Urinary proteome profiles associated with cognitive decline in community elderly residents—A pilot study

Watanabe, Yumi; Hirao, Yoshitoshi; Kasuga, Kensaku; Kitamura, Kaori; Nakamura, Kazutoshi; Yamamoto, Tadashi

doi:10.3389/fneur.2023.1134976

Cited by 3 publications

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The urine proteome is an interesting source for non-invasive biomarker development, as it is readily available in large quantities, the majority of its´ content independent of diurnal variability and proteolytic degradation, and accessible to mass spectrometer measurement 42 . First studies have capitalised on these characteristics for biomarker detection in patients with cognitive decline 43,44 , neural ceroid lipofuscinosis 45 , kidney disease 46 , cancer 47 , dermatomyositis 48 but also Parkinson´s disease 49,50 .…”

Section: Introductionmentioning

confidence: 99%

Detection of prodromal Parkinson’s disease using a urine proteomics panel and machine learning

Hällqvist,

Schreglmann,

Kulcsarova

et al. 2023

Preprint

View full text Add to dashboard Cite

Parkinson's disease is a progressive neurological disorder that affects movement and can cause a wide range of symptoms. Idiopathic REM-sleep behaviour disorder (iRBD) has been identified as the single most specific early symptom of Parkinson's disease to date. In order to facilitate the screening of individuals at high risk to develop Parkinson's disease, we developed a multiplexed panel of urine proteomics using machine learning and targeted mass spectrometry to detect iRBD. Random urine samples from clinically and genetically well characterized patients with iRBD, idiopathic and hereditary forms of Parkinson's disease, as well as matching controls, were collected in two academic centres in a standardized way and analysed using our assay. In the first instance, a biomarker discovery and exploratory comparison of samples from randomly selected idiopathic Parkinson's patients and age/sex-matched healthy controls were proteomic profiled and quantified (> 2500 proteins). The most differentially expressed biomarkers were combined into a high-throughput, multiplexed assay using targeted proteomics and specifically designed for use of tandem mass spectrometers for potential translation into clinical practise. This test was then validated on independent patient and control samples (n=184). After detecting a major influence of sex on the proteome, we focused subsequent analyses on the larger group of available male samples (n = 114) and report results based on iRBD (n = 14), idiopathic (n = 35) and young-onset Parkinson's disease (n = 15), carriers of LRRK2 (n = 10), PARKIN-gene mutations (n = 5), and healthy control subjects (n = 35). After establishing excellent compatibility between the two study sites, orthogonal partial least squares discriminant analysis (OPLS-DA) excluded a relevant effect of ageing, but detected significant differences between iRBD and healthy controls (ANOVA-CV P = 0.002), as well as the combination of iPD/iRBD and healthy controls (ANOVA-CV P = 0.01). Uni- and multivariate analyses detected a shared expression pattern for the protein biomarkers UBC, NCAM1, MIEN1, SPP2, REG1B, ITIH2, BCHE and C3 between iRBD and idiopathic Parkinson's disease. Utilizing split train/test-datasets in a multiple-regression classifier model resulted in a mean accuracy of 78% to differentiate iRBD from controls, matching the clinical conversion rate for iRBD to Parkinson's disease. Hierarchical clustering revealed greater similarities in urine proteomic changes between iRBD and idiopathic than monogenic Parkinson's disease. Several proteins identified correlated either with clinical severity (e.g. VCAM1, MSN, HPX), or risk for future conversion to Parkinson's disease (VCAM1, MSN, MYO10, HSPAIL). This assay demonstrates the power of machine learning and urine biomarkers to identify iRBD patients. As we develop new therapies and interventions, the ability to detect individuals at-risk of neurodegeneration in very early disease stage will be invaluable in treating Parkinson's disease.

show abstract

Section: Introductionmentioning

confidence: 99%

Detection of prodromal Parkinson’s disease using a urine proteomics panel and machine learning

Hällqvist,

Schreglmann,

Kulcsarova

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Proteomic and computational analyses followed by functional validation of protective effects of trigonelline against calcium oxalate-induced renal cell deteriorations

Peerapen,

Boonmark,

Putpeerawit

et al. 2023

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

Recent progress in mass spectrometry-based urinary proteomics

Joshi,

Garapati,

Ghose

et al. 2024

Clin Proteom

View full text Add to dashboard Cite

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis—from characterizing diseases to discovering predictive, diagnostic and prognostic markers—continues to drive investigations of the urinary proteome.

show abstract

Urinary proteome profiles associated with cognitive decline in community elderly residents—A pilot study

Cited by 3 publications

References 51 publications

Detection of prodromal Parkinson’s disease using a urine proteomics panel and machine learning

Detection of prodromal Parkinson’s disease using a urine proteomics panel and machine learning

Proteomic and computational analyses followed by functional validation of protective effects of trigonelline against calcium oxalate-induced renal cell deteriorations

Recent progress in mass spectrometry-based urinary proteomics

Contact Info

Product

Resources

About