What accounts for ethnic differences in newborn skinfold thickness comparing South Asians and White Caucasians? Findings from the START and FAMILY Birth Cohorts
Bibliographic record
Abstract
OBJECTIVE: South Asians are a high-risk group for type 2 diabetes and coronary heart disease. We sought to determine ethnic differences in newborn adiposity comparing South Asians (SA) to White Caucasians (Whites). METHODS: Seven hundred ninety pregnant women (401 SA, 389 Whites) and their full-term offspring from two birth cohorts in Canada were analyzed. Pregnant women completed a health assessment including a 75-g oral glucose tolerance test to assess for dysglycemia. Birthweight, length, waist and hip circumference, and triceps and subscapular skinfold thickness (a surrogate measure of body adiposity) were measured in all newborns. Multivariate regression was used to identify maternal factors associated with newborn skinfold measurements. RESULTS: South Asian women were younger (30.1 vs 31.8 years, P<0.001), their prepregnancy body mass index was lower (23.7 vs 26.2, P<0.0001) and gestational diabetes was substantially higher (21% vs 13%, P=0.005) compared with Whites. Among full-term newborns, South Asians had lower birthweight (3283 vs 3517 g, P=0.0001), had greater skinfold thickness (11.7 vs 10.6 mm; P=0.0001) and higher waist circumference (31.1 vs 29.9 cm, P=0.0001) compared with Whites. Risk factors for newborn skinfold thickness included South Asian ethnicity (standardized estimate (s.e.): 0.24; P<0.0001), maternal glucose (s.e.: 0.079; P=0.04) and maternal body fat (s.e.: 0.14; P=0.0002). CONCLUSIONS: South Asian newborns are lower birthweight and have greater skinfold thickness, compared with White newborns, and this is influenced by maternal body fat and glucose. Interventions aimed at reducing body fat prior to pregnancy and gestational diabetes during pregnancy in South Asians may favorably alter newborn body composition and require evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".