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Background Premature and early menopause involve ovarian hormone deprivation, which may contribute to later-life vulnerability to cognitive decline and neurodegenerative diseases. Multimodal neuroimaging biomarkers provide a means of detecting structural, microstructural, functional, and molecular brain changes before clinical symptoms emerge. Methods Following PRISMA guidelines, we conducted a systematic search of PubMed. Study quality was assessed using the Newcastle–Ottawa Scale. Results Neuroimaging modalities included in vivo structural magnetic resonance imaging (MRI) ( n = 16), functional MRI ( n = 2), diffusion tensor imaging ( n = 4), and positron emission tomography ( n = 3). Women with premature or early menopause showed regionally specific structural and microstructural brain abnormalities, including lower gray-matter volume or cortical thickness in medial temporal, frontal, and basal forebrain regions; white-matter microstructural abnormalities in the anterior corona radiata, corpus callosum, and fronto-occipital tracts; and reduced posterior hippocampal activation during memory encoding. In one study, earlier age at menopause was associated with higher amyloid- β (Aβ) burden in women who underwent premenopausal bilateral oophorectomy (PBO), and elevated tau deposition was reported predominantly among women with higher Aβ levels. Conclusion Converging multimodal neuroimaging evidence suggests that premature and early menopause may be associated with Alzheimer’s disease-relevant brain changes. However, findings remain heterogeneous, and longitudinal studies are needed to clarify the temporal relationships between ovarian hormone deprivation and neurodegeneration, and to determine the clinical implication of these findings.

Importance Sex differences are increasingly recognized as modifiers of Alzheimer disease and related dementias, with women exhibiting greater tau burden and faster cognitive decline than men. Even though α-synuclein copathology frequently occurs in Alzheimer disease, its contribution to sex differences in disease progression is unclear. Objective To test whether α-synuclein positivity, measured using cerebrospinal fluid seed amplification assay (SAA), is differentially associated with tau accumulation in women vs men across the Alzheimer disease continuum. Design, Setting, and Participants This cohort study used longitudinal tau positron emission tomography from the Alzheimer’s Disease Neuroimaging Initiative collected between 2015 and 2023, with a median (IQR) follow-up of 1.23 (0.00-3.84) years. Participants were stratified by cerebrospinal fluid α-synuclein seed amplification assay status and sex. Participants were cognitively unimpaired or cognitively impaired (mild cognitive impairment or dementia) at baseline. Exposure Cerebrospinal fluid α-synuclein status determined by SAA and dichotomized as SAA negative or SAA positive. Main Outcomes and Measures Tau burden was quantified as standardized uptake value ratio (SUVr) in the medial temporal composite region of interest. Linear mixed-effects models tested SAA by sex by time interactions on longitudinal tau accumulation, adjusting for baseline age, baseline cognitive status, apolipoprotein E ε4 carrier status, and site. Sample size estimates were calculated to detect 25% and 50% treatment effects with 80% power in those with cognitive impairment. Results Among 415 participants (mean [SD] age, 72.3 [7.6] years; 220 women [53%]; 69 SAA positive [17%] and 346 SAA negative [83%]), there was a significant interaction between SAA status, sex, and time on tau accumulation (β, 0.061; 95% CI, 0.030-0.093; P < .001). Women with positive SAA results exhibited the fastest tau accumulation compared with other groups (0.066 SUVr per year; 95% CI, 0.043 to 0.089 SUVr per year; P < .001). Clinical trials targeting tau pathology in cognitively impaired individuals with 18-month follow-up would require 129 SAA-positive women to detect a 25% treatment effect with 80% power, compared with 518 SAA-negative women. Conclusions and Relevance In this cohort study of participants across the Alzheimer disease continuum, α-synuclein copathology was associated with faster tau accumulation in women than men. These findings may inform sex-specific interpretation of α-synuclein biomarkers and trial design.

Abstract Background The associations between bilateral oophorectomy or hysterectomy with ovarian conservation and the risk of Parkinson's disease (PD) remain inconclusive. Objectives To evaluate the evidence on these two associations. Methods We conducted a systematic search across Embase, PubMed, Scopus, MEDLINE, and Web of Science through May 15, 2024. Data were extracted independently by two reviewers, and random‐effects meta‐analyses were performed. Results Eight studies with a total of 242,275 women were included. For bilateral oophorectomy, the effect size (ES) was greater than 1.0 in 5 of 8 studies. The pooled analysis showed a significant association between premenopausal bilateral oophorectomy and an increased risk of PD in cohort studies (hazard ratio [HR] 1.32; 95% CI 1.12–1.56) but not in case–control studies or combining both cohort and case–control studies. There was a suggestive trend of increasing risk with younger age at bilateral oophorectomy. For hysterectomy with ovarian conservation, the ES was greater than 1.0 in all six studies. The pooled analysis showed a non‐significant association in cohort studies and in case–control studies; however, the association was significant when combining both cohort studies and case–control studies (ES 1.19; 95% CI 1.01–1.41). Conclusions Current studies provided low to moderate evidence for the associations between bilateral oophorectomy and hysterectomy with ovarian conservation and increased risk of PD. However, the evidence was only observational and there was important heterogeneity across the case–control studies. Future studies should focus on surgeries performed at younger ages and on the possible effects of estrogen replacement therapy.

Abstract Objective To compare the long‐term risk of subsequent abdominopelvic surgery in women who underwent hysterectomy with ovarian conservation (H‐OC) versus referent women, and in women who underwent abdominal H‐OC versus vaginal H‐OC. Methods We conducted a population‐based cohort study in Olmsted County, Minnesota, USA including 1573 women who underwent H‐OC for a benign indication at age 18–44 years between January 1, 1980, and December 31, 2002, and 1573 age‐matched (±1 year) referent women who had not undergone hysterectomy or any oophorectomy as of the matching date. For both the H‐OC women and the referent women, we defined the index date as the H‐OC or matching date plus 6 months. The index date served as the starting point for follow‐up. Results Overall, we found an increased risk of subsequent lysis of adhesions in women who underwent H‐OC compared to referent women (hazard ratio [HR], 1.57 [95% confidence interval [CI]: 1.09–2.27]). Women undergoing H‐OC also had an increased risk of subsequent gastrointestinal tract procedures (HR, 1.18 [95% CI: 1.05–1.31]) compared to referent women. When comparing surgical route of H‐OC, we identified an increased risk of subsequent adnexal surgeries in women undergoing abdominal compared with vaginal H‐OC (HR, 1.91 [95% CI: 1.40–2.61]). Conclusion Women who underwent H‐OC have an increased risk of subsequent abdominopelvic surgeries compared to referent women. In addition, women who underwent abdominal H‐OC have an increased risk of subsequent adnexal procedures compared to women who underwent vaginal H‐OC.

Objectives: To determine the risk of developing de novo carpal tunnel syndrome (CTS), arthritis, or rheumatoid arthritis in women who underwent hysterectomy with bilateral ovarian conservation (H-OC) before reaching spontaneous menopause. Methods: We identified 1,573 women undergoing H-OC for a benign indication at age 18 to 44 years between 1980 and 2002 in Olmsted County, Minnesota. Each woman was age-matched (±1 y) to a referent woman who had not undergone hysterectomy or oophorectomy before the matching date (index date). Diagnostic codes for CTS and arthritis were extracted from the Rochester Epidemiology Project medical records-linkage system through 2019. Hazard ratios (HRs) and 95% CIs were estimated using Cox proportional hazards models and the two cohorts were balanced for 19 chronic conditions and for several potential confounders present at index date. Results: Over a median 25.8-year follow-up, women undergoing H-OC had an increased risk of developing CTS (HR: 1.33; 95% CI: 1.13-1.56) compared with referent women. The risk was similar in strata by age at H-OC and was higher for women with endometriosis and menstrual disorders indications. Women undergoing H-OC also had an increased risk of developing arthritis (HR: 1.34; 95% CI: 1.23-1.47). The risk was similar in strata by age at H-OC and by indication. Rheumatoid arthritis considered separately was also associated with H-OC (HR: 1.55; 95% CI: 1.05-2.30). Conclusions: H-OC was associated with an increased risk of de novo CTS, arthritis (all types), and rheumatoid arthritis. Further studies are needed to confirm these associations and to explore potential biological mechanisms.

Objective To study the reliability and validity of adverse childhood experiences (ACE) scores measured using a questionnaire versus abstraction of medical records, and to test whether the scores vary by history of bilateral oophorectomy, or by age and presence of anxiety or depressive symptoms at the time of questionnaire administration. Design The study involved a reliability component and a predictive validity component. Setting A population-based sample in Olmsted County, Minnesota, was derived from the Mayo Clinic Cohort Study of Oophorectomy and Aging-2. Participants We included 198 women who underwent premenopausal bilateral oophorectomy for a non-malignant indication between 1988 and 2007 and 174 referent women of the same age randomly sampled from the general population (total of 372 women). At a later time (median of 22.7 years later), the women were contacted and invited to self-administer the ACE questionnaire during an inperson visit. Independent of the visit, their medical records were abstracted for ACE by a physician. Interventions Questionnaire and abstraction-based ACE scores. Main outcome measures Agreement between the two ACE scores (reliability; weighted kappa statistics) and comparison of incidence of multimorbidity in women with ACE scores ≥1 vs 0 (predictive validity; survival analyses). Data were analysed in March and April 2024. Results The 372 women in the study had a median age of 65 years at the time of ACE questionnaire self-administration (IQR, 62–69). Questionnaire-based ACE scores showed moderate agreement with abstracted ACE scores (weighted kappa 0.44 (95% CI 0.34 to 0.54)). The cut-off score of ACE ≥1 showed an overall fair agreement between the two scores (kappa 0.33 (95% CI 0.24 to 0.43)). A comparison of the cumulative incidence of multimorbidity in women with an ACE score ≥1 versus women with ACE score of 0 yielded a HR of 1.13 (95% CI 1.00 to 1.27) for abstracted ACE and 1.13 (95% CI 1.01 to 1.27) for questionnaire-based ACE. The best predictive validity was for ACE measured by both methods combined (either or) with a HR of 1.29 (95% CI 1.13 to 1.46). Conclusions and relevance Questionnaire-based ACE scores have moderate agreement with medical records abstracted ACE scores. However, both sets of ACE scores are predictive of the accumulation of multimorbidity at older age and should be considered complementary.

Objective: To explore potential differences in physical activity and sedentary behavior volumes and patterns among postmenopausal women with and without premenopausal bilateral oophorectomy (PBO). Methods: Women with a history of PBO (n = 50) and age-matched postmenopausal referent women (n = 50) were recruited. Participants wore accelerometers on both ankles for 7 days. Volume metrics of sedentary behavior and physical activity, such as step counts, active time, and sedentary time, as well as the sedentary behavior and habitual physical activity distribution, and accumulation patterns, were quantified from the accelerometer data and compared between groups. Results: Metrics indicating volume of sedentary behavior and physical activity were not statistically different between groups. PBO was significantly associated with higher variability in stepping bout time ( P = 0.022), indicating a potentially more complex walking pattern. In addition, PBO was significantly associated with lower variability in sedentary break time ( P = 0.012), and lower activity time Gini index ( Z = −2.428, P = 0.015). This suggests that women with PBO may have broken up their sedentary time with shorter and less variable activity bouts, and because they had relatively shorter average daily active time, they might be at a higher risk of subsequent adverse health outcomes such as low bone mineral density. Conclusions: Although there were no differences in overall activity volume, some differences in activity patterns emerged between women with PBO and referent women. The study highlights the need for longitudinal research to understand how physical activity and sedentary behavior patterns evolve in postmenopausal women with a history of PBO.

AbstractINTRODUCTIONIdentifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lacking. We developed novel set algorithms using both structured and unstructured electronic health record (EHR) data, applying Diagnostic and Statistical Manual of Mental Disorders criteria for dementia case identification.METHODSOur cohort (n = 1082) included individuals aged ≥ 60 with dementia identified through specialist clinics and a comparison group without dementia. Clinicians from Australia and the United States informed predictor selection. We developed algorithms through a biostatistics stream for structured data and a natural language processing (NLP) stream for text, synthesizing results via logistic regression.RESULTSThe final structured model retained 16 variables (area under the receiver operating characteristic curve [AUC] 0.853, specificity 72.2%, sensitivity 80.6%). NLP classifiers (logistic regression, support vector machine, and random forest models) performed comparably. The final, combined model outperformed all others (AUC = 0.951, P < 0.001 for comparison to structured model).DISCUSSIONEmbedding text‐derived insights within algorithms trained on structured medical data significantly enhances dementia identification capacity.Highlights Algorithmic tools for detection of individuals with dementia are available; however, previous work has used heterogeneous case definitions which are not clinically meaningful, and has relied on proxies such as diagnostic codes or medications for case ascertainment. We used a novel, dual‐stream algorithmic development approach, simultaneously and separately modeling a clinically meaningful outcome (diagnosis of dementia according to specialized clinical impression) using structured and unstructured electronic health record datasets. Our clinically grounded case definition supported the inclusion of key structured variables (such as dementia International Classification of Disease codes and medications) as modeling predictors rather than outcomes. Our algorithms, published in detail to support validation and replication, represent a major step forward in the use of routinely collected data for detection of diagnosed dementia.

Abstract The impact of early-life traumatic experiences on late-life morbidity remains partly unknown. We tested the hypothesis that adverse childhood experiences (ACE) and specifically abuse (physical, verbal or emotional, or sexual) experienced during childhood or early adulthood are associated with a higher rate of accumulation of multi-morbidity in women. Here we show strong evidence in support of the hypothesis. We studied 1,026 women aged 21–45 years randomly selected from the general population in Olmsted County, Minnesota and used the Rochester Epidemiology Project medical records-linkage system to measure the rate of development of 18 chronic conditions. The women had a median age of 41 years at inclusion in the study and were followed historically for a median of 21 years. Women with an ACE score ≥ 2 had higher incidence of 10 of the 18 chronic conditions considered separately and an accelerated accumulation of multi-morbidity measured as a score compared to women with ACE score 0. In addition, women exposed specifically to abuse in childhood or early adulthood had accelerated accumulation of multi-morbidity. We excluded the possible confounding effect of socioeconomic status and explored a series of possible mediation events or characteristics. We discuss several possible biological and social or behavioral mechanisms underlying these associations.

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.