Menopause Live - IMS Updates

Date of release: 22 June, 2015

Urine proteomics: a non-invasive diagnostic tool

As a clinician, I was not aware of the growing interest in analyzing urine proteomics. The routine urine analysis includes urine protein levels but, in fact, in almost all cases we are interested in the urine albumin as a marker of kidney disease (nephropathy of all sorts, mainly diabetic). A full screen of urinary proteins and peptides (the urine proteome) is not available for routine clinical work. The reason for this has been two-fold: lack of data to establish the value of the test, and technical issues related to preparing the samples for the best and most accurate analysis. However, many recent studies show that characteristic changes in healthy elderly people's urine proteomes appear to reflect certain physiological processes associated with metabolic changes and the aging process [1]. Furthermore, documenting the urine proteome may facilitate earlier detection of disease, improve assessment of prognosis and allow closer monitoring of response to therapy [2]. So, which of the urinary proteins may serve as biomarkers of specific diseases?


It is obvious that researchers were looking at the potential contribution of the urine protein profile in the case of urinary tract or prostate diseases [3]. Urine proteomic contents are different in people with normal kidney function as compared to those with chronic renal failure [4]. Another typical set-up is IgA nephropathy which is manifested by tubulointerstitial damage and segmental glomerulosclerosis and by the expression of a whole gamut of peptides and proteins in the urine [5]. In regard to prostate cancer, the engrailed-2 protein was investigated being a tumor-specific urinary biomarker for the early diagnosis of prostate cancer and for estimation of tumor volume [6]. Urinary vinculin, prostatic acid phosphatase and galectin-3 may serve to differentiate between patients with and without prostate cancer and patients with and without relapse of prostate cancer [7]. Many studies have explored the urine proteome in diabetic patients, but findings probably just reflect the kidney involvement and renal complications rather than the diabetes itself [3]. Urinary tract malignancies, such as upper tract urothelial carcinoma or bladder cancer, were targets for a search for successful proteomic candidates. Several of them (calreticulin, annexin A2, and annexin A3) were found to have potential value in upper tract urothelial carcinoma [8], and a 10-biomarkers panel achieved good prediction of bladder cancer [9].


While it is logical to look for urinary biomarkers in urinary tract diseases, an intriguing question is whether there could be peptides or proteins secreted into the urine and becoming good markers for extra-urinary tract illnesses. Several such molecules were detected in breast cancer patients, such as MMP-9 and ADAM-12, but by the end of the day there is still no validated urinary biomarker available for use in routine clinical practice, as data are conflicting and the same biomarker may occur in several cancer types, such as bladder, colorectal, liver, lung, ovarian, and pancreatic cancer [3,10,11]. Attempts were made to identify certain proteome profiles that could be associated with atherosclerosis and coronary artery disease, but data so far are not sufficient to be used in real-life clinical work [12].


Will urine proteomics be of any help in the diagnosis and management of gynecological diseases? Cytokeratin-19 was found to be highly up-regulated in the urine of women with endometriosis, and therefore it was suggested that it may serve as a biomarker of the disease [13]. However, a recent study was not able to replicate those results [14]. This probably points at one of the greatest challenges in utilizing such diagnostic tests, since biomarkers are often shared by several pathologies and thus are not specific to one disease. Therefore, the trend has been to shift towards implementing a whole panel of biomarkers, which may increase specificity. Testing urine samples from women with or without endometriosis revealed 133 proteins which were different between the two groups [15]. Another study identified six statistically significant putative urinary peptide markers while comparing controls with moderate/severe endometriosis patients [16]. These relevant biomarkers showed a good sensitivity and specificity profile, and thus it was concluded that they might be used for diagnosis and staging of endometriosis. A small study demonstrated, in all women with interstitial cystitis/painful bladder syndrome (IC/PBS), the presence of alpha-1B-glycoprotein and orosomucoid-1, and the majority of these patients had elevated expression of these two proteins compared to control subjects [17]. Contrarily, transthyretin and hemopexin were detected in all control individuals, while the majority of the IC/PBS patients had decreased expression levels of these two proteins. These results may indicate that cell adhesion and response to stimuli are down-regulated whereas response to inflammation, wounding, and tissue degradation are up-regulated in IC/PBS. 


Urinary proteins were tested in obstetrics as well. Comparison of pregnant with non-pregnant controls yielded a panel of 284 pregnancy-specific proteomic biomarkers [18]. Subsequently, a model of 50 biomarkers was developed from urinary specimens obtained at week 28 that was associated with the risk of future pre-eclampsia. The urinary markers appeared to predict pre-eclampsia at gestational week 28 with good confidence but were not reliable at earlier time points. The association of certain proteomic profiles with pre-eclampsia was demonstrated in other studies as well, one of which was able to correctly distinguish between severe and mild pre-eclampsia [19]. 


Many diseases can be accurately diagnosed or monitored by invasive procedures. But modern medicine looks for ways to achieve similar data or forecasts without the need to be invasive. Building clinical risk scores is one alternative but more sophisticated methodologies, such as gene profiling or determination of serum or urine proteomics are the best next-generation choices. Still, these have to be better developed and validated before we could use them routinely.

Amos Pines
Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel


  1. Decramer S, Gonzalez de Peredo A, Breuil B, et al. Urine in clinical proteomics. Mol Cell Proteomics 2008;7:1850-62

  2. Barratt J, Topham P. Urine proteomics: the present and future of measuring urinary protein components in disease. CMAJ 2007;177:361-8

  3. Pedroza-Díaz J, Röthlisberger S. Advances in urinary protein biomarkers for urogenital and non-urogenital pathologies. Biochem Med (Zagreb) 2015;25:22-35

  4. Good DM, Zurbig P, Argiles A, et al. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics 2010;9:242437

  5. Haubitz M, Wittke S, Weissinger EM, et al. Urine protein patterns can serve as diagnostic tools in patients with IgA nephropathy. Kidney Int 2005;67:231320

  6. Morgan R, Boxall A, Bhatt A, et al. Engrailed-2 (EN2): a tumor specific urinary biomarker for the early diagnosis of prostate cancer. Clin Cancer Res 2011;17:10908

  7. Geisler C, Gaisa NT, Pfister D, et al. Identification and validation of potential new biomarkers for prostate cancer diagnosis and prognosis using 2D-DIGE and MS. Biomed Res Int 2015;2015:454256

  8. Lu CM, Lin JJ, Huang HH, et al. A panel of tumor markers, calreticulin, annexin A2, and annexin A3 in upper tract urothelial carcinoma identified by proteomic and immunological analysis. BMC Cancer 2014;14:363

  9. Chen LM, Chang M, Dai Y, et al. External validation of a multiplex urinary protein panel for the detection of bladder cancer in a multicenter cohort. Cancer Epidemiol Biomarkers Prev 2014;23:1804-12

  10. Kveiborg M, Frohlich C, Albrechtsen R, et al. A role for ADAM12 in breast tumor progression and stromal cell apoptosis. Cancer Res 2005;65:475461

  11. Pories SE, Zurakowski D, Roy R, et al. Urinary metalloproteinases: noninvasive biomarkers for breast cancer risk assessment. Cancer Epidemiol Biomarkers Prev 2008;17:103442

  12. Delles C, Schiffer E, von Zur Muhlen C, et al. Urinary proteomic diagnosis of coronary artery disease: identification and clinical validation in 623 individuals. J Hypertens 2010;28:2316-22

  13. Gjavotchanoff R. CYFRA 21-1 in urine: a diagnostic marker for endometriosis? Int J Womens Health 2015;7: 205211

  14. Kuessel L, Jaeger-Lansky A, Pateisky P, et al. Cytokeratin-19 as a biomarker in urine and in serum for the diagnosis of endometriosis--a prospective study. Gynecol Endocrinol 2014;30:38-41

  15. Tokushige N, Markham R, Crossett B, et al. Discovery of a novel biomarker in the urine in women with endometriosis. Fertil Steril 2011;95:46-9

  16. El-Kasti MM, Wright C, Fye HK, Roseman F, Kessler BM, Becker CM. Urinary peptide profiling identifies a panel of putative biomarkers for diagnosing and staging endometriosis. Fertil Steril 2011;95:1261-6

  17. Goo YA, Tsai YS, Liu AY, Goodlett DR, Yang CC. Urinary proteomics evaluation in interstitial cystitis/painful bladder syndrome: a pilot study. Int Braz J Urol 2010;36:464-78

  18. Carty DM, Siwy J, Brennand JE, Zürbig P, et al. Urinary proteomics for prediction of preeclampsia. Hypertension 2011;57:561-9

  19. Lee SM, Park JS, Norwitz ER, et al. Characterization of discriminatory urinary proteomic biomarkers for severe preeclampsia using SELDI-TOF mass spectrometry. J Perinat Med 2011;39:391-6

El siguiente comentario es una traduccin de una contribucin original en Ingls enviada a los miembros el Septiembre 30, 2013. La traduccin ha sido gentilmente efectuada por el

Dr Peter Chedraui

Influencia del uso reciente y duración de glucocorticoides sobre la densidad mineral ósea y el riesgo de fracturas

Majunder y colegas han informado recientemente sobre la influencia del uso reciente y la duración de glucocorticoides orales sistémicos sobre la densidad mineral ósea (DMO) y el riesgo de fractura, independientemente de la dosis. Mediante un estudio de cohorte basado en la población y la utilización de un conjunto de datos validados que incluía todos los residentes de Manitoba, se logró identificar a todos los adultos mayores de 40 años que se habían sometido a una densitometría (DXA) entre 1998 y 2007, y que fuesen seguidos durante 5 años. De los 50,000 sujetos, 25% informó el uso previo de los glucocorticoides. A los 5 años de seguimiento, 5% de ellos reportó una fractura osteoporótica mayor y 1% sufrió una fractura de cadera. En un análisis ajustado por factores de confusión, se informó que el uso reciente (hasta 1 año) y prolongado (más de 90 días) de glucocorticoides sistémicos se asoció significativamente a menor DMO o mayor riesgo de fractura. La exposición de corta duración, ya sea remota o reciente, no tuvo efecto.


Este reporte apoya el concepto de que el tamaño muestral no importa! Se acepta que, en general, la exposición a glucocorticoides tiene un efecto negativo sobre el hueso expresado a través de los osteoclastos. Esto resulta en una rápida pérdida de DMO y mayor riesgo de fractura, pero que generalmente se revierte a los 12 meses después de cesar su uso. Se deben tomar medidas preventivas con el tiempo (más temprano que tarde). La primera impresión es que el riesgo aumenta a medida que aumenta la dosis, pero no se ha establecido cual es la dosis más baja y segura a usar. Aunque este estudio tiene algunas limitaciones (como ha sido señalado por los autores), apoyo su conclusión. Al iniciar terapia con glucocorticoides sistémicos, se debe considerar el uso adicional de agentes protectores del hueso, si la duración prevista de la terapia es más de 90 días o al menos cuando la duración de la exposición excede 90 días, independientemente de la dosis o de los valores de la DMO.

Tobie J. de Villiers

Department of Gynaecology, Faculty of Health Sciences, University of Stellenbosch, South Africa


  1. Majumdar SR, Morin SN, Lix LM, Leslie WD. Influence of recency and duration of glucocorticoid use on bone mineral density and risk of fractures: population-based cohort study. Osteoporos Int 2013;24:2493-8

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