Presence of chronic morbidities alters skeletal muscle health and amino acid kinetics in mild cognitive impairment
Bibliographic record
Abstract
BackgroundSkeletal muscle weakness and mild cognitive impairment (MCI) commonly occur with aging.ObjectiveWe examined whether presence of chronic morbidities in MCI is associated with specific alterations in muscle health, functional capacity, and whole body amino acid kinetics.MethodsA group of 247 older adults were stratified into MCI/non-MCI (Montreal Cognitive Assessment) and presence/absence of chronic diseases. We measured lean mass by dual-energy x-ray absorptiometry, strength by dynamometry, and functional capacity by 6-min walk test. Postabsorptive whole body production (WBP) of amino acids were assessed by pulse administration of a mixture of 18 amino acid stable isotopes.ResultsMCI was associated with lower lean mass, functional capacity (p < 0.003), and WBP of arginine, glycine, leucine, and phenylalanine to tyrosine conversion (reflecting net protein breakdown (net PB)) but higher WBP of taurine (all p < 0.05). Presence of chronic morbidities was associated with lower muscle strength, WBP of glycine, and net PB (p < 0.0001), but higher WBP of phenylalanine, glutamate, taurine, tryptophan, and leucine (all p < 0.05). MCI*chronic morbidity interactions were found for muscle strength and net PB (p < 0.0001), with the lowest values in MCI with chronic morbidities.ConclusionsPresence of MCI and chronic morbidities in the older population affect different markers of muscle health and functional decline. Individuals with both MCI and chronic morbidities are at increased risk for severe muscle weakness likely related to a severe downregulation of glycine production and net protein breakdown. Therefore, it is important to consider the presence of chronic morbidities when investigating muscle health and functional capacity in MCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".