Socioeconomic Status at Birth and Breast Tissue Composition in Adolescence and Adulthood
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status (SES) at birth is associated with breast cancer risk. Whether this association is driven by changes in breast tissue composition (BTC) prior to adulthood remains unclear. METHODS: We used multivariable linear regression models to examine whether SES at birth is associated with BTC in adolescence and adulthood using data from a New York City cohort of daughters (n = 165, 11-20 years) and mothers (n = 160, 29-55 years). We used maternal-reported data on daughters' household income and maternal education at birth, analyzed individually and in combination (SES index). Women also reported their own mothers' education at birth. We used optical spectroscopy to evaluate BTC measures that positively (water content, collagen content, optical index) and negatively (lipid content) correlate with mammographic breast density, a recognized breast cancer risk factor. RESULTS: Being in the highest versus lowest category of the SES index was associated with lower lipid content [βadjusted (βadj) = -0.80; 95% confidence interval (CI), -1.30 to -0.31] and higher collagen content (βadj = 0.54; 95% CI, 0.09-0.99) in adolescence. In women with a body mass index (BMI) <30 kg/m2, higher maternal education at birth (≥ vs. < high school degree) was associated with lower lipid content (βadj = -0.57; 95% CI, -0.97 to -0.17), higher water content (βadj = 0.70; 95% CI, 0.26-1.14), and higher optical index (βadj = 0.53; 95% CI, 0.10-0.95). CONCLUSIONS: This study supports that SES at birth is associated with BTC in adolescence and adulthood, although the latter association may depend on adult BMI. IMPACT: Further research is needed to identify the socially patterned early life factors influencing BTC.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".