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Breen C, Papale LA, Clark LR, Bergmann PE, Madrid A, Asthana S, Johnson SC, Keleş S, Alisch RS, Hogan KJ. Whole genome methylation sequencing in blood identifies extensive differential DNA methylation in late-onset dementia due to Alzheimer's disease. Alzheimers Dement 2024; 20:1050-1062. [PMID: 37856321 PMCID: PMC10916976 DOI: 10.1002/alz.13514] [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: 06/23/2023] [Revised: 08/17/2023] [Accepted: 09/25/2023] [Indexed: 10/21/2023]
Abstract
INTRODUCTION DNA microarray-based studies report differentially methylated positions (DMPs) in blood between late-onset dementia due to Alzheimer's disease (AD) and cognitively unimpaired individuals, but interrogate < 4% of the genome. METHODS We used whole genome methylation sequencing (WGMS) to quantify DNA methylation levels at 25,409,826 CpG loci in 281 blood samples from 108 AD and 173 cognitively unimpaired individuals. RESULTS WGMS identified 28,038 DMPs throughout the human methylome, including 2707 differentially methylated genes (e.g., SORCS3, GABA, and PICALM) encoding proteins in biological pathways relevant to AD such as synaptic membrane, cation channel complex, and glutamatergic synapse. One hundred seventy-three differentially methylated blood-specific enhancers interact with the promoters of 95 genes that are differentially expressed in blood from persons with and without AD. DISCUSSION WGMS identifies differentially methylated CpGs in known and newly detected genes and enhancers in blood from persons with and without AD. HIGHLIGHTS Whole genome DNA methylation levels were quantified in blood from persons with and without Alzheimer's disease (AD). Twenty-eight thousand thirty-eight differentially methylated positions (DMPs) were identified. Two thousand seven hundred seven genes comprise DMPs. Forty-eight of 75 independent genetic risk loci for AD have DMPs. One thousand five hundred sixty-eight blood-specific enhancers comprise DMPs, 173 of which interact with the promoters of 95 genes that are differentially expressed in blood from persons with and without AD.
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Affiliation(s)
- Coleman Breen
- Department of StatisticsUniversity of Wisconsin, Medical Sciences CenterMadisonWisconsinUSA
| | - Ligia A. Papale
- Department of Neurological SurgeryUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Lindsay R. Clark
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Geriatric Research Education and Clinical CenterWilliam S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Phillip E. Bergmann
- Department of Neurological SurgeryUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Andy Madrid
- Department of Neurological SurgeryUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Sanjay Asthana
- Geriatric Research Education and Clinical CenterWilliam S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Geriatric Research Education and Clinical CenterWilliam S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Sündüz Keleş
- Department of StatisticsUniversity of Wisconsin, Medical Sciences CenterMadisonWisconsinUSA
- Department of Biostatistics and Medical InformaticsUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Reid S. Alisch
- Department of Neurological SurgeryUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Kirk J. Hogan
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Department of AnesthesiologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
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Du L, Hermann BP, Jonaitis EM, Cody KA, Rivera-Rivera L, Rowley H, Field A, Eisenmenger L, Christian BT, Betthauser TJ, Larget B, Chappell R, Janelidze S, Hansson O, Johnson SC, Langhough R. Harnessing cognitive trajectory clusterings to examine subclinical decline risk factors. Brain Commun 2023; 5:fcad333. [PMID: 38107504 PMCID: PMC10724051 DOI: 10.1093/braincomms/fcad333] [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: 06/20/2023] [Revised: 10/23/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023] Open
Abstract
Cognitive decline in Alzheimer's disease and other dementias typically begins long before clinical impairment. Identifying people experiencing subclinical decline may facilitate earlier intervention. This study developed cognitive trajectory clusters using longitudinally based random slope and change point parameter estimates from a Preclinical Alzheimer's disease Cognitive Composite and examined how baseline and most recently available clinical/health-related characteristics, cognitive statuses and biomarkers for Alzheimer's disease and vascular disease varied across these cognitive clusters. Data were drawn from the Wisconsin Registry for Alzheimer's Prevention, a longitudinal cohort study of adults from late midlife, enriched for a parental history of Alzheimer's disease and without dementia at baseline. Participants who were cognitively unimpaired at the baseline visit with ≥3 cognitive visits were included in trajectory modelling (n = 1068). The following biomarker data were available for subsets: positron emission tomography amyloid (amyloid: n = 367; [11C]Pittsburgh compound B (PiB): global PiB distribution volume ratio); positron emission tomography tau (tau: n = 321; [18F]MK-6240: primary regions of interest meta-temporal composite); MRI neurodegeneration (neurodegeneration: n = 581; hippocampal volume and global brain atrophy); T2 fluid-attenuated inversion recovery MRI white matter ischaemic lesion volumes (vascular: white matter hyperintensities; n = 419); and plasma pTau217 (n = 165). Posterior median estimate person-level change points, slopes' pre- and post-change point and estimated outcome (intercepts) at change point for cognitive composite were extracted from Bayesian Bent-Line Regression modelling and used to characterize cognitive trajectory groups (K-means clustering). A common method was used to identify amyloid/tau/neurodegeneration/vascular biomarker thresholds. We compared demographics, last visit cognitive status, health-related factors and amyloid/tau/neurodegeneration/vascular biomarkers across the cognitive groups using ANOVA, Kruskal-Wallis, χ2, and Fisher's exact tests. Mean (standard deviation) baseline and last cognitive assessment ages were 58.4 (6.4) and 66.6 (6.6) years, respectively. Cluster analysis identified three cognitive trajectory groups representing steep, n = 77 (7.2%); intermediate, n = 446 (41.8%); and minimal, n = 545 (51.0%) cognitive decline. The steep decline group was older, had more females, APOE e4 carriers and mild cognitive impairment/dementia at last visit; it also showed worse self-reported general health-related and vascular risk factors and higher amyloid, tau, neurodegeneration and white matter hyperintensity positive proportions at last visit. Subtle cognitive decline was consistently evident in the steep decline group and was associated with generally worse health. In addition, cognitive trajectory groups differed on aetiology-informative biomarkers and risk factors, suggesting an intimate link between preclinical cognitive patterns and amyloid/tau/neurodegeneration/vascular biomarker differences in late middle-aged adults. The result explains some of the heterogeneity in cognitive performance within cognitively unimpaired late middle-aged adults.
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Affiliation(s)
- Lianlian Du
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bruce P Hermann
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Neurology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53705, USA
| | - Erin M Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Karly Alex Cody
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Leonardo Rivera-Rivera
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53705, USA
| | - Howard Rowley
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Aaron Field
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Laura Eisenmenger
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bradley T Christian
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53705, USA
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Tobey J Betthauser
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bret Larget
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Rick Chappell
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA
| | | | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund 205 02, Sweden
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Rebecca Langhough
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
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Vasiljevic E, Koscik RL, Jonaitis E, Betthauser T, Johnson SC, Engelman CD. Cognitive trajectories diverge by genetic risk in a preclinical longitudinal cohort. Alzheimers Dement 2023; 19:3108-3118. [PMID: 36723444 PMCID: PMC10390653 DOI: 10.1002/alz.12920] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/06/2022] [Accepted: 12/12/2022] [Indexed: 02/02/2023]
Abstract
INTRODUCTION We sought to characterize the timing of changes in cognitive trajectories related to genetic risk using the apolipoprotein E (APOE) score, a continuous measure of Alzheimer's disease (AD) risk. We also aimed to determine whether that timing was different when genetic risk was measured using an AD polygenic risk score (PRS) that contains APOE. METHODS We analyzed trajectories (N ≈1135) for four neuropsychological composite scores using mixed effects regression for longitudinal change across APOE scores and PRS of participants in the Wisconsin Registry for Alzheimer's Prevention, a longitudinal study of adults aged 40 to 70 at baseline, with a median participant follow-up time of 7.8 years. RESULTS We found a significant non-linear age-by-APOE score interaction in predicting cognitive decline. Cognitive trajectories diverged by APOE score at approximately 65 years of age. A 0.5 standard deviation difference in cognition between extreme percentiles of the PRS was predicted to occur 1 to 2 years before that of the APOE score. DISCUSSION Cognitive decline differs across time and APOE score. Estimates did not substantially shift with the AD PRS. HIGHLIGHTS The apolipoprotein E (APOE) score, a continuous measure, accounts for non-linear genetic risk of Alzheimer's disease. Non-linear age interacts with the APOE score to affect cognition. Cognitive decline starts to differ by APOE score levels at approximately age 65. Cognitive decline timing by polygenic risk (including APOE) is similar to APOE alone.
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Affiliation(s)
- Eva Vasiljevic
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, 610 Walnut Dr., Madison, WI 53726, USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, 1180 Observatory Drive Madison, WI 53706, USA
| | - Rebecca Langhough Koscik
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
| | - Erin Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
| | - Tobey Betthauser
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, USA
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, USA
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, 610 Walnut Dr., Madison, WI 53726, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, 610 Walnut Street, 9th Floor, Madison, WI 53726, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, MC 2420, Madison, Wisconsin 53792, USA
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Akushevich I, Kravchenko J, Yashkin A, Doraiswamy PM, Hill CV. Expanding the scope of health disparities research in Alzheimer's disease and related dementias: Recommendations from the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" Workshop Series. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12415. [PMID: 36935764 PMCID: PMC10020680 DOI: 10.1002/dad2.12415] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 03/18/2023]
Abstract
Topics discussed at the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" workshop, held by Duke University and the Alzheimer's Association with support from the National Institute on Aging, are summarized. Ways in which existing data resources paired with innovative applications of both novel and well-known methodologies can be used to identify the effects of multi-level societal, community, and individual determinants of race/ethnicity, sex, and geography-related health disparities in Alzheimer's disease and related dementia are proposed. Current literature on the population analyses of these health disparities is summarized with a focus on identifying existing gaps in knowledge, and ways to mitigate these gaps using data/method combinations are discussed at the workshop. Substantive and methodological directions of future research capable of advancing health disparities research related to aging are formulated.
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Affiliation(s)
- Igor Akushevich
- Social Science Research InstituteBiodemography of Aging Research UnitDuke UniversityDurhamNorth CarolinaUSA
| | - Julia Kravchenko
- Duke University School of MedicineDepartment of SurgeryDurhamNorth CarolinaUSA
| | - Arseniy Yashkin
- Social Science Research InstituteBiodemography of Aging Research UnitDuke UniversityDurhamNorth CarolinaUSA
| | - P. Murali Doraiswamy
- Departments of Psychiatry and MedicineDuke University School of MedicineDurhamNorth CarolinaUSA
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5
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Porter T, Sim M, Prince RL, Schousboe JT, Bondonno C, Lim WH, Zhu K, Kiel DP, Hodgson JM, Laws SM, Lewis JR. Abdominal aortic calcification on lateral spine images captured during bone density testing and late-life dementia risk in older women: A prospective cohort study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2022; 26:100502. [PMID: 36213133 PMCID: PMC9535408 DOI: 10.1016/j.lanwpc.2022.100502] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND Dementia after the age of 80 years (late-life) is increasingly common due to vascular and non-vascular risk factors. Identifying individuals at higher risk of late-life dementia remains a global priority. METHODS In prospective study of 958 ambulant community-dwelling older women (≥70 years), lateral spine images (LSI) captured in 1998 (baseline) from a bone density machine were used to assess abdominal aortic calcification (AAC). AAC was classified into established categories (low, moderate and extensive). Cardiovascular risk factors and apolipoprotein E (APOE) genotyping were evaluated. Incident 14.5-year late-life dementia was identified from linked hospital and mortality records. FINDINGS At baseline women were 75.0 ± 2.6 years, 44.7% had low AAC, 36.4% had moderate AAC and 18.9% had extensive AAC. Over 14.5- years, 150 (15.7%) women had a late-life dementia hospitalisation (n = 132) and/or death (n = 58). Compared to those with low AAC, women with moderate and extensive AAC were more likely to suffer late-life dementia hospitalisations (9.3%, 15.5%, 18.3%, respectively) and deaths (2.8%, 8.3%, 9.4%, respectively). After adjustment for cardiovascular risk factors and APOE, women with moderate and extensive AAC had twice the relative hazards of late-life dementia (moderate, aHR 2.03 95%CI 1.38-2.97; extensive, aHR 2.10 95%CI 1.33-3.32), compared to women with low AAC. INTERPRETATION In community-dwelling older women, those with more advanced AAC had higher risk of late-life dementia, independent of cardiovascular risk factors and APOE genotype. Given the widespread use of bone density testing, simultaneously capturing AAC information may be a novel, non-invasive, scalable approach to identify older women at risk of late-life dementia. FUNDING Kidney Health Australia, Healthway Health Promotion Foundation of Western Australia, Sir Charles Gairdner Hospital Research Advisory Committee Grant, National Health and Medical Research Council of Australia.
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Key Words
- AAC, abdominal aortic calcification
- AAC24, abdominal aortic calcification 24 scale scores
- AD, Alzheimer's disease
- APOE, apolipoprotein E
- ASVD, atherosclerotic vascular disease
- AUC, area under the curve
- Aging
- CAC, coronary artery calcification
- CVD, cardiovascular disease
- DXA, dual-energy X-ray absorptiometry
- Dementia
- Epidemiology
- FRS, Framingham General Cardiovascular Risk Scores
- IDI, integrated discrimination improvement
- Imaging
- LSI, lateral spine imaging
- NRI, net reclassification improvement
- ROC, receiver operator characteristics
- Vascular disease
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Affiliation(s)
- Tenielle Porter
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia
| | - Marc Sim
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Medical School, University of Western Australia, Crawley, WA, Australia
| | - Richard L. Prince
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Medical School, University of Western Australia, Crawley, WA, Australia
| | - John T. Schousboe
- Park Nicollet Clinic and HealthPartners Institute, HealthPartners, Minneapolis, MN, USA
- Division of Health Policy and Management, University of Minnesota, Minneapolis, MN, USA
| | - Catherine Bondonno
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Medical School, University of Western Australia, Crawley, WA, Australia
| | - Wai H. Lim
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Renal Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Kun Zhu
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Douglas P. Kiel
- Marcus Institute for Aging Research, Hebrew SeniorLife, Department of Medicine Beth, Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Jonathan M. Hodgson
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Medical School, University of Western Australia, Crawley, WA, Australia
| | - Simon M. Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia
| | - Joshua R. Lewis
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Medical School, University of Western Australia, Crawley, WA, Australia
- Centre for Kidney Research, Children's Hospital at Westmead, School of Public Health, Sydney Medical School, the University of Sydney, Sydney, NSW, Australia
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Kaźmierski J, Miler P, Pawlak A, Woźniak J, Frankowska E, Nowakowska K, Kuchta K, Pazdrak M, Woźniak K, Magierski R, Krejca M, Wilczyński M. Lower Preoperative Verbal Memory Performance Is Associated with Delirium after Coronary Artery Bypass Graft Surgery: A Prospective Cohort Study. Arch Clin Neuropsychol 2022; 38:49-56. [PMID: 35915987 PMCID: PMC9868524 DOI: 10.1093/arclin/acac064] [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: 02/17/2022] [Revised: 06/22/2022] [Accepted: 07/07/2022] [Indexed: 01/26/2023] Open
Abstract
OBJECTIVE Cognitive impairment constitutes one of the major risk factors of delirium after coronary artery bypass graft (CABG) surgery; however, it is unclear whether only patients with global cognitive decline are at increased risk for delirium or if individuals with preserved global cognitive functions but impairments in specific cognitive domains are also more vulnerable to developing delirium. Thus, this study aimed to analyze the neurocognitive status of patients scheduled for CABG surgery with the use of an advanced computerized cognitive battery (CNS Vital Signs) and to investigate possible associations between impaired performance in selective cognitive areas and the risk of postoperative delirium development. METHODS The study enrolled 127 participants with a median age of 67 years (IQR: 63-71). Postoperative delirium developed in 32 (25%) patients.Before surgery, the patients were screened for global cognitive impairment with the use of the Mini-Mental State Examination Test, and the individuals were asked to perform the CNS Vital Signs battery to investigate 12 specific cognitive domains. The Confusion Assessment Method and the Memorial Delirium Assessment Scale were used to screen for a diagnosis of delirium postoperatively. RESULTS In multivariate models, a lower score of verbal memory-assessed preoperatively was independently associated with the risk of postoperative delirium development. Other independent predictors of delirium included more advanced age, gender female, depression, postoperative pyrexia, and the presence of extracorporeal circulation. CONCLUSIONS As decreased verbal memory constitutes an independent risk factor for postoperative delirium, a verbal memory test may be a useful predictor of postoperative delirium development.
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Affiliation(s)
- Jakub Kaźmierski
- Corresponding author at: Department of Old Age Psychiatry and Psychotic Disorders, Faculty of Gerontology, Medical University of Lodz, Czechoslowacka 8/10, 92-216 Lodz, Poland. Tel.: 48 42 675 73 72; fax: 00 48 42 675 77 29. E-mail address: (J. Kaźmierski)
| | - Piotr Miler
- Central Clinical Hospital, Medical University of Lodz, Lodz, Poland
| | - Agnieszka Pawlak
- Central Clinical Hospital, Medical University of Lodz, Lodz, Poland
| | - Joanna Woźniak
- Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, Poland
| | - Emilia Frankowska
- Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, Poland
| | - Karina Nowakowska
- Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, Poland
| | - Katarzyna Kuchta
- Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, Poland
| | - Michał Pazdrak
- Central Clinical Hospital, Medical University of Lodz, Lodz, Poland
| | - Katarzyna Woźniak
- Department of Cardiac Surgery, Central Clinical Hospital, Medical University of Lodz, Lodz, Poland
| | - Radosław Magierski
- Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, Poland
| | - Michał Krejca
- Department of Cardiac Surgery, Central Clinical Hospital, Medical University of Lodz, Lodz, Poland
| | - Mirosław Wilczyński
- Department of Cardiac Surgery, Central Clinical Hospital, Medical University of Lodz, Lodz, Poland
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