Multi-Omics Integrative Analyses Identified Two Endotypes of Hip Osteoarthritis
Bibliographic record
Abstract
(1) Background: Osteoarthritis (OA) is a heterogeneous disorder, and subgroup classification of OA remains elusive. The aim of our study was to identify endotypes of hip OA and investigate the altered pathways in the different endotypes. (2) Methods: Metabolomic profiling and genome-wide genotyping were performed on fasting blood. Transcriptomic profiling was performed on RNA extracted from cartilage samples. Machine learning methods were used to identify endotypes of hip OA. Pathway analysis was used to identify the altered pathways between hip endotypes and controls. GWAS was performed on each of the identified metabolites. Transcriptomic data was used to examine the expression levels of identified genes in cartilage. (3) Results: 180 hip OA patients and 120 OA-free controls were classified into three clusters based on metabolomic data. The combination of arginine, ornithine, and the average value of 7 lysophosphatidylcholines had an area under the curve (AUC) of 0.97 (95% CI: 0.96–0.99) to discriminate hip OA from controls, and the combination of γ-aminobutyric acid, spermine, aconitic acid, and succinic acid had an AUC of 0.96 (95% CI: 0.94–0.99) to distinguish two hip OA endotypes. GWAS identified 236 SNPs to be associated with identified metabolites at GWAS significance level. Pro-inflammatory cytokine levels were significantly different between two endotypes (all p < 0.05). (4) Conclusions: Hip OA could be classified into two distinct molecular endotypes. The primary differences between the two endotypes involve changes in pro-inflammatory factors and energy metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".