Specific features associated with SCD may help to identify individuals at high risk of fast conversion to MCI. These results highlight the importance of a close follow-up of subjects with SCD-P and include them in early intervention programs because of their increased risk for the development of MCI.

IntroductionAlzheimer’s disease (AD) is a major threat for the well-being of an increasingly aged world population. The physiopathological mechanisms of late-onset AD are multiple, possibly heterogeneous, and not well understood. Different combinations of variables from several domains (i.e., clinical, neuropsychological, structural, and biochemical markers) may predict dementia conversion, according to distinct physiopathological pathways, in different groups of subjects.MethodsWe launched the Vallecas Project (VP), a cohort study of non-demented people aged 70–85, to characterize the social, clinical, neuropsychological, structural, and biochemical underpinnings of AD inception. Given the exploratory nature of the VP, multidimensional and machine learning techniques will be applied, in addition to the traditional multivariate statistical methods.ResultsA total of 1169 subjects were recruited between October 2011 and December 2013. Mean age was 74.4 years (SD 3.9), 63.5% of the subjects were women, and 17.9% of the subjects were carriers of at least one ε4 allele of the apolipoprotein E gene. Cognitive diagnoses at inclusion were as follows: normal cognition 93.0% and mild cognitive impairment (MCI) 7.0% (3.1% amnestic MCI, 0.1% non-amnestic MCI, 3.8% mixed MCI). Blood samples were obtained and stored for future determinations in 99.9% of the subjects and 3T magnetic resonance imaging study was conducted in 89.9% of the volunteers. The cohort is being followed up annually for 4 years after the baseline.ConclusionWe have established a valuable homogeneous single-center cohort which, by identifying groups of variables associated with high risk of MCI or dementia conversion, should help to clarify the early physiopathological mechanisms of AD and should provide avenues for prompt diagnosis and AD prevention.

The MAPT H1 haplotype has been linked to several disorders, but its relationship with Alzheimer's disease (AD) remains controversial. A rare variant in MAPT (p.A152T) has been linked with frontotemporal dementia (FTD) and AD. We genotyped H1/H2 and p.A152T MAPT in 11,572 subjects from Spain (4,327 AD, 563 FTD, 648 Parkinson's disease (PD), 84 progressive supranuclear palsy (PSP), and 5,950 healthy controls). Additionally, we included 101 individuals from 21 families with genetic FTD. MAPT p.A152T was borderline significantly associated with FTD [odds ratio (OR) = 2.03; p = 0.063], but not with AD. MAPT H1 haplotype was associated with AD risk (OR = 1.12; p = 0.0005). Stratification analysis showed that this association was mainly driven by APOE ɛ4 noncarriers (OR = 1.14; p = 0.0025). MAPT H1 was also associated with risk for PD (OR = 1.30; p = 0.0003) and PSP (OR = 3.18; p = 8.59 × 10-8) but not FTD. Our results suggest that the MAPT H1 haplotype increases the risk of PD, PSP, and non-APOE ɛ4 AD.

Alzheimer’s Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.