Plasma Insulin-like Growth Factor–Binding Protein-7 Is Positively Associated with Age, Obesity, Mortality, and Cancer in Postmenopausal Women
Bibliographic record
Abstract
BACKGROUND: Predictors of premature death and cancer development are needed to more precisely identify individuals who may warrant preventive intervention. Circulating insulin-like growth factor (IGF)-binding protein-7 (IGFBP7) and, to a lesser extent, the IGFBP7/IGF-1 ratio are emerging biomarkers of renal and cardiovascular morbidity. However, their relationships with aging, obesity, mortality, and cancer risk remain unclear. METHODS: This hypothesis-generating study investigated plasma IGFBP7, IGF-1, and their ratio as predictors of all-cause mortality and the incidence of any cancer (excluding nonmelanoma skin cancer), obesity-related cancer (composite of 13 cancer types), and breast cancer in a large longitudinal cohort of postmenopausal women. We assessed the relationships of each biomarker with age, body mass index, and each outcome (bivariately and controlling for age, body mass index, race, physical activity, education, income, marital status, alcohol intake, smoking, diabetes, and hormone therapy) in 793 Women's Health Initiative Observational Study participants (mean, 19.4-year follow-up). RESULTS: In adjusted analyses, IGFBP7 increased with age and obesity and was positively associated with risks of all-cause mortality [HR = 2.42 (95% confidence interval, 1.37-4.26); P = 0.002], any cancer [HR = 2.04 (1.05-3.94); P = 0.035], and obesity-related cancer [HR = 1.58 (0.99-2.51); P = 0.053]. Also in adjusted analyses, the IGFBP7/IGF-1 ratio increased with age and was positively associated with all-cause mortality [HR = 1.51 (1.14-1.99); P = 0.004] and any cancer incidence [HR = 5.44 (1.13-26.1); P = 0.034]. CONCLUSIONS: Plasma IGFBP7 and the IGFBP7/IGF-1 ratio are positively associated with age, obesity (IGFBP7 only), mortality, and cancer in postmenopausal women. IMPACT: Plasma IGFBP7 may represent an age- and obesity-sensitive biomarker of increased risk of developing cancer and/or dying prematurely.
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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 | 0.001 | 0.002 |
| 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".