Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study

Abstract

Background: CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Subtyping CKD with multidimensional patient data holds the key to precision medicine. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes.

Methods: We used unsupervised consensus clustering on 72 baseline characteristics among 2696 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) study to identify novel CKD subgroups that best represent the data pattern. Calculation of the standardized difference of each parameter used the cutoff of ±0.3 to show subgroup features. CKD subgroup associations were examined with the clinical end points of kidney failure, the composite outcome of cardiovascular diseases, and death.

Results: The algorithm revealed three unique CKD subgroups that best represented patients' baseline characteristics. Patients with relatively favorable levels of bone density and cardiac and kidney function markers, with lower prevalence of diabetes and obesity, and who used fewer medications formed cluster 1 (n=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (n=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (n=395). These three subgroups, when linked with future clinical end points, were associated with different risks of CKD progression, cardiovascular disease, and death. Furthermore, patient heterogeneity among predefined subgroups with similar baseline kidney function emerged.

Conclusions: Consensus clustering synthesized the patterns of baseline clinical and laboratory measures and revealed distinct CKD subgroups, which were associated with markedly different risks of important clinical outcomes. Further examination of patient subgroups and associated biomarkers may provide next steps toward precision medicine.

Keywords: CKD subgroups; clustering analysis; patient heterogeneity; survival.

Graphical abstract

Figure 1.

Consensus matrix heatmaps using all baseline predictors. The consensus matrix heat maps of K=2 to K=8 using all 72 baseline parameters (n=2696) The darkest blue color represents perfect consensus where two individuals always group together, the white color represents perfect consensus where two individuals always group separately, and the blue color scales in between represent ambiguous consensus where two individuals are grouped together in some runs but separately in others. (A) K=2. (B) K=3. (C) K=4. (D) K=5. (E) K=6. (F) K=7. (G) K=8.

Figure 2.

Cluster consensus score and proportion of ambiguously clustered (PAC) pair for consensus clustering using 72 predictors. The bar plot in (A) represents the mean consensus score for different numbers of clusters (K ranges from two to eight) on the basis of 100 repeated resamplings of 80% of the 2696 CRIC participants. The black line in (B) shows the PAC values using the strict criteria with the predetermined boundary of (0, 1) as the definition for ambiguously clustered pairs, and the red line in (B) represents the PAC values using the relaxed criteria with the predetermined boundary of (0.1, 0.9) as the definition for ambiguously clustered pairs.

Figure 3.

The Manhattan plot of the standardized differences across three CKD subgroups for each of the 72 baseline parameters. The y axis is the standardized differences value, and the x axis shows the eight categories of the baseline parameters. The dashed vertical lines represent the standardized differences cutoffs of >0.3 or <−0.3. The gray horizontal lines lay out the category to which each marker belongs, including bone and mineral markers, cardiac markers, diabetes markers, factors of health status, inflammation markers, kidney markers, lipids markers, and use of medications. Cluster 1: well-educated individuals with relatively high baseline kidney function and lower levels of nonfavorable risk factors of bone and mineral markers and cardiac markers. Cluster 2: individuals who are older in age, are obese/overweight, have diabetes, and use more medications. Cluster 3: individuals with higher levels of inflammation, cardiac, and bone and mineral markers; lower levels of serum albumin, serum bicarbonate, and serum calcium; and a lower level of kidney function, along with lower SES. Alka. Phosphat., alkaline phosphatase; BMI, body mass index; CBC, complete blood count; CRP, high-sensitivity C-reactive protein; CVD, cardiovascular disease; CXCL12, C-X-C motif chemokine ligand 12; FGF-23, fibroblast growth factor 23; HbA1C, hemoglobin A1C; HS, high-sensitivity; NGAL, neutrophil gelatinase–associated lipocalin; NSAID, nonsteroidal anti-inflammatory drug; NTproBNP, N-terminal prohormone of brain natriuretic peptide; PTH, parathyroid hormone; UPCR, urine protein-creatinine ratio.

Figure 4.

The scatterplot of eGFR and UACR, colored by CKD cluster membership. Each dot represents one individual and is colored by the CKD cluster membership. The two horizontal lines represent the conventional UACR cutoffs of 30 and 300 mg/g; the vertical line represents the conventional eGFR cutoff of 45 ml/min per 1.73 m². Cluster 1: well-educated individuals with relatively high baseline kidney function and lower levels of nonfavorable risk factors of bone and mineral markers and cardiac markers. Cluster 2: individuals who are older in age, are obese/overweight, have diabetes, and use more medications. Cluster 3: individuals with higher levels of inflammation, cardiac, and bone and mineral markers; lower levels of serum albumin, serum bicarbonate, and serum calcium; and a lower level of kidney function, along with lower SES. ACR, albumin-creatinine ratio.

Figure 5.

Kaplan–Meier survival plots of the three clusters defined by 72 baseline variables and six clinical outcomes. (A) CKD progression; (B) ESKD; (C) CHF; (D) composite cardiovascular disease outcome of MI, stroke, or PAD; (E) composite cardiovascular disease outcome of CHF, MI, stroke, or PAD; and (F) death. The log rank P values for all comparisons were <0.001. Cluster 1: well-educated individuals with relatively high baseline kidney function and lower levels of nonfavorable risk factors of bone and mineral markers and cardiac markers. Cluster 2: individuals who are older in age, are obese/overweight, have diabetes, and use more medications. Cluster 3: individuals with higher levels of inflammation, cardiac, and bone and mineral markers; lower levels of serum albumin, serum bicarbonate, and serum calcium; and a lower level of kidney function, along with lower SES.