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Don J, Schork AJ, Glusman G, Rappaport N, Cummings SR, Duggan D, Raju A, Hellberg KLG, Gunn S, Monti S, Perls T, Lapidus J, Goetz LH, Sebastiani P, Schork NJ. The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank. GeroScience 2024; 46:3911-3927. [PMID: 38451433 PMCID: PMC11226417 DOI: 10.1007/s11357-024-01107-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/21/2024] [Indexed: 03/08/2024] Open
Abstract
Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.
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Affiliation(s)
- Janith Don
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Andrew J Schork
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | | | | | - Steve R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - David Duggan
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Anish Raju
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Kajsa-Lotta Georgii Hellberg
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | - Sophia Gunn
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Stefano Monti
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Thomas Perls
- Department of Medicine, Section of Geriatrics, Boston University, Boston, MA, USA
| | - Jodi Lapidus
- Department of Biostatistics, Oregon Health & Science University, Portland, OR, USA
| | - Laura H Goetz
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Veterans Affairs Loma Linda Health Care, Loma Linda, CA, USA
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Tufts University School of Medicine and Data Intensive Study Center, Boston, MA, USA
| | - Nicholas J Schork
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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Danish M, Dratva MA, Lui KK, Heyworth N, Wang X, Malhotra A, Hartman SJ, Lee EE, Sundermann EE, Banks SJ. Intersections of Modifiable Risks: Loneliness is Associated with Poor Subjective Sleep Quality in Older Women at Risk for Alzheimer's Disease. Int J Aging Hum Dev 2024:914150241255888. [PMID: 39054949 DOI: 10.1177/00914150241255888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
We examined the relationship between subjective and objective sleep outcomes and loneliness in older women at risk for Alzheimer's disease (AD). Our sample consisted of 39 participants (aged 65+) with mild cognitive deficits who completed the UCLA Loneliness Scale, the Pittsburgh Sleep Quality Index (PSQI), and an at home sleep test, to determine presence of obstructive sleep apnea. Based on sleep quality scores, individuals categorized as "poor sleepers" had significantly higher loneliness scores than "good sleepers." However, total loneliness scores did not significantly differ between groups with or without sleep apnea. We found that higher loneliness was significantly associated to lower habitual sleep efficiency and sleep duration and was also influenced by use of sleep medication. Our findings suggest that increased loneliness relates to worse subjective sleep quality, but not to sleep apnea. These findings suggest that combined interventions targeting loneliness and sleep quality may be important for older women.
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Affiliation(s)
- Madina Danish
- MADURA ADAR Program, University of California, San Diego, La Jolla, CA, USA
| | - Melanie A Dratva
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Kitty K Lui
- Joint Doctoral Program in Clinical Psychology, SDSU/UC San Diego, San Diego, CA, USA
| | - Nadine Heyworth
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Xin Wang
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Atul Malhotra
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Sheri J Hartman
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Ellen E Lee
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Erin E Sundermann
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Sarah J Banks
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
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Gong B, Zhang W, Cong W, Gu Y, Ji W, Yin T, Zhou H, Hu H, Zhuang J, Luo Y, Liu Y, Gao J, Yin Y. Systemic Administration of Neurotransmitter-Derived Lipidoids-PROTACs-DNA Nanocomplex Promotes Tau Clearance and Cognitive Recovery for Alzheimer's Disease Therapy. Adv Healthc Mater 2024:e2400149. [PMID: 39007278 DOI: 10.1002/adhm.202400149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/10/2024] [Indexed: 07/16/2024]
Abstract
Alzheimer's disease (AD) poses a significant burden on the economy and healthcare systems worldwide. Although the pathophysiology of AD remains debatable, its progression is strongly correlated with the accumulation of tau aggregates. Therefore, tau clearance from brain lesions can be a promising strategy for AD therapy. To achieve this, the present study combined proteolysis-targeting chimera (PROTAC), a novel protein-degradation technique that mediates degradation of target proteins via the ubiquitin-proteasome system, and a neurotransmitter-derived lipidoid (NT-lipidoid) nanoparticle delivery system with high blood-brain barrier-penetration activity, to generate a novel nanomedicine named NPD. Peptide 1, a cationic tau-targeting PROTAC is loaded onto the positively charged nanoparticles using DNA-intercalation technology. The resulting nanomedicine displayed good encapsulation efficiency, serum stability, drug release profile, and blood-brain barrier-penetration capability. Furthermore, NPD potently induced tau clearance in both cultured neuronal cells and the brains of AD mice. Moreover, intravenous injection of NPD led to a significant improvement in the cognitive function of the AD mice, without any remarkable abnormalities, thereby supporting its clinical development. Collectively, the novel nanomedicine developed in this study may serve as an innovative strategy for AD therapy, since it effectively and specifically induces tau protein clearance in brain lesions, which in turn enhances cognition.
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Affiliation(s)
- Baofeng Gong
- Department of Neurology, Second Affiliated Hospital (Shanghai Changzheng Hospital) of Naval Medical University, Shanghai, 200003, China
| | - Weicong Zhang
- School of Pharmacy, University College London, London, WC1N 1AX, UK
| | - Wei Cong
- School of Medicine, Shanghai University, Shanghai, 200444, China
| | - Yuankai Gu
- Department of Neurology, Second Affiliated Hospital (Shanghai Changzheng Hospital) of Naval Medical University, Shanghai, 200003, China
| | - Wenbo Ji
- Department of Neurology, Second Affiliated Hospital (Shanghai Changzheng Hospital) of Naval Medical University, Shanghai, 200003, China
| | - Tong Yin
- Department of Neurology, Second Affiliated Hospital (Shanghai Changzheng Hospital) of Naval Medical University, Shanghai, 200003, China
| | - Honglei Zhou
- Department of General Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Honggang Hu
- School of Medicine, Shanghai University, Shanghai, 200444, China
| | - Jianhua Zhuang
- Department of Neurology, Second Affiliated Hospital (Shanghai Changzheng Hospital) of Naval Medical University, Shanghai, 200003, China
| | - Yi Luo
- New Drug Discovery and Development, Biotheus Inc., Zhuhai, 519080, China
- Clinical Pharmacy Innovation Institute, Shanghai Jiao Tong University of Medicine, Shanghai, 200240, China
| | - Yan Liu
- Clinical Pharmacy Innovation Institute, Shanghai Jiao Tong University of Medicine, Shanghai, 200240, China
- Department of Clinical Pharmacy, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Jie Gao
- Changhai Clinical Research Unit, Shanghai Changhai Hospital, Naval Medical University, Shanghai, 200433, China
| | - You Yin
- Department of Neurology, Second Affiliated Hospital (Shanghai Changzheng Hospital) of Naval Medical University, Shanghai, 200003, China
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
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4
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Farhadieh ME, Mozafar M, Sanaaee S, Sodeifi P, Kousha K, Zare Y, Zare S, Maleki Rad N, Jamshidi-Goharrizi F, Allahverdloo M, Rahimi A, Sadeghi M, Shafie M, Mayeli M. Polygenic hazard score predicts synaptic and axonal degeneration and cognitive decline in Alzheimer's disease continuum. Arch Gerontol Geriatr 2024; 127:105576. [PMID: 39096557 DOI: 10.1016/j.archger.2024.105576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 07/08/2024] [Accepted: 07/13/2024] [Indexed: 08/05/2024]
Abstract
BACKGROUND Growth associated protein-43 (GAP-43) and neurofilaments light (NFL) are biomarkers of synaptic and axonal injury, and are associated with cognitive decline in Alzheimer's disease (AD) contiuum. We investigated whether Polygenic Hazard Score (PHS) is associated with specific biomarkers and cognitive measures, and if it can predict the relationship between GAP-43, NFL, and cognitive decline in AD. METHOD We enrolled 646 subjects: 93 with AD, 350 with mild cognitive impairment (MCI), and 203 cognitively normal controls. Variables included GAP-43, plasma NFL, and PHS. A PHS of 0.21 or higher was considered high risk while a PHS below this threshold was considered low risk. A subsample of 190 patients with MCI with four years of follow-up cognitive assessments were selected for longitudinal analysis . We assessed the association of the PHS with AD biomarkers and cognitive measures, as well as the predictive power of PHS on cognitive decline and the conversion of MCI to AD. RESULTS PHS showed high diagnostic accuracy in distinguishing AD, MCI, and controls. At each follow-up point, high risk MCI patients showed higher level of cognitive impairment compared to the low risk group. GAP-43 correlated with all follow-up cognitive tests in high risk MCI patients which was not detected in low risk MCI patients. Moreover, high risk MCI patients progressed to dementia more rapidly compared to low risk patients. CONCLUSION PHS can predict cognitive decline and impacts the relationship between neurodegenerative biomarkers and cognitive impairment in AD contiuum. Categorizing patients based on PHS can improve the prediction of cognitive outcomes and disease progression.
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Affiliation(s)
- Mohammad-Erfan Farhadieh
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Sciences and Technology, University of Isfahan, Isfahan, Iran
| | - Mehrdad Mozafar
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; NeuroTRACT Association, Tehran University of Medical Sciences, Tehran, Iran
| | - Saameh Sanaaee
- Department of Cellular and Molecular Biology, School of Advanced Sciences and Technology, Islamic Azad University of Medical Sciences, Tehran, Iran
| | - Parastoo Sodeifi
- School of Medicine, Azad University of Medical Sciences, Tehran, Iran
| | - Kiana Kousha
- Department of Plant and Animal Biology, Faculty of Biological Sciences and Technology, University of Isfahan, Isfahan, Iran
| | - Yeganeh Zare
- Department of Psychology, Faculty of Literature, Humanities and Social Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Shahab Zare
- Department of Educational Psychology, Faculty of Psychology and Educational Science, Allameh Tabatabai University, Tehran, Iran
| | - Nooshin Maleki Rad
- Department of Linguistic, Faculty of Literatures and Human Sciences, University of Ferdowsi, Mashhad, Iran
| | - Faezeh Jamshidi-Goharrizi
- Department of Sociology-Cultural Sociology, Islamic Azad University, Kish International Branch, Hormozgan, Iran; Department of Psychology, Faculty of Psychology, Payame Noor University, Tehran, Iran
| | - Mohammad Allahverdloo
- Department of Psychology, Faculty of Literature, Humanities and Social Sciences, Islamic Azad University, Zanjan, Iran
| | - Arman Rahimi
- Institue of Translational Medicine, Semmelwis, Budapest, Hungary
| | - Mohammad Sadeghi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; NeuroTRACT Association, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahan Shafie
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; NeuroTRACT Association, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahsa Mayeli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
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Hahn G, Prokopenko D, Hecker J, Lutz SM, Mullin K, Tanzi RE, DeSantis S, Lange C. Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model. Genet Epidemiol 2024. [PMID: 38982682 DOI: 10.1002/gepi.22581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 04/23/2024] [Accepted: 06/25/2024] [Indexed: 07/11/2024]
Abstract
The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.
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Affiliation(s)
- Georg Hahn
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Dmitry Prokopenko
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Sharon M Lutz
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Kristina Mullin
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Stacia DeSantis
- The University of Texas Health Science Center, Houston, Texas, USA
| | - Christoph Lange
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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Asp I, Cawley-Bennett ATJ, Frascino JC, Golshan S, Bondi MW, Smith CN. News event memory in amnestic and non-amnestic MCI, heritable risk for dementia, and subjective memory complaints. Neuropsychologia 2024; 199:108887. [PMID: 38621578 DOI: 10.1016/j.neuropsychologia.2024.108887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/09/2024] [Accepted: 04/09/2024] [Indexed: 04/17/2024]
Abstract
Robust and sensitive clinical measures are needed for more accurate and earlier detection of Alzheimer's disease (AD), for staging preclinical AD, and for gauging the efficacy of treatments. Mild impairment on episodic memory tests is thought to indicate a cognitive risk of developing AD and mild cognitive impairment (MCI), considered to be a transitional stage between normal aging and AD. Novel tests of semantic memory, such as memory for news events, are also impaired early on but have received little clinical attention even though they may provide a novel way to assess cognitive risk for AD. We examined memory for news events in older adults with normal cognition (NC, N = 34), amnestic MCI (aMCI, N = 27), or non-aMCI (N = 10) using the Retrograde Memory News Events Test (RM-NET). We asked if news event memory was sensitive to 1) aMCI and also non-aMCI, which has rarely been examined, 2) genetic risk for dementia (positive family history of any type of dementia, presence of an APOE-4 allele, or polygenic risk for AD), and 3) subjective memory functioning judgments about the past. We found that both MCI subgroups exhibited impaired RM-NET Lifespan accuracy scores together with temporally-limited retrograde amnesia. For the aMCI group amnesia extended back 45 years prior to testing, but not beyond that time frame. The extent of retrograde amnesia could not be reliably estimated in the small non-aMCI group. The effect sizes of having MCI on the RM-NET were medium for the non-aMCI group and large for the aMCI group, whereas the effect sizes of participant characteristics on RM-NET accuracy scores were small. For the combined MCI group (N = 37), news event memory was significantly related to positive family history of dementia but was not related to the more specific genetic markers of AD risk. For the NC group, news event memory was not related to any measure of genetic risk. Objective measures of past memory from the RM-NET were not related to subjective memory judgements about the present or the recent past in either group. By contrast, when individuals subjectively compared their present versus past memory abilities, there was a significant association between this judgment and objective measures of the past from the RM-NET (direct association for the NC group and inverse for the MCI group). The RM-NET holds significant promise for early identification of those with cognitive and genetic risk factors for AD and non-AD dementias.
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Affiliation(s)
- Isabel Asp
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA
| | | | - Jennifer C Frascino
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Shahrokh Golshan
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Mark W Bondi
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Christine N Smith
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA; Center for the Neurobiology of Learning and Memory, University of California Irvine, CA, USA.
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7
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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Yang X, Sullivan PF, Li B, Fan Z, Ding D, Shu J, Guo Y, Paschou P, Bao J, Shen L, Ritchie MD, Nave G, Platt ML, Li T, Zhu H, Zhao B. Multi-organ imaging-derived polygenic indexes for brain and body health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.04.18.23288769. [PMID: 38883759 PMCID: PMC11177904 DOI: 10.1101/2023.04.18.23288769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.
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Affiliation(s)
- Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Patrick F. Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- UCLA Samueli School of Engineering, Los Angeles, CA 90095, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dezheng Ding
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yuxin Guo
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Gideon Nave
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael L. Platt
- Marketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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9
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Hahn G, Prokopenko D, Hecker J, Lutz SM, Mullin K, Sejour L, Hide W, Vlachos I, DeSantis S, Tanzi RE, Lange C. Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data. Brief Bioinform 2024; 25:bbae267. [PMID: 38836403 PMCID: PMC11151121 DOI: 10.1093/bib/bbae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/17/2024] [Accepted: 05/16/2024] [Indexed: 06/06/2024] Open
Abstract
In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.
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Affiliation(s)
- Georg Hahn
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, 02115, Boston, MA, USA
| | - Dmitry Prokopenko
- Department of Neurology, Genetics and Aging Research Unit, McCance Center for Brain Health, Massachusetts General Hospital, 55 Fruit Street, 02114, Boston, MA, USA
| | - Julian Hecker
- Channing Divsion of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis Street, 02115, Boston, MA, USA
| | - Sharon M Lutz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, 02115, Boston, MA, USA
| | - Kristina Mullin
- Department of Neurology, Genetics and Aging Research Unit, McCance Center for Brain Health, Massachusetts General Hospital, 55 Fruit Street, 02114, Boston, MA, USA
| | - Leinal Sejour
- Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, 02215, Boston, MA, USA
| | - Winston Hide
- Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, 02215, Boston, MA, USA
| | - Ioannis Vlachos
- Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, 02215, Boston, MA, USA
| | - Stacia DeSantis
- Houston Campus, The University of Texas Health Science Center, 1200 Pressler Street, 77030, Houston, TX, USA
| | - Rudolph E Tanzi
- Department of Neurology, Genetics and Aging Research Unit, McCance Center for Brain Health, Massachusetts General Hospital, 55 Fruit Street, 02114, Boston, MA, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, 02115, Boston, MA, USA
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10
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Ahmadi N, Dratva MA, Heyworth N, Wang X, Blennow K, Banks SJ, Sudermann EE. Moving Beyond Depression: Mood Symptoms Across the Spectrum Relate to Tau Pathology in Older Women at Risk for Alzheimer's Disease. Int J Aging Hum Dev 2024:914150241253257. [PMID: 38751054 DOI: 10.1177/00914150241253257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
We examined how symptoms across the mood spectrum relate to Alzheimer's disease (AD) biomarkers in older women at high risk for AD. Participants included 25 women aged 65+ with mild cognitive deficits and elevated AD genetic risk. The Profile of Mood States Questionnaire measured mood symptoms and a total mood disturbance (TMD) score. Tau burden in the meta-temporal region of interest was measured using MK-6240 Tau positron emission tomography (PET) imaging. A subset (n = 12) also had p-Tau181, and Aß40/42 levels measured in plasma. Higher TMD scores related to higher tau PET standardized uptake value ratio (SUVR). Greater negative mood symptoms correlated with higher tau PET SUVR, while greater vigor correlated with lower SUVR. Similar results were seen with plasma p-Tau181 levels, but not with Aβ40/42 levels. In conclusion, positive and negative mood symptoms related to tau pathology in older women at high risk for AD, highlighting a role of mental well-being in AD risk.
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Affiliation(s)
| | - Melanie A Dratva
- Department of Neurosciences, University of California, San Diego, USA
| | - Nadine Heyworth
- Department of Neurosciences, University of California, San Diego, USA
| | - Xin Wang
- Department of Neurosciences, University of California, San Diego, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Sarah J Banks
- Department of Neurosciences, University of California, San Diego, USA
- Department of Psychiatry, University of California, San Diego, USA
| | - Erin E Sudermann
- Department of Psychiatry, University of California, San Diego, USA
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Zhuang X, Xu G, Amei A, Cordes D, Wang Z, Oh EC. Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal genome-wide association studies model. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12597. [PMID: 38855650 PMCID: PMC11157162 DOI: 10.1002/dad2.12597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/19/2024] [Accepted: 04/17/2024] [Indexed: 06/11/2024]
Abstract
INTRODUCTION The development and progression of Alzheimer's disease (AD) is a complex process, during which genetic influences on phenotypes may also change. Incorporating longitudinal phenotypes in genome-wide association studies (GWAS) could unmask these genetic loci. METHODS We conducted a longitudinal GWAS using a varying coefficient test to identify age-dependent single nucleotide polymorphisms (SNPs) in AD. Data from 1877 Alzheimer's Neuroimaging Data Initiative participants, including impairment status and amyloid positron emission tomography (PET) scan standardized uptake value ratio (SUVR) scores, were analyzed using a retrospective varying coefficient mixed model association test (RVMMAT). RESULTS RVMMAT identified 244 SNPs with significant time-varying effects on AD impairment status, with 12 SNPs on chromosome 19 validated using National Alzheimer's Coordinating Center data. Age-stratified analyses showed these SNPs' effects peaked between 70 and 80 years. Additionally, 73 SNPs were linked to longitudinal amyloid accumulation changes. Pathway analyses implicated immune and neuroinflammation-related disruptions. DISCUSSION Our findings demonstrate that longitudinal GWAS models can uncover time-varying genetic signals in AD. Highlights Identify time-varying genetic effects using a longitudinal GWAS model in AD.Illustrate age-dependent genetic effects on both diagnoses and amyloid accumulation.Replicate time-varying effect of APOE in a second dataset.
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Affiliation(s)
- Xiaowei Zhuang
- Interdisciplinary Neuroscience PhD ProgramUniversity of Nevada Las VegasLas VegasNevadaUSA
- Laboratory of Neurogenetics and Precision MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
- Lou Ruvo Center for Brain HealthCleveland ClinicLas VegasNevadaUSA
| | - Gang Xu
- Department of BiostatisticsYale School of Public HealthNew HavenConnecticutUSA
- Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasNevadaUSA
| | - Amei Amei
- Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasNevadaUSA
| | - Dietmar Cordes
- Lou Ruvo Center for Brain HealthCleveland ClinicLas VegasNevadaUSA
- Institute of Cognitive ScienceUniversity of Colorado BoulderBoulderColoradoUSA
| | - Zuoheng Wang
- Department of BiostatisticsYale School of Public HealthNew HavenConnecticutUSA
| | - Edwin C. Oh
- Interdisciplinary Neuroscience PhD ProgramUniversity of Nevada Las VegasLas VegasNevadaUSA
- Laboratory of Neurogenetics and Precision MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
- School of MedicineDepartment of Internal MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
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12
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Soni N, Hohsfield LA, Tran KM, Kawauchi S, Walker A, Javonillo D, Phan J, Matheos D, Da Cunha C, Uyar A, Milinkeviciute G, Gomez‐Arboledas A, Tran K, Kaczorowski CC, Wood MA, Tenner AJ, LaFerla FM, Carter GW, Mortazavi A, Swarup V, MacGregor GR, Green KN. Genetic diversity promotes resilience in a mouse model of Alzheimer's disease. Alzheimers Dement 2024; 20:2794-2816. [PMID: 38426371 PMCID: PMC11032575 DOI: 10.1002/alz.13753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a neurodegenerative disorder with multifactorial etiology, including genetic factors that play a significant role in disease risk and resilience. However, the role of genetic diversity in preclinical AD studies has received limited attention. METHODS We crossed five Collaborative Cross strains with 5xFAD C57BL/6J female mice to generate F1 mice with and without the 5xFAD transgene. Amyloid plaque pathology, microglial and astrocytic responses, neurofilament light chain levels, and gene expression were assessed at various ages. RESULTS Genetic diversity significantly impacts AD-related pathology. Hybrid strains showed resistance to amyloid plaque formation and neuronal damage. Transcriptome diversity was maintained across ages and sexes, with observable strain-specific variations in AD-related phenotypes. Comparative gene expression analysis indicated correlations between mouse strains and human AD. DISCUSSION Increasing genetic diversity promotes resilience to AD-related pathogenesis, relative to an inbred C57BL/6J background, reinforcing the importance of genetic diversity in uncovering resilience in the development of AD. HIGHLIGHTS Genetic diversity's impact on AD in mice was explored. Diverse F1 mouse strains were used for AD study, via the Collaborative Cross. Strain-specific variations in AD pathology, glia, and transcription were found. Strains resilient to plaque formation and plasma neurofilament light chain (NfL) increases were identified. Correlations with human AD transcriptomics were observed.
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Affiliation(s)
- Neelakshi Soni
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Lindsay A. Hohsfield
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Kristine M. Tran
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Shimako Kawauchi
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Transgenic Mouse Facility, ULAROffice of ResearchUniversity of CaliforniaIrvineCaliforniaUSA
| | - Amber Walker
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Transgenic Mouse Facility, ULAROffice of ResearchUniversity of CaliforniaIrvineCaliforniaUSA
| | - Dominic Javonillo
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Jimmy Phan
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Dina Matheos
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Celia Da Cunha
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Asli Uyar
- The Jackson LaboratoryBar HarborMaineUSA
| | - Giedre Milinkeviciute
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Angela Gomez‐Arboledas
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Katelynn Tran
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | | | - Marcelo A. Wood
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Andrea J. Tenner
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Molecular Biology and BiochemistryUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Pathology and Laboratory MedicineUniversity of CaliforniaIrvineCaliforniaUSA
| | - Frank M. LaFerla
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | | | - Ali Mortazavi
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Developmental and Cellular BiologyUniversity of CaliforniaIrvineCaliforniaUSA
- Center for Complex Biological SystemsUniversity of CaliforniaIrvineCaliforniaUSA
| | - Vivek Swarup
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Grant R. MacGregor
- Transgenic Mouse Facility, ULAROffice of ResearchUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Developmental and Cellular BiologyUniversity of CaliforniaIrvineCaliforniaUSA
| | - Kim N. Green
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
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Vilor‐Tejedor N, Genius P, Rodríguez‐Fernández B, Minguillón C, Sadeghi I, González‐Escalante A, Crous‐Bou M, Suárez‐Calvet M, Grau‐Rivera O, Brugulat‐Serrat A, Sánchez‐Benavides G, Esteller M, Fauria K, Molinuevo JL, Navarro A, Gispert JD. Genetic characterization of the ALFA study: Uncovering genetic profiles in the Alzheimer's continuum. Alzheimers Dement 2024; 20:1703-1715. [PMID: 38088508 PMCID: PMC10984507 DOI: 10.1002/alz.13537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 09/12/2023] [Accepted: 10/11/2023] [Indexed: 03/16/2024]
Abstract
INTRODUCTION In 2013, the ALzheimer's and FAmilies (ALFA) project was established to investigate pathophysiological changes in preclinical Alzheimer's disease (AD), and to foster research on early detection and preventive interventions. METHODS We conducted a comprehensive genetic characterization of ALFA participants with respect to neurodegenerative/cerebrovascular diseases, AD biomarkers, brain endophenotypes, risk factors and aging biomarkers. We placed particular emphasis on amyloid/tau status and assessed gender differences. Multiple polygenic risk scores were computed to capture different aspects of genetic predisposition. We additionally compared AD risk in ALFA to that across the full disease spectrum from the Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS Results show that the ALFA project has been successful at establishing a cohort of cognitively unimpaired individuals at high genetic predisposition of AD. DISCUSSION It is, therefore, well-suited to study early pathophysiological changes in the preclinical AD continuum. Highlights Prevalence of ε4 carriers in ALzheimer and FAmilies (ALFA) is higher than in the general European population The ALFA study is highly enriched in Alzheimer's disease (AD) genetic risk factors beyond APOE AD genetic profiles in ALFA are similar to clinical groups along the continuum ALFA has succeeded in establishing a cohort of cognitively unimpaired individuals at high genetic AD risk ALFA is well suited to study pathogenic events/early pathophysiological changes in AD.
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Affiliation(s)
- Natalia Vilor‐Tejedor
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Department of Clinical GeneticsErasmus University Medical CenterRotterdamNetherlands
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
| | - Patricia Genius
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
| | - Blanca Rodríguez‐Fernández
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
| | - Carolina Minguillón
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
| | - Iman Sadeghi
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
| | - Armand González‐Escalante
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Department of Medicine and Life SciencesUniversitat Pompeu FabraBarcelonaSpain
| | - Marta Crous‐Bou
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Department of EpidemiologyHarvard T.H. Chan School of Public Health. School of Public Health 2BostonMassachusettsUSA
- Catalan Institute of Oncology (ICO)‐Bellvitge Biomedical Research Center (IDIBELL)Hospital Duran i ReynalsBarcelonaSpain
| | - Marc Suárez‐Calvet
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
- Servei de NeurologiaHospital del MarBarcelonaSpain
| | - Oriol Grau‐Rivera
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
- Servei de NeurologiaHospital del MarBarcelonaSpain
| | - Anna Brugulat‐Serrat
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
- Global Brain Health InstituteSan FranciscoCaliforniaUSA
| | - Gonzalo Sánchez‐Benavides
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
| | - Manel Esteller
- Cancer Epigenetics, Josep Carreras Leukaemia Research Institute (IJC)BarcelonaSpain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Instituto de Salud Carlos IIIMadridSpain
- Integrated Pharmacology and Systems NeurosciencesIMIM‐Hospital del Mar Medical Research InstituteBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Physiological Sciences DepartmentSchool of Medicine and Health SciencesUniversity of Barcelona (UB)BarcelonaSpain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Experimental Medicine, H. Lundbeck A/SKøbenhavnDenmark
| | - Arcadi Navarro
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Department of Medicine and Life SciencesUniversitat Pompeu FabraBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Department of Experimental and Health SciencesInstitute of Evolutionary Biology (CSIC‐UPF)BarcelonaSpain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Department of Medicine and Life SciencesUniversitat Pompeu FabraBarcelonaSpain
- Centro de Investigación Biomédica en Red BioingenieríaBiomateriales y Nanomedicina. Instituto de Salud carlos IIIMadridSpain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
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14
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Gunter NB, Gebre RK, Graff-Radford J, Heckman MG, Jack CR, Lowe VJ, Knopman DS, Petersen RC, Ross OA, Vemuri P, Ramanan VK. Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes. Neurol Genet 2024; 10:e200120. [PMID: 38250184 PMCID: PMC10798228 DOI: 10.1212/nxg.0000000000200120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/01/2023] [Indexed: 01/23/2024]
Abstract
Background and Objectives Alzheimer disease (AD) has a polygenic architecture, for which genome-wide association studies (GWAS) have helped elucidate sequence variants (SVs) influencing susceptibility. Polygenic risk score (PRS) approaches show promise for generating summary measures of inherited risk for clinical AD based on the effects of APOE and other GWAS hits. However, existing PRS approaches, based on traditional regression models, explain only modest variation in AD dementia risk and AD-related endophenotypes. We hypothesized that machine learning (ML) models of polygenic risk (ML-PRS) could outperform standard regression-based PRS methods and therefore have the potential for greater clinical utility. Methods We analyzed combined data from the Mayo Clinic Study of Aging (n = 1,791) and the Alzheimer's Disease Neuroimaging Initiative (n = 864). An AD PRS was computed for each participant using the top common SVs obtained from a large AD dementia GWAS. In parallel, ML models were trained using those SV genotypes, with amyloid PET burden as the primary outcome. Secondary outcomes included amyloid PET positivity and clinical diagnosis (cognitively unimpaired vs impaired). We compared performance between ML-PRS and standard PRS across 100 training sessions with different data splits. In each session, data were split into 80% training and 20% testing, and then five-fold cross-validation was used within the training set to ensure the best model was produced for testing. We also applied permutation importance techniques to assess which genetic factors contributed most to outcome prediction. Results ML-PRS models outperformed the AD PRS (r2 = 0.28 vs r2 = 0.24 in test set) in explaining variation in amyloid PET burden. Among ML approaches, methods accounting for nonlinear genetic influences were superior to linear methods. ML-PRS models were also more accurate when predicting amyloid PET positivity (area under the curve [AUC] = 0.80 vs AUC = 0.63) and the presence of cognitive impairment (AUC = 0.75 vs AUC = 0.54) compared with the standard PRS. Discussion We found that ML-PRS approaches improved upon standard PRS for prediction of AD endophenotypes, partly related to improved accounting for nonlinear effects of genetic susceptibility alleles. Further adaptations of the ML-PRS framework could help to close the gap of remaining unexplained heritability for AD and therefore facilitate more accurate presymptomatic and early-stage risk stratification for clinical decision-making.
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Affiliation(s)
- Nathaniel B Gunter
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Robel K Gebre
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Jonathan Graff-Radford
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Michael G Heckman
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Clifford R Jack
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Val J Lowe
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - David S Knopman
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Ronald C Petersen
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Owen A Ross
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Prashanthi Vemuri
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Vijay K Ramanan
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
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15
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Zammit AR, Bennett DA, Buchman AS. From theory to practice: translating the concept of cognitive resilience to novel therapeutic targets that maintain cognition in aging adults. Front Aging Neurosci 2024; 15:1303912. [PMID: 38283067 PMCID: PMC10811007 DOI: 10.3389/fnagi.2023.1303912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/06/2023] [Indexed: 01/30/2024] Open
Abstract
While the concept of cognitive resilience is well-established it has not been defined in a way that can be measured. This has been an impediment to studying its underlying biology and to developing instruments for its clinical assessment. This perspective highlights recent work that has quantified the expression of cortical proteins associated with cognitive resilience, thus facilitating studies of its complex underlying biology and the full range of its clinical effects in aging adults. These initial studies provide empirical support for the conceptualization of resilience as a continuum. Like other conventional risk factors, some individuals manifest higher-than-average cognitive resilience and other individuals manifest lower-than-average cognitive resilience. These novel approaches for advancing studies of cognitive resilience can be generalized to other aging phenotypes and can set the stage for the development of clinical tools that might have the potential to measure other mechanisms of resilience in aging adults. These advances also have the potential to catalyze a complementary therapeutic approach that focuses on augmenting resilience via lifestyle changes or therapies targeting its underlying molecular mechanisms to maintain cognition and brain health even in the presence of untreatable stressors like brain pathologies that accumulate in aging adults.
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Affiliation(s)
- Andrea R. Zammit
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, United States
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
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16
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Koch E, Pardiñas AF, O'Connell KS, Selvaggi P, Camacho Collados J, Babic A, Marshall SE, Van der Eycken E, Angulo C, Lu Y, Sullivan PF, Dale AM, Molden E, Posthuma D, White N, Schubert A, Djurovic S, Heimer H, Stefánsson H, Stefánsson K, Werge T, Sønderby I, O'Donovan MC, Walters JTR, Milani L, Andreassen OA. How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry. Biol Psychiatry 2024:S0006-3223(24)00003-9. [PMID: 38185234 DOI: 10.1016/j.biopsych.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.
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Affiliation(s)
- Elise Koch
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pierluigi Selvaggi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - José Camacho Collados
- CardiffNLP, School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | | | | | - Erik Van der Eycken
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Cecilia Angulo
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden; Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, California; Departments of Radiology, Psychiatry, and Neurosciences, University of California, San Diego, La Jolla, California
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nathan White
- CorTechs Laboratories, Inc., San Diego, California
| | | | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; The Norwegian Centre for Mental Disorders Research Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Hakon Heimer
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Nordic Society of Human Genetics and Precision Medicine, Copenhagen, Denmark
| | | | | | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark; Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark; Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Ida Sønderby
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia; Genetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, Estonia
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway.
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17
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Liu C, Hou J, Li W, Chen J, Li Y, Zhang J, Zhou W, Zhang W, Deng F, Wang Y, Chen L, Qin S, Meng X, Lu S. Construction and optimization of a polygenic risk model for venous thromboembolism in the Chinese population. J Vasc Surg Venous Lymphat Disord 2024; 12:101666. [PMID: 37619711 DOI: 10.1016/j.jvsv.2023.08.007] [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: 03/15/2023] [Revised: 08/06/2023] [Accepted: 08/12/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND Venous thromboembolism (VTE) has both environmental and genetic risk factors. It is regulated by polygenes and multisites. The polygenic risk score (PRS) has been widely used because any single genetic biomarker failed to accurately predict the genetic risk of VTE. However, no polygenic risk model has been proposed for VTE in the Chinese population. Thus, we aimed to construct a PRS model for the first episode of VTE in the Chinese population. METHODS First, single nucleotide polymorphisms (SNPs) associated with VTE in genome-wide association studies, meta-analyses, and candidate gene studies were screened as variables for the PRS. The logarithm of the odds ratio was used to weight the variables. Second, a training set with simulated data from 1000 cases of VTE and 1000 controls was created with different genotypes and frequencies. Finally, we calculated the area under the receiver operating characteristic curve (AUC) to evaluate the discriminatory ability of the PRS model. RESULTS We screened 53 SNPs potentially associated with the first episode of VTE in the Chinese population. The AUC of the PRS-53 model (containing 53 SNPs) was 0.748 (95% confidence interval, 0.727-0.770) in the training set. From the largest weight to the smallest weight, SNPs were incrementally added to the model to calculate the AUC for model optimization. The AUC of the PRS-10 model (containing 10 SNPs) was 0.718 (95% confidence interval, 0.696-0.740), with no statistically significant difference from the AUC for the PRS-53 model. CONCLUSIONS The PRS-10 and PRS-53 models showed similar predictive abilities and satisfactory discriminatory power and can be used to predict the genetic risk of the first episode of VTE in the Chinese population. The simplified PRS-10 model is more efficient in clinical practice.
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Affiliation(s)
- Chao Liu
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Jiaxuan Hou
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Weiming Li
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Jinxing Chen
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Yane Li
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Jiawei Zhang
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Wei Zhou
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Wei Zhang
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Fenni Deng
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Yu Wang
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Luan Chen
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Shengying Qin
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiaohong Meng
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Shaoying Lu
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China.
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Kagerer SM, Awasthi S, Ripke S, Maceski A, Benkert P, Fall AB, Riederer P, Fischer P, Walitza S, Grünblatt E, Kuhle J, Unschuld PG. Polygenic risk for Alzheimer's disease is associated with neuroaxonal damage before onset of clinical symptoms. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12504. [PMID: 38213949 PMCID: PMC10776830 DOI: 10.1002/dad2.12504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 09/25/2023] [Accepted: 10/19/2023] [Indexed: 01/13/2024]
Abstract
INTRODUCTION Establishing valid diagnostic strategies is a precondition for successful therapeutic intervention in Alzheimer's disease (AD). METHODS One hundred forty-four healthy 75-year-old participants from the Vienna-Transdanube-Aging longitudinal cohort study were tested for neuroaxonal damage by single molecular array (Simoa) plasma neurofilament light chain (NfL) levels at baseline, 30, 60, and 90 months, and onset of AD dementia. Individual risk for sporadic AD was estimated by continuous shrinkage polygenic risk score (PRS-CS, genome-wide association study). RESULTS Nineteen participants developed AD after a median of 60 months (interquartile range 30). In participants with AD, baseline NfL plasma levels correlated with PRS-CS (r = 0.75, p < 0.001; difference to controls: Fisher's r-to-z: z = 3.89, p < 0.001). PRS-CS combined with baseline plasma NfL predicted onset of AD (p < 0.01). DISCUSSION Our data suggest that polygenic risk for AD and plasma NfL closely interact years before onset of clinical symptoms. Peripheral NfL may serve as a diagnostic measure supporting early therapeutic intervention and secondary prevention in AD.
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Affiliation(s)
- Sonja M. Kagerer
- Department of Geriatric PsychiatryPsychiatric University Hospital Zurich (PUK)ZurichSwitzerland
- Institute for Regenerative Medicine (IREM)University of ZurichSchlierenSwitzerland
| | - Swapnil Awasthi
- Department of Psychiatry and PsychotherapyCharité‐UniversitätsmedizinBerlinGermany
| | - Stephan Ripke
- Department of Psychiatry and PsychotherapyCharité‐UniversitätsmedizinBerlinGermany
- Analytic and Translational Genetics UnitMassachusetts General HospitalBostonMassachusettsUSA
- Stanley Center for Psychiatric ResearchBroad Institute of MIT and HarvardCambridgeMassachusettsUSA
| | - Aleksandra Maceski
- Department of NeurologyUniversity Hospital and University of BaselBaselSwitzerland
- Multiple Sclerosis Centre and Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB)Departments of Biomedicine and Clinical ResearchUniversity Hospital and University of BaselBaselSwitzerland
| | - Pascal Benkert
- Multiple Sclerosis Centre and Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB)Departments of Biomedicine and Clinical ResearchUniversity Hospital and University of BaselBaselSwitzerland
- Departments of Clinical ResearchUniversity Hospital and University of BaselBaselSwitzerland
| | - Aïda B. Fall
- Geriatric Psychiatry ServiceUniversity Hospitals of Geneva (HUG)ThônexSwitzerland
- Department of PsychiatryUniversity of Geneva (UniGE)GenevaSwitzerland
| | - Peter Riederer
- Center of Mental HealthClinic and Policlinic of PsychiatryPsychosomatics and PsychotherapyUniversity Hospital of WürzburgWürzburgGermany
- Department of PsychiatryUniversity of Southern Denmark OdenseOdenseDenmark
| | - Peter Fischer
- Department of PsychiatrySocial Medicine Center East‐DonauspitalViennaAustria
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and PsychotherapyPsychiatric University Hospital ZurichUniversity of ZurichZurichSwitzerland
- Zurich Center for Integrative Human PhysiologyUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichSwiss Federal Institute of Technology and University of ZurichZurichSwitzerland
| | - Edna Grünblatt
- Department of Child and Adolescent Psychiatry and PsychotherapyPsychiatric University Hospital ZurichUniversity of ZurichZurichSwitzerland
- Zurich Center for Integrative Human PhysiologyUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichSwiss Federal Institute of Technology and University of ZurichZurichSwitzerland
| | - Jens Kuhle
- Department of NeurologyUniversity Hospital and University of BaselBaselSwitzerland
- Multiple Sclerosis Centre and Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB)Departments of Biomedicine and Clinical ResearchUniversity Hospital and University of BaselBaselSwitzerland
| | - Paul G. Unschuld
- Geriatric Psychiatry ServiceUniversity Hospitals of Geneva (HUG)ThônexSwitzerland
- Department of PsychiatryUniversity of Geneva (UniGE)GenevaSwitzerland
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Cruciani F, Aparo A, Brusini L, Combi C, Storti SF, Giugno R, Menegaz G, Boscolo Galazzo I. Identifying the joint signature of brain atrophy and gene variant scores in Alzheimer's Disease. J Biomed Inform 2024; 149:104569. [PMID: 38104851 DOI: 10.1016/j.jbi.2023.104569] [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: 02/22/2023] [Revised: 11/20/2023] [Accepted: 12/07/2023] [Indexed: 12/19/2023]
Abstract
The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.
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Affiliation(s)
- Federica Cruciani
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy.
| | - Antonino Aparo
- Department of Computer Science, University of Verona, Verona, Italy
| | - Lorenza Brusini
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Carlo Combi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Silvia F Storti
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Verona, Italy
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
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Najar J, Thorvaldsson V, Kern S, Skoog J, Waern M, Zetterberg H, Blennow K, Skoog I, Zettergren A. Polygenic risk scores for Alzheimer's disease in relation to cognitive change: A representative sample from the general population followed over 16 years. Neurobiol Dis 2023; 189:106357. [PMID: 37977433 DOI: 10.1016/j.nbd.2023.106357] [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: 04/27/2023] [Revised: 09/22/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Polygenic risk scores for Alzheimer's disease (AD-PRSs) have been associated with cognition. However, few studies have examined the effect of AD-PRS beyond the APOE gene, and the influence of genetic variants related to level of cognitive ability (COG-PRS) on cognitive performance over time in the general older population. METHOD A population-based sample of 965 individuals born in 1930, with genetic and standardized cognitive data on six psychometric tests (Thurstone's picture memory, immediate recall of 10 words, Block design, word fluency, figure identification, delayed recall of 12 items), were examined at age 70, 75, 79, and 85 years. Non-APOE AD-PRSs and COG-PRSs (P < 5e-8, P < 1e-5, P < 1e-3, P < 1e-1) were generated from recent genome-wide association studies. Linear mixed effect models with random intercepts and slope were used to analyze the effect of APOE ε4 allele, AD-PRSs, and COG-PRSs, on cognitive performance and rate of change. Analyses were repeated in samples excluding dementia. RESULTS APOE ε4 and AD-PRS predicted change in cognitive performance (APOE ε4*age: β = -0.03, P < 0.0001 and AD-PRS *age: β = -0.01, P = 0.02). The results remained similar in the sample excluding those with dementia. COG-PRS predicted level of cognitive performance, while APOE ε4 and AD-PRS did not. COG-PRSs did not predict change in cognitive performance. CONCLUSION We found that genetic predisposition of AD predicted cognitive decline among 70-year-olds followed over 16 years, regardless of dementia status, while polygenic risk for general cognitive performance did not.
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Affiliation(s)
- Jenna Najar
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden; Department of Human Genetics, Genomics of Neurodegenerative Diseases and Aging at the Amsterdam University Medical Center, Amsterdam, the Netherlands.
| | - Valgeir Thorvaldsson
- Department of Psychology, and Centre for Ageing and Health (AGECAP), at the University of Gothenburg, Sweden.
| | - Silke Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden.
| | - Johan Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Sweden.
| | - Margda Waern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, Gothenburg, Sweden.
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden.
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Sweden.
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Yu C, Ryan J, Orchard SG, Robb C, Woods RL, Wolfe R, Renton AE, Goate AM, Brodtmann A, Shah RC, Chong TTJ, Sheets K, Kyndt C, Sood A, Storey E, Murray AM, McNeil JJ, Lacaze P. Validation of newly derived polygenic risk scores for dementia in a prospective study of older individuals. Alzheimers Dement 2023; 19:5333-5342. [PMID: 37177856 PMCID: PMC10640662 DOI: 10.1002/alz.13113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/27/2023] [Accepted: 04/03/2023] [Indexed: 05/15/2023]
Abstract
INTRODUCTION Recent genome-wide association studies identified new dementia-associated variants. We assessed the performance of updated polygenic risk scores (PRSs) using these variants in an independent cohort. METHODS We used Cox models and area under the curve (AUC) to validate new PRSs (PRS-83SNP, PRS-SBayesR, and PRS-CS) compared with an older PRS-23SNP in 12,031 initially-healthy participants ≥70 years of age. Dementia was rigorously adjudicated according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria. RESULTS PRS-83SNP, PRS-SBayesR, and PRS-CS were associated with incident dementia, with fully adjusted (including apolipoprotein E [APOE] ε4) hazard ratios per standard deviation (SD) of 1.35 (1.23-1.47), 1.37 (1.25-1.50), and 1.42 (1.30-1.56), respectively. The AUC of a model containing conventional/non-genetic factors and APOE was 74.7%. This was improved to 75.7% (p = 0.007), 76% (p = 0.004), and 76.1% (p = 0.003) with addition of PRS-83SNP, PRS-SBayesR, and PRS-CS, respectively. The PRS-23SNP did not improve AUC (74.7%, p = 0.95). CONCLUSION New PRSs for dementia significantly improve risk-prediction performance, but still account for less risk than APOE genotype overall.
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Affiliation(s)
- Chenglong Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Suzanne G. Orchard
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Catherine Robb
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rory Wolfe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Alan E. Renton
- Department Genetics and Genomic Sciences and Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alison M. Goate
- Department Genetics and Genomic Sciences and Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Raj C. Shah
- Department of Family & Preventive Medicine and the Rush Alzheimer’s Disease Center, Chicago, Illinois, USA
| | - Trevor T.-J. Chong
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Clinical Neurosciences, St. Vincent’s Hospital, Melbourne, Victoria, Australia
| | - Kerry Sheets
- Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
- Division of Geriatrics, Hennepin Healthcare, Minneapolis, Minnesota, USA
| | - Christopher Kyndt
- Department of Neurology, Melbourne Health, Parkville, Victoria, Australia
- Department of Neuroscience, Eastern Health, Box Hill, Victoria, Australia
| | - Ajay Sood
- Department of Neurology and the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Anne M. Murray
- Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
- Division of Geriatrics, Hennepin Healthcare, Minneapolis, Minnesota, USA
- Berman Center for Outcomes and Clinical Research, Hennepin Healthcare Research Institute, Hennepin Healthcare, and University of Minnesota, Minneapolis, Minnesota, USA
| | - John J. McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Paul Lacaze
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Akyol O, Akyol S, Chou MC, Chen S, Liu CK, Selek S, Soares JC, Chen CH. Lipids and lipoproteins may play a role in the neuropathology of Alzheimer's disease. Front Neurosci 2023; 17:1275932. [PMID: 38033552 PMCID: PMC10687420 DOI: 10.3389/fnins.2023.1275932] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Alzheimer's disease (AD) and other classes of dementia are important public health problems with overwhelming social, physical, and financial effects for patients, society, and their families and caregivers. The pathophysiology of AD is poorly understood despite the extensive number of clinical and experimental studies. The brain's lipid-rich composition is linked to disturbances in lipid homeostasis, often associated with glucose and lipid abnormalities in various neurodegenerative diseases, including AD. Moreover, elevated low-density lipoprotein (LDL) cholesterol levels may be related to a higher probability of AD. Here, we hypothesize that lipids, and electronegative LDL (L5) in particular, may be involved in the pathophysiology of AD. Although changes in cholesterol, triglyceride, LDL, and glucose levels are seen in AD, the cause remains unknown. We believe that L5-the most electronegative subfraction of LDL-may be a crucial factor in understanding the involvement of lipids in AD pathology. LDL and L5 are internalized by cells through different receptors and mechanisms that trigger separate intracellular pathways. One of the receptors involved in L5 internalization, LOX-1, triggers apoptotic pathways. Aging is associated with dysregulation of lipid homeostasis, and it is believed that alterations in lipid metabolism contribute to the pathogenesis of AD. Proposed mechanisms of lipid dysregulation in AD include mitochondrial dysfunction, blood-brain barrier disease, neuronal signaling, inflammation, and oxidative stress, all of which lead ultimately to memory loss through deficiency of synaptic integration. Several lipid species and their receptors have essential functions in AD pathogenesis and may be potential biomarkers.
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Affiliation(s)
- Omer Akyol
- Molecular Cardiology, Vascular and Medicinal Research, The Texas Heart Institute, Houston, TX, United States
| | | | - Mei-Chuan Chou
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Shioulan Chen
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ching-Kuan Liu
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Salih Selek
- Department of Psychiatry and Behavioral Sciences, UTHealth Houston McGovern Medical School, Houston, TX, United States
| | - Jair C. Soares
- Department of Psychiatry and Behavioral Sciences, UTHealth Houston McGovern Medical School, Houston, TX, United States
| | - Chu-Huang Chen
- Molecular Cardiology, Vascular and Medicinal Research, The Texas Heart Institute, Houston, TX, United States
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23
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Ding Y, Chen H, Yan Y, Qiu Y, Zhao A, Li B, Xu W, Deng Y. Relationship Between FERMT2, CELF1, COPI, CHRNA2, and ABCA7 Genetic Polymorphisms and Alzheimer's Disease Risk in the Southern Chinese Population. J Alzheimers Dis Rep 2023; 7:1247-1257. [PMID: 38025799 PMCID: PMC10657721 DOI: 10.3233/adr-230072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
Background Alzheimer's disease (AD) is a multi-gene inherited disease, and apolipoprotein E (APOE) ɛ4 is a strong risk factor. Other genetic factors are important but limited. Objective This study aimed to investigate the relationship between 17 single-nucleotide polymorphisms (SNPs) and AD in the Southern Chinese populations. Methods We recruited 242 AD patients and 208 controls. The SNaPshot technique was used to detect the SNPs. Results Adjusted for sex and age, we found rs6572869 (FERMT2), rs11604680 (CELF1), and rs1317149 (CELF1) were associated with AD risk in the dominant (rs6572869: p = 0.022, OR = 1.55; rs11604680: p = 0.007, OR = 1.68; rs1317149: p = 0.033, OR = 1.50) and overdominant models (rs6572869: p = 0.001, OR = 1.96; rs11604680: p = 0.002, OR = 1.82; rs1317149: p = 0.003, OR = 1.80). rs9898218 (COPI) was associated with AD risk in the overdominant model (p = 0.004, OR = 1.81). Further, rs2741342 (CHRNA2) was associated with AD protection in the dominant (p = 0.002, OR = 0.5) and additive models (p = 0.002, OR = 0.64). Mutations in rs10742814 (CELF1), rs11039280 (CELF1), and rs3752242 (ABCA7) contributed to AD protection. Among them, rs10742814 (CELF1), rs3752242 (ABCA7), and rs11039280 (CELF1) were more significantly associated with AD carrying APOE ɛ4, whereas rs1317149 (CELF1) showed an opposite trend. Interestingly, rs4147912 (ABCA7) and rs2516049 (HLA-DRB1) were identified to be relevant with AD carrying APOE ɛ4. Using expression quantitative trait locus analysis, we found polymorphisms in CELF1 (rs10742814 and rs11039280), ABCA7 (rs4147912), HLA-DRB1 (rs2516049), and ADGRF4 (rs1109581) correlated with their corresponding gene expression in the brain. Conclusions We identified four risk and four protective SNPs associated with AD in the Southern Chinese population, with different correlations between APOE ɛ4 carriers and non-carriers. rs4147912 (ABCA7) and rs2516049 (HLA-DRB1) were associated with AD carrying APOE ɛ4.
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Affiliation(s)
- Yanfei Ding
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haijuan Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Yan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinghui Qiu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Aonan Zhao
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Binyin Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Xu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yulei Deng
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, Ruijin Hospital, Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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24
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Bakalar D, Gavrilova O, Jiang SZ, Zhang HY, Roy S, Williams SK, Liu N, Wisser S, Usdin TB, Eiden LE. Constitutive and conditional deletion reveals distinct phenotypes driven by developmental versus neurotransmitter actions of the neuropeptide PACAP. J Neuroendocrinol 2023; 35:e13286. [PMID: 37309259 PMCID: PMC10620107 DOI: 10.1111/jne.13286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/11/2023] [Accepted: 04/25/2023] [Indexed: 06/14/2023]
Abstract
Neuropeptides may exert trophic effects during development, and then neurotransmitter roles in the developed nervous system. One way to associate peptide-deficiency phenotypes with either role is first to assess potential phenotypes in so-called constitutive knockout mice, and then proceed to specify, regionally and temporally, where and when neuropeptide expression is required to prevent these phenotypes. We have previously demonstrated that the well-known constellation of behavioral and metabolic phenotypes associated with constitutive pituitary adenylate cyclase-activating peptide (PACAP) knockout mice are accompanied by transcriptomic alterations of two types: those that distinguish the PACAP-null phenotype from wild-type (WT) in otherwise quiescent mice (cPRGs), and gene induction that occurs in response to acute environmental perturbation in WT mice that do not occur in knockout mice (aPRGs). Comparing constitutive PACAP knockout mice to a variety of temporally and regionally specific PACAP knockouts, we show that the prominent hyperlocomotor phenotype is a consequence of early loss of PACAP expression, is associated with Fos overexpression in hippocampus and basal ganglia, and that a thermoregulatory effect previously shown to be mediated by PACAP-expressing neurons of medial preoptic hypothalamus is independent of PACAP expression in those neurons in adult mice. In contrast, PACAP dependence of weight loss/hypophagia triggered by restraint stress, seen in constitutive PACAP knockout mice, is phenocopied in mice in which PACAP is deleted after neuronal differentiation. Our results imply that PACAP has a prominent role as a trophic factor early in development determining global central nervous system characteristics, and in addition a second, discrete set of functions as a neurotransmitter in the fully developed nervous system that support physiological and psychological responses to stress.
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Affiliation(s)
- Dana Bakalar
- Section on Molecular Neuroscience, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
| | - Oksana Gavrilova
- Mouse Metabolism Core Laboratory, National Institute of Diabetes and Kidney Disease- Intramural Research Program, Bethesda, Maryland, USA
| | - Sunny Z Jiang
- Section on Molecular Neuroscience, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
| | - Hai-Ying Zhang
- Section on Molecular Neuroscience, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
| | - Snehashis Roy
- Systems Neuroscience Imaging Resource, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
| | - Sarah K Williams
- Systems Neuroscience Imaging Resource, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
| | - Naili Liu
- Mouse Metabolism Core Laboratory, National Institute of Diabetes and Kidney Disease- Intramural Research Program, Bethesda, Maryland, USA
| | - Stephen Wisser
- Systems Neuroscience Imaging Resource, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
| | - Ted B Usdin
- Systems Neuroscience Imaging Resource, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
| | - Lee E Eiden
- Section on Molecular Neuroscience, National Institute of Mental Heath - Intramural Research Program, Bethesda, Maryland, USA
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25
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Chung J, Sahelijo N, Maruyama T, Hu J, Panitch R, Xia W, Mez J, Stein TD, Saykin AJ, Takeyama H, Farrer LA, Crane PK, Nho K, Jun GR. Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles. Alzheimers Dement 2023; 19:5173-5184. [PMID: 37166019 PMCID: PMC10638468 DOI: 10.1002/alz.13069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 05/12/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity. METHODS We conducted co-expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD-related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs). We computed the module-based PRSs (mbPRSs) for each module and tested associations with mbPRSs for cognitive test scores, cognitively defined AD subgroups, and brain imaging data. RESULTS Of the modules significantly associated with NPs and/or NFTs, the mbPRSs from two modules (M6 and M9) showed distinct associations with language and visuospatial functioning, respectively. They matched clinical subtypes and brain atrophy at specific regions. DISCUSSION Our findings demonstrate that polygenic profiling based on co-expressed gene sets can explain heterogeneity in AD patients, enabling genetically informed patient stratification and precision medicine in AD. HIGHLIGHTS Co-expression gene-network analysis in Alzheimer's disease (AD) brains identified gene sets (modules) associated with AD heterogeneity. AD-associated modules were selected when genes in each module were enriched for neuritic plaques and neurofibrillary tangles. Polygenic risk scores from two selected modules were linked to the matching cognitively defined AD subgroups (language and visuospatial subgroups). Polygenic risk scores from the two modules were associated with cognitive performance in language and visuospatial domains and the associations were confirmed in regional-specific brain atrophy data.
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Affiliation(s)
- Jaeyoon Chung
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Nathan Sahelijo
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Toru Maruyama
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Junming Hu
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Rebecca Panitch
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Weiming Xia
- Department of Pharmacology & Experimental Therapeutics, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Veterans Affairs Medical Center, Bedford, MA 01730, USA
| | - Jesse Mez
- Department of Neurology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Thor D. Stein
- Department of Veterans Affairs Medical Center, Bedford, MA 01730, USA
- Department of Pathology & Laboratory Medicine, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Boston VA Healthcare Center, Boston, MA 02130, USA
| | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Haruko Takeyama
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Japan, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Research Organization for Nano and Life Innovations, Waseda University, 513, Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
| | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Gyungah R. Jun
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
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26
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Reas ET, Shadrin A, Frei O, Motazedi E, McEvoy L, Bahrami S, van der Meer D, Makowski C, Loughnan R, Wang X, Broce I, Banks SJ, Fominykh V, Cheng W, Holland D, Smeland OB, Seibert T, Selbæk G, Brewer JB, Fan CC, Andreassen OA, Dale AM. Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures. Alzheimers Dement 2023; 19:5151-5158. [PMID: 37132098 PMCID: PMC10620101 DOI: 10.1002/alz.13112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/21/2023] [Accepted: 04/04/2023] [Indexed: 05/04/2023]
Abstract
INTRODUCTION There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). METHODS Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. RESULTS The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. DISCUSSION The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. HIGHLIGHTS A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.
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Affiliation(s)
- Emilie T. Reas
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, PO box 1080, Blindern, 0316 Oslo, Norway
| | - Ehsan Motazedi
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Linda McEvoy
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Dennis van der Meer
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Carolina Makowski
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert Loughnan
- University of California, San Diego, La Jolla, California, USA
| | - Xin Wang
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Iris Broce
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Sarah J. Banks
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Vera Fominykh
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Dominic Holland
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Olav B. Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Tyler Seibert
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - James B. Brewer
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Chun C. Fan
- Population Neuroscience and Genetics Lab, University of California, La Jolla, CA 92093, USA
- Center for Human Development, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Anders M. Dale
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
- Population Neuroscience and Genetics Lab, University of California, La Jolla, CA 92093, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
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Hou XH, Suckling J, Shen XN, Liu Y, Zuo CT, Huang YY, Li HQ, Wang HF, Tan CC, Cui M, Dong Q, Tan L, Yu JT. Multipredictor risk models for predicting individual risk of Alzheimer's disease. J Transl Med 2023; 21:768. [PMID: 37904154 PMCID: PMC10614397 DOI: 10.1186/s12967-023-04646-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/22/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Early prevention of Alzheimer's disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features. METHODS A total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction. RESULTS During the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer's continuum model was developed which could predict the Alzheimer's continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91). CONCLUSIONS The risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.
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Affiliation(s)
- Xiao-He Hou
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Xue-Ning Shen
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Chuan-Tao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Hong-Qi Li
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Hui-Fu Wang
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Chen-Chen Tan
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Mei Cui
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Lan Tan
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
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28
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Suh EH, Lee G, Jung SH, Wen Z, Bao J, Nho K, Huang H, Davatzikos C, Saykin AJ, Thompson PM, Shen L, Kim D. An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification. Front Aging Neurosci 2023; 15:1281748. [PMID: 37953885 PMCID: PMC10637854 DOI: 10.3389/fnagi.2023.1281748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/06/2023] [Indexed: 11/14/2023] Open
Abstract
Introduction Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.
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Affiliation(s)
- Erica H. Suh
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Garam Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Heng Huang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Meder B, Melén E, Wheelock CE, Jacobsson B. Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification. J Intern Med 2023; 294:378-396. [PMID: 37093654 PMCID: PMC10523928 DOI: 10.1111/joim.13640] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala university, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2200 Copenhagen, Denmark
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Sweden
- Novo Nordisk Foundation, Denmark
| | - Harald Hedman
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Ruth J.F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Meder
- Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University Of Heidelberg, Germany
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm
- Sachś Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden
- Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
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Mantyh WG, Cochran JN, Taylor JW, Broce IJ, Geier EG, Bonham LW, Anderson AG, Sirkis DW, Joie RL, Iaccarino L, Chaudhary K, Edwards L, Strom A, Grant H, Allen IE, Miller ZA, Gorno‐Tempini ML, Kramer JH, Miller BL, Desikan RS, Rabinovici GD, Yokoyama JS. Early-onset Alzheimer's disease explained by polygenic risk of late-onset disease? ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12482. [PMID: 37780862 PMCID: PMC10535074 DOI: 10.1002/dad2.12482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023]
Abstract
Early-onset Alzheimer's disease (AD) is highly heritable, yet only 10% of cases are associated with known pathogenic mutations. For early-onset AD patients without an identified autosomal dominant cause, we hypothesized that their early-onset disease reflects further enrichment of the common risk-conferring single nucleotide polymorphisms associated with late-onset AD. We applied a previously validated polygenic hazard score for late-onset AD to 193 consecutive patients diagnosed at our tertiary dementia referral center with symptomatic early-onset AD. For comparison, we included 179 participants with late-onset AD and 70 healthy controls. Polygenic hazard scores were similar in early- versus late-onset AD. The polygenic hazard score was not associated with age-of-onset or disease biomarkers within early-onset AD. Early-onset AD does not represent an extreme enrichment of the common single nucleotide polymorphisms associated with late-onset AD. Further exploration of novel genetic risk factors of this highly heritable disease is warranted.Highlights: There is a unique genetic architecture of early- versus late-onset Alzheimer's disease (AD).Late-onset AD polygenic risk is not an explanation for early-onset AD.Polygenic risk of late-onset AD does not predict early-onset AD biology.Unique genetic architecture of early- versus late-onset AD parallels AD heterogeneity.
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Affiliation(s)
- William G. Mantyh
- Department of NeurologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | | | | | - Iris J. Broce
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Ethan G. Geier
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Luke W. Bonham
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | - Daniel W. Sirkis
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Renaud La Joie
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Leonardo Iaccarino
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Kiran Chaudhary
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Lauren Edwards
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Amelia Strom
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Harli Grant
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Isabel E. Allen
- Department of Epidemiology and BiostatisticsUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Zachary A. Miller
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Marilu L. Gorno‐Tempini
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Joel H. Kramer
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Bruce L. Miller
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Rahul S. Desikan
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Gil D. Rabinovici
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Life Sciences DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Jennifer S. Yokoyama
- Memory and Aging CenterDepartment of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
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Sun T, Ding Y. Neural network on interval-censored data with application to the prediction of Alzheimer's disease. Biometrics 2023; 79:2677-2690. [PMID: 35960189 PMCID: PMC10177011 DOI: 10.1111/biom.13734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 08/01/2022] [Indexed: 11/28/2022]
Abstract
Alzheimer's disease (AD) is a progressive and polygenic disorder that affects millions of individuals each year. Given that there have been few effective treatments yet for AD, it is highly desirable to develop an accurate model to predict the full disease progression profile based on an individual's genetic characteristics for early prevention and clinical management. This work uses data composed of all four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, including 1740 individuals with 8 million genetic variants. We tackle several challenges in this data, characterized by large-scale genetic data, interval-censored outcome due to intermittent assessments, and left truncation in one study phase (ADNIGO). Specifically, we first develop a semiparametric transformation model on interval-censored and left-truncated data and estimate parameters through a sieve approach. Then we propose a computationally efficient generalized score test to identify variants associated with AD progression. Next, we implement a novel neural network on interval-censored data (NN-IC) to construct a prediction model using top variants identified from the genome-wide test. Comprehensive simulation studies show that the NN-IC outperforms several existing methods in terms of prediction accuracy. Finally, we apply the NN-IC to the full ADNI data and successfully identify subgroups with differential progression risk profiles. Data used in the preparation of this article were obtained from the ADNI database.
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Affiliation(s)
- Tao Sun
- Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ying Ding
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Juul Rasmussen I, Frikke-Schmidt R. Modifiable cardiovascular risk factors and genetics for targeted prevention of dementia. Eur Heart J 2023; 44:2526-2543. [PMID: 37224508 PMCID: PMC10481783 DOI: 10.1093/eurheartj/ehad293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/22/2023] [Accepted: 05/04/2023] [Indexed: 05/26/2023] Open
Abstract
Dementia is a major global challenge for health and social care in the 21st century. A third of individuals >65 years of age die with dementia, and worldwide incidence numbers are projected to be higher than 150 million by 2050. Dementia is, however, not an inevitable consequence of old age; 40% of dementia may theoretically be preventable. Alzheimer's disease (AD) accounts for approximately two-thirds of dementia cases and the major pathological hallmark of AD is accumulation of amyloid-β. Nevertheless, the exact pathological mechanisms of AD remain unknown. Cardiovascular disease and dementia share several risk factors and dementia often coexists with cerebrovascular disease. In a public health perspective, prevention is crucial, and it is suggested that a 10% reduction in prevalence of cardiovascular risk factors could prevent more than nine million dementia cases worldwide by 2050. Yet this assumes causality between cardiovascular risk factors and dementia and adherence to the interventions over decades for a large number of individuals. Using genome-wide association studies, the entire genome can be scanned for disease/trait associated loci in a hypothesis-free manner, and the compiled genetic information is not only useful for pinpointing novel pathogenic pathways but also for risk assessments. This enables identification of individuals at high risk, who likely will benefit the most from a targeted intervention. Further optimization of the risk stratification can be done by adding cardiovascular risk factors. Additional studies are, however, highly needed to elucidate dementia pathogenesis and potential shared causal risk factors between cardiovascular disease and dementia.
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Affiliation(s)
- Ida Juul Rasmussen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
| | - Ruth Frikke-Schmidt
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Wang X, Broce I, Qiu Y, Deters KD, Fan CC, Dale AM, Edland SD, Banks SJ. A simple genetic stratification method for lower cost, more expedient clinical trials in early Alzheimer's disease: A preliminary study of tau PET and cognitive outcomes. Alzheimers Dement 2023; 19:3078-3086. [PMID: 36701211 PMCID: PMC10368787 DOI: 10.1002/alz.12952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Identifying individuals who are most likely to accumulate tau and exhibit cognitive decline is critical for Alzheimer's disease (AD) clinical trials. METHODS Participants (N = 235) who were cognitively normal or with mild cognitive impairment from the Alzheimer's Disease Neuroimaging Initiative were stratified by a cutoff on the polygenic hazard score (PHS) at 65th percentile (above as high-risk group and below as low-risk group). We evaluated the associations between the PHS risk groups and tau positron emission tomography and cognitive decline, respectively. Power analyses estimated the sample size needed for clinical trials to detect differences in tau accumulation or cognitive change. RESULTS The high-risk group showed faster tau accumulation and cognitive decline. Clinical trials using the high-risk group would require a fraction of the sample size as trials without this inclusion criterion. DISCUSSION Incorporating a PHS inclusion criterion represents a low-cost and accessible way to identify potential participants for AD clinical trials.
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Affiliation(s)
- Xin Wang
- University of California, San Diego, California, USA
| | - Iris Broce
- University of California, San Diego, California, USA
| | - Yuqi Qiu
- East China Normal University, Shanghai, China
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Li F, Liu W, Hu C, Tang M, Zhang Y, Ho HC, Peng S, Li Z, Wang Q, Li X, Xu B, Li F. Global association of greenness exposure with risk of nervous system disease: A systematic review and meta-analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 877:162773. [PMID: 36933739 DOI: 10.1016/j.scitotenv.2023.162773] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 05/06/2023]
Abstract
Nervous system disease (NSD) is a global health burden with increasing prevalence in the last 30 years. There is evidence that greenness can improve nervous system health through a variety of mechanisms; however, the evidence is inconsistent. In the present systematic review and meta-analysis, we examined the relationship between greenness exposure and NSD outcomes. Studies on the relationship between greenness and NSD health outcomes published till July 2022 were searched in PubMed, Cochrane, Embase, Scopus, and Web of Science. In addition, we searched the cited literature and updated our search on Jan 20, 2023, to identify any new studies. We included human epidemiological studies that assess the association of greenness exposure with the risk of NSD. Greenness exposure was measured using NDVI (the normalized difference vegetation index) and the outcome was the mortality or morbidity of NSD. The pooled relative risks (RRs) were estimated using a random effects model. Of 2059 identified studies, 15 studies were included in our quantitative evaluation, in which 11 studies found a significant inverse relationship between the risk of NSD mortality or incidence/prevalence and an increase in surrounding greenness. The pooled RRs for cerebrovascular diseases (CBVD), neurodegenerative diseases (ND), and stroke mortality were 0.98 (95 % CI: 0.97, 1.00), 0.98 (95 % CI: 0.98, 0.99), and 0.96 (95 % CI: 0.93, 1.00), respectively. The pooled RRs for PD incidence and stroke prevalence/incidence were 0.89 (95 % CI: 0.78, 1.02) and 0.98 (95 % CI: 0.97, 0.99), respectively. The confidence of evidence for ND mortality, stroke mortality, and stroke prevalence/incidence was downgraded to "low", while CBVD mortality and PD incidence were downgraded to "very low" due to inconsistency. We found no evidence of publication bias and the sensitivity analysis results of all subgroups are robust except for the stroke mortality subgroup. This is the first comprehensive meta-analysis of greenness exposure and NSD outcomes in which an inverse relationship was observed. It is necessary to conduct further research to ascertain the role greenness exposure plays in various NSDs and the management of greenness should be considered a public health strategy.
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Affiliation(s)
- Fangzheng Li
- School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.
| | - Wei Liu
- School of Art, Qufu Normal University, Rizhao 276826, China
| | - Chengyang Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Mingcheng Tang
- School of Landscape Architecture and Forestry, Qingdao Agricultural University, Qingdao 266109, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Hung Chak Ho
- Department of Anaesthesiology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Shijia Peng
- Charles Davis's Lab Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Zhouyuan Li
- School of Grassland Science, Beijing Forestry University, Beijing 100083, China
| | - Qing Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Xiong Li
- School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
| | - Bing Xu
- Department of Earth System Science, Tsinghua University, 10084 Beijing, China
| | - Fengyi Li
- School of Landscape Architecture and Forestry, Qingdao Agricultural University, Qingdao 266109, China.
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Avelar-Pereira B, Belloy ME, O'Hara R, Hosseini SMH. Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks. Mol Psychiatry 2023; 28:2423-2432. [PMID: 36539525 PMCID: PMC10279806 DOI: 10.1038/s41380-022-01886-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 09/19/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.
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Affiliation(s)
- Bárbara Avelar-Pereira
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA.
| | - Michael E Belloy
- Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA
| | - Ruth O'Hara
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA
| | - S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA.
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Ochneva AG, Soloveva KP, Savenkova VI, Ikonnikova AY, Gryadunov DA, Andryuschenko AV. Modern Approaches to the Diagnosis of Cognitive Impairment and Alzheimer's Disease: A Narrative Literature Review. CONSORTIUM PSYCHIATRICUM 2023; 4:53-62. [PMID: 38239570 PMCID: PMC10790729 DOI: 10.17816/cp716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND The aging of the worlds population leads to an increase in the prevalence of age-related diseases, including cognitive impairment. At the stage of dementia, therapeutic interventions become usually ineffective. Therefore, researchers and clinical practitioners today are looking for methods that allow for early diagnosis of cognitive impairment, including techniques that are based on the use of biological markers. AIM The aim of this literature review is to delve into scientific papers that are centered on modern laboratory tests for Alzheimers disease, including tests for biological markers at the early stages of cognitive impairment. METHODS The authors have carried out a descriptive review of scientific papers published from 2015 to 2023. Studies that are included in the PubMed and Web of Science electronic databases were analyzed. A descriptive analysis was used to summarized the gleaned information. RESULTS Blood and cerebrospinal fluid (CSF) biomarkers, as well as the advantages and disadvantages of their use, are reviewed. The most promising neurotrophic, neuroinflammatory, and genetic markers, including polygenic risk models, are also discussed. CONCLUSION The use of biomarkers in clinical practice will contribute to the early diagnosis of cognitive impairment associated with Alzheimers disease. Genetic screening tests can improve the detection threshold of preclinical abnormalities in the absence of obvious symptoms of cognitive decline. The active use of biomarkers in clinical practice, in combination with genetic screening for the early diagnosis of cognitive impairment in Alzheimers disease, can improve the timeliness and effectiveness of medical interventions.
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Pettigrew C, Nazarovs J, Soldan A, Singh V, Wang J, Hohman T, Dumitrescu L, Libby J, Kunkle B, Gross AL, Johnson S, Lu Q, Engelman C, Masters CL, Maruff P, Laws SM, Morris JC, Hassenstab J, Cruchaga C, Resnick SM, Kitner-Triolo MH, An Y, Albert M. Alzheimer's disease genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals. Alzheimers Res Ther 2023; 15:66. [PMID: 36978190 PMCID: PMC10045505 DOI: 10.1186/s13195-023-01206-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/12/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND Both Alzheimer's disease (AD) genetic risk factors and indices of cognitive reserve (CR) influence risk of cognitive decline, but it remains unclear whether they interact. This study examined whether a CR index score modifies the relationship between AD genetic risk factors and long-term cognitive trajectories in a large sample of individuals with normal cognition. METHODS Analyses used data from the Preclinical AD Consortium, including harmonized data from 5 longitudinal cohort studies. Participants were cognitively normal at baseline (M baseline age = 64 years, 59% female) and underwent 10 years of follow-up, on average. AD genetic risk was measured by (i) apolipoprotein-E (APOE) genetic status (APOE-ε2 and APOE-ε4 vs. APOE-ε3; N = 1819) and (ii) AD polygenic risk scores (AD-PRS; N = 1175). A CR index was calculated by combining years of education and literacy scores. Longitudinal cognitive performance was measured by harmonized factor scores for global cognition, episodic memory, and executive function. RESULTS In mixed-effects models, higher CR index scores were associated with better baseline cognitive performance for all cognitive outcomes. APOE-ε4 genotype and AD-PRS that included the APOE region (AD-PRSAPOE) were associated with declines in all cognitive domains, whereas AD-PRS that excluded the APOE region (AD-PRSw/oAPOE) was associated with declines in executive function and global cognition, but not memory. There were significant 3-way CR index score × APOE-ε4 × time interactions for the global (p = 0.04, effect size = 0.16) and memory scores (p = 0.01, effect size = 0.22), indicating the negative effect of APOE-ε4 genotype on global and episodic memory score change was attenuated among individuals with higher CR index scores. In contrast, levels of CR did not attenuate APOE-ε4-related declines in executive function or declines associated with higher AD-PRS. APOE-ε2 genotype was unrelated to cognition. CONCLUSIONS These results suggest that APOE-ε4 and non-APOE-ε4 AD polygenic risk are independently associated with global cognitive and executive function declines among individuals with normal cognition at baseline, but only APOE-ε4 is associated with declines in episodic memory. Importantly, higher levels of CR may mitigate APOE-ε4-related declines in some cognitive domains. Future research is needed to address study limitations, including generalizability due to cohort demographic characteristics.
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Affiliation(s)
- Corinne Pettigrew
- Johns Hopkins University School of Medicine, 1600 McElderry St, Baltimore, MD, 21205, USA.
| | - Jurijs Nazarovs
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Anja Soldan
- Johns Hopkins University School of Medicine, 1600 McElderry St, Baltimore, MD, 21205, USA
| | - Vikas Singh
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Jiangxia Wang
- Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, 1207 17th Ave South, Nashville, TN, 37212, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, 1207 17th Ave South, Nashville, TN, 37212, USA
| | - Julia Libby
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, 1207 17th Ave South, Nashville, TN, 37212, USA
| | - Brian Kunkle
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alden L Gross
- Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA
| | - Sterling Johnson
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Qiongshi Lu
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Corinne Engelman
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Colin L Masters
- The Florey Institute, University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Paul Maruff
- The Florey Institute, University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Simon M Laws
- Centre for Precision Health and Collaborative Genomics and Translation Group, Edith Cowan University, 270 Jundaloop Drive, Jundaloop, WA, 6027, Australia
- Curtin Medical School, Curtin University, Kent Street, Bentley, WA, 6102, Australia
| | - John C Morris
- Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Jason Hassenstab
- Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Carlos Cruchaga
- Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Susan M Resnick
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Melissa H Kitner-Triolo
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Yang An
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Marilyn Albert
- Johns Hopkins University School of Medicine, 1600 McElderry St, Baltimore, MD, 21205, USA
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Tomaszowski KH, Roy S, Guerrero C, Shukla P, Keshvani C, Chen Y, Ott M, Wu X, Zhang J, DiNardo CD, Schindler D, Schlacher K. Hypomorphic Brca2 and Rad51c double mutant mice display Fanconi anemia, cancer and polygenic replication stress. Nat Commun 2023; 14:1333. [PMID: 36906610 PMCID: PMC10008622 DOI: 10.1038/s41467-023-36933-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 02/10/2023] [Indexed: 03/13/2023] Open
Abstract
The prototypic cancer-predisposition disease Fanconi Anemia (FA) is identified by biallelic mutations in any one of twenty-three FANC genes. Puzzlingly, inactivation of one Fanc gene alone in mice fails to faithfully model the pleiotropic human disease without additional external stress. Here we find that FA patients frequently display FANC co-mutations. Combining exemplary homozygous hypomorphic Brca2/Fancd1 and Rad51c/Fanco mutations in mice phenocopies human FA with bone marrow failure, rapid death by cancer, cellular cancer-drug hypersensitivity and severe replication instability. These grave phenotypes contrast the unremarkable phenotypes seen in mice with single gene-function inactivation, revealing an unexpected synergism between Fanc mutations. Beyond FA, breast cancer-genome analysis confirms that polygenic FANC tumor-mutations correlate with lower survival, expanding our understanding of FANC genes beyond an epistatic FA-pathway. Collectively, the data establish a polygenic replication stress concept as a testable principle, whereby co-occurrence of a distinct second gene mutation amplifies and drives endogenous replication stress, genome instability and disease.
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Affiliation(s)
- Karl-Heinz Tomaszowski
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Sunetra Roy
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Carolina Guerrero
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Poojan Shukla
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Caezaan Keshvani
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Yue Chen
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Martina Ott
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Xiaogang Wu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Courtney D DiNardo
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA
| | - Detlev Schindler
- Institut fuer Humangenetik, University of Wuerzburg, Wuerzburg, Germany
| | - Katharina Schlacher
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA.
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Kondo T, Yada Y, Ikeuchi T, Inoue H. CDiP technology for reverse engineering of sporadic Alzheimer's disease. J Hum Genet 2023; 68:231-235. [PMID: 35680997 PMCID: PMC9968655 DOI: 10.1038/s10038-022-01047-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/11/2022] [Indexed: 11/09/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that causes cognitive impairment for which neither treatable nor preventable approaches have been confirmed. Although genetic factors are considered to contribute to sporadic AD, for the majority of AD patients, the exact causes of AD aren't fully understood. For AD genetics, we developed cellular dissection of polygenicity (CDiP) technology to identify the smallest unit of AD, i.e., genetic factors at a cellular level. By CDiP, we found potential therapeutic targets, a rare variant for disease stratification, and polygenes to predict real-world AD by using the real-world data of AD cohort studies (Alzheimer's Disease Neuroimaging Initiative: ADNI and Japanese Alzheimer's Disease Neuroimaging Initiative: J-ADNI). In this review, we describe the components and results of CDiP in AD, induced pluripotent stem cell (iPSC) cohort, a cell genome-wide association study (cell GWAS), and machine learning. And finally, we discuss the future perspectives of CDiP technology for reverse engineering of sporadic AD toward AD eradication.
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Affiliation(s)
- Takayuki Kondo
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
- Medical-risk Avoidance based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project (AIP), Kyoto, Japan
- iPSC-based Drug Discovery and Development Team, RIKEN BioResource Research Center (BRC), Kyoto, Japan
| | - Yuichiro Yada
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
- iPSC-based Drug Discovery and Development Team, RIKEN BioResource Research Center (BRC), Kyoto, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Haruhisa Inoue
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.
- Medical-risk Avoidance based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project (AIP), Kyoto, Japan.
- iPSC-based Drug Discovery and Development Team, RIKEN BioResource Research Center (BRC), Kyoto, Japan.
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan.
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40
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Brenowitz WD, Fornage M, Launer LJ, Habes M, Davatzikos C, Yaffe K. Alzheimer's Disease Genetic Risk, Cognition, and Brain Aging in Midlife. Ann Neurol 2023; 93:629-634. [PMID: 36511390 PMCID: PMC9974745 DOI: 10.1002/ana.26569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/10/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
Abstract
We examined associations of an Alzheimer's disease (AD) Genetic Risk Score (AD-GRS) and midlife cognitive and neuroimaging outcomes in 1,252 middle-aged participants (311 with brain MRI). A higher AD-GRS based on 25 previously identified loci (excluding apolipoprotein E [APOE]) was associated with worse Montreal Cognitive Assessment (-0.14 standard deviation [SD] [95% confidence interval {CI}: -0.26, -0.02]), older machine learning predicted brain age (2.35 years[95%CI: 0.01, 4.69]), and white matter hyperintensity volume (0.35 SD [95% CI: 0.00, 0.71]), but not with a composite cognitive outcome, total brain, or hippocampal volume. APOE ε4 allele was not associated with any outcomes. AD risk genes beyond APOE may contribute to subclinical differences in cognition and brain health in midlife. ANN NEUROL 2023;93:629-634.
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Affiliation(s)
- Willa D Brenowitz
- Departments of Psychiatry and Behavioral Sciences, Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
| | - Myriam Fornage
- The University of Texas, Health Science Center at Houston, Houston, Texas, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, Maryland, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kristine Yaffe
- Departments of Psychiatry and Behavioral Sciences, Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- San Francisco VA Medical Center, San Francisco, California, USA
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41
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Ren P, Ding W, Li S, Liu G, Luo M, Zhou W, Cheng R, Li Y, Wang P, Li Z, Yao L, Jiang Q, Liang X. Regional transcriptional vulnerability to basal forebrain functional dysconnectivity in mild cognitive impairment patients. Neurobiol Dis 2023; 177:105983. [PMID: 36586468 DOI: 10.1016/j.nbd.2022.105983] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/07/2022] [Accepted: 12/28/2022] [Indexed: 12/30/2022] Open
Abstract
Nucleus basalis of Meynert (NbM), one of the earliest targets of Alzheimer's disease (AD), may act as a seed for pathological spreading to its connected regions. However, the underlying basis of regional vulnerability to NbM dysconnectivity remains unclear. NbM functional dysconnectivity was assessed using resting-state fMRI data of health controls and mild cognitive impairment (MCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI2/GO phase). Transcriptional correlates of NbM dysconnectivity was explored by leveraging public intrinsic and differential post-mortem brain-wide gene expression datasets from Allen Human Brain Atlas (AHBA) and Mount Sinai Brain Bank (MSBB). By constructing an individual-level tissue-specific gene set risk score (TGRS), we evaluated the contribution of NbM dysconnectivity-correlated gene sets to change rate of cerebral spinal fluid (CSF) biomarkers during preclinical stage of AD, as well as to MCI onset age. An independent cohort of health controls and MCI patients from ADNI3 was used to validate our main findings. Between-group comparison revealed significant connectivity reduction between the right NbM and right middle temporal gyrus in MCI. This regional vulnerability to NbM dysconnectivity correlated with intrinsic expression of genes enriched in protein and immune functions, as well as with differential expression of genes enriched in cholinergic receptors, immune, vascular and energy metabolism functions. TGRS of these NbM dysconnectivity-correlated gene sets are associated with longitudinal amyloid-beta change at preclinical stages of AD, and contributed to MCI onset age independent of traditional AD risks. Our findings revealed the transcriptional vulnerability to NbM dysconnectivity and their crucial role in explaining preclinical amyloid-beta change and MCI onset age, which offer new insights into the early AD pathology and encourage more investigation and clinical trials targeting NbM.
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Affiliation(s)
- Peng Ren
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin 150001, China
| | - Wencai Ding
- Department of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Siyang Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin 150001, China
| | - Guiyou Liu
- Beijing Institute for Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing 100069, China; Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Meng Luo
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Rui Cheng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Yiqun Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Zhipeng Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin 150001, China
| | - Lifen Yao
- Department of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Key Laboratory of Biological Big Data (Harbin Institute of Technology), Ministry of Education, Harbin 150001, China.
| | - Xia Liang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China; Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin 150001, China.
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Zhang Z, Chen L, Wang X, Wang C, Yang Y, Li H, Cai M, Lin H. Associations of Air Pollution and Genetic Risk With Incident Dementia: A Prospective Cohort Study. Am J Epidemiol 2023; 192:182-194. [PMID: 36269005 DOI: 10.1093/aje/kwac188] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 08/02/2022] [Accepted: 10/18/2022] [Indexed: 02/07/2023] Open
Abstract
Evidence on the association between air pollution and dementia is accumulating but still inconclusive, and the potential effect modification by genetics is unclear. We investigated the joint effects of air pollution exposure and genetic risk on incident dementia in a prospective cohort study, the UK Biobank study. Land use regression models were used to estimate exposure to ambient particulate matter (PM) in 3 fraction sizes (PM with diameter < 2.5 μm (PM2.5), coarse particles (PM with diameter 2.5-10 μm (PMc)), and PM with diameter < 10 μm (PM10)), PM2.5 absorbance, nitrogen dioxide levels, and nitrogen oxide levels at each individual's baseline residence. A polygenic risk score was calculated as a quantitative measure of genetic dementia risk. Incident cases of dementia were ascertained through linkage to health administrative data sets. Among the 227,840 participants included in the analysis, 3,774 incident dementia cases (including 1,238 cases of Alzheimer disease and 563 cases of vascular dementia) were identified. After adjustment for a variety of covariates, including genetic factors, positive associations were found between exposure to air pollution-particularly PM10, PM2.5 absorbance, and nitrogen dioxide-and incident all-cause dementia and Alzheimer disease but not vascular dementia. No significant interaction between air pollution and genetics was found, either on the multiplicative scale or on the additive scale. Exposure to air pollution was associated with a higher risk of developing dementia regardless of genetic risk.
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Gao XR, Chiariglione M, Qin K, Nuytemans K, Scharre DW, Li YJ, Martin ER. Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer's disease prediction. Sci Rep 2023; 13:450. [PMID: 36624143 PMCID: PMC9829871 DOI: 10.1038/s41598-023-27551-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Alzheimer's disease (AD) is the most common late-onset neurodegenerative disorder. Identifying individuals at increased risk of developing AD is important for early intervention. Using data from the Alzheimer Disease Genetics Consortium, we constructed polygenic risk scores (PRSs) for AD and age-at-onset (AAO) of AD for the UK Biobank participants. We then built machine learning (ML) models for predicting development of AD, and explored feature importance among PRSs, conventional risk factors, and ICD-10 codes from electronic health records, a total of > 11,000 features using the UK Biobank dataset. We used eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), which provided superior ML performance as well as aided ML model explanation. For participants age 40 and older, the area under the curve for AD was 0.88. For subjects of age 65 and older (late-onset AD), PRSs were the most important predictors. This is the first observation that PRSs constructed from the AD risk and AAO play more important roles than age in predicting AD. The ML model also identified important predictors from EHR, including urinary tract infection, syncope and collapse, chest pain, disorientation and hypercholesterolemia, for developing AD. Our ML model improved the accuracy of AD risk prediction by efficiently exploring numerous predictors and identified novel feature patterns.
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Affiliation(s)
- Xiaoyi Raymond Gao
- Department of Ophthalmology and Visual Sciences, The Ohio State University, Columbus, OH, USA.
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
- Division of Human Genetics, The Ohio State University, Columbus, OH, USA.
- Ohio State University Physicians Inc., Columbus, OH, USA.
| | - Marion Chiariglione
- Department of Ophthalmology and Visual Sciences, The Ohio State University, Columbus, OH, USA
| | - Ke Qin
- Department of Ophthalmology and Visual Sciences, The Ohio State University, Columbus, OH, USA
| | - Karen Nuytemans
- John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Douglas W Scharre
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Yi-Ju Li
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
- Duke Molecular Physiology Institute, Durham, NC, USA
| | - Eden R Martin
- John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL, USA
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Linear Mixed Model Analysis of Polygenic Hazard Score on Verbal Memory Decline in Alzheimer's Disease. Nurs Res 2023; 72:66-73. [PMID: 36097266 DOI: 10.1097/nnr.0000000000000623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a chronic, progressive, degenerative disease characterized by cognitive dysfunction, including verbal memory loss. Studies were lacking in examining the longitudinal effect of polygenic hazard score on the Rey Auditory Verbal Learning Test-Delayed Total (AVDELTOT) score (a common measure of verbal memory). A key step in analyzing longitudinal changes in cognitive measures using a linear mixed model (LMM) is choosing a suitable covariance structure. OBJECTIVES The study aims to determine the association between the polygenic hazard score and the AVDELTOT score accounting for repeated measures (the covariance structure). METHODS The AVDELTOT scores were collected at baseline, 12 months, 24 months, 36 months, and 48 months from 283 participants with AD, 347 with cognitive normal, and 846 with mild cognitive impairment in the Alzheimer's Disease Neuroimaging Initiative. The Bayesian information criterion statistic was used to select the best covariance structure from 10 covariance structures in longitudinal analysis of AVDELTOT scores. The multivariable LMM was used to investigate the effect of polygenic hazard score status (low vs. medium vs. high) on changes in AVDELTOT scores while adjusted for age, gender, education, APOE-ε4 genotype, and baseline Mini-Mental State Examination score. RESULTS One-way analysis of variance revealed significant differences in AVDELTOT scores, Mini-Mental State Examination scores, and polygenic hazard scores among AD diagnoses at baseline. Bayesian information criterion favored the compound symmetry covariance structure in the LMM analysis. Using the multivariate LMM, the APOE-ε4 allele and high polygenic hazard score value was significantly associated with AVDELTOT declines. Significant polygenic hazard score status by follow-up visit interactions was discovered. CONCLUSION Our findings provide the first evidence of the effect of polygenic hazard score status and APOE-ε4 allele on declines in verbal memory in people with AD.
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Panyard DJ, Deming YK, Darst BF, Van Hulle CA, Zetterberg H, Blennow K, Kollmorgen G, Suridjan I, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:395-409. [PMID: 36744333 PMCID: PMC10050104 DOI: 10.3233/jad-220599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models. OBJECTIVE We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers. METHODS We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD. RESULTS The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ42:Aβ40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5). CONCLUSION These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.
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Affiliation(s)
- Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Yuetiva K. Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Burcu F. Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States of America
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | | | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI 53726, United States of America
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, United States of America
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Feldman HH, Belleville S, Nygaard HB, Montero-Odasso M, Durant J, Lupo JL, Revta C, Chan S, Cuesta M, Slack PJ, Winer S, Brewster PWH, Hofer SM, Lim A, Centen A, Jacobs DM, Anderson ND, Walker JD, Speechley MR, Zou GY, Chertkow H. Protocol for the Brain Health Support Program Study of the Canadian Therapeutic Platform Trial for Multidomain Interventions to Prevent Dementia (CAN-THUMBS UP): A Prospective 12-Month Intervention Study. J Prev Alzheimers Dis 2023; 10:875-885. [PMID: 37874110 PMCID: PMC10258470 DOI: 10.14283/jpad.2023.65] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/19/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND/OBJECTIVES CAN-THUMBS UP is designed as a comprehensive and innovative fully remote program to 1) develop an interactive and compelling online Brain Health Support Program intervention, with potential to positively influence dementia literacy, self-efficacy and lifestyle risk factors; 2) enroll and retain a community-dwelling Platform Trial Cohort of individuals at risk of dementia who will participate in the intervention; 3) support an open platform trial to test a variety of multidomain interventions that might further benefit individuals at risk of dementia. This manuscript presents the Brain Health Support Program Study protocol. DESIGN/SETTING Twelve-month prospective multi-center longitudinal study to evaluate a fully remote web-based educational intervention. Participants will subsequently be part of a Platform Trial Cohort and may be eligible to participate in further dementia prevention clinical trials. PARTICIPANTS Three hundred fifty older adults who are cognitively unimpaired or have mild cognitive impairment, with at least 1 well established dementia risk factor. INTERVENTION Participants engage in the Brain Health Support Program intervention for 45-weeks and complete pre/post intervention measures. This intervention is designed to convey best available evidence for dementia prevention, consists of 181 chapters within 8 modules that are progressively delivered, and is available online in English and French. The program has been developed as a collaborative effort by investigators with recognized expertise in the program's content areas, along with input from older-adult citizen advisors. MEASUREMENTS This study utilizes adapted remote assessments with accessible technologies (e.g. videoconferencing, cognitive testing via computer and mobile phone, wearable devices to track physical activity and sleep, self-administered saliva sample collection). The primary outcome is change in dementia literacy, as measured by the Alzheimer's Disease Knowledge Scale. Secondary outcomes include change in self-efficacy; engagement using the online program; user satisfaction ratings; and evaluation of usability and acceptance. Exploratory outcomes include changes in attitudes toward dementia, modifiable risk factors, performance on the Neuropsychological Test Battery, performance on self-administered online cognitive assessments, and levels of physical activity and sleep; success of the national recruitment plan; and the distribution of age adjusted polygenic hazard scores. CONCLUSIONS This fully remote study provides an accessible approach to research with all study activities being completed in the participants' home environment. This approach may reduce barriers to participation, provide an easier and less demanding participant experience, and reach a broader geography with recruitment from all regions of Canada. CAN-THUMBS UP represents a Canadian contribution to the global World-Wide FINGERS program (alz.org/wwfingers).
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Affiliation(s)
- H H Feldman
- Howard H Feldman, MD, University of California, San Diego, 9500 Gilman Drive, MC 0949, La Jolla, CA 92037-0949,
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Wu BS, Zhang YR, Yang L, Zhang W, Deng YT, Chen SD, Feng JF, Cheng W, Yu JT. Polygenic Liability to Alzheimer's Disease Is Associated with a Wide Range of Chronic Diseases: A Cohort Study of 312,305 Participants. J Alzheimers Dis 2023; 91:437-447. [PMID: 36442194 DOI: 10.3233/jad-220740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) patients rank among the highest levels of comorbidities compared to persons with other diseases. However, it is unclear whether the conditions are caused by shared pathophysiology due to the genetic pleiotropy for AD risk genes. OBJECTIVE To figure out the genetic pleiotropy for AD risk genes in a wide range of diseases. METHODS We estimated the polygenic risk score (PRS) for AD and tested the association between PRS and 16 ICD10 main chapters, 136 ICD10 level-1 chapters, and 377 diseases with cases more than 1,000 in 312,305 individuals without AD diagnosis from the UK Biobank. RESULTS After correction for multiple testing, AD PRS was associated with two main ICD10 chapters: Chapter IV (endocrine, nutritional and metabolic diseases) and Chapter VII (eye and adnexa disorders). When narrowing the definition of the phenotypes, positive associations were observed between AD PRS and other types of dementia (OR = 1.39, 95% CI [1.34, 1.45], p = 1.96E-59) and other degenerative diseases of the nervous system (OR = 1.18, 95% CI [1.13, 1.24], p = 7.74E-10). In contrast, we detected negative associations between AD PRS and diabetes mellitus, obesity, chronic bronchitis, other retinal disorders, pancreas diseases, and cholecystitis without cholelithiasis (ORs range from 0.94 to 0.97, FDR < 0.05). CONCLUSION Our study confirms several associations reported previously and finds some novel results, which extends the knowledge of genetic pleiotropy for AD in a range of diseases. Further mechanistic studies are necessary to illustrate the molecular mechanisms behind these associations.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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Eissman JM, Wells G, Khan OA, Liu D, Petyuk VA, Gifford KA, Dumitrescu L, Jefferson AL, Hohman TJ. Polygenic resilience score may be sensitive to preclinical Alzheimer's disease changes. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:449-460. [PMID: 36540999 PMCID: PMC9888419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Late-onset Alzheimer's disease (LOAD) is a polygenic disorder with a long prodromal phase, making early diagnosis challenging. Twin studies estimate LOAD as 60-80% heritable, and while common genetic variants can account for 30% of this heritability, nearly 70% remains "missing". Polygenic risk scores (PRS) leverage combined effects of many loci to predict LOAD risk, but often lack sensitivity to preclinical disease changes, limiting clinical utility. Our group has built and published on a resilience phenotype to model better-than-expected cognition give amyloid pathology burden and hypothesized it may assist in preclinical polygenic risk prediction. Thus, we built a LOAD PRS and a resilience PRS and evaluated both in predicting cognition in a dementia-free cohort (N=254). The LOAD PRS had a significant main effect on baseline memory (β=-0.18, P=1.68E-03). Both the LOAD PRS (β=-0.03, P=1.19E-03) and the resilience PRS (β=0.02, P=0.03) had significant main effects on annual memory decline. The resilience PRS interacted with CSF Aβ on baseline memory (β=-6.04E-04, P=0.02), whereby it predicted baseline memory among Aβ+ individuals (β=0.44, P=0.01) but not among Aβ- individuals (β=0.06, P=0.46). Excluding APOE from PRS resulted in mainly LOAD PRS associations attenuating, but notably the resilience PRS interaction with CSF Aβ and selective prediction among Aβ+ individuals was consistent. Although the resilience PRS is currently somewhat limited in scope from the phenotype's cross-sectional nature, our results suggest that the resilience PRS may be a promising tool in assisting in preclinical disease risk prediction among dementia-free and Aβ+ individuals, though replication and fine-tuning are needed.
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Affiliation(s)
- Jaclyn M. Eissman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Greyson Wells
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Omair A. Khan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Dandan Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Vladislav A. Petyuk
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest, National Laboratory, Richland, WA 99354, USA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37212, USA,
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Pala D, Lee B, Ning X, Kim D, Shen L. Mediation Analysis and Mixed-Effects Models for the Identification of Stage-specific Imaging Genetics Patterns in Alzheimer's Disease. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:2667-2673. [PMID: 36824222 PMCID: PMC9942815 DOI: 10.1109/bibm55620.2022.9995405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Alzheimer's disease (AD) is one of the most common and severe forms of Senile Dementia. Genome-wide association studies (GWAS) have identified dozens of AD susceptible loci. To better understand potential mechanism-of-action for AD, quantitative brain imaging features have been studied as mediators linking genetic variants to AD outcomes. In this study, Mediation analysis, Chow test and Mixed-effects Models are used to investigate the biological pathways by which genetic variants affect both brain structures/functions and disease diagnosis. We analyzed the imaging and genetics data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project, including a Polygenic Hazard Score (PHS) and 13 imaging quantitative traits (QTs) extracted from the AV45 PET scans quantifying the amyloid deposition in different brain regions of subjects from four separate diagnostic groups. Mediation analysis assessed the mediating effects of image QTs between PHS and diagnosis, whereas Chow test and Linear Mixed-Effects models were used to characterize intra-group differences in the associations between genetic scores and imaging QTs for different disease stages. Results show that promising stage-specific imaging QTs that mediate the genetic effect of the studied PHS on disease status have been identified, providing novel insights into the predictive power of the PHS and the mediating power of amyloid imaging QTs with respect to multiple stages over the AD progression.
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Affiliation(s)
- Daniele Pala
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Brian Lee
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
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Pasqualetti G, Thayanandan T, Edison P. Influence of genetic and cardiometabolic risk factors in Alzheimer's disease. Ageing Res Rev 2022; 81:101723. [PMID: 36038112 DOI: 10.1016/j.arr.2022.101723] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 08/18/2022] [Accepted: 08/21/2022] [Indexed: 01/31/2023]
Abstract
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder. Cardiometabolic and genetic risk factors play an important role in the trajectory of AD. Cardiometabolic risk factors including diabetes, mid-life obesity, mid-life hypertension and elevated cholesterol have been linked with cognitive decline in AD subjects. These potential risk factors associated with cerebral metabolic changes which fuel AD pathogenesis have been suggested to be the reason for the disappointing clinical trial results. In appreciation of the risks involved, using search engines such as PubMed, Scopus, MEDLINE and Google Scholar, a relevant literature search on cardiometabolic and genetic risk factors in AD was conducted. We discuss the role of genetic as well as established cardiovascular risk factors in the neuropathology of AD. Moreover, we show new evidence of genetic interaction between several genes potentially involved in different pathways related to both neurodegenerative process and cardiovascular damage.
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Affiliation(s)
| | - Tony Thayanandan
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, UK
| | - Paul Edison
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, UK; School of Medicine, Cardiff University, UK.
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