Cord blood fatty acid binding protein 4 and lipids in infants born small- or large-for-gestational-age
Bibliographic record
Abstract
Aim Adverse (poor or excessive) fetal growth “programs” an elevated risk of type 2 diabetes. Fatty acid binding protein 4 (FABP4) has been implicated in regulating insulin sensitivity and lipid metabolism relevant to fetal growth. We sought to determine whether FABP4 is associated with poor or excessive fetal growth and fetal lipids. Methods In a nested case-control study in the Shanghai Birth Cohort including 60 trios of small-for-gestational-age (SGA, an indicator of poor fetal growth), large-for-gestational-age (LGA, an indicator of excessive fetal growth) and optimal-for-gestational-age (OGA, control) infants, we measured cord blood concentrations of FABP4 and lipids [high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterols, triglycerides (TG)]. Results Adjusting for maternal and neonatal characteristics, higher cord blood FABP4 concentrations were associated with a lower odds of SGA [OR = 0.29 (0.11–0.77) per log unit increment in FABP4, P = 0.01], but were not associated with LGA (P = 0.46). Cord blood FABP4 was positively correlated with both LDL (r = 0.29, P = 0.025) and HDL (r = 0.33, P = 0.01) in LGA infants only. Conclusion FABP4 was inversely associated with the risk of SGA. The study is the first to demonstrate LGA-specific positive correlations of cord blood FABP4 with HDL and LDL cholesterols, suggesting a role of FABP4 in fetal lipid metabolism in subjects with excessive fetal growth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".