Childhood obesity is rising globally. Yet, few studies have examined the microbiome and proteome in early childhood in relation to this outcome, and most are cross-sectional by design. Early-life factors in the ABIS birth cohort (n = 16,683) were associated with obesity up to age 26 (mean follow-up 25.3 years, range 23.7-26.5 years): psychosocial stressors, smoking, infections, and diet in the first year. We assessed biomarkers, including cord blood metabolome (n = 290) and proteome (n = 358), by liquid chromatography, mass spectrometry, and Olink. Gut microbial composition at age one (n = 1,743) was assessed using stool samples and 16S rRNA sequencing. In this prospective longitudinal cohort study, significant differences were found in infants with future obesity, including elevated angiopoietin-like 4 (ANGPTL4), follistatin, and hepatocyte growth factor (independently of maternal weight) and reduced isocaproic acid, tryptophan, and oleic acid, with prenatal mediation. Akkermansia, asaccharolytic bacteria (Phascolarctobacterium and Senegalimassiliensia), and equol-producers (Adlercreutzia and Slackia) were depleted. Machine learning models selecting 40 most predictive features showed long-term prediction from birth proteomics and bacterial taxa at age one (area under the curve [AUC] = 0.83 ± .05, n = 1,877) and additional metrics, for example, parental and child body mass index in the first 8 years (AUC = 0.89 ± .02, n = 1,877), suggesting durable biological encoding. Proteomic markers across folds included fibroblast growth factor 19, ANGPTL4, sulfotransferase family 2A member 1, and interleukin 20. These findings suggest clinically relevant biomarkers indicating early-life regulation of bile acid metabolism, lipid storage vs. oxidation, and immune-metabolic signaling and pathways to prospectively prevent childhood- and adult-onset obesity across a 26-year predictive gap.ImportanceUnderstanding the origins of obesity is critical for developing preventive strategies, and early life represents a particularly sensitive window. This study leverages a large, general-population cohort with prospectively collected data, including parental body mass index (BMI), cord blood proteomics, and the gut microbiome at age one, linked to obesity outcomes over 26 years. Using integrated machine learning models, we show that in addition to parental BMI, specific proteomic and microbial markers present in infancy can predict long-term obesity risk, highlighting the role of early metabolic programming. Several key markers point to bile acid signaling as a mechanism connecting early microbiome development with fat accumulation and insulin regulation. By identifying these early-life predictors long before obesity manifests, these results provide new insights into intergenerational risk and suggest measurable targets for preventing obesity and related metabolic disorders from the earliest stages of life.

Historically, glycomics has lagged behind other omics fields, owing to analytical challenges. However, recent technological advances are rapidly transforming the field, enabling a growing number of large-scale studies to harness the wealth of information encoded by glycans. In this Review, we provide an overview of the current state of large-scale glycomics and its role in understanding the functional importance of glycans. We discuss the main challenges in high-throughput glycoanalytical methods and highlight recent progress, with a particular emphasis on standardization and reproducibility. Special attention is given to the necessity of large, well-designed and ideally longitudinal and multicohort studies that use rigorous statistical approaches to uncover robust biological associations. Finally, we conclude with selected applications and perspectives, emphasizing the potential of large-scale glycomics to drive biomarker discovery and enhance the understanding of glycan-mediated biology.

Naïve T cell output from the thymus varies across the human lifespan and is a key determinant of health, differing between individuals by age, sex, and genetics. How thymic output is dynamically regulated early in life in response to initial microbial colonization remains unclear. We report longitudinal thymic output dynamics, measured as T cell receptor excision circles (TRECs), in 136 newborns from Stockholm, Sweden. Thymic output increases after birth following initial microbial colonization, peaking at 3-4 mo. Peak height correlates with plasma levels of RANKL and lymphotoxin-α and with a common genetic variant in the TCRD locus previously linked to adult thymopoiesis. B cell lymphopoiesis measured by KRECs reveals divergent dynamics between B and T cell branches of the adaptive immune system in early life. Findings are corroborated by thymic tissue analyses, in which local RANKL secretion correlates with medullary, but not cortical, epithelial cell numbers. These results illuminate the establishment of healthy immune-microbe interactions in early human life.

Microbial contamination in food processing remains a persistent and complex challenge. Understanding the sources, contributing factors, and control measures is essential for effective mitigation. In this study we employed a combination of metagenomic sequencing, targeted culturomics, and whole-genome sequencing of key isolates to gain a comprehensive view of bacterial dynamics and functional capabilities throughout a working shift in a beef slaughter and cutting facility. This allowed us to identify which bacteria are i) most prevalent in the clean facility before the start of the work, ii) able to establish themselves over time, and iii) detectable in the final product. We further generated a functional profile of the microbial community within the facility, with a particular focus on antimicrobial resistance and biofilm formation genes, and the presence of specific pathogens and spoilage organisms. Both culture-based and sequencing data showed that Psychrobacter and Pseudomonas strains present in the final product were also detected on the membrane skinner, a machine used to remove all the excess tissues from meat, and in the drains even after cleaning. We found a high number of genes involved in biofilm formation in Psychrobacter immobilis, a characteristic that may explain their biofilm capacity and the survival of this species during the cleaning process and persistence throughout the facility. Taken together, our findings suggest potential sources of contamination and highlight the advantages of integrating culture-dependent methods with high-throughput sequencing technologies to enhance microbial monitoring and control strategies in food production environments.

Emerging evidence suggests a bidirectional relationship between sleep and the gut microbiome. In this study, we explore the associations of sleep characteristics with lifestyle factors and gut microbiome composition in 6941 participants from the Lifelines Dutch Microbiome Project. We show that lower alpha diversity is associated with poorer sleep quality, later chronotype, and greater social jet lag, while beta diversity is linked to both sleep quality and social jet lag. Of the 137 bacterial species associated with sleep, 35.6% are validated in an independent cohort. Mediation analyses indicate that, while changes in species abundance are largely a consequence of sleep behavior, certain species may mediate diet's influence on sleep. For example, we find that Clostridia species UC5_1_1E11 and SGB14844 mediate the effect of coffee intake on social jet lag. These findings highlight the intricate relationship between diet, the gut microbiome, and sleep, suggesting the potential for microbiome-targeted interventions to improve sleep health.

Type 1 diabetes (T1D) is increasing globally, yet the earliest biological determinants remain poorly defined, particularly in general population studies. We studied the Swedish population-based ABIS birth cohort (n = 16,683) to identify early-life risk factors. Olink proteomic analysis (n = 286 controls, n = 146 cases) of inflammatory signals at birth shows differential abundance years before diagnosis (mean age 12.6 years), with proteins enriched for neutrophil migration, cytotoxicity, extracellular matrix remodeling, and immune regulation. Several markers remain significant in spite of prenatal and perinatal factors including family history of diabetes, and are associated with differences in compounds like stearic acid, lysine, glutamine, and persistent, environmental toxicants perfluorodecylethanoic acid and perfluorooctane sulfonate (PFOS). Using machine learning, we identify a protein subset that predicts T1D with high accuracy (AUC = 0.89 ± 0.02), independently of HLA genetic risk. These findings suggest that innate and tissue-remodeling pathways are perturbed at birth, possibly reflecting early β-cell vulnerability. Identifying these disruptions at birth with a non-invasive method opens a window for prevention, protecting β-cells before the inflammatory attack on islets begins.

Early-life microbial exposures are essential for optimal development of human physiology. Yet, understanding of the human microbiome during pregnancy and childhood is still far from being complete. To identify knowledge gaps and establish research priorities, a multidisciplinary expert panel used the Delphi method for consensus development and conducted a literature search on early-life microbiome determinants. Responses from 55 researchers from an online survey were analyzed alongside keyword frequency from 20 501 publications. This approach enabled us to categorize existing evidence and highlight areas requiring investigation. While the main routes for mother-to-child bacterial transmission and their contributions to the newborn microbiome have been studied, many gaps remain. Priority areas include non-bacterial microbes, ecological principles of colonization, environmental and social influences, body sites beyond the gut, and factors affecting the maternal microbiome and its effects on the child's microbiome. Significance of factors such as hygiene habits, non-antibiotic medications, and pollution remains to be uncovered. Knowledge is also limited on postnatal microbial sharing via household contacts and shared environments (e.g. family members, peers) and the contribution of these pathways to microbiome assembly. We hope this report will guide and inspire future research into the early-life microbiome as a modifiable factor in reducing disease risk.

Emerging studies reveal that gut microbes can conjugate diverse amino acids to bile acids, known as microbially conjugated bile acids. However, their regulation and health effects remain unclear. Here, we analyzed early-life microbially conjugated bile acid patterns and their link to islet autoimmunity. We quantified 110 microbial bile acids in 303 stool samples collected longitudinally (3-36 months) from children who developed one or more islet autoantibodies and controls who remained autoantibody-negative. We identified distinct age-dependent trajectories of these bile acid amidates and correlated them with gut microbiome composition. We found that altered levels of ursodeoxycholic and deoxycholic acid conjugates were linked to islet autoimmunity as well as modulated monocyte activation in response to immunostimulatory lipopolysaccharide and Th17/Treg cell balance. These findings suggest that microbially conjugated bile acids influence immune development and type 1 diabetes risk.

Horizontal gene transfer (HGT) is a major driver of bacterial evolution, but its role in shaping the human gut microbiome over time remains poorly understood. Here, we present a longitudinal metagenomic analysis of 676 fecal samples from 338 individuals in the Lifelines-DEEP study collected ~4 years apart, using a newly developed workflow to detect recent HGT events from metagenome-assembled genomes. We identified 5,644 high-confidence HGT events occurring within the past ~10,000 years across 116 gut bacterial species. We find that species pairs with an HGT relationship were significantly more likely to maintain stable co-abundance relationships over the 4-year period, suggesting that gene exchange contributes to community stability. Notably, HGT and strain replacement act together to disseminate mobile genes in the population. Furthermore, our observation that an individual's mobile gene pool remains highly personalized and stable over time indicates that host lifestyles drive specific gene transfer. For example, proton pump inhibitor usage is linked to increased transfer of multidrug transporter genes. Our findings demonstrate, at the individual gut microbiome level, that HGT is both an integral and stabilizing force in the human gut ecosystem and an important mechanism for disseminating adaptive functions, underscoring HGT potential for tracking host lifestyle.