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  • Antonio Maceo

Predicting Metabolic Syndrome

A new groundbreaking study led by research institutions in the field of obesity medicine, imaging, and clinical care. The study, led by Dr. John Shepard and associate Dr. John Bennett, demonstrates that 3D imaging can now predict Metabolic Syndrome (MetS) with 92% accuracy.


Metabolic syndrome impacts one-third of all Americans, and is is often considered the precursor to many non-communicable diseases, such as Heart Disease, Diabetes and Cancer. Detecting MetS early can give patients and consumers an opportunity to lower or even reverse health trajectories before symptoms appear and treatment costs are incurred. A positive result for MetS is correlated with a 5x greater risk of Diabetes and 3X greater risk of Cardiovascular Disease (CVD).


The study is the culmination of 2 years of work with UHCC and ShapeUp principal investigator Dr. John Shepard and associate Dr. John Bennett. The research includes data gathered and analyzed by partner institutes including Pennington Biomedical Research Center and UCSF Medicine.


What is Metabolic Syndrome?

MetS is defined as the detection of at least 3 of 5 conditions, including poor cholesterol, high blood pressure, and high glucose in the blood. Clinically testing for MetS has been expensive and time consuming. Typically, a physician would order a blood test, measure a patient's blood pressure, and use a tape measure to determine waist circumference. This arduous process can lead to low patient engagement, causing many people to go untested. Our 3D predictive model technology allows for broader testing without the invasiveness of a blood test or the time and cost of meeting a physician. This predictor now broadens the reach for health screening by making the software available at your local gym, physical training office, med-spa, and weight-loss center. With the growing adoption from primary care practices and hospitals (Mt. Sinai, Holy Name Medical, and more) physicians will find the screening to be a valuable way to drive patient engagement.

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