Abstract: FR-PO400
CKD Self-Management: Identifying Phenotypes and Associations with Renal and Cardiovascular Outcomes
Session Information
- CKD: Risk Factors for Incidence and Progression - I
November 03, 2017 | Location: Hall H, Morial Convention Center
Abstract Time: 10:00 AM - 10:00 AM
Category: Chronic Kidney Disease (Non-Dialysis)
- 301 CKD: Risk Factors for Incidence and Progression
Authors
- Schrauben, Sarah J., U Penn, Philadelphia, Pennsylvania, United States
- Hsu, Jesse Yenchih, U Penn, Philadelphia, Pennsylvania, United States
- Rosas, Sylvia E., CRIC, Bethesda, Maryland, United States
- Deo, Rajat, CRIC, Bethesda, Maryland, United States
- Jaar, Bernard G., CRIC, Bethesda, Maryland, United States
- Saab, Georges, CRIC, Bethesda, Maryland, United States
- Lederer, Swati, CRIC, Bethesda, Maryland, United States
- Chen, Jing, CRIC, Bethesda, Maryland, United States
- Ricardo, Ana C., CRIC, Bethesda, Maryland, United States
- Lash, James P., CRIC, Bethesda, Maryland, United States
- Feldman, Harold I., U Penn, Philadelphia, Pennsylvania, United States
- Anderson, Amanda Hyre, U Penn, Philadelphia, Pennsylvania, United States
Background
In the effort to slow chronic kidney disease (CKD) progression and its complications, patients need to engage in self-management behaviors. This study evaluated CKD self-management behaviors (CKD-SMB) by identifying patterns of engagement into groups (or phenotypes), and evaluating the association of these phenotypes with renal and cardiovascular outcomes, and death.
Methods
Data from the Chronic Renal Insufficiency Cohort (CRIC) Study were analyzed using a clustering technique, latent class analysis (LCA), to identify CKD-SMB phenotypes stratified by diabetes status. The original CRIC cohort (N=3939) was the derivation cohort, and 1,560 participants subsequently recruited served as the validation cohort. LCA was based on the following measures of CKD-SMB: BMI, diet, physical activity, blood pressure, smoking status, and hemoglobin A1c (if diabetic), which were dichotomized into “recommended” and “not recommended”. Cox proportional hazards models calculated hazard ratios (HRs, 95% CI) of phenotypes for CKD progression, atherosclerotic and heart failure (HF) events, and death.
Results
Three CKD-SMB phenotypes were identified separately among diabetics (DM) and non-diabetics (ND) that varied by level of engagement in recommended CKD-SMB, with Phenotype I being the most engaged, Phenotype II moderately engaged, and Phenotype III, the least engaged. In multivariable-adjusted models, Phenotype III was strongly associated with CKD progression, atherosclerotic events, and death among both DM and ND, and Phenotype II was associated with atherosclerotic events in DM and with death in ND (Table).
Conclusion
This study demonstrates there are potentially three CKD-SMB phenotypes that distinguish risk for clinical outcomes. Given the rise of CKD and its complications, CKD-SMB phenotypes could identify high-risk groups and guide management.
Associations (HR, 95% CI) of Clinical Outcomes by CKD-SMB Phenotypes
| Phenotype II-DM | Phenotype-II-ND | Phenotype III-DM | Phenotype III-ND | |
| CKD Progression | 1.11 (0.91-1.35) | 1.01 (0.82-1.25) | 1.82 (1.32-2.52) | 1.49 (1.06-2.09) |
| Atherosclerotic Events | 1.40 (1.04-1.89) | 1.15 (0.83-1.60) | 2.54 (1.61-3.99) | 1.90 (1.18-3.06) |
| HF Events | 1.08 (0.84-1.40) | 1.35 (0.97-1.86) | 0.90 (0.56-1.45) | 1.12 (0.66-1.92) |
| Death | 1.22 (0.97-1.55) | 2.80 (1.60-4.89) | 1.95 (1.35-2.81) | 4.14 (2.06-8.33) |
Phenotype I as referent group.
Funding
- NIDDK Support