대한치매학회지 (Dementia and Neurocognitive Disorders)

대한치매학회 (Korean Dementia Association)
권호 표지
DOI QR코드

DOI QR Code

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

  • SeongHee Ho (Department of Neurology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Yun Jeong Hong (Department of Neurology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Jee Hyang Jeong (Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University School of Medicine) ;
  • Kee Hyung Park (Department of Neurology, College of Medicine, Gachon University Gil Medical Center) ;
  • SangYun Kim (Department of Neurology, Seoul National University College of Medicine) ;
  • Min Jeong Wang (ROA Neurology Clinic) ;
  • Seong Hye Choi (Department of Neurology, Inha University, School of Medicine) ;
  • SeungHyun Han (ROWAN Inc.) ;
  • Dong Won Yang (Department of Neurology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea)
  • 투고 : 2022.08.29
  • 심사 : 2022.10.28
  • 발행 : 2022.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

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.

키워드

과제정보

Statistical consultation was supported by the Department of Biostatistics of the Catholic Research Coordinating Center.

참고문헌

  1. Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chetelat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement 2014;10:844-852.
  2. Reisberg B, Prichep L, Mosconi L, John ER, Glodzik-Sobanska L, Boksay I, et al. The pre-mild cognitive impairment, subjective cognitive impairment stage of Alzheimer's disease. Alzheimers Dement 2008;4:S98-S108.
  3. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. 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 2011;7:280-292.
  4. Parnetti L, Chipi E, Salvadori N, D'Andrea K, Eusebi P. Prevalence and risk of progression of preclinical Alzheimer's disease stages: a systematic review and meta-analysis. Alzheimers Res Ther 2019;11:7.
  5. Dhillon S. Aducanumab: first approval. Drugs 2021;81:1437-1443.
  6. Gauthier S, Albert M, Fox N, Goedert M, Kivipelto M, Mestre-Ferrandiz J, et al. Why has therapy development for dementia failed in the last two decades? Alzheimers Dement 2016;12:60-64.
  7. Sabbagh MN, Hendrix S, Harrison JE. FDA position statement "early Alzheimer's disease: developing drugs for treatment, guidance for industry". Alzheimers Dement (N Y) 2019;5:13-19.
  8. Hong YJ, Yoon B, Shim YS, Kim SO, Kim HJ, Choi SH, et al. 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 2015;40:158-165.
  9. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington: American Psychiatric Association, 2013.
  10. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256:183-194.
  11. Jessen F, Amariglio RE, Buckley RF, van der Flier WM, Han Y, Molinuevo JL, et al. The characterisation of subjective cognitive decline. Lancet Neurol 2020;19:271-278.
  12. Kang YJ, Na DL. Seoul Neuropsychological Screening Battery (SNSB-II). Seoul: Human Brain Research & Consulting Co., 2012.
  13. Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 2008;27:685-691.
  14. Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P, et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 1992;55:967-972.
  15. Hong YJ, Park JW, Lee SB, Kim SH, Kim Y, Ryu DW, et al. 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 2021;50:437-445.
  16. Sabri O, Sabbagh MN, Seibyl J, Barthel H, Akatsu H, Ouchi Y, et al. Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer's disease: phase 3 study. Alzheimers Dement 2015;11:964-974.
  17. Delorme A, Palmer J, Onton J, Oostenveld R, Makeig S. Independent EEG sources are dipolar. PLoS One 2012;7:e30135.
  18. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004;134:9-21.
  19. Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 2002;24 Suppl D:5-12.
  20. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968-980.
  21. Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 2004;115:2292-2307.
  22. Youn YC, Lee BS, Kim GJ, Ryu JS, Lim K, Lee R, et al. Blood amyloid-β oligomerization as a biomarker of Alzheimer's disease: a blinded validation study. J Alzheimers Dis 2020;75:493-499.
  23. Song M, Lee SH, Jahng S, Kim SY, Kang Y. Validation of the Korean-Everyday Cognition (K-ECog). J Korean Med Sci 2019;34:e67.
  24. Ventry IM, Weinstein BE. The hearing handicap inventory for the elderly: a new tool. Ear Hear 1982;3:128-134.
  25. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606-613.
  26. Huh BY, Yim JH, Bae JM, Choi SS, Kim SW, Hwang HS. The validity of modified Korean-translated BEPSI (Brief Encounter Psychosocial Instrument) as instrument of stress measurement in outpatient clinic. J Korean Acad Fam Med 1996;17:42-53.
  27. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193-213.
  28. Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, Scheltens P, Verhey FR, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 2015;313:1924-1938.
  29. Risacher SL, Kim S, Nho K, Foroud T, Shen L, Petersen RC, et al. APOE effect on Alzheimer's disease biomarkers in older adults with significant memory concern. Alzheimers Dement 2015;11:1417-1429.
  30. Ebenau JL, Timmers T, Wesselman LM, Verberk IM, Verfaillie SC, Slot RE, et al. ATN classification and clinical progression in subjective cognitive decline: the SCIENCe project. Neurology 2020;95:e46-e58.
  31. Walker KA, Sharrett AR, Wu A, Schneider AL, Albert M, Lutsey PL, et al. Association of midlife to late-life blood pressure patterns with incident dementia. JAMA 2019;322:535-545.
  32. Hogan DB, Ebly EM, Rockwood K. Weight, blood pressure, osmolarity, and glucose levels across various stages of Alzheimer's disease and vascular dementia. Dement Geriatr Cogn Disord 1997;8:147-151.
  33. Javanshiri K, Waldo ML, Friberg N, Sjovall F, Wickerstrom K, Haglund M, et al. Atherosclerosis, hypertension, and diabetes in Alzheimer's disease, vascular dementia, and mixed dementia: prevalence and presentation. J Alzheimers Dis 2018;65:1247-1258.
  34. Perrotin A, Mormino EC, Madison CM, Hayenga AO, Jagust WJ. Subjective cognition and amyloid deposition imaging: a Pittsburgh Compound B positron emission tomography study in normal elderly individuals. Arch Neurol 2012;69:223-229.
  35. Amariglio RE, Becker JA, Carmasin J, Wadsworth LP, Lorius N, Sullivan C, et al. Subjective cognitive complaints and amyloid burden in cognitively normal older individuals. Neuropsychologia 2012;50:2880-2886.
  36. Zhou M, Zhang F, Zhao L, Qian J, Dong C. Entorhinal cortex: a good biomarker of mild cognitive impairment and mild Alzheimer's disease. Rev Neurosci 2016;27:185-195.
  37. Varon D, Loewenstein DA, Potter E, Greig MT, Agron J, Shen Q, et al. Minimal atrophy of the entorhinal cortex and hippocampus: progression of cognitive impairment. Dement Geriatr Cogn Disord 2011;31:276-283.
  38. Zhao W, Luo Y, Zhao L, Mok V, Su L, Yin C, et al. Automated brain MRI volumetry differentiates early stages of Alzheimer's disease from normal aging. J Geriatr Psychiatry Neurol 2019;32:354-364.
  39. Luchsinger JA, Gustafson DR. Adiposity, type 2 diabetes, and Alzheimer's disease. J Alzheimers Dis 2009;16:693-704.
  40. Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obes Rev 2011;12:e426-e437.
  41. Chang KV, Hsu TH, Wu WT, Huang KC, Han DS. Association between sarcopenia and cognitive impairment: a systematic review and meta-analysis. J Am Med Dir Assoc 2016;17:1164.e7-1164.e15.
  42. Peng TC, Chen WL, Wu LW, Chang YW, Kao TW. Sarcopenia and cognitive impairment: a systematic review and meta-analysis. Clin Nutr 2020;39:2695-2701.
  43. Zhang J, Peng M, Jia J. Plasma amyloid-β oligomers and soluble tumor necrosis factor receptors as potential biomarkers of AD. Curr Alzheimer Res 2014;11:325-331.
  44. Sun L, Zhong Y, Gui J, Wang X, Zhuang X, Weng J. A hydrogel biosensor for high selective and sensitive detection of amyloid-beta oligomers. Int J Nanomedicine 2018;13:843-856.
  45. Schreiter-Gasser U, Gasser T, Ziegler P. Quantitative EEG analysis in early onset Alzheimer's disease: a controlled study. Electroencephalogr Clin Neurophysiol 1993;86:15-22.
  46. Peel NM, Alapatt LJ, Jones LV, Hubbard RE. 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 2019;74:943-948.
  47. Lin FR, Yaffe K, Xia J, Xue QL, Harris TB, Purchase-Helzner E, et al. Hearing loss and cognitive decline in older adults. JAMA Intern Med 2013;173:293-299.