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Abdul Manap AS, Almadodi R, Sultana S, Sebastian MG, Kavani KS, Lyenouq VE, Shankar A. Alzheimer's disease: a review on the current trends of the effective diagnosis and therapeutics. Front Aging Neurosci 2024; 16:1429211. [PMID: 39185459 PMCID: PMC11341404 DOI: 10.3389/fnagi.2024.1429211] [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: 05/07/2024] [Accepted: 07/25/2024] [Indexed: 08/27/2024] Open
Abstract
The most prevalent cause of dementia is Alzheimer's disease. Cognitive decline and accelerating memory loss characterize it. Alzheimer's disease advances sequentially, starting with preclinical stages, followed by mild cognitive and/or behavioral impairment, and ultimately leading to Alzheimer's disease dementia. In recent years, healthcare providers have been advised to make an earlier diagnosis of Alzheimer's, prior to individuals developing Alzheimer's disease dementia. Regrettably, the identification of early-stage Alzheimer's disease in clinical settings can be arduous due to the tendency of patients and healthcare providers to disregard symptoms as typical signs of aging. Therefore, accurate and prompt diagnosis of Alzheimer's disease is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms. There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Understanding the evolution of disease-modifying therapies and putting effective diagnostic and therapeutic management into practice requires an understanding of this concept. The outcomes of this study will enhance in-depth knowledge of the current status of Alzheimer's disease's diagnosis and treatment, justifying the necessity for the quest for potential novel biomarkers that can contribute to determining the stage of the disease, particularly in its earliest stages. Interestingly, latest clinical trial status on pharmacological agents, the nonpharmacological treatments such as behavior modification, exercise, and cognitive training as well as alternative approach on phytochemicals as neuroprotective agents have been covered in detailed.
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Affiliation(s)
- Aimi Syamima Abdul Manap
- Department of Biomedical Science, College of Veterinary Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Reema Almadodi
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | - Shirin Sultana
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | | | | | - Vanessa Elle Lyenouq
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | - Aravind Shankar
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
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Ciesla M, Pobst J, Gomes-Osman J, Lamar M, Barnes LL, Banks R, Jannati A, Libon D, Swenson R, Tobyne S, Bates D, Showalter J, Pascual-Leone A. Estimating dementia risk in an African American population using the DCTclock. Front Aging Neurosci 2024; 15:1328333. [PMID: 38274984 PMCID: PMC10810014 DOI: 10.3389/fnagi.2023.1328333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 12/22/2023] [Indexed: 01/27/2024] Open
Abstract
The prevalence of Alzheimer's disease (AD) and related dementias (ADRD) is increasing. African Americans are twice as likely to develop dementia than other ethnic populations. Traditional cognitive screening solutions lack the sensitivity to independently identify individuals at risk for cognitive decline. The DCTclock is a 3-min AI-enabled adaptation of the well-established clock drawing test. The DCTclock can estimate dementia risk for both general cognitive impairment and the presence of AD pathology. Here we performed a retrospective analysis to assess the performance of the DCTclock to estimate future conversion to ADRD in African American participants from the Rush Alzheimer's Disease Research Center Minority Aging Research Study (MARS) and African American Clinical Core (AACORE). We assessed baseline DCTclock scores in 646 participants (baseline median age = 78.0 ± 6.4, median years of education = 14.0 ± 3.2, 78% female) and found significantly lower baseline DCTclock scores in those who received a dementia diagnosis within 3 years. We also found that 16.4% of participants with a baseline DCTclock score less than 60 were significantly more likely to develop dementia in 5 years vs. those with the highest DCTclock scores (75-100). This research demonstrates the DCTclock's ability to estimate the 5-year risk of developing dementia in an African American population. Early detection of elevated dementia risk using the DCTclock could provide patients, caregivers, and clinicians opportunities to plan and intervene early to improve cognitive health trajectories. Early detection of dementia risk can also enhance participant selection in clinical trials while reducing screening costs.
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Affiliation(s)
| | | | - Joyce Gomes-Osman
- Linus Health, Boston, MA, United States
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Melissa Lamar
- Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Lisa L. Barnes
- Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Russell Banks
- Linus Health, Boston, MA, United States
- Department of Communicative Sciences and Disorders, College of Arts and Sciences, Michigan State University, East Lansing, MI, United States
| | - Ali Jannati
- Linus Health, Boston, MA, United States
- Department of Neurology, Harvard Medical School, Boston, MA, United States
| | - David Libon
- Linus Health, Boston, MA, United States
- Department of Geriatrics and Gerontology, New Jersey Institute for Successful Aging, Rowan University School of Osteopathic Medicine, Stratford, NJ, United States
| | - Rodney Swenson
- Linus Health, Boston, MA, United States
- University of North Dakota School of Medicine and Health Sciences, Fargo, ND, United States
| | | | | | | | - Alvaro Pascual-Leone
- Linus Health, Boston, MA, United States
- Department of Neurology, Harvard Medical School, Boston, MA, United States
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, United States
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Giannopoulou P, Vrahatis AG, Papalaskari MA, Vlamos P. The RODI mHealth app Insight: Machine-Learning-Driven Identification of Digital Indicators for Neurodegenerative Disorder Detection. Healthcare (Basel) 2023; 11:2985. [PMID: 37998477 PMCID: PMC10671821 DOI: 10.3390/healthcare11222985] [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: 10/18/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
Neurocognitive Disorders (NCDs) pose a significant global health concern, and early detection is crucial for optimizing therapeutic outcomes. In parallel, mobile health apps (mHealth apps) have emerged as a promising avenue for assisting individuals with cognitive deficits. Under this perspective, we pioneered the development of the RODI mHealth app, a unique method for detecting aligned with the criteria for NCDs using a series of brief tasks. Utilizing the RODI app, we conducted a study from July to October 2022 involving 182 individuals with NCDs and healthy participants. The study aimed to assess performance differences between healthy older adults and NCD patients, identify significant performance disparities during the initial administration of the RODI app, and determine critical features for outcome prediction. Subsequently, the results underwent machine learning processes to unveil underlying patterns associated with NCDs. We prioritize the tasks within RODI based on their alignment with the criteria for NCDs, thus acting as key digital indicators for the disorder. We achieve this by employing an ensemble strategy that leverages the feature importance mechanism from three contemporary classification algorithms. Our analysis revealed that tasks related to visual working memory were the most significant in distinguishing between healthy individuals and those with an NCD. On the other hand, processes involving mental calculations, executive working memory, and recall were less influential in the detection process. Our study serves as a blueprint for future mHealth apps, offering a guide for enhancing the detection of digital indicators for disorders and related conditions.
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Affiliation(s)
- Panagiota Giannopoulou
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (P.G.); (A.G.V.)
| | - Aristidis G. Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (P.G.); (A.G.V.)
| | | | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (P.G.); (A.G.V.)
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