Genomic and metabolomic studies for better understanding of osteoarthritis pathogenesis
Bibliographic record
Abstract
Osteoarthritis is the most common form of arthritis and one of the ten most disabling diseases in developed countries. The main objective of my thesis was to employ genomic and metabolomic approaches to improve our understanding of the OA pathogenesis. I carried out a metabolomics analysis and identified three distinct endotypes of OA patients. Butyrylcarnitine, arginine, and a number of glycerophosphlipids were the major contributing metabolites for the differentiation between the three endotypes suggesting that the primary OA patients can be classified as muscle weakness, arginine deficient, and low inflammatory OA. Using the same metabolomics approach, I found that the elevated blood level of the ADMA and uric acid were associated with the muscle weakness over 10-years and may elevate the study participants risk for developing OA. Additionally, I conducted an independent GWAS analysis in OA patients from NL and identified novel genes significantly associated with OA. These genes are involved in cartilage deterioration; inflammatory signaling; innate immune pathway; abnormal bone growth and remodeling; panic disorder; and pain mechanisms. Further, I performed a genetic variant annotation study using WES data and identified deleterious variants in the IGSF3, ZNF717, PRSS1, AQP7, and ESRRA genes in OA patients that have not been reported in previous OA GWAS studies. These genes act in the ECM homeostasis and degradation. With the same WES data, I conducted a genome wide digenic interaction test in OA patients and identified aggregated variants in each of the CDH19, SOGA1, MORC4, TMTC4, and ANK3 genes to be significantly interacting with rs56158521 in the HLA-DRB1 gene. Our findings suggested the implication of the immunoinflammatory pathway in the pathogenesis of OA. Also, I conducted a GWAS analysis and found variants in the MC5R gene to be significantly increasing the TJR pain, and variants adjacent to the TPTE gene to be significantly increasing the TJR disability. These genes are involved in immunoinflammatory reactions and may play a significant role in the pain and function mechanisms following TJR. While confirmation is required, these findings provided new insights into better understanding of the OA pathogenesis and hold promising as druggable targets for developing OA therapies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".