Compositional analysis of dietary patterns

Advances in methods for characterizing dietary patterns: A scoping review

There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.

Tutorial on statistical data reduction methods for exploring dietary patterns

Diet is one of the most important factors affecting human health and it is different for each person. Examining individual foods in the diet does not provide sufficient information to the researcher, so we need food patterns to obtain more complete information. Food pattern analysis is also a complementary approach that is carried out by statistical methods and provides additional evidence in this regard. In this tutorial article, we have tried to briefly explain all statistical analyses which can used for dietary pattern analysis.

Chinese Dietary Indices and Glioma: New Insights of a Case-Control Study in the Chinese Population

Identifying modifiable factors in primary prevention strategies is a typical goal of glioma epidemiology. Among many glioma risk factors, diet was always considered as one. Most of the relevant studies thus far were concentrated on the West. It was crucial to investigate the connection between the Chinese diet and gliomas given the stark variations between western and eastern diets. A food frequency questionnaire including 114 items was used to investigate the food intake of the study subjects. The Chinese Dietary Quality Index (CDQI), the Chinese Dietary Balance Index (CDBI), the Dietary Antioxidant Index (DAI), the Dietary Inflammation Index (DII), and the Chinese Healthy Eating Index (CHEI) were calculated based on the data provided by the food frequency questionnaire to evaluate dietary quality, dietary balance, dietary antioxidants, dietary inflammation and adherence to the Chinese dietary guidelines in 506 glioma patients and 506 controls, respectively. After adjusting covariates, CHEI (OR = 0.90, 95% CI: 0.88-0.93) and DAI (OR = 0.61, 95% CI: 0.54-0.70) were correlated to a reduced glioma risk, and CDBI-based undernutrition (OR = 1.08, 95% CI: 1.06-1.12) and overnutrition (OR = 1.14, 95% CI: 1.09-1.20) and DII (OR = 2.20, 95% CI: 1.81-2.68) were correlated to an elevated glioma risk. Moreover, restrictive cubic spline analysis showed that there were significant nonlinear dose-response relationships between CHEI, CDBI, DAI, DII, and glioma. Therefore, adhering to the Chinese dietary guidelines was connected with a lower glioma risk, and undernutrition and overnutrition in the Chinese diet were associated with an increased risk of glioma.

Exploring the association of dietary patterns with the risk of hypertension using principal balances analysis and principal component analysis

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-h 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 3892 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: OR = 0·74 (95 % CI 0·57, 0·95); Pfor 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.

Dietary Patterns and Prostate Cancer: CAPLIFE Study

The etiology of prostate cancer (PCa) remains uncertain, and the role of diet is unclear. We aimed to evaluate the role of diet, through dietary patterns, on PCa, considering tumor aggressiveness and extension. The CAPLIFE study is a population-based case-control study including a total of 428 incident PCa cases and 393 controls aged 40-80 years. Dietary information was collected through a validated food frequency questionnaire. Three dietary patterns were identified through principal component analysis: "Mediterranean," "Western," and "Unhealthy," which were categorized into tertiles according to the control group cutoff points. Tumor aggressiveness and extension was determined. Logistic regression models were used to assess the association between dietary patterns and PCa. High adherence to an unhealthy dietary pattern was associated with higher odds of PCa, OR_T3vsT1 = 1.52 (95% CI 1.02-2.27), especially for cases with ISUP 1-2 and localized PCa tumors. This association was not observed with a Western or Mediterranean pattern. In conclusion, adherence to an unhealthy diet appears to be associated with higher odds of PCa, especially for cases with ISUP 1-2 and localized PCa tumors.

Long-term association of vegetable and fruit intake with risk of dementia in Japanese older adults: the Hisayama study

Background

Several prospective Western studies have reported an inverse association of vegetable and fruit intake with dementia risk. However, there is limited epidemiologic evidence in Asians. This study investigated the association of intakes of vegetables, fruits, and their nutrients on the risk of incident dementia and its subtypes in a Japanese community.

Methods

A total of 1071 participants (452 men and 619 women) aged ≥60 years without dementia at baseline were prospectively followed up for 24 years. Intakes of vegetables, fruits, and nutrients were evaluated using a 70-item semiquantitative food frequency questionnaire at baseline and were categorized into quartiles separately by gender. The outcome measure was the development of dementia and its subtypes—namely, Alzheimer’s disease (AD) and vascular dementia (VaD). The risk estimates of incident dementia were computed using a Cox proportional hazards model.

Results

During the long-term follow-up period, 464 subjects developed dementia, of whom 286 had AD and 144 had VaD. Higher vegetable intake was associated gradually with lower risk of developing dementia and AD (both P-trend < 0.05), but not VaD, after adjusting for confounders. Subjects allocated the highest quartile of vegetable intake had 27 and 31% lower risk of dementia and AD, respectively, than those with the lowest quartile. The risk of dementia decreased significantly with higher intakes of vitamin A, riboflavin, vitamin C, magnesium, calcium, and potassium (all P-trend < 0.05). Subjects with higher total dietary fiber intake tended to be at decreased risk for total dementia (P-trend = 0.07). Meanwhile, there were no significant associations between fruit intake and the risk of dementia and its subtypes.

Conclusion

Higher intakes of vegetables and their constituent nutrients were associated with a lower risk of dementia in Japanese older adults. A diet rich in vegetables may be beneficial in reducing the dementia risk in Asians.

A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project

BACKGROUND

Individual diet components and specific dietary regimens have been shown to impact the gut microbiome.

OBJECTIVES

Here, we explored the contribution of long-term diet by searching for dietary patterns that would best associate with the gut microbiome in a population-based cohort.

METHODS

Using a priori and a posteriori approaches, we constructed dietary patterns from an FFQ completed by 1800 adults in the American Gut Project. Dietary patterns were defined as groups of participants or combinations of food variables (factors) driven by criteria ranging from individual nutrients to overall diet. We associated these patterns with 16S ribosomal RNA-based gut microbiome data for a subset of 744 participants.

RESULTS

Compared to individual features (e.g., fiber and protein), or to factors representing a reduced number of dietary features, 5 a posteriori dietary patterns based on food groups were best associated with gut microbiome beta diversity (P ≤ 0.0002). Two patterns followed Prudent-like diets-Plant-Based and Flexitarian-and exhibited the highest Healthy Eating Index 2010 (HEI-2010) scores. Two other patterns presented Western-like diets with a gradient in HEI-2010 scores. A fifth pattern consisted mostly of participants following an Exclusion diet (e.g., low carbohydrate). Notably, gut microbiome alpha diversity was significantly lower in the most Western pattern compared to the Flexitarian pattern (P ≤ 0.009), and the Exclusion diet pattern was associated with low relative abundance of Bifidobacterium (P ≤ 1.2 × 10-7), which was better explained by diet than health status.

CONCLUSIONS

We demonstrated that global-diet a posteriori patterns were more associated with gut microbiome variations than individual dietary features among adults in the United States. These results confirm that evaluating diet as a whole is important when studying the gut microbiome. It will also facilitate the design of more personalized dietary strategies in general populations.

Association between animal source foods consumption and risk of hypertension: a cohort study

PURPOSE

This study assessed the association between animal source foods (ASF) consumption and hypertension, a recognised risk factor for cardiovascular disease. Adverse effects of red and processed meat (RPM) consumption and beneficial effects of the consumption of dairy products and other ASF have been discovered separately; however, the constrained nature of food intake has been typically ignored. We assessed the effects of substituting RPM and other ASF.

METHODS

We followed-up 5394 Chinese adults (age 18-60 years) at baseline using the China Health and Nutrition Survey from 2004 to 2011. Food consumption was assessed using individual-based consecutive 24-h recall and household-based food weighing approaches. Both traditional substitution analysis and substitution analysis based on compositional transformation were used to assess substitution effects.

RESULTS

In total, 1267 participants were newly diagnosed with hypertension during the median follow-up time of 6.81 years (range, 2.97-6.99 years). The traditional substitution analysis found that substituting eggs for RPM was associated with a lower risk of hypertension. The compositional transformation substitution analysis revealed that replacing RPM with any other ASF was associated with a lower risk of hypertension; it implemented substitutions of one or many ASF for RPM; it also revealed different substitution effects of RPM and dairy products, and substituting dairy products for RPM was associated with reduced hypertension risks.

CONCLUSION

The compositional transformation substitution analysis considers the constrained and relative nature of food consumption. It is a flexible approach to estimating substitution effects using different patterns to obtain personalised estimation effects and provide individualised dietary recommendations.

Effects of Dietary Patterns during Pregnancy on Preterm Birth: A Birth Cohort Study in Shanghai

The objective of this study was to analyse representative dietary patterns during pregnancy in Shanghai and explore the effects of dietary patterns during pregnancy on preterm birth. Data were derived from the ‘Iodine Status in Pregnancy and Offspring Health Cohort’ (ISPOHC) study. Multistage, stratified random sampling was used to select survey participants from 16 districts in Shanghai, which were divided into five sampling areas; 40–70 pregnant women were selected from each area. A total of 4361 pregnant women and their offspring were involved in the study. The male-to-female ratio of the babies was 1.04:1, and the incidence of single preterm birth was 4.2%. Three dietary patterns were extracted by factor analysis: a ‘Vegetarian Pattern’, an ‘Animal Food Pattern’ (AFP), and a ‘Dairy and Egg Pattern’. These patterns explained 40.513% of the variance in dietary intake. Binary logistic regression, which was used to analyse the association between birth outcomes and scores measuring maternal dietary patterns, found only the AFP was a significant risk factor for preterm birth. Higher AFP scores were positively associated with preterm birth (Q2 vs. Q1 OR = 1.487, 95% CI: 1.002–2.207; Q3 vs. Q1 OR = 1.885, 95% CI: 1.291–2.754). After adjusting for other potential contributors, a higher AFP score was still a significant risk factor for preterm birth (Q2 vs. Q1 OR = 1.470, 95% CI: 0.990–2.183; Q3 vs. Q1 OR = 1.899, 95% CI: 1.299–2.776). The incidence of preterm birth was 4.2%. A higher score of AFP was significantly associated with a higher risk of preterm birth. The animal food intake of pregnant women should be reasonably consumed during pregnancy.

A review of statistical methods for dietary pattern analysis

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.

How Does Time Use Differ between Individuals Who Do More versus Less Foodwork? A Compositional Data Analysis of Time Use in the United Kingdom Time Use Survey 2014-2015

BACKGROUND

Increased time spent on home food preparation is associated with higher diet quality, but a lack of time is often reported as a barrier to this practice. We compared time use in individuals who do more versus less foodwork (tasks required to feed ourselves and our households, including home food preparation).

METHODS

Cross-sectional analysis of the UK Time Use Survey 2014-15, participants aged 16+ (N = 6143). Time use over 24 h was attributed to seven compositional parts: personal care; sleep; eating; physical activity; leisure screen time; work (paid and unpaid); and socialising and hobbies. Participants were categorised as doing no, 'some' (<70 min), or 'more' foodwork (≥70 min). We used compositional data analysis to test whether time-use composition varied between these participant groups, determine which of the parts varied between groups, and test for differences across population subgroups.

RESULTS

Participants who spent more time on foodwork spent less time on sleep, eating, and personal care and more time on work. Women who did more foodwork spent less time on personal care, socialising, and hobbies, which was not the case for men.

CONCLUSION

Those who seek to encourage home food preparation should be aware of the associations between foodwork and other activities and design their interventions to guard against unintended consequences.