Cerebrospinal Fluid Biomarkers in the Workup of Cognitive Symptoms: A Four Year Prospective Cohort: Diagnostic Reclassification and Predictive Performance of ATN Profiling in Memory Clinic Patients

Dr. Almas Qureshi, Dolly, Dr. Arun Kumar

Alzheimer’s Disease; CSF Biomarkers; Amyloid-Beta; Phospho-Tau; ATN Classification; Mild Cognitive Impairment; Dementia Conversion

Cerebrospinal fluid (CSF) biomarkers for amyloid-beta (Aβ42, Aβ42/Aβ40 ratio) and tau (total tau, phospho-tau-181) have transformed the diagnostic workup of cognitive symptoms by enabling in-vivo characterisation of underlying Alzheimer pathology. The ATN classification framework amyloid (A), tau (T), and neurodegeneration (N) has emerged as the operational structure for biomarker interpretation. We undertook a prospective cohort study of 224 patients with cognitive symptoms referred for CSF biomarker testing at a tertiary memory clinic, with 4-year follow-up to characterise diagnostic reclassification rates, conversion trajectories, and predictive performance of the ATN framework. The A+T+ profile was identified in 96 patients (42.9%), A+T- in 32 (14.3%), A-T+ in 22 (9.8%), and A-T- in 54 (24.1%). The remaining 20 patients (8.9%) had indeterminate samples. CSF biomarkers led to diagnostic reclassification in 68 patients (30.4%), most commonly from probable AD to non-AD (removal of an AD label in patients with A-T- profile) or from non-specific MCI to prodromal AD (A+T+ profile). Cumulative dementia conversion at 4 years was 55% in A+T+ MCI patients compared with 17% in A-T- MCI patients. Strongest independent predictors of conversion included biomarker profile, lower baseline MoCA, hippocampal atrophy, APOE ε4 status, and advanced age. The findings support routine CSF biomarker assessment in suspected early Alzheimer disease.

[1] Agarwal, A., Kumar, D., & S, P. M. (2026). Optimizing clinical effectiveness of enhanced recovery after surgery (ERAS): Multidisciplinary pathways, patient-centered outcomes, and data-driven performance analytics. International Innovations & Scholarly Trends Journal, 2(2).
[2] Bhatnagar, M., Kumar, N., & Shivam. (2026). Quality improvement frameworks in modern surgical practice: Evidence-based models, implementation science, and outcome-oriented performance evaluation. International Journal of Scientific Research and Engineering Development, 9(2).
[3] Catherine, S., Gupta, N., Gopi, E., & Swadhi, R. (2025). Enhancing patient engagement and outcomes through digital transformation: Machine learning in medical marketing. In Impact of digital transformation on business growth and performance (pp. 285–312).
[4] Deepa, R., Swadhi, R., Udayavani, V., Lakshmi, R., & Rafiq, S. (2026). Motion-controlled wearables for physiological monitoring and predictive diagnostics. In R. Vettriselvan & N. Suresh (Eds.), Intelligent motion control for human-centered systems (pp. 1–28).
[5] A. S. Aneeshkumar & C. JothiVenkateswaran (2026), Relevance Study of Data Mining for the Identification of Negatively Influenced Factors in Sick Groups, Procedia Computer Science, Elsevier, 47, pp.101-108
[6] Gautam, M., Samyal, M., & Chaudhary, S. (2026). Preoperative risk stratification and surgical outcome prediction: Integrating clinical scoring systems, data-driven models, and patient-centered optimization. International Innovations & Scholarly Trends J., 2(3).
[7] Jha, S. C., Kumar, P., & Neha. (2026). Artificial intelligence-assisted decision support in internal medicine: Enhancing clinical judgment, precision care, and health system performance. Inter. Journal of Scientific Development and Research, 11(2).
[8] Kumar, P., Gautam, S., & Maitiy, S. (2026). Diagnostic utility of biomarkers in early disease stratification: Clinical applications, predictive value, and emerging innovations. Journal of Emerging Technologies and Innovative Research, 13(2).
[9] Kumar, R., Sharma, K., & Gupta, S. K. (2026). Multimorbidity patterns and therapeutic complexity in adult medical practice: Implications for polypharmacy and patient-centred care. International Journal of Creative Research Thoughts, 14(2).
[10] Rasi, R. A., & Ashifa, K. M. (2019). Role of community-based programmes for active ageing: Elders self-help group in Kerala. Indian Journal of Public Health Research & Development, 10(12).
[11] Sahu, R. L., Sharma, K., & Gupta, S. K. (2026). Biological and mechanical determinants of fracture healing: An integrated mechano-biological, systemic, and translational framework. International Journal of Recent Development in Engineering and Technology, 15(3).
[12] Selvi, K., Anbarasan, P., Madhumita, G., Janaki, L., & Devi, K. K. (2026). Governance, security, and ethical considerations in AI-driven motion control systems. In Methodologies and applications of intelligent motion control systems (pp. 217–242). IGI Global.
[13] Subramani, M., Chillagattu, V., Gayathri, K., Rastogi, V., & Ranganathan, S. (2026). Digital twin integration for predictive and real-time motion control in infrastructure engineering. In Methodologies and applications of intelligent motion control systems (pp. 189–216). IGI Global.
[14] Swadhi, R., Gayathri, K., Suresh, N. V., Catherine, S., & Velmurugan, P. R. (2025). Leveraging machine learning for enhanced patient engagement and outcomes: Revolutionizing healthcare marketing. In Impact of digital transformation on business growth and performance (pp. 313–340). IGI Global.
[15] Vettriselvan, R., Ramya, R., Selvalakshmi, V., Jyothi, P., & Velmurugan, P. R. (2026). Empowering patients through knowledge: Educational strategies in rehabilitation. In Holistic approaches to health recovery (pp. 263–290). IGI Global.
[16] Vettriselvan, R., Velmurugan, P. R., Varshney, K. R., EP, J., & Deepika, R. (2025). Health impacts of smartphone and internet addictions across age groups: Physical and mental health across generations. In Impacts of digital technologies across generations (pp. 187–210). IGI Global.
[17] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Velmurugan, P. R., & Hasine, J. (2025). Strategic collaborations in medical innovation and AI-driven globalization: Advancing healthcare startups. In Navigating strategic partnerships for sustainable startup growth (pp. 85–110). IGI Global.
[18] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026). Transforming the future of management and medical education: AI-driven innovations in curriculum design. In AI education strategies for future-proofing curriculum design (pp. 459–476). IGI Global.
[19] Yatish, Khatoon, N., & Kumar, A. (2026). Advancing preventive strategies for chronic disease management: Clinical, behavioral, and population-level perspectives. International Journal of Novel Trends and Innovation, 4(2).