Poor muscle quality: A hidden and detrimental health condition in obesity

Artificial Intelligence-Assisted Muscular Ultrasonography for Assessing Inflammation and Muscle Mass in Patients at Risk of Malnutrition

BACKGROUND

Malnutrition, influenced by inflammation, is associated with muscle depletion and body composition changes. This study aimed to evaluate muscle mass and quality using Artificial Intelligence (AI)-enhanced ultrasonography in patients with inflammation.

METHODS

This observational, cross-sectional study included 502 malnourished patients, assessed through anthropometry, electrical bioimpedanciometry, and ultrasonography of the quadriceps rectus femoris (QRF). AI-assisted ultrasonography was used to segment regions of interest (ROI) from transversal QRF images to measure muscle thickness (RFMT) and area (RFMA), while a Multi-Otsu algorithm was used to extract biomarkers for muscle mass (MiT) and fat mass (FatiT). Inflammation was defined as C-reactive protein (CRP) levels above 3 mg/L.

RESULTS

The results showed a mean patient age of 63.72 (15.95) years, with malnutrition present in 82.3% and inflammation in 44.8%. Oncological diseases were prevalent (46.8%). The 44.8% of patients with inflammation (CRP > 3) exhibited reduced RFMA (2.91 (1.11) vs. 3.20 (1.19) cm2, p < 0.01) and RFMT (0.94 (0.28) vs. 1.01 (0.30) cm, p < 0.01). Muscle quality was reduced, with lower MiT (45.32 (9.98%) vs. 49.10 (1.22%), p < 0.01) and higher FatiT (40.03 (6.72%) vs. 37.58 (5.63%), p < 0.01). Adjusted for age and sex, inflammation increased the risks of low muscle area (OR = 1.59, CI: 1.10-2.31), low MiT (OR = 1.49, CI: 1.04-2.15), and high FatiT (OR = 1.44, CI: 1.00-2.06).

CONCLUSIONS

AI-assisted ultrasonography revealed that malnourished patients with inflammation had reduced muscle area, thickness, and quality (higher fat content and lower muscle percentage). Elevated inflammation levels were associated with increased risks of poor muscle metrics. Future research should focus on exploring the impact of inflammation on muscles across various patient groups and developing AI-driven biomarkers to enhance the diagnosis, monitoring, and treatment of malnutrition and sarcopenia.

Sarcopenic obesity and weight loss-induced muscle mass loss

PURPOSE OF REVIEW

Sarcopenic obesity is a clinical condition characterized by the coexistence of excess adiposity and impaired muscle function, associated with heightened cardiometabolic risk and frailty. The emergence of new incretin-based obesity management medications (OMMs), which allow unprecedented weight loss, has raised concerns regarding weight loss-induced fat-free mass (FFM) reduction, including skeletal muscle mass (SMM). This review examines recent findings on the prevalence, diagnosis, and implications of sarcopenic obesity, explores the effects of weight-loss interventions on body composition and their impact on health, and discusses strategies to preserve muscle mass.

RECENT FINDINGS

Weight loss induced by incretin-based OMMs results in a variable but significant reduction in FFM. The extent to which this loss affects SMM and function remains uncertain. Nutritional strategies, particularly adequate protein intake, and structured exercise interventions, especially resistance training, play a key role in mitigating FFM loss. Digital health interventions and telemedicine-based exercise programs offer promising approaches for maintaining muscle health during weight loss.

SUMMARY

The clinical significance of FFM loss during weight reduction remains debated. Future research should refine sarcopenic obesity diagnostic criteria, assess the long-term impact of FFM/SMM reduction during intentional weight loss, and evaluate interventions that optimize body composition while preserving functional health.