Background: Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately.Methods: This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation.Results: While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods’ performance in terms of reproducibility, validity, and ability to predict different outcomes.Conclusion: Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.

Background: Several observational studies have investigated the association of insomnia with psychiatric disorders. Such studies yielded mixed results, and whether these associations are causal remains unclear. Thus, we aimed to identify the causal relationships between insomnia and five major psychiatric disorders.Methods: The analysis was implemented with six genome-wide association studies; one for insomnia and five for psychiatric disorders (attention-deficit/hyperactivity disorder, autism spectrum disorder, major depressive disorder, schizophrenia, and bipolar disorder). A heterogeneity in dependent instrument (HEIDI) approach was used to remove the pleiotropic instruments, Mendelian randomization (MR)-Egger regression was adopted to test the validity of the screened instruments, and bidirectional generalized summary data-based MR was performed to estimate the causal relationships between insomnia and these major psychiatric disorders.Results: We observed significant causal effects of insomnia on the risk of autism spectrum disorder and bipolar disorder, with odds ratios of 1.739 (95% confidence interval: 1.217–2.486, p = 0.002) and 1.786 (95% confidence interval: 1.396–2.285, p = 4.02 × 10⁻⁶), respectively. There was no convincing evidence of reverse causality for insomnia with these two disorders (p = 0.945 and 0.546, respectively). When insomnia was considered as either the exposure or outcome variable, causal estimates for the remaining three psychiatric disorders were not significant.Conclusions: Our results suggest a causal role of insomnia in autism spectrum disorder and bipolar disorder. Future disease models should include insomnia as a factor for these two disorders to develop effective interventions. More detailed mechanism studies may also be inspired by this causal inference.

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Background: Diet has long been hypothesized to play an important role in hyperuricemia, and weight gain is a factor that is strongly associated with the rise in serum urate. We aimed to clarify the mediating role of obesity in the relationship between diet and hyperuricemia and to determine whether a weight-loss diet is an effective way to prevent hyperuricemia. Methods: This cross-sectional study analysed representative samples of United States (n = 20,081; NHANES 2007–2016) adults. Nutrient patterns were derived with two methods: principal component analysis (PCA) and reduced rank regression (RRR) with obesity. Logistic regression and multivariable linear regression were applied to analyse the association between nutrient patterns in obesity and hyperuricemia. Mediation analyses were used to determine whether four obesity indicators, including body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI) and lipid accumulation product index (LAP), mediated the relationship between nutrient patterns and hyperuricemia. Results: PCA revealed three nutrient patterns (including “Low energy diet”, “Lower vitamin A, C, K pattern” and “Vitamin B group”), and only Vitamin B group had a total effect on hyperuricemia. RRR revealed one main nutrient pattern associated with obesity, which was characterized by High fat and low vitamin levels and was significantly associated with hyperuricemia. Mediation analysis showed that obesity mostly or even completely mediated the relationship between nutrient patterns and hyperuricemia, especially traditional obesity indicators, which played a key intermediary effect. The proportions of indirect effects for BMI and WC were as high as 53.34 and 59.69, respectively. Conclusions: Our findings suggest that the direct effect of diet on hyperuricemia is weak, and obesity plays a critical mediating role in the relationship between diet and hyperuricemia, which confirms that a weight-loss diet such as a “Low fat and high vitamin diet” may be useful in preventing hyperuricemia.

Few studies have investigated gender differences in dietary intake. The objective of this cross-sectional study was to examine gender differences in dietary patterns and their association with the prevalence of metabolic syndrome. The food intakes of 3794 subjects enrolled by a two-stage cluster stratified sampling method were collected using a valid semi-quantitative food frequency questionnaire (FFQ). Metabolic syndrome (MetS) was defined according to the International Diabetes Federation (IDF) and its prevalence was 35.70% in the sample (37.67% in men and 24.67% in women). Dietary patterns were identified using factor analysis combined with cluster analysis and multiple group confirmatory factor analysis was used to assess the factorial invariance between gender groups. The dominating dietary pattern for men was the “balanced” dietary pattern (32.65%) and that for women was the “high-salt and energy” dietary pattern (34.42%). For men, the “animal and fried food” dietary pattern was related to higher risk of MetS (odds ratio: 1.27; 95% CI: 1.01–1.60), after adjustment for age, marital status, socioeconomic status and lifestyle factors. For women, the “high-salt and energy” dietary pattern was related to higher risk of MetS (odds ratio: 2.27; 95% CI: 1.24–4.14). We observed gender differences in dietary patterns and their association with the prevalence of MetS. For men, the “animal and fried food” dietary pattern was associated with enhancive likelihood of MetS. For women, it was the “high-salt and energy” dietary pattern.

Zhao CN, et al. Allergy Asthma Immunol Res. 2015 Mar;7(2):175-185. https://doi.org/10.4168/aair.2015.7.2.175