medRxiv : the preprint server for health sciences最新文献

Introduction: CDR-SB is a reliable and clinically meaningful composite for assessing treatment effects in Alzheimer's disease (AD) clinical trials. Small CDR-SB differences at the end of a trial often lead to controversy in deriving clinically meaningful interpretations.

Methods: We estimated progression-free time participants remained at each 0.5-unit CDR-SB increment in dominantly inherited AD (DIAD) and sporadic AD populations, evaluating its potential as an alternative measure of treatment effects.

Results: Progression-free time is longer at CDR-SB ≤ 2.0 (1-2 years) and shorter at CDR-SB ≥ 5 (0.33 or less) in the ADNI cohort. The DIAD cohort showed similar but shorter times. Using progression-free time, continuous lecanemab treatment for three years is estimated to delay disease progression by 0.7 years in the sporadic population.

Discussion: Progression-free time provides a benchmark for expressing clinical progression and treatment effects and can be applied particularly during open-label extensions and singlearm trials without placebo comparisons.

Purpose: While school gun violence (SGV) incidents in the United States (U.S.) have risen sharply over the past decade, limited research has examined adolescent worries about SGV. We examined the frequency and correlates of SGV worry in a U.S. nationally representative sample of adolescents.

Methods: Data were from adolescent participants (14-17 years; N = 1017) in the 2022 National Survey of Sexual Health and Behavior, a nationally representative study of sexual health experiences of people in the U.S. SGV worry was a single 5-point item (not at all worried - extremely worried). We used both weighted descriptive statistics to examine SGV frequency and random intercept mixed effects ordinal regression to examine demographic and background impact on SGV worry.

Results: Nearly 75% of adolescents reported some degree of SGV worry; of these, one in five were very or extremely worried. SGV worry was significantly higher for adolescents in younger grades and among racial/ethnic minority youth, as well as for cisgender female and gender minority teens. Adolescents in higher income homes were less worried about SGV. Both teens living in metropolitan locations and teens who reported higher anxiety in the past two weeks noted higher SGV worry.

Conclusions: U.S. adolescents have a substantial level of worry about school gun violence. Structural interventions are needed to reduce SGV itself. Moreover, because there are detrimental long-term impacts of prolonged worry, targeted interventions are important for reaching those who are at greatest worry risk, including lower income, race/ethnic minority and gender minority teens.

Implications and conclusions: Three out of every four adolescents in the United States worry about being a victim of school gun violence. Structural interventions to both reduce SGV itself and to reach adolescents at greatest risk for SGV worry are warranted.

Endometriosis is a prevalent, complex, inflammatory condition associated with a diverse range of symptoms and comorbidities. Despite its substantial burden on patients, population-level studies that explore its comorbid patterns and heterogeneity are limited. In this retrospective case-control study, we analyzed comorbidities from over forty thousand endometriosis patients across six University of California medical centers using de-identified electronic health record (EHR) data. We found hundreds of conditions significantly associated with endometriosis, including genitourinary disorders, neoplasms, and autoimmune diseases, with strong replication across datasets. Clustering analyses identified patient subpopulations with distinct comorbidity patterns, including psychiatric and autoimmune conditions. This study provides a comprehensive analysis of endometriosis comorbidities and highlights the heterogeneity within the patient population. Our findings demonstrate the utility of EHR data in uncovering clinically meaningful patterns and suggest pathways for personalized disease management and future research on biological mechanisms underlying endometriosis.

In studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole genome sequencing of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls. To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of BD association with single-variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD GWAS loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

Background: Carotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke risk and plaque burden, its long-term effects on plaque dynamics remain unclear. This study aimed to evaluate the long-term impact of IPH on carotid plaque burden progression using deep learning-based segmentation on multi-contrast magnetic resonance vessel wall imaging (VWI).

Methods: Twenty-eight asymptomatic subjects with carotid atherosclerosis underwent an average of 4.7 ± 0.6 VWI scans over 5.8 ± 1.1 years. Deep learning pipelines were developed and validated to segment the carotid vessel walls and IPH. Bilateral plaque progression was analyzed using generalized estimating equations, and linear mixed-effects models evaluated long-term associations between IPH occurrence, IPH volume, and plaque burden (%WV) progression.

Results: IPH was detected in 23/50 of arteries. Of arteries without IPH at baseline, 11/39 developed new IPH that persisted, while 5/11 arteries with baseline IPH exhibited it throughout the study. Bilateral plaque growth was significantly correlated (r = 0.54, p < 0.001), but this symmetry was weakened with IPH presence. The progression rate for arteries without IPH was -0.001 %/year (p = 0.90). However, IPH presence or development at any point was associated with a 2.3% absolute increase in %WV on average (p < 0.001). The volume of IPH was also positively associated with increased %WV (p = 0.005).

Conclusions: Deep learning-based segmentation pipelines were utilized to identify IPH, quantify IPH volume, and measure their effects on carotid plaque burden during long-term follow-up. Findings demonstrated that IPH may persist for extended periods. While arteries without IPH demonstrated minimal progression under contemporary treatment, presence of IPH and greater IPH volume significantly accelerated long-term plaque growth.

The detection of norm deviations is fundamental to clinical decision making and impacts our ability to diagnose and treat diseases effectively. Current normative modeling approaches rely on generic comparisons and quantify deviations in relation to the population average. However, generic models interpolate subtle nuances and risk the loss of critical information, thereby compromising effective personalization of health care strategies. To acknowledge the substantial heterogeneity among patients and support the paradigm shift of precision medicine, we introduce Nearest Neighbor Normativity (N ³ ), which is a strategy to refine normativity evaluations in diverse and heterogeneous clinical study populations. We address current methodological shortcomings by accommodating several equally normative population prototypes, comparing individuals from multiple perspectives and designing specifically tailored control groups. Applied to brain structure in 36,896 individuals, the N ³ framework provides empirical evidence for its utility and significantly outperforms traditional methods in the detection of pathological alterations. Our results underscore N ³ 's potential for individual assessments in medical practice, where norm deviations are not merely a benchmark, but an important metric supporting the realization of personalized patient care.

Psychiatry lags in adopting etiological approaches to diagnosis, prognosis, and outcome prediction compared to the rest of medicine. Etiological factors such as childhood trauma (CHT), substance use (SU), and socioeconomic status (SES) significantly affect psychotic disorder symptoms. This study applied an agnostic clustering approach to identify exposome clusters "Exposotypes (ETs)" and examine their relationship with clinical, cognitive, and functional outcomes. Using data from individuals with psychotic disorders (n=1,350), and controls (n=623), we assessed the relationship between the exposotypes and outcomes. Four exposotypes were identified: ET1 characterized by high CHT and SU; ET2, high CHT; ET3, high SU; ET4, low exposure. Compared to ET4, ET1 demonstrated higher positive and general symptoms, anxiety, depression, impulsivity, and mania; ET2 had higher anxiety, depression, and impulsivity; ET3 had better cognitive and functional outcomes with lower negative symptoms. Intracranial volume was largest in ET3, and smallest in ET2. No group differences in schizophrenia polygenic risk scores were found. The age of onset was 5 years earlier in ET1 than in ET4. These findings provide insight into the complex etiological interplay between trauma, and SU, as well as their unique effects on clinical symptoms, cognition, neurobiology, genetic risk, and functioning.

The G1 and G2 variants of the APOL1 gene increase the risk of chronic kidney disease (CKD) in individuals of African descent. In the presence of secondary stressors such as inflammation and hypoxia, these gain-of-function variants can induce podocyte dysfunction and cell death through mechanisms that are not fully understood. To identify genes that influence the cytotoxic effects of APOL1 variants under hypoxic conditions, we conducted a comprehensive whole-genome RNA interference (RNAi) screen. We found that silencing several peroxisomal (PEX) genes significantly intensified the cytotoxicity associated with the G1 and G2 variants, revealing the previously unknown role of peroxisomes in APOL1-related cytotoxicity. Importantly, enhancing peroxisomal function through both genetic and pharmacological approaches led to a significant reduction in cytotoxicity linked to these variants. We also identified a peroxisomal targeting signal at the C-terminus of APOL1 that facilitates its translocation to peroxisomes during hypoxia, and mutations in this signal were found to reduce the cytotoxic effects of the variants. Collectively, our findings underscore the importance of peroxisomal function in the pathogenesis of CKD associated with APOL1 variants and suggest that targeting peroxisomes may represent an effective therapeutic strategy to mitigate CKD risk in vulnerable populations.

Translational statement: Peroxisomal function is necessary to protect from APOL1 (kidney risk variant) KRV-mediated cytotoxicity in the presence of hypoxia, and targeting peroxisomes may represent an effective therapeutic strategy to mitigate CKD risk.

The COVID-19 pandemic has highlighted the importance of psychological resilience for well-being. However, no systematic review has been synthesizing global literature on this topic. This review examines psychological resilience across diverse populations, focusing on measurement, associated factors, and future public health preparedness strategies. Guided by the PRISMA, a thorough search of PsycINFO, PubMed, ScienceDirect, and Web of Science was conducted in February 2024 including empirical studies considering psychological resilience during the COVID-19 pandemic published in peer-reviewed English journals from 2020 to 2024, using search terms relevant to psychological resilience and COVID-19. Sixty-eight studies included, mostly using CD-RISC to measure psychological resilience. Multiple factors at various socio-ecological levels were associated with psychological resilience. Effective strategies in promoting resilience included promoting social connectedness through government policies and health communication campaigns and implementing personalized interventions such as telehealth services, resilience-training programs, and mindfulness-based interventions. For preparedness for future public health crises, long-term strategies emphasized investments in mental health services, resilience-building activities, and providing robust support for mental health across diverse populations. Community and educational initiatives should focus on mental health promotion programs in colleges and schools and life skills training to improve interpersonal skills and job crafting. Technological solutions like remote consultations, mHealth applications, and telehealth were also recommended. This review suggests that improving preparedness for future public health crises requires prioritizing investments in mental health services, resilience-building activities, and mHealth applications. These insights provide a foundational resource for research, policy, and interventions to strengthen resilience globally at individual, community, and structural levels.

Objective: The primary objective was to use network analysis to characterize maternal transport patterns in the state of Georgia and compare them with the state's designated perinatal regions (DPRs).

Study design: Using 2017-2022 birth records in Georgia, we constructed network graphs of maternal transport routes among obstetric facilities. We used multivariate logistic regression to identify factors associated with inter-DPR transports. We applied a community-detection algorithm to cluster facilities and compared the clusters to Georgia's DPRs.

Results: Among 774 639 deliveries, 2 757 (0.36%) involved transports among obstetric facilities. 8 facility clusters were identified and strongly aligned with DPRs (p < 0.001). Inter-DRP transports tended to occur between neighboring DPRs and between facilities belonging to the same healthcare system (p < 0.001).

Conclusion: Network analysis reveals patterns of maternal transports among obstetric facilities. States can improve the design of perinatal regionalization systems by formalizing existing partnership among obstetric facilities.