Proteome-wide mendelian randomization reveals circulating proteins causally associated with childhood body mass index
Bibliographic record
Abstract
Abstract Childhood obesity is a major public health problem, affecting one in 5 youths. We aimed to characterize biomarkers for pediatric obesity among circulating proteins using Mendelian randomization (MR). We utilized genome-wide significant cis-protein quantitative trait loci (pQTL) from three large adult proteomic GWAS (N total>58,000) and a small childhood proteomic GWAS (N=2,147) as genetic instruments for circulating protein levels. Using two-sample Mendelian randomization, we estimated causal effects of the circulating proteins on childhood body mass index (BMI) in a European GWAS of 39,620 children. MR Wald ratios were calculated to estimate the causal effect of each protein on childhood BMI. Sensitivity analyses testing the MR assumptions included colocalization and phenome-wide association studies (PheWAS). Replication was conducted using independent GWAS datasets, complemented by reverse MR and tissue enrichment analyses. Among 535 tested proteins, three colocalized and demonstrated decreasing effects on BMI per standard deviation increase in their level: endoglin (ENG; MR beta: -0.07, 95% CI [-0.10, -0.04], P=4.4×10⁻ 5 ), fatty acid binding protein 4 (FABP4; MR beta: -0.33, 95% CI [-0.50, -0.16], P=1.3×10⁻ 4 ), and cell adhesion molecule 1 (CADMI1; MR beta: -0.26, 95% CI [-0.37, -0.15], P=5.45×10⁻ 5 ). All three proteins showed evidence of colocalization (posterior probability >75%) and were identified using adult proteomic GWAS, given a limited statistical power using the pediatric proteomic GWAS data. Reverse causation was identified for FABP4, suggesting a compensatory mechanism. In conclusion, we identified three circulating proteins as potential blood biomarkers or drug targets for pediatric obesity, warranting further functional validation to elucidate biological mechanisms and assess therapeutic potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".