Prevalence of MMP-1 rs1799750 Polymorphism in Androgenetic Alopecia: A Cross-Sectional Study in an Indonesia Population
Bibliographic record
Abstract
Background: Matrix metalloproteinase-1 (MMP-1) is an enzyme responsible for degrading extracellular matrix (ECM) components, particularly collagen. Overexpression of MMP-1 can accelerate ECM degradation, contributing to various pathological conditions. The most studied polymorphism in the promoter region of the MMP-1 gene is rs1799750, which has been linked to several diseases in previous studies. Androgenetic alopecia (AGA) is a condition characterized by the progressive miniaturization of hair follicles, influenced by androgen signaling and ECM remodeling. This study aimed to investigate the prevalence of MMP-1 gene polymorphism and its potential association with AGA.Materials and methods: This study included 50 subjects diagnosed with AGA and 50 subjects without AGA. All subjects completed a questionnaire that included gender, age, BMI, and ethnicity. DNA was extracted from blood samples for genotyping of the MMP-1 rs1799750 gene. Genotyping was performed using the PCR-RFLP method with AluI as the restriction enzyme. For validation, several samples were sequenced at Apical Scientific Laboratory, Malaysia.Results: Among the 50 subjects with AGA, 9 had the 1G/1G genotype, 26 had the 1G/2G genotype, and 15 had the 2G/2G genotype. Similarly, among the 50 subjects without AGA, 8 had the 1G/1G genotype, 27 had the 1G/2G genotype, and 15 had the 2G/2G genotype. The allele frequencies of 1G and 2G in the AGA group were 0.44 and 0.56, respectively, while in the non-AGA group, they were 0.43 and 0.57, respectively. Chi-square analysis of AGA and MMP-1 genotype yielded a p-value of 0.96, indicating no significant association between AGA and the MMP-1 genotype.Conclusion: In this study, the association between the MMP-1 gene polymorphisms rs1799750 with AGA was not observed.Keywords: Androgenetic Alopecia, matrix metalloproteinase-1, polymorphisms, rs1799750
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".