Relationships between adiposity distribution and metabolic health in preconception women in South Africa
Bibliographic record
Abstract
Abstract Objective Adipose tissue is a central regulator of metabolic health and a contributor to systemic inflammation. Patterns of adiposity deposition are important to understand for optimizing health. This study aimed to asses relationships between adiposity deposition and metabolic and inflammatory biomarkers in South African women prior to conception. Methods Non‐pregnant, healthy women (n = 298) were recruited for this cross‐sectional study via home visits. Body composition was measured by Dual X‐ray Absorptiometry. Inflammation markers C‐reactive protein (CRP), alpha1‐acid glycoprotein (AGP), hemoglobin A1c (HbA1c), and blood pressure were scored according to risk. A summative metabolic health risk score was created for women with obesity. Generalized regression models assessed relationships between adiposity deposition and outcomes with adjustment for potential confounders. Results Obesity was present in 22% of women (mean age = 20.93 years). Fat mass index was associated with inflammation and metabolic health risk (β = 0.58; p < 0.01). Visceral fat, trunk:limb ratio, android:gynoid ratio, body mass index, weight, and waist circumference were positively associated with CRP, AGP, and metabolic health risk (p < 0.01). Weight was associated with Hba1c (β < 0.01; p < 0.05). Participants with obesity and low metabolic health risk had lower fat mass index and visceral fat than participants with obesity and higher metabolic health risk. Conclusions Black South African women accumulated excess adipose tissue in abdominal regions. While fat mass and body mass were associated with inflammation and metabolic health risk, women with obesity and with lower fat mass index and lower visceral adipose tissue were metabolically protected. Identification of women at risk for metabolic disease preconception could help ensure future healthy pregnancies and prevent transference of risk to offspring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".