Nutrition, Exercise, and Pharmaceutical Therapies for Sarcopenic Obesity
Bibliographic record
Abstract
Sarcopenia is characterized by progressive and generalized loss of skeletal muscle mass and strength, with a risk of adverse outcomes such as physical disability, poor quality of life, and death. Sarcopenic obesity is defined as having both sarcopenia and obesity, a condition reported to be associated with a higher risk for adverse outcomes including functional disability, frailty, poor quality of life, longer hospitalization, and higher mortality rates. The definition and diagnostic criteria for sarcopenia have been described by several working groups on the disease; however, there is no standardized definition and diagnostic criteria for sarcopenic obesity. In this review, we summarize nutrition, exercise, and pharmaceutical therapies for counteracting sarcopenic obesity in humans. Although there are some pharmaceutical therapies for both sarcopenia (i.e., testosterone, growth hormone, ghrelin, and vitamin D) and obesity (orlistat, lorcaserin, phentermine-topiramate, and vitamin D), therapies combining nutrition and exercise remain the first-line choice for preventing and treating sarcopenic obesity. Resistance training combined with supplements containing amino acids are considered most effective for treating sarcopenia. Low-calorie, high-protein diets combined with aerobic exercise and resistance training are recommended for preventing and treating obesity. Therefore, nutrition therapies (low-calorie, high-protein diets, protein and amino acid supplementation) and exercise therapies (resistance training and aerobic exercise) would be expected to be the most effective option for preventing and treating sarcopenic obesity. In cases of severe sarcopenic obesity or failure to achieve muscle gain and weight loss through nutrition and exercise therapies, it is necessary to add pharmaceutical therapies to treat the condition.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".