Your could try figuring it out yourselves, but ask your competent? doctor instead.
- Age
- Gender
- Body Mass Index (BMI)
- Waist circumference
- Systolic blood pressure
- Upper arm circumference
- Upper leg (thigh) length [1]
One body measurement could help reveal your diabetes risk—it's not weight
The length of a person’s thigh could help reveal whether they are at risk of type 2 diabetes, according to a new study from researchers in Denmark who have built an at-home test based on the finding.
The tool, called MEDWACS, was developed to help identify people with prediabetes or undiagnosed type 2 diabetes without requiring a blood test or a doctor’s visit, ultimately enabling earlier access to vital health information and steps to improve their health. Users have to answer a short set of questions, several of which can be measured at home with nothing more than a tape measure, a bathroom scale and a standard blood pressure monitor.
The push to develop MEDWACS stems from a gap in diagnosis.
According to the Danish Diabetes Association, roughly 100,000 Danes have type 2 diabetes without knowing it, and about half a million more are estimated to have prediabetes, a precursor condition. Type 2 diabetes accounts for 80 percent of all diabetes cases in Denmark, making early detection a public health priority.
In the U.S., around 31.3 million people aged 65 years or older have prediabetes.
How MEDWACS Works
The test was built using artificial intelligence trained on 30 years of U.S. health data from the National Health and Nutrition Examination Survey, covering adults aged 18 and older of both sexes. From nearly 3,700 possible health indicators, researchers narrowed the test down to seven parameters, including age, gender and body mass index.
The tool was then externally validated using separate data sets from the U.S. and South Korea, where actual diabetes rates were already known.
According to the findings, published in the Journal of Clinical Epidemiology, MEDWACS performed on par with, or better than, other established screening methods at identifying people with prediabetes or undiagnosed type 2 diabetes.
The Thigh Connection
Among the seven parameters, the one researchers describe as most unexpected is thigh length.
Umberto Maggiore, an Italian physician and kidney disease specialist who worked on the tool, explained the link in a statement. He said the length of the femur reflects nutrition in early childhood, and that poor nutrition in the first years of life slightly restricts bone growth, a pattern that is closely tied to a higher risk of diabetes decades later.
Maggiore also pointed to muscle mass. The body’s largest muscle group sits in the thighs, and those muscles play a key role in clearing sugar from the blood. Shorter legs, he said, generally mean less muscle available to absorb that sugar, raising diabetes risk.
statement. “Poor nutrition in the first years of life slightly inhibits bone growth and is closely linked to an increased risk of diabetes decades later.
“Furthermore, the body’s largest muscle group is also located in the thighs, and these muscles are responsible for removing sugar from the blood. Overall, shorter legs mean that a person has less muscle mass to absorb that sugar, which increases the risk of diabetes.”
Daniel Yoo, data scientist at the Technical University of Denmark, spoke with Newsweek about his team’s findings.
“We wanted to create an early warning system that requires zero blood tests or doctor visits,” Yoo said. “By removing those barriers, we can empower people to check their risk right from their living room and prompt them to seek clinical testing before serious complications develop.
“While traditional clinical screening guidelines often target middle-aged or older adults, MEDWACS is validated for anyone aged 18 and up. This is a crucial advantage because type 2 diabetes is increasingly affecting younger populations who might otherwise slip under the radar.”
The researchers built MEDWACS on U.S. and South Korean data, so they say the thigh measurement’s relevance to the Danish population is still an assumption rather than a confirmed fact. To be fully certain the tool works as effectively in Denmark, it would need to be validated against Danish health data specifically.
Even so, the team behind the test says it has good reason to believe the tool will prove accurate for Danes as well, given how consistently it performed across the two very different populations already tested.
Reference
Yoo, D., Maggiore, U., Jolliet, O. (2026). Enhancing prediabetes and diabetes detection through a machine learning-enabled self-assessment approach. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2026.112266.
Contact Newsweek editors on this story: Kara Dolman and Gray R. Thomas
Update, 08/03/2026 09:55 a.m. ET: This article was updated with comment from Daniel Yoo.
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