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INTRODUCTION: Patients with subjective memory complaints (SMC) may include subgroups with different neuropsychological profiles and risks of cognitive impairment. METHODS: Cluster analysis was performed on two datasets (n: 630 and 734) comprising demographic and neuropsychological data from SMC and healthy controls (HC). Survival analyses were conducted on clusters. Bayesian model averaging assessed the predictive utility of clusters and other biomarkers. RESULTS: Two clusters with higher and lower than average cognitive performance were detected in SMC and HC. Assignment to the lower performance cluster increased the risk of cognitive impairment in both datasets (hazard ratios: 1.78 and 2.96; Plog‐rank: 0.04 and <0.001) and was associated with lower hippocampal volumes and higher tau/amyloid beta 42 ratios in cerebrospinal fluid. The effect of SMC was small and confounded by mood. DISCUSSION: This study provides evidence of the presence of cognitive clusters that hold biological significance and predictive value for cognitive decline in SMC and HC. Highlights: Patients with subjective memory complaints include two cognitive clusters.Assignment to the lower performance cluster increases risk of cognitive impairment.This cluster shows a pattern of biomarkers consistent with incipient Alzheimer's disease pathology.The same cognitive cluster structure is found in healthy controls.The effect of memory complaints on risk of cognitive decline is small and confounded. [ABSTRACT FROM AUTHOR]

Background: Alzheimer disease (AD) is the most common cause of dementia worldwide. It is known that cerebral amyloid aggregation and neurodegeneration are first steps in the physiopathology of AD. In the prodromal phase, a clinical diagnosis of amnesic mild cognitive impairment (aMCI) can be stablished, but not all subjects with MCI have a prodromal AD. The objective of this study was to determinate the impact of amyloid and FDG PET on the progression from aMCI to AD dementia. Methods: From October 2013 to March 2021, we prospectively selected 145 patients with a diagnosis aMCI. We excluded subjects older than 85 and those with structural brain lesion on brain MRI. Participants went through an amyloid and an FDG‐PET scans and were classified as positive or negative for amyloid and neurodegeneration. Patients were followed to stablish progression to AD dementia, 118 patients were followed up more than 36 months. Statistical analyses were done with STATA with significance set at p<0.05. All analyses were adjusted by age, gender, and education. Results: Demographics are shown on table 1. Sensibility, specificity, negative predictive value, and positive predictive value during the 4 years are shown on table 2. 88 patients were A+ and 74 patients were A‐. We divided the patients in 4 groups: A+/FDG+ (n:54), A+/FDG‐ (n:34), A‐/FDG‐ (40), A‐/FDG+(4). The mean of conversion were 39.95 months, this time depends on the result of the biomarkers. A‐ patients converted less and latter with a mean of 75.07 (IC 56.54→81) month, than A+ patients, that converted earlier (chi2 = 20.37 Pr>chi2 = 0.0000). A+/FDG+ converted with a mean of 27.79 (20.40‐33.21) months while A+/FDG‐ converted earlier with a mean of 37.38 (20.73‐48.26) month. (chi2 = 4.16 Pr>chi2 = 0.0415). Conclusions: Amyloid and FDG PET can help us to stablish prognosis in aMCI patients. We have observed that a positive amyloid PET is associated with an increased risk of progression to AD dementia. Even more, aMCI patients with both, amyloid PET and FDG, positive PETs progressed more often and earlier to AD dementia than those who have a positive amyloid PET but a negative FDG‐PET. [ABSTRACT FROM AUTHOR]