Seeing beyond BMI: Estimating cardiometabolic risk with

What happened

Insulin resistance is one of the most critical yet underdiagnosed drivers of modern metabolic disease. Predating the clinical onset of type 2 diabetes by years, impaired insulin sensitivity stealthily impairs vascular health, liver function, and energy metabolism long before fasting blood sugar rises into diagnostic ranges. 9 is considered insulin resistant based on epidemiological reviews.

PhotoScan bypasses the clinical measurements by extracting geometric body information directly from smartphone images. We built and evaluated this framework in three key phases: Next, we compared how well different combinations of data predicted insulin resistance, stacking our baseline demographics against combining it with standard tape measurements, smartwatch BIA sensors, PhotoScan, and gold-standard DXA scans.

We tested our models on the MetabolicMosaic cohort using a gradient boosting classifier to identify subjects with insulin resistance. To ensure our results were completely unbiased and leak-free, we implemented a rigorous testing process that repeatedly evaluated the model on unseen data. We also made sure each test group was evenly balanced by both BMI and insulin resistance status, ensuring a fair and realistic performance test.

With this robust framework in place, we systematically fed the classifier five distinct feature sets to compare their predictive power, baseline demographics like age, sex, and body mass index, standard tape measure anthropometrics, smartwatch bioelectrical impedance, our smartphone PhotoScan metrics, and the clinical gold-standard DXA scans. By comparing how the model performed with each of these isolated inputs, we established the clinical value of our smartphone optical phenotyping.

To evaluate our models, we focused on two key metrics: the Area Under the Receiver Operating Characteristic curve (AUROC) and the Net Reclassification Index (NRI). Simply put, AUROC measures how accurately a model can distinguish between someone who has insulin resistance and someone who does not (higher is better).

Why it matters

Recent studies demonstrate that multimodal machine learning frameworks integrating wearable sensor data with routine lab tests can accurately predict HOMA-IR to flag early metabolic risk. Integrating objective measures of body composition offers a vital complement to wearable technology; while wearables track daily physiological behaviors, body composition provides a distinct structural assessment of adiposity to form a complete picture of metabolic risk.

While knowing your total body fat percentage is a good baseline to measure adiposity versus lean mass, additional body composition biomarkers provide much deeper clinical insights.

For instance, the Android-to-Gynoid fat ratio (A/G ratio) compares the fat stored in your trunk (an "apple" shape) versus your hips and thighs (a "pear" shape); the Visceral-to-Subcutaneous fat area ratio (V/S ratio) distinguishes between the highly metabolic internal fat surrounding your organs and the subcutaneous fat stored just beneath your skin. Elevated A/G ratios and higher visceral fat mass strongly correlate with insulin resistance prevalence.

Currently, the gold standard for measuring true body composition is Dual-Energy X-Ray Absorptiometry (DXA) scans. These scans are incredibly precise, but aren't built for everyday screening because they are expensive, require specialized clinical infrastructure, and expose patients to low doses of radiation.

Building on the growing capability of smartphones to passively monitoring user health during daily use, such as continuous heart-rate monitoring, we introduce PhotoScan: an investigational deep learning framework that estimates three-dimensional body composition metrics including body fat percentage (BF%), A/G ratio and V/S ratio, directly from standard 2D smartphone photos.

To build this, we pre-trained a deep neural network on over 35,000 participant records from the UK Biobank and fine-tuned it with a diverse new cohort of 677 adults. Validated across clinical cohorts, PhotoScan demonstrates higher body fat percentage accuracy than smartwatch-based bioelectrical impedance analysis (BIA) sensors while unlocking A/G and V/S ratios beyond BIA's capabilities, offering a scalable, non-invasive framework to predict insulin resistance with near-DXA accuracy.

What to watch

NRI, on the other hand, quantifies exactly how much our new digital metrics improve our ability to correctly categorize people compared to our old baseline model. 692. 748.

In contrast, adding BIA with demographics (demo + bia below) yielded no improvement in AUROC or NRI for insulin resistance classification for IR classification, as BIA only provides BF% estimation, whose feature importance is significantly lower than A/G ratio and V/S ratio in the demo + photoscan model.