AIMC Topic: Sarcopenia

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Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis.

Clinical nutrition ESPEN
BACKGROUND & AIMS: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in tr...

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BMC musculoskeletal disorders
BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both...

Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models.

The American journal of clinical nutrition
BACKGROUND: Sarcopenia is known as a decline in skeletal muscle quality and function that is associated with age. Sarcopenia is linked to diverse health problems, including endocrine-related diseases. Environmental chemicals (ECs), a broad class of c...

Machine-learning classifier models for predicting sarcopenia in the elderly based on physical factors.

Geriatrics & gerontology international
AIM: As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X-rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose ...

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data.

Journal of neuroengineering and rehabilitation
BACKGROUND: In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adu...

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosi...

Automated evaluation of masseter muscle volume: deep learning prognostic approach in oral cancer.

BMC cancer
BACKGROUND: Sarcopenia has been identified as a potential negative prognostic factor in cancer patients. In this study, our objective was to investigate the relationship between the assessment method for sarcopenia using the masseter muscle volume me...

Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

ESMO open
BACKGROUND: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and my...

Role of irisin and myostatin on sarcopenia in malnourished patients diagnosed with GLIM criteria.

Nutrition (Burbank, Los Angeles County, Calif.)
OBJECTIVES: Sarcopenia is characterized by the loss of muscle mass. Skeletal muscle can produce and secrete different molecules called myokines. Irisin and myostatin are antagonistic myokines, and to our knowledge, no studies of both myokines have be...