Genetic determinants of adiponectin regulation revealed by pregnancy
Bibliographic record
Abstract
Objective This study investigated genetic determinants of adiponectin during pregnancy to reveal novel biology of adipocyte regulation. Methods A genome‐wide association study was conducted in 1,322 pregnant women from the Hyperglycemia and Adverse Pregnancy Outcome Study with adiponectin measured at ∼28 weeks of gestation. Variants reaching P < 5×10−5 for de novo genotyping in two replication cohorts (Genetics of Glycemic regulation in Gestation and Growth N = 522; ECOGENE‐21 N = 174) were selected. Results In the combined meta‐analysis, the maternal T allele of rs900400 located on chr3q25 (near LEKR1/CCNL1) was associated with lower maternal adiponectin (β ± standard error [SE] = −0.18 ± 0.03 standard deviation [SD] of adiponectin per risk allele; P = 1.5 10−8; N = 2,004; multivariable adjusted models). In contrast, rs900400 showed only nominal association with adiponectin in a large sample of nonpregnant women (β ± SE = −0.012 ± 0.006; P = 0.05; N = 16,678 women from the ADIPOgen consortium). The offspring rs900400 T risk allele was associated with greater neonatal skinfold thickness (β ±SE = 0.19 ± 0.04 SD per risk allele; P = 4.1×10−8; N = 1,489) and higher cord blood leptin (β ± SE = 0.28 ± 0.05 log‐leptin per risk allele; P = 8.2 10−9; N = 502), but not with cord blood adiponectin (P = 0.23; N = 495). The T allele of rs900400 was associated with higher expression of TIPARP in adipocytes. Conclusions These investigations of adipokines during pregnancy and early life suggest that rs900400 has a role in adipocyte function.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".