Diagnosis and Monitoring of Disease Progression Using Deep Neuro Signatures

Mild Cognitive Impairment Clinical Trial

— DNS  

Official title:

DNS Cohort Study Aimed at Understanding the Pathophysiology of AD and AD Related Disorders (ADRD)

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05153941
• Other study ID #: 4151/19-10-2021
• Status: Recruiting
• Start date: January 31, 2022
• Est. completion date: January 31, 2030

Study information

• Verified date: September 2023
• Source: Altoida
• Contact: Carol Murphy, PhD
• Phone: 1 (815) 409-2745
• Email: carol.murphy[@]altoida.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Alzheimer's disease (AD) clinically characterized by the cognitive impairment and lowering of various functional abilities lead to staggering costs and suffering, which are particularly related to the social impacts of caring for increasingly disabled individuals. Some of these changes can be almost undetectable in the early stages of the disease, worsening over time often and at a varying rate of progression in different people. The traditional clinical scales or questionnaires such as ADCS (Alzheimer's Disease Cooperative Study) - ADL (Activities of Daily Living) for detecting such functional disabilities are typically blunt and rely on direct observation or caregiver recall. Digital technologies, particularly those based on the use of smart phones, wearable and/or home-based monitoring devices, here defined as 'Remote Measurement Technologies' (RMTs), provide an opportunity to change radically the way in which functional assessment is undertaken in AD, RMTs have potential to obtain better measurements of behavioral and biological parameters associated with individual Activities of Daily Living (ADL) when compared to the current subjective scales or questionnaires. Divergence from normative ADL profiles could objectively indicate the presence of incipient functional impairment at the very early stages of AD. Therefore, the main hypothesis of this study is that RMTs should allow the detection of impairments in functional components of ADLs that occur below the detection threshold of clinical scale or questionnaires.

Description:

Relevant outcome measurements for the study have been selected through the following longitudinal process: Identification of functional domains that meet one or more of the following criteria: Predicts conversion of MCI due to AD to mild AD dementia. Impaired in mild AD dementia. Predicts functional decline in AD dementia. Reported as important by an AD dementia patient advisory board. Identification of candidate RMTs to cover real-life measurement of the functional domains identified in step 1. Identification of candidate digital biomarkers, which are life-log data such as steps, sleep, heart rate and exercise collected by RMTs, to cover clinical measurement of the functional domains identified in step 1. Functional domains, RMTs, and clinical assessments that resulted from the selection process above are listed in Table 1, and 2. The selection process described in step 1 resulted in the identification of the following functional domains, sorted by relevance [HR (highly relevant), R (relevant), N (neutral), and LR (least relevant)]. The central assumption of the study is that functional disabilities proportionally increase with the progressive worsening of the AD. A recent classification proposed by the FDA 2018 draft guidance for the development of novel treatments in AD identifies the early disease progression in 3 stages: (a) Patients with characteristic pathophysiologic changes of Preclinical AD but no evidence of clinical impact (Stage 1) (b) Patients with characteristic pathophysiologic changes of AD and subtle or more apparent detectable abnormalities on sensitive neuropsychological measures, but no functional impairment (Stage 2), and (c) Patients with characteristic pathophysiologic changes of AD and subtle or more apparent detectable abnormalities on sensitive neuropsychological measures, but mild and detectable functional impairment (Stage 3). The aim of the DNS study is to assess the feasibility, utility and performance of selected RMTs in profiling ADL in real-world settings. This goal will be achieved by evaluating digital signals collected either continuously, or daily, or weekly at home, either using wearable devices or home-located ambient devices, determining as much as possible the specific context-dependent conditions in which the signals are measured. As a general hypothesis, RMTs will deliver more sensitive and less variable measurements when compared to standard clinical assessments, questionnaires and tests measuring specific functional capabilities in the clinics. The most important results of the study will be (1) the evidence that some RMT parameters will provide insights for lower variance than the standard scales or questionnaires, (2) the capacity of Altoida, Inc. NMI and/or RMTs to significantly differentiate the preclinical AD stages 1 & 2 when compared to healthy volunteers with negative AD biomarkers as a control, and (3) a similar capabilities of Altoida, Inc. NMI and/or RMTs to detect monotonic change in the mild cognitive impairment (MCI) due to AD group and, even more, in mild, moderate and severe AD dementia groups, proportional to the specific functional impairment known to worsen during the disease progression.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 3500
• Est. completion date: January 31, 2030
• Est. primary completion date: January 31, 2029
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 50 Years and older

Eligibility

• Inclusion criteria Subjects enrolled in this study are diagnosed based on the established criteria as described below by physicians/medical doctors with expertise in Alzheimer's disease and other neurodegenerative disorders. To be eligible to participate in this study, a subject must meet the following criteria: 1a. For subjects in the Alzheimer's continuum and those with non-AD pathologic changes,

(n=3,110):

• Male or female over 50 years of age.
• Approximately age and gender matched among groups as classified below.
• A study partner (caregiver/family member) is available to collaborate to visit the site together with the subject and give necessary information on the subject.
• Physician's clinical judgement of individuals by classifying into three syndromal stages of cognitive continuum: cognitively unimpaired, mild cognitive impairment, and dementia as described in 2018 NIA-AA research framework [39], [40] while taking into account of clinical assessment performance such as MMSE and CDR scores. This syndromic staging is applicable to all members of a research cohort independent from biomarker profiles.
• Numeric clinical staging in 2018 NIA-AA research framework may also be applied to cognitive staging in the Alzheimer's continuum [40] .
• cognitively unimpaired
• mild cognitive impairment
• mild dementia
• moderate dementia
• severe dementia
• AT(N) biomarker profile as evidenced by CSF test results is combined with the clinical staging for the classification of each subject.
• Aß biomarker positive subjects without cognitive impairment, those with MCI, and those with dementia are considered as preclinical AD, MCI due to AD, and dementia due to AD, respectively. In case otherwise stated, these nomenclatures are used throughout this study.
• Informed consent signed by the subject and/or study partner.
• Study partners should be able to read and communicate in the language of the Hospital site and available to actively engage in tests and questionnaires.
• Subject or the study partner owns a smart phone.
• Their house should allow appropriate Wi-Fi and/or phone line connectivity.
• For those participating in the sub-study from each intended respective subject grouping described in Sub-study section, signature on an additional Informed Consent

1b. Healthy volunteer subjects (n=400):

• Male or female over 50 years of age.
• Individuals with all AT(N) biomarkers at normal levels (i.e., A-,T-, (N)-) as confirmed by negative status of respective AD biomarker test utilized in this study.
• Approximately age and gender matched to subjects in the Alzheimer's continuum and those with non-AD pathologic changes on a group level.
• A study partner is available to collaborate.
• Cognitively unimpaired as defined by syndrome staging of cognitive continuum in 2018 NIA-AA research framework [40], [41] supported by each of the following test scores: MMSE =27, and CDR 0.
• In otherwise good health conditions, or with diagnosis mild chronic disorders (of metabolic, respiratory, immunological, cardiologic, and metabolic origin) or any other affections that are controlled by the therapy and do not importantly limit ADLs or social interactions.
• Able to read and to communicate in the language of the recruitment center.
• Informed consent signed by the subject and study partner.
• Subject or study partner owns a smart phone.
• Their house should allow appropriate Wi-Fi and/or phone line connectivity.
• For those participating in the sub-study from each intended respective subject grouping described in Sub-study section, signature on an additional Informed Consent Exclusion criteria A potential subject who meets any of the following criteria will be excluded from participation in this study. Those criteria would be applied at the subject screening:

2a. For subjects in the Alzheimer's continuum and those with non-AD pathologic changes:

• Presence of an additional neurological, psychiatric, or chronic disease that may affect ADL, cognitive function or social interactions.
• Abnormal VB12 value.
• Any other kind of disorders that relevantly affect mobility and/or ADL, cognitive function or social interactions (e.g., immune-mediated inflammatory disorders, recovery from recent trauma, stroke, etc.). MRI assessment should be utilized for verifying those disorders.
• TSH above normal range
• T3 or T4 outside normal range with clinically significant.
• Positive test for SARS-CoV-2 on a nasopharyngeal swab
• Failure to show negative PCR results for Covid19 or proof of vaccination

2b. Healthy volunteer subjects:

• Presence of an additional neurological, psychiatric, or chronic disease that may affect ADL, cognitive function or social interactions.
• Diagnosis of any disorders or post traumatic conditions that are not fully controlled by the therapy and produce relevant limitations of ADL, cognitive function or social interactions.
• Positive test for SARS-CoV-2 on a nasopharyngeal swab
• Failure to show negative PCR results for Covid19 or proof of vaccination

Related Conditions & MeSH terms

• Alzheimer Disease
• Alzheimer's Disease
• Cognitive Dysfunction
• Mild Cognitive Impairment

Locations

Country	Name	City	State
Greece	Nikaia Ag Panteleimon Hospital	Athens

Sponsors (3)

Lead Sponsor	Collaborator
Altoida	Eisai Co., Ltd., Ionian University

Country where clinical trial is conducted

Greece, 

References & Publications (34)

10. McLeod, D.R., et al., An automated version of the digit symbol substitution test (DSST). Behavior Research Methods & Instrumentation, 1982. 14(5): p. 463-466.

Barker J, Smith Byrne K, Doherty A, Foster C, Rahimi K, Ramakrishnan R, Woodward M, Dwyer T. Physical activity of UK adults with chronic disease: cross sectional analysis of accelerometer measured physical activity in 96 706 UK Biobank participants. Int J Epidemiol. 2019 Aug 1;48(4):1386. doi: 10.1093/ije/dyz148. No abstract available. — View Citation

Barker J, Smith Byrne K, Doherty A, Foster C, Rahimi K, Ramakrishnan R, Woodward M, Dwyer T. Physical activity of UK adults with chronic disease: cross-sectional analysis of accelerometer-measured physical activity in 96 706 UK Biobank participants. Int J Epidemiol. 2019 Aug 1;48(4):1167-1174. doi: 10.1093/ije/dyy294. Erratum In: Int J Epidemiol. 2019 Aug 1;48(4):1386. — View Citation

Bertens D, Tijms BM, Vermunt L, Prins ND, Scheltens P, Visser PJ. The effect of diagnostic criteria on outcome measures in preclinical and prodromal Alzheimer's disease: Implications for trial design. Alzheimers Dement (N Y). 2017 Sep 21;3(4):513-523. doi: 10.1016/j.trci.2017.08.005. eCollection 2017 Nov. — View Citation

Birchwood M, Smith J, Cochrane R, Wetton S, Copestake S. The Social Functioning Scale. The development and validation of a new scale of social adjustment for use in family intervention programmes with schizophrenic patients. Br J Psychiatry. 1990 Dec;157:853-9. doi: 10.1192/bjp.157.6.853. — View Citation

Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, Wykes T, Richardson MP; RADAR-CNS Consortium. Wearable technology in epilepsy: The views of patients, caregivers, and healthcare professionals. Epilepsy Behav. 2018 Aug;85:141-149. doi: 10.1016/j.yebeh.2018.05.044. Epub 2018 Jun 23. — View Citation

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 May;28(2):193-213. doi: 10.1016/0165-1781(89)90047-4. — View Citation

Doherty A, Smith-Byrne K, Ferreira T, Holmes MV, Holmes C, Pulit SL, Lindgren CM. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat Commun. 2018 Dec 10;9(1):5257. doi: 10.1038/s41467-018-07743-4. — View Citation

Doherty AR, Hodges SE, King AC, Smeaton AF, Berry E, Moulin CJ, Lindley S, Kelly P, Foster C. Wearable cameras in health: the state of the art and future possibilities. Am J Prev Med. 2013 Mar;44(3):320-3. doi: 10.1016/j.amepre.2012.11.008. No abstract available. — View Citation

Ferguson A, Lyall LM, Ward J, Strawbridge RJ, Cullen B, Graham N, Niedzwiedz CL, Johnston KJA, MacKay D, Biello SM, Pell JP, Cavanagh J, McIntosh AM, Doherty A, Bailey MES, Lyall DM, Wyse CA, Smith DJ. Genome-Wide Association Study of Circadian Rhythmicity in 71,500 UK Biobank Participants and Polygenic Association with Mood Instability. EBioMedicine. 2018 Sep;35:279-287. doi: 10.1016/j.ebiom.2018.08.004. Epub 2018 Aug 14. — View Citation

Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975 Nov;12(3):189-98. doi: 10.1016/0022-3956(75)90026-6. No abstract available. — View Citation

Galasko D, Bennett D, Sano M, Ernesto C, Thomas R, Grundman M, Ferris S. An inventory to assess activities of daily living for clinical trials in Alzheimer's disease. The Alzheimer's Disease Cooperative Study. Alzheimer Dis Assoc Disord. 1997;11 Suppl 2:S33-9. — View Citation

Hughes TF, Chang CC, Vander Bilt J, Ganguli M. Engagement in reading and hobbies and risk of incident dementia: the MoVIES project. Am J Alzheimers Dis Other Demen. 2010 Aug;25(5):432-8. doi: 10.1177/1533317510368399. — View Citation

Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J, Liu E, Molinuevo JL, Montine T, Phelps C, Rankin KP, Rowe CC, Scheltens P, Siemers E, Snyder HM, Sperling R; Contributors. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018 Apr;14(4):535-562. doi: 10.1016/j.jalz.2018.02.018. — View Citation

Jutten RJ, Peeters CFW, Leijdesdorff SMJ, Visser PJ, Maier AB, Terwee CB, Scheltens P, Sikkes SAM. Detecting functional decline from normal aging to dementia: Development and validation of a short version of the Amsterdam IADL Questionnaire. Alzheimers Dement (Amst). 2017 Mar 31;8:26-35. doi: 10.1016/j.dadm.2017.03.002. eCollection 2017. — View Citation

Kaufer DI, Cummings JL, Ketchel P, Smith V, MacMillan A, Shelley T, Lopez OL, DeKosky ST. Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. J Neuropsychiatry Clin Neurosci. 2000 Spring;12(2):233-9. doi: 10.1176/jnp.12.2.233. — View Citation

Koster N, Knol DL, Uitdehaag BM, Scheltens P, Sikkes SA. The sensitivity to change over time of the Amsterdam IADL Questionnaire((c)). Alzheimers Dement. 2015 Oct;11(10):1231-40. doi: 10.1016/j.jalz.2014.10.006. Epub 2015 Jan 15. — View Citation

Matcham F, Barattieri di San Pietro C, Bulgari V, de Girolamo G, Dobson R, Eriksson H, Folarin AA, Haro JM, Kerz M, Lamers F, Li Q, Manyakov NV, Mohr DC, Myin-Germeys I, Narayan V, Bwjh P, Ranjan Y, Rashid Z, Rintala A, Siddi S, Simblett SK, Wykes T, Hotopf M; RADAR-CNS consortium. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol. BMC Psychiatry. 2019 Feb 18;19(1):72. doi: 10.1186/s12888-019-2049-z. — View Citation

Miller, N.E., et al., Accuracy Of Behavioral Assessment With A Wearable Camera in Semi-structured And Free Living Conditions In Older Adults. Medicine and Science in Sports and Exercise, 2017. 49(5): p. 651-651.

Narayan VA, Manji HK. Moving from 'diagnose and treat' to 'predict and pre-empt' in neuropsychiatric disorders. Nat Rev Drug Discov. 2016 Feb;15(2):71-2. doi: 10.1038/nrd.2015.20. Epub 2015 Nov 27. — View Citation

Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971 Mar;9(1):97-113. doi: 10.1016/0028-3932(71)90067-4. No abstract available. — View Citation

Owens AP, Hinds C, Manyakov NV, Stavropoulos TG, Lavelle G, Gove D, Diaz-Ponce A, Aarsland D. Selecting Remote Measurement Technologies to Optimize Assessment of Function in Early Alzheimer's Disease: A Case Study. Front Psychiatry. 2020 Nov 5;11:582207. doi: 10.3389/fpsyt.2020.582207. eCollection 2020. — View Citation

Pedrosa H, De Sa A, Guerreiro M, Maroco J, Simoes MR, Galasko D, de Mendonca A. Functional evaluation distinguishes MCI patients from healthy elderly people--the ADCS/MCI/ADL scale. J Nutr Health Aging. 2010 Oct;14(8):703-9. doi: 10.1007/s12603-010-0102-1. — View Citation

Petersen RC, Wiste HJ, Weigand SD, Fields JA, Geda YE, Graff-Radford J, Knopman DS, Kremers WK, Lowe V, Machulda MM, Mielke MM, Stricker NH, Therneau TM, Vemuri P, Jack CR Jr. NIA-AA Alzheimer's Disease Framework: Clinical Characterization of Stages. Ann Neurol. 2021 Jun;89(6):1145-1156. doi: 10.1002/ana.26071. Epub 2021 Apr 6. — View Citation

Ries JD, Echternach JL, Nof L, Gagnon Blodgett M. Test-retest reliability and minimal detectable change scores for the timed "up & go" test, the six-minute walk test, and gait speed in people with Alzheimer disease. Phys Ther. 2009 Jun;89(6):569-79. doi: 10.2522/ptj.20080258. Epub 2009 Apr 23. — View Citation

Robert P, Ferris S, Gauthier S, Ihl R, Winblad B, Tennigkeit F. Review of Alzheimer's disease scales: is there a need for a new multi-domain scale for therapy evaluation in medical practice? Alzheimers Res Ther. 2010 Aug 26;2(4):24. doi: 10.1186/alzrt48. — View Citation

Sikkes SA, Knol DL, Pijnenburg YA, de Lange-de Klerk ES, Uitdehaag BM, Scheltens P. Validation of the Amsterdam IADL Questionnaire(c), a new tool to measure instrumental activities of daily living in dementia. Neuroepidemiology. 2013;41(1):35-41. doi: 10.1159/000346277. Epub 2013 May 25. — View Citation

Stavropoulos, T.G., G. Meditskos, and I. Kompatsiaris, DemaWare2 AND Integrating sensors, multimedia and semantic analysis for the ambient care of dementia. Pervasive and Mobile Computing, 2017. 34 : p. 126-145.

Swaminathan A, Jicha GA. Nutrition and prevention of Alzheimer's dementia. Front Aging Neurosci. 2014 Oct 20;6:282. doi: 10.3389/fnagi.2014.00282. eCollection 2014. — View Citation

Tarnanas I, Tsolaki A, Wiederhold M, Wiederhold B, Tsolaki M. Five-year biomarker progression variability for Alzheimer's disease dementia prediction: Can a complex instrumental activities of daily living marker fill in the gaps? Alzheimers Dement (Amst). 2015 Nov 14;1(4):521-32. doi: 10.1016/j.dadm.2015.10.005. eCollection 2015 Dec. — View Citation

van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimaki M, Frayling TM, Pack AI, Bucan M, Trenell MI, Mazzotti DR, Gehrman PR, Singh-Manoux BA, Weedon MN. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep. 2018 Aug 28;8(1):12975. doi: 10.1038/s41598-018-31266-z. — View Citation

Vermunt L, Sikkes SAM, van den Hout A, Handels R, Bos I, van der Flier WM, Kern S, Ousset PJ, Maruff P, Skoog I, Verhey FRJ, Freund-Levi Y, Tsolaki M, Wallin AK, Olde Rikkert M, Soininen H, Spiru L, Zetterberg H, Blennow K, Scheltens P, Muniz-Terrera G, Visser PJ; Alzheimer Disease Neuroimaging Initiative; AIBL Research Group; ICTUS/DSA study groups. Duration of preclinical, prodromal, and dementia stages of Alzheimer's disease in relation to age, sex, and APOE genotype. Alzheimers Dement. 2019 Jul;15(7):888-898. doi: 10.1016/j.jalz.2019.04.001. Epub 2019 Jun 1. — View Citation

Weintraub S, Carrillo MC, Farias ST, Goldberg TE, Hendrix JA, Jaeger J, Knopman DS, Langbaum JB, Park DC, Ropacki MT, Sikkes SAM, Welsh-Bohmer KA, Bain LJ, Brashear R, Budur K, Graf A, Martenyi F, Storck MS, Randolph C. Measuring cognition and function in the preclinical stage of Alzheimer's disease. Alzheimers Dement (N Y). 2018 Feb 13;4:64-75. doi: 10.1016/j.trci.2018.01.003. eCollection 2018. — View Citation

Wright, S.P., et al., An analysis of how consumer physical activity monitors are used in biomedical research. Faseb Journal, 2017. 31.

* Note: There are 34 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	ADL using selected RMTs	Assess statistically significant difference between healthy volunteers, preclinical AD, MCI due to AD, mild, moderate and severe AD dementia in outcome measures of ADL using selected RMTs.	5 years
Primary	Neuropsychological assessment like the Clinical Dementia Rating (CDR) scale	Assess statistically significant difference between healthy volunteers, preclinical AD, MCI due to AD, mild, moderate and severe AD dementia using RMTs, neuropsychological assessment like CDR.	5 years
Primary	Neuropsychological assessment like Altoida, Inc. Neuro Motor Index (NMI) medical device	Assess statistically significant difference between healthy volunteers, preclinical AD, MCI due to AD, mild, moderate and severe AD dementia using RMTs, neuropsychological assessment like the Altoida, Inc. Neuro Motor Index (NMI) medical device.	5 years
Primary	Demographics, medical history, physical status, life-habits, and medication from the analysis of neuropsychological assessments.	Assess statistically significant difference between healthy volunteers, preclinical AD, MCI due to AD, mild, moderate and severe AD dementia focusing on their demographics, medical history, physical status, life-habits, and medication from the analysis of neuropsychological assessments.	5 years
Primary	Demographics, medical history, physical status, life-habits, and medication from the analysis of biomarker measurements.	Assess statistically significant difference between healthy volunteers, preclinical AD, MCI due to AD, mild, moderate and severe AD dementia focusing on their demographics, medical history, physical status, life-habits, and medication from the analysis of biomarker measurements.	5 years
Primary	Demographics, medical history, physical status, life-habits, and medication from the analysis of RMTs	Assess statistically significant difference between healthy volunteers, preclinical AD, MCI due to AD, mild, moderate and severe AD dementia focusing on their demographics, medical history, physical status, life-habits, and medication from the analysis of RMTs.	5 years
Primary	Demographics, medical history, physical status, life-habits, and medication from the analysis of Altoida NMI medical device.	Assess statistically significant difference between healthy volunteers, preclinical AD, MCI due to AD, mild, moderate and severe AD dementia focusing on their demographics, medical history, physical status, life-habits, and medication from the analysis of Altoida NMI medical device.	5 years

External Links

•   European Medicines Agency
•   Functional Activities Questionnaire
•   Guideline clinical investigation medicines treatment Alzheimer's disease other dementias