Glucose intolerance in aging is mediated by the Gpcpd1-GPC metabolic axis
Bibliographic record
Abstract
Abstract Skeletal muscle plays a central role in the regulation of systemic metabolism during lifespan. With aging, muscle mediated metabolic homeostasis is perturbed, contributing to the onset of multiple chronic diseases. Our knowledge on the mechanisms responsible for this age-related perturbation is limited, as it is difficult to distinguish between correlation and causality of molecular changes in muscle aging. Glycerophosphocholine phosphodiesterase 1 (GPCPD1) is a highly abundant muscle enzyme responsible for the hydrolysis of the lipid glycerophosphocholine (GPC). The physiological function of GPCPD1 remained largely unknown. Here, we report that the GPCPD1-GPC metabolic pathway is dramatically perturbed in the aged muscle. Muscle-specific inactivation of Gpcpd1 resulted in severely affected glucose metabolism, without affecting muscle development. This pathology was muscle specific and did not occur in white fat-, brown fat- and liver-deficient Gpcpd1 deficient mice. Moreover, in the muscle specific mutant mice, glucose intolerance was markedly accelerated under high sugar and high fat diet. Mechanistically, Gpcpd1 deficiency results in accumulation of GPC, without any other significant changes in the global lipidome. This causes an “aged-like” transcriptomic signature in young Gpcpd1 deficient muscles and impaired insulin signaling. Finally, we report that GPC levels are markedly perturbed in muscles from both aged humans and patients with Type 2 diabetes, with a high correlation between GPC levels and increased chronological age. Our findings show the novel and critical physiological function of GPCPD1-GPC metabolic pathway to glucose metabolism, and the perturbation of this pathway with aging, which may contribute to glucose intolerance in aging.
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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 | 0.001 |
| 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".