Metabolically Active Visceral Fat May be Tied to Advanced Endometrial Cancer: Study Suggests - Video

New research presented at the 38th Annual Congress of the European Association of Nuclear Medicine (EANM'25) suggests that the metabolic activity of visceral fat, rather than its volume alone, may be linked to more aggressive forms of endometrial cancer. The study highlights a potential shift in how clinicians may assess cancer risk and progression in obese patients.

While obesity has long been recognized as a key risk factor for endometrial cancer, the new findings suggest that it’s not just the presence of fat but how metabolically active it is that could influence disease severity. Visceral fat, the fat surrounding internal organs, has been shown in prior research to play a significant role in metabolic health and systemic inflammation.

In this study, researchers analyzed PET/CT scans from 274 women diagnosed with endometrial cancer. Using glucose uptake as a marker of metabolic activity, they specifically measured how active visceral fat tissue was in each patient. The results showed that women with higher glucose uptake in their visceral fat were more likely to present with advanced-stage disease and lymph node metastases.

“Higher metabolic activity in visceral fat was significantly associated with more advanced cancer stages and the presence of lymph node metastases,” said lead author Jostein Sæterstøl, PhD candidate and MSc. “Notably, we saw no strong correlation between the volume of visceral fat and its metabolic activity, suggesting that the link to disease aggressiveness is not merely related to the quantity of fat, but also to its biological activity.”

Sæterstøl added that chronic inflammation, insulin resistance, and adipokine signaling from visceral fat may promote tumour growth and metastasis, highlighting complex biological interactions between fat tissue and cancer cells.

Although PET/CT imaging of fat metabolism is not yet practical for routine clinical use, researchers are optimistic that advancements such as AI-based segmentation and quantitative imaging techniques could make it a valuable tool in the future.

Reference: https://eanm25.eanm.org/