Alzheimer’s disease (AD) is the most common form of dementia worldwide, being characterized by the deposition of senile plaques, neurofibrillary tangles (enriched in the amyloid beta (Aβ) peptide and hyperphosphorylated tau (p-tau), respectively) and memory loss. Aging, type 2 diabetes (T2D) and female sex (especially after menopause) are risk factors for AD, but their crosslinking mechanisms remain unclear. Most clinical trials targeting AD neuropathology failed and it remains incurable. However, evidence suggests that effective anti-T2D drugs, such as the GLP-1 mimetic and neuroprotector liraglutide, can be also efficient against AD. Thus, we aimed to study the benefits of a peripheral liraglutide treatment in AD female mice. We used blood and brain cortical lysates from 10-month-old 3xTg-AD female mice, treated for 28 days with liraglutide (0.2 mg/kg, once/day) to evaluate parameters affected in AD (e.g., Aβ and p-tau, motor and cognitive function, glucose metabolism, inflammation and oxidative/nitrosative stress). Despite the limited signs of cognitive changes in mature female mice, liraglutide only reduced their cortical Aβ1–42 levels. Liraglutide partially attenuated brain estradiol and GLP-1 and activated PKA levels, oxidative/nitrosative stress and inflammation in these AD female mice. Our results support the earlier use of liraglutide as a potential preventive/therapeutic agent against the accumulation of the first neuropathological features of AD in females.

BACKGROUND Twitter is a social media platform popularly used by health practitioners, a trend that has been followed by medical journals. The impact of Twitter in bibliometrics of stroke-related literature is yet to be determined. AIMS We aimed to qualitatively assess the usage of Twitter by stroke journals and study the relationship between Twitter activity and citation rates of stroke articles. METHODS We used Journal Citation Reports to identify stroke journals. We collected the 2021 Impact Factor (IF) and the top 50 articles contributing to each journal IF. Relevant metrics were collected through Twitonomy, Altmetric and Web of Science. The association between Twitter activity and citation rates was tested by a negative binomial regression model adjusted to journal’s IF. A bivariate correlation and a log-linear regression model adjusted to journal’s IF tested the relationship between number of tweets, tweeters and the number of citations. RESULTS We collected 450 articles across 9 stroke-dedicated journals, five of which had a Twitter account. Only 95 (21%) articles had no Twitter mentions. The median number of citations in articles with versus without Twitter activity was 19 (10-39) vs 11(7-17) (P<0.001). Twitter activity was associated with higher citation rates controlling for the IF (OR: 2.7, 95%CI 2.12-3.38, P<0.001). We found number of tweets to be predicted by the number of citations controlling for the IF (B=0.33, 95%CI 0.29-0.40, β=0.54, P<0.001). CONCLUSIONS Tweeted stroke articles tend to have higher citation rates which can be predicted by the number of tweets.

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