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 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.

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BackgroundDiet-related cardiovascular diseases have produced a large health burden in China. Coal miners are a high-risk population for cardiovascular disease, but there is little evidence concerning associations between coal miners’ dietary patterns and their 10-year cardiovascular disease risk score levels.MethodsThe study included 2632 participants and focused on dietary patterns associated with higher 10-year cardiovascular disease risk score levels. A valid semi-quantitative food frequency questionnaire was used to collect data regarding dietary intake, and dietary patterns were identified using factor analysis combined with cluster analysis. Logistic regression was used to assess associations between dietary patterns and 10-year cardiovascular disease risk score levels.ResultsFor ground workers, compared with the ‘Healthy’ pattern, the ‘High-salt’ and ‘Refined grains’ patterns were significantly associated with higher 10-year atherosclerotic cardiovascular disease risk score level (OR: 1.50, 95% CI: 1.02–2.21; OR: 1.92, 95% CI: 1.26–2.93) and 10-year ischemic cardiovascular disease risk score level (OR: 2.18, 95% CI: 1.25–3.80; OR: 2.64, 95% CI: 1.48–4.72) adjusted for gender, and behavioural and socioeconomic factors. The ‘High-fat and salt’ pattern was significantly associated with higher 10-year ischemic cardiovascular disease risk score level (OR: 1.97, 95% CI: 1.13–3.42). For underground workers, the ‘High-salt’ pattern was significantly associated with higher 10-year atherosclerotic cardiovascular disease risk score level (OR: 1.65, 95% CI: 1.16–2.36) and 10-year ischemic cardiovascular disease risk score level (OR: 1.76, 95% CI: 1.09–2.84).ConclusionsThis study provides evidence for dietary patterns associated with higher 10-year cardiovascular disease risk score levels in Chinese miners, and facilitates relevant departments in designing effective dietary guidelines to ameliorate dietary structures.

Objective: In the field of nutritional epidemiology, principal component analysis (PCA) has been used extensively in identifying dietary patterns. Recently, compositional data analysis (CoDA) has emerged as an alternative approach for obtaining dietary patterns. We aimed to directly compare and evaluate the ability of PCA and principal balances analysis (PBA), a data-driven method in CoDA, in identifying dietary patterns and their associations with the risk of hypertension. Design: Cohort study. A 24-hour dietary recall questionnaire was used to collect dietary data. Multivariate logistic regression analysis was used to analyse the association between dietary patterns and hypertension. Setting: 2004 and 2009 China Health and Nutrition Survey. Participants: A total of 3,892 study participants aged 18–60 years were included as the subjects. Results: PCA and PBA identified five patterns each. PCA patterns comprised a linear combination of all food groups, whereas PBA patterns included several food groups with zero loadings. The coarse cereals pattern identified by PBA was inversely associated with hypertension risk (highest quintile: odds ratio=0.74 [95% confidence interval=0.57–0.95]; P for trend=0.037). None of the five PCA patterns was associated with hypertension. Compared with the PCA patterns, the PBA patterns were clearly interpretable and accounted for a higher percentage of variance in food intake. Conclusions: Findings showed that PBA might be an appropriate and promising approach in dietary pattern analysis. Higher adherence to the coarse cereals dietary pattern was associated with a lower risk of hypertension. Nevertheless, the advantages of PBA over PCA should be confirmed in future studies.