Insulin resistance—best known as a driver of diabetes and a contributor to cardiovascular, kidney, and liver disease—may also play a far larger role in cancer risk than previously understood. In a study of half a million UK Biobank participants, researchers led by the University of Tokyo used a machine‑learning model to show that insulin resistance is a risk factor for 12 types of cancer, offering the first population‑scale evidence of this long‑suspected link.
Insulin resistance affects daily life in ways that often go unnoticed. When the body stops responding properly to insulin—a hormone that regulates blood glucose—blood sugar levels rise, metabolic pathways shift, and long‑term damage accumulates. The condition is a fundamental cause of type 2 diabetes and is tightly associated with obesity, but it also contributes to other diseases. Despite its broad impact, insulin resistance has been notoriously difficult to measure directly in clinical settings, limiting researchers’ ability to understand its full consequences.
That challenge prompted Yuta Hiraike, MD, PhD, and colleagues at the University of Tokyo to turn to artificial intelligence. The team recently developed a machine‑learning tool called AI‑IR, which predicts insulin resistance using nine standard clinical measurements collected during routine health checkups. “We recently made a tool, AI‑IR, for predicting insulin resistance in individuals based on nine different pieces of medical information,” Hiraike said. “It proved successful and made us think we could apply this tool to related concerns.”
One of those concerns was cancer. Although scientists have long suspected a link between insulin resistance and certain cancers, gathering large‑scale evidence has been difficult because direct measurement requires specialized testing available only in advanced diabetes clinics. By applying AI‑IR to 500,000 UK Biobank participants, the team was able to estimate insulin resistance at a population level and examine its relationship to cancer incidence.
The results, published in Nature Communications in a paper titled “Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer,” were striking. “With AI‑IR, we have provided the first population‑scale evidence that insulin resistance is a risk factor for cancer,” Hiraike said. Because the model relies on routine clinical data, he added, “AI‑IR could be easily implemented to identify high‑risk individuals and enable focused screening of diabetes, cardiovascular disease, and cancer.”
The study also highlights the limitations of relying on body mass index (BMI) as a proxy for metabolic health. BMI can misclassify individuals—labeling some obese people as metabolically healthy while overlooking insulin resistance in people with normal weight. By combining multiple clinical parameters into a single metric, AI‑IR captures metabolic dysfunction that BMI alone cannot explain.
“When compared with directly measured insulin resistance in validation datasets, AI‑IR achieved strong predictive performance,” Hiraike said. “AI‑IR provides a robust and scalable alternative for evaluating insulin resistance at the population scale.”
The team now plans to explore how genetic differences influence insulin‑resistance‑related cancer risk and to integrate large‑scale human data with molecular biology studies. Their goal is to develop better strategies to detect, understand, and ultimately overcome insulin resistance—an everyday metabolic disturbance that may have far‑reaching consequences.
