The American Journal of Clinical Nutrition

BackgroundThe 2019 Canadas Food Guide (CFG) provides universal recommendations to individuals aged 2 years or older. The extent to which these recommendations positively influence key health outcomes in older adults is unknown. ObjectiveIn community-dwelling adults aged 67-84 years and compared with habitual diet and physical activity, our objective was to estimate the 3-year difference in muscle strength, physical function, cardiometabolic health, and cognition according to the adherence to CFG recommendations (CFG), enhancements with additional protein foods (CFG-PRO), physical activity (PA) and both (CFG-PLUS). MethodsLongitudinal non-experimental data from the NuAge study (n=1753, mean [SD] age, 74 [4], 52% females) were used to emulate a target trial based on a total of four annual measurements of dietary intakes and covariates. Dietary intakes were measured using up to three interviewer-administered 24-hour recalls at each time. Physical activity was estimated using the Physical Activity Scale for the Elderly (PASE) questionnaire. Strength outcomes included handgrip, elbow flexor and quadriceps strength; function outcomes included "Timed-Up-and-Go", walking speed; cardiometabolic health outcomes included waist circumference, blood pressure and fasting glucose, estimated glomerular filtration rate (eGFR); cognition was evaluated with the Modified Mini-Mental State exam. The hypothetical intervention was modelled using the parametric g-formula to account for confounding (e.g., individual factors, propensity to health-seeking behaviors, disease burden) and loss to follow-up. Results1561 participants were eligible for the target trial emulation. Compared with no change, CFG would have increased elbow flexor strength by 0.8 kg (95%CI: 0.2, 1.3), quadriceps strength by 0.8 kg (95%CI: 0.0, 1.7), walking speed by 0.03 m/s (95%CI: 0.00, 0.05), eGFR by 2.8 mL/min/1.73m{superscript 2} (95%CI: 0.7, 4.5) and reduced waist circumference by 1.0 cm (95%CI: -1.7, -0.3). Effect estimates were similar for the CFG-PRO intervention. However, the CFG-PLUS intervention would have slightly amplified these differences, especially for walking speed (vs. CFG, +0.06 m/s; 95%CI: 0.03, 0.08) and waist circumference (vs. CFG, -0.8 cm; 95%CI: -1.5, -0.2). The hypothetical interventions did not meaningfully affect handgrip strength. ConclusionUnder strong assumptions, compared with no change, adhering to CFG 2019 recommendations over a 3-year follow-up improved strength and walking speed of adults aged 67 and older. Further increasing physical activity and protein food intake together improved walking speed to a clinically relevant extent. In view of the typical declines observed with no change in diet and physical activity, the improvements described would translate into attenuations of declines in strength and walking speed.

Background: Black adults bear a disproportionate burden of cardiometabolic dysfunction, yet most dietary trial evidence comes from predominantly White cohorts. Objective: To evaluate whether a personalized whole-food dietary intervention improves cardiometabolic outcomes more in Black than White young adults with overweight or obesity. Methods: In this 8-week randomized, controlled trial (ClinicalTrials.gov: NCT04635917), 112 Black and White adults (18-35 years; BMI 25-45 kg/m2) were block-randomized by race to a personalized dietary intervention providing whole foods (PD, n=57) or conventional dietary counseling at baseline (BL) using MyPlate guidelines (CD, n=55). Primary outcomes were Matsuda Index and fasting and OGTT-derived glucose, insulin, and non-esterified fatty acids. Other glucoregulatory, cardiovascular, anthropometric, appetite, and cognitive outcomes were also assessed. Outcomes were analyzed using baseline-adjusted linear models with sensitivity analyses adjusting for baseline BMI and food security score. Results: Compliance with study food consumption was 85-91%. Diet quality was higher in PD than CD (P < 0.05), with larger gains in vegetable-related outcomes among Black participants (group x race, P < 0.05). HOMA-{beta} was lower in PD than CD overall (P < 0.05). In sensitivity analyses, Black PD participants had greater fasting insulin reductions than White, especially in the latter half of intervention (week x group x race, P < 0.05), with a similar tendency for HOMA-IR. Glucose AUC 0-30 min was higher in White than Black PD participants (group x race, P < 0.05). Concentration performance was higher in PD than CD overall (P < 0.05), with larger gains in processing speed and accuracy among Black than White participants (group x race, P < 0.05). No effects were observed for cardiovascular or appetite outcomes. Conclusions: The personalized whole-food intervention produced differential effects in fasting insulin and early-phase glucose handling, and greater benefits in attention, in Black compared with White young adults with overweight or obesity during weight maintenance.

BackgroundMedical nutrition therapy is fundamental for managing glycemia and weight in type 1 diabetes, yet dietary guidance specific to this population and relevant subgroups is lacking. PurposeWe synthesized the interventional literature investigating diet patterns for glycemic and weight management in youth and adults with type 1 diabetes, with attention to interindividual variation that suggests the need for precision approaches. Data SourcesAMED, CINAHL, Cochrane Library, Ovid MEDLINE, Ovid Embase, Google Scholar, and Web of Science Core Collection were searched from January 2011 to June 2024. Study SelectionClinical trials [&ge;]4 weeks with [&ge;]10 youth and/or adults diagnosed with type 1 diabetes [&ge;]6 months prior and reported HbA1c or weight were included. Data SynthesisTwelve studies with 668 participants were included. Data were pooled by random-effects models for HbA1c and weight. Studies with insufficient data and subgroup differences were narratively synthesized per Synthesis Without Meta-analysis guidelines. Pooled results of very low to moderate certainty evidence showed no advantage of any particular diet pattern relative to routine care in randomized trials. Very low-quality evidence from single-arm low carbohydrate trials suggested improved HbA1c over time (-0.63% [95%CI -0.99, -0.27]; -6.0 mmol/mol [-10.8, -3.0]). Wide pooled confidence intervals demonstrated between-person heterogeneity; however, stratification of results by participant characteristics was rarely performed. LimitationsLimited evidence precluded subgroup analyses that can inform precision nutrition approaches. ConclusionsRandomized trials designed to investigate treatment response heterogeneity are needed to determine whether precision nutrition therapies optimize glycemia and weight in persons with type 1 diabetes. Disclosure SummaryLaura Nally receives research support from the National Institutes of Health and is a consultant for Medtronic, WebMD, and Calm. Jennifer Sherr serves, or has served, on advisory panels for Bigfoot Biomedical, Cecelia Health, Insulet Corporation, Mannkind, Medtronic Diabetes, StartUp Health Diabetes Moonshot, and Vertex. Dr. Sherr has served as a consultant to Abbott Diabetes, Bigfoot Biomedical, Insulet, Medtronic Diabetes, and Zealand. Yale School of Medicine has received research support for Dr. Sherr from Abbott Diabetes, JAEB Center for Health Research, JDRF, Insulet, Medtronic, NIH, and Prevention Bio. Chantal Mathieu serves or has served on the advisory panel for Novo Nordisk, Sanofi, Eli Lilly and Company, Novartis, Dexcom, Boehringer Ingelheim, Bayer, Roche, Abbott, Medtronic, Insulet, Biomea Fusion, SAB Bio and Vertex. Financial compensation for these activities has been received by KU Leuven; KU Leuven has received research support for Chantal Mathieu from Medtronic, Novo Nordisk, and Sanofi; Chantal Mathieu serves or has served on the speakers bureau for Novo Nordisk, Sanofi, Eli Lilly and Company, Medtronic, Dexcom, Insulet, Abbott, Vertex and Boehringer Ingelheim. Financial compensation for these activities has been received by KU Leuven. Irl Hirsch reports research funding from Tandem and Dexcom; and consulting fees from Abbott, Roche, GWave, and Vertex. Lori Laffel reports consulting for Dexcom, Boehringer Ingelheim, Medtronic, Provention Bio, Sanofi, Medtronic, Sequel MedTech, Vertex, and Tandem Diabetes. Peter Gottlieb has served as an advisor to Viacyte/Vertex, Imcyse, JDRF T1D Fund, and GentiBio; has received research support from Novo Nordisk, Imcyse, Novartis, Mercia/Nova, Provention Bio, ActoBio Therapeutics, Helmsley Foundation, JDRF, and NIH; and is a co-founder, Chief Medical Officer, and shareholder of ImmunoMolecular Therapeutics, Inc. Mustafa Tosur served as an advisory board member for Provention Bio in 2020 and 2021. Richard Pratley has received the following (thru 12/31/2023 directed to his institution; as of 1/1/2024 directed to Dr. Pratley personally): speaker fees from Lilly, Merck and Novo Nordisk; consulting fees from Bayer AG, Bayer HealthCare Pharmaceuticals, Inc., Corcept Therapeutics Incorporated, Dexcom, Endogenex, Inc., Gasherbrum Bio, Inc., Genprex, Getz Pharma, Hanmi Pharmaceutical Co., Hengrui (USA) Ltd., Intas Pharmaceuticals, Inc., Lilly, Merck, Novo Nordisk, Pfizer, Rivus Pharmaceuticals Inc., Sanofi, and Sun Pharmaceutical Industries; and grants from Biomea Fusion, Carmot Therapeutics, Dompe, Endogenex, Inc., Fractyl, Lilly, Novo Nordisk, and Sanofi. All other authors report no conflict of interest.

BackgroundCaloric restriction (CR) improves markers of biological aging, yet long-term effects on the human metabolome remain unclear. ObjectiveThis study examined the effects of CR (2 years) in healthy adults without obesity on circulating metabolites linked to aging and metabolic adaptations. MethodsUntargeted metabolomics was performed using fasted plasma samples collected at baseline, 12, and 24 months (BL, 12M, 24M) from CALERIE participants randomized to CR or ad libitum (AL) control. A total of 864 known metabolites were identified and grouped into nine biologically coherent super pathways to support pathway-level interpretation (amino acid, peptide, carbohydrate, energy, lipid, nucleotide, cofactors and vitamins, xenobiotics, and partially characterized molecules). Principal component analysis (PCA) summarized metabolite variation, and linear mixed models assessed intervention effects on each PC in group-by-time interactions. ResultsThree principal components showed significant group-by-time interactions: PC2 (carbohydrate), PC5 (partially characterized molecules), and PC4 (lipid). Carbohydrate (PC2) and partially characterized metabolites (PC5) decreased from baseline to 12M in both groups; from 12M to 24M, levels stabilized in CR but increased in AL for PC2, while PC5 continued to decline in CR and increased in AL. Lipid metabolites (PC4) decreased in CR and increased in AL at 12M, with the pattern reversing from 12M to 24M. Key contributors included malto-saccharides and related carbohydrate intermediates for PC2, glutamine degradants and lactone sulfates for PC5, and sphingolipids for PC4. ConclusionThis study provided insights into metabolic changes during CR, particularly for carbohydrate and lipid metabolism. Carbohydrate and lipid metabolites that were reduced by CR during the weight loss phase (BL to 12M) followed by stabilization or compensatory responses during the weight maintenance phase (12M to 24M) may link CR-induced changes in metabolism to inflammation. Future research is needed to tease out CR adaptations versus diet related changes in metabolites and explore the functional significance of these metabolic changes during CR for aging and long-term metabolic health. ConclusionCR produced distinct, time-dependent shifts in carbohydrate and lipid pathways. Early reductions during weight loss followed by stabilization or compensatory responses during weight maintenance suggest dynamic metabolic remodeling that may relate to inflammation-linked mechanisms. Further work is needed to distinguish CR-specific adaptations from dietary influences and to clarify the functional significance of these metabolic changes for aging and long-term metabolic health.

Background & aims: Autophagy activation is a promising strategy to counteract age-related cellular dysfunction. While preclinical studies suggest dietary protein restriction can induce autophagy via mTORC1 inhibition, direct human evidence using dynamic, flux-based measurements remains limited. The aim of this study was to determine whether a low protein diet could modulate autophagic flux in humans. Methods: We conducted a randomized crossover trial in which 74 healthy adults were randomized to receive two 4-week interventions of either average-protein (20% energy) or reduced-protein (10% energy) diets prescribed to maintain calculated energy balance, separated by a 4-week washout period. The primary outcome was autophagic flux measured in whole blood using a validated assay that preserves PBMCs in their physiological environment during lysosomal inhibition. Secondary outcomes included metabolic markers, body composition, and self-reported health metrics. Results: Sixty-three participants completed both interventions (mean {+/-} SD age 29.5 {+/-} 7.2 yrs; BMI 24.0 {+/-} 3.2 kg/m2). Reducing protein intake did not alter autophagic flux (adjusted mean difference: -8.46 ng LC3B-II/mg protein/h; 95% CI: -24.06 to 7.14; p = 0.28). Metabolomic profiling confirmed effective dietary separation, with lower circulating urea following reduced protein intake. Small differences in body weight and muscle mass were observed, while fat mass was unaffected. Fasting glucose, insulin, lipids, blood pressure, and quality of life did not differ between the two diets. Conclusions: Moderate protein restriction does not increase basal autophagy in circulating immune cells of healthy adults, suggesting protein reduction alone, without caloric deficit, may be insufficient to activate autophagy in human blood.

BackgroundCollege-attending young adults frequently experience declines in diet quality, physical activity, and psychological well-being during the transition to independent living, contributing to weight gain during the first year of college. Although multicomponent lifestyle interventions have been developed to address these behaviors, the responsiveness to such programs could differ across demographic factors associated with health behaviors, such as sex, race, and ethnicity. Hence, this secondary analysis of large-scale college health trials evaluated whether the effectiveness of such interventions differed by these demographic factors. MethodsData were combined from two multi-site randomized controlled trials: Young Adults Eating and Active for Health (YEAH) trial and the Get FRUVED trial. Both interventions used theory-based approaches to promote healthy weight management through improvements in diet quality, physical activity, and stress management. Baseline-adjusted linear regression models evaluated the effects of group (intervention, control) and its interactions with sex, race (White, Black, Other), or Hispanic ethnicity. Models were adjusted for baseline outcome values, baseline BMI, study (YEAH vs. FRUVED), and state of data collection. ResultsIntervention participants reported higher fruit and vegetable intake, lower processed meat intake, and longer sleep duration compared with controls. However, there was significant heterogeneity in these dietary outcomes by ethnicity, race, and sex. Non-Hispanic participants in the intervention group had higher fruit and vegetable intake compared to controls (p < 0.05). And, within the intervention group, Hispanic females had lower bacon/sausage intake than Hispanic males and non-Hispanic females (p < 0.05). With respect to race, Black participants reported higher total processed meat intake than White and Other race participants in the intervention group (p <0.05). These demographic factors did not moderate the interventions impact on physical activity, sleep duration, and perceived stress. Overall, the intervention appeared to be the least effective for Hispanic males who exhibited higher body weight and waist circumference compared with Hispanic females and non-Hispanic males (p < 0.05). ConclusionsMulticomponent lifestyle interventions can improve selected dietary outcomes among college students, but effectiveness may differ across demographic subgroups. Culturally and sex-tailored strategies that consider the intersecting influences of sex, race, and ethnicity may enhance intervention effectiveness during the transition to college.

The Goldberg method has been suggested for identifying energy intake (EI) under-reporting in nutritional epidemiology. Its implementation, however, is limited by challenges associated with estimating physical activity levels (PAL). We quantified the accuracy of the Sedentary Time and Activity Reporting Questionnaire (STAR-Q) derived PAL (PALSTAR-Q) combined with the Goldberg method (Goldberg-PALSTARQ) to identify EI misreporting as compared with doubly labeled water (DLW) derived total energy expenditure (TEEDLW). Between 2009 and 2011, 99 men and women completed a two-week DLW protocol, a food frequency questionnaire, and the STAR-Q. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of the Goldberg-PALSTAR-Q were determined. Fifty-eight percent of men and women were classified as under-reporters by Goldberg-PALSTAR-Q compared with 60% of men and 56% of women by TEEDLW. Among men, sensitivity, specificity, PPV, NPV and accuracy and 95% confidence intervals were 88% (68%-97%), 87% (61%-98%), 91% (72%-99%), 81% (56%-94%), and 87% (72%-95%), respectively; and among women 79% (62%-90%), 69% (50%-84%), 77% (60%-88%), 72% (52%-86%), and 75% (62%-84%), respectively. Validated individual level PALs used with the Goldberg method can be informative in sensitivity analyses to gain insight into EI misreporting in nutritional epidemiology studies lacking in objective EI measures.

BackgroundRising rates of diet-related chronic diseases and disparities in disease prevalence by sociodemographic factors highlight the need to improve the diet quality of Americans. Understanding how the Healthy Eating Index-2020 (HEI-2020) can be used as a measure of diet quality to create benchmarks for national nutrition monitoring can assist in surveillance and improve the design of nutrition programs and policies. ObjectiveTo examine the utility of the HEI-2020 to create benchmarks for national diet quality monitoring. MethodsThis serial cross-sectional study used data from the 2009-2018 National Health and Nutrition Examination Surveys (NHANES). Nationally representative data for 22,168 US adults ([&ge;]20 years) who completed two 24-hour recalls were analyzed. We derived HEI-2020 scores (0-100) from participants two 24-hour dietary recalls. Diet quality categories were established: high diet quality (>70-100), marginal (>60-70), low (>50-60), and very low (0-50). ResultsOnly 13% of US adults had high diet quality while nearly two-thirds had low or very low diet quality. Diet quality was higher for older adults, female, "Other" race or ethnicity, born outside of the US, have higher education attainment, higher income, and food security. Compared to adults with high diet quality, adults with very low diet quality had lower intakes of unprocessed or minimally processed foods, fruits, vegetables, whole grains, and seafood and the highest intakes of ultra-processed foods, refined grains, and red and processed meats (all P-trends <0.01). Adults with very low diet quality were more likely to have elevated adiposity, lower HDL cholesterol, and higher triglycerides, fasting glucose, and hemoglobin A1c (all P-trends [&le;]0.01). ConclusionsThe HEI-2020 is a robust measure of diet quality that can be directly linked to biological measures associated with chronic disease risk. Using evidence-based HEI categories could allow policymakers, public health practitioners, and nutrition professionals to set benchmarks and nationwide targets for achieving improved diet quality.

ImportanceEating timing has been increasingly linked to human health, yet national trends in macronutrient/food group timing and their health implications remain unclear. ObjectiveTo characterize trends in timing of energy, macronutrient, and food group intake among US adults and examine their associations with mortality. DesignCross-sectional analysis of eating timing trends using National Health and Nutrition Examination Survey (NHANES, 1999-March 2020) data; longitudinal analysis of mortality through December 2019. SettingA nationally representative sample. ParticipantsAdults aged [&ge;]20 years with valid 24-hour dietary recall data. ExposuresTiming of energy, macronutrient, and food group intake across predefined 4-hour blocks: 2:00-5:59 am (predawn), 6:00-9:59 am (morning), 10:00 am-1:59 pm (noon), 2:00-5:59 pm (afternoon), 6:00-9:59 pm (evening), and 10:00 pm-1:59 am (midnight). Main Outcomes and MeasuresSecular trends in eating timing and mortality. ResultsAmong 50,264 adults (mean age, 47.5 years; 51.2% women), evening accounted for the largest proportion of daily energy intake (weighted mean proportions across years, 31.9%-33.3%), followed by noon (24.7%-26.8%), afternoon (19.9%-21.8%), morning (13.5%-14.9%), and overnight (midnight and predawn; 5.6%-6.5%), with 23.4%-28.0% of adults consuming foods at midnight; similar distribution patterns were observed for macronutrient and food intake, except whole grain intake peaked in the morning and fruit, egg, and dairy intake distributed more evenly. Over time, energy intake proportions declined at noon and midnight but increased in the afternoon; while the secular trends varied by macronutrients and food groups. On average, fasting started at 8:34-8:51 pm and ended at 8:41-8:52 am. Mean midpoint and duration for energy intake were 2:38-2:48 pm and 11.9-12.2 hours, respectively. Male, non-Hispanic Black, and socioeconomically disadvantaged groups had larger midnight intake proportions and later intake midpoints. Reallocating 5% of daily energy intake from other blocks to midnight was associated with higher cardiovascular mortality (HR, 1.09; 95% CI, 1.02-1.17), driven by carbohydrates and related foods; reallocating 5% to predawn was associated with higher cancer mortality (1.22; 1.05-1.41), driven by proteins and related foods. Each 1-hour delay in the start/end of fasting and intake midpoint was associated with an 8%-9% higher risk of cardiovascular mortality. Conclusion and RelevanceFrom 1999 to March 2020, US adults consistently consumed the highest energy, macronutrients, and most foods in the evening and started fasting relatively late, with a quarter of adults having midnight consumption. Higher overnight intake and delayed eating timing were associated with higher mortality, particularly for specific macronutrients and foods, highlighting the need to devise optimal eating timing recommendations incorporating food compositions. Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the eating timing patterns for macronutrients and major food groups among US adults from 1999 to March 2020, and how are they associated with mortality? FindingsIn this nationally representative study of 50,264 adults, evening (6-10 pm) consistently contributed to the highest intake proportions of energy, macronutrients, and most food groups, followed by noon (10 am-2 pm), afternoon (2-6 pm), morning (6-10 am), midnight (10 pm-2 am), and predawn (2-6 am). Notably, midnight intake contributed [~]5% of daily energy intake, with a quarter of adults consuming foods at midnight, a nontrivial amount given its potential health risk. The mean durations for energy and macronutrient intake remained [~]12.0 hours, and fasting began after 20:34. Higher midnight and predawn energy intake might be associated with higher mortality; however, associations between eating timing and mortality varied by macronutrients/foods. Males and adults with lower socioeconomic status had higher midnight intake and delayed intake midpoints. MeaningHigh evening intake proportions, midnight consumption, and late fasting onset among US adults raise concerns given the health risk of late eating timing, particularly among males, non-Hispanic Black adults, and adults with low socioeconomic status.

ObjectiveTraditional pairwise meta-analyses indicated that nuts consumption can improve blood pressure. We iamed to determine the dose-dependent effect of nuts on systolic (SBP) and diastolic blood pressure (DBP) in adults. MethodsA systematic search was undertaken in PubMed, Scopus, and ISI Web of Science till March 2021. Randomized controlled trials (RCT) evaluating the effects of nuts on SBP and DBP in adults were included. We estimated change in blood pressure for each 20 g/d increment in nut consumption in each trial and then, calculated mean difference (MD) and 95%CI using a random-effects model. We estimated dose-dependent effect using a dose-response meta-analysis of differences in means. The certainty of evidence was rated using the GRADE instrument, with the minimal clinically important difference being considered 2 mmHg. ResultsA total of 31 RCTs with 2784 participants were included. Each 20 g/d increase in nut consumption reduced SBP (MD: -0.50 mmHg, 95%CI: -0.79, -0.21; I2 = 12%, n = 31; GRADE = moderate certainty) and DBP (MD: -0.23 mmHg, 95%CI: -0.38, -0.08; I2 = 0%, n = 31; GRADE = moderate certainty). The effect of nuts on SBP was more evident in patients with type 2 diabetes (MD: -1.31, 95%CI: -2.55, -0.05; I2 = 31%, n = 6). The results were robust in the subgroup of trials with low risk of bias. Levels of SBP decreased proportionally with the increase in nuts consumption up to 40 g/d (MD40g/d: -1.60, 95%CI: -2.63, -0.58), and then appeared to plateau with a slight upward curve. A linear dose-dependent reduction was seen for DBP, with the greatest reduction at 80 g/d (MD80g/d: -0.80, 95%CI: -1.55, -0.04). ConclusionsThe available evidence provides a good indication that nut consumption can result in a small improvement in blood pressure in adults. Well-designed trials are needed to confirm the findings in long term follow-up.

Background: Recent food-based recommendations for flavan-3-ols highlight a growing need to understand the breadth of our dietary polyphenol exposure. However, estimation of dietary polyphenol intake remains challenging, requiring custom computational tools that are often difficult to implement or not fully reproducible. Objective: We aimed to an automated, user-friendly tool to estimate polyphenol intake from diet recalls and records. Methods: We developed Polyphenol Estimator, a tool that processes dietary data from the Automated Self-Administered 24-Hour (ASA24) Dietary Assessment Tool or the Automated Multiple-Pass Method from the National Health and Examination Survey (NHANES). Polyphenol Estimator disaggregates foods using the FDA Food Disaggregation Database into ingredients, matches these ingredients to FooDB, and estimates polyphenol intake at the total, class, and compound level. Optionally, these polyphenol estimates can be used to calculate the Dietary Inflammatory Index (DII). Polyphenol Estimator is freely available online (https://swi1.github.io/polyphenol_estimator) with a tutorial for users with limited programming experience. Results: To illustrate Polyphenol Estimator, we applied it to two days of diet recalls from adults ([&ge;] 20 years) in NHANES 2021-2023 (n = 2778). For 97.7% of participants, less than 2.5% of reported foods went unmapped, with 75.7% of participants having complete mappings. Total polyphenol intake was 517 +/- 439 (mean +/- SD) mg/1000 kcal, largely from green tea, coffee, black tea, apples, wine, oranges, and blueberries. At the class level, polyphenols classified as organooxygen compounds, flavonoids, and cinnamic acids and derivatives were top intake contributors. At the compound level, cyptochlorogenic acid, neocholorogenic acid, and caffeic acid were top contributors. Lastly, the DII was 1.4 +/- 1.9, indicating the average diet had proinflammatory potential. Conclusions: Polyphenol Estimator offers an automated method to obtain total, class, and compound-level polyphenol estimates from dietary data to aid future efforts to understand polyphenol intake exposures and their biological impact on health.

BackgroundCholine is an essential nutrient, and insufficient intake negatively affects organs such as the liver, brain, and muscles. In the United States, average choline intake remains below the Adequate Intake (AI) (550 mg/day men, 425 mg/day women). Although conventional dietary assessment tools can identify people who are eating diets low in choline, no metabolite biomarkers have been proven to reliably assess choline intake. ObjectiveWe tested whether plasma concentrations of choline and its metabolites could determine dietary choline intake. We also assessed whether liver elastography (Fibroscan) could detect diet-induced changes in liver fat. MethodsIn a double-blind, randomized, crossover feeding study, participants adhered to 3 distinct dietary patterns for 2-wk intervals, delivering approximately 100%, 50%, and 25% of the choline AI. On Day 12 of each dietary arm, in addition to the food supplied, subjects consumed a single bolus of 2.2 mmol trimethyl-d9-choline. Plasma concentrations of choline, betaine and phosphatidylcholine (PtdCho) were measured using mass spectrometry. Targeted assays quantified choline, betaine, phosphatidylcholine and total homocysteine concentrations. Liver fat content was evaluated non-invasively using Fibroscan. ResultsPlasma concentrations of d9-choline, betaine, and their isotopic enrichment ratio (IER) varied with dietary intake (q<0.0001), and PtdCho IER also differed significantly (q=0.001). In targeted analysis, choline and betaine concentrations were highly responsive to dietary choline intake, while PtdCho and tHcy were not. Receiver Operator Characteristic (ROC) analysis showed strong accuracy using plasma choline (AUC=0.81) and betaine (AUC=0.80), with improved accuracy when combined (AUC= 0.83). Fibroscan identified a subset of participants with increased liver fat in response to the 25% AI vs. 100% AI choline diet, though patterns varied among individuals. ConclusionPlasma choline and betaine concentrations are robust biomarkers of dietary choline intake under controlled feeding. These findings support targeted metabolite profiling to improve choline intake assessment and reveal induvial variability in liver response to low choline intake.

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.

BackgroundTime-restricted eating (TRE) has gained popularity for weight loss and metabolic health. While some evidence suggests greater benefits when TRE aligns with circadian rhythms--characterized by early daytime eating and avoidance of nighttime intake, often referred to as early TRE (eTRE), other studies report no meaningful differences between eTRE, other TRE approaches with or without exercise, or calorie restriction (CR), and robust comparative evidence remains limited. AimTherefore, the aim of this network meta-analysis (NMA) is to evaluate the physiological effects of eTRE, midday time-restricted eating (mTRE), late time-restricted eating (lTRE), with and without exercise, CR, and control (without prescribed energy or fasting windows) on anthropometric measures and cardiometabolic markers in adults with cardiometabolic risk factors. MethodsA comprehensive literature search was conducted in four major databases (PubMed, Web of Science, Scopus, and Embase) up to April 24, 2025. A Bayesian NMA was performed, using a control group as the reference comparator across interventions. Treatment effects were expressed as mean differences with 95% confidence intervals. The relative ranking of the included arms on the outcomes was assessed using surface under the cumulative ranking curve, values derived from the NMA, where higher values reflect a higher probability of superior effectiveness. Resultsa total of 40 trials comprising 3259 subjects were included in the analysis. There were significant reductions in most anthropometric measures in all intervention groups compared to control group. Whereas eTRE and eTRE + exercise (EX) significantly improved glucoregulatory outcomes compared to control, eTRE + EX showed superior results over other interventions. ConclusionWhile our results did not detect statistically significant differences between TRE patterns and CR, the consistent SUCRA rankings in favor of eTRE (particularly with exercise) suggest that meal timing may play an important role in metabolic regulation.

BackgroundUltra-processed food (UPF) accounts for a majority of calories consumed in the United States, but the impact on human health remains unclear. ObjectiveTo identify poly-metabolite scores in blood and urine that are predictive of UPF intake. MethodsIDATA participants (n=718) with serially collected blood and urine and up to 6 24-hour dietary recalls (ASA-24s), collected over 12-months, were selected for metabolomics analysis using ultra-high performance liquid chromatography with tandem mass spectrometry. Average daily UPF intake was estimated as percentage energy according to the Nova system. Partial Spearman correlations and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to estimate UPF-metabolite correlations and build poly-metabolite scores of UPF intake, respectively. Scores were tested post hoc in a previously conducted randomized, controlled, crossover-feeding trial of 20 domiciled participants who consumed ad libitum diets that were 80% and 0% energy from UPF for 2 weeks each. ResultsIDATA participants were 51% female, and 97% completed [&ge;]4 ASA-24s. Mean intake was 50% energy from UPF. UPF intake was correlated with 187 (of 952) serum and 284 (of 1111) 24-hour urine metabolites (p.FDR [&le;] 0.01), including lipid (n=53 serum, n=21 24-hour urine), amino acid (n=33, 59), carbohydrate (n=3, 8), xenobiotic (n=33, 69), cofactor and vitamin (n= 9, 11), peptide (n=7, 6), and nucleotide (n=6, 8) metabolites. Using LASSO regression, 28 serum and 33 24-hour urine metabolites were selected as predictors of UPF intake; biospecimen-specific scores were calculated as a linear combination of selected metabolites. Overlapping metabolites included (S)C(S)S-S-methyl cysteine sulfoxide (rs= -0.19, -0.23), N2-N5-diacetylornithine (rs= - 0.26, -0.27), pentoic acid (rs= -0.28, -0.31), and N6-carboxymethylysine (rs=0.15, 0.21). Within the cross-over feeding trial, the poly-metabolite scores discriminated, within individual, between UPF diet phases (P paired t-test<1x10-5). ConclusionsPoly-metabolite scores, developed in IDATA participants with varying diets, are predictive of UPF intake and could advance epidemiological research on UPF and health.

Background: Chronic kidney disease (CKD) is associated with alterations in the gut microbiome that promote the accumulation of gut-derived uremic solutes and contribute to systemic inflammation, vascular dysfunction, and disease progression. Dietary fiber has emerged as a promising modulator of gut microbial metabolism, yet the influence of fiber physicochemical properties, particularly fermentability and viscosity, on uremic metabolite production in CKD remains poorly understood. Objective: To systematically evaluate the effects of isolated dietary fiber interventions, classified by fermentability and viscosity, on gut microbial metabolites in CKD across experimental rodent models and randomized clinical trials, and to determine whether these fiber properties modify microbial metabolites. Methods: A systematic search of PubMed, Embase, CINAHL, and Cochrane Library (through June 2026) identified randomized controlled trials and controlled rodent studies assessing isolated dietary fiber in CKD. Eligible studies reported at least one gut-derived metabolite (i.e., indoxyl sulfate (IS), p-cresyl sulfate (PCS), trimethylamine-N-oxide (TMAO), tryptophan-derived indoles, or short-chain fatty acids (SCFAs)). Random-effects models were used for pooled estimates using weighted mean differences (WMD) for human studies and standardized mean differences (SMD) for animal studies. Subgroup analyses evaluated fiber fermentability, viscosity, intervention dose, duration, and CKD stage. Risk of bias was assessed with ROB-2 and SYRCLE, and evidence certainty with GRADE. Results: Twenty-eight studies (13 human, 15 animal) met eligibility criteria, comprising 511 participants and 312 animals with CKD. Isolated fiber supplementation, primarily fermentable and non-viscous fibers, reduced IS (human: -0.13 mg/dL; 95% CI: -0.25, -0.01; p = 0.03; animal: -1.99; 95% CI: -3.06, -0.92; p < 0.0001) and pCS (human: -0.23 mg/dL; 95% CI: -0.46, 0.001; p = 0.051; animal: -1.56; 95% CI: -2.08, -1.03; p < 0.0001). SCFAs increased in animal studies, including cecal acetate (2.00, 95% CI: 0.78 to 3.22; p = 0.001) and circulating propionate (1.51, 95% CI: 0.054 to 2.96; p=0.04). There were no dose-dependent effects, but longer interventions (>8 weeks) tended to lower pCS (-0.26 mg/dL, 95% CI: -0.55 to 0.02; p=0.06). Some heterogeneity and low-to-moderate certainty were observed. Conclusion: Isolated dietary fiber reduces major gut-derived uremic solutes in CKD, with fermentability influencing metabolic responsiveness, but with minimal studies on viscous fibers. Larger, longer-duration trials with standardized reporting of total fiber intake and clinical endpoints are needed to guide evidence-based dietary recommendations in CKD.

BackgroundDietary patterns consist of multiple interrelated components, while individuals have numerous characteristics that may jointly influence dietary patterns. Studies to assess associations between sociodemographic characteristics and dietary patterns typically do not consider this complexity. ObjectiveThe objective of this study was to examine joint relationships between dietary patterns and sociodemographic characteristics among adults in Canada. Methods24-hour dietary recall data for adults [&ge;]18 years were drawn from the 2015 Canadian Community Health Survey Nutrition (n=14 097). Three mixed graphical models were developed to explore networks of sociodemographic characteristics, dietary components, and sociodemographic characteristics and dietary components together. Networks included 30 log-transformed food groups (grams), sex, age, household food security status, income, employment status, education, geographic region, and smoking status. Results are expressed as (edge weight; [95% CI]). ResultsThe strongest pairwise relationships were observed among dietary components and among sociodemographic characteristics. Positive linear relationships were observed among vegetable groupings; for example, between green and orange vegetables (0.12; [0,08, 0.16]). Negative relationships were observed among subgroups of each of animal foods, beverages, and grains; for example, between refined and whole grains (-0.30; [-0.33, -0.26]). In the model including dietary components and sociodemographic characteristics, age was associated with grains (other) (-0.12; [-0.16, -0.09]), coffee/tea (0.21; 95% CI [0.17, 0.24]), and whole grains (0.12; [0.08, 0.15]). Sex was associated with sweet beverages (0.11; [0.06, 0.17]), alcohol (0.18; [0.13, 0.24]), cured meat (0.20; [0.15, 0.26]), and red meat (0.16; [0.11, 0.21]). ConclusionsIn some cases, pairwise relationships between dietary components suggest displacement, for example, of whole grains by refined grains. Age and sex were the characteristics most strongly connected to dietary components. Statement of significanceExploring joint relationships between intersecting sociodemographic characteristics and multidimensional dietary patterns can assist with better understanding dietary heterogeneity to inform policies and programs that support healthy eating.

BackgroundMaternal nutritional interventions aim to address nutrient deficiencies in pregnancy, a leading cause of maternal and neonatal morbidity and mortality worldwide. How these interventions influence the placenta, which plays a vital role in fetal growth and nutrient supply, is not well understood. This is a major gap in understanding how such interventions could influence pregnancy outcomes and fetal health. We hypothesised that nutritional interventions influence placental phenotype, and that these placental changes relate to how successful, or not, the intervention is in improving pregnancy outcomes. MethodsWe conducted a systematic review and followed PRISMA-2020 reporting guidelines. Articles were retrieved from PubMed, Clinicaltrials.gov, and ICTRP-WHO using pre-defined search terms and screened by two reviewers using a 3-level process. Inclusion criteria considered articles published from January 2001-September 2021 that reported on clinical trials in humans, which administered a maternal nutritional intervention during the periconceptional or pregnancy period and reported on placental phenotype (shape and form, function or placental disorders). FindingsFifty-three eligible articles reported on (multiple) micronutrient- (n=33 studies), lipid- (n=11), protein- (n=2), and diet-/lifestyle-based (n=8) interventions. Of the micronutrient-based interventions, 16 (48%) associated with altered placental function, namely altered nutrient transport/metabolism (n=9). Nine (82%) of the lipid-based interventions associated with altered placental phenotype, including elevated placental fatty acid levels (n=5), altered nutrient transport/metabolism gene expression (n=4), and decreased inflammatory biomarkers (n=2). Of the protein-based interventions, two (66%) associated with altered placental phenotype, including increased placental efficiency (n=1) or decreased preeclampsia risk (n=1). Three (38%) of diet and lifestyle-based interventions associated with placental changes, namely placental gene expression (n=1) and disease (n=2). In studies with data on maternal (n=30) or offspring (n=20) outcomes, interventions that influenced placental phenotype were more likely to have also associated with improved maternal outcomes (11/15 [73%]) and offspring birth outcomes (6/11 [54%]), compared to interventions that did not associate with placental changes (2/15 [13%] and 1/9 [11%], respectively). ConclusionsPericonceptional and prenatal nutritional interventions to improve maternal/pregnancy health associate with altered placental development and function. These placental adaptations likely benefit the pregnancy and improve offspring outcomes. Understanding the placentas role in the success of interventions to combat nutrient deficiencies is critical for improving interventions and reducing maternal and neonatal morbidity and mortality globally.

BackgroundLow energy diets (LEDs) are effective for body weight (BW) loss and improvement of type 2 diabetes (T2D) risk biomarkers. Emerging evidence suggests that whole fruit feijoa powder, rich in polyphenols and abscisic acid, may further support T2D prevention. The FERDINAND study investigated whether daily consumption of a whole fruit feijoa powder enhanced LED-driven improvements in fasting plasma glucose (FPG), BW, and other metabolic markers during a 6-month intervention in adults with overweight and prediabetes. MethodsAt in-clinic screening 97 participants were enrolled into the trial and randomised to receive 1.15 g/day of whole fruit feijoa powder (Feiolix(R), Fx, n=48) or Placebo treatment (n=49) for 6 months. All participants underwent 2-months of LED-induced weight loss, followed by 4 months of dietary advice for weight loss maintenance. FPG, BW and secondary outcomes including blood pressure (BP) and body composition, were assessed at 0 (baseline), 2 (post-LED), 4 and 6 months (end of study). Data was analysed using linear mixed-effects models as both raw/observed data (OD) and intention-to-treat (ITT) with imputation for missing not-at-random data points. Extreme outliers were removed prior to analyses. Statistical significance was set to P[&le;]0.05. ResultsBW significantly decreased in both treatments during the 2 month LED, with rebound towards baseline by 6 months (time0-6m: P<0.001), but with no difference between Fx and Placebo groups over the full intervention (treatmentxtime0-6m, ns). FPG followed the weight loss trajectory and also decreased and rebounded in both treatment groups (time0-6m: P<0.001), but the interaction between time and treatment over 6 months was significant (ITT: treatmentxtime0-6m, P=0.05). At month 2 FPG was lower than baseline in both treatments, but at month 6 only Fx treatment remained significantly lower than baseline (P<0.05) and was also lower than Placebo (P<0.05). In some participants, FPG reverted to normal or worsened to T2D between enrolment and baseline assessment. When excluded, the sub-group with confirmed prediabetes showed similar outcomes for pattern of change in trajectory for Fx vs. Placebo treatment (ITT: treatmentxtime0-6m, P=0.04) but with no significant difference between the two treatments at study end. Systolic BP (SBP), but not diastolic BP, also changed significantly over 6 months between treatments (treatmentxtime0-6m, OD: P=0.009, ITT: P=0.01), with SBP lower in Fx compared to Placebo at all timepoints (P<0.05). However, no difference between treatments was apparent in the sub-group of participants confirmed as normotensive at baseline (treatmentxtime, ns). ConclusionsIn adults with overweight and prediabetes, Fx supplementation may enhance improvements in glycaemia and blood pressure achieved through weight loss. The findings should be interpreted with some caution, given the influence in FERDINAND of individuals with FPG and blood pressure outside prediabetic and normotensive ranges. Nevertheless, these results warrant further investigation in larger cohorts at risk of T2D to fully evaluate the contribution that whole fruit feijoa powder can make to improved metabolic health.

Background: Dietary fiber comprises a heterogeneous group of compounds with distinct physicochemical properties and biological effects. As such, functional outcomes observed for one fiber cannot be generalized to others. Some fermentable fibers, such as arabinoxylan, may exert biologically selective effects across multiple physiological domains, highlighting the need to evaluate individual ingredients for their domain-specific activity in controlled human studies. Methods: In this randomized, double-blind, parallel, 3-arm, placebo-controlled trial, healthy, overweight adults were assigned to consume one of two low doses of an arabinoxylan dietary fiber (3.5g or 5g) or placebo over the intervention period. Self-reported appetite sensations were assessed as the primary outcome using validated visual analogue scales. Secondary and exploratory endpoints included lipid parameters, gastrointestinal outcomes, mood-related measures, and gut microbiota composition and fermentation-derived metabolites. Analyses were conducted in the full analysis set and a high-compliance population to assess responses under sustained intake conditions, as per the intended dosing regimen. Results: The primary endpoint of appetite sensations did not differ between either arabinoxylan group and placebo. In contrast, evidence of microbial fermentation and selective microbiota engagement was observed. These responses occurred alongside consistent and favorable changes in lipid parameters under conditions of sustained intake, including reductions in low-density lipoprotein cholesterol and triglycerides. Additional outcomes, including gastrointestinal symptoms and mood, demonstrated domain-specific responses. Conclusion: This study demonstrates that supplementation with low doses of arabinoxylan dietary fiber elicit biologically selective, domain-specific effects across metabolic, microbial, gastrointestinal, and behavioral outcomes, particularly under conditions of sustained intake. These responses occurred independently of changes in appetite sensation, indicating that functional effects were not mediated through appetite-related pathways. Collectively, the findings highlight the ingredient's biological versatility and contextual responsiveness across physiological systems, and suggest its prebiotic potential through alignment with ISAPP's definition of a prebiotic, supporting further investigation of specific mechanistic pathways. Clinical trial registration: https://clinicaltrials.gov/study/NCT06884449, identifier: NCT06884449