Current Developments in Nutrition

Background & aims Food- and nutrient-based inflammatory scores may capture different dietary features. We compared the Food Inflammation Score for Individuals (FISI) and Dietary Inflammatory Index (DII) in relation to cognitive performance and incremental discrimination. Methods This cross-sectional analysis included 2,455 adults aged [&ge;]60 years in NHANES 2011-2014 with four cognitive tests and two reliable 24-h recalls. Daily FISI summed [(food FII per 100 g) x grams/100] across foods; two days were averaged. DII used 33 parameters averaged across the same days. Survey-weighted linear and logistic models incorporated strata, primary sampling units, and WTDR2D/2. Discrimination used survey-weighted AUROCs and 500 stratified primary-sampling-unit bootstrap replicates. Results After full adjustment, a 1-SD higher DII was associated with a 0.091 lower global cognitive Z-score ({beta} = -0.091; 95% CI, -0.143 to -0.040; P = 0.005), whereas FISI was not ({beta} = -0.019; 95% CI, -0.064 to 0.026; P = 0.341). The DII quartile trend was significant (P = 0.007), but nominal domain associations did not survive false-discovery-rate correction. Neither score was associated with low cognitive performance. The base AUROC was 0.8242; adding FISI changed it by <0.0001 (95% bootstrap CI, -0.0002 to 0.0007), and adding DII changed it by 0.0005 (-0.0003 to 0.0021). Conclusions Higher DII, but not FISI, was associated with lower global cognitive performance. Neither score materially improved discrimination for low cognitive performance. The two scores should not be assumed equivalent or interchangeable.

Objective: Observational studies have provided evidence for positive associations between inflammatory dietary patterns (IDP) and mental health, which might be mediated by immune activation. However, a causal relationship has not yet been established. Here we aim to investigate the causal nature of associations between IDP and mental health traits (depressed affect, mood swings, neuroticism, feed-up feelings, worry, irritability) using Mendelian Randomization (MR) analyses. Method: In the UK Biobank dataset, IDP was identified by conducting a partial least squares regression (PLSR) on the items of the food frequency questionnaire along with three inflammatory biomarkers as response variables: C-reactive protein, platelets, and white blood cell (WBC) counts. An individual-level genome-wide association study (GWAS) of IDP was performed within an unrelated European subsample from the UK Biobank (n=320,137) and summary-level GWAS data for mental health traits were utilized for bi-directional two-step MR to test the association between genetically predicted IDP and mental health traits. Result: The first PLSR component was retained for subsequent analysis, with a higher IDP score indicating a more frequent consumption of processed meat, beef, pork, lamb/mutton, and poultry. Genome-wide association analysis identified 101 independent genomic loci. MR analyses indicated a uni-directional positive relationship from IDP to neuroticism and a positive bi-directional relationships between IDP and depressed affect, mood swings, fed-up feelings, and irritability. The mediation effect of total white blood cell count on neuroticism score was also significant (adjusted P<0.05). Conclusion: Our findings identified genetic loci and functional properties of IDP and provided evidence for causal pathways with depressed affect, mood swings, irritability, and fed-up feelings. Implementing dietary advice and interventions should become part of a public mental health approach. Keywords: Inflammatory dietary pattern; Partial least squares regression; Genome-wide association study; Mendelian Randomization; Mental health.

Micronutrient deficiencies are prevalent in Madagascar, where diets rely heavily on starchy staples and access to animal-source foods is limited. Small dried fish (SDF) are widely available, yet their nutritional value and health risks remain poorly documented. We combined market surveys, taxonomic identification, and micronutrient and heavy metal analyses of nine SDF types collected along National Road 7. The samples encompassed 33 fish families, were dominated by small pelagic species (Clupeidae and Engraulidae), and were appreciated by consumers. A daily portion (5 g for infants; 10 g for young children and women of childbearing age) contributed substantially to Recommended Nutrient Intakes (RNIs). Across samples and groups, SDF were rich (>30% of RNI) in selenium and, for infants and young children, in calcium. All samples were a source of (>15% of RNI), or rich in, phosphorus, whereas iron contributions were more variable but often substantial. Several samples exceeded 100% of RNIs for selenium, calcium, iron, or manganese in infants and young children, and some were also sources of magnesium and, less frequently, zinc. Vitamin A was absent from sun-dried samples but detected in a smoked freshwater type. Heavy metal concentrations varied markedly, and portions of several types led to estimated exposures to inorganic arsenic or cadmium exceeding reference values, whereas freshwater species and some pelagic types showed a more favorable nutrition-risk balance. Overall, SDF are affordable, nutrient-dense foods with strong potential to alleviate micronutrient deficiencies in Madagascar, while highlighting the need for type-specific guidance to balance nutritional benefits and contamination risks.

Background: The U.S. Food and Nutrient Database for Dietary Studies (FNDDS) is updated across NHANES dietary cycles and is central to U.S. nutrition surveillance. However, multi-cycle food-code-level changes in nutrient composition have not been comprehensively characterized across the full WWEIA nutrient panel. Objective: To characterize ten-year temporal patterns in nutrient composition across five FNDDS cycles, evaluate pandemic-period food-code compositional stability, and distinguish exploratory mean-level signals from distributional heterogeneity that may reflect reformulation, database coverage, or food-code definition changes. Methods: We analyzed five consecutive FNDDS biennial releases: 2013-14, 2015-16, 2017-18, 2019-20, and 2021-23. Nutrient values were extracted from the public FNDDS/FoodData Central release files and standardized to per-100-g food-code-level records. Cycle midpoints, 2013.5, 2015.5, 2017.5, 2019.5, and 2022.0, served as the independent variable in an exploratory ordinary least squares (OLS) regression. Mann-Kendall testing assessed monotonic rank trends, Welch's ANOVA assessed food-code-level distributional heterogeneity, and pairwise Welch comparisons with Cohen's d summarized pre-pandemic, pandemic-period, and post-pandemic differences. Equivalence testing using TOST with +/-10% bounds was restricted to the 2019-20 versus 2021-23 stability comparison. OLS sensitivity analyses were repeated after excluding the structurally atypical 2017-18 cycle. Results: Sixty-three nutrients were analyzed. Eight nutrients showed nominal OLS trends, p < 0.05, but none remained significant after Bonferroni correction. Mann-Kendall testing identified two nominal monotonic signals, and none after adjustment. Welch's ANOVA detected cycle-level distributional differences for 61 of 63 nutrients at nominal p < 0.05 and 57 of 63 after adjustment. Pairwise pandemic-period analyses showed many adjusted differences when the pre-pandemic baseline was compared with 2019-20 or 2021-23, but standardized effects were small, with all absolute Cohen's d values < 0.20. No nutrient differed after adjustment between 2019-20 and 2021-23, and 39 of 48 primary analytes met +/-10% TOST equivalence criteria for that comparison. Slope estimates were directionally stable after excluding 2017-18, but nominal significance status remained sensitive to the short time series. Conclusions: FNDDS food composition varied across cycles, but there was no clear decade-long linear trend for most nutrients. The main signal was a possible increase in total PUFA and linoleic acid, which may reflect changes in fat quality. The 2021-23 cycle was very similar to 2019-20, suggesting no major post-pandemic shift in the foods represented. These findings should be interpreted as food-database signals, not as direct estimates of what people consumed.

Background: The growing burden of lifestyle-related chronic diseases has increased the need for clinically interpretable decision-support tools capable of integrating artificial intelligence with evidence-based preventive nutrition. Although machine learning has shown considerable potential for health risk prediction, most existing approaches remain limited to isolated predictive models or conventional nutritional software, with little integration of multidimensional clinical assessment and personalized recommendations. Objective: To develop and internally validate NutrIA, a hybrid web-based Clinical Decision Support System (CDSS) that combines machine learning, validated clinical assessment, structured clinical reasoning and personalized nutritional recommendations for preventive medicine. Methods: NutrIA was developed using harmonized data from the National Health and Nutrition Examination Survey (NHANES, 1988 to 2018). A supervised machine learning model was trained to estimate 5-, 10- and 20-year all-cause mortality risk and subsequently integrated with an adaptive clinical questionnaire, validated screening instruments, nutritional indicators, dietary clustering, clinical phenotyping and a transparent rule-based recommendation engine within a unified web-based platform. Results: The predictive model achieved ROC-AUC values of 0.894, 0.914 and 0.923 for 5-, 10- and 20-year mortality prediction, respectively. The implemented CDSS incorporates an adaptive questionnaire (151 items), 39 validated clinical assessment instruments, 17 clinical phenotypes and 31 dietary clustering modules to generate individualized nutritional and lifestyle recommendations together with an automated clinical report. The integrated framework translates probabilistic risk estimates into clinically interpretable decision support for personalized preventive nutrition. Conclusions: NutrIA demonstrates the technical feasibility of integrating machine learning with knowledge-based clinical reasoning within a single web-based CDSS for preventive nutrition. Although external validation and prospective clinical evaluation are required before routine implementation, the proposed architecture represents a promising step toward clinically interpretable artificial intelligence for personalized nutritional care.

Low protein (LP) diets improve metabolic health in rodents and humans. In rodents, LP diets are typically implemented by replacing protein with carbohydrates like sucrose or cornstarch, keeping diets isocaloric. However, humans can choose from many different types of carbohydrate, and how dietary carbohydrate quality - the precise composition of the dietary sugars - impacts the response to dietary protein remains largely unexplored. Here, mice were fed control (21% protein) or LP (7% protein) diets with four different carbohydrate sources: sucrose, a 1:1 glucose/fructose mixture, glucose, or fructose. While LP diets improved metabolic health across all groups in male mice, carbohydrate quality also significantly altered specific health outcomes, with fructose-fed mice having the lowest body weight and adiposity of all control diets. In female mice, responses to LP diets were influenced by carbohydrate quality, with certain sugars inducing a stronger metabolic response to LP diets than previously seen. Finally, in female APP/PS1 mice, a model of Alzheimer's disease, we find that although LP diets reduce A-beta; plaque burden irrespective of carbohydrate type, dietary sugar type does influence spatial memory. Together, these results demonstrate that while dietary protein is a critical determinant of metabolic and neurological health, carbohydrate quality influences these outcomes in a sex-specific manner.

Plant-based diets may benefit planetary and human health. However, the molecular pathways linking plant-based diet quality to chronic diseases remain unclear. In 4,372 participants from the European Prospective Investigation into Cancer and Nutrition, we assessed 7,285 SomaScan-measured aptamers to identify circulating proteins associated with healthful (hPDI) and unhealthful (uPDI) plant-based dietary patterns using complementary statistical and machine-learning approaches. We then applied cis-pQTL Mendelian randomization (MR) and colocalization to prioritize diet-associated proteins with genetic evidence for associations with overall and site-specific cancers, type 2 diabetes (T2D), and cardiovascular disease (CVD). Among diet-associated proteins with MR evidence, 8 hPDI- and 12 uPDI-related protein-disease associations showed strong colocalization, including EGFR-breast cancer, MMP10-endometrial cancer, NCAN-T2D, and PCSK9-CVD. These findings identify candidate proteins that may link plant-based diet quality to chronic diseases and provide biological insights into the potential health benefits of healthful plant-based diets, which are increasingly relevant to public and planetary health.

Objective To develop and evaluate an automated large language model (LLM)-based framework for conducting meta-analyses of nutrition-related exposures and the risk of breast, ovarian, and uterine cancers. Design We developed MetaFemina, an automated evidence-synthesis pipeline for women's cancers that integrates keyword-based literature retrieval, LLM-assisted evidence extraction, and random-effects meta-analysis. We evaluated its performance against two recently published peer-reviewed meta-analyses and compared exposure-outcome associations across the three cancer types. Data sources PubMed articles identified through keyword-based searches of titles and abstracts. Methods MetaFemina was developed as a web platform that identifies relevant scientific articles, automatically extracts relevant information using LLMs, and synthesizes extracted evidence using random-effects meta-analysis. Additional analyses included assessment of heterogeneity, publication bias, and leave-one-out sensitivity analyses. The platform also provides sample size calculations based on synthesized effect sizes and generates visual summaries and plain-language interpretations. Results Compared with two recent peer-reviewed meta-analyses of folate and vitamin E intake in relation to breast cancer risk, MetaFemina demonstrated high sensitivity (81.82% and 80%, respectively) in identifying eligible studies and additionally retrieved relevant articles that had been missed by manual screening (27 and 13, respectively). Among 226 exposures considered, lutein and beta-carotene were significantly associated with lower risks of breast, ovarian, and uterine cancers. Vitamin D, antioxidants, and soy were significantly associated with lower risks of both breast and ovarian cancers, whereas calcium and folic acid were significantly associated with lower risks of both breast and uterine cancers. In contrast, iron, red meat, and copper were significantly associated with higher risks of both breast and uterine cancers. omega-6 fatty acids showed contrasting associations, being significantly associated with higher breast cancer risk but lower ovarian cancer risk. After restriction to dietary-intake studies, these cross-cancer significant associations remained statistically significant except for copper, which no longer met the two-study threshold for either breast or uterine cancer. Additionally, calcium became significantly associated with lower ovarian cancer risk, resulting in significant negative associations across all three cancer types, while vitamin E became significantly associated with lower breast cancer risk and remained significantly associated with lower ovarian cancer risk. Conclusions MetaFemina demonstrated high sensitivity for identifying relevant scientific literature, extracts key evidence, and performs statistically rigorous automated meta-analyses. The framework may facilitate more rapid evidence synthesis in nutritional epidemiology and may support researchers in study design, hypothesis generation, and interpretation of emerging evidence.

Returning individual results to research participants is increasingly expected in genomic studies. Yet, genomic dietary data have unique characteristics. New DNA sequencing techniques reconstruct diet from the residual plant and animal DNA recoverable in stool, reporting not nutrients or calories but a list of the species a person ate. Turning that list into something a participant can understand does not yet have an established framework. The Everyone EATS (Edible Atlas Through Sequencing) study returned personalized genomic dietary profiles to a non-clinical pilot cohort. Participants collected a stool sample at home, samples were processed with FoodSeq, and each participant received an interactive Diet Data Return Report that translated detected taxa into familiar food groups and benchmarked them against prior FoodSeq cohorts. No monetary compensation was offered, and the Diet Data Return Report was the sole incentive. Of 111 kits mailed, 80 were returned (72.1%) and sequenced (100% sequencing success for plant DNA; 98.7% for animal). Among report recipients with engagement data (n = 73), all accessed their report and 74% engaged interactively with its content. Among 16 feedback survey completers, 13/16 (81.2%) found the report easy to understand, 13/16 (81.2%) reported improved understanding of their dietary variety, and 5/16 (31.2%) reported a dietary behavior change. Curiosity about personal dietary data and supporting broader research goals were the most common reasons for enrolling (79.8%, respectively). Notably, the foods participants flagged as missing or unexpected clustered among herbs, spices, seafood, and processed items, the foods most easily forgotten in self-report and least readily assigned from sequence. Overall, our results suggest that genomic dietary data can be returned in a form participants find understandable and engaging, and that curiosity alone can motivate participation without payment.

BackgroundObesity is a complex multifactorial disease associated with chronic low grade inflammation, gut microbiota dysbiosis, and impaired gut-brain communication. Oligomeric procyanidins from grape seed extracts (GSE) are promising prebiotic candidates, capable of modulating host metabolism through interactions with the gut microbiota. MethodsC57Bl/6J male mice were rendered obese by feeding them a high fat, high sucrose diet and were orally administered GSE at a dose of 1or 2 g/kg/day for 12 weeks. We assessed body weight, adiposity, glucose tolerance, insulin sensitivity, circulating hormones, brain homeostasis markers, colonic and liver gene expression, 16S rRNA gene sequencing of the gut microbiota profiles, and untargeted cecal metabolomics. ResultsGSE reduced body weight gain, visceral adiposity, adipocyte hypertrophy, and improved oral glucose tolerance and insulin sensitivity. It normalized circulating lipid and glucose levels and lowered fasting insulin and leptin while increasing endogenous GLP-1. Hepatic gene expression analysis revealed a dose-dependent restoration of antioxidant defenses (SOD, CAT) and lipogenic transcription factors (SREBP, ChREBP). In the colon, GSE attenuated pro-inflammatory IL6 cytokine expression and strikingly upregulated GLP-1 and GLP-1 receptor expression. Microbiota analysis revealed a profound, dose-dependent remodeling of gut microbiota composition and diversity, with an expansion of health-associated taxa, such as Akkermansia muciniphila. Brain analyses revealed restoration of NAA and BDNF levels together with markers consistent with improved mitochondrial function. Cecal metabolomics revealed normalization of secondary bile acid metabolism, restoration of arginine bioavailability, and reduction in the accumulation of L-DOPA and spermidine. ConclusionsAn oligomeric procyanidin-rich grape seed extract acts as a multitarget prebiotic that alleviates diet-induced obesity and is associated with coordinated restoration of gut microbiota composition, GLP-1 signaling, and gut-brain and gut-liver communication pathways. Convergent dose-dependent effects on Akkermansia muciniphila abundance, GLP-1, NAA, and BDNF identify key mechanisms underlying its metabolic benefits.

Background. Obesity is a modifiable risk factor for osteoarthritis and may contribute to pain, functional impairment, inflammation, and cartilage degradation. Resveratrol has potential anti-inflammatory and chondroprotective effects, but its efficacy as an adjunct to dietary intervention remains unclear. Objective. This study evaluated whether resveratrol supplementation provides additional benefits when combined with a low-calorie diet in postmenopausal women with obesity and knee osteoarthritis. Methods. A total of 97 postmenopausal women with obesity and knee osteoarthritis were included in this randomized controlled clinical study. Participants received either a 10-day low-calorie diet alone or the same diet combined with 150 mg/day trans-resveratrol. Anthropometric parameters, body composition, biochemical markers, pain intensity, functional status, and urinary CTX-II were assessed at baseline and follow-up. Results. Both interventions were associated with reductions in body weight, BMI, waist and hip circumferences, fat mass, glucose, HOMA-IR, lipid parameters, hsCRP, VAS, WOMAC, LAI, and urinary CTX-II. Compared with diet alone, resveratrol supplementation did not provide additional benefits for anthropometric parameters, glucose metabolism, lipid profile, or WOMAC score. However, the resveratrol group showed a greater reduction in hsCRP and urinary CTX-II. The obesity class did not modify the treatment effect. Conclusion. A short-term low-calorie diet improved metabolic, inflammatory, and osteoarthritis-related parameters in postmenopausal women with obesity and knee osteoarthritis. The addition of resveratrol did not enhance weight loss or improve most metabolic outcomes but was associated with greater reductions in hsCRP and urinary CTX-II. These findings suggest a potential anti-inflammatory and cartilage-related effect of resveratrol, which requires confirmation in longer randomized trials.

Efficiently summarizing dietary records at scale remains a persistent bottleneck in nutritional epidemiology. We present FoodScribe, which translates free-text meal descriptions into quantitative nutrient profiles by combining ingredient parsing with nutrient retrieval by querying the USDA FoodData Central (FDC) database. Benchmarked using three LLM providers using Nutribench dataset, FoodScribe completed annotation of 3,807 meal descriptions in 2.5 hours, a task otherwise requiring substantial manual effort from trained nutritionists. FoodScribe achieved accuracy across macronutrient estimation (F1=0.79-0.89), with models performing better for protein than fat estimation. Application to a Mediterranean diet intervention cohort indicated dietary shifts consistent with the intervention pattern based on model-derived estimates. Integration with metabolomics data suggested that fiber and vegetable intake were positively associated with a fecal metabolite cluster.

Background: Many epidemiological studies rely on dietary exposures taken from baseline only. This limits our understanding of diet-disease associations because it requires assuming a level of temporal stability, either by individuals or dietary pattern composition. Objectives: This study aimed to evaluate these analytic assumptions of pattern structure consistency and baseline adherence using a cohort of US Black women with repeated measures of food frequency questionnaires (FFQ). Methods: Data from 6151 Black women aged 21-69 from the Black Women's Health Study with complete FFQ data in 1995, 2001, 2013, and 2021 were evaluated for temporal stability. Baseline dietary patterns were derived using an overfitted latent class model. Parameter estimates from the baseline model were then applied to subsequent waves to track individual transitions between existing patterns. Dietary patterns were also derived at each time point using an overfitted latent class model and assessed for changes in pattern composition over time. Results: Five baseline dietary patterns were identified in 1995. Only 18% of participants remained in the same baseline dietary pattern across all four time points, while all others transitioned to a different baseline-derived pattern. Dietary patterns derived independently at subsequent time points, yielded a different number of dietary patterns at each time point (2001: 6 patterns, 2013: 5 patterns, 2021: 4 patterns). Correlation strength of subsequent derived patterns and baseline patterns significantly weakened in strength after 2001 (40% pairings > 0.5), with no patterns correlated greater than 0.5 in 2021. Conclusion: Prospective studies that rely on baseline dietary exposure data cannot assume stability of pattern composition or individual pattern adherence over time, as it ignores changes in dietary habits and may bias our understanding of the diet-disease pathway.

The Healthy Eating Index (HEI-2020) is conventionally computed by aggregating intake across days before scoring. Digital food logging enables an alternative: scoring each day and averaging daily scores. These methods are not equivalent. The HEI's density-based structure and component caps cause aggregation to inflate adequacy scores when intake is irregular. Using Food & You data, we show daily HEI correlates more strongly with microbiome diversity, and recommend co-reporting both metrics.

Type 2 diabetes (T2D) affects more than 16 million Brazilians and has nearly doubled in the last 20 years. Lifestyle interventions reduce T2D incidence in people at risk of developing the disease; however, no large-scale trials have evaluated diabetes prevention programs in Brazil or compared telehealth and hybrid delivery in middle-income settings. These are key gaps that must be addressed to enable nationwide scale-up, particularly in primary care settings and remote areas. This article presents the protocol for the PROVEN-DIA trial, designed to address these evidence gaps. PROVEN-DIA is a multicenter, open-label, randomized controlled superiority trial with a parallel design enrolling 1,305 adults with prediabetes at 30 sites across all five Brazilian regions (ClinicalTrials.gov: NCT06426277). Participants will be randomly assigned in equal numbers to one of three groups. All groups receive lifestyle guidance targeting diet, physical activity, sleep, stress, alcohol consumption, and smoking, in accordance with Brazilian national guidelines. The two intervention groups receive PROVEN-DIA, a structured 36-month lifestyle program with 43 scheduled contacts encompassing individual counseling sessions, structured support contacts, and group education sessions, delivered either in a hybrid format (PROVEN-DIA) or telehealth only (TelePROVEN-DIA). The control group receives the same lifestyle guidance through unstructured individual visits every six months, without predefined content or ongoing support. The primary outcome is the cumulative incidence of type 2 diabetes at 36 months. Secondary outcomes include body weight, fasting glucose, glycated hemoglobin (HbA1c), dietary quality, physical activity, sedentary behavior, sleep quality, perceived stress, alcohol consumption, smoking behavior, and health-related quality of life. Analyses will follow the intention-to-treat principle.

Importance: Food and non-alcoholic beverage marketing drives children's dietary intake, yet updated evidence quantifying effects by marketing medium and sociodemographic factors is needed to inform policy. Objective: To quantify the effect of food marketing on dietary intake among children and adolescents (0-19 years) and examine variations by age, sex, socioeconomic position (SEP), weight status, marketing medium, and exposure duration. Data Sources: Nineteen electronic databases were searched for articles published from April 2020 to February 2026, complemented by World Health Organization-commissioned reviews covering 1970 to March 2020. Study Selection: Two reviewers independently selected peer-reviewed primary studies that assessed the association between food marketing and dietary intake, following PRISMA guidelines, with no language restrictions. Data Extraction and Synthesis: Two reviewers independently extracted data and assessed the risk of bias. Random-effects meta-analyses were conducted. The certainty of evidence was assessed using GRADE. Main Outcomes and Measures: Dietary intake (energy, quantity, or number of items consumed). Results: A total of 55 studies (N = 6,877; range 2-18 years) were included. Food marketing was associated with higher dietary intake (mean difference [MD], 34.8 kcal; 95% CI, 20.2-49.4) compared with no or less marketing. Unhealthy marketing via television (20 studies; MD, 44.5 kcal; 95% CI, 11.2-77.8), digital media (11 studies; MD, 37.5 kcal; 95% CI, 20.1-54.9), and packaging (11 studies; MD, 20.5 kcal; 95% CI, 0.7-40.3) all increased intake; the difference across media was significant (p < .001). Higher intake was observed in males (3 studies; MD, 51.9 kcal; 95% CI, 45.4-58.3) but not in females (MD, -6.8 kcal; 95% CI, -60.3-46.6); difference was not significant (p = .082). Differences by weight status (p = .012) were seen (5 studies; normal weight: MD, 55.6 kcal; 95% CI, -51.3-162.5; overweight/obese: 146.9 kcal; 95% CI, 34.1-259.7). Effects varied by age (p = .003) and by digital media exposure duration (p = .044). One study examined ethnicity; none studied SEP. Conclusions and Relevance: Food marketing is associated with increased dietary intake, with low certainty of evidence. Variations were observed across age, sex, weight status, and marketing medium. Further research is needed for adolescents and the role of SEP.

Background: Iron Deficiency Anemia (IDA) is one of the most prevalent nutritional disorders globally and a leading cause of Disability Adjusted Life Years (DALYs). Conventional diagnostic methods fail to detect deficiencies at an early stage and rarely account for individual genetic5 predisposition. Methods: This study proposes an end-to-end AI-driven precision nutrition pipeline integrating public Genome-Wide Association Study (GWAS) data and NHANES phenotypic data encompassing demographics, dietary intake, anthropometrics, and hematology. A synthetic genotype matrix was simulated for 400 GWAS-filtered SNPs using Hardy-Weinberg Equilibrium. Data preprocessing included missing value imputation, feature engineering, and SMOTE class balancing. Four machine learning models namely, Logistic Regression, Random Forest, Artificial Neural Network (ANN), and XGBoost were implemented and evaluated for both IDA classification and haemoglobin regression tasks. Results: XGBoost achieved state-of-the-art performance with ROC-AUC = 0.9981 for classification and R2 = 0.9903 for haemoglobin prediction. Polygenic Risk Score (PRS) stratification classified participants into low (73%), moderate (18%), and high (9%) risk tiers. Pathway burden analysis identified the Hepcidin Regulation pathway as the highest burden pathway in high-risk individuals. Conclusion: The integration of genomics, machine learning, and nutritional science through a Pathway-Burden Precision Nutrition Engine produced gene-specific, evidence-graded dietary recommendations, demonstrating significant potential for early and personalised IDA prevention.

Child-targeted marketing on packaged foods can shape children's food preferences and parents' purchasing decisions, yet many products with child-targeted marketing are ultra-processed foods (UPFs) and contain cosmetic additives such as food colorings, which have raised concerns about adverse effects on children's health and behavior. This mixed-methods study examined the prevalence of food colorings in child-directed UPFs and explored parents' perceptions and knowledge of these additives in beverages commonly consumed by children. Quantitative data were obtained from the Mintel Global New Products Database to identify child-directed products launched in Brazil between 2018 and 2021, measured as having at least one child-targeted marketing strategy in the food package, and whether they contained food colorings. Qualitative data came from seven focus groups with parents of children aged 2-5 and 6-11 years in Brazil, alongside a brief survey assessing participants' ability to identify food colorings on product labels. Among 5,078 UPFs launched during the study period, 23.0% contained child-targeted marketing, and 40.3% of these had food colorings. The highest prevalence was observed in carbonated beverages, candies, and ice creams, in which more than half of products contained food colorings. Parents generally understood that food colorings are used to make products more attractive to children and associated them with potential health risks, but reported difficulties avoiding them. These findings highlight the widespread presence of food colorings in child-targeted UPFs in Brazil and underscore the need for stronger regulatory measures to restrict the use of food colorings and improve labelling on food packages.

BackgroundDespite the advancements in infant nutrition, a gap still exists in the nutritional composition bioactive ingredients between infant formula and human milk. We developed a next-generation, proof-of-concept infant formula that contains recombinant human milk proteins. ObjectiveTo determine the impact of a novel infant formula (H1) on organ growth and development, and intestinal function compared to donor human milk (DHM) and standard infant formula (S) in a term piglet model. MethodsTerm piglets delivered via cesarean section were fed either a donor human milk (DHM) control, the investigational formula (H1), or infant formula (S) for 10 days. On d 10, a blood sample and tissues were collected. ResultsThere was no difference (P > 0.05) in piglet growth, although H1 piglets had a smaller relative stomach and liver than DHM and S piglets. H1 piglets had higher (P < 0.05) interleukins in the distal ileum, but no other systemic cytokines were elevated compared to the DHM and S piglets. H1 piglet small intestinal histology was similar (P > 0.05) to that of DHM and S piglets. Additionally, H1 piglets had either the same (P > 0.05) or higher (P < 0.05) amino acids in circulation compared to DHM and S piglets. Recombinant human proteins had either similar (P > 0.05) or lower (P < 0.05) activity compared to the native human proteins when assessing the individual ingredients in the H1 formula. ConclusionH1 formula was noninferior to DHM and S based on growth, small intestinal histology and plasma amino acid endpoints when fed to neonatal piglets. These findings warrant further studies to use the neonatal piglet as a model to evaluate more in-depth outcomes of health and safety for new infant formulas. Lay SummaryA novel piglet study shows a hypoallergenic, next-generation infant formula containing recombinant human milk proteins rivals donor human milk and standard formula for growth, gut health, and nutrient status.

Introduction: Cash transfer programs could reduce diabetes risk by decreasing chronic stress and increasing food security, physical activity, and preventive care, but there is an evidence gap on the relationship between cash transfer access and diabetes and prediabetes incidence. Methods: We used data from the Health and Ageing in Africa: Longitudinal Studies in South Africa (HAALSA) Indepth cohort of Black South Africans ages 40+ (N=5059). We fit log binomial models to estimate the relationship between household cash transfer eligibility (HCT) and cumulative incidence of diabetes and prediabetes between 2014/15 and 2021/22. We performed quantile regression to estimate change in continuous glucose values across the glucose distribution with additional HCT. Results: No association was observed between HCT and diabetes risk. Each additional unit of HCT was associated with reduced prediabetes risk [aCIR (95% CI): 0.94 (0.90, 0.98); p=0.007]. The largest reduction in glucose values associated with additional HCT was at the highest end of the glucose distribution. Discussion: Our observations suggest household cash transfer access reduces risk of prediabetes but not diabetes. However, household cash transfer access was associated with the largest decrease in glucose for the most severely glucose-impaired, implying the potential for HCT to reduce risk of hyperglycemic complications.