A diagnostic proposal for sarcopenic obesity in adults based on body composition phenotypes. (December 2022)

• Record Type: Journal Article
• Authors: Zambon Azevedo, Vittoria; Ponnaiah, Maharajah; Bel Lassen, Pierre; Ratziu, Vlad; Oppert, Jean-Michel
• Is Part Of: Clinical nutrition ESPEN. Volume 52(2022)
• Page Start: 119
• Page End: 130
• Publication Date: 2022-12
• Subjects: Obesity -- Sarcopenic obesity -- Body composition -- Screening -- Diagnosis assessment; Nutritionally induced diseases -- Periodicals; Metabolism -- Disorders -- Periodicals; 616.39005
• DOI: 10.1016/j.clnesp.2022.10.010
• Languages: English
• ISSNs: 2405-4577

Abstract

Summary: Background & aims: Sarcopenic obesity (SO) associates a decrease in lean body mass (LBM) with an excessive increase in fat mass (FM). A number of diagnostic methods, definitions criteria, and thresholds have been proposed for SO resulting in markedly discordant prevalence estimates in populations with obesity. In this study, we first assessed several previously described SO diagnostic criteria and their limitations, and then we propose an innovative approach for identifying SO. Methods: Data were from a cross-sectional study of a cohort of overweight/obese patients who underwent clinical, laboratory, and body composition assessments by dual-energy X-ray absorptiometry (DXA). We performed unsupervised machine learning through clustering analysis to discriminate lean and fat compartments, and multivariate logistic regressions which provided prognostic variables applied on sex-specific models for SO diagnosis evaluation based on a training dataset (80% of total sample, n = 1165). The predicted models were validated by random forest (RF) machine learning algorithm in the validation dataset (20% of total sample, n = 262). Results: Data from 1427 subjects were analyzed, 79.8% women, mean (±s.d.) age 45.0 (±12.9) years, grade III obesity (BMI over 40 kg/m 2 ) in 42.7%, diabetes in 20.7%, dyslipidemia in 86.3%, and arterial hypertension in 30.3%. Patients with grade III obesity had higher amounts of LBM, FM, and bone mass than subjects with overweight (BMI between 25.0 and 29.9 kg/m 2 ) (p-values < 0.001). When published definitions of SO were applied to this cohort, the prevalence ranged from 0.6% to 96.6%. We built a model that identified 62 (4.3%) individuals as SO, 1125 (78.9%) as non-SO, and 240 (16.8%) as borderline-SO. SO patients showed higher body weight, FM, bone mass, leptin levels, and hepatic steatosis index, but lower LBM and all muscle indexes than non-SO subjects (p-values ≤ 0.001). Patients in the SO and borderline-SO categories were more often females than males (4.5% vs . 3.8% and 16.9% vs . 16.7% respectively, p-value < 0.001) and had significantly higher prevalence of metabolic syndrome and hypertension than non-SO subjects. Males with SO also had higher cardiovascular risk score, while females had higher prevalence of respiratory disorders (p-values < 0.05 for all). Conclusions: Current diagnostic criteria for SO result in widely discrepant prevalence values leading to diagnosis uncertainty. We developed and validated diagnostic criteria based on body composition phenotypes, specifically for overweight/obese subjects, which identified patients at risk of cardio-metabolic complications. This approach may improve the identification of sarcopenia in subjects with obesity. Highlights: The available operative definitions of sarcopenic obesity (SO) result in markedly discordant prevalence estimates. We developed an unbiased body composition-based diagnostic approach using machine-learning algorithms to diagnose SO. This approach demonstrated a clinical relevance on the assessment of the clinical phenotype and cardiometabolic risk of SO. This diagnostic approach should allow an improved identification of SO and thus help raise awareness of this condition.