Developers and software architects are often looking for design patterns to apply, new algorithms to implement, reusable components that are easy to use and maintain, and new ways to improve development. It's not always easy to find a unique or perfect solution and it's necessary to use different technologies and methodologies to accomplish the goal of having an application that runs and never fails.
Introduction and Objective: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease, and its prevalence in patients with T2DM is estimated to be over 60%. The American Diabetes Association (ADA) recommends that adults with T2DM with an indeterminate or high Fibrosis-4 (FIB-4) score should have additional risk stratification by liver stiffness measurement to detect liver disease early and prevent future liver complications, such as fibrosis and cirrhosis. Our study aims to describe the implementation of ADA recommended liver fibrosis screening in the primary care setting. Methods: This was a chart review of adults with T2DM from two primary care clinics within an academic medical center who were seen for a routine visit between 7/31/2023 to 12/31/2024. Adults diagnosed with hepatic steatosis, alcohol use disorder, or liver disease were excluded. Age, platelets, and liver function tests were collected to calculate FIB-4 score to estimate risk of liver fibrosis. Descriptive statistics were used to summarize patient demographics, clinical characteristics, and risk stratification. Results: A total of 751 patients were included in the analysis. The population was majority male (51.9%), White (74.2%), and Hispanic (62.3%). The mean age was 61 ± 12 years and 49.8% of patients had obesity. FIB-4 scores were unavailable for 52 (6.9%) patients due to missing labs. In those with calculable FIB-4 scores, 39.3% had indeterminate FIB-4 and 6.6% had high FIB-4. Therefore, a total of 45.9% of adults with calculable FIB-4 score and T2DM should undergo additional fibrosis screening by liver stiffness measurement based off ADA guidelines. Conclusion: This study emphasizes the importance of implementation of ADA recommended liver fibrosis screening in adults with T2DM and highlights the opportunity for early detection of liver disease in the primary care setting. V. Nguyen: None. J. Rocco: None. T. Oberg: None. K. Gallegos Aragon: None. P.D. Deming: None. G. Ray: None.
Introduction and Objective: Brown adipose tissue (BAT) functions as a metabolic sink, efficiently processing fatty acids (FAs), glucose, and amino acids, playing a pivotal role in metabolic regulation and energy homeostasis. However, the metabolic adaptations enabling BAT to respond to fasting and refeeding cycles are not well understood. Methods: We employed mass spectrometry techniques, including Liquid Chromatography (LC), Capillary Electrophoresis (CE), and Spatially Resolved Imaging. Results: We demonstrate that BAT exhibits a distinct free fatty acid (FFA) and lipid-bound fatty acid profile, with enrichment of very long-chain polyunsaturated fatty acids (VLC-PUFAs) and C13-C14 fatty acids compared to white adipose tissue (WAT). Alternate day fasting (ADF) triggered a dynamic change of these free fatty acids (FFAs) in BAT, accompanied by selective alterations of upper glycolysis, glyceroneogenesis, and triglyceride synthesis, a shift less pronounced in WAT. Additionally, several types of BAT phospholipid, including lysophosphatidic acid, phosphatidylcholine, phosphatidylserine, and phosphatidylinositol-ceramide, transitioned from highly unsaturated to more saturated lipids, alongside significant spatial and dynamic reprogramming. Mechanistically, periodic fasting and refeeding activated mTORC1, and genetic inactivation of mTORC1 in BAT diminished ADF-induced lipid saturation, storage, and redistribution. Conclusion: These findings reveal that while BAT generally prefers unsaturated fats, it undergoes substantial lipid saturation and spatially dynamic reprogramming in response to fasting and refeeding, offering new insights into BAT’s adaptive role in metabolic homeostasis. C. Wang: None. M. Liu: None.
