Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification
This study introduces the NHANES Accelerometry Cardiometabolic Benchmark using data from 1,381 adults (2003-2006). It evaluates ridge regression, XGBoost, and TabPFN v2 for predicting HbA1c, triglycerides, and CRP from accelerometry and lifestyle data. TabPFN v2 performs best for HbA1c and CRP, while triglycerides remain largely unpredictable. Conformal prediction shows marginal coverage targets are met, but subgroup inequities exist.
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[Submitted on 29 Jun 2026]
Title:Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification
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Abstract:Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness. We introduce the NHANES Accelerometry Cardiometabolic Benchmark, derived from NHANES 2003-2006, comprising 1,381 adults with hip-worn accelerometry, fasting laboratory biomarkers, dietary intake, and anthropometrics. We evaluate three tabular learning methods -- ridge regression, XGBoost, and the foundation model TabPFN v2 -- to predict glycated haemoglobin (HbA1c), fasting triglycerides, and C-reactive protein (CRP) from activity phenotypes and lifestyle covariates. TabPFN v2 achieves the best overall performance (HbA1c R^2=0.156, CRP R^2=0.383), while triglycerides remain largely unpredictable (R^2
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