC3972766 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Using exploratory factor analysis of FFQ data to identify dietary patterns among Yup'ik people.

Ryman Tove K TK Austin Melissa A MA Hopkins Scarlett S Philip Jacques J O'Brien Diane D Thummel Kenneth K Boyer Bert B BB

Public health nutrition 20130104 3

<h4>Objective</h4>An FFQ developed by the Center for Alaska Native Health Research for studies in Yup'ik people includes market foods and subsistence foods such as moose, seal, waterfowl and salmon that may be related to disease risk. Because the FFQ contains >100 food items, we sought to characterize dietary patterns more simply for use in ongoing pharmacogenomics studies.<h4>Design</h4>Exploratory factor analysis was used to derive a small number of 'factors' that explain a substantial amount ...[more]

PMID: 23290469

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Project description:BackgroundPrincipal components analysis (PCA) has been the most widely used method for deriving dietary patterns to date. However, PCA requires arbitrary ad hoc decisions for selecting food variables in interpreting dietary patterns and does not easily accommodate covariates. Sparse latent factor models can be utilized to address these issues.ObjectiveThe objective of this study was to compare Bayesian sparse latent factor models with PCA for identifying dietary patterns among young adults.MethodsHabitual food intake was estimated in 2730 sedentary young adults from the Training Interventions and Genetics of Exercise Response (TIGER) Study [aged 18-35 y; body mass index (BMI; in kg/m2): 26.5 ± 6.1] who exercised <30 min/wk during the previous 30 d without restricting caloric intake before study enrollment. A food-frequency questionnaire was used to generate the frequency intakes of 102 food items. Sparse latent factor modeling was applied to the standardized food intakes to derive dietary patterns, incorporating additional covariates (sex, race/ethnicity, and BMI). The identified dietary patterns via sparse latent factor modeling were compared with the PCA derived dietary patterns.ResultsSeven dietary patterns were identified in both PCA and sparse latent factor analysis. In contrast to PCA, the sparse latent factor analysis allowed the covariate information to be jointly accounted for in the estimation of dietary patterns in the model and offered probabilistic criteria to determine the foods relevant to each dietary pattern. The derived patterns from both methods generally described common dietary behaviors. Dietary patterns 1-4 had similar food subsets using both statistical approaches, but PCA had smaller sets of foods with more cross-loading elements between the 2 factors. Overall, the sparse latent factor analysis produced more interpretable dietary patterns, with fewer of the food items excluded from all patterns.ConclusionSparse latent factor models can be useful in future studies of dietary patterns by reducing the intrinsic arbitrariness involving the choice of food variables in interpreting dietary patterns and incorporating covariates in the assessment of dietary patterns.

Project description:To target food security interventions for smallholder households, decision makers need large-scale information, such as maps on poverty, food security and key livelihood activities. Such information is often based on expert knowledge or aggregated data, despite the fact that food security and poverty are driven largely by processes at the household level. At present, it is unclear if and how household level information can contribute to the spatial prediction of such welfare indicators or to what extent local variability is ignored by current mapping efforts. A combination of geo-referenced household level information with spatially continuous information is an underused approach to quantify local and large-scale variation, while it can provide a direct estimate of the variability of welfare indicators at the most relevant scale. We applied a stepwise regression kriging procedure to translate point information to spatially explicit patterns and create country-wide predictions with associated uncertainty estimates for indicators on food availability and related livelihood activities using household survey data from Uganda. With few exceptions, predictions of the indicators were weak, highlighting the difficulty in capturing variability at larger scale. Household explanatory variables identified little additional variation compared to environmental explanatory variables alone. Spatial predictability was strongest for indicators whose distribution was determined by environmental gradients. In contrast, indicators of crops that were more ubiquitously present across agroecological zones showed large local variation, which often overruled large-scale patterns. Our procedure adds to existing approaches that often only show large-scale patterns by revealing that local variation in welfare is large. Interventions that aim to target the poor must recognise that diversity in livelihood activities for income generation within any given area often overrides the variability of livelihood activities between distant regions in the country.

Project description:ObjectivesObesity among Pasifika people living in New Zealand is a serious health problem with prevalence rates more than twice those of the general population (67% vs 33%, respectively). Due to the high risk of developing obesity for this population, we investigated diet quality of Pacific youth and their parents and grandparents. Therefore, we examined the dietary diversity of 30 youth and their parents and grandparents (n=34) to identify whether there are generational differences in dietary patterns and investigate the relationship between acculturation and dietary patterns.MethodsThe study design of the overarching study was cross-sectional. Face-to-face interviews were conducted with Pasifika youth, parents and grandparents to investigate dietary diversity, that included both nutritious and discretionary food items and food groups over a 7 day period. Study setting was located in 2 large urban cities, New Zealand. Exploratory factor analyses were used to calculate food scores (means) from individual food items based on proportions consumed over the week, and weights were applied to calculate a standardised food score. The relationship between the level of acculturation and deprivation with dietary patterns was also assessed.ResultsThree distinctive dietary patterns across all participants were identified from our analyses. Healthy diet, processed diet and mixed diet. Mean food scores indicated statistically significant differences between the dietary patterns for older and younger generations. Older generations showed greater diversity in food items consumed, as well as eating primarily a 'healthy diet'. The younger generation was more likely to consume a 'processed diet'. There was significant association between acculturation and deprivation with the distinctive dietary patterns.ConclusionOur investigation highlighted generational differences in consuming a limited range of food items. Identified dietary components may, in part, be explained by specific acculturation modes (assimilation and marginalised) and high socioeconomic deprivation among this particular study population.

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Using exploratory factor analysis of FFQ data to identify dietary patterns among Yup'ik people.

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Using exploratory factor analysis of FFQ data to identify dietary patterns among Yup'ik people.

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