The Lipid Accumulation Product for the Early Prediction of Gestational Insulin Resistance and Glucose Dysregulation
Bibliographic record
Abstract
BACKGROUND: Recent insights linking insulin resistance and lipid overaccumulation suggest a novel approach for the early identification of women who may soon experience glucose dysregulation. Among women without a history of gestational diabetes, we tested the association between the lipid accumulation product (LAP) obtained in early pregnancy and glucose dysregulation or insulin resistance in the second trimester. METHODS: A total of 180 white pregnant women of French-Canadian origin were included in this study. At 11–14 weeks' gestation, fasting insulin, glucose, C-peptide concentrations, and estimated insulin resistance (HOMA-IR) were obtained. The waist circumference (WC) and fasting triglycerides (TG) were measured to calculate LAP as(WC[cm] - 58) · TG[mmol/L]. At 24–28 weeks' gestation, glucose was measured 2 hours after a 75-g oral glucose challenge and other fasting variables were repeated. RESULTS: Among the nulliparous women tested at the end of the second trimester, fasting insulin, C-peptide, insulin resistance (HOMA-IR index), fasting glucose, and 2-hour glucose progressively increased ( p £ 0.002)according to their first-trimester LAP tertiles. Similar results were observed in parous women except for the glucose variables. The first-trimester LAP tended to show a stronger correlation to the second-trimester HOMAIR index (r = 0.56) than fasting triglyceride levels alone (r = 0.40) or waist circumference alone (r = 0.44) among nulliparous women. Similar associations were observed for parous women. Adjustment for body mass index weakened these associations, especially among parous women. CONCLUSIONS: An increased value of LAP at the beginning of a pregnancy could be associated with an increased risk of insulin resistance or hyperglycemia later in gestation.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".