Neural Regeneration Research ›› 2026, Vol. 21 ›› Issue (8): 3779-3787.doi: 10.4103/NRR.NRR-D-25-00308

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Cognitive heterogeneity in mild cognitive impairment due to Alzheimer’s disease pathology

Siyun Chen1, David P. Salmon2, 3, Howard H. Feldman3, 4, Karen Messer5, Mark W. Bondi3, 6, Dongsheng Xu7, Yuqi Qiu8, *, Diane M. Jacobs2, 3, *, The Alzheimer’s Disease Neuroimaging Initiative   

  1. 1Department of Rehabilitation Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China; 
    2Department of Neurosciences, University of California San Diego, La Jolla, CA, USA; 
    3UC San Diego Shiley-Marcos Alzheimer’s Disease Research Center, University of California San Diego, La Jolla, CA, USA; 
    4Alzheimer’s Disease Cooperative Study, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA; 
    5Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA; 
    6Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; 
    7College of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; 
    8KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China
  • Online:2026-08-18 Published:2026-04-27
  • Contact: Diane M. Jacobs, PhD, djacobs@ucsd.edu; Yuqi Qiu, PhD, yqqiu@fem.ecnu.edu.cn.
  • Supported by:
    SC was funded by Shanghai Baiyulan Pujiang Project (No. 24PJD087). YQ was funded by the National Natural Science Foundation of China (No. 12401347), Shanghai “Science and Technology Innovation Action Plan” Computational Biology Key Project (Nos. 23JS1400500 and 23JS1400800), Chinese MOE Foundation on Humanities and Social Sciences (No. 23YJC910006), and the Natural Science Foundation of Shanghai (No. 24ZR1420400). MWB was funded by NIH/NIA (Nos. R01AG082073, R01AG079280, and P30AG062429). HHF was funded by NIH/NIA (Nos. U19AG079774-01, R01AG061146, P30AG062429, R01AG076634-01, CIHR 137794), and the Epstein Family Alzheimer’s Research Collaboration; reports service agreements with LuMind Foundation, Novo Nordisk Inc., Axon Neuroscience, Arrowhead Pharmaceuticals, and Biosplice Therapeutics Inc.; travel support from Novo Nordisk Inc., Royal Society of Canada, Translating Research for Elder Care, Association for Frontotemporal Dementia, and Rainwater Charitable Foundation; service on a DMC for Roche/Genentech Pharmaceuticals and Janssen Research & Development LLC; and service on the Tau Consortium SAB. All funds are directed to UC Regents with no personal funds received. HHF personally receives royalties for patent “Detecting and Treating Dementia” (Serial Number 12/3-2691 U.S. Patent No. PCT/US2007/07008. Washington, DC: U.S. Patent and Trademark Office). DMJ was funded by NIH/NIA R01AG064002, P30AG062429, R01AG076634, and the Epstein Family Alzheimer’s Research Collaboration.

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Abstract: Traditional clinical subtype classifications (such as amnestic and non-amnestic mild cognitive impairment) rely on subjective interpretations of overlapping patterns of performance on cognitive tests, which may lead to unreliable categorization. A more precise and objective classification of mild cognitive impairment subtypes can be achieved through data-driven clustering techniques. However, because previous studies have not restricted their cohorts to patients who have mild cognitive impairment with the pathology of Alzheimer’s disease, the nature of cognitive variability and its impact on disease progression in a strictly defined biomarker-positive preclinical Alzheimer’s disease cohort remains unknown. We examined cognitive heterogeneity among participants with mild cognitive impairment due to Alzheimer’s disease and evaluated its prognostic utility. Neuropsychological test data from 389 patients with mild cognitive impairment in whom the cerebrospinal fluid biomarker confirmed Alzheimer’s disease were obtained from the Alzheimer’s Disease Neuroimaging Initiative cohorts. Principal component analysis and model-based clustering were used to identify cognitive profiles, which were then validated through a 100-time bootstrap analysis. Pairwise comparisons tested for differences between the identified subgroups in participant characteristics, scores on cognitive and clinical outcomes, levels of cerebrospinal fluid biomarkers, and magnetic resonance imaging-derived brain volumes. Longitudinal analyses evaluated differences in rate of change of magnetic resonance imaging volumetric measurements and clinical outcomes over 48 months. Survival analysis assessed risk for conversion to dementia. Alpha-synuclein levels and white matter hyperintensity volumes were considered for sensitivity analysis. Two distinct cognitive profiles were identified: a “typical” group (56.04% of the sample) that demonstrated relatively poorer scores on memory testing than non-memory tests, and an “atypical” group (43.96% of the sample) with smaller differences between memory and non-memory measures, indicating a more uniform pattern of impairment across cognitive domains. While the groups had comparable levels of overall cognitive impairment and cerebrospinal fluid biomarkers of Alzheimer’s disease, the typical group displayed accelerated atrophy rates every 6 months across multiple brain regions (hippocampus: 29.02 mm3, standard error [SE] = 10.13, P = 0.005; whole brain: 1799.85 mm3, SE = 781.57, P = 0.023; entorhinal cortex: 22.26 mm3, SE = 11.15, P = 0.048; fusiform gyrus: 66.24 mm3, SE = 28.53, P = 0.021). Survival analysis revealed markedly higher dementia conversion risk (hazard ratio: 1.70, 95% confidence interval: 1.27, 2.27, P < 0.001) and shorter progression time in the typical group. These findings persisted after controlling for comorbid pathologies. In conclusion, this data-driven approach identified two distinct cognitive subtypes of mild cognitive impairment due to Alzheimer’s disease that differed in their rates of clinical decline and neurodegeneration. These findings could be used to improve prognostic models and inform clinical trial stratification.

Key words: Alzheimer’s disease, biomarkers, cluster analysis, cognitive heterogeneity, cognitive subtypes, dementia conversion, mild cognitive impairment, neurodegeneration, neuroimaging, neuropsychology