Carbohydrate and lipid metabolism in neonates and children born SGA. A systematic review and metanalysis
Bibliographic record
Abstract
PURPOSE: Being born small for gestational age (SGA) is a marker of adverse intrauterine environment and is associated with metabolic disorders in adulthood. The present metanalysis compares the carbohydrate and lipid metabolism between neonates and pre- and post- pubertal children born SGA and their appropriate for gestational age (AGA) -born peers. METHODS: A systematic search was conducted in PubMed, Cochrane, and Scopus databases to identify observational studies comparing carbohydrate and lipidemic profiles in neonates and children born SGA vs. AGA. Data were extracted on insulin, glucose, total cholesterol, HDL, LDL cholesterol and triglyceride concentration. Standardized mean differences (SMD) were calculated using random-effects models. The risk of bias was assessed using Newcastle-Ottawa Scale (NOS). Heterogeneity and publication bias were assessed using the I² statistic and Egger’s test, respectively. RESULTS: Twenty-eight studies (N = 8453) were analyzed. SGA neonates had greater triglycerides (SMD: 0.41, 95% CI: 0.19–0.63) and lower HDL cholesterol (SMD: -0.29, 95% CI: -0.45 to -0.13) concentration than AGA neonates. Prepubertal children born SGA showed significantly greater insulin concentration (SMD: 0.33, 95% CI: 0.10–0.57) than those born AGA. No significant differences were found between AGA and SGA neonates, pre- and post-pubertal children in glucose and LDL cholesterol concentration. CONCLUSION: Neonates born SGA show greater circulating triglycerides and lower HDL concentration compared to their AGA peers, while prepubertal children born SGA show greater circulating insulin concentration, potentially predisposing them to insulin resistance. These findings showcase the long-term metabolic consequences of adverse intrauterine conditions, which result to SGA offspring, and emphasize the importance of monitoring SGA neonates and children for potential metabolic disorders during their life.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".