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Liu Y, Hou W, Gao T, Yan Y, Wang T, Zheng C, Zeng P. Influence and role of polygenic risk score in the development of 32 complex diseases. J Glob Health 2025; 15:04071. [PMID: 40063714 PMCID: PMC11893022 DOI: 10.7189/jogh.15.04071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2025] Open
Abstract
Background The polygenic risk score (PRS) has been perceived as advantageous in predicting the risk of complex diseases compared to other measures. We aimed to systematically evaluate the influence of PRS on disease outcome and to explore its predictive value. Methods We comprehensively assessed the relationship between PRS and 32 complex diseases in the UK Biobank. We used Cox models to estimate the effects of PRS on the incidence risk. Then, we constructed prediction models to assess the clinical utility of PRS in risk prediction. For 16 diseases, we further compared the disease risk and prediction capability of PRS across early and late-onset cases. Results Higher PRS led to greater incident risk, with hazard ratio (HR) ranging from 1.07 (95% confidence interval (CI) = 1.06-1.08) for panic/anxiety disorder to 4.17 (95% CI = 4.03-4.31) for acute pancreatitis. This effect was more pronounced in early-onset cases for 12 diseases, increasing by 52.8% on average. Particularly, the early-onset risk of heart failure associated with PRS (HR = 3.02; 95% CI = 2.53-3.59) was roughly twice compared to the late-onset risk (HR = 1.48; 95% CI = 1.46-1.51). Compared to average PRS (20-80%), individuals positioned within the top 2.5% of the PRS distribution exhibited varying degrees of elevated risk, corresponding to a more than five times greater risk on average. PRS showed additional value in clinical risk prediction, causing an average improvement of 6.1% in prediction accuracy. Further, PRS demonstrated higher predictive accuracy for early-onset cases of 11 diseases, with heart failure displaying the most significant (37.5%) improvement when incorporating PRS into the prediction model (concordance index (C-index) = 0.546; standard error (SE) = 0.011 vs. C-index = 0.751; SE = 0.010, P = 2.47 × 10-12). Conclusions As a valuable complement to traditional clinical risk tools, PRS is closely related to disease risk and can further enhance prediction accuracy, especially for early-onset cases, underscoring its potential role in targeted prevention for high-risk groups.
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Affiliation(s)
- Yuxin Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Wenyan Hou
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Tongyu Gao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yu Yan
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Chu Zheng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Jiangsu Engineering Research Centre of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Jiangsu Engineering Research Centre of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
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Bader I, Groot C, Tan HS, Milongo JMA, Haan JD, Verberk IMW, Yong K, Orellina J, Campbell S, Wilson D, van Harten AC, Kok PHB, van der Flier WM, Pijnenburg YAL, Barkhof F, van de Giessen E, Teunissen CE, Bouwman FH, Ossenkoppele R. Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer's disease pathology in the eye clinic. Alzheimers Res Ther 2024; 16:190. [PMID: 39169442 PMCID: PMC11340081 DOI: 10.1186/s13195-024-01545-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 07/25/2024] [Indexed: 08/23/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups. METHODS The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aβ-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments. RESULTS We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aβ- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding. CONCLUSIONS We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection. TRIAL REGISTRATION The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23-09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.
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Affiliation(s)
- Ilse Bader
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands.
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands.
- Department of Ophthalmology, Bergman Clinics, Amsterdam, 1101 BM, The Netherlands.
| | - Colin Groot
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - H Stevie Tan
- Department of Ophthalmology, Bergman Clinics, Amsterdam, 1101 BM, The Netherlands
- Department of Ophthalmology, Amsterdam UMC, Amsterdam, 1081 HV, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
- Amsterdam UMC Location VUmc, Amsterdam Reproduction and Development Research Institute, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Jean-Marie A Milongo
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
- Department of Ophthalmology, Bergman Clinics, Amsterdam, 1101 BM, The Netherlands
| | - Jurre den Haan
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Inge M W Verberk
- Neurochemistry Laboratory, Laboratory Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081 HV, The Netherlands
| | - Keir Yong
- Queen Square Institute of Neurology, Dementia Research Centre, London, WC1N 3BG, UK
| | | | | | | | - Argonde C van Harten
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Pauline H B Kok
- Department of Ophthalmology, Bergman Clinics, Amsterdam, 1101 BM, The Netherlands
| | - Wiesje M van der Flier
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
- Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081 HV, The Netherlands
| | - Yolande A L Pijnenburg
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Frederik Barkhof
- Amsterdam Neuroscience, Brain Imaging, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081 HZ, The Netherlands
- UCL Queen Square Institute of Neurology and Centre for Medical Image Computing, University College, London, WC1N 3BG, UK
| | - Elsmarieke van de Giessen
- Amsterdam Neuroscience, Brain Imaging, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Charlotte E Teunissen
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
- Neurochemistry Laboratory, Laboratory Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081 HV, The Netherlands
| | - Femke H Bouwman
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Rik Ossenkoppele
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, 1081 HV, The Netherlands.
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands.
- Clinical Memory Research Unit, Lund University, Lund, Sweden.
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Li K, Hou Y, Han J, Lv C, Liu W, Du J, Sun W, Fan J, Peng X. A non-peptide-based fluorescent probe capable of sensitively visualizing asparagine endopeptidase. Chem Commun (Camb) 2024; 60:3031-3034. [PMID: 38391081 DOI: 10.1039/d3cc05419e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The non-peptide-based fluorescent probe QMC11 is capable of specifically targeting asparagine endopeptidase (AEP) and imaging cellular endogenous AEP. The motion of the probe can be restricted by AEP to activate fluorescence while keeping a low background signal.
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Affiliation(s)
- Kang Li
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Yanxian Hou
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Jinliang Han
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Chengyuan Lv
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Wenkai Liu
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Jianjun Du
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Wen Sun
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
| | - Jiangli Fan
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
- Ningbo Institute of Dalian University of Technology, Ningbo 315016, China
| | - Xiaojun Peng
- State Key Laboratory of Fine Chemicals, Frontiers Science Centre for Smart Materials Oriented Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
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