Diagnóstico Precoz en la Enfermedad de Alzheimer y su Impacto sobre el Deterioro Cognitivo y Dependencia
Palabras clave:
enfermedad de alzheimer, diagnóstico precoz, biomarcadores, deterioro cognitivo leve, actividades de la vida diaria
Resumen
Introducción: La enfermedad de Alzheimer es la principal causa de demencia a nivel global. El diagnóstico tardío limita la ventana de oportunidad terapéutica, acelerando la pérdida de autonomía del paciente. El uso de biomarcadores moleculares y neuroimagen en fases prodrómicas permite identificar la proteinopatía antes de la instauración de daño neuronal irreversible. Objetivo: Sintetizar la evidencia científica terciaria y secundaria sobre la precisión de los biomarcadores moleculares y técnicas de neuroimagen en el diagnóstico prodrómico de la enfermedad de Alzheimer, evaluando su impacto en el control del deterioro cognitivo y la preservación de la funcionalidad. Métodos: Se desarrolló una revisión en paraguas (umbrella review) siguiendo los lineamientos del Instituto Joanna Briggs (JBI) y la declaración PRISMA. La calidad metodológica y el riesgo de sesgo se evaluaron mediante la herramienta AMSTAR 2. La síntesis de los datos se realizó mediante análisis cualitativo y narrativo estructurado. Resultados: La cuantificación de la razón Abeta42/Abeta40 en líquido cefalorraquídeo y la determinación de biomarcadores plasmáticos, particularmente la isoforma p-tau217, alcanzaron una precisión diagnóstica superior al 90% (AUC > 0.95), equiparable a la tomografía por emisión de positrones de amiloide. La intervención terapéutica con anticuerpos monoclonales anti-amiloide durante el deterioro cognitivo leve demostró una desaceleración de hasta el 27%-35% en el declive cognitivo global medido por escalas CDR-SB e iADRS. La detección oportuna prolongó la independencia en actividades instrumentales de la vida diaria, mitigando la sobrecarga del cuidador y reduciendo los costos proyectados por institucionalización y dependencia severa. Conclusiones: La identificación biológica de la enfermedad de Alzheimer en etapas prodrómicas mediante biomarcadores plasmáticos y de neuroimagen optimiza la estratificación del riesgo y posibilita la intervención médica precoz. Esta estrategia frena la progresión del deterioro cognitivo, retrasa la dependencia funcional y alivia de forma significativa la carga socioeconómica sobre los sistemas sanitarios.
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2. Hansson O, Blennow K, Zetterberg H, Dage J. Blood biomarkers for Alzheimer’s disease in clinical practice and trials. Nat Aging. 2023 May 1;3(5):506–19. doi:10.1038/S43587-023-00403-3 PubMed PMID: 37202517.
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4. Gamble LD, Clare L, Opdebeeck C, Martyr A, Jones RW, Rusted JM, et al. Cognitive reserve and its impact on cognitive and functional abilities, physical activity and quality of life following a diagnosis of dementia: longitudinal findings from the Improving the experience of Dementia and Enhancing Active Life (IDEAL) study. Age Ageing. 2025 Jan 1;54(1). doi:10.1093/AGEING/AFAE284 PubMed PMID: 39775724.
5. Öksüz N, Ghouri R, Taşdelen B, Uludüz D, Özge A. Mild Cognitive Impairment Progression and Alzheimer’s Disease Risk: A Comprehensive Analysis of 3553 Cases over 203 Months. J Clin Med. 2024 Jan 1;13(2):518. doi:10.3390/JCM13020518 PubMed PMID: 38256652.
6. Dyck CH van, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, et al. Lecanemab in Early Alzheimer’s Disease. New England Journal of Medicine. 2023 Jan 5;22(3):142–3. doi:10.1056/NEJMOA2212948 PubMed PMID: 36449413.
7. Sims JR, Zimmer JA, Evans CD, Lu M, Ardayfio P, Sparks JD, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial. JAMA. 2023 Aug 8;330(6):512–27. doi:10.1001/JAMA.2023.13239 PubMed PMID: 37459141.
8. Maji SK, Anoop A, Singh PK, Jacob RS. CSF Biomarkers for Alzheimer’s Disease Diagnosis. Int J Alzheimers Dis. 2010;2010:606802. doi:10.4061/2010/606802 PubMed PMID: 20721349.
9. Ghahremani M, Smith EE, Ismail Z. Persistent functional impairment as an early indicator of cognitive decline and dementia in cognitively normal older adults. J Alzheimers Dis. 2026 Feb 1;109(3):1415–27. doi:10.1177/13872877251406661 PubMed PMID: 41428488.
10. Sheikh-Bahaei N, Sajjadi SA, Manavaki R, Gillard JH. Imaging Biomarkers in Alzheimer’s Disease: A Practical Guide for Clinicians. J Alzheimers Dis Rep. 2017;1(1):71. doi:10.3233/ADR-170013 PubMed PMID: 30480230.
11. Nandi A, Counts N, Chen S, Seligman B, Tortorice D, Vigo D, et al. Global and regional projections of the economic burden of Alzheimer’s disease and related dementias from 2019 to 2050: A value of statistical life approach. EClinicalMedicine. 2022 Sep 1;51:101580. doi:10.1016/J.ECLINM.2022.101580 PubMed PMID: 35898316.
12. Chandler J, Done N, Desai U, Georgieva M, Gomez-Lievano A, Ye W, et al. Potential Implications of Slowing Disease Progression in Amyloid-Positive Early Alzheimer’s Disease: Estimates from Real-World Data. Journal of Prevention of Alzheimer’s Disease. 2024 Mar 1;11(2):310–9. doi:10.14283/JPAD.2024.27 PubMed PMID: 38374737.
13. Chandler JM, Ye W, Mi X, Doty EG, Johnston JA. Potential Impact of Slowing Disease Progression in Early Symptomatic Alzheimer’s Disease on Patient Quality of Life, Caregiver Time, and Total Societal Costs: Estimates Based on Findings from GERAS-US Study. Journal of Alzheimer’s Disease. 2024 Jul 16;100(2):563–78. doi:10.3233/JAD-231166 PubMed PMID: 38875031.
14. Raphael D de L. Neuropsychiatric symptoms in mild cognitive impairment and early Alzheimer’s disease: Clinical pattern and diagnostic implications. AIMS Neurosci. 2025;12(4):676–705. doi:10.3934/NEUROSCIENCE.2025032
15. Govindarajan R, Thirunadanasikamani K, Napa KK, Sathya S, Murugan JS, Priya KGC. Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features. MethodsX. 2025 Dec 1;15. doi:10.1016/J.MEX.2025.103491
16. Patel D, Patel T, Patel PN. Integrative Analysis of Pharmacological and Non-pharmacological Interventions in Alzheimer’s Dementia. Cureus. 2026 Apr 20. doi:10.7759/CUREUS.107386
17. Gosalia J, Spicuzza JMD, Bowlus CK, Pawelczyk JA, Proctor DN. Linking metabolic syndrome, cerebral small vessel disease, and cognitive health: insights from a subclinical population study using TriNetX. Geroscience. 2025 Jun 1;47(3):3791–806. doi:10.1007/S11357-024-01456-X PubMed PMID: 39708217.
18. Ruthirakuhan M, Guan DX, Mortby M, Gatchel J, Babulal GM. Updates and future perspectives on neuropsychiatric symptoms in Alzheimer’s disease. Alzheimer’s and Dementia. 2025 Mar 1;21(3). doi:10.1002/ALZ.70079 PubMed PMID: 40145329.
19. Devanarayan V, Donohue MC, Sperling RA, Johnson KA, Ye Y, Charil A, et al. Multimodal prognostic modeling of individual cognitive trajectories to enhance trial efficiency in preclinical Alzheimer’s disease. Alzheimer’s and Dementia. 2025 Sep 1;21(9). doi:10.1002/ALZ.70702 PubMed PMID: 40990131.
20. Abuhantash F, Welsch R, Finkelstein S, AlShehhi A. Alzheimer’s disease risk prediction using machine learning for survival analysis with a comorbidity-based approach. Sci Rep. 2025 Dec 1;15(1). doi:10.1038/S41598-025-14406-0 PubMed PMID: 40770222.
21. Jabeen T, Karimi F, Zomorrodi AR, Khalilpour K. Multi-modal machine learning and gut microbiome pathway analysis for Alzheimer’s risk prediction. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring. 2026 Apr 1;18(2). doi:10.1002/DAD2.70340
22. Anselmo A, Corallo F, Pagano M, Cardile D, Marra A, Maresca G, et al. Beyond Diagnosis: Exploring Residual Autonomy in Dementia Through a Systematic Review. Medicina (Lithuania). 2025 May 1;61(5). doi:10.3390/MEDICINA61050895 PubMed PMID: 40428853.
23. Hayes CA, Abdullah L, Gills J, Odden MC. Population intervention models of racial ethnic disparities in cognitive outcomes from cardiometabolic risk factors – HABS-HD. Alzheimer’s Research and Therapy . 2025 Dec 1;17(1). doi:10.1186/S13195-025-01866-9 PubMed PMID: 41035086.
24. Shi Y, Fang Z, Chen Y, Lin R, Huang C, Li H, et al. Prevalence and risk factors of loneliness in older adults with preclinical and prodromal Alzheimer’s disease: the impact of depression, frailty-weight loss and quality of life. BMC Geriatr. 2026 Dec 1;26(1). doi:10.1186/S12877-026-07432-8 PubMed PMID: 41965547.
25. Morozova I, Zorkina Y, Berdalin A, Ikonnikova A, Emelyanova M, Fedoseeva E, et al. Dynamics of Cognitive Impairment in MCI Patients over a Three-Year Period: The Informative Role of Blood Biomarkers, Neuroimaging, and Genetic Factors. Diagnostics. 2024 Sep 1;14(17). doi:10.3390/DIAGNOSTICS14171883
26. Manzano Palomo MS, Terrón Cuadrado C, Gil Moreno MJ, Fernández García C, Alonso Bouzón C, Besga Basterra A, et al. Mild functional impairment: Definition and diagnostic criteria. Neurología (English Edition). 2026 May;502093. doi:10.1016/J.NRLENG.2026.502093
27. Feyissa AM. Before It’s Too Late: Recognizing the Impact of Seizures and Risk Factors for Late-Onset Epilepsy in Dementia and Mild Cognitive Impairment. Epilepsy Curr. 2025 Dec 1;25(6):392–4. doi:10.1177/15357597251366616
28. Zahr NM. Alcohol Use Disorder and Dementia: A Review. Alcohol Res. 2024;44(1). doi:10.35946/ARCR.V44.1.03 PubMed PMID: 38812709.
29. Cavaco MA, Jang SR, Olsen C, Bodnar C, Ferko N. Global Societal Burden of Alzheimer’s Disease by Severity: a Targeted Literature Review. Neurol Ther. 2025 Oct 1;14(5):1797–826. doi:10.1007/S40120-025-00815-W
30. Altuna M, Rodríguez E, Núñez M, Trueba-Saiz Á, Lizan L, Díaz-Cerezo S. A Systematic Literature Review of the Epidemiological, Diagnostic Workup, Humanistic, and Economic Burden of Alzheimer’s Disease in Spain. Cureus. 2026 Jan 15. doi:10.7759/CUREUS.101581
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Publicado
2026-10-23
Cómo citar
Blacio Arce, E. M., & Chu Lee, A. J. (2026). Diagnóstico Precoz en la Enfermedad de Alzheimer y su Impacto sobre el Deterioro Cognitivo y Dependencia. Ciencia Latina Revista Científica Multidisciplinar, 10(5), 4008-4030. https://doi.org/10.37811/cl_rcm.v10i5.26272
Número
Sección
Ciencias de la Salud
Derechos de autor 2026 Elias Moisés Blacio Arce, Angel Jose Chu Lee
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
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