Alzheimer’s disease (AD) is a devastating neurodegenerative disorder leading to age-associated dementia. For the first time in the last 20 years causative therapeutic strategies are on the horizon. However, since changes in cognitive function develop slowly over time in these patients, they are often diagnosed at an advanced stage of molecular pathology, a time point when causative treatments fail. Thus, there is great need for the identification of inexpensive and minimal invasive approaches that could be used for screening with the aim to identify individuals at risk for cognitive decline that can then undergo further diagnostics and eventually stratified therapies. A recent line of research indicates that circulating microRNAs might serve as promising diagnostic biomarkers but the current data is often inconsistent. In this study we analyzed more than 1000 deeply phenotypes probands (controls, subjective cognitive impairment, mild cognitive impairment, AD, AD relatives without AD) via small RNA sequencing of blood samples. We performed differential expression, WGCNA as well as machine learning analysis to identify microRNAs that correlate with diagnosis and disease phenotypes. Candidate microRNAs are also analyzed at the functional level.