Abstract
<title>Abstract</title> <p>Background The triglyceride-glucose (TyG) index is a well-established surrogate marker of insulin resistance, yet its transition from childhood metabolic phenotypes to adult trajectories and their joint association with metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiometabolic outcomes remains poorly characterized. Methods We conducted a dual-cohort study integrating cross-sectional latent profile analysis (LPA) of 4,901 children (12–19 years) from NHANES 1999–2020 with longitudinal group-based trajectory modeling of 7,473 adults (≥ 45 years) from CHARLS 2011–2020. LPA was performed on eight metabolic-inflammatory variables (TyG index, HOMA-IR, CRP, PNI, NLR, ALT, BMI z-score, WHtR). TyG index trajectories were identified using latent class growth analysis (LCGM). Survey-weighted logistic regression, Cox proportional hazards models, and restricted cubic spline analyses were applied. Results Four distinct pediatric metabolic phenotypes were identified: Metabolically Healthy (49.2%), Intermediate Risk (34.4%), Inflammation-Predominant (8.4%), and Mixed High Risk (7.9%). Compared to the Metabolically Healthy class, the Mixed High Risk group had 64.4% MASLD prevalence (ORadj 3,850.7, 95% CI 535.4–27,698). Four TyG index trajectories emerged in adults: Low-Stable (46.0%), Decreasing (21.5%), Increasing (22.7%), and Very-High-Stable (9.8%). The Increasing trajectory showed the strongest risk for incident MASLD (OR 15.10, 95% CI 9.21–24.78) and incident diabetes (OR 1.82, 95% CI 1.39–2.38). The Very-High-Stable trajectory carried the highest prevalent MASLD risk (OR 9.00, 95% CI 5.38–15.07) and marginally elevated mortality (HR 1.40, 95% CI 0.95–2.06). Cross-cohort analysis revealed parallel TyG distributions across pediatric phenotypes and adult trajectory classes. Conclusions Metabolic phenotypes established in childhood persist into adulthood through distinct TyG index trajectories. The rising TyG trajectory confers the highest cardiometabolic risk, suggesting that early identification of at-risk metabolic phenotypes could guide preventive strategies across the lifespan. (Word count: 267)</p>