Nutrient Database Research Articles

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.

Surface Temperature Changes and Effects on Cocoa Yield in the Top Four Cocoa Producing Countries of Africa: A Comparative Analysis

Cocoa is a leading export crop to the world market and Cote D’Ivoire, Ghana, Nigeria and Cameroon in Africa, accounts for about 70% of the global supply. However, increasing global warming with attendant high ambient temperature had affected yields in the tropical regions of Africa. The data of 61 years (1961 – 2021), area harvested (ha), yield (kg/ha) and temperature (OC) were sourced from the database of Food and Agricultural Organisation Corporate Statistical (FAOSTAT). Least squares regression and correlation analysis models were used for data analysis. Results showed that mean ambient temperature in Cote D’Ivoire, Ghana, Nigeria and Cameroon have increased by 0.7661, 0.7507, 0.7333 and 0.6400 OC for a total of 51, 51, 50 and 49 years respectively, while mean decreases were 0.1541, 0.1693, 0.1710, 0.1392oC for 10, 10, 11 and 12 years respectively. Annual average temperature increase was highest (0.0243oC) in Cote D’Ivoire with regression coefficient (R2) of 0.7232 and least (0.0200oC) in Nigeria at R2 of 0.6380. However, in both countries, annual temperature decreased by 0.0012o and 0.0007o with R2 of 0.0052 and 0.0014 respectively. The mean annual temperature increase in Ghana and Cameroon were 0.023o and 0.0204o with R2 of 0.7364 and 0.6965 respectively, while the decreases were 0.0019o and -0.0011o with R2 of 0.0152 and 0.0431 respectively. The cocoa cumulative yield (kg/ha) reduced by 105.8, 90.8 and 75.0kg/ha respectively in Cote D’Ivoire, Ghana and Cameroon when temperature decreased, but increased by 12.5kg/ha in Nigeria during same temperature decreasing trend. In Ghana and Cameroon, cocoa annual yield was higher in the years of temperature increase, while decreased ambient temperature enhanced cocoa yield in Nigeria and Cote D’Ivoire. Hence, to enhance cocoa yield in the top four cocoa producing countries of Africa, the ambient temperatures should be controlled through the adoption of Good Agricultural Practices (GAP).

Instrument failures for the AirSeal device: A Food and Drug Administration MAUDE database study

ObjectiveThis study aims to analyze instances of AirSeal device failures reported in the Food and Drug Administration's MAUDE (Manufacturer and User Facility Device) database, focusing on identifying trends in failure types and locations. Summary Background DataThe AirSeal device has revolutionized minimally invasive surgery by providing stable pneumoperitoneum and optimal visualization. However, concerns about its reliability and safety have surfaced. MethodsRetrospective analysis of the MAUDE database. A search for ‘AirSeal’ device-related events from 2014 to 2023. A total of 411 events resulting in injury, death, or other complications were identified, and data were extracted from these reports. Data extraction included report number, event date, event type, manufacturer, patient problem, and number of events. It should be noted that detailed information such as the specific organs involved or the exact surgical procedures were not available in the MAUDE database reports. ResultsOver the 9-year period, 411 AirSeal device-related events were reported in MAUDE. The predominant failure mechanisms included device breakage 266 (64.7%), defective failures 16 (3.9%), flow failures 13 (3.2%), leak failures 8 (1.9%), patient-device interaction issues 5 (1.2%), and pressure-related failures 4 (1.0%). Among the reports, incidents related to the valveless-trocar included 261 (63.5%), whereas the insufflator was implicated in 150 reports (36.5%). The most common event reported involving the patient was embolism listed in 35 cases. There is also an increase in the number of cases reported annually. ConclusionFurther research is warranted to understand the root cause of device failure and implement targeted interventions aimed at mitigating any risks associated with the AirSeal device. Ultimately, collective efforts are directed towards upholding the highest standards of patient safety in this era of minimally invasive surgery.

How Often Does Technology Fail in Robotic-Assisted Arthroplasty? A Comprehensive Analysis of a United States Food and Drug Administration Database

BackgroundRobotic-assisted arthroplasty is a growing alternative to conventionally instrumented arthroplasty; however, the incidence of adverse events (AEs) associated with this technology reported to the United States Food and Drug Administration (FDA) remains poorly quantified. The objective of this study was to categorize AEs associated with robotic-assisted arthroplasty and calculate their annual incidence as reported to the FDA. MethodsThe FDA’s Manufacturer and User Facility Device Experience database was queried for AEs from January 1, 2017 to December 31, 2021 associated with the most prevalent robotic-arthroplasty system. The AEs were calculated using national surgical numbers provided by the manufacturer and grouped by total hip arthroplasty (THA), total knee arthroplasty (TKA), or partial knee arthroplasty (PKA). ResultsThere were 1,710 unique AEs across the study period, with 436 THA, 1,005 TKA, and 269 PKA, representing incidence rates of 0.37, 0.30, and 0.40%, respectively. All procedures demonstrated lower rates of AEs in the final year of the study, compared to the first year. Most complications were related to mechanical problems, not software issues. Surgical delays due to AEs occurred in THA (0.13% cumulative incidence, 14.0 minutes average delay), TKA (0.13%, 20.6 minutes), and PKA (0.22%, 19.4 minutes). No cases were canceled due to AEs in THA, though a few TKA (0.003%) and PKA (0.02%) cases were not performed. Patient injuries occurred in 0.05, 0.05, and 0.08% of THA, TKA, and PKA, respectively. Surgical reintervention was necessary in 0.004, 0.003, and 0.01% of THA, TKA, and PKA, respectively. ConclusionsRobotic-assisted arthroplasty has a small number and relatively low rate of AEs reported to the FDA as measured through the Manufacturer and User Facility Device Experience database, with rates steadily decreasing over the study period. Patient injury, case delay, and reoperation represent only a small fraction of the already rare AEs in the database.

Phosphorous intake in foods and phosphorus status markers in circulation in the Boston Puerto Rican Health Study

Phosphorus (P) additives may be deleterious for health. We measured the P content of key foods, and associations of P intake with biomarkers in the Boston Puerto Rican Health Study (BPRHS). Direct chemical analysis of 92 foods was done with the molybdenum blue spectrophotometric method and inductively coupled plasma mass spectrometry (ICP-MS). A novel algorithm was used to determine bioavailable, natural, and added P. We estimated P intakes from foods in 1323 participants, aged 45–75 y, and associations of these with serum P, fibroblast growth factor 23 (FGF23), parathyroid hormone (PTH), and Klotho. Relationships between intakes and status markers were assessed with Pearson’s correlations and t-tests. Our food analyses generally support P values in the USDA nutrient database, with the exceptions of American and cheddar cheese, which had more P than in the database. Women had higher added P intake than men, and younger participants had higher added P than those older. Total P intake tended to be positively associated with serum P and klotho, and inversely associated with PTH, but relationships were not strong. Puerto Rican adults have high intake of additive P. Culturally sensitive interventions that highlight dietary quality are needed.

Application of environmentally extended input-output data to estimate greenhouse gas emissions attributable to packaged foods and beverages in Australia

Input-output tables for national economies are widely available and may provide an established mechanism for estimating environmental impacts of industry sectors. We used environmentally extended input-output (EEIO) analysis to estimate greenhouse gas emissions (GHGe) for Australian packaged foods and beverages and compared the findings to those derived from life cycle analysis (LCA). GHGe intensities in purchaser prices were used alongside corresponding median prices per kilogram from NielsenIQ Homescan, a nationally representative database with food prices. Applying GHGe estimates to a 2019 Australian packaged food database, we reported median and interquartile range (IQR) GHGe per kilogram, comparing them with LCA-derived estimates. EEIO-derived intensities were estimated for 23,550 products, and the median overall GHGe based on EEIO data was 6.87 kg CO2eq / kg (IQR 4.20 to 10.5). LCA-derived estimates were comparatively lower, showing a median overall GHGe of 2.42 kg CO2eq / kg (IQR 1.41 to 5.00) using Poore and Nemecek data and 2.35 kg CO2eq / kg (IQR 1.24 to 4.53) using FoodSwitch. Despite differences in magnitude, EEIO-derived data discriminated between the highest emitting categories in alignment with the LCA-based approaches. Future work is required to understand the discrepancies and make the EEIO method a compelling alternative to LCA-based approaches.

The U.S. Food and Drug Administration (FDA) Food Disaggregation Database (FDA-FDD): a new tool for U.S. dietary exposure assessment

Dietary exposure to a food chemical (e.g. contaminant, nutrient, or other natural constituent) is a function of the concentration of the chemical in foods and the quantity of each food consumed. Exposures to food chemicals can be estimated using intake data from What We Eat in America (WWEIA), the food consumption survey portion of the National Health and Nutrition Examination Survey (NHANES). To estimate exposures to chemicals in foods consumed by NHANES/WWEIA respondents, the consumption data must be mapped to chemical concentration data on the same or similar foods. However, food chemical data are generally not available on all the foods and food mixtures that are reported in NHANES/WWEIA. To address this, we developed the FDA Food Disaggregation Database (FDA-FDD), a ‘recipe’ database with estimates of ingredient percentages. FDA-FDD allows mapping to food chemical data based on ingredients in NHANES/WWEIA foods rather than on food mixtures, resulting in more accurate exposure estimates. Using FDA-FDD, FDA mapped over 11,000 NHANES/WWEIA foods to FDA’s Total Diet Study (TDS) foods. FDA-FDD is available as part of a publicly available interactive application that also allows access to the TDS mapping.