Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental condition often accompanied by metabolic and nutritional imbalances. Conventional dietary interventions, such as the gluten-free, casein-free diet, typically fail to consider individual genetic variations. Nutrigenomics, the study of gene-nutrient interactions, offers a promising framework for exploring personalized dietary interventions that may help address the metabolic and neurological complexities associated with ASD, although current evidence remains preliminary. This research note offers recommendations for integrating nutrigenomics into special education through a multidisciplinary approach that combines clinical nutrition, genetics, and educational practice via a 3-phase agenda. Stage 1 focuses on identifying behavioral subgroups within special education settings and using validated tools such as the Child Behavior Checklist Scale to analyze nutritional intake. Stage 2 involves the development and pilot-testing of behavior-specific nutrition protocols that are tailored to these subgroups, incorporating input from practice experts in nutrigenomics. Lastly, in Stage 3, a personalized nutrition model that incorporates genetic screening and metabolic profiling is constructed in collaboration with dietitians, educators, and caregivers. By bridging clinical and educational domains, this study seeks to establish nutrigenomics-based nutrition therapy as a viable and equitable intervention for improving health and developmental outcomes among students with ASD.
Cataracts are a major cause of visual impairment worldwide, particularly among older adults, with an increasing prevalence due to population aging. Surgery is the primary treatment; however, preventive strategies are crucial for reducing the disease burden. This study aimed to investigate dietary and health-related factors associated with cataract occurrence and develop a predictive model using machine learning. Data were derived from the Korea National Health and Nutrition Examination Survey 2015–2017. The study included 190 women aged 60–79 years: 124 with cataracts and 66 controls. Analyzed variables included sociodemographic, behavioral, chronic disease, and dietary intake factors. After data preprocessing, 4 machine learning algorithms: support vector machine (SVM), random forest (RF), eXtreme gradient boosting, and multilayer perceptron were used. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC) and precision-recall curves. Among the tested models, the SVM achieved the best performance under stratified 10-fold cross-validation, with 71% accuracy, 86% precision, 73% recall, 79% F1-score, 65% AUROC, and 81% AUPRC. According to our findings, the odds of having cataracts can be effectively predicted using dietary and health data without relying on specialized ophthalmic equipment. The proposed model demonstrates the potential of machine learning-based tools for early identification and prevention of cataracts. Future studies with larger and more diverse samples, as well as integrating additional data sources such as genomics and lifestyle factors, are warranted to refine predictive accuracy and enhance personalized nutrition-based interventions.
This study examined meal patterns and protein-rich food utilization in the foodservice practices of public and private geriatric long-term care hospitals in South Korea. Over a period of 6 months, a total of 612 daily menus (306 from each hospital type, breakfast, lunch, and dinner) were collected from four hospitals (two public, two private). Each menu was categorized by meal composition, included staple food, soup, main dish, side dishes, and kimchi. The most common meal pattern consisted of a staple food, soup, main dish, two side dishes, and kimchi. Compared with private hospitals, public hospitals offered a greater variety in meal composition, staple foods, soups, and main dishes. However, no significant differences were observed in protein foods of main dishes. Overall, meat accounted for about half, whereas fish accounted for one-third. Approximately 30% of protein foods in main dishes were processed. In side dish 1, the proportion of protein-rich foods was lower in public than in private hospitals, whereas the proportion of processed foods exceeded two-thirds in both hospital, but was significantly higher in public hospitals. Soup was the second most important protein source after the main dish, with fish as the most often used; however, processed protein foods were also common. These findings indicate that the main dish and soup are the principal protein sources, and the relatively high inclusion of fish reflects a favorable pattern. However, to ensure intake of high-quality proteins by older adults, the high reliance on processed protein foods highlights the need to reconsider foodservice practices.
Chronic obstructive pulmonary disease (COPD) is a major respiratory disorder characterized by irreversible airflow limitation. The role of diet in the prevention and management of COPD is receiving increasing attention. This study aimed to examine the association between the composite intake of vegetables, fruits, meat, and fish and pulmonary function as well as COPD prevalence in a representative sample of Korean adults aged ≥ 40 years using data from the 7th Korea National Health and Nutrition Examination Survey. Higher vegetable intake was associated with significantly better pulmonary function parameters, including forced vital capacity (p < 0.001), forced vital capacity percent predicted (p = 0.050), forced expiratory volume (FEV) in 1 second (FEV1; p < 0.001), FEV1 percent predicted (p = 0.038), FEV in 6 seconds (p < 0.001), and peak expiratory flow (p < 0.001). Furthermore, individuals with a high combined intake of vegetables, fruits, meat, and fish demonstrated a 0.261-fold lower COPD prevalence than those without such intake (p = 0.039). The dietary inflammatory index (DII) was significantly lower among participants without COPD than among those with COPD (mean DII = −3.6947, p = 0.002), indicating that a diet rich in anti-inflammatory nutrients can help reduce COPD risk. These findings suggest that vegetable consumption supports improved respiratory function, and a composite dietary pattern incorporating various food groups may help reduce the prevalence of COPD in the adult population.
We investigated adolescents’ perceptions of meat alternatives and examined the relationships among their views on various types of these alternatives. A survey was conducted with 372 middle and high school students, focusing on their perceptions of 3 categories of meat alternatives: plant-based meats, edible insects, and cultured meats. The relationships among these perceptions were subsequently analyzed. Overall, 77.4% of respondents were aware of meat alternatives, and 38.7% reported having consumed them. Perception levels differed by category, with plant-based meats receiving the highest scores, followed by cultured meats and edible insects. Notably, perceptions across the different categories of meat alternatives were significantly correlated. These findings suggest that increasing awareness about meat alternatives—particularly through education—may help promote sustainable and healthy eating behaviors among adolescents.
This study was conducted to analyze diet and health-related factors based on the income level of single-adult households using data from the Korea National Health and Nutrition Survey (KNHANES). Among those who participated in the 2016–2018 KNHANES, 951 single-adult households aged 19 to 64 were selected, and factors such as general characteristics, physical characteristics, dietary behaviors, health behaviors, and the prevalence of chronic diseases were analyzed. The high-income group had higher frequency of eating out, better dietary status generally, higher recognition rate of nutrition labels than the other groups. The rate of alcohol consumption and smoking was higher in the high-income group of single-adult households, while the rates of anxiety and depression were higher in the low-income group. Additionally, the use of dietary supplements decreased as income level decreased. Among chronic diseases, hypertension, diabetes, and dyslipidemia had the lowest prevalence in the middle-income group and the highest prevalence in the low-income group. These results suggest that diet and health behaviors vary by income level in single-adult households and may be associated with the prevalence of chronic diseases. Future systematic studies should be conducted to determine the causal relationships between these factors.
Nutrition fact labels (NFLs) have advantages because they are an intuitive tool that provides unified information regulated by the government and does not require any devices or special skills. During pandemic, with increased interest in information about healthy food choices and optimum nutrition, frequent exposure to NFLs on pre-packaged foods and dietary supplements may have helped consumers become aware of and/or use NFLs. We aimed to evaluate NFL usage changes from the pre- and early to the late pandemic years in the Korean adult population, using data from the Korean Community Health Survey (3-year total respondents n = 687,610) conducted from 2019 to 2021. NFL awareness, effect, and utilization ratios in each subgroup (sex, age, diabetes mellitus/hypertension, subjective health status, and physical activity) were analyzed for the 3 years by the cross-tabulation test of weighted complex sample analysis. Despite the declining awareness of NFLs in the Korean population, the proportion of individuals who were affected by the NFL content in the entire population and the utilization ratio among those who were aware of NFLs increased continuously during the early and late pandemic periods. Thus, Nutrition experts and policy-makers need to increase efforts to maintain interest in NFLs that emerged during the pandemic. NFLs, a conventional but well-regulated and effective tool, may have enabled the Korean population to make healthy food choices during the pandemic.
This study compared the effects of 12 weeks of intensive nutrition education (IE) using the 5A's behavioral change model and basic nutrition education (BE) on nutritional knowledge and nutrient intake among Korean adolescent athletes. This study included elite adolescent athletes (IE group: n = 65, BE group: n = 65) at a physical education high school in Seoul. In this prospective, randomized, controlled trial, the athletes' body composition, nutritional knowledge, nutrient intake, and self-management practices were evaluated at the beginning and end of the intervention. Both groups had increased levels of nutrition knowledge between pre- and post-test, but the change in total score for nutrition knowledge was significantly higher in the IE group than in the BE group (p < 0.001). Energy intake post-test increased significantly in the IE group (from 2,185 to 2,651 kcal/day, p < 0.001) but not in the BE group. The intake of carbohydrates, protein, and fat also increased significantly in the IE group (carbohydrates: from 298 to 352 g/day, protein: from 86 to 106 g/day, fat: from 71 to 88 g/day, all p < 0.001), but the change in the BE group was not significant. Additionally, the IE group showed a significant overall increase in vitamins and minerals compared to the BE group at post-test. Adolescent athletes in the IE group showed improved nutritional knowledge and intake compared to those in the BE group 12 weeks after the intervention.
This study analyzed the 2019 Community Health Survey data to compare and analyze the health levels and life satisfaction of single-person elderly households based on food security. The final study subjects were 15,606 single-person elderly individuals aged 65 and above. These subjects were classified based on their response to food security into three groups: food sufficient-diverse, food sufficient-not diverse, and food insufficient. The study results showed that the proportion of the food insufficient group among single-person elderly households was 7.4% for men and 10.6% for women, with a slightly higher rate for female elderly. Both male and female elderly over 80 years of age, with low education levels, and basic living support recipients showed significantly higher proportions in the sufficient-not diverse and food insufficient groups. For male elderly, significant differences were observed in subjective health status and oral health level in the food insufficient group, and for female elderly, stress levels also showed significant differences. Life satisfaction scores were generally lower for female elderly compared to male, and significant differences were found in both male and female elderly based on food security. Common factors that significantly influence life satisfaction among single-person elderly households, both male and female, include food security, subjective health status, and living environment satisfaction, with food security being the most impactful factor. The study suggests that it is necessary to include these significant factors in the development of various social activity programs, such as dietary programs, to enhance life satisfaction and food security of single-person elderly households.
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The aim of this study was to investigate whether dairy intake was associated with the severity of coronavirus disease 2019 (COVID-19) disease and the probability of hospitalization of patients. This cross-sectional study was conducted on 141 patients with COVID-19 with an average age of 46.23 ± 15.88 years. The number of men (52.5%) participating in this study was higher than that of women. The association between dairy intake and COVID-19 was evaluated by multivariable logistic regression analysis. The risk of hospitalization in the highest tertile of dairy intake was 31% lower than in the lowest tertile (odds ratio [OR], 0.69; 95% confidence interval [CI], 0.37–1.25, p trend = 0.023). Higher milk and yogurt intake was associated with a reduced risk of hospitalization due to COVID-19. Patients in the third tertiles were about 65% (p for trend = 0.014) and 12% (p for trend = 0.050) less likely to be hospitalized than those in the first tertile, respectively. Dairy consumption, especially low-fat ones, was associated with a lower risk of hospitalization due to COVID-19 and lower severity of COVID-19.
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People with higher genetic predisposition to obesity are more susceptible to cardiovascular diseases (CVDs) and healthy plant-based foods may be associated with reduced risks of obesity and other metabolic markers. We investigated whether healthy plant-foods-rich dietary patterns might have inverse associations with cardiometabolic risk factors in participants at genetically elevated risk of obesity. For this cross-sectional study, 377 obese and overweight women were chosen from health centers in Tehran, Iran. We calculated a healthy plant-based diet index (h-PDI) in which healthy plant foods received positive scores, and unhealthy plant and animal foods received reversed scores. A genetic risk score (GRS) was developed based on 3 polymorphisms. The interaction between GRS and h-PDI on cardiometabolic traits was analyzed using a generalized linear model (GLM). We found significant interactions between GRS and h-PDI on body mass index (BMI) (p = 0.02), body fat mass (p = 0.04), and waist circumference (p = 0.056). There were significant gene-diet interactions for healthful plant-derived diets and BMI-GRS on high-sensitivity C-reactive protein (p = 0.03), aspartate aminotransferase (p = 0.04), alanine transaminase (p = 0.05), insulin (p = 0.04), and plasminogen activator inhibitor 1 (p = 0.002). Adherence to h-PDI was more strongly related to decreased levels of the aforementioned markers among participants in the second or top tertile of GRS than those with low GRS. These results highlight that following a plant-based dietary pattern considering genetics appears to be a protective factor against the risks of cardiometabolic abnormalities.
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Recent studies have evaluated the association between specific beverage intake and metabolic risks in adults. However, more evidence is needed to examine the association between the Healthy Beverage Index (HBI) and metabolic factors. Therefore, this study investigated the relationship between HBI and metabolic factors in adults. In this cross-sectional study, 338 overweight and obese individuals living in Tabriz, Iran were selected. Data on beverage consumption, demographics, physical activity, and anthropometric characteristics were evaluated using validated standard protocols. The predefined HBI was calculated based on previous studies. The mean value of HBI index among all of the participants was 59.76 ± 6.51. Those at the higher HBI scores had significantly lower waist circumference, waist-to-hip ratio, fat mass, and weight (p < 0.05). HBI and triglyceride scores also had a significant relationship. It has been shown that at higher HBI scores compared to lower scores, high-density lipoprotein cholesterol levels increase while homeostatic model assessment for insulin resistance, low-density lipoprotein cholesterol, total cholesterol, and blood pressure decrease. HBI scores higher among Iranian adults were associated with a better chance of losing weight and weight loss and a better lipid profile, and lower blood pressure. Therefore, HBI can be a useful and helpful tool for assessing the overall quality of beverages adults consume. However, further studies are warranted to confirm the possible health effects of healthy beverage index.
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The present study sought to examine the association between an infant’s anthropometric outcomes with maternal Dietary Inflammatory Index (DII) and Alternate Healthy Eating Index for Pregnancy (AHEI-P) scores during the third trimester of pregnancy. This prospective cohort study was applying 130 pregnant women, at the pregnancy training center in west Tehran, Iran (November 2020 to July 2021). The maternal dietary intake, and body mass index (BMI), and social economic level were evaluated. The data about birth weight, birth height, head circumference, and, gestational age at birth were extracted from each child’s health records. The ultimate sample included 122 (93.8%) pairs of women/newborn children. The participants, mean age was 28.13 ± 4.66 years with gestational age between 28 to 40 weeks and the mean of BMI was 24.62 ± 3.51. Our outcomes, after adjustment for confounding factors, suggested that those newborn infants in the highest quartile of maternal DII score had a significantly lower weight (p < 0.001) and height (p = 0.05), in comparison to those in the lowest quartile, but not head circumference (p = 0.18). Moreover, after adjustment for confounding factors, results suggested that those newborn infants in the First quartile of maternal AHEI-P score had a significantly lower weight (p = 0.018) and, in comparison to those in the higher quartile. It appears that newborn infants with lower maternal DII and higher AHEI-P scores may have a better anthropometric outcome. Further longitudinal and in-depth qualitative and quantitative studies, with a longer-term follow-up, is warranted to support the integrity of our outcomes.
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Hemodialysis (HD) patients face a common problem of malnutrition due to poor appetite. This study aims to verify the appetite alteration model for malnutrition in HD patients through quantitative data and the International Classification of Functioning, Disability, and Health (ICF) framework. This study uses the Mixed Method-Grounded Theory (MM-GT) method to explore various factors and processes affecting malnutrition in HD patients, create a suitable treatment model, and validate it systematically by combining qualitative and quantitative data and procedures. The demographics and medical histories of 14 patients were collected. Based on the theory, the research design is based on expansion and confirmation sequence. The usefulness and cut-off points of the creatinine index (CI) guidelines for malnutrition in HD patients were linked to significant categories of GT and the domain of ICF. The retrospective CIs for 3 months revealed patients with 3 different levels of appetite status at nutrition assessment and 2 levels of uremic removal. In the same way, different levels of dry mouth, functional support, self-efficacy, and self-management were analyzed. Poor appetite, degree of dryness, and degree of taste change negatively affected CI, while self-management, uremic removal, functional support, and self-efficacy positively affected CI. This study identified and validated the essential components of appetite alteration in HD patients. These MM-GT methods can guide the selection of outcome measurements and facilitate the perspective of a holistic approach to self-management and intervention.
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The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40–69 years from the Korea National Health and Nutrition Examination Survey (2013–2018). We set MetS (3–5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = −2.0545] and saturated fatty acid [β = −2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.
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