Research

JAMA | Original Investigation

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes
An Individual-Participant Data Meta-Analysis
Writing Group for the CKD Prognosis Consortium
Supplemental content
IMPORTANCE Chronic kidney disease (low estimated glomerular filtration rate [eGFR] or
albuminuria) affects approximately 14% of adults in the US.
OBJECTIVE To evaluate associations of lower eGFR based on creatinine alone, lower eGFR

based on creatinine combined with cystatin C, and more severe albuminuria with adverse
kidney outcomes, cardiovascular outcomes, and other health outcomes.
DESIGN, SETTING, AND PARTICIPANTS Individual-participant data meta-analysis of 27 503 140
individuals from 114 global cohorts (eGFR based on creatinine alone) and 720 736 individuals
from 20 cohorts (eGFR based on creatinine and cystatin C) and 9 067 753 individuals from114 cohorts (albuminuria) from 1980 to 2021.
EXPOSURES The Chronic Kidney Disease Epidemiology Collaboration 2021 equations for eGFR
based on creatinine alone and eGFR based on creatinine and cystatin C; and albuminuria
estimated as urine albumin to creatinine ratio (UACR).
MAIN OUTCOMES AND MEASURES The risk of kidney failure requiring replacement therapy,
all-cause mortality, cardiovascular mortality, acute kidney injury, any hospitalization,
coronary heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease.
The analyses were performed within each cohort and summarized with random-effects
meta-analyses.
RESULTS Within the population using eGFR based on creatinine alone (mean age, 54 years[SD, 17 years]; 51% were women; mean follow-up time, 4.8 years [SD, 3.3 years]), the meaneGFR was 90 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 11 mg/g
(IQR, 8-16 mg/g). Within the population using eGFR based on creatinine and cystatin C(mean age, 59 years [SD, 12 years]; 53% were women; mean follow-up time, 10.8 years[SD, 4.1 years]), the mean eGFR was 88 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the
median UACR was 9 mg/g (IQR, 6-18 mg/g). Lower eGFR (whether based on creatinine alone
or based on creatinine and cystatin C) and higher UACR were each significantly associatedwith higher risk for each of the 10 adverse outcomes, including those in the mildest
categories of chronic kidney disease. For example, among people with a UACR less than
10 mg/g, an eGFR of 45 to 59 mL/min/1.73 m2 based on creatinine alone was associatedwith significantly higher hospitalization rates compared with an eGFR of 90 to
104 mL/min/1.73 m2 (adjusted hazard ratio, 1.3 [95% CI, 1.2-1.3]; 161 vs 79 events per 1000
person-years; excess absolute risk, 22 events per 1000 person-years [95% CI, 19-25 events
per 1000 person-years]).
CONCLUSIONS AND RELEVANCE In this retrospective analysis of 114 cohorts, lower eGFR basedon creatinine alone, lower eGFR based on creatinine and cystatin C, and more severe UACR
were each associated with increased rates of 10 adverse outcomes, including adverse kidney
outcomes, cardiovascular diseases, and hospitalizations.

Group Information: The members of
the Writing Group for the CKD
Prognosis Consortium appear at theend of the article. The members of
the CKD Prognosis Consortium arelisted in Supplement 3.

JAMA. 2023;330(13):1266-1277. doi:10.1001/jama.2023.17002
1266

Corresponding Author: Morgan E.
Grams, MD, PhD, 227 E 30th St,
Unit 825, New York, NY 10016(ckdpc@jhmi.edu).
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Estimated Glomerular Filtration Rate, Albuminuria, and Adverse OutcomesChronic kidney disease (CKD), which is defined by albuminuria (urine albumin to creatinine ratio [UACR]≥30 mg/g/d) or a glomerular filtration rate (GFR) lessthan 60 mL/min/1.73 m2 that persists for at least 3 months, affects approximately 14% of adults in the US.1 Both lower estimated GFR (eGFR) values and more severe albuminuria havebeen associated with higher rates of kidney failure with replacement therapy, acute kidney injury, all-cause mortality, andcardiovascular mortality.2-6This study evaluated associations of albuminuria, eGFR,
and the combination of albuminuria and eGFR with 10 adverse health outcomes, consisting of incident kidney failure
with replacement therapy, all-cause mortality, cardiovascular mortality, acute kidney injury, hospitalization, coronary
heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease. Associations were evaluated within subgroups of age, sex, and presence of diabetes and cardiovascular disease. The eGFR was assessed using race-free equations
that incorporated creatinine alone or both creatinine and cystatin C.7 The prespecified analyses included evaluating whether
eGFR based on creatinine and cystatin C was more strongly associated with adverse outcomes compared with eGFR basedon creatinine alone.

Methods
Study Population
Investigators in the CKD Prognosis Consortium (ckdpc.org)
were invited to participate in the current meta-analysis if
their represented cohorts included individuals with botheGFR and albuminuria as well as having 50 events or more forat least 1 of the selected outcomes. For measures of prevalence and absolute incidence of adverse outcomes, we useddata from the Optum Labs Data Warehouse, which is a data set
with deidentified administrative claims and electronic healthrecords for patients who were followed up longitudinally. The
data derived from the electronic health records included a subset that was normalized and standardized into a single
database.8 The study was approved by the institutional review board (IRB) at the Bloomberg School of Public Health,
Johns Hopkins University. The data were preexisting anddeidentified; however, in accordance with individual cohort
policies, the study underwent expedited IRB approval. The IRB
waived the requirement for informed consent.

Kidney Measures
All individuals had serum creatinine measurements with eGFR
based on creatinine alone estimated using the racefree Chronic Kidney Disease Epidemiology Collaboration(CKD-EPI) 2021 creatinine equation.7 A subset of the population had serum creatinine and cystatin C measurementswith eGFR estimated using the CKD-EPI 2021 creatininecystatin C equation. The methods for the creatinine and cystatin C measurements for each cohort are described ineAppendix 1 in Supplement 1.9-11 The categories of eGFR were
105 mL/min/1.73 m2 or greater, 90 to 104 mL/min/1.73 m2,
60 to 89 mL/min/1.73 m2, 45 to 59 mL/min/1.73 m2, 30 toOriginal Investigation Research

Key Points
Question Are lower values for estimated glomerular filtration rate(eGFR based on either creatinine alone or creatinine and cystatinC) and more severe albuminuria associated with adverse kidneyand cardiovascular outcomes?
Findings In this retrospective individual-level data analysis of
27 503 140 individuals from 114 cohorts, lower eGFR and more
severe albuminuria were each associated with higher rates ofadverse kidney outcomes, including kidney failure withreplacement therapy and acute kidney injury. Lower eGFR andmore severe albuminuria also were associated with adverse
cardiovascular outcomes, including cardiovascular mortality, heart
failure, and atrial fibrillation.
Meaning Lower eGFR values and more severe albuminuria wereassociated with multiple adverse outcomes.

44 mL/min/1.73 m2, 15 to 29 mL/min/1.73 m2, and less than
15 mL/min/1.73 m2.
Albuminuria was measured and calculated as UACR, urine
protein to creatinine ratio, or dipstick proteinuria. For the former 2 methods, both spot and 24-hour collections were accepted. For the latter 2 methods, the values were extrapolated to the UACR using a previously published multivariable
conversion equation.12 In the categorical analyses, dipstick proteinuria categories of negative, trace, 1+, 2+, and 3+ or 4+ wereclassified into the UACR categories of less than 10 mg/g, 10 to
29 mg/g, 30 to 299 mg/g, 300 to 999 mg/g, and 1000 mg/g or
greater, respectively. In the sensitivity analyses without dipstick values, all dipstick measures were classified in the missing UACR category.

Outcomes
The following outcomes were requested for each cohort: allcause mortality, cardiovascular mortality (death due to cardiovascular disease), kidney failure with replacement therapy(receipt of chronic dialysis or kidney transplant), all-cause hospitalization, and hospitalizations for stroke (ischemic or hemorrhagic), myocardial infarction, heart failure (any hospitalization or death with heart failure), acute kidney injury, atrial
fibrillation, and peripheral artery disease. Some of the cohorts were linked to the US Renal Data System13 to ascertain
kidney failure with replacement therapy, some of the cohorts
performed expert adjudication for specific outcomes, and someof the cohorts identified outcomes based on coding alone using
the International Classification of Diseases, Ninth Revision,or the International Statistical Classification of Diseases andRelated Health Problems, Tenth Revision. Cohort-specific outcome definitions appear in eAppendix 1 in Supplement 1. Individuals with a history of the outcome were excluded fromthe analyses of incident events. Each cohort contributed between 1 and 10 analyses, depending on the outcomes available for each cohort. General population cohorts with fewer
than 50 events for a specific outcome and CKD cohorts withfewer than 25 events were excluded from the meta-analysis
for the corresponding outcome.

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Research Original Investigation

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Statistical Analyses
Cox proportional hazards models were used to relate kidney
measurements to adverse outcomes separately in each cohort. Random-effects models were used for the metaanalysis of the hazard ratios (HRs). The kidney measurements were collected at a single visit. In the categoricalanalyses, individuals were classified by the categories of
eGFR (<15, 15-29, 30-44, 45-59, 60-89, 90-104, and≥105 mL/min/1.73 m2) and UACR (<10, 10-29, 30-299, 300999, and ≥1000 mg/g). The models included interaction
terms for all combinations of the eGFR and UACR categories (eg, the product terms of eGFR <15 mL/min/1.73 m2
and UACR <10 mg/g; eGFR of 15-29 mL/min/1.73 m 2 and
UACR <10 mg/g; and eGFR of 30-44 mL/min/1.73 m2 and UACR
<10 mg/g, etc). The reference group was set as an eGFR of 90
to 104 mL/min/1.73 m2 and a UACR less than 10 mg/g. Because the CKD cohorts lacked individuals in the referencegroup, only general population and electronic health record
cohorts were used in the categorical analyses.
Because fewer individuals had data to contribute to theeGFR analyses based on creatinine and cystatin C, the less common categories of eGFR and UACR were combined to ensure
adequate numbers of events. Hence, the 2 lowest categories
of eGFR were combined (<15 and 15-29 mL/min/1.73 m2) andthe 2 highest categories of UACR were combined (300-999 and≥1000 mg/g). Model adjustment differed depending on the outcome and included a subset of the following covariates: age,sex, smoking status (current, former, never), systolic bloodpressure, total cholesterol, high-density lipoprotein cholesterol, body mass index, use of antihypertensive medications,and a history of diabetes, coronary heart disease, stroke, heart
failure, atrial fibrillation, peripheral artery disease, cancer, andchronic obstructive pulmonary disease when relevant (eg, ananalysis of incident peripheral artery disease as an outcome
would not include peripheral artery disease as an adjustment
variable). All covariate definitions and models appear in eAppendix 1 in Supplement 1.
Quantitative covariates were included in the model using
a continuous scale. The missing data for albuminuria weretreated as a separate category when the missingness exceeded
10% in a given cohort, otherwise a complete case analysis was
performed. For other variables, the extent and handling of missing data are detailed in eTable 1 in Supplement 2 and in eAppendix 1 in Supplement 1. Models were run overall and by stratum for age (<65 years, ≥65 years) and sex (female, male).
To facilitate comparison of associations across cohorts, outcomes, and by filtration marker (eGFR based on creatinine alone
vs eGFR based on creatinine and cystatin C), eGFR and UACR
were also modeled continuously with linear spline terms andknots at eGFRs of 60 mL/min/1.73 m2 and 105 mL/min/1.73 m2
and log transformation for UACR. The model parameters wereotherwise identical to those of the categorical analyses. Continuous analyses were performed in all cohorts, including thegeneral population, electronic health record, and CKD cohorts. The meta-analysis of β coefficients from the Cox proportional hazards models used random effects as detailed above.
Forest plots were examined to assess the heterogeneity ofthe effect sizes across cohorts and cohort characteristics. Sub1268

group analyses were performed by age, sex, diabetes, and presence of cardiovascular disease. In the sensitivity analyses, continuous associations also were examined using other estimating
equations for GFR, including previous CKD-EPI equations14,15(CKD-EPI 2009 equation for eGFR based on creatinine alone
and CKD-EPI 2012 equation for eGFR based on creatinine andcystatin C), but only using the non-Black (NB) race value, andEuropean Kidney Function Collaboration (EKFC) equations16(EKFC equations for eGFR based on creatinine alone and for
eGFR based on creatinine and cystatin C).
After the meta-analysis of the β coefficients (log HRs), we
compared log HRs within each combined category of eGFR and
UACR across populations or subgroups of populations using
matched-pair Wilcoxon signed rank tests. The betweenpopulation differences were summarized using medians and
IQRs. A P value <.05 was considered statistically different.
The largest cohort, the Optum Labs Data Warehouse (electronic health records for populations of patients in the US) wasused to estimate the prevalence for the eGFR and UACR categories and the unadjusted incidence rates for each adverse
outcome within the categories of eGFR and UACR. For theseanalyses, single measurements for eGFR and albuminuria wereused. The incidence of adverse outcomes was estimated individually within each of the 39 health systems and summarized as a median cohort value across each health system(eg, 19 health systems had higher incidence rates and 19 healthsystems had lower incidence rates). Adjusted excess incidence (ie, the difference in incidence comparing 1 combined
eGFR and UACR category vs the reference category) was estimated by treating incidence rates in the median health system in the reference group among the Optum Labs Data Warehouse cohorts as a constant and combining the HRs from themeta-analysis for each group in the categorical analysis of eGFR
based on creatinine alone.
All analyses were conducted using Stata MP version 16.1
(StataCorp). All statistical testing was 2-sided. Statistical significance was determined by P < .05.

Results
Study Population
A total of 120 cohorts were evaluated. Of these 120 cohorts,
the individual cohort principal investigators refused to provide data for 2 cohorts and were unable to send data or
perform the analysis within the time allotted for 4 cohorts,
leaving 114 cohorts in this individual-participant data metaanalysis. The data sources included 37 observational studies
or clinical trials of individuals identified from the general
population, 49 electronic health record databases, and 28
observational studies or clinical trials of adults with CKD.
Additional information on the included cohorts appears ineAppendix 1 and eAppendix 2 in Supplement 1.
Population With eGFR Based on Creatinine Alone
Of the 120 cohorts evaluated for inclusion, 114 cohorts
including 27 503 140 individuals had data available for
eGFR based on creatinine alone and were included in the

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jama.comEstimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Original Investigation Research

Table 1. Participant Characteristics for the Estimated Glomerular Filtration Rate (eGFR)
Based on Creatinine Alone or Creatinine and Cystatin C
eGFR based on creatinine aloneeGFR based on creatinineand cystatin C

No. of cohorts

114

20

No. of individualsa

27 503 140

721 394

Age, mean (SD), y

54 (17)59 (12)

Female

51

53

Male

49

47

4.8 (3.3)

10.8 (4.1)

Sex, %

Follow-up, mean (SD), y
Comorbid conditions, %Taking medications for hypertension

16.6

27.0

Diabetes

15.2

9.4

Former smoking

13.1

35.0

Current smoking

10.6

11.5

Coronary heart disease

9.9

6.3

History of cancer

13.0

10.8

Chronic obstructive pulmonary disease

7.5

2.4

Atrial fibrillation

4.5

4.7

History of heart failure

3.5

3.2

History of stroke

3.2

3.5

Peripheral artery disease

1.6

1.0

126 (17)138 (20)29 (7)

28 (5)

Total

4.7 (1.3)

5.0 (1.1)

High-density lipoprotein

1.3 (0.4)

1.3 (0.4)

90 (22)89 (20)c

11 (8-16)

9 (6-18)

SI conversion factors: To convert
high-density and total cholesterol to
mg/dL, divide by 0.0259.
a

Not necessarily the denominator foreach characteristic. The proportion
with missing data for eachcharacteristic appears in eTable 1 in
Supplement 2. Detailed definitionsof each of these characteristics
appear in eAppendix 1 in
Supplement 1.

bCalculated as weight in kilograms
divided by height in meterssquared.

Vital signs and laboratory studies
Systolic blood pressure, mean (SD),mm HgbBody mass index, mean (SD)

Cholesterol, mean (SD), mmol/L

2

eGFR, mean (SD), mL/min/1.73 m
Based on creatinine alone

Based on creatinine and cystatin C
Urine albumin to creatinine ratio,
median (IQR), mg/gd

88 (22)analyses (Table 1). Among these individuals, the mean age
was 54 years (SD, 17 years), 51% were women, the meaneGFR based on creatinine alone was 90 mL/min/1.73 m2 (SD,22 mL/min/1.73 m2), and 33.0% had measures of albuminuria. Of those with albuminuria measures, the median
UACR was 11 mg/g (IQR, 8-16 mg/g). The number of cohorts
contributing to each outcome ranged from 52 (for acute kidney injury) to 108 (for all-cause mortality). The rates ofadverse outcomes were lowest for peripheral artery disease(median rate among 62 cohorts, 1.4 events per 1000 personyears) and kidney failure with replacement therapy (medianrate among 83 cohorts, 1.3 events per 1000 person-years)
and highest for hospitalizations (median rate among 52
cohorts, 94 events per 1000 person-years) (eTable 2 in
Supplement 2).
Population With eGFR Based on Creatinine and Cystatin C
There were 20 cohorts included with 721 394 individuals that
had data for cystatin C (eGFR based on creatinine and cystatin C population). Among these individuals, the mean age was
59 years (SD, 12 years), 53% were women, the mean eGFRcBased on creatinine alone.

d

These data for measurement ofalbuminuria represent less than
50% of the analytic population(9 067 753 [33.0%] for eGFR basedon creatinine alone and 320 443[44.4%] for eGFR based on
creatinine and cystatin C).

was 89 mL/min/1.73 m2 (SD, 20 mL/min/1.73 m2) based on creatinine alone vs 88 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2)
based on creatinine and cystatin C, and 44.4% had measuresof albuminuria. Of those with albuminuria measures, the median UACR was 9 mg/g (IQR, 6-18 mg/g).
Both Populations
The clinical characteristics for each cohort appear in eTable 3
in Supplement 2 (eGFR based on creatinine alone population) and in eTable 4 in Supplement 2 (eGFR based on creatinine and cystatin C population). Most individuals who were
missing albuminuria data came from the electronic health record cohorts (95.4% of the population for eGFR based on creatinine alone and 99.8% of the population for eGFR based on
creatinine and cystatin C). For the analyses of eGFR basedon creatinine alone, the mean follow-up time was 4.8 years(SD, 3.3 years). For the analyses of eGFR based on creatinineand cystatin C, the mean follow-up was 10.8 years (SD, 4.1 years)
and the number of cohorts contributing data ranged from 3(for hospitalizations) to 20 (for all-cause mortality) (eTable 5
in Supplement 2).

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Research Original Investigation

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Analyses According to eGFR Based on Creatinine Aloneand UACR
In the categorical analyses of eGFR based on creatinine
alone, compared with the reference category of 90 to 104
mL/min/1.73 m2, the lower categories (eGFR ≤60 mL/min/
1.73 m2) were significantly associated with higher risk foreach outcome. Compared with the reference UACR categoryof less than 10 mg/g, the higher categories were associatedwith higher rates for each outcome (Figure 1). Risks amongpeople with missing UACR data were comparable with thosewithin the UACR category of 10 to 29 mg/g (median difference in log HRs, −0.03 [IQR, −0.11 to 0.09], P = .39; eTable 6
in Supplement 2). The patterns of risk associations weresimilar across each age category and among men andwomen, although the relative risks (RRs) were weaker in theolder age (≥65 years) category compared with the youngerage (<65 years) category (median difference in log HRs,
−0.45 [interquartile interval {IQI}, −0.70 to −0.14], P < .001)
and very slightly stronger in women compared with men(median difference in log HRs, 0.04 [IQI, −0.05 to 0.13],
P < .001; eTable 7 in Supplement 2).
Compared with an eGFR of 90 to 104 mL/min/1.73 m2 basedon creatinine alone, the CKD category of G3a (an eGFR of
45-59 mL/min/1.73 m2 based on creatinine alone) was significantly associated with higher adjusted HRs for each outcome, even among people with a UACR less than 10 mg/g, a
UACR of 10 mg/g to less than 30 mg/g, or in those with missing UACR (Table 2). When stratified by age and sex, the RRs
for the CKD category of G3a were smaller among older adults(≥65 years of age) compared with younger adults (<65 yearsof age) (median difference in log HRs, −0.36 [IQI, −0.49 to−0.27], P < .001); however, all remained statistically significant except for hospitalizations among older adults withmissing data for UACR. The RR comparisons between men
and women were not significantly different (median difference in log HRs, 0.02 [IQI, −0.04 to 0.07], P = .19; eTable 8 in
Supplement 2).
In the continuous analyses, the HRs for the spline term forlower eGFR (<60 mL/min/1.73 m2) and an 8-fold higher UACR
were highest for kidney failure with replacement therapy(HR for eGFR <60 per 15 mL/min/1.73 m2, 3.89 [95% CI, 3.734.06]), and all (for eGFR) or nearly all (for UACR) associations
were statistically significant in the individual cohorts (eTable 9
in Supplement 2 and the eFigure in Supplement 1). In thesensitivity analyses excluding albuminuria measured bydipstick, the UACR associations were not statistically different from those when the dipstick measures were included(median difference in log HRs, −0.02 [IQI, −0.02 to −0.004],
P = .06; eTable 10 in Supplement 2). The HRs by subgroup ofage, sex, diabetes, and cardiovascular disease appear ineTable 11 in Supplement 2.

Analyses According to eGFR Based on Creatinine
and Cystatin C and UACR
In the categorical analyses of eGFR based on creatinine andcystatin C, compared with the reference category of 90 to
104 mL/min/1.73 m2, the eGFR categories below 60 mL/min/
1.73 m2 were significantly associated with higher risk for
1270

each outcome. Compared with the UACR reference categoryof less than 10 mg/g, higher UACR categories were associated with higher rates for each outcome (Figure 2 andeTable 12 in Supplement 2). Associations remained statistically significant in subset analyses by age and sex; there
were weaker RRs in older adults (≥65 years of age) compared with younger adults (<65 years of age) (median difference in log HRs, −0.14 [IQI, −0.36 to 0.03], P < .001), but not
in women compared with men (median difference in log
HRs, −0.002 [IQI, −0.10 to 0.11], P = .53; eTable 13 in
Supplement 2).
The differences in the adjusted HRs for eGFR based on creatinine and cystatin C between the older and younger age
groups were smaller than those seen with eGFR based on creatinine alone (median difference in differences, −0.16 [IQI,−0.34 to −0.01], P < .001; eTable 13 in Supplement 2). The risk
for all outcomes was increased in the CKD category of G3a(eGFR category of 45-59 mL/min/1.73 m2 based on creatinineand cystatin C) even among people with a UACR less than
10 mg/g, and these risks remained statistically significant in
the subset analyses by age and sex (eTable 14 in Supplement 2). Compared with the analyses using eGFR based on creatinine alone, the risk associations with eGFR based on
creatinine and cystatin C were stronger and less U-shaped(median difference in log HRs, 0.10 [IQI, 0.02 to 0.21], P < .001;
Figure 3).
Associations with alternative estimating equations for GFR
appear in eTable 15 in Supplement 2. The alternative estimating equations were highly correlated with eGFR using the
CKD-EPI 2021 equation in all cohorts (range of Pearson correlation coefficients, 0.98-1.00 for eGFR based on creatinine
alone using the CKD-EPI 2021 and EKFC equations; range ofPearson correlation coefficients, 0.93-0.99 for eGFR based on
creatinine and cystatin C using the CKD-EPI 2021 and EKFC
equations; range of Pearson correlation coefficients, 0.991.00 for eGFR based on creatinine alone using the CKD-EPI 2021
and CKD-EPI 2009 NB equations; and range of Pearson correlation coefficients, 0.996-1.00 for eGFR based on creatinineand cystatin C using the CKD-EPI 2021 and CKD-EPI 2012
NB equations).

Prevalence of CKD and Incidence of Adverse Outcomes
Of the included individuals in the cohorts from the Optum
Labs Data Warehouse, 63% were missing a measure of albuminuria (including dipstick measures). The prevalence ofeach category of eGFR based on creatinine alone was similar
with and without the inclusion of those missing a measureof albuminuria. For example, the prevalence of eGFR lessthan 60 mL/min/1.73 m2 (based on creatinine alone) was
9.6% when individuals missing a measure of albuminuria
were included and was 10% when those missing a measureof albuminuria were excluded. Among those with measuresof albuminuria, the prevalence of the UACR category of 30
to 299 mg/g (category A2) was 9.9%, 3.1% had the UACR category of 300 to 999 mg/g, and 1.2% had the UACR categoryof 1000 mg/g or greater (eTable 16 in Supplement 2).
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jama.comEstimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Original Investigation Research

Figure 1. Categorical Analysis of the Associations of Estimated Glomerular Filtration Rate (eGFR) and Albuminuria With Subsequent Adverse Outcomesin the Population Based on Creatinine Alone
Urine albumin to creatinine ratio, mg/g
Overall

<10

10-29

eGFR, mL/min/1.73 m2
using creatinine alone30-299Urine albumin to creatinine ratio, mg/g

300-999

≥1000

<10

All-cause mortality: 82 cohorts
26 444 384 participants; 2 604 028 events

10-29

30-299300-999≥1000

Myocardial infarction: 64 cohorts
22 838 356 participants; 451 063 events

≥105

1.6

2.2

2.9

4.3

5.8

1.1

1.4

2.0

2.7

3.8

90-104

Reference

1.3

1.8

2.6

3.1

Reference

1.3

1.6

2.2

3.2

60-89

1.0

1.3

1.7

2.2

2.8

1.1

1.3

1.6

2.2

3.1

45-59

1.3

1.6

2.0

2.4

3.1

1.4

1.7

2.0

2.8

3.7

30-44

1.8

2.0

2.5

3.2

3.9

1.9

2.0

2.4

3.2

4.3

15-29

2.8

2.8

3.3

4.1

5.6

2.7

3.1

3.1

4.2

5.1

<15

4.6

5.0

5.3

6.0

7.0

4.6

5.6

4.8

6.0

6.0

eGFR, mL/min/1.73 m2
using creatinine alone

Cardiovascular mortality: 76 cohorts
26 022 346 participants; 776 441 events

Stroke: 68 cohorts
24 746 436 participants; 461 785 events

≥105

1.4

2.0

3.0

4.1

5.4

1.2

1.6

2.2

3.1

4.3

90-104

Reference

1.3

1.9

2.7

3.6

Reference

1.3

1.6

2.4

3.1

60-89

1.0

1.4

1.7

2.4

3.2

1.1

1.3

1.7

2.2

3.0

45-59

1.4

1.7

2.2

2.8

3.8

1.4

1.6

1.9

2.3

2.9

30-44

2.0

2.3

2.8

3.7

4.6

1.6

1.7

2.0

2.4

3.0

15-29

3.2

3.1

3.5

5.0

6.5

1.8

2.1

2.1

2.7

3.0

<15

6.1

6.4

6.4

7.3

8.2

3.2

2.8

2.9

3.2

3.8

eGFR, mL/min/1.73 m2
using creatinine alone

Kidney failure with replacement therapy: 57 cohorts
25 466 956 participants; 158 846 events

Heart failure: 61 cohorts
24 603 016 participants; 1 132 443 events

≥105

0.5

1.2

2.9

7.7

25

1.2

1.7

2.7

4.2

6.9

90-104

Reference

1.8

4.3

12

43

Reference

1.3

2.0

2.8

4.2

60-89

2.3

4.9

10

27

85

1.1

1.4

1.9

2.7

4.2

45-59

13

19

37

89

236

1.6

1.8

2.4

3.4

5.0

30-44

50

58

115

240

463

2.2

2.5

3.1

4.2

6.5

15-29

283

301

443

796

1253

3.6

3.5

4.1

5.8

8.1

<15

770

1040

1618

2297

2547

5.1

5.7

5.8

7.9

9.9

eGFR, mL/min/1.73 m2
using creatinine alone

Acute kidney injury: 49 cohorts
23 914 614 participants; 1 408 929 events

Atrial fibrillation: 50 cohorts
22 886 642 participants; 1 068 701 events

≥105

1.0

1.6

2.4

3.7

5.5

1.1

1.3

1.7

2.4

3.5

90-104

Reference

1.4

2.1

3.2

5.0

Reference

1.2

1.5

1.9

2.3

60-89

1.6

2.2

3.1

4.3

6.7

1.0

1.2

1.4

1.7

2.2

45-59

3.5

4.0

5.1

6.9

9.0

1.2

1.3

1.5

1.8

2.4

30-44

5.6

5.9

6.8

8.6

11

1.4

1.5

1.7

2.0

2.4

15-29

8.3

8.0

8.5

9.9

10

1.9

1.8

2.0

2.6

3.0

<15

8.5

11

7.9

5.5

5.7

2.6

2.5

3.1

3.6

4.2

eGFR, mL/min/1.73 m2
using creatinine alone

Hospitalization: 49 cohorts
25 426 722 participants; 8 398 637 events

Peripheral artery disease: 54 cohorts
24 830 794 participants; 378 924 events

≥105

1.4

1.7

2.1

2.1

2.3

0.9

1.4

1.9

2.8

5.0

90-104

Reference

1.1

1.3

1.5

1.7

Reference

1.3

1.9

2.8

4.3

60-89

1.0

1.1

1.3

1.5

1.8

1.0

1.3

1.8

2.5

3.8

45-59

1.3

1.3

1.5

1.7

2.1

1.5

1.7

2.1

2.9

4.2

30-44

1.5

1.5

1.6

1.9

2.3

2.0

1.9

2.5

3.6

5.0

15-29

1.8

1.8

1.9

2.4

2.8

3.3

3.3

3.8

5.7

8.1

<15

2.7

2.8

3.0

3.2

3.8

9.1

9.0

9.6

13

14

The numbers in the boxes reflect the adjusted hazard ratio vs the reference category.
The adjustment variables appear in the legend for Figure 2. The sample sizes include
individuals who are missing a measure of albuminuria. The percentile shaded the darkest green color corresponds to the proportion of cells in the grid without chronic kid-

neydisease(eg,6of35cellswitheGFRⱖ60mL/min/1.73m2 andurinealbumintocreatinine ratio <30 mg/g), and the percentile shaded the darkest red color corresponds
to the proportion expected to be at the highest risk for adverse outcomes (eg, 11 of 35
cellswitheGFR<15mL/min/1.73m2 andurinealbumintocreatinineratioⱖ1000mg/g).

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Research Original Investigation

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Table 2. Adjusted Hazard Ratios of Subsequent Adverse Outcomes for Individuals With Mild to Moderate Kidney Disease
Estimated glomerular filtration rate (eGFR), adjusted hazard ratio (95% CI)aeGFR based on creatinine aloneeGFR of90-104 mL/
min/1.73 m2
and UACR<10 mg/g

eGFR of45-59 mL/
min/1.73 m2
and UACR<10 mg/g

eGFR based on creatinine and cystatin C
eGFR of45-59 mL/
min/1.73 m2
and UACRof 10-29 mg/g

eGFR of45-59 mL/
min/1.73 m2
and UACR <10 mg/g

eGFRof 45-59 mL/
min/1.73 m2
and UACRof 10-29 mg/g

1 [Reference]

1.3 (1.2-1.4)

1.6 (1.5-1.7)

1.6 (1.5-1.7)

1 [Reference]

1.7 (1.5-1.8)

2.2 (2.1-2.3)

Cardiovascularmortality

1 [Reference]

1.4 (1.3-1.4)

1.7 (1.5-1.9)

1.6 (1.4-1.8)

1 [Reference]

1.9 (1.6-2.2)

2.7 (2.4-3.0)

Kidney failure
with replacement
therapy

1 [Reference]

12.7 (11.1-14.6)

19.0 (15.6-23.1)

17.7 (14.2-22.1)

1 [Reference]

5.8 (2.4-14.2)

12.5 (5.4-29.1)

Acute kidney injury

1 [Reference]

3.5 (3.3-3.7)

4.0 (3.7-4.3)

3.8 (3.5-4.2)

1 [Reference]

3.9 (3.5-4.4)

4.7 (4.2-5.2)

Hospitalization

1 [Reference]

1.3 (1.2-1.3)

1.3 (1.3-1.4)

1.2 (1.2-1.3)

1 [Reference]

1.3 (1.1-1.5)

1.4 (1.1-1.7)

Coronary heartdisease1 [Reference]

1.4 (1.3-1.5)

1.7 (1.6-1.8)

1.6 (1.4-1.8)

1 [Reference]

1.6 (1.3-2.0)

2.0 (1.7-2.3)

Stroke1 [Reference]

1.4 (1.3-1.5)

1.6 (1.5-1.7)

1.5 (1.4-1.7)

1 [Reference]

1.6 (1.4-1.9)

1.8 (1.5-2.2)

Heart failure

1 [Reference]

1.6 (1.5-1.7)

1.8 (1.7-2.0)

1.9 (1.8-2.1)

1 [Reference]

1.5 (1.1-2.1)

2.2 (1.8-2.8)

Atrial fibrillation

1 [Reference]

1.2 (1.2-1.3)

1.3 (1.3-1.4)

1.4 (1.3-1.5)

1 [Reference]

1.3 (1.1-1.5)

1.6 (1.5-1.9)

Peripheral arterydisease1 [Reference]

1.5 (1.3-1.6)

1.8 (1.6-2.0)

1.9 (1.6-2.1)

1 [Reference]

2.5 (1.5-4.2)

3.7 (2.7-4.9)

The adjustment variables included age, sex, smoking status (current, former,
never), systolic blood pressure, total cholesterol, high-density lipoproteincholesterol, body mass index, use of antihypertensive medications, and ahistory of diabetes, coronary heart disease, stroke, heart failure, atrial
fibrillation, peripheral artery disease, cancer, and chronic obstructive
pulmonary disease when relevant. All models appear in eAppendix 1 inand UACR. Hospitalizations were the most common adverse
outcome. In the reference group (eGFR 90-104 mL/min/1.73
m2 and UACR <10 mg/g), the rate of hospitalizations was 79
per 1000 person-years, the rate for all-cause mortality was
11 per 1000 person-years, the rate for acute kidney injury
was 4.5 per 1000 person-years, the rate for atrial fibrillation
was 4.0 per 1000 person-years, the rate for heart failure was
3.9 per 1000 person-years, the rate for cardiovascular mortality was 2.3 per 1000 person-years, the rate for stroke was
2.1 per 1000 person-years, the rate for myocardial infarction
was 1.7 per 1000 person-years, the rate for peripheral arterydisease was 0.6 per 1000 person-years, and the rate for kidney failure with replacement therapy was 0.1 per 1000
person-years (eTable 17 in Supplement 2).
Among people with a UACR less than 10 mg/g, an eGFR of
45 to 59 mL/min/1.73 m2 based on creatinine alone was associated with significantly higher hospitalization rates comparedwith an eGFR of 90 to 104 mL/min/1.73 m2 (adjusted HR, 1.3[95% CI, 1.2-1.3]; 161 vs 79 events per 1000 person-years; excess absolute risk, 22 events per 1000 person-years [95% CI,
19-25 events per 1000 person-years]). For the most severe CKD
categories (eGFR <15 mL/min/1.73 m2 and UACR ≥1000 mg/g),
the highest rates of adverse outcomes were for hospitalizations (562 per 1000 person-years), kidney failure with replacement therapy (325 per 1000 person-years), and mortality (148per 1000 person-years). The adjusted excess mortality appears in eTable 18 in Supplement 2. The unadjusted incidence
rates by age and sex appear in eTables 19-22 in Supplement 2.
1272

eGFR of90-104 mL/
min/1.73 m2
and UACR<10 mg/g

All-cause mortality

Abbreviation: UACR, urine albumin to creatinine ratio.
aeGFR of45-59 mL/
min/1.73 m2
and missing
UACR

Supplement 1. The cohorts used in these analyses are the general populationand electronic health record cohorts (the chronic kidney disease cohorts did
not have sufficient individuals in the reference groups) and those missing ameasure of albuminuria were included. All comparisons yielded P < .001. The
number of cohorts appears in Figure 1 and Figure 2 and the cell-specific sample
sizes and number of events appear in eTables 6 and 12 in Supplement 2.

Discussion
This individual-participant data meta-analysis of more than
27 million adults evaluated associations of eGFR and albuminuria with 10 adverse outcomes that included kidney outcomes, all-cause mortality, cardiovascular mortality, hospitalizations, and other cardiovascular events. There were strong
graded associations with lower eGFR and adverse outcomes
for the new, race-free 2021 CKD-EPI equation for eGFR basedon creatinine alone7 and also when cystatin C was included as
an additional filtration marker in eGFR (based on creatinineand cystatin C). The pattern of associations persisted irrespective of age, sex, diabetes, and cardiovascular disease and werestronger for eGFR based on creatinine and cystatin C compared with eGFR based on creatinine alone. This work supports recent recommendations to increase the use of cystatin
C in clinical practice.17,18
Prior meta-analyses of eGFR and albuminuria with adverse outcomes evaluated only 5 adverse outcomes in 1.2 million individuals within 21 cohorts from 14 countries.2-6 These
reports used eGFR based on creatinine alone; the Modification of Diet in Renal Disease study equation, which includes
race; and an unvalidated equation to impute UACR from theratio of urine protein to creatinine. In the current study, eGFR
was calculated using the race-free estimating equations for botheGFR based on creatinine alone and eGFR based on creatinine and cystatin C per 2021 recommendations from the

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jama.comEstimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Original Investigation Research

Figure 2. Categorical Analysis of the Associations of Estimated Glomerular Filtration Rate (eGFR) and Albuminuria
With Subsequent Adverse Outcomes in the Population Based on Creatinine and Cystatin C

OveralleGFR, mL/min/1.73 m2 using
creatinine and cystatin C

Urine albumin to creatinine ratio, mg/g

Urine albumin to creatinine ratio, mg/g

<10

<10

10-29

30-299≥300

All-cause mortality: 11 cohorts
692 802 participants; 97 006 events

10-29

30-299≥300

Myocardial infarction: 10 cohorts
649 365 participants; 17 926 events

≥105

1.0

1.3

1.6

2.5

0.9

1.2

1.4

2.8

90-104

Reference

1.3

1.5

2.0

Reference

1.2

1.4

1.8

60-89

1.2

1.5

1.9

2.5

1.2

1.4

1.5

1.9

45-59

1.7

2.2

2.5

3.3

1.6

1.9

2.3

3.3

30-44

2.3

2.6

3.4

4.4

2.1

2.6

3.1

3.3

<30

3.6

4.0

5.5

7.1

5.1

3.0

4.9

5.0

eGFR, mL/min/1.73 m2 using
creatinine and cystatin C

Cardiovascular mortality: 11 cohorts
692 322 participants; 25 322 events

Stroke: 9 cohorts
662 605 participants; 16 909 events

≥105

1.0

1.4

1.8

4.1

1.0

1.2

1.6

90-104

Reference

1.5

1.6

2.9

Reference

1.2

1.5

2.3

60-89

1.2

1.7

2.3

3.4

1.2

1.4

1.8

2.5

45-59

1.9

2.7

3.2

4.6

1.6

1.7

2.1

2.7

30-44

2.5

3.5

4.5

5.9

1.7

2.0

2.3

2.6

<30

5.8

5.0

6.1

8.7

1.9

2.3

2.8

4.4

eGFR, mL/min/1.73 m2 using
creatinine and cystatin C

Kidney failure with replacement therapy:
5 cohorts, 630 370 participants; 4306 events

2.5

Heart failure: 9 cohorts
641 298 participants; 27 406 events

≥105

0.6

0.8

2.3

10

0.9

1.2

1.7

3.7

90-104

Reference

1.5

4.5

11

Reference

1.3

1.4

2.5

60-89

1.9

3.7

8.3

31

1.2

1.6

1.9

3.0

45-59

5.8

13

25

73

1.5

2.2

3.0

4.1

30-44

20

23

78

191

2.5

2.9

4.1

5.7

<30

111

261

343

580

5.3

4.8

6.5

7.7

eGFR, mL/min/1.73 m2 using
creatinine and cystatin C

Acute kidney injury: 5 cohorts
630 370 participants; 24 062 events

Atrial fibrillation: 5 cohorts
607 102 participants; 37 278 events

≥105

0.8

1.0

1.4

3.5

0.9

1.0

1.1

1.9

90-104

Reference

1.3

1.7

2.8

Reference

1.2

1.4

2.2

60-89

1.6

2.5

2.9

5.3

1.1

1.3

1.5

2.0

45-59

3.9

4.7

5.5

7.5

1.3

1.6

1.8

2.2

30-44

5.8

7.0

8.4

10

1.6

2.0

2.2

2.5

<30

11

12

12

21

2.0

2.0

2.7

4.4

eGFR, mL/min/1.73 m2 using
creatinine and cystatin C

Hospitalization: 3 cohorts
630 489 participants; 464 894 events

Peripheral artery disease: 6 cohorts
642 624 participants; 3943 events

≥105

1.0

1.1

1.1

1.6

0.9

1.9

1.8

2.9

90-104

Reference

1.1

1.3

1.4

Reference

1.5

2.0

3.2

60-89

1.1

1.2

1.3

1.6

1.3

1.8

2.1

3.9

45-59

1.3

1.4

1.5

1.7

2.5

3.7

3.3

4.0

30-44

1.5

1.5

1.6

2.1

4.0

3.7

4.5

6.9

<30

1.8

2.0

2.1

3.0

7.8

4.5

9.0

12

National Kidney Foundation and the American Society ofNephrology.17,18 The UACR was imputed from the ratio of urine
protein to creatinine or the urine dipstick protein using a validated equation.12,19 The current study adds to the literature byproviding strong evidence for the classification and risk stratification of CKD using the most up-to-date estimates of GFR,
more categories of albuminuria, and additional cardiovascu-

The numbers in the boxes reflect theadjusted hazard ratio compared withthe reference category. The
adjustment variables included age,sex, smoking status (current, former,
never), systolic blood pressure, totalcholesterol, high-density lipoproteincholesterol, body mass index, use ofantihypertensive medications, and ahistory of diabetes, coronary heartdisease, stroke, heart failure, atrial
fibrillation, peripheral artery disease,cancer, and chronic obstructive
pulmonary disease when relevant.
The cohorts used in these analyses
are the general population andelectronic health record cohorts (thechronic kidney disease [CKD] cohorts
did not have sufficient individuals in
the reference cells). The sample sizes
include individuals who are missing
a measure of albuminuria. The
percentile shaded the darkest green
color corresponds to the proportionof cells in the grid without CKD(eg, 6 of 24 cells), and the percentile
shaded the darkest red color
corresponds to the proportion
expected to be at the highest risk foradverse outcomes (eg, 5 of 24 cells).

lar outcomes. The use of 114 cohorts from across the world enhances the generalizability of the results.
The results underscore the importance of albuminuria inrisk assessment. Even mildly elevated UACR (category A2;
UACR of 30-299 mg/g) was statistically significantly associated with increased risk for all outcomes. The adjusted excess risk of mortality associated with a UACR of 300 to 999 mg/g

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1273

Research Original Investigation

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Figure 3. Hazard Ratios for Adverse Outcomes Using a Continuous Model of Estimated Glomerular Filtration Rate (eGFR)
A Associations of eGFR based on creatinine alone with all-cause mortality,

B

Associations of eGFR based on creatinine and cystatin C with all-cause
mortality, cardiovascular mortality, all-cause hospitalizations, myocardial
infarction, stroke, heart failure, atrial fibrillation, and peripheral artery disease

All-cause mortality (721 394 participants; 102 910 events)

Stroke (711 293 participants; 17 609 events)

Cardiovascular mortality (719 987 participants; 27 051 events)

Heart failure (674 255 participants; 28 530 events)

All-cause hospitalization (676 519 participants; 7862 events)

Atrial fibrillation (653 507 participants; 38 224 events)

Myocardial infarction (711 478 participants; 18 659 events)

Peripheral artery disease (660 412 participants; 4458 events)

5.0

5.0

4.0

4.0

3.0

3.0

Hazard ratio

Hazard ratio

cardiovascular mortality, all-cause hospitalizations, myocardial infarction,
stroke, heart failure, atrial fibrillation, and peripheral artery disease

2.0
1.5

2.0
1.5

1.0

1.0

0.7

0.7
15

30

45

60

75

90

105

120

15

eGFR, mL/min/1.73 m2 using creatinine alone
C

30

45

60

75

90

105

120

eGFR, mL/min/1.73 m2 using creatinine and cystatin C
D Associations of eGFR based on creatinine and cystatin C with kidney failure

Associations of eGFR based on creatinine alone with kidney failure
with replacement therapy and acute kidney injury

with replacement therapy and acute kidney injury

128

128

32

32

Hazard ratio

Hazard ratio

Kidney failure with replacement therapy (637 387 participants; 24 342 events)
Acute kidney injury (632 452 participants; 466 201 events)

8
4

8
4

2

2

1

1

0.50

0.50

0.25

0.25
15

30

45

60

75

90

105

120

15

eGFR, mL/min/1.73 m2 using creatinine alone

30

45

60

75

90

105

120

eGFR, mL/min/1.73 m2 using creatinine and cystatin C

The diamond indicates the reference point at eGFR of 90 mL/min/1.73 m2. The dots indicate that the 95% CI for the hazard ratio from this spline model does notinclude 1.0.

and an eGFR of 90 to 104 mL/min/1.73 m2 was comparable withthat of stage 1 colon cancer (17 deaths per 1000 person-years
and a 5-year survival rate of 91%). 20 Similar to previous
observations,21 the current study demonstrates low rates of albuminuria measurement in electronic health records.
Some guidelines recommend cystatin C testing in patients with CKD, and others discourage measurement of cystatin C.2,22 The current study provides evidence for the potential utility of eGFR based on creatinine and cystatin C. WitheGFR based on creatinine alone, there was a U-shaped association with the study outcomes, indicating a higher risk withboth lower eGFR (<60 mL/min/1.73 m2) and higher eGFR(>105 mL/min/1.73 m2). This finding may indicate imprecision and systematic overestimation of GFR among people who
1274

progress to adverse events (thus contributing to the U-shaped
curve). There was a more linear risk relationship for eGFR whenbased on both creatinine and cystatin C.
Both creatinine and cystatin C values are affected by clinical characteristics independent of GFR,23 and the most widely
recognized non-GFR determinant for creatinine is muscle
mass.24 Persons with low muscle mass, on average, have higher
eGFR based on creatinine alone than eGFR based on creatinine and cystatin C.25 Differences in RRs between eGFR basedon creatinine alone and eGFR based on creatinine and cystatin C were observed among older adults (≥65 years of age), suggesting that when clinically available, additional use of cystatin C could better identify high-risk individuals, particularly
among older populations.

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jama.comEstimated Glomerular Filtration Rate, Albuminuria, and Adverse OutcomesThis study has several strengths. First, the sample size waslarge and included adults from multiple countries. Second, themost recent eGFR equations were evaluated. Third, the results suggested that deviations in risk associations across type,geographic location, and cohort characteristics were unlikely. Fourth, the subgroup analyses demonstrated the higher
risk for adverse outcomes associated with lower eGFR andhigher UACR across categories of age, sex, presence of diabetes, and history of cardiovascular disease.

Original Investigation Research

Third, cystatin C was available in only a subset of cohorts.
Fourth, the data used in the analyses were observational andcausal inferences should not be made.26 Fifth, although the findings support the use of eGFR based on creatinine and cystatin C
in the detection and staging of CKD, cystatin C is not widely available and may be expensive to routinely measure. Sixth, some variables such as baseline heart failure were missing from several cohorts and may have confounded the relationship between eGFR
and the outcomes, particularly for acute kidney injury.

Limitations
This study has several limitations. First, other estimating
equations of GFR were not comprehensively tested.14,16 Second, the included cohorts used different study designs andprotocols for outcome ascertainment. The outcomes were
often based on International Classification of Diseases codes,
which have variable sensitivity and specificity for each outcome measure.

ARTICLE INFORMATION
Accepted for Publication: August 15, 2023.
Writing Group for the CKD Prognosis
Consortium: Morgan E. Grams, MD, PhD; Josef
Coresh, MD, PhD; Kunihiro Matsushita, MD, PhD;
Shoshana H. Ballew, PhD; Yingying Sang, MS; Aditya
Surapaneni, PhD; Natalia Alencar de Pinho, PhD;
Amanda Anderson, PhD; Lawrence J. Appel, MD;
Johan Ärnlöv, MD, PhD; Fereidoun Azizi, MD; Nisha
Bansal, MD; Samira Bell, MD; Henk J. G. Bilo, MD,
PhD; Nigel J. Brunskill, MBChB, PhD; Juan J.
Carrero, PhD; Steve Chadban, MD, PhD; John
Chalmers, MD, PhD; Jing Chen, MSc, MD; Elizabeth
Ciemins, MPH, PhD; Massimo Cirillo, MD; Natalie
Ebert, MPH, MD; Marie Evans, MD, PhD; Alejandro
Ferreiro, MD; Edouard L. Fu, PhD; Masafumi
Fukagawa, MD, PhD; Jamie A. Green, MD; Orlando
M. Gutierrez, MD; William G. Herrington, MD;
Shih-Jen Hwang, PhD; Lesley A. Inker, MD;
Kunitoshi Iseki, MD; Tazeen Jafar, MPH, MD;
Simerjot K. Jassal, MD; Vivekanand Jha, MD, DM;
Aya Kadota, MD, PhD; Ronit Katz, DPhil; Anna
Köttgen, MD, MPH; Tsuneo Konta, MD; Florian
Kronenberg, MD; Brian J. Lee, MD; Jennifer Lees,
MBChB, PhD; Adeera Levin, MD; Helen C. Looker,
MBBS; Rupert Major, MD, PhD; Cheli Melzer Cohen,
MSc; Makiko Mieno, PhD; Mariko Miyazaki, MD,
PhD; Olivier Moranne, MD, PhD; Isao Muraki, MD,
PhD; David Naimark, MD, MSc; Dorothea Nitsch,
MD; Wonsuk Oh, PhD; Michelle Pena, MPH, PhD;
Tanjala S. Purnell, MPH, PhD; Charumathi
Sabanayagam, MPH, MD, PhD; Michihiro Satoh,
PhD; Simon Sawhney, MD, PhD; Elke Schaeffner,
MSc, MD; Ben Schöttker, PhD; Jenny I. Shen, MD;
Michael G. Shlipak, MPH, MD; Smeeta Sinha,
MBChB, PhD; Benedicte Stengel, MD, PhD; Keiichi
Sumida, MPH, MD, PhD; Marcello Tonelli, MD;
Jose M. Valdivielso, PhD; Arjan D. van Zuilen, MD;
Frank L. J. Visseren, MD, PhD; Angela Yee-Moon
Wang, MD, PhD; Chi-Pang Wen, MD, DrPH;David C. Wheeler, MD; Hiroshi Yatsuya, MD, PhD;
Kunihiro Yamagata, MD, PhD; Jae won Yang, MD;
Ann Young, MD, PhD; Haitao Zhang, PhD; Luxia
Zhang, MPH, MD; Andrew S. Levey, MD; Ron T.
Gansevoort, MD, PhD.
Affiliations of Writing Group for the CKD
Prognosis Consortium: Division of Precision
Medicine, Department of Medicine, Grossman

Conclusions
In this retrospective analysis of 114 cohorts, lower eGFR basedon creatinine alone, lower eGFR based on creatinine and cystatin C, and more severe UACR were each associated with increased rates of 10 adverse outcomes, including adverse kidney outcomes, cardiovascular diseases, and hospitalizations.

School of Medicine, New York University, New York,New York (Grams, Surapaneni); Department of
Epidemiology and Welch Center for Prevention,
Epidemiology, and Clinical Research, Bloomberg
School of Public Health, Johns Hopkins University,
Baltimore, Maryland (Grams, Coresh, Matsushita,
Ballew, Sang, Appel, Purnell); Centre for Research in
Epidemiology and Population Health, Paris-Saclay
University, Inserm U1018, Versailles Saint-Quentin
University, Clinical Epidemiology Team, Villejuif,
France (Alencar de Pinho, Stengel); School of Public
Health and Tropical Medicine, Tulane University,
New Orleans, Louisiana (Anderson); School of
Health and Social Studies, Dalarna University, Falun,
Sweden (Ärnlöv); Department of Neurobiology,
Care Sciences, and Society, Family Medicine andPrimary Care Unit, Karolinska Institutet, Huddinge,
Sweden (Ärnlöv); Endocrine Research Center,Research Institute for Endocrine Sciences, Shahid
Beheshti University of Medical Sciences, Tehran,
Iran (Azizi); Division of Nephrology, University ofWashington, Seattle (Bansal); Division ofPopulation Health and Genomics, School of
Medicine, University of Dundee, Dundee, Scotland(Bell); Diabetes Centre and Department of Internal
Medicine, University of Groningen, University
Medical Center Groningen, Groningen, theNetherlands (Bilo); Department of Cardiovascular
Sciences, University of Leicester, and John Walls
Renal Unit, Leicester General Hospital, University
Hospitals of Leicester NHS Trust, Leicester, England(Brunskill, Major); Department of Medical
Epidemiology and Biostatistics, Karolinska
Institutet, and Department of Clinical Science,
Danderyd Hospital, Stockholm, Sweden (Carrero);
Department of Renal Medicine, Royal Prince Alfred
Hospital, Sydney, Australia (Chadban); George
Institute for Global Health, University of New South
Wales, Sydney, Australia (Chalmers); School of
Public Health, Imperial College, London, England(Chalmers); Prasanna School of Public Health,
Manipal Academy of Higher Education, Manipal,
India (Chalmers); Department of Medicine, Schoolof Medicine, Tulane University, New Orleans,Louisiana (Chen); AMGA (American Medical Group
Association), Alexandria, Virginia (Ciemins);
Department Scuola Medica Salernitana, Universityof Salerno, Fisciano, Italy (Cirillo); Institute of Public
Health, Charité Universitätsmedizin Berlin, Berlin,

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Germany (Ebert, Schaeffner); Department of Renal
Medicine, Karolinska Institutet, Karolinska
University Hospital, Stockholm, Sweden (Evans);
Departamento de Nefrología, Facultad de Medicina,
Universidad de la República, Montevideo, Uruguay(Ferreiro); Division of Pharmacoepidemiology andPharmacoeconomics, Department of Medicine,
Brigham and Women’s Hospital and Harvard
Medical School, Boston, Massachusetts (Fu);Division of Nephrology, Endocrinology, andMetabolism, School of Medicine, Tokai University,
Isehara, Japan (Fukagawa); Department ofNephrology, Geisinger Commonwealth School of
Medicine, Danville, Pennsylvania (Green); Center
for Kidney Health Research, Geisinger, Danville,
Pennsylvania (Green); Division of Nephrology,
University of Alabama at Birmingham (Gutierrez);
Medical Research Council Population HealthResearch Unit, University of Oxford, Oxford,
England (Herrington); Clinical Trial Service Unit andEpidemiological Studies Unit, University of Oxford,
Oxford, England (Herrington); Framingham Heart
Study, Framingham, Massachusetts (Hwang);
Population Sciences Branch, Division of Intramural
Research, National Heart, Lung, and Blood
Institute, Bethesda, Maryland (Hwang); Division of
Nephrology, Tufts Medical Center, Boston,
Massachusetts (Inker, Levey); Okinawa Heart andRenal Association, Okinawa, Japan (Iseki);
Programme in Health Services and Systems
Research, Duke-NUS Medical School, Singapore(Jafar); Duke Global Health Institute, Duke
University, Durham, North Carolina (Jafar);
University of California–San Diego, La Jolla (Jassal);
San Diego VA Health Care System, San Diego,
California (Jassal); George Institute for Global
Health India, New Delhi, India (Jha); George
Institute for Global Health, School of Public Health,
Imperial College, London, England (Jha);
Department of Public Health, NCD Epidemiology
Research Center, Shiga University of Medical
Science, Otsu, Japan (Kadota); Department of
Obstetrics and Gynecology, University ofWashington, Seattle (Katz); Institute of Genetic
Epidemiology, Faculty of Medicine and Medical
Center, University of Freiburg, Freiburg, Germany(Köttgen); Department of Public Health andHygiene, Yamagata University Faculty of Medicine,
Yamagata, Japan (Konta); Institute of Genetic(Reprinted) JAMA October 3, 2023 Volume 330, Number 13© 2023 American Medical Association. All rights reserved.

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Research Original Investigation

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Epidemiology, Medical University of Innsbruck,
Innsbruck, Austria (Kronenberg); Kaiser
Permanente, Hawaii Region, and Moanalua Medical
Center, Honolulu, Hawai‘i (Lee); School of
Cardiovascular and Metabolic Health, University ofGlasgow, Glasgow, Scotland (Lees); Glasgow Renal
and Transplant Unit, Queen Elizabeth University
Hospital, Glasgow, Scotland (Lees); Division of
Nephrology, University of British Columbia,
Vancouver, Canada (Levin); Chronic Kidney Disease
Section, National Institute of Diabetes andDigestive and Kidney Diseases, Phoenix, Arizona(Looker); Maccabi Institute for Research andInnovation, Maccabi Healthcare Services, Tel-Aviv,
Israel (Melzer Cohen); Department of Medical
Informatics, Center for Information, Jichi Medical
University, Tochigi, Japan (Mieno); Department ofNephrology, Endocrinology, and Vascular Medicine,
Tohoku University Graduate School of Medicine,
Sendai, Japan (Miyazaki); Service de Néphrologie
Dialyse Aphérèse, Nîmes Hôpital Universitaire,Nîmes, France (Moranne); IDESP, UMR-INSERM,
Universite de Montpellier, Montpellier, France(Moranne); Public Health, Osaka University
Graduate School of Medicine, Suita, Japan (Muraki);Department of Medicine and Institute of Health
Policy, Management, and Evaluation, University ofToronto, Toronto, Ontario, Canada (Naimark);
London School of Hygiene and Tropical Medicine,
London, England (Nitsch); Department of Genetics
and Genomic Sciences, Icahn School of Medicine atMount Sinai, New York, New York (Oh);
Department of Clinical Pharmacy andPharmacology, University of Groningen, University
Medical Center Groningen, Groningen, theNetherlands (Pena); Division of Transplantation,
Department of Surgery, School of Medicine, Johns
Hopkins University, Baltimore, Maryland (Purnell);Center for Health Equity, Johns Hopkins University,
Baltimore, Maryland (Purnell); Singapore Eye
Research Institute, Singapore National Eye Centre,
Singapore (Sabanayagam); Ophthalmology and
Visual Sciences Academic Clinical Programme,
Duke-NUS Medical School, Singapore(Sabanayagam); Division of Public Health, Hygiene,and Epidemiology, Tohoku Medical andPharmaceutical University, Sendai, Japan (Satoh);
Aberdeen Centre for Health Data Science, School of
Medicine, Medical Sciences, and Nutrition,
University of Aberdeen, Aberdeen, Scotland(Sawhney); NHS Grampian, Aberdeen, Scotland(Sawhney); Division of Clinical Epidemiology andAging Research, German Cancer Research Center,
Heidelberg, Germany (Schöttker); Department ofMedicine, David Geffen School of Medicine,
University of California, Los Angeles (Shen);
Lundquist Institute, Harbor-UCLA Medical Center,
Torrance, California (Shen); Kidney Health Research
Collaborative, Department of Medicine, Universityof California, San Francisco (Shlipak); General
Internal Medicine Division, Medical Service,
San Francisco Veterans Affairs Health Care System,
San Francisco, California (Shlipak); Salford Royal
Hospital, Northern Care Alliance NHS Foundation
Trust, Salford, England (Sinha); Division of
Nephrology, Department of Medicine, University ofTennessee Health Science Center, Memphis(Sumida); Department of Medicine, University ofCalgary, Calgary, Alberta, Canada (Tonelli); Vascular
and Renal Translational Research Group, Biomedical
Research Institute of Lleida, IRBLleida andUniversity of Lleida, Lleida, Spain (Valdivielso);
Department of Nephrology and Hypertension,

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University Medical Center Utrecht, Utrecht, theNetherlands (van Zuilen); Department of Vascular
Medicine, University Medical Center Utrecht,
Utrecht University, Utrecht, the Netherlands(Visseren); Department of Medicine, Queen Mary
Hospital, University of Hong Kong, Hong Kong,
China (Wang); Institute of Population Health
Science, National Health Research Institutes,
Zhunan, Taiwan/China Medical University Hospital,
Taichung, Taiwan (Wen); Department of Renal
Medicine, University College London, London,
England (Wheeler); Department of Public Healthand Health Systems, Nagoya University Graduate
School of Medicine, Nagoya, Japan (Yatsuya);
Department of Nephrology, University of Tsukuba,
Tsukuba, Japan (Yamagata); Department of Internal
Medicine, Wonju College of Medicine, Yonsei
University, Wonju, South Korea (Yang); Division of
Nephrology, Unity Health Toronto, University ofToronto, Toronto, Ontario, Canada (Young); ICES
Western, London, Ontario, Canada (Young);
National Clinical Research Center of Kidney
Diseases, Jinling Hospital, Medical School ofNanjing University, Nanjing, China (H. Zhang);
Peking University First Hospital, Beijing, China(L. Zhang); Department of Nephrology, University
Medical Center Groningen, University of Groningen,
Groningen, the Netherlands (Gansevoort).
Author Contributions: Dr Grams had full access toall of the data in the study and takes responsibility
for the integrity of the data and the accuracy of thedata analysis.
Concept and design: Grams, Coresh, Matsushita,
Carrero, Chadban, Fukagawa, Iseki, Jafar, Levin,
Melzer Cohen, Nitsch, Shlipak, Wheeler, Levey,
Gansevoort.
Acquisition, analysis, or interpretation of data: All
authors.
Drafting of the manuscript: Grams, Ballew, Bansal,
Iseki, Miyazaki, Shlipak, van Zuilen.
Critical review of the manuscript for important
intellectual content: All authors.
Statistical analysis: Sang, Surapaneni, Bell, Carrero,
Hwang, Kadota, Lee, Mieno, Sawhney, van Zuilen.
Obtained funding: Grams, Coresh, Ballew,
Anderson, Appel, Chen, Major, Tonelli, Valdivielso,
Levey.
Administrative, technical, or material support:
Grams, Coresh, Ballew, Alencar de Pinho, Anderson,
Appel, Azizi, Bansal, Brunskill, Chadban, Ciemins,
Ferreiro, Fu, Fukagawa, Green, Iseki, Jafar, Jha,
Köttgen, Konta, Major, Melzer Cohen, Moranne,
Nitsch, Purnell, Sabanayagam, Satoh, Sawhney,Shen, Valdivielso, Wang, Wen, Wheeler, Yang,
H. Zhang.
Supervision: Grams, Coresh, Anderson, Appel,
Carrero, Kadota, Levin, Major, Shlipak, Visseren,
Yamagata, Gansevoort.
Conflict of Interest Disclosures: Dr Grams
reported receiving nonfinancial support fromKDIGO (Kidney Disease: Improving Global
Outcomes) and the Korean Society of Nephrology
and receiving personal fees from the Nephrology
Self-Assessment Program. Dr Coresh reported
receiving personal fees from Healthy.io.
Dr Matsushita reported receiving personal feesfrom Kowa, Kyowa Kirin, Akebia, and AMGA.
Dr Alencar de Pinho reported receiving grants from
Fresenius Medical Care, GSK (formerly
GlaxoSmithKline), Vifor France, Boehringer
Ingelheim, and AstraZeneca. Dr Ärnlöv reported
receiving personal fees from AstraZeneca, Astellas,
Novartis, and Boehringer Ingelheim. Dr Bellreported receiving personal fees from GSK,
AstraZeneca, and Bayer. Dr Brunskill reported
receiving grants from Kidney Research UK.
Dr Carrero reported receiving nonfinancial support
from KDIGO. Dr Chalmers reported receiving grants
from the National Health and Medical ResearchCouncil of Australia. Dr Ebert reported receiving
personal fees from Bayer Leverkusen. Dr Evans
reported receiving grants and personal fees from
Astellas; receiving personal fees from AstraZeneca,
Vifor, Fresenius Medical Care, and Baxter; andserving on a steering committee for the Swedish
Renal Registry. Dr Fu reported receiving grants fromthe Dutch Scientific Organization. Dr Fukagawa
reported receiving grants from Kyowa-Kirin andreceiving personal fees from Bayer Yakuhin.
Dr Gutierrez reported receiving personal fees from
Amgen, Tarsus Cardio Inc, and Klick Health.
Dr Herrington reported receiving grants from the
UK Medical Research Council, Boehringer
Ingelheim, and Eli Lilly. Dr Jha reported receiving
personal fees from GSK, Boehringer Ingelheim,
Travere, Vera, Zydus Cadilla, Bayer, AstraZeneca,
Baxter Healthcare, Visterra, and George Clinical.
Dr Konta reported receiving personal fees from
Tanabe-Mitsubishi, AstraZeneca, Daiichi-Sankyo,
Boehringer Ingelheim, Sanwakagaku, Chugai, Pfizer,
Mochida, Bayer, Kowa, Novartis, Kyowa-Kirin,
Asteras, and Ono and receiving grants from
Daiichi-Sankyo, Mochida, Tanabe-Mitsubishi,
Chugai, and Novartis. Dr Lees reported receiving
personal fees from AstraZeneca. Dr Major reported
receiving grants from Kidney Research UK andreceiving personal fees from AstraZeneca UK.
Dr Miyazaki reported receiving grants from Astellas,
Kyowa-Kirin, Torii Pharmaceutical, and Chugai
Pharmaceutical. Dr Nitsch reported serving on asteering committee for GSK; receiving grants from
the Medical Research Council, the National Institutefor Health and Care Research, and the HealthFoundation; and being the UK Kidney Association
director of informatics research. Dr Sawhney
reported receiving grants from the Academy ofMedical Sciences. Dr Schaeffner reported receiving
grants from Bayer and a stipend from the National
Kidney Foundation for editorial work for theAmerican Journal of Kidney Diseases. Dr Shenreported receiving personal fees from Healthmap
Solutions, Outset Medical, and Spectral Medical.
Dr Shlipak reported receiving grants from Bayer
Pharmaceuticals and receiving personal fees from
Cricket Health, Intercept Pharmaceuticals,
Boehringer Ingelheim, AstraZeneca, and Bayer
Pharmaceuticals. Dr Sinha reported receiving
personal fees from AstraZeneca, Amenarini, Bayer,
Boehringer Ingelheim, CSL Vifor, GSK, Johnson &Johnson, Novartis, and Sanofi-Genzyme andreceiving grants from AstraZeneca, CSL Vifor, GSK,
Sanofi-Genzyme, and Johnson & Johnson.
Dr Stengel reported receiving grants from GSK,
Fresenius Medical Care, Boehringer Ingelheim, andVifor Fresenius. Dr Wheeler reported receiving
personal fees from Astellas, AstraZeneca, Bayer,
Boehringer Ingelheim, Eledon, Galderma, GSK,
Gilead, Janssen, Merck Sharp and Dohme,
ProKidney, Tricida, Vifor, and Zydus. Dr Levey
reported receiving personal fees from AstraZeneca.
No other disclosures were reported.
Funding/Support: The CKD Prognosis Consortium
data coordinating center is funded in part by
program grant R01DK100446 from the National
Institute of Diabetes and Digestive and Kidney
Diseases and funding from the US National Kidney

JAMA October 3, 2023 Volume 330, Number 13 (Reprinted)© 2023 American Medical Association. All rights reserved.

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jama.comEstimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Original Investigation Research

Foundation. A variety of sources have supported
enrollment and data collection including laboratory
measurements and follow-up in the collaborating
cohorts of the CKD Prognosis Consortium. Thesefunding sources include government agencies suchas national institutes of health and medical researchcouncils as well as foundations and industry
sponsors listed in eAppendix 3 in Supplement 1.

6. van der Velde M, Matsushita K, Coresh J, et al;
Chronic Kidney Disease Prognosis Consortium.
Lower estimated glomerular filtration rate andhigher albuminuria are associated with all-cause
and cardiovascular mortality: a collaborative
meta-analysis of high-risk population cohorts.
Kidney Int. 2011;79(12):1341-1352. doi:10.1038/ki.
2010.536

Role of the Funder/Sponsor: The funders/sponsors had no role in the design and conduct of
the study; collection, management, analysis, andinterpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit
the manuscript for publication.

7. Inker LA, Eneanya ND, Coresh J, et al; Chronic
Kidney Disease Epidemiology Collaboration. New
creatinine- and cystatin C-based equations toestimate GFR without race. N Engl J Med. 2021;385(19):1737-1749. doi:10.1056/NEJMoa2102953

Group Information: The members of the CKD
Prognosis Consortium are listed in Supplement 3.
Disclaimer: Some of the data were supplied by the
US Renal Data System and some of the authors
work for the US government (at institutions
including the National Heart, Lung, and Blood
Institute, the National Institute of Diabetes andDigestive and Kidney Diseases, and others). Theinterpretation and reporting of these data are theresponsibility of the authors and in no way should
be seen as an official policy or interpretation of the
US government.
Data Sharing Statement: See Supplement 4.
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doi:10.1056/NEJMra2201153(Reprinted) JAMA October 3, 2023 Volume 330, Number 13© 2023 American Medical Association. All rights reserved.

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