Latent class models and latent transition models for dietary pattern analysis

Dietary patterns (DP) are used to study the effects of overall diet on health outcomes as opposed to the effects of individual nutrients or foods. DP are empirically derived mostly using factor and cluster analysis. Latent class models (LCM) have been shown empirically to be more appropriate to derive DP than cluster analysis, but they have not been compared yet to those derived by factor analysis. We derive DP using LCM and factor analysis on food-items, test how well the resulting classes are characterized by the factor scores, and compare subjects' direct classification from LCM versus two a posteriori classifications from factor scores: one possible classification using tertiles and a two-step classification using LCM on previously derived factor scores. In order to study changes in dietary patterns over time, we propose using latent transition models to study change as characterized by the movement between discrete dietary patterns. Latent transition models directly classify subjects into mutually exclusive DP at each time point and allow predictors for class membership and for probabilities of changing classes over time. There are several challenges particular to DP analysis: a large ([greater than or equal to]80) number of food-items, non-standard mixture distributions (continuous with a mass point at zero for non-consumption), and typical assumptions (conditional independence given the class and time point, time-invariant conditional responses, and invariant transition probabilities) may not be realistic. We compare performance, capabilities and flexibility between two software packages (Mplus and a user's derived procedure in SAS) that allow fitting latent transition models. A key decision involved when deriving DP is whether or not to collapse the primary dietary data into a smaller number of items called food groups. Advantages for collapsing include dimension reduction and decreasing the number of non-consumers to reduce the mass-point at zero. However, not collapsing helps our understanding of which combinations of specific foods are consumed. Further, food-grouping may have an impact on the association between DP and health outcomes. We explore via a Monte Carlo simulation study whether food-grouping makes a difference when deriving DP using LCM. Methods are illustrated using data from the Pregnancy, Infection and Nutrition (PIN) Study

Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults

Abstract Objective We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose. Design We measured diet over a 3 d period with 24 h recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Setting Adults ( n 4316) from the China Health and Nutrition Survey. Results The adjusted odds ratio for diabetes prevalence (HbA1c≥6·5 %), comparing the highest dietary pattern score quartile with the lowest, was 1·26 (95 % CI 0·76, 2·08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0·76 (95 % CI 0·49, 1·17) for a traditional southern pattern (PCA; rice, meat, poultry and fish) and 2·37 (95 % CI 1·56, 3·60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviourally meaningful. It combined the deleterious effects of the modern high-wheat pattern (high intakes of wheat buns and breads, deep-fried wheat and soya milk) with the deleterious effects of consuming the opposite of the traditional southern pattern (low intakes of rice, poultry and game, fish and seafood). Conclusions Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes

Latent Transition Models to Study Women's Changing of Dietary Patterns From Pregnancy to 1 Year Postpartum

Latent class models are useful for classifying subjects by dietary patterns. Our goals were to use latent transition models to identify dietary patterns during pregnancy and postpartum, to estimate the prevalence of these dietary patterns, and to model transition probabilities between dietary patterns as a function of covariates. Women who were enrolled in the Pregnancy, Infection, and Nutrition Study (University of North Carolina, 2000–2005) were followed for 1 year postpartum, and their diets were assessed in the second trimester and at 3 and 12 months postpartum (n = 519, 484, and 374, respectively) by using a food frequency questionnaire. After adjusting for energy intake, parity, smoking status, race, and education, we identified 3 dietary patterns and named them “prudent,” “health conscious Western,” and “Western.” Nulliparas were 2.9 and 2.1 times more likely to be in the “prudent” class than the “health conscious Western” or the “Western” class, respectively. The 3 dietary patterns were very stable, with the “health conscious Western” class being the least stable; the probability for staying in the same class was 0.74 and 0.87 at 3 and 12 months postpartum, respectively. Breastfeeding mothers were more likely than nonbreastfeeding mothers to switch dietary pattern class (P = 0.0286). Except for breastfeeding mothers, most women did not switch dietary patterns from pregnancy to postpartum

Latent Class Analysis Is Useful to Classify Pregnant Women into Dietary Patterns

Empirical dietary patterns are derived predominantly using principal components, exploratory factor analysis (EFA), or cluster analysis. Interestingly, latent variable models are less used despite their being more flexible to accommodate important characteristics of dietary data and despite dietary patterns being recognized as latent variables. Latent class analysis (LCA) has been shown empirically to be more appropriate to derive dietary patterns than k-means clustering but has not been compared yet to confirmatory factor analysis (CFA). In this article, we derived dietary patterns using EFA, CFA, and LCA on food items, tested how well the classes from LCA were characterized by the factors from CFA, and compared participants’ direct classification from LCA on food items compared with 2 a posteriori classifications from factor scores. Methods were illustrated with the Pregnancy, Infection and Nutrition Study, North Carolina, 2000–2005 (n = 1285 women). From EFA and CFA, we found that food items were grouped into 4 factors: Prudent, Prudent with coffee and alcohol, Western, and Southern. From LCA, pregnant women were classified into 3 classes: Prudent, Hard core Western, and Health-conscious Western. There was high agreement between the direct classification from LCA on food items and the classification from the 2-step LCA on factor scores [κ=0.70 (95% CI = 0.66, 0.73)] despite factors explaining only 25% of the total variance. We suggest LCA on food items to study the effect for mutually exclusive classes and CFA to understand which foods are eaten in combination. When interested in both benefits, the 2-step classification using LCA on previously derived factor scores seems promising

The association of trajectories of protein intake and age-specific protein intakes from 2 to 22 years with BMI in early adulthood

Abstract No study has analysed how protein intake from early childhood to young adulthood relate to adult BMI in a single cohort. To estimate the association of protein intake at 2, 11, 15, 19 and 22 years with age- and sex-standardised BMI at 22 years (early adulthood), we used linear regression models with dietary and anthropometric data from a Filipino birth cohort (1985–2005, n 2586). We used latent growth curve analysis to identify trajectories of protein intake relative to age-specific recommended daily allowance (intake in g/kg body weight) from 2 to 22 years, then related trajectory membership to early adulthood BMI using linear regression models. Lean mass and fat mass were secondary outcomes. Regression models included socioeconomic, dietary and anthropometric confounders from early life and adulthood. Protein intake relative to needs at age 2 years was positively associated with BMI and lean mass at age 22 years, but intakes at ages 11, 15 and 22 years were inversely associated with early adulthood BMI. Individuals were classified into four mutually exclusive trajectories: (i) normal consumers (referent trajectory, 58 % of cohort), (ii) high protein consumers in infancy (20 %), (iii) usually high consumers (18 %) and (iv) always high consumers (5 %). Compared with the normal consumers, ‘usually high’ consumption was inversely associated with BMI, lean mass and fat mass at age 22 years whereas ‘always high’ consumption was inversely associated with male lean mass in males. Proximal protein intakes were more important contributors to early adult BMI relative to early-childhood protein intake; protein intake history was differentially associated with adulthood body size

Maternal Dietary Patterns during the Second Trimester Are Associated with Preterm Birth

Background: Preterm birth is one of the leading causes of neonatal morbidity in the United States. Despite decades of research, the etiology is largely unknown

Dietary pattern trajectories during 15 years of follow-up and HbA1c, insulin resistance and diabetes prevalence among Chinese adults

Most research on dietary patterns and health outcomes does not include longitudinal exposure data. We used an innovative technique to capture dietary pattern trajectories and their association with hemoglobin A1c (HbA1c), homeostasis model of insulin resistance (HOMA-IR), and prevalence of newly diagnosed diabetes

Maternal Dietary Patterns during Pregnancy Are Associated with Child Growth in the First 3 Years of Life

Background: Child obesity is a major problem in the United States. Identifying early-life risk factors is necessary for prevention. Maternal diet during pregnancy is a primary source of fetal energy and might influence risk of child obesity

Shifts in the Recent Distribution of Energy Intake among U.S. Children Aged 2–18 Years Reflect Potential Abatement of Earlier Declining Trends

Recent national surveys suggest that child obesity in the United States may have reached a plateau, but corresponding trends in energy intake have not been examined in depth. This article evaluates medium-term trends in children’s reported energy intake by using 4 waves of national dietary surveillance from 2003–2004 to 2009–2010. The analysis uses up to 2 24-h dietary recalls, incorporating methods that address challenges in estimating usual intake, accounting for intraindividual variance and covariates such as the presence of atypical consumption days. Quantile regression was used to assess disparities in intake among sociodemographic subgroups at extremes of the distribution as well as at the median, and the potential influence of misreporting was evaluated. Results indicated that after an initial decline in intakes across all age groups through 2007–2008, there were significant increases of ∼90 kcal/d at the median among adolescents in 2009–2010, whereas intakes in younger children remained steady. Among adolescent boys, the recent increase was larger at the 90th percentile than at the median. Intake trends did not vary by race/ethnic group, among whom intakes were similar at the upper end of the distribution. Misreporting did not influence trends over time, but intakes were lower in younger children and higher in older children after excluding misreporters. Overall, findings suggest that declines in children’s energy intake from 2003–2004 through 2007–2008 were consistent with the obesity plateau observed in most age and gender subgroups through 2009–2010. However, there is evidence of increased intakes among adolescents in 2009–2010, which may threaten the earlier abatement in overweight in this older age group

Longitudinal analysis of dietary patterns in Chinese adults from 1991 to 2009

Our aims were to identify the changes or stability in the structure of dietary patterns and the tracking, trends and factors related to the adherence of these patterns in China from 1991 to 2009. We used seven waves of the China Health and Nutrition Survey and included 9,253 adults with ≥3 waves complete. Diet was measured over a 3-day period with 24-hr recalls and a household food inventory. Using factor analysis in each wave we found that the structure of the two dietary patterns identified, remained stable over the studied period. The traditional southern pattern was characterized by high intake of rice, fresh leafy vegetables, low-fat red meat, pork, organ meats, poultry and fish/seafood and low intakes of wheat flour, corn/coarse grains; and the modern high-wheat pattern was characterized by high intake of wheat buns/breads, cakes/cookies/pastries, deep-fried wheat, nuts/seeds, starchy roots/tubers products, fruits, eggs/eggs products, soy milk, animal-based milk and instant noodles/frozen dumplings. Temporal tracking (maintenance of a relative position over time) was higher for the traditional southern, whereas adherence to the modern high-wheat had an upward trend over time. Higher income, education and urbanicity level were positively associated with both dietary patterns, but the association became smaller in the later years. These results suggest that even in the context of rapid economic changes in China; the way people chose to combine their foods remained relatively stable. However, the increasing popularity of the modern high-wheat pattern, a pattern associated with several energy-dense foods is cause of concern