posted on 2022-03-15, 16:29authored bySeokhun Yang, Soongu Kwak, You-Hyun Song, Seung Seok Han, Hye Sun Lee, Shinae Kang, Seung-Pyo Lee
<b>Objective</b> To analyze the relationship between time-serial changes in insulin resistance and
renal outcomes.
<p><b>Research design and methods </b>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/1.73m<sup>2 </sup>in ≥2 consecutive check-ups or albumin ≥1+ on urine strip.</p>
<p><b>Results</b> 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, <i>P</i> <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.</p>
<p><b>Conclusion</b> 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.</p>
Funding
This work was supported by a National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT; No. 2019R1A2C2084099).