ABSTRACT
INTRODUCTION: Sarcopenic obesity and its association with nonalcoholic fatty liver disease (NAFLD) is under-recognized by many healthcare providers in Western medicine due to the lack of awareness and diagnostic guidelines. The result is delayed recognition and treatment, which leads to further health deterioration and increased healthcare costs. Sarcopenic obesity is characterized by the presence of increased fat mass in combination with muscle catabolism related to chronic inflammation and/or inactivity. Previous research has recommended evaluating body composition and physical function performance to adequately diagnose sarcopenic obesity. Body composition analysis can be performed by imaging applications through magnetic resonance imaging, computed tomography, and dual-energy x-ray absorptiometry. Due to the cost of each device and radiation exposure for patients as evidenced in all three modalities, bioelectrical impedance analysis offers a noninvasive approach capable of providing quick and reliable estimates of lean body and fat mass. METHODS AND RESULTS: This review analyzes the current evidence-based literature, indicating a lower skeletal muscle mass and increased visceral adipose tissue correlation to the advancement of fibrosis in fatty liver disease. CONCLUSION: Given the substantial promising research conducted in predominantly Asian populations regarding body tissue distribution and NAFLD, additional prospective research is needed to extend these findings in Western populations.
ABSTRACT
OBJECTIVE: We sought to validate an algorithm designed to identify patients with post-gastric bypass hypoglycemia (PGBH) using clinician chart review. METHODS: We conducted a chart review study of non-diabetic patients who underwent Roux-en-Y gastric bypass (RYGB) at our institution from 2004 to 2013. The electronic medical record (EMR) algorithm was based on any post-operative glucose <60 mg/dl, diagnosis of hypoglycemia, or medication use for treatment of PGBH and identified 158 charts as PGBH and 1048 charts without PGBH. Two clinicians independently reviewed a random selection of 80 cases and 80 control charts and determined the presence or absence of PGBH by searching the chart using keywords and reviewing laboratory results, medications, and clinic notes. RESULTS: Of the 160 charts reviewed, the EMR algorithm agreed with the chart review for 130 (accuracy = 80%, 95% CI = 75-87%) with sensitivity of 89% (95% CI = 83-96%) and specificity of 86% (95% CI = 78-93%). We improved the algorithm's accuracy to 90% by limiting the search to data obtained 3 months or more following RYGB. CONCLUSION: The EMR algorithm has high sensitivity, specificity, and accuracy to identify post-gastric bypass hypoglycemia within our patient cohort. The use EMR-based algorithms may be a useful tool for future research to improve our understanding of epidemiology and risk factors for post-bariatric surgery hypoglycemia.
