We aimed to examine the profile and severity of mild behavioral impairment (MBI) in a sample of β-amyloid positive individuals with amnestic mild cognitive impairment (aMCI)compared to cognitively normal older adults (CN). Within aMCI, we further examined the potential influence of APOE and BDN Frisk genetic polymorphisms on MBI severity.

We included 64 β-amyloid positive aMCI participants and 50 CN older adults from the Czech Brain Aging Study. The participants underwent neurological, comprehensive neuropsychological examination, APOE and BDNF genotyping, and magnetic resonance imaging.MBI was diagnosed with the Mild behavioral impairment checklist (MBI-C) developed for MBI case detection, and the diagnosis was based on the MBI-C total score ≥7. Additionally, self-report instruments for anxiety (the Beck Anxiety Inventory) and depressive symptoms (the Geriatric Depression Scale-15) were administered. The participants were stratified based on the presence of at least one risk allele in genes for APOE (i.e., e4 carriers and non-carriers) and BDNF (i.e., Met carriers and non-carriers). We used linear regressions to examine the between-group differences.

MBI symptoms (MBI-C total score ≥1) were present in 28% CN and 83% aMCI. Almost half (48.4%) of the aMCI individuals met the criteria for the MBI syndrome. Compared to the CN, the aMCI group displayed more affective, apathy, and impulse dyscontrol symptoms (p<0.001) but not social inappropriateness or psychotic symptoms. Furthermore, aMCI participants reported more depressive (p<0.01) but similar anxiety symptoms to CN on self-report measures. Within the aMCI group, APOE e4 and BDNF Met carriers did not differ from non-carriers in the severity of NPS in either instrument. However, the results suggested that an interaction between these polymorphisms influenced self-reported anxiety (p=0.034), with Met carriers/e4 non-carriers reporting the highest anxiety levels.

MBI is frequent in prodromal Alzheimer´s disease and characterized by affective, apathy, and impulse dyscontrol symptoms. APOE and BDNF risk genetic polymorphisms did not influence the NPS severity when considered separately; however, their interaction might influence anxiety, which warrants further investigation.

The research has received funding from the EEA/ Norway Grants 2014-2021 and the Technology Agency of the Czech Republic – project number TO01000215, Ministry of Health of the Czech Republic, grant no. 19-04-00560, National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107) - funded by the European Union – Next Generation EU and GAČR 22-33968S.

To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning.

Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error.

Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose–response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified).

Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.

Research shows that lipid levels may be associated with cognitive function, particularly among women. We aimed to examine total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein (HDL), and HDL/LDL ratio in relation to cognitive performance, measured with six well-established cognitive domains and a composite cognitive score (CCS).

In this cross-sectional study, biomarkers and neuropsychological assessment were available for 141 adults with MMSE scores ≥ 24 (mean age = 69 years, 47% female, mean education = 14.4 years) attending a neuropsychological evaluation. Ordinary least squares regressions were adjusted for age, gender, education, and depressive symptoms in Model 1 and also for apolipoprotein E4 (APOE4) status in Model 2.

High-density lipoprotein cholesterol (HDL-C) was associated with better CCS (β = 0.24; p = 0.014). This association was significant among women (β = 0.30; p = 0.026) and not among men (β = 0.20; p = 0.124). HDL-C was also related to attention/working memory (β = 0.24; p = 0.021), again only among women (β = 0.37; p = 0.012) and not men (β = 0.15; p = 0.271). Adjusting for APOE4 yielded significance for high HDL-C and CCS (β = 0.24; p = 0.022).

HDL-C was the main lipoprotein affecting cognitive function, with results somewhat more pronounced among women. Research should investigate the possibility of finding ways to boost HDL-C levels to potentially promote cognitive function.