Linking obesity-associated genotype to child language development: the role of early-life neurology-related proteomics and brain myelination
Bibliographic record
Abstract
BACKGROUND: The association between childhood obesity and language development may be confounded by socio-environmental factors and attributed to comorbid pathways. METHODS: In a longitudinal Singaporean mother-offspring cohort, we leveraged trans-ancestry polygenic predictions of body mass index (BMI) to interrogate the causal effects of early-life BMI on child language development and its effects on molecular and neuroimaging measures. Leveraging large genome-wide association studies, we examined whether the link between obesity and language development is causal or due to a shared genetic basis. FINDINGS: We found an inverse association between polygenic risk for obesity, which is less susceptible to confounding, and language ability assessed at age 9. Our findings suggested a shared genetic basis between obesity and language development rather than a causal effect of obesity on language development. Interrogating early-life mechanisms including neurology-related proteomics and language-related white matter microstructure, we found that EFNA4 and VWC2 expressions were associated with language ability as well as fractional anisotropy of language-related white matter tracts, suggesting a role in brain myelination. Additionally, the expression of the EPH-Ephrin signalling pathway in the hippocampus might contribute to language development. Polygenic risk for obesity was nominally associated with EFNA4 and VWC2 expression. However, we did not find support for mediating mechanisms via these proteins. INTERPRETATION: This study demonstrates the potential of examining early-life proteomics in conjunction with deep genotyping and phenotyping and provides biological insights into the shared genomic links between obesity and language development. FUNDING: Singapore National Research Foundation and Agency for Science, Technology and Research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".