Glomerular Filtration Rate, Albuminuria, and Reported Kidney Disease in Comparison: Results From the German National Cohort (NAKO)

PD Dr rer nat Peggy Sekula; Elena Butz; Prof Dr med Anna Köttgen; Prof Dr med Elke Schaeffner; PD Dr Dr med Janis M Nolde; Dr med Insa M Schmidt; Dr rer nat Lara Kim Brackmann; Dr oec-troph Beate Fischer; Prof Dr med Matthias Girndt; Dr rer nat Kathrin Günther; Dr rer med Anke Hannemann; Univ-Prof Dr med Volker Harth; Dr med Torben Heinsohn; Univ-Prof Dr med André Karch; Prof Dr med Thomas Keil; PD Dr med Lilian Krist; Prof Dr med Berit Lange; Prof em Dr med Dr PH Michael Leitzmann; Dr rer med Claudia Meinke-Franze; Dr med Jaroslawna Meister; Prof Dr med Rafael Mikolajczyk; Univ-Prof Dr sc hum Ute Mons; Dr sc hum Katharina Nimptsch; Dr rer nat Nadia Obi; Dr oec troph Cara Övermöhle; Prof Dr med Tobias Pischon; Prof Dr rer san Tamara Schikowski; Prof Dr Ben Schöttker; Prof Dr PH Matthias B Schulze; Julia Schwichtenberg; Prof Dr med Andreas Stang; Dr phil Henning Teismann; Prof Dr med Henry Völzke; Prof Dr med Karlhans Endlich; Prof Dr med Matthias Nauck; Prof Dr Markus Scholz; Prof Dr rer biol hum Iris M Heid; Prof Dr med Wolfgang Lieb

doi:10.3238/arztebl.m2026.0053

. 2026 Jun 26;123(13):345–352. doi: 10.3238/arztebl.m2026.0053

Glomerular Filtration Rate, Albuminuria, and Reported Kidney Disease in Comparison

Results From the German National Cohort (NAKO)

Peggy Sekula ^1,^*1,^✉, Elena Butz ^1,^*1, Anna Köttgen ¹, Elke Schaeffner ³, Janis M Nolde ², Insa M Schmidt ⁴, Lara Kim Brackmann ⁵, Beate Fischer ⁶, Matthias Girndt ⁷, Kathrin Günther ⁵, Anke Hannemann ⁸, Volker Harth ⁹, Torben Heinsohn ¹⁰, André Karch ¹¹, Thomas Keil ¹², Lilian Krist ¹², Berit Lange ¹⁰, Michael Leitzmann ⁶, Claudia Meinke-Franze ¹⁴, Jaroslawna Meister ¹⁵, Rafael Mikolajczyk ¹⁶, Ute Mons ¹⁷, Katharina Nimptsch ¹⁸, Nadia Obi ⁹, Cara Övermöhle ¹⁹, Tobias Pischon ^18,²⁰, Tamara Schikowski ²¹, Ben Schöttker ²², Matthias B Schulze ²³, Julia Schwichtenberg ²⁴, Andreas Stang ²⁴, Henning Teismann ¹¹, Henry Völzke ¹⁴, Karlhans Endlich ²⁵, Matthias Nauck ⁸, Markus Scholz ²⁶, Iris M Heid ²⁷, Wolfgang Lieb ¹⁹

¹Institute of Epidemiology and Prevention, Medical Center – University of Freiburg, Medical Faculty, University of Freiburg, Freiburg, Germany

²Institute of Epidemiology and Prevention and Department of Internal Medicine IV, Nephrology and General Practice, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany

³Institute of Public Health, Charitè – Universtitätsmedizin Berlin, Berlin, Germany

⁴Department of Internal Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA, and Department of Medicine III, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

⁵Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany

⁶Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany

⁷Department of Internal Medicine II, University Medicine Halle (UMH), Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

⁸Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, and DZHK (German Centre for Cardiovascular Research), partnersite North, Greifswald, Germany

⁹Institute for Occupational Medicine and Maritime Medicine (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

¹⁰Department of Epidemiology, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany

¹¹Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany

¹²Institute of Social Medicine, Epidemiology and Health Economics, Charité-University Medical Center Berlin, Berlin, Germany

Institute for Clinical Epidemiology and Biometry, University of Würzburg, und State Institute Health I, Bavarian State Office for Health and Food Safety, Erlangen, Germany

¹⁴Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

¹⁵Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany

¹⁶Institute of Medical Epidemiology, Biometry, and Informatics, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

¹⁷Division of Primary Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, and Heidelberg University, Medical Faculty Mannheim, Mannheim, Germany

¹⁸Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC): Dr. sc. hum. Katharina Nimptsch, Prof. Dr. med. Tobias Pischon

¹⁹Institute of Epidemiology, Kiel University, Kiel, Germany

²⁰Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Biobank Technology Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC): Prof. Dr. med. Tobias Pischon

²¹IUF – Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany

²²Division of Clinical Epidemiology of Early Cancer Detection, German Cancer Research Center, Heidelberg, Germany

²³Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany, and Institute of Human Nutrition, University of Potsdam, Nuthetal, Germany

²⁴Institute for Medical Informatics, Biometry, and Epidemiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany

²⁵Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany

²⁶Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany

²⁷Chair of Genetic Epidemiology, University of Regensburg, Regensburg, Germany

^✉

Corresponding author PD Dr. Peggy Sekula peggy.sekula@uniklinik-freiburg.de

^*1

These two authors share first authorship.

^*2

The remaining authors of this publication are listed in the citation and at the end of the article, where their affiliations can be found.

Translated from the original German by Ralf Thoene, M.D.

^✉

Corresponding author.

PMCID: PMC13399007 PMID: 42028694

Summary

Background:

Chronic kidney disease (CKD) can be asymptomatic for many years and is often diagnosed late. Given the availability of new treatments, the early identification of relevant findings from screening of the kidney markers estimated glomerular filtration rate (eGFR) and albuminuria in the general population is becoming increasingly important.

Methods:

In the NAKO study, self-reported medical diagnoses of kidney disease in 195 182 participants were compared with relevant findings from screening biomarkers (eGFR < 60 mL/min/1.73 m² and albuminuria). For the purpose of comparison, various equations for assessing kidney function were evaluated as well.

Results:

2% of the participants reported having received a medical diagnosis of kidney disease, and 2% had an eGFR below 60 mL/min/1.73 m². There was, however, little overlap between these two groups: more than 80% of participants with an eGFR between 30 and 59 mL/min/1,73 m² did not report any diagnosis of kidney disease. The additional inclusion of data on albuminuria did not materially affect this discrepancy: 6213 persons (17.5% of the cohort) with an abnormal eGFR or urinary albumin-to-creatinine ratio (UACR) did not report any diagnosis of kidney disease. Even among participants whose eGFR was in the range of 30–59 mL/min/1.73 m² and whose UACR was above 300 mg/g, less than half reported having a medically diagnosed kidney disease.

Conclusion:

These findings indicate a low level of awareness regarding the possible presence of CKD in the general population. Many people with abnormal screening findings needing further investigation due to their potential clinical relevance are unaware that they might be suffering from a kidney disease. As more effective treatments for kidney disease are now available, these findings indicate a need for structured screening and evaluation strategies to promote kidney health.

Hundreds of millions of people worldwide live with chronic kidney disease (CKD) (1–3). The prevalence of CKD continues to rise sharply and it is now ranked among the ten leading causes of death (1, 4). New WHO guidelines recognize kidney health as a growing global priority (5); the number of deaths caused by CKD is expected to further increase in the future (6).

Many people with CKD are unaware of their disorder, because CKD is typically asymptomatic in its early stages and thus goes undiagnosed initially (2, 7). Various studies have in fact shown that a large proportion of people with CKD, including those in high-risk groups, are not formally diagnosed with CKD (8–17). With a paradigm shift in CKD management underway, early detection is now more important than ever: Slowing disease progression was long viewed as the only realistic management strategy, until recent randomized trials and meta-analyses have shown that, with modern combination therapy and in selected patient populations, remission is actually achievable (18, 19).

In this light, early diagnosis of CKD is becoming ever more important, so that people affected can benefit from the therapeutic potential of modern treatment strategies (8, 9, 20, 21). In routine clinical practice, the markers most commonly used to diagnose kidney disease include the estimated glomerular filtration rate (eGFR) for assessing kidney function and albumin in the urine for identifying kidney damage (2). Equations commonly used to estimate GFR rely on serum measurements of creatinine and/or cystatin C and are selected considering age and the intended use, amongst other things (2, 22, 23). Most of the currently available information on the need for expanded screening for CKD originates from population-based surveys, databases maintained by treating physicians, and prospective cohort studies (9–7, 20). There is also various data on CKD available from German studies with up to about 10 000 participants—from regional studies and studies focusing on older adults (3, 17, 24–27).

Given Germany’s aging population and the importance of CKD as a widespread disease, nationwide data are highly relevant. Large, population-based cohort studies with biospecimen collection and long-term follow-up—such as the German National Cohort (NAKO) (https://nako.de) with over 200 000 participants—today represent a key research infrastructure, offering adequate statistical power (28).

The primary aim of our analysis was to determine, based on cross-sectional data from the NAKO study, the number of participants with screening findings that would require further diagnostic testing to rule out CKD (eSupplement Figure 1). In particular, the reported diagnoses were to be compared with the available laboratory results for eGFR and albuminuria in order to establish how many persons have abnormal laboratory findings (eGFR < 60 mL/min/1.73 m² or UACR ≥ 30 mg/g), yet are unaware of potentially having kidney disease. The second aim was to compare different equations for calculating eGFR.

eSupplement Figure 1: — Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; KRT, kidney replacement therapy; UACR, urinary albumin-to-creatinine ratio.

Methods

Study population and data

The German National Cohort (NAKO study) is a prospective, population-based cohort study (DRKS00037328) conducted in Germany (28). Between 2014 and 2019, a total of 205 415 adults aged 19 to 74 years (overall response rate: 15.6%) were included in the study (29). Approvals from the relevant ethics committees and written informed consent from the participants were obtained (30).

As part of collecting information on pre-existing conditions, the following question was asked: “Has a doctor ever diagnosed you with impaired kidney function or chronic kidney disease?“ (eSupplement Table 1) (31). Further questions concerned information about dialysis treatment and about a previous kidney transplant. In this project, a reported kidney disease was considered to be present if the response to one of these three questions was “yes.” Dialysis treatment or a previous kidney transplant was combined into the category of “kidney replacement therapy” (KRT).

eSupplement Table 1: Definition of the NAKO variables used and derived variables.

Variable name	Definition	Range of observations	% valid observation in study population N=195,182
self-reported information
Sex	Participant’s sex	male, female	100
Age	Participant’s age at baseline visit in years	19-75	100
Educational status	Question: What is the highest level of general education you have completed? Please tell me using this list:
	“Schüler/-in, besuche eine allgemeinbildende Vollzeitschule” Student currently attending a general full-time school	low	98.83
	“Von der Schule abgegangen ohne Hauptschulabschluss [Volksschulabschluss]” Left school without a basic secondary school-leaving certificate	low
	“Hauptschulabschluss [Volksschulabschluss]” Basic secondary school certificate	low
	“Polytechnische Oberschule der DDR mit Abschluss der 8. oder 9. Klasse” Former German Democratic Republic Polytechnic Secondary School (completion of 8th or 9th grade)	low
	“Realschulabschluss [Mittlere Reife]” Intermediate secondary school certificate	middle
	“Polytechnische Oberschule der DDR mit Abschluss der 10. Klasse” Former German Democratic Republic Polytechnic Secondary School (completion of 10th grade)	middle
	“Fachhochschulreife, Abschluss einer Fachoberschule” Entrance qualification for universities of applied sciences	high
	“Allgemeine oder fachgebundene Hochschulreife/Abitur [Gymnasium bzw. EOS, auch EOS mit Lehre]” General or subject-specific higher education entrance qualification (Abitur)	high
	“Abitur über zweiten Bildungsweg nachgeholt” Higher education entrance qualification obtained via second-track education	high
Smoking status	Participant’s smoking status	non-smoker, ex-smoker, smoker	96.17
Body mass index (BMI)	BMI (based on cleaned measurements and self-reports), kg/m²	11.4-78.7 kg/m²	99.61
Kidney disease	Question: Have you ever been diagnosed by a doctor with impaired kidney function or chronic kidney disease?	yes/no	100
- Dialysis	If yes: Have you ever undergone dialysis (blood purification)?	yes/no	99.99
Transplantation	Question: Have you ever had an organ transplant? Please also consider bone marrow, stem cells or skin	yes/no	99.89
- Kidney transplantation	If yes: Which organ was transplanted in your case? - Kidney	yes/no	99.89
aggregated variables, centrally provided
Hypertension	Systolic blood pressure≥ 140 mmHg or question ‘Have you ever been diagnosed by a doctor with any of the following cardiovascular conditions? – High blood pressure’ was answered with ‘Yes’	yes/no	99.76
Diabetes	HbA1C ≥ 6,5% or Question ‘Have you ever been diagnosed by a doctor with any of the following metabolic disorders? – Diabetes mellitus’ answered with ‘Yes’	yes/no	99.31
laboratory measurements
Serum creatinine^*	Serum creatinine levels were measured at the central laboratory for approximately 57% of the participants and at local laboratories for approximately 43% of the participants. At the central laboratory, creatinine levels were measured using an enzymatic method on the DIMENSION VISTA 1500 (Siemens Healthineers, Erlangen, Germany). The local laboratories used either enzymatic methods or the Jaffé reaction for measurement. The following platforms were used in the local laboratories: Dimension Vista 1500 and Advia 2400 (Siemens Healthineers, Erlangen, Germany), Cobas 8100, Cobas 8000, Cobas c701, Cobas 6000 (Roche Diagnostics, Rotkreuz, Switzerland), and AU680, AU5800, DxC800, AU5822 (Beckman Coulter, Brea, USA). Overall, the measured values were well comparable between the local and central study laboratories. However, a systematically higher range of values was observed in the local laboratories of the study centers in Essen, Freiburg, Regensburg, and Leipzig. It could not be conclusively determined whether the observed differences in measured values were attributable to laboratory analysis or to ‘true’ differences among the study participants. However, three of these four study centers (Essen, Freiburg, Regensburg) had only a small number of samples analyzed in local laboratories; the majority of measurements for these study centers were also performed at the central study laboratory. Details can be found in the corresponding quality report: https://www2.unimedizin-greifswald.de/klinchem/index.php?id=453.	0.08 – 10.65 mg/dL	100
Serum cystatin C^*	Cystatin C concentrations were measured exclusively in the central laboratory using nephelometry on the Dimension Vista 1500 (Siemens Healthineers, Erlangen, Germany).	0.21 - 8.36 mg/L	57.46
Urinary albumin-to-creatinine ratio (UACR)	The albumin-to-creatinine ratio was measured in spontaneous urine samples at the study centers in Augsburg, Freiburg, and Neubrandenburg. CLINITEK Microalbumin 9 test strips were used for the measurement with the CLINITEK Status+ analyzer (both from Siemens Healthineers, Erlangen, Germany).	1: <3.4 mg/mmol 2: 3.4-33.9 mg/mmol 3: >33.9 mg/mmol	18.19
derived variables
Reported kidney disease	Yes, if participants reported kidney disease, dialysis, or kidney transplants.	yes/no	100
Estimated glomerular filtration rate (eGFR)	Calculation using serum creatinine and/or serum cystatin C, as well as age and sex See eSupplement Table 2 for details on the formulas used	>0 (unit: mL/min/1.73m²)	depending on the availability of laboratory results

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Creatinine (all laboratories) and cystatin C (central laboratory only) were measured as part of an immediate analysis using fresh material without a freeze-thaw cycle. In accordance with standard protocols, samples were processed locally after collection (Schipf et al., PMID: 32047976):

This includes centrifugation of the samples after storage for 30 minutes at room temperature, as well as their continued storage at room temperature until transport. The serum tubes contain a separating gel, which forms a barrier between the serum and cellular components, thereby supporting the stability and quality of the sample.

Laboratory samples were shipped to the central study laboratory via standard mail. Transport of laboratory samples to the local study laboratories was arranged on a case-by-case basis. As part of quality assurance, an investigation was conducted to determine whether the duration of transport had an impact on creatinine levels. This was not the case. The corresponding quality reports from the central study laboratory are publicly available and can be viewed at the following address: https://www2.unimedizin-greifswald.de/klinchem/index.php?id=453.

Based on available serum measurements of creatinine (96%) and cystatin C (55%) in the study population, eGFR was calculated using various estimation equations (eSupplement Table 2). The primarily used equation in this study was the creatinine-based European Kidney Function Consortium equation according to Pottel et al. (32).

eSupplement Table 2: Implemented eGFR equations.

Short name^*	PMID	Consortium	Equation		Comment
eGFR equation with serum creatinine (mg/dL)
Levey 2009 (Crea)	19414839	CKD-EPI	GFR = 141 × min(Crea/κ, 1)^α × max(Crea/κ, 1)^-1.209 × 0.993^age × 1.018 [if female] × 1.159 [if black] κ = 0.7 for women and 0.9 for men, α = -0.329 for women and -0.411 for men		NAKO: implementation of ‘Race’ term under the assumption that all participants are ‘non-Black’
Inker 2021 (Crea)	34554658	CKD-EPI	GFR = 142 × min(Crea/κ; 1)^α × max(Crea/κ; 1)^-1.200 × 0.994^age × 1.012 [if female] κ = 0.7 for women and 0.9 for men, α = -0.241 for women and -0.302 for men
Pottel 2021 (Crea)	33166224	EKFC	age 2-40 years, Crea/Q <1: 107.3 × (Crea/Q)^-0.322	age 2-40 years, Crea/Q ≥1: 107.3 × (Crea/Q)^-1.132	equation primarily used in this project
			age >40 years, Crea/Q <1: 107.3 x (Crea/Q)^-0.322 × 0.990^(age-40)	age >40 years, Crea/Q ≥ 1: 107.3 × (Crea/Q)^-1.132 × 0.990^(age-40)
			Q values: age 2–25 years: for men ln(Q) = 3.200 + 0.259 × age - 0.543 × ln(age) - 0.00763 × age² + 0.0000790 × age³ ; for women ln(Q) = 3.080 + 0.177 × age - 0.223 × ln(age) - 0.00596 × age² + 0.0000686 × age³ age >25 years: for men Q = 80 μmol/L (0.90 mg/dL); for women Q = 62 μmol/L (0.70 mg/dL)
eGFR equation with serum cystatin C (mg/L)
Inker 2012 (Cys)	22762315	CKD-EPI	GFR = 133 × min(Cys/0.8; 1)^-0.499 × max(Cys/0.8; 1)^-1.328 × 0.996^age × 0.932 [if female]		NAKO: implementation of ‘Race’ term under the assumption that all participants are ‘non-Black’
Pottel 2023 (Cys)	36720134	EKFC	age 18-40 years, Cys/0.83 <1: 107.3 x (Cys/0.83)^-0.322 age >40 years, Cys/0.83 <1: 107.3 x (Cys/0.83)^-0.322 x 0.990^(age-40) age >50 years, Cys/Q < 1: 107.3 x (Cys/Q)^-0.322 x 0.990^(age-40) >Q = 0.83 + 0.005 x (Age – 50)	age 18-40 years, Cys/0.83 ≥1: 107.3 x (Cys/0.83)^-1.132 age >40 years, Cys/0.83 ≥1: 107.3 x (Cys/0.83)^-1.132 x 0.990^(age-40) age >50 years, Cys/Q ≥1: 107.3 x (Cys/Q)^-1.132 x 0.990^(age-40)
eGFR equation with serum creatinine (mg/dL) and cystatin C (mg/L)
Inker 2012 (Crea + Cys)	22762315	CKD-EPI	GFR = 135 × min(Crea/κ; 1)^α × max(Crea/κ; 1)^-0.601 × min(Cys/0.8; 1)^-0.375 × max(Cys/0.8; 1)^-0.711 × 0.995^age × 0.969 [if female] × 1.08 [if black] κ = 0.7 for women and 0.9 for men, α = -0.248 for women and -0.207 for men		NAKO: implementation of ‘Race’ term under the assumption that all participants are ‘non-Black’
Inker 2021 (Crea + Cys)	34554658	CKD-EPI	GFR = 135 × min(Crea/κ; 1)^α × max(Crea/κ; 1)^-0.544 × min(Cys/0.8; 1)^-0.323 × max(Cys/0.8; 1)^-0.778 × 0.996^age × 0.963 [if female] κ = 0.7 for women and 0.9 for men, α = -0.219 for women and -0.144 for men
Pottel 2023 (Crea + Cys)	36720134	EKFC	defined as arithmetic mean of eGFR calculated from serum creatinine [Pottel 2021 (Crea) ] and eGFR calculated from serum cystatin C [Pottel 2023 (Cys)]

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Unit: mL/min/1.73m²

Short name: surname of first author, year of publication and used biomarker in brackets

Abbreviations: eGFR, estimated glomerular filtration rate; PMID, PubMed ID; Crea, serum creatinine; Cys, serum cystatin C; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; EKFC, European Kidney Function Consortium

The urinary albumin-to-creatinine ratio (UACR) as an indicator of kidney damage was determined using urine test strips (Siemens Clinitek) specifically designed to detect microalbuminuria. The categories recorded (<30, 30–300 [microalbuminuria] and >300 mg/g [macroalbuminuria] correspond to the KDIGO risk classification for kidney damage (A1–A3) (2). KDIGO risk categories were also used to classify eGFR values: ≥ 60 (G1–2: normal to mildly impaired), 30–59 (G3: mildly to moderately impaired), and < 30 mL/min/1.73 m² (G4–5: severely impaired/kidney failure) (2). Impaired kidney function was defined as an eGFR < 60 mL/min/1.73 m².

Statistical analysis

Based on a data export from September 2023 (N = 204 770), complete data on reported kidney disease and creatinine levels were available from 195 182 participants (eSupplement Figure 1). Additional information on albuminuria was available for 35 504 participants (18%) and information on cystatin C levels was available for 112 151 participants (57%). Given that KRT affects the interpretability of kidney function parameters, participants receiving KRT were excluded from figures and analyses related to eGFR and UACR.

Apart from unweighted information on means and proportions, weighted values were also calculated using the NAKO study weights to better describe the composition of the underlying population (29, 33, 34). The weighted statistics take into account that the probability of being selected for the study varied between participants depending on their age and sex, and that selected persons with specific characteristics more frequently consented to participation than others (29, 35).

GFR estimation equations were compared using pairwise concordance correlation coefficients (36). Logistic regression models were used to identify factors associated with non-reporting of a kidney disease.

The statistical software R version >4.0.5 was used for all statistical analyses.

Results

The mean age in the study population was 50 ± 13 years, with an equal sex distribution (Table 1). Of the participants, 41.2% had hypertension and 6.8% had diabetes mellitus.

Table 1. Description of the study population.

Unweighted statistics	Total cohort (N = 195 182)	Subcohort with urine test data (N = 35 504)	Subcohort with cystatin C data (N = 112 151)
Age, years Mean (SD)	50 (± 13)	49 (± 13)	50 (± 13)
Sex Male	97 328 (49.9%)	18 315 (51.6%)	56 419 (50.3%)
Educational level Low Intermediate High	29 802 (15.4%) 61 308 (31.8%) 101 786 (52.8%)	6 676 (19.0%) 14 459 (41.1%) 14 035 (39.9%)	16 793 (15.2%) 33 213 (30.0%) 60 839 (54.9%)
Body Mass Index Median (Q1; Q3)	25.9 (23.1; 29.3)	26.3 [23.5; 29.8]	25.9 (23.1; 29.2)
Smoking status Never smoked No longer smokes Smoking	86 351 (46.0%) 61 313 (32.7%) 40 036 (21.3%)	16 215 (46.5%) 10 743 (30.8%) 7 895 (22.7%)	49 766 (46.3%) 35 318 (32.9%) 22 348 (20.8%)
Diabetes mellitus	13 258 (6.8%)	2 428 (6.9%)	7 411 (6.6%)
Hypertension	80 229 (41.2%)	15 264 (43.1%)	45 520 (40.7%)
*Reported kidney disease^1**	4224 (2.2%)	823 (2.3%)	2423 (2.2%)
*Kidney replacement therapy^2**	227 (0.1%)	43 (0.1%)	116 (0.1%)
*Serum creatinine, mg/dL^3** Median (Q1; Q3)	0.81 (0.71; 0.92)	0.81 [0.71; 0.93]	0.80 (0.70; 0.92)
*Serum cystatin C, mg/L^3** Median (Q1; Q3)			0.80 (0.72; 0.89)
*eGFR, mL/min/1.73 m^23** Mean (SD)	91 (± 15)	91 (± 14)	91 (± 14)
*eGFR < 60 mL/min/1.73 m^23**	5079 (2.6%)	871 (2.5%)	2581 (2.3%)
*Urinary albumin-to-creatinine ratio^3 (urine dipsticks)** <30 mg/g 30–300 mg/g >300 mg/g		29 542 (83.3%) 5638 (15.9%) 281 (0.8%)

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The dataset in the main analysis includes individuals with valid creatinine measurements and information on the presence of reported impaired kidney function. For the subgroup analyses, the dataset was restricted to participants with urine test data/cystatin C data (see eSupplement Table 1 for a detailed description of the variables)

^*1

Also includes people who report having received a kidney transplant

^*2

Includes treatment with dialysis (blood purification) or a kidney transplant

^*3

For these calculations, persons receiving kidney replacement therapy were excluded.

eGFR, estimated glomerular filtration rate according to Pottel et al. 2021 (32); Q, quartile; SD, standard deviation

Frequency of reported kidney disease and of eGFR < 60 mL/min/1.73 m²

A medically diagnosed kidney disease was reported by 2.2% (weighted: 2.1%) of the participants. This proportion increased, as expected, with age, from 0.8% among 19– to 29-year-olds to 3.5% among 60– to 75-year-olds (eSupplement Table 3). A total of 227 participants (0.1 %) reported to have KRT (Table 1).

eSupplement Table 3: Description of the study population by age group.

unweighted statistics main study population	19-29 years (N=18987)	30-39 years (N=20675)	40-49 years (N=50847)	50-59 years (N=52424)	60-75 years (N=52249)	Total (N=195182)
Sex, male	9407 (49.5%)	10273 (49.7%)	25450 (50.1%)	25943 (49.5%)	26255 (50.2%)	97328 (49.9%)
Educational status
low	802 (4.3%)	1510 (7.4%)	5148 (10.3%)	8079 (15.6%)	14263 (27.6%)	29802 (15.4%)
middle	3427 (18.2%)	5020 (24.6%)	16932 (33.8%)	19320 (37.3%)	16609 (32.1%)	61308 (31.8%)
high	14617 (77.6%)	13909 (68.1%)	28085 (56.0%)	24383 (47.1%)	20792 (40.2%)	101786 (52.8%)
Body mass index
median (Q1; Q3)	23.4 (21.3; 26.1)	24.5 (22.1; 27.6)	25.5 (23; 28.8)	26.3 (23.6; 29.6)	27.3 (24.5; 30.6)	25.9 (23.1; 29.3)
Smoking status
non-smoker	11413 (61.2%)	9310 (46.2%)	23523 (47.7%)	21049 (41.7%)	21056 (42.9%)	86351 (46.0%)
ex-smoker	2628 (14.1%)	5544 (27.5%)	14459 (29.3%)	17866 (35.4%)	20816 (42.4%)	61313 (32.7%)
smoker	4603 (24.7%)	5285 (26.2%)	11372 (23.0%)	11538 (22.9%)	7238 (14.7%)	40036 (21.3%)
Diabetes	152 (0.8%)	478 (2.3%)	1892 (3.7%)	3438 (6.6%)	7298 (14.1%)	13258 (6.8%)
Hypertenstion	2168 (11.5%)	3477 (16.9%)	15207 (30.0%)	24774 (47.4%)	34603 (66.4%)	80229 (41.2%)
Reported kidney disease¹	158 (0.8%)	220 (1.1%)	834 (1.6%)	1178 (2.2%)	1829 (3.5%)	4224 (2.2%)
Kidney replacement therapy²	11 (0.1%)	10 (0.0%)	53 (0.1%)	70 (0.1%)	83 (0.2%)	227 (0.1%)
Serum creatinine, mg/dL^*
median (Q1; Q3)	0.79 (0.7; 0.9)	0.79 (0.69; 0.9)	0.8 (0.71; 0.92)	0.8 (0.71; 0.92)	0.83 (0.71; 0.94)	0.81 (0.71; 0.92)
Serum cystatin C, mg/L^*
median (Q1; Q3)	0.75 (0.69; 0.82)	0.75 (0.68; 0.82)	0.77 (0.7; 0.85)	0.81 (0.74; 0.9)	0.87 (0.79; 0.97)	0.8 (0.72; 0.89)
eGFR, mL/min/1.73m^2*
mean (SD)	100 (± 11)	100 (± 11)	97 (± 11)	88 (± 11)	78 (± 11)	91 (± 15)
eGFR <60 mL/min/1.73m²^*	21 (0.1%)	29 (0.1%)	172 (0.3%)	801 (1.5%)	4056 (7.8%)	5079 (2.6%)
unweighted statistics Subcohort with urine test data	19-29 years (N=3694)	30-39 years (N=4240)	40-49 years (N=9587)	50-59 years (N=9286)	60-75 years (N=8654)	Total (N=35461)
UACR (urin test strip, mg/g)
<30 mg/g	3184 (86.2%)	3669 (86.5%)	8097 (84.5%)	7744 (83.4%)	6848 (79.1%)	29542 (83.3%)
30 - 300 mg/g	493 (13.3%)	562 (13.3%)	1451 (15.1%)	1472 (15.9%)	1660 (19.2%)	5638 (15.9%)
>300 mg/g	17 (0.5%)	9 (0.2%)	39 (0.4%)	70 (0.8%)	146 (1.7%)	281 (0.8%)

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Se eSupplement Table 1 for details on variable definitions.

Reported kidney disease also includes people who have had a kidney transplant;

Kidney replacement therapy comprises dialysis or kidney transplantation.

For these cacluations, participants with kidney replacement therapy were excluded.

Abbreviations: SD, standard deviation; Q1, 1st quartile; Q3, 3rd quartile; eGFR: estimated glomerular filtration rate (Pottel 2021, creatinine-based; see eSupplement Table 2 for details); UACR, urinary albumin-tocreatinine ratio.

Among the 194 955 participants not having KRT, the mean eGFR was 91 ± 15 mL/min/1.73 m² (weighted: 94 ± 15), which was within the reference range and reflected the study design (Table 1). The proportion of participants with an eGFR < 60 mL/min/1.73 m² increased from 0.1% among those aged 19 to 29 years to 7.8% among those aged 60 to 75 years (overall: 2.6%; weighted: 2.2%; eSupplement Table 3). The results were confirmed in a sensitivity analysis, testing the creatinine-based results for possible batch effects (eSupplement Methods 1).

Concordance between reported kidney disease and abnormal laboratory test results

Of the participants without KRT, 8201 reported having been diagnosed with kidney disease or had an eGFR < 60 mL/min/1.73 m². However, both criteria were met in only 875 participants. The subcohort with urine test data (N = 35 504; without KRT, N = 35 461) was used to investigate the low concordance in more detail, taking into account the possible presence of kidney damage in persons with a normal eGFR. The characteristics of the subcohort were very similar to those of the overall cohort (Table 1) and the subgroup had virtually identical weighted percentages of reported kidney disease (2.3%) and eGFR < 60 mL/min/1.73 m² (2.2%). Microalbuminuria (UACR 30–300 mg/g) was present in 15.9 % (weighted: 16.6%) of participants and macroalbuminuria (UACR > 300 mg/g) in 0.8% (weighted: 0.8%).

Figure 1 illustrates the concordance of reported kidney disease, eGFR < 60 mL/min/1.73 m² and UACR ≥ 30 mg/g in participants without KRT. While at least one of the three criteria applied to 6 993 participants, only 304 out of 35 461 persons (0.9%) reported a kidney disease that was in line with the screening findings. A total of 6213 persons (17.5%) reported no kidney disease despite abnormal eGFR or UACR values. Notably, in the KDIGO risk category G3 (30–59 mL/min/1.73 m²), the proportion of reported kidney disease across categories A1 through A3 was considerably below expectations (Table 2). Even in persons with an eGFR of 30–59 mL/min/1.73 m² and an UACR > 300 mg/g, this proportion was less than 50%. Women (Figure 2) and older participants (eSupplement Figure 2) very often reported no kidney disease despite abnormal findings.

**Group sizes and overlap among NAKO participants** with regard to all combinations of reported kidney disease, eGFR < 60 mL/min/1.73 m² and UACR ≥ 30 mg/g. Based on the subcohort with urine test data (exclusive of participants on kidney replacement therapy, N = 35 461), the Figure shows the sizes of the various groups, which are determined by combinations of the presence or absence of a reported kidney disease, an eGFR < 60 mL/min/1.73 m² and a UACR ≥ 30 mg/g. A dark-filled dot indicates the presence of a characteristic, while a light-filled dot indicates its absence. For example, the third bar from the left shows that 527 participants (1.5% of the subcohort with urine test data) have an eGFR < 60 mL/min/1.73 m², but do not report a kidney disease, and have no albuminuria.

eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio

Table 2. Percentage of persons with reported kidney disease in the respective KDIGO risk category.

		UACR (in mg/g)			Total
		< 30	30–300	> 300	Total
eGFR (in mL/min/1.73 m²)	> 90	1.1 (187/16 552)	1.8 (54/2 922)	13.5 (12/89)	1.3 (253/19 563)
	60–90	2.3 (289/12 392)	2.8 (71/2 497)	11.6 (16/138)	2.5 (376/15 027)
	30–59	11.8 (70/595)	20.7 (45/217)	46.2 (18/39)	15.6 (133/851)
	15–29	50 (1/2)	100 (2/2)	100 (14/14)	94.4 (17/18)
	< 15	0 (0/1)	– (0/0)	100 (1/1)	50 (1/2)
	Total	1.9 (547/29 542)	3.1 (172/5638)	21.7 (91/281)	2.2 (780/35 461)

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Based on the subcohort with urine test data (exclusive of participants on kidney replacement therapy, N = 35 461), the table shows the percentages of participants with reported kidney disease by KDIGO risk category. Based on the baseline eGFR and UACR values collected, the NAKO participants were assigned to their respective groups. The cell colors are based on the KDIGO color scheme for the progression of kidney disease (green: low risk – red: high risk) (2). In the cells for categories G3–G5 (eGFR < 60 mL/min/1.73 m²) and A2–A3 (UACR ≥ 30 mg/g), one would expect high values for these percentages in an ideal scenario. The sometimes very low percentages, particularly in the group with an eGFR of 30–59 mL/min/1.73 m², indicate a lack of awareness of the possible presence of CKD. eGFR, estimated glomerular filtration rate; KDIGO, Kidney Disease Improving Global Outcomes; UACR, urinary albumin-to-creatinine ratio

**Sex-specific numbers and proportions** of NAKO participants in KDIGO risk categories with abnormal eGFR or UACR values who reported having kidney disease or no kidney disease. For the Figure, the cohort participants with urine test data (excluding those on kidney replacement therapy, N = 35 461) were assigned to three groups based on the KDIGO risk categories (1: G4/5 or A3; 2: G3 or A2; 3: G1/2 or A1).

For the risk groups 1 and 2, the figure illustrates that a large proportion of the participants, particularly those in the group with moderately impaired kidney function and the group of women, did not report having kidney disease. In the interest of clarity, the group with normal laboratory results is not shown. Participants were assigned to their respective groups based on the most severe single finding, i.e., individuals were assigned to the group with the highest risk based on their eGFR or UACR values.

eGFR, estimated glomerular filtration rate; KDIGO, Kidney Disease Improving Global Outcomes; UACR, urinary albumin-to-creatinine ratio

eSupplement Figure 2: — The figure shows values for participants in the cohort with albuminuria data (excluding participants on kidney replacement therapy, N=35,461). The data suggest an age-dependent relationship between the report of a kidney disease diagnosis and abnormalities in eGFR or UACR.

Abbreviations: eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio.

Among participants with an eGFR < 60 mL/min/1.73 m² or a UACR ≥ 30 mg/g (N = 1,032), increasing age was associated with a higher frequency of participants reporting no kidney disease (odds ratio [OR]: 1.29 per 10 years; 95% confidence interval: [1.05; 1.57]; eSupplement Table 4). On the other hand, men (OR 0.64; [0.44; 0.91]) and participants with an eGFR < 30 mL/min/1.73 m² (OR 0.01; [0.00; 0.04]) or a UACR ≥ 30 mg/g (OR 0.42; [0.28; 0.62]) reported less frequently that they had not received such a diagnosis compared to their comparison groups. Similarly, the probability of not being diagnosed with kidney disease was lower in participants with diabetes mellitus (OR 0.7; [0.44; 1.01]) or hypertension (OR 0.65; [0.4; 1.03]) compared to participants without these diseases; however; these differences were not significant (eSupplement Table 4).

eSupplement Table 4: Associations between demographic factors and unreported kidney disease among NAKO participants with abnormal laboratory results.

(A) Cohort: participants with eGFR <60 mL/min/1.73m² and UACR >30 mg/g of the subcohort with data on albuminuria, and complete data regarding considered variables (N=1,032)
Variable	Univariate	Multivariate (MV)	MV + educational status	MV + smoking	MV + body mass index	MV + diabetes	MV + hypertension
Sex, male	0.56 [0.39; 0.78]	0.64 [0.44; 0.91]	0.62 [0.43; 0.89]	0.67 [0.46; 0.97]	0.65 [0.45; 0.93]	0.67 [0.46; 0.96]	0.68 [0.47; 0.98]
Age/10 years	1.12 [0.94; 1.33]	1.29 [1.05; 1.57]	1.26 [1.02; 1.55]	1.31 [1.06; 1.60]	1.34 [1.09; 1.65]	1.35 [1.09; 1.65]	1.37 [1.10; 1.69]
eGFR <30 mL/min/1.73m²	0.02 [0.00; 0.06]	0.01 [0.00; 0.04]	0.01 [0.00; 0.04]	0.01 [0.00; 0.04]	0.01 [0.00; 0.04]	0.01 [0.00; 0.04]	0.01 [0.00; 0.04]
eGFR 30-59 mL/min/1.73m²	0.71 [0.43; 1.11]	0.31 [0.17; 0.53]	0.31 [0.17; 0.53]	0.31 [0.17; 0.53]	0.31 [0.17; 0.53]	0.30 [0.17; 0.51]	0.3 [0.17; 0.52]
UACR ≥ 30 mg/g	0.50 [0.35; 0.70]	0.42 [0.28; 0.62]	0.41 [0.28; 0.62]	0.41 [0.28; 0.62]	0.42 [0.28; 0.63]	0.44 [0.29; 0.66]	0.43 [0.29; 0.63]
Educational status: middle	0.75 [0.50; 1.12]	-	0.75 [0.49; 1.15]	-	-	-	-
Educational status: low	1.03 [0.66; 1.61]	-	1.03 [0.64; 1.66]	-	-	-	-
Smoking status: ex-smoker	0.64 [0.44; 0.91]	-	-	0.78 [0.52; 1.16]	-	-	-
Smoking status: smoker	0.82 [0.50; 1.36]	-	-	1.02 [0.60; 1.78]	-	-	-
Body mass index	0.97 [0.95; 1.00]	-	-	-	0.97 [0.94; 1.00]	-	-
Diabetes	0.61 [0.42; 0.89]	-	-	-	-	0.67 [0.44; 1.01]	-
Hypertension	0.61 [0.39; 0.91]	-	-	-	-	-	0.65 [0.4; 1.03]
[B] Cohort: participants with eGFR <60 mL/min/1.73m² of the study population, and complete data regarding considered variables [N=5,017]
Variable	Univariat	Multivariat [MV]	MV + Bildungsstand	MV + Rauchen	MV + Body Mass Index	MV + Diabetes	MV + Bluthochdruck
Sex, male	0.55 [0.47; 0.64]	0.55 [0.47; 0.64]	0.54 [0.46; 0.63]	0.52 [0.44; 0.62]	0.54 [0.47; 0.64]	0.59 [0.50; 0.69]	0.58 [0.50; 0.68]
Age/10 years	1.30 [1.17; 1.43]	1.26 [1.14; 1.39]	1.27 [1.14; 1.40]	1.30 [1.17; 1.44]	1.30 [1.17; 1.44]	1.34 [1.20; 1.48]	1.37 [1.23; 1.52]
Educational status: middle	0.93 [0.78; 1.11]	-	0.85 [0.71; 1.02]	-	-	-	-
Educational status: low	1.05 [0.87; 1.26]	-	0.92 [0.76; 1.12]	-	-	-	-
Smoking status: ex-smoker	0.79 [0.67; 0.94]	-	-	0.89 [0.75; 1.06]	-	-	-
Smoking status: smoker	0.75 [0.59; 0.96]	-	-	0.89 [0.69; 1.16]	-	-	-
Body mass index	0.97 [0.96; 0.98]	-	-	-	0.96 [0.95; 0.98]	-	-
Diabetes	0.48 [0.41; 0.56]	-	-	-	-	0.50 [0.42; 0.60]	-
Hypertension	0.53 [0.44; 0.64]	-	-	-	-	-	0.53 [0.43; 0.65]

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The reported results are based on logistic regression analysis of data from NAKO participants with (A) an eGFR <60 mL/min/1.73 m² or a UACR ≥30 mg/g, excluding those receiving renal replacement therapy and participants with incomplete information on the variables considered here (N=1,032). In the sensitivity analysis (B), data from the entire study population were used without taking Urinstix data into account (N=5,017).

Using logistic regression with unreported kidney disease as the outcome variable, the table presents odds ratios together with their respective 95% confidence intervals for various models (listed by column).

All statistically significant results are highlighted in bold (i.e. the 95% confidence interval does not include 1).

Reference categories of categorical variables:

(A) eGFR >59 mL/min/1.73m², high educational status, non-smoker

(B) eGFR 30-59 mL/min/1.73m², high educational status, non-smoker

Abbreviations: eGFR, estimated glomerular filtration rate (Pottel 2021, creatinine-based, see eSupplement Table 2 for details); UACR, urinary albumin-to-creatinine ratio.

Interpretation guide: The association between diabetes and the non-reporting of kidney disease shows an odds ratio <1, which is borderline significant in the main analysis (upper limit of the 95% CI = 1) and significant in the sensitivity analysis (upper limit of the 95% CI <1; highlighted in bold). In participants with diabetes, the odds of not reporting kidney disease, despite abnormal laboratory findings, are therefore lower than in the group with abnormal laboratory findings but without diabetes. As diabetes is one of the main causes of chronic kidney disease, the result indicates that people with diabetes are screened more effectively in this regard.

Comparability of eGFR calculations

The evaluation of eight different estimation equations in the subcohort with cystatin-C measurements (N = 112 151; eSupplement Tables 2 and 5) revealed differences in a given person’s eGFR values, amounting to a median of 17.0 mL/min/1.73 m² (Q3: 22.1). The concordance correlation coefficients were good to excellent for equations that used the same biomarker(s) (range: 0.74–0.97) and less good when comparing creatinine-based equations with cystatin C-based equations (range: 0.53–0.66; eSupplement Figure 3). The estimates differed, in some cases systematically, particularly in the range of higher eGFR values (eSupplement Figure 4).

eSupplement Table 5: Weighted percentiles of eGFR distributions in the NAKO for various GFR estimation equations.

Percentile (unit: mL/min/1.73m²)	Levey 2009 (Crea)	Inker 2021 (Crea)	Pottel 2021 (Crea)	Inker 2012 (Crea + Cys)	Inker 2021 (Crea + Cys)	Pottel 2023 (Crea + Cys)	Inker 2012 (Cys)	Pottel 2023 (Cys)	Minimum	Maximum
0%	6.6	7.1	7.2	11.9	12.5	15.3	6.4	9	6.4	15.3
1%	56.6	59.9	54.2	57.6	60.7	58.4	52.1	55.9	52.1	60.7
2.5%	64.6	68.3	61.9	66.6	70.5	66.4	62.3	64.8	61.9	70.5
5%	71.2	75	67.9	73.4	77.7	72.3	69.4	71.3	67.9	77.7
10%	78.1	82.2	74.5	80.6	85.2	78.1	77.6	78.4	74.5	85.2
25%	89.6	94	84.8	92	96.9	87	91.9	87.9	84.8	96.9
50%	100.1	103.8	95.5	103.7	108.4	97.3	105.9	99.4	95.5	108.4
75%	111.4	114.2	106.7	114.5	118.1	107.1	116.3	109.9	106.7	118.1
90%	120.6	122.7	111.5	123.2	125.5	112.1	123.8	114.4	111.5	125.5
95%	124.7	126.1	114	127.7	129.4	114.2	128	117.3	114	129.4
97.5%	127.9	128.5	116.3	131.7	132.7	116	131.9	119.1	116	132.7
99%	131.3	131.3	118.9	136.5	136.7	118.4	137.2	122.5	118.4	137.2
100%	198.4	173.3	167.8	178.5	173.3	135.4	206.3	154	135.4	206.3

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The reported statistics (weighted) were derived from the analysis of dataset 2 (individuals with valid creatinine and cystatin C measurements and self-reported kidney disease). Participants with kidney replacement therapy were excluded (N=112.035).

eGFR equations: see eSupplement Table 2.

Abbreviations: eGFR, estimated glomerular filtration rate; Crea: serum creatinine; Cys: cystatin C.

eSupplement Figure 3: — For NAKO participants with complete eGFR values (N=112,151), concordance correlation coefficients were calculated for each combination of eGFR values and illustrated as a heatmap. See **eSupplement Table 2** for information on the implemented eGFR equations. Values ≥0.75 indicate good agreement, and values ≥0.95 indicate excellent agreement.

Abbreviations: eGFR: estimated glomerular filtration rate; Crea: serum creatinine; Cys: cystatin C

eSupplement Figure 4: — Each point represents the mean of two eGFR values for an individual (x-axis) and their difference (y-axis). The solid black horizontal line marks the mean difference, whilst the dotted lines indicate the upper and lower limits of agreement (±1.96 SD). The red line indicates a difference of zero. For clarity, 5,000 individuals have been randomly selected for display. See **eSupplement Table 2** for information on the eGFR equations implemented.

It is striking that, for example, eGFR values from Inker 2012 (Cys) and Pottel 2023 (Cys) systematically differ, particularly at higher eGFR values (see bottom right).

Abbreviations: eGFR: estimated glomerular filtration rate; Crea: serum creatinine; Cys: cystatin C

The means for eGFR and the respective proportion of participants with eGFR < 60 mL/min/1.73 m² ranged between 91.4 ± 14.3 (weighted: 94.2 ± 14.7) and 2.3% (weighted: 2.0%) according to Pottel et al. (32) and 104.1 ± 15.4 (weighted: 106.6 ± 16.2) mL/min/1.73 m² and 0.9% (weighted: 0.9%) according to Inker et al. (22) (Table 3). However, the lack of concordance between reported kidney disease and abnormal laboratory findings was observed regardless of the GFR estimation equation used (eSupplement Figure 5).

Table 3. Mean eGFR values and proportion of NAKO participants with eGFR values < 60 mL/min/1.73 m² by estimation equation.

Unweighted (weighted) statistics		Mean	Standard deviation	Proportion of participants with eGFR < 60 mL/min/1.73 m² (%)
Estimation equation^*	Levey 2009 (Crea)	95.9 (99.6)	15.5 (16.4)	1.6 (1.4)
	Inker 2021 (Crea)	99.5 (102.9)	14.9 (15.7)	1.1 (1.0)
	Pottel 2021 (Crea)	91.4 (94.2)	14.3 (14.7)	2.3 (2.0)
	Inker 2012 (Crea + Cys)	99.7 (102.6)	16.0 (16.9)	1.3 (1.3)
	Inker 2021 (Crea + Cys)	104.1 (106.6)	15.4 (16.2)	0.9 (0.9)
	Pottel 2023 (Crea + Cys)	93.5 (96.0)	13.1 (13.6)	1.2 (1.2)
	Inker 2012 (Cys)	100.9 (103.1)	17.5 (18.4)	2.0 (2.0)
	Pottel 2023 (Cys)	95.7 (97.8)	14.2 (14.9)	1.5 (1.5)

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The weighted results are based on the dataset comprising participants with complete serum creatinine and cystatin C data, from which 116 participants receiving renal replacement therapy were excluded (N = 112 035). The equation primarily used in this project is marked in bold.

See eSupplement Table 2 for references of the GFR estimation equations

Cys, cystatin; eGFR, estimated glomerular filtration rate; Crea, creatinine

eSupplement Figure 5: — This presentation utilised the cystatin C subcohort for which urine test strip results were also available (excluding participants on kidney replacement therapy, N=21,385).

Due to the use of a different eGFR equation (**eSupplement Table 2**), individual participants are assigned to different combinations, which may also have altered the order of the combinations. Both the proportion of participants meeting at least one of the criteria (range: 19.6–20.7%) and the proportion of participants whose abnormal laboratory findings correspond to a reported diagnosis of kidney disease (range: 0.9–1.4%) vary only slightly.

Abbreviations: eGFR: estimated glomerular filtration rate; Crea: serum creatinine; Cys: cystatin C

Discussion

The aim of this study was to generate previously missing information on the occurrence of abnormal laboratory results for kidney function parameters and reported diagnosed kidney disease, based on data from the NAKO study which recruited participants from across Germany (28). The analysis reveals clear evidence of a relevant gap in awareness that CKD may be present.

In the overall cohort of 195 182 participants (without KRT, N = 194 955), weighted 2.1% reported a medically diagnosed kidney disease. The weighted proportion of participants with an eGFR < 60 mL/min/1.73 m² was very similar (2.2%). In the cohort with urine test data (N = 35 504; without KRT, N = 35 461), weighted 2.3% reported a kidney disease. Weighted 2.2% had an eGFR < 60 mL/min/1.73 m² and weighted 17.4% had a UACR ≥ 30 mg/g.

Three German studies reported CKD prevalence rates of 11.2%, 12.7% and 17.3%, respectively, and 2.2%, 2.3% and 5.9%, respectively, of those affected had an eGFR < 60 mL/min/1.73 m² (3, 24, 27). These figures are similar to the NAKO findings observed in our study and are based on a similar age and sex distribution, with also one laboratory value for eGFR or UACR in each study. Minor discrepancies are likely due to differences in the sampling method, the sample size or the methods used.

The reported prevalence rates for CKD vary significantly between countries (3, 4, 16). Taking into account the age and sex distributions in Europe, the proportion of persons with an eGFR < 60 mL/min/1.73 m² ranges from 1% in Italy to 5.9% in northeastern Germany, and when albuminuria is also taken into account, the proportion ranges from 3.3% in Norway to 17.3% in northeastern Germany (3). This variation can likely be attributed to both methodological differences (e.g., selection of the study population, measurement methods) and regional differences in lifestyles, risk factors and healthcare (3).

As expected, the proportion of NAKO participants who reported kidney disease as well as those with an eGFR < 60 mL/min/1.73 m² and a UACR ≥ 30 mg/g increased with age (3, 24, 37). Using a comparable eGFR equation, the Berlin Initiative Study (BIS) reported an eGFR-based CKD prevalence of 38% among people aged 70 and older (25), a finding that supports our observation.

In the NAKO study, the overlap between a reported kidney disease and abnormal laboratory test results was small. The proportion of participants without abnormal laboratory findings who nevertheless reported medically diagnosed impaired kidney function could, for example, be explained by transient kidney dysfunction, diagnoses based on criteria not covered in this study, biological and analytical variability in laboratory parameters, and misunderstandings in the communication of medical findings. Of particular note is that only a minority of NAKO participants with objectively abnormal laboratory test results reported ever having been diagnosed with kidney disease.

Given the lack of repeat testing, this cross-sectional study does not allow for a formal diagnosis of CKD. Nevertheless, it can be assumed that, besides persons with transiently abnormal findings, there is a significant subgroup in which the observed abnormalities would be confirmed upon repeat testing. Earlier population-based studies have consistently shown that, among a significant proportion of people with abnormal screening results, an abnormal eGFR or albuminuria finding is confirmed when the test is repeated (27, 38, 39). Depending on the study population, the parameter examined and the time interval until the follow-up measurement, approximately 40–60% of individuals with an initial eGFR < 60 mL/min/1.73 m² showed persistent abnormalities, with some cohorts reporting even higher confirmation rates of up to approximately 70–80% (38, 39).

Based on these data, it appears that a structured, population-wide implementation of CKD screening strategies is needed to close any potential diagnostic gaps as early as possible. In the event of abnormal initial findings, repeat testing should be performed in a timely manner and consistently, so that a diagnosis of CKD can be established in accordance with the guidelines. While the aim of CKD treatment has long been to slow the loss of kidney function, recent randomized trials and meta-analyses show that modern combination therapy can stabilize eGFR courses within the range of normal age-related decline and substantially reduce or even normalize albuminuria (18, 19). With these advances, the concept of CKD remission is moving increasingly closer to reality. However, in order to achieve this, affected persons must be identified at an early stage, before irreversible structural kidney damage has occurred. With that in mind, it is necessary to close potential diagnostic gaps, not only from an epidemiological viewpoint, but increasingly also from a prognosis and health economics perspective. The figures reported in the international literature regarding the lack of concordance are well aligned with the findings of our study (2, 14, 24). Similar to what we observed in our study, Tangri et al. reported associations between a lack of awareness of having CKD and older age, female sex, and persons without diabetes mellitus or hypertension (14).

The discrepancy between reported kidney disease and an eGFR < 60 mL/min/1.73 m² could also be attributable to the estimation of GFR, which can lead to both false-positive diagnoses and CKD being overlooked (26, 40). Although the choice of equation is important both for the individual patient and for determining the prevalence of people with reduced kidney function, the lack of concordance between self-reported kidney disease and abnormal laboratory test results was observed regardless of the estimation equation used.

The strengths of our study include its size and wide geographical coverage as well as the availability of data on creatinine and cystatin-C levels and albuminuria. In addition, standardized instruments were used for both the data collection and the measurements. We were able to include study weights that adjust the results for differences in selection probabilities and selective nonresponse in the study population. The data reported here should not be interpreted as a representative prevalence estimate for the general population of Germany. The NAKO study intentionally recruited adults between the ages of 19 and 74 years from predefined regions to allow for long-term prospective observation. The reported proportions thus apply to these age groups within the study regions and cannot, for example, be extrapolated to very elderly persons. Another limitation is the lack of timely repeat measurements, which are impractical in observational studies, making it impossible to formally diagnose CKD.

Conclusion

In the large German NAKO study, approximately 2% of participants reported a kidney disease. There was, however, little overlap with abnormal eGFR or UACR values. This finding illustrates a lack of awareness of the possibility of having CKD among the general population. The onset of CKD is insidious. The key to better early detection is therefore to raise awareness of this problem among patients and physicians and to consistently follow up on abnormal screening results with repeat measurements.

Acknowledgement

This analysis was conducted with data from the German National Cohort (NAKO) study (www.nako.de). The NAKO study has been funded by the German Federal Ministry of Research, Technology and Space (BMFTR, Bundesministerium für Forschung, Technologie und Raumfahrt) (grant numbers: 01ER1301A/B/C, 01ER1511D, 01ER1801A/B/C/D and 01ER2301A/B/C), the Federal States (“Länder”) and the Helmholtz Association, as well as the participating universities and institutes of the Leibniz Association. We thank all participants and staff members of the NAKO study.

Footnotes

Funding: The work by PS and AK was funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) through SPP 2177 (3598/6–2) and DFG project ID431984000 (SFB 1453). PS received funding from the DFG to support the work of EB (SE 2407/3–1). IMH was funded by TRR 374 TP C6 (Project ID: 509149993). JMN receives funding from the Berta-Ottenstein program for Clinician Scientists of the University Freiburg.

Conflict of interest statement: EB received travel expense support from DGfN.

AKa is the President of the German Society for Epidemiology (DGEpi).

RM is a member of the board of the German Society for Epidemiology.

ES receives consulting fees from AstraZeneca and a fee from the National Kidney Foundation for editorial work at the American Journal of Kidney Diseases. She is a spokesperson of the European Kidney Function Consortiums and was a member of the working group of the KDIGO Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. She is the vice president of the German Society of Nephrology (DGfN).

MS states ongoing collaboration with Owkin on the topic of heart failure.

KE is co-inventor and holds a patent (EP3396380B1) for a diagnostic instrument used to identify podocyte foot-process effacement. He is the treasurer of the Förderverein Nordverbund Niere.

TP and WL are volunteer members of the board of NAKO e.V. which oversees the NAKO study.

MG is a member of the extended board of the German Society of Nephrology and Subject Editor for the Clinical Kidney Journal. He has received lecture fees from Boehringer-Ingelheim, AstraZeneca, Novartis, and Glaxo Smith Kline.

The remaining authors declare no conflict of interest.

Data sharing

The data of the NAKO study are made available to researchers with approved research applications. More information on data requests can be found here: https://transfer.nako.de/transfer/index

Supplementary material

Complete list of full references

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Supplementary Material (eSupplement)

eSupplement Method 1

Sensitivity analysis to investigate possible bias in creatinine-based results

The serum creatinine measurements used in this project were carried out at the central laboratory for approximately 57% of participants and at local laboratories for approximately 43% of participants (eSupplement Table 1). IDMS-traceable calibrators were used for the majority of these measurements.

Regardless of the laboratory, all measurements were subjected to stringent quality control at a central level. The corresponding quality reports from the central study laboratory are publicly available and can be viewed at the following address:

https://www2.unimedizin-greifswald.de/klinchem/index.php?id=453.

The results of the quality control showed that the measured values from the local and central study laboratories were broadly comparable, although unexplained, systematically higher values were observed in the local laboratories at the study centers in Essen, Freiburg, Regensburg and Leipzig.

To investigate a possible batch effect in the reported results based on creatinine measurements, the study cohort was divided into two groups: (i) study centers that mainly had measured creatinine centrally (>80%) and (ii) study centers that had a larger proportion of measured creatinine locally (>20%). In these two groups, key analyses were repeated and compared with the results of the main analysis:

Table: Comparison of the key figures obtained from the overall cohort, including all study centers, and obtained from the analyses of the two groups that had measured creatinine mainly centrally or locally.

	All study centers (main analysis)	Study centers with larger proportion measured locally	Study centers with larger proportion measured centrally
	(N=195,182)	(N=89,315)	(N=105,867)
Sex, male	97,328 (49.9%)	44,499 (49.8%)	52,829 (49.9%)
Age, years
mean (SD)	50 (± 13)	50 (± 13)	50 (± 13)
Diabetes	13,258 (6.8%)	6,069 (6.8%)	7,189 (6.8%)
Hypertension	80,229 (41.2%)	36,446 (40.9%)	43,783 (41.5%)
Reported kidney disease	4,224 (2.2%) (weighted: 2.1%)	1,890 (2.1%) (weighted: 1.9%)	2,334 (2.2%) (weighted: 2.2%)
Creatinine, mg/dL^*
median [Q1, Q3]	81 [0.71, 0.92] weighted: 0.81 [0.70, 0.90]	0.81 [0.71, 0.93] weighted: 0.82 [0.70, 0.92]	0.8 [0.7, 0.92] weighted: 0.81 [0.69, 0.90]
eGFR, Creatinine, Pottel 2021^*
mean (SD)	91 (± 15) weighted: 94 (±15)	90 (± 15) weighted: 94 (±15)	91 (± 14) weighted: 94 (±15)
eGFR <60 mg/dL/1.73m^2*	5,079 (2.6%) (weighted: 2.2%)	2,454 (2.8%) (weighted: 2.2%)	2,625 (2.5%) (weighted: 2.2%)

Open in a new tab

Participants with kidney replacement therapy were excluded from these analyses.

Study centers that had a larger proportion of creatinine measured locally (>20%): Augsburg, Mannheim, Halle, Leipzig, all of Berlin’s city centers, Hamburg;

Study centers that mainly had creatinine measured centrally (>80%): Regensburg, Freiburg, Essen, Saarbruecken, Muenster, Duesseldorf, Hannover, Bremen, Kiel, Neubrandenburg

Conclusion

The high degree of consistency in the results between these groups suggests that our findings are robust in the face of potential center effects.

References (abbreviated)

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PERMALINK

Glomerular Filtration Rate, Albuminuria, and Reported Kidney Disease in Comparison

PD Dr rer nat Peggy Sekula

Elena Butz, M.Sc.

Prof Dr med Anna Köttgen, M.P.H.

Prof Dr med Elke Schaeffner

PD Dr Dr med Janis M Nolde

Dr med Insa M Schmidt, M.P.H.

Dr rer nat Lara Kim Brackmann

Dr oec-troph Beate Fischer

Prof Dr med Matthias Girndt

Dr rer nat Kathrin Günther

Dr rer med Anke Hannemann

Univ-Prof Dr med Volker Harth, M.P.H.

Dr med Torben Heinsohn

Univ-Prof Dr med André Karch, MSc

Prof Dr med Thomas Keil

PD Dr med Lilian Krist, M.P.H.

Prof Dr med Berit Lange

Prof em Dr med Dr PH Michael Leitzmann

Dr rer med Claudia Meinke-Franze

Dr med Jaroslawna Meister

Prof Dr med Rafael Mikolajczyk

Univ-Prof Dr sc hum Ute Mons, M.A.

Dr sc hum Katharina Nimptsch

Dr rer nat Nadia Obi

Dr oec troph Cara Övermöhle

Prof Dr med Tobias Pischon

Prof Dr rer san Tamara Schikowski

Prof Dr Ben Schöttker

Prof Dr PH Matthias B Schulze

Julia Schwichtenberg, M.A.

Prof Dr med Andreas Stang

Dr phil Henning Teismann, Dipl.-Psych.

Prof Dr med Henry Völzke

Prof Dr med Karlhans Endlich

Prof Dr med Matthias Nauck

Prof Dr Markus Scholz

Prof Dr rer biol hum Iris M Heid

Prof Dr med Wolfgang Lieb, M.Sc.

Summary

Background:

Methods:

Results:

Conclusion:

eSupplement Figure 1: Analysis datasets and research questions.

Methods

Study population and data

eSupplement Table 1: Definition of the NAKO variables used and derived variables.

eSupplement Table 2: Implemented eGFR equations.

Statistical analysis

Results

Table 1. Description of the study population.

Frequency of reported kidney disease and of eGFR < 60 mL/min/1.73 m2

eSupplement Table 3: Description of the study population by age group.

Concordance between reported kidney disease and abnormal laboratory test results

Figure 1.

Table 2. Percentage of persons with reported kidney disease in the respective KDIGO risk category.

Figure 2.

eSupplement Figure 2: Age-specific Proportions of (not) reporting kidney disease and (ab)normal eGFR and/or UACR values.

eSupplement Table 4: Associations between demographic factors and unreported kidney disease among NAKO participants with abnormal laboratory results.

Comparability of eGFR calculations

eSupplement Table 5: Weighted percentiles of eGFR distributions in the NAKO for various GFR estimation equations.

eSupplement Figure 3: Concordance correlation between eGFR values calculated from different equations.

eSupplement Figure 4: Bland-Altman plots for the pairwise comparison of eGFR values using different equations.

Table 3. Mean eGFR values and proportion of NAKO participants with eGFR values < 60 mL/min/1.73 m2 by estimation equation.

eSupplement Figure 5: Group sizes and overlap among NAKO participants with regard to all combinations of reported kidney disease, eGFR <60 mL/min/1.73 m2 and UACR ≥30 mg/g, using various GFR estimation equations.

Discussion

Conclusion

Acknowledgement

Footnotes

Data sharing

Supplementary material

Complete list of full references

Supplementary Material (eSupplement)

eSupplement Method 1

Table: Comparison of the key figures obtained from the overall cohort, including all study centers, and obtained from the analyses of the two groups that had measured creatinine mainly centrally or locally.

Conclusion

References (abbreviated)

ACTIONS

Frequency of reported kidney disease and of eGFR < 60 mL/min/1.73 m²

Table 3. Mean eGFR values and proportion of NAKO participants with eGFR values < 60 mL/min/1.73 m² by estimation equation.

eSupplement Figure 5: Group sizes and overlap among NAKO participants with regard to all combinations of reported kidney disease, eGFR <60 mL/min/1.73 m² and UACR ≥30 mg/g, using various GFR estimation equations.