Accurate, Race-Free LDL-C Estimation in Non-Fasting Settings: A Machine-Learning Study in 3,477 Adults

Traditional LDL-C testing barriers—mandatory 9–12 hour fasting and inperson visits—disproportionately limit access for rural populations (60% of US counties lack cardiology services), shift workers, patients with diabetes, and caregivers. These barriers persist despite 2016 European guidelines endorsing non-fasting lipid panels and explosive post-COVID telehealth growth. To determine whether machine learning can maintain and enhance LDL-C accuracy in real-world access-expanding scenarios: non-fasting collection, remote assessment without vital signs, and race-free inputs with equity audit. Cross-sectional analysis of All of Us Research Program (n=3,477 adults; 40.1% tested outside traditional fasting windows). We evaluated accuracy across fasting proxies, labs-only configurations, and racial/ethnic groups using 80/20 train/test split with bootstrap confidence intervals. Mean absolute error (MAE) and calibration in likely non-fasting states (blood drawn after 10:00 local time, triglycerides (TG) ≥ 200 mg dL−1, glucose > 110 mg dL−1); labs-only configuration (no blood pressure); racial equity (Black-/African American vs. White MAE gap); economic impact of single-visit workflows. Among 696 test participants, 279 (40.1%) were tested in likely non-fasting states. Standard equations degraded substantially in non-fasting conditions (Friedewald MAE 29.05 mg dL−1vs. 25.87 mg dL−1fasting; calibration slope remained far from ideal at 0.58–0.61), while the ML system maintained accuracy (MAE 24.04 mg dL−1non-fasting vs. 23.18 mg dL−1fasting; slope 0.99–1.07)—yielding 17.2% improvement over Friedewald in non-fasting states vs. 10.4% when fasting. Removing blood pressure measurements changed MAE by only −0.12 mg dL−1(90% CI −0.33 to 0.08), meeting non-inferiority within ±0.5 mg dL−1margin and enabling telehealth workflows. The system achieved racial equity without race input (Black/African American–White gap −0.19 mg dL−1, 95% CI −4.12 to 3.74, indicating no meaningful difference). Economic modeling showed eliminating fasting requirements prevents 4,000 repeat visits annually per 10,000 tests. Base-case net savings of $185,000 per 10,000 tests (40% repeat-visit rate at $50/visit minus $15,000 implementation/QA costs). Break-even occurs at 750 tests in year one, then 250 tests/year thereafter. Machine learning enables accurate LDL-C assessment without fasting or in-person visits, addressing critical access barriers for rural, diabetic, and underserved populations. With roughly 40% of real-world testing already occurring outside fasting windows, single-visit workflows can substantially reduce repeat visits while maintaining measurement quality and achieving racial equity.