The Fully Understanding Eating and Lifestyle Behaviors (FUEL) trial: Protocol for a cohort study harnessing digital health tools to phenotype dietary non-adherence behaviors during lifestyle intervention.

Objective: Lifestyle intervention can produce clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Dietary lapses, instances of non-adherence to the recommended dietary goal(s) in lifestyle intervention, are associated with less weight loss and higher energy intake. There are distinct "types" of dietary lapse (e.g., eating an off-plan food, eating a larger portion), and behavioral, psychosocial, and contextual mechanisms may differ across dietary lapse types. Some lapse types also appear to impact weight more than others. Elucidating clear lapse types thus has potential for understanding and improving adherence to lifestyle intervention. Methods: This 18-month observational cohort study will use real-time digital assessment tools within a multi-level factor analysis framework to uncover "lapse phenotypes" and understand their impact on clinical outcomes. Adults with overweight/obesity (n = 150) will participate in a 12-month online lifestyle intervention and 6-month weight loss maintenance period. Participants will complete 14-day lapse phenotyping assessment periods at baseline, 3, 6, 12, and 18 months in which smartphone surveys, wearable devices, and geolocation will assess dietary lapses and relevant phenotyping characteristics. Energy intake (via 24-h dietary recall) and weight will be collected at each assessment period. Results: This trial is ongoing; data collection began on 31 October 2022 and is scheduled to complete by February 2027. Conclusion: Results will inform novel precision tools to improve dietary adherence in lifestyle intervention, and support updated theoretical models of adherence behavior. Additionally, these phenotyping methods can likely be leveraged to better understand non-adherence to other health behavior interventions. Trial Registration: This study was prospectively registered https://clinicaltrials.gov/study/NCT05562427.

Disaggregation of Dairy in Composite Foods in the United Kingdom.

Dairy, especially cheese, is associated with high levels of greenhouse gas emissions. Accurate estimates of dairy consumption are therefore important for monitoring dietary transition targets. Previous studies found that disaggregating the meat out of composite foods significantly impacts estimates of meat consumption. Our objective was to determine whether disaggregating the dairy out of composite foods impacts estimates of dairy consumption in Scotland. Approximately 32% of foods in the UK Nutrient Databank contain some dairy. In the 2021 Scottish Health Survey, mean daily intakes of dairy with and without disaggregation of composite foods were 238.6 and 218.4 g, respectively. This translates into an 8% underestimation of dairy consumption when not accounting for dairy in composite foods. In particular, milk was underestimated by 7% and cheese and butter by 50%, whereas yogurt was overestimated by 15% and cream by 79%. Failing to disaggregate dairy from composite foods may underestimate dairy consumption.

Development of the Diet Quality Questionnaire for Measurement of Dietary Diversity and Other Diet Quality Indicators.

Background: To monitor trends toward healthy and sustainable diets, there is a need for feasible survey tools, with cross-cultural validity, low-cost, and low-expertise requirements. Objectives: The objective of this research was to develop a method to gather data suitable for monitoring diet quality in the general population (women and men of all ages) that is feasible within multitopic surveys, low burden for both enumerators and respondents, valid at population level, and that captures the information necessary for understanding diet quality at global and local levels. Methods: A literature review was conducted to identify constructs of diet quality with existing consensus, indicators with existing global demand, and methods that may be feasible and valid. Results were presented to a technical advisory group for debate, resulting in consensus on a set of constructs to be measured, desired indicators, viable data collection platforms, and an approach for testing and piloting. Results: Food group-based indicators and 24-h recall period were selected as the most feasible and valid approach for population-level monitoring. A 29-item Diet Quality Questionnaire (DQQ) was developed, where each yes/no question asks about the consumption of a distinct food group on the previous day or night. The food groups were selected for the purpose of deriving indicators to capture the constructs for which there was consensus: nutrient adequacy, and protection against noncommunicable diseases, including both positive and negative risk factors. Conclusions: The DQQ is low cost and feasible to administer in existing large-scale surveys, overcoming barriers to diet data collection that have precluded the routine monitoring of diet quality in the past. This novel approach has now been used across >85 countries in the Gallup World Poll and other surveys, generating the first nationally representative available datasets on Minimum Dietary Diversity for Women and complementary diet quality indicators.

Fine-scale Dietary Polyphenol Intake is Associated with Systemic and Gastrointestinal Inflammation in Healthy Adults.

BACKGROUND: Polyphenols are dietary bioactive compounds, many of which have anti-inflammatory properties. However, information on the intake of dietary polyphenols at the class and compound level and their associations with gastrointestinal (GI) and systemic inflammation is lacking. OBJECTIVE: Estimate dietary polyphenol intake in healthy adults and examine its relationship with GI and systemic inflammation markers. METHODS: Healthy adults (n = 350) completed the USDA Nutritional Phenotyping Study, an observational, cross-sectional study balanced for age, sex, and body mass index. Dietary intake, assessed via multiple 24-hour recalls, was ingredientized and mapped to FooDB, a comprehensive food composition database. Dietary polyphenol intake (total, class, compound) was estimated and examined for its relationship to GI and systemic inflammation markers using linear models and random forest regressions. RESULTS: Mean total polyphenol intake was approximately 914 mg/1000 kcal per day with flavonoids as the greatest class contributor (495 mg/1000 kcal per day). Tea, coffee, and fruits were among the largest food contributors to polyphenol intake. Total polyphenol intake negatively associated with the GI inflammation marker, fecal calprotectin (ß=-0.004, p=0.04). At the class level, polyphenols categorized as prenol lipids (ß=-0.94, p<0.01) and phenylpropanoic acids (ß=-0.92, p<0.01) negatively associated with plasma lipopolysaccharide-binding protein, a proxy for GI permeability. Food sources of these two classes included mainly olive products. We further detected a positive association between C-Reactive protein and polyphenols in the "cinnamic acids and derivatives" class using hierarchical feature engineering and random forest modeling. CONCLUSION: Even in healthy adults, dietary polyphenol intake was negatively associated with GI inflammation and intake of prenol lipids and phenylpropanoic acids were negatively associated with GI permeability. Relationships between polyphenol intake and inflammatory outcomes varied with the resolution - total, class, compound - of polyphenol intake, suggesting a nuanced impact of polyphenols on GI and systemic inflammation. CLINICAL TRIAL REGISTRY: NCT02367287, ClinicalTrials.gov.

The Carbon Stable Isotope Ratio as an Objective Biomarker of Added Sugar Intake: A Scoping Review of Current Evidence in Human Nutrition.

Objective biomarkers of dietary intake are needed to advance nutrition research. The carbon stable isotope ratio (C13/C12; CIR) holds promise as an objective biomarker of added sugar (AS) and sugar-sweetened beverage (SSB) intake. This systematic scoping review presents the current evidence on CIRs from human studies. Search results (through April 12, 2024) yielded 6297 studies and 24 final articles. Studies were observational (n = 12), controlled feeding (n = 10), or dietary interventions (n = 2). CIRs were sampled from blood (n = 23), hair (n = 5), breath (n = 2), and/or adipose tissue (n = 1). Most (n = 17) conducted whole tissue (that is, bulk) analysis, 8 used compound specific isotope analysis (CSIA), and/or 2 studies used methods appropriate for analyzing breath. Studies were conducted in 3 concentrated geographic regions of the United States (n = 7 Virginia; n = 5 Arizona; n = 4 Alaska), with only 2 studies conducted in other countries. Studies that used CSIA to examine the CIR from the nonessential amino acid alanine (CIR-Ala; n = 4) and CIR analyzed from breath (n = 2) provided the most robust evidence for CIR as an objective biomarker of AS and SSBs (R2 range 0.36-0.91). Studies using bulk analysis of hair or blood showed positive, but modest and more variable associations with AS and SSBs (R2 range 0.05-0.48). Few studies showed no association, particularly in non-United States populations and those with low AS and SSB intakes. Two studies provided evidence for CIR to detect changes in SSB intake in response to dietary interventions. Overall, the most compelling evidence supports CIR-Ala as an objective indicator of AS intake and breath CIR as an indicator of short-term AS intake. Considering how to adjust for underlying dietary patterns remains an important area of future work and emerging methods using breath and CSIA warrant additional investigation. More evidence is needed to refine the utility and specificity of CIRs to measure AS and SSB intake.

Identifying the Leading Sources of Saturated Fat and Added Sugar in U.S. Adults.

The 2020-2025 Dietary Guidelines for Americans recommend limiting intakes of saturated fat and added sugars (SF/AS) to <10% total energy. Data-driven approaches to identify sources of SF/AS are needed to meet these goals. We propose using a population-based approach to identify the leading food and beverage sources of SF/AS consumed by US adults. Foods and beverages reported as consumed were assessed from two, 24 h dietary recalls (24HRDR) from 36,378 adults aged 19 years and older from the 2005-2018 National Health and Nutrition Examination Survey. Intakes of SF/AS were aggregated across both 24HRDR to identify What We Eat in America food categories accounting for ≥90% of SF/AS, respectively, by the total population and within population subgroups. Data were weighted to estimate a nationally representative sample. Ninety-five discrete food categories accounted for ≥90% of the total SF/AS intakes for >88% of the representative sample of U.S. adults. The top sources of SF were cheese, pizza, ice cream, and eggs. The leading sources of AS were soft drinks, tea, fruit drinks, and cakes and pies. This analysis reflects a parsimonious approach to reliably identify foods and beverages that contribute to SF/AS intakes in U.S. adults.

Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care.

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps' features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia's Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: -1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods.

Relative validity of an intelligent ordering system to estimate dietary intake among university students from a medical school in Shanghai, China.

BACKGROUND: Dietary assessment methods have limitations in capturing real-time eating behaviour accurately. Equipped with automated dietary-data-collection capabilities, the "intelligent ordering system" (IOS) has potential applicability in obtaining long-term consecutive, relatively detailed on-campus dietary records among university students with little resource consumption. We investigated (1) the relative validity of IOS-derived nutrient/food intakes compared to those from the 7-day food diary (7DFD); (2) whether including a supplemental food frequency questionnaire (SFFQ) improves IOS accuracy; and (3) sex differences in IOS dietary intake estimation. METHODS: Medical students (n = 221; age = 22.2 ± 2.4 years; 38.5% male and 61.5% female) completed the 7DFD and SFFQ. During the consecutive 7-day survey period, students weighed and photographed each meal before and after consumption. Then, students reviewed their 3-month diet and completed the SFFQ, which includes eight underprovided school-canteen food items (e.g., dairy, fruits, nuts). Meanwhile, 9385 IOS dietary data entries were collected. We used Spearman coefficients and linear regression models to estimate the associations among the different dietary intake assessment methods. Individual- and group-level agreement was assessed using the Wilcoxon signed-rank test, cross-classification, and Bland‒Altman analysis. RESULTS: IOS mean daily energy, protein, fat, and carbohydrate intake estimations were significantly lower (-15-20%) than those of the 7DFD. The correlation coefficients varied from 0.52 (for added sugar) to 0.88 (for soybeans and nuts), with fruits (0.37) and dairy products (0.29) showing weaker correlations. Sixty-two (milk and dairy products) to 97% (soybeans and nuts) of participants were classified into the same or adjacent dietary intake distribution quartile using both methods. The energy and macronutrient intake differences between the IOS + SFFQ and 7DFD groups decreased substantially. The separate fruit intake measurements from each assessment method did not significantly differ from each other (p > 0.05). IOS and IOS + SFFQ regression models generally yielded higher R2 values for males than for females. CONCLUSION: Despite estimation differences, the IOS can be reliable for medical student dietary habit assessment. The SFFQ is useful for measuring consumption of foods that are typically unavailable in school cafeterias, improving the overall dietary evaluation accuracy. The IOS assessment was more accurate for males than for females.

The Nigerian Dietary Screening Tool: A Step toward Improved Patient-Clinician Communication in Nigerian Hospitals: A Pilot Implementation Study.

Implementing dietary screening tools into clinical practice has been challenging, including in Nigeria. This study evaluated the impact of the Nigerian dietary screening tool (NiDST) on patient-clinician communication and barriers to and facilitators of implementation. A mixed methods approach was used to collect data from patients (n = 151) and clinicians (n = 20) from outpatient clinics in Nigeria. Patients completed the validated 25-item NiDST prior to outpatient consultations. Both patients and clinicians completed the Measurement Instrument for Determinants of Innovations (MIDI) questionnaire to assess implementation determinants post-consultation. Semi-structured interviews were conducted for in-depth feedback. The fidelity of implementation was 92% for NiDST-reported dietary discussion, with a mean completion time of <6 min and an accepted marginal increase in consultation time (<10 min). For clinicians, 25% reported time constraints and their additional nutritional knowledge as barriers, while facilitators of NiDST implementation were the clarity and completeness of the NiDST, clinical relevance and improved patient-clinician communication, as reported by all the clinicians. Over 96% of patients reported the NiDST as quick to complete, with 90.7% reporting self-reflection on dietary intake. This study demonstrated the NiDST's potential to enhance patient-clinician communication and highlighted major facilitators of implementation in clinical practice to improve dietary discussion in Nigeria.

Micronutrient intake and status in young vegans, lacto-ovo-vegetarians, pescatarians, flexitarians, and omnivores.

PURPOSE: Whether youth who follow plant-based diets in Nordic countries meet their dietary needs for micronutrients remains unclear. This study aims to evaluate micronutrient intake and status in Norwegian youth following vegan, lacto-ovo-vegetarian, pescatarian, flexitarian and omnivore diets. METHODS: Cross-sectional design, with healthy 16-to-24-year-olds (n = 165). Participants were asked to complete a questionnaire and four 24-hour dietary recalls. Dried blood spots (DBS) and spot-urine samples were collected for analysis of methyl malonic acid (MMA) (n = 65), haemoglobin (Hb) (n = 164) and urinary iodine concentration (UIC) (n = 163). RESULTS: Vegans reported highest habitual supplement usage of multivitamin (58%), B12 (90%) and macroalgae consumption (32%), while flexitarians reported highest habitual usage of omega-3 supplements (56%). For daily supplement usage, vegans reported highest use of multivitamins (42%), B12 (79%), iodine (37%) and iron (63%). Increased risk of inadequate intake (energy-adjusted) were found for vitamin D (60% within lacto-ovo-vegetarians), selenium (70% within lacto-ovo-vegetarians, 65% within omnivores), and iodine (63% within vegans). Median MMA levels suggest low risk of insufficient B12 status across all groups (MMA 0.04‒0.37µmol/l) and 2% had MMA levels indicating possible B12 deficiency and 8% had elevated levels. Median Hb levels indicated low risk of anemia across all groups (≥ 12.0 females, ≥ 13.0 g/dl males), though 7% had Hb values indicating risk of mild anemia and 4% risk of moderate anemia. The median UIC indicates mild iodine deficiency in all groups (UIC < 100 µg/l), except vegans, who were moderately iodine deficient (UIC < 50 µg/l). CONCLUSIONS: Our study indicated that the participating youth had low risk of inadequate intake of most micronutrients, partly due to high supplement usage. However, for iodine, vitamin D, and selenium higher risk of inadequate intake was found. UIC corroborated the low iodine intake among vegans. Thus, we suggest iodine status of youth in Norway should be monitored, especially among young fertile women who omits dietary iodine sources, until a mandatory iodine fortification program is implemented. Furthermore, we suggest that food education on how to secure sufficient nutrients from food in general should be provided to the Norwegian youth population, especially how to secure adequate intake of vitamin D, selenium and iodine.

Validity of a digital photo-based dietary assessment tool: Development and initial evaluation.

Background and aim: To evaluate the validity and user satisfaction of a digital photo-based dietary assessment tool as an alternative to the hand-written paper record method that assists researchers during the pandemic. This study compared nutrient intake and users' satisfaction with methods between a digital photo-based dietary assessment tool, known as the Nutrinote Gama app, and food weighing as the gold standard. Methods: Fifty college students majoring in food and nutrition (90% were women; median age, 21 years) took pictures of their foods and beverages before and after consumption and then uploaded them to the Nutrinote Gama application. Trained nutritionists evaluated plate wastes, and nutritional content was revealed on the Nutrinote Gama application. Parallel to the photo-based method, they kept a weight dietary record and sent it to the researcher. A questionnaire was used to assess participants' satisfaction. Results: No statistical differences (p = 0.89) were observed in the measurement of energy intake between Nutrinote Gama (mean ± standard deviation [SD] = 582.8 ± 131) and food weighing (mean ± SD = 566.1 ± 133). No statistical differences (p = 0.59) were also observed in the measurement of protein, fat (p = 0.434), and carbohydrate (p = 230). The energy, protein, fat, and carbohydrate intakes estimated from the two methods were significantly correlated (r = 0.86, 0.870, 0.811, 0.738, respectively). Over 70% of participants were satisfied with the photo-based record. Conclusion: The results indicate that this digital photo-based dietary assessment tool is valid and user-friendly to estimate nutrient intake.

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review.

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.

Validity and Reproducibility of an Electronic Food Frequency Questionnaire in Argentinian Adults.

This study aimed to validate a semiquantitative electronic food frequency questionnaire (eFFQ) in estimating the intake of a comprehensive list of nutrients and bioactive compounds among adults from six regions of Argentina using multiple 24 h dietary recall (24HR) as a reference. A total of 163 adults completed two administrations of the eFFQ and four 24HRs. The paired t-test/Wilcoxon signed-rank test, Spearman/Pearson correlations, cross-classification, weighted kappa statistics, and Bland-Altman plots were employed to determine relative validity. To determine reproducibility, intraclass correlations (ICC), cross-classification, and weighted kappa statistics were calculated. For relative validity, crude correlations ranged from 0.15 to 0.57; energy adjustment and de-attenuation slightly improved most of these correlations. In cross-classification analysis, agreements within one quintile adjacent to exact agreement (EA ± 1) ranged from 52.2% to ~74%; extreme misclassifications were < 7%. For reproducibility, the crude ICC ranged from 0.29 to 0.85, showing moderate to good correlations for most nutrients. Cross-classification analysis showed agreement levels for the EA ± 1 quintile of 70.6% to 87.7%. Weighted kappa values ranged from 0.21 to 0.62. The results show that this eFFQ is relatively valid in ranking adults according to their nutrient intake and has an acceptable reproducibility, yet it slightly overestimates the intake of most nutrients.

An Improved Assessment Method to Estimate (Poly)phenol Intake in Adults with Chronic Pancreatitis.

BACKGROUND: Insights into (poly)phenol exposure represent a modifiable factor that may modulate inflammation in chronic pancreatitis (CP), yet intake is poorly characterized and methods for assessment are underdeveloped. AIMS: The aims are to develop and test a method for estimating (poly)phenol intake from a 90-day food frequency questionnaire (FFQ) using the Phenol-Explorer database and determine associations with dietary patterns in CP patients versus controls via analysis of previously collected cross-sectional data. METHODS: Fifty-two CP patients and 48 controls were recruited from an ambulatory clinic at a large, academic institution. To assess the feasibility of the proposed methodology for estimating dietary (poly)phenol exposure, a retrospective analysis of FFQ data was completed. Mann-Whitney U tests were used to compare (poly)phenol intake by group; Spearman correlations and multivariable-adjusted log-linear associations were used to compare (poly)phenol intakes with dietary scores within the sample. RESULTS: Estimation of (poly)phenol intake from FFQs was feasible and produced estimates within a range of intake previously reported. Total (poly)phenol intake was significantly lower in CP vs controls (463 vs. 567mg/1000kcal; p = 0.041). In adjusted analyses, higher total (poly)phenol intake was associated with higher HEI-2015 (r = 0.34, p < 0.001), aMED (r = 0.22, p = 0.007), EDIH (r = 0.29, p < 0.001), and EDIP scores (r = 0.35, p < 0.001), representing higher overall diet quality and lower insulinemic and anti-inflammatory dietary potentials, respectively. CONCLUSIONS: Using enhanced methods to derive total (poly)phenol intake from an FFQ is feasible. Those with CP have lower total (poly)phenol intake and less favorable dietary pattern indices, thus supporting future tailored dietary intervention studies in this population.

Development of a Population-Level Dichotomous Indicator of Minimum Dietary Diversity as a Proxy for Micronutrient Adequacy in Adolescents Aged 10-19 Y in the United States.

BACKGROUND: Diversity is a key component of diet quality and health, but no indicator exists for adolescents under the age of 15 y. OBJECTIVES: To establish a dichotomous indicator for population-level assessment of adolescent dietary diversity as a proxy for micronutrient adequacy. METHODS: We used the probability approach to construct mean probability of adequacy (MPA) of 11 micronutrients from 2 d of 24-h dietary recall data from NHANES, 2007-2018. For each micronutrient, probability of adequacy (PA) was calculated using the best linear unbiased predictor of usual intake. Adolescent dietary diversity score (ADDS) was derived with a maximum score of 10 food groups. Generalized linear mixed models were used to examine associations between ADDS and MPA. Receiver operating characteristic analysis was used to establish a cutoff for minimum dietary diversity for adolescents (MDD-A) using an energy-adjusted logistic model with ADDS predicting MPA > 0.6. RESULTS: PA was >80% for all nutrients except vitamin C (42.1%), folate (65.7%), and calcium (23.8%). Population MPA was 79.4%, and nearly 92% of adolescents had an MPA > 0.6. ADDS was positively associated with MPA, and energy was a significant confounder. The area under the curve was >0.8 on both days with sensitivity and specificity ranging from 0.71 to 0.80. The MDD-A cutoff was calculated as 5.12 and 5.10 food groups on days 1 and 2, respectively. CONCLUSIONS: In U.S. adolescents, the best cutoff for a dichotomous indicator of dietary diversity as a proxy for micronutrient adequacy is 6 food groups in a given day. Future research could validate MDD-A and its associated cutoff for use across country contexts.

Reliance on self-reports and estimated food composition data in nutrition research introduces significant bias that can only be addressed with biomarkers.

The chemical composition of foods is complex, variable, and dependent on many factors. This has a major impact on nutrition research as it foundationally affects our ability to adequately assess the actual intake of nutrients and other compounds. In spite of this, accurate data on nutrient intake are key for investigating the associations and causal relationships between intake, health, and disease risk at the service of developing evidence-based dietary guidance that enables improvements in population health. Here, we exemplify the importance of this challenge by investigating the impact of food content variability on nutrition research using three bioactives as model: flavan-3-ols, (-)-epicatechin, and nitrate. Our results show that common approaches aimed at addressing the high compositional variability of even the same foods impede the accurate assessment of nutrient intake generally. This suggests that the results of many nutrition studies using food composition data are potentially unreliable and carry greater limitations than commonly appreciated, consequently resulting in dietary recommendations with significant limitations and unreliable impact on public health. Thus, current challenges related to nutrient intake assessments need to be addressed and mitigated by the development of improved dietary assessment methods involving the use of nutritional biomarkers.

Studies about the health benefits of foods or nutrients are often inconsistent. One study may find a health benefit of a particular food and may recommend that people increase their consumption of this food to reduce their disease risk. Yet another study may find the opposite. Inconsistent study results fuel confusion and frustration, and reduce trust in research. Limitations in the studies' designs are likely to be blamed for the inconsistent findings. For example, many studies rely on participants to self-report their food intake and on databases of the nutritional content of food. But people may not accurately report their food intake. Foods vary in their nutritional content, even between two items of the same food such as two apples. And how individuals metabolize foods can further affect the nutrients they receive. Nutritional biomarkers are a potential alternative to measuring dietary intake of specific nutrients. Biomarkers are compounds the body produces when it metabolizes a specific nutrient. Measuring biomarkers therefore give scientists a more accurate and unbiased assessment of nutrient intake. Ottaviani et al. conducted a study to test the differences when estimating nutrient intake using nutritional biomarkers compared with more conventional tools. They analyzed data from a nutrition study that involved over 18,000 participants. The experiments used computer modelling to assess study results using self-reported food intake in combination with food composition database information, or measures of three biomarkers estimating the intake of flavan-3-ols, epicatechin, and nitrates. The models showed that self-reported intake and food database information often led to inaccurate results that did not align well with biomarker measurements. Measuring nutritional biomarkers provides a more accurate and unbiased assessment of nutritional intake. Using these measurements instead of traditional methods for measuring nutrient intake may help increase the reliability of nutrition research. Scientists must work to identify and confirm biomarkers of nutrients to facilitate this work. Using these more precise nutrient measurements in studies may result in more consistent results. It may also lead to more trustworthy recommendations for consumers.

Validity of the PortionSize application compared with that of MyFitnessPal for accurately estimating intake: a randomized crossover laboratory-based evaluation.

BACKGROUND: PortionSize offers real-time feedback on dietary intake, including intake of MyPlate food groups but requires further evaluation on a larger sample in a laboratory-based setting. MyFitnessPal (MFP) is a commonly used commercial dietary assessment application, and to our knowledge, no known studies have evaluated MFP in a laboratory setting. OBJECTIVES: The overall objective was to test the validity of PortionSize and MFP to accurately measure intake compared with that of weighed food (WB) and to compare error between applications. A secondary objective was to test usability, satisfaction, and user preference between applications. METHODS: This randomized crossover study was completed between February and October 2021. Participants (N = 43) used both applications to estimate intake in a laboratory setting. Participants were provided with a preweighed plated meal and plated leftovers. Two 1-sided t tests assessed equivalence (±21% bounds) between simulated intake from PortionSize and WB, and MFP and WB. The primary outcome was energy intake, and secondary outcome measures were portion size (in grams), food groups, and other nutrients. Differences in relative absolute error, usability, satisfaction, and user preference between applications were evaluated using dependent samples t tests. Cohen d assessed effect size. RESULTS: For PortionSize, energy and portion size were underestimated by 13.3% and 14.0%, respectively, and were not equivalent to WB. For MFP, energy was overestimated by 7.0%, and equivalent to WB (P = 0.04). Relative absolute error for energy did not differ between applications. For PortionSize, Cohen d was small (<0.2) for fruits, grains, protein foods, and specific nutrients. No differences were seen with usability, and the only difference for satisfaction was that participants found it easier to use MFP to find foods consumed (P = 0.019), and participants preferred using MFP (P = 0.014). CONCLUSIONS: PortionSize requires further updates to improve energy estimates and usability but demonstrates clinical utility for tracking food group and nutrient intake. PortionSize did not outperform MFP for measuring energy intake. CLINICAL TRIAL REGISTRY: This trial was registered at clinicaltrials.gov as NCT04700904 (https://classic. CLINICALTRIALS: gov/ct2/show/NCT04700904).

Design, Methods, and Participation in Riksmaten Young Children-A Swedish National Dietary Survey.

Background: National dietary surveys provide essential data for risk benefit assessments of foods and nutrients, for management and policy development. Physical activity measurement and biomonitoring can provide important complementary data but are less commonly included. Objectives: This study aimed to describe the study design and methods of the cross-sectional Swedish national dietary survey Riksmaten Young Children (Riksmaten småbarn), of children aged 9 mo, 18 mo, and 4 y. Participation/dropout rates for the 2 older age groups are also presented. The impact of different recruitment strategies is discussed. Methods: Children (N = 16,655) were randomly selected from the population register; invitations to guardians were sent by post and where possible, followed up by telephone. Food intake was assessed by a 2-d food diary and/or questionnaire. Height and weight were reported after measurement. Physical activity (accelerometery, 7 d) and stool, blood, and urine samples were assessed in subgroups. Results: Food consumption data were collected in 1828 children (11% of the invited; 18 mo: n = 1078, and 4 y: n = 750). Of participants also in subgroups, 71% provided physical activity data (n = 1307), 60% stool samples (n = 630), and 51% blood and/or urine samples (n = 593). The study population represented all geographic regions and types of municipalities in Sweden, but participating households had both higher education level and higher income than the target population. Only minor differences were seen in participation rates between recruitment via post and telephone compared with those through post only (12% compared with 10%). Repeated contact attempts were needed for the majority of participants (65%). Despite the low-participation rate, 99% of the participants completed the study once started. Conclusions: Although it was a challenge to recruit participants, Riksmaten Young Children provides a substantial amount of information at national level, representative in terms of sex, geography, and family structure. The underrepresentation of households with lower socioeconomic position must be considered when generalizing results.

Automated Artificial Intelligence-Based Thai Food Dietary Assessment System: Development and Validation.

Background: Dietary assessment is a fundamental component of nutrition research and plays a pivotal role in managing chronic diseases. Traditional dietary assessment methods, particularly in the context of Thai cuisine, often require extensive training and may lead to estimation errors. Objectives: To address these challenges, Institute of Nutrition, Mahidol University (INMU) iFood, an innovative artificial intelligence-based Thai food dietary assessment system, allows for estimating the nutritive values of dishes from food images. Methods: INMU iFood leverages state-of-the-art technology and integrates a validated automated Thai food analysis system. Users can use 3 distinct input methods: food image recognition, manual input, and a convenient barcode scanner. This versatility simplifies the tracking of dietary intake while maximizing data quality at the individual level. The core improvement in INMU iFood can be attributed to 2 key factors, namely, the replacement of Yolov4-tiny with Yolov7 and the expansion of noncarbohydrate source foods in the training image data set. Results: This combination significantly enhances the system's ability to identify food items, especially in scenarios with closely packed food images, thus improving accuracy. Validation results showcase the superior performance of the INMU iFood integrated V7-based system over its predecessor, V4-based, with notable improvements in protein and fat estimation. Furthermore, INMU iFood addresses limitations by offering users the option to import additional food products via a barcode scanner, thus providing access to a vast database of nutritional information through Open Food Facts. This integration ensures users can track their dietary intake effectively, with expanded access to over 3000 food items added to or updated in the Open Food Facts database covering a wide variety of dietary choices. Conclusions: INMU iFood is a promising tool for researchers, health care professionals, and individuals seeking to monitor their dietary intake within the context of Thai cuisine and for ultimately promoting better health outcomes and facilitating nutrition-related research.

Intake Biomarkers for Nutrition and Health: Review and Discussion of Methodology Issues.

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.