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Association of Longitudinal Trajectories of Insulin Resistance With Adverse Renal Outcomes

posted on 15.03.2022, 16:29 by Seokhun Yang, Soongu Kwak, You-Hyun Song, Seung Seok Han, Hye Sun Lee, Shinae Kang, Seung-Pyo Lee
Objective To analyze the relationship between time-serial changes in insulin resistance and renal outcomes.

Research design and methods A prospective cohort of subjects from the general population without chronic kidney disease (CKD) underwent a biennial check-up for 12 years (n=5,347). The 12-year duration was divided into a 6-year exposure period, where distinct homeostatic model assessment for insulin resistance (HOMA-IR) trajectories were identified using latent variable mixture modeling, followed by a 6-year event accrual period, from which the renal outcome data were analyzed. The primary endpoint was adverse renal outcomes, defined as a composite of eGFR <60 mL/min/1.73m2 in ≥2 consecutive check-ups or albumin ≥1+ on urine strip.

Results Two distinct groups of HOMA-IR trajectories were identified during the exposure period: stable (n=4,770) and increasing (n=577). During the event accrual period, 449 (8.4%) patients developed adverse renal outcomes, and the risk was higher in the increasing HOMA-IR trajectory group than in the stable group (hazard ratio 2.06, 95% confidence interval 1.62–2.60, P <0.001). The results were similar after adjustment for baseline clinical characteristics, comorbidities, anthropometric and laboratory findings, eGFR, and HOMA-IR. The clinical significance of increasing HOMA-IR trajectory was similar in three or four HOMA-IR trajectories. The increasing tendency of HOMA-IR was persistently associated with a higher incidence of adverse renal outcomes, irrespective of the prevalence of diabetes.

Conclusion An increasing tendency of insulin resistance was associated with a higher risk of adverse renal outcomes. Time-serial tracking of insulin resistance may help identify patients at high risk for CKD.


This work was supported by a National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT; No. 2019R1A2C2084099).