Biochemical evaluation of Beta-Trace Protein: A newer glomerular filtration Biomarker for Early Detection and Risk Assessment of Chronic Kidney Disease over the older creatinine

Biochemical evaluation of Beta-Trace Protein: A newer glomerular filtration Biomarker for Early Detection and Risk Assessment of Chronic Kidney Disease over the older creatinine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biochemical evaluation of Beta-Trace Protein: A newer glomerular filtration Biomarker for Early Detection and Risk Assessment of Chronic Kidney Disease over the older creatinine Oras Kadhim, Alaa Ibraheem LazimAlsaedi Al-Asadi, Firas S. Al-Jabban, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8951086/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Early detection of chronic kidney disease (CKD) is essential for improving clinical outcomes. However, serum creatinine (Scr), the conventional biomarker for estimating glomerular filtration rate (GFR), has notable limitations, particularly in identifying early-stage CKD. This study evaluates beta-trace protein (BTP) as an alternative biomarker for GFR estimation. Both Scr and serum BTP levels were significantly elevated in CKD patients compared to the control group. Notably, serum BTP exhibited a stronger inverse correlation with measured GFR (mGFR) (r = − 0.934) than Scr (r = − 0.46), indicating superior sensitivity to renal function decline. Receiver operating characteristic (ROC) curve analysis further demonstrated that BTP had higher diagnostic accuracy, with an optimal cutoff concentration of 0.63 mg/L yielding a sensitivity of 98.9%, specificity of 100%, and area under the curve (AUC) of 0.993. In contrast, Scr at 0.775 mg/dL showed lower diagnostic performance (sensitivity = 83.3%, specificity = 42.9%, AUC = 0.74). These findings suggest that BTP may offer improved diagnostic precision and earlier detection of CKD compared to Scr, with potential benefits for more accurate GFR estimation and timely clinical intervention. chronic kidney disease (CKD) beta trace protein (BTP) serum creatinine (Scr) glomerular filtration rate (GFR) Figures Figure 1 Figure 2 Figure 3 1. Introduction Chronic kidney disease (CKD) is a chronic illness marked by persistent renal damage or a gradual decrease of kidney function that cannot be reversed[ 1 ].The term CKD refers to a variety of conditions that impact the structure and function of the kidney, such as persistent abnormalities in urine, abnormalities in the kidney's structure, or reduced excretory renal function that may indicate the loss of functioning nephrons[ 2 ]. CKD is a global public health concern. Kidney failure is becoming more widespread throughout the United States; the consequences are serious and the expenses are substantial. It is anticipated that the number of renal failure patients receiving dialysis and transplantation would rise significantly in the period between 2000 and 2010. Regardless of the underlying etiology, the main consequences of chronic kidney disease include cardiovascular disease (CVD), problems from reduced renal function, and the progression to kidney failure. A growing body of research suggests that early identification and therapy may be able to postpone or avoid some of these unfavorable effects. Regrettably, there are missed chances for prevention due to chronic renal disease's under-diagnosis and under-treatment, may due to a lack of awareness through early detector biomarker and/or technique[ 3 ]. A global public health concern, kidney failure is distinguished by rising incidence and prevalence, exorbitant expenses, and unfavorable outcomes[ 4 ]. Even the early phases of CKD, which can have detrimental effects such as loss of renal function, CVD, and early mortality, are far more common. An international effort focused on the early stages of CKD will be necessary for utilizing strategies to enhance outcomes. The National Kidney Foundation (NKF)'s Kidney Disease Outcomes Quality Initiative (K/DOQI) defines CKD as GFR < 60 ml/min/1.73 m² or the presence of kidney damage through clinical assessment for a period longer than 90 days[ 5 ] (Figure-1). 1.1 .Glomerular Filtration Rate (GFR) The physiological mechanism that turns blood into an ultrafiltrate as it passes through glomerular capillaries is known as glomerular filtration. GFR is, in theory, calculated by multiplying the average single-nephron GFR by the number of nephrons. Hemodynamic variables within the glomerular capillary network and the hydraulic characteristics of the capillary wall are factors that determine single-nephron GFR. In humans, the abundance of glomeruli (1 million per kidney), rich renal tissue, and high GFR (180 L/d) assure[ 6 ]. Serum creatinine (Scr) is a waste product that is filtered out of the blood by the kidneys. Elevated levels of creatinine in the blood can be an indicator of renal problems and CKD. A blood test to measure serum creatinine levels is commonly used to assess kidney health and monitor changes in kidney function over time in people with CKD. Several biomarkers performance have been assessed as predictors of renal illness. Serum creatinine is the most often used biomarker; however, it has a number of drawbacks when it comes to renal disease monitoring. The main drawback is that serum creatinine is nonspecific (factors such as diet, exercise, routes “other than kidney” for creatinine excretion, and certain medications can affect creatinine levels), which makes it more difficult to diagnose the underlying cause of CKD and AKI. Until the damage is well-established, creatinine frequently does not rise. We could just be observing the tip of the iceberg in terms of renal disorders if we merely use blood creatinine as a screening tool. In a significant proportion of patients, we could be overlooking subclinical renal disease because we are waiting for their serum creatinine to increase before intervening. As a result, researchers are looking for disease-specific biomarkers for screening and early diagnosis, the early diagnosis make a difference[ 7 ], [ 8 ]. This accordingly represents limitations that reduce creatinine sensitivity and specificity making creatinine a rough index as early predictor of CKD. However, Monitoring creatinine levels is a key part of diagnosing, staging, and managing CKD to help slow its progression and reduce the risk of complications[ 9 ]. 1.2. Beta trace protein (BTP) Lipocalin prostaglandin D2 synthase (L-PGDS), which is encoded by the PTGDS gene, is another name for β-Trace protein (BTP), a low-molecular-weight glycoprotein that is becoming a new and unique indicator of glomerular filtration rate. BTP is present in blood at far lower amounts than in cerebral spinal fluid, where it is an essential component[ 2 ], [ 10 ],[ 11 ]. Its renal processing and serum origin are still poorly known. In contrast to serum creatinine, BTP is physiologically active. It has both enzymatic and ligand-binding characteristics. Prostaglandin H2 (PGH2) is converted to PGD2 by BTP. An important range of physiological activities, such as platelet aggregation, vasodilation, inflammation, adipogenesis, and bone remodeling, are mediated by the eicosanoid PGD2. BTP is now strongly linked to end-stage renal disease, cardiovascular illness, glomerular filtration rate, and mortality in a number of different patient groups[ 12 ]. Numerous research have used a range of statistical approaches to investigate the relative performance of BTP against creatinine in determining GFR. The majority of early research used receiver operating curve analysis to investigate how well BTP could identify different GFR cutoff points in a range of patient populations, such as children, CKD patients, and kidney transplant recipients. Some discovered that serum creatinine level was not as effective as BTP level in identifying decreased GFR[ 13 ], [ 14 ], [ 10 ], [ 7 ]. 2. Materials and Methods Research methodology This study is a case control study. The individuals were advised and were collected from outpatient nephrology and urology clinics and from renal transplant center from Merjan city/ Babylon. They signed a written informed consent form before to testing. The study was carried out on 160 subjects divided into two groups, 90 subjects (35 females and 55 males) categorized as patients have CKD with age mean (year) ± SD (46.54 ± 10.05), and 70 others (19 females and 51 males) categorized as apparently healthy control group aged mean (year) ± SD (46.41 ± 9.237). The patients were diagnosed and imaging studies and blood and urine tests (biochemical abnormalities) were necessary for the diagnosis. Every patient had serum samples taken. On the day of blood collection, the concentrations of creatinine, and BTP were determined in serum samples that had been kept at -80°C. 2.1. Methods BioVendor - Laboratorni medicina a.s. used the enzyme-linked immunosorbent test (ELISA) to assess BTP. A kinetic alkaline picrate technique was used to quantify creatinine[ 15 ]. GFR (mGFR) was measured using the 99ᵐTc-DTPA radio isotope technique as standard approach. Ethical approval The Ethics committee, College of Dentistry, University of Babylon, gave ethical approval for this study . The study was conducted in accordance with the ethical principles that have their origin in Declaration of Helsinki . it was carried out with patients, verbal and analytic approval before the sample was taken , the study protocol and the subject information and consent form were reviewed and approved by local ethics committee to the document number ( Ref # 546, date : January 19, 2025 ) to get this approval . 2.2. Statistical Analysis All statistical analyses were performed using IBM Corp.'s 2012 SPSS program. IBM Excel (2016, Microsoft Corp. USA) and IBM SPSS Statistics for Windows (Version 21.0, Armonk, NY: IBM Corp. USA). Considering a significant result to be P < 0.05. Unpaired-Sample t-test was used to determine if there were any significant differences in the measured parameters between the two comparative groups under study. Pearson's pearson analysis to evaluate the presence of correlations. ROC analysis for assessing cutoff value specificity and sensitivity. 3. Results The general characteristics of the two comparative groups were demonstrated in table (1) bellow, the mean ± SD and difference significance of standard glomerular filtration rate (mGFR), serum BTP, and Scr concentrations were also showed in the same table. The results indicated that, there were a significant level difference between the two comparative groups regarding the levels of mGFR, Scr, and BTP; (P < 0.05). The mGFR level was significantly lower in CKD individuals compared to control group. Meanwhile, BTP and Scr concentrations were significantly higher in CKD individuals compared to control group as expected. According to the pearson correlation, Scr and the biomarker candidate BTP were showed significant negative correlation with standard mGFR. While, significant positive correlation between each other. But, what was the interesting in this regard was the stronger inverse correlation of BTP with mGFR (r = - 0.934) compared to Scr with mGFR (r = - 0.46). Table (2), and figure (2) summarized these correlations. Table 1. Groups characteristics and serum concentration of parameters formulated as mean ± SD with significant differences explained as P-value (total-160). Parameters Control mean±SD CKD PATIENTS mean±SD T.test P-value Participants female 19 35 male 51 55 Age 46.41±9.237 46.54±10.05 -0.084 0.933 BTP mg/ml 0.51±0.064 1.19±0.16 -36.49 0.000 Scr mg/dl 0.75± 0.097 0.87± 0.15 -6.097 0.000 mGFR (standard) ml/min./1.73m² 112.59 ± 8.481 42.39 ± 18.2 32.345 0.000 Table 2. Correlation significance and intensity of BTP and Scr with mGFR and with each other (total-160). Parameter Correlation characteristics BTP mg/l Cr mg/dl mGFR ml/min/1.73m² BTP mg/l r 1 0.459 ** -0.934 ** P-value 0.000 0.000 Cr mg/dl r 0.459 ** 1 -0.460 ** P-value 0.000 0.000 mGFR ml/min/1.73m² r -0.934 ** -0.460 ** 1 P-value 0.000 0.000 **. Correlation is significant at the 0.01 level (2-tailed). The ROC Curve analyzed that Scr at a concentration of (0.775 mg/dl) considered the best cutoff value for diagnostic performance to all other Scr concentrations that showed a sensitivity = 0.833% and specificity = 0.429% with AUC = 0.74. While, BTP at a concentration of (0.63 mg/l) indicated a sensitivity = 0.989% and specificity = 1.0% with AUC = 0.993. Table (3), and figure (3), summarized these results. According to ROC Curve analysis, BTP indicated a better diagnostic accuracy performance towards CKD than Scr. Table 3. An analysis comparing the diagnostic accuracy of BTP as a new biomarker with older Scr. diagnosis outcome predicted Biomarker Cutoff value Sensitivity % Specificity % Accuracy by area under ROC curve CKD Cr (0.775 mg/dl) 0.833 0.429 0.74 BTP (0.63 mg/l) 0.989 1.0 0.993 4. Discussion Accurate evaluation of GFR is crucial for CKD identification, staging, and assessment of progression, prognosis, therapy, and medication dose modification[10], [16]. Under conditions when a more precise determination of GFR would influence treatment decisions, the Kidney Disease: Improving Global Outcomes recommendations recommend measuring GFR with an exogenous filtration marker[17]. Additionally, in 2014, the European Medicines Agency revised its recommendations, advising that pharmacokinetic studies including people with reduced renal function should employ a technique that accurate assesses GFR using an exogenous marker. In daily practice, GFR-estimating equations (eGFR ) are most frequently utilized. They have the benefit of being affordable and providing results right away. Their reliance on endogenous biomarkers, which are complicated by non-GFR determinants including age, sex, muscle mass, medications, specific chronic illnesses, nutrition, and probably a host of other factors, consider a drawback[18]. The results of this study indicated that there were a significant decline (P < 0.05) regarding mGFR in patients with CKD compare to control group, the mean and standard deviation were (42.39 ± 18.2 ml/min/1.73m² Compare to 112.59 ± 8.481 ml/min/1.73m²) (Table 1). These results were expected since for the detection of CKD, GFR assessment is essential[4], [19]. Creatinine and BTP whom represents endogenous biomarkers, both showed significant elevation directed with renal function decline (P < 0.05). However, what was the interesting in this regard was the stronger correlation of BTP with mGFR (r = - 0.934) compared to Cr with mGFR (r = - 0.46). Table (2), and figure (1) summarized these correlations. Moreover, the results of ROC-Curve analysis indicated that BTP have a stronger performance diagnostic accuracy than Cr. regarding CKD diagnosis, (Table 3, figure 3, summarized these results). Scr have many limitations in estimating and early sensitizing renal function decline and so, lack of ability to make early diagnosis ability[20], [21]. In addition, the concentration of Scr only respond and starting to elevate when 40–50% of the renal parenchyma is injured[22]. This is because Scr is not specific [23], [24] and, more importantly, insensitive as an early predictor marker of renal function because it stays within the normal reference interval. It has a flat slope throughout a large portion of the GFR range until GFR is reduced by around 75%. Interpreting variations in serum creatinine is challenging [25], [26]. About 7 to 10% of the creatinine found in urine can be attributed to "tubular secretion," which is a tiny but substantial and variable proportion[27], [28]. However, this amount increases when renal insufficiency is present. Non-GFR determining variables such as age, gender, muscle mass [29], and food have an impact on Scr [30], [31]. With increased renal excretion in CKD. Extra renal creatinine clearance becomes crucial for patients with chronic kidney disease [32], [33], [34], [35]. Consequently, many individuals with stage 3 CKD (GFR, 30-59 mL/min/1.73 m2) and "stage 2 CKD" (GFR, 60-89 mL/min/1.73 m2) would go unidentified by Scr testing. Therefore, while a normal blood creatinine level may not always correspond to normal kidney function, a higher Scr concentration is often associated with decreased renal function. From a clinical practice point of view, decline kidney function is estimated by eGFR, based on serum levels of creatinine and cystatin C, and the presence of albuminuria. However, eGFR using these biomarkers often vary from mGFR by remarkable percentages, that is taken into consideration, that eGFR values incorrectly stage CKD in approximately half of patients, and that eGFR and mGFR give different rates of GFR impairment. There is a nonlinear correlation between creatinine/cystatin C and the GFR, and, consequently, relatively tiny initial elevations in these biomarkers represent significant decreases in the GFR. Errors were similar and unexpected for formulas based on cystatin C and/or creatinine. The fact that these inaccuracies continue to occur indicates that the issue is not with the mathematical techniques used to estimate GFR, but rather with the use of creatinine and/or cystatin C as an endogenous biomarker of renal function[6], [36], [37]. The higher molecular weight isoform BTP [2] on the other hand showed a close results to mGFR than Scr as stated before and also over other biomarkers[7], this may due to that BTP extracted from the plasma by the kidneys' primary excretion route, glomerular filtration; tubular reabsorption and catabolism inside tubular cells can complete the renal handling process with no extra renal clearance as Scr, it also less impacted by age and gender than serum creatinine, and not impacted by race[12]. BTP was not impacted by hemodialysis and was hence not eliminated. Therefore, including BTP as an endogenous filtration measure for GFR degradation might be seen as an extra benefit[38]. Also, Because of its stable production rate, anionic nature, low molecular mass, and capacity to predict kidney impairment "earlier" than albuminuria, urinary BTP may be a better predictor of kidney damage [161]. Additionally, in CKD patients, it can forecast an early decline in GFR[39], [40]. 5. Conclusion For many years, creatinine restrictions have been widely recognized. However, in clinical practice, creatinine remains the gold standard biomarker for assessing CKD. But, BTP as a glomerular filtration marker in this study, demonstrated its ability to raise the early predictive diagnostic value in CKD and the accuracy of GFR estimates over creatinine. Declarations Ethics approval and consent to participate The Ethics committee, College of Dentistry, University of Baghdad, gave ethical approval for this study . The study was conducted in accordance with the ethical principles that have their origin in Declaration of Helsinki . it was carried out with patients , verbal and analytic approval before the sample was taken , the study protocol and the subject information and consent form were reviewed and approved by local ethics committee to the document number ( Ref # 559, date : January 19, 2025) to get this approval . Consent for publication I, the undersigned, grant Springer Nature the right to publish the manuscript titled: “Evaluation of Stress Biomarkers in Individuals with Migraine or Tension-Type Headache Before and After Botulinum Toxin Injection” In all forms and media now known or later developed. I/We confirm: Original work and not under consideration elsewhere. Copyright/permissions in order; no conflicting third-party rights. Open Access terms as applicable. Editorial changes allowed; substantive changes with author approval. Compliance with ethical standards and disclosures. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions and confidentiality agreements. Competing Interests The authors declare that they have no competing interests. Funding The authors declare that they have no competing interests. Authors' contributions Oras Kadhim Baqer Al-Asadi, Alaa Ibraheem Lazim Alsaedi, and Firas S. Al-Jabban conceived and designed the study. Oras Kadhim Baqer Al-Asadi, Hafidh I. Al-Sadi, and Ghazi Mohamad RAMADAN analyzed and interpreted the data. Ahmed Mohammed Attyah Zheoat, Haneen Mohanad Mohammed, and Lamis Khidher Mohammed collected and processed the samples. Oras Kadhim Baqer Al-Asadi and Alaa Ibraheem Lazim Alsaedi drafted the manuscript. Firas S. Al-Jabban, Hafidh I. Al-Sadi, and Ghazi Mohamad RAMADAN critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the staff and management of Al-Manara University for Medical Sciences, Al-Mustaqbal University, University of Mashreq, Al Taff University College, Al–Hillah Teaching Hospital, and University of Babylon for their support and cooperation during this study. We also appreciate the participants who contributed to this research. References Levey AS et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8951086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619783261,"identity":"9794db47-a405-4d6b-8754-22dc87f3d405","order_by":0,"name":"Oras Kadhim","email":"","orcid":"","institution":"Al-Manara University for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Oras","middleName":"","lastName":"Kadhim","suffix":""},{"id":619783262,"identity":"939160a4-f512-467a-aa09-774f2f505f5d","order_by":1,"name":"Alaa Ibraheem LazimAlsaedi Al-Asadi","email":"","orcid":"","institution":"Al-Manara University for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alaa","middleName":"Ibraheem LazimAlsaedi","lastName":"Al-Asadi","suffix":""},{"id":619783263,"identity":"6254be09-eec3-412f-b7ef-9b6e73d45315","order_by":2,"name":"Firas S. Al-Jabban","email":"","orcid":"","institution":"Al- Mustaqbal University","correspondingAuthor":false,"prefix":"","firstName":"Firas","middleName":"S.","lastName":"Al-Jabban","suffix":""},{"id":619783264,"identity":"65c464e0-c90d-47c0-944d-45c6d75938c0","order_by":3,"name":"Hafidh I. Al-Sadi","email":"","orcid":"","institution":"the University of Mashreq","correspondingAuthor":false,"prefix":"","firstName":"Hafidh","middleName":"I.","lastName":"Al-Sadi","suffix":""},{"id":619783265,"identity":"97fc2733-67da-4e46-a2ae-0d8c620d1032","order_by":4,"name":"Ghazi RAMADAN","email":"","orcid":"","institution":"Al Taff University College","correspondingAuthor":false,"prefix":"","firstName":"Ghazi","middleName":"","lastName":"RAMADAN","suffix":""},{"id":619783266,"identity":"febeeaae-b47e-4e6a-9cfb-a4d639287e49","order_by":5,"name":"Ahmed Mohammed Attyah Zheoat Zheoat","email":"","orcid":"","institution":"Al-Manara University for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Mohammed Attyah Zheoat","lastName":"Zheoat","suffix":""},{"id":619783267,"identity":"a83b9ea3-65c1-4522-9822-2e6a9ac94ee1","order_by":6,"name":"Haneen Mohanad Mohammed","email":"","orcid":"","institution":"Al-Manara University for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Haneen","middleName":"Mohanad","lastName":"Mohammed","suffix":""},{"id":619783268,"identity":"defc5f8e-3946-43c9-b306-5c94acbcd4f4","order_by":7,"name":"Lamis Khidher Mohammed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie3PMarCMBjA8S8UdAntquAhBA/gQVwMgpPBtYNKQdDFAziI7woVoXNKIA4WXAM6KIKboLhk9Gs3BVvf9uDlDwkJ5EcSAJvtD+biECAEBY2rto+T4wS5pPRCjklKSDFBJCAl5DRJd0WkvGWxSQ41dz9WN7YctrwpEuNHnwnloaT6QqsH1ZmzaMPnkgRkluxzHoYEbpLWda8BLFI8QOKQSQ7xrmFsMtJ/AFso/lNIKjwUVGe3OMCCAV5aSK74l0TSqu42oK0EXyGJ8/7ieXx9N0o2Xd05EzMY8eVOxkfjfybvyWwWX5/HRr85bLPZbP+kJ8e5aI3ZEgYVAAAAAElFTkSuQmCC","orcid":"","institution":"Al- Mustaqbal University","correspondingAuthor":true,"prefix":"","firstName":"Lamis","middleName":"Khidher","lastName":"Mohammed","suffix":""}],"badges":[],"createdAt":"2026-02-23 23:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8951086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8951086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106635558,"identity":"60f5848a-7999-44ef-b6ae-d722e4c6f6eb","added_by":"auto","created_at":"2026-04-10 16:48:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82602,"visible":true,"origin":"","legend":"\u003cp\u003eAn evidence-based model for the phases of chronic kidney disease (CKD) onset, progression, and treatment interventions[3].\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8951086/v1/4f6bcaa79c0d73d6c70b8330.jpg"},{"id":106635559,"identity":"7c9e60d1-3f28-4d95-8802-78f4d5e32776","added_by":"auto","created_at":"2026-04-10 16:48:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegression line of correlation of BTP, Scr with mGFR and with each other.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8951086/v1/37360cb5e7480e999d23b6ab.jpg"},{"id":106635560,"identity":"796f4f0f-edd3-4630-bcc5-c7de82667e06","added_by":"auto","created_at":"2026-04-10 16:48:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve-analysis comparing the diagnostic accuracy of BTP as a new biomarker with older Sr expressed as AUC.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8951086/v1/d2b97d4baab2ef711b16e05c.jpg"},{"id":107929434,"identity":"4c75d80c-51ee-4a2b-ac0d-b69697794321","added_by":"auto","created_at":"2026-04-27 16:15:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":506703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8951086/v1/1aced6ff-e8c3-4386-a2fa-bb1026c62d23.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biochemical evaluation of Beta-Trace Protein: A newer glomerular filtration Biomarker for Early Detection and Risk Assessment of Chronic Kidney Disease over the older creatinine","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is a chronic illness marked by persistent renal damage or a gradual decrease of kidney function that cannot be reversed[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].The term CKD refers to a variety of conditions that impact the structure and function of the kidney, such as persistent abnormalities in urine, abnormalities in the kidney's structure, or reduced excretory renal function that may indicate the loss of functioning nephrons[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCKD is a global public health concern. Kidney failure is becoming more widespread throughout the United States; the consequences are serious and the expenses are substantial. It is anticipated that the number of renal failure patients receiving dialysis and transplantation would rise significantly in the period between 2000 and 2010. Regardless of the underlying etiology, the main consequences of chronic kidney disease include cardiovascular disease (CVD), problems from reduced renal function, and the progression to kidney failure. A growing body of research suggests that early identification and therapy may be able to postpone or avoid some of these unfavorable effects. Regrettably, there are missed chances for prevention due to chronic renal disease's under-diagnosis and under-treatment, may due to a lack of awareness through early detector biomarker and/or technique[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA global public health concern, kidney failure is distinguished by rising incidence and prevalence, exorbitant expenses, and unfavorable outcomes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Even the early phases of CKD, which can have detrimental effects such as loss of renal function, CVD, and early mortality, are far more common. An international effort focused on the early stages of CKD will be necessary for utilizing strategies to enhance outcomes. The National Kidney Foundation (NKF)'s Kidney Disease Outcomes Quality Initiative (K/DOQI) defines CKD as GFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73 m\u0026sup2; or the presence of kidney damage through clinical assessment for a period longer than 90 days[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] (Figure-1).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 .Glomerular Filtration Rate (GFR)\u003c/h2\u003e \u003cp\u003eThe physiological mechanism that turns blood into an ultrafiltrate as it passes through glomerular capillaries is known as glomerular filtration. GFR is, in theory, calculated by multiplying the average single-nephron GFR by the number of nephrons. Hemodynamic variables within the glomerular capillary network and the hydraulic characteristics of the capillary wall are factors that determine single-nephron GFR. In humans, the abundance of glomeruli (1\u0026nbsp;million per kidney), rich renal tissue, and high GFR (180 L/d) assure[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eSerum creatinine (Scr)\u003c/b\u003e is a waste product that is filtered out of the blood by the kidneys. Elevated levels of creatinine in the blood can be an indicator of renal problems and CKD. A blood test to measure serum creatinine levels is commonly used to assess kidney health and monitor changes in kidney function over time in people with CKD. Several biomarkers performance have been assessed as predictors of renal illness. Serum creatinine is the most often used biomarker; however, it has a number of drawbacks when it comes to renal disease monitoring. The main drawback is that serum creatinine is nonspecific (factors such as diet, exercise, routes \u0026ldquo;other than kidney\u0026rdquo; for creatinine excretion, and certain medications can affect creatinine levels), which makes it more difficult to diagnose the underlying cause of CKD and AKI. Until the damage is well-established, creatinine frequently does not rise. We could just be observing the tip of the iceberg in terms of renal disorders if we merely use blood creatinine as a screening tool. In a significant proportion of patients, we could be overlooking subclinical renal disease because we are waiting for their serum creatinine to increase before intervening. As a result, researchers are looking for disease-specific biomarkers for screening and early diagnosis, the early diagnosis make a difference[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This accordingly represents limitations that reduce creatinine sensitivity and specificity making creatinine a rough index as early predictor of CKD. However, Monitoring creatinine levels is a key part of diagnosing, staging, and managing CKD to help slow its progression and reduce the risk of complications[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Beta trace protein (BTP)\u003c/h2\u003e \u003cp\u003eLipocalin prostaglandin D2 synthase (L-PGDS), which is encoded by the PTGDS gene, is another name for β-Trace protein (BTP), a low-molecular-weight glycoprotein that is becoming a new and unique indicator of glomerular filtration rate. BTP is present in blood at far lower amounts than in cerebral spinal fluid, where it is an essential component[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e],[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Its renal processing and serum origin are still poorly known. In contrast to serum creatinine, BTP is physiologically active. It has both enzymatic and ligand-binding characteristics. Prostaglandin H2 (PGH2) is converted to PGD2 by BTP. An important range of physiological activities, such as platelet aggregation, vasodilation, inflammation, adipogenesis, and bone remodeling, are mediated by the eicosanoid PGD2. BTP is now strongly linked to end-stage renal disease, cardiovascular illness, glomerular filtration rate, and mortality in a number of different patient groups[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Numerous research have used a range of statistical approaches to investigate the relative performance of BTP against creatinine in determining GFR. The majority of early research used receiver operating curve analysis to investigate how well BTP could identify different GFR cutoff points in a range of patient populations, such as children, CKD patients, and kidney transplant recipients. Some discovered that serum creatinine level was not as effective as BTP level in identifying decreased GFR[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eResearch methodology\u003c/p\u003e \u003cp\u003eThis study is a case control study. The individuals were advised and were collected from outpatient nephrology and urology clinics and from renal transplant center from Merjan city/ Babylon. They signed a written informed consent form before to testing.\u003c/p\u003e \u003cp\u003eThe study was carried out on 160 subjects divided into two groups, 90 subjects (35 females and 55 males) categorized as patients have CKD with age mean (year)\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (46.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05), and 70 others (19 females and 51 males) categorized as apparently healthy control group aged mean (year)\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (46.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.237). The patients were diagnosed and imaging studies and blood and urine tests (biochemical abnormalities) were necessary for the diagnosis.\u003c/p\u003e \u003cp\u003eEvery patient had serum samples taken. On the day of blood collection, the concentrations of creatinine, and BTP were determined in serum samples that had been kept at -80\u0026deg;C.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Methods\u003c/h2\u003e \u003cp\u003eBioVendor - Laboratorni medicina a.s. used the enzyme-linked immunosorbent test (ELISA) to assess BTP. A kinetic alkaline picrate technique was used to quantify creatinine[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGFR (mGFR) was measured using the 99ᵐTc-DTPA radio isotope technique as standard approach.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics committee, College of Dentistry, University of Babylon, gave ethical approval for this study . The study was conducted in accordance with the ethical principles that have their origin in Declaration of Helsinki . \u0026nbsp;it was carried out with patients, verbal and analytic approval before the sample was taken , the study protocol and the subject information and consent form were reviewed and approved by local ethics committee to the document number ( Ref # 546, date : January 19, 2025 ) to get this approval . \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using IBM Corp.'s 2012 SPSS program. IBM Excel (2016, Microsoft Corp. USA) and IBM SPSS Statistics for Windows (Version 21.0, Armonk, NY: IBM Corp. USA). Considering a significant result to be P \u0026lt; 0.05. Unpaired-Sample t-test was used to determine if there were any significant differences in the measured parameters between the two comparative groups under study. Pearson's pearson analysis to evaluate the presence of correlations. ROC analysis for assessing cutoff value specificity and sensitivity.\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003eThe general characteristics of the two comparative groups were demonstrated in table (1) bellow, the mean \u0026plusmn; SD and difference significance of standard glomerular filtration rate (mGFR), serum BTP, and Scr concentrations were also showed in the same table. The results indicated that, there were a significant level difference between the two comparative groups regarding the levels of mGFR, Scr, and BTP;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(P \u0026lt; 0.05). The mGFR level was significantly lower in CKD individuals compared to control group. Meanwhile, BTP and Scr concentrations were significantly higher in CKD individuals compared to control group as expected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the pearson correlation, Scr and the biomarker candidate BTP were showed significant negative correlation with standard mGFR. While, significant positive correlation between each other. But, what was the interesting in this regard was the stronger inverse correlation of BTP with mGFR (r = - 0.934) compared to Scr with mGFR (r = - 0.46). Table (2), and figure (2) summarized these correlations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Groups characteristics and serum concentration of parameters formulated as mean \u0026plusmn; SD with significant differences explained as P-value (total-160).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD PATIENTS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT.test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.41\u0026plusmn;9.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.54\u0026plusmn;10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBTP mg/ml\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u0026plusmn;0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-36.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScr mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75\u0026plusmn; 0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87\u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emGFR (standard)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eml/min./1.73m\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112.59 \u0026plusmn; 8.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.39 \u0026plusmn; 18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Correlation significance and intensity of BTP and Scr with mGFR and with each other (total-160).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBTP mg/l\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emGFR ml/min/1.73m\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBTP mg/l\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.459\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.934\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.459\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.460\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emGFR ml/min/1.73m\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.934\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.460\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe ROC Curve analyzed that Scr at a concentration of (0.775 mg/dl) considered the best cutoff value for diagnostic performance to all other Scr concentrations that showed a sensitivity = 0.833% and specificity = 0.429% with AUC = 0.74. While, BTP at a concentration of (0.63 mg/l) indicated a sensitivity = 0.989% and specificity = 1.0% with AUC = 0.993. Table (3), and figure (3), summarized these results. According to ROC Curve analysis, BTP indicated a better diagnostic accuracy performance towards CKD than Scr. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. An analysis comparing the diagnostic accuracy of BTP as a new biomarker with older Scr.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ediagnosis outcome predicted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCutoff value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity %\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy by area under ROC curve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eCr (0.775 mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eBTP (0.63 mg/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eAccurate evaluation of GFR is crucial for CKD identification, staging, and assessment of progression, prognosis, therapy, and medication dose modification[10], [16]. Under conditions when a more precise determination of GFR would influence treatment decisions, the Kidney Disease: Improving Global Outcomes recommendations recommend measuring GFR with an exogenous filtration marker[17]. Additionally, in 2014, the European Medicines Agency revised its recommendations, advising that pharmacokinetic studies including people with reduced renal function should employ a technique that accurate assesses GFR using an exogenous marker. In daily practice, GFR-estimating equations (eGFR ) are most frequently utilized. They have the benefit of being affordable and providing results right away. Their reliance on endogenous biomarkers, which are complicated by non-GFR determinants including age, sex, muscle mass, medications, specific chronic illnesses, nutrition, and probably a host of other factors, consider a drawback[18]. The results of this study indicated that there were a significant decline (P \u0026lt; 0.05) regarding mGFR in patients with CKD compare to control group, the mean and standard deviation were (42.39 \u0026plusmn; 18.2 ml/min/1.73m\u0026sup2; Compare to 112.59 \u0026plusmn; 8.481 ml/min/1.73m\u0026sup2;) (Table 1). These results were expected since for the detection of CKD, GFR assessment is essential[4], [19]. Creatinine and BTP whom represents endogenous biomarkers, both showed significant elevation directed with renal function decline (P \u0026lt; 0.05). However, what was the interesting in this regard was the stronger correlation of BTP with mGFR (r = - 0.934) compared to Cr with mGFR (r = - 0.46). Table (2), and figure (1) summarized these correlations. Moreover, the results of ROC-Curve analysis indicated that BTP have a stronger performance diagnostic accuracy than Cr. regarding CKD diagnosis, (Table 3, figure 3, summarized these results). Scr have many limitations in estimating and early sensitizing renal function decline and so, lack of ability to make early diagnosis ability[20], [21]. In addition, the concentration of Scr only respond and starting to elevate when 40\u0026ndash;50% of the renal parenchyma is injured[22]. This is because Scr is not specific [23], [24] and, more importantly, insensitive as an early predictor marker of renal function because it stays within the normal reference interval. It has a flat slope throughout a large portion of the GFR range until GFR is reduced by around 75%. Interpreting variations in serum creatinine is challenging [25], [26]. About 7 to 10% of the creatinine found in urine can be attributed to \u0026quot;tubular secretion,\u0026quot; which is a tiny but substantial and variable proportion[27], [28]. However, this amount increases when renal insufficiency is present. Non-GFR determining variables such as age, gender, muscle mass [29], and food have an impact on Scr [30], [31]. With increased renal excretion in CKD. Extra renal creatinine clearance becomes crucial for patients with chronic kidney disease [32], [33], [34], [35]. Consequently, many individuals with stage 3 CKD (GFR, 30-59 mL/min/1.73 m2) and \u0026quot;stage 2 CKD\u0026quot; (GFR, 60-89 mL/min/1.73 m2) would go unidentified by Scr testing. Therefore, while a normal blood creatinine level may not always correspond to normal kidney function, a higher Scr concentration is often associated with decreased renal function. From a clinical practice point of view, decline kidney function is estimated by eGFR, based on serum levels of creatinine and cystatin C, and the presence of albuminuria. However, eGFR using these biomarkers often vary from mGFR by remarkable percentages, that is taken into consideration, that eGFR values incorrectly stage CKD in approximately half of patients, and that eGFR and mGFR give different rates of GFR impairment. There is a nonlinear correlation between creatinine/cystatin C and the GFR, and, consequently, relatively tiny initial elevations in these biomarkers represent significant decreases in the GFR. Errors were similar and unexpected for formulas based on cystatin C and/or creatinine. The fact that these inaccuracies continue to occur indicates that the issue is not with the mathematical techniques used to estimate GFR, but rather with the use of creatinine and/or cystatin C as an endogenous biomarker of renal function[6], [36], [37]. The higher molecular weight isoform BTP [2] on the other hand showed a close results to mGFR than Scr as stated before and also over other biomarkers[7], this may due to that BTP extracted from the plasma by the kidneys\u0026apos; primary excretion route, glomerular filtration; tubular reabsorption and catabolism inside tubular cells can complete the renal handling process with no extra renal clearance as Scr, it also less impacted by age and gender than serum creatinine, and not impacted by race[12]. BTP was not impacted by hemodialysis and was hence not eliminated. Therefore, including BTP as an endogenous filtration measure for GFR degradation might be seen as an extra benefit[38]. Also, Because of its stable production rate, anionic nature, low molecular mass, and capacity to predict kidney impairment \u0026quot;earlier\u0026quot; than albuminuria, urinary BTP may be a better predictor of kidney damage [161]. Additionally, in CKD patients, it can forecast an early decline in GFR[39], [40].\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eFor many years, creatinine restrictions have been widely recognized. However, in clinical practice, creatinine remains the gold standard biomarker for assessing CKD. But, BTP as a glomerular filtration marker in this study, demonstrated its ability to raise the early predictive diagnostic value in CKD and the accuracy of GFR estimates over creatinine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics committee, College of Dentistry, University of Baghdad, gave ethical approval for this study . The study was conducted in accordance with the ethical principles that have their origin in Declaration of Helsinki . \u0026nbsp;it was carried out with patients\u003csup\u003e,\u0026nbsp;\u003c/sup\u003everbal and analytic approval before the sample was taken , the study protocol and the subject information and consent form were reviewed and approved by local ethics committee to the document number ( Ref # 559, date : January 19, 2025) to get this approval .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI, the undersigned, grant Springer Nature the right to publish the manuscript titled:\u003c/p\u003e\n\u003cp\u003e“Evaluation of Stress Biomarkers in Individuals with Migraine or Tension-Type Headache Before and After Botulinum Toxin Injection”\u003c/p\u003e\n\u003cp\u003eIn all forms and media now known or later developed. I/We confirm:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eOriginal work\u003c/strong\u003e and not under consideration elsewhere.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCopyright/permissions\u003c/strong\u003e in order; no conflicting third-party rights.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOpen Access\u003c/strong\u003e terms as applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEditorial changes\u003c/strong\u003e allowed; substantive changes with author approval.\u003c/li\u003e\n \u003cli\u003eCompliance with ethical standards and disclosures.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions and confidentiality agreements.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOras Kadhim Baqer Al-Asadi, Alaa Ibraheem Lazim Alsaedi, and Firas S. Al-Jabban conceived and designed the study. Oras Kadhim Baqer Al-Asadi, Hafidh I. Al-Sadi, and Ghazi Mohamad RAMADAN analyzed and interpreted the data. Ahmed Mohammed Attyah Zheoat, Haneen Mohanad Mohammed, and Lamis Khidher Mohammed collected and processed the samples. Oras Kadhim Baqer Al-Asadi and Alaa Ibraheem Lazim Alsaedi drafted the manuscript. Firas S. Al-Jabban, Hafidh I. Al-Sadi, and Ghazi Mohamad RAMADAN critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff and management of Al-Manara University for Medical Sciences, Al-Mustaqbal University, University of Mashreq, Al Taff University College, Al–Hillah Teaching Hospital, and University of Babylon for their support and cooperation during this study. We also appreciate the participants who contributed to this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLevey AS et al. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. 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Am J Kidney Dis. 2015;65(1):131\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhavsar NA, et al. Comparison of measured GFR, serum creatinine, cystatin C, and beta-trace protein to predict ESRD in African Americans with hypertensive CKD. Am J Kidney Dis. 2011;58(6):886\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabb\u0026eacute; D et al. Method selected for the determination of creatinine in plasma or serum. Choice of optimal conditions of measurement. Ann de Biol clinique, 1996, pp. 285\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRathi N, et al. Predicting GFR after radical nephrectomy: the importance of split renal function. World J Urol. 2022;40(4):1011\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Boer IH, et al. 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Am J Physiology-Renal Physiol, 307, 8, pp. F939\u0026ndash;F948, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWuerzner G, Firsov D, Bonny O. Circadian glomerular function: from physiology to molecular and therapeutical aspects. Nephrol Dialysis Transplantation. 2014;29(8):1475\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuijsen JGM, Van Acker BAC, Koomen GCM, Koopman MG, Arisz L. Circadian rhythm of glomerular filtration rate in patients after kidney transplantation. Nephrol Dialysis Transplantation. 1994;9(9):1330\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTardo DT, Briggs C, Ahern G, Pitman A, Sinha S. Anatomical variations of the renal arterial vasculature: An Australian perspective. J Med Imaging Radiat Oncol. 2017;61(5):643\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitch WE, Collier VU, Walser M. Creatinine metabolism in chronic renal failure. Clin Sci. 1980;58(4):327\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavaneethan SD, Mitch WE. Does Serum Creatinine Reflect Muscle Mass in Patients with Kidney Failure? LWW; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiller G, Ferris M, Gattineni J. Assessment of kidney function in children, adolescents, and young adults. in Pediatric Nephrology. Springer; 2022. pp. 145\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStehl\u0026eacute; T, et al. Development and validation of a new equation based on plasma creatinine and muscle mass assessed by CT scan to estimate glomerular filtration rate: a cross-sectional study. Clin Kidney J. 2023;16(8):1265\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorrini E, et al. Estimated GFR: time for a critical appraisal. Nat Rev Nephrol. 2019;15(3):177\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasala S, Carmody JB. How to use\u0026hellip; serum creatinine, cystatin C and GFR. Archives Disease Childhood-Education Pract. 2017;102(1):37\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen HH. β-trace protein versus cystatin C: which is a better surrogate marker of renal function versus prognostic indicator in cardiovascular diseases? American College of Cardiology Foundation Washington, DC; 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUehara Y, Makino H, Seiki K, Urade Y, Kidney L-PCRG. Urinary excretions of lipocalin-type prostaglandin D synthase predict renal injury in type-2 diabetes: a cross-sectional and prospective multicentre study, Nephrology Dialysis Transplantation, vol. 24, no. 2, pp. 475\u0026ndash;482, 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonadio C. Serum and urinary markers of early impairment of GFR in chronic kidney disease patients: diagnostic accuracy of urinary β-trace protein. Am J Physiology-Renal Physiol. 2010;299(6):F1407\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chronic kidney disease (CKD), beta trace protein (BTP), serum creatinine (Scr), glomerular filtration rate (GFR)","lastPublishedDoi":"10.21203/rs.3.rs-8951086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8951086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarly detection of chronic kidney disease (CKD) is essential for improving clinical outcomes. However, serum creatinine (Scr), the conventional biomarker for estimating glomerular filtration rate (GFR), has notable limitations, particularly in identifying early-stage CKD. This study evaluates beta-trace protein (BTP) as an alternative biomarker for GFR estimation.\u003c/p\u003e \u003cp\u003eBoth Scr and serum BTP levels were significantly elevated in CKD patients compared to the control group. Notably, serum BTP exhibited a stronger inverse correlation with measured GFR (mGFR) (r = \u0026minus;\u0026thinsp;0.934) than Scr (r = \u0026minus;\u0026thinsp;0.46), indicating superior sensitivity to renal function decline. Receiver operating characteristic (ROC) curve analysis further demonstrated that BTP had higher diagnostic accuracy, with an optimal cutoff concentration of 0.63 mg/L yielding a sensitivity of 98.9%, specificity of 100%, and area under the curve (AUC) of 0.993. In contrast, Scr at 0.775 mg/dL showed lower diagnostic performance (sensitivity\u0026thinsp;=\u0026thinsp;83.3%, specificity\u0026thinsp;=\u0026thinsp;42.9%, AUC\u0026thinsp;=\u0026thinsp;0.74).\u003c/p\u003e \u003cp\u003eThese findings suggest that BTP may offer improved diagnostic precision and earlier detection of CKD compared to Scr, with potential benefits for more accurate GFR estimation and timely clinical intervention.\u003c/p\u003e","manuscriptTitle":"Biochemical evaluation of Beta-Trace Protein: A newer glomerular filtration Biomarker for Early Detection and Risk Assessment of Chronic Kidney Disease over the older creatinine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 16:48:21","doi":"10.21203/rs.3.rs-8951086/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd5a20a7-ab4c-42b9-9563-d8a9b9b0876a","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:13:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 16:48:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8951086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8951086","identity":"rs-8951086","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}