Search

Alzheimers Disease (AD) is marked by pronounced sex differences in pathophysiology and progression. However, the field has yet to fully recognize AD as a womens health issue, delaying the development of targeted preventative strategies and treatments. This perspective explores the elements impacting AD in women, identifying sex specificity in risk factors, highlighting new diagnostic approaches with electronic health records, and reviewing key molecular studies to underscore the need for integrative precision medicine approaches. Established AD risk factors such as advancing age, the apolipoprotein E4 allele, and poorer cardiovascular health affect women differently. We also shed light on sociocultural risk factors, focusing on the gender disparities that may play a role in AD development. From a biological perspective, sex differences in AD are apparent in biomarkers and transcriptomics, further emphasizing the need for targeted diagnostics and treatments. The convergence of novel multiomics data and cutting-edge computational tools provides a unique opportunity to study the molecular underpinnings behind sex dimorphism in AD. Thus, precision medicine emerges as a promising framework for understanding AD pathogenesis through the integration of genetics, sex, environment, and lifestyle. By characterizing AD as a womens health challenge, we can catalyze a transformative shift in AD research and care, marked by improved diagnostic accuracy, targeted interventions, and ultimately, enhanced clinical outcomes.

Objective: To assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults., Study Setting and Design: We extracted HRSN information using the NLP system Clinical Text Analysis and Knowledge Extraction System (cTAKES), combined with Concept Unique Identifiers and Systematized Nomenclature for Medicine codes. We validated cTAKES performance, via manual chart review, on two HRSNs: food insecurity, which was included in the healthcare system's HRSN screening tool, and housing insecurity, which was not., Data Sources and Analytic Sample: De-identified EHRs in a large California healthcare system (January 2013 through October 2022) from 119,127 patients aged 55+ in primary and emergency care settings (n = 1,385,259 clinical notes)., Principal Findings: Although cTAKES had a moderate positive predictive value (77.5%) for housing insecurity, housing challenges among older adults frequently did not align with the concepts the algorithm recognized. cTAKES performed poorly for food insecurity (positive predictive value: 18.5%) because this NLP system incorrectly flagged structured fields from the screening tool., Conclusion: Unstandardized terminology and poor integration of HRSN screeners in EHR remain important barriers to identifying older adults' food and housing insecurity using cTAKES., (© 2025 The Author(s). Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Introduction: The impact of cholesterol on late-life cognition remains controversial. We investigated the association of high-density lipoprotein cholesterol (HDL-C) and non-HDL-C with memory in a nationally representative cohort., Methods: Health and Retirement Study (HRS) participants ( N  = 13,258) aged 50+ (mean age: 67.2 years) followed from 2006 to 2020 provided cholesterol measures every 4 years and cognitive assessments biennially. Linear mixed models predicted memory scores using both baseline and time-updated cholesterol values., Results: Higher baseline HDL-C (mean: 53.9 mg/dL) predicted better memory scores ( β : 0.05, 95% confidence interval [CI]: 0.03 to 0.08), but not memory change. Baseline non-HDL-C (mean: 143 mg/dL) predicted poorer memory scores ( β : -0.01, 95% CI: -0.02 to 0.00), but not memory change. Time-updated HDL-C predicted better memory ( β : 0.02, 95% CI: 0.00 to 0.04), but non-HDL-C showed no such associations., Discussion: While higher peripheral HDL-C is linked to better memory, the small effect sizes and absence of associations of HDL-C and non-HD-CL with memory change suggests that peripheral cholesterol has a small effect on the variation of memory scores., Highlights: Higher HDL-C levels predict better memory scores but not memory change across 14 years of follow-up.Baseline higher LDL-C levels predict poorer memory scores across time, but not memory change.The small effects and absence of consistent association between cholesterol levels and memory change suggest that cholesterol plays a minor role in cognitive decline., Competing Interests: The authors report no relevant conflicts of interest or disclosures. Author disclosures are available in the Supporting Information., (© 2025 The Author(s). Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

With the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information. Recognizing and addressing these limitations can inform the design and interpretation of EHR-based informatics studies that avoid confusing or incorrect conclusions, particularly when applied to population or precision medicine. Here we discuss key considerations in the design, implementation and interpretation of EHR-based informatics studies, drawing from examples in the literature across hypothesis generation, hypothesis testing and machine learning applications. We outline the growing opportunities for EHR-based informatics studies, including association studies and predictive modeling, enabled by evolving AI capabilities-while addressing limitations and potential pitfalls to avoid., (© 2024. Springer Nature America, Inc.)

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses., (© 2024. The Author(s).)

Background and Objectives: Rapid developments in Alzheimer disease (AD) biomarker research suggest that predictive testing may become widely available. To ensure equal access to AD predictive testing, it is important to understand factors that affect testing interest. Discrimination may influence attitudes toward AD testing, particularly among racially and ethnically minoritized populations, because of structural racism in health care systems. This study examined whether everyday or lifetime discrimination experiences shape interest in AD predictive testing., Methods: In the 2010 and 2012 biennial Health and Retirement Study waves, respondents were randomly selected to complete questions on interest in receiving free testing that could determine whether they would develop AD in the future. The exposures were everyday discrimination (6 items) and lifetime discrimination (7 items); both were transformed into a binary variable. Logistic regression models predicting interest in AD testing were controlled for deciles of propensity scores for each discrimination measure. Odds ratios were re-expressed as risk differences (RDs)., Results: Our analytic sample included 1,499 respondents. The mean age was 67 (SD = 10.2) years, 57.4% were women, 65.7% were White, and 80% endorsed interest in AD predictive testing. Most of the participants (54.7%) experienced everyday discrimination in at least one domain; 24.1% experienced major lifetime discrimination in at least one domain. Those interested in predictive testing were younger (66 vs 70 years) and more likely to be Black (20% vs 15%) or Latinx (14% vs 8%) than participants uninterested in testing. The probability of wanting an AD test was not associated with discrimination for Black (RD everyday discrimination = -0.026; 95% CI [-0.081 to 0.029]; RD lifetime discrimination = -0.012; 95% CI [-0.085 to 0.063]) or Latinx (RD everyday discrimination = -0.023, 95% CI [-0.082 to 0.039]; RD lifetime discrimination = -0.011; 95% CI [-0.087 to 0.064]) participants., Discussion: Despite historical and contemporary experiences of discrimination, Black and Latinx individuals express interest in AD testing. However, Black and Latinx individuals remain underrepresented in AD research, including research on AD testing. Interest in personalized information about dementia risk may be a pathway to enhance their inclusion in research and clinical trials.