Yeshiva University

ABSTRACT Background Suicide is the second leading cause of death among youth ages 15–24. Sexual minority (SM) youth are at higher risk for suicidal thoughts, behavior, and non-suicidal self- injury than their heterosexual peers. Part of understanding this disparity includes accounting for the role of social drivers of health. The increased risk of suicide and self-harm for SM youth is driven by experiences of minority stress and discrimination that is embedded in society. Recognizing the impact of these factors is essential to inform the delivery of evidence-based interventions for SM youth who are receiving care for suicide prevention. Objective To describe the process for developing a behavioral fit analysis when working with SM youth who are at risk for suicide, with an emphasis on the role of social drivers of health that may overlap with SM identity as it contributes to suicide risk. Method A brief review of the literature is provided on the topics of minority stress and existing screening and assessment approaches for suicidal youth. The benefits of a behavioral fit analysis are described and how this supplements existing evidence-based tools. A fictional case example is included to illustrate the direct application of this organizational framework. Results A case example of a fictional SM youth is provided with an accompanying fit analysis and treatment plan. Implications for generalization to clinical practice are described, complemented with resources and suggestions for clinicians. Conclusions The use of a behavioral fit analysis balances a nomothetic and idiographic approach to a case conceptualization for SM youth at risk for suicide and incorporates the impact of social drivers of health on the use of evidence-based treatments.

BackgroundWomen are at greater risk of developing sporadic Alzheimer's disease (AD). While their longer lifespans contribute to AD‐risk, this factor does not fully explain the observed sex differences. Increased glial activation and neuroinflammation associated with female‐sex may play a role. Glial fibrillary acidic protein (GFAP), a marker of astrocyte activation and neuroinflammation, has been linked to AD‐ risk. In preclinical and prodromal AD, higher brain GFAP levels are associated with cognitive decline, Aβ deposition, tau spread, and neurodegeneration associated with AD. Studies have reported that plasma GFAP levels are higher in women compared to men, but few have thoroughly examined this influence. This prompted us to examine sex‐differences in plasma GFAP in cognitively unimpaired older adults and its relationship to cognitive performance and other plasma AD biomarkers, both at baseline and longitudinally.MethodParticipants were enrolled in the Memory Education and Research Initiative (MERI) program and completed a neuropsychological battery, clinical and psychiatric evaluation, and blood draw. Plasma GFAP, Aβ42, Aβ40 concentrations were measured using single‐molecule array (SIMOA) platform. Participants were included if they gave blood (2+ timepoints), MMSE>23 at Baseline, and age 50‐85. Analysis of covariance (ANCOVA) was conducted to assess for sex differences, and longitudinal analyses using linear mixed models were conducted to examine associations with cognition.ResultA total 325 MERI (mean age=72 years; 59% females) were included in the analysis. ANCOVA, controlling for age, showed significantly higher plasma GFAP levels in females compared to males (p = 0.001). Longitudinal analysis of 229 MERI revealed that higher baseline GFAP is associated with lower MMSE scores over time (Figure 1, p = 0.002). When stratified by sex, these associations remained significant in females but not males (p = 0.002). GFAP was also inversely associated with Aβ42/40 over time (Figure 2, p <0.001) in females.ConclusionCognitively unimpaired females had higher levels of plasma GFAP compared to males. Moreover, elevated GFAP levels associated with greater decline in global cognition and lower Aβ42/40 in females but not males. These findings suggest increased astrocytic activation that may lead to cognitive decline in females. More studies are needed to understand how sex‐differences in neuroinflammation may impact future AD‐risk. Potential mechanisms will be discussed.

We present iDT-diet, an intelligent digital twin prototype designed to model the long-term influence of diet quality on health biomarkers and chronic conditions. The system integrates three novel components: (i) a random forest learning model enhanced with Choquet LASSO feature selection for capturing complex, nonlinear interactions in temporal health data; (ii) a translation module that converts predictive outputs into natural language narratives of physical and biomarker states; and (iii) a generative 3D visualization engine that produces dynamic, personalized digital twins reflecting evolving health trajectories. This integration uniquely links advanced machine learning, interpretable communication, and immersive visualization within a single framework. While the current implementation focuses on retrospective digital twin generation, the system architecture supports real-time data integration, enabling continuous monitoring, predictive simulation, and personalized recommendation delivery for diet and lifestyle management.