Associations between maternal pre-pregnancy BMI and infant striatal mean diffusivity
Bibliographic record
Abstract
Abstract Background/Objectives It is well-established that parental obesity is a strong risk factor for associates with offspring obesity. Further, a converging body of evidence now suggests that maternal weight profiles may affect the developing offspring brain in a manner that confers future obesity risk. Here, we investigated how pre-pregnancy maternal weight status influences the reward-related striatal areas of the offspring brain during in utero development. Methods We used diffusion tensor imaging to quantify the microstructure of the striatal brain regions of interest in neonates (N = 116 mean gestational weeks at birth 39.88, SD = 1.14; and at scan 43.56, SD = 1.05). Linear regression was used to test the associations between maternal pre-pregnancy body mass index and infant striatal mean diffusivity. Results A strong positive association was found between the maternal pre-pregnancy body mass index and newborn left caudate nucleus mean diffusivity. Results remained unchanged after the adjustment for covariates. Conclusions In utero exposure to maternal adiposity might have a growth impairing impact on the mean diffusivity of infant left caudate nucleus. Considering the involvement of caudate nucleus in regulating eating behaviour and food-related reward processing later in life, this finding calls for further investigations to define the prognostic relevance of early life caudate development and weight trajectories of the offspring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".