Cardiovascular diseases (CVDs) remain a major cause of health burden for all regions of the world.1 Although CVD mortality has declined owing to reductions in risk factors, such as cholesterol, BP, and smoking, in high-income countries,2–4 the decline has slowed down significantly in recent years,5 and the health loss due to CVD in low- and middle-income countries has been continuously rising.6 It was estimated that >80% of the global burden of CVD occurred in low- and middle-income countries.7 CVD in China has increased alarmingly during past decades, and it was estimated that 290 million Chinese adults were living with CVD in 2016.8 The trend was expected to continue into the future.9,10
Risk stratification and prediction of CVD events are important in CVD prevention. Risk estimation using traditional CVD risk factors, such as age, sex, smoking, cholesterol, BP, and glucose, has been widely used, such as the Framingham risk score and the more recent atherosclerotic cardiovascular disease (ASCVD) risk score.11,12 Risk stratification using nontraditional CVD risk factors, such as the urinary albumin-creatinine ratio (ACR) and the eGFR, was used infrequently, although they are proven to associate with and well predict future CVD development in diverse populations.13 Indeed, the Kidney Disease Improving Global Outcomes (KDIGO) 2012 clinical practice guideline used eGFR and ACR to categorize a population into low, intermediate, high, or very high risk for CKD prognosis on the basis of evidence from the Chronic Kidney Disease Prognosis Consortium (CKD-PC), which currently consists of >70 prospective cohorts.1415 The KDIGO risk stratification of CKD prognosis focused on various outcomes, including all-cause mortality, cardiovascular mortality, disease (ESKD), progressive CKD, and AKI.16 Its stratification of major CVD risks, including both fatal and nonfatal CVD events, and whether its stratification and predictive ability remain beyond traditional CVD risk prediction scores are unknown. In fact, the American College of Cardiology (ACC) and the American Heart Association (AHA) recently recommended to consider additional individual risk-enhancing clinical factors when making preventive treatment decisions, such as statin therapy.17 However, whether the KDIGO risk category defined by eGFR and urinary ACR levels can be used as a good risk-enhancing factor and what the cardiovascular risks are on the basis of the traditional ASCVD score and the nontraditional KDIGO risk category are unknown.
The China Cardiometabolic Disease and Cancer Cohort (4C) study is a large, nationwide, multicenter, prospective cohort study investigating the determinants and risk factors of cardiometabolic diseases in Chinese community residents aged ≥40 years. The objectives of this report are to evaluate the CVD risks within categories of traditional and nontraditional estimators, such as the ASCVD risk score and the KDIGO risk categories, and to examine the improvement in prediction and reclassification of CVD risks by adding ACR and eGFR (individually, together, and in combination using the KDIGO risk categories) to the ASCVD risk score.
Study Design and Population
The study protocol and standard of procedures of the 4C study have been published previously.18,19 In 2010–2012, 193,846 adults from 20 study sites across mainland China participated in a comprehensive baseline examination, which included a standard questionnaire, anthropometric measurements, blood and urine sampling, and biochemical determination. In 2014–2016, participants were asked to come back for a follow-up examination to record changes in lifestyle factors and metabolic characteristics, as well as the occurrence of major CVDs. A total of 170,240 participants were followed up. After excluding participants with a history of CVD (n=11,083), with missing ASCVD risk score (n=11,010), with missing eGFR or ACR levels (n=13,285), or with missing data on incident CVD events (n=19,496), 115,366 participants were included for the main analysis.
The 4C study was approved by the Ethical Review Committee of Ruijin Hospital. Written informed consent was obtained from all study participants.
Data were collected at local hospitals or community clinics in the participants’ residential area by trained staff according to a standard protocol. A questionnaire including information on demographic characteristics, histories of chronic diseases, current medications, and lifestyle factors was administered by face-to-face interview. Current smoking was defined as smoking at least one cigarette per day or at least seven cigarettes per week during past 6 months. Current drinking was defined as drinking alcohol at least once per week during past 6 months. Physical activity was evaluated using the International Physical Activity Questionnaire,20 and being physically active was defined as moderate physical activity ≥150 min/wk, vigorous physical activity ≥75 min/wk, or moderate plus vigorous physical activity ≥150 min/wk.21 Healthy diet was defined as intakes of fruits and vegetables ≥4.5 cups/d.10 Anthropometric measurements, including height, weight, and waist and hip circumferences, were conducted, and body mass index was calculated as weight in kilograms divided by height in meters squared. BP was measured three times with 1-minute intervals between measurements after at least 5-minute sitting rest using a calibrated automatic electronic device (OMRON Model HEM-752). Alcohol, tea, coffee, and exercise were strictly avoided 30 minutes before BP measurements. The average of the three measurements was used for analysis.
All participants were asked to fast overnight for at least 10 hours before blood sampling at the next early morning. Participants without a diabetes history underwent an oral glucose tolerance test, and blood sampling was repeated 2 hours after the glucose challenge. Fasting and postload plasma glucose levels were analyzed locally by using glucose oxidase or hexokinase methods. Fasting blood samples were centrifuged on site; serum samples were aliquoted and frozen at −80°C within 2 hours of collection, and they were shipped in dry ice to the central laboratory in Shanghai, which was accredited by the College of American Pathologists and where serum insulin, total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides were measured by using an ARCHITECT ci16200 autoanalyzer (Abbott Laboratories, Abbott Park, IL). Serum creatinine concentrations were detected using the kinetic Jaffé method on the same autoanalyzer with calibration traceable to an isotope dilution mass spectrometry reference measurement.
The first void urine sample in early morning was collected from each participant. Urine samples were frozen at local hospitals and shipped in dry ice to the central laboratory, where urinary albumin concentrations were measured by immunonephelometry using the Siemens BNII nephelometer (Siemens Healthcare Diagnostics, Marburg, Germany) and urinary creatinine concentrations were determined by an enzymatic method (ADVIA Chemistry XPT System; Siemens Healthcare, Erlangen, Germany).
KDIGO and ASCVD Risk Categories
The eGFR was estimated from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration equation.22 Urinary ACR was calculated by dividing urinary albumin concentrations in milligrams by urinary creatinine concentrations in grams. CKD was defined by eGFR<60 ml/min per 1.73 m2 or urinary ACR ≥30 mg/g. The low KDIGO risk was defined by eGFR≥60 ml/min per 1.73 m2 and urinary ACR<30 mg/g. The intermediate KDIGO risk was defined by eGFR≥60 ml/min per 1.73 m2 and urinary ACR =30–299 mg/g or eGFR=45–59 ml/min per 1.73 m2 and urinary ACR <30 mg/g. The high KDIGO risk was defined by eGFR≥60 ml/min per 1.73 m2 and urinary ACR ≥300 mg/g, eGFR=45–59 ml/min per 1.73 m2 and urinary ACR =30–299 mg/g, or eGFR=30–44 ml/min per 1.73 m2 and urinary ACR <30 mg/g. The very high KDIGO risk was defined by eGFR<30 ml/min per 1.73 m2, eGFR=30–44 ml/min per 1.73 m2 and urinary ACR ≥30 mg/g, or eGFR=45–59 ml/min per 1.73 m2 and urinary ACR ≥300 mg/g (Table 1).1415
Table 1. -
Numbers of participants (proportions) in KDIGO risk categories in the 4C study
|eGFR, ml/min per 1.73 m2
||Urinary ACR, mg/g
||30 to <300
|60 to <90
|45 to <60
|30 to <45
|15 to <30
A total of 13,526 (11.72%) participants had CKD defined by eGFR<60 ml/min per 1.73 m2 or urinary ACR ≥30 mg/g.
aIn total, 101,840 (88.28%) participants had low risk.
bIn total, 11,340 (9.83%) participants had intermediate risk.
cIn total, 1647 (1.43%) participants had high risk.
dIn total, 539 (0.47%) participants had very high risk.
The ASCVD risk score evaluates 10-year risk of developing a first ASCVD event, defined as nonfatal myocardial infarction or coronary heart disease death or fatal or nonfatal stroke.12 It was calculated using the Pooled Cohort Equations with traditional cardiovascular risk determinants, including sex, antihypertensive treatment, age, total cholesterol, HDL cholesterol, systolic BP, current smoking status, and diabetes.12 Diabetes was defined according to the 1999 World Health Organization criteria. The ASCVD risk strata included ASCVD risk scores of <5.0%, 5.0% to <7.5%, 7.5% to <10.0%, 10.0% to <15.0%, 15.0% to <20.0%, and ≥20.0%.
The occurrence of major CVDs, including myocardial infarction and stroke, was obtained at the follow-up visit.18,19 If participants reported hospitalization or emergency department visit, their medical records, including medical history, physical examination findings, laboratory tests, treatments, and diagnosis at discharge, were abstracted by trained staff using a standard form. Photocopies of the participant’s inpatient record, discharge summary, electrocardiogram, and imaging reports were obtained. Information on vital status and clinical outcomes was also collected from the local death and disease registries of the National Disease Surveillance Point System and the National Health Insurance System. Two members of the 4C Morbidity and Mortality Adjudication Committee independently adjudicated each clinical events using hospital records and death certificates according to a standard protocol. Major cardiovascular events in this study included nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death. Myocardial infarction was defined by changes in levels of troponin T and creatine-kinase-MB isoform, symptoms of myocardial ischemia, changes in electrocardiogram results, or a combination of these. Stroke was defined as a fixed neurologic deficit lasting >24 hours because of a presumed vascular cause. Cardiovascular death was defined as death due to cardiovascular causes. All members of the adjudication committee were unaware of the baseline risk factors of study participants. Discrepancies were resolved by discussion involving other members of the committee.
Baseline characteristics of the overall study population and of participants with low, intermediate, high, or very high KDIGO risks are described with continuous variables in means ± SDs or medians (25%–75% quartile) and categorical variables in numbers (percentages). Categories of high and very high KDIGO risk were combined for analysis due to a limited number of participants in these categories. The incidence risk (95% confidence interval [95% CI]) and incidence rate (95% CI) of major CVD events were examined in different combinations of KDIGO and ASCVD risk categories. Follow-up time was calculated as the time from the baseline visit until the first CVD event date, the follow-up date, or the death date, whichever occurred first. Kaplan–Meier curves for incident CVD according to KDIGO risk categories within ASCVD risk strata were constructed, and log-rank tests for risks of developing major CVD events were conducted. Hazard ratios (HRs) and 95% CIs for incident CVD with increasing KDIGO risk categories were calculated using Cox proportional hazards models adjusted for other CVD risk factors. The risks of different combinations of KDIGO and ASCVD risk categories for incident CVD compared with the lowest-risk combination (low KDIGO risk and ASCVD risk score <5.0%) were also calculated using multivariable Cox regression models. In addition, changes in c statistics of CVD risk prediction models after adding eGFR, log(ACR), or both to a base model including demographic characteristics, lifestyle variables, and the ASCVD score were evaluated in the overall study population and in subgroups of men and women, age <60 and ≥60 years, with and without diabetes, and with and without hypertension. Finally, the improvement in reclassification and discrimination of major CVD events using a model with the ASCVD score, log(ACR), and eGFR compared with a model with only the ASCVD score was evaluated using the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI).23 The categorical NRI was calculated with cutoff points 7.5% and 20.0% to correspond with the low-risk (<7.5%), intermediate-risk (7.5%–19.9%), and high-risk (≥20.0%) categories over 10 years defined in the ACC/AHA guideline.17 The continuous NRI was also calculated and did not depend on the choice of cutoff values. Because all participants in the 4C study had <10 years of follow-up, an exponential survival function was used to scale participants’ risk at their current length of follow-up to risk at 10 years.24 We then repeated the NRI and IDI assessments using CVD risk factors instead of the ASCVD risk score. Bootstrap resampling with 500 repetitions was done to obtain 95% CIs for NRI and IDI.
All analyses were conducted using SAS version 9.4 (SAS Institute Inc, Cary, NC) and the R statistical computing environment (http://www.r-project.org). All reported P values are two sides, and P values of <0.05 were considered as statistically significant.
Proportions of Chinese adults aged ≥40 years by eGFR and urinary ACR categories are shown in Table 1. Overall, 101,840 (88.28%) participants had a low KDIGO risk, 11,340 (9.83%) had an intermediate KDIGO risk, 1647 (1.43%) had a high KDIGO risk, and 539 (0.47%) had a very high KDIGO risk. In addition, 61,626 (53.42%), 12,514 (10.85%), 8716 (7.56%), 11,654 (10.10%), 7232 (6.27%), and 13,624 (11.81%) participants had ASCVD risk scores of <5.0%, 5.0% to <7.5%, 7.5% to <10.0%, 10.0% to <15.0%, 15.0% to <20.0%, and ≥20.0%, respectively.
Generally, levels of continuous variables, such as age, body mass index, waist circumference, systolic and diastolic BP, fasting and 2-hour postload plasma glucose, the homeostatic measurement assessment of insulin resistance, total cholesterol, LDL cholesterol, triglycerides, and urinary ACR, and proportions of categorical variables, such as overweight or obesity, central obesity, hypertension, and diabetes, increased significantly across KDIGO risk categories (Table 2). In addition, proportions of participants who had high school or more education or were current smokers or current drinkers and levels of eGFR decreased significantly across KDIGO risk categories. Numbers and proportions of participants in each ASCVD risk stratum by KDIGO risk categories are shown in Table 2.
Table 2. -
Characteristics of study participants by KDIGO risk categories
||KDIGO Risk Categories
||High or Very High Risk
|No. of participants, n (%)
|Men, n (%)
|High school education or above, n (%)
|Current smokers, n (%)
|Current drinkers, n (%)
|Body mass index, kg/m2
|Waist circumference, cm
|Systolic BP, mm Hg
|Diastolic BP, mm Hg
|Fasting glucose, mg/dl
|2-h postload glucose, mg/dl
|Total cholesterol, mg/dl
|LDL cholesterol, mg/dl
|HDL cholesterol, mg/dl
|eGFR, ml/min per 1.73 m2
|Urinary ACR, mg/g
|Overweight or obesity, n (%)
|Central obesity, n (%)
|Hypertension, n (%)
|Diabetes, n (%)
|ASCVD<5.0%, n (%)
|ASCVD 5.0% to <7.5%, n (%)
|ASCVD 7.5% to <10.0%, n (%)
|ASCVD 10.0% to <15.0%, n (%)
|ASCVD 15.0% to <20.0%, n (%)
|ASCVD≥20.0%, n (%)
There were 1053 missing values for drinking status, 4292 missing values for body mass index, and 1780 missing values for waist circumference. HOMA-IR, homeostatic model assessment of insulin resistance.
Incident CVD Events
A total of 2866 major CVD events occurred during 415,111 person-years of follow-up for a median (range) of 3.1 (0–6.9) years, including 419 nonfatal myocardial infarction, 1812 nonfatal stroke, and 635 cardiovascular deaths. The incidence risk and incidence rate of major CVD events increased dramatically across both KDIGO and ASCVD risk categories. The incidence risk (95% CI) ranged from 0.90% (95% CI, 0.82% to 0.98%) in participants with a low KDIGO risk and an ASCVD risk score <5.0% to 16.74% (95% CI, 14.27% to 19.22%) in participants with a high or very high KDIGO risk and an ASCVD risk score ≥20.0%. The incidence risk of major CVD events scaled to 10 years was generally consistent with the risk estimated by the 10-year ASCVD score, especially in lower-risk categories (Table 3). The incidence rate (95% CI) ranged from 2.48 (95% CI, 2.28 to 2.71) per 1000 person-years in participants with a low KDIGO risk and an ASCVD risk score <5.0% to 48.61 (95% CI, 41.33 to 57.17) per 1000 person-years in participants with a high or very high KDIGO risk and an ASCVD risk score ≥20.0% (Supplemental Figure 1). Kaplan–Meier survival curves revealed significantly increased rates of major CVD events across the low, intermediate, and high or very high KDIGO risk categories in each ASCVD risk stratum, and the effect of including the KDIGO risk category was greatest in the high ASCVD risk groups (all P<0.01 by the log-rank test) (Figure 1).
Table 3. -
Incidence risk (95% CI) of major CVD events according to the ASCVD and the KDIGO risk categories
||Scaled 10-yr Risk of Major CVD Events
||High or Very High Risk
||0.90 (0.82 to 0.98)
||2.12 (1.68 to 2.56)
||2.26 (0.94 to 3.58)
||3.16 (2.92 to 3.41)
|ASCVD 5.0% to <7.5%
||1.94 (1.68 to 2.20)
||3.55 (2.51 to 4.59)
||2.86 (0.39 to 5.33)
||6.65 (5.87 to 7.42)
|ASCVD 7.5% to <10.0%
||2.55 (2.20 to 2.91)
||3.55 (2.34 to 4.75)
||5.81 (2.32 to 9.31)
||8.51 (7.47 to 9.54)
|ASCVD 10.0% to <15.0%
||2.93 (2.60 to 3.26)
||5.47 (4.27 to 6.66)
||8.70 (5.22 to 12.17)
||10.42 (9.44 to 11.40)
|ASCVD 15.0% to <20.0%
||3.98 (3.49 to 4.47)
||6.60 (5.06 to 8.14)
||9.25 (5.48 to 13.02)
||13.83 (12.43 to 15.21)
||6.44 (5.96 to 6.92)
||8.98 (7.91 to 10.05)
||16.74 (14.27 to 19.22)
||22.54 (21.33 to 23.74)
Numbers are percentages (95% CIs) of major CVD events in each cell.
aAn exponential survival function was used to scale participants’ risk at their current length of follow-up to risk at 10 yr.
Risks of CVD Events
The associations between the ASCVD risk score and KDIGO risk category as well as their components with cardiovascular outcomes are described in Supplemental Tables 1 and 2. A significant interaction was found between the ASCVD risk score and KDIGO risk category in association with CVD risks (P for interaction <0.001). Risks of major CVD events increased significantly across ACR and eGFR categories (Table 4). In combination, participants with an intermediate KDIGO risk and with a high or very high KDIGO risk were at significantly increased risks of developing major CVD events compared with participants with a low KDIGO risk in most ASCVD risk strata, including the stratum of an ASCVD risk score ≥20.0% (Figure 2). In this stratum, participants with an intermediate KDIGO risk had a 42% increased risk of developing major CVD events (HR, 1.42; 95% CI, 1.22 to 1.67), and participants with a high or very high KDIGO risk had a 144% increased risk of developing major CVD events (HR, 2.44; 95% CI, 2.01 to 2.98) compared with participants with a low KDIGO risk. In addition, using participants with a low KDIGO risk and an ASCVD risk score <5.0% as the reference, participants with a high or very high KDIGO risk and an ASCVD risk score ≥20.0% had a 8.95-fold increase in risks of developing major CVD events (HR, 9.95; 95% CI, 7.72 to 12.84) (Figure 2).
Table 4. -
HRs (95% CIs) of major CVD events in association with eGFR and urinary ACR categories
|eGFR, ml/min per 1.73 m2
||Urinary ACR, mg/g
||30 to <300
||2.00 (1.71 to 2.34)
||2.47 (1.60 to 3.80)
|60 to <90
||1.44 (1.30 to 1.58)
||2.02 (1.73 to 2.37)
||3.27 (2.43 to 4.40)
|45 to <60
||1.75 (1.37 to 2.23)
||2.93 (2.14 to 4.00)
||3.56 (2.19 to 5.78)
|30 to <45
||1.57 (0.89 to 2.80)
||2.25 (1.23 to 4.11)
||2.17 (1.03 to 4.58)
|15 to <30
||2.60 (0.65 to 10.43)
||2.02 (0.50 to 8.11)
||5.31 (2.84 to 9.93)
||3.33 (0.47 to 23.66)
||8.42 (1.18 to 59.84)
||3.13 (1.01 to 9.75)
Participants with eGFR≥90 ml/min per 1.73 m2 and urinary ACR <30 mg/g were used as reference. The analysis was adjusted for education, current drinking, fruit and vegetable intake, physical activity, body mass index, and the ASCVD score.
Prediction and Reclassification of CVD Risks
Changes in c statistic after adding eGFR, log(ACR), or both to a model including demographic and lifestyle variables as well as the ASCVD risk score were 0.00 (−0.00 to 0.01), 0.01 (0.00 to 0.01), and 0.01 (0.01to 0.02), respectively, in the overall study population (Supplemental Figure 2). In most subgroups, prediction of major CVD events was improved significantly by adding log(ACR) on top of the ASCVD risk score and was improved even further by adding both eGFR and log(ACR), especially in participants with diabetes. The c-statistic changes were 0.02 (0.01 to 0.03) by adding log(ACR) and 0.03 (0.01 to 0.04) by adding both eGFR and log(ACR) in participants with diabetes.
Among the 2866 participants who developed major CVD events, 444 participants (15.49%) were “correctly” reclassified upward and 230 participants (8.03%) were “incorrectly” reclassified downward after adding eGFR and log(ACR) to the model of the ASCVD score (NRI for events, 7.47%; 95% CI, 5.67% to 9.16%). Meanwhile, among the 112,500 participants who did not experience major CVD events, 6350 participants (5.64%) were “correctly” reclassified downward and 9375 participants (8.33%) were “incorrectly” reclassified upward (NRI for nonevents, −2.69%; 95% CI, −2.91% to −2.50%). The overall continuous NRI was 30.17% (26.70% to 33.89%), and the overall categorical NRI was 4.78% (3.03% to 6.41%). The IDI was 1.55% (1.32% to 1.79%) (Table 5). NRI and IDI were also significantly improved when KDIGO risk category was added to the ASCVD risk score (Supplemental Table 3) or when assessed using CVD risk factors instead of the ASCVD risk score (Table 5).
Table 5. -
Improvement in reclassification and discrimination of major CVD events by adding eGFR and ACR to the ASCVD risk score or ASCVD risk factors
||Continuous NRI, % (95% CI)
||Categorical NRI, % (95% CI)
||IDI, % (95% CI)
|ASCVD risk score
||22.17 (18.16 to 25.98)
||2.02 (0.49 to 3.39)
||0.63 (0.49 to 0.77)
||25.90 (22.10 to 29.43)
||4.41 (2.49 to 6.21)
||1.16 (0.95 to 1.37)
| +log(ACR) + eGFR
||30.17 (26.70 to 33.89)
||4.78 (3.03 to 6.41)
||1.55 (1.32 to 1.79)
|ASCVD risk factors
||6.80 (3.16 to 10.51)
||1.87 (0.82 to 2.92)
||0.24 (0.16 to 0.32)
||12.00 (8.56 to 15.64)
||3.25 (1.73 to 4.63)
||0.86 (0.68 to 1.03)
| +log(ACR) + eGFR
||10.34 (6.70 to 13.92)
||3.66 (2.26 to 5.05)
||0.99 (0.81 to 1.16)
ASCVD risk factors were variables used to calculate the ASCVD risk score, including sex, age, current smoking, systolic BP, antihypertensive drugs, total cholesterol, HDL cholesterol, and diabetes.
Using data from a large, nationwide, prospective cohort study of Chinese community residents aged ≥40 years, we found that the KDIGO risk categories using nontraditional CVD risk factors, such as eGFR and urinary ACR, could further stratify CVD risks above and beyond the ASCVD risk score of traditional CVD risk factors. The combination of the ASCVD and the KDIGO risk categories made better stratification of the study population. In addition, adding ACR and eGFR on top of the ASCVD score significantly improved the prediction of CVD development. Finally, adding ACR and eGFR to the ASCVD score improved reclassification of CVD risks for participants who developed major CVD events. These findings highlighted the importance of eGFR and urinary ACR (individually, together, and in combination using the KDIGO risk categories) as nontraditional risk factors in the stratification and prediction of major CVD events in Chinese population.
The associations of increased urinary ACR and decreased eGFR with the development of CVD events have been well established.25,26 Significantly increased risks of major CVD events were observed in this study across ACR and eGFR groups individually, and the increase was more striking across combined ACR and eGFR groups. The predictive abilities of ACR and eGFR for incident CVD have also been demonstrated using c-statistic changes in the CKD-PC cohorts.13 They reported that the combination of eGFR and ACR outperforms most single modifiable traditional risk factors, such as systolic BP, diabetes, cholesterol, and smoking, in predicting cardiovascular mortality, stroke, and heart failure. In addition to prediction of CVD risks assessed by c-statistic changes, we used NRI and IDI to evaluate reclassifications of CVD risks by ACR and eGFR adding to the ASCVD risk score. The significant improvement in CVD risk prediction and reclassification by ACR and eGFR on top of the ASCVD score found in this study further highlighted their importance in CVD prevention. Furthermore, although CKD-PC cohorts had the advantages of a large sample size and a wide coverage of diverse populations, there are important measurement issues regarding the uniformity of urine albumin/protein assay, and data for Asian individuals were predominantly from cohorts with dipstick proteinuria,13 which provides only semiquantitative results. Therefore, urinary albumin and urinary creatinine of all 4C participants measured in the central laboratory using standard methods and strict quality control offered important and reliable data from a large, nationwide, and well-characterized Chinese general population.
In their most recent guideline on the primary prevention of CVD, the ACC and the AHA recommended to consider additional individual risk-enhancing clinical factors when making decisions of interventions.17 An eGFR=15–59 ml/min per 1.73 m2 with or without albuminuria was recommended as one of the risk enhancers. However, depending on eGFR levels alone without regard to albuminuria can miss important information because urinary ACR or albuminuria is predictive of CVD events as well as, if not better than, eGFR levels.13 The adjusted CVD risk was almost two-fold higher among individuals with albuminuria and eGFR≥60 ml/min per 1.73 m2 as compared with those with eGFR 45–59 ml/min per 1.73 m2 and normal protein excretion.27,28 Findings from this study also revealed that ACR improved prediction and reclassification of CVD risk better than eGFR. Therefore, the KDIGO risk categories defined by both eGFR and ACR might serve as a better risk-enhancing factor than eGFR alone.
The KDIGO risk categories combining urinary ACR and eGFR levels were defined in 2012 for CKD prognosis.1415 The ASCVD risk score was developed in 2013 by integrating traditional CVD risk factors and has been widely used to predict risk and recommend management strategies for those at risks of ASCVD,12 although overestimation of the score has been reported in diverse populations.29 The ASCVD score tended to overestimate CVD risk at higher risk categories in the 4C population. Incidences and risks of CVD events associated with a given ASCVD risk score vary substantially when the KDIGO risk was different. In fact, we found that individuals with a high or very high KDIGO risk but with an intermediate ASCVD risk score (7.5% to <10.0%) appeared to have similar CVD risk as individuals with a low KDIGO risk and a high ASCVD risk score (≥20%). In addition, adding KDIGO risk category to the ASCVD risk score significantly improved reclassification and discrimination of major CVD events. Therefore, nontraditional risk estimation using the KDIGO risk might be recommended before making preventive treatment decisions, especially when eGFR and ACR are already measured for clinical purposes. For example, in individuals with a borderline or intermediate ASCVD score but a high KDIGO risk, the cardiovascular risk can be “correctly” reclassified upward, leading to potential interventions that might benefit those individuals in the prevention of future CVD events. Although evaluating CVD risks using the ASCVD score and KDIGO category separately, efforts could be made to develop new risk stratifications or categories and new risk prediction scores incorporating both traditional and nontraditional risk factors in different ethnic populations.
Strengths of this study included the large and representative sample of Chinese adults selected from 20 study sites of both urban and rural areas across mainland China, the standardized and uniformed collection of comprehensive baseline and follow-up data, the centralized measurement and calculation of eGFR and ACR, and the wide coverage of the KDIGO and ASCVD risk levels, which made this analysis possible in people with very low across very high CVD risks. There are also limitations. The follow-up duration was relatively short, and observed risks at 10 years had to be assessed on the basis of extrapolation. ACR and eGFR were evaluated at a single point in time, which often results in an overestimation of CKD prevalence and the KDIGO risk. Individual CVD outcomes, such as myocardial infarction, stroke, and cardiovascular mortality, were not examined due to limited numbers of each outcomes. A relatively high proportion of participants with missing data was excluded from this analysis. Examination of participants’ characteristics revealed a modestly increased KDIGO risk and ASCVD risk score in participants who were excluded compared with those who were included. Therefore, this association between high KDIGO risk and CVD might be conservative. In addition, generalization of these findings to adults <40 years, adults who are institutionalized, or adults of other ethnicities is limited.
In conclusion, consideration of nontraditional CVD risk factors, such as eGFR and urinary ACR, could further stratify future CVD risks on top of the ASCVD risk score calculated by traditional CVD risk factors. In addition, prediction and reclassification of CVD risks were improved significantly by adding ACR and eGFR to the ASCVD risk score. Clinical applications of the KDIGO risk categories for CVD prevention should be evaluated by interventional studies and in other ethnic populations.
Y. Mu reports speakers bureau from Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi-Aventis. All remaining authors have nothing to disclose.
This work was supported by National Key R&D Program of China grants 2017YFC1310700, 2016YFC1305600, 2018YFC1311800, 2016YFC0901200, 2016YFC1305202, and 2016YFC1304904; National Natural Science Foundation of China grants 81870560 and 81561128019; Shanghai Municipal Government grant 18411951800; Shanghai Shenkang Hospital Development Center grant SHDC12019101; Shanghai Jiaotong University School of Medicine grant DLY201801; and Ruijin Hospital grant 2018CR002.
The funding agencies had no role in the design and conduct of the study; in the collection, management, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.
Y. Bi, M. Li, Y. Mu, G. Ning, W. Wang, Y. Xu, and J. Zhao conceptualized and designed the study; M. Li, J. Lu, G. Qin, Y. Xu, and L. Yan drafted the manuscript; Y. Bi, G. Chen, Li Chen, Lulu Chen, Y. Chen, Z. Gao, R. Hu, Y. Huo, M. Li, Q. Li, C. Liu, J. Lu, Z. Luo, Y. Mu, G. Ning, G. Qin, Y. Qin, F. Shen, L. Shi, Q. Su, X. Tang, Q. Wan, G. Wang, W. Wang, Y. Wang, S. Wu, Y. Xu, L. Yan, T. Yang, X. Yu, Y. Zhang, and J. Zhao were responsible for critical revision of the manuscript for important intellectual content; Y. Bi, G. Ning, W. Wang, and Y. Xu had full access to all of the data in the study and take responsibility for its integrity of the data and the accuracy of the data analysis; and Y. Bi, G. Chen, Li Chen, Lulu Chen, Y. Chen, M. Dai, Z. Gao, R. Hu, Y. Huo, M. Li, Q. Li, C. Liu, J. Lu, Z. Luo, Y. Mu, G. Ning, G. Qin, Y. Qin, F. Shen, L. Shi, Q. Su, X. Tang, Q. Wan, G. Wang, T. Wang, W. Wang, Y. Wang, S. Wu, M. Xu, Y. Xu, L. Yan, T. Yang, X. Yu, D. Zhang, Y. Zhang, J. Zhao, and Z. Zhao were responsible for the acquisition, analysis, or interpretation of data and approved the final version of the manuscript.
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2020060856/-/DCSupplemental.
Supplemental Table 1. Incidence rates (95% CIs) of major cardiovascular disease events per 1000 person-years according to eGFR and urinary ACR categories.
Supplemental Table 2. Hazard ratios (95% CIs) of major cardiovascular disease events in association with the ASCVD risk score and its components.
Supplemental Table 3. Improvement in reclassification and discrimination of major cardiovascular disease events by adding KDIGO risk category to the ASCVD risk score.
Supplemental Figure 1. Incidence rates (95% CIs) of major cardiovascular disease events according to the ASCVD and KDIGO risk categories.
Supplemental Figure 2. The c-statistic changes after adding eGFR, log(ACR), or eGFR and log(ACR) to the base model in prediction of major cardiovascular disease events.
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