Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Biofortified cassava increases β-carotene and vitamin A concentrations in the TAG-rich plasma layer of American women

Biofortification of cassava with the provitamin A carotenoid β-carotene is a potential mechanism for alleviating vitamin A deficiency. Cassava is a staple food in the African diet, but data regarding the human bioavailability of b-carotene from this food are scarce. The objective of the present study was to evaluate provitamin A-enhanced cassava as a source of β-carotene and vitamin A for healthy adult women. The study was a randomised, cross-over trial of ten American women. The subjects consumed three different porridges separated by 2 week washout periods. Treatment meals (containing 100 g cassava) included: biofortified cassava (2mg β-carotene) porridge with added oil (15 ml peanut or rapeseed oil, 20 g total fat); biofortified cassava porridge without added oil (6 g total fat); unfortified white cassava porridge with a 0·3 mg retinyl palmitate reference dose and added oil (20 g total fat). Blood was collected six times from 20·5 to 9·5 h post-feeding. TAG-rich lipoprotein (TRL) plasma was separated by ultracentrifugation and analysed using HPLC with coulometric array electrochemical detection. The AUC for retinyl palmitate increased after the biofortified cassava meals were fed (P,0·05). Vitamin A conversion was 4·2 (SD 3·1) and 4·5 (SD 3·1) μg β-carotene:1 μg retinol, with and without added oil, respectively. These results show that biofortified cassava increases β-carotene and retinyl palmitate TRL plasma concentrations in healthy well-nourished adult women, suggesting that it is a viable intervention food for preventing vitamin A deficiency

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients (“Nutrient-Only”) or the nutrient and food descriptions (“Nutrient + Text”). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24

Liking and Acceptability of Whole Grains Increases with a 6-Week Exposure but Preferences for Foods Varying in Taste and Fat Content Are Not Altered: A Randomized Controlled Trial

BackgroundSince 2005, the Dietary Guidelines for Americans have recommended consuming at least half of total grains as whole grains (WGs) for optimal health benefits; however, consumption of WGs falls far short of recommended amounts.ObjectiveThis study aimed to evaluate the effect of mere exposure to WGs on liking, acceptability, and consumption of WG foods and to determine if exposure to WG would influence liking and wanting for other foods varying in fat content and sweet taste.MethodsHealthy, self-identified low WG consumers (n = 45) were randomly assigned to either a 6-wk WG intervention or a refined grain (RG) control condition during which they received a weekly market basket of grain products to incorporate into daily meals and snacks. Consumption of grain products was measured by weekly logs and weigh-backs. A sensory evaluation protocol was conducted at baseline and week 6 to evaluate changes in perception of grain products. Computer tasks designed to measure liking and wanting for other foods varying in high/low-fat content and sweet/savory taste were also completed at baseline and week 6.ResultsParticipants in the WG group significantly increased WG consumption. Exposure to WG products resulted in improved ratings of liking, flavor, texture, and willingness to include WG in the regular diet. No significant changes in liking or wanting for foods representing high-fat sweet (HFSW), low-fat sweet (LFSW), high-fat savory (HFSA), or low-fat savory (LFSA) categories were found in the WG group. In contrast, exposure to RG foods resulted in an increased explicit wanting for HFSW and LFSW and a decreased wanting for HFSA foods.ConclusionsMere exposure to WG foods represents a feasible and easily applied behavioral strategy for increasing consumption of WGs. Encouraging consumers to focus on enjoyment of the taste may be more effective than emphasizing the health benefits of WG consumption. This trial was registered at clinicaltrials.gov as NCT01403857

A Potential Tool for Clinicians; Evaluating a Computer-Led Dietary Assessment Method in Overweight and Obese Women during Weight Loss.

Many Americans are attempting to lose weight with the help of healthcare professionals. Clinicians can improve weight loss results by using technology. Accurate dietary assessment is crucial to effective weight loss. The aim of this study was to validate a computer-led dietary assessment method in overweight/obese women. Known dietary intake was compared to Automated Self-Administered 24-h recall (ASA24) reported intake in women (n = 45), 19-50 years, with body mass index of 27-39.9 kg/m². Participants received nutrition education and reduced body weight by 4%-10%. Participants completed one unannounced dietary recall and their responses were compared to actual intake. Accuracy of the recall and characteristics of respondent error were measured using linear and logistic regression. Energy was underreported by 5% with no difference for most nutrients except carbohydrates, vitamin B12, vitamin C, selenium, calcium and vitamin D (p = 0.002, p < 0.0001, p = 0.022, p = 0.010, p = 0.008 and p = 0.001 respectively). Overall, ASA24 is a valid dietary assessment tool in overweight/obese women participating in a weight loss program. The automated features eliminate the need for clinicians to be trained, to administer, or to analyze dietary intake. Computer-led dietary assessment tools should be considered as part of clinician-supervised weight loss programs

Challenges in Designing and Delivering Diets and Assessing Adherence: A Randomized Controlled Trial Evaluating the 2010 Dietary Guidelines for Americans.

BackgroundControlled-feeding trials are challenging to design and administer in a free-living setting. There is a need to share methods and best practices for diet design, delivery, and standard adherence metrics.ObjectivesThis report describes menu planning, implementing, and monitoring of controlled diets for an 8-wk free-living trial comparing a diet pattern based on the Dietary Guidelines for Americans (DGA) and a more typical American diet (TAD) pattern based on NHANES 2009-2010. The objectives were to 1) provide meals that were acceptable, portable, and simple to assemble at home; 2) blind the intervention diets to the greatest extent possible; and 3) use tools measuring adherence to determine the success of the planned and implemented menu.MethodsMenus were blinded by placing similar dishes on the 2 intervention diets but changing recipes. Adherence was monitored using daily food checklists, a real-time dashboard of scores from daily checklists, weigh-backs of containers returned, and 24-h urinary nitrogen recoveries. Proximate analyses of diet composites were used to compare the macronutrient composition of the composite and planned menu.ResultsMeeting nutrient intake recommendations while scaling menus for individual energy intake amounts and food portions was most challenging for vitamins D and E, the sodium-to-potassium ratio, dietary fiber, and fatty acid composition. Dietary adherence for provided foods was >95%, with no differences between groups. Urinary nitrogen recoveries were ∼80% relative to nitrogen intake and not different between groups. Composite proximate analysis matched the plan for dietary fat, protein, and carbohydrates. Dietary fiber was ∼2.5 g higher in the TAD composite compared with the planned menu, but ∼7.4 g lower than the DGA composite.ConclusionsBoth DGA and TAD diets were acceptable to most participants. This conclusion was supported by self-reported consumption, quantitative weigh-backs of provided food, and urinary nitrogen recovery. Dietary adherence measures in controlled-feeding trials would benefit from standard protocols to promote uniformity across studies. The trial is registered at clinicaltrials.gov as NCT02298725