Urinary peptidomic biomarkers of renal function in heart transplant recipients

Peptides as "better biomarkers"? Value, challenges, and potential solutions to facilitate implementation

Peptides carry important functions in normal physiological and pathophysiological processes and can serve as clinically useful biomarkers. Given the ability to diffuse passively across endothelial barriers, endogenous peptides can be examined in several body fluids, including among others urine, blood, and cerebrospinal fluid. This review article provides an update on the recently published literature that reports on investigating native peptides in body fluids using mass spectrometry-based platforms, specifically those studies that focus on the application of peptides as biomarkers to improve clinical management. We emphasize on the critical evaluation of their clinical value, how close they are to implementation, and the associated challenges and potential solutions to facilitate clinical implementation. During the last 5 years, numerous studies have been published, demonstrating the increased interest in mass spectrometry for the assessment of endogenous peptides as potential biomarkers. Importantly, the presence of few successful examples of implementation in patients' management and/or in the context of clinical trials indicates that the peptide biomarker field is evolving. Nevertheless, most studies still report evidence based on small sample size, while validation phases are frequently missing. Therefore, a gap between discovery and implementation still exists.

Statistical approaches applicable in managing OMICS data: Urinary proteomics as exemplary case

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented.

OSTEO18, a novel urinary proteomic signature, associated with osteoporosis in heart transplant recipients

Background

Immunosuppressive treatment in heart transplant (HTx) recipient causes osteoporosis. The urinary proteomic profile (UPP) includes peptide fragments derived from the bone extracellular matrix. Study aims were to develop and validate a multidimensional UPP biomarker for osteoporosis in HTx patients from single sequenced urinary peptides identifying the parent proteins.

Methods

A single-center HTx cohort was analyzed. Urine samples were measured by capillary electrophoresis coupled with mass spectrometry. Cases with osteoporosis and matching controls were randomly selected from all available 389 patients. In derivation case-control dataset, 1576 sequenced peptides detectable in ≥30 % of patients. Applying statistical analysis on these, an 18-peptide multidimensional osteoporosis UPP biomarker (OSTEO18) was generated by support vector modeling. The 2 replication datasets included 118 and 94 patients. For further validation, the whole cohort was analyzed. Statistical methods included logistic regression and receiver operating characteristic curve (ROC) analysis.

Results

In derivation dataset, the AUC, sensitivity and specificity of OSTEO18 were 0.83 (95 % CI: 0.76-0.90), 74.3 % and 87.1 %, respectively. In replication datasets, results were confirmatory. In the whole cohort (154 osteoporotic patients [39.6 %]), the ORs for osteoporosis increased (p < 0.0001) across OSTEO18 quartiles from 0.39 (95 % CI: 0.25-0.61) to 3.14 (2.08-4.75). With full adjustment for known osteoporosis risk factors, OSTEO18 improved AUC from 0.708 to 0.786 (p = 0.0003) for OSTEO18 categorized (optimized threshold: 0.095) and to 0.784 (p = 0.0004) for OSTEO18 as continuously distributed classifier.

Conclusion

OSTEO18 is a clinically meaningful novel biomarker indicative of osteoporosis in HTx recipients and is being certified as in-vitro diagnostic.

Contribution of Proteomics in Transplantation: Identification of Injury and Rejection Markers

Solid organ transplantation saves thousands of lives suffering from end-stage diseases. Although early transplants experienced acute organ injury, medical breakthroughs, such as tissue typing, and use of immunosuppressive agents have considerably improved graft survival. However, the overall incidence of allograft injury and chronic rejection remains high. Often the clinical manifestations of organ injury or rejection are nonspecific and late. Current requirement for successful organ transplantation is the identification of reliable, accurate, disease-specific, noninvasive methods for the early diagnosis of graft injury or rejection. Development of noninvasive techniques is important to allow routine follow-ups without the discomfort and risks associated with a graft biopsy. Multiple biofluids have been successfully tested for the presence of potential proteomic biomarkers; these include serum, plasma, urine, and whole blood. Kidney transplant research has provided significant evidence to the potential of proteomics-based biomarkers for acute and chronic kidney rejection, delayed graft function, early detection of declining allograft health. Multiple proteins have been implicated as biomarkers; however, recent observations implicate the use of similar canonical pathways and biofunctions associated with graft injury/rejection with altered proteins as potential biomarkers. Unfortunately, the current biomarker studies lack high sensitivity and specificity, adding to the complexity of their utility in the clinical space. In this review, we first describe the high-throughput proteomics technologies and then discuss the outcomes of proteomics profiling studies in the transplantation of several organs. Existing literature provides hope that novel biomarkers will emerge from ongoing efforts and guide physicians in delivering specific therapies to prolong graft survival.

A set of urinary peptides can predict early renal damage in primary hypertension

OBJECTIVES

Renal diseases caused by primary hypertension (HTN) are often asymptomatic without sensitive markers for early diagnosis and prediction, easily progressing to severe and irreversible renal damage in patients with clinical manifestations. This study explored whether a set of urinary peptides could serve as a potential biomarker for early prediction of renal damage in HTN.

METHODS

Urinary peptides level of healthy individuals, HTN + normoalbuminuric and HTN + albuminuria patients were compared, and 22 baseline data including sex, age, renal function, hypertensive fundus lesions were collected. Patients diagnosed with HTN, albuminuria, and normal renal function were followed up. According to the follow-up results, the cut-off value of a set of urinary peptides in predicting hypertensive renal injury was calculated and analyzed in the high-risk and low-risk groups of HTN patients for its performance in detecting early hypertensive renal injury.

RESULTS

Among a sum of 319 participants, average urinary peptides level was significantly higher in patients with HTN than in normal individuals. A total of 147 HTN patients with normal albuminuria were followed up for a mean of 3.8 years. Thirty-five patients showed urinary albumin-to-creatinine ratio (uACR) at least 30 mg/g for three consecutive times. The receiver-operating characteristic (ROC) curve showed that the urinary peptides cut-off value for evaluating new-onset proteinuria in patients with HTN was 0.097. Based on this cut-off value, 39 and 108 patients were included in the high-risk and low-risk groups, respectively. Specifically, compared with patients in the low-risk group, those in the high-risk group showed significantly longer duration of HTN, higher proportions of hypertensive fundus lesions and at least 30 mg/g uACR, and higher levels of homocysteine (Hcy), cystatin C (CysC), beta-2 microglobulin (β2-MG), and uACR. 76.9% of high-risk patients had significantly higher new-onset proteinuria than the low-risk group. Correlation analysis demonstrated a positive correlation between urinary peptides and UACR ( r  = 0.494, P  < 0.001). The incidence of new-onset albuminuria was significantly higher in the high-risk group than in the low-risk group, as shown by Cox regression analysis. The areas under the curve of urinary peptides, Hcy, β2-MG and CysC were 0.925, 0.753, 0.796 and 0.769, respectively.

CONCLUSION

A set of urinary peptides is a predictor of new-onset proteinuria in patients with HTN, therefore, it can be used for diagnosing patients with early renal injury in patients with HTN, contributing to early prevention and treatment of hypertensive nephropathy.

A classifier based on 273 urinary peptides predicts early renal damage in primary hypertension

OBJECTIVES

Renal diseases caused by primary hypertension (HTN) are often asymptomatic without sensitive markers for early diagnosis and prediction, easily progressing to severe and irreversible renal damage in patients with clinical manifestations. This study explored whether a classifier developed based on 273 urinary peptides (CKD273) could serve as a potential biomarker for early prediction of renal damage in HTN.

METHODS

Urinary CKD273 level of healthy individuals, HTN + normoalbuminuric and HTN + albuminuria patients were compared, and 22 baseline data including sex, age, renal function, and hypertensive fundus lesions were collected. Patients diagnosed with HTN, albuminuria, and normal renal function were followed up. According to the follow-up results, the cut-off value of CKD273 in predicting hypertensive renal injury was calculated and analyzed in the high-risk and low-risk groups of HTN patients for its performance in detecting early hypertensive renal injury.

RESULTS

Among a sum of 319 participants, average urinary CKD273 level was significantly higher in patients with HTN than in normal individuals. A total of 147 HTN patients with normal albuminuria were followed up for a mean of 3.8 years. Thirty-five patients showed urinary albumin-to-creatinine ratio (uACR) at least 30 mg/g for three consecutive times. The receiver-operating characteristic (ROC) curve showed that the urinary CKD273 cut-off value for evaluating new-onset proteinuria in patients with HTN was 0.097. Based on this cut-off value, 39 and 108 patients were included in the high-risk and low-risk groups, respectively. Specifically, compared with patients in the low-risk group, those in the high-risk group showed significantly longer duration of HTN, higher proportions of hypertensive fundus lesions and at least 30 mg/g uACR, and higher levels of homocysteine (Hcy), cystatin C (CysC), beta-2 microglobulin (β2-MG), and uACR. 76.9% of high-risk patients had significantly higher new-onset proteinuria than the low-risk group. Correlation analysis demonstrated a positive correlation between urinary CKD273 and UACR ( r  = 0.494, P  = 0.000). The incidence of new-onset albuminuria was significantly higher in the high-risk group than in the low-risk group, as shown by Cox regression analysis. The areas under the curve of CKD273, Hcy, β2-MG, and CysC were 0.925, 0.753, 0.796, and 0.769, respectively.

CONCLUSION

Urinary CKD273 is a predictor of new-onset proteinuria in patients with HTN, therefore, it can be used for diagnosing patients with early renal injury in patients with HTN, contributing to early prevention and treatment of hypertensive nephropathy.

Serum Galectin-3 and Mucin-1 (CA15-3) in Relation to Renal Function in Untreated Chinese Patients

BACKGROUND

Galectin-3 is a multi-functional lectin protein and a ligand of mucin-1 (CA15-3), and has been linked to renal fibrosis in animal models and renal function in humans. However, no population study has ever explored the associations with both ligand and receptor. We therefore investigate the independent association of renal function with serum galectin-3 and mucin-1 (CA15-3) in untreated Chinese patients.

METHODS

The study participants were outpatients who were suspected of hypertension, but had not been treated with antihypertensive medication. Serum galectin-3 and mucin-1 (CA15-3) concentrations were both measured by the enzyme-linked immunosorbent assay (ELISA) method. Estimated glomerular filtration rate (eGFR) was calculated from serum creatinine by the use of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.

RESULTS

The 1,789 participants included 848 (47.4%) men. Mean (±SD) age was 51.3 ± 10.7 years. Multiple regression analyses showed that eGFR was significantly associated with serum galectin-3 and mucin-1 (CA15-3) concentration (0.68 and 1.32 ml/min/1.73 m2 decrease per 1-SD increase in log transformed serum galectin-3 and mucin-1 (CA15-3) concentration, respectively; P ≤ 0.006). The association of eGFR with serum mucin-1 (CA15-3) concentration was significantly stronger in the overweight (BMI 24.0-27.9 kg/m2) and obese (BMI ≥ 28.0 kg/m2) than in normal weight subjects (BMI < 24.0 kg/m2, P for interaction 0.018). Path analysis showed that serum galectin-3 concentration had both a direct (P = 0.016) and a mucin-1 mediated indirect effect (P = 0.014) on eGFR.

CONCLUSIONS

Both circulating galectin-3 and mucin-1 (CA15-3) were significantly associated with renal function. The role of galectin-3 on renal function might be partially via mucin-1.

The novel proteomic signature for cardiac allograft vasculopathy

AIMS

Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV.

METHODS AND RESULTS

We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75-0.91, P < 0.001] and 0.71 (95% CI: 0.60-0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07-1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5-15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7-119.5, P < 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility.

CONCLUSIONS

This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets.

Urinary peptidomic profiles to address age-related disabilities: a prospective population study

BACKGROUND

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 called for innovation in addressing age-related disabilities. Our study aimed to identify and validate a urinary peptidomic profile (UPP) differentiating healthy from unhealthy ageing in the general population, to test the UPP predictor in independent patient cohorts, and to search for targetable molecular pathways underlying age-related chronic diseases.

METHODS

In this prospective population study, we used data from participants in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO), done in northern Belgium from 1985 to 2019, and invited participants to a follow-up examination in 2005-10. Participants were eligible if their address was within 15 km of the examination centre and if they had not withdrawn consent in any of the previous examination cycles (1985-2004). All participants (2005-10) were also invited to an additional follow-up examination in 2009-13. Participants who took part in both the 2005-10 follow-up examination and in the additional 2009-13 follow-up visit constituted the derivation dataset, which included their 2005-10 data, and the time-shifted internal validation dataset, which included their 2009-13 data. The remaining participants who only had 2005-10 data constituted the synchronous internal validation dataset. Participants were excluded from analyses if they were incapacitated, had not undergone UPP, or had either missing or outlying (three SDs greater than the mean of all consenting participants) values of body-mass index, plasma glucose, or serum creatinine. The UPP was assessed by capillary electrophoresis coupled with mass spectrometry. The multidimensional UPP signature reflecting ageing was generated from the derivation dataset and validated in the time-shifted internal validation dataset and the synchronous validation dataset. It was further validated in patients with diabetes, COVID-19, or chronic kidney disease (CKD). In FLEMENGHO, the mortality endpoints were all-cause, cardiovascular, and non-cardiovascular mortality; other endpoints were fatal or non-fatal cancer and musculoskeletal disorders. Molecular pathway exploration was done using the Reactome and Kyoto Encyclopedia of Genes and Genomes databases.

FINDINGS

778 individuals (395 [51%] women and 383 [49%] men; aged 16·2-82·1 years; mean age 50·9 years [SD 15·8]) from the FLEMENGHO cohort had a follow-up examination between 2005 and 2010, of whom 559 participants had a further follow-up from Oct 28, 2009, to March 19, 2013, and made up the derivation (2005-10) and time-shifted internal validation (2009-13) datasets. 219 were examined once and constituted the synchronous internal validation dataset (2005-10). With correction for multiple testing and multivariable adjustment, chronological age was associated with 210 sequenced peptides mainly showing downregulation of collagen fragments. The trained model relating chronological age to UPP, derived by elastic net regression, included 54 peptides from 17 proteins. The UPP-age prediction model explained 76·3% (r=0·87) of chronological age in the derivation dataset, 54·4% (r=0·74) in the time-shifted validation dataset, and 65·3% (r=0·81) in the synchronous internal validation dataset. Compared with chronological age, the predicted UPP-age was greater in patients with diabetes (chronological age 50·8 years [SE 0·37] vs UPP-age 56·9 years [0·30]), COVID‑19 (53·2 years [1·80] vs 58·5 years [1·67]), or CKD (54·6 years [0·97] vs 62·3 years [0·85]; all p<0·0001). In the FLEMENGHO cohort, independent of chronological age, UPP-age was significantly associated with various risk markers related to cardiovascular, metabolic, and renal disease, inflammation, and medication use. Over a median of 12·4 years (IQR 10·8-13·2), total mortality, cardiovascular mortality, and osteoporosis in the population was associated with UPP-age independent of chronological age, with hazard ratios per 10 year increase in UPP-age of 1·54 (95% CI 1·22-1·95) for total mortality, 1·72 (1·20-2·47) for cardiovascular mortality, and 1·40 (1·06-1·85) for osteoporosis and fractures. The most relevant molecular pathways informed by the proteins involved deregulation of collagen biology and extracellular matrix maintenance.

INTERPRETATION

The UPP signature indicative of ageing reflects fibrosis and extracellular matrix remodelling and was associated with risk factors and adverse health outcomes in the population and with accelerated ageing in patients. Innovation in addressing disability should shift focus from the ontology of diseases to shared disease mechanisms, in particular ageing-related fibrotic degeneration.

FUNDING

European Research Council, Ministry of the Flemish Community, OMRON Healthcare.

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease

Urinary peptides gained significant attention as potential biomarkers especially in the context of kidney and cardiovascular disease. In this manuscript the recent literature since 2015 on urinary peptide investigation in human kidney and cardiovascular disease is reviewed. The technology most commonly used in this context is capillary electrophoresis coupled mass spectrometry, in part owed to the large database available and the well-defined dataspace. Several studies based on over 1000 subjects are reported in the recent past, especially examining CKD273, a classifier for assessment of chronic kidney disease based on 273 urine peptides. Interestingly, the most abundant urinary peptides are generally collagen fragments, which may have gone undetected for some time as they are typically modified via proline hydroxylation. The data available suggest that urinary peptides specifically depict inflammation and fibrosis, and may serve as a non-invasive tool to assess fibrosis, which appears to be a key driver in kidney and cardiovascular disease. The recent successful completion of the first urinary peptide guided intervention trial, PRIORITY, is expected to further spur clinical application of urinary peptidomics, aiming especially at early detection of chronic diseases, prediction of progression, and prognosis of drug response.