Vertex balding, plasma insulin-like growth factor 1, and insulin-like growth factor binding protein 3
Bibliographic record
Abstract
BACKGROUND: A recent report suggested that men with vertex balding have higher levels of plasma insulin-like growth factor 1 (IGF-1). The association of its major carrier protein, insulin-like growth factor binding protein 3 (IGFBP-3), with male pattern hair loss has not been examined. OBJECTIVE: We evaluated the relations of plasma concentrations of IGF-1 and IGFBP-3 with vertex balding in middle-aged and elderly men. METHODS: Participants were 431 male members of the Health Professionals Follow-up Study who responded to a question in 1992 on their hair pattern at 45 years of age and who were 47 to 81 years old when they provided a blood specimen in 1993-1994. Odds ratios (ORs) of vertex balding associated with IGF-1 and IGFBP-3 were estimated from logistic regression models mutually adjusting for each other and controlling for age at blood draw. RESULTS: Of the 431 men, 128 had vertex balding at age 45. Compared with men who were not balding, for a 1 standard deviation increase in plasma IGF-1 level (72.4 ng/mL), the OR for vertex balding was 1. 31 (95% CI, 0.95-1.81). For a 1 standard deviation increase in plasma IGFBP-3 (957 ng/mL), the OR for vertex balding was 0.62 (95% CI, 0.44-0.88). CONCLUSION: Older men with vertex balding have lower circulating levels of IGFBP-3 and higher levels of IGF-1 when controlling for IGFBP-3 level.
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 | 0.003 |
| 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) | 0.003 | 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".