Identifying Nutrient Patterns in South African Foods to Support National Nutrition Guidelines and Policies

Impact of Dietary Patterns on Metabolic Syndrome in Young Adults: A Cross-Sectional Study

Metabolic syndrome has become a significant public health concern. This study aims to investigate the impact of dietary patterns on metabolic syndrome in young adults and how physical activity modulates this effect. A cross-sectional study was conducted at a health management center in Tianjin, China, from September 2022 to March 2023. Participants aged 18-35 years were recruited using convenience sampling. Dietary intake was assessed using a validated food frequency questionnaire. Logistic regression models evaluated associations between these patterns and metabolic syndrome, adjusting for potential confounders. Among 442 participants, four dietary patterns were identified: Legume-Nut, Alcohol-Meat, Sugar-Processed, and Egg-Vegetable. The Legume-Nut dietary pattern was associated with a higher risk of metabolic syndrome (OR = 2.63, 95% CI: 1.08-6.37), while the Egg-Vegetable dietary pattern was associated with a lower risk (OR = 0.26, 95% CI: 0.10-0.70). No significant associations were found for the Sugar-Processed and Alcohol-Meat patterns. Subgroup analysis revealed that the Legume-Nut pattern increased the risk of metabolic syndrome among those with irregular physical activity, whereas the Egg-Vegetable pattern decreased the risk. These findings highlight the significant influence of dietary patterns on the risk of metabolic syndrome in young adults and the modifying effect of regular physical activity, underscoring the need for targeted dietary and lifestyle interventions to prevent metabolic syndrome in this population.

Metabolomic Insights on Potassium Excretion, Blood Pressure, and Glucose Homeostasis: The African-PREDICT Study

BACKGROUND

Low-potassium intake is associated with a higher risk of type 2 diabetes and hypertension. Both conditions occur more frequently in Black populations, who also consume less potassium-rich foods.

OBJECTIVES

Using metabolomics to identify dysregulated metabolic pathways associated with low-potassium excretion may procure more accurate entry points for nutritional prevention and intervention for type 2 diabetes and hypertension.

METHODS

A total of 440 White and 350 Black adults from the African-PREDICT study (aged 20-30 y) were included. Twenty-four-hour blood pressure (BP) was measured. Potassium, sodium, and fasting glucose concentrations were analyzed in 24-h urine and plasma samples. Liquid chromatography-tandem mass spectrometry-based metabolomics included the analyses of amino acids and acylcarnitines in spot urine samples.

RESULTS

Black participants had lower urinary potassium concentrations than Whites (36.6 compared with 51.1 mmol/d; P < 0.001). In White but not Black adults, urinary potassium correlated positively with 2-aminoadipic acid (2-AAA) (r = 0.176), C3-[propionyl]carnitine (r = 0.137), C4-[butyryl]carnitine (r = 0.169) and C5-[isovaleryl]carnitine (r = 0.167) in unadjusted and 2-AAA (r = 0.158) and C4-carnitine (r = 0.160) in adjusted analyses (all P < 0.05 and q < 0.05). Elevated C0-, C3-, and C5-carnitine in turn were positively associated with systolic BP (Black and White groups), diastolic BP (Black group), and glucose (White group) (all P < 0.05).

CONCLUSIONS

Racial differences are an important consideration when investigating nutrient-metabolite relationships and the role thereof in cardiovascular disease. Only in White adults did urinary potassium associate with 2-AAA and short-chain acylcarnitines. These metabolites were positively related to BP and fasting plasma glucose concentrations. In White adults, the metabolomic profiles related to potassium excretion may contribute to BP regulation and glucose homeostasis. This trial was registered at clinicaltrials.gov as NCT03292094.

Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping

Worldwide, several food-based dietary guidelines, with diverse food-grouping methods in various countries, have been developed to maintain and promote public health. However, standardized international food-grouping methods are scarce. In this study, we used two-dimensional mapping to classify foods based on their nutrient composition. The Standard Tables of Food Composition in Japan were used for mapping with a novel technique-t-distributed stochastic neighbor embedding-to visualize high-dimensional data. The mapping results showed that most foods formed food group-based clusters in the Standard Tables of Food Composition in Japan. However, the beverages did not form large clusters and demonstrated scattered distribution on the map. Green tea, black tea, and coffee are located within or near the vegetable cluster whereas cocoa is near the pulse cluster. These results were ensured by the k-nearest neighbors. Thus, beverages made from natural materials can be categorized based on their origin. Visualization of food composition could enable an enhanced comprehensive understanding of the nutrients in foods, which could lead to novel aspects of nutrient-value-based food classifications.

Is There a Convergence between the Food Classification Adopted by Food-Based Dietary Guidelines and Food Science and Technology?

The World Health Organization (WHO) proposed the dietary guidelines presented as the Food-based Dietary Guidelines (FBDG). The FBDG classify foods according to their origin, nature, nutrient source, food group, and processing level. Food science and technology (FST) ranks food according to its origin, perishability, nutrient source, processing, food group, and formulation. This paper aimed to compare the convergence points for food classification according to the FBDG and FST. This study was carried out in two phases. The first step was identifying the Food-Based Dietary Guidelines (FBDG). For each of the FBDG, food items were grouped as fruits, vegetables, cereals, sugars, fat and oils, legumes, foods from animals, dairy products, and others. The second step aimed to identify and describe the different food classification systems. The search was performed on PubMed®, Science Direct, and Web of Science and websites of international organizations such as the Food and Agriculture Organization of the United Nations (FAO), the World Health Organization (WHO), and the Codex Alimentarius. Our results show that the points of convergence between the classifications were the classification in terms of origin (animal and vegetable), nutrient sources, and food groups. However, inconsistencies were observed for the distribution of food items in each group in the 98 surveyed FBDG. As for nature, there was a convergence for in natura, minimally processed, and processed foods. However, the criteria adopted for minimally processed and processed foods described in the FBDG differ from those considered by the FST. FST also does not recognize the classification of foods concerning the level of processing.

Determining classes of food items for health requirements and nutrition guidelines using Gaussian mixture models

Introduction

The identification of classes of nutritionally similar food items is important for creating food exchange lists to meet health requirements and for informing nutrition guidelines and campaigns. Cluster analysis methods can assign food items into classes based on the similarity in their nutrient contents. Finite mixture models use probabilistic classification with the advantage of taking into account the uncertainty of class thresholds.

Methods

This paper uses univariate Gaussian mixture models to determine the probabilistic classification of food items in the South African Food Composition Database (SAFCDB) based on nutrient content.

Results

Classifying food items by animal protein, fatty acid, available carbohydrate, total fibre, sodium, iron, vitamin A, thiamin and riboflavin contents produced data-driven classes with differing means and estimates of variability and could be clearly ranked on a low to high nutrient contents scale. Classifying food items by their sodium content resulted in five classes with the class means ranging from 1.57 to 706.27 mg per 100 g. Four classes were identified based on available carbohydrate content with the highest carbohydrate class having a mean content of 59.15 g per 100 g. Food items clustered into two classes when examining their fatty acid content. Foods with a high iron content had a mean of 1.46 mg per 100 g and was one of three classes identified for iron. Classes containing nutrient-rich food items that exhibited extreme nutrient values were also identified for several vitamins and minerals.

Discussion

The overlap between classes was evident and supports the use of probabilistic classification methods. Food items in each of the identified classes were comparable to allowed food lists developed for therapeutic diets. This data-driven ranking of nutritionally similar classes could be considered for diet planning for medical conditions and individuals with dietary restrictions.

Urinary metabolomics, dietary salt intake and blood pressure: the African-PREDICT study

In Black populations excessive salt intake may exacerbate the genetic predisposition to hypertension and promote the early onset of cardiovascular disease. Ethnic differences in the interaction between sodium intake and the metabolome may play a part in hypertension and cardiovascular disease development. We determined (1) urinary amino acid and acylcarnitine profiles of young Black and White adults according to low, moderate, and high dietary salt intake, and (2) investigated the triad of salt intake, systolic blood pressure (SBP), and the associated metabolomics profile. This study included 447 White and 380 Black adults aged 20-30 years from the African-PREDICT study. Estimated salt intake was determined from 24-hour urinary sodium levels. Urinary amino acids and acylcarnitines were measured using liquid chromatography-tandem mass spectrometry. Black adults exhibited no significant differences in SBP, amino acids, or acylcarnitines across low (<5g/day), moderate (5-10g/day), and high (>10g/day) salt intake. White adults with a high salt intake had elevated SBP compared to those with low or moderate intakes (p < 0.001). Furthermore, gamma-aminobutyric acid (GABA) (q = 0.020), citrulline (q = 0.020), glutamic acid (q = 0.046), serine (q = 0.054) and proline (q = 0.054) were lowest in those with higher salt intake. Only in White and not Black adults did we observe inverse associations of clinic SBP with GABA (Adj. R2 = 0.34; Std. β = -0.133; p = 0.003), serine (Adj. R2 = 0.33; Std. β = -0.109; p = 0.014) and proline (Adj. R2 = 0.33; Std. β = -0.109; p = 0.014). High salt intake in White, but not in black adults, were related to metabolomic changes and may contribute to pathophysiological mechanisms associated with increased BP.

Statistical Methods for the Analysis of Food Composition Databases: A Review

Evidence-based knowledge of the relationship between foods and nutrients is needed to inform dietary-based guidelines and policy. Proper and tailored statistical methods to analyse food composition databases (FCDBs) could assist in this regard. This review aims to collate the existing literature that used any statistical method to analyse FCDBs, to identify key trends and research gaps. The search strategy yielded 4238 references from electronic databases of which 24 fulfilled our inclusion criteria. Information on the objectives, statistical methods, and results was extracted. Statistical methods were mostly applied to group similar food items (37.5%). Other aims and objectives included determining associations between the nutrient content and known food characteristics (25.0%), determining nutrient co-occurrence (20.8%), evaluating nutrient changes over time (16.7%), and addressing the accuracy and completeness of databases (16.7%). Standard statistical tests (33.3%) were the most utilised followed by clustering (29.1%), other methods (16.7%), regression methods (12.5%), and dimension reduction techniques (8.3%). Nutrient data has unique characteristics such as correlated components, natural groupings, and a compositional nature. Statistical methods used for analysis need to account for this data structure. Our summary of the literature provides a reference for researchers looking to expand into this area.