High-fat diet-induced acceleration of osteoarthritis is associated with a distinct and sustained plasma metabolite signature
Bibliographic record
Abstract
Metabolic changes induced by high fat diet (HFD) that contribute to osteoarthritis (OA) are poorly understood. We investigated longitudinal changes to metabolites and their contribution to OA pathogenesis in response to HFD. HFD-fed mice exhibited acceleration of spontaneous age-related and surgically-induced OA compared to lean diet (LD)-fed mice. Using metabolomics, we identified that HFD-fed mice exhibited a distinct and sustained plasma metabolite signature rich in phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPCs), even after resumption of normal chow diet. Using receiver operator curve analysis and prediction modelling, we showed that the concentration of these identified metabolites could efficiently predict the type of diet and OA risk with an accuracy of 93%. Further, longitudinal evaluation of knee joints of HFD- compared to LD- fed mice showed a greater percentage of leptin-positive chondrocytes. Mechanistic data showed that leptin-treated human OA chondrocytes exhibited enhanced production of lysoPCs and expression of autotaxin and catabolic MMP-13. Leptin-induced increased MMP13 expression was reversed by autotaxin inhibition. Together, this study is the first to describe a distinct and sustained HFD-induced metabolite signature. This study suggests that in addition to increased weight, identified metabolites and local leptin-signaling may also contribute in part, towards the accelerated OA-phenotype observed in HFD mice.
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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".