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van der Veere PJ, Hoogland J, Visser LNC, Van Harten AC, Rhodius-Meester HF, Sikkes SAM, Venkatraghavan V, Barkhof F, Teunissen CE, van de Giessen E, Berkhof J, Van Der Flier WM. Predicting Cognitive Decline in Amyloid-Positive Patients With Mild Cognitive Impairment or Mild Dementia. Neurology 2024; 103:e209605. [PMID: 38986053 PMCID: PMC11238942 DOI: 10.1212/wnl.0000000000209605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Cognitive decline rates in Alzheimer disease (AD) vary greatly. Disease-modifying treatments may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid-positive patients with mild cognitive impairment (MCI) or mild dementia. METHODS From the Amsterdam Dementia Cohort, we selected amyloid-positive participants with MCI or mild dementia and at least 2 longitudinal Mini-Mental State Examination (MMSE) measurements. Amyloid positivity was based on CSF AD biomarker concentrations or amyloid PET. We used linear mixed modeling to predict MMSE over time, describing trajectories using a cubic time curve and interactions between linear time and the baseline predictors age, sex, baseline MMSE, APOE ε4 dose, CSF β-amyloid (Aβ) 1-42 and pTau, and MRI total brain and hippocampal volume. Backward selection was used to reduce model complexity. These models can predict MMSE over follow-up or the time to an MMSE value. MCI and mild dementia were modeled separately. Internal 5-fold cross-validation was performed to calculate the explained variance (R2). RESULTS In total, 961 participants were included (age 65 ± 7 years, 49% female), 310 had MCI (MMSE 26 ± 2) and 651 had mild dementia (MMSE 22 ± 4), with 4 ± 2 measurements over 2 (interquartile range 1-4) years. Cognitive decline rates increased over time for both MCI and mild dementia (model comparisons linear vs squared vs cubic time fit; p < 0.05 favoring a cubic fit). For MCI, backward selection retained age, sex, and CSF Aβ1-42 and pTau concentrations as time-varying effects altering the MMSE trajectory. For mild dementia, retained time-varying effects were Aβ1-42, age, APOE ε4, and baseline MMSE. R2 was 0.15 for the MCI model and 0.26 for mild dementia in internal cross-validation. A hypothetical patient with MCI, baseline MMSE 28, and CSF Aβ1-42 of 925 pg/mL was predicted to reach an MMSE of 20 after 6.0 years (95% CI 5.4-6.7) and after 8.6 years with a hypothetical treatment reducing decline by 30%. DISCUSSION We constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and the remaining uncertainty and aid in conversations about individualized potential treatment effects.
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Affiliation(s)
- Pieter J van der Veere
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Jeroen Hoogland
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Leonie N C Visser
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Argonde C Van Harten
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Hanneke F Rhodius-Meester
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Sietske A M Sikkes
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Vikram Venkatraghavan
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Frederik Barkhof
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Charlotte E Teunissen
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Elsmarieke van de Giessen
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Johannes Berkhof
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
| | - Wiesje M Van Der Flier
- From the Alzheimer Center and Department of Neurology (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., S.A.M.S., V.V., W.M.V.D.F.), and Department of Epidemiology and Biostatistics (P.J.v.d.V., J.H., L.N.C.V., J.B., W.M.V.D.F.), Amsterdam Neuroscience, VU University Medical Center; Amsterdam Neuroscience (P.J.v.d.V., L.N.C.V., A.C.V.H., H.F.R.-M., V.V., C.E.T., E.G., W.M.V.D.F.), Neurodegeneration the Netherlands; Division of Clinical Geriatrics (L.N.C.V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Medical Psychology (L.N.C.V.), Amsterdam UMC Location AMC, University of Amsterdam; Amsterdam Public Health (L.N.C.V.), Quality of Care, Personalized Medicine; Internal Medicine (H.F.R.-M.), Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc; Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Movement and Behavioral Sciences, VU University; Department of Radiology & Nuclear Medicine (F.B., E.G.), Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; and Neurochemistry Laboratory and Biobank (C.E.T.), Department of Clinical Chemistry, Amsterdam Neuroscience, VU University Medical Center, the Netherlands
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Flyborg J, Renvert S, Anderberg P, Sanmartin-Berglund J. The long-term effect on oral health and quality of life using a powered toothbrush in individuals with mild cognitive impairment. An intervention trial. SPECIAL CARE IN DENTISTRY 2024. [PMID: 38994574 DOI: 10.1111/scd.13040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND The number of older individuals with mild cognitive impairment and neurocognitive diseases is increasing, which may rapidly deteriorate oral health and Quality of life. Therefore, removing dental biofilm is essential for maintaining good oral health. The present study aimed to investigate whether introducing a powered toothbrush reduces the presence of dental plaque, bleeding on probing, and periodontal pockets ≥4 mm, leading to maintained or improved oral health and improved Quality of life in a group of older individuals with mild cognitive impairment. METHODS Two hundred and thirteen individuals aged 55 or older living without official home care with a Mini-Mental State Examination (MMSE) score between 20 and 28 and a history of memory problems in the previous 6 months were recruited and screened for the study. The individuals received a powered toothbrush and thorough instructions on how to use it. Clinical oral examinations, Quality of life examinations, and MMSE tests were conducted at baseline, 6, 12, and 24 months. The intervention group was compared to control groups at baseline and 24-month examination. It was divided into an MMSE high group with a score of more than 26 and an MMSE low group with a score of 26 and lower or decreasing two steps or more for 12 months. RESULTS PI, BOP, and PPD≥4 mm improved continuously in both MMSE groups during the 24 months of the study. The values for QoL-AD deteriorated over time, while the oral health-related Quality of life did not show any statistically significant changes. CONCLUSIONS Introducing a powered toothbrush improved PI, BOP, and PPD≥4 mm over 24 months, even among individuals with low or declining MMSE scores. Improved oral health is associated with a preserved OHR-QoL.
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Affiliation(s)
- Johan Flyborg
- Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden
| | - Stefan Renvert
- Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden
- Faculty of Health Sciences, Kristianstad University, Kristianstad, Sweden
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden
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Wang X, Zhou S, Ye N, Li Y, Zhou P, Chen G, Hu H. Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal. BMC Geriatr 2024; 24:531. [PMID: 38898411 PMCID: PMC11188292 DOI: 10.1186/s12877-024-05044-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 05/06/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Mild cognitive impairment has received widespread attention as a high-risk population for Alzheimer's disease, and many studies have developed or validated predictive models to assess it. However, the performance of the model development remains unknown. OBJECTIVE The objective of this review was to provide an overview of prediction models for the risk of Alzheimer's disease dementia in older adults with mild cognitive impairment. METHOD PubMed, EMBASE, Web of Science, and MEDLINE were systematically searched up to October 19, 2023. We included cohort studies in which risk prediction models for Alzheimer's disease dementia in older adults with mild cognitive impairment were developed or validated. The Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed to assess model bias and applicability. Random-effects models combined model AUCs and calculated (approximate) 95% prediction intervals for estimations. Heterogeneity across studies was evaluated using the I2 statistic, and subgroup analyses were conducted to investigate sources of heterogeneity. Additionally, funnel plot analysis was utilized to identify publication bias. RESULTS The analysis included 16 studies involving 9290 participants. Frequency analysis of predictors showed that 14 appeared at least twice and more, with age, functional activities questionnaire, and Mini-mental State Examination scores of cognitive functioning being the most common predictors. From the studies, only two models were externally validated. Eleven studies ultimately used machine learning, and four used traditional modelling methods. However, we found that in many of the studies, there were problems with insufficient sample sizes, missing important methodological information, lack of model presentation, and all of the models were rated as having a high or unclear risk of bias. The average AUC of the 15 best-developed predictive models was 0.87 (95% CI: 0.83, 0.90). DISCUSSION Most published predictive modelling studies are deficient in rigour, resulting in a high risk of bias. Upcoming research should concentrate on enhancing methodological rigour and conducting external validation of models predicting Alzheimer's disease dementia. We also emphasize the importance of following the scientific method and transparent reporting to improve the accuracy, generalizability and reproducibility of study results. REGISTRATION This systematic review was registered in PROSPERO (Registration ID: CRD42023468780).
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Affiliation(s)
- Xiaotong Wang
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Shi Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Niansi Ye
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Yucan Li
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Pengjun Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Gao Chen
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Hui Hu
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China.
- Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Wuhan, China.
- Hubei Shizhen Laboratory, Wuhan, China.
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Cabreira V, Alty J, Antic S, Araújo R, Aybek S, Ball HA, Baslet G, Bhome R, Coebergh J, Dubois B, Edwards M, Filipović SR, Frederiksen KS, Harbo T, Hayhow B, Howard R, Huntley J, Isaacs J, LaFrance WC, Larner AJ, Di Lorenzo F, Main J, Mallam E, Marra C, Massano J, McGrath ER, McWhirter L, Moreira IP, Nobili F, Pennington C, Tábuas-Pereira M, Perez DL, Popkirov S, Rayment D, Rossor M, Russo M, Santana I, Schott J, Scott EP, Taipa R, Tinazzi M, Tomic S, Toniolo S, Tørring CW, Wilkinson T, Frostholm L, Stone J, Carson A. Perspectives on the diagnosis and management of functional cognitive disorder: An international Delphi study. Eur J Neurol 2024:e16318. [PMID: 38700361 DOI: 10.1111/ene.16318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/18/2024] [Accepted: 04/11/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND Current proposed criteria for functional cognitive disorder (FCD) have not been externally validated. We sought to analyse the current perspectives of cognitive specialists in the diagnosis and management of FCD in comparison with neurodegenerative conditions. METHODS International experts in cognitive disorders were invited to assess seven illustrative clinical vignettes containing history and bedside characteristics alone. Participants assigned a probable diagnosis and selected the appropriate investigation and treatment. Qualitative, quantitative and inter-rater agreement analyses were undertaken. RESULTS Eighteen diagnostic terminologies were assigned by 45 cognitive experts from 12 countries with a median of 13 years of experience, across the seven scenarios. Accurate discrimination between FCD and neurodegeneration was observed, independently of background and years of experience: 100% of the neurodegenerative vignettes were correctly classified and 75%-88% of the FCD diagnoses were attributed to non-neurodegenerative causes. There was <50% agreement in the terminology used for FCD, in comparison with 87%-92% agreement for neurodegenerative syndromes. Blood tests and neuropsychological evaluation were the leading diagnostic modalities for FCD. Diagnostic communication, psychotherapy and psychiatry referral were the main suggested management strategies in FCD. CONCLUSIONS Our study demonstrates the feasibility of distinguishing between FCD and neurodegeneration based on relevant patient characteristics and history details. These characteristics need further validation and operationalisation. Heterogeneous labelling and framing pose clinical and research challenges reflecting a lack of agreement in the field. Careful consideration of FCD diagnosis is advised, particularly in the presence of comorbidities. This study informs future research on diagnostic tools and evidence-based interventions.
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Affiliation(s)
- Verónica Cabreira
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jane Alty
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia
| | - Sonja Antic
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Rui Araújo
- Department of Neurology, Centro Hospitalar Universitário São João, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine University of Porto, Porto, Portugal
| | - Selma Aybek
- Neurology, Faculty of Sciences and Medicine, Fribourg University, Fribourg, Switzerland
| | - Harriet A Ball
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Gaston Baslet
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Rohan Bhome
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Jan Coebergh
- Department of Neurology, St George's University of London, London, UK
| | - Bruno Dubois
- Department of Neurology, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), AP-HP, Brain Institute, Sorbonne University, Paris, France
| | - Mark Edwards
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry Psychology and Neurosciences, Kings College London, London, UK
| | - Saša R Filipović
- University of Belgrade Institute for Medical Research, Belgrade, Serbia
| | - Kristian Steen Frederiksen
- Clinical Trial Unit, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Harbo
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Bradleigh Hayhow
- Department of Neurology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
- School of Medicine, University of Notre Dame Australia, Fremantle, Western Australia, Australia
| | - Robert Howard
- Division of Psychiatry, University College London, London, UK
| | - Jonathan Huntley
- Division of Psychiatry, University College London, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
| | - Jeremy Isaacs
- Department of Neurology, St George's University of London, London, UK
| | - William Curt LaFrance
- Alpert Medical School, Brown University, Providence, Rhode Island, USA
- Neuropsychiatry and Behavioral Neurology, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Andrew J Larner
- Cognitive Function Clinic, Walton Centre for Neurology and Neurosurgery, Liverpool, UK
| | - Francesco Di Lorenzo
- Department of Clinical and Behavioural Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - James Main
- Bristol Dementia Wellbeing Service, Devon Partnership NHS Trust, Bristol, UK
| | | | - Camillo Marra
- Department of Neuroscience, Catholic University of the Sacred Heart, Memory Clinic - Fondazione Policlinico Agostino Gemelli IRCCS, Rome, Italy
| | - João Massano
- Department of Neurology, Centro Hospitalar Universitário São João, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine University of Porto, Porto, Portugal
| | - Emer R McGrath
- School of Medicine, University of Galway, Galway, Ireland
| | - Laura McWhirter
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Isabel Portela Moreira
- Neurology Department, Private Hospital of Gaia of the Trofa Saúde Group, Vila Nova de Gaia, Portugal
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Catherine Pennington
- Clinical Lecturer, University of Edinburgh, Edinburgh, UK
- Neurology Department, NHS Forth Valley, Larbert, UK
- Department of Clinical Neurosciences, NHS Lothian, Edinburgh, UK
| | - Miguel Tábuas-Pereira
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Praceta Prof. Mota Pinto, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - David L Perez
- Department of Neurology and Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stoyan Popkirov
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Dane Rayment
- Rosa Burden Centre for Neuropsychiatry, Southmead Hospital, Bristol, UK
| | - Martin Rossor
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Isabel Santana
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Jonathan Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Emmi P Scott
- Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ricardo Taipa
- Neuropathology Department, Centro Hospitalar Universitário de Santo António, Porto, Portugal
| | - Michele Tinazzi
- Department of Neurosciences, Biomedicine and Movement, University of Verona, Verona, Italy
| | - Svetlana Tomic
- Department of Neurology, University Hospital Center Osijek, Medical School on University of Osijek, Osijek, Croatia
| | - Sofia Toniolo
- Cognitive Disorder Clinic, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Tim Wilkinson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Lisbeth Frostholm
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Functional Disorders and Psychosomatics, Aarhus University Hospital, Aarhus, Denmark
| | - Jon Stone
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Alan Carson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Griswold AJ, Rajabli F, Gu T, Arvizu J, Golightly CG, Whitehead PL, Hamilton-Nelson KL, Adams LD, Sanchez JJ, Mena PR, Starks TD, Illanes-Manrique M, Silva C, Bush WS, Cuccaro ML, Vance JM, Cornejo-Olivas MR, Feliciano-Astacio BE, Byrd GS, Beecham GW, Haines JL, Pericak-Vance MA. Generalizability of Tau and Amyloid Plasma Biomarkers in Alzheimer's Disease Cohorts of Diverse Genetic Ancestries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.10.24305617. [PMID: 38645114 PMCID: PMC11030471 DOI: 10.1101/2024.04.10.24305617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Introduction Plasma phosphorylated threonine-181 of Tau and amyloid beta are biomarkers for differential diagnosis and preclinical detection of Alzheimer disease (AD). Given differences in AD risk across diverse populations, generalizability of existing biomarker data is not assured. Methods In 2,086 individuals of diverse genetic ancestries (African American, Caribbean Hispanic, and Peruvians) we measured plasma pTau-181 and Aβ42/Aβ40. Differences in biomarkers between cohorts and clinical diagnosis groups and the potential discriminative performance of the two biomarkers were assessed. Results pTau-181 and Aβ42/Aβ40 were consistent across cohorts. Higher levels of pTau181 were associated with AD while Aβ42/Aβ40 had minimal differences. Correspondingly, pTau-181 had greater predictive value than Aβ42/Aβ40, however, the area under the curve differed between cohorts. Discussion pTau-181 as a plasma biomarker for clinical AD is generalizable across genetic ancestries, but predictive value may differ. Combining genomic and biomarker data from diverse individuals will increase understanding of genetic risk and refine clinical diagnoses.
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Affiliation(s)
- Anthony J Griswold
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Farid Rajabli
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Tianjie Gu
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Jamie Arvizu
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Charles G Golightly
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Patrice L Whitehead
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Kara L Hamilton-Nelson
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Jose Javier Sanchez
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Pedro R Mena
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
| | - Takiyah D Starks
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC, 27102, USA
| | | | - Concepcion Silva
- Department of Internal Medicine, Universidad Central Del Caribe, Bayamón, Puerto Rico, 00960, USA
| | - William S Bush
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
- Cleveland Institute for Computational Biology, Cleveland, OH, 44106, USA
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Mario R Cornejo-Olivas
- Neurogenetics Research Center, Instituto Nacional de Ciencias Neurologicas, Lima, 15003, Peru
| | | | - Goldie S Byrd
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC, 27102, USA
| | - Gary W Beecham
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
| | - Jonathan L Haines
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
- Cleveland Institute for Computational Biology, Cleveland, OH, 44106, USA
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, 33136, USA
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Aspö M, Sundell M, Protsiv M, Wiggenraad F, Rydén M, Mangialasche F, Kivipelto M, Visser LNC. The expectations and experiences of patients regarding the diagnostic workup at a specialized memory clinic: An interview study. Health Expect 2024; 27:e14021. [PMID: 38515262 PMCID: PMC10958124 DOI: 10.1111/hex.14021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/07/2024] [Accepted: 03/10/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Because of the shift towards earlier diagnosis of dementia and/or Alzheimer's disease (AD), increasing numbers of individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are seen in memory clinics. Yet, evidence indicates that there is room for improvement when it comes to tailoring of the diagnostic work-up to the needs of individual patients. To optimize the quality of care, we explored patients' perspectives regarding the diagnostic work-up at a specialized memory clinic. METHODS This interview study was conducted at Karolinska University Hospital (Sweden). The comprehensive diagnostic work-up for dementia at the memory clinic in Solna is conducted within 1 week. A sample of 15 patients (8 female; mean age = 61 years [range 50-72]; 11 SCD, 1 MCI and 3 AD dementia) was purposively selected for a series of three semistructured interviews, focussing on (1) needs and expectations (during the week of diagnostic testing), (2) experiences (within 2 weeks after test-result disclosure) and (3) reflections and evaluation (3 months after disclosure). Transcribed audio-recorded data were analyzed using thematic content analysis (using MaxQDA software). RESULTS Three key themes were identified: (1) the expectations and motivations of individuals for visiting the memory clinic strongly impacted their experience; (2) the diagnostic work-up impacted individuals psychosocially and (3) the diagnostic work-up provided an opportunity to motivate individuals to adopt a healthier lifestyle. CONCLUSION Our findings underscore the importance of enquiring about the expectations and needs of individuals referred to a specialized memory clinic, allowing for expectation management and personalization of provided information/advice, and potentially informing the selection of patients in need of a comprehensive diagnostic work-up. Structural guidance might be needed to support those with SCD and MCI to help them cope with uncertainty, potentially resolve their issues, and/or stimulate brain health. PATIENT OR PUBLIC CONTRIBUTION We gathered the perspectives of 15 individuals who had been referred to the memory clinic at three different time points through semistructured interviews, and these interviews were the primary data source.
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Affiliation(s)
- Malin Aspö
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer ResearchKarolinska InstitutetStockholmSweden
- Theme Inflammation and Aging, Medical Unit AgingKarolinska University HospitalStockholmSweden
| | - Maria Sundell
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer ResearchKarolinska InstitutetStockholmSweden
- Theme Inflammation and Aging, Medical Unit AgingKarolinska University HospitalStockholmSweden
| | - Myroslava Protsiv
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer ResearchKarolinska InstitutetStockholmSweden
| | - Fleur Wiggenraad
- Theme Inflammation and Aging, Medical Unit AgingKarolinska University HospitalStockholmSweden
| | - Marie Rydén
- Theme Inflammation and Aging, Medical Unit AgingKarolinska University HospitalStockholmSweden
| | - Francesca Mangialasche
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer ResearchKarolinska InstitutetStockholmSweden
- Theme Inflammation and Aging, Medical Unit AgingKarolinska University HospitalStockholmSweden
| | - Miia Kivipelto
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer ResearchKarolinska InstitutetStockholmSweden
- Theme Inflammation and Aging, Medical Unit AgingKarolinska University HospitalStockholmSweden
- The Ageing Epidemiology Research Unit, School of Public HealthImperial College LondonLondonUK
- Institute of Public Health and Clinical NutritionUniversity of Eastern FinlandKuopioFinland
| | - Leonie N. C. Visser
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer ResearchKarolinska InstitutetStockholmSweden
- Department of Medical Psychology, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Amsterdam Public Health Research InstituteQuality of CareAmsterdamThe Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical CenterVU University Medical CenterAmsterdamThe Netherlands
- Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands
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Kim SK, Kim H, Kim SH, Kim JB, Kim L. Electroencephalography-based classification of Alzheimer's disease spectrum during computer-based cognitive testing. Sci Rep 2024; 14:5252. [PMID: 38438453 PMCID: PMC10912091 DOI: 10.1038/s41598-024-55656-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive disease leading to cognitive decline, and to prevent it, researchers seek to diagnose mild cognitive impairment (MCI) early. Particularly, non-amnestic MCI (naMCI) is often mistaken for normal aging as the representative symptom of AD, memory decline, is absent. Subjective cognitive decline (SCD), an intermediate step between normal aging and MCI, is crucial for prediction or early detection of MCI, which determines the presence of AD spectrum pathology. We developed a computer-based cognitive task to classify the presence or absence of AD pathology and stage within the AD spectrum, and attempted to perform multi-stage classification through electroencephalography (EEG) during resting and memory encoding state. The resting and memory-encoding states of 58 patients (20 with SCD, 10 with naMCI, 18 with aMCI, and 10 with AD) were measured and classified into four groups. We extracted features that could reflect the phase, spectral, and temporal characteristics of the resting and memory-encoding states. For the classification, we compared nine machine learning models and three deep learning models using Leave-one-subject-out strategy. Significant correlations were found between the existing neurophysiological test scores and performance of our computer-based cognitive task for all cognitive domains. In all models used, the memory-encoding states realized a higher classification performance than resting states. The best model for the 4-class classification was cKNN. The highest accuracy using resting state data was 67.24%, while it was 93.10% using memory encoding state data. This study involving participants with SCD, naMCI, aMCI, and AD focused on early Alzheimer's diagnosis. The research used EEG data during resting and memory encoding states to classify these groups, demonstrating the significance of cognitive process-related brain waves for diagnosis. The computer-based cognitive task introduced in the study offers a time-efficient alternative to traditional neuropsychological tests, showing a strong correlation with their results and serving as a valuable tool to assess cognitive impairment with reduced bias.
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Affiliation(s)
- Seul-Kee Kim
- Bionics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sang Hee Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
| | - Laehyun Kim
- Bionics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea.
- Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, Republic of Korea.
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8
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Hill-Jarrett TG, Choi M, Buto PT, Miramontes S, Thomas MD, Yang Y, Kim MH, Sims KD, Glymour MM. Associations of Everyday and Lifetime Experiences of Discrimination With Willingness to Undergo Alzheimer Disease Predictive Testing. Neurology 2024; 102:e208005. [PMID: 38266219 DOI: 10.1212/wnl.0000000000208005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/13/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Rapid developments in Alzheimer disease (AD) biomarker research suggest that predictive testing may become widely available. To ensure equal access to AD predictive testing, it is important to understand factors that affect testing interest. Discrimination may influence attitudes toward AD testing, particularly among racially and ethnically minoritized populations, because of structural racism in health care systems. This study examined whether everyday or lifetime discrimination experiences shape interest in AD predictive testing. METHODS In the 2010 and 2012 biennial Health and Retirement Study waves, respondents were randomly selected to complete questions on interest in receiving free testing that could determine whether they would develop AD in the future. The exposures were everyday discrimination (6 items) and lifetime discrimination (7 items); both were transformed into a binary variable. Logistic regression models predicting interest in AD testing were controlled for deciles of propensity scores for each discrimination measure. Odds ratios were re-expressed as risk differences (RDs). RESULTS Our analytic sample included 1,499 respondents. The mean age was 67 (SD = 10.2) years, 57.4% were women, 65.7% were White, and 80% endorsed interest in AD predictive testing. Most of the participants (54.7%) experienced everyday discrimination in at least one domain; 24.1% experienced major lifetime discrimination in at least one domain. Those interested in predictive testing were younger (66 vs 70 years) and more likely to be Black (20% vs 15%) or Latinx (14% vs 8%) than participants uninterested in testing. The probability of wanting an AD test was not associated with discrimination for Black (RD everyday discrimination = -0.026; 95% CI [-0.081 to 0.029]; RD lifetime discrimination = -0.012; 95% CI [-0.085 to 0.063]) or Latinx (RD everyday discrimination = -0.023, 95% CI [-0.082 to 0.039]; RD lifetime discrimination = -0.011; 95% CI [-0.087 to 0.064]) participants. DISCUSSION Despite historical and contemporary experiences of discrimination, Black and Latinx individuals express interest in AD testing. However, Black and Latinx individuals remain underrepresented in AD research, including research on AD testing. Interest in personalized information about dementia risk may be a pathway to enhance their inclusion in research and clinical trials.
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Affiliation(s)
- Tanisha G Hill-Jarrett
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - Minhyuk Choi
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - Peter T Buto
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - Silvia Miramontes
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - Marilyn D Thomas
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - Yulin Yang
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - Min Hee Kim
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - Kendra D Sims
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
| | - M Maria Glymour
- From the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco
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9
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Platero C, Tohka J, Strange B. Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients. Curr Alzheimer Res 2024; 20:778-790. [PMID: 38425106 DOI: 10.2174/0115672050295317240223162312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Mild Cognitive Impairment (MCI) usually precedes the symptomatic phase of dementia and constitutes a window of opportunities for preventive therapies. OBJECTIVES The objective of this study was to predict the time an MCI patient has left to reach dementia and obtain the most likely natural history in the progression of MCI towards dementia. METHODS This study was conducted on 633 MCI patients and 145 subjects with dementia through 4726 visits over 15 years from Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A combination of data from AT(N) profiles at baseline and longitudinal predictive modeling was applied. A data-driven approach was proposed for categorical diagnosis prediction and timeline estimation of cognitive decline progression, which combined supervised and unsupervised learning techniques. RESULTS A reduced vector of only neuropsychological measures was selected for training the models. At baseline, this approach had high performance in detecting subjects at high risk of converting from MCI to dementia in the coming years. Furthermore, a Disease Progression Model (DPM) was built and also verified using three metrics. As a result of the DPM focused on the studied population, it was inferred that amyloid pathology (A+) appears about 7 years before dementia, and tau pathology (T+) and neurodegeneration (N+) occur almost simultaneously, between 3 and 4 years before dementia. In addition, MCI-A+ subjects were shown to progress more rapidly to dementia compared to MCI-A- subjects. CONCLUSION Based on proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicated that only a single cerebrospinal fluid sample is necessary during the prodromal phase of AD. Prediction from MCI into dementia and its timeline can be achieved exclusively through neuropsychological measures.
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Affiliation(s)
- Carlos Platero
- Health Science Technology Group, Technical University of Madrid, 28012 Madrid, Spain
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, FI-70211 Kuopio, Finland
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, CTB, Technical University of Madrid, IdISSC, Madrid, Spain
- Alzheimer Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid, Spain
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10
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Dai Y, Hsu YC, Fernandes BS, Zhang K, Li X, Enduru N, Liu A, Manuel AM, Jiang X, Zhao Z. Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach. J Alzheimers Dis 2024; 97:1807-1827. [PMID: 38306043 DOI: 10.3233/jad-231020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Background The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yu-Chun Hsu
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Brisa S Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kai Zhang
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoyang Li
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Astrid M Manuel
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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11
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Sánchez-Ortí JV, Correa-Ghisays P, Balanzá-Martínez V, Selva-Vera G, Vila-Francés J, Magdalena-Benedito R, San-Martin C, Victor VM, Escribano-Lopez I, Hernandez-Mijares A, Vivas-Lalinde J, Crespo-Facorro B, Tabarés-Seisdedos R. Inflammation and lipid metabolism as potential biomarkers of memory impairment across type 2 diabetes mellitus and severe mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110817. [PMID: 37327846 DOI: 10.1016/j.pnpbp.2023.110817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/20/2023] [Accepted: 06/12/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Neurocognitive impairment is a transdiagnostic feature across several psychiatric and cardiometabolic conditions. The relationship between inflammatory and lipid metabolism biomarkers and memory performance is not fully understood. This study aimed to identify peripheral biomarkers suitable to signal memory decline from a transdiagnostic and longitudinal perspective. METHODS Peripheral blood biomarkers of inflammation, oxidative stress and lipid metabolism were assessed twice over a 1-year period in 165 individuals, including 30 with schizophrenia (SZ), 42 with bipolar disorder (BD), 35 with major depressive disorder (MDD), 30 with type 2 diabetes mellitus (T2DM), and 28 healthy controls (HCs). Participants were stratified by memory performance quartiles, taking as a reference their global memory score (GMS) at baseline, into categories of high memory (H; n = 40), medium to high memory (MH; n = 43), medium to low memory (ML; n = 38) and low memory (L; n = 44). Exploratory and confirmatory factorial analysis, mixed one-way analysis of covariance and discriminatory analyses were performed. RESULTS L group was significantly associated with higher levels of tumor necrosis factor-alpha (TNF-α) and lower levels of apolipoprotein A1 (Apo-A1) compared to those from the MH and H groups (p < 0.05; η2p = 0.06-0.09), with small to moderate effect sizes. Moreover, the combination of interleukin-6 (IL-6), TNF-α, c-reactive protein (CRP), Apo-A1 and Apo-B compounded the transdiagnostic model that best discriminated between groups with different degrees of memory impairment (χ2 = 11.9-49.3, p < 0.05-0.0001). CONCLUSIONS Inflammation and lipid metabolism seem to be associated with memory across T2DM and severe mental illnesses (SMI). A panel of biomarkers may be a useful approach to identify individuals at greater risk of neurocognitive impairment. These findings may have a potential translational utility for early intervention and advance precision medicine in these disorders.
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Affiliation(s)
- Joan Vicent Sánchez-Ortí
- INCLIVA - Health Research Institute, Valencia, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Faculty of Psychology, University of Valencia, Valencia, Spain
| | - Patricia Correa-Ghisays
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Faculty of Psychology, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain.
| | - Vicent Balanzá-Martínez
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain; Mental Health Unit of Catarroja, Valencia, Spain.
| | - Gabriel Selva-Vera
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Joan Vila-Francés
- IDAL - Intelligent Data Analysis Laboratory, University of Valencia, Valencia, Spain
| | | | - Constanza San-Martin
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Víctor M Victor
- Service of Endocrinology and Nutrition, University Hospital Dr. Peset, Spain; Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia, Spain; Department of Physiology, University of Valencia, Valencia, Spain
| | | | | | | | - Benedicto Crespo-Facorro
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; Department of Psychiatry, Faculty of Medicine, University of Sevilla, HU Virgen del Rocío IBIS, Spain
| | - Rafael Tabarés-Seisdedos
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
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12
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Jin S, Li C, Miao J, Sun J, Yang Z, Cao X, Sun K, Liu X, Ma L, Xu X, Liu Z. Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study. J Am Med Dir Assoc 2023; 24:1959-1966.e7. [PMID: 37716705 DOI: 10.1016/j.jamda.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 09/18/2023]
Abstract
OBJECTIVES Mild cognitive impairment (MCI) is a transitional stage between normal cognitive aging and dementia that increases the risk of progressive cognitive decline. Early prediction of MCI could be beneficial for identifying vulnerable individuals in the community and planning primary and secondary prevention to reduce the incidence of MCI. DESIGN A narrative review and cohort study. SETTING AND PARTICIPANTS We review the MCI prediction based on the assessment of sociodemographic factors. We included participants from 3 surveys: 8915 from wave 2011/2012 of the China Health and Retirement Longitudinal Study (CHARLS), 9765 from the 2011 Chinese Longitudinal Healthy Longevity Survey (CLHLS), and 1823 from the 2014 Rugao Longevity and Ageing Study (RuLAS). METHODS We searched in PubMed, Embase, and Web of Science Core Collection between January 1, 2019, and December 30, 2022. To construct the composite risk score, a multivariate Cox proportional hazards regression model was used. The performance of the score was assessed using receiver operating characteristic (ROC) curves. Furthermore, the composite risk score was validated in 2 longitudinal cohorts, CLHLS and RuLAS. RESULTS We concluded on 20 articles from 892 available. The results suggested that the previous models suffered from several defects, including overreliance on cross-sectional data, low predictive utility, inconvenient measurement, and inapplicability to developing countries. Our empirical work suggested that the area under the curve for a 5-year MCI prediction was 0.861 in CHARLS, 0.797 in CLHLS, and 0.823 in RuLAS. We designed a publicly available online tool for this composite risk score. CONCLUSIONS AND IMPLICATIONS Attention to these sociodemographic factors related to the incidence of MCI can be beneficially incorporated into the current work, which will set the stage for better early prediction of MCI before its incidence and for reducing the burden of the disease.
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Affiliation(s)
- Shuyi Jin
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chenxi Li
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jiani Miao
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingyi Sun
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhenqing Yang
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xingqi Cao
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kaili Sun
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lina Ma
- Department of Geriatrics, Xuanwu Hospital Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Xin Xu
- Department of Big Data in Health Science School of Public Health, and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, School of Medicine, Zhejiang University, China.
| | - Zuyun Liu
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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13
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Bivona G, Iemmolo M, Ghersi G. Cerebrospinal and Blood Biomarkers in Alzheimer's Disease: Did Mild Cognitive Impairment Definition Affect Their Clinical Usefulness? Int J Mol Sci 2023; 24:16908. [PMID: 38069230 PMCID: PMC10706963 DOI: 10.3390/ijms242316908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Despite Alzheimer's Disease (AD) being known from the times of Alois Alzheimer, who lived more than one century ago, many aspects of the disease are still obscure, including the pathogenesis, the clinical spectrum definition, and the therapeutic approach. Well-established biomarkers for AD come from the histopathological hallmarks of the disease, which are Aβ and phosphorylated Tau protein aggregates. Consistently, cerebrospinal fluid (CSF) Amyloid β (Aβ) and phosphorylated Tau level measurements are currently used to detect AD presence. However, two central biases affect these biomarkers. Firstly, incomplete knowledge of the pathogenesis of diseases legitimates the search for novel molecules that, reasonably, could be expressed by neurons and microglia and could be detected in blood simpler and earlier than the classical markers and in a higher amount. Further, studies have been performed to evaluate whether CSF biomarkers can predict AD onset in Mild Cognitive Impairment (MCI) patients. However, the MCI definition has changed over time. Hence, the studies on MCI patients seem to be biased at the beginning due to the imprecise enrollment and heterogeneous composition of the miscellaneous MCI subgroup. Plasma biomarkers and novel candidate molecules, such as microglia biomarkers, have been tentatively investigated and could represent valuable targets for diagnosing and monitoring AD. Also, novel AD markers are urgently needed to identify molecular targets for treatment strategies. This review article summarizes the main CSF and blood AD biomarkers, underpins their advantages and flaws, and mentions novel molecules that can be used as potential biomarkers for AD.
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Affiliation(s)
- Giulia Bivona
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy
| | - Matilda Iemmolo
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, 90128 Palermo, Italy
| | - Giulio Ghersi
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, 90128 Palermo, Italy
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14
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Wang Y, Sun Y, Wang Y, Jia S, Qiao Y, Zhou Z, Shao W, Zhang X, Guo J, Zhang B, Niu X, Wang Y, Peng D. Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer's disease: findings based on urine proteomics and machine learning. Alzheimers Res Ther 2023; 15:191. [PMID: 37925455 PMCID: PMC10625308 DOI: 10.1186/s13195-023-01324-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/04/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Alzheimer's disease is a prevalent disease with a heavy global burden. Proteomics is the systematic study of proteins and peptides to provide comprehensive descriptions. Aiming to obtain a more accurate and convenient clinical diagnosis, researchers are working for better biomarkers. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels. METHODS We firstly enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples, and conducted an LC-MS/MS analysis. In parallel, clinical data were collected, and clinical examinations were performed. After statistical and bioinformatics analyses, significant risk factors and differential urinary proteins were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM. RESULTS Fifty-seven AD patients, 43 MCI patients, and 62 CN subjects were enrolled. A total of 3366 proteins were identified, and 608 urine proteins were finally included in the analysis. There were 33 significantly differential proteins between the AD and CN groups and 15 significantly differential proteins between the MCI and CN groups. AD diagnostic panel included DDC, CTSC, EHD4, GSTA3, SLC44A4, GNS, GSTA1, ANXA4, PLD3, CTSH, HP, RPS3, CPVL, age, and APOE ε4 with an AUC of 0.9989 in the training test and 0.8824 in the test set while MCI diagnostic panel included TUBB, SUCLG2, PROCR, TCP1, ACE, FLOT2, EHD4, PROZ, C9, SERPINA3, age, and APOE ε4 with an AUC of 0.9985 in the training test and 0.8143 in the test set. Besides, diagnostic proteins were weakly correlated with cognitive functions. CONCLUSIONS In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN.
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Affiliation(s)
- Yuye Wang
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Yu Sun
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Yu Wang
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Shuhong Jia
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Yanan Qiao
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Zhi Zhou
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Wen Shao
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Xiangfei Zhang
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Jing Guo
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Bin Zhang
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Xiaoqian Niu
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Dantao Peng
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Department of Neurology, China-Japan Friendship Hospital, Beijing, 100029, China.
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
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15
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Cabreira V, McWhirter L, Carson A. Functional Cognitive Disorder: Diagnosis, Treatment, and Differentiation from Secondary Causes of Cognitive Difficulties. Neurol Clin 2023; 41:619-633. [PMID: 37775194 DOI: 10.1016/j.ncl.2023.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
Functional cognitive disorder is an increasingly common cause of referral to the memory clinic. As a substantial source of disability, clinicians involved in the management of patients with cognitive complaints need to familiarize themselves with this important differential diagnosis. Our approach focuses on the identification of positive features of internal inconsistency (historical and clinical clues alongside patterns of performance) instead of an exclusionary approach. Although effective treatments are desperately needed, promising therapies include metacognitive retraining and cognitive-behavioral therapy modalities. Future research should focus on a better understanding of disease trajectories and outcomes as well as the development of evidence-based interventions.
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Affiliation(s)
- Verónica Cabreira
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Laura McWhirter
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Alan Carson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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16
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Hou XH, Suckling J, Shen XN, Liu Y, Zuo CT, Huang YY, Li HQ, Wang HF, Tan CC, Cui M, Dong Q, Tan L, Yu JT. Multipredictor risk models for predicting individual risk of Alzheimer's disease. J Transl Med 2023; 21:768. [PMID: 37904154 PMCID: PMC10614397 DOI: 10.1186/s12967-023-04646-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/22/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Early prevention of Alzheimer's disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features. METHODS A total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction. RESULTS During the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer's continuum model was developed which could predict the Alzheimer's continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91). CONCLUSIONS The risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.
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Affiliation(s)
- Xiao-He Hou
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Xue-Ning Shen
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Chuan-Tao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Hong-Qi Li
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Hui-Fu Wang
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Chen-Chen Tan
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Mei Cui
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Lan Tan
- Department of Neurology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, WHO Collaborating Center for Research and Training in Neurosciences, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
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17
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Frölich L, von Arnim C, Bohlken J, Pantel J, Peters O, Förstl H. [Mild cognitive impairment in geriatric practice: patient orientation, diagnostics, treatment and ethics]. Z Gerontol Geriatr 2023; 56:492-497. [PMID: 36006476 DOI: 10.1007/s00391-022-02098-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2022] [Indexed: 10/15/2022]
Abstract
Mild cognitive impairment (MCI) is a common problem in old people, which can be distressing for patients and their families. The main feature of MCI is a decrease in cognitive performance with activities of daily living still unimpaired. The identification of treatable risk factors, recognition of early cognitive changes and a timely differential diagnosis, comprehensive information and counselling are important tasks in geriatric medicine. The aim of this article is to present practical recommendations to support physicians working with geriatric patients in recognizing cognitive deficits at an early stage, provide high-quality care focusing on counselling, treatment, and comorbidity management and to maximize the potential of the available treatment options.
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Affiliation(s)
- Lutz Frölich
- Abteilung Gerontopsychiatrie, Zentralinstitut für Seelische Gesundheit, 68163, Mannheim, Deutschland.
| | | | - Jens Bohlken
- Institut für Arbeitsmedizin, Sozialmedizin und Public Health, Universitätklinikum Leipzig, Leipzig, Deutschland
| | - Johannes Pantel
- Bereich Altersmedizin, Institut für Allgemeinmedizin, Universität Frankfurt, Frankfurt, Deutschland
| | - Oliver Peters
- Zentrum für Demenzprävention, Klinik für Psychiatrie und Psychotherapie CBF, Charité, Berlin, Deutschland
| | - Hans Förstl
- Klinik für Psychiatrie und Psychotherapie, TU München, München, Deutschland
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18
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Dai Y, Yu-Chun H, Fernandes BS, Zhang K, Xiaoyang L, Enduru N, Liu A, Manuel AM, Jiang X, Zhao Z. Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach. RESEARCH SQUARE 2023:rs.3.rs-3328861. [PMID: 37720047 PMCID: PMC10503860 DOI: 10.21203/rs.3.rs-3328861/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Background The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points. Methods We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS. Conclusion Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.
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Affiliation(s)
- Yulin Dai
- The University of Texas Health Science Center at Houston
| | - Hsu Yu-Chun
- The University of Texas Health Science Center at Houston
| | | | - Kai Zhang
- The University of Texas Health Science Center at Houston
| | - Li Xiaoyang
- The University of Texas Health Science Center at Houston
| | - Nitesh Enduru
- The University of Texas Health Science Center at Houston
| | - Andi Liu
- The University of Texas Health Science Center at Houston
| | | | - Xiaoqian Jiang
- The University of Texas Health Science Center at Houston
| | - Zhongming Zhao
- The University of Texas Health Science Center at Houston
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19
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Hendriksen HMA, van Gils AM, van Harten AC, Hartmann T, Mangialasche F, Kamondi A, Kivipelto M, Rhodius-Meester HFM, Smets EMA, van der Flier WM, Visser LNC. Communication about diagnosis, prognosis, and prevention in the memory clinic: perspectives of European memory clinic professionals. Alzheimers Res Ther 2023; 15:131. [PMID: 37543608 PMCID: PMC10404377 DOI: 10.1186/s13195-023-01276-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/19/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND The paradigm shift towards earlier Alzheimer's disease (AD) stages and personalized medicine creates new challenges for clinician-patient communication. We conducted a survey among European memory clinic professionals to identify opinions on communication about (etiological) diagnosis, prognosis, and prevention, and inventory needs for augmenting communication skills. METHODS Memory clinic professionals (N = 160) from 21 European countries completed our online survey (59% female, 14 ± 10 years' experience, 73% working in an academic hospital). We inventoried (1) opinions on communication about (etiological) diagnosis, prognosis, and prevention using 11 statements; (2) current communication practices in response to five hypothetical cases (AD dementia, mild cognitive impairment (MCI), subjective cognitive decline (SCD), with ( +) or without ( -) abnormal AD biomarkers); and (3) needs for communication support regarding ten listed communication skills. RESULTS The majority of professionals agreed that communication on diagnosis, prognosis, and prevention should be personalized to the individual patient. In response to the hypothetical patient cases, disease stage influenced the inclination to communicate an etiological AD diagnosis: 97% would explicitly mention the presence of AD to the patient with AD dementia, 68% would do so in MCI + , and 29% in SCD + . Furthermore, 58% would explicitly rule out AD in case of MCI - when talking to patients, and 69% in case of SCD - . Almost all professionals (79-99%) indicated discussing prognosis and prevention with all patients, of which a substantial part (48-86%) would personalize their communication to patients' diagnostic test results (39-68%) or patients' anamnestic information (33-82%). The majority of clinicians (79%) would like to use online tools, training, or both to support them in communicating with patients. Topics for which professionals desired support most were: stimulating patients' understanding of information, and communicating uncertainty, dementia risk, remotely/online, and with patients not (fluently) speaking the language of the country of residence. CONCLUSIONS In a survey of European memory clinic professionals, we found a strong positive attitude towards communication with patients about (etiological) diagnosis, prognosis, and prevention, and personalization of communication to characteristics and needs of individual patients. In addition, professionals expressed a need for supporting tools and skills training to further improve their communication with patients.
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Affiliation(s)
- Heleen M A Hendriksen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
| | - Aniek M van Gils
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Argonde C van Harten
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Tobias Hartmann
- Experimental Neurology, Saarland University, 66424, Homburg, Germany
- Deutsches Institut Für DemenzPrävention, Saarland University, 66424, Homburg, Germany
| | - Francesca Mangialasche
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Medical Unit Aging, Theme Inflammation and Aging, Stockholm, Sweden
| | - Anita Kamondi
- Department of Neurology, Neurology and Neurosurgery, National Institute of Mental Health, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Miia Kivipelto
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Medical Unit Aging, Theme Inflammation and Aging, Stockholm, Sweden
- Ageing and Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Helsinki, Finland
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
- Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Ellen M A Smets
- Medical Psychology, Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Quality of Care, Personalized Medicine, , Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Leonie N C Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Medical Psychology, Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Quality of Care, Personalized Medicine, , Amsterdam, The Netherlands
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20
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Waldemar G. Data-driven care for patients with neurodegenerative disorders. Nat Rev Neurol 2023:10.1038/s41582-023-00828-9. [PMID: 37400548 DOI: 10.1038/s41582-023-00828-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Affiliation(s)
- Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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21
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van der Flier WM, de Vugt ME, Smets EMA, Blom M, Teunissen CE. Towards a future where Alzheimer's disease pathology is stopped before the onset of dementia. NATURE AGING 2023; 3:494-505. [PMID: 37202515 DOI: 10.1038/s43587-023-00404-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/21/2023] [Indexed: 05/20/2023]
Abstract
Alzheimer's disease (AD) is a major healthcare challenge with no curative treatment at present. To address this challenge, we need a paradigm shift, where we focus on pre-dementia stages of AD. In this Perspective, we outline a strategy to move towards a future with personalized medicine for AD by preparing for and investing in effective and patient-orchestrated diagnosis, prediction and prevention of the dementia stage. While focusing on AD, this Perspective also discusses studies that do not specify the cause of dementia. Future personalized prevention strategies encompass multiple components, including tailored combinations of disease-modifying interventions and lifestyle. By empowering the public and patients to be more actively engaged in the management of their health and disease and by developing improved strategies for diagnosis, prediction and prevention, we can pave the way for a future with personalized medicine, in which AD pathology is stopped to prevent or delay the onset of dementia.
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Affiliation(s)
- Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands.
- Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands.
| | - Marjolein E de Vugt
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, the Netherlands
| | - Ellen M A Smets
- Medical Psychology, Amsterdam UMC location AMC, Amsterdam, the Netherlands
| | - Marco Blom
- Alzheimer Nederland, Amersfoort, Utrecht, the Netherlands
| | - Charlotte E Teunissen
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
- Neurochemistry Laboratory, Clinical Chemistry, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
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22
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Dreves MAE, van Harten AC, Visser LNC, Rhodius‐Meester H, Köhler S, Kooistra M, Papma JM, Honey MIJ, Blom MM, Smets EMA, de Vugt ME, Teunissen CE, van der Flier WM. Rationale and design of the ABOARD project (A Personalized Medicine Approach for Alzheimer's Disease). ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2023; 9:e12401. [PMID: 37287472 PMCID: PMC10242186 DOI: 10.1002/trc2.12401] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/10/2023] [Accepted: 05/18/2023] [Indexed: 06/09/2023]
Abstract
The key to stopping Alzheimer's disease (AD) lies in the pre-dementia stages, with the goal to stop AD before dementia has started. We present the rationale and design of the ABOARD (A Personalized Medicine Approach for Alzheimer's Disease) project, which aims to invest in personalized medicine for AD. ABOARD is a Dutch public-private partnership of 32 partners, connecting stakeholders from a scientific, clinical, and societal perspective. The 5-year project is structured into five work packages on (1) diagnosis, (2) prediction, (3) prevention, (4) patient-orchestrated care, and (5) communication and dissemination. ABOARD functions as a network organization in which professionals interact cross-sectorally. ABOARD has a strong junior training program "Juniors On Board." Project results are shared with society through multiple communication resources. By including relevant partners and involving citizens at risk, patients, and their care partners, ABOARD builds toward a future with personalized medicine for AD. Highlights ABOARD (A Personalized Medicine Approach for Alzheimer's Disease) is a public-private research project executed by 32 partners that functions as a network organization.Together, the project partners build toward a future with personalized medicine for Alzheimer's disease.Although ABOARD is a Dutch consortium, it has international relevance.ABOARD improves diagnosis, prediction, prevention, and patient-orchestrated care.
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Affiliation(s)
- Maria A. E. Dreves
- Alzheimer Center AmsterdamNeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Amsterdam NeuroscienceAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Argonde C. van Harten
- Alzheimer Center AmsterdamNeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Amsterdam NeuroscienceAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Neurochemistry LaboratoryDepartment of Clinical ChemistryVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Leonie N. C. Visser
- Alzheimer Center AmsterdamNeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Department of Medical PsychologyAmsterdam UMC location AMCUniversity of AmsterdamAmsterdamthe Netherlands
- Amsterdam Public Health Research InstituteQuality of CareAmsterdam UMC location AMCUniversity of AmsterdamAmsterdamthe Netherlands
- Division of Clinical GeriatricsCenter for Alzheimer ResearchDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Hanneke Rhodius‐Meester
- Alzheimer Center AmsterdamNeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Amsterdam NeuroscienceAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Department of Internal MedicineGeriatric Medicine sectionVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Department of Geriatric MedicineThe Memory ClinicOslo University HospitalOsloNorway
| | - Sebastian Köhler
- Department of Psychiatry and NeuropsychologySchool for Mental Health and NeuroscienceMaastricht UniversityMaastrichtthe Netherlands
| | | | - Janne M. Papma
- Department of Neurology and Alzheimer Center Erasmus MCErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Madison I. J. Honey
- Amsterdam NeuroscienceAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Neurochemistry LaboratoryDepartment of Clinical ChemistryVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | | | - Ellen M. A. Smets
- Department of Medical PsychologyAmsterdam UMC location AMCUniversity of AmsterdamAmsterdamthe Netherlands
- Amsterdam Public Health Research InstituteQuality of CareAmsterdam UMC location AMCUniversity of AmsterdamAmsterdamthe Netherlands
| | - Marjolein E. de Vugt
- Department of Psychiatry and NeuropsychologySchool for Mental Health and NeuroscienceMaastricht UniversityMaastrichtthe Netherlands
| | - Charlotte E. Teunissen
- Amsterdam NeuroscienceAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Neurochemistry LaboratoryDepartment of Clinical ChemistryVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center AmsterdamNeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Amsterdam NeuroscienceAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Department of Epidemiology and Data ScienceAmsterdam UMC location VUmc, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - the ABOARD Consortium
- Alzheimer Center AmsterdamNeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamthe Netherlands
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23
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Castro-Aldrete L, Moser MV, Putignano G, Ferretti MT, Schumacher Dimech A, Santuccione Chadha A. Sex and gender considerations in Alzheimer’s disease: The Women’s Brain Project contribution. Front Aging Neurosci 2023; 15:1105620. [PMID: 37065460 PMCID: PMC10097993 DOI: 10.3389/fnagi.2023.1105620] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/15/2023] [Indexed: 04/01/2023] Open
Abstract
The global population is expected to have about 131.5 million people living with Alzheimer’s disease (AD) and other dementias by 2050, posing a severe health crisis. Dementia is a progressive neurodegenerative condition that gradually impairs physical and cognitive functions. Dementia has a variety of causes, symptoms, and heterogeneity concerning the influence of sex on prevalence, risk factors, and outcomes. The proportion of male-to-female prevalence varies based on the type of dementia. Despite some types of dementia being more common in men, women have a greater lifetime risk of developing dementia. AD is the most common form of dementia in which approximately two-thirds of the affected persons are women. Profound sex and gender differences in physiology and pharmacokinetic and pharmacodynamic interactions have increasingly been identified. As a result, new approaches to dementia diagnosis, care, and patient journeys should be considered. In the heart of a rapidly aging worldwide population, the Women’s Brain Project (WBP) was born from the necessity to address the sex and gender gap in AD. WBP is now a well-established international non-profit organization with a global multidisciplinary team of experts studying sex and gender determinants in the brain and mental health. WBP works with different stakeholders worldwide to help change perceptions and reduce sex biases in clinical and preclinical research and policy frameworks. With its strong female leadership, WBP is an example of the importance of female professionals’ work in the field of dementia research. WBP-led peer-reviewed papers, articles, books, lectures, and various initiatives in the policy and advocacy space have profoundly impacted the community and driven global discussion. WBP is now in the initial phases of establishing the world’s first Sex and Gender Precision Medicine Institute. This review highlights the contributions of the WBP team to the field of AD. This review aims to increase awareness of potentially important aspects of basic science, clinical outcomes, digital health, policy framework and provide the research community with potential challenges and research suggestions to leverage sex and gender differences. Finally, at the end of the review, we briefly touch upon our progress and contribution toward sex and gender inclusion beyond Alzheimer’s disease.
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Affiliation(s)
- Laura Castro-Aldrete
- Women’s Brain Project, Guntershausen bei Aadorf, Switzerland
- *Correspondence: Laura Castro-Aldrete,
| | | | - Guido Putignano
- Women’s Brain Project, Guntershausen bei Aadorf, Switzerland
| | | | - Annemarie Schumacher Dimech
- Women’s Brain Project, Guntershausen bei Aadorf, Switzerland
- Faculty of Medicine and Health Sciences, University of Lucerne, Lucerne, Switzerland
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24
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Heinzinger N, Maass A, Berron D, Yakupov R, Peters O, Fiebach J, Villringer K, Preis L, Priller J, Spruth EJ, Altenstein S, Schneider A, Fliessbach K, Wiltfang J, Bartels C, Jessen F, Maier F, Glanz W, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Killimann I, Göerß D, Laske C, Munk MH, Spottke A, Roy N, Heneka MT, Brosseron F, Dobisch L, Ewers M, Dechent P, Haynes JD, Scheffler K, Wolfsgruber S, Kleineidam L, Schmid M, Berger M, Düzel E, Ziegler G. Exploring the ATN classification system using brain morphology. Alzheimers Res Ther 2023; 15:50. [PMID: 36915139 PMCID: PMC10009950 DOI: 10.1186/s13195-023-01185-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/08/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort. METHODS We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/-), CSF phospho-tau (T+/-), and adjusted hippocampal volume or CSF total-tau (N+/-). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A-T-N- towards A+T+N+ including also non-AD continuum ATN groups. RESULTS The ACH-based progression A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead. CONCLUSION Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy. TRIAL REGISTRATION DRKS00007966, 04/05/2015, retrospectively registered.
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Affiliation(s)
- Nils Heinzinger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. .,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Jochen Fiebach
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Lukas Preis
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany.,Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.,University of Edinburgh and UK DRI, Edinburgh, UK
| | - Eike Jacob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany.,Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Killimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Doreen Göerß
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, Göttingen, Germany
| | - John Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Berlin, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Schmid
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Moritz Berger
- Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
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25
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Silva-Spínola A, Lima M, Leitão MJ, Bernardes C, Durães J, Duro D, Tábuas-Pereira M, Santana I, Baldeiras I. Blood biomarkers in mild cognitive impairment patients: Relationship between analytes and progression to Alzheimer disease dementia. Eur J Neurol 2023; 30:1565-1573. [PMID: 36880887 DOI: 10.1111/ene.15762] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND AND PURPOSE Blood-based biomarkers are promising tools for the diagnosis of Alzheimer disease (AD) at prodromal stages (mild cognitive impairment [MCI]) and are hoped to be implemented as screening tools for patients with cognitive complaints. In this work, we evaluated the potential of peripheral neurological biomarkers to predict progression to AD dementia and the relation between blood and cerebrospinal fluid (CSF) AD markers in MCI patients referred from a general neurological department. METHODS A group of 106 MCI patients followed at the Neurology Department of Coimbra University Hospital was included. Data regarding baseline neuropsychological evaluation, CSF levels of amyloid β 42 (Aβ42), Aβ40, total tau (t-Tau), and phosphorylated tau 181 (p-Tau181) were available for all the patients. Aβ42, Aβ40, t-Tau, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) levels were determined in baseline stored serum and plasma samples by commercial SiMoA (Single Molecule Array) assays. Progression from MCI to AD dementia was assessed at follow-up (mean = 5.8 ± 3.4 years). RESULTS At baseline, blood markers NfL, GFAP, and p-Tau181 were significantly increased in patients who progressed to AD at follow-up (p < 0.001). In contrast, plasma Aβ42/40 ratio and t-Tau showed no significant differences between groups. NfL, GFAP, and p-Tau181 demonstrated good diagnostic accuracy to identify progression to AD dementia (area under the curve [AUC] = 0.81, 0.80, and 0.76, respectively), which improved when combined (AUC = 0.89). GFAP and p-Tau181 were correlated with CSF Aβ42. Association of p-Tau181 with NfL was mediated by GFAP, with a significant indirect association of 88% of the total effect. CONCLUSIONS Our findings highlight the potential of combining blood-based GFAP, NfL, and p-Tau181 to be applied as a prognostic tool in MCI.
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Affiliation(s)
- Anuschka Silva-Spínola
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,Center for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Marisa Lima
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Center for Research in Neuropsychology and Cognitive Behavioral Intervention, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - Maria João Leitão
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Catarina Bernardes
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - João Durães
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Diana Duro
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Miguel Tábuas-Pereira
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Isabel Santana
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Inês Baldeiras
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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26
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Maheux E, Koval I, Ortholand J, Birkenbihl C, Archetti D, Bouteloup V, Epelbaum S, Dufouil C, Hofmann-Apitius M, Durrleman S. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun 2023; 14:761. [PMID: 36765056 PMCID: PMC9918533 DOI: 10.1038/s41467-022-35712-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.
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Affiliation(s)
- Etienne Maheux
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Igor Koval
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Juliette Ortholand
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Colin Birkenbihl
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Damiano Archetti
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Vincent Bouteloup
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Stéphane Epelbaum
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences, Paris, France
| | - Carole Dufouil
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Martin Hofmann-Apitius
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
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27
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Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, Lei Y, Fu W, Wang X, Xiao X, Liu X, Yang Q, Liao X, Zhou Y, Fang L, Dong Y, Yang Y, Jiang H, Huang S, Shen L. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology. Alzheimers Res Ther 2023; 15:32. [PMID: 36765411 PMCID: PMC9912534 DOI: 10.1186/s13195-023-01181-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/06/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer's disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD. METHODS A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants' EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual's cognitive function. RESULTS The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction. CONCLUSIONS Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.
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Affiliation(s)
- Bin Jiao
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China ,grid.216417.70000 0001 0379 7164National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China ,grid.216417.70000 0001 0379 7164Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China ,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China ,grid.216417.70000 0001 0379 7164Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Rihui Li
- grid.168010.e0000000419368956Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA ,Brainup Institute of Science and Technology, Chongqing, China
| | - Hui Zhou
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Kunqiang Qing
- Brainup Institute of Science and Technology, Chongqing, China
| | - Hui Liu
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Hefu Pan
- Brainup Institute of Science and Technology, Chongqing, China
| | - Yanqin Lei
- Brainup Institute of Science and Technology, Chongqing, China
| | - Wenjin Fu
- Brainup Institute of Science and Technology, Chongqing, China
| | - Xiaoan Wang
- Brainup Institute of Science and Technology, Chongqing, China
| | - Xuewen Xiao
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xixi Liu
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xinxin Liao
- grid.216417.70000 0001 0379 7164Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Yafang Zhou
- grid.216417.70000 0001 0379 7164Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Liangjuan Fang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yanbin Dong
- Brainup Institute of Science and Technology, Chongqing, China
| | - Yuanhao Yang
- grid.1003.20000 0000 9320 7537Mater Research Institute, The University of Queensland, Woolloongabba, Queensland 4102 Australia
| | - Haiyan Jiang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Sha Huang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China. .,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China. .,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China. .,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China. .,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China. .,Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China.
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28
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Debray TPA, Collins GS, Riley RD, Snell KIE, Van Calster B, Reitsma JB, Moons KGM. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ 2023; 380:e071058. [PMID: 36750236 PMCID: PMC9903176 DOI: 10.1136/bmj-2022-071058] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 02/09/2023]
Affiliation(s)
- Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
- National Institute for Health and Care Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Kym I E Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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29
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The Synergic Effect of AT(N) Profiles and Depression on the Risk of Conversion to Dementia in Patients with Mild Cognitive Impairment. Int J Mol Sci 2023; 24:ijms24021371. [PMID: 36674881 PMCID: PMC9865785 DOI: 10.3390/ijms24021371] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/02/2023] [Accepted: 01/06/2023] [Indexed: 01/13/2023] Open
Abstract
Few studies have addressed the impact of the association between Alzheimer's disease (AD) biomarkers and NPSs in the conversion to dementia in patients with mild cognitive impairment (MCI), and no studies have been conducted on the interaction effect of these two risk factors. AT(N) profiles were created using AD-core biomarkers quantified in cerebrospinal fluid (CSF) (normal, brain amyloidosis, suspected non-Alzheimer pathology (SNAP) and prodromal AD). NPSs were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q). A total of 500 individuals with MCI were followed-up yearly in a memory unit. Cox regression analysis was used to determine risk of conversion, considering additive and multiplicative interactions between AT(N) profile and NPSs on the conversion to dementia. A total of 224 participants (44.8%) converted to dementia during the 2-year follow-up study. Pathologic AT(N) groups (brain amyloidosis, prodromal AD and SNAP) and the presence of depression and apathy were associated with a higher risk of conversion to dementia. The additive combination of the AT(N) profile with depression exacerbates the risk of conversion to dementia. A synergic effect of prodromal AD profile with depressive symptoms is evidenced, identifying the most exposed individuals to conversion among MCI patients.
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30
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Mank A, van Maurik IS, van Harten AC, Rhodius‐Meester HFM, Teunissen CE, van Berckel BNM, Berkhof J, van der Flier WM, Rijnhart JJM. Life satisfaction across the entire trajectory of Alzheimer's disease: A mediation analysis. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12389. [PMID: 36579132 PMCID: PMC9780509 DOI: 10.1002/dad2.12389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 11/16/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
Introduction We studied life satisfaction across Alzheimer's disease (AD) stages and studied mobility and meaningful activities as mediators of the associations between these AD stages and life satisfaction. Methods In this cross-sectional study, we included n = 269 amyloid-positive patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD dementia from the Amsterdam Dementia Cohort. Life satisfaction was measured with the satisfaction with life scale. The mediating role of transportation, work, sports, and hobbies on life satisfaction was examined in single and multiple mediator models. Results Patients with dementia are less satisfied with life compared to SCD and MCI. These differences in life satisfaction are explained by reduced participation in meaningful activities, which in turn, was largely attributable to decreased transportation use. Discussion Our findings suggest that improving access to transportation, therewith allowing participation in meaningful activities help to maintain life satisfaction and may be an important target for intervention.
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Affiliation(s)
- Arenda Mank
- Alzheimer Center Amsterdam, NeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamThe Netherlands,Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands,Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Epidemiology and Data ScienceAmsterdam Public Health InstituteAmsterdamThe Netherlands
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, NeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamThe Netherlands,Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands,Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Epidemiology and Data ScienceAmsterdam Public Health InstituteAmsterdamThe Netherlands
| | - Argonde C. van Harten
- Alzheimer Center Amsterdam, NeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamThe Netherlands,Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands
| | - Hanneke F. M. Rhodius‐Meester
- Alzheimer Center Amsterdam, NeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamThe Netherlands,Department of Internal MedicineGeriatric Medicine SectionVrije Universiteit AmsterdamAmsterdam UMCAmsterdamThe Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry LaboratoryDepartment of Clinical ChemistryAmsterdam UMC location VUmcVrije UniversiteitAmsterdamThe Netherlands
| | - Bart N. M. van Berckel
- Alzheimer Center Amsterdam, NeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamThe Netherlands,Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands,Department of Radiology & Nuclear Medicine Amsterdam Neuroscience Vrije Universiteit AmsterdamAmsterdam UMCAmsterdamThe Netherlands
| | - Johannes Berkhof
- Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Epidemiology and Data ScienceAmsterdam Public Health InstituteAmsterdamThe Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, NeurologyAmsterdam UMC location VUmcVrije Universiteit AmsterdamAmsterdamThe Netherlands,Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands,Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Epidemiology and Data ScienceAmsterdam Public Health InstituteAmsterdamThe Netherlands
| | - Judith J. M. Rijnhart
- Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Epidemiology and Data ScienceAmsterdam Public Health InstituteAmsterdamThe Netherlands
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31
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van Maurik IS, Broulikova HM, Mank A, Bakker ED, de Wilde A, Bouwman FH, Stephens AW, van Berckel BNM, Scheltens P, van der Flier WM. A more precise diagnosis by means of amyloid PET contributes to delayed institutionalization, lower mortality, and reduced care costs in a tertiary memory clinic setting. Alzheimers Dement 2022; 19:2006-2013. [PMID: 36419238 DOI: 10.1002/alz.12846] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 10/03/2022] [Accepted: 10/05/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION We aim to study the effect of a more precise diagnosis, by means of amyloid positron emission tomography (PET), on institutionalization, mortality, and health-care costs. METHODS Between October 27, 2014 and December 31, 2016, we offered amyloid PET to all patients as part of their diagnostic work-up. Patients who accepted to undergo amyloid PET (n = 449) were propensity score matched with patients without amyloid PET (n = 571, i.e., no PET). Matched groups (both n = 444) were compared on rate of institutionalization, mortality, and health-care costs in the years after diagnosis. RESULTS Amyloid PET patients had a lower risk of institutionalization (10% [n = 45] vs. 21% [n = 92]; hazard ratio [HR] = 0.48 [0.33-0.70]) and mortality rate (11% [n = 49] vs. 18% [n = 81]; HR = 0.51 [0.36-0.73]) and lower health-care costs in the years after diagnosis compared to matched no-PET patients (β = -4573.49 [-6524.76 to -2523.74], P-value < 0.001). DISCUSSION A more precise diagnosis in tertiary memory clinic patients positively influenced the endpoints of institutionalization, death, and health-care costs.
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Affiliation(s)
- Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam Epidemiology and Data Science Amsterdam the Netherlands
- Amsterdam Public Health Methodology Amsterdam the Netherlands
| | - Hana M. Broulikova
- Department of Health Sciences Faculty of Science Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute Amsterdam the Netherlands
| | - Arenda Mank
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam Epidemiology and Data Science Amsterdam the Netherlands
- Amsterdam Public Health Methodology Amsterdam the Netherlands
| | - Els D. Bakker
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
| | - Arno de Wilde
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- EQT Life Sciences Amsterdam the Netherlands
| | - Femke H. Bouwman
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
| | | | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- EQT Life Sciences Amsterdam the Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam the Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam the Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam Epidemiology and Data Science Amsterdam the Netherlands
- Amsterdam Public Health Methodology Amsterdam the Netherlands
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Kandiah N, Choi SH, Hu CJ, Ishii K, Kasuga K, Mok VC. Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion. J Alzheimers Dis Rep 2022; 6:699-710. [PMID: 36606209 PMCID: PMC9741748 DOI: 10.3233/adr-220059] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) poses a substantial healthcare burden in the rapidly aging Asian population. Early diagnosis of AD, by means of biomarkers, can lead to interventions that might alter the course of the disease. The amyloid, tau, and neurodegeneration (AT[N]) framework, which classifies biomarkers by their core pathophysiological features, is a biomarker measure of amyloid plaques and neurofibrillary tangles. Our current AD biomarker armamentarium, comprising neuroimaging biomarkers and cerebrospinal fluid biomarkers, while clinically useful, may be invasive and expensive and hence not readily available to patients. Several studies have also investigated the use of blood-based measures of established core markers for detection of AD, such as amyloid-β and phosphorylated tau. Furthermore, novel non-invasive peripheral biomarkers and digital biomarkers could potentially expand access to early AD diagnosis to patients in Asia. Despite the multiplicity of established and potential biomarkers in AD, a regional framework for their optimal use to guide early AD diagnosis remains lacking. A group of experts from five regions in Asia gathered at a meeting in March 2021 to review the current evidence on biomarkers in AD diagnosis and discuss best practice around their use, with the goal of developing practical guidance that can be implemented easily by clinicians in Asia to support the early diagnosis of AD. This article summarizes recent key evidence on AD biomarkers and consolidates the experts' insights into the current and future use of these biomarkers for the screening and early diagnosis of AD in Asia.
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Affiliation(s)
- Nagaendran Kandiah
- Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,Correspondence to: Nagaendran Kandiah, Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore 308232. Tel.: +65 6592 2653; Fax: +65 6339 2889; E-mail: ; ORCID: 0000-0001-9244-4298
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Republic of Korea
| | - Chaur-Jong Hu
- Department of Neurology, Dementia Center, Shuang Ho Hospital, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kenji Ishii
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Center for Bioresources, Brain Research Institute, Niigata University, Niigata, Japan
| | - Vincent C.T. Mok
- Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, China
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Zetterberg H, Schott JM. Objectifying Subjective Cognitive Decline: The Prognostic Role of Alzheimer Biomarkers. Neurology 2022; 99:735-736. [PMID: 36240103 DOI: 10.1212/wnl.0000000000201172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 07/13/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Henrik Zetterberg
- From the Department of Psychiatry and Neurochemistry (H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z., J.M.S.), UCL Institute of Neurology, Queen Square; UK Dementia Research Institute at UCL (H.Z., J.M.S.), London; Hong Kong Center for Neurodegenerative Diseases (H.Z.), Clear Water Bay, China; and Dementia Research Centre (J.M.S.), Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom.
| | - Jonathan M Schott
- From the Department of Psychiatry and Neurochemistry (H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z., J.M.S.), UCL Institute of Neurology, Queen Square; UK Dementia Research Institute at UCL (H.Z., J.M.S.), London; Hong Kong Center for Neurodegenerative Diseases (H.Z.), Clear Water Bay, China; and Dementia Research Centre (J.M.S.), Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom
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Deng Z, Jiang J, Wang J, Pan D, Zhu Y, Li H, Zhang X, Liu X, Xu Y, Li Y, Tang Y. Angiotensin Receptor Blockers Are Associated With a Lower Risk of Progression From Mild Cognitive Impairment to Dementia. Hypertension 2022; 79:2159-2169. [PMID: 35766029 DOI: 10.1161/hypertensionaha.122.19378] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Previous studies found that antihypertensive medications (AHMs) acting on the renin-angiotensin system had the potential to reduce the progression from mild cognitive impairment to dementia. However, it remains unclear whether this association differs between ACE (angiotensin-converting enzyme) inhibitors and angiotensin receptor blockers. METHODS We conducted a retrospective cohort study in the Alzheimer's Disease Neuroimaging Initiative among 403 participants with hypertension and mild cognitive impairment at baseline. Information on AHMs received during the follow-up period, including angiotensin receptor blockers, ACE inhibitors, beta-blockers, calcium channel blockers, and diuretics, were self-reported. Cox proportional hazards models adjusted for potential confounders were used in the time to event analysis with progression to dementia as outcome. RESULTS Of the 403 participants, the mean (SD) age was 74.0 (7.3) years, 152 (37.7%) were female, 158 (39.2%) progressed to dementia over a median follow-up time of 3.0 years. Angiotensin receptor blockers were associated with a lower risk of progression to dementia as compared to ACE inhibitors (adjusted hazard ratio=0.45 [95% CI, 0.25-0.81]; P=0.023), other classes of AHMs (beta-blockers, calcium channel blockers, diuretics; adjusted hazard ratio, 0.49 [95% CI, 0.27-0.89]; P=0.037), and none of AHMs (adjusted hazard ratio, 0.31 [95% CI, 0.16-0.58]; P=0.001). CONCLUSIONS In patients with hypertension and mild cognitive impairment, angiotensin receptor blockers were associated with a lower risk of progression to dementia compared with ACE inhibitors and other classes of AHMs. Our findings may have important implications for clinical practice but still warrant further investigations in larger prospective cohorts or clinical trials.
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Affiliation(s)
- Zhenhong Deng
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jingru Jiang
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jia Wang
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Dong Pan
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yingying Zhu
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Division of Clinical Research Design (Y.Z., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Honghong Li
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoni Zhang
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaohuan Liu
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yongteng Xu
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi Li
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Division of Clinical Research Design (Y.Z., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation (Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yamei Tang
- Department of Neurology (Z.D., J.J., J.W., D.P., Y.Z., H.L., X.Z.,X.L., Y.X., Y.L., Y.T.), Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China (Y.T.)
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Sexton C, Solis M, Aharon-Peretz J, Alexopoulos P, Apostolova LG, Bayen E, Birkenhager B, Cappa S, Constantinidou F, Fortea J, Gerritsen DL, Hassanin HI, Ibanez A, Ioannidis P, Karageorgiou E, Korczyn A, Leroi I, Lichtwarck B, Logroscino G, Lynch C, Mecocci P, Molinuevo JL, Papatriantafyllou J, Papegeorgiou S, Politis A, Raman R, Ritchie K, Sanchez-Juan P, Sano M, Scarmeas N, Spiru L, Stathi A, Tsolaki M, Yener G, Zaganas I, Zygouris S, Carrillo M. Alzheimer's disease research progress in the Mediterranean region: The Alzheimer's Association International Conference Satellite Symposium. Alzheimers Dement 2022; 18:1957-1968. [PMID: 35184367 PMCID: PMC11066754 DOI: 10.1002/alz.12588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 01/28/2023]
Abstract
As research and services in the Mediterranean region continue to increase, so do opportunities for global collaboration. To support such collaborations, the Alzheimer's Association was due to hold its seventh Alzheimer's Association International Conference Satellite Symposium in Athens, Greece in 2021. Due to the COVID-19 pandemic, the meeting was held virtually, which enabled attendees from around the world to hear about research efforts in Greece and the surrounding Mediterranean countries. Research updates spanned understanding the biology of, treatments for, and care of people with Alzheimer's disease (AD_ and other dementias. Researchers in the Mediterranean region have outlined the local epidemiology of AD and dementia, and have identified regional populations that may expedite genetic studies. Development of biomarkers is expected to aid early and accurate diagnosis. Numerous efforts have been made to develop culturally specific interventions to both reduce risk of dementia, and to improve quality of life for people living with dementia.
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Affiliation(s)
- Claire Sexton
- Alzheimer's Association, 225 N Michigan Avenue, 17th Fl, Chicago, Illinois, USA
| | | | | | - Panagiotis Alexopoulos
- Department of Psychiatry, Patras University Hospital, Faculty of Medicine, School of Health Sciences, University of Patras, Patras, Greece
| | | | - Eléonore Bayen
- Laboratoire d'imagerie biomédicale, Sorbonne Université, department of physical rehabilitation medicine, Pitié-Salpêtrière hospital, AP-HP, Paris, France
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA
| | - Betty Birkenhager
- Department of General Practice and Elderly Care Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Stefano Cappa
- University School for Advanced Studies (IUSS-Pavia) and IRCCS Mondino Foundation, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, PV, Italy
| | - Fofi Constantinidou
- Department of Psychology & Center for Applied Neuroscience, University of Cyprus, Nicosia, Cyprus
| | - Juan Fortea
- Sant Pau Memory Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau- Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Hany I Hassanin
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA
- Geriatric Medicine Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Agustin Ibanez
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA
- Trinity College Dublin, Dublin, Ireland
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
- Universidad de San Andres & National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | | | | | | | - Iracema Leroi
- Trinity College Dublin, Global Brain Health Institute, Dublin, Ireland
| | - Bjorn Lichtwarck
- The Centre for Age-related Functional Decline and Disease, Innlandet Hospital Trust, Ottestad, Norway
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain Department of Clinical Research in Neurology of the University of Bari at "Pia Fondazione Card G. Panico" Hospital Tricase (Le), Bari, Italy
- Department of Basic Medicine Neuroscience and Sense Organs University Aldo Moro Bari, Bari, Italy
| | - Chris Lynch
- Alzheimer's Disease International, London, UK
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | | | - John Papatriantafyllou
- Third Age Center IASIS, Athens-Glyfada, Athens, Greece
- 1st University Neurology Department, Eginitio Hospital, Athens, Greece
- Ana Aslan International Foundation
| | - Sokratis Papegeorgiou
- 1st University Neurology Department, Eginitio Hospital, Athens, Greece
- National and Kapodistrian University of Athens, Athens, Greece
| | - Antonis Politis
- 1st Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
| | - Rema Raman
- Alzheimer's Therapeutic Research Institute, University of Southern California, CA, USA
| | | | - Pascual Sanchez-Juan
- Institute for Research Marqués de Valdecilla (IDIVAL), CIBERNED, University of Cantabria and Department of Neurology, Marqués de Valdecilla University Hospital, Santander, Spain
| | - Mary Sano
- The Mount Sinai Hospital, New York, NY, USA
| | - Nikolas Scarmeas
- National and Kapodistrian University of Athens, Athens, Greece
- Columbia University, New York, NY, USA
| | - Luiza Spiru
- Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania
- Ana Aslan International Foundation
| | - Afroditi Stathi
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Magda Tsolaki
- 1st Department of Neurology, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Makedonia, Greece
| | - Görsev Yener
- Faculty of Medicine, Izmir University of Economics, Izmir, Turkey
| | - Ioannis Zaganas
- Neurogenetics Laboratory, Medical School, University of Crete
| | - Stelios Zygouris
- Centre for Research and Technology Hellas/ Information Technologies Institute, Thessaloniki, Greece
| | - Maria Carrillo
- Alzheimer's Association, 225 N Michigan Avenue, 17th Fl, Chicago, Illinois, USA
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Mank A, van Maurik IS, Rijnhart JJM, bakker ED, Bouteloup V, Le Scouarnec L, Teunissen CE, Barkhof F, Scheltens P, Berkhof J, van der Flier WM. Development of multivariable prediction models for institutionalization and mortality in the full spectrum of Alzheimer’s disease. Alzheimers Res Ther 2022; 14:110. [PMID: 35932034 PMCID: PMC9354423 DOI: 10.1186/s13195-022-01053-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 07/27/2022] [Indexed: 11/15/2022]
Abstract
Background Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer’s disease (AD) is characterized by a long pre-dementia stage. Therefore, we developed prediction models to predict institutionalization and mortality along the AD continuum of cognitively normal to dementia. Methods This study included SCD/MCI patients (subjective cognitive decline (SCD) or mild cognitive impairment (MCI)) and patients with AD dementia from the Amsterdam Dementia Cohort. We developed internally and externally validated prediction models with biomarkers and without biomarkers, stratified by dementia status. Determinants were selected using backward selection (p<0.10). All models included age and sex. Discriminative performance of the models was assessed with Harrell’s C statistics. Results We included n=1418 SCD/MCI patients (n=123 died, n=74 were institutionalized) and n=1179 patients with AD dementia (n=413 died, n=453 were institutionalized). For both SCD/MCI and dementia stages, the models for institutionalization and mortality included after backward selection clinical characteristics, imaging, and cerebrospinal fluid (CSF) biomarkers. In SCD/MCI, the Harrell’s C-statistics of the models were 0.81 (model without biomarkers: 0.76) for institutionalization and 0.79 (model without biomarker: 0.76) for mortality. In AD-dementia, the Harrell’s C-statistics of the models were 0.68 (model without biomarkers: 0.67) for institutionalization and 0.65 (model without biomarker: 0.65) for mortality. Models based on data from amyloid-positive patients only had similar discrimination. Conclusions We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01053-0.
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Gleason CE, Zuelsdorff M, Gooding DC, Kind AJH, Johnson AL, James TT, Lambrou NH, Wyman MF, Ketchum FB, Gee A, Johnson SC, Bendlin BB, Zetterberg H. Alzheimer's disease biomarkers in Black and non-Hispanic White cohorts: A contextualized review of the evidence. Alzheimers Dement 2022; 18:1545-1564. [PMID: 34870885 PMCID: PMC9543531 DOI: 10.1002/alz.12511] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 09/10/2021] [Accepted: 09/15/2021] [Indexed: 02/06/2023]
Abstract
Black Americans are disproportionately affected by dementia. To expand our understanding of mechanisms of this disparity, we look to Alzheimer's disease (AD) biomarkers. In this review, we summarize current data, comparing the few studies presenting these findings. Further, we contextualize the data using two influential frameworks: the National Institute on Aging-Alzheimer's Association (NIA-AA) Research Framework and NIA's Health Disparities Research Framework. The NIA-AA Research Framework provides a biological definition of AD that can be measured in vivo. However, current cut-points for determining pathological versus non-pathological status were developed using predominantly White cohorts-a serious limitation. The NIA's Health Disparities Research Framework is used to contextualize findings from studies identifying racial differences in biomarker levels, because studying biomakers in isolation cannot explain or reduce inequities. We offer recommendations to expand study beyond initial reports of racial differences. Specifically, life course experiences associated with racialization and commonly used study enrollment practices may better account for observations than exclusively biological explanations.
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Affiliation(s)
- Carey E. Gleason
- Division of Geriatrics and GerontologyDepartment of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's Disease Research CenterMadisonWisconsinUSA
- Geriatric ResearchEducation and Clinical Center (11G)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Megan Zuelsdorff
- Wisconsin Alzheimer's Disease Research CenterMadisonWisconsinUSA
- University of Wisconsin School of NursingMadisonWisconsinUSA
| | - Diane C. Gooding
- Department of PsychologyUniversity of Wisconsin, MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Amy J. H. Kind
- Division of Geriatrics and GerontologyDepartment of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's Disease Research CenterMadisonWisconsinUSA
- Geriatric ResearchEducation and Clinical Center (11G)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
- Center for Health Disparities ResearchDepartment of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Adrienne L. Johnson
- Center for Tobacco Research and InterventionUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Taryn T. James
- Division of Geriatrics and GerontologyDepartment of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's Disease Research CenterMadisonWisconsinUSA
| | - Nickolas H. Lambrou
- Geriatric ResearchEducation and Clinical Center (11G)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Mary F. Wyman
- Geriatric ResearchEducation and Clinical Center (11G)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
- Department of PsychologyUniversity of Wisconsin, MadisonMadisonWisconsinUSA
| | - Fred B. Ketchum
- Department of NeurologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Alexander Gee
- Nehemiah Center for Urban Leadership DevelopmentMadisonWisconsinUSA
| | - Sterling C. Johnson
- Division of Geriatrics and GerontologyDepartment of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's Disease Research CenterMadisonWisconsinUSA
- Geriatric ResearchEducation and Clinical Center (11G)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Division of Geriatrics and GerontologyDepartment of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's Disease Research CenterMadisonWisconsinUSA
| | - Henrik Zetterberg
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and Physiologythe Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative DiseaseUCL Institute of Neurology, Queen SquareLondonUK
- UK Dementia Research Institute at UCLLondonUK
- Hong Kong Center for NeurodegenerationHong KongChina
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Orellana A, García-González P, Valero S, Montrreal L, de Rojas I, Hernández I, Rosende-Roca M, Vargas L, Tartari JP, Esteban-De Antonio E, Bojaryn U, Narvaiza L, Alarcón-Martín E, Alegret M, Alcolea D, Lleó A, Tárraga L, Pytel V, Cano A, Marquié M, Boada M, Ruiz A. Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort. Int J Mol Sci 2022; 23:ijms23136891. [PMID: 35805894 PMCID: PMC9266894 DOI: 10.3390/ijms23136891] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Clinical diagnosis of Alzheimer’s disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST® ELISA and Lumipulse G® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer’s disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815−27). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.
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Affiliation(s)
- Adelina Orellana
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Pablo García-González
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Laura Montrreal
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Itziar de Rojas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Isabel Hernández
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Maitee Rosende-Roca
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Liliana Vargas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Juan Pablo Tartari
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Ester Esteban-De Antonio
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Urszula Bojaryn
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Leire Narvaiza
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Emilio Alarcón-Martín
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Montserrat Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Daniel Alcolea
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, 08029 Barcelona, Spain
| | - Alberto Lleó
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, 08029 Barcelona, Spain
| | - Lluís Tárraga
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Amanda Cano
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
- Correspondence:
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Harms RL, Ferrari A, Meier IB, Martinkova J, Santus E, Marino N, Cirillo D, Mellino S, Catuara Solarz S, Tarnanas I, Szoeke C, Hort J, Valencia A, Ferretti MT, Seixas A, Santuccione Chadha A. Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach. EPMA J 2022; 13:299-313. [PMID: 35719134 PMCID: PMC9203627 DOI: 10.1007/s13167-022-00284-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
Abstract
Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00284-3.
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Affiliation(s)
| | | | | | - Julie Martinkova
- Women’s Brain Project, Guntershausen, Switzerland
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Enrico Santus
- Women’s Brain Project, Guntershausen, Switzerland
- Bayer, NJ USA
| | - Nicola Marino
- Women’s Brain Project, Guntershausen, Switzerland
- Dipartimento Di Scienze Mediche E Chirurgiche, Università Degli Studi Di Foggia, Foggia, Italy
| | - Davide Cirillo
- Women’s Brain Project, Guntershausen, Switzerland
- Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034 Barcelona, Spain
| | | | | | - Ioannis Tarnanas
- Altoida Inc., Houston, TX USA
- Global Brain Health Institute, Dublin, Ireland
| | - Cassandra Szoeke
- Women’s Brain Project, Guntershausen, Switzerland
- Centre for Medical Research, Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Australia
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
- International Clinical Research Center, St Anne’s University Hospital Brno, Brno, Czech Republic
| | - Alfonso Valencia
- Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034 Barcelona, Spain
- ICREA - Institució Catalana de Recerca I Estudis Avançats, Pg. Lluís Companys 23, 08010 Barcelona, Spain
| | | | - Azizi Seixas
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL 33136 USA
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Investigating the combination of plasma amyloid-beta and geroscience biomarkers on the incidence of clinically meaningful cognitive decline in older adults. GeroScience 2022; 44:1489-1503. [PMID: 35445358 DOI: 10.1007/s11357-022-00554-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 03/22/2022] [Indexed: 11/04/2022] Open
Abstract
We investigated combining a core AD neuropathology measure (plasma amyloid-beta [Aβ] 42/40) with five plasma markers of inflammation, cellular stress, and neurodegeneration to predict cognitive decline. Among 401 participants free of dementia (median [IQR] age, 76 [73-80] years) from the Multidomain Alzheimer Preventive Trial (MAPT), 28 (7.0%) participants developed dementia, and 137 (34.2%) had worsening of clinical dementia rating (CDR) scale over 4 years. In the models utilizing plasma Aβ alone, a tenfold increased risk of incident dementia (nonsignificant) and a fivefold increased risk of worsening CDR were observed as each nature log unit increased in plasma Aβ levels. Models incorporating Aβ plus multiple plasma biomarkers performed similarly to models included Aβ alone in predicting dementia and CDR progression. However, improving Aβ model performance for composite cognitive score (CCS) decline, a proxy of dementia, was observed after including plasma monocyte chemoattractant protein 1 (MCP1) and growth differentiation factor 15 (GDF15) as covariates. Participants with abnormal Aβ, GDF15, and MCP1 presented higher CCS decline (worsening cognitive function) compared to their normal-biomarker counterparts (adjusted β [95% CI], - 0.21 [- 0.35 to - 0.06], p = 0.005). In conclusion, our study found limited added values of multi-biomarkers beyond the basic Aβ models for predicting clinically meaningful cognitive decline among non-demented older adults. However, a combined assessment of inflammatory and cellular stress status with Aβ pathology through measuring plasma biomarkers may improve the evaluation of cognitive performance.
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA,Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA,Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Kambeitz-Ilankovic L, Koutsouleris N, Upthegrove R. The potential of precision psychiatry: what is in reach? Br J Psychiatry 2022; 220:175-178. [PMID: 35354501 DOI: 10.1192/bjp.2022.23] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.
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Affiliation(s)
- Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Germany; and Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; and Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Rachel Upthegrove
- Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, UK; and Institute for Mental Health, University of Birmingham, UK
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Pichet Binette A, Palmqvist S, Bali D, Farrar G, Buckley CJ, Wolk DA, Zetterberg H, Blennow K, Janelidze S, Hansson O. Combining plasma phospho-tau and accessible measures to evaluate progression to Alzheimer's dementia in mild cognitive impairment patients. Alzheimers Res Ther 2022; 14:46. [PMID: 35351181 PMCID: PMC8966264 DOI: 10.1186/s13195-022-00990-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/16/2022] [Indexed: 02/06/2023]
Abstract
Background Up to now, there are no clinically available minimally invasive biomarkers to accurately identify mild cognitive impairment (MCI) patients who are at greater risk to progress to Alzheimer’s disease (AD) dementia. The recent advent of blood-based markers opens the door for more accessible biomarkers. We aimed to identify which combinations of AD related plasma biomarkers and other easily accessible assessments best predict progression to AD dementia in patients with mild cognitive impairment (MCI). Methods We included patients with amnestic MCI (n = 110) followed prospectively over 3 years to assess clinical status. Baseline plasma biomarkers (amyloid-β 42/40, phosphorylated tau217 [p-tau217], neurofilament light and glial fibrillary acidic protein), hippocampal volume, APOE genotype, and cognitive tests were available. Logistic regressions with conversion to amyloid-positive AD dementia within 3 years as outcome was used to evaluate the performance of different biomarkers measured at baseline, used alone or in combination. The first analyses included only the plasma biomarkers to determine the ones most related to AD dementia conversion. Second, hippocampal volume, APOE genotype and a brief cognitive composite score (mPACC) were combined with the best plasma biomarker. Results Of all plasma biomarker combinations, p-tau217 alone had the best performance for discriminating progression to AD dementia vs all other combinations (AUC 0.84, 95% CI 0.75–0.93). Next, combining p-tau217 with hippocampal volume, cognition, and APOE genotype provided the best discrimination between MCI progressors vs. non-progressors (AUC 0.89, 0.82–0.95). Across the few best models combining different markers, p-tau217 and cognition were consistently the main contributors. The most parsimonious model including p-tau217 and cognition had a similar model fit, but a slightly lower AUC (0.87, 0.79–0.95, p = 0.07). Conclusion We identified that combining plasma p-tau217 and a brief cognitive composite score was strongly related to greater risk of progression to AD dementia in MCI patients, suggesting that these measures could be key components of future prognostic algorithms for early AD. Trial registration NCT01028053, registered December 9, 2009.
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Affiliation(s)
- Alexa Pichet Binette
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden.
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, SE-20502, Malmö, Sweden
| | - Divya Bali
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
| | | | | | - David A Wolk
- Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK.,UK Dementia Research Institute at UCL, London, UK.,Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden. .,Memory Clinic, Skåne University Hospital, SE-20502, Malmö, Sweden.
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Pelkmans W, Vromen EM, Dicks E, Scheltens P, Teunissen CE, Barkhof F, van der Flier WM, Tijms BM. Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline. Brain Commun 2022; 4:fcac026. [PMID: 35310828 PMCID: PMC8924646 DOI: 10.1093/braincomms/fcac026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/22/2021] [Accepted: 02/07/2022] [Indexed: 11/25/2022] Open
Abstract
Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.
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Affiliation(s)
- Wiesje Pelkmans
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ellen M. Vromen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, UCL, London, UK
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology & Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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Leuzy A, Mattsson‐Carlgren N, Palmqvist S, Janelidze S, Dage JL, Hansson O. Blood-based biomarkers for Alzheimer's disease. EMBO Mol Med 2022; 14:e14408. [PMID: 34859598 PMCID: PMC8749476 DOI: 10.15252/emmm.202114408] [Citation(s) in RCA: 129] [Impact Index Per Article: 64.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 11/02/2021] [Accepted: 11/05/2021] [Indexed: 12/01/2022] Open
Abstract
Neurodegenerative disorders such as Alzheimer's disease (AD) represent a mounting public health challenge. As these diseases are difficult to diagnose clinically, biomarkers of underlying pathophysiology are playing an ever-increasing role in research, clinical trials, and in the clinical work-up of patients. Though cerebrospinal fluid (CSF) and positron emission tomography (PET)-based measures are available, their use is not widespread due to limitations, including high costs and perceived invasiveness. As a result of rapid advances in the development of ultra-sensitive assays, the levels of pathological brain- and AD-related proteins can now be measured in blood, with recent work showing promising results. Plasma P-tau appears to be the best candidate marker during symptomatic AD (i.e., prodromal AD and AD dementia) and preclinical AD when combined with Aβ42/Aβ40. Though not AD-specific, blood NfL appears promising for the detection of neurodegeneration and could potentially be used to detect the effects of disease-modifying therapies. This review provides an overview of the progress achieved thus far using AD blood-based biomarkers, highlighting key areas of application and unmet challenges.
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Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
| | - Niklas Mattsson‐Carlgren
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Department of NeurologySkåne University HospitalLundSweden
- Wallenberg Centre for Molecular MedicineLund UniversityLundSweden
| | - Sebastian Palmqvist
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Shorena Janelidze
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
| | - Jeffrey L Dage
- Stark Neuroscience Research InstituteIndiana University School of MedicineIndianapolisINUSA
| | - Oskar Hansson
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Memory ClinicSkåne University HospitalLundSweden
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Ingala S, van Maurik IS, Altomare D, Wurm R, Dicks E, van Schijndel RA, Zwan M, Bouwman F, Schoonenboom N, Boelaarts L, Roks G, van Marum R, van Harten B, van Uden I, Claus J, Wottschel V, Vrenken H, Wattjes MP, van der Flier WM, Barkhof F. Clinical applicability of quantitative atrophy measures on MRI in patients suspected of Alzheimer's disease. Eur Radiol 2022; 32:7789-7799. [PMID: 35639148 PMCID: PMC9668763 DOI: 10.1007/s00330-021-08503-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 11/03/2021] [Accepted: 12/01/2021] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Neurodegeneration in suspected Alzheimer's disease can be determined using visual rating or quantitative volumetric assessments. We examined the feasibility of volumetric measurements of gray matter (GMV) and hippocampal volume (HCV) and compared their diagnostic performance with visual rating scales in academic and non-academic memory clinics. MATERIALS AND METHODS We included 231 patients attending local memory clinics (LMC) in the Netherlands and 501 of the academic Amsterdam Dementia Cohort (ADC). MRI scans were acquired using local protocols, including a T1-weighted sequence. Quantification of GMV and HCV was performed using FSL and FreeSurfer. Medial temporal atrophy and global atrophy were assessed with visual rating scales. ROC curves were derived to determine which measure discriminated best between cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD). RESULTS Patients attending LMC (age 70.9 ± 8.9 years; 47% females; 19% CN; 34% MCI; 47% AD) were older, had more cerebrovascular pathology, and had lower GMV and HCV compared to those of the ADC (age 64.9 ± 8.2 years; 42% females; 35% CN, 43% MCI, 22% AD). While visual ratings were feasible in > 95% of scans in both cohorts, quantification was achieved in 94-98% of ADC, but only 68-85% of LMC scans, depending on the software. Visual ratings and volumetric outcomes performed similarly in discriminating CN vs AD in both cohorts. CONCLUSION In clinical settings, quantification of GM and hippocampal atrophy currently fails in up to one-third of scans, probably due to lack of standardized acquisition protocols. Diagnostic accuracy is similar for volumetric measures and visual rating scales, making the latter suited for clinical practice. In a real-life clinical setting, volumetric assessment of MRI scans in dementia patients may require acquisition protocol optimization and does not outperform visual rating scales. KEY POINTS • In a real-life clinical setting, the diagnostic performance of visual rating scales is similar to that of automatic volumetric quantification and may be sufficient to distinguish Alzheimer's disease groups. • Volumetric assessment of gray matter and hippocampal volumes from MRI scans of patients attending non-academic memory clinics fails in up to 32% of cases. • Clinical MR acquisition protocols should be optimized to improve the output of quantitative software for segmentation of Alzheimer's disease-specific outcomes.
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Affiliation(s)
- Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Radiology and Nuclear Medicine, Noordwest Hospital Group, Alkmaar, The Netherlands
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Daniele Altomare
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland ,Memory Clinic, University Hospitals of Geneva, Geneva, Switzerland
| | - Raphael Wurm
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Ronald A. van Schijndel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Marissa Zwan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Femke Bouwman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Niki Schoonenboom
- Geriatric Department, Noordwest Ziekenhuis Groep, Alkmaar, The Netherlands
| | - Leo Boelaarts
- Geriatric Department, Noordwest Ziekenhuis Groep, Alkmaar, The Netherlands
| | - Gerwin Roks
- Department of Neurology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, The Netherlands
| | - Rob van Marum
- Department of Geriatrics, Jeroen Bosch Hospital, ‘S-Hertogenbosch, The Netherlands ,Department of Family Medicine and Elderly Care Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Barbera van Harten
- Department of Neurology, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Inge van Uden
- Department of Neurology, Catharina Hospital, Eindhoven, The Netherlands
| | - Jules Claus
- Department of Neurology, Tergooi Hospital, Blaricum, The Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Mike P. Wattjes
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Institutes of Neurology and Healthcare Engineering, UCL, London, UK
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van Gils AM, Visser LNC, Hendriksen HMA, Georges J, van der Flier WM, Rhodius‐Meester HFM. Development and design of a diagnostic report to support communication in dementia: Co‐creation with patients and care partners. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2022; 14:e12333. [PMID: 36092691 PMCID: PMC9446898 DOI: 10.1002/dad2.12333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/10/2022] [Accepted: 05/10/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Aniek M. van Gils
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
| | - Leonie N. C. Visser
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
- Department of Neurobiology Care Sciences and Society Division of Clinical Geriatrics Center for Alzheimer Research, Karolinska Institutet Stockholm Sweden
- Department of Medical Psychology Amsterdam Public Health Research Institute Amsterdam UMC location AMC Amsterdam The Netherlands
| | - Heleen M. A. Hendriksen
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
| | | | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
- Department of Epidemiology and Biostatistics Amsterdam Neuroscience VU University Medical Center Amsterdam UMC Amsterdam The Netherlands
| | - Hanneke F. M. Rhodius‐Meester
- Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc Amsterdam The Netherlands
- Amsterdam Neuroscience Neurodegeneration Amsterdam The Netherlands
- Department of Internal Medicine Geriatric Medicine Section Amsterdam Cardiovascular Sciences Institute Amsterdam UMC location VUmc Amsterdam The Netherlands
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Study on Adjuvant Medication for Patients with Mild Cognitive Impairment Based on VR Technology and Health Education. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:1187704. [PMID: 34949967 PMCID: PMC8670913 DOI: 10.1155/2021/1187704] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/18/2021] [Accepted: 11/24/2021] [Indexed: 12/02/2022]
Abstract
In order to improve the efficiency of auxiliary medication for patients with mild cognitive impairment, this paper proposes a method based on VR technology and health education. Sixty elderly patients with COPD and MCI admitted to a hospital from January 2019 to February 2020 were randomly divided into a control group and study group, with 50 cases in each group. On the basis of conventional drug therapy, health education, and respiratory muscle training, patients in the control group received routine lung rehabilitation training, while patients in the study group received lung rehabilitation training using the BioMaster virtual scene interactive rehabilitation training system. Both groups continued training for 12 weeks. Lung function indexes, 6-minute walking distance, COPD assessment test (CAT) score, and Montreal Cognitive Function Assessment Scale (MoCA) score were compared between the 2 groups before training and 4, 8, and 12 weeks after training. The experimental results show that, in the study group, the percentage of FEV1 in the predicted value at 8 weeks after training, the percentage of FEV1 in the predicted value at 12 weeks after training, and FEV1/FVC were higher than those in the control group (P < 0.05). There was no significant difference in 6-minute walking distance, CAT score, and MoCA score between the two groups before training (P > 0.05). Twelve weeks after training, patients in the study group had a longer 6-minute walking distance, a lower CAT score, and a higher MoCA score than those in the control group (P < 0.05). It is proved that the application of virtual reality technology in lung rehabilitation training of elderly COPD patients with MCI is effective, which can effectively improve the lung function, cognitive function, and exercise tolerance of the patients and reduce the symptoms of dyspnea and the efficiency of medication.
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van Gils AM, Visser LN, Hendriksen HM, Georges J, Muller M, Bouwman FH, van der Flier WM, Rhodius-Meester HF. Assessing the Views of Professionals, Patients, and Care Partners Concerning the Use of Computer Tools in Memory Clinics: International Survey Study. JMIR Form Res 2021; 5:e31053. [PMID: 34870612 PMCID: PMC8686488 DOI: 10.2196/31053] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/07/2021] [Accepted: 09/18/2021] [Indexed: 12/20/2022] Open
Abstract
Background Computer tools based on artificial intelligence could aid clinicians in memory clinics in several ways, such as by supporting diagnostic decision-making, web-based cognitive testing, and the communication of diagnosis and prognosis. Objective This study aims to identify the preferences as well as the main barriers and facilitators related to using computer tools in memory clinics for all end users, that is, clinicians, patients, and care partners. Methods Between July and October 2020, we sent out invitations to a web-based survey to clinicians using the European Alzheimer’s Disease Centers network and the Dutch Memory Clinic network, and 109 clinicians participated (mean age 45 years, SD 10; 53/109, 48.6% female). A second survey was created for patients and care partners. They were invited via Alzheimer Europe, Alzheimer’s Society United Kingdom, Amsterdam Dementia Cohort, and Amsterdam Aging Cohort. A total of 50 patients with subjective cognitive decline, mild cognitive impairment, or dementia (mean age 73 years, SD 8; 17/34, 34% female) and 46 care partners (mean age 65 years, SD 12; 25/54, 54% female) participated in this survey. Results Most clinicians reported a willingness to use diagnostic (88/109, 80.7%) and prognostic (83/109, 76.1%) computer tools. User-friendliness (71/109, 65.1%); Likert scale mean 4.5, SD 0.7), and increasing diagnostic accuracy (76/109, 69.7%; mean 4.3, SD 0.7) were reported as the main factors stimulating the adoption of a tool. Tools should also save time and provide clear information on reliability and validity. Inadequate integration with electronic patient records (46/109, 42.2%; mean 3.8, SD 1.0) and fear of losing important clinical information (48/109, 44%; mean 3.7, SD 1.2) were most frequently indicated as barriers. Patients and care partners were equally positive about the use of computer tools by clinicians, both for diagnosis (69/96, 72%) and prognosis (73/96, 76%). In addition, most of them thought favorably regarding the possibility of using the tools themselves. Conclusions This study showed that computer tools in memory clinics are positively valued by most end users. For further development and implementation, it is essential to overcome the technical and practical barriers of a tool while paying utmost attention to its reliability and validity.
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Affiliation(s)
- Aniek M van Gils
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Leonie Nc Visser
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Heleen Ma Hendriksen
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | | | - Majon Muller
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Femke H Bouwman
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Hanneke Fm Rhodius-Meester
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
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