The link between sarcopenic obesity and Alzheimer's disease: a brain‐derived neurotrophic factor point of view
Bibliographic record
Abstract
Age-related diseases are becoming more prominent as the lifespan of the global population rises. Many of these diseases coincide with each other and can even influence the onset of additional comorbidities. Sarcopenic obesity is described as age-related loss of muscle mass that concurs with excessive weight gain and tends to increase the risk of comorbidity development, including Alzheimer's disease (AD). Though the exact link between sarcopenic obesity and AD is not known, this review explores the possibility that reduced levels of brain-derived neurotrophic factor (BDNF) throughout the body may serve as the underlying commonality. In AD, reductions in BDNF signalling through its receptor promote the activation of glycogen synthase kinase 3 beta (GSK3β), which subsequently increases the production of amyloid beta (Aβ) peptides and neurofibrillary tangles (NFTs). In the skeletal muscle, lower BDNF concentrations are linked to impaired muscle fibre repair and regeneration, increasing the likelihood of sarcopenia. Furthermore, the absence of BDNF impairs mitochondrial function, leading to insulin resistance and increased adiposity. BDNF concentration has a negative relationship with obesogenic markers in adipose tissue, and as such, lower concentrations of BDNF lead to weight gain. Collectively, current literature suggests that BDNF attenuates AD pathology while improving skeletal muscle mitochondrial function, whole-body insulin resistance and facilitating adipocyte browning. Therefore, BDNF may be a viable target for multiple age-related diseases, but more research is required to substantiate this claim, with a particular focus on examining any potential influence of biological sex, as women are at a higher risk for both AD and sarcopenic obesity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".