MétaCan
Menu
Back to cohort
Record W2913739853 · doi:10.1158/1538-7755.disp17-a36

Abstract A36: Serum insulin-like growth factor (IGF)-I and IGF binding protein-3 in relation to terminal duct lobular unit involution in Caucasian and African American women: The Susan G. Komen Tissue Bank

2018· article· en· W2913739853 on OpenAlexaff
Hannah Oh, Ruth M. Pfeiffer, Roni T. Falk, Hisani N. Horne, Jackie Xiang, Michaël Pollak, Louise A. Brinton, Anna Maria Storniolo, Mark E. Sherman, Gretchen L. Gierach, Jonine D. Figueroa

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2018
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsMcGill University
Fundersnot available
KeywordsInsulin-like growth factor-binding proteinMedicineInternal medicineInvolution (esoterism)EndocrinologyInsulin-like growth factorBreast cancerConfoundingGrowth factorCancerBiologyReceptor

Abstract

fetched live from OpenAlex

Abstract Background: Insulin-like growth factor (IGF)-I and IGF binding protein (IGFBP)-3 play important roles in carcinogenesis, particularly for breast cancer. However, little is known about whether the IGF system influences histologic characteristics of normal glandular tissue and whether relationships vary by race. Lesser degrees of age-related terminal duct lobular unit (TDLU) involution, as reflected by higher numbers of TDLUs and acini per TDLU, have been associated with higher breast cancer risk. We examined the associations of IGF measures with TDLU involution of normal breast using standardized TDLU measures. Methods: Among 715 Caucasian and 283 African American (AA) women with normal breast tissue samples from the Komen Tissue Bank, serum concentrations of IGF-I and IGFBP-3 were quantified using enzyme-linked immunosorbent assay (ELISA). Hematoxilyn and eosin-stained tissue sections were assessed for numbers of TDLUs (“TDLU count”) and acini/TDLU. Zero-inflated Poisson regression models with a robust variance estimator were used to estimate associations of IGF-I, IGFBP-3, and IGF-I:IGFBP-3 molar ratio (tertiles) with TDLU count by race and menopausal status, adjusting for potential confounders. We also tested for interactions by race using likelihood ratio tests. Results: AA (vs. Caucasian) women had higher age-adjusted mean levels of serum IGF-I (137 vs. 131 ng/mL, p=0.07) and lower levels of IGFBP-3 (4165 vs. 4684 ng/mL, p<0.0001); the differences persisted after adjustment for additional covariates including BMI and parity/age at first birth. Postmenopausal IGFBP-3 was inversely associated with TDLU count among both AA (RR T3vs.T1=0.49, 95% CI=0.28-0.84, p-trend=0.04) and Caucasian (RR T3vs.T1=0.63, 95% CI=0.41-0.99, p-trend=0.04) women. In premenopausal women, higher IGF-I:IGFBP-3 ratios were associated with higher TDLU count in Caucasians (OR T3vs.T1=1.33, 95% CI=1.01-1.31, p-trend=0.04) but not in AA (OR T3vs.T1=0.65, 95% CI=0.42-1.00, p-trend=0.05) women. There was no statistically significant interaction by race (p-interaction≥0.10). Conclusions: Our data suggest the potential role of the IGF system, particularly IGFBP-3, in TDLU involution of the normal breast among both Caucasian and AA women. Citation Format: Hannah OH, Ruth M. Pfeiffer, Roni T. Falk, Hisani N. Horne, Jackie Xiang, Michael Pollak, Louise A. Brinton, Anna Maria V. Storniolo, Mark E. Sherman, Gretchen L. Gierach, Jonine D. Figueroa. Serum insulin-like growth factor (IGF)-I and IGF binding protein-3 in relation to terminal duct lobular unit involution in Caucasian and African American women: The Susan G. Komen Tissue Bank [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr A36.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.038
GPT teacher head0.332
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCancer Epidemiology Biomarkers & PreventionSame topicGrowth Hormone and Insulin-like Growth FactorsFrench-language works237,207