Peer-reviewed article
Early-life proteomic and microbiome features signal obesity risk across 26 years of follow-up.
Angelica P Ahrens, Raquel Dias, Tuulia Hyötyläinen, Pär Anderson White, Matej Orešič et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study followed 16,683 children from birth to age 26 to identify early-life biological markers that predict obesity risk. Researchers measured proteins in cord blood and gut bacteria composition at age one, then tracked which children developed obesity. They found that certain proteins and bacterial species present in infancy were associated with later obesity, suggesting that children's metabolism and microbiome are already programmed early in life in ways that influence weight gain decades later.
Key points
- Specific proteins elevated at birth (ANGPTL4, follistatin, hepatocyte growth factor) and reduced metabolites (tryptophan, oleic acid) were associated with obesity up to age 26
- Children who later developed obesity had depleted beneficial gut bacteria species like Akkermansia at age one
- Machine learning models using birth proteins and age-one bacteria could predict obesity with 83% accuracy, improving to 89% when including parental and childhood BMI
- Key biological pathways involved bile acid metabolism, lipid storage, and immune-metabolic signaling
- Results suggest early-life microbiome and metabolic programming provide a 26-year predictive window for obesity prevention
Limitations
This is an observational cohort study that identifies associations rather than proving cause-and-effect relationships. The predictive biomarkers have not yet been validated in independent populations or tested as targets for intervention to prevent obesity.
Abstract
UNLABELLED: 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. IMPORTANCE: Understanding 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.