Wantanee Kriengsinyos

In Thailand, meals are commonly consumed as combinations of multiple dishes, yet no established nutritional criteria exist to evaluate the healthfulness of meals. This study aimed to develop healthy meal criteria for Thai diets using a nutrient profile model. The Nutrient Profiling System for Meals (NPS-M) was developed with six nutritional components: energy, protein, vegetables, total fat, added sugar, and sodium. A scoring algorithm was created. Meal combinations were considered healthier if they achieved scores ≥70%, with no zero score in any one component. A total of 72,149 meals were constructed and evaluated. Criterion validity was assessed by comparing NPS-M with the Nutrient-Rich Foods Index 6.3 (NRF6.3), Health Star Rating (HSR), and Thailand's Healthier Choice logo for ready-to-eat foods (HCL-RTE) using Spearman's rank correlation. The median NPS-M score of all meal combinations was 58 (interquartile range: 45.0-72.0). After excluding meals with zero scores in any component, 10,148 meals (14.1% of total) remained. Among these, 7,252 meals (10.1% of total) achieved scores ≥70%. Sodium was a predominant limiting factor, disqualifying 80% of meals. Criterion validity test-ing showed moderate correlation with NRF6.3 (ρ = 0.566, p <0.001), moderate reverse correlation with HSR (ρ = -0.527, p <0.001), and strong correlation with HCL-RTE (ρ = 0.796, p <0.001). The NPS-M provides a practical framework for evaluating meal healthfulness in the Thai context. The model has potential for use in restaurants, canteens, and public health programs to improve meal quality and support healthier diets in Thailand.

The essentiality of protein in the human diet is unequivocal. Yet researchers, clinicians, and lay people often believe numerous propositions about dietary protein despite insufficient supporting or refuting data in some instances. To address this disconnect, and to "pressure-test" current beliefs about dietary protein, the Indiana University School of Public Health-Bloomington convened a workshop in February 2025 titled "Human Dietary Protein Needs and Benefits: A Critical Assessment of Postulated Propositions." More than 20 international experts discussed (1) methodologic issues affecting data acquisition and interpretation; (2) "optimal" dietary protein intakes and effects on muscle protein synthesis rates, muscle protein accretion, muscle growth, and muscle repair; (3) protein needs during weight loss; (4) acute protein intake thresholds above and below which protein is no longer related to anabolism; and (5) dietary protein intakes above which protein may be detrimental to health. The experts rated each proposition on a scale from "existing evidence strongly supports the proposition" to "existing evidence seems sufficient to rule out the viability of the proposition." In most instances, the experts believed additional research was warranted. For many propositions the research base was insufficient in terms of quality (rigor), quantity (sample size, study duration), or pertinence (e.g., use of surrogate markers).

The field of nutrition and dietetics is evolving, requiring continuous education and professional development for dietitians to maintain competencies. Purs

Objective: This study compared dietary diversity and nutrient adequacy across age groups and seasons within an indigenous Karen community. Design: Cross-sectional survey. Setting: Dietary intake was assessed using a single-day 24-hour dietary recall among Karen community members living in two villages of Laiwo subdistrict, Sangkhlaburi district, Kanchanaburi province, Thailand. Participants: In total, 312 Karen people participated during the rainy season and 344 during the dry season, including school-age children (6-12 years), working-age people (19-59 years), and older people (≥60 years). Results: Dietary diversity scores (DDS) and food variety scores (FVS) significantly differed across age groups for both seasons. However, seasonal DDS differences were not observed within any group, except for the FVS of school-age children. Over 70% of participants in all age groups had inadequate intake of key micronutrients—calcium, iron, vitamin A, vitamin C, zinc, vitamin B6, and vitamin B12—as indicated by nutrient adequacy ratios (NARs) < 0.75. Moderate to strong positive correlations between DDS and NARs for energy, vitamin B2, vitamin C, niacin, and mean adequacy ratio (MAR) (r = 0.418–0.691, p < 0.001) were observed exclusively in the dry season and across all age groups. Conclusion: Among the Karen people, who are also facing triple burden malnutrition, dietary diversity is limited, micronutrient inadequacy is prevalent, and overall dietary quality is insufficient despite frequent vegetable consumption. Findings highlight the need to address systemic challenges related to food variety and to promote education on appropriate food quantities, preparation methods, and sustainable traditional food systems to improve nutrition.

Global economic development has been associated with an increased prevalence of obesity and related health problems. Increased caloric intake and reduced energy expenditure are both cited as development-related contributors to the obesity crisis, but their relative importance remains unresolved. Here, we examine energy expenditure and two measures of obesity (body fat percentage and body mass index, BMI) for 4,213 adults from 34 populations across six continents and a wide range of lifestyles and economies, including hunter-gatherer, pastoralist, farming, and industrialized populations. Economic development was positively associated with greater body mass, BMI, and body fat, but also with greater total, basal, and activity energy expenditure. Body size–adjusted total and basal energy expenditures both decreased approximately 6 to 11% with increasing economic development, but were highly variable among populations and did not correspond closely with lifestyle. Body size–adjusted total energy expenditure was negatively, but weakly, associated with measures of obesity, accounting for roughly one-tenth of the elevated body fat percentage and BMI associated with economic development. In contrast, estimated energy intake was greater in economically developed populations, and in populations with available data (n = 25), the percentage of ultraprocessed food in the diet was associated with body fat percentage, suggesting that dietary intake plays a far greater role than reduced energy expenditure in obesity related to economic development.

Previous studies have highlighted differences in faecal short-chain fatty acids (SCFAs) between hypercholesterolemic (HC) and normocholesterolemic (NC) individuals; however, the impact of nutritional factors on SCFAs remains underexplored. This cross-sectional observational study involved 42 HC and 38 NC Thai adults and examined faecal SCFA concentrations and their associations with nutritional factors. Aliphatic (acetic, propionic, butyric acids) and branched (isobutyric and isovaleric acids) SCFAs were analysed. Although NC participants exhibited higher aliphatic and total SCFAs than HC participants, these differences were insignificant. Carbohydrate intake was positively associated with acetic and propionic acid, in the NC group. Sugar intake was negatively correlated with isobutyric and isovaleric acids in the NC group but positively correlated in the HC group. These findings suggest less efficient sugar and carbohydrate fermentation in the HC group, potentially affecting SCFA profiles. Future studies should investigate gut microbiota composition, bile acid secretion and gut pH as contributing factors.

Thailand's implementation of the “Healthier Choice” logo (THCL) as a front-of-pack nutritional labeling (FOPNL) scheme aims to promote healthier consumer food choices. In response, the beverage industry has increasingly replaced nutritive sweeteners (NS) with non-nutritive sweeteners (NNS) in their products. This study evaluates the energy and sugar content, sweetener usage, and overall healthfulness of pre-packaged beverages in Thai supermarkets, while also assessing their compliance with WHO “free sugar” consumption guidelines and exploring appropriate sugar thresholds for revising the THCL criteria. A cross-sectional audit was conducted in a major Bangkok supermarket between March and April 2022, collecting data on 881 pre-packaged beverages. To ensure a comprehensive evaluation, these beverages were further categorized based on product ingredients, THCL status, and sweetener type. Healthfulness was assessed using three validated nutrient profiling systems (NPS), and sugar threshold scenarios were evaluated using receiver operating characteristic analysis and area under the curve metrics. The median energy and sugar content per 100 ml were 37.9 kcal (21.9–52.2) and 5.00 g (2.0–7.8), respectively. Overall, pre-packaged beverages in Thai supermarkets were predominantly classified as “less healthy,” with 40.1% containing at least one NNS. Products carrying the THCL logo (27.7%) had lower energy and sugar content, indicating a “healthier” profile. However, these beverages also exhibited a higher prevalence of NNS compared to unlabeled products. Regardless of beverage type, consuming a typical bottle containing NS or a combination of NS and NNS in a single sitting often exceeded the WHO's recommended daily limit of 5% of total energy intake from free sugars. Thus, the study also proposes a phased approach to sugar reduction, initially lowering the sugar threshold to 5.0 g/100 ml, followed by a further reduction to 4.0 g/100 ml. In conclusion, the study underscores the widespread use of NNS in Thai pre-packaged beverages and identifies an overall “less healthy” nutritional profile across product categories. Reducing sugar content and implementing incremental sugar benchmarks in the THCL scheme are essential steps toward improving the healthfulness of pre-packaged beverages in Thailand and aligning these products more closely with WHO guidelines on sugar consumption.

Background: To accurately capture an individual’s food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client’s memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely. Objective: This review aims to fill the gap by providing a current overview of AI’s integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems’ strengths and weaknesses and propose enhancements to improve IADA’s accuracy and adoption in the nutrition community. Methods: This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021. Results: A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance. Conclusions: This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field.

Background: The purpose of nutrient profiling models (NPM) is to evaluate and categorize food products, in order to make it easier for consumers to choose healthier options, to encourage the development of nutritious foods, and to restrict the excessive advertising of products with poor nutritional value. In Thailand, a front-of-package nutrition labeling scheme (FOPNL), known as the “Healthier Choice” symbol (HCL), has been adopted. For composite meal products, the HCL uses a nutrient profiling model (HCL–NPM) to determine which product qualifies as ‘healthier’ and is allowed to display the HCL symbol. This research aims to assess the convergent validity of this scoring algorithm. Methods: A secondary data analysis, using the Mintel Global New Products Database, was conducted. The nutrient composition of newly launched composite meals marketed in Thailand from 2016 to 2021 was included. An analysis of convergent validity was conducted using the following tests: (1) the Pearson correlation test with the Nutri-Score Nutrient Profiling Model (NS–NPM), Health Star Rating Nutrient Profiling Model (HSR–NPM), and Nutrient-Rich Food Index 6.3 (NRF6.3); (2) the Cohen’s kappa coefficient agreement analysis, to identify ‘healthier’ products with the Nutri-Score FOPNL (NS–FOPNL), Health Star Rating FOPNL (HSR–FOPNL), Chilean Warning Label (CWL–FOPNL), and Choices International (CI–FOPNL). Results: A total of 589 composite meal products were included. The median score of HCL–NPM was 18.0 points (with an interquartile range of 14.0–22.0), and the mean +/− standard deviation score was 18.3+/−5.4 points. The Pearson’s correlation coefficient revealed that the HCL–NPM had a moderate negative correlation with the NS–NPM (r = −0.458; 95%CI: −0.520 to −0.391) and with HSR–NPM (r = −0.524; 95%CI: −0.520 to −0.391), while a weak positive correlation was found with NRF6.3 (r = 0.369; 95%CI: 0.297 to 0.436). In the agreement analysis, HCL–FOPNL had a fair pair-wise agreement with CI–FOPNL (κ = 0.335; 95%CI: 0.218 to 0.453), whereas a slight agreement was reported when compared to CWL–FOPNL (κ = 0.187; 95%CI: 0.145 to 0.228), NS–FOPNL (κ = 0.172; 95%CI: 0.132 to 0.211), and HSR–FOPNL (κ = 0.157; 95%CI: 120 to 0.193). Conclusion: The HCL algorithm system could be used to evaluate culture-specific packaged composite meal products in Thailand. It exhibits sufficient correlation and agreement with other international FOPNLs. These results may be helpful in informing improvements to the “Healthier Choice” scheme in Thailand.