Women's Brain Project

BackgroundAlzheimer’s disease (AD) exhibits sex differences in prevalence, symptoms and risk factors. Understanding the effect of sex in AD cerebrospinal fluid (CSF) biomarkers and their association with amyloid-beta (Aβ) pathology in preclinical stages have important implications for their use in prevention trials. The objective of this study was to examine sex differences in core AD CSF biomarkers used in early diagnosis and prevention trials, as well as in CSF biomarkers reflecting downstream pathophysiological mechanisms, and in their associations with Aβ pathology as measured by Positron Emission Tomography (PET).MethodsCognitively Unimpaired (CU) participants from the ALFA + (N = 400) and the WRAP/WADRC (N = 548) cohorts were included in the study. CSF biomarkers for core AD pathology (Aβ42, Aβ42/40, p-tau181/Aβ42, p-tau181, p-tau217 and p-tau231), neurodegeneration (NfL, t-tau), synaptic dysfunction (neurogranin, GAP-43, SNAP25, synaptotagmin-1, α-synuclein), glial reactivity (GFAP, S100B, sTREM2, YKL-40), neuroinflammation (IL-6, MCP-1), and vascular dysregulation (sICAM-1, sVCAM-1) were measured. Participants underwent Aβ PET at baseline and follow-up visit. We used Analyses of Covariance (ANCOVA) to evaluate sex differences in CSF biomarker levels and performed sex-stratified Receiver-Operating Characteristic (ROC) analyses to test their performance to identify Aβ PET-positive individuals. Additionally, we run linear regression models to study the modifying effect of sex on the association of baseline CSF biomarkers with cross-sectional and longitudinal Aβ PET uptake.ResultsMen had higher CSF NfL, glial reactivity and vascular dysregulation biomarkers (Cohen’s d ranging from -0.22 to -0.44, P < 0.05), and lower synaptic biomarkers (Cohen’s d ranging from 0.18 to 0.30, P < 0.05) compared to women at baseline. There were no sex differences in the core AD CSF biomarkers’ performance to identify Aβ PET-positive individuals (DeLong's test P values > 0.05), with CSF p-tau181/Aβ42 and p-tau217 showing the highest performance in both sexes (Areas Under the Curve (AUCs) ranging from 87.1 to 96.3). However, sex modified the associations of baseline CSF biomarkers with Aβ PET uptake, which were more pronounced in women than in men.ConclusionsOur results suggest that tailoring core AD CSF biomarkers by sex is not necessary for detecting Aβ PET positivity in CU individuals. However, sex differences in their association with Aβ deposition could influence their prognostic or monitoring applications.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13195-025-01844-1.

Sex differences in neuropsychological (NP) test performance might have important implications for the diagnosis of Alzheimer's disease (AD). This study investigates sex differences in neuropsychological performance among individuals without dementia at baseline. Neuropsychological assessment data, both standard test scores and process coded responses, from Framingham Heart Study participants were analyzed for sex differences using regression model and Cox proportional hazards model. Optimal NP profiles were identified by machine learning methods for men and women. Sex differences were observed in both summary scores and composite process scores of NP tests in terms of adjusted means and their associations with AD incidence. The optimal NP profiles for men and women have 10 and 8 measures, respectively, and achieve 0.76 mean area under the curve for AD prediction. These results suggest that NP tests can be leveraged for developing more sensitive, sex-specific indices for the diagnosis of AD.

Women represent two-thirds of patients with Alzheimer disease (AD), and sex differences might affect results of randomized clinical trials (RCTs). However, little information exists on differences in sex as reported in RCTs for AD. To assess the ratio of females to males and the reporting of sex-stratified data in large pharmaceutical RCTs for AD. A search for pharmaceutical RCTs for AD was conducted on September 4, 2019, using ClinicalTrials.gov with the key word Alzheimer disease, and articles related to those trials were identified using the PubMed, Scopus, and Google Scholar databases. Searches were conducted between September 4 and October 31, 2019, and between April 15 and May 31, 2020. Controlled RCTs that had more than 100 participants and tested the efficacy of drugs or herbal extracts were included. Of 1047 RCTs identified, 409 were published and therefore screened. A total of 77 articles were included in the final analysis, including 56 primary articles on AD, 13 secondary articles on AD, and 8 articles on mild cognitive impairment. The location and date of publication; number, sex, and age of patients enrolled; disease severity; experimental or approved status of the drug; and whether the study included a sex-stratified analysis in the protocol, methods, or results were extracted by 1 reviewer for each article, and the meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Data were analyzed using a mixed-effects model. The mean proportion of women enrolled in the trials and the associations between prespecified variables were analyzed. The proportion of articles that included sex-stratified results and the temporal trends in the reporting of these results were also studied. In this review of 56 RCTs for AD involving 39 575 participants, 23 348 women (59.0%) were included. The mean (SD) proportion of women in RCTs of approved drugs was 67.3% (6.9%), and in RCTs of experimental drugs was 57.9% (5.9%). The proportion of women in RCTs of experimental drugs was significantly lower than the proportion of women in the general population with AD in the US (62.1%; difference, -4.56% [95% CI, -6.29% to -2.87%]; P < .001) and Europe (68.2%; difference, -10.67% [95% CI, -12.39% to -8.97%]; P < .001). Trials of approved drugs had a higher probability of including women than trials of experimental drugs (odds ratio [OR], 1.26; 95% CI, 1.05-1.52; P = .02). Both the severity of AD at baseline and the trial location were associated with the probability of women being enrolled in trials (severity: OR, 0.98; 95% CI, 0.97-1.00; P = .02; location in Europe: OR, 1.26; 95% CI, 1.05-1.52; P = .01; location in North America: OR, 0.81; 95% CI, 0.71-0.93; P = .002). Only 7 articles (12.5%) reported sex-stratified results, with an increasing temporal trend (R, 0.30; 95% CI, 0.05-0.59; P = .03). In this systematic review and meta-analysis, the proportion of women in RCTs for AD, although higher than the proportion of men, was significantly lower than that in the general population. Only a small proportion of trials reported sex-stratified results. These findings support strategies to improve diversity in enrollment and data reporting in RCTs for AD.

Abstract BackgroundFDA has made great efforts to promote inclusion of women in clinical trials. The aim is to protect specific biological gender and sex issues and encourage drug developers to consider specific clinical outcomes and drug‐safety profiles in terms of gender and sex. Alzheimer’s disease (AD) affects predominantly women, who double men in the number of patients diagnosed with dementia. Therefore, we explored gender and sex bias in the inclusion criteria for clinical trials in AD. Here our aim was to compare main inclusion criteria for clinical trials in MCI and dementia patients in terms of sex.MethodPatients evaluated at FACE from 1996 to 2019 with CDR 0,5 to 2 were analysed. Then, we used the main inclusion criteria for AD clinical trials to compare the sample in terms of sex and syndromic diagnosis. The chosen variables were age, sex, education, comorbidities, treatment and MMSE score. Logistic regression analysis accounting for age, years of education, MMSE, treatment and comorbidities was used.ResultFrom a total sample of 20,203 MCI and dementia patients, we selected potential candidates for clinical trials with complete information regarding age, age, years of education, MMSE, comorbidities and treatment. After adjusting for age, years of education, MMSE, comorbidities and treatment, in the dementia sample only age and years of education showed statistically significant differences. Whereas in the MCI sample: age, years of education, treatment and comorbidities showed statistically significant differences (Tables 1 and 2)ConclusionWomen with MCI and dementia are less likely to participate in a clinical trial due to older age and less years of education than men. Another factor that prevent women for participating in clinical trials is their comorbidities when compared to men, whereas men are excluded due to treatment criteria more often than women. These results show that there is a pre‐selection bias against women in AD clinical trials because of demographic characteristics. Therefore, participation of women in clinical trials does not reflect the prevalence of AD among women and is not representative of reality. Efforts should be made to provide equal opportunities to potential clinical trials candidates regardless of gender.

In this issue of the European Journal of Neurology, Phan and colleagues present data on sex differences in health-related quality of life (HRQoL) after stroke from a first-ever stroke registered in the Australian Stroke Clinical Registry from 2010 - 2014.1 HRQoL was assessed with the European Quality of Life-5 Dimensions (EQ-5D-3L). Older women and young men reported poorer health- related quality of life post-stroke as compared to other groups. The results were influenced by pre-stroke factors, such as stroke severity and comorbidities, but not by clinical care or socioeconomic status. For instance, older women had more dementia and atrial fibrillation, they were more often dependent pre-stroke and more often lived alone prior to admission.1 This is in line with several results in the field. It is well established that older women are less likely to have adequate blood pressure control, treated with antithrombotic drug and to be anticoagulated for atrial fibrillation when compared with other groups, all associated with risk of cognitive decline.

UNSTRUCTURED Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.