Identification of an Epigenetic Signature of Osteoporosis in Blood DNA of Postmenopausal Women
Bibliographic record
Abstract
Osteoporosis is one of the most common age-related progressive bone diseases in elderly people. Approximately one in three women and one in five men are predisposed to developing osteoporosis. In postmenopausal women, a reduction in BMD leads to an increased risk of fractures. In the current study, we delineated the DNA methylation signatures in whole blood samples of postmenopausal osteoporotic women. We obtained whole blood DNA from 22 normal women and 22 postmenopausal osteoporotic women (51 to 89 years old) from the Canadian Multicenter Osteoporosis Study (CaMos) cohort. These DNA samples were subjected to Illumina Infinium human methylation 450 K analysis. Illumina 450K raw data were analyzed by Genome Studio software. Analysis of the female participants with early and advanced osteoporosis resulted in the generation of a list of 1233 differentially methylated CpG sites when compared with age-matched normal women. T test, ANOVA, and post hoc statistical analyses were performed, and 77 significantly differentially methylated CpG sites were identified. From the 13 most significant genes, ZNF267, ABLIM2, RHOJ, CDKL5, and PDCD1 were selected for their potential role in bone biology. A weighted polygenic DNA methylation score of these genes predicted osteoporosis at an early stage with high sensitivity and specificity and correlated with measures of bone density. Pyrosequencing analysis of these genes was performed to validate the results obtained from Illumina 450 K methylation analysis. The current study provides proof of principal for the role of DNA methylation in osteoporosis. Using whole blood DNA methylation analysis, women at risk of developing osteoporosis can be identified before a diagnosis of osteoporosis is made using BMD as a screening method. Early diagnosis will help to select patients who might benefit from early therapeutic intervention. © 2018 American Society for Bone and Mineral Research.
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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 | 0.000 |
| 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".