[2603.27017] Beyond BMI: Smartphone Body Composition Phenotyping for Cardiometabolic Risk Assessment

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Quantitative Biology > Quantitative Methods

arXiv:2603.27017 (q-bio)

[Submitted on 27 Mar 2026 (v1), last revised 6 Apr 2026 (this version, v2)]

Title:Beyond BMI: Smartphone Body Composition Phenotyping for Cardiometabolic Risk Assessment

Authors:Menglian Zhou, Arno Charton, Emily Blanchard, Lawrence Cai, Tracy Giest, Herschel Watkins, Mohamed Bouterfa, Jackie Wasson, Keerthana Natarajan, Aniket Deshpande, Jiening Zhan, Shelten Yuen, Xavi Prieto, Jacqueline Shreibati, Mark Malhotra, Shwetak Patel, Lindsey Sunden, Cathy Speed, Alicia Kokoszka, Aravind Natarajan, Alexandros Pantelopoulos, Ahmed Metwally<br>View a PDF of the paper titled Beyond BMI: Smartphone Body Composition Phenotyping for Cardiometabolic Risk Assessment, by Menglian Zhou and 21 other authors

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Abstract:Body Mass Index (BMI) is a widely accessible but imprecise proxy of cardiometabolic health. While assessing true body composition is superior, gold-standard methods like Dual-Energy X-ray Absorptiometry (DXA) are not scalable. We address this gap by developing and validating "PhotoScan," a method to estimate body composition from smartphone imagery. We pretrained a deep learning model on UK Biobank participants (N=35,323) and fine-tuned on a newly recruited clinical cohort (PhotoBIA cohort, N=677) with diverse ethnicity, age, and body fat distribution, achieving high accuracy against DXA for total body fat percentage (BF%, MAE = 2.15%), Android-to-Gynoid fat ratio (A/G, MAE = 0.11), and visceral-to-subcutaneous fat area ratio (V/S, MAE = 0.09). Generalizability of the model was demonstrated on an independent metabolic health study cohort (MetabolicMosaic cohort, N=132 participants), achieving MAEs of 2.13% for BF%, 0.09 for A/G, and 0.09 for V/S. We then evaluated the clinical utility of these metrics in the MetabolicMosaic cohort by predicting insulin resistance (IR). Adding PhotoScan-derived body composition metrics to baseline demographics model (Age, Sex, BMI) significantly improved insulin resistance classification (Area Under the Receiver Operating Characteristic Curve "AUROC" 76.0% vs 69.2%, DeLong test p=0.002, Net Reclassification Index "NRI" 0.593). Crucially, this accessible smartphone method achieved performance nearly equivalent to adding clinical-grade DXA data to baseline demographics model (AUROC 77.3% vs 69.2%, DeLong test p=0.004, NRI 0.748). These findings demonstrate that smartphone-based phenotyping captures clinically meaningful risk signals missed by BMI and anthropometrics, offering a scalable alternative to DXA for cardiometabolic risk stratification.

Subjects:

Quantitative Methods (q-bio.QM)

Cite as:<br>arXiv:2603.27017 [q-bio.QM]

(or<br>arXiv:2603.27017v2 [q-bio.QM] for this version)

https://doi.org/10.48550/arXiv.2603.27017

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arXiv-issued DOI via DataCite

Submission history<br>From: Menglian Zhou [view email]<br>[v1]<br>Fri, 27 Mar 2026 22:12:16 UTC (7,454 KB)

[v2]<br>Mon, 6 Apr 2026 23:23:21 UTC (7,452 KB)

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