Background <p>Chronic obstructive pulmonary disease (COPD), emphysema, bronchiectasis, and pulmonary heart disease are chronic lung diseases that pose a global public health challenge. However, there remains a lack of accurate assessment and predictive indicators in large-scale epidemiological screenings. The triglyceride-glucose (TyG) index serves as a reliable indicator of insulin resistance (IR). IR is associated with an increased incidence, prevalence, or severity of chronic lung diseases. Therefore, this study aims to investigate the relationship between the TyG index and the risk of chronic lung diseases, as well as to assess the predictive role of the TyG index in chronic lung diseases.</p> Methods <p>Based on data collected from the China Health and Aging Longitudinal Study (CHARLS) from 2011 to 2020, a total of 3,776 research subjects were included for data analysis. The TyG index of each subject was recorded, with self-reported and hospital-diagnosed chronic lung diseases as the observation outcomes. K-means clustering analysis was employed to categorize the subjects into three Clusters. The Kaplan–Meier curve was used to compare the survival rates of chronic lung diseases events among the Clusters. Multivariate Cox proportional hazards regression analysis was conducted to examine the relationship between the TyG index and chronic lung diseases events across the Clusters. A restricted cubic splines (RCS) regression model was utilized to explore potential linear associations between the TyG index and chronic lung diseases events. The Receiver Operating Characteristic Curve (ROC) was used to evaluate the predictive value of the TyG index for chronic lung diseases events.</p> Results <p>A total of 3,776 subjects were included in the study, during the follow-up period from 2013 to 2020, 940 subjects were diagnosed with chronic lung diseases. Based on baseline characteristics, the K-means clustering analysis identified three Clusters of subjects: the elderly Cluster with multiple comorbidities and low metabolic risk, the young Cluster with good baseline health and moderate metabolic risk, and the middle-aged Cluster with multiple comorbidities and high metabolic risk. The Kaplan–Meier curve indicated statistically significant survival differences among the Clusters (<i>p =</i> 0.0064). After a follow-up period exceeding 50&#xa0;months, Cluster 1 exhibited the fastest decline and the lowest rate of disease-free survival. Multivariate Cox proportional hazards analysis revealed that in the unadjusted model, the TyG index of Cluster 1 was significantly associated with chronic lung diseases events (HR, 1.58 [95% CI 1.18–2.13], <i>p &lt;</i> 0.05). This association remained significant in models adjusted for demographic factors (HR, 1.61 [95% CI 1.18–2.20], <i>p &lt;</i> 0.05) and in models adjusted for both demographic factors and disease status (HR, 1.64 [95% CI 1.19–2.26], <i>p &lt;</i> 0.05). Similarly, the TyG index in Cluster 3 showed a significant association with chronic lung diseases events in both the unadjusted (HR, 1.62 [95% CI 1.12–2.32], <i>p &lt;</i> 0.05) and adjusted models (HR, 1.66 [95% CI 1.15–2.39], <i>p &lt;</i> 0.05; HR, 1.66 [95% CI 1.14–2.41], <i>p &lt;</i> 0.05). RCS curves demonstrated a positive association between the TyG index and chronic lung diseases events in Clusters 1 and 3. ROC curves for the entire population and each Cluster indicated that the predictive value of the TyG index for chronic lung diseases events was limited (AUC = 0.511–0.548).</p> Conclusion <p>Research indicates a positive association between the TyG index and chronic lung diseases in specific populations. The TyG index serves as a predictive factor for chronic lung diseases events, although it is not an independent predictor. The calculation and monitoring of the TyG index can aid in risk stratification and the development of intervention strategies for populations. Furthermore, baseline information can be utilized for further subCluster analyses and exploration of interactions.</p>