Abstract
Background and Aims
The estimated glomerular filtration rate (eGFR) and other kidney function markers are associated with cardiovascular disease (CVD) mortality [1]. It remains unclear whether integrating multiple kidney markers together can improve CVD mortality risk prediction and what would be an appropriate method of integration. In a small general population sample, we recently showed that confirmatory factor analysis (CFA) may predict CVD risk better than single markers, but it did not outperform cystatin C-based eGFR (eGFRcys) [2]. To assess whether our findings were context-dependent and to which extent they may extend to mortality risk assessment, we applied CFA and exploratory factor analysis (EFA), integrating five kidney function markers in the UK Biobank (UKBB) study, comparing risk discrimination for CVD mortality and renal failure mortality versus established eGFR formulas.
Method
We analyzed data from 366,758 UKBB participants (mean age 56.6 years; females 53.7%) without clinical history of kidney failure at baseline. Information on participants’ mortality was collected from the National Health System registry, using ICD-10 codes I00-I99 and N17-N19 to identify CVD mortality and renal failure mortality. We applied CFA and EFA to creatinine-based estimated glomerular filtration rate (eGFRcre), eGFRcys, blood urea nitrogen (BUN), uric acid (UA), and serum albumin (Alb). EFA was fitted using maximum likelihood. Promax rotation was then applied. We fitted Cox regression models to examine the associations of mortality with kidney markers: CFA-based kidney index [CFA]; 1st EFA-based kidney index [EFA1]; 2nd EFA-based kidney index [EFA2]; eGFRcre; eGFRcys; and creatinine- and cystatin C-based eGFR (eGFRcrecys). Models were adjusted for sex, age, body mass index, education, self-reported ancestry, hypertension, diabetes, and tobacco smoking. The receiver operating characteristics (ROC) curve and the DeLong test were used to compare discriminatory ability of each index.
Results
CFA standardized factor loadings (λ) of eGFRcre, eGFRcys, BUN, UA and Alb were 0.81, 0.73, -0.55, -0.39, and 0.12, respectively. EFA identified two factors: EFA1, largely dependent on eGFRcys (λ = 0.85), and EFA2, reflecting BUN (λ = 0.88), eGFRcre (λ = -0.55) and UA (λ = 0.36). Over a median follow-up of 12.5 years, we observed 26,327mortality cases, of which 5,376 and 45 were related to CVD and renal failure. The hazard ratios (HR) and 95% confidence intervals (CI) for CVD mortality and renal failure mortality per each standard deviation change were of 1.22 (1.19–1.25) and 3.53 (2.98–4.18) for eGFRcre, 1.62 (1.57–1.67) and 7.34 (5.82–9.27) for eGFRcys, 1.47 (1.43–1.51) and 4.82 (4.04–5.76) for eGFRcrecys, 1.38 (1.34–1.41) and 2.94 (2.63–3.29) for CFA, 1.61 (1.56–1.66) and 7.54 (5.90–9.64) for EFA1, and 1.33 (1.30–1.35) and 1.98 (1.85–2.12) for EFA2. The area under the curve (AUC) for CVD mortality risk was higher for EFA1 (0.706, 0.699–0.713) than for any other kidney marker, except for eGFRcys (0.709, 0.702–0.716). The kidney failure mortality AUC of EFA1 (0.936, 0.901–0.972) was similar to that of eGFRcys (0.939, 0.905–0.972).
Conclusion
To assess the contribution of kidney function to CVD-related mortality in general population studies of mainly healthy individuals, EFA is a better way than using single markers. However, EFA does not outperform eGFRcys, which, being based on a simpler calculation, remains a better choice for CVD and renal failure mortality risk prediction.