Palmitic Acid and DGAT1 Deficiency Enhance Osteoclastogenesis, while Oleic Acid-Induced Triglyceride Formation Prevents It
Bibliographic record
Abstract
Both obesity and diabetes mellitus are associated with alterations in lipid metabolism as well as a change in bone homeostasis and osteoclastogenesis. We hypothesized that increased fatty acid levels affect bone health by altering precursor cell differentiation and osteoclast activation. Here we show that palmitic acid (PA, 16:0) enhances receptor activator of NF-κB ligand (RANKL)-stimulated osteoclastogenesis and is sufficient to induce osteoclast differentiation even in the absence of RANKL. TNFα expression is crucial for PA-induced osteoclastogenesis, as shown by increased TNFα mRNA levels in PA-treated cells and abrogation of PA-stimulated osteoclastogenesis by TNFα neutralizing antibodies. In contrast, oleic acid (OA, 18:1) does not enhance osteoclast differentiation, leads to increased intracellular triglyceride accumulation, and inhibits PA-induced osteoclastogenesis. Adenovirus-mediated expression of diacylglycerol acyl transferase 1 (DGAT1), a gene involved in triglyceride synthesis, also inhibits PA-induced osteoclastogenesis, suggesting a protective role of DGAT1 for bone health. Accordingly, Dgat1 knockout mice have larger bone marrow-derived osteoclasts and decreased bone mass indices. In line with these findings, mice on a high-fat PA-enriched diet have a greater reduction in bone mass and structure than mice on a high-fat OA-enriched diet. Thus, we propose that TNFα mediates saturated fatty acid-induced osteoclastogenesis that can be prevented by DGAT activation or supplementation with OA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".