Risk of Alzheimer's Disease Following Influenza Vaccination: A Claims-Based Cohort Study Using Propensity Score Matching.

BACKGROUND: Prior studies have found a reduced risk of dementia of any etiology following influenza vaccination in selected populations, including veterans and patients with serious chronic health conditions. However, the effect of influenza vaccination on Alzheimer's disease (AD) risk in a general cohort of older US adults has not been characterized. OBJECTIVE: To compare the risk of incident AD between patients with and without prior influenza vaccination in a large US claims database. METHODS: Deidentified claims data spanning September 1, 2009 through August 31, 2019 were used. Eligible patients were free of dementia during the 6-year look-back period and≥65 years old by the start of follow-up. Propensity-score matching (PSM) was used to create flu-vaccinated and flu-unvaccinated cohorts with similar baseline demographics, medication usage, and comorbidities. Relative risk (RR) and absolute risk reduction (ARR) were estimated to assess the effect of influenza vaccination on AD risk during the 4-year follow-up. RESULTS: From the unmatched sample of eligible patients (n = 2,356,479), PSM produced a sample of 935,887 flu-vaccinated-unvaccinated matched pairs. The matched sample was 73.7 (SD, 8.7) years of age and 56.9% female, with median follow-up of 46 (IQR, 29-48) months; 5.1% (n = 47,889) of the flu-vaccinated patients and 8.5% (n = 79,630) of the flu-unvaccinated patients developed AD during follow-up. The RR was 0.60 (95% CI, 0.59-0.61) and ARR was 0.034 (95% CI, 0.033-0.035), corresponding to a number needed to treat of 29.4. CONCLUSION: This study demonstrates that influenza vaccination is associated with reduced AD risk in a nationwide sample of US adults aged 65 and older.

Multimodal Phenotyping of Alzheimer's Disease with Longitudinal Magnetic Resonance Imaging and Cognitive Function Data.

Alzheimer's disease (AD) varies a great deal cognitively regarding symptoms, test findings, the rate of progression, and neuroradiologically in terms of atrophy on magnetic resonance imaging (MRI). We hypothesized that an unbiased analysis of the progression of AD, regarding clinical and MRI features, will reveal a number of AD phenotypes. Our objective is to develop and use a computational method for multi-modal analysis of changes in cognitive scores and MRI volumes to test for there being multiple AD phenotypes. In this retrospective cohort study with a total of 857 subjects from the AD (n = 213), MCI (n = 322), and control (CN, n = 322) groups, we used structural MRI data and neuropsychological assessments to develop a novel computational phenotyping method that groups brain regions from MRI and subsets of neuropsychological assessments in a non-biased fashion. The phenotyping method was built based on coupled nonnegative matrix factorization (C-NMF). As a result, the computational phenotyping method found four phenotypes with different combination and progression of neuropsychologic and neuroradiologic features. Identifying distinct AD phenotypes here could help explain why only a subset of AD patients typically respond to any single treatment. This, in turn, will help us target treatments more specifically to certain responsive phenotypes.