Population-based reference values for kidney function and kidney function decline in 25- to 95-year-old Germans without and with diabetes
Authors: Janina M. Herold, Simon Wiegrebe, Jana Nano, Bettina Jung, Mathias Gorski, Barbara Thorand, Wolfgang Koenig, Tanja Zeller, Martina E. Zimmermann, Ralph Burkhardt, Bernhard Banas, Helmut Küchenhoff, Klaus J. Stark, Annette Peters, Carsten A. Böger, Iris M. Heid
Journal: Kidney International (2024)

Understanding normal aging of kidney function is pivotal to help distinguish individuals at particular risk for chronic kidney disease. Glomerular filtration rate (GFR) is typically estimated via serum creatinine (eGFRcrea) or cystatin C (eGFRcys). Since population-based age-group-specific reference values for eGFR and eGFR-decline are scarce, we aimed to provide such reference values from population-based data of a wide age range. In four German population-based cohorts (KORA-3, KORA-4, AugUR, DIACORE), participants underwent medical exams, interview, and blood draw up to five times within up to 25 years. We analyzed eGFRcrea and eGFRcys cross-sectionally and longitudinally (12,000 individuals, age 25-95 years). Cross-sectionally, we found age-group-specific eGFRcrea to decrease approximately linearly across the full age range, for eGFRcys up to the age of 60 years. Within age-groups, there was little difference by sex or diabetes status. Longitudinally, linear mixed models estimated an annual eGFRcrea decline of -0.80 [95% confidence interval -0.82, -0.77], -0.79 [-0.83, -0.76], and -1.20 mL/min/1.73m2 [-1.33, -1.08] for the general population, "healthy" individuals, or individuals with diabetes, respectively. Reference values for eGFR using cross-sectional data were shown as percentile curves for "healthy" individuals and for individuals with diabetes. Reference values for eGFR-decline using longitudinal data were presented as 95% prediction intervals for "healthy" individuals and for individuals with diabetes, obesity, and/or albuminuria. Thus, our results can help clinicians to judge eGFR values in individuals seen in clinical practice according to their age and to understand the expected range of annual eGFR-decline based on their risk profile. Understanding normal aging of kidney function is pivotal to help distinguish individuals at particular risk for chronic kidney disease. Glomerular filtration rate (GFR) is typically estimated via serum creatinine (eGFRcrea) or cystatin C (eGFRcys). Since population-based age-group-specific reference values for eGFR and eGFR-decline are scarce, we aimed to provide such reference values from population-based data of a wide age range. In four German population-based cohorts (KORA-3, KORA-4, AugUR, DIACORE), participants underwent medical exams, interview, and blood draw up to five times within up to 25 years. We analyzed eGFRcrea and eGFRcys cross-sectionally and longitudinally (12,000 individuals, age 25-95 years). Cross-sectionally, we found age-group-specific eGFRcrea to decrease approximately linearly across the full age range, for eGFRcys up to the age of 60 years. Within age-groups, there was little difference by sex or diabetes status. Longitudinally, linear mixed models estimated an annual eGFRcrea decline of -0.80 [95% confidence interval -0.82, -0.77], -0.79 [-0.83, -0.76], and -1.20 mL/min/1.73m2 [-1.33, -1.08] for the general population, "healthy" individuals, or individuals with diabetes, respectively. Reference values for eGFR using cross-sectional data were shown as percentile curves for "healthy" individuals and for individuals with diabetes. Reference values for eGFR-decline using longitudinal data were presented as 95% prediction intervals for "healthy" individuals and for individuals with diabetes, obesity, and/or albuminuria. Thus, our results can help clinicians to judge eGFR values in individuals seen in clinical practice according to their age and to understand the expected range of annual eGFR-decline based on their risk profile. Lay SummaryKidney function, assessed as estimated glomerular filtration rate (eGFR), declines by age. In clinical practice, it is important to understand whether a person has an eGFR value as expected given the person's age, or whether the value is lower than expected and potentially a reason for concern. Although chronic kidney disease is defined as eGFR <60 ml/min per 1.73 m2, the question arises whether a value of, for example, 58 ml/min per 1.73 m2 for an 80-year-old person is indicative of disease or age appropriate. We collected data from >12,000 individuals, aged 25 to 95 years, from population-based German studies. We provide age-specific reference values for eGFR usable in clinical practice to answer this question. Longitudinal information on eGFR decline was analyzed to also provide reference values for eGFR-decline by risk profile groups. Advanced regression models were applied for these analyses. Our results are interpretable and usable to help in clinical routine. Kidney function, assessed as estimated glomerular filtration rate (eGFR), declines by age. In clinical practice, it is important to understand whether a person has an eGFR value as expected given the person's age, or whether the value is lower than expected and potentially a reason for concern. Although chronic kidney disease is defined as eGFR <60 ml/min per 1.73 m2, the question arises whether a value of, for example, 58 ml/min per 1.73 m2 for an 80-year-old person is indicative of disease or age appropriate. We collected data from >12,000 individuals, aged 25 to 95 years, from population-based German studies. We provide age-specific reference values for eGFR usable in clinical practice to answer this question. Longitudinal information on eGFR decline was analyzed to also provide reference values for eGFR-decline by risk profile groups. Advanced regression models were applied for these analyses. Our results are interpretable and usable to help in clinical routine. Kidney function undergoes a natural decline by aging. The number of nephrons, the smallest units of the kidney and responsible for the filtration process, starts decreasing at the age of 30 years.1Denic A. Lieske J.C. Chakkera H.A. et al.The substantial loss of nephrons in healthy human kidneys with aging.J Am Soc Nephrol. 2017; 28: 313-320Crossref PubMed Scopus (279) Google Scholar Glomerular filtration rate (GFR) is an established parameter to assess kidney function, typically estimated via serum creatinine (eGFRcrea), cystatin C (eGFRcys), or both (eGFRcrea-cys). Values of eGFR <60 ml/min per 1.73 m2 define chronic kidney disease (CKD).2Kidney Disease: Improving Global Outcomes (KDIGO) CKD-MBD Work GroupKDIGO clinical practice guideline for the diagnosis, evaluation, prevention, and treatment of chronic kidney disease-mineral and bone disorder (CKD-MBD).Kidney Int Suppl. 2009; 113: S1-S130PubMed Google Scholar,3Levey A.S. Coresh J. Bolton K. et al.K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification.Am J Kidney Dis. 2002; 39: S1-S266PubMed Google Scholar Approximately 10% of the world's population4Cockwell P. Fisher L.-A. The global burden of chronic kidney disease.Lancet. 2020; 395: 662-664Abstract Full Text Full Text PDF PubMed Scopus (277) Google Scholar and 10% to 13% in Germany5Weckmann G. Chenot J.-F. Stracke S. The management of non-dialysis-dependent chronic kidney disease in primary care.Dtsch Arztebl Int. 2020; 117: 745-751PubMed Google Scholar are affected by CKD. Elderly individuals often have eGFR <60 ml/min per 1.73 m2 because of natural kidney aging,6Jha V. Garcia-Garcia G. Iseki K. et al.Chronic kidney disease: global dimension and perspectives.Lancet. 2013; 382: 260-272Abstract Full Text Full Text PDF PubMed Scopus (3084) Google Scholar,7Couser W.G. Remuzzi G. Mendis S. Tonelli M. The contribution of chronic kidney disease to the global burden of major noncommunicable diseases.Kidney Int. 2011; 80: 1258-1270Abstract Full Text Full Text PDF PubMed Scopus (1071) Google Scholar causing a substantial debate on whether age-dependent CKD definitions are warranted.8Delanaye P. Jager K.J. Bökenkamp A. et al.CKD: a call for an age-adapted definition.J Am Soc Nephrol. 2019; 30: 1785-1805Crossref PubMed Scopus (199) Google Scholar Clinicians are typically faced with the question of whether an observed eGFR of, for example, 58 ml/min per 1.73 m2 is within the normal range for a healthy 80-year-old individual. Another question is what annual eGFR decline can be expected for individuals with a certain risk profile (e.g., for individuals with obesity or with diabetes and microalbuminuria). Reference values for eGFR using cross-sectional data from general populations, and particularly longitudinal data to derive reference values for eGFR decline, are limited. Some studies provide reference values for middle-aged adults,9Wetzels J.F.M. Kiemeney L.A.L.M. Swinkels D.W. et al.Age- and gender-specific reference values of estimated GFR in Caucasians: the Nijmegen Biomedical Study.Kidney Int. 2007; 72: 632-637Abstract Full Text Full Text PDF PubMed Scopus (307) Google Scholar, 10Berg U.B. Differences in decline in GFR with age between males and females: reference data on clearances of inulin and PAH in potential kidney donors.Nephrol Dial Transplant. 2006; 21: 2577-2582Crossref PubMed Scopus (177) Google Scholar, 11Waas T. Schulz A. Lotz J. et al.Distribution of estimated glomerular filtration rate and determinants of its age dependent loss in a German population-based study.Sci Rep. 2021; 1110165Crossref PubMed Scopus (45) Google Scholar and few include individuals aged >80 years,12Ebert N. Jakob O. Gaedeke J. et al.Prevalence of reduced kidney function and albuminuria in older adults: the Berlin Initiative Study.Nephrol Dial Transplant. 2017; 32: 997-1005PubMed Google Scholar, 13Eriksen B.O. Palsson R. Ebert N. et al.GFR in healthy aging: an individual participant data meta-analysis of iohexol clearance in European population-based cohorts.J Am Soc Nephrol. 2020; 31: 1602-1615Crossref PubMed Scopus (69) Google Scholar, 14Hemmelgarn B.R. Zhang J. Manns B.J. et al.Progression of kidney dysfunction in the community-dwelling elderly.Kidney Int. 2006; 69: 2155-2161Abstract Full Text Full Text PDF PubMed Scopus (322) Google Scholar, 15Schaeffner E.S. Ebert N. Kuhlmann M.K. et al.Age and the course of GFR in persons aged 70 and above.Clin J Am Soc Nephrol. 2022; 17: 1119-1128Crossref PubMed Scopus (0) Google Scholar including 2 German studies.11Waas T. Schulz A. Lotz J. et al.Distribution of estimated glomerular filtration rate and determinants of its age dependent loss in a German population-based study.Sci Rep. 2021; 1110165Crossref PubMed Scopus (45) Google Scholar,15Schaeffner E.S. Ebert N. Kuhlmann M.K. et al.Age and the course of GFR in persons aged 70 and above.Clin J Am Soc Nephrol. 2022; 17: 1119-1128Crossref PubMed Scopus (0) Google Scholar Furthermore, many studies provide only eGFRcrea due to higher costs when measuring cystatin C, but eGFRcys or eGFRcrea-cys are considered more suitable for individuals at old age.16Potok O.A. Rifkin D.E. Ix J.H. et al.Estimated GFR accuracy when cystatin C- and creatinine-based estimates are discrepant in older adults.Kidney Med. 2023; 5100628Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar There is thus a lack of reference values for eGFR or eGFR decline for individuals over a wide age range and limited data on cystatin-based eGFR. There is also no consensus on how to generate and present such reference values in an interpretable manner. We thus aimed to provide population-based reference values for eGFR and eGFR decline based on both creatinine and cystatin C in adult individuals of a wide age range (25–95 years), for healthy individuals, and for individuals with diabetes. Furthermore, we aimed to derive estimates of the association of sex, obesity, diabetes, and albuminuria with eGFR levels and annual eGFR decline and to use these to generate eGFR-decline reference values by risk groups. For this, we evaluated data from 4 comparably designed population-based cohorts from Germany enabling the analysis of >12,000 individuals cross-sectionally and >26,000 eGFRcrea and eGFRcys assessments over up to 25 years longitudinally. We analyzed 4 population-based cohorts from South Germany: (i–ii) 2 studies for the middle-aged adult population (KORA-3, KORA-4), (iii) 1 study for the old-aged population (AugUR), and (iv) 1 study on individuals with diabetes (DIACORE). In the following, we used the term KORA-3 for individuals in KORA-S3 with follow-up (F3, Fit) and KORA-4 for individuals in S4 (F4, FF4, Fit). Studies were comparable in terms of recruitment, study conduct, and standard operating procedures. Detailed study descriptions were published previously17Holle R. Happich M. Löwel H. Wichmann H.E. KORA--a research platform for population based health research.Gesundheitswesen. 2005; 67: S19-S25Crossref PubMed Scopus (606) Google Scholar, 18Dörhöfer L. Lammert A. Krane V. et al.Study design of DIACORE (DIAbetes COhoRtE) - a cohort study of patients with diabetes mellitus type 2.BMC Med Genet. 2013; 14: 25Crossref PubMed Scopus (24) Google Scholar, 19Stark K. Olden M. Brandl C. et al.The German AugUR study: study protocol of a prospective study to investigate chronic diseases in the elderly.BMC Geriatr. 2015; 15: 130Crossref PubMed Scopus (27) Google Scholar (Supplementary Note S1.1). Processing of biomaterial for was equivalent across the 4 studies, as described previously20Seissler J. Feghelm N. Then C. et al.Vasoregulatory peptides pro-endothelin-1 and pro-adrenomedullin are associated with metabolic syndrome in the population-based KORA F4 study.Eur J Endocrinol. 2012; 167: 847-853Crossref PubMed Scopus (0) Google Scholar, 21Rheinberger M. Jung B. Segiet T. et al.Poor risk factor control in outpatients with diabetes mellitus type 2 in Germany: the DIAbetes COhoRtE (DIACORE) study.PLoS One. 2019; 14e0213157Crossref Scopus (6) Google Scholar, 22Donhauser F.J. Zimmermann M.E. Steinkirchner A.B. et al.Cardiovascular risk factor control in 70- to 95-year-old individuals: cross-sectional results from the population-based AugUR study.J Clin Med. 2023; 12: 2102Crossref Scopus (1) Google Scholar (Supplementary Note S1.2). Biomarkers were measured by certified laboratories with different arrays, where comparability of methods was assessed following Clinical and Laboratory Standards Institute guidelines. Serum creatinine concentrations were measured by enzymatic assays or modified Jaffé (if applicable, corrected by factor 0.9523Goek O.-N. Prehn C. Sekula P. et al.Metabolites associate with kidney function decline and incident chronic kidney disease in the general population.Nephrol Dial Transplant. 2013; 28: 2131-2138Crossref PubMed Scopus (106) Google Scholar) and standardized to information display measurements standard. Because KORA-S3 creatinine measurements lacked assay manufacturer's documentation and differed from the other KORA surveys (Supplementary Figure S1), we excluded these values from analyses and considered KORA-F3 baseline for analyses using creatinine. Cystatin C was measured via nephelometric methods or immunoassays and standardized according to the International Federation of Clinical Chemistry. Glycated hemoglobin was measured from ethylenediamine tetraacetic acid anticoagulated whole blood via ion-exchange high-performance liquid chromatographic assay (KORA, AugUR) or immunoassay (DIACORE). Urine albumin and creatinine were measured in each study and at each time point, except KORA-S4, KORA-Fit3, and KORA-Fit4. A detailed overview of blood processing and biomarker measurements is provided in Supplementary Table S1. The outcome of interest was GFR, and various formulas estimate GFR from creatinine and/or cystatin to fit eGFR as closely as possible to measured GFR. For our primary analyses, we derived eGFRcrea, eGFRcys, and eGFRcrea-cys using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation,24Inker L.A. Eneanya N.D. Coresh J. et al.New creatinine- and cystatin C-based equations to estimate GFR without race.N Engl J Med. 2021; 385: 1737-1749Crossref PubMed Scopus (1424) Google Scholar the CKD-EPI 2012 equation,25Inker L.A. Schmid C.H. Tighiouart H. et al.Estimating glomerular filtration rate from serum creatinine and cystatin C.N Engl J Med. 2012; 367: 20-29Crossref PubMed Scopus (3058) Google Scholar or the combined equation from 2021,24Inker L.A. Eneanya N.D. Coresh J. et al.New creatinine- and cystatin C-based equations to estimate GFR without race.N Engl J Med. 2021; 385: 1737-1749Crossref PubMed Scopus (1424) Google Scholar respectively. CKD-EPI 2021 includes sex-specific coefficients and an age term (e.g., 0.9938age) and avoids the race term from CKD-EPI 2009.26Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (19447) Google Scholar CKD-EPI 2021 was used by the recent Kidney Disease: Improving Global Outcomes (KDIGO) guidelines.27Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work GroupKDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.Kidney Int. 2024; 105: S117-S314PubMed Google Scholar However, most European laboratories still derive eGFRcrea by CKD-EPI 2009,26Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (19447) Google Scholar and European societies recommended to stall the update to CKD-EPI 2021 because of limited advantages for European populations.28Delanaye P. Schaeffner E. Cozzolino M. et al.The new, race-free, Chronic Kidney Disease Epidemiology Consortium (CKD-EPI) equation to estimate glomerular filtration rate: is it applicable in Europe? a position statement by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM).Clin Chem Lab Med. 2023; 61: 44-47Crossref PubMed Scopus (32) Google Scholar As potential update, alternative equations for eGFRcrea29Pottel H. Björk J. Courbebaisse M. et al.Development and validation of a modified full age spectrum creatinine-based equation to estimate glomerular filtration rate: a cross-sectional analysis of pooled data.Ann Intern Med. 2021; 174: 183-191Crossref PubMed Scopus (182) Google Scholar and eGFRcys30Pottel H. Björk J. Rule A.D. et al.Cystatin C-based equation to estimate GFR without the inclusion of race and sex.N Engl J Med. 2023; 388: 333-343Crossref PubMed Scopus (3) Google Scholar are suggested by the European Kidney Function Consortium (EKFC; sex-specific coefficients, no age term until 40 years; e.g., 0.990age-40 for age >40 years). We thus applied also CKD-EPI 2009 and EKFC for sensitivity analyses. From each study center visit, time-dependent covariables were obtained in a similar manner across studies. Albuminuria was derived from urinary albumin-to-creatinine ratio (UACR) as microalbuminuria (UACR ≥30 and <300 mg/g) or macroalbuminuria (UACR ≥300 mg/g).31Stevens P.E. Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline.Ann Intern Med. 2013; 158: 825-830Crossref PubMed Google Scholar Diabetes was defined via self-report, intake of antidiabetic medication (using Anatomical Therapeutic Chemical classification32Collaborating Centre for Drug Statistics MethodologyGuidelines for ATC classification and DDD assignment 2013.2012Google Scholar), or glycated hemoglobin ≥6.5%. DIACORE was restricted to individuals with diabetes assessed via health insurance provider. History of cardiovascular disease was defined as self-report of any prior myocardial infarction or stroke (or interventional revascularization in AugUR and DIACORE). Body mass index was computed using measured weight (from each visit) divided by squared height (kg/m2; from baseline visit). Body mass index ≥25 and <30 kg/m2 was defined as overweight, and body mass index ≥30 kg/m2 as obese. Blood pressure was measured 3 times at each study center visit, and the mean of second and third measurements was used for analyses. For our analyses, we included participants aged ≥25 years (minimum age in KORA studies), with neither renal replacement therapy (dialysis or kidney transplantation) nor history of severe kidney disease (end-stage kidney disease, acute kidney injury, or disease requiring nephrectomy reported at baseline). For cross-sectional analyses, we excluded individuals without available eGFR assessment at baseline (Supplementary Figure S2A). For longitudinal analyses, we excluded eGFR values after an eGFR <15 ml/min per 1.73 m2 or after onset of renal replacement therapy or severe kidney disease; we excluded individuals without any available measurement of eGFRcrea at any time point (Supplementary Figure S2B). We analyzed the data focused on general population individuals (i.e., KORA-3, KORA-4, and AugUR), their healthy subgroup, or individuals with diabetes (adding DIACORE). For the healthy subgroup, eGFR values were excluded when the individual had diabetes, history of cardiovascular disease, systolic/diastolic blood pressure ≥140/90 mm Hg, or UACR ≥30 mg/g at baseline (cross-sectional analyses) or at the respective time point (longitudinal analyses); the healthy-defining variables were nonmissing in >99% individuals at baseline or any time point where eGFR was available (except for UACR in KORA). For the diabetes subgroup, we analyzed eGFR values when individuals had ascertained diabetes at baseline (cross-sectionally) or at 1 time point (longitudinally; excluding eGFR values before diabetes was observed). We analyzed eGFRcrea, eGFRcys, and eGFRcrea-cys (CKD-EPI 2021 and 2012) as outcome on the original scale (winsorized at 15 and 200 ml/min per 1.73 m2). Although studies were comparable in design and conduct, creatinine and cystatin were measured by different laboratories and assays. Therefore, we performed study-specific analyses and then evaluated whether fixed-effect meta-analyses or joint data analyses were applicable. All statistical analyses were performed using R, version 4.3.1. For all regression models, age was centered at 50 years. In cross-sectional data (using baseline), we derived mean values of eGFRcrea and eGFRcys and 95% confidence intervals (CIs) per sex and age group. In longitudinal data, we estimated eGFRcrea decline over age without linearity assumption (generalized additive model [GAM], penalized splines to model age, f[age]) and with linearity assumption (linear mixed model [LMM]). The models included random intercepts (RIs), sex, interaction of sex with f(age) or age, study membership if applicable, and, in sensitivity analyses, random slopes (RI + RS; Supplementary Note S2.1). We analyzed eGFRcys decline analogously. Both GAM and LMM enabled the inclusion of all individuals with at least 1 eGFR value while accounting for intrasubject variation caused by repeated measurements. In longitudinal data, we applied a further multivariable LMM to estimated risk factor association with eGFRcrea levels (main effects) and eGFRcrea decline (interaction with age): the LMM included RI, age, all risk factors (sex, diabetes, overweight, obesity, microalbuminuria, and macroalbuminuria), their interaction with age, and study membership if applicable (Supplementary Note S2.2); the model included time-constant (sex) and time-varying covariate effects (all other risk factors). We analyzed eGFRcys analogously. To generate reference values for eGFRcrea, we used cross-sectional data for the healthy subgroup and for individuals with diabetes. We derived 2.5th, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 97.5th percentile curves as age-appropriate reference values (using generalized additive mixed model for location, scale, and shape [GAMLSS]; Supplementary Note S2.3). The use of GAMLSS allowed us to model eGFRcrea over age without linearity or normality assumption. We repeated this for eGFRcrea-cys, because this is judged by practitioners when cystatin is available. To generate reference values for eGFRcrea decline or eGFRcys decline, we used longitudinal data and risk factor association estimates from the LMM described above (here: RI + RS). By risk profile, we derived 95% prediction intervals that account for the variability in person-specific slopes (Supplementary Note S2.4). We compared individuals' eGFRcrea (eGFRcys) values derived by CKD-EPI 202124Inker L.A. Eneanya N.D. Coresh J. et al.New creatinine- and cystatin C-based equations to estimate GFR without race.N Engl J Med. 2021; 385: 1737-1749Crossref PubMed Scopus (1424) Google Scholar (CKD-EPI 201225Inker L.A. Schmid C.H. Tighiouart H. et al.Estimating glomerular filtration rate from serum creatinine and cystatin C.N Engl J Med. 2012; 367: 20-29Crossref PubMed Scopus (3058) Google Scholar) with values derived by CKD-EPI 200926Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (19447) Google Scholar or EKFC 202129Pottel H. Björk J. Courbebaisse M. et al.Development and validation of a modified full age spectrum creatinine-based equation to estimate glomerular filtration rate: a cross-sectional analysis of pooled data.Ann Intern Med. 2021; 174: 183-191Crossref PubMed Scopus (182) Google Scholar (EKFC 202330Pottel H. Björk J. Rule A.D. et al.Cystatin C-based equation to estimate GFR without the inclusion of race and sex.N Engl J Med. 2023; 388: 333-343Crossref PubMed Scopus (3) Google Scholar). We also evaluated the impact of using these alternative eGFR equations on cross-sectional and longitudinal analyses results described above. There is a substantial debate on the use of age-independent versus age-dependent eGFR cutoff values to define CKD.8Delanaye P. Jager K.J. Bökenkamp A. et al.CKD: a call for an age-adapted definition.J Am Soc Nephrol. 2019; 30: 1785-1805Crossref PubMed Scopus (199) Google Scholar We derived the proportion of CKD by age group based on eGFRcrea <60 ml/min per 1.73 m2, UACR ≥30 mg/g, or their combination. We contrasted these with CKD proportions that would be yielded if age-specific cutoff values for eGFR were based on our GALMSS-derived reference values (using midpoint age per age group and corresponding modeled 2.5th percentile). The AugUR study was approved by the Ethics Committee of the University of Regensburg, Germany (vote 12-101-0258). The study complies with the 1964 Declaration of Helsinki and its later amendments. The KORA-S3 study was approved by the local authorities and conducted in accordance with the data protection regulations as part of the World Health Organization MONICA project. All other KORA studies were approved by the Ethics Committee of the Bavarian Chamber of Physicians (KORA-F3 EC number 03097, KORA-S4 EC number 99186, KORA-F4/FF4 EC number 06068, KORA-Fit EC number 17040). The DIACORE study and its protocol have been approved by the participating universities' Ethics Committees and is in accordance with the Declaration of Helsinki. The study is registered at the German Registry of Clinical Trials (DRKS00010498) and at the International Clinical Trials Registry Platform of the World Health Organization. The study complies with the 1964 Declaration of Helsinki and its later amendments, and all participants provided written informed consent. Our cross-sectional analyses included 12,014 or 12,125 individuals with available eGFRcrea or eGFRcys at baseline, respectively. Participants of the general population studies (KORA-3, KORA-4, and AugUR) covered a baseline age of 25 to 95 years, and 8%, 5%, or 24% had diabetes, respectively; individuals from the diabetes study (DIACORE) were aged 27 to 92 years (Table 1; by sex, Supplementary Table S2).Table 1Characteristics of cross-sectionally analyzed individuals by studyVariableKORA 3KORA 4AugURDIACORE(n = 2906)(n = 3732)(n = 2385)(n = 2991)Demographic characteristics Age, mean (SD), yr57 (13)50 (14)78 (5)65 (9) Men, % (n)48 (1422)48 (1823)48 (1151)60 (1795) Never smoked, % (n)44 (1282)41 (1539)55 (1311)42 (1260) Ever smoked, % (n)37 (1075)33 (1240)38 (921)45 (1342) BMI, mean (SD), kg/m227.7 (4.6)27.2 (4.7)27.7 (4.5)31.4 (5.7)Clinical characteristics Obesity, % (n)27 (772)23 (858)26 (624)55 (1623) Overweight, % (n)44 (1255)43 (1609)46 (1091)35 (1032) Diabetes, % (n)8 (241)5 (197)24 (534)100 (2991) Time since diabetes, mean (SD), yr10 (10)10 (8)NA10 (8) Systolic BP, mean (SD), mm Hg130 (20)128 (19)132 (18)139 (18) Diastolic BP, mean (SD), mm Hg82 (11)80 (10)76 (11)77 (11) Hypertension, % (n)34 (979)29 (1068)31 (739)45 (1329) CVD, % (n)5 (137)0.2 (7)22 (516)26 (773)Medication intake, % (n) Glucose-lowering6 (182)3 (122)16 (385)88 (2616) Blood pressure lowering32 (916)18 (674)68 (1609)78 (2324) Lipid lowering11 (318)6 (224)35 (828)50 (1477)Laboratory measurements, mean (SD) HbA1c, %5.4 (0.5)5.6 (0.6)5.8 (0.7)6.9 (1.1) LDL cholesterol, mg/dl128.1 (32.8)137.3 (41.4)141.2 (34.9)118.1 (37.0) HDL cholesterol, mg/dl58.6 (17.1)57. 9 (17.0)61.3 (15.5)52.9 (15.3) Hemoglobin, g/dl14.2 (1.2)14.3 (1.3)13.8 (1.3)14.2 (1.3) UACR, mg/gaUACR and albuminuria are shown for KORA-F4.17.5 (137.1)25.5 (199.3)42.9 (127.8)75.8 (342.4) Creatinine, mg/dl0.88 (0.28)0.85 (0.24)0.97 (0.31)0.96 (0.36) Cystatin C, mg/LbCystatin C and eGFRcys are shown for KORA-S3.0.93 (0.24)0.86 (0.23)1.20 (0.31)1.10 (0.39)Kidney function eGFRcrea, mean (SD), ml/min per 1.73 m290.6 (17.2)96.6 (16.0)72.6 (16.7)82.5 (20.6) eGFRcys, mean (SD), ml/min per 1.73 m2bCystatin C and eGFRcys are shown for KORA-S3.90.0 (19.9)97.2 (19.5)61.1 (16.9)74.6 (22.5) eGFRcrea-cys, mean (SD), ml/min per 1.73 m277.4 (21.3)100.4 (16.8)69.4 (17.2)81.5 (22.3) Microalbuminuria, % (n)7 (189)8 (241)21 (476)21 (617) Macroalbuminuria, % (n)0.8 (21)1.1 (32)2.9 (66)4.3 (130)AugUR, xxx; BMI, body mass index; BP, blood pressure; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; CVD, cardiovascular disease; DIACORE, xxx; eGFR, estimated glomerular filtration rate; eGFRcrea, estimated glomerular filtration rate based on creatinine; eGFRcrea-cys, estimated glomerular filtration rate based on creatinine and cystatin C; eGFRcys, estimated glomerular filtration