Comparison of Serum Creatinine– and Cystatin C–Based eGFR at Baseline and Their Prediction of Incident Moderate Albuminuria in Individuals With Type 1 Diabetes Free

Despite significant advances in diabetes treatment and kidney disease prevention, there is still a substantial residual risk of diabetic kidney disease (DKD). About one-third of individuals with type 1 diabetes (T1D) develop moderate albuminuria during the first 30 years of having diabetes (1). Even at the early stage of albuminuria, the risk of cardiovascular disease is more than sixfold compared with the risk for individuals without diabetes, and the risk increases substantially with worsening of albuminuria (2). Notably, those with severe albuminuria face the same risk of kidney failure irrespective of decade of diabetes onset (1). There is, therefore, an urgent need to focus efforts on the early detection and reversal of DKD.

The current risk assessment of DKD relies primarily on the conventional risk factors of dyslipidemia, hypertension, poor glycemic control, smoking, albuminuria, and reduced glomerular filtration rate (GFR). An early diagnosis of DKD currently is based on an increased urinary albumin excretion rate (UAER) that indicates diabetes-induced glomerular damage in individuals with diabetes (3). Another common diagnostic measure in clinical practice is serum creatinine (SCr)-based estimated GFR (eGFRcr), a key indicator of kidney function. Various equations have been developed over the years to improve the accuracy of the GFR estimation (4). Despite this, the performance of equations based on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in the setting of diabetes, especially in people with relatively well-preserved kidney function, has been shown to be suboptimal (5).

However, SCr is affected by factors beyond the kidney or the used equation, including age, muscle mass, body size, and nutritional status, leading to inaccuracies in the estimated GFR (eGFR). Additionally, the SCr concentrations begin to increase only when kidney disease is advanced (6). Therefore, early diagnosis of DKD can be missed and treatment consequently delayed. Instead, serum cystatin C (SCysC)-based eGFR (eGFRcys) has been suggested to be more precise because SCysC is unrelated to muscle mass, is freely filtered by the glomerulus, is largely reabsorbed, and is fully catabolized by the proximal tubules, making it an ideal marker of GFR (7). Given these differences between SCr and SCysC, discrepancies between eGFRcr and eGFRcys also are common (8–11). However, an accurate estimation of the kidney function is crucial for the diagnosis and treatment of DKD and also for decision-making regarding several other health conditions.

Studies comparing the accuracy of GFR estimation with SCr and SCysC against direct measurement of GFR have yielded conflicting results, many of them indicating a superiority of eGFRcys over eGFRcr (8), but it is of note that also opposite findings exist (12). The discrepancies are further reflected by their distinct differences to predict DKD or other long-term clinical outcomes. Our previous study showed the benefit of eGFRcys for the evaluation of risk of kidney failure in T1D (13). However, the emphasis in exploring risk factors and biomarkers should ideally be directed toward an early detection of DKD. Some cross-sectional studies have shown that SCysC is significantly higher in individuals with moderate albuminuria than in those with normal UAER (14) and that eGFRcys might be more sensitive to detect early DKD (15). Furthermore, SCysC per se has also been suggested as a useful biomarker for the detection of cardiovascular disease and heart failure, in addition to early kidney disease (16). To date, large-scale studies on the discrepancies between eGFRcr and eGFRcys in individuals with T1D are lacking.

Based on these gaps in the knowledge, the first aim of this study was to assess the concordance between Scr- and SCysC-based eGFRs in individuals with T1D at different stages of albuminuria and to explore the variables associated with the discordance. Because comparing two different GFR estimates, eGFRcr and eGFRcys, does not allow for conclusions regarding the superiority of one estimate over the other, we investigated their potential differences in association with the incidence of albuminuria.

The target population was a subset of the Finnish Diabetic Nephropathy (FinnDiane) study who had both Scr and SCysC data available, and who did not have kidney failure (n = 3,769). The FinnDiane study was established to study genetic and environmental risk factors for diabetic micro- and macrovascular complications. It covers adult participants (aged ≥18 years) with T1D recruited from more than 90 hospitals and primary health care centers throughout Finland (a list of the FinnDiane Study centers is given in the Supplementary Material). T1D was defined as diabetes onset when younger than 40 years and permanent insulin treatment initiated within a year of the diabetes diagnosis. The research plan was approved by the Ethics Committee of the Helsinki and Uusimaa Hospital District, and the study was performed according to the Declaration of Helsinki. All participants provided their written informed consent.

A detailed FinnDiane research plan has been presented previously (17). In brief, the baseline study visits have been conducted since 1997 and are ongoing. At the baseline study visit, all participants undergo a thorough clinical examination and complete questionnaires regarding health and medical history with their attending physician. Serum and urine samples are collected for biochemical measurements. UAER is measured from 24 h (mg/24 h) or timed overnight (µg/min) urine collections. Normal UAER is defined as a UAER <20 µg/min or <30 mg/24 h in at least two of three consecutive urine collections. Moderate albuminuria is defined as UAER ≥20 and <200 µg/min or ≥30 and <300 mg/24 h, and severe albuminuria as UAER ≥200 µg/min or ≥300 mg/24 h.

Serum creatinine was measured centrally using the Jaffe method until 2002 and later by isotope dilution–mass spectrometry (IDMS) in the laboratory of the Helsinki University Hospital. Samples were randomly selected for parallel analyses with the Jaffe and IDMS methods, and values before 2002 were transformed to IDMS values before the estimation of the eGFRcr, using the following formula: Scr (IDMS) = [0.953 × Scr (Jaffe)] − 7.261. CysC was measured by an immunoprecipitation method (Thermo Fisher Scientific, Vantaa, Finland).

Data on progression of albuminuria were obtained either by reexamining the individuals at a FinnDiane follow-up visit or by reviewing their medical records and available routine laboratory data. Such data were available from 1,920 individuals with normal UAER at baseline. Based on Scr and SCysC concentrations, eGFRcr was calculated using the Scr equation CKD-EPI 2009, and eGFRcys was calculated using the SCysC equation CKD-EPI 2012 (18).

Continuous variables were compared with the Student t test (two groups) and expressed as mean (SD) if the data were normally distributed, or with the Mann-Whitney U test and are expressed as median (interquartile range [IQR]) if the data distribution was skewed. Categorical variables are expressed as n (%) and the χ² test to compare differences between groups. The linear correlation between eGFRcr and eGFRcys was assessed using the Pearson correlation coefficient.

The modified Bland-Altman plot method was used to show the relationship between eGFRcys and eGFRcr, with eGFRcr plotted on the x-axis and the difference (eGFRdiff = eGFRcys minus eGFRcr) on the y-axis. There are not established cutoff values for defining concordance and discordance. However, various definitions have been used in published studies (9–11,19–22). We assessed concordance in several ways. First, for comparability with previous studies, we classified eGFRdiff into concordant (−15 ≤ eGFRdiff < 15 mL/min/1.73 m²), negatively discordant (eGFRdiff < −15 mL/min/1.73 m²), and positively discordant (eGFRdiff ≥15 mL/min/1.73 m²) categories, in accordance with the categorization used in recent studies (9,20). Second, we computed percent differences as ([eGFRcys minus eGFRcr]/eGFRcr) × 100% and classified the percent eGFRdiff as <−20%, −20% to 20%, and ≥20% (19). Finally, we evaluated concordance between eGFRcys and eGFRcr categories using the Kidney Disease Improving Global Outcomes chronic kidney disease (CKD) categories (G5: <15; G4: 15–29; G3: 30–59; G2: 60–89; and G1: ≥90 mL/min per 1.73 m²) (21).

Multivariable logistic regression analyses were performed using concordant, negatively (eGFRcys < eGFRcr) or positively (eGFRcys > eGFRcr) discordant as outcomes, and the independent variables included age, sex, albuminuria stage, coronary artery disease, stroke, retinal laser photocoagulation, smoking status, systolic blood pressure (SBP), HbA_1c, HDL cholesterol (HDL-C), triglycerides, BMI, waist-to-height ratio (WHR), insulin dose per kilogram, and high-sensitivity C-reactive protein (hs-CRP). A stepwise procedure was used to select the variables associated with discordance.

Individuals with normal UAER were followed from the baseline study visit until progression to moderate albuminuria, death, or the end of 2020. Before the Cox regression analyses, the shapes of the association between baseline SCysC, eGFRcr, eGFRcys, and the progression were assessed allowing for nonlinearity by using restricted cubic splines with three knots. The Wald test for linearity was used to test for the presence of nonlinearity in these analyses. The assumptions of proportional hazards were tested with Schoenfeld residuals. Because the proportional hazard assumption was violated for UAER, the follow-up time was stratified into distinct intervals with turning point at 9 years. The associations were analyzed adjusting for UAER, age at onset, duration of diabetes, sex, and the well-established modifiable risk factors HbA_1c, SBP, smoking status, HDL-C, triglycerides, and BMI. The UAER value used in the analyses was obtained from a 24-h urine collection. Because some of the individuals had an overnight instead of a 24-h timed urine collection, the number of individuals in these analyses was reduced to 1,788.

There were 1,954 men (51.8%) and 1,815 women (48.2%) in this study. The median age was 36.6 (27.7–46.4) years and the median duration of diabetes was 19.5 (10.9–29.2) years. The distributions of eGFRcr and eGFRcys are shown in the Supplementary Fig. 1. The median eGFRcr was 102.6 (85.6–116.2) mL/min/1.73 m² and the median eGFRcys 103.6 (85.7–114.4) mL/min/1.73 m². The characteristics of the participants divided into concordant and discordant eGFRdiff groups are shown in Table 1. Those in the negatively discordant eGFRdiff group (eGFRcys < eGFRcr) were younger, had lower age at onset of diabetes, shorter duration of diabetes, and were more likely men, current smokers, had a worse lipid profile, higher hs-CRP value and insulin dose, and lower BMI and SBP compared with the concordant group. Those in the positively discordant eGFRdiff group (eGFRcys > eGFRcr) were more likely women, were of older age at onset of diabetes, normal UAER, less laser treatment, were less likely to be current smokers, and had better lipid profiles than those in the concordant group.

The overall correlation between eGFRcys and eGFRcr was 0.74: 0.77 in men and 0.71 in women. This correlation increased along with worsening albuminuria, being the weakest, 0.47, in the individuals with normal UAER, and 0.62 and 0.82 in those with moderate and severe albuminuria, respectively (Supplementary Fig. 2).

Figure 1 shows modified Bland-Altman plots of the relationship between eGFRcr and eGFRdiff by albuminuria groups. In all groups, there was a negative linear relationship between eGFRcr and eGFRdiff. The mean absolute difference between eGFRcys and eGFRcr was 13.6 ± 12.2 mL/min/1.73 m² when eGFRcys was greater than the eGFRcr and 14.4 ± 12.1 mL/min/1.73 m² when it was lower than the eGFRcr.

The overall number of individuals in the concordant eGFRdiff group was 2,371 (concordance rate 62.9%) (Supplementary Table 1); there were 768 (20.4%) in the negatively discordant group and 630 (16.7%) in the positively discordant group. The concordance rate was similar in men and women, but the direction of discordance was different, as shown in Table 1. Individuals with severe albuminuria had a concordance rate of 72.5% and the positively discordant rate was the lowest, 8.0%. The concordance rate was 61.4% in those with normal UAER and 61.6% in those with moderate albuminuria.

The concordance was higher, 73.1%, when using percent eGFRdiff groups (Supplementary Table 2). Opposite to concordance classification based on classification by differences of 15 mL/min/1.73 m², concordance was the highest, 75.3%, in individuals with normal UAER; it was 61.2% in the severe albuminuria group. In the CKD stage classification, 2,777 individuals (73.7%) remained at the same stage, whereas the rest were classified to a more (13.9%) or less (12.4%) advanced stage by eGFRcys compared with eGFRcr (Supplementary Table 3). Men had higher concordance rate (78.4%) than women (68.6%). Concordance based on the CKD classification was highest, 75.9%, in individuals with normal UAER; it was 70.3% in moderate albuminuria group, and 64.4% in the severe albuminuria group.

Table 2 shows the variables that were associated with discordance between eGFRcys and eGFRcr in multivariable logistic regression analyses as well as the odds ratios of the associations. Variables associated with discordance were age, sex, albuminuria status, smoking status, retinal laser photocoagulation treatment, hs-CRP level, and insulin dose.

During a median of 11.0 (IQR = 5.9–15.0) follow-up years, 225 individuals (12.6%) progressed from normal UAER to moderate albuminuria. Supplementary Table 4 presents the clinical characteristics of the individuals with normal UAER at baseline stratified by incident moderate albuminuria, and Supplementary Table 5 presents clinical characteristics by SCysC concentration quartiles. The individuals who progressed from normal AER to moderate albuminuria were more often men and current smokers, had younger age at onset of diabetes, higher HbA_1c, BMI, WHR, SBP, and UAER, and more unfavorable lipid profile compared with the individuals who did not progress.

Both eGFRcys and SCysC were significantly associated with the progression to moderate albuminuria, after adjustment for UAER, sex, age at onset of diabetes, duration of diabetes, HbA_1c, SBP, smoking status, HDL-C and triglyceride levels, and BMI. The risk of developing moderate albuminuria increased linearly by 19% per each 0.1 increase in SCysC level (hazard ratio [HR] 1.19 [95% CI 1.08–1.31]; P = 0.0005) in the fully adjusted model. eGFRcys was also associated with the progression, with an HR of 1.01 (95% CI 1.01–1.02; P = 0.003) for a 10-unit decrease. In contrast, eGFRcr was not associated with the progression (P = 0.78). Figure 2 shows the relationship between the progression and SCysC, eGFRcys, and eGFRcr, allowing for possible nonlinearity.

We found notable discrepancies between eGFRcr and eGFRcys in individuals with T1D in this study. The overall concordance rate was 62.9% when it was defined as an eGFRdiff between −15 and 15 mL/min/1.73 m²; the discordance rate was 37.1%. Several factors, such as sex, albuminuria status, smoking, laser treatment, HbA_1c, HDL-C and hs-CRP levels, and insulin dose contributed to the discordance. Furthermore, baseline SCysC and eGFRcys were both associated with the progression to moderate albuminuria from normal UAER, whereas eGFRcr was not.

The results related to the discrepancy between eGFRcys and eGFRcr in our study are consistent with previous studies that also repeatedly found substantial discrepancies in individuals without diabetes and in individuals with type 2 diabetes or T1D (8–11,23). However, most of the previous studies included individuals without diabetes, the sample size in those few studies including individuals with T1D have been very small, and the studies did not differentiate individuals by albuminuria status (8,24–26). To our knowledge, the present study is the largest to include adult individuals with T1D, cover a wide range of albuminuria, and explore factors associated with discordance between eGFRcr and eGFRcys.

Larger positive discordance in our study was seen more frequently in women, indicating that eGFRcys tends to be higher than eGFRcr in women. This contradicts the findings in the general population, where women typically have higher or similar eGFRcr than eGFRcys (9–11). Notably, the higher eGFRcr seen in these studies has generally been explained by lower muscle mass in women. Given that creatinine is a waste turnover product of skeletal muscle, its serum concentrations are linked to muscle mass, making its influence on eGFRcr apparent. In contrast, muscle mass does not have an effect on SCysC (27). The reason for the discrepancy might be because our study population comprised solely individuals with T1D. Factors such as insulin treatment, glycemic control, inflammation, insulin resistance, and hormonal abnormalities affect muscle health in T1D (28). Accelerating aging of skeletal muscle mass in T1D has also been suggested (29). Testosterone concentrations are decreased in men with T1D (30). Testosterone deficiency leads to a decline in muscle protein synthesis (31) in men with T1D; thus, they may have lower muscle mass compared with men in the general population. Such T1D-related factors might mitigate the traditionally observed sex differences in muscle mass and, therefore, the contradictive finding in this study.

Many of the other factors that were associated with the discrepancy between eGFRcys and eGFRcr may also be connected through their effect on muscle mass. We found that current smoking was associated with higher eGFRcr than eGFRcys. This is also in concordance with findings of previous studies (11,32). Smoking can have both direct and indirect effects on muscle mass by disturbing muscle cell metabolism and causing chronic inflammation and sarcopenia (33). Smoking has also been associated with lower exercise levels (32). Because regular physical activity is crucial to maintain muscle mass, smoking may instead reduce the muscle mass. Therefore, it is possible that smoking may decrease SCr concentrations and thereby lead to erroneously high eGFRcr values.

The discordance rate was the greatest in individuals with normal UAER. At the normal UAER stage, there is profound heterogeneity in the kidney function. Some individuals have a high GFR and hyperfiltration in the early course of diabetes, whereas some individuals develop a reduced GFR (<60 mL/min per 1.73 m²) despite normal UAER (i.e., nonalbuminuric kidney disease), although this is rare in T1D (34). The general view is that the SCr concentration begins to increase only when GFR is reduced by 50% (6). Thus, given that SCysC is a more sensitive biomarker of kidney function, it may react much earlier than SCr, as reflected by the substantial difference we found between eGFRcr and eGFRcys.

There is a paucity of studies assessing the predictive ability of SCysC and eGFRcys with respect to incident albuminuria. However, our follow-up study of individuals with T1D and the availability of Scr and SCysC values from the same time point gave us the opportunity to compare eGFRcys and eGFRcr as well as SCysC per se as independent predictors of albuminuria. We found that both SCysC and eGFRcys were independent predictors of incident moderate albuminuria in addition to the baseline UAER and after further adjustment for sex, age at onset and duration of diabetes, HbA_1c, SBP, smoking status, and HDL-C and triglyceride levels. The risk of developing moderate albuminuria increased linearly by 20% per each 0.1 increase in SCysC, adjusted for clinical risk factors. eGFRcys was associated with 2% risk of progression per 10-unit decrease. These findings are clinically important and emphasize that SCysC can be a clinically useful early biomarker of DKD at the stage when there are no signs of albuminuria.

Bjornstad et al. (35) investigated relationships between kidney injury biomarkers and the development of impaired GFR and albuminuria in 527 adults with T1D. They demonstrated that plasma CysC was associated with the development of impaired GFR over a period of 12 years and the development of albuminuria in a univariable manner, but the association disappeared after adjustment for age, sex, HbA_1c, LDL cholesterol level, SBP, and baseline albumin-to-creatinine ratio (35). However, the number of individuals with incident DKD was relatively low. Therefore, there might have been lack of power to detect an association between SCysC and albuminuria after adjustments.

There are, however, more studies comparing the accuracy of eGFRcr and eGFRcys against directly measured GFR. A common conclusion from these studies has been that eGFRcys is more accurate than eGFRcr for estimating GFR in individuals with T1D (8,24,25). In contrast, some studies did not find any differences at all, especially the studies in type 2 diabetes or obesity (12,36), or the studies recommending the use of an equation combining SCr and SCysC to estimate GFR (37). In the present study, we were unable to conclude that one estimate was superior to the other because we did not have directly measure GFR. On the other hand, eGFRcys was associated with the incidence of albuminuria, whereas eGFRcr was not.

The differences seen between eGFRcys and eGFRcr have led to the observations that the eGFRcys-to-eGFRcr ratio differs between varying clinical settings. SCysC concentration is usually higher in individuals with diabetes, morbid obesity, and the metabolic syndrome (38), but beyond these clinical conditions, higher SCysC level most probably indicates alterations in glomerular filtration. This is supported by the simultaneous increase in the serum concentrations of β-2-microglobulin, β-trace protein, and retinol-binding protein (39). Consequently, Grubb et al. (39) suggested that a eGFRcys-to-eGFRcr ratio is <0.60 may be due to a reduction in the pore diameter of the glomerular filtration barrier, and they designated this pathophysiological state the “shrunken pore syndrome,” although it remains hypothetical.

In addition to indicating alterations in the glomerular filtration, SCysC also is a marker of inflammation, with elevated levels observed in various inflammatory states (40). It is also well established that chronic low-grade inflammation plays a central role in the pathophysiology of the development of albuminuria (41). Our finding that SCysC is associated with incident albuminuria may reflect the underlying inflammatory processes that drive the onset of albuminuria.

The main limitation of our study is that we did not have access to measured GFRs or to serial SCysC concentrations. Another limitation is that we did not have follow-up data on incident albuminuria for all individuals with normal UAER. Finally, we acknowledge that more research is needed before SCysC level can be applied in clinical practice. A strength of the study is that the we included many well-characterized individuals with T1D with both SCysC and SCr measurements taken at the same time point. This allowed us to explore the predictive value of either baseline eGFRcr or eGFRcys and SCysC with respect to new-onset moderate albuminuria.

In summary, our results show that in a large population of individuals with T1D, discordant eGFRcr and eGFRcys values are frequent. The reasons are not known, but several clinical factors, such as sex, albuminuria status, smoking, laser treatment, HbA_1c, HDL-C and hs-CRP levels, and insulin dose contribute to the discrepancies. Clinically, the most important finding of this study is that SCysC and eGFRcys were independently associated with incident moderate albuminuria, whereas eGFRcr was not. Our findings, therefore, suggest that SCysC may facilitate early identification of individuals at risk for increase in albuminuria.

Acknowledgments. The authors thank all physicians and nurses at each FinnDiane center participating in recruitment and characterization of the participants. A complete list of the physicians and nurses is presented in the Supplementary Material.

Funding. The FinnDiane study was supported by grants from the Folkhälsan Research Foundation, Wilhelm and Else Stockmann Foundation, Liv och Hälsa Society, Helsinki University Central Hospital Research Funds (grant TYH2023403), Novo Nordisk Foundation (grant NNFOC0013659), Academy of Finland (grant 316664), Finnish Diabetes Research Foundation, and Sigrid Jusélius Foundation.

Duality of Interest. P.-H.G. has received investigator-initiated research grants from Eli Lilly and Roche; is an advisory board member for AbbVie, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Cebix, Eli Lilly, Janssen, Medscape, Merck Sharp & Dohme, Mundipharma, Nestlé, Novartis, Novo Nordisk, and Sanofi; and has received lecture fees from Astellas, AstraZeneca, Bayer, Berlin Chemie, Boehringer Ingelheim, Eli Lilly, Elo Water, Genzyme, Merck Sharp & Dohme, Medscape, Menarini, Novartis, Novo Nordisk, PeerVoice, Sanofi, and Sciarc. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. V.H. contributed the study design, performed the statistical analyses, and contributed to writing the first draft of the manuscript. L.M.T. interpreted the data and critically reviewed the manuscript. P.-H.G. interpreted the data and contributed to writing and critically review of the manuscript. V.H. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented at the 60th EASD Annual Meeting, Madrid, Spain, 10–13 September 2024.

Handling Editors. The journal editors responsible for overseeing the review of the manuscript were Elizabeth Selvin and Neda Laiteerapong.