BACKGROUND:Minimal research has assessed the adequacy of baby-led weaning (BLW) as a complementary feeding (CF) approach in infant nutrient intake and growth. OBJECTIVE:This study assesses growth between infants following BLW and conventional weaning (CW) and compares at-home energy, macronutrient, and micronutrient intake by weaning approach and relative to the Dietary Reference Intakes. DESIGN:This is a secondary analysis of 5- to 12-month-old full-term infants from Denver, CO, recruited for an ongoing longitudinal randomized controlled trial. PARTICIPANTS/SETTING:One hundred forty-four participants with complete dietary intake and anthropometric data from August 2021 through August 2024 from the Maternal and Infant Nutrition Trial were included in this analysis. Infants were classified as following BLW if puréed foods accounted for ≤10% of total energy intake (n = 52); all other infants were classified as CW (n = 92). MAIN OUTCOME MEASURES:Dietary intakes of energy, macronutrients, and selected micronutrients at ages 5, 9, and 12 months using weighed 3-day diet records and anthropometrics, including odds of rapid weight gain. STATISTICAL ANALYSIS PERFORMED:The χ2 test was used to compare demographic data between BLW and CW groups. Linear regression and independent t tests were utilized to compare unadjusted and adjusted dietary intake means and growth z scores between groups. Logistic regression was used to calculate odds ratios for rapid weight gain. RESULTS:No significant differences in nutrient intakes were observed at baseline (infant age 5 months). Total energy intake was not different between groups at any age. At age 9 months, BLW infants had higher reported dietary sodium (437 vs 294 mg; P < .001), a higher percent energy from fat (47% vs 43%; P < .001), and a lower percent energy from carbohydrate (43% vs 50%; P < .001) than CW infants, with no significant differences at age 12 months for any nutrient. Both groups had reported dietary sodium higher than the Adequate Intake at age 12 months. No significant differences in rapid weight gain or growth z scores were observed. Results were consistent after adjusting for mode of feeding, infant sex, and study arm. CONCLUSIONS:The findings indicate that BLW and CW are associated with different macronutrient and micronutrient intakes at infant age 9 months but not age 12 months. BLW and CW may promote similar growth outcomes during complementary feeding.

Thyroid diseases are common and highly heritable. We performed a meta-analysis of genome-wide association studies from 19 biobanks for five thyroid diseases: thyroid cancer (ThC), benign nodular goiter, Graves’ disease, lymphocytic thyroiditis and primary hypothyroidism. We analyzed genetic association data from ~2.9 million genomes and identified 313 known and 570 new independent loci linked to thyroid diseases. We discovered genetic correlations between ThC, benign nodular goiter and autoimmune thyroid diseases ( rg = 0.16–0.97). Telomere maintenance genes contributed to benign and malignant thyroid nodular disease risk, whereas cell cycle, DNA repair and damage response genes were associated with ThC. We propose a paradigm that explains genetic predisposition to benign and malignant thyroid nodules. We found polygenic risk score associations with ThC risk of structural disease recurrence, tumor size, multifocality, lymph node metastases and extranodal extension. Polygenic risk scores identified individuals with aggressive ThC in a biobank, creating an opportunity for genetically informed population screening.

Genetic summary statistics can be used in a variety of analyses, such as causal inference, genetic correlation, and risk scores, to provide insights into the genetic architecture of conditions and traits. However, complete statistics are often not reported, limiting the utility of these data. Indeed, many post hoc analyses of diseases require case and control allele frequencies (AFs), which are not always published. Here, we present methods and software to derive case and control AFs from genome-wide association study (GWAS) summary statistics using the odds ratio, case and control sample sizes, and either the total (case and control aggregated) AF or the standard error (SE). In simulations and real data, derivations of case and control AFs using total AFs are highly accurate, whereas using SE underestimates AFs when covariates were included in the GWAS. While estimating case and control AFs using the total AF is preferred due to its high accuracy, the SE is more commonly available. Thus, we developed a bias adjustment using gnomAD AFs as a proxy for true AFs, reducing bias when using the SE. The methods and software provided here expand the utility of publicly available genetic summary statistics and promote the reusability of genomic data. The R package Case-Control Allele Frequency Estimation (CCAFE) is freely available on Bioconductor and GitHub.

Background:IgA is the dominant antibody in the human gut and a key regulator of host-microbe interactions. Infants begin to produce IgA at around 6 months old and receive large quantities of IgA via human milk, but technical limitations have prevented species-level characterization of IgA binding in early life. This has left basic knowledge gaps about which species are targeted by IgA in infancy, and how modifiable lifestyle factors like breastfeeding and complementary feeding impact IgA targeting. Results:Here we adapt Metagenomic Immunoglobulin Sequencing (MIg-Seq) for low-biomass infant fecal samples and apply this optimized protocol to 32 longitudinal samples from 16 infants enrolled in the MINT trial, a four-arm randomized controlled trial comparing meat-based, dairy-based, plant-based, and reference complementary feeding patterns, with fecal sampling at 6 and 12 months (pre and post intervention). Infant IgA targeting mirrors adults at the phylum level, with both age groups showing significantly higher IgA targeting of Pseudomonadota and lower targeting of Bacteroidota relative to other phyla. During the substantial microbiome compositional shifts noted between 6 and 12 months, IgA targeting is significantly more stable than the microbiome itself. Among persistent colonizers, IgA targeting strengthens significantly from 6 to 12 months, with the most pronounced effect observed for Bifidobacterium , a finding robust across all dietary arms and feeding modes. The feeding arm to which infants were enrolled was not significantly associated with IgA binding, but several nutrient-specific associations were discovered. Animal-derived nutrients, particularly cholesterol, are strongly positively correlated with IgA targeting of Bifidobacterium longum , while plant-derived carotenoids are positively associated with IgA targeting of Flavonifractor plautii and Ruminococcus gnavus . Conclusions:This study introduces an experimental and computational framework for species-level IgA profiling in the infant gut. The progressive strengthening of IgA targeting of Bifidobacterium and other beneficial persistent colonizers suggests a role for IgA in reinforcing beneficial microbes during infancy. The nutrient-specific dietary effects on IgA targeting reveal the immunological consequences of the complementary feeding period, and highlight a contrast between animal-versus plant-based diets. Together, these findings point to early nutritional interventions and IgA-based therapeutics as promising tools for promoting healthy immune-microbiome development.

Despite considerable advances in identifying risk factors for obesity, gaps remain in our understanding about its etiology. Genetic variants explain only a small portion of variation in obesity-related traits such as body mass index (BMI). Epigenetic regulation, which controls gene expression and is influenced by environmental and genetic factors, may account for additional variability in BMI. Epigenetic studies of BMI have largely been conducted in European ancestry populations, despite the disproportionate burden of obesity in African Americans (AAs). We conducted a sex-stratified BMI epigenome-wide association study meta-analysis in AA participants from the Jackson Heart Study (n = 1,604) and the Multi-Ethnic Study of Atherosclerosis (n = 179) with Illumina EPIC (850,000) array data. Linear regression models with methylation as the outcome and continuous BMI as the predictor were stratified by study and sex and meta-analyzed. We identified 208 methylation sites (CpGs, p < 8.72 × 10-8) significantly associated with BMI; 151 had not been previously reported in the literature. Replication was performed in a separate sample of AA participants with 450,000 array data, which lacks many CpGs present in the 850,000 array. Replication testing was possible for only 29 of the 151 CpGs; 19 were statistically significant (p < 1.72 × 10-3). Sex-specific results showed 4 female-only and 3 male-only BMI-CpGs not identified in the sex-combined results. Differentially methylated region (DMR) analysis resulted in 66 DMRs, including several regions near genes previously implicated for obesity (e.g., SOCS3, TGFB1). Further analyses showed enrichment of genes and traits related to the immune system and inflammation-related pathways (e.g., the IL-6/JAK/STAT pathway).

Background:While studies have explored differences in gut microbiome development for infant liquid diets (breastmilk, formula), little is known about the impact of complementary foods on infant gut microbiome development. Here, we investigated how different protein-rich foods (i.e., meat vs. dairy) affect fecal metagenomics and metabolomics during early complementary feeding from 5-12 months in U.S. formula-fed infants from a randomized controlled feeding trial. Results:We used a novel network representation learning approach to model the time-dependent, complex interactions between microbiome features, metabolite compounds, and diet. We then used the embedded space to detect features associated with age and diet type and found the meat diet group was enriched with microbial genes encoding amino acid, nucleic acid, and carbohydrate metabolism. Compared to a more traditional differential abundance analysis, which analyzes features independently and found no significant diet associations, network node embedding represents the infant samples, microbiome features, and metabolites in a single transformed space revealing otherwise undetected associations between infant diet and the gut microbiome. Conclusions:Our findings generate new hypotheses regarding the interplay between complementary feeding practices, microbial-metabolic interactions, and infant physiological outcomes. This work highlights the impact of complementary foods on infant gut microbiome development and the potential of using network representation learning to integrate multi-omic data, allowing for greater insight into complex diet, microbial, and metabolite interactions.

Astaxanthin, a marine carotenoid with antioxidant and anti-inflammatory properties, exists in various isomers in salmon. Despite salmon being a key dietary source of astaxanthin in American diets, the isomer contents across salmon types, processing methods, and human plasma post-consumption remains underexplored. Using mass spectrometry, we analyzed astaxanthin isomers, EPA, and DHA in wild and farmed salmon, processed salmon products, and human plasma following a feeding study. Results showed higher levels of 3S,3 ' S-all-trans-astax-anthin, EPA, and DHA in wild versus farmed salmon. Cooking did not affect 3S,3 ' S-all-trans-astaxanthin levels, but they were lower in processed forms like canned salmon. Plasma concentrations of 3S,3 ' S-all-trans-astaxanthin increased significantly in humans after consuming a Mediterranean diet with two servings of farmed salmon per week for five weeks. Notably, 13-cis-astaxanthin, but not 9-cis-astaxanthin, was detected in plasma. These findings demonstrate that food processing and farming practices affect astaxanthin levels, and that plasma astaxanthin concentrations reflect dietary salmon intake.

Background: Data regarding effects of small-quantity-lipid-based nutrient supplements (SQ-LNS) on maternal serum zinc concentrations (SZC) in pregnancy and lactation are limited. Objectives: The objectives of this study were to evaluate the effect of preconception compared with prenatal zinc supplementation (compared with control) on maternal SZC and hypozincemia during pregnancy and early lactation in women in low-resource settings, and assess associations with birth anthropometry. Methods: From similar to 100 women/arm at each of 3 sites (Guatemala, India, and Pakistan) of the Women First Preconception Maternal Nutrition trial, we compared SZC at 12- and 34-wk gestation (n = 651 and 838, respectively) and 3-mo postpartum (n = 742) in women randomly assigned to daily SQ-LNS containing 15 mg zinc from >= 3 mo before conception (preconception, arm 1), from similar to 12 wk gestation through delivery (early pregnancy, arm 2) or not at all (control, arm 3). Birth anthropometry was examined for newborns with ultrasound-determined gestational age. Statistical analyses were performed separately for each time point. Results: At 12-wk gestation and 3-mo postpartum, no statistical differences in mean SZC were observed among arms. At 34-wk, mean SZC for arms 1 and 2 were significantly higher than for arm 3 (50.3, 50.8, 47.8 mu g/dL, respectively; P = 0.005). Results were not impacted by correction for inflammation or albumin concentrations. Prevalence of hypozincemia at 12-wk (<56 mu g/dL) was 23% in Guatemala, 26% in India, and 65% in Pakistan; at 34 wk (<50 mu g/dL), 36% in Guatemala, 48% in India, and 74% in Pakistan; and at 3-mo postpartum (<66 mu g/dL) 79% in Guatemala, 91% in India, and 92% in Pakistan. Maternal hypozincemia at 34-wk was associated with lower birth length-for-age Z-scores (all sites P = 0.013, Pakistan P = 0.008) and weight-for-age Z-scores (all sites P = 0.017, Pakistan P = 0.022). Conclusions: Despite daily zinc supplementation for >= 7 mo, high rates of maternal hypozincemia were observed. The association of hypozincemia with impaired fetal growth suggests widespread zinc deficiency in these settings.

Diet is among the most influential lifestyle factors impacting chronic disease risk. Nutrimetabolomics, the application of metabolomics to nutrition research, allows for the detection of food-specific compounds (FSCs) that can be used to connect dietary patterns, such as a Mediterranean-style (MED) diet, to health. This validation study is based upon analyses from a controlled feeding MED intervention, where our team identified FSCs from eight foods that can be detected in biospecimens after consumption and may therefore serve as food intake biomarkers. Individuals with overweight/obesity who do not habitually consume a MED dietary pattern will complete a 16-week randomized, multi-intervention, semi-controlled feeding study of isocaloric dietary interventions: (1) MED-amplified dietary pattern, containing 500 kcal/day from eight MED target foods: avocado, basil, cherry, chickpea, oat, red bell pepper, walnut, and a protein source (alternating between salmon or unprocessed, lean beef), and (2) habitual/Western dietary pattern, containing 500 kcal/day from six non-MED target foods: cheesecake, chocolate frozen yogurt, refined grain bread, sour cream, white potato, and unprocessed, lean beef. After a 2-week washout, participants complete four, 4-week intervention periods, with biospecimen sampling and outcome assessments at baseline and at intervention weeks 4, 8, 12, and 16. The primary outcome is change in the relative abundance of FSCs from the eight MED target foods in participant biospecimens from baseline to the end of each intervention period. Secondary outcomes include mean change in cardiometabolic health indicators, inflammatory markers, and adipokines. Exploratory outcomes include change in diversity and community composition of the gut microbiota. Our stepwise strategy, beginning with identification of FSCs in whole diets and biospecimens, followed by relating these to health indicators will lead to improved methodology for assessment of dietary patterns and a better understanding of the relationship between food and health. This study will serve as a first step toward validating candidate food intake biomarkers and allow for assessment of relationships with cardiometabolic health. The identification of food intake biomarkers is critical to future research and has implications spanning health promotion and disease prevention for many chronic conditions. Registered at ClinicalTrials.gov: NCT05500976 ; Date of registration: August 15, 2022.

While studies have explored differences in gut microbiome development regarding infant liquid diets (breastmilk, formula), surprisingly little is known about the impact of complementary foods on the infant gut microbiome. Indeed, current dietary recommendations for infants and toddlers are not formulated with knowledge of how the developing gut microbiome metabolizes complementary foods. Here, we investigated how different protein-rich foods (i.e., meat vs. dairy) affect fecal metagenomics and metabolomics during early complementary feeding from 5-12 months in formula-fed infants. We used a network node embedding to model the time-dependent, complex interactions between microbiome features, metabolomic compound features, and diet. We then used the embedded space to detect features associated with baseline or endpoint and meat or dairy diet- finding enriched networks of microbiome and metabolomic features, and compared the results to those found using a more traditional differential abundance analysis. ### Competing Interest Statement The authors have declared no competing interest.

Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations. There is a need for methods to enable the harmonization of summary data where the underlying substructure is matched between datasets. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to enable the harmonization of genetic summary data by estimating and adjusting for substructure. In extensive simulations and application to public data, we show that Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and scans for potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse, publicly available summary data, resulting in improved and more equitable research.

Motivation: The quantity of statistical tools designed for omics data analysis has grown rapidly with the ability to collect large sets of human health data, particularly longitudinal data sets. Most tools are assessed for performance using simulated datasets constructed to mimic a handful of relevant characteristics from real world data sets. Consequently, the simulated data sets, and their respective simulation frameworks, are too narrow in scope to qualify as a standard for assessment in longitudinal omics analyses. Results: Here we present the flexible and accessible simulation framework and software package called SimMiL (Simulating Microbiome Longitudinal data) capturing three general components of longitudinal microbiome data: (i) absence/presence of microbes, (ii) individual microbe abundance, and (iii) microbiome community composition over time. The framework is assessed by replicating the Type I error and Power analyses of a broad range of statistical tools (MirKAT, repeated measures permANOVA, and a modified kernel association test). Software Avaliability: The simulation framework is at https://github.com/nweaver111/SimMiL ### Competing Interest Statement The authors have declared no competing interest.

Extreme polymorphism of HLA and killer-cell immunoglobulin-like receptors (KIR) differentiates immune responses across individuals. Additional to T cell receptor interactions, subsets of HLA class I act as ligands for inhibitory and activating KIR, allowing natural killer (NK) cells to detect and kill infected cells. We investigated the impact of HLA and KIR polymorphism on the severity of COVID-19. High resolution HLA class I and II and KIR genotypes were determined from 403 non-hospitalized and 1575 hospitalized SARS-CoV-2 infected patients from Italy collected in 2020. We observed that possession of the activating KIR2DS4*001 allotype is associated with severe disease, requiring hospitalization (OR = 1.48, 95% CI 1.20-1.85, pc  = 0.017), and this effect is greater in individuals homozygous for KIR2DS4*001 (OR = 3.74, 95% CI 1.75-9.29, pc  = 0.003). We also observed the HLA class II allotype, HLA-DPB1*13:01 protects SARS-CoV-2 infected patients from severe disease (OR = 0.49, 95% CI 0.33-0.74, pc  = 0.019). These association analyses were replicated using logistic regression with sex and age as covariates. Autoantibodies against IFN-α associated with COVID-19 severity were detected in 26% of 156 hospitalized patients tested. HLA-C*08:02 was more frequent in patients with IFN-α autoantibodies than those without, and KIR3DL1*01502 was only present in patients lacking IFN-α antibodies. These findings suggest that KIR and HLA polymorphism is integral in determining the clinical outcome following SARS-CoV-2 infection, by influencing the course both of innate and adaptive immunity.

BACKGROUND:Nutrimetabolomics allows for the comprehensive analysis of foods and human biospecimens to identify biomarkers of intake and begin to probe their associations with health. Salmon contains hundreds of compounds that may provide cardiometabolic benefits. OBJECTIVES:We used untargeted metabolomics to identify salmon food-specific compounds (FSCs) and their predicted metabolites that were found in plasma after a salmon-containing Mediterranean-style (MED) diet intervention. Associations between changes in salmon FSCs and changes in cardiometabolic health indicators (CHIs) were also explored. METHODS:For this secondary analysis of a randomized, crossover, controlled feeding trial, 41 participants consumed MED diets with 2 servings of salmon per week for 2 5-wk periods. CHIs were assessed, and fasting plasma was collected pre- and postintervention. Plasma, salmon, and 99 MED foods were analyzed using liquid chromatography-mass spectrometry-based metabolomics. Compounds were characterized as salmon FSCs if detected in all salmon replicates but none of the other foods. Metabolites of salmon FSCs were predicted using machine learning. For salmon FSCs and metabolites found in plasma, linear mixed-effect models were used to assess change from pre- to postintervention and associations with changes in CHIs. RESULTS:Relative to the other 99 MED foods, there were 508 salmon FSCs with 237 unique metabolites. A total of 143 salmon FSCs and 106 metabolites were detected in plasma. Forty-eight salmon FSCs and 30 metabolites increased after the intervention (false discovery rate <0.05). Increases in 2 annotated salmon FSCs and 2 metabolites were associated with improvements in CHIs, including total cholesterol, low-density lipoprotein cholesterol, triglycerides, and apolipoprotein B. CONCLUSIONS:A data-driven nutrimetabolomics strategy identified salmon FSCs and their predicted metabolites that were detectable in plasma and changed after consumption of a salmon-containing MED diet. Findings support this approach for the discovery of compounds in foods that may serve, upon further validation, as biomarkers or act as bioactive components influential to health. The trials supporting this work were registered at NCT02573129 (Mediterranean-style diet intervention) and NCT05500976 (ongoing clinical trial).