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Abstract: TH-PO1008

Predictors of Differences in Cystatin C- and Creatinine-Based eGFR

Session Information

Category: CKD (Non-Dialysis)

  • 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

  • Chen, Debbie, University of California San Francisco, San Francisco, California, United States
  • Lu, Kaiwei, Northern California Institute for Research and Education, San Francisco, California, United States
  • Scherzer, Rebecca, University of California San Francisco, San Francisco, California, United States
  • Potok, O. Alison, University of California San Diego School of Medicine, La Jolla, California, United States
  • Lees, Jennifer S., University of Glasgow, Glasgow, Glasgow, United Kingdom
  • Mark, Patrick, University of Glasgow, Glasgow, Glasgow, United Kingdom
  • Ix, Joachim H., University of California San Diego School of Medicine, La Jolla, California, United States
  • Shlipak, Michael, San Francisco VA Health Care System, San Francisco, California, United States
  • Estrella, Michelle M., San Francisco VA Health Care System, San Francisco, California, United States
Background

Large differences between eGFR based on cystatin C (eGFRcys) and creatinine (eGFRcr) are common. A comprehensive evaluation of factors that account for these differences is needed to guide the interpretation of discrepant eGFR values.

Methods

We randomly divided the study population of 468,969 UK Biobank participants into 2 nonoverlapping cohorts, 80% for model training and 20% for model testing. In the training set, we used Bayesian model averaging to identify a parsimonious set of predictors among 34 candidate variables, including sociodemographic, lifestyle, physical and clinical factors of large eGFRdiff. Large eGFRdiff was defined as eGFRcys minus eGFRcr <-15 (negative eGFRdiff) or ≥15 mL/min/1.73 m2 (positive eGFRdiff). We incorporated the identified set of predictors into a multinomial logistic regression model to estimate the odds of negative or positive eGFRdiff. We assessed model performance in the test set.

Results

The mean age was 56y; 46% were male.The overall mean ± SD of eGFRcys was 88±16 and eGFRcr was 95±13 mL/min/1.73 m2; 25% of participants had negative eGFRdiffs and 5% had positive eGFRdiff. Strong predictors of negative eGFRdiff included older age, male sex, South Asian ethnicity, current smoker (vs. never smoker); history of thyroid dysfunction, chronic inflammatory disease, steroid use, higher waist circumference and body fat, and UACR >300 mg/g (Table 1). Odds ratio estimates for these predictors were largely inverse of those for positive eGFRdiff. The model’s AUC was 0.75 in the test set, with good calibration (1.00).

Conclusion

This study highlights the multitude of demographic, lifestyle, and health characteristics associated with wide eGFRdiff. These results may help clinicians to interpret discrepant eGFRcys and eGFRcr values.

Funding

  • NIDDK Support