Establishing hemolysis, icterus, and lipemia interference limits for body fluid chemistry analytes measured on the Roche cobas instrument.

OBJECTIVES: The aims of this study were to (1) establish the maximum allowable interference limits for hemolysis, lipemia, and icterus for chemistry analytes tested in body fluid samples and (2) assess the effectiveness of serial dilution to mitigate spectral interferences. METHODS: Residual body fluids from clinically ordered testing were mixed (<10% by volume) with stock solutions of interferent (spiked) and compared with a control spiked with an equal volume of 0.9% saline. The analytes were measured on the Roche cobas c501 instrument. Difference and percentage difference were calculated and compared with allowable total error limits. A subset of samples were serially diluted with 0.9% saline. Mean (SD) difference and percentage difference were calculated. RESULTS: The interference thresholds were lower than the package insert for lactate dehydrogenase, cholesterol, triglycerides, and total protein for hemolysis; amylase, cholesterol, and total protein for icterus; and albumin for lipemia. Only cholesterol and triglyceride results returned to baseline upon dilution of icteric samples. CONCLUSIONS: Interference thresholds in body fluids were lower than blood for 6 analytes. Diluting interferences that surpass these limits does not produce reliable results that are comparable to the baseline results before spiking in the interferent.

Discordance Between Very Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol Increases Cardiovascular Disease Risk in a Geographically Defined Cohort.

BACKGROUND: Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low-density lipoprotein cholesterol (VLDL-C) and lipid discordance could be contributors to the residual risk of ASCVD. METHODS AND RESULTS: Cardiovascular disease-free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low-density lipoprotein cholesterol (LDL-C) and VLDL-C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow-up. The association of LDL-C and VLDL-C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL-C and VLDL-C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL-C (per 10-mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05-1.09]; P<0.001]) after adjustment for traditional risk factors. The interaction between LDL-C and VLDL-C was not statistically significant (P=0.11). Discordant individuals with high VLDL-C and low LDL-C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person-years, during follow-up. CONCLUSIONS: VLDL-C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Association of cardiac biomarkers with long-term cardiovascular events in a community cohort.

MATERIALS AND METHODS: The study assessed major adverse cardiac events (MACE) (myocardial infarction, coronary artery bypass graft, percutaneous intervention, stroke, and death. Cox proportional hazards models assessed apolipoprotein AI (ApoA1), apolipoprotein B (ApoB), ceramide score, cystatin C, galectin-3 (Gal3), LDL-C, Non-HDL-C, total cholesterol (TC), N-terminal B-type natriuretic peptide (NT proBNP), high-sensitivity cardiac troponin (HscTnI) and soluble interleukin 1 receptor-like 1. In adjusted models, Ceramide score was defined by from N-palmitoyl-sphingosine [Cer(16:0)], N-stearoyl-sphingosine [Cer(18:0)], N-nervonoyl-sphingosine [Cer(24:1)] and N-lignoceroyl-sphingosine [Cer(24:0)]. Multi-biomarker models were compared with C-statistics and Integrated Discrimination Index (IDI). RESULTS: A total of 1131 patients were included. Adjusted NT proBNP per 1 SD resulted in a 31% increased risk of MACE/death (HR = 1.31) and a 31% increased risk for stroke/MI (HR = 1.31). Adjusted Ceramide per 1 SD showed a 13% increased risk of MACE/death (HR = 1.13) and a 29% increased risk for stroke/MI (HR = 1.29). These markers added to clinical factors for both MACE/death (p = 0.003) and stroke/MI (p = 0.034). HscTnI was not a predictor of outcomes when added to the models. DISCUSSION: Ceramide score and NT proBNP improve the prediction of MACE and stroke/MI in a community primary prevention cohort.

In a community cohort, where a wide range of biomarkers were evaluated, Ceramide score provided additive value over traditional cardiac risk factors alone for predicting stroke/MI. NT ProBNP provided additive value in prediction of MACE/death. Other biomarkers failed to improve the discrimination of these models.

Clinical Impacts of Implementing the 2021 Race-Free Chronic Kidney Disease Epidemiology Collaboration Estimated Glomerular Filtration Rate.

BACKGROUND: Estimated glomerular filtration rate (eGFR) has become incorporated into multiple clinical management situations. Historically, equations included a Black race coefficient, which lacked biological plausibility and created potential to exacerbate health disparities. A new equation created in 2021 changed the weighting of age, sex, and creatinine by modeling against a diverse cohort and removing the Black race coefficient. CONTENT: A variety of clinical outcomes including kidney disease risk stratification, medication dosing, patient eligibility for clinical trials, and kidney donation are impacted by implementation of the new equation. Nearly 2 years after its initial publication, many studies have reported on observed analytical performance of the 2021 eGFR determined as diagnostic concordance and percentage of estimates within 30% of measured GFR. Additionally, the potential clinical impacts following adoption of the new eGFR among different patient populations has also been reported. Here we review these studies with a focus on assessing the data associated with the transition from 2009 to 2021 Chronic Kidney Disease Epidemiology Collaboration equations. SUMMARY: The reported interindividual variation in eGFR performance is significantly larger than any potential benefit derived from race coefficients. Both the 2021 eGFR and the 2009 eGFR analytical performance fall short of the validation cohort performance in most cohorts. However, the 2021 analytical is similar or better than the 2009 eGFR in most cohorts. Implementing the 2021 eGFR will remove a systematic overestimation of kidney function among Black patients.

Assessing GFR With Proenkephalin.

Introduction: In clinical practice, kidney (dys)function is monitored through creatinine-based estimations of glomerular filtration rate (eGFR: Modification of Diet in Renal Disease [MDRD], Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]). Creatinine is recognized as a late and insensitive biomarker of glomerular filtration rate (GFR). The novel biomarker proenkephalin (PENK) may overcome these limitations, but no PENK-based equation for eGFR is currently available. Therefore, we developed and validated a PENK-based equation to assess GFR. Methods: In this international multicenter study in 1354 stable and critically ill patients, GFR was measured (mGFR) through iohexol or iothalamate clearance. A generalized linear model with sigmoidal nonlinear transfer function was used for equation development in the block-randomized development set. Covariates were selected in a data-driven fashion. The novel equation was assessed for bias, precision (mean ± SD), and accuracy (eGFR percentage within ±30% of mGFR, P30) in the validation set and compared with MDRD and CKD-EPI. Results: Median mGFR was 61 [44-81] ml/min per 1.73 m2. In order of importance, PENK, creatinine, and age were included, and sex or race did not improve performance. The PENK-based equation mean ± SD bias of the mGFR was 0.5 ± 15 ml/min per 1.73 m2, significantly less compared with MDRD (8 ± 17, P < 0.001) and 2009 CKD-EPI (5 ± 17, P < 0.001), not reaching statistical significance compared with 2021 CKD-EPI (1.3 ± 16, P = 0.06). The P30 accuracy of the PENK-based equation was 83%, significantly higher compared with MDRD (68%, P < 0.001) and 2009 CKD-EPI (76%, P < 0.001), similar to 2021 CKD-EPI (80%, P = 0.13). Conclusion: Overall, the PENK-based equation to assess eGFR performed better than most creatinine-based equations without using sex or race.

Estimating glomerular filtration rate with new equations: can one size ever fit all?

Glomerular filtration rate (GFR) is thought to be the best overall indicator of kidney health. On an individual patient basis, a working knowledge of GFR is important to understand the future risk for chronic kidney disease (CKD) progression, enhanced risk for cardiovascular disease and death, and for optimal medical management including the dosing of certain drugs. Although GFR can be directly measured using exogenous compounds that are eliminated by the kidney, these methods are not scalable for repeated and routine use in clinical care. Thus, in most circumstances GFR is estimated, termed estimated GFR (eGFR), using serum biomarkers that are eliminated by the kidney. Of these, serum creatinine, and to a lesser extent cystatin C, are most widely employed. However, the resulting number is simply a population average for an individual of that age and sex with a given serum creatinine and/or cystatin C, while the range of potential GFR values is actually quite large. Thus, it is important to consider characteristics of a given patient that might make this estimate better or worse in a particular case. In some circumstances, cystatin C or creatinine might be the better choice. Ultimately it is difficult, if not impossible, to have an eGFR equation that performs equally well in all populations. Thus, in certain cases it might be appropriate to directly measure GFR for high consequence medical decision-making, such as approval for kidney donation or prior to certain chemotherapeutic regimens. In all cases, the eGFR thresholds of CKD stage should not be viewed as absolute numbers. Thus, clinical care should not be determined solely by CKD stage as determined by eGFR alone, but rather by the combination of an individual patient's likely kidney function together with their current clinical situation.

Performance of Nuclear Magnetic Resonance-Based Estimated Glomerular Filtration Rate in a Real-World Setting.

An accurate estimate of glomerular filtration rate (eGFR) is essential for proper clinical management, especially in patients with kidney dysfunction. This prospective observational study evaluated the real-world performance of the nuclear magnetic resonance (NMR)-based GFRNMR equation, which combines creatinine, cystatin C, valine, and myo-inositol with age and sex. We compared GFRNMR performance to that of the 2021 CKD-EPI creatinine and creatinine-cystatin C equations (CKD-EPI2021Cr and CKD-EPI2021CrCys), using 115 fresh routine samples of patients scheduled for urinary iothalamate clearance measurement (mGFR). Median bias to mGFR of the three eGFR equations was comparably low, ranging from 0.4 to 2.0 mL/min/1.73 m2. GFRNMR outperformed the 2021 CKD-EPI equations in terms of precision (interquartile range to mGFR of 10.5 vs. 17.9 mL/min/1.73 m2 for GFRNMR vs. CKD-EPI2021CrCys; p = 0.01) and accuracy (P15, P20, and P30 of 66.1% vs. 48.7% [p = 0.007], 80.0% vs. 60.0% [p < 0.001] and 95.7% vs. 86.1% [p = 0.006], respectively, for GFRNMR vs. CKD-EPI2021CrCys). Clinical parameters such as etiology, comorbidities, or medications did not significantly alter the performance of the three eGFR equations. Altogether, this study confirmed the utility of GFRNMR for accurate GFR estimation, and its potential value in routine clinical practice for improved medical care.

Impact of race-independent equations on estimating glomerular filtration rate for the assessment of kidney dysfunction in liver disease.

BACKGROUND: Altered hemodynamics in liver disease often results in overestimation of glomerular filtration rate (GFR) by creatinine-based GFR estimating (eGFR) equations. Recently, we have validated a novel eGFR equation based on serum myo-inositol, valine, and creatinine quantified by nuclear magnetic resonance spectroscopy in combination with cystatin C, age and sex (GFRNMR). We hypothesized that GFRNMR could improve chronic kidney disease (CKD) classification in the setting of liver disease. RESULTS: We conducted a retrospective multicenter study in 205 patients with chronic liver disease (CLD), comparing the performance of GFRNMR to that of validated CKD-EPI eGFR equations, including eGFRcr (based on creatinine) and eGFRcr-cys (based on both creatinine and cystatin C), using measured GFR as reference standard. GFRNMR outperformed all other equations with a low overall median bias (-1 vs. -6 to 4 ml/min/1.73 m2 for the other equations; p < 0.05) and the lowest difference in bias between reduced and preserved liver function (-3 vs. -16 to -8 ml/min/1.73 m2 for other equations). Concordant classification by CKD stage was highest for GFRNMR (59% vs. 48% to 53%) and less biased in estimating CKD severity compared to the other equations. GFRNMR P30 accuracy (83%) was higher than that of eGFRcr (75%; p = 0.019) and comparable to that of eGFRcr-cys (86%; p = 0.578). CONCLUSIONS: Addition of myo-inositol and valine to creatinine and cystatin C in GFRNMR further improved GFR estimation in CLD patients and accurately stratified liver disease patients into CKD stages.

Serum myo-inositol and valine improve metabolomic-based estimated glomerular filtration rate among kidney transplant recipients.

Background: Close monitoring of glomerular filtration rate (GFR) is essential for the management of patients post kidney transplantation. Measured GFR (mGFR), the gold standard, is not readily accessible in most centers. Furthermore, the performance of new estimated GFR (eGFR) equations based upon creatinine and/or cystatin C have not been validated in kidney transplant patients. Here we evaluate a recently published eGFR equation using cystatin C, creatinine, myo-inositol and valine as measured by nuclear magnetic resonance (eGFRNMR). Methods: Residual sera was obtained from a cohort of patients with clinically ordered iothalamate renal clearance mGFR (n = 602). Kidney transplant recipients accounted for 220 (37%) of participants. Results: Compared to mGFR, there was no significant bias for eGFRcr or eGFRNMR, while eGFRcr-cys significantly underestimated mGFR. P30 values were similar for all eGFR. P15 was significantly higher for eGFRNMR compared to eGFRcr, while the P15 for eGFRcr-cys only improved among patients without a kidney transplant. Agreement with mGFR CKD stages of <15, 30, 45, 60, and 90 ml/min/1.73 m2 was identical for eGFRcr and eGFRcr-cys (61.8%, both cases) while eGFRNMR was significantly higher (66.4%) among patients with a kidney transplant. Conclusion: The 2021 CKD-EPI eGFRcr and eGFRcr-cys have similar bias, P15, and agreement while eGFRNMR more closely matched mGFR with the strongest improvement among kidney transplant recipients.

Short- and Long-Term Biological Variability of Small Dense LDL, HDL3, and Triglyceride-Rich Lipoprotein Cholesterol.

BACKGROUND: Measurement of cholesterol within lipoprotein subfractions may aid in cardiovascular disease prediction. Simple, homogenous enzymatic assays for the direct measurement of lipoprotein subfractions have been developed to measure small dense low-density lipoprotein cholesterol (sdLDL-C), high-density lipoprotein-3 cholesterol (HDL3-C), and triglyceride-rich lipoprotein (TRL-C) cholesterol. The objective of this study was to determine biological variability for sdLDL-C, HDL3-C, and TRL-C in a healthy reference population to facilitate interpretation of these analytes. METHODS: Serum samples were collected from 24 healthy subjects (n = 14 female/10 male) daily for 3 days while non-fasting, and daily for 5 days, weekly for 4 weeks, and monthly for 6 months after overnight fasting. sdLDL-C, HDL3-C, and TRL-C cholesterol were measured by homogenous enzymatic assays. Sources of variability (between-subject, within-subject, and analytical) were calculated using random-effects regression models. Reference change value (RCV) and index of individuality (II) for each time period were determined from the variance components. RESULTS: Analytic variability (daily, weekly, and monthly CVA) was <3% for each analyte. Monthly within-subject variability (CVI) was 17.1% for sdLDL-C, 7.4% for HDL3-C, and 25.7% for TRL-C. Most of the monthly variation was attributed to between-subject variation for all 3 analytes. Overall RCVs for monthly measurements were 18.1 mg/dL for sdLDL-C, 6.1 mg/dL for HDL3-C, and 16.0 mg/dL for TRL-C. IIs were <0.6 for sdLDL-C and HDL3-C, and 0.81 for TRL-C. CONCLUSIONS: sdLDL-C, HDL3-C, and TRL-C showed moderate within-subject variability, but high between-subject variability, in a healthy reference population. Given the high individuality of each analyte, population-based reference intervals may be inadequate to detect clinically significant changes.

Analytical Validation of GFRNMR: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.

Accurate and precise monitoring of kidney function is critical for a timely and reliable diagnosis of chronic kidney disease (CKD). The determination of kidney function usually involves the estimation of the glomerular filtration rate (eGFR). We recently reported the clinical performance of a new eGFR equation (GFRNMR) based on the nuclear magnetic resonance (NMR) measurement of serum myo-inositol, valine, and creatinine, in addition to the immunoturbidometric quantification of serum cystatin C, age and sex. We now describe the analytical performance evaluation of GFRNMR according to the Clinical and Laboratory Standards Institute guidelines. Within-laboratory coefficients of variation (CV%) of the GFRNMR equation did not exceed 4.3%, with a maximum CV% for repeatability of 3.7%. Between-site reproducibility (three sites) demonstrated a maximum CV% of 5.9%. GFRNMR stability was demonstrated for sera stored for up to 8 days at 2-10°C and for NMR samples stored for up to 10 days in the NMR device at 6 ± 2°C. Substance interference was limited to 4/40 (10.0%) of the investigated substances, resulting in an underestimated GFRNMR (for glucose and metformin) or a loss of results (for naproxen and ribavirin) for concentrations twice as high as usual clinical doses. The analytical performances of GFRNMR, combined with its previously reported clinical performance, support the potential integration of this NMR method into clinical practice.

The Biological Variability of Plasma Ceramides in Healthy Subjects.

BACKGROUND: Ceramides are bioactive lipid species that mediate numerous cell-signaling events. Elevated plasma ceramides concentration constitutes a risk factor for several pathologies. Multiple studies have affirmed the plasma concentrations of 4 specific ceramides (Cer16:0, Cer18:0, Cer24:0, and Cer24:1) can predict cardiovascular disease risk. Furthermore, these ceramides can be altered by many lipid-lowering therapies. Understanding the biological variability within an individual, and within a population, will further inform the clinical use of plasma ceramides as a biomarker. In this study, we aimed to define the intra- and interbiological variability of ceramides in a healthy reference population in a weekly and monthly manner. METHODS: Fasting plasma from 24 healthy adults was collected daily (5 days), weekly (4 weeks), and monthly (7 months). Ceramide concentrations were measured with liquid chromatography-mass spectrometry (LC-MS). For analysis, we used random-effects regression models to estimate variance components. RESULTS: The analytical variability was smaller compared to the biological variability overall. The greatest variation reported was between-subject variation for all ceramide species. The critical difference-reference change value (RCV) for within-subject variations monthly were 0.07 mcmol/L (Cer16:0), 0.04 mcmol/L (Cer18:0), 1.09 mcmol/L (Cer24:0), and 0.27 mcmol/L (Cer24:1). The index of individuality (IOI) of ceramides were 0.82 (Cer16:0), 0.96 (Cer18:0), 1.06 (Cer24:0), and 0.89 (Cer24:1). The most consistent ceramide species was Cer18:0 with the lowest within- and between-subject critical differences in weekly and monthly measurements. CONCLUSIONS: Overall, this study demonstrates that the variability of ceramide concentrations at different time points is minimal within individuals, allowing a single draw to be sufficient at least in a yearly time frame.

Clinical Impact of the Refit CKD-EPI 2021 Creatinine-Based eGFR Equation.

BACKGROUND: The National Kidney Foundation recently endorsed the refit Chronic Kidney Disease Collaboration (CKD-EPI) equation for estimated glomerular filtration rate (eGFR) using creatinine, age and sex [2021 eGFRCr(AS)] without a coefficient for race. We evaluated the impact of adopting the 2021 eGFRCr(AS) equation or a variation of the 2009 CKD-EPI eGFR equation without race [2009 CKD-EPI eGFRCr(ASR-NB)] compared to the original CKD-EPI eGFR [2009 eGFRCr(ASR)]. METHODS: The studied population included patients with a clinically ordered iothalamate clearance (n = 33 889). Bias was assessed as the difference between measured and estimated GFR, P30 was defined as the percentage of estimates within 30% of measured GFR, and concordance was determined according to relevant clinical thresholds. RESULTS: Among Black patients, the median bias for 2009 eGFRCr(ASR), 2009 eGFRCr(ASR-NB), and 2021 eGFRCr(AS) was -1.32 mL min-1 (1.73 m2)-1 (95CI -2.46 to -0.26), -8.81 mL min-1 (1.73 m2)-1 (95CI -9.93 to -7.58), and -6.08 mL min-1 (1.73 m2)-1 (95CI -7.18 to -4.92), respectively. The median bias among non-Black patients was -0.15 m min-1 (1.73 m2)-1 (95CI -0.84 to -0.08) for 2021 eGFRcr(AS) compared to -3.09 mL min-1 (1.73 m2)-1 (95CI -3.17 to -3.03) for the 2009 eGFRCr(ASR). P30 and concordance were not significantly different in either racial group. The net reclassification improvement at a measured GFR <20 mL min-1 (1.73 m2)-1 was 6.4% (95CI 0.36 to 12.4) for Black patients and -5.1% (95CI -6.0 to -4.1) for non-Black patients using the 2021 eGFRCr(AS) equation. CONCLUSIONS: Overall, the change in reported eGFR was minimal. However, these changes led to significant reclassification improvements at lower eGFR, which will indirectly improve equitable access to CKD resources.

Assessing the Accuracy of Estimated Lipoprotein(a) Cholesterol and Lipoprotein(a)-Free Low-Density Lipoprotein Cholesterol.

Background Accurate measurement of the cholesterol within lipoprotein(a) (Lp[a]-C) and its contribution to low-density lipoprotein cholesterol (LDL-C) has important implications for risk assessment, diagnosis, and treatment of atherosclerotic cardiovascular disease, as well as in familial hypercholesterolemia. A method for estimating Lp(a)-C from particle number using fixed conversion factors has been proposed (Lp[a]-C from particle number divided by 2.4 for Lp(a) mass, multiplied by 30% for Lp[a]-C). The accuracy of this method, which theoretically can isolate "Lp(a)-free LDL-C," has not been validated. Methods and Results In 177 875 patients from the VLDbL (Very Large Database of Lipids), we compared estimated Lp(a)-C and Lp(a)-free LDL-C with measured values and quantified absolute and percent error. We compared findings with an analogous data set from the Mayo Clinic Laboratory. Error in estimated Lp(a)-C and Lp(a)-free LDL-C increased with higher Lp(a)-C values. Median error for estimated Lp(a)-C <10 mg/dL was -1.9 mg/dL (interquartile range, -4.0 to 0.2); this error increased linearly, overestimating by +30.8 mg/dL (interquartile range, 26.1-36.5) for estimated Lp(a)-C ≥50 mg/dL. This error relationship persisted after stratification by overall high-density lipoprotein cholesterol and high-density lipoprotein cholesterol subtypes. Similar findings were observed in the Mayo cohort. Absolute error for Lp(a)-free LDL-C was +2.4 (interquartile range, -0.6 to 5.3) for Lp(a)-C<10 mg/dL and -31.8 (interquartile range, -37.8 to -26.5) mg/dL for Lp(a)-C≥50 mg/dL. Conclusions Lp(a)-C estimations using fixed conversion factors overestimated Lp(a)-C and subsequently underestimated Lp(a)-free LDL-C, especially at clinically relevant Lp(a) values. Application of inaccurate Lp(a)-C estimations to correct LDL-C may lead to undertreatment of high-risk patients.

Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C.

Assessment of renal function relies on the estimation of the glomerular filtration rate (eGFR). Existing eGFR equations, usually based on serum levels of creatinine and/or cystatin C, are not uniformly accurate across patient populations. In the present study, we expanded a recent proof-of-concept approach to optimize an eGFR equation targeting the adult population with and without chronic kidney disease (CKD), based on a nuclear magnetic resonance spectroscopy (NMR) derived 'metabolite constellation' (GFRNMR). A total of 1855 serum samples were partitioned into development, internal validation and external validation datasets. The new GFRNMR equation used serum myo-inositol, valine, creatinine and cystatin C plus age and sex. GFRNMR had a lower bias to tracer measured GFR (mGFR) than existing eGFR equations, with a median bias (95% confidence interval [CI]) of 0.0 (-1.0; 1.0) mL/min/1.73 m2 for GFRNMR vs. -6.0 (-7.0; -5.0) mL/min/1.73 m2 for the Chronic Kidney Disease Epidemiology Collaboration equation that combines creatinine and cystatin C (CKD-EPI2012) (p < 0.0001). Accuracy (95% CI) within 15% of mGFR (1-P15) was 38.8% (34.3; 42.5) for GFRNMR vs. 47.3% (43.2; 51.5) for CKD-EPI2012 (p < 0.010). Thus, GFRNMR holds promise as an alternative way to assess eGFR with superior accuracy in adult patients with and without CKD.