Nutrient Database Research Articles

OptimAAPP, a smartphone insulin dose calculator for carbohydrate, fat, and protein: A cross-over, randomised controlled trial in adolescents and adults with type 1 diabetes using multiple daily injection therapy.

To (1) evaluate the efficacy of OptimAAPP, a smartphone insulin dose calculator for carbohydrate, fat, and protein in managing glycaemia compared with carbohydrate counting in adolescents and adults with type 1 diabetes using flexible multiple daily injection therapy (MDI, ≥4 injections/day) and (2) assess user acceptability of OptimAAPP. In this free-living trial, participants aged 12-50 years were randomised to use carbohydrate counting or OptimAAPP for meal insulin dose calculation for 3 months, then use the alternate method for 3 months. The primary outcome, time-in-range (3.9-10.0 mmol/L) was measured in weeks 3-4 of each arm using continuous glucose monitoring. The acceptability of OptimAAPP was assessed at end intervention using a purpose-designed questionnaire. An intention-to-treat analysis of 41 participants, mean age 28 ± 12 years and HbA1c 56 ± 10 mmol/mol (7.3 ± 0.9%) found no significant difference in glycaemic outcomes when using OptimAAPP compared with carbohydrate counting including time-in-range (70.5 vs. 67.6%, p = 0.102), above range (24.5% vs. 28.0%, p = 0.068), below range (4.9% vs. 4.4%, p = 0.318), and coefficient of variation (32.2% vs. 33.3%, p = 0.136). There was no severe hypoglycaemia. Participants reported that OptimAAPP was easy to use (79%), and they were confident in giving the recommended doses (82%). Barriers to use were the small food database and the time associated with food entry. In adolescents and adults using flexible MDI therapy, OptimAAPP use did not produce glycaemic outcomes that were significantly different from carbohydrate counting. Participant views of OptimAAPP indicate a high level of acceptability. Increasing the size of the food database will likely enhance the user experience.

Classifying sources of low- and no-calorie sweeteners within the Canadian food composition database

Low- and no-calorie sweeteners are sugar substitutes that impart sweetness. Examining exposure to low- and no-calorie sweeteners is challenging because the amounts of sweeteners in food and beverage products are not standard elements of food composition databases. We identified food codes representing sources of low- and no-calorie sweeteners in the food composition database used for Canadian surveillance data using multiple approaches. First, food code descriptions were searched for keywords (e.g. low calorie) potentially representing low and no-calorie sweeteners. Next, the U.S. Food and Nutrient Database for Dietary Surveys food code descriptions, matched to food codes within the Canadian database, were examined for keywords representing confirmed sweetener sources. Finally, using websites for three Canadian grocers, ingredient lists for brand-specific products were examined for sources of low- and no-calorie sweeteners. Recipe codes often required an examination of ingredient-level food codes. Of 5180 food codes, 76 were classified as sources of low- and no-calorie sweeteners and an additional 46 recipe codes were identified as containing a source of sweetener. The classification system can be applied to national survey data to describe exposure to low- and no-calorie sweeteners and identify key sources of sweeteners. Standardized identification of food codes as sources of low- and no-calorie sweeteners will contribute to an evidence base that can be synthesized to inform nutrition policy.

The association between anthocyanin intake and myopia in adolescents: a cross-sectional study of NHANES.

The study aimed to explore the relationship of anthocyanin and its subtypes with myopia in adolescents aged 12-17 years. Adolescents data for this cross-sectional study were extracted from the National Health and Nutrition Examination Survey (NHANES) 2007-2008. Anthocyanin and subtypes were obtained using the Nutrient Database for Dietary Studies codes. Myopia was defined as a spherical equivalent of -1.0 diopters or less. The relationships between anthocyanin and subtypes intake and myopia were determined utilizing weighted univariate and multivariate logistic regression models. The relationships were also explored in gender, leisure time, physical activity, sedentary activity, BMI, and serum cotinine subgroups. A total of 839 adolescents were included for further analysis, among them 245 have myopia. Malvidin (34.98%) was the subtype with the largest anthocyanin intake, followed by cyanidin (22.94%). Compared to adolescents without anthocyanin intake, total anthocyanin intake was related to a lower incidence of myopia (OR = 0.69, 95%CI: 0.51-0.92). Higher intake of cyanidin (OR = 0.69, 95%CI: 0.52-0.92), petunidin (OR = 0.64, 95%CI: 0.42-0.97), and delphinidin (OR = 0.71, 95%CI: 0.51-0.99) were associated with lower odds of myopia in adolescents. Higher total anthocyanin intake was related to lower odds of myopia in those females, leisure time physical activity ≥60 min/day, sedentary time <8 h/day, overweight or obese, and serum cotinine ≥0.05 ng/ml. Higher total anthocyanin intake, particularly cyanidin, petunidin, and delphinidin, was related to a lower incidence of myopia in adolescents. Increasing dietary anthocyanin intake may be an effective prevention strategy for ocular health.

Potential Biomass Energy Generation from Agricultural Production in South Sudan

The aim of this study is to assess the capacity of agricultural residues in South Sudan in terms of their types and quantities. The country had limited access to modern energy sources, and most of the population relied on traditional biomass for cooking and heating. However, efforts were being made to explore and develop alternative and sustainable energy sources, including biomass energy. Biomass energy involves the use of organic materials, such as wood, crop residues, and animal waste, to generate heat or electricity. In the context of South Sudan, biomass energy could play a crucial role in meeting the energy needs of the population, especially in rural areas. The quantities of crops cultivated in South Sudan, measured in tons of dry matter per year, were determined through calculations based on production data from the Food and Agriculture Organization Statistical Databases of the United Nations (FAOSTAT) for the 2021 seasonal year. The annual gross potential of agricultural residues was calculated by employing a residue to product ratio. The energy potential of crop residues was computed by multiplying the calorific values of agricultural residues with the available amount of residue. The total energy potential of agricultural residues during the 2021 production period in South Sudan amounted to approximately 112.7 TJ. The total amount of unused agricultural crop residues was estimated to be approximately 8.6 kilo tons (Kt). The proportion of overall residue quantity comprises major crops such as sorghum (78.71%), maize (11.06%), rice (5.66%), and millet (4.36%).

Food and Drug Administration Database Secondary Analysis: Difference in Operative Hysteroscopy Device Safety Profiles

Food and Drug Administration Database Secondary Analysis: Difference in Operative Hysteroscopy Device Safety Profiles

Refining Ounce-Equivalents Using the EAA-9 Approach for Protein Scoring and Dietary Guidance

The USDA Protein Food Ounce-equivalents are designed to identify plant sources of protein foods and provide serving size substitutions. While the ounce-equivalent concept is simple, it fails to generate equivalent exchanges for protein or essential amino acids (EAAs). To accurately define the EAA content of USDA protein food ounce-equivalents, to develop a more accurate food exchange list, and to evaluate the EAA-9 protein quality framework as a tool for determining precise EAA-equivalent substitutions. The USDA National Nutrient Database (SR Legacy) and the EAA-9 protein quality model were used to evaluate the validity of the USDA Protein Food ounce-equivalents for creating equivalent protein and EAA substitutions. The EAA-9 framework then established EAA-9 Equivalence serving sizes to meet EAA requirements. EAA composition in protein foods was assessed. EAA-9 Equivalence servings were developed. EAA composition was calculated for USDA protein food ounce-equivalents. EAA-9 scores were calculated for protein foods and compared using an egg's EAA composition as a standard. MyPlate Kitchen Recipes were used to apply USDA protein food ounce-equivalent exchanges and EAA-9 equivalence servings. The USDA protein food ounce-equivalents are not equivalent in protein or EAAs with the disparity ranging from one ounce-equivalent of chicken breast with 9.1 g of protein and 4.0 g of EAAs to one ounce-equivalent of almonds with 3.0 g of protein and 0.9 g of EAAs. Using the USDA serving of one egg as a standard for comparing protein food groups, less than 15% of beans, peas, and lentils and 0% of nuts and seed ounce-equivalents achieve the EAA composition of an egg. EAA-9 Equivalence servings are truly equivalent, with each serving providing a reliable and interchangeable protein source. The EAA-9 Equivalence servings have been calculated and are now available for all USDA SR Legacy foods with a complete EAA profile, offering a resource for exchanges that ensure EAA requirements are met. Creating ounce-equivalent substitutions for protein foods requires creating food exchanges that assure EAA requirements are met. The USDA Protein Food ounce-equivalents provide inadequate guidance for balancing EAA requirements.

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes.

An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.

Proportions and types of different sweeteners in the Hungarian packaged food supply

Abstract Branded food databases facilitate the analysis of sweetener use in commercial packaged food products. The purpose of this study was to map the use of sweeteners in relevant packaged food categories and to assess and compare the nutrient profile of non-sugar (including sugar alcohol) vs. added sugar sweetened food products in Hungary. The foodDB software, developed by University of Oxford, enables automated online data collection, and was used to compile data of all foods and drinks available from four major Hungarian supermarket websites, selected by market share in March 2021. Only products containing either added sugar and/or non-sugar sweeteners were included in the study. These were categorized into 12 food groups using specific search terms. After data cleaning the final product number was 2016. 72% of the products was sweetened with added sugars only, 15% of them with non-sugar sweeteners only and 13% contained both sugars and non-sugar sweeteners. Chewing gums (92%), soft drinks (60%), energy drinks (50%) and pickles (35%) contained non-sugar sweeteners in the largest proportion. More than 90% of flavoured alcoholic beers, chocolate bars, breakfast cereals and yogurts were sweetened with added sugar only. Nine categories included food items containing multiple non-sugar sweeteners per product. The most common non-sugar sweeteners were acesulfame-potassium, sucralose, aspartame present in 15%, 10%, 9% of the total products respectively. Muesli bars with non-sugar sweeteners contained less energy but also less fibre than muesli bars with added sugars only; whereas pickles with non-sugar sweeteners contained less energy but were significantly higher in salt than those products with added sugars only. The presence of non-sugar sweeteners is widespread in the Hungarian food supply; however, their use does not guarantee a more beneficial nutrient profile. Branded food databases are useful tools to analyse the nutrient composition of packaged foods. Key messages • Branded food databases are useful tools to monitor the composition of the packaged food supply. • The nutrition profile of foods with non-sugar sweeteners can be less favourable than foods with added sugars.

Real-world safety profile of zanubrutinib: a disproportionality analysis based on the FAERS database

ObjectiveZanubrutinib is a second-generation Bruton’s tyrosine kinase inhibitor that has been approved for the treatment of several B cell malignancies. The aim of this study was to evaluate adverse events...

Comparison of the nutritional composition and calculated Nutri-score classifications of the Dutch food retail supply in 2018 and 2020.

In the Netherlands, reformulation strategies have been established for several years, whereas Nutri-Score was implemented in 2024. Besides being a helpful tool for consumers to make healthier food choices, Nutri-Score also aims to stimulate food reformulation by food manufacturers. The present study investigates whether changes in food composition could have led to different calculated Nutri-Score classifications. Food compositions and Nutri-Score classifications were calculated using the updated Nutri-Score algorithm. Food groups with the largest change in the distribution of Nutri-Score classifications were analysed in-depth by plotting frequency distributions and calculating median contents for nutrient contents that relatively changed the most in 2020. Food composition data were available from the Dutch Branded Food database in 2018 (n 38 295) and 2020 (n 48 091). Not applicable. In general, median nutrient contents and calculated Nutri-Score classifications were similar for 2018 and 2020. The median sugar and SFA contents were lower for some food groups (e.g. breakfast cereals, meat preserves, sweets and sweet goods) in 2020 compared to 2018. The median SFA content for meat preserves and sweets and sweet goods was relatively low in Nutri-Score classification A ascending towards higher median content in Nutri-Score classification E. Although food reformulation was not substantial in the Dutch food retail supply in 2018 and 2020, some differences in Nutri-Score classifications were observed. When implemented, Nutri-Score may encourage food manufacturers to increase their reformulation efforts. Repeated monitoring of food compositions and Nutri-Score classifications is recommended to establish reformulation efforts by food manufacturers.

The impact of different dietary flavonoids on the risk of coronary heart disease in cancer patients and that on the prognosis of patients with cancer and coronary heart disease.

The purpose of this study is to explore the risk of coronary heart disease (CHD) in cancer patients who consume different flavonoids, and the impact of flavonoids on the prognosis of cancer patients with CHD. We extracted dietary flavonoids data on 1454 patients diagnosed with cancer from the National Health and Nutrition Examination Survey and Food and Nutrient Database for Dietary Studies. Logistic regression analysis was used to explore the relationship between the intake of flavonoids and the risk of CHD. Cox proportional hazard model was used to explore the impact of flavonoids intake on prognosis in 148 patients with cancer and CHD. Malvidin intake increased the risk of CHD by 1% [odds ratio (OR) = 1.01, 95% confidence interval (CI): 1.00-1.02, P < 0.05] in cancer patients, while epicatechin and isorhamnetin reduced the risk of CHD by 3% (OR = 0.97, 95% CI: 0.94-1.00, P < 0.05) and 15% (OR = 0.85, 95% CI: 0.72-1.00, P < 0.05), respectively. Adjusted by age, sex, and race, malvidin intake increased the risk of CHD in cancer patients by 1% (OR = 1.01, 95% CI: 1.00-1.02, P < 0.05), isorhamnetin decreased the risk by 15% (OR = 0.85, 95% CI: 0.72-1.00, P < 0.05), and epicatechin showed no effect on the risk of CHD (P > 0.05). No flavonoids had impact on the prognosis of patients with cancer and CHD (P > 0.05). For patients with cancer, consuming malvidin increases the risk of CHD, while isorhamnetin reduces the risk. Consuming flavonoids has no impact on the prognosis of patients with cancer and CHD.

Association of niacin intake and metabolic dysfunction-associated steatotic liver disease: findings from National Health and Nutrition Examination Survey

AimThis study aims to explore the relationship between niacin intake and the prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) within a large, multi-ethnic cohort.MethodsA total of 2946 participants from the National Health and Nutrition Examination Survey (NHANES) were carefully selected based on strict inclusion and exclusion criteria. Participants meeting the eligibility criteria underwent two dietary recall interviews, and niacin intake was calculated using the USDA’s Food and Nutrient Database for Dietary Studies (FNDDS). Liver steatosis was diagnosed using a Controlled Attenuation Parameter (CAP) of 248 dB/m, and MASLD diagnosis was based on metabolic indicators. Weighted multivariate logistic regression was utilized to analyze the correlation between niacin intake and MASLD prevalence, with potential nonlinear relationships explored through restricted cubic spline (RCS) regression.ResultsAnalysis of baseline data revealed that MASLD patients had lower niacin intake levels and poorer metabolic biomarker profiles. Both RCS analysis and multivariate logistic regression indicated a U-shaped association between niacin intake and MASLD prevalence. Specifically, there was a non-linear dose-response relationship, with the odds of MASLD gradually decreasing with increasing niacin intake until reaching a threshold of 23.6 mg, beyond which the odds of MASLD began to increase.ConclusionThis study confirms a U-shaped nonlinear relationship between niacin intake and MASLD prevalence within the diverse American population.

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Database. The caloric content of foods was estimated using the nutritional composition (i.e., carbohydrates, protein, total lipid, and total sugar content) and classified based on their weighted health risks, considering their carbohydrate, lipid, and glycemic index levels. The algorithms used for modeling include multiple linear regression (MLR), K-nearest neighbors, support vector machine, random forest regression (RFR), gradient-boosted regression, decision trees (DT), and deep neural networks. The MLR model demonstrated high accuracy on the training dataset (R2=0.99, mean absolute error [MAE]=7.71kcal, and root mean squared error [RMSE]=17.89kcal) and testing dataset (R2=0.99, MAE=7.75kcal, and RMSE=18kcal) in energy estimation, indicating its effectiveness in dietary assessment. The RFR and DT models were useful in categorizing foods into low-health-risk foods, but their performance was reduced in medium and high-health-risk groups. This research contributes to developing tools that could aid in personalized dietary planning and public health interventions to mitigate diet-related health risks. PRACTICAL APPLICATION: This study applies machine learning to estimate how many calories are in food and to understand the health risks different foods might have. By investigating the fats, cholesterol, and sugars in food items listed in a public database, we can better plan diets or develop apps that help people make healthier eating choices. This work aims to improve public access to nutritional information, supporting efforts to combat diet-related diseases through educational materials and applications that guide dietary choices in various settings.

Evaluation of Trend Models Performance and Forecasting Onion Production: A Comparative Study

Onions, a significant spice crop in Bangladesh, frequently experience price surges due to an imbalance between demand and supply, compelling the government to import onions. The volatility of the onion market in Bangladesh and the measures needed to stabilize the situation require intensive policy attention. Therefore, this study was carried out to forecast the onion production over time and to evaluate the efficacy of various trend models. The secondary data was collected from the Food and Agriculture Organization's Statistical Database (FAOSTAT) from 1972 to 2022. The autoregressive integrated moving average (ARIMA) model was used to predict the onion production over time. Additionally, metrics such as modified and the coefficient of determination were used to evaluate the effectiveness of eight trend models comprehensively. The findings indicate that the cubic model is superior to others since it has larger and adjusted values. The models' accuracy is also determined utilizing mean absolute percentage error (MAPE) and mean absolute deviation (MAD) metrics. The cubic trend model exhibits lower MAPE and MAD values of 29.43% and 80638.67, respectively. Therefore, it is concluded that the ARIMA (2, 2, 2) model is the best model for forecasting the annual production of onion in Bangladesh. A five-year trend analysis spanning from 2023-2027 indicates an upward trend. Such a result offers a critical insight for stakeholders and policymakers in formulating sustainable onion production strategies. J Bangladesh Agril Univ 22(3): 386-395, 2024

Food Database Meets Artificial Intelligence: A New Method for Personalized Patient Eating Plans

Food Database Meets Artificial Intelligence: A New Method for Personalized Patient Eating Plans

Environmental impact of dietary patterns in 10 European countries; a cross-sectional analysis of nationally representative dietary surveys.

Changing dietary patterns is essential to reducing the substantial environment impact of agriculture and food production systems. We performed a cross-country comparison of dietary patterns and their associated environmental impact in Europe, including by sociodemographic factors. We analyzed pooled cross-sectional dietary records collected during 2010-18 from 10 European countries using the European Food Safety Authority (EFSA) Comprehensive European Food Database (16 508 adults; aged 18-79 years). Each food consumed was mapped to the corresponding environmental impact data using the SHARP Indicators Database, which provides greenhouse gas emission (GHGE) and land use (LU) values of approximately 900 foods. Total diet-associated environmental impact was calculated for each person and averaged across multiple days. Multivariable linear regression models were used to compare diet-associated GHGE and LU between population subgroups (gender, age, education and diet type) with country-level fixed effects. The mean dietary GHGE and LU per capita ranged from 4.0 kgCO2/day and 5.0 m2*year/day in Spain to 6.5 kgCO2eq/day and 8.2 m2*year/day in France. Diet-related GHGE and LU (per kg/food) were lower among females (2.6 kgCO2eq/day, B = -0.08, P < 0.01; 3.2 m2*year/day, B = -0.11, P < 0.01), older population aged 66-79 (2.6 kgCO2eq/day, B = -0.03, P < 0.01; 3.4 m2*year/day, B = -0.4, P < 0.01), people following vegetarian diets (1.7 kgCO2eq/day, B = -0.07, P < 0.01; 2.0 m2*year/day, B = -0.07, P < 0.01), and higher among individuals with secondary education (2.7 kgCO2eq/day, B = 0.05, P < 0.01; 3.6 m2*year/day, B = -0.05, P < 0.01). Environmental footprints vary substantially across countries, dietary patterns and between different sociodemographic groups in Europe. These findings are crucial for the development of country-specific food policies aimed at promoting environmentally sustainable diets.

녹차 가공 과자류의 제품명 연구

This study was to analyze the product name of the green tea processed confectionery and identify the characteristics reflected in those names, thereby providing insights necessary for developing such product names. For this purpose, this study collected data on 427 confectionery items with product names containing ‘green tea’ by searching the processed food database on the Ministry of Food and Drug Safety website from June 1 to June 16, 2024. The main research findings from the collected data analysis are as follows. First, the structural characteristics of confectionery product names included the prevalence of combinations of Sino-Korean words and loanwords, the use of neologisms or native words, and the need for improvement in foreign language expressions. Second, the semantic characteristics of the product names revealed the rise of foreign-style baked goods, the application of diversified confectionery ingredients, and the emergence of high-protein and vegan confectionery. In conclusion, green tea processed confectionery products should be named carefully compared to other products, as their names play a crucial role in conveying a healthy image and influencing consumer choice.

Contextualizing toxic elements in the diet: a case for integration of toxic element data into food databases.

Food composition data plays a key role in the practice of nutrition. However, nutrition professionals may currently lack the resources they need to integrate information about toxic elements - such as arsenic, cadmium, and lead - in food into the advice they give consumers. Geographic, sociocultural, and individual factors may impact not only the toxic element content of food, but also how the balance between potentially toxic and health-promoting components of food must be weighed. Better integration and contextualization of toxic element data into key food databases could allow for more nuanced, comprehensive nutrition guidance.

Antibiotics-induced pulmonary embolism: A disproportionality analysis in Food and Drug Administration database of Adverse Event Reporting System using data mining algorithms

Antibiotics-induced pulmonary embolism: A disproportionality analysis in Food and Drug Administration database of Adverse Event Reporting System using data mining algorithms

Decoding heterogeneity in data-driven self-monitoring adherence trajectories in digital lifestyle interventions for weight loss: a qualitative study

BackgroundData-driven trajectory modeling approaches have been used to identify participant subgroups with differing responses to digital lifestyle interventions. Identifying contributing factors to different participant subgroups can inform tailored strategies to early “rescue” intervention non-responders. Self-monitoring (SM) is a central mechanism in lifestyle interventions for driving behavior change and can serve as an early indicator for later intervention response. This qualitative study aimed to compare SM experiences between intervention response subgroups and to identify contributing factors to intervention response subgroups in a 6-month digital lifestyle intervention for adults with overweight or obesity.ResultsParticipants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Responder Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Non-responder Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.ConclusionsOur study indicates that qualitative analysis is valuable for translating data-driven findings to actionable intervention improvement strategies. Our findings may inform the development of practical SM improvement strategies in future digital lifestyle interventions for weight loss. Notably, building problem solving skills emerge as a key approach to prevent potential non-responders from intervention disengagement.