Platelet bioenergetics correlate with skeletal muscle respiration in a murine model of type II diabetes
Bibliographic record
Abstract
Mitochondrial bioenergetic research in skeletal muscle is limited by the need for biopsies. We executed a proof-of-concept study to evaluate whether blood platelets could serve as a minimally invasive surrogate for skeletal muscle mitochondrial respiration in mice. Using Seahorse extracellular flux analysis, platelet respiration was measured in healthy C57BL/6J and leptin receptor-null db/db mice, while high-resolution respirometry (Oroboros O2k) assessed mitochondrial function in white gastrocnemius muscle of the same animals. A critical component of this study was extensive methodological optimization for platelet bioenergetics analysis in mice. We provide comprehensive methodological details and guiding principles for performing Seahorse bioenergetic assays on mouse platelets. Our foundational findings also suggest platelet mitochondria can reflect tissue-level mitochondrial health, pointing to a potential “liquid biopsy” approach for assessing metabolic status. Multiple key metrics of respiration showed significant correlations between platelets and muscle in the same animals, indicating that platelet bioenergetic profiles mirror the metabolic status of skeletal muscle in healthy and genetically diabetic mice. This work lays the conceptual and methodological foundation for future studies in human metabolic diseases where muscle bioenergetic dysfunction is implicated but current methods are not implementable for clinical surveillance. This study provides foundational proof-of-concept in healthy and diabetic mice, motivating validation in human studies as the next step toward biomarker development and precision medicine strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".