Nonlinear thresholds in lipid-frailty interplay: Precision targets for severe airflow limitation in aging adults

To explore the association between fat metabolism, frailty phenotype, and Severe Airflow Limitation（SAL） in middle-aged and elderly populations, identify non-linear thresholds and sociodemographic modification effects.

This cross-sectional study included 2,907 participants (556 SAL cases) from the China Health and Retirement Longitudinal Study (CHARLS). Associations between lipid indices (AIP, residual cholesterol, etc.), frailty index, and SAL were examined using multivariate logistic regression. Nonlinear relationships were assessed using piecewise regression with the segmented package to identify thresholds. Subgroup and interaction analyses were conducted to evaluate effect modifications by age, gender, education, and other factors.

AIP showed an inverse association with SAL (fully adjusted OR = 0.556, 95% CI: 0.394-0.787; P < 0.001). Residual cholesterol exhibited a nonlinear association with a threshold at 0.329 mmol/L: below this threshold, the inverse association was substantially stronger (OR = 0.003, 95% CI: 0.000-0.473; P = 0.024); above the threshold, the inverse association was attenuated but remained significant (OR = 0.758, 95% CI: 0.595-0.967; P = 0.026). Frailty status was positively associated with SAL (OR = 1.816, 95% CI: 1.467-2.248; P < 0.001). Education level modified the associations of AIP (interaction P = 0.036) and residual cholesterol (interaction P = 0.009), with the strongest inverse associations observed in the highest education group. Rural residents had a higher prevalence of SAL than urban residents (74.8% vs. 69.3%, P = 0.010).

In this cross-sectional study, lower AIP and residual cholesterol levels below 0.329 mmol/L, as well as frailty, were associated with SAL, particularly among older and rural populations. The observed lipid profiles may reflect disease-related metabolic alterations. The modification of lipid-SAL associations by education level suggests that social factors may be relevant for identifying high-risk populations.