Why Do Diabetes and Obesity Often Co-occur? A Panoramic Analysis from Shared Genes to Metabolic Pathways(2025 Edition)

Introduction: Decoding the Coexistence of “Sugar” and “Fat”

November 14 is World Diabetes Day. In both clinics and laboratories, one observation stands out: obesity and type 2 diabetes (T2DM) often co-occur—the “Twin Epidemics.”

We move beyond appearances to explore shared mechanisms and show how the RDDC platform and its AI bioinformatics tools support systematic analysis.

Shared “Soil”: Insulin Resistance

What Is Insulin Resistance

Insulin acts like a key opening the cellular door for glucose. Increased adiposity—especially visceral fat—releases inflammatory factors that disrupt this interaction, leading to insulin resistance and hyperglycemia.

• Obesity is the major driver of insulin resistance
• Insulin resistance is central to T2DM pathogenesis
• Both are tightly linked via this pathway

Beyond Phenotypes: Shared Genetics and Pathways

If insulin resistance is the surface, shared genetic susceptibility and molecular pathways are the roots. Scientists seek hub genes and crossroads pathways regulating energy storage and utilization.

RDDC offers integrated Gene, Disease, Mutation, and Model databases, with AI tools for systematic exploration.

Using RDDC to Identify Shared Genes

Traditionally, researchers curate diabetes-related genes (e.g., Lep, PPARG) and obesity-related genes (e.g., FTO, MC4R), then compare them.

On RDDC, visit the Disease Database to search “Diabetes Mellitus, Type 2” and “Obesity,” then use tools to intersect gene sets for a shared list.

Figure 1: RDDC T2DM disease page

Figure 2: RDDC Obesity disease page

Figure 3: PPARG gene details

Validating Mechanisms with Models

Research heavily relies on mouse models such as db/db and ob/ob, which exhibit severe obesity, insulin resistance, and hyperglycemia—classic for “obesity-induced diabetes.”

Use the Model Database to find suitable models and detailed phenotypes. Commercial access example: db/db mouse.

Figure 4: PPARG-related model example

AI Tools to Illuminate Metabolic Pathways

When large gene sets are obtained, the Pathway Enrichment Tool quickly reveals glucose/lipid metabolism pathways. RDDC offers 22 cutting-edge AI tools that elevate gene lists into insights.

Figure 5: Pathway enrichment tool

Figure 6: AI bioinformatics tools list

Future Outlook: From Shared Mechanisms to Precision Therapies

If both share roots, efficient strategies may target common nodes. GLP-1 receptor agonists (e.g., semaglutide) began as glucose-lowering agents and later showed strong weight-loss effects—validating the paradigm.

Content Source and Disclaimer

This page is for research and educational reference only and does not constitute medical advice. Descriptions of databases and tools are illustrative; conclusions require rigorous experimental validation and peer review.

References