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Chun MY, Chae W, Seo SW, Jang H, Yun J, Na DL, Kang D, Lee J, Hammers DB, Apostolova LG, Jang SI, Kim HJ. Effects of risk factors on the development and mortality of early- and late-onset dementia: an 11-year longitudinal nationwide population-based cohort study in South Korea. Alzheimers Res Ther 2024; 16:92. [PMID: 38664771 PMCID: PMC11044300 DOI: 10.1186/s13195-024-01436-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Early-onset dementia (EOD, onset age < 65) and late-onset dementia (LOD, onset age ≥ 65) exhibit distinct features. Understanding the risk factors for dementia development and mortality in EOD and LOD respectively is crucial for personalized care. While risk factors are known for LOD development and mortality, their impact on EOD remains unclear. We aimed to investigate how hypertension, diabetes mellitus, hyperlipidemia, atrial fibrillation, and osteoporosis influence the development and mortality of EOD and LOD, respectively. METHODS Using the Korean National Health Insurance Service (NHIS) database, we collected 546,709 dementia-free individuals and followed up for 11 years. In the two study groups, the Younger group (< 65 years old) and the Older group (≥ 65 years old), we applied Cox proportional hazard models to assess risk factors for development of EOD and LOD, respectively. Then, we assessed risk factors for mortality among EOD and LOD. RESULTS Diabetes mellitus and osteoporosis increased the risk of EOD and LOD development. Hypertension increased the risk of EOD, while atrial fibrillation increased the risk of LOD. Conversely, hyperlipidemia exhibited a protective effect against LOD development. Additionally, diabetes mellitus increased mortality in EOD and LOD. Hypertension and atrial fibrillation increased mortality in LOD, while hyperlipidemia decreased mortality in EOD and LOD. CONCLUSIONS Risk factors influencing dementia development and mortality differed in EOD and LOD. Targeted public health interventions addressing age-related risk factors may reduce dementia incidence and mortality.
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Affiliation(s)
- Min Young Chun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Neurology, Yonsei University College of Medicine, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, 363 Dongbaekjukjeon-daero, Giheung-gu, , Yongin-si, Gyeonggi-do, 16995, South Korea
| | - Wonjeong Chae
- Office of Strategic Planning, Healthcare Policy and Strategy Task Force, Yonsei University Health System, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Neurology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, South Korea
| | - Jihwan Yun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Neurology, Soonchunhyang University Bucheon Hospital, 170, Jomaru-ro, Wonmi-Gu, Bucheon-si, Gyeonggi-do, 14574, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Dongwoo Kang
- Department of Data Science, Hanmi Pharm. Co., Ltd, 14, Wiryeseong-daero, Songpa-gu, Seoul, South Korea
| | - Jungkuk Lee
- Department of Data Science, Hanmi Pharm. Co., Ltd, 14, Wiryeseong-daero, Songpa-gu, Seoul, South Korea
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, 355 W 16th St, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, 355 W 16th St, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, 355W 16th St, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, 355W 16th St, Indianapolis, IN, USA
| | - Sung-In Jang
- Department of Preventive Medicine, College of Medicine, Yonsei University, 50-1, Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
- Department of Neurology, Indiana University School of Medicine, 355 W 16th St, Indianapolis, IN, USA.
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Hall MG, Wollman SC, Haines ME, Katschke JL, Boyle MA, Richardson HK, Hammers DB. Clinical validation of an aggregate learning ratio from the neuropsychological assessment battery. Appl Neuropsychol Adult 2024:1-10. [PMID: 38527375 DOI: 10.1080/23279095.2024.2329974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Quantifying learning deficits provides valuable information in identifying and diagnosing mild cognitive impairment and dementia. Previous research has found that a learning ratio (LR) metric, derived from the list learning test from the Neuropsychological Assessment Battery (NAB), was able to distinguish between those with normal cognition versus memory impairment. The current study furthers the NAB LR research by validating a NAB story LR, as well as an aggregate LR. The aggregate LR was created by combining the individual list and story LRs. Participants were classified as those with normal cognition (n = 51), those with MCI (n = 39) and those with dementia (n = 35). Results revealed the story LR was able to accurately distinguish normal controls from those with mild cognitive impairment and those with dementia and offers enhanced discriminability beyond the story immediate recall score (sum of trial 1 and trial 2). Further, the aggregate LR provided superior discriminability beyond the individual list and story LRs and accounted for additional variance in diagnostic group classification. The NAB aggregate LR provides improved sensitivity in detecting declines in impaired learning, which may assist clinicians in making diagnoses earlier in a disease process, benefiting the individual through earlier interventions.
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Affiliation(s)
- Matthew G Hall
- PM&R, The University of Toledo - Health Science Campus, Toledo, OH, USA
| | | | - Mary E Haines
- PM&R, The University of Toledo - Health Science Campus, Toledo, OH, USA
| | | | - Mellisa A Boyle
- PM&R, The University of Toledo - Health Science Campus, Toledo, OH, USA
| | | | - Dustin B Hammers
- Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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Kantarci K, Tosakulwong N, Lesnick TG, Kara F, Kendall-Thomas J, Kapoor E, Fields JA, James TT, Lobo RA, Manson JE, Pal L, Hammers DB, Malek-Ahmadi M, Cedars MI, Naftolin FN, Santoro N, Miller VM, Harman SM, Dowling NM, Gleason CE. Cardiometabolic outcomes in Kronos Early Estrogen Prevention Study continuation: 14-year follow-up of a hormone therapy trial. Menopause 2024; 31:10-17. [PMID: 37989141 PMCID: PMC10756493 DOI: 10.1097/gme.0000000000002278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
OBJECTIVE This study aimed to determine long-term cardiometabolic effects of hormone therapies initiated within 3 years of onset of menopause after a 14-year follow-up study of participants of the Kronos Early Estrogen Prevention Study (KEEPS). METHODS KEEPS was a multisite clinical trial that recruited recently menopausal women with good cardiovascular health for randomization to oral conjugated equine estrogens (Premarin, 0.45 mg/d) or transdermal 17β-estradiol (Climara, 50 μg/d) both with micronized progesterone (Prometrium, 200 mg/d) for 12 d/mo, or placebo pills and patch for 4 years. KEEPS continuation recontacted KEEPS participants 14 years after randomization and 10 years after the completion of the 4-year clinical trial to attend in-person clinic visits. RESULTS Participants of KEEPS continuation (n = 299 of the 727 KEEPS participants; 41%) had an average age of 67 years (range, 58-73 y). Measurements of systolic and diastolic blood pressures, waist-to-hip ratio, fasting levels of glucose, insulin, lipid profiles, and homeostasis model assessment of insulin resistance were not different among the treatment groups at either KEEPS baseline or at KEEPS continuation visits, or for change between these two visits. The frequency of self-reported diabetes ( P = 0.007) and use of diabetes medications was higher in the placebo than the oral conjugated equine estrogens ( P = 0.045) or transdermal 17β-estradiol ( P = 0.02) groups, but these differences were not supported by the laboratory measurements of glycemia or insulin resistance. CONCLUSIONS There was no evidence of cardiovascular and/or metabolic benefits or adverse effects associated with 4 years use of oral or transdermal forms of hormone therapy by recently menopausal women with good cardiovascular health after 10 years.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - JoAnn E. Manson
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | | | | | - Sherman M. Harman
- Phoenix VA Health University of Arizona College of Medicine, Phoenix, AZ
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Duff K, Hammers DB, Koppelmans V, King JB, Hoffman JM. Short-Term Practice Effects on Cognitive Tests Across the Late Life Cognitive Spectrum and How They Compare to Biomarkers of Alzheimer's Disease. J Alzheimers Dis 2024; 99:321-332. [PMID: 38669544 DOI: 10.3233/jad-231392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Background Practice effects on cognitive testing in mild cognitive impairment (MCI) and Alzheimer's disease (AD) remain understudied, especially with how they compare to biomarkers of AD. Objective The current study sought to add to this growing literature. Methods Cognitively intact older adults (n = 68), those with amnestic MCI (n = 52), and those with mild AD (n = 45) completed a brief battery of cognitive tests at baseline and again after one week, and they also completed a baseline amyloid PET scan, a baseline MRI, and a baseline blood draw to obtain APOE ɛ4 status. Results The intact participants showed significantly larger baseline cognitive scores and practice effects than the other two groups on overall composite measures. Those with MCI showed significantly larger baseline scores and practice effects than AD participants on the composite. For amyloid deposition, the intact participants had significantly less tracer uptake, whereas MCI and AD participants were comparable. For total hippocampal volumes, all three groups were significantly different in the expected direction (intact > MCI > AD). For APOE ɛ4, the intact had significantly fewer copies of ɛ4 than MCI and AD. The effect sizes of the baseline cognitive scores and practice effects were comparable, and they were significantly larger than effect sizes of biomarkers in 7 of the 9 comparisons. Conclusion Baseline cognition and short-term practice effects appear to be sensitive markers in late life cognitive disorders, as they separated groups better than commonly-used biomarkers in AD. Further development of baseline cognition and short-term practice effects as tools for clinical diagnosis, prognostic indication, and enrichment of clinical trials seems warranted.
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Affiliation(s)
- Kevin Duff
- Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, OR, USA
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indiana, USA
| | | | - Jace B King
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - John M Hoffman
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
- University of Utah Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, Salt Lake City, UT, USA
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Bae J, Logan PE, Acri DJ, Bharthur A, Nho K, Saykin AJ, Risacher SL, Nudelman K, Polsinelli AJ, Pentchev V, Kim J, Hammers DB, Apostolova LG. A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression. Alzheimers Dement 2023; 19:5690-5699. [PMID: 37409680 PMCID: PMC10770299 DOI: 10.1002/alz.13319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/25/2023] [Accepted: 05/12/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. METHODS We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. RESULTS Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. DISCUSSION The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
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Affiliation(s)
- Jinhyeong Bae
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Paige E. Logan
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Dominic J. Acri
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Apoorva Bharthur
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Angelina J. Polsinelli
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Valentin Pentchev
- Department of Information Technology, Indiana University Network Science Institute, Bloomington, IN, 47408, United States
| | - Jungsu Kim
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Dustin B. Hammers
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Liana G. Apostolova
- Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
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Hammers DB, Nemes S, Diedrich T, Eloyan A, Kirby K, Aisen P, Kramer J, Nudelman K, Foroud T, Rumbaugh M, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Weintraub S, Wingo TS, Wolk DA, Wong B, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Learning slopes in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S19-S28. [PMID: 37243937 PMCID: PMC10806757 DOI: 10.1002/alz.13159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/16/2023] [Accepted: 02/16/2023] [Indexed: 05/29/2023]
Abstract
OBJECTIVE Investigation of learning slopes in early-onset dementias has been limited. The current study aimed to highlight the sensitivity of learning slopes to discriminate disease severity in cognitively normal participants and those diagnosed with early-onset dementia with and without β-amyloid positivity METHOD: Data from 310 participants in the Longitudinal Early-Onset Alzheimer's Disease Study (aged 41 to 65) were used to calculate learning slope metrics. Learning slopes among diagnostic groups were compared, and the relationships of slopes with standard memory measures were determined RESULTS: Worse learning slopes were associated with more severe disease states, even after controlling for demographics, total learning, and cognitive severity. A particular metric-the learning ratio (LR)-outperformed other learning slope calculations across analyses CONCLUSIONS: Learning slopes appear to be sensitive to early-onset dementias, even when controlling for the effect of total learning and cognitive severity. The LR may be the learning measure of choice for such analyses. HIGHLIGHTS Learning is impaired in amyloid-positive EOAD, beyond cognitive severity scores alone. Amyloid-positive EOAD participants perform worse on learning slopes than amyloid-negative participants. Learning ratio appears to be the learning metric of choice for EOAD participants.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Sára Nemes
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Taylor Diedrich
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, California, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steve Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Sandra Weintraub
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Bonnie Wong
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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Polsinelli AJ, Wonderlin RJ, Hammers DB, Pena Garcia A, Eloyan A, Taurone A, Thangarajah M, Beckett L, Gao S, Wang S, Kirby K, Logan PE, Aisen P, Dage JL, Foroud T, Griffin P, Iaccarino L, Kramer JH, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Soleimani-Meigooni DN, Rumbaugh M, Toga AW, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Womack K, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Baseline neuropsychiatric symptoms and psychotropic medication use midway through data collection of the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort. Alzheimers Dement 2023; 19 Suppl 9:S42-S48. [PMID: 37296082 PMCID: PMC10709525 DOI: 10.1002/alz.13344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION We examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). METHODS Baseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70). RESULTS Affective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD. Tension and impulse control behaviors were more common in EOnonAD. A minority of participants were using psychotropic medications, and use was higher in EOnonAD. DISCUSSION Overall NPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants. Future research will investigate moderators and etiological drivers of NPS, and NPS differences in EOAD versus late-onset AD.
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Affiliation(s)
- Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Ryan J. Wonderlin
- Marian University College of Osteopathic Medicine, Indianapolis, Indiana, 46222, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Alex Pena Garcia
- Marian University College of Osteopathic Medicine, Indianapolis, Indiana, 46222, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, 95616, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Sophia Wang
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, 92121, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Joel H. Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, 98195, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, 90033, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55123, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, 33140, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, 10032, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55123, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, 77030, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Kyle Womack
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, 63110, USA
| | - Erik Musiek
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, 63110, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Meghan Riddle
- Department of Psychiatry, Alpert Medical School, Brown University, Providence, Rhode Island, 02912, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, USA
| | - Steven Salloway
- Department of Psychiatry, Alpert Medical School, Brown University, Providence, Rhode Island, 02912, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, 94304, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., 20057, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, 30307, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, 92121, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, 46202, USA
| | - LEADS Consortium
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
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8
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Nemes S, Logan PE, Manchella MK, Mundada NS, Joie RL, Polsinelli AJ, Hammers DB, Koeppe RA, Foroud TM, Nudelman KN, Eloyan A, Iaccarino L, Dorsant-Ardón V, Taurone A, Maryanne Thangarajah, Dage JL, Aisen P, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Womack KB, Wolk DA, Rabinovici GD, Carrillo MC, Dickerson BC, Apostolova LG. Sex and APOE ε4 carrier effects on atrophy, amyloid PET, and tau PET burden in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S49-S63. [PMID: 37496307 PMCID: PMC10811272 DOI: 10.1002/alz.13403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION We used sex and apolipoprotein E ε4 (APOE ε4) carrier status as predictors of pathologic burden in early-onset Alzheimer's disease (EOAD). METHODS We included baseline data from 77 cognitively normal (CN), 230 EOAD, and 70 EO non-Alzheimer's disease (EOnonAD) participants from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS). We stratified each diagnostic group by males and females, then further subdivided each sex by APOE ε4 carrier status and compared imaging biomarkers in each stratification. Voxel-wise multiple linear regressions yielded statistical brain maps of gray matter density, amyloid, and tau PET burden. RESULTS EOAD females had greater amyloid and tau PET burdens than males. EOAD female APOE ε4 non-carriers had greater amyloid PET burdens and greater gray matter atrophy than female ε4 carriers. EOnonAD female ε4 non-carriers also had greater gray matter atrophy than female ε4 carriers. DISCUSSION The effects of sex and APOE ε4 must be considered when studying these populations. HIGHLIGHTS Novel analysis examining the effects of biological sex and apolipoprotein E ε4 (APOE ε4) carrier status on neuroimaging biomarkers among early-onset Alzheimer's disease (EOAD), early-onset non-AD (EOnonAD), and cognitively normal (CN) participants. Female sex is associated with greater pathology burden in the EOAD cohort compared to male sex. The effect of APOE ε4 carrier status on pathology burden was the most impactful in females across all cohorts.
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Affiliation(s)
- Sára Nemes
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Mohit K. Manchella
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Department of Chemistry, University of Southern Indiana, Evansville, Indiana, 47712, USA
| | - Nidhi S. Mundada
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Renaud La Joie
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Robert A. Koeppe
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, 48105, USA
| | - Tatiana M. Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kelly N. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Valérie Dorsant-Ardón
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Jeffery L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, 92121, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California, San Francisco, California, 94158, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA, 98195, USA
| | - Melissa E. Murray
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, 90033, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, 85315, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Ranjan Duara
- Department of Neurology, Center for Mind/Brain Medicine, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, 02115, USA
- Wein Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, 33140, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, 10032, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, 559095, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, 77030, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Sharon J. Sha
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, 94304, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown Universit, Washington, DC, 20007, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - David A. Wolk
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,19104, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
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9
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Hammers DB, Eloyan A, Taurone A, Thangarajah M, Beckett L, Gao S, Kirby K, Aisen P, Dage JL, Foroud T, Griffin P, Grinberg LT, Jack CR, Kramer J, Koeppe R, Kukull WA, Mundada NS, Joie RL, Soleimani-Meigooni DN, Iaccarino L, Murray ME, Nudelman K, Polsinelli AJ, Rumbaugh M, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Womack K, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Profiling baseline performance on the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort near the midpoint of data collection. Alzheimers Dement 2023; 19 Suppl 9:S8-S18. [PMID: 37256497 PMCID: PMC10806768 DOI: 10.1002/alz.13160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 06/01/2023]
Abstract
OBJECTIVE The Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) seeks to provide comprehensive understanding of early-onset Alzheimer's disease (EOAD; onset <65 years), with the current study profiling baseline clinical, cognitive, biomarker, and genetic characteristics of the cohort nearing the data-collection mid-point. METHODS Data from 371 LEADS participants were compared based on diagnostic group classification (cognitively normal [n = 89], amyloid-positive EOAD [n = 212], and amyloid-negative early-onset non-Alzheimer's disease [EOnonAD; n = 70]). RESULTS Cognitive performance was worse for EOAD than other groups, and EOAD participants were apolipoprotein E (APOE) ε4 homozygotes at higher rates. An amnestic presentation was common among impaired participants (81%), with several clinical phenotypes present. LEADS participants generally consented at high rates to optional trial procedures. CONCLUSIONS We present the most comprehensive baseline characterization of sporadic EOAD in the United States to date. EOAD presents with widespread cognitive impairment within and across clinical phenotypes, with differences in APOE ε4 allele carrier status appearing to be relevant. HIGHLIGHTS Findings represent the most comprehensive baseline characterization of sporadic early-onset Alzheimer's disease (EOAD) to date. Cognitive impairment was widespread for EOAD participants and more severe than other groups. EOAD participants were homozygous apolipoprotein E (APOE) ε4 carriers at higher rates than the EOnonAD group. Amnestic presentation predominated in EOAD and EOnonAD participants, but other clinical phenotypes were present.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Lea T. Grinberg
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steven Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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10
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Cho H, Mundada NS, Apostolova LG, Carrillo MC, Shankar R, Amuiri AN, Zeltzer E, Windon CC, Soleimani-Meigooni DN, Tanner JA, Heath CL, Lesman-Segev OH, Aisen P, Eloyan A, Lee HS, Hammers DB, Kirby K, Dage JL, Fagan A, Foroud T, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Nudelman K, Toga A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski EJ, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Koeppe R, Iaccarino L, Dickerson BC, La Joie R, Rabinovici GD. Amyloid and tau-PET in early-onset AD: Baseline data from the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S98-S114. [PMID: 37690109 PMCID: PMC10807231 DOI: 10.1002/alz.13453] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023]
Abstract
INTRODUCTION We aimed to describe baseline amyloid-beta (Aβ) and tau-positron emission tomograrphy (PET) from Longitudinal Early-onset Alzheimer's Disease Study (LEADS), a prospective multi-site observational study of sporadic early-onset Alzheimer's disease (EOAD). METHODS We analyzed baseline [18F]Florbetaben (Aβ) and [18F]Flortaucipir (tau)-PET from cognitively impaired participants with a clinical diagnosis of mild cognitive impairment (MCI) or AD dementia aged < 65 years. Florbetaben scans were used to distinguish cognitively impaired participants with EOAD (Aβ+) from EOnonAD (Aβ-) based on the combination of visual read by expert reader and image quantification. RESULTS 243/321 (75.7%) of participants were assigned to the EOAD group based on amyloid-PET; 231 (95.1%) of them were tau-PET positive (A+T+). Tau-PET signal was elevated across cortical regions with a parietal-predominant pattern, and higher burden was observed in younger and female EOAD participants. DISCUSSION LEADS data emphasizes the importance of biomarkers to enhance diagnostic accuracy in EOAD. The advanced tau-PET binding at baseline might have implications for therapeutic strategies in patients with EOAD. HIGHLIGHTS 72% of patients with clinical EOAD were positive on both amyloid- and tau-PET. Amyloid-positive patients with EOAD had high tau-PET signal across cortical regions. In EOAD, tau-PET mediated the relationship between amyloid-PET and MMSE. Among EOAD patients, younger onset and female sex were associated with higher tau-PET.
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Affiliation(s)
- Hanna Cho
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
- Global Brain Health Institute, University of California, San Francisco, California, USA
| | - Nidhi S Mundada
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Ranjani Shankar
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Alinda N Amuiri
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Ehud Zeltzer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Charles C Windon
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - David N Soleimani-Meigooni
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Jeremy A Tanner
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Courtney Lawhn Heath
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Orit H Lesman-Segev
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Diagnostic Imaging, Sheba Medical Center, Tel HaShomer, Israel
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Rhode Island, USA
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lea T Grinberg
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Pathology, University of California - San Francisco, San Francisco, California, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joel Kramer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Emily J Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Thomas S Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Koeppe
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Renaud La Joie
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Gil D Rabinovici
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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11
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Dage JL, Eloyan A, Thangarajah M, Hammers DB, Fagan AM, Gray JD, Schindler SE, Snoddy C, Nudelman KNH, Faber KM, Foroud T, Aisen P, Griffin P, Grinberg LT, Iaccarino L, Kirby K, Kramer J, Koeppe R, Kukull WA, Joie RL, Mundada NS, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga AW, Touroutoglou A, Vemuri P, Atri A, Beckett LA, Day GS, Graff-Radford NR, Duara R, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Womack KB, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Cerebrospinal fluid biomarkers in the Longitudinal Early-onset Alzheimer's Disease Study. Alzheimers Dement 2023; 19 Suppl 9:S115-S125. [PMID: 37491668 PMCID: PMC10877673 DOI: 10.1002/alz.13399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION One goal of the Longitudinal Early Onset Alzheimer's Disease Study (LEADS) is to define the fluid biomarker characteristics of early-onset Alzheimer's disease (EOAD). METHODS Cerebrospinal fluid (CSF) concentrations of Aβ1-40, Aβ1-42, total tau (tTau), pTau181, VILIP-1, SNAP-25, neurogranin (Ng), neurofilament light chain (NfL), and YKL-40 were measured by immunoassay in 165 LEADS participants. The associations of biomarker concentrations with diagnostic group and standard cognitive tests were evaluated. RESULTS Biomarkers were correlated with one another. Levels of CSF Aβ42/40, pTau181, tTau, SNAP-25, and Ng in EOAD differed significantly from cognitively normal and early-onset non-AD dementia; NfL, YKL-40, and VILIP-1 did not. Across groups, all biomarkers except SNAP-25 were correlated with cognition. Within the EOAD group, Aβ42/40, NfL, Ng, and SNAP-25 were correlated with at least one cognitive measure. DISCUSSION This study provides a comprehensive analysis of CSF biomarkers in sporadic EOAD that can inform EOAD clinical trial design.
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Affiliation(s)
- Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Julia D. Gray
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Casey Snoddy
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelly N. H. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelley M. Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Laurel A. Beckett
- Department of Public Health Sciences, University of California-Davis, Davis, California, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington, D.C., USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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Bushnell J, Hammers DB, Aisen P, Dage JL, Eloyan A, Foroud T, Grinberg LT, Iaccarino L, Jack CR, Kirby K, Kramer J, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG, Clark DG. Influence of amyloid and diagnostic syndrome on non-traditional memory scores in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S29-S41. [PMID: 37653686 PMCID: PMC10855009 DOI: 10.1002/alz.13434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 09/02/2023]
Abstract
INTRODUCTION The Rey Auditory Verbal Learning Test (RAVLT) is a useful neuropsychological test for describing episodic memory impairment in dementia. However, there is limited research on its utility in early-onset Alzheimer's disease (EOAD). We assess the influence of amyloid and diagnostic syndrome on several memory scores in EOAD. METHODS We transcribed RAVLT recordings from 303 subjects in the Longitudinal Early-Onset Alzheimer's Disease Study. Subjects were grouped by amyloid status and syndrome. Primacy, recency, J-curve, duration, stopping time, and speed score were calculated and entered into linear mixed effects models as dependent variables. RESULTS Compared with amyloid negative subjects, positive subjects exhibited effects on raw score, primacy, recency, and stopping time. Inter-syndromic differences were noted with raw score, primacy, recency, J-curve, and stopping time. DISCUSSION RAVLT measures are sensitive to the effects of amyloid and syndrome in EOAD. Future work is needed to quantify the predictive value of these scores. HIGHLIGHTS RAVLT patterns characterize various presentations of EOAD and EOnonAD Amyloid impacts raw score, primacy, recency, and stopping time Timing-based scores add value over traditional count-based scores.
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Affiliation(s)
- Justin Bushnell
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lea T. Grinberg
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Nidhi S. Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steven Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - David G. Clark
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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13
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Nudelman KNH, Jackson T, Rumbaugh M, Eloyan A, Abreu M, Dage JL, Snoddy C, Faber KM, Foroud T, Hammers DB, Taurone A, Thangarajah M, Aisen P, Beckett L, Kramer J, Koeppe R, Kukull WA, Murray ME, Toga AW, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Pathogenic variants in the Longitudinal Early-onset Alzheimer's Disease Study cohort. Alzheimers Dement 2023; 19 Suppl 9:S64-S73. [PMID: 37801072 PMCID: PMC10783439 DOI: 10.1002/alz.13482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION One goal of the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is to investigate the genetic etiology of early onset (40-64 years) cognitive impairment. Toward this goal, LEADS participants are screened for known pathogenic variants. METHODS LEADS amyloid-positive early-onset Alzheimer's disease (EOAD) or negative early-onset non-AD (EOnonAD) cases were whole exome sequenced (N = 299). Pathogenic variant frequency in APP, PSEN1, PSEN2, GRN, MAPT, and C9ORF72 was assessed for EOAD and EOnonAD. Gene burden testing was performed in cases compared to similar-age cognitively normal controls in the Parkinson's Progression Markers Initiative (PPMI) study. RESULTS Previously reported pathogenic variants in the six genes were identified in 1.35% of EOAD (3/223) and 6.58% of EOnonAD (5/76). No genes showed enrichment for carriers of rare functional variants in LEADS cases. DISCUSSION Results suggest that LEADS is enriched for novel genetic causative variants, as previously reported variants are not observed in most cases. HIGHLIGHTS Sequencing identified eight cognitively impaired pathogenic variant carriers. Pathogenic variants were identified in PSEN1, GRN, MAPT, and C9ORF72. Rare variants were not enriched in APP, PSEN1/2, GRN, and MAPT. The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is a key resource for early-onset Alzheimer's genetic research.
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Affiliation(s)
- Kelly N. H. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
| | - Trever Jackson
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Marco Abreu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Jeffrey L. Dage
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Casey Snoddy
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Kelley M. Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - DIAN/DIAN-TU Clinical/Genetics Committee
- Washington University School of Medicine in St. Louis, MO, USA, 63110
- Icahn School of Medicine at Mount Sinai, New York, NY, USA, 10029
- Mayo Clinic College of Medicine, Jacksonville, FL, USA, 32224
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, 92121
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA, 95616
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, CA, USA, 94143
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA, 48109
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, WA, USA, 98195
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA, 32224
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA, 90033
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, AZ, USA, 85315
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA, 32224
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA, 33140
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA, 10032
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
- Department of Neurology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, TX, USA, 77030
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA, 90095
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63110
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21295
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, RI Island, USA, 02912
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA , 60611
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, RI Island, USA, 02912
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA, 94304
| | - R. Scott Turner
- Department of Neurology, Georgetown University, DC, USA, 20057
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, USA, 30307
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 19104
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, IL, USA, 60603
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, 02114
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, CA, USA, 94143
| | - Liana G. Apostolova
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, 92121
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, IN, USA, 46202
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Calamia M, Hammers DB. Process scores on measures of learning and memory: issue 2. J Clin Exp Neuropsychol 2023; 45:759-762. [PMID: 38373209 DOI: 10.1080/13803395.2024.2307218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Affiliation(s)
- Matthew Calamia
- Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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Spencer RJ, Williams TF, Kordovski VM, Patrick SD, Lengu K, Gradwohl BD, Hammers DB. A quantitative review of competing learning slope metrics: effects of age, sex, and clinical diagnosis. J Clin Exp Neuropsychol 2023; 45:744-757. [PMID: 38357915 DOI: 10.1080/13803395.2024.2314741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 12/27/2023] [Indexed: 02/16/2024]
Abstract
INTRODUCTION In learning and memory tests that involve multiple presentations of the same material, learning slope refers to the degree to which examinees improve performances over successive learning trials. We aimed to quantitatively review the traditional raw learning slope (RLS), and the newly created learning ratio (LR) to understand the effects of demographic variables and clinical diagnoses on learning slope (e.g., limited improvement over multiple trials), and to develop demographically sensitive norms. METHOD A systematic literature search was conducted to evaluate the potential for these aims to be examined across the most popular contemporary multi-trial learning tests. Two databases were searched. Following this, hierarchical linear modeling was used to examine how demographic variables predict learning slope indices. These results were in turn used to contrast the performance of clinical groups with the predicted performance of demographically similar healthy controls. Finally, preliminary normative estimates for learning slope indices were presented. RESULTS A total of 82 studies met criteria for inclusion in this study. However, the Rey Auditory Verbal Learning Test (RAVLT) was the only test to have sufficient trial-level learning and demographic data. Fifty-eight samples from 19 studies were quantitatively examined. Hierarchical linear models provided evidence of sex differences and a curvilinear decline in learning slope with age, with strongest and most consistent effects for LR relative to RLS. Regression-based norms for demographically corrected RLS and LR scores for the RAVLT are presented. The effect of clinical diagnoses was consistently stronger for LR, and Alzheimer's disease had the strongest effect, followed by invalid performances, severe traumatic brain injury, and seizures/epilepsy. CONCLUSION Overall, LR enjoys both conceptual and demonstrated psychometric advantages over RLS. Replication of these findings can be completed by reanalyzing existing datasets. Further work may focus on the utility of using LR in diagnosis and prediction of clinical prognosis.
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Affiliation(s)
- Robert J Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USA
| | - Trevor F Williams
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Victoria M Kordovski
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Physical Medicine and Rehabilitation, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Sarah D Patrick
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
| | - Ketrin Lengu
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Department of Physical Medicine and Rehabilitation, The MetroHealth System, Cleveland, OH, USA
| | - Brian D Gradwohl
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Trinity Health Hauenstein Neurosciences, Trinity Health, Muskegon, MI, USA
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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16
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Ikanga J, Reyes A, Kaba D, Akilimali P, Mampunza S, Epenge E, Gikelekele G, Kavugho I, Tshengele N, Hammers DB, Alonso A. Prevalence of suspected dementia in a sample of adults living in Kinshasa-Democratic Republic of the Congo. Alzheimers Dement 2023; 19:3783-3793. [PMID: 36880714 PMCID: PMC10483015 DOI: 10.1002/alz.13003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 01/10/2023] [Accepted: 01/22/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND The prevalence of dementia in Sub-Saharan Africa, particularly in French-speaking countries, has received limited attention. This study investigates the prevalence and risk factors of suspected dementia in elderly adults in Kinshasa, Democratic Republic of the Congo (DRC). METHODS A community-based sample of 355 individuals over 65 years old was selected using multistage probability sampling in Kinshasa. Participants were screened using the Community Screening Instrument for Dementia, Alzheimer's Questionnaire, Geriatric Depression Scale, Beck Anxiety Inventory, and Individual Fragility Questionnaire, followed by clinical interview and neurological examination. Suspected dementia diagnoses were made based on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria including significant cognitive and functional impairments. Prevalence and odds ratios (ORs) with 95% confidence interval (CI) were calculated using, respectively, regression and logistic regression. RESULTS Among 355 participants (mean age 74, SD = 7; 51% male), the crude prevalence of suspected dementia was 6.2% (9.0% in women and 3.8% in men). Female sex was a significant factor associated with suspected dementia [OR = 2.81, 95% CI (1.08-7.41)]. The prevalence of dementia increased with age (14.0% after 75 years and 23.1% after 85 years), with age being significantly associated with suspected dementia [OR = 5.42, 95% CI (2.86-10.28)]. Greater education was associated with a lower prevalence of suspected dementia [OR = 2.36, 95% CI (2.14-2.94), comparing those with ≥7.3 years of education to those with <7.3 years of education]. Other factors associated with the prevalence of suspected dementia included being widowed (OR = 1.66, 95% CI (1.05-2.61), being retired or semi-retired (OR = 3.25, 95% CI (1.50-7.03)], a diagnosis of anxiety [OR = 2.56, 95% CI (1.05-6.13)], and death of a spouse or a relative after age 65 [OR = 1.73, 95% CI (1.58-1.92)]. In contrast, depression [OR = 1.92, 95% CI (0.81-4.57)], hypertension [OR = 1.16, 95% CI (0.79-1.71)], body mass index (BMI) [OR = 1.06, 95% CI (0.40-2.79)], and alcohol consumption [OR = 0.83, 95% CI (0.19-3.58)] were not significantly associated with suspected dementia. CONCLUSIONS This study found a prevalence of suspected dementia in Kinshasa/DRC similar to other developing countries and Central African countries. Reported risk factors provide information to identify high-risk individuals and develop preventive strategies in this setting.
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Affiliation(s)
- Jean Ikanga
- Emory University School of Medicine, Department of Rehabilitation Medicine, Atlanta, Georgia, 30322, USA
- University of Kinshasa and Catholic University of Congo, School of Medicine, Kinshasa, Department of Psychiatry, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Anny Reyes
- Emory University School of Medicine, Department of Rehabilitation Medicine, Atlanta, Georgia, 30322, USA
| | - Didine Kaba
- University of Kinshasa, School of Public Health, Department of Epidemiology, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Pierre Akilimali
- University of Kinshasa, School of Public Health, Department of Epidemiology, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Samuel Mampunza
- University of Kinshasa and Catholic University of Congo, School of Medicine, Kinshasa, Department of Psychiatry, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Emmanuel Epenge
- University of Kinshasa, Department of neurology, Kinshasa, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Guy Gikelekele
- University of Kinshasa and Catholic University of Congo, School of Medicine, Kinshasa, Department of Psychiatry, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Immaculee Kavugho
- Memory clinic of Kinshasa, Kinshasa, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Nathan Tshengele
- University of Kinshasa and Catholic University of Congo, School of Medicine, Kinshasa, Department of Psychiatry, B.P. 7463 Kinshasa I, Democratic Republic of Congo
| | - Dustin B. Hammers
- Indiana university, Department of neurology, Indianapolis, IN 46202, USA
| | - Alvaro Alonso
- Emory University, Department of Epidemiology, Rollins School of Public Health, Atlanta, GA, 30307, USA
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17
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Hammers DB, Pentchev JV, Kim HJ, Spencer RJ, Apostolova LG. The relationship between learning slopes and Alzheimer's Disease biomarkers in cognitively unimpaired participants with and without subjective memory concerns. J Clin Exp Neuropsychol 2023; 45:727-743. [PMID: 37676258 PMCID: PMC10916703 DOI: 10.1080/13803395.2023.2254444] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023]
Abstract
OBJECTIVE Learning slopes represent serial acquisition of information during list-learning tasks. Although several calculations for learning slopes exist, the Learning Ratio (LR) has recently demonstrated the highest sensitivity toward changes in cognition and Alzheimer's disease (AD) biomarkers. However, investigation of learning slopes in cognitively unimpaired individuals with subjective memory concerns (SMC) has been limited. The current study examines the association of learning slopes to SMC, and the role of SMC in the relationship between learning slopes and AD biomarkers in cognitively unimpaired individuals. METHOD Data from 950 cognitively unimpaired participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 89) were used to calculate learning slope metrics. Learning slopes among those with and without SMC were compared with demographic correction, and the relationships of learning slopes with AD biomarkers of bilateral hippocampal volume and β-amyloid pathology were determined. RESULTS Learning slopes were consistently predictive of hippocampal atrophy and β-amyloid deposition. Results were heightened for LR relative to the other learning slopes. Additionally, interaction analyses revealed different associations between learning slopes and hippocampal volume as a function of SMC status. CONCLUSIONS Learning slopes appear to be sensitive to SMC and AD biomarkers, with SMC status influencing the relationship in cognitively unimpaired participants. These findings advance our knowledge of SMC, and suggest that LR - in particular - can be an important tool for the detection of AD pathology in both SMC and in AD clinical trials.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Julian V. Pentchev
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA, 48105
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA, 48105
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA, 46202
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18
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Hammers DB, Calamia M. Process scores on measures of learning and memory: Issue 1. J Clin Exp Neuropsychol 2023; 45:647-651. [PMID: 38266187 DOI: 10.1080/13803395.2024.2307219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Matthew Calamia
- Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
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19
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Hammers DB, Kostadinova RV, Spencer RJ, Ikanga JN, Unverzagt FW, Risacher SL, Apostolova LG. Sensitivity of memory subtests and learning slopes from the ADAS-Cog to distinguish along the continuum of the NIA-AA Research Framework for Alzheimer's Disease. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2023; 30:866-884. [PMID: 36074015 PMCID: PMC9992455 DOI: 10.1080/13825585.2022.2120957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/30/2022] [Indexed: 10/14/2022]
Abstract
Despite extensive use of the Alzheimer's Disease (AD) Assessment Scale - Cognitive Subscale (ADAS-Cog) in AD research, exploration of memory subtests or process scores from the measure has been limited. The current study sought to establish validity for the ADAS-Cog Word Recall Immediate and Delayed Memory subtests and learning slope scores by showing that they are sensitive to AD biomarker status. Word Recall subtest and learning slope scores were calculated for 441 participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 90). All participants were categorized using the NIA-AA Research Framework - based on PET-imaging of β-amyloid (A) and tau (T) deposition - as Normal AD Biomarkers (A-T-), Alzheimer's Pathologic Change (A + T-), or Alzheimer's disease (A + T+). Memory subtest and learning slope performances were compared between biomarker status groups, and with regard to how well they discriminated samples with (A + T+) and without (A-T-) biomarkers. Lower Word Recall memory subtest scores - and scores for a particular learning slope calculation, the Learning Ratio - were observed for the AD (A + T+) group than the other biomarker groups. Memory subtest and Learning Ratio scores further displayed fair to good receiver operator characteristics when differentiating those with and without AD biomarkers. When comparing across learning slopes, the Learning Ratio metric consistently outperformed others. ADAS-Cog memory subtests and the Learning Ratio score are sensitive to AD biomarker status along the continuum of the NIA-AA Research Framework, and the results offer criterion validity for use of these subtests and process scores as unique markers of memory capacity.
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Affiliation(s)
- Dustin B. Hammers
- Indiana University School of Medicine, Department of Neurology, Indianapolis, IN, USA
| | | | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA
| | - Jean N. Ikanga
- Emory University, School of Medicine, Department of Rehabilitation Medicine, GA, USA
- University of Kinshasa, Department of Psychiatry, Democratic Republic of Congo (DRC)
| | | | - Shannon L. Risacher
- Indiana University School of Medicine, Department of Radiology, Indianapolis, IN, USA
| | - Liana G. Apostolova
- Indiana University School of Medicine, Department of Neurology, Indianapolis, IN, USA
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20
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Hall MG, Wollman SC, Haines ME, Boyle MA, Richardson HK, Hammers DB. Novel learning ratio from the NAB list learning test distinguishes between clinical groups: clinical validation and sex-related differences. J Clin Exp Neuropsychol 2023; 45:715-726. [PMID: 37477412 DOI: 10.1080/13803395.2023.2236772] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/09/2023] [Indexed: 07/22/2023]
Abstract
List-learning tasks provide a wealth of information about an individual's cognitive abilities: attention, encoding, storage, retrieval, recognition. A more recently developed metric, the Learning Ratio (LR), supplements information about cognitive ability and can assist the clinician in determining whether an individual has cognitive impairment. The LR is calculated by taking the difference between the individuals' raw score on the first learning trial and their raw score on the last learning trial, which is then divided by the number of words left to be learned after the first learning trial. A LR derived from the list-learning task from the Neuropsychological Assessment Battery (NAB) was evaluated to determine ability to distinguish those with normal cognition from those with mild cognitive impairment (MCI) and dementia. Results from the present study indicate the NAB LR is able to distinguish between clinical groups; recommended cutoffs for the NAB LR scores are provided. We also found a significant female sex-advantage for the NAB LR in those with normal memory ability and demonstrated the female sex advantage decreased with increasing memory impairment. Taken together, the NAB LR may assist clinicians in making an accurate and early diagnosis and may be helpful for tracking learning and functioning across multiple assessments. .
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Affiliation(s)
- Matthew G Hall
- University of Toledo Medical Center Rehabilitation Services, Toledo, Ohio, USA
| | | | - Mary E Haines
- University of Toledo Medical Center Rehabilitation Services, Toledo, Ohio, USA
| | - Mellisa A Boyle
- University of Toledo Medical Center Rehabilitation Services, Toledo, Ohio, USA
| | | | - Dustin B Hammers
- Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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21
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Hammers DB, Miranda M, Abildskov TJ, Tate DF, Wilde EA, Spencer RJ. Consideration of different scoring approaches for a verbal incidental learning measure from the WAIS-IV using hippocampal volumes. Appl Neuropsychol Adult 2023; 30:43-53. [PMID: 33882772 DOI: 10.1080/23279095.2021.1909592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Objective: While Spencer's verbal incidental learning (IL) task-from Vocabulary and Similarities subtests of the WAIS-has been validated relative to traditional memory measures and Alzheimer's disease (AD) pathology, the effectiveness of the particular scoring method used has not been assessed relative to alternative scoring weightings. The purpose of this study was to compare original and alternative scoring methods of this IL task by using an AD biomarker-benchmark to arrive at an optimal approach. Methods: Fifty-five memory-clinic patients aged 59-87 received neuropsychological assessment, measures of IL, and quantitative brain imaging. Partial correlation coefficients with total hippocampal volume-controlling for age, sex, and intracranial volume-were assessed across several IL scoring methods, and partial correlations with measures of memory were examined to evaluate convergent validity.Results: IL scoring methods maximizing the contribution of paired-associate-recall-performance were significantly correlated with both hippocampal volumes and traditional memory measures, whereas discrimination-emphasized scoring methods were not.Conclusions: IL scoring methods emphasizing memory paired-associate recall appeared to be preferable to those emphasizing memory discrimination. Administration of the IL- Similarities subtest alone, without IL- Vocabulary, may strike a balance between strength of relationships with both hippocampal volumes and standard memory measures, while also limiting administration time.
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Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Michelle Miranda
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Tracy J Abildskov
- Department of Neurology, Traumatic Brain Injury and Concussion Center, University of Utah, Salt Lake City, UT, USA
| | - David F Tate
- Department of Neurology, Traumatic Brain Injury and Concussion Center, University of Utah, Salt Lake City, UT, USA.,George E. Whalen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Elisabeth A Wilde
- Department of Neurology, Traumatic Brain Injury and Concussion Center, University of Utah, Salt Lake City, UT, USA.,George E. Whalen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Robert J Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA.,Department of Psychiatry, Neuropsychology Section, Michigan Medicine, Ann Arbor, MI, USA
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22
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Duff K, Suhrie KR, Hammers DB, Dixon AM, King JB, Koppelmans V, Hoffman JM. Repeatable battery for the assessment of neuropsychological status and its relationship to biomarkers of Alzheimer's disease. Clin Neuropsychol 2023; 37:157-173. [PMID: 34713772 PMCID: PMC9271322 DOI: 10.1080/13854046.2021.1995050] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/14/2021] [Indexed: 02/07/2023]
Abstract
The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has been associated with commonly used biomarkers of Alzheimer's disease (AD). However, prior studies have typically utilized small and poorly characterized samples, and they have not analyzed the subtests of the RBANS. The current study sought to expand on prior work by examining the relationship between the Indexes and subtest scores of the RBANS and three AD biomarkers: amyloid deposition via positron emission tomography, hippocampal volume via magnetic resonance imaging, and APOE ε4 status. One-hundred twenty-one older adults across the AD continuum (intact, amnestic Mild Cognitive Impairment, mild AD), who were mostly Caucasian and well-educated, underwent assessment with the RBANS and collection of the three biomarkers. Greater amyloid deposition was significantly related to lower scores on all five Indexes and the Total Scale score of the RBANS, as well as 11 of 12 subtests. For bilateral hippocampal volume, significant correlations were observed for 4 of the 5 Indexes, Total Scale score, and 9 of 12 subtests, with smaller hippocampi being related to lower RBANS scores. Participants with at least one APOE ε4 allele had significantly lower scores on 3 of the 5 Indexes, Total Scale score, and 8 of the 12 subtests. In this sample of participants across the dementia spectrum, most RBANS Indexes and subtests showed relationships with the amyloid deposition, hippocampal volumes, and APOE status, with poorer performance on the RBANS being associated with biomarker positivity. Although memory scores on the RBANS have traditionally been linked to biomarkers in AD, other Index and subtest scores also hold promise as indicators of AD. Replication in a more diverse sample is needed.
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Affiliation(s)
- Kevin Duff
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah
| | - Kayla R. Suhrie
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah
| | - Dustin B. Hammers
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah
| | - Ava M. Dixon
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah
| | - Jace B. King
- Department of Radiology and Imaging Sciences, University of Utah, United States
| | | | - John M. Hoffman
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah
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23
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Hammers DB, Lin JH, Polsinelli AJ, Logan PE, Risacher SL, Schwarz AJ, Apostolova LG. Criterion Validation of Tau PET Staging Schemes in Relation to Cognitive Outcomes. J Alzheimers Dis 2023; 96:197-214. [PMID: 37742649 PMCID: PMC10825758 DOI: 10.3233/jad-230512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND Utilization of NIA-AA Research Framework requires dichotomization of tau pathology. However, due to the novelty of tau-PET imaging, there is no consensus on methods to categorize scans into "positive" or "negative" (T+ or T-). In response, some tau topographical pathologic staging schemes have been developed. OBJECTIVE The aim of the current study is to establish criterion validity to support these recently-developed staging schemes. METHODS Tau-PET data from 465 participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 90) were classified as T+ or T- using decision rules for the Temporal-Occipital Classification (TOC), Simplified TOC (STOC), and Lobar Classification (LC) tau pathologic schemes of Schwarz, and Chen staging scheme. Subsequent dichotomization was analyzed in comparison to memory and learning slope performances, and diagnostic accuracy using actuarial diagnostic methods. RESULTS Tau positivity was associated with worse cognitive performance across all staging schemes. Cognitive measures were nearly all categorized as having "fair" sensitivity at classifying tau status using TOC, STOC, and LC schemes. Results were comparable between Schwarz schemes, though ease of use and better data fit preferred the STOC and LC schemes. While some evidence was supportive for Chen's scheme, validity lagged behind others-likely due to elevated false positive rates. CONCLUSIONS Tau-PET staging schemes appear to be valuable for Alzheimer's disease diagnosis, tracking, and screening for clinical trials. Their validation provides support as options for tau pathologic dichotomization, as necessary for use of NIA-AA Research Framework. Future research should consider other staging schemes and validation with other outcome benchmarks.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Joshua H. Lin
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam J. Schwarz
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Takeda Pharmaceuticals Ltd., Cambridge, MA, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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24
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Logan PE, Nemes S, Iaccarino L, Mundada NS, La Joie R, Aisen P, Dage JL, Eloyan A, Fagan AM, Foroud TM, Gatsonis C, Hammers DB, Jack CR, Kramer JH, Koeppe R, Saykin AJ, Toga AW, Vemuri P, Atri A, Day GS, Duara R, Graff‐Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez MF, Onyike CU, Rogalski EJ, Sha S, Turner RW, Womack KB, Carrillo MC, Rabinovici GD, Dickerson BC, Apostolova LG. Sex and
APOE‐
ε
4
carrier effects on early‐onset Alzheimer’s disease pathology. Alzheimers Dement 2022. [DOI: 10.1002/alz.068743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Paige E. Logan
- Indiana University School of Medicine Indianapolis IN USA
| | - Sára Nemes
- Indiana University School of Medicine Indianapolis IN USA
| | | | | | - Renaud La Joie
- University of California, San Francisco San Francisco CA USA
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California San Diego CA USA
| | | | | | - Anne M. Fagan
- Washington University School of Medicine St. Louis MO USA
| | | | | | | | | | - Joel H. Kramer
- University of California, San Francisco San Francisco CA USA
| | | | | | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), University of Southern California Los Angeles CA USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute/Banner Health Sun City AZ USA
| | | | | | | | | | | | | | | | | | | | - Sharon Sha
- Stanford University School of Medicine Stanford CA USA
| | | | - Kyle B. Womack
- Washington University School of Medicine St. Louis MO USA
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25
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Hammers DB, Duff K, Spencer RJ. Demographically-corrected normative data for the RBANS learning ratio in a sample of older adults. Clin Neuropsychol 2022; 36:2221-2236. [PMID: 34313182 PMCID: PMC8792095 DOI: 10.1080/13854046.2021.1952308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/01/2021] [Indexed: 01/27/2023]
Abstract
BACKGROUND A novel learning slope score - the Learning Ratio (LR) - has recently been developed that appears to be sensitive to memory performance and AD pathology more optimally than traditional learning slope calculations. While promising, this research to date has been both experimental and based on group differences, and therefore does not aid in the interpretation of individual LR performance for either clinical or research settings. The objective of the current study was to develop demographically-corrected normative data on these LR learning slopes on verbal learning measures from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). METHOD The current study examined the influence of age and education on LR metrics for the List Learning, Story Memory, and an Aggregated RBANS score in 200 cognitively intact adults aged 65 or older using linear regression. RESULTS Age and education correlated with most LR metrics, but no sex differences were observed. Linear regression permitted the prediction of LR values from age and education, which are then compared to observed LR values. The result is demographically-corrected T scores for these LR metrics. CONCLUSIONS By comparing observed and predicted LR scores calculated from regression-based prediction equations, this represents the first step towards interpretation of individual performances on this metric for clinical decision making and treatment planning purposes. With future replication in diverse and heterogenous samples, we hope to offer a new clinical tool for the examination of learning slopes in older adults.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kevin Duff
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA
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Hammers DB, Suhrie K, Dixon A, Gradwohl BD, Archibald ZG, King JB, Spencer RJ, Duff K, Hoffman JM. Relationship between a novel learning slope metric and Alzheimer's disease biomarkers. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2022; 29:799-819. [PMID: 33952156 PMCID: PMC8568738 DOI: 10.1080/13825585.2021.1919984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 04/18/2021] [Indexed: 01/07/2023]
Abstract
The Learning Ratio (LR) is a novel learning score examining the proportion of information learned over successive learning trials relative to information available to be learned. Validation is warranted to understand LR's sensitivity to Alzheimer's disease (AD) pathology. One-hundred twenty-three participants across the AD continuum underwent memory assessment, quantitative brain imaging, and genetic analysis. LR scores were calculated from the HVLT-R, BVMT-R, RBANS List Learning, and RBANS Story Memory, and compared to total hippocampal volumes,18F-Flutemetamol composite SUVR uptake, and APOE ε4 status. Lower LR scores were consistently associated with smaller total hippocampal volumes, greater cerebral β-amyloid deposition, and APOE ε4 positivity. This LR score outperformed a traditional learning slope calculation in all analyses. LR is sensitive to AD pathology along the AD continuum - more so than a traditional raw learning score - and reducing the competition between the first trial and subsequent trials can better depict learning capacity.
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Affiliation(s)
- Dustin B. Hammers
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Kayla Suhrie
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Ava Dixon
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Brian D. Gradwohl
- Mercy Health Hauenstein Neurosciences, Mercy Health, Muskegon, MI, USA
| | - Zane G. Archibald
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Jace B. King
- Utah Center for Advanced Imaging Research, Department of Radiology & Imaging Sciences, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, USA
| | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA
| | - Kevin Duff
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - John M. Hoffman
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
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Reyes A, De Wit L, Winston MR, Hammers DB, Alonso A, Ikanga JN. A-3 Examining The Sociodemographic Factors Associated with Performance on The Community Screening Instrument for Dementia in Congolese Older Adults. Arch Clin Neuropsychol 2022. [DOI: 10.1093/arclin/acac060.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Objective: Given the lack of comprehensive neuropsychological tools in Sub-Saharan Africa, there is a need to examine the clinical utility of dementia cognitive screeners in this setting. We examined the contribution of sociodemographic factors to performance on the Community Screening Instrument for Dementia (CSID) in Congolese older adults.
Methods: 354 participants (mean age = 73.6 ± 6.7, mean education (years) =7.3 ± 4.7; 50% female) were randomly recruited. All participants completed the CSID (mean = 25.23 ± 4.19). Multiple linear regressions were conducted to examine the contribution of age, education, sex, and the interactions between education, school type, and participant income to CSID raw scores. Raw scores were demographically adjusted for education and sex by adding 1 point for ≤12 years of education and 1 point for female.
Results: Older age (β = −0.362, p < 0.001), fewer years of education (β = −0.335, p = 0.022), female sex (β = −0.223, p = 0.035), and public schools (β = −0.185, p = 0.008) were associated with lower CSID scores. There was a trend for lower-income associated with lower CSID scores (β = −0.185, p = 0.062). The interaction between education and school was significant (p = 0.007), with education having a stronger effect on CSID scores for private (β = 0.25) relative to public schools (β = 0.16). The effect of education and sex decreased in the education- and sex-adjusted CSID scores (education β = 0.121; sex β = −0.0.056).
Conclusion: We demonstrate that the effects of education on CSID varied based on sociodemographic characteristics in Congolese older adults, with private school associated with better performance. Given the effects of education and sex on performance, future studies should examine if demographically adjusted scores improve the sensitivity and specificity of the CSID in Congolese populations.
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Hammers DB, Spencer RJ, Apostolova LG. Validation of and Demographically Adjusted Normative Data for the Learning Ratio Derived from the RAVLT in Robustly Intact Older Adults. Arch Clin Neuropsychol 2022; 37:981-993. [PMID: 35175287 PMCID: PMC9618160 DOI: 10.1093/arclin/acac002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The learning ratio (LR) is a novel learning slope score that was developed to identify learning more accurately by considering the proportion of information learned after the first trial of a multi-trial learning task. Specifically, LR is the number of items learned after trial one divided by the number of items yet to be learned. Although research on LR has been promising, convergent validation, clinical characterization, and demographic norming of this LR metric are warranted to understand its clinical utility when derived from the Rey Auditory Verbal Learning Test (RAVLT). METHOD Data from 674 robustly cognitively intact older participants from the Alzheimer's Disease Neuroimaging Initiative (aged 54- 89) were used to calculate the LR metric. Comparison of LR's relationship with standard memory measures was undertaken relative to other traditional learning slope metrics. In addition, retest reliability at 6, 12, and 24 months was examined, and demographically adjusted normative comparisons were developed. RESULTS Lower LR scores were associated with poorer performances on memory measures, and LR scores outperformed traditional learning slope calculations across all analyses. Retest reliability exceeded acceptability thresholds across time, and demographically adjusted normative equations suggested better performance for cognitively intact participants than those with mild cognitive impairment. CONCLUSIONS These results suggest that this LR score possesses sound retest reliability and can better reflect learning capacity than traditional learning slope calculations. With the added development and validation of regression-based normative comparisons, these findings support the use of the RAVLT LR as a clinical tool to inform clinical decision-making and treatment.
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Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Robert J Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Department of Psychiatry, Michigan Medicine, Neuropsychology Section, Ann Arbor MI, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
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Duff K, Ying J, Suhrie KR, Dalley BCA, Atkinson TJ, Porter SM, Dixon AM, Hammers DB, Wolinsky FD. Computerized Cognitive Training in Amnestic Mild Cognitive Impairment: A Randomized Clinical Trial. J Geriatr Psychiatry Neurol 2022; 35:400-409. [PMID: 33783254 DOI: 10.1177/08919887211006472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Computerized cognitive training has been successful in healthy older adults, but its efficacy has been mixed in patients with amnestic Mild Cognitive Impairment (MCI). METHODS In a randomized, placebo-controlled, double-blind, parallel clinical trial, we examined the short- and long-term efficacy of a brain-plasticity computerized cognitive training in 113 participants with amnestic MCI. RESULTS Immediately after 40-hours of training, participants in the active control group who played computer games performed better than those in the experimental group on the primary cognitive outcome (p = 0.02), which was an auditory memory/attention composite score. There were no group differences on 2 secondary outcomes (global cognitive composite and rating of daily functioning). After 1 year, there was no difference between the 2 groups on primary or secondary outcomes. No adverse events were noted. CONCLUSIONS Although the experimental cognitive training program did not improve outcomes in those with MCI, the short-term effects of the control group should not be dismissed, which may alter treatment recommendations for these patients.
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Affiliation(s)
- Kevin Duff
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, 14434University of Utah, UT, USA
| | - Jian Ying
- Department of Internal Medicine, 14434University of Utah, UT, USA
| | - Kayla R Suhrie
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, 14434University of Utah, UT, USA
| | - Bonnie C A Dalley
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, 14434University of Utah, UT, USA
| | - Taylor J Atkinson
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, 14434University of Utah, UT, USA.,School of Aging Studies, 7831University of South Florida, FL, USA
| | - Sariah M Porter
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, 14434University of Utah, UT, USA
| | - Ava M Dixon
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, 14434University of Utah, UT, USA
| | - Dustin B Hammers
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, 14434University of Utah, UT, USA
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Hammers DB, Kostadinova R, Unverzagt FW, Apostolova LG. Assessing and validating reliable change across ADNI protocols. J Clin Exp Neuropsychol 2022; 44:85-102. [PMID: 35786312 PMCID: PMC9308719 DOI: 10.1080/13803395.2022.2082386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/23/2022] [Indexed: 10/17/2022]
Abstract
OBJECTIVE Reliable change methods can aid in determining whether changes in cognitive performance over time are meaningful. The current study sought to develop and cross-validate 12-month standardized regression-based (SRB) equations for the neuropsychological measures commonly administered in the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal study. METHOD Prediction algorithms were developed using baseline score, retest interval, the presence/absence of a 6-month evaluation, age, education, sex, and ethnicity in two different samples (n = 192 each) of robustly cognitively intact community-dwelling older adults from ADNI - matched for demographic and testing factors. The developed formulae for each sample were then applied to one of the samples to determine goodness-of-fit and appropriateness of combining samples for a single set of SRB equations. RESULTS Minimal differences were seen between Observed 12-month and Predicted 12-month scores on most neuropsychological tests from ADNI, and when compared across samples the resultant Predicted 12-month scores were highly correlated. As a result, samples were combined and SRB prediction equations were successfully developed for each of the measures. CONCLUSIONS Establishing cross-validation for these SRB prediction equations provides initial support of their use to detect meaningful change in the ADNI sample, and provides the basis for future research with clinical samples to evaluate potential clinical utility. While some caution should be considered for measuring true cognitive change over time - particularly in clinical samples - when using these prediction equations given the relatively lower coefficients of stability observed, use of these SRBs reflects an improvement over current practice in ADNI.
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Affiliation(s)
- Dustin B. Hammers
- Indiana University School of Medicine, Department of Neurology, Indianapolis, IN, USA
| | - Ralitsa Kostadinova
- Indiana University School of Medicine, Department of Neurology, Indianapolis, IN, USA
| | | | - Liana G. Apostolova
- Indiana University School of Medicine, Department of Neurology, Indianapolis, IN, USA
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Hammers DB, Duff K, Apostolova LG. Examining the role of repeated test exposure over 12 months across ADNI protocols. Alzheimers Dement (Amst) 2022; 14:e12289. [PMID: 35233441 PMCID: PMC8868516 DOI: 10.1002/dad2.12289] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Received: 11/17/2021] [Revised: 01/07/2022] [Accepted: 01/10/2022] [Indexed: 11/21/2022]
Abstract
Objective: Changes to study protocols during longitudinal research may alter cognitive testing schedules over time. Unlike in prior Alzheimer's Disease Neuroimaging Initiative (ADNI) protocols, where testing occurred twice annually, participants enrolled in the ADNI-3 are no longer exposed to cognitive materials at 6 months. This may affect their 12-month performance relative to earlier ADNI cohorts, and potentially confounds data harmonization attempts between earlier and later ADNI protocols. Method: Using data from participants enrolled across multiple ADNI protocols, this study investigated whether test exposure during 6-month cognitive evaluation influenced scores on subsequent 12-month evaluation. Results: No interaction effects were observed between test exposure group and time at 12 months on cognitive performance. No improvements, and limited declines, were seen between baseline and 12-month follow-up scores on most measures. Conclusions: The 6-month testing session had minimal impact on 12-month performance in ADNI. Collapsing longitudinal data across ADNI protocols in future research appears appropriate.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of MedicineIndianapolisIndianaUSA
| | - Kevin Duff
- Center for Alzheimer's CareImaging, and Research, Department of NeurologyUniversity of UtahSalt Lake CityUtahUSA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of MedicineIndianapolisIndianaUSA
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Hammers DB, Suhrie K, Dixon A, Gradwohl BD, Duff K, Spencer RJ. Validation of HVLT-R, BVMT-R, and RBANS Learning Slope Scores along the Alzheimer's Continuum. Arch Clin Neuropsychol 2022; 37:78-90. [PMID: 33899087 DOI: 10.1093/arclin/acab023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/23/2021] [Accepted: 03/27/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The learning ratio (LR) is a novel learning slope score that has been developed to reduce the inherent competition between the first trial and subsequent trials in traditional learning slopes. In essence, the LR is the number of items learned after the first trial divided by the number of items yet to be learned. Criterion and convergent validation of this LR score is warranted to understand its sensitivity along the Alzheimer's disease (AD) continuum. METHOD The LR metric was calculated for 123 participants from standard measures of memory, including the Hopkins Verbal Learning Test-Revised, Brief Visuospatial Memory Test-Revised, Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) list learning, and RBANS story memory. All participants were categorized as normal cognition, mild cognitive impairment (MCI), or AD. LR performances were compared between groups, among other standard memory measures, and with regards to how well they discriminated cognitively impaired from unimpaired samples-and within diagnostic subgroups. RESULTS Lower LR scores were observed for the MCI and AD groups than the normal cognition group, with the AD group performing worse than the MCI group for several slope calculations. Lower LR scores were also consistently associated with poorer performances on traditional memory measures. LR scores further displayed excellent receiver operator characteristics when differentiating those with and without cognitive impairment-and MCI from normal cognition. Overall, LR scores consistently outperformed traditional learning slope calculations across all analyses. CONCLUSIONS This LR score is sensitive to memory dysfunction along the AD continuum, and results offer criterion and convergent validity for use of the LR metric to understand learning capacity.
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Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Kayla Suhrie
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Ava Dixon
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Brian D Gradwohl
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor, MI, USA
| | - Kevin Duff
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Robert J Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor, MI, USA
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Kara F, Reid RI, Schwarz CG, Tosakulwong N, Lesnick TG, Zuk SM, Kendall‐Thomas J, Thostenson K, Reyes DA, Fields JA, Senjem ML, Min H, Lowe VJ, Jack CR, Bailey KR, James TT, Lobo RA, Manson JE, Pal L, Hammers DB, Malek‐Ahmadi MH, Cedars MI, Naftolin F, Miller VM, Harman SM, Dowling NM, Gleason CE, Kantarci K. Higher systolic and diastolic blood pressures are associated with loss of white matter integrity in postmenopausal women of the KEEPS Continuation Study. Alzheimers Dement 2021. [DOI: 10.1002/alz.053874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - JoAnn E. Manson
- Department of Preventive Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA
| | - Lubna Pal
- Yale School of Medicine New Haven CT USA
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Suhrie KR, Hammers DB, Porter SM, Dixon AM, King JB, Anderson JS, Duff K, Hoffman JM. Predicting biomarkers in intact older adults and those with amnestic Mild Cognitive Impairment, and mild Alzheimer's Disease using the Repeatable Battery for the Assessment of Neuropsychological Status. J Clin Exp Neuropsychol 2021; 43:861-878. [PMID: 35019815 DOI: 10.1080/13803395.2021.2023476] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 12/23/2021] [Indexed: 10/19/2022]
Abstract
INTRODUCTION The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has been associated, to varying degrees, with commonly used biomarkers of Alzheimer's disease (AD). Given the ease of RBANS administration as a screening tool for clinical trials and other applications, a better understanding of how RBANS performance is associated with presence of APOE ε4 allele[s], cerebral amyloid burden, and hippocampal volume is warranted. METHOD One hundred twenty-one older adults who were classified as intact, amnestic Mild Cognitive Impairment, or mild AD underwent cognitive assessment with the RBANS, genetic analysis, and quantitative brain imaging. APOE ε4 carrier status, 18F-Flutemetamol composite standardized uptake value ratio (SUVR), and hippocampal volume were each regressed on demographic variables and RBANS Total Scale score, Index scores, and subtest scores. RESULTS Lower RBANS Total Scale score or Delayed Memory Index (DMI) predicted the presence of APOE ε4 allele[s], higher cerebral amyloid burden, and lower hippocampal volumes. DMI was a slightly better predictor than Total Scale score for most AD biomarkers. No demographic variables consistently contributed to these models. CONCLUSIONS The RBANS - DMI in particular - is sensitive to AD pathology. As such, it could be used as a predictive tool, particularly in clinical drug trials to enrich samples prior to less accessible AD biomarker investigation.
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Affiliation(s)
- Kayla R Suhrie
- Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Dustin B Hammers
- Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Sariah M Porter
- Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Ava M Dixon
- Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Jace B King
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Jeffrey S Anderson
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Kevin Duff
- Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - John M Hoffman
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
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Hammers DB, Gradwohl BD, Kucera A, Abildskov TJ, Wilde EA, Spencer RJ. Preliminary Validation of the Learning Ratio for the HVLT-R and BVMT-R in Older Adults. Cogn Behav Neurol 2021; 34:170-181. [PMID: 34473668 DOI: 10.1097/wnn.0000000000000277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/13/2020] [Indexed: 11/25/2022]
Abstract
BACKGROUND The learning slope is typically represented as the raw difference between the final score and the score of the first learning trial. A new method for calculating the learning slope, the learning ratio (LR), was recently developed; it is typically represented as the number of items that are learned after the first trial divided by the number of items that are yet to be learned. OBJECTIVE To evaluate the convergent and criterion validity of the LR in order to understand its sensitivity to Alzheimer disease (AD) pathology. METHOD Fifty-six patients from a memory clinic underwent standard neuropsychological assessment and quantitative brain imaging. LR scores were calculated from the Hopkins Verbal Learning Test-Revised and the Brief Visuospatial Memory Test-Revised and were compared with both standard memory measures and total hippocampal volumes, as well as between individuals with AD and those with mild cognitive impairment. RESULTS Lower LR scores were consistently associated with poorer performances on standard memory measures and smaller total hippocampal volumes, generally more so than traditional learning slope scores. The LR scores of the AD group were smaller than those of the group with mild cognitive impairment. Furthermore, the aggregation of LR scores into a single metric was partially supported. CONCLUSION The LR is sensitive to AD pathology along the AD continuum. This result supports previous claims that the LR score can reflect learning capacity better than traditional learning calculations can by considering the amount of information that is learned at trial 1.
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Affiliation(s)
- Dustin B Hammers
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, Utah
| | - Brian D Gradwohl
- Mental Health Service, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
- Department of Psychiatry, Neuropsychology Section, Michigan Medicine, Ann Arbor, Michigan
| | | | - Tracy J Abildskov
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, Utah
| | - Elisabeth A Wilde
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, Utah
- George E. Whalen Veterans Affairs Medical Center, Salt Lake City, Utah
| | - Robert J Spencer
- Mental Health Service, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
- Department of Psychiatry, Neuropsychology Section, Michigan Medicine, Ann Arbor, Michigan
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Hammers DB, Suhrie KR, Dixon A, Porter S, Duff K. Validation of one-week reliable change methods in cognitively intact community-dwelling older adults. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2021; 28:472-492. [PMID: 32613913 PMCID: PMC7775875 DOI: 10.1080/13825585.2020.1787942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/23/2020] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Reliable change methods can assist the determination of whether observed changes in performance are meaningful. The current study sought to validate previously published standardized regression-based (SRB) equations for commonly administered cognitive tests using a cognitively intact sample of older adults, and extend findings by including relevant demographic and test-related variables known to predict cognitive performance. Method: This study applied previously published SRB prediction equations to 107 cognitively intact older adults assessed twice over one week. Prediction equations were also updated by pooling the current validation sample with 93 cognitively intact participants from original development sample to create a combined development sample. Results: Significant improvements were seen between observed baseline and follow-up scores on most measures. However, few differences were seen between observed follow-up scores and those predicted from these SRB algorithms, and the level of practice effects observed based on these equations were consistent with expectations. When SRBs were re-calculated from this combined development sample, predicted follow-up scores were mostly comparable with these equations, but standard errors of the estimate were consistently smaller. Conclusions: These results help support the validity of of these SRB equations to predict cognitive performance on these measures when repeated administration is necessary over short intervals. Findings also highlight the utility of expanding SRB models when predicting follow-up performance serially to provide more accurate assessment of reliable change at the level of the individual. As short-term practice effects are shown to predict cognitive performance annually, they possess the potential to inform clinical decision-making about individuals along the Alzheimer's continuum.
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Affiliation(s)
- Dustin B. Hammers
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah
- Center on Aging, University of Utah
| | - Kayla R. Suhrie
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah
| | - Ava Dixon
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah
| | - Sariah Porter
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah
| | - Kevin Duff
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah
- Center on Aging, University of Utah
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Hammers DB, Suhrie KR, Dixon A, Porter S, Duff K. Reliable change in cognition over 1 week in community-dwelling older adults: a validation and extension study. Arch Clin Neuropsychol 2021; 36:347-358. [PMID: 32026948 PMCID: PMC8245079 DOI: 10.1093/arclin/acz076] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 11/15/2019] [Accepted: 11/18/2019] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVE Reliable change methods can aid neuropsychologists in understanding if performance differences over time represent clinically meaningful change or reflect benefit from practice. The current study sought to externally validate the previously published standardized regression-based (SRB) prediction equations developed by Duff for commonly administered cognitive measures. METHOD This study applied Duff's SRB prediction equations to an independent sample of community-dwelling participants with amnestic mild cognitive impairment (MCI) assessed twice over a 1-week period. A comparison of MCI subgroups (e.g., single v. multi domain) on the amount of change observed over 1 week was also examined. RESULTS Using pairwise t-tests, large and statistically significant improvements were observed on most measures across 1 week. However, the observed follow-up scores were consistently below expectation compared with predictions based on Duff's SRB algorithms. In individual analyses, a greater percentage of MCI participants showed smaller-than-expected practice effects based on normal distributions. In secondary analyses, smaller-than-expected practice effects were observed in participants with worse baseline memory impairment and a greater number of impaired cognitive domains, particularly for measures of executive functioning/speeded processing. CONCLUSIONS These findings help to further support the validity of Duff's 1-week SRB prediction equations in MCI samples and extend previous research by showing incrementally smaller-than-expected benefit from practice for increasingly impaired amnestic MCI subtypes.
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Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah
- Center on Aging, University of Utah
| | - Kayla R Suhrie
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah
| | - Ava Dixon
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah
| | - Sariah Porter
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah
| | - Kevin Duff
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah
- Center on Aging, University of Utah
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Hammers DB, Duff K. Application of Different Standard Error Estimates in Reliable Change Methods. Arch Clin Neuropsychol 2021; 36:339-346. [PMID: 31732736 PMCID: PMC8060987 DOI: 10.1093/arclin/acz054] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/16/2019] [Accepted: 09/04/2019] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE This study attempted to clarify the applicability of standard error (SE) terms in clinical research when examining the impact of short-term practice effects on cognitive performance via reliable change methodology. METHOD This study compared McSweeney's SE of the estimate (SEest) to Crawford and Howell's SE for prediction of the regression (SEpred) using a developmental sample of 167 participants with either normal cognition or mild cognitive impairment (MCI) assessed twice over 1 week. One-week practice effects in older adults: Tools for assessing cognitive change. Using these SEs, previously published standardized regression-based (SRB) reliable change prediction equations were then applied to an independent sample of 143 participants with MCI. RESULTS This clinical developmental sample yielded nearly identical SE values (e.g., 3.697 vs. 3.719 for HVLT-R Total Recall SEest and SEpred, respectively), and the resultant SRB-based discrepancy z scores were comparable and strongly correlated (r = 1.0, p < .001). Consequently, observed follow-up scores for our sample with MCI were consistently below expectation compared to predictions based on Duff's SRB algorithms. CONCLUSIONS These results appear to replicate and extend previous work showing that the calculation of the SEest and SEpred from a clinical sample of cognitively intact and MCI participants yields similar values and can be incorporated into SRB reliable change statistics with comparable results. As a result, neuropsychologists utilizing reliable change methods in research investigation (or clinical practice) should carefully balance mathematical accuracy and ease of use, among other factors, when determining which SE metric to use.
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Affiliation(s)
- Dustin B Hammers
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Center on Aging, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Center on Aging, University of Utah, Salt Lake City, UT, USA
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Hammers DB, Duff K, Spencer RJ. Demographically-corrected normative data for the HVLT-R, BVMT-R, and Aggregated Learning Ratio values in a sample of older adults. J Clin Exp Neuropsychol 2021; 43:290-300. [PMID: 33899697 PMCID: PMC8259561 DOI: 10.1080/13803395.2021.1917523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/10/2021] [Indexed: 10/21/2022]
Abstract
Background: The Learning Ratio (LR) is a novel learning slope score that has been developed to reduce the inherent competition between the first trial and subsequent trials in traditional learning slopes. Recent findings suggest that LR is sensitive to AD pathology along the AD continuum - more so than the traditional learning calculations that employ raw changes across trials. However, research is still experimental and not yet directly applicable to clinical settings. Consequently, the objective of the current study was to develop demographically-corrected normative data on these LR learning slopes.Method: The current study examined the influence of age and education on LR scores for the HVLT-R, BVMT-R, and an Aggregated HVLT-R/BVMT-R in 200 cognitively intact adults aged 65 years and older using linear regression.Results: Age negatively correlated with all LR metrics, and education positively correlated with most. No sex differences were identified. LR values were predicted from age and education, which can be compared to observed LR values and converted into demographically-corrected T scores.Conclusions: By comparing observed and predicted LR scores calculated from regression-based prediction equations, interpretations are permitted that aid in clinical decision making and treatment planning. Co-norming of the HVLT-R and BVMT-R also allows for comparisons between verbal and visual learning slope scores in individual patients. We hope normative data for LR enhances its utility as a clinical tool for examining learning slopes in older adults administered the HVLT-R and/or BVMT-R.
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Affiliation(s)
- Dustin B. Hammers
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- Center for Alzheimer’s Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Robert J. Spencer
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor MI, USA
- Michigan Medicine, Department of Psychiatry, Neuropsychology Section, Ann Arbor MI, USA
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Hammers DB, Porter S, Dixon A, Suhrie KR, Duff K. Validating 1-Year Reliable Change Methods. Arch Clin Neuropsychol 2021; 36:87-98. [PMID: 32885234 PMCID: PMC7809650 DOI: 10.1093/arclin/acaa055] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/24/2020] [Accepted: 07/01/2020] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE reliable change methods can assist in the determination of whether observed changes in performance are meaningful. The current study sought to validate previously published 1-year standardized regression-based (SRB) equations for commonly administered neuropsychological measures that incorporated baseline performances, demographics, and 1-week practice effects. METHOD Duff et al.'s SRB prediction equations were applied to an independent sample of 70 community-dwelling older adults with either normal cognition or mild cognitive impairment, assessed at baseline, at 1 week, and at 1 year. RESULTS minimal improvements or declines were seen between observed baseline and observed 1-year follow-up scores, or between observed 1-year and predicted 1-year scores, on most measures. Relatedly, a high degree of predictive accuracy was observed between observed 1-year and predicted 1-year scores across cognitive measures in this repeated battery. CONCLUSIONS these results, which validate Duff et al.'s SRB equations, will permit clinicians and researchers to have more confidence when predicting cognitive performance on these measures over 1 year.
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Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
- Center on Aging, University of Utah, Salt Lake City, UT, USA
| | - Sariah Porter
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Ava Dixon
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Kayla R Suhrie
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- Department of Neurology, Center for Alzheimer’s Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
- Center on Aging, University of Utah, Salt Lake City, UT, USA
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Hammers DB, Stolwyk R, Harder L, Cullum CM. A survey of international clinical teleneuropsychology service provision prior to and in the context of COVID-19. Clin Neuropsychol 2020; 34:1267-1283. [PMID: 32844714 DOI: 10.1080/13854046.2020.1810323] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Objective: Despite expansion of telecommunication strategies across health services and data supporting feasibility of videoconference-based neuropsychological assessment, relatively little is known about teleneuropsychology (TeleNP) use in practice. The current COVID-19 pandemic provides an opportunity for greater use of TeleNP and understanding of neuropsychologists' experience with this unique assessment medium.Methods: During the course of a no-cost global webinar related to practical/ethical considerations of TeleNP practice, attendees were invited to engage in a 26-question survey about their TeleNP use and related COVID-19 concerns. TeleNP practices before the COVID-19 pandemic and early on during the global outbreak were queried among survey participants, along with examination of TeleNP intentions following COVID-19.Results: Multiple countries were represented across five continents, with two-thirds of respondents being from the United States. Approximately one-fourth of respondents reported using TeleNP for clinical interview, feedback, and intervention prior to the onset of the COVID-19 pandemic, and approximately one-tenth of individuals used TeleNP for testadministration. Increased use of TeleNP for clinical interview, feedback, and intervention was reported within the first few weeks of the global COVID-19 outbreak, though the use of TeleNP for testing remained relatively unchanged. Most respondents indicated an intention for future use of TeleNP.Conclusions: Our findings suggest the use of TeleNP is increasing, although use of remote TeleNP testing is still developing. Findings also illustrate increasing use of TeleNP in the context of the COVID-19 pandemic and encourage follow-up investigation in future studies to understand the changing practices and rates of TeleNP provision over time.
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Affiliation(s)
- Dustin B Hammers
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah.,Center on Aging, University of Utah
| | - Renerus Stolwyk
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia.,Monash-Epworth Rehabilitation Research Centre, Monash University, Melbourne, Australia
| | - Lana Harder
- Children's Health, Children's Medical Center, Dallas, Texas.,Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas.,Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - C Munro Cullum
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas.,Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas.,Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, Texas
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Hammers DB, Suhrie KR, Porter SM, Dixon AM, Duff K. Validation of one-year reliable change in the RBANS for community-dwelling older adults with amnestic mild cognitive impairment. Clin Neuropsychol 2020; 36:1304-1327. [PMID: 32819188 PMCID: PMC7909751 DOI: 10.1080/13854046.2020.1807058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
OBJECTIVE The current study sought to externally validate previously published standardized regression-based (SRB) equations for the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Indexes administered twice over a one-year period. Method: Hammers and colleagues' SRB prediction equations were applied to two independent samples of community-dwelling older adults with amnestic Mild Cognitive Impairment (MCI), including those recruited from the community (n = 64) and those recruited from a memory disorders clinic (n = 58). Results: While Observed Baseline and Observed Follow-up performances were generally comparable for both MCI samples over one year, both samples possessed significantly lower Observed One-Year Follow-up scores than were predicted based on Hammers and colleagues' development sample across many RBANS Indexes. Relatedly, both amnestic MCI samples possessed a greater percentage of participants either "declining" or failing to exhibit a long-term practice effect over one year relative to expectation across most Indexes. Further, the clinic-recruited amnestic MCI sample displayed worse baseline performances, smaller long-term practice effects, and greater proportions of individual participants exhibiting a decline across one year relative to the community amnestic MCI sample. Conclusions: These findings validate Hammers and colleagues' SRB prediction equations by (1) indicating their ability to identify clinically meaningful change across RBANS Indexes in independent samples, and (2) discriminating rates of cognitive change among cognitively nuanced samples.
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Affiliation(s)
- Dustin B Hammers
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA.,Center on Aging, University of Utah, Salt Lake City, UT, USA
| | - Kayla R Suhrie
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Sariah M Porter
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Ava M Dixon
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA.,Center on Aging, University of Utah, Salt Lake City, UT, USA
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43
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Spencer RJ, Gradwohl BD, Williams TF, Kordovski VM, Hammers DB. Developing learning slope scores for the repeatable battery for the assessment of neuropsychological status. Appl Neuropsychol Adult 2020; 29:584-590. [PMID: 32654521 DOI: 10.1080/23279095.2020.1791870] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Initial learning and learning slope are often acknowledged as important qualitative aspects of learning, but the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) contains discrete indices for neither. The traditional method of calculating learning slope involves a difference score between the last trial and first trial, which is referred to as raw learning score (RLS). However, this method does not account for initial Trial One performance and produces a ceiling effect that penalizes efficient first learners. We propose an alternative method of calculating learning score that accounts for initial learning performance, called learning ratio (LR), and we compared the psychometric and predictive properties of these methods. Performances from the List Learning and Story Memory subtests were used to create the indices, and composite learning scores were calculated by combining List Learning and Story Memory. The sample included 289 military veterans (mean age = 65.9 [12.6], education = 13.3 [2.4]), most of whom were male, undergoing neuropsychological assessments that included the RBANS. Results indicated that LR demonstrated superior correlations with criterion measures of memory when compared with RLS, and the LR composite score better discriminated between those with and without a neurocognitive diagnosis, AUC = 0.81 (0.76-0.87), than the RLS composite, AUC = 0.70 (0.64-0.76). We concluded that scores from the RBANS can be computed for initial learning and learning slope and that the LR method of calculating learning is superior to RLS in this older veteran sample.
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Affiliation(s)
- Robert J Spencer
- Mental Health, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
| | - Brian D Gradwohl
- Mental Health, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
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Abstract
Objective: In a meta-analysis examining practice effects on repeated neuropsychological testing, Calamia et al. (2012) provided information to predict practice effects in healthy and clinical samples across a range of cognitive domains. However, these estimates have not been validated.Method: This study used these prediction estimate calculations to predict follow-up scores across one year on a brief battery of neuropsychological tests in a sample of 93 older adults with amnestic mild cognitive impairment. The predicted follow-up scores were compared to observed follow-up scores.Results: Using Calamia et al. model's intercept, age, retest interval, clinical status, and specific cognitive tests, three of the seven observed follow-up scores in this cognitive battery were significantly lower than the Calamia et al. predicted follow-up scores. Differences between individual participants' observed and predicted follow-up scores were more striking. For example, on Delayed Recall of the Hopkins Verbal Learning Test - Revised, 40% of the sample had Calamia et al. predicted scores that were one or more standard deviations above their observed scores. These differences were most notable on tests that were not in Calamia et al.'s cognitive battery, suggesting the meta-analysis results may not generalize as well to other tests.Conclusions: Although Calamia et al. provided a method for predicting practice effects and follow-up scores, these results raise caution when using them in MCI, especially on cognitive tests that were not in their meta-analysis.
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Affiliation(s)
- Kevin Duff
- Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Dustin B Hammers
- Center for Alzheimer's Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
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Hammers DB, Kucera A, Spencer RJ, Abildskov TJ, Archibald ZG, Hoffman JM, Wilde EA. Examining the Relationship between a Verbal Incidental Learning Measure from the WAIS-IV and Neuroimaging Biomarkers for Alzheimer's Pathology. Dev Neuropsychol 2020; 45:95-109. [PMID: 32374196 DOI: 10.1080/87565641.2020.1762602] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Convergent validation of a verbal incidental learning (IL) task from the WAIS-IV using neuroimaging biomarkers is warranted to understand its sensitivity to Alzheimer's disease (AD) pathology. Fifty-five memory clinic patients aged 59 to 87 years received neuropsychological assessment, and measures of IL and quantitative brain imaging. Worse IL-Total Score and IL-Similarities performances were significantly associated with smaller hemispheric hippocampal volumes. IL measures were not significantly correlated with cerebral β-amyloid burden, though a trend was present and effect sizes were mild. These hippocampal volume results suggest that this IL task may be sensitive to AD pathology along the AD continuum.
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Affiliation(s)
- Dustin B Hammers
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah , Salt Lake City, UT, USA
| | - Amanda Kucera
- University of Utah Health Care , Salt Lake City, UT, USA
| | - Robert J Spencer
- Mental Health Service, VA Ann Arbor Healthcare System , Ann Arbor, MI, USA
| | - Tracy J Abildskov
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah , Salt Lake City, UT, USA
| | - Zane G Archibald
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah , Salt Lake City, UT, USA
| | - John M Hoffman
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah , Salt Lake City, UT, USA
| | - Elizabeth A Wilde
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah , Salt Lake City, UT, USA.,George E. Wahlen Veterans Affairs Medical Center , Salt Lake City, UT, USA
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Hammers DB, Suhrie KR, Porter SM, Dixon AM, Duff K. Generalizability of reliable change equations for the RBANS over one year in community-dwelling older adults. J Clin Exp Neuropsychol 2020; 42:394-405. [PMID: 32212958 DOI: 10.1080/13803395.2020.1740654] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Objective: Reliable change methods can assist neuropsychologists in determining whether observed changes in a patient's performance are clinically meaningful. The current study sought to validate previously published standardized regression-based (SRB) equations for the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Indexes and subtests.Methods: Duff and colleagues's SRB prediction equations, developed from 223 cognitively intact primary care patients, were applied to an independent sample of robustly cognitively intact (n = 129) community-dwelling older adults assessed with the RBANS twice over a one-year period.Results: Results suggest that the cognitively intact participants in the current validation sample possessed significantly better Observed Follow-up scores than was predicted based on Duff's developmental sample across most RBANS Indexes and many RBANS subtests, though significantly lower Observed Follow-up scores were observed for the Visuospatial/Constructional Index than was predicted. As a result of these findings, the current study calculated updated prediction algorithms for the RBANS Index and subtest scores from the sample of 129 cognitively intact participants.Conclusions: Duff's 2004 and 2005 SRB prediction equations for the RBANS Index and subtest scores failed to generalize to a sample of cognitively intact community-dwelling participants recruited from senior living centers and independent assisted living facilities. These updated SRB prediction equations - being developed from a more medically "clean" sample of cognitively intact older adults who remained stable over 12 months - have the potential to provide a more accurate assessment of reliable change in an individual patient.
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Affiliation(s)
- Dustin B Hammers
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA.,Center on Aging, University of Utah, Salt Lake City, UT, USA
| | - Kayla R Suhrie
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Sariah M Porter
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Ava M Dixon
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- Center for Alzheimer's Care, Imaging, and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA.,Center on Aging, University of Utah, Salt Lake City, UT, USA
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Hammers DB, Weisenbach S. Questioning the Effort Hypothesis That Depressed Patients Perform Disproportionately Worse on Effortful Cognitive Tasks. Percept Mot Skills 2020; 127:401-414. [PMID: 31928391 DOI: 10.1177/0031512519898356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The debate over Hasher and Zacks’ effort hypothesis—that performance on effortful tasks by patients with depression will be disproportionately worse than their performance on automatic tasks—shows a need for additional research to settle whether or not this notion is “clinical lore.” In this study, we categorized 285 outpatient recipients of neuropsychological evaluations into three groups—No Depression, Mild-to-Moderate Depression, and Severe Depression—based on their Beck Depression Inventory-2 self-reports. We then compared these groups’ performances on both “automatic” and “effortful” versions of the Ruff 2 & 7 Selective Attention Test Total Speed and Total Accuracy Indices, the Digit Span subtest from the Wechsler Adult Intellectual Scale—Fourth Edition, and Trail Making Test Parts A and B, using a two-way (3 × 2) mixed multivariate analysis of variance. Patients with Mild-to-Moderate Depression or Severe Depression performed disproportionately worse than patients with No Depression in our sample on more effortful versions of only one of the four attention or executive functioning measures (Trail Making Test). Thus, these data failed to fully support a hypothesis of disproportionately worse performance on more effortful tasks. While this study failed to negate the effort hypothesis in some specific instances, particularly for use in the Trail Making Test, there is cause for caution in routinely applying the effort hypothesis when interpreting test findings in most clinical settings and for most measures.
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Affiliation(s)
- Dustin B Hammers
- Department of Neurology, Center for Alzheimer's Care, Imaging, and Research, University of Utah, Salt Lake City, UT, USA
| | - Sara Weisenbach
- Department of Psychiatry, Renaissance School of Medicine, Stony Brook University, NY, USA
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48
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Hammers DB, Foster NL, Hoffman JM, Greene TH, Duff K. Neuropsychological, Psychiatric, and Functional Correlates of Clinical Trial Enrollment. J Prev Alzheimers Dis 2019; 6:242-247. [PMID: 31686096 DOI: 10.14283/jpad.2019.38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Screen failure rates in Alzheimer's disease (AD) clinical trial research are unsustainable, with participant recruitment being a top barrier to AD research progress. The purpose of this project was to understand the neuropsychological, psychiatric, and functional features of individuals who failed screening measures for AD trials. Previously collected clinical data from 38 patients (aged 50-83) screened for a specific industry-sponsored clinical trial of MCI/early AD (Biogen 221AD302, [EMERGE]) were analyzed to identify predictors of AD trial screen pass/fail status. Worse performance on non-memory cognitive domains like crystalized knowledge, executive functioning, and attention, and higher self-reported anxiety, was associated with failing the screening visit for the EMERGE AD clinical trial, whereas we were not able to detect a relationship between screening status and memory performance, self-reported depression, or self-reported daily functioning. By identifying predictors of AD trial screen passing/failure, this research may influence decision-making about which patients are most likely to successfully enroll in a trial, thereby potentially lowering participant burden, maximizing study resources, and reducing costs.
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Affiliation(s)
- D B Hammers
- Dustin B. Hammers, PhD, ABPP(CN), Center for Alzheimer's Care, Imaging and Research, University of Utah, Department of Neurology, 650 Komas Drive #106-A, Salt Lake City, UT 84108, Tel: 801-585-6546. Fax: 801-581-2483. E-mail:
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Duff K, Dalley BCA, Suhrie KR, Hammers DB. Predicting Premorbid Scores on the Repeatable Battery for the Assessment of Neuropsychological Status and their Validation in an Elderly Sample. Arch Clin Neuropsychol 2018; 34:395-402. [DOI: 10.1093/arclin/acy050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 05/01/2018] [Accepted: 05/15/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Kevin Duff
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Bonnie C A Dalley
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Kayla R Suhrie
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Dustin B Hammers
- Center for Alzheimer’s Care, Imaging and Research, Department of Neurology, University of Utah, Salt Lake City, UT, USA
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50
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Hammers DB, Atkinson TJ, Dalley BCA, Suhrie KR, Horn KP, Rasmussen KM, Beardmore BE, Burrell LD, Duff K, Hoffman JM. Amyloid Positivity Using [18F]Flutemetamol-PET and Cognitive Deficits in Nondemented Community-Dwelling Older Adults. Am J Alzheimers Dis Other Demen 2017; 32:320-328. [PMID: 28403622 DOI: 10.1177/1533317517698795] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Little research exists examining the relationship between beta-amyloid neuritic plaque density via [18F]flutemetamol binding and cognition; consequently, the purpose of the current study was to compare cognitive performances among individuals having either increased amyloid deposition (Flute+) or minimal amyloid deposition (Flute-). Twenty-seven nondemented community-dwelling adults over the age of 65 underwent [18F]flutemetamol amyloid-positron emission tomography imaging, along with cognitive testing using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and select behavioral measures. Analysis of variance was used to identify the differences among the cognitive and behavioral measures between Flute+/Flute- groups. Flute+ participants performed significantly worse than Flute- participants on RBANS indexes of immediate memory, language, delayed memory, and total scale score, but no significant group differences in the endorsed level of depression or subjective report of cognitive difficulties were observed. Although these results are preliminary, [18F]flutemetamol accurately tracks cognition in a nondemented elderly sample, which may allow for better prediction of cognitive decline in late life.
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Affiliation(s)
- Dustin B Hammers
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Taylor J Atkinson
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Bonnie C A Dalley
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Kayla R Suhrie
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Kevin P Horn
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kelli M Rasmussen
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Britney E Beardmore
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Lance D Burrell
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- 1 Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - John M Hoffman
- 2 Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
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