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Gordon MN, Heneka MT, Le Page LM, Limberger C, Morgan D, Tenner AJ, Terrando N, Willette AA, Willette SA. Impact of COVID-19 on the Onset and Progression of Alzheimer's Disease and Related Dementias: A Roadmap for Future Research. Alzheimers Dement 2022; 18:1038-1046. [PMID: 34874605 PMCID: PMC9011667 DOI: 10.1002/alz.12488] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/29/2021] [Indexed: 12/12/2022]
Abstract
COVID-19 causes lasting neurological symptoms in some survivors. Like other infections, COVID-19 may increase risk of cognitive impairment. This perspective highlights four knowledge gaps about COVID-19 that need to be filled to avoid this possible health issue. The first is the need to identify the COVID-19 symptoms, genetic polymorphisms and treatment decisions associated with risk of cognitive impairment. The second is the absence of model systems in which to test hypotheses relating infection to cognition. The third is the need for consortia for studying both existing and new longitudinal cohorts in which to monitor long term consequences of COVID-19 infection. A final knowledge gap discussed is the impact of the isolation and lack of social services brought about by quarantine/lockdowns on people living with dementia and their caregivers. Research into these areas may lead to interventions that reduce the overall risk of cognitive decline for COVID-19 survivors.
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Affiliation(s)
- Marcia N. Gordon
- Dept of Translational NeuroscienceMichigan State University400 Monroe Ave NWGrand RapidsMI49503USA
| | - Michael T. Heneka
- Dept. of Neurodegenerative Disease and Geriatric Psychiatry/NeurologyUniversity of Bonn Medical CenterSigmund‐Freud Str. 25, 53127 BonnGermany
| | - Lydia M. Le Page
- Departments of Physical Therapy and Rehabilitation Science, and Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoUSA
| | - Christian Limberger
- Graduate Program in Biological Sciences: BiochemistryUniversidade Federal do Rio Grande do SulPorto AlegreRSBrazil
| | - David Morgan
- Dept of Translational NeuroscienceMichigan State University400 Monroe Ave NWGrand RapidsMI49503USA
| | - Andrea J. Tenner
- Molecular Biology and Biochemistry, Neurobiology and Behavior and Pathology and Laboratory MedicineUniversity of CaliforniaIrvineUSA
| | - Niccolò Terrando
- Department of Anesthesiology, Cell Biology, and ImmunologyDuke University Medical CenterDurhamNC27710USA
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Srinivas ML, Shim H, Jones D, Hansen PR, Willette SA, Willette A, Li-Rodenborn ER, Perencevich EN, Goto M. 381. The Importance of Data Accuracy and Transparency for Policymaking During a Public Health Crisis: A Case Study in the State of Iowa. Open Forum Infect Dis 2021. [PMCID: PMC8644118 DOI: 10.1093/ofid/ofab466.582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background High-quality data are necessary for decision-making during the SARS-CoV-2 pandemic. Lack of transparency and accuracy in data reporting can erode public confidence, mislead policymakers, and endanger safety. Two major data errors in Iowa impacted critical state- and county-level decision-making. Methods The Iowa Department of Public Health (IDPH) publishes daily COVID-19 data. Authors independently tracked daily data from IDPH and other publicly available sources (i.e., county health departments, news media, and social networks). Data include: number and type of tests, results, hospitalizations, intensive care unit admissions, and deaths at state/county levels. Results Discrepancies were identified between IDPH and non-IDPH data, with at least two confirmed by IDPH: (1) The backdating of test results identified on May 28, 2020. IDPH labeled results as occurring up to four months before the actual test date. IDPH confirmed that if a person previously tested for SARS-CoV-2, a new test result was attributed to the initial test’s date. Corrections on August 19, 2020 increased positivity rates in 31 counties, but decreased the state’s overall rate (9.1% to 7.5%). (2) The selective exclusion of antigen test results noted on August 20, 2020. Antigen testing was included in the total number of tests reported in metric denominators, but their results were being excluded from their respective numerators. Thus, positive antigen results were interpreted as de facto negative tests, artificially lowering positivity rates. Corrections increased Iowa’s positivity rate (5.0% to 14.2%). In July 2020, the Iowa Department of Education mandated in-person K-12 learning for counties with < 15% positivity. These data changes occurred during critical decision-making, altering return-to-learn plans in seven counties. The Center for Medicare and Medicaid Services’ requirements also caused nursing homes to urgently revise testing strategies. ![]()
Timeline of changes to Iowa state COVID-19 testing through the end of August 2020. ![]()
Change in positive and overall test results due to IDPH data corrections. These graphs represent the difference in cumulative total reported test results when pulled from the IDPH website on September 29, 2020 compared to data for the same dates when pulled on August 19, 2020 before the announced adjustment. The adjustment and subsequent daily changes in reported data amount to a dramatic change in the number of reported positive cases (A) with an increase of nearly 3,000 cases by April 25, as well as the loss of tens of thousands of data points when tracking total resulted tests (B). Conclusion Data availability, quality, and transparency vary widely across the US, hindering science-based policymaking. Independent audit and curations of data can contribute to better public health policies. We urge all states to increase the availability and transparency of public health data. Disclosures All Authors: No reported disclosures
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Affiliation(s)
| | - HyungSub Shim
- University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Dana Jones
- University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | | | | | | | | | | | - Michihiko Goto
- University of Iowa Carver College of Medicine, Iowa City, Iowa
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Willette AA, Pappas C, Hoth N, Wang Q, Klinedinst B, Willette SA, Larsen B, Pollpeter A, Li T, Le S, Collazo-Martinez AD, Mochel JP, Allenspach K, Dantzer R. Inflammation, negative affect, and amyloid burden in Alzheimer's disease: Insights from the kynurenine pathway. Brain Behav Immun 2021; 95:216-225. [PMID: 33775832 PMCID: PMC8187283 DOI: 10.1016/j.bbi.2021.03.019] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Depressive symptoms in Alzheimer's disease (AD) predict worse cognitive and functional outcomes. Both AD and major depression inflammatory processes are characterized by shunted tryptophan metabolism away from serotonin (5-HT) and toward the neuroinflammatory kynurenine (Kyn) pathway. The present study assessed associations between Kyn and behavioral, neuroanatomical, neuropathological, and physiological outcomes common to both AD and negative affect across the AD continuum. METHODS In 58 cognitively normal, 396 mild cognitive impairment, and 112 AD participants from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI1) cohort, serum markers of 5-HT, tryptophan, and Kyn were measured and their relationships investigated with immunologic markers, affect and functional outcomes, CSF markers of beta-amyloid (Aβ) and tau, and regional gray matter. RESULTS A higher Kyn/Tryptophan ratio was linked to many inflammatory markers, as well as lower functional independence and memory scores. A higher Kyn/5-HT ratio showed similar associations, but also strong relationships with negative affect and neuropsychiatric disturbance, executive dysfunction, and global cognitive decline. Further, gray matter atrophy was seen in hippocampus, anterior cingulate, and prefrontal cortices, as well as greater amyloid and total tau deposition. Finally, using moderated-mediation, several pro-inflammatory factors partially mediated Kyn/5-HT and negative affect scores in participants with subclinical Aβ (i.e., Aβ-), whereas such associations were fully mediated by Complement 3 in Aβ+ participants. CONCLUSION These findings suggest that inflammatory signaling cascades may occur during AD, which is associated with increased Kyn metabolism that influences the pathogenesis of negative affect. Aβ and the complement system may be critical contributing factors in this process.
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Affiliation(s)
- Auriel A. Willette
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA,Neuroscience Graduate Program, Iowa State University, Ames, IA,Department of Psychology, Iowa State University, Ames, IA,Department of Neurology, University of Iowa, Iowa City, IA,Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA,Department of Biomedical Sciences, Iowa State University, Ames, IA, USA,Address Correspondence to: Auriel A. Willette, Ph.D., M.S., 1109 HNSB, 706 Morrill Rd., Ames, IA 50011, Phone: (515) 294-3110,
| | - Colleen Pappas
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA
| | - Nathan Hoth
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA
| | - Qian Wang
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA
| | | | - Sara A. Willette
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA
| | - Brittany Larsen
- Neuroscience Graduate Program, Iowa State University, Ames, IA
| | - Amy Pollpeter
- Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA
| | - Tianqi Li
- Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA
| | - Scott Le
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA
| | | | | | - Karin Allenspach
- Department of Veterinary Clinical Sciences, Iowa State University, Ames, IA, USA
| | - Robert Dantzer
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston,TX
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Willette AA, Willette SA, Wang Q, Pappas C, Klinedinst BS, Le S, Larsen B, Pollpeter A, Li T, Brenner N, Waterboer T. Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study. medRxiv 2021:2020.06.09.20127092. [PMID: 32577673 PMCID: PMC7302228 DOI: 10.1101/2020.06.09.20127092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization). METHODS Among aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4,510 participants with 7,539 test cases. We downloaded baseline data from 10-14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean. RESULTS The "best-fit" model for predicting COVID-19 risk achieved excellent discrimination (AUC=0.969, 95% CI=0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC=0.803, 95% CI=0.663-0.943) and included only serology titers. CONCLUSIONS Accurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.
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Affiliation(s)
- Auriel A. Willette
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
- Department of Neurology, University of Iowa, Iowa City, IA, USA
- Iowa COVID-19 Tracker, Ames, IA, USA
| | | | - Qian Wang
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Colleen Pappas
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Brandon S. Klinedinst
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Scott Le
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Brittany Larsen
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Amy Pollpeter
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Tianqi Li
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Nicole Brenner
- Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tim Waterboer
- Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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