Key result
AI-derived accelerated cardiovascular aging links to ~10-point higher metabolic syndrome prevalence on FoCUS.
Why the study?
AI-based biological age models offer a non-invasive way to quantify CV aging beyond chronological age, prompting evaluation of the clinical and cardiometabolic correlates of accelerated CV aging estimated from AI-augmented focused cardiac ultrasound in a preventive screening cohort.
Does AI-estimated accelerated cardiovascular biological age from focused cardiac ultrasound identify adverse clinical and cardiometabolic parameters compared to expected aging in asymptomatic adults undergoing preventive screening?
Cross-Sectional (n=1,506)
No
Does AI-estimated accelerated cardiovascular biological age from focused cardiac ultrasound identify adverse clinical and cardiometabolic parameters compared to expected aging in asymptomatic adults undergoing preventive screening?
AI-derived cardiovascular biological age from focused cardiac ultrasound identifies subclinical cardiometabolic vulnerability—including higher rates of metabolic syndrome, obesity, and dyslipidemia—independent of chronological age in asymptomatic adults.
May flag subclinical cardiometabolic risk via AI ultrasound in screening; leaves open incremental value beyond traditional factors.
Background Artificial intelligence (AI)-based biological age models offer a non-invasive means to quantify cardiovascular (CV) aging, potentially capturing risk beyond chronological age. We evaluated the clinical and cardiometabolic correlates of accelerated CV aging estimated from AI-augmented focused cardiac ultrasound (FoCUS) in a preventive screening cohort. Methods Asymptomatic self-referred adults undergoing annual screening in a hospital-based program underwent FoCUS imaging. An AI model estimated CV biological age from FoCUS images, with accelerated aging defined as the top quintile of the delta (CV biological age minus chronological age) after Beheshti bias correction. Participants were stratified into accelerated (quintile 5, Q5) versus expected aging (quintiles 1–4). Demographic, anthropometric, body composition, and laboratory parameters were compared between groups. Results Among 1,506 participants (mean age 66 years; 38% women), chronological age was similar between groups (p=0.659). As expected, the Accelerated Aging group had higher CV biological age (68±5 vs. 60±6 years, p<0.001). Compared with expected aging, accelerated agers showed higher BMI (28±4 vs. 27±4 kg/m², p<0.001), waist circumference (98±14 vs. 95±12 cm, p=0.005), and body fat percentage (36±8% vs. 33±8%, p<0.001). They also had higher resting heart rate (71±11 vs. 68±11 bpm, p<0.001), diastolic blood pressure (81±9 vs. 79±8 mmHg, p=0.001) and triglycerides (120±62 vs. 105±50 mg/dL, p<0.001). Metabolic syndrome was more prevalent in Q5 (27% vs. 17%, p<0.001). In participants aged 40–69 years (N=963), accelerated agers trended toward higher SCORE2 CV risk (5.6±2 vs. 5.3±2, p=0.07). Conclusions AI-derived CV biological age from FoCUS identifies subclinical cardiometabolic vulnerability independent of chronological age. This rapid, point-of-care tool may enhance personalized risk profiling and guide early preventive interventions in asymptomatic adults.
No takes yet. Share an insight, caveat, or question.
Amster et al. (2026) conducted a cross-sectional in Cardiometabolic aging in asymptomatic adults (n=1,506). Accelerated cardiovascular aging (top quintile of AI-derived CV biological age minus chronological age from FoCUS) vs. Expected aging (quintiles 1–4) was evaluated on Prevalence of metabolic syndrome (p=<0.001). AI-derived accelerated cardiovascular aging from FoCUS was associated with higher metabolic syndrome prevalence (27% vs. 17%, p<0.001) and worse cardiometabolic profiles.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: