Acute allograft rejection remains a major complication in kidney transplantation, highlighting the need for accurate and early diagnostic tools to enable prompt treatment. Standard diagnostic methods, such as serum creatinine monitoring, lack sufficient sensitivity and specificity. As a result, subclinical rejection may go undetected, or unnecessary biopsies may be performed, posing additional risks. Previously, a novel, non-invasive urine test based on metabolomic profiling was developed to detect renal allograft rejection. However, the early post-transplant period remains particularly challenging, as reliable biomarker-based detection in this critical window is not yet well established. This study, conducted within the UMBRELLA project, analyzed 682 urine samples from 109 kidney transplant recipients. The test utilized a specific urinary metabolite constellation based on alanine, citrate, lactate, and urea. A total of 29 clinical and transplant-related parameters, including donor and recipient characteristics, ischemia times, and donor type, were evaluated for their effect on the test’s ability to detect biopsy-confirmed rejection within the first 14 days after transplantation. Univariate analysis identified 10 significant confounding factors, including lower residual urine volume before transplantation, reduced eGFR, use of induction therapy, longer warm and cold ischemia times, deceased donor status, younger recipient age, and certain HLA mismatches. Multivariate analysis confirmed the relevance of living donation. Subgroup analysis revealed the highest diagnostic accuracy in recipients of living donor kidneys, with an AUC of 0.720 (95% CI, 0.62–0.82), followed by recipients with a short warm ischemia time (<30 min), who achieved an AUC of 0.702 (95% CI, 0.61–0.79). Clinical complications often coincided with abnormal metabolite test results. In conclusion, this study underscores the importance of considering donor type and ischemia times when interpreting urinary metabolite constellations for rejection monitoring in the early post-transplant period. The findings suggest a distinct metabolic profile in recipients of deceased donor kidneys within the first 2 weeks after transplantation. Understanding these influencing factors may enhance the accuracy of non-invasive rejection detection and support timely clinical interventions to improve patient outcomes.

Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1 H-NMR-based metabolomics can identify cancer with high accuracy. SCAN2 tested whether integrating metabolomics with glycomics improves discrimination in a clinically complex, real-world population. Serum from 369 SCAN patients (59 cancers) was analysed using AXINON®lipoFIT® -derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. Integration of glycomics with metabolomics improved discrimination, achieving an AUC of 0.88 in a refined cohort excluding dominant comorbidities. Cancer-associated bi- and tri-antennary glycans, including FA2G2S1, FA2BG1, and M5A1G1S1, differentiated cancer cases. A classifier targeting metastatic disease achieved an AUC of 0.80. Joint probability analysis preserved cancer-associated metabolic signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.8%. These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and demonstrate that integrating metabolomics with glycomics enhances cancer detection in patients with non-specific symptoms. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing.

Background: Mild cognitive impairment (MCI) is a heterogeneous state between normal ageing and dementia, often considered prodromal to Alzheimer's disease (AD). Progression is variable, and distinguishing stable from progressive MCI remains difficult, particularly in the presence of mixed neuropathology. Blood biomarkers such as phosphorylated tau181 (pTau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) demonstrate prognostic value in established AD, but limited performance for prognosticating progression from MCI. Methods: Blood protein biomarkers (pTau181, GFAP, NfL) were integrated with NMR- and LC-MS-derived metabolomic features. In a deeply phenotyped MCI cohort (VITACOG; n=68) with two-year MRI follow-up, cross-validated logistic regression identified discriminative multi-analyte panels to distinguish stable from progressive MCI. Disease progression was defined by worsening cortical atrophy, measured via annualised brain volume loss. Generalisability was tested in a larger community-based cohort from UK Biobank (n=223) and two Oxford Project to Investigate Memory and Ageing (OPTIMA) subsets with histopathological diagnosis (n=61, n=37). Results: Integration of pTau181 with six metabolite features yielded the highest prognostic performance (AUC 0.91; accuracy 80%), with metabolomic findings independently validated in the OPTIMA cohort. A complementary GFAP-NMR panel also performed strongly (AUC 0.80; accuracy 75%). In contrast, individual metabolites, including the atrophy marker homocysteine, and standalone protein biomarkers performed poorly (AUC ≤0.66), as well their combination (AUC 0.68), highlighting the added value of multi-omic integration. In an asymptomatic ageing population (UK Biobank), the models served as a population-level stress test, confirming that multi-omic integration improved specificity for MRI-derived atrophy measures and captured atrophy-related risk in community cohorts. Conclusion: Multi-omic integration of protein and metabolic features markedly improved prognostication of MCI progression by capturing early neurodegenerative signatures, yielding translational panels suitable for scalable risk stratification and early therapeutic intervention in clinical practice. ### Competing Interest Statement A.D.S. and H.R. are named as inventors on two patents held by the University of Oxford on the use of B-vitamins to treat cognitive disorders (US9364497 and US10966947). F.J. is named as an inventor on US10966947. These patents have been licensed to Elysium Health, NY. J.S.O.M. has a research contract and equipment loan from ThermoFisher Scientific, which manufactures IC-MS systems. S. de J., Q. G. and E. S. are employees of Numares AG (Am Biopark 9, 93053 Regensburg-Grass, Germany). All other authors report no conflicts of interest. N.J.A. received consultancy or speaker fees from BioArtic, Biogen, Lilly, Quanterix and Alamar Biosciences. H.Z. has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZpath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Enigma, LabCorp, Merck Sharp & Dohme, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Quanterix, Red Abbey Labs, reMYND, Roche, Samumed, ScandiBio Therapeutics AB, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures sponsored by Alzecure, BioArctic, Biogen, Cellectricon, Fujirebio, LabCorp, Lilly, Novo Nordisk, Oy Medix Biochemica AB, Roche, and WebMD, is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, and is a shareholder of CERimmune Therapeutics (outside submitted work). S. M. S. is co-founder and part-owner of SBGneuro. ### Funding Statement T.K. is funded by an EPSRC Postdoctoral Pathway Scheme (EP/Z534870/1) and EPSRC talent and skills funding and Numares AG (Am Biopark 9, 93053 Regensburg-Grass, Germany). This research was supported in part by the Aqua-Synapse EU framework (2022-2026) to D.A., funded by the European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 101086453. The authors are solely responsible for the content of this publication, which does not necessarily represent the official views of the European Union or the European Research Executive Agency. H.Z. is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018 and #2019-02397), the European Union's Horizon Europe research and innovation programme under grant agreement No 101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320). The original VITACOG trial was supported by grants from Charles Wolfson Charitable Trust, Medical Research Council, Alzheimer's Research Trust, Henry Smith Charity, John Coates Charitable Trust, Thames Valley Dementias and Neurodegenerative Diseases Research Network of the National Institute for Health Research, UK, and the Sidney and Elizabeth Corob Charitable Trust. The original OPTIMA cohort was supported by grants from Bristol-Myers Squibb, Medical Research Council and the Charles Wolfson Charitable Trust. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The VITACOG trial was approved by a local NHS Research Ethics Committee (COREC 04/Q1604/100). For the OPTIMA cohort, ethical approval was obtained from the Frenchay Research Ethics Committee (REC Ref 09/H0107/9). All participants provided written informed consent in accordance with the Declaration of Helsinki. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Anonymised data not published within this article will be made available by request from any qualified investigator.

Hepatocellular carcinoma is frequently unrecognized in its early stage limiting the access to the first therapeutic steps resulting in a low cure rate. Therefore, an early diagnosis is crucial. In this scenario the analysis of lipidome and metabolome emerged as a promising tool for early detection. Aim of the study was to characterize metabolomic profiles as novel markers of early hepatocellular carcinoma. Serum basal levels of metabolites, isolated from a cohort of 90 patients (n = 30 early stage; n = 30 advanced stage; n = 30 liver cirrhosis) were analysed using a nuclear magnetic resonance spectroscopy platform. To assess the predictive value of nuclear magnetic resonance profiles, we included the magnetic resonance imaging follow up of control patients with liver cirrhosis. Significant differences were observed in the levels of individual parameters that included total cholesterol, LDL and HDL subclasses, Isoleucine, Valine, Triglycerides, Lactate, Alanine, Albumin, alpha Fetoprotein, Dimethylamine, Glycerol, and total Bilirubin levels in cancer compared to liver cirrhosis (p < 0.05). Furthermore, a significant difference in glycerol levels (p < 0.05) and a decreasing trend in dimethylamine were observed in cirrhotic patients who later developed HCC (16%, n = 5). Retrospective MRI analysis revealed precursor lesions in 3/5 patients, initially not classified as HCC due to their size and hemodynamic features. Nuclear magnetic resonance based assessment of lipidomic and metabolomic profiles permit the differentiation of cancer from liver cirrhosis. The data obtained suggests a possible role of lipidomic based serum profiles for early detection.

INTRODUCTION:Elevated total homocysteine (tHcy) is a major predictor of brain atrophy, cognitive decline, and Alzheimer's disease (AD) progression. The VITACOG trial, a randomized, placebo-controlled study in mild cognitive impairment (MCI), previously showed that B vitamin supplementation lowered tHcy, slowing brain atrophy and cognitive decline; however, the underlying mechanisms remained unclear. METHODS:We used untargeted, multi-platform metabolomics, with nuclear magnetic resonance and liquid chromatography-mass spectrometry to analyze serum samples from 89 B vitamin-treated and 84 placebo-treated MCI participants over a 2 year follow-up period. RESULTS:Multivariate modeling distinguished treated from placebo groups with 91.2 ± 1.8% accuracy. B vitamin supplementation induced significant metabolic reprogramming, lowering quinolinic acid, α-ketoglutarate, α-ketobutyrate, glucose, and glutamate. DISCUSSION:These findings reveal that B vitamins influence metabolic pathways beyond tHcy reduction, particularly the tricarboxylic acid cycle and glutamine-glutamate cycling, critical for brain energy homeostasis and neurotransmission. This metabolic signature supports B vitamin supplementation as a strategy for slowing MCI progression. HIGHLIGHTS:Nuclear magnetic resonance and multi-platform liquid chromatography tandem mass spectrometry metabolomics were performed on serum samples from 89 B vitamin-treated and 84 placebo participants in the VITACOG trial. Multi-platform metabolomics revealed B vitamin-driven metabolic reprogramming, achieving 91% classification accuracy. B vitamin supplementation modulates key neuroprotective metabolic pathways. Regulation of energy metabolism and neurotransmission by B vitamins contributes to brain health in elderly individuals. B vitamins demonstrate potential as an adjunct therapy in mild cognitive impairment, potentially mitigating progression to Alzheimer's disease.

In the original publication [...].

Abstract Background An accurate and reliable measurement of glomerular filtration rate (GFR) is essential to monitor for rejection and disease progression post-kidney transplantation. Measured GFR (mGFR) is the current gold standard. However, it has scarce availability at many centers and is labor intensive. Newer estimated GFR (eGFR) methods containing cystatin-C and/or other biomarkers have not been clinically validated in a post-transplant cohort. In this study, we evaluated a new NMR-based GFR (GFRNMR) equation that contains serum creatinine, cystatin C, myoinositol, and valine in post-kidney transplant recipients in a clinical routine setting. Methods Venipuncture for serum collection was performed immediately prior to mGFR measurement with urinary iothalamate clearance in 67 post-kidney transplant recipients. Serum was stored at 4°C and measured by NMR no later than four days after collection. eGFR was assessed using the following equations: i) GFRNMR, ii) CKD-EPI2021Cr (creatinine) and iii) CKD-EPI2021Cr,Cys (creatinine and cystatin-C). Bias was calculated as “eGFR—mGFR” for all equations. The Bootstrap method was employed to assess pairwise significance levels between bias distributions interquartile ranges (IQR), and P-value correction was determined with the Benjamini & Hochberg method. Precision, which was defined as percentage of samples within ±30% and ±15% from the mGFR value (P30 and P15, respectively), was calculated for each equation. Pairwise comparisons were made using McNemar’s Chi-squared and Benjamini & Hochberg correction. Precision was defined as the proportion of eGFR values concordant with mGFR values by CKD clinical stage. Results P30 for GFRNMR was significantly higher than CKD-EPI2021 Cr,Cys (97% vs 84%, P < 0.01) and higher than CKD-EPI2021 Cr (88%, P = 0.08). Similarly, P15 for GFRNMR was 63%; higher than for CKD-EPI2021 Cr (54%, P = 0.35) and CKD-EPI2021 Cr,Cys (51%, P = 0.19). Bias IQR for GFRNMR was 9.5 mL/min/1.73 m2 (median bias 2.0 mL/min/1.73 m2); significantly smaller than for CKD-EPI2021 Cr (Bias IQR 15.55 mL/min/1.73 m2, P < 0.05; median bias 1.47 mL/min/1.73 m2), and smaller than CKD-EPI2021 Cr,Cys (Bias IQR 14.4 mL/min/1.73 m2, P = 0.09; median bias 2.3 mL/min/1.73 m2). A broad range of medications such as immune modulators, and comorbidities such as hypertension, thyroid dysfunction, and coronary artery disease, did not interfere with GFRNMR performance. Conclusion NMR-based eGFR was less biased, more precise, and more accurate compared to creatinine and cystatin-C CKD-EPI eGFR equations in a post-kidney transplant setting. These findings suggest GFRNMR may more closely resemble mGFR in post-transplant patients and warrants further study on its potential use to improve clinical management in this patient group.

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 GFRNMRcould 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 GFRNMRto 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 m2for 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.

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.

Abstract Background The cytotoxic agent methotrexate (MTX) is mainly used in oncology and rheumatology. MTX is 80%–90% excreted unchanged in the urine, thus, impaired renal excretion because of kidney disease leads to accumulation and prolonged exposure, with a consequent increased risk of myelosuppressive and other toxic adverse effects. Direct kidney damage from MTX crystal precipitation and tubular injury may also occur. Therefore, precise and accurate measurement of renal function prior to MTX administration is of paramount importance to determine accurate dosing and to minimize such adverse risks. Commonly used estimated glomerular filtration rate (eGFR) equations perform poorly in patients with cachexia and at later stages of life—the typical oncology patient. Here we evaluate the feasibility of applying the recently introduced nuclear magnetic resonance (NMR)-based GFR equation (GFRNMR) using creatinine, cystatin C, myo-inositol, and valine, in oncology patients receiving high-dose MTX. Methods A series of residual sera from patients scheduled to receive high-dose MTX infusion for oncological indications were collected from two sites: n = 52 sera from the University Hospital Regensburg (Regensburg, Germany), at four time points (t0h, t24h, t42h, and t48h), and n = 10 cross-sectionally from the Mayo Clinic (MN). Standard drug-monitoring trough levels were used as a reference. Serum was prepared and NMR-measured in five replicates for all samples. GFRNMR was compared with the current standard GFR equations: i) CKD-EPI2021Cr (creatinine) and ii) CKD-EPI2021CrCys (creatinine and cystatin-C). Coefficient of determination for linear regression, kappa coefficient for categorical regression and bootstrapped confidence intervals were calculated. Results MTX was shown not to increase the GFRNMR failure rate or affect the intra-assay precision of GFRNMR. When monitoring eGFR during high-dose MTX treatment, MTX affected GFR according to the RIFLE criteria for AKI in 13 cycles in 9 patients; 1/13 were classified as ‘risk’ (creatinine increased 1.5-fold) and 1/13 were classified as ‘injury’ (creatinine increased 2.0-fold). Using MTX clearance as a surrogate for measured GFR, GFRNMR was shown to reflect the MTX plasma clearance constant k more accurately (using a fitting function c = c0*e−kt) with r = 0.758 (95% CI 0.36–0.92, P = 0.004) compared to CKD-EPI2021Cr (r = 0.401, 95%CI −0.19–0.78, P = 0.176) and CKD-EPI2021CrCyc (r = 0.632, 95% CI 0.12–0.88). Conclusion The robustness of GFRNMR test results is not affected by HD-MTX treatment. Compared to standard eGFR equations, GFRNMR is more closely associated with MTX renal clearance rates and may therefore be a novel method to improve the accuracy of MTX dosing in the future.

Supplementary Data from Prediction of Muscle-invasive Bladder Cancer Using Urinary Proteomics

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.

Background: In an earlier monocentric study, we have developed a novel non-invasive test system for the prediction of renal allograft rejection, based on the detection of a specific urine metabolite constellation. To further validate our results in a large real-world patient cohort, we designed a multicentric observational prospective study (PARASOL) including six independent European transplant centers. This article describes the study protocol and characteristics of recruited better patients as subjects. Methods: Within the PARASOL study, urine samples were taken from renal transplant recipients when kidney biopsies were performed. According to the Banff classification, urine samples were assigned to a case group (renal allograft rejection), a control group (normal renal histology), or an additional group (kidney damage other than rejection). Results: Between June 2017 and March 2020, 972 transplant recipients were included in the trial (1,230 urine samples and matched biopsies, respectively). Overall, 237 samples (19.3%) were assigned to the case group, 541 (44.0%) to the control group, and 452 (36.7%) samples to the additional group. About 65.9% were obtained from male patients, the mean age of transplant recipients participating in the study was 53.7 ± 13.8 years. The most frequently used immunosuppressive drugs were tacrolimus (92.8%), mycophenolate mofetil (88.0%), and steroids (79.3%). Antihypertensives and antidiabetics were used in 88.0 and 27.4% of the patients, respectively. Approximately 20.9% of patients showed the presence of circulating donor-specific anti-HLA IgG antibodies at time of biopsy. Most of the samples (51.1%) were collected within the first 6 months after transplantation, 48.0% were protocol biopsies, followed by event-driven (43.6%), and follow-up biopsies (8.5%). Over time the proportion of biopsies classified into the categories Banff 4 (T-cell-mediated rejection [TCMR]) and Banff 1 (normal tissue) decreased whereas Banff 2 (antibody-mediated rejection [ABMR]) and Banff 5I (mild interstitial fibrosis and tubular atrophy) increased to 84.2 and 74.5%, respectively, after 4 years post transplantation. Patients with rejection showed worse kidney function than patients without rejection. Conclusion: The clinical characteristics of subjects recruited indicate a patient cohort typical for routine renal transplantation all over Europe. A typical shift from T-cellular early rejections episodes to later antibody mediated allograft damage over time after renal transplantation further strengthens the usefulness of our cohort for the evaluation of novel biomarkers for allograft damage.

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.