Characterizing substructure via mixture modeling in large-scale genetic summary statistics.

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 groups masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted substructure limits summary data usability, especially for understudied or admixed populations. Here, we present Summix2 , a comprehensive set of methods and software based on a computationally efficient mixture model to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and identifies 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.

Zinc Supplementation Initiated Prior to or During Pregnancy Modestly Impacted Maternal Status and High Prevalence of Hypozincemia in Pregnancy and Lactation: The Women First Preconception Maternal Nutrition Trial.

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 ∼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 ∼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 µg/dL, respectively; P = 0.005). Results were not impacted by correction for inflammation or albumin concentrations. Prevalence of hypozincemia at 12-wk (<56 µg/dL) was 23% in Guatemala, 26% in India, and 65% in Pakistan; at 34 wk (<50 µg/dL), 36% in Guatemala, 48% in India, and 74% in Pakistan; and at 3-mo postpartum (<66 µg/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. This trial is registered at clinicaltrials.gov as #NCT01883193.

mini-MED: study protocol for a randomized, multi-intervention, semi-controlled feeding trial of a Mediterranean-amplified vs. habitual Western dietary pattern for the evaluation of food-specific compounds and cardiometabolic health.

BACKGROUND: 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. METHODS: 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. DISCUSSION: 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. TRIAL REGISTRATION: Registered at ClinicalTrials.gov: NCT05500976 ; Date of registration: August 15, 2022.

HLA-DPB1*13:01 associates with enhanced, and KIR2DS4*001 with diminished protection from developing severe COVID-19.

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.

Salmon Food-Specific Compounds and Their Metabolites Increase in Human Plasma and Are Associated with Cardiometabolic Health Indicators Following a Mediterranean-Style Diet Intervention.

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).

Maternal nutritional status modifies heat-associated growth restriction in women with chronic malnutrition.

Rapid changes in the global climate are deepening existing health disparities from resource scarcity and malnutrition. Rising ambient temperatures represent an imminent risk to pregnant women and infants. Both maternal malnutrition and heat stress during pregnancy contribute to poor fetal growth, the leading cause of diminished child development in low-resource settings. However, studies explicitly examining interactions between these two important environmental factors are lacking. We leveraged maternal and neonatal anthropometry data from a randomized controlled trial focused on improving preconception maternal nutrition (Women First Preconception Nutrition trial) conducted in Thatta, Pakistan, where both nutritional deficits and heat stress are prevalent. Multiple linear regression of ambient temperature and neonatal anthropometry at birth (n = 459) showed a negative association between daily maximal temperatures in the first trimester and Z-scores of birth length and head circumference. Placental mRNA-sequencing and protein analysis showed transcriptomic changes in protein translation, ribosomal proteins, and mTORC1 signaling components in term placenta exposed to excessive heat in the first trimester. Targeted metabolomic analysis indicated ambient temperature associated alterations in maternal circulation with decreases in choline concentrations. Notably, negative impacts of heat on birth length were in part mitigated in women randomized to comprehensive maternal nutritional supplementation before pregnancy suggesting potential interactions between heat stress and nutritional status of the mother. Collectively, the findings bridge critical gaps in our current understanding of how maternal nutrition may provide resilience against adverse effects of heat stress in pregnancy.

Longitudinal Reduction in Diversity of Maternal Gut Microbiota During Pregnancy Is Observed in Multiple Low-Resource Settings: Results From the Women First Trial.

Objective: To characterize the changes in gut microbiota during pregnancy and determine the effects of nutritional intervention on gut microbiota in women from sub-Saharan Africa (the Democratic Republic of the Congo, DRC), South Asia (India and Pakistan), and Central America (Guatemala). Methods: Pregnant women in the Women First (WF) Preconception Maternal Nutrition Trial were included in this analysis. Participants were randomized to receive a lipid-based micronutrient supplement either ≥3 months before pregnancy (Arm 1); started the same intervention late in the first trimester (Arm 2); or received no nutrition supplements besides those self-administered or prescribed through local health services (Arm 3). Stool and blood samples were collected during the first and third trimesters. Findings presented here include fecal 16S rRNA gene-based profiling and systemic and intestinal inflammatory biomarkers, including alpha (1)-acid glycoprotein (AGP), C-reactive protein (CRP), fecal myeloperoxidase (MPO), and calprotectin. Results: Stool samples were collected from 640 women (DRC, n = 157; India, n = 102; Guatemala, n = 276; and Pakistan, n = 105). Gut microbial community structure did not differ by intervention arm but changed significantly during pregnancy. Richness, a measure of alpha-diversity, decreased over pregnancy. Community composition (beta-diversity) also showed a significant change from first to third trimester in all four sites. Of the top 10 most abundant genera, unclassified Lachnospiraceae significantly decreased in Guatemala and unclassified Ruminococcaceae significantly decreased in Guatemala and DRC. The change in the overall community structure at the genus level was associated with a decrease in the abundances of certain genera with low heterogeneity among the four sites. Intervention arms were not significantly associated with inflammatory biomarkers at 12 or 34 weeks. AGP significantly decreased from 12 to 34 weeks of pregnancy, whereas CRP, MPO, and calprotectin did not significantly change over time. None of these biomarkers were significantly associated with the gut microbiota diversity. Conclusion: The longitudinal reduction of individual genera (both commensals and potential pathogens) and alpha-diversity among all sites were consistent and suggested that the effect of pregnancy on the maternal microbiota overrides other influencing factors, such as nutrition intervention, geographical location, diet, race, and other demographical variables.

Opportunities and challenges for the use of common controls in sequencing studies.

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

Accessible analysis of longitudinal data with linear mixed effects models.

Longitudinal studies are commonly used to examine possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe the linear mixed effects (LME) model and how to use it for longitudinal studies. We re-analyze a dataset published by Blanton et al. in 2016 that modeled growth trajectories in mice after microbiome implantation from nourished or malnourished children. We compare the fit and stability of different parameterizations of ANOVA and LME models; most models found that the nourished versus malnourished growth trajectories differed significantly. We show through simulation that the results from the two-way ANOVA and LME models are not always consistent. Incorrectly modeling correlated data can result in increased rates of false positives or false negatives, supporting the need to model correlated data correctly. We provide an interactive Shiny App to enable accessible and appropriate analysis of longitudinal data using LME models.

RAREsim: A simulation method for very rare genetic variants.

Identification of rare-variant associations is crucial to full characterization of the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirror the distribution of rare variants and haplotype structure in real data. Additionally, importing real-variant annotation enables in silico comparison of methods, such as rare-variant association tests and polygenic scoring methods, that focus on putative causal variants. Existing simulation methods are either unable to employ real-variant annotation or severely under- or overestimate the number of singletons and doubletons, thereby reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare-variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real-variant annotations. We highlight RAREsim's utility across various genetic regions, sample sizes, ancestries, and variant classes.

Genome-wide analysis of copy number variants and normal facial variation in a large cohort of Bantu Africans.

Similarity in facial characteristics between relatives suggests a strong genetic component underlies facial variation. While there have been numerous studies of the genetics of facial abnormalities and, more recently, single nucleotide polymorphism (SNP) genome-wide association studies (GWASs) of normal facial variation, little is known about the role of genetic structural variation in determining facial shape. In a sample of Bantu African children, we found that only 9% of common copy number variants (CNVs) and 10-kb CNV analysis windows are well tagged by SNPs (r2 ≥ 0.8), indicating that associations with our internally called CNVs were not captured by previous SNP-based GWASs. Here, we present a GWAS and gene set analysis of the relationship between normal facial variation and CNVs in a sample of Bantu African children. We report the top five regions, which had p values ≤ 9.35 × 10-6 and find nominal evidence of independent CNV association (p < 0.05) in three regions previously identified in SNP-based GWASs. The CNV region with strongest association (p = 1.16 × 10-6, 55 losses and seven gains) contains NFATC1, which has been linked to facial morphogenesis and Cherubism, a syndrome involving abnormal lower facial development. Genomic loss in the region is associated with smaller average lower facial depth. Importantly, new loci identified here were not identified in a SNP-based GWAS, suggesting that CNVs are likely involved in determining facial shape variation. Given the plethora of SNP-based GWASs, calling CNVs from existing data may be a relatively inexpensive way to aid in the study of complex traits.

Summix: A method for detecting and adjusting for population structure in genetic summary data.

Publicly available genetic summary data have high utility in research and the clinic, including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure, resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. Although several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies (AFs) from summary data. Using continental reference ancestry, African (AFR), non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v.2.1 exome and genome groups and subgroups, finding heterogeneous continental ancestry for several groups, including African/African American (∼84% AFR, ∼14% EUR) and American/Latinx (∼4% AFR, ∼5% EAS, ∼43% EUR, ∼46% IAM). Compared to the unadjusted gnomAD AFs, Summix's ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds, allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities.

Improved first trimester maternal iodine status with preconception supplementation: The Women First Trial.

Maternal iodine (I) status is critical in embryonic and foetal development. We examined the effect of preconception iodine supplementation on maternal iodine status and on birth outcomes. Non-pregnant women in Guatemala, India and Pakistan (n ~ 100 per arm per site) were randomized ≥ 3 months prior to conception to one of three intervention arms: a multimicronutrient-fortified lipid-based nutrient supplement containing 250-µg I per day started immediately after randomization (Arm 1), the same supplement started at ~12 weeks gestation (Arm 2) and no intervention supplement (Arm 3). Urinary I (µg/L) to creatinine (mg/dl) ratios (I/Cr) were determined at 12 weeks for Arm 1 versus Arm 2 (before supplement started) and 34 weeks for all arms. Generalized linear models were used to assess the relationship of I/Cr with arm and with newborn anthropometry. At 12 weeks gestation, adjusted mean I/Cr (µg/g) for all sites combined was significantly higher for Arm 1 versus Arm 2: (203 [95% CI: 189, 217] vs. 163 [95% CI: 152, 175], p < 0.0001). Overall adjusted prevalence of I/Cr < 150 µg/g was also lower in Arm 1 versus Arm 2: 32% (95% CI: 26%, 38%) versus 43% (95% CI: 37%, 49%) (p = 0.0052). At 34 weeks, adjusted mean I/Cr for Arm 1 (235, 95% CI: 220, 252) and Arm 2 (254, 95% CI: 238, 272) did not differ significantly but were significantly higher than Arm 3 (200, 95% CI: 184, 218) (p < 0.0001). Nominally significant positive associations were observed between I/Cr at 12 weeks and birth length and head circumference z-scores (p = 0.028 and p = 0.005, respectively). These findings support the importance of first trimester iodine status and suggest need for preconception supplementation beyond salt iodization alone.

Genome-wide copy number variations in a large cohort of bantu African children.

BACKGROUND: Copy number variations (CNVs) account for a substantial proportion of inter-individual genomic variation. However, a majority of genomic variation studies have focused on single-nucleotide variations (SNVs), with limited genome-wide analysis of CNVs in large cohorts, especially in populations that are under-represented in genetic studies including people of African descent. METHODS: We carried out a genome-wide copy number analysis in > 3400 healthy Bantu Africans from Tanzania. Signal intensity data from high density (> 2.5 million probes) genotyping arrays were used for CNV calling with three algorithms including PennCNV, DNAcopy and VanillaICE. Stringent quality metrics and filtering criteria were applied to obtain high confidence CNVs. RESULTS: We identified over 400,000 CNVs larger than 1 kilobase (kb), for an average of 120 CNVs (SE = 2.57) per individual. We detected 866 large CNVs (≥ 300 kb), some of which overlapped genomic regions previously associated with multiple congenital anomaly syndromes, including Prader-Willi/Angelman syndrome (Type1) and 22q11.2 deletion syndrome. Furthermore, several of the common CNVs seen in our cohort (≥ 5%) overlap genes previously associated with developmental disorders. CONCLUSIONS: These findings may help refine the phenotypic outcomes and penetrance of variations affecting genes and genomic regions previously implicated in diseases. Our study provides one of the largest datasets of CNVs from individuals of African ancestry, enabling improved clinical evaluation and disease association of CNVs observed in research and clinical studies in African populations.

Lipidomics-Based Comparison of Molecular Compositions of Green, Yellow, and Red Bell Peppers.

Identifying and annotating the molecular composition of individual foods will improve scientific understanding of how foods impact human health and how much variation exists in the molecular composition of foods of the same species. The complexity of this task includes distinct varieties and variations in natural occurring pigments of foods. Lipidomics, a sub-field of metabolomics, has emerged as an effective tool to help decipher the molecular composition of foods. For this proof-of-principle research, we determined the lipidomic profiles of green, yellow and red bell peppers (Capsicum annuum) using liquid chromatography mass spectrometry and a novel tool for automated annotation of compounds following database searches. Among 23 samples analyzed from 6 peppers (2 green, 1 yellow, and 3 red), over 8000 lipid compounds were detected with 315 compounds (106 annotated) found in all three colors. Assessments of relationships between these compounds and pepper color, using linear mixed effects regression and false discovery rate (<0.05) statistical adjustment, revealed 11 compounds differing by color. The compound most strongly associated with color was the carotenoid, ß-cryptoxanthin (p-value = 7.4 × 10-5; FDR adjusted p-value = 0.0080). These results support lipidomics as a viable analytical technique to identify molecular compounds that can be used for unique characterization of foods.

Different Blood Metabolomics Profiles in Infants Consuming a Meat- or Dairy-Based Complementary Diet.

BACKGROUND: Research is limited in evaluating the mechanisms responsible for infant growth in response to different protein-rich foods; Methods: Targeted and untargeted metabolomics analysis were conducted on serum samples collected from an infant controlled-feeding trial that participants consumed a meat- vs. dairy-based complementary diet from 5 to 12 months of age, and followed up at 24 months. RESULTS: Isoleucine, valine, phenylalanine increased and threonine decreased over time among all participants; Although none of the individual essential amino acids had a significant impact on changes in growth Z scores from 5 to 12 months, principal component heavily weighted by BCAAs (leucine, isoleucine, valine) and phenylalanine had a positive association with changes in length-for-age Z score from 5 to 12 months. Concentrations of acylcarnitine-C4, acylcarnitine-C5 and acylcarnitine-C5:1 significantly increased over time with the dietary intervention, but none of the acylcarnitines were associated with infant growth Z scores. Quantitative trimethylamine N-oxide increased in the meat group from 5 to 12 months; Conclusions: Our findings suggest that increasing total protein intake by providing protein-rich complementary foods was associated with increased concentrations of certain essential amino acids and short-chain acyl-carnitines. The sources of protein-rich foods (e.g., meat vs. dairy) did not appear to differentially impact serum metabolites, and comprehensive mechanistic investigations are needed to identify other contributors or mediators of the diet-induced infant growth trajectories.

Effects of Complementary Feeding With Different Protein-Rich Foods on Infant Growth and Gut Health: Study Protocol.

Background: An urgent need exists for evidence-based dietary guidance early in life, particularly regarding protein intake. However, a significant knowledge gap exists in the effects of protein-rich foods on growth and development during early complementary feeding. Methods: This is a randomized controlled trial of infant growth and gut health (primary outcomes). We directly compare the effects of dietary patterns with common protein-rich foods (meat, dairy, plant) on infant growth trajectories and gut microbiota development (monthly assessments) during early complementary feeding in both breast- and formula-fed infants. Five-month-old infants (up to n = 300) are randomized to a meat-, dairy-, plant-based complementary diet or a reference group (standard of care) from 5 to 12 months of age, with a 24-month follow-up assessment. Infants are matched for sex, mode of delivery and mode of feeding using stratified randomization. Growth assessments include length, weight, head circumference and body composition. Gut microbiota assessments include both 16S rRNA profiling and metagenomics sequencing. The primary analyses will evaluate the longitudinal effects of the different diets on both anthropometric measures and gut microbiota. The secondary analysis will evaluate the potential associations between gut microbiota and infant growth. Discussion: Findings are expected to have significant scientific and health implications for identifying beneficial gut microbial changes and dietary patterns and for informing dietary interventions to prevent the risk of overweight and later obesity, and promote optimal health. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT05012930.

Exome Sequencing Identifies Genes and Gene Sets Contributing to Severe Childhood Obesity, Linking PHIP Variants to Repressed POMC Transcription.

Obesity is genetically heterogeneous with monogenic and complex polygenic forms. Using exome and targeted sequencing in 2,737 severely obese cases and 6,704 controls, we identified three genes (PHIP, DGKI, and ZMYM4) with an excess burden of very rare predicted deleterious variants in cases. In cells, we found that nuclear PHIP (pleckstrin homology domain interacting protein) directly enhances transcription of pro-opiomelanocortin (POMC), a neuropeptide that suppresses appetite. Obesity-associated PHIP variants repressed POMC transcription. Our demonstration that PHIP is involved in human energy homeostasis through transcriptional regulation of central melanocortin signaling has potential diagnostic and therapeutic implications for patients with obesity and developmental delay. Additionally, we found an excess burden of predicted deleterious variants involving genes nearest to loci from obesity genome-wide association studies. Genes and gene sets influencing obesity with variable penetrance provide compelling evidence for a continuum of causality in the genetic architecture of obesity, and explain some of its missing heritability.

Longitudinal Changes of One-Carbon Metabolites and Amino Acid Concentrations during Pregnancy in the Women First Maternal Nutrition Trial.

BACKGROUND: Maternal dietary restriction and supplementation of one-carbon (1C) metabolites can impact offspring growth and DNA methylation. However, longitudinal research of 1C metabolite and amino acid (AA) concentrations over the reproductive cycle of human pregnancy is limited. OBJECTIVE: To investigate longitudinal 1C metabolite and AA concentrations prior to and during pregnancy and the effects of a small-quantity lipid-based nutrition supplement (LNS) containing >20 micronutrients and prepregnancy BMI (ppBMI). METHODS: This study was an ancillary study of the Women First Trial (NCT01883193, clinicaltrials.gov) focused on a subset of Guatemalan women (n = 134), 49% of whom entered pregnancy with a BMI ≥25 kg/m2. Ninety-five women received LNS during pregnancy (+LNS group), while the remainder did not (-LNS group). A subset of women from the Pakistan study site (n = 179) were used as a replication cohort, 124 of whom received LNS. Maternal blood was longitudinally collected on dried blood spot (DBS) cards at preconception, and at 12 and 34 wk gestation. A targeted metabolomics assay was performed on DBS samples at each time point using LC-MS/MS. Longitudinal analyses were performed using linear mixed modeling to investigate the influence of time, LNS, and ppBMI. RESULTS: Concentrations of 23 of 27 metabolites, including betaine, choline, and serine, changed from preconception across gestation after application of a Bonferroni multiple testing correction (P < 0.00185). Sixteen of those metabolites showed similar changes in the replication cohort. Asymmetric and symmetric dimethylarginine were decreased by LNS in the participants from Guatemala. Only tyrosine was statistically associated with ppBMI at both study sites. CONCLUSIONS: Time influenced most 1C metabolite and AA concentrations with a high degree of similarity between the 2 diverse study populations. These patterns were not significantly altered by LNS consumption or ppBMI. Future investigations will focus on 1C metabolite changes associated with infant outcomes, including DNA methylation. This trial was registered at clinicaltrials.gov as NCT01883193.

Nutrimetabolomics reveals food-specific compounds in urine of adults consuming a DASH-style diet.

Although health benefits of the Dietary Approaches to Stop Hypertension (DASH) diet are established, it is not understood which food compounds result in these benefits. We used metabolomics to identify unique compounds from individual foods of a DASH-style diet and determined if these Food-Specific Compounds (FSC) are detectable in urine from participants in a DASH-style dietary study. We also examined relationships between urinary compounds and blood pressure (BP). Nineteen subjects were randomized into 6-week controlled DASH-style diet interventions. Mass spectrometry-based metabolomics was performed on 24-hour urine samples collected before and after each intervention and on 12 representative DASH-style foods. Between 66-969 compounds were catalogued as FSC; for example, 4-hydroxydiphenylamine was found to be unique to apple. Overall, 13-190 of these FSC were detected in urine, demonstrating that these unmetabolized food compounds can be discovered in urine using metabolomics. Although linear mixed effects models showed no FSC from the 12 profiled foods were significantly associated with BP, other endogenous and food-related compounds were associated with BP (N = 16) and changes in BP over time (N = 6). Overall, this proof of principle study demonstrates that metabolomics can be used to catalog FSC, which can be detected in participant urine following a dietary intervention.