📄 genetic and genomic medicine

The Genetic and Proteomic Determinants of Pediatric Stature Development and their link to adult height and Type 2 Diabetes

This study integrates genomic and proteomic analyses of over 72,000 Norwegian children to reveal that childhood stature is governed by distinct, stage-specific genetic and metabolic mechanisms that independently influence adult height and increase the risk of type 2 diabetes.

Original authors: Fragoso-Bargas, N., Lupu, A. E., Campillo-Pereda, I., Huang, Y., Sundfjord, J., Karimi, R., Lind, T., Holm, J.-C., Holm, L. A., Havdahl, A., Andreassen, O. A., O'Rahilly, S., Sole-Navais, P., Jacobsso

Published 2026-06-22

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Fragoso-Bargas, N., Lupu, A. E., Campillo-Pereda, I., Huang, Y., Sundfjord, J., Karimi, R., Lind, T., Holm, J.-C., Holm, L. A., Havdahl, A., Andreassen, O. A., O'Rahilly, S., Sole-Navais, P., Jacobsson, B., Bratland, E., Davey Smith, G., Juliusson, P. B., Ong, K. K., Hansen, T., Njolstad, P. R., Vaudel, M., Johansson, S.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine your height as a long, winding road trip from the moment you are born until you become an adult. For a long time, scientists thought this road was driven by the same engine the whole way. But this new study suggests that the journey actually has three different "drivers" who take turns at the wheel, and the type of car you're in changes depending on which stage of the trip you're on.

Here is a simple breakdown of what the researchers found:

1. The Three Drivers of Growth

The researchers looked at the DNA of over 72,000 children in Norway, tracking their height from birth up to age 8. They discovered that the genetic instructions for growing aren't all the same. Instead, they fall into three distinct groups, or "clusters," that act like different drivers:

The Infant Driver (Birth to 1 year): This driver is in charge of the very early miles. They found 60 specific genetic signals that mostly work during infancy and then fade away. Interestingly, some of these signals are linked to how the body handles sugar and appetite (like a gene called GLP1R), suggesting that the way a baby grows in the first year is closely tied to their metabolism.
The Childhood Driver (1 to 8 years): This driver takes over as the child grows older. There are 71 genetic signals that are strongest during these years. Like the infant driver, this group is also heavily linked to how the body manages energy and sugar.
The Lifetime Driver (The Whole Trip): This is the steady, long-haul driver. There are 48 signals that keep working from childhood all the way through to adulthood. These are mostly linked to the skeleton and bones—the actual "frame" of the car.

2. The "Adult Height" Map vs. The "Child" Map

Scientists have a very detailed map for how tall adults will be, based on millions of people. The researchers tested if this "Adult Map" could predict how tall a child would be.

The Result: The Adult Map works surprisingly well, even for babies! It explains about 4% of a baby's height, but by age 8, it explains about 20%.
The Catch: The map gets a bit "foggy" during puberty (around ages 10–15). Just like a GPS losing signal in a tunnel, the adult map isn't perfect for predicting the growth spurt that happens during puberty. This suggests that puberty has its own unique set of genetic rules that the adult map doesn't fully capture yet.

3. The Protein Fuel

The researchers also looked at the "fuel" in the bloodstream—specifically proteins that help you grow. They found that children with a genetic tendency to be taller had higher levels of specific growth proteins (like IGF1 and IGF2).

The Analogy: Think of these proteins as the fuel injectors. The study found that the "Childhood Driver" genes are better at predicting how much of this specific growth fuel is in the blood than the "Adult Driver" genes are. This confirms that childhood growth relies on a specific set of biological tools that are slightly different from what keeps us tall as adults.

4. The Surprising Link to Diabetes

One of the most interesting findings is a twist in the story about height and Type 2 Diabetes (a condition where the body struggles to manage sugar).

The Old Story: Usually, being a taller adult is linked to a lower risk of diabetes.
The New Twist: The study found that if a child grows very tall very quickly before puberty, it might actually increase the risk of diabetes later in life.
The Explanation: It seems that the "fuel" that makes a baby grow long and tall in the womb (fetal growth) is protective against diabetes. However, if a child shoots up in height after birth but before puberty, it might be a sign that their body is struggling to manage sugar, which could lead to diabetes later. It's like the difference between a car that was built strong from the factory (good) versus a car that had to be stretched quickly later on (risky).

5. Boys vs. Girls

The study checked if boys and girls have different "drivers." They found that for the most part, the genetic rules are the same for both. The road looks identical for boys and girls until puberty hits, at which point the paths diverge slightly (mostly because boys hit their growth spurt a bit later than girls).

The Bottom Line

Growing up isn't just one big process; it's a relay race with different genetic runners passing the baton at different times.

Early life is driven by genes linked to metabolism and appetite.
Later life is driven by genes linked to bone structure.
The connection to health: How fast a child grows before puberty might be a warning sign for future sugar problems, even if they end up being a healthy, normal-sized adult.

This study helps us understand that to truly know a child's health, we need to look at when they are growing, not just how tall they are.

Technical Summary: The Genetic and Proteomic Determinants of Pediatric Stature Development and their link to adult height and Type 2 Diabetes

Problem Statement
While stature is a fundamental indicator of pediatric health and the genetic architecture of adult height is comprehensively mapped (explaining ~40% of variance), the molecular and metabolic underpinnings of growth during infancy and early childhood remain poorly characterized. Existing literature suggests complex, often contradictory relationships between growth trajectories and later metabolic health, specifically Type 2 Diabetes (T2D). For instance, taller adult height is generally associated with lower T2D risk, whereas rapid height gain during childhood has been linked to higher risk. Furthermore, the proteomic landscape of healthy childhood growth and its specific genetic drivers are largely unexplored compared to adult populations. This study aims to define the genetic and proteomic architecture of stature from birth to childhood, characterize life-stage shifts in these mechanisms, and elucidate their causal links to adult height and metabolic disease.

Methodology
The study leveraged a multi-cohort approach centered on the Norwegian Mother, Father and Child (MoBa) study, the largest longitudinal birth cohort utilized for this purpose.

Genetic Discovery: Genome-wide association studies (GWAS) were performed on 574,580 standardized length/height measurements from 72,704 children in MoBa across 12 time points (birth to 8 years). These results were integrated with UK Biobank data (comparative height at age 10 and measured adult height).
Statistical Analysis: Researchers employed hierarchical clustering to group 179 independent genetic signals based on their effect trajectories across ages. Polygenic scores (PGS) were constructed for specific clusters (Infancy, Childhood, Lifetime) and compared against an adult-height-based PGS. Sex-stratified analyses and sex-interaction tests were conducted to assess divergence.
Functional Annotation: Gene enrichment analyses were performed using GLAD4U and UK rare variant panels to link signals to Mendelian disorders and biological pathways. Tissue enrichment was assessed via FUMA using GTEx datasets.
Proteomics and Metabolomics: Associations between height and circulating proteins were investigated in three independent cohorts: ALSPAC (UK), HOLBAEK (Denmark), and OTIS (Sweden). This included targeted analysis of growth factors (IGF1, IGF2, IGFBP3) and hypothesis-free screens of 1,365 circulating proteins using mass spectrometry and Olink panels.
Causal Inference: Mendelian Randomization (MR) and Multivariable MR (MVMR) were utilized to disentangle the causal effects of birth length, childhood height, and adult height on T2D and glucose-related traits.

Key Contributions and Results

Dynamic Genetic Architecture: The study revealed that SNP-based heritability of stature increases markedly from birth ( $h^2 = 0.16$ ) to age 8 ( $h^2 = 0.41$ ), approaching adult levels. Genetic correlations between time points are strong but decrease as the time interval widens, indicating both shared and distinct genetic mechanisms.
Temporal Clustering of Genetic Signals: Hierarchical clustering identified three distinct clusters of genetic effects:
- Infancy Cluster (60 signals): Predominant effects from birth to 1 year. Enriched for endocrine and metabolic pathways.
- Childhood Cluster (71 signals): Stronger effects from 1 to 8 years, attenuating by puberty. Also enriched for metabolic and endocrine disorders.
- Lifetime Cluster (48 signals): Consistent effects across ages 1–10 years and larger effects on adult height. Enriched for skeletal biology and bone diseases.
Novel Genetic Findings: The study identified 19 novel signals not previously associated with height in GWAS catalogs. Notably, two signals near GLP1R (rs72857945 and rs579786) showed strong, BMI-independent effects restricted to infancy. These variants are linked to appetite regulation and are in linkage disequilibrium with variants affecting semaglutide response (weight loss and nausea).
Sex Differences: No robust evidence was found for sex-specific genetic architectures in stature from birth to 8 years; genetic correlations between sexes remained close to 1.0 until puberty, after which performance of adult-height PGS diverged slightly, reflecting the later timing of the pubertal growth spurt in boys.
Proteomic Associations:
- Genetic Drivers: Childhood height PGS (based on the 179 MoBa SNPs) showed stronger associations with circulating IGF1, IGF2, and IGFBP3 levels than adult-height PGS, despite the latter containing far more SNPs.
- Measured Height: A proteome-wide screen in the HOLBAEK cohort identified 106 proteins associated with measured childhood height. Key associations included IGF-axis proteins (IGF1, IGFBP3, IGFBP5, IGFALS) and bone formation markers (COL1A1, SPP1, ACAN). Leptin (LEP) and Placenta Growth Factor (PGF) were also significantly associated with height, independent of waist-to-height ratio.
Mendelian Randomization and Metabolic Links:
- T2D Risk: Univariable MR confirmed that shorter birth length and shorter adult height are associated with higher T2D risk. However, MVMR revealed that the protective effect of taller adult height is largely mediated by fetal growth mechanisms; when adjusting for birth length, the direct effect of adult height on T2D disappears.
- Childhood Height Risk: Conversely, taller prepubertal height (at 7 and 10 years) was causally associated with increased risk of T2D and higher fasting insulin, independent of adult stature. This supports the hypothesis that rapid childhood growth may predispose to metabolic dysfunction.
Predictive Modeling: An adult-height-based PGS explained increasing variance in childhood height over time (from ~~4% at birth to ~22% at age 8). While parental height remained a stronger predictor than PGS alone, the combination of parental height and PGS yielded the highest predictive power (~~26% variance explained at age 7), suggesting genetic data complements traditional clinical monitoring.

Significance
The paper claims to provide the first comprehensive characterization of the genetic and proteomic architecture of stature from birth through childhood. By identifying life-stage-specific genetic clusters, the study demonstrates that the biological mechanisms governing growth are not static but evolve, with early-life growth heavily influenced by metabolic and endocrine pathways distinct from the skeletal biology dominating lifelong growth.

The findings clarify the complex relationship between stature and Type 2 Diabetes, suggesting that the "protective" effect of tall adult height is a proxy for favorable fetal growth, whereas rapid prepubertal growth may independently increase metabolic risk. Furthermore, the identification of GLP1R variants influencing infancy height offers potential pharmacogenomic insights, linking early growth genetics to adult drug response (e.g., semaglutide). Finally, the study establishes a robust link between childhood height genetics and the IGF axis, reinforcing the role of these pathways in both growth and metabolic health. The authors conclude that conducting GWAS at specific developmental stages is essential for understanding the etiology of complex traits and their associated disease risks.