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Hernandez H, Baez S, Medel V, Moguilner S, Cuadros J, Santamaria-Garcia H, Tagliazucchi E, Valdes-Sosa PA, Lopera F, OchoaGómez JF, González-Hernández A, Bonilla-Santos J, Gonzalez-Montealegre RA, Aktürk T, Yıldırım E, Anghinah R, Legaz A, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, García AM, Huepe D, Caterina GD, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel C, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark R, Herzog R, Yerlikaya D, Güntekin B, Parra MA, Prado P, Ibanez A. Brain health in diverse settings: How age, demographics and cognition shape brain function. Neuroimage 2024; 295:120636. [PMID: 38777219 DOI: 10.1016/j.neuroimage.2024.120636] [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: 02/08/2024] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.
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Affiliation(s)
- Hernan Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sandra Baez
- Universidad de los Andes, Bogota, Colombia; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland
| | - Vicente Medel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Harvard Medical School, Boston, MA, USA
| | - Jhosmary Cuadros
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile; Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Hernando Santamaria-Garcia
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia; Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; University of Buenos Aires, Argentina
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia
| | | | | | | | | | - Tuba Aktürk
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Ebru Yıldırım
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil; Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil
| | - Agustina Legaz
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Görsev G Yener
- Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey; Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey; Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Alberto A Fernández Lucas
- Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center, Universidad de San Andréss, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez
| | - Gaetano Di Caterina
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
| | | | - Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | | | - Carlos Coronel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ruaridh Clark
- Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
| | - Ruben Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey; Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| | - Mario A Parra
- Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Trinity College Dublin, The University of Dublin, Dublin, Ireland.
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Tang AS, Woldemariam SR, Miramontes S, Norgeot B, Oskotsky TT, Sirota M. Harnessing EHR data for health research. Nat Med 2024; 30:1847-1855. [PMID: 38965433 DOI: 10.1038/s41591-024-03074-8] [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: 01/03/2024] [Accepted: 05/17/2024] [Indexed: 07/06/2024]
Abstract
With the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information. Recognizing and addressing these limitations can inform the design and interpretation of EHR-based informatics studies that avoid confusing or incorrect conclusions, particularly when applied to population or precision medicine. Here we discuss key considerations in the design, implementation and interpretation of EHR-based informatics studies, drawing from examples in the literature across hypothesis generation, hypothesis testing and machine learning applications. We outline the growing opportunities for EHR-based informatics studies, including association studies and predictive modeling, enabled by evolving AI capabilities-while addressing limitations and potential pitfalls to avoid.
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Affiliation(s)
- Alice S Tang
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah R Woldemariam
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Silvia Miramontes
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | | | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
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Schliep KC, Thornhill J, Tschanz J, Facelli JC, Østbye T, Sorweid MK, Smith KR, Varner M, Boyce RD, Brown CJC, Meeks H, Abdelrahman S. Predicting the onset of Alzheimer's disease and related dementia using Electronic Health Records: Findings from the Cache County Study on Memory in Aging (1995-2008). RESEARCH SQUARE 2024:rs.3.rs-4414498. [PMID: 38883755 PMCID: PMC11177999 DOI: 10.21203/rs.3.rs-4414498/v1] [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
Introduction Clinical notes, biomarkers, and neuroimaging have been proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict Alzheimer's disease (AD) and Alzheimer's disease related dementias (ADRD) in a well-phenotyped, population-based cohort using a machine learning approach. Methods Administrative healthcare data (k=163 diagnostic features), in addition to Census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008). Results Among successfully linked UPDB-CCS participants (n=4206), 522 (12.4%) had incident AD/ADRD as per the CCS "gold standard" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49). DISCUSSION Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict AD/ADRD, corroborated by prior research.
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Miramontes S, Pereda Serras C, Woldemariam SR, Khan U, Li Y, Tang AS, Tsoy E, Oskotsky TT, Sirota M. Alzheimer's disease as a women's health challenge: a call for action on integrative precision medicine approaches. NPJ WOMEN'S HEALTH 2024; 2:17. [PMID: 38778871 PMCID: PMC11106001 DOI: 10.1038/s44294-024-00021-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's Disease (AD) is marked by pronounced sex differences in pathophysiology and progression. However, the field has yet to fully recognize AD as a women's health issue, delaying the development of targeted preventative strategies and treatments. This perspective explores the elements impacting AD in women, identifying sex specificity in risk factors, highlighting new diagnostic approaches with electronic health records, and reviewing key molecular studies to underscore the need for integrative precision medicine approaches. Established AD risk factors such as advancing age, the apolipoprotein E4 allele, and poorer cardiovascular health affect women differently. We also shed light on sociocultural risk factors, focusing on the gender disparities that may play a role in AD development. From a biological perspective, sex differences in AD are apparent in biomarkers and transcriptomics, further emphasizing the need for targeted diagnostics and treatments. The convergence of novel multiomics data and cutting-edge computational tools provides a unique opportunity to study the molecular underpinnings behind sex dimorphism in AD. Thus, precision medicine emerges as a promising framework for understanding AD pathogenesis through the integration of genetics, sex, environment, and lifestyle. By characterizing AD as a women's health challenge, we can catalyze a transformative shift in AD research and care, marked by improved diagnostic accuracy, targeted interventions, and ultimately, enhanced clinical outcomes.
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Affiliation(s)
- S. Miramontes
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
| | - C. Pereda Serras
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
| | - S. R. Woldemariam
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
| | - U. Khan
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
| | - Y. Li
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
| | - A. S. Tang
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
| | - E. Tsoy
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA USA
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA USA
| | - T. T. Oskotsky
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
| | - M. Sirota
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA USA
- Department of Pediatrics, University of California San Francisco, San Francisco, CA USA
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5
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Veneziani I, Grimaldi A, Marra A, Morini E, Culicetto L, Marino S, Quartarone A, Maresca G. Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline-A Systematic Review. J Clin Med 2024; 13:2307. [PMID: 38673581 PMCID: PMC11051247 DOI: 10.3390/jcm13082307] [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: 03/18/2024] [Revised: 04/09/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024] Open
Abstract
Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.
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Affiliation(s)
- Isabella Veneziani
- Department of Nervous System and Behavioural Sciences, Psychology Section, University of Pavia, Piazza Botta, 11, 27100 Pavia, Italy (A.G.)
| | - Alessandro Grimaldi
- Department of Nervous System and Behavioural Sciences, Psychology Section, University of Pavia, Piazza Botta, 11, 27100 Pavia, Italy (A.G.)
| | - Angela Marra
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Elisabetta Morini
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Laura Culicetto
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Angelo Quartarone
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Giuseppa Maresca
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
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6
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Tang AS, Rankin KP, Cerono G, Miramontes S, Mills H, Roger J, Zeng B, Nelson C, Soman K, Woldemariam S, Li Y, Lee A, Bove R, Glymour M, Aghaeepour N, Oskotsky TT, Miller Z, Allen IE, Sanders SJ, Baranzini S, Sirota M. Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights. NATURE AGING 2024; 4:379-395. [PMID: 38383858 PMCID: PMC10950787 DOI: 10.1038/s43587-024-00573-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024]
Abstract
Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.
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Affiliation(s)
- Alice S Tang
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, San Francisco and Berkeley, CA, USA.
| | - Katherine P Rankin
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Gabriel Cerono
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Silvia Miramontes
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Hunter Mills
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Jacquelyn Roger
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Billy Zeng
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Charlotte Nelson
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Karthik Soman
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah Woldemariam
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Yaqiao Li
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Albert Lee
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Riley Bove
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Maria Glymour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Zachary Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Isabel E Allen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Stephan J Sanders
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Sergio Baranzini
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pediatrics, University of California, San Francisco, CA, USA.
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Disease insights from medical data using interpretable risk prediction models. NATURE AGING 2024; 4:293-294. [PMID: 38383859 DOI: 10.1038/s43587-024-00585-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
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Villablanca A, Dugger BN, Nuthikattu S, Chauhan J, Cheung S, Chuah CN, Garrison SL, Milenkovic D, Norman JE, Oliveira LC, Smith BP, Brown SD. How cy pres promotes transdisciplinary convergence science: an academic health center for women's cardiovascular and brain health. J Clin Transl Sci 2024; 8:e16. [PMID: 38384925 PMCID: PMC10880003 DOI: 10.1017/cts.2023.705] [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: 07/30/2023] [Revised: 11/15/2023] [Accepted: 12/15/2023] [Indexed: 02/23/2024] Open
Abstract
Cardiovascular disease (CVD) is largely preventable, and the leading cause of death for men and women. Though women have increased life expectancy compared to men, there are marked sex disparities in prevalence and risk of CVD-associated mortality and dementia. Yet, the basis for these and female-male differences is not completely understood. It is increasingly recognized that heart and brain health represent a lifetime of exposures to shared risk factors (including obesity, hyperlipidemia, diabetes, and hypertension) that compromise cerebrovascular health. We describe the process and resources for establishing a new research Center for Women's Cardiovascular and Brain Health at the University of California, Davis as a model for: (1) use of the cy pres principle for funding science to improve health; (2) transdisciplinary collaboration to leapfrog progress in a convergence science approach that acknowledges and addresses social determinants of health; and (3) training the next generation of diverse researchers. This may serve as a blueprint for future Centers in academic health institutions, as the cy pres mechanism for funding research is a unique mechanism to leverage residual legal settlement funds to catalyze the pace of scientific discovery, maximize innovation, and promote health equity in addressing society's most vexing health problems.
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Affiliation(s)
- Amparo Villablanca
- Department of Internal Medicine, University of California, Davis, CA, USA
| | - Brittany N. Dugger
- Department of Pathology and Laboratory Medicine, University of California, Davis, CA, USA
| | | | - Joohi Chauhan
- Department of Pathology and Laboratory Medicine, University of California, Davis, CA, USA
- Department of Computer Engineering, University of California, Davis, CA, USA
| | - Samson Cheung
- Department of Computer Engineering, University of California, Davis, CA, USA
| | - Chen-Nee Chuah
- Department of Computer Engineering, University of California, Davis, CA, USA
| | - Siedah L. Garrison
- Department of Internal Medicine, University of California, Davis, CA, USA
| | | | - Jennifer E. Norman
- Department of Internal Medicine, University of California, Davis, CA, USA
| | - Luca Cerny Oliveira
- Department of Computer Engineering, University of California, Davis, CA, USA
| | - Bridgette P. Smith
- Department of Internal Medicine, University of California, Davis, CA, USA
| | - Susan D. Brown
- Department of Internal Medicine, University of California, Davis, CA, USA
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Kodama L, Woldemariam S, Tang A, Li Y, Oskotsky T, Raphael E, Sirota M. Sex-stratified phenotyping of comorbidities associated with an inpatient delirium diagnosis using real world data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.02.23297925. [PMID: 37961487 PMCID: PMC10635265 DOI: 10.1101/2023.11.02.23297925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Delirium is a heterogeneous and detrimental mental condition often seen in older, hospitalized patients and is currently hard to predict. In this study, we leverage large-scale, real- world data using the electronic health records (EHR) to identify two cohorts comprised of 7,492 UCSF patients and 19,417 UC health system patients (excluding UCSF patients) with an inpatient delirium diagnosis and the same number of propensity score-matched control patients without delirium. We found significant associations between comorbidities or laboratory test values and an inpatient delirium diagnosis which were validated independently. Most of these associations were those previously-identified as risk factors for delirium, including metabolic abnormalities, mental health diagnoses, and infections. Some of the associations were sex- specific, including those related to dementia subtypes and infections. We further explored the diagnostic associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis of the risk factor to development of delirium demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to dramatic increases in mortality outcome across both cohorts. These results demonstrate the powerful application of leveraging EHR data to shed insights into prior diagnoses and laboratory test values that could help predict development of inpatient delirium and emphasize the importance of considering patient demographic characteristics including documented sex when making these assessments. One Sentence Summary Longitudinal analysis of electronic health record data reveals associations between inpatient delirium, comorbidities, and mortality.
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Grabowska ME, Huang A, Wen Z, Li B, Wei WQ. Drug repurposing for Alzheimer's disease from 2012-2022-a 10-year literature review. Front Pharmacol 2023; 14:1257700. [PMID: 37745051 PMCID: PMC10512468 DOI: 10.3389/fphar.2023.1257700] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.
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Affiliation(s)
- Monika E. Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Annabelle Huang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhexing Wen
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, United States
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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Woldemariam SR, Tang AS, Oskotsky TT, Yaffe K, Sirota M. Similarities and differences in Alzheimer's dementia comorbidities in racialized populations identified from electronic medical records. COMMUNICATIONS MEDICINE 2023; 3:50. [PMID: 37031271 PMCID: PMC10082816 DOI: 10.1038/s43856-023-00280-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 03/24/2023] [Indexed: 04/10/2023] Open
Abstract
BACKGROUND Alzheimer's dementia (AD) is a neurodegenerative disease that is disproportionately prevalent in racially marginalized individuals. However, due to research underrepresentation, the spectrum of AD-associated comorbidities that increase AD risk or suggest AD treatment disparities in these individuals is not completely understood. We leveraged electronic medical records (EMR) to explore AD-associated comorbidities and disease networks in racialized individuals identified as Asian, Non-Latine Black, Latine, or Non-Latine White. METHODS We performed low-dimensional embedding, differential analysis, and disease network-based analyses of 5664 patients with AD and 11,328 demographically matched controls across two EMR systems and five medical centers, with equal representation of Asian-, Non-Latine Black-, Latine-, and Non-Latine White-identified individuals. For low-dimensional embedding and disease network comparisons, Mann-Whitney U tests or Kruskal-Wallis tests followed by Dunn's tests were used to compare categories. Fisher's exact or chi-squared tests were used for differential analysis. Spearman's rank correlation coefficients were used to compare results between the two EMR systems. RESULTS Here we show that primarily established AD-associated comorbidities, such as essential hypertension and major depressive disorder, are generally similar across racialized populations. However, a few comorbidities, including respiratory diseases, may be significantly associated with AD in Black- and Latine- identified individuals. CONCLUSIONS Our study revealed similarities and differences in AD-associated comorbidities and disease networks between racialized populations. Our approach could be a starting point for hypothesis-driven studies that can further explore the relationship between these comorbidities and AD in racialized populations, potentially identifying interventions that can reduce AD health disparities.
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Affiliation(s)
- Sarah R Woldemariam
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA
| | - Alice S Tang
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA
- School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Kristine Yaffe
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA.
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA.
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Xu J, Mao C, Hou Y, Luo Y, Binder JL, Zhou Y, Bekris LM, Shin J, Hu M, Wang F, Eng C, Oprea TI, Flanagan ME, Pieper AA, Cummings J, Leverenz JB, Cheng F. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell Rep 2022; 41:111717. [PMID: 36450252 PMCID: PMC9837836 DOI: 10.1016/j.celrep.2022.111717] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/01/2022] [Accepted: 11/02/2022] [Indexed: 12/03/2022] Open
Abstract
Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.
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Affiliation(s)
- Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jessica L Binder
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lynn M Bekris
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Jiyoung Shin
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Margaret E Flanagan
- Department of Pathology and Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA; Department of Neuroscience, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - James B Leverenz
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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Demographic reporting across a decade of neuroimaging: a systematic review. Brain Imaging Behav 2022; 16:2785-2796. [DOI: 10.1007/s11682-022-00724-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 11/25/2022]
Abstract
Abstract
Diversity of participants in biomedical research with respect to race, ethnicity, and biological sex is crucial, particularly given differences in disease prevalence, recovery, and survival rates between demographic groups. The objective of this systematic review was to report on the demographics of neuroimaging studies using magnetic resonance imaging (MRI). The Web of Science database was used and data collection was performed between June 2021 to November 2021; all articles were reviewed independently by at least two researchers. Articles utilizing MR data acquired in the United States, with n ≥ 10 human subjects, and published between 2010–2020 were included. Non-primary research articles and those published in journals that did not meet a quality control check were excluded. Of the 408 studies meeting inclusion criteria, approximately 77% report sex, 10% report race, and 4% report ethnicity. Demographic reporting also varied as function of disease studied, participant age range, funding, and publisher. We anticipate quantitative data on the extent, or lack, of reporting will be necessary to ensure inclusion of diverse populations in biomedical research.
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Neuroimmune contributions to Alzheimer's disease: a focus on human data. Mol Psychiatry 2022; 27:3164-3181. [PMID: 35668160 PMCID: PMC9168642 DOI: 10.1038/s41380-022-01637-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 05/05/2022] [Accepted: 05/18/2022] [Indexed: 12/12/2022]
Abstract
The past decade has seen the convergence of a series of new insights that arose from genetic and systems analyses of Alzheimer's disease (AD) with a wealth of epidemiological data from a variety of fields; this resulted in renewed interest in immune responses as important, potentially causal components of AD. Here, we focus primarily on a review of human data which has recently yielded a set of robust, reproducible results that exist in a much larger universe of conflicting reports stemming from small studies with important limitations in their study design. Thus, we are at an important crossroads in efforts to first understand at which step of the long, multiphasic course of AD a given immune response may play a causal role and then modulate this response to slow or block the pathophysiology of AD. We have a wealth of new experimental tools, analysis methods, and capacity to sample human participants at large scale longitudinally; these resources, when coupled to a foundation of reproducible results and novel study designs, will enable us to monitor human immune function in the CNS at the level of complexity that is required while simultaneously capturing the state of the peripheral immune system. This integration of peripheral and central perturbations in immune responses results in pathologic responses in the central nervous system parenchyma where specialized cellular microenvironments composed of multiple cell subtypes respond to these immune perturbations as well as to environmental exposures, comorbidities and the impact of the advancing life course. Here, we offer an overview that seeks to illustrate the large number of interconnecting factors that ultimately yield the neuroimmune component of AD.
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