The present study sought to evaluate the validity and generality of the Mini-Mental State Examination (MMSE) and its subsection scores. We gave the MMSE and other neuropsychological tests to 51 patients with probable Alzheimer's disease. On the basis of correlational and factor analyses, overall performance on the MMSE proved to have good concurrent validity with other comprehensive neuropsychological assessment instruments. However, the MMSE subsections should not be viewed as highly specific measures of cognition or memory.

ObjectivesTo understand the relation between risk genes for Alzheimer’s disease (AD) and their influence on biomarkers for AD, we examined the association of AD in the Finnish cohort with single nucleotide polymorphisms (SNPs) from top AlzGene loci, genome-wide association studies (GWAS), and candidate gene studies; and tested the correlation between these SNPs and AD markers Aβ1–42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF).MethodsWe tested 25 SNPs for genetic association with clinical AD in our cohort comprised of 890 AD patients and 701-age matched healthy controls using logistic regression. For the correlational study with biomarkers, we tested 36 SNPs in a subset of 222 AD patients with available CSF using mixed models. Statistical analyses were adjusted for age, gender and APOE status. False discovery rate for multiple testing was applied. All participants were from academic hospital and research institutions in Finland.Results APOE-ε4, CLU rs11136000, and MS4A4A rs2304933 correlated with significantly decreased Aβ1–42 (corrected p<0.05). At an uncorrected p<0.05, PPP3R1 rs1868402 and MAPT rs2435211 were related with increased t-tau; while SORL1 rs73595277 and MAPT rs16940758, with increased p-tau. Only TOMM40 rs2075650 showed association with clinical AD after adjusting for APOE-ε4 (p = 0.007), but not after multiple test correction (p>0.05).ConclusionsWe provide evidence that APOE-ε4, CLU and MS4A4A, which have been identified in GWAS to be associated with AD, also significantly reduced CSF Aβ1–42 in AD. None of the other AlzGene and GWAS loci showed significant effects on CSF tau. The effects of other SNPs on CSF biomarkers and clinical AD diagnosis did not reach statistical significance. Our findings suggest that APOE-ε4, CLU and MS4A4A influence both AD risk and CSF Aβ1–42.

BackgroundWe developed multifactorial models for predicting incident dementia and brain pathology in the oldest old using the Vantaa 85+ cohort.MethodsWe included participants without dementia at baseline and at least 2 years of follow-up (N = 245) for dementia prediction or with autopsy data (N = 163) for pathology. A supervised machine learning method was used for model development, considering sociodemographic, cognitive, clinical, vascular, and lifestyle factors, as well as APOE genotype. Neuropathological assessments included β-amyloid, neurofibrillary tangles and neuritic plaques, cerebral amyloid angiopathy (CAA), macro- and microscopic infarcts, α-synuclein pathology, hippocampal sclerosis, and TDP-43.ResultsPrediction model performance was evaluated using AUC for 10 × 10-fold cross-validation. Overall AUCs were 0.73 for dementia, 0.64–0.68 for Alzheimer’s disease (AD)- or amyloid-related pathologies, 0.72 for macroinfarcts, and 0.61 for microinfarcts. Predictors for dementia were different from those in previous reports of younger populations; for example, age, sex, and vascular and lifestyle factors were not predictive. Predictors for dementia versus pathology were also different, because cognition and education predicted dementia but not AD- or amyloid-related pathologies. APOE genotype was most consistently present across all models. APOE alleles had a different impact: ε4 did not predict dementia, but it did predict all AD- or amyloid-related pathologies; ε2 predicted dementia, but it was protective against amyloid and neuropathological AD; and ε3ε3 was protective against dementia, neurofibrillary tangles, and CAA. Very few other factors were predictive of pathology.ConclusionsDifferences between predictors for dementia in younger old versus oldest old populations, as well as for dementia versus pathology, should be considered more carefully in future studies.Electronic supplementary materialThe online version of this article (10.1186/s13195-018-0450-3) contains supplementary material, which is available to authorized users.

AD risk loci polygenically contribute to Aβ pathology in the CSF and temporal cortex, and this effect is potentially associated with increased γ-secretase activity.

The results reveal that the prediction performance of the combined cohort is close to the average of the individual cohorts. It is feasible to use different cohorts as training sets for the DSI, if they are sufficiently similar.

Our aim was to investigate the association between behavioral symptoms of agitation, disinhibition, irritability, elation, and aberrant motor behavior to frontal brain volumes in a cohort with various neurodegenerative diseases. A total of 121 patients with mild cognitive impairment (MCI, n = 58), Alzheimer's disease (AD, n = 45) and behavioral variant frontotemporal dementia (bvFTD, n = 18) were evaluated with a Neuropsychiatric Inventory (NPI). A T1-weighted MRI scan was acquired for each participant and quantified with a multi-atlas segmentation method. The volumetric MRI measures of the frontal lobes were associated with neuropsychiatric symptom scores with a linear model. In the regression model, we included CDR score and TMT B time as covariates to account for cognitive and executive functions. The brain volumes were corrected for age, gender and head size. The total behavioral symptom score of the five symptoms of interest was negatively associated with the volume of the subcallosal area (β = −0.32, p = 0.002). High disinhibition scores were associated with reduced volume in the gyrus rectus (β = −0.30, p = 0.002), medial frontal cortex (β = −0.30, p = 0.002), superior frontal gyrus (β = −0.28, p = 0.003), inferior frontal gyrus (β = −0.28, p = 0.005) and subcallosal area (β = −0.28, p = 0.005). Elation scores were associated with reduced volumes of the medial orbital gyrus (β = −0.30, p = 0.002) and inferior frontal gyrus (β = −0.28, p = 0.004). Aberrant motor behavior was associated with atrophy of frontal pole (β = −0.29, p = 0.005) and the subcallosal area (β = −0.39, p < 0.001). No significant associations with frontal brain volumes were found for agitation and irritability. We conclude that the subcallosal area may be common neuroanatomical area for behavioral symptoms in neurodegenerative diseases, and it appears to be independent of disease etiology.

Later life lifestyle factors had no impact on key AD biomarkers in individuals with SCD and MCI or on conversion to AD-type dementia in MCI.

Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC).Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype.Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.