It's not just how much fat you have-it's where it hides. New research suggests that hidden fat patterns in the body may accelerate brain aging and raise the risk of cognitive decline—even in people who don’t appear obese.
Researchers at The Affiliated Hospital of Xuzhou Medical University in China analyzed MRI scans from nearly 26,000 participants in the UK Biobank. They identified two previously unrecognized fat distribution patterns linked to brain shrinkage and neurological risk. The first, “pancreatic-predominant,” involves unusually high fat levels in the pancreas, while the second, “skinny fat,” describes people with high overall fat despite a normal or only slightly elevated BMI.
Both groups showed greater gray matter loss, faster brain aging, cognitive decline, and elevated risk of neurological diseases. People in the pancreatic-predominant group had about 30% fat in the pancreas—up to six times higher than lean individuals—yet did not necessarily have high liver fat, highlighting a hidden risk often overlooked in clinical exams.
“Most radiology assessments focus on fatty liver,” said Dr. Kai Liu, the study’s coauthor. “But pancreatic fat may be an even stronger indicator of cognitive risk.”
The skinny fat group accumulated fat mainly in the abdominal area, with a high weight-to-muscle ratio, even if their BMI looked normal. Both patterns underline that brain health depends not just on total body fat but also on its location and composition.
These findings could help doctors provide more personalized advice and earlier interventions to protect brain function. Dr. Liu concluded, “Where fat goes matters as much as how much you have—understanding these patterns could reshape how we assess long-term brain health.”
REFERENCE: Miao Yu, Libin Yao, Sanjeev Shahi, Yingqianxi Xu, Meizi Li, Qingtong Zheng, Di Ma, Qi Zhang, Dan Wang, Yang Wu, Xiao Zhou, Haitao Ge, Chunfeng Hu, Yanjia Deng, Kai Liu. Association of Body Fat Distribution Patterns at MRI with Brain Structure, Cognition, and Neurologic Diseases. Radiology, 2026; 318 (1) DOI: 10.1148/radiol.252610
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