Appendicular Skeletal Muscle Mass in Older Adults Can Be Estimated With a Simple Equation Using a Few Zero-Cost Variables.

Background And Purpose: Assessing appendicular skeletal muscle (ASM) mass is crucial for the diagnosis of numerous pathologies related to the decline of muscle mass in old age, such as sarcopenia, malnutrition, or cachexia. The dual-energy X-ray absorptiometer (DEXA) radiological technique, which is the gold standard for its assessment, is particularly costly and not routinely used in clinical practice. The aim of this study was to derive computationally simple equations capable of estimating the DEXA-measured ASM at zero cost in older adult populations.

Can artificial intelligence simplify the screening of muscle mass loss?

Background: Sarcopenia is a risk factor for morbidity and preventable mortality in old age, with consequent high costs for the national health system. Its diagnosis requires costly radiological examinations, such as the DEXA, which complicate screening in medical centers with a high prevalence of sarcopenia.

Objectives: Developing a nearly zero-cost screening tool to emulate the performance of DEXA in identifying patients with muscle mass loss.

Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes.

Aims: The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia.

Methods: We use NHANES cross-sectional data over 10 years (2007-2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia.