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Metabolic Connections: Rethinking MASLD, MASH, and Obesity in an Evolving Management Landscape 

Slideset

Metabolic dysfunction–associated steatotic liver disease (MASLD) and metabolic dysfunction–associated steatohepatitis (MASH) are closely linked with obesity and cardiometabolic disease, creating opportunities for earlier identification and more comprehensive management. In this downloadable slideset, expert faculty review the shared pathophysiology connecting obesity, liver health, and cardiovascular risk, along with current recommendations for risk assessment, fibrosis screening, and patient stratification. Learners will also explore the latest evidence supporting obesity-directed and MASH-directed therapies, including incretin-based treatments, resmetirom, and emerging investigational agents, and examine strategies for multidisciplinary care across the disease continuum. 

Released: November 05, 2026

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Provided by

Provided by Clinical Care Options, LLC dba Decera Clinical Education

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Supporters

This activity is supported by Boehringer Ingelheim Pharmaceuticals, Inc.

Boehringer Ingelheim Pharmaceuticals, Inc.

Target Audience

This activity is intended for hepatologists, endocrinologists, primary care physicians, nurse practitioners, physician associates, pharmacists, and other healthcare providers interested in improving the care of patients living with obesity and MASLD/MASH.

Learning Objectives

Upon completion of this activity, participants should be able to:

  • Analyze current evidence describing the interconnected metabolic pathways linking obesity, liver health, and cardiovascular risk 

  • Implement recommended screening and risk assessments to improve timely detection of MASLD/MASH in patients at risk 

  • Incorporate the latest efficacy and safety data for new and emerging treatments into integrated management decisions for patients with obesity and MASLD/MASH 

Additional Disclosures

Generative artificial intelligence (AI) tools were used for drafting educational content, revising or refining educational content, generating clinical case(s) or scenario(s), and drafting assessment question(s) and/or rationale(s). All AI-assisted content underwent human review, editing, and validation by qualified faculty and accredited education staff to ensure scientific accuracy, balance, and compliance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education.