The correlation between the trajectory of plasma atherosclerosis-inducing index in the examination population and the risk of developing metabolic dysfunction-associated steatotic liver disease.
Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a prevalent metabolic disorder with rising global incidence and substantial clinical implications. The atherogenic index of plasma (AIP), a novel marker of lipid metabolism, has shown potential in predicting metabolic diseases, but its longitudinal association with MASLD remains unclear.
This longitudinal study included 1,894 participants from the health examination database of the First Affiliated Hospital of Xi'an Jiaotong University (2020-2024). Group-based trajectory modeling (GBTM) identified three AIP trajectory patterns: low-stable, moderate-stable, and high-stable. Cox proportional hazards models were used to assess the association between AIP trajectories and incident MASLD, adjusting for demographic, lifestyle, and metabolic factors. Subgroup and sensitivity analyses were conducted to assess effect modification and robustness.
Three AIP trajectory patterns were identified: low-stable (33.7%), moderate-stable (49.1%), and high-stable (17.3%). Over a median follow-up of 2.97 years, the high-stable group exhibited a 2.23-fold increased risk of incident MASLD (adjusted HR = 2.23, 95% CI: 1.51-3.30; P < 0.001) compared with the low-stable group, even after adjusting for metabolic mediators including BMI and fasting glucose. The moderate-stable group also showed an increased risk (HR = 1.42, 95% CI: 1.02-1.98; P = 0.038). Subgroup analyses demonstrated consistent associations, with more pronounced effects among non-diabetic and non-hypertensive individuals. Sensitivity analyses further confirmed the robustness of these findings.
Sustained high AIP levels are independently associated with an increased risk of MASLD. AIP trajectory monitoring may offer a valuable tool for early identification and targeted prevention of MASLD.
This longitudinal study included 1,894 participants from the health examination database of the First Affiliated Hospital of Xi'an Jiaotong University (2020-2024). Group-based trajectory modeling (GBTM) identified three AIP trajectory patterns: low-stable, moderate-stable, and high-stable. Cox proportional hazards models were used to assess the association between AIP trajectories and incident MASLD, adjusting for demographic, lifestyle, and metabolic factors. Subgroup and sensitivity analyses were conducted to assess effect modification and robustness.
Three AIP trajectory patterns were identified: low-stable (33.7%), moderate-stable (49.1%), and high-stable (17.3%). Over a median follow-up of 2.97 years, the high-stable group exhibited a 2.23-fold increased risk of incident MASLD (adjusted HR = 2.23, 95% CI: 1.51-3.30; P < 0.001) compared with the low-stable group, even after adjusting for metabolic mediators including BMI and fasting glucose. The moderate-stable group also showed an increased risk (HR = 1.42, 95% CI: 1.02-1.98; P = 0.038). Subgroup analyses demonstrated consistent associations, with more pronounced effects among non-diabetic and non-hypertensive individuals. Sensitivity analyses further confirmed the robustness of these findings.
Sustained high AIP levels are independently associated with an increased risk of MASLD. AIP trajectory monitoring may offer a valuable tool for early identification and targeted prevention of MASLD.