Lecithin Alleviates Memory Deficits and Muscle Attenuation in Chinese Older Adults and SAMP8 Mice
Bibliographic record
Abstract
Identifying the mechanistic targets of crosstalk between sarcopenia (SA) and mild cognitive impairment (MCI) is critical for screening high-risk populations and exploring effective prevention and treatment strategies. In a nationwide multicenter prospective cohort study combined with an RCT study, it is found that indexes of muscle health reveal a strong predictive relationship with cognitive performance assessed using the Montreal Cognitive Assessment (MoCA). Furthermore, Random Forest models suggest that lecithin can predict both diseases. Erythrocyte lipid analysis and RCT study indicate the protective function of lecithin and the potential involvement of irisin in that process. In rodent models, phosphocholine (PC) alleviates learning and memory impairments and muscle attenuation in SAMP8 mice, while FNDC5/irisin knockdown accelerates brain and muscle damage or eliminates the protective effects of PC. Transcriptome analysis shows that PGC1α (the regulator of FNDC5) is regulated by PC treatment, and the results of knocking out PGC1α and FNDC5/irisin are consistent. Here it is found that muscle-secreted FNDC5/irisin is a key target of "muscle-brain" crosstalk, and lecithin may postpone the progression of MCI and SA by stimulating PGC1α-FNDC5/irisin-mediated cross-protection of cognition and skeletal muscle.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".