Study Design and Baseline Results in a Cohort Study to Identify Predictors for the Clinical Progression to Mild Cognitive Impairment or Dementia From Subjective Cognitive Decline (CoSCo) Study
2
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
Background and Purpose: Subjective cognitive decline (SCD) refers to the self-perception of cognitive decline with normal performance on objective neuropsychological tests. SCD, which is the first help-seeking stage and the last stage before the clinical disease stage, can be considered to be the most appropriate time for prevention and treatment. This study aimed to compare characteristics between the amyloid positive and amyloid negative groups of SCD patients. Methods: A cohort study to identify predictors for the clinical progression to mild cognitive impairment (MCI) or dementia from subjective cognitive decline (CoSCo) study is a multicenter, prospective observational study conducted in the Republic of Korea. In total, 120 people aged 60 years or above who presented with a complaint of persistent cognitive decline were selected, and various risk factors were measured among these participants. Continuous variables were analyzed using the Wilcoxon rank-sum test, and categorical variables were analyzed using the χ2 test or Fisher’s exact test. Logistic regression models were used to assess the predictors of amyloid positivity. Results: The multivariate logistic regression model indicated that amyloid positivity on PET was related to a lack of hypertension, atrophy of the left temporal lateral and entorhinal cortex, low body mass index, low waist circumference, less body and visceral fat, fast gait speed, and the presence of the apolipoprotein E ε4 allele in amnestic SCD patients. Conclusions: The CoSCo study is still in progress, and the authors aim to identify the risk factors that are related to the progression of MCI or dementia in amnestic SCD patients through a two-year follow-up longitudinal study.
Abstract
Background and Purpose: Subjective cognitive decline (SCD) refers to the self-perception of cognitive decline with normal performance on objective neuropsychological tests. SCD, which is the first help-seeking stage and the last stage before the clinical disease stage, can be considered to be the most appropriate time for prevention and treatment. This study aimed to compare characteristics between the amyloid positive and amyloid negative groups of SCD patients. Methods: A cohort study to identify predictors for the clinical progression to mild cognitive impairment (MCI) or dementia from subjective cognitive decline (CoSCo) study is a multicenter, prospective observational study conducted in the Republic of Korea. In total, 120 people aged 60 years or above who presented with a complaint of persistent cognitive decline were selected, and various risk factors were measured among these participants. Continuous variables were analyzed using the Wilcoxon rank-sum test, and categorical variables were analyzed using the χ2 test or Fisher’s exact test. Logistic regression models were used to assess the predictors of amyloid positivity. Results: The multivariate logistic regression model indicated that amyloid positivity on PET was related to a lack of hypertension, atrophy of the left temporal lateral and entorhinal cortex, low body mass index, low waist circumference, less body and visceral fat, fast gait speed, and the presence of the apolipoprotein E ε4 allele in amnestic SCD patients. Conclusions: The CoSCo study is still in progress, and the authors aim to identify the risk factors that are related to the progression of MCI or dementia in amnestic SCD patients through a two-year follow-up longitudinal study.
참고문헌 (47)
- Jessen F, "A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease", Alzheimers Dement, 10, pp.844~852 (2014)
- Reisberg B, "The pre-mild cognitive impairment, subjective cognitive impairment stage of Alzheimer’s disease", Alzheimers Dement, 4, pp.S98~S108 (2008)
- Sperling RA, "Toward defining the preclinical stages of Alzheimer’s disease : recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease", Alzheimers Dement, 7, pp.280~292 (2011)
- Parnetti L, "Prevalence and risk of progression of preclinical Alzheimer’s disease stages : a systematic review and meta-analysis", Alzheimers Res Ther, 11, pp.7~ (2019)
- Dhillon S, "Aducanumab : first approval", Drugs, 81, pp.1437~1443 (2021)
- Gauthier S, "Why has therapy development for dementia failed in the last two decades?", Alzheimers Dement, 12, pp.60~64 (2016)
- Sabbagh MN, "FDA position statement “early Alzheimer’s disease: developing drugs for treatment, guidance for industry”", Alzheimers Dement(N Y), 5, pp.13~19 (2019)
- Hong YJ, "Predictors of clinical progression of subjective memory impairment in elderly subjects : data from the Clinical Research Centers for Dementia of South Korea(CREDOS)", Dement Geriatr Cogn Disord, 40, pp.158~165 (2015)
- American Psychiatric Association, "Diagnostic and Statistical Manual of Mental Disorders. 5th ed", American Psychiatric Association, ~ (2013)
- Petersen RC, "Mild cognitive impairment as a diagnostic entity", J Intern Med, 256, pp.183~194 (2004)
- Jessen F, "The characterisation of subjective cognitive decline", Lancet Neurol, 19, pp.271~278 (2020)
- Kang YJ, "Seoul Neuropsychological Screening Battery (SNSB-II)" (2012), Human Brain Research & Consulting Co
- Jack CR Jr, "The Alzheimer’s Disease Neuroimaging Initiative(ADNI) : MRI methods", J Magn Reson Imaging, 27, pp.685~691 (2008)
- Scheltens P, "Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates", J Neurol Neurosurg Psychiatry, 55, pp.967~972 (1992)
- Hong YJ, "The influence of amyloid burden on cognitive decline over 2 years in older adults with subjective cognitive decline : a prospective cohort study", Dement Geriatr Cogn Disord, 50, pp.437~445 (2021)
- Sabri O, "Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer’s disease : phase 3 study", Alzheimers Dement, 11, pp.964~974 (2015)
- Delorme A, "Independent EEG sources are dipolar", PLoS One, 7, pp.e30135~ (2012)
- Delorme A, "EEGLAB : an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis", J Neurosci Methods, 134, pp.9~21 (2004)
- Pascual-Marqui RD, "Standardized low-resolution brain electromagnetic tomography(sLORETA) : technical details", Methods Find Exp Clin Pharmacol, 24(Suppl D), pp.5~12 (2002)
- Desikan RS, "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest", Neuroimage, 31, pp.968~980 (2006)
- Nolte G, "Identifying true brain interaction from EEG data using the imaginary part of coherency", Clin Neurophysiol, 115, pp.2292~2307 (2004)
- Youn YC, "Blood amyloid-β oligomerization as a biomarker of Alzheimer’s disease : a blinded validation study", J Alzheimers Dis, 75, pp.493~499 (2020)
- 송민지, "Validation of the Korean-Everyday Cognition (K-ECog)", Journal of Korean Medical Science, 34(9), pp.1~14 (2019), 대한의학회
- Ventry IM, "The hearing handicap inventory for the elderly : a new tool", Ear Hear, 3, pp.128~134 (1982)
- Kroenke K, "The PHQ-9 : validity of a brief depression severity measure", J Gen Intern Med, 16, pp.606~613 (2001)
- Huh BY, "The validity of modified Korean-translated BEPSI(Brief Encounter Psychosocial Instrument)as instrument of stress measurement in outpatient clinic", J Korean Acad Fam Med, 17, pp.42~53 (1996)
- Buysse DJ, "The Pittsburgh Sleep Quality Index : a new instrument for psychiatric practice and research", Psychiatry Res, 28, pp.193~213 (1989)
- Jansen WJ, "Prevalence of cerebral amyloid pathology in persons without dementia : a meta-analysis", JAMA, 313, pp.1924~1938 (2015)
- Risacher SL, "APOE effect on Alzheimer’s disease biomarkers in older adults with significant memory concern", Alzheimers Dement, 11, pp.1417~1429 (2015)
- Ebenau JL, "ATN classification and clinical progression in subjective cognitive decline : the SCIENCe project", Neurology, 95, pp.e46~e58 (2020)
- Walker KA, "Association of midlife to late-life blood pressure patterns with incident dementia", JAMA, 322, pp.535~545 (2019)
- Hogan DB, "Weight, blood pressure, osmolarity, and glucose levels across various stages of Alzheimer’s disease and vascular dementia", Dement Geriatr Cogn Disord, 8, pp.147~151 (1997)
- Javanshiri K, "Atherosclerosis, hypertension, and diabetes in Alzheimer’s disease, vascular dementia, and mixed dementia : prevalence and presentation", J Alzheimers Dis, 65, pp.1247~1258 (2018)
- Perrotin A, "Subjective cognition and amyloid deposition imaging : a Pittsburgh Compound B positron emission tomography study in normal elderly individuals", Arch Neurol, 69, pp.223~229 (2012)
- Amariglio RE, "Subjective cognitive complaints and amyloid burden in cognitively normal older individuals", Neuropsychologia, 50, pp.2880~2886 (2012)
- Zhou M, "Entorhinal cortex : a good biomarker of mild cognitive impairment and mild Alzheimer’s disease", Rev Neurosci, 27, pp.185~195 (2016)
- Varon D, "Minimal atrophy of the entorhinal cortex and hippocampus : progression of cognitive impairment", Dement Geriatr Cogn Disord, 31, pp.276~283 (2011)
- Zhao W, "Automated brain MRI volumetry differentiates early stages of Alzheimer’s disease from normal aging", J Geriatr Psychiatry Neurol, 32, pp.354~364 (2019)
- Luchsinger JA, "Adiposity, type 2 diabetes, and Alzheimer’s disease", J Alzheimers Dis, 16, pp.693~704 (2009)
- Anstey KJ, "Body mass index in midlife and late-life as a risk factor for dementia : a meta-analysis of prospective studies", Obes Rev, 12, pp.e426~e437 (2011)
- Chang KV, "Association between sarcopenia and cognitive impairment : a systematic review and meta-analysis", J Am Med Dir Assoc, 17, pp.1164.e7~1164.e15 (2016)
- Peng TC, "Sarcopenia and cognitive impairment : a systematic review and meta-analysis", Clin Nutr, 39, pp.2695~2701 (2020)
- Zhang J, "Plasma amyloid-β oligomers and soluble tumor necrosis factor receptors as potential biomarkers of AD", Curr Alzheimer Res, 11, pp.325~331 (2014)
- Sun L, "A hydrogel biosensor for high selective and sensitive detection of amyloid-beta oligomers", Int J Nanomedicine, 13, pp.843~856 (2018)
- Schreiter-Gasser U, "Quantitative EEG analysis in early onset Alzheimer’s disease : a controlled study", Electroencephalogr Clin Neurophysiol, 86, pp.15~22 (1993)
- Peel NM, "The association between gait speed and cognitive status in community-dwelling older people : a systematic review and meta-analysis", J Gerontol A Biol Sci Med Sci, 74, pp.943~948 (2019)
- Lin FR, "Hearing loss and cognitive decline in older adults", JAMA Intern Med, 173, pp.293~299 (2013)
