Revised: February 16, 2026
Accepted: May 9, 2026
Published online: June 26, 2026
Processing time: 143 Days and 21.5 Hours
A simple method for evaluating renal function is the estimated glomerular filtration rate (eGFR), which reveals prognostic implications. However, it is not yet known which equation should be applied to elderly Chinese individuals.
To compare the ability of Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), and the Asian/Chinese-modified equations and their predictive performance for clinical outcomes.
A total of 3257 community-dwelling elderly Chinese participants (≥ 65 years) in northern Shanghai were prospectively recruited in the present study. The GFR was estimated by creatinine-based MDRD and CKD-EPI and modified Asi
The prevalence of eGFR < 60 mL/minute, based on different eGFR equations, ranged from 5.7% (c-aMDRD) to 16.5% (cCKD-EPI). The rate and risk of adverse outcomes were higher in patients with eGFR < 60 mL/minute (P < 0.05). Low eGFR (< 60 mL/minute) estimated by the c-aMDRD equation was associated with MACE and car
eGFR is an independent predictor of long-term outcomes. When estimated by the c-aMDRD equation, eGFR predicts MACE and cardiovascular mortality, while all equations predict all-cause mortality. The CKD-EPI, aCKD-EPI, and cCKD-EPI equations might be better than MDRD and c-aMDRD for risk stratification in the elderly Chinese population.
Core Tip: For risk stratification in elderly Chinese adults, the choice of estimated glomerular filtration rate equation is crucial. This study finds that while the modified Chinese-abbreviated Modification of Diet in Renal Disease equation specifically predicts major adverse cardiovascular events and cardiovascular death, the newer Chronic Kidney Disease Epidemiology Collaboration and its Chinese-adjusted versions are superior overall, offering the best predictive power for all-cause mortality.
- Citation: Aili A, Li RY, Mohammed AA, Zhao S, Li MR, Li HN, Xu YW, Zhang Y. Prognostic value of glomerular-filtration-rate estimated by common and modified Asian/Chinese equations for cardiovascular outcomes in elderly Chinese individuals. World J Cardiol 2026; 18(6): 119376
- URL: https://www.wjgnet.com/1949-8462/full/v18/i6/119376.htm
- DOI: https://dx.doi.org/10.4330/wjc.119376
Chronic kidney disease (CKD) is a major public health concern and is associated with an increased risk of cardiovascular events and all-cause mortality[1-3]. Impaired renal function contributes to endothelial dysfunction, vascular calcification, inflammation, and accelerated atherosclerosis, thereby substantially increasing cardiovascular morbidity and mortality in affected individuals[1,4]. A simple method for evaluating renal function is the estimated glomerular filtration rate (eGFR) by several equations. For decades, the most widely used eGFR equations, including Modification of Diet in Renal Disease (MDRD) and the more recent CKD Epidemiology Collaboration (CKD-EPI), were primarily developed and validated in Caucasian populations[5,6]. Given ethnic differences in muscle mass, diet, genetic background, and creatinine meta
Aging is associated with physiological declines in renal function and an increased prevalence of cardiovascular disease, making risk prediction in older adults especially challenging and clinically important[10-13]. Importantly, the optimal eGFR equation for predicting cardiovascular risk in elderly Chinese individuals remains uncertain[14,15]. Whether Asian/Chinese-modified equations offer superior prognostic value compared with traditional MDRD or CKD-EPI equations has not been systematically evaluated in large community-based elderly cohorts.
Therefore, in the current study, we used data from a Chinese community-based elderly cohort to examine the associations between eGFR calculated using MDRD, CKD-EPI, and Asian/Chinese-modified equations and adverse clinical outcomes. Our primary aim was to determine which eGFR equation provides the most robust prognostic information for cardiovascular risk prediction in elderly Chinese individuals.
This analysis was part of the Northern Shanghai Study (ClinicalTrials.gov, No. NCT02368938; February 15, 2015), officially titled “Prognosis Factors of Mortality and Cardiovascular Diseases in the Elderly Chinese: The Northern Shanghai Study”[16]. The study design has been described previously in detail[16]. The inclusion criteria for the present study were: (1) Age ≥ 65 years; and (2) Ability to complete follow-up. The exclusion criteria were: (1) Missing data on baseline serum creatinine (Scr); (2) Presence of New York Heart Association class IV heart failure or end-stage renal disease; (3) Malignant tumour or life expectancy < 5 years; (4) History of stroke within the past 3 months; (5) Withdrawal from the trial due to other diseases; and (6) Protocol violations.
Data collected via questionnaire included gender, age, weight, height, smoking habits, and history of diabetes, hy
The eGFR was calculated using five creatinine-based equations: The MDRD, the CKD-EPI, the derived Chinese-abbreviated MDRD (c-aMDRD), the Asian-adapted CKD-EPI (aCKD-EPI), and the Chinese-adapted CKD-EPI (cCKD-EPI) equations. Scr is expressed in mg/dL, age in years, and eGFR in mL/minute/1.73 m²[5-8,15,17-19].
MDRD equation: eGFR MDRD = 175 × (Scr)-1.154 × (age)-0.203 × (0.742 if female).
CKD-EPI equation: eGFR CKD-EPI = 141 × min (Scr/κ, 1)α × max (Scr/κ, 1)-1.209 × 0.993age × (1.018 if female), where κ = 0.7 for females and 0.9 for males, and α = -0.329 for females and -0.411 for males.
c-aMDRD equation: eGFR c-aMDRD = 175 × (Scr)-1.234 × (age)-0.179 × (0.79 if female).
aCKD-EPI equation: eGFR aCKD-EPI = eGFR CKD-EPI × Asian correction factor, where the Asian correction factor is 0.813.
cCKD-EPI equation: eGFR cCKD-EPI = eGFR CKD-EPI × Chinese correction coefficient, where the Chinese-specific coefficient is 1.1.
The primary endpoint of this study was the occurrence of major adverse cardiovascular events (MACE). MACE were defined as a composite endpoint of non-fatal myocardial infarction, non-fatal stroke, cardiovascular revascularization stent or coronary artery bypass grafting, and cardiovascular death. The secondary endpoint was the all-cause death. All-cause death was defined as mortality from any cause, including cardiac-related. Cardiovascular death was identified as mortality due to fatal arrhythmia, cardiac arrest, myocardial infarction, or heart failure. Mortality data and causes of death up to October 25, 2022, were obtained from the Shanghai Health Statistics Center. We obtained hospitalization records for all participants from the Shanghai Medical Insurance Bureau up to October 25, 2022. MACE events were identified using International Classification of Diseases, 10th Revision codes, such as I63 for ischemic stroke, I60-I61 for haemorrhagic stroke, I64 for unspecified stroke, and I21 for myocardial infarction. For participants without follow-up data from the Shanghai Medical Insurance Bureau and Shanghai Health Statistics Center, follow-up was conducted via telephone.
Baseline demographic and clinical characteristics are presented as medians (interquartile ranges) for continuous variables and n (%) for categorical variables. Comparisons of categorical variables between groups were performed using the χ2 test, while non-normally distributed continuous variables were compared using the non-parametric tests. The Kaplan Meier time-to-event curve and the log-rank test were used to determine the significance of time to endpoints in patients with eGFR ≥ and < 60 mL/minute. Cox proportional hazard models were used to assess the risk associated with different CKD categories, based on various GFR estimation equations, for MACE, cardiovascular death, and all-cause mortality. The models were adjusted for significant known risk factors (covariates with P < 0.1 in the univariate models), including gender, smoking status, hypertension, diabetes, body mass index, age, coronary heart disease, blood leukocyte, serum albumin, uric acid, and stroke history. Odds ratios for the absence of MACE during follow-up were estimated using logistic regression, with the same model adjustments. The prognostic performance of the five eGFR equations was compared by evaluating goodness-of-fit and discrimination of different models after adjusting for confounding variables. To quantify the goodness-of-fit, the Akaike information criterion (AIC) was calculated. The discriminative power of the eGFR equations was assessed using the area under of the receiver operating characteristic curve (AUC). Differences in AUCs between equations were calculated and compared using the z-test. Two-tailed P values < 0.05 were considered statistically significant. All statistical analyses were conducted using SPSS v.26.0 and GraphPad v.8.0.1.
From July 2014 to September 2019, the research team invited 3590 individuals to participate in the North Shanghai Study. After applying the exclusion criteria and obtaining informed consent, 3363 (93.7%) participants were initially enrolled. For the current analysis, we further excluded participants who were younger than 65 years (n = 61), lacked baseline creatinine measurements (n = 39), or were lost to follow-up (n = 6). The final analytic cohort comprised 3257 participants (Figure 1).
The median age of the cohort was 70 years (interquartile range: 67-75 years), and 44% were male. The prevalence of eGFR < 60 mL/minute was 7.7% using the MDRD, 5.7% using the c-aMDRD, 10.2% using the CKD-EPI, 7.7% using the aCKD-EPI, and 16.5% using the cCKD-EPI equations. Table 1 and Supplementary Table 1 summarize the baseline characteristics of the study population, stratified by eGFR ≥ 90 and < 90 mL/minute. Compared with participants with eGFR ≥ 90 mL/minute, those with eGFR < 90 mL/minute were older and more frequently male, current smokers, and had a higher prevalence of hypertension and coronary heart disease. They also had higher leukocyte counts, uric acid levels, and body mass index, but a lower prevalence of diabetes (all P < 0.05). Total cholesterol and low-density lipoprotein differed significantly between groups only when eGFR was estimated using the MDRD equation.
| Variable | Total | MDRD, < 90 mL/minute (n = 2069, 63.5%) | c-aMDRD, < 90 mL/minute (n = 1466, 45.0%) | CKD-EPI, < 90 mL/minute (n = 2436, 74.8%) | aCKD-EPI, < 90 mL/minute (n = 1878, 57.7%) | cCKD-EPI, < 90 mL/minute (n = 3112, 95.5%) |
| Age | 70 (67-75) | 71a (68-77) | 72a (68-78) | 71a (68-77) | 72a (68-78) | 70a (67-75) |
| Male | 1430 (43.9) | 906 (43.8) | 788a (53.1) | 1125a (46.2) | 855a (45.5) | 1428a (45.9) |
| Hypertension | 1740 (53.4) | 1147a (55.4) | 848a (57.8) | 1349a (55.4) | 1074a (57.2) | 1674 (53.8) |
| Diabetes | 639 (19.6) | 374a (18.1) | 283 (19.3) | 454a (18.6) | 351 (18.7) | 599a (19.2) |
| CHD | 1050 (32.2) | 703a (34) | 511a (34.9) | 838a (34.4) | 665a (35.4) | 1008 (32.4) |
| Stroke | 612 (18.8) | 394 (19) | 285 (19.4) | 458 (18.8) | 355 (18.9) | 588 (18.9) |
| Smoking | 807 (24.8) | 518 (25) | 430a (29.3) | 606 (24.9) | 475 (25.3) | 805a (25.9) |
| Alcoholic | 556 (17.1) | 334 (16.1) | 236 (16.1) | 409 (16.8) | 303 (16.1) | 537 (17.3) |
| BMI | 24.4 (22.2-26.6) | 24.4a (22.4-26.8) | 24.6a (22.5-26.9) | 24.4a (22.3-26.7) | 24.5a (22.5-26.9) | 24.4 (22.2-26.6) |
| TC | 5.04 (4.41-5.73) | 5.06a (4.45-5.75) | 5.02 (4.40-5.71) | 5.04 (4.42-5.74) | 5.04 (4.44-5.74) | 5.03 (4.40-5.73) |
| LDL | 3.09 (2.53-3.70) | 3.11a (2.57-3.72) | 3.08 (2.55-3.68) | 3.09 (2.55-3.71) | 3.10 (2.56-3.71) | 3.08 (2.53-3.69) |
| HDL | 1.35 (1.13-1.61) | 1.34 (1.13-1.61) | 1.34 (1.11-1.62) | 1.34 (1.13-1.62) | 1.34 (1.12-1.63) | 1.34 (1.13-1.61) |
| TG | 1.38 (1.04-1.90) | 1.35 (1.03-1.87) | 1.36 (1.03-1.88) | 1.36 (1.03-1.88) | 1.36 (1.03-1.88) | 1.38 (1.04-1.90) |
| WBC | 5.66 (4.83-6.66) | 5.74a (4.92-6.72) | 5.78a (4.99-6.84) | 5.7a (4.87-6.71) | 5.75a (4.93-6.74) | 5.66 (4.83-6.66) |
| Albumin | 47 (45-49) | 47a (45-49) | 47a (45-48) | 47a (45-49) | 47a (45-48) | 47 (45-49) |
| Uric acid | 325 (275-380) | 340a (290-400) | 355a (301-416) | 335a (286-393) | 343a (292-403) | 327a (279-382) |
During a median follow-up of 5.4 years (interquartile ranges: 4.1-7.3), 343 participants (10.53%) experienced MACE including non-fatal myocardial infarction (36, 1.11%), non-fatal stroke (93, 2.86%), cardiovascular revascularization (137, 4.21%), and cardiovascular death (77, 2.36%). The secondary endpoint (all-cause mortality) occurred in 202 participants (6.20%), including non-cardiovascular death (125, 3.84%) and cardiovascular death (77, 2.36%). The incidence of MACE, cardiovascular mortality, and all-cause mortality increased with decreasing eGFR across all five equations (all P < 0.05; Table 2). Kaplan-Meier analyses demonstrated that participants with a low eGFR (< 60 mL/minute) had a significantly higher risk of MACE, cardiovascular mortality, and all-cause mortality compared with those with eGFR ≥ 60 mL/minute for all equations (log-rank P < 0.05), as shown in Figure 2 and Supplementary Figures 1 and 2.
| Equation | Event | eGFR(mL/minute) | P value | ||
| ≥ 90 | 60-90 | < 60 | |||
| MDRD | MACE | 113 (9.5) | 190 (10.5) | 40 (15.9)1 | 0.011 |
| Cardiovascular mortality | 16 (1.3) | 42 (2.3) | 19 (7.6)1 | < 0.001 | |
| All-cause mortality | 54 (4.5) | 95 (5.2) | 53 (21.1)1 | < 0.001 | |
| Event-free | 1037 (87.3) | 1575 (86.6) | 177 (70.5)1 | < 0.001 | |
| c-aMDRD | MACE | 170 (9.5) | 137 (10.7) | 36 (19.4)1 | < 0.001 |
| Cardiovascular mortality | 32 (1.8) | 27 (2.1) | 18 (9.7)1 | < 0.001 | |
| All-cause mortality | 82 (4.6) | 75 (5.9) | 45 (24.2)1 | < 0.001 | |
| Event-free | 1571 (87.7) | 1095 (85.5) | 123 (66.1)1 | < 0.001 | |
| CKD-EPI | MACE | 71 (8.6) | 224 (10.7) | 48 (14.4)1 | 0.015 |
| Cardiovascular mortality | 7 (0.9)2 | 46 (2.2) | 24 (7.2)1 | < 0.001 | |
| All-cause mortality | 26 (3.2)2 | 111 (5.3) | 65 (19.5)1 | < 0.001 | |
| Event-free | 731 (89)2 | 1814 (86.3) | 244 (73.3)1 | < 0.001 | |
| aCKD-EPI | MACE | 122 (8.8)2 | 180 (11.1) | 41 (16.3)1 | 0.001 |
| Cardiovascular mortality | 12 (0.9)2 | 45 (2.8) | 20 (7.9)1 | < 0.001 | |
| All-cause mortality | 45 (3.3)2 | 102 (6.3) | 55 (21.8)1 | < 0.001 | |
| Event-free | 1224 (88.8)2 | 1389 (85.4) | 176 (69.8)1 | < 0.001 | |
| cCKD-EPI | MACE | 12 (8.3) | 254 (9.9) | 77 (14.3)1 | 0.006 |
| Cardiovascular mortality | 1 (0.7) | 41 (1.6) | 35 (6.5)1 | < 0.001 | |
| All-cause mortality | 6 (4.1) | 109 (4.2) | 87 (16.2)1 | < 0.001 | |
| Event-free | 128 (88.3) | 2252 (87.5) | 409 (76.0)1 | < 0.001 | |
Table 3 and Figure 3 depict the association between CKD stage and MACE. Compared to participants with an eGFR of 60-90 mL/minute, those with an eGFR < 60 mL/minute estimated using the c-aMDRD equation had a significantly increased risk of MACE [hazard ratio (HR) = 1.59, 95% confidence interval (CI): 1.06-2.38, P = 0.026]. In contrast, the risk of MACE associated with eGFR < 60 mL/minute was not statistically significant when estimated using the MDRD, CKD-EPI, aCKD-EPI, or cCKD-EPI equations. Likewise, MACE risk did not differ significantly between participants with an eGFR ≥ 90 and 60-90 mL/minute across all equations. Furthermore, participants with an eGFR < 60 mL/minute estimated using the c-aMDRD equation had a significantly higher risk of cardiovascular mortality compared to those with an eGFR of 60-90 mL/minute (HR = 2.86, 95%CI: 1.47-5.56; Supplementary Table 2 and Supplementary Figure 3A). This association was not observed with other equations.
| eGFR (mL/minute) | ||||
| Equation | Cox | ≥ 90 | 60-90 | < 60 |
| MDRD | Event | 113/1188 | 190/1818 | 40/251 |
| HR | 1.08 | 1 | 1.15 | |
| 95%CI | 0.84-1.39 | 0.79-1.67 | ||
| P value | 0.54 | 0.46 | ||
| c-aMDRD | Event | 170/1791 | 137/1280 | 36/186 |
| HR | 1.18 | 1 | 1.59 | |
| 95%CI | 0.92-1.51 | 1.06-2.38 | ||
| P value | 0.18 | 0.026 | ||
| CKD-EPI | Event | 71/821 | 224/2103 | 48/333 |
| HR | 1.21 | 1 | 0.87 | |
| 95%CI | 0.89-1.65 | 0.62-1.24 | ||
| P value | 0.21 | 0.45 | ||
| aCKD-EPI | Event | 122/1379 | 180/1626 | 41/252 |
| HR | 1.14 | 1 | 1.12 | |
| 95%CI | 0.87-1.48 | 0.77-1.62 | ||
| P value | 0.34 | 0.55 | ||
| cCKD-EPI | Event | 12/145 | 254/2574 | 77/538 |
| HR | 1.17 | 1 | 0.79 | |
| 95%CI | 0.62-2.18 | 0.58-1.08 | ||
| P value | 0.63 | 0.14 | ||
Interestingly, participants with an eGFR ≥ 90 mL/minute estimated using the c-aMDRD equation also exhibited a higher cardiovascular mortality risk compared to those with an eGFR of 60-90 mL/minute (HR = 1.85, 95%CI: 1.05-3.25), whereas no significant differences were observed with other equations. For all-cause mortality (Supplementary Table 3 and Supplementary Figure 3B), eGFR < 60 mL/minute was consistently associated with an increased risk across all equations: MDRD (HR = 2.45; 95%CI: 1.69-3.56), c-aMDRD (HR = 2.82; 95%CI: 1.86-4.27), CKD-EPI (HR = 1.86; 95%CI: 1.31-2.64), aCKD-EPI (HR = 2.11; 95%CI: 1.47-3.03), and cCKD-EPI (HR = 1.57; 95%CI: 1.11-2.22). In contrast, eGFR ≥ 90 mL/minute was not associated with all-cause mortality compared with the reference group across all equations.
Table 4 summarizes the comparative discrimination performance of the eGFR equations. Compared to the MDRD equation, the CKD-EPI, aCKD-EPI, and cCKD-EPI equations demonstrated significantly better discrimination for MACE, cardiovascular mortality, and all-cause mortality, with AUC differences ranging from 0.024 to 0.059 (all P < 0.001; Figure 4A). Similarly, compared to the c-aMDRD equation, the CKD-EPI, aCKD-EPI, and cCKD-EPI equations showed superior discrimination for all outcomes (AUC differences, 0.018 to 0.061; all P < 0.001; Figure 4B).
| MACE | CV mortality | All-cause mortality | Event-free | |||||
| ΔAUC | P value | ΔAUC | P value | ΔAUC | P value | ΔAUC | P value | |
| MDRD and c-aMDRD | -0.06 | 0.089 | 0.002 | 0.744 | -0.004 | 0.372 | -0.007 | 0.027 |
| MDRD and CKD-EPI | -0.025 | < 0.001 | -0.059 | < 0.001 | -0.051 | < 0.001 | -0.032 | < 0.001 |
| MDRD and aCKD-EPI | -0.024 | < 0.001 | -0.059 | < 0.001 | -0.049 | < 0.001 | -0.030 | < 0.001 |
| MDRD and cCKD-EPI | -0.034 | < 0.001 | -0.057 | < 0.001 | -0.050 | < 0.001 | -0.041 | < 0.001 |
| c-aMDRD and CKD-EPI | -0.019 | < 0.001 | -0.061 | < 0.001 | -0.047 | < 0.001 | -0.025 | < 0.001 |
| c-aMDRD and aCKD-EPI | -0.018 | 0.001 | -0.061 | < 0.001 | -0.046 | < 0.001 | -0.024 | < 0.001 |
| c-aMDRD and cCKD-EPI | -0.028 | < 0.001 | -0.059 | < 0.001 | -0.047 | < 0.001 | -0.035 | < 0.001 |
| CKD-EPI and aCKD-EPI | 0.001 | 0.075 | 0.000 | 0.876 | 0.001 | 0.069 | 0.002 | 0.003 |
| CKD-EPI and cCKD-EPI | -0.009 | 0.118 | 0.002 | 0.870 | 0.000 | 0.981 | -0.009 | 0.067 |
| aCKD-EPI and cCKD-EPI | -0.010 | 0.093 | 0.002 | 0.887 | -0.001 | 0.885 | -0.011 | 0.038 |
Model fit was assessed using the AIC and the Bayesian Information Criterion across different endpoints (Supp
This study examined the impact of renal impairment on long-term outcomes in an elderly Chinese population. Several key findings emerged. First, when different equations were used to calculate eGFR, the prevalence of eGFR < 60 mL/minute/1.73 m2 varied widely, ranging from 5.7% to 16.5%. Second, irrespective of the equation applied and despite differences in prevalence across equations, participants with an eGFR < 60 mL/minute/1.73 m2 had a significantly higher risk of MACE, cardiovascular mortality, and all-cause mortality. Third, a significant association between eGFR and outcomes was observed when using the c-aMDRD equation, with lower eGFR independently associated with MACE and cardiovascular mortality. In addition, reduced eGFR was consistently associated with all-cause mortality across all equations. Finally, the CKD-EPI, aCKD-EPI, and cCKD-EPI equations demonstrated superior prognostic performance for MACE, cardiovascular mortality, all-cause mortality, and event-free survival compared with the MDRD and c-aMDRD equations.
Interest in the association between renal impairment and the risks of mortality and cardiovascular disease has increased substantially in recent years. GFR is the most comprehensive index of renal function in both healthy and diseased states[20]. Although eGFR is an established predictor of clinical outcomes in older populations, evidence regarding the comparative prognostic performance of different eGFR equations remains limited and inconsistent. Prior studies have suggested that the CKD-EPI equation provides more accurate GFR estimation and superior risk prediction compared with the MDRD equation[7,21,22]. However, to our knowledge, no previous study compared the prognostic value of Asian- or Chinese-modified equations with conventional equations in an elderly Chinese population. Our findings provide novel evidence that, in older Chinese individuals, all eGFR equations independently predicted all-cause mortality, whereas only the c-aMDRD equation retained independent prognostic value for MACE and cardiovascular mortality. These results highlight potential differences in equation performance for cardiovascular vs non-cardiovascular outcomes and underscore the need for population-specific calibration when using eGFR for cardiovascular risk stratification.
In Western populations, the prevalence of CKD is approximately 10% at age 65 and increases to nearly 60% among individuals aged ≥ 80 years[12,13]. A large retrospective cohort study in Spain (n = 130233; median age 70 years) reported that 13.5% of participants overall and 29% of those aged ≥ 75 years had an eGFR < 60 mL/minute using the CKD-EPI equation[10]. Similarly, in a Belgian cohort of individuals aged ≥ 80 years, the prevalence of eGFR < 60 mL/minute was 44%-45% based on the MDRD and CKD-EPI equations[23]. Moreover, the mean eGFR estimated by the MDRD and CKD-EPI equations were 64 ± 22 mL/minute and 61 ± 19 mL/minute, respectively. In our study, the prevalence of eGFR < 60 mL/minute ranged from 5.7% to 16.5% across equations and the mean eGFR was 94 ± 24, 85 ± 20, 83 ± 15, 79 ± 14, and 71 ± 12 mL/minute when using the c-aMDRD, MDRD, aCKD-EPI, CKD-EPI, and cCKD-EPI equations, respectively, suggesting age- and equation-dependent variability. In contrast, Matsushita et al[21] reported that the median eGFR calculated using the CKD-EPI equation was higher than that calculated using the MDRD equation (97.6 mL/minute vs 88.8 mL/minute) in the Atherosclerosis Risk in Communities Study. Scr, the basis of these equations, is influenced not only by GFR but also by muscle mass, diet, sex, race, and aging, which may partly explain inter-equation and population differences in estimated renal function.
Previous studies have consistently demonstrated that reduced eGFR is associated with increased all-cause mortality and cardiovascular morbidity[24-26]. However, findings in older populations have been inconsistent. In NHANES II, eGFR < 70 mL/minute (MDRD) was associated with increased all-cause and cardiovascular mortality[24], whereas in the Atherosclerosis Risk in Communities study, an eGFR of 15-59 mL/minute was linked to higher cardiovascular risk[26]. Similar results were obtained in a cohort of older adults (≥ 65 years old) for an eGFR of 15-59 mL/minute (MDRD)[25]. In contrast, NHANES I did not observe a significant association between eGFR 30-60 mL/minute and mortality[27]. Large population-based studies from the United States, Iceland, and the United Kingdom further demonstrated that eGFR < 60 mL/minute was independently associated with mortality and cardiovascular outcomes[1,28,29]. Using the CKD-EPI equation, Shastri et al[2] reported that eGFR < 43 mL/minute predicted mortality in older adults, and Van Pottelbergh et al[23] showed that risks were particularly pronounced when eGFR < 30 mL/minute.
In our study, eGFR < 60 mL/minute estimated by all equations was significantly associated with all-cause mortality, whereas eGFR < 60 mL/minute predicted MACE and cardiovascular mortality only when calculated using the c-aMDRD equation. These discrepancies may reflect differences in eGFR equations and population characteristics. Notably, we observed a U-shaped association between eGFR (c-aMDRD) and cardiovascular mortality, consistent with prior studies reporting increased mortality at higher eGFR levels[2,10], potentially reflecting confounding by frailty, hyperfiltration, or creatinine-related bias.
Our findings indicate that the CKD-EPI, aCKD-EPI, and cCKD-EPI equations demonstrated superior discrimination for MACE, cardiovascular mortality, and all-cause mortality compared with the MDRD and c-aMDRD equations in an elderly Chinese community-based population. Consistent with our results, previous work from the North Shanghai Study showed that eGFR estimated using the aCKD-EPI and cCKD-EPI equations was more strongly associated with preclinical target organ damage[9]. These differences are not unexpected, as some equations were derived and validated in Western populations, whereas others were developed in Asian or Chinese cohorts.
The CKD-EPI equation, a newer GFR estimation method, has consistently shown greater accuracy than the MDRD equation across diverse populations and clinical settings[6]. The four-level ethnicity-based CKD-EPI equation further reduced estimation bias in Asian populations[7], including Chinese individuals, and CKD-EPI-derived eGFR has been shown to better predict adverse outcomes in Asian cohorts[3,30,31]. Accordingly, the aCKD-EPI equation may provide more accurate risk stratification in elderly Chinese individuals. The superior performance of the cCKD-EPI equation may reflect its derivation in a Chinese population with an age distribution like our cohort, highlighting the importance of population- and age-specific calibration of eGFR equations for cardiovascular risk prediction.
This study is the first to compare eGFR equations in relation to adverse outcomes in elderly Chinese individuals. A major advantage of our study is that it utilized data from a population-based, prospective cohort study. This study also has limitations. The Scr concentrations were measured using the enzymatic method and the eGFR was based on the initial creatinine value observed at admission, but variations in creatinine concentrations within subjects should be considered. The absence of a reference standard for measuring the true GFR, as well as the eGFR cutoff of 60 mL/minute utilized to define CKD in older people in this study, is frequently debated because some age-related decline renal function might result from physiological changes rather than true pathology. Our participants were recruited from northern Shanghai; therefore, the findings may not apply to other races. Finally, the cross-sectional design of this study cautions against overinterpretation of the data.
The different eGFR values showed poor agreement, with the prevalence of renal dysfunction (eGFR < 90 mL/minute) ranging from 45%-95%. A low eGFR estimated by c-aMDRD is associated with MACE and cardiovascular mortality and with all-cause mortality by all equations in community-dwelling elderly Chinese individuals. The CKD-EPI, aCKD-EPI, and cCKD-EPI equations demonstrated better predictive ability for long-term outcomes than the MDRD and c-aMDRD equations in this populations.
| 1. | Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296-1305. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 9341] [Cited by in RCA: 8464] [Article Influence: 384.7] [Reference Citation Analysis (5)] |
| 2. | Shastri S, Katz R, Rifkin DE, Fried LF, Odden MC, Peralta CA, Chonchol M, Siscovick D, Shlipak MG, Newman AB, Sarnak MJ. Kidney function and mortality in octogenarians: Cardiovascular Health Study All Stars. J Am Geriatr Soc. 2012;60:1201-1207. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 37] [Cited by in RCA: 39] [Article Influence: 2.8] [Reference Citation Analysis (0)] |
| 3. | Aiumtrakul N, Kittithaworn A, Supasyndh O, Krittayaphong R, Phrommintikul A, Satirapoj B. Prediction of cardiovascular outcome by estimated glomerular filtration rate among high-risk patients: a Thai nationwide cohort study. Clin Exp Nephrol. 2022;26:1180-1193. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 3] [Reference Citation Analysis (0)] |
| 4. | Guérin AP, Pannier B, Marchais SJ, London GM. Arterial structure and function in end-stage renal disease. Curr Hypertens Rep. 2008;10:107-111. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 28] [Cited by in RCA: 30] [Article Influence: 1.7] [Reference Citation Analysis (0)] |
| 5. | Beck GJ, Berg RL, Coggins CH, Gassman JJ, Hunsicker LG, Schluchter MD, Williams GW. Design and statistical issues of the Modification of Diet in Renal Disease Trial. The Modification of Diet in Renal Disease Study Group. Control Clin Trials. 1991;12:566-586. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 81] [Cited by in RCA: 91] [Article Influence: 2.6] [Reference Citation Analysis (0)] |
| 6. | Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604-612. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 22035] [Cited by in RCA: 21011] [Article Influence: 1235.9] [Reference Citation Analysis (33)] |
| 7. | Stevens LA, Claybon MA, Schmid CH, Chen J, Horio M, Imai E, Nelson RG, Van Deventer M, Wang HY, Zuo L, Zhang YL, Levey AS. Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int. 2011;79:555-562. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 422] [Cited by in RCA: 411] [Article Influence: 27.4] [Reference Citation Analysis (0)] |
| 8. | Liu X, Gan X, Chen J, Lv L, Li M, Lou T. A new modified CKD-EPI equation for Chinese patients with type 2 diabetes. PLoS One. 2014;9:e109743. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 16] [Cited by in RCA: 25] [Article Influence: 2.1] [Reference Citation Analysis (0)] |
| 9. | Ji H, Zhang H, Xiong J, Yu S, Chi C, Bai B, Li J, Blacher J, Zhang Y, Xu Y. eGFRs from Asian-modified CKD-EPI and Chinese-modified CKD-EPI equations were associated better with hypertensive target organ damage in the community-dwelling elderly Chinese: the Northern Shanghai Study. Clin Interv Aging. 2017;12:1297-1308. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 7] [Cited by in RCA: 18] [Article Influence: 2.0] [Reference Citation Analysis (0)] |
| 10. | Salvador-González B, Gil-Terrón N, Cerain-Herrero MJ, Subirana I, Güell-Miró R, Rodríguez-Latre LM, Cunillera-Puértolas O, Elosua R, Grau M, Vila J, Pascual-Benito L, Mestre-Ferrer J, Ramos R, Baena-Díez JM, Soler-Vila M, Alonso-Bes E, Ruipérez-Guijarro L, Álvarez-Funes V, Freixes-Villaró E, Rodríguez-Pascual M, Martínez-Castelao A. Estimated Glomerular Filtration Rate, Cardiovascular Events and Mortality Across Age Groups Among Individuals Older Than 60 Years in Southern Europe. Rev Esp Cardiol (Engl Ed). 2018;71:450-457. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 2] [Reference Citation Analysis (0)] |
| 11. | Sandholm N, Valo E, Tuomilehto J, Harjutsalo V, Groop PH; FinnDiane Study. Rate of Kidney Function Decline is Associated With Kidney and Heart Failure in Individuals With Type 1 Diabetes. Kidney Int Rep. 2023;8:2043-2055. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 5] [Reference Citation Analysis (0)] |
| 12. | Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298:2038-2047. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 3821] [Cited by in RCA: 3469] [Article Influence: 182.6] [Reference Citation Analysis (3)] |
| 13. | Van Pottelbergh G, Bartholomeeusen S, Buntinx F, Degryse J. The prevalence of chronic kidney disease in a Flemish primary care morbidity register. Age Ageing. 2012;41:231-233. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 18] [Cited by in RCA: 15] [Article Influence: 1.1] [Reference Citation Analysis (0)] |
| 14. | Van Pottelbergh G, Van Heden L, Matheï C, Degryse J. Methods to evaluate renal function in elderly patients: a systematic literature review. Age Ageing. 2010;39:542-548. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 49] [Cited by in RCA: 45] [Article Influence: 2.8] [Reference Citation Analysis (0)] |
| 15. | Lamb EJ, Stevens PE. Estimating and measuring glomerular filtration rate: methods of measurement and markers for estimation. Curr Opin Nephrol Hypertens. 2014;23:258-266. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 47] [Cited by in RCA: 53] [Article Influence: 4.4] [Reference Citation Analysis (0)] |
| 16. | Ji H, Xiong J, Yu S, Chi C, Fan X, Bai B, Zhou Y, Teliewubai J, Lu Y, Xu H, Zhang Y, Xu Y. Northern Shanghai Study: cardiovascular risk and its associated factors in the Chinese elderly-a study protocol of a prospective study design. BMJ Open. 2017;7:e013880. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 21] [Cited by in RCA: 34] [Article Influence: 3.8] [Reference Citation Analysis (0)] |
| 17. | Ma YC, Zuo L, Chen JH, Luo Q, Yu XQ, Li Y, Xu JS, Huang SM, Wang LN, Huang W, Wang M, Xu GB, Wang HY. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol. 2006;17:2937-2944. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1523] [Cited by in RCA: 1588] [Article Influence: 79.4] [Reference Citation Analysis (0)] |
| 18. | Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL, Coresh J, Levey AS; CKD-EPI Investigators. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20-29. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 3582] [Cited by in RCA: 3361] [Article Influence: 240.1] [Reference Citation Analysis (4)] |
| 19. | Schaeffner ES, Ebert N, Delanaye P, Frei U, Gaedeke J, Jakob O, Kuhlmann MK, Schuchardt M, Tölle M, Ziebig R, van der Giet M, Martus P. Two novel equations to estimate kidney function in persons aged 70 years or older. Ann Intern Med. 2012;157:471-481. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 392] [Cited by in RCA: 443] [Article Influence: 31.6] [Reference Citation Analysis (3)] |
| 20. | Rivera-Caravaca JM, Ruiz-Nodar JM, Tello-Montoliu A, Esteve-Pastor MA, Quintana-Giner M, Véliz-Martínez A, Orenes-Piñero E, Romero-Aniorte AI, Vicente-Ibarra N, Pernias-Escrig V, Carrillo-Alemán L, Candela-Sánchez E, Hortelano I, Villamía B, Sandín-Rollán M, Nuñez-Martínez L, Valdés M, Marín F. Disparities in the Estimation of Glomerular Filtration Rate According to Cockcroft-Gault, Modification of Diet in Renal Disease-4, and Chronic Kidney Disease Epidemiology Collaboration Equations and Relation With Outcomes in Patients With Acute Coronary Syndrome. J Am Heart Assoc. 2018;7:e008725. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 14] [Cited by in RCA: 18] [Article Influence: 2.3] [Reference Citation Analysis (0)] |
| 21. | Matsushita K, Selvin E, Bash LD, Astor BC, Coresh J. Risk implications of the new CKD Epidemiology Collaboration (CKD-EPI) equation compared with the MDRD Study equation for estimated GFR: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis. 2010;55:648-659. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 256] [Cited by in RCA: 247] [Article Influence: 15.4] [Reference Citation Analysis (0)] |
| 22. | Ballo P, Chechi T, Spaziani G, Fibbi V, Conti D, Ferro G, Nigrelli S, Dattolo P, Fazi A, Santoro GM, Zuppiroli A, Pizzarelli F. Prognostic comparison between creatinine-based glomerular filtration rate formulas for the prediction of 10-year outcome in patients with non-ST elevation acute coronary syndrome treated by percutaneous coronary intervention. Eur Heart J Acute Cardiovasc Care. 2018;7:689-702. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 6] [Cited by in RCA: 11] [Article Influence: 1.4] [Reference Citation Analysis (0)] |
| 23. | Van Pottelbergh G, Vaes B, Adriaensen W, Matheï C, Legrand D, Wallemacq P, Degryse JM. The glomerular filtration rate estimated by new and old equations as a predictor of important outcomes in elderly patients. BMC Med. 2014;12:27. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 24] [Cited by in RCA: 35] [Article Influence: 2.9] [Reference Citation Analysis (0)] |
| 24. | Muntner P, He J, Hamm L, Loria C, Whelton PK. Renal insufficiency and subsequent death resulting from cardiovascular disease in the United States. J Am Soc Nephrol. 2002;13:745-753. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 577] [Cited by in RCA: 575] [Article Influence: 24.0] [Reference Citation Analysis (0)] |
| 25. | Manjunath G, Tighiouart H, Coresh J, Macleod B, Salem DN, Griffith JL, Levey AS, Sarnak MJ. Level of kidney function as a risk factor for cardiovascular outcomes in the elderly. Kidney Int. 2003;63:1121-1129. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 300] [Cited by in RCA: 301] [Article Influence: 13.1] [Reference Citation Analysis (0)] |
| 26. | Manjunath G, Tighiouart H, Ibrahim H, MacLeod B, Salem DN, Griffith JL, Coresh J, Levey AS, Sarnak MJ. Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community. J Am Coll Cardiol. 2003;41:47-55. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 704] [Cited by in RCA: 648] [Article Influence: 28.2] [Reference Citation Analysis (0)] |
| 27. | Garg AX, Clark WF, Haynes RB, House AA. Moderate renal insufficiency and the risk of cardiovascular mortality: results from the NHANES I. Kidney Int. 2002;61:1486-1494. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 218] [Cited by in RCA: 199] [Article Influence: 8.3] [Reference Citation Analysis (0)] |
| 28. | Di Angelantonio E, Chowdhury R, Sarwar N, Aspelund T, Danesh J, Gudnason V. Chronic kidney disease and risk of major cardiovascular disease and non-vascular mortality: prospective population based cohort study. BMJ. 2010;341:c4986. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 203] [Cited by in RCA: 187] [Article Influence: 11.7] [Reference Citation Analysis (0)] |
| 29. | Quinn MP, Cardwell CR, Kee F, Maxwell AP, Savage G, McCarron P, Fogarty DG. The finding of reduced estimated glomerular filtration rate is associated with increased mortality in a large UK population. Nephrol Dial Transplant. 2011;26:875-880. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 11] [Cited by in RCA: 13] [Article Influence: 0.8] [Reference Citation Analysis (0)] |
| 30. | Sudchada P, Laehn S. Comparisons of GFR estimation using the CKD Epidemiology Collaboration (CKD-EPI) equation and other creatinine-based equations in Asian population: a systematic review. Int Urol Nephrol. 2016;48:1511-1517. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 22] [Cited by in RCA: 24] [Article Influence: 2.4] [Reference Citation Analysis (0)] |
| 31. | Xie P, Huang JM, Lin HY, Wu WJ, Pan LP. CDK-EPI equation may be the most proper formula based on creatinine in determining glomerular filtration rate in Chinese patients with chronic kidney disease. Int Urol Nephrol. 2013;45:1057-1064. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 24] [Cited by in RCA: 30] [Article Influence: 2.1] [Reference Citation Analysis (0)] |