medRxiv : the preprint server for health sciences最新文献

{"title":"Body Image and Its Associated Factors among People Living with HIV: A Comprehensive Systematic Review and implications for integrated care.","authors":"Atena Pasha, Mohammad Jahanaray, Xiaoming Li, Shan Qiao","doi":"10.1101/2025.05.04.25326771","DOIUrl":"10.1101/2025.05.04.25326771","url":null,"abstract":"<p><strong>Objectives: </strong>People living with HIV (PLWH) face unique psychosocial challenges due to both infection and antiretroviral therapy (ART), one of which is body image disruption. Yet, a comprehensive synthesis of existing research on body image among PLWH is lacking. This study systematically reviewed relevant studies to explore body image issues, identify associated factors, and describe assessment methods and interventions targeting body image in this population.</p><p><strong>Methods: </strong>Guided by the PRISMA, a thorough search of PsycINFO, PubMed, Embase, and Web of Science was conducted in January 2024, including empirical studies considering Body Image among PLWH published in peer-reviewed English journals, using search terms relevant to HIV and Body image. To include the latest articles, we conducted another round of searches in November 2024. NIH Study Quality Assessment Tools were used to assess the quality of the included studies, and a narrative synthesis was conducted to identify common themes, including definitions of body image, associated factors, measurement instruments, and interventions targeting body image among PLWH.</p><p><strong>Results: </strong>From 2197 publications, 26 studies from 2004 to 2024 met the inclusion criteria, comprising a sample of 4095 PLWH aged 8 to 65 from different countries. Most of the studies were cross-sectional in design and varied in focus. Findings reveal that body image issues are prevalent among PLWH. The majority of studies demonstrated an association between negative body image and psychological comorbidities, including depression, anxiety, social withdrawal, and substance use. Body image dissatisfaction was also associated with physical health factors such as lipodystrophy. BMI measures reported in twelve studies indicated that BMI tends to increase with age in PLWH. Sixteen distinct body image measurement tools were used across studies. CBT-BISC was the only target intervention that showed effectiveness in mitigating body image disturbance and improving ART adherence among PLWH.</p><p><strong>Conclusion: </strong>Body image issues represent a critical but often overlooked component of the biopsychosocial challenges faced by PLWH. This is the first comprehensive literature review to exclusively consider body image, associated factors, measurements, and target interventions among PLWH, which highlighted the need for comprehensive, culturally sensitive interventions that address both the physical and psychological dimensions of body image concerns.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Bacterial Vaginosis Development using Artificial Neural Networks.","authors":"Jacob H Elnaggar, John W Lammons, Caleb M Ardizzone, Kristal J Aaron, Clayton Jacobs, Keonte J Graves, Sheridan D George, Meng Luo, Ashutosh Tamhane, Paweł Łaniewski, Alison J Quayle, Melissa M Herbst-Kralovetz, Nuno Cerca, Christina A Muzny, Christopher M Taylor","doi":"10.1101/2025.05.02.25326872","DOIUrl":"10.1101/2025.05.02.25326872","url":null,"abstract":"<p><p>Bacterial vaginosis (BV) is a dysbiosis of the vaginal microbiome, characterized by the depletion of protective <i>Lactobacillus</i> spp. and overgrowth of anaerobes. Artificial neural network (ANN) modeling of vaginal microbial communities offers an opportunity for early detection of incident BV (iBV). 16S rRNA gene sequencing and quantitative PCR was performed on longitudinal vaginal specimens collected from participants within 14 days of iBV or from healthy participants to calculate the inferred absolute abundance (IAA) of vaginal bacterial taxa. ANNs were trained using the IAA of vaginal taxa from 420 vaginal specimens to classify individual vaginal specimens as either pre-iBV (collected before iBV onset) or Healthy. Feature importance was assessed to understand how specific vaginal micro-organisms contributed to model predictions. ANN modeling accurately classified >97% of specimens as either pre-iBV or Healthy (sensitivity >96%, specificity >98%) using IAA of 20 vaginal taxa. Model prediction accuracy was maintained when training models using only a few key vaginal taxa. Models trained using only the top five most important features achieved an accuracy of >97%, sensitivity >92%, and specificity >99%. Model predictive accuracy was further improved by training models on specimens from white and black participants separately; using only three feature models achieved an accuracy >96%, sensitivity >91%, and specificity >91%. Feature analysis found that <i>Lactobacillus</i> species <i>L. gasseri</i> and <i>L. jensenii</i> differed in how they contributed to model predictions in models trained with data stratified by race. A total of 420 vaginal specimens were analyzed, providing a robust dataset for model training and validation.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Migraine Trigger Surprisal Predictions: A Bayesian Approach to Establishing Prospective Expectations.","authors":"Dana P Turner, Emily Caplis, Twinkle Patel, Timothy T Houle","doi":"10.1101/2025.05.03.25326924","DOIUrl":"10.1101/2025.05.03.25326924","url":null,"abstract":"<p><strong>Objective: </strong>To extend the application of surprisal theory for predicting migraine attack risk by developing methods to estimate trigger variable likelihood in real time, under conditions of limited personal observation.</p><p><strong>Background: </strong>Prior work has demonstrated that higher surprisal, a measure quantifying the unexpectedness of a trigger exposure, predicts headache onset over 12 to 24 hours. However, these analyses relied on retrospective expectations of trigger exposure formed after extended data collection. To operationalize surprisal prospectively, Bayesian methods could update expectations dynamically over time.</p><p><strong>Methods: </strong>In a prospective daily diary study of individuals with migraine (N = 104), data were collected over 28 days, including stress, sleep, and exercise exposures. Bayesian models were applied to estimate daily expectations for each variable under uninformative and empirical priors derived from the sample. Stress was modeled using a hurdle-Gamma distribution, sleep using a rounded Normal distribution, and exercise using a Bernoulli distribution. Surprisal was calculated based on the predictive distribution at each time point and compared to static empirical surprisal values obtained after full data collection.</p><p><strong>Results: </strong>Dynamic Bayesian surprisal values systematically differed from retrospective empirical estimates, particularly early in the observation period. Divergence was larger and more variable under uninformative priors but attenuated over time. Empirically informed priors produced more stable, lower-bias surprisal trajectories. Substantial individual variability was observed across exposure types, especially for exercise behavior.</p><p><strong>Conclusions: </strong>Prospective surprisal modeling is feasible but highly sensitive to prior specification, especially in sparse data contexts (e.g., a binary exposure). Incorporating empirical or individually informed priors may improve early model calibration, though individual learning remains essential. These methods offer a foundation for real-time headache forecasting and dynamic modeling of brain-environment interactions.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-center study to discern the diurnal variation of wearable device-based heart rate variability (HRV) in the Chronic Renal Insufficiency Cohort (CRIC) Study.","authors":"Carsten Skarke, Wei Yang, Daohang Sha, Nicholas F Lahens, Tamara Isakova, Mark Unruh, Rajat Deo, Eunice Carmona-Powell, John H Holmes, Elaine Ficarra, Jing Chen, Jiang He, Hernan Rincon-Choles, Vallabh Shah, Chi-Yuan Hsu, Amanda H Anderson, James P Lash, Mahboob Rahman","doi":"10.1101/2025.04.30.25326177","DOIUrl":"10.1101/2025.04.30.25326177","url":null,"abstract":"<p><p>Little is known about the prognostic value of out-of-clinic biometric monitoring of cardiovascular function in chronic kidney disease (CKD). Using real-world sampling strategies, a mean (±SD) of 50.3±9.3 hours of ECG recordings from wearable BioPatch ECG devices was collected in a cohort consisting of 458 participants from seven Chronic Renal Insufficiency Cohort (CRIC) centers. The presence of diabetes was associated with a 7.4 ms lower Standard Deviation of NN Intervals (SDNN) compared to non-diabetic participants (<i>p</i>=0.001). Multivariable linear regression revealed that participants without proteinuria (uPCR<0.2) had a 5.15 ms higher SDNN compared to participants with proteinuria (uPCR≥0.2, <i>p</i>=0.027). Cosinor modeling suggested differences in SDNN acrophase quartiles for diabetes (<i>p</i>=0.02), history of cardiovascular disease (<i>p</i>=0.003), eGFR (<i>p</i>=0.04), systolic blood pressure (<i>p</i>=0.04), and beta blocker use (<i>p</i>=0.0002). In the spline analysis, the SDNN curve differed between participants with and without cardiovascular disease (<i>p</i>=0.0005). This study assembled the largest dataset to date of SDNN as an index for heart rate variability from wearable digital health technology in the CRIC. The study demonstrates that several clinical and demographic factors are associated with SDNN in participants with CKD. This sets the stage to determine the predictiveness of time-specific HRV metrics for future clinical outcomes.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular risk management in adults with diagnosed diabetes in Mexico from 2016-2023: A retrospective analysis of nationally representative surveys.","authors":"Daniel Ramírez-García, Jerónimo Perezalonso-Espinosa, Padme Nailea Méndez-Labra, Carlos A Fermín-Martínez, Juan Pablo Díaz-Sánchez, César Daniel Paz-Cabrera, Arsenio Vargas-Vázquez, Miriam Teresa López-Teros, David Flood, Jennifer Manne-Goehler, Neftali Eduardo Antonio-Villa, Goodarz Danaei, Jacqueline A Seiglie, Omar Yaxmehen Bello-Chavolla","doi":"10.1101/2024.09.18.24313926","DOIUrl":"10.1101/2024.09.18.24313926","url":null,"abstract":"<p><strong>Background: </strong>Effective cardiovascular disease (CVD) risk management is a cornerstone of optimal diabetes care. Here, we estimated the prevalence and determinants of CVD risk factor control amongst individuals with diagnosed diabetes in Mexico.</p><p><strong>Methods: </strong>We analyzed data from individuals with diagnosed diabetes ≥20 years from the 2016-2023 Mexican National Health and Nutrition Surveys. We estimated the prevalence of glycemic, blood pressure (BP), non-current smoking, and combined CVD risk factor control. LDL-C control was assessed using SCORE2-Diabetes risk categories. We estimated the prevalence of BP-lowering, cholesterol-lowering, and glucose-lowering medication use, and explored determinants of control achievement using logistic regression.</p><p><strong>Results: </strong>We analyzed data representing 43.2 million adults with diagnosed diabetes during 2016-2023. In 2023, glycemic control was 29% (95%CI 21%-38%), BP control 22.9% (95%CI 14%-31%), and non-current smoking 89% (95%CI 81%-96%). The proportion of people classified as high or very-high CVD risk increased from 59.8% (95%CI 52.1%-67.0%) in 2016 to 68.4% (95%CI 55.6%-78.9%) in 2023, representing ~5.1 million adults. LDL-C control prevalence increased from 2.8% (95%CI 1.2%-4.4%) in 2016 to 6.6% (95%CI 1.9%-11.2%) in 2023. Combined risk factor control achievement was low primarily due to suboptimal LDL-C control, despite high medication use; this was more likely achieved in females, younger individuals, and those with college education or living in states with higher socioeconomic position.</p><p><strong>Conclusions: </strong>Despite increasing CVD risk during this period, comprehensive glycemic and CVD risk factor management for adults with diabetes in Mexico remains suboptimal. Our findings highlight the need for strategies to address gaps in CVD risk management to reduce premature mortality in this population.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic and Epigenetic Dysregulation of CR1 is Associated with Catastrophic Antiphospholipid Syndrome (CAPS).","authors":"Nikhil Ranjan, Michael Cole, Gloria F Gerber, Mark A Crowther, Evan M Braunstein, Daniel Flores-Guerrero, Kathy Haddaway, Alexis Reed, Michael B Streiff, Keith R McCrae, Michelle Petri, Shruti Chaturvedi, Robert A Brodsky","doi":"10.1101/2025.05.01.25326429","DOIUrl":"10.1101/2025.05.01.25326429","url":null,"abstract":"<p><strong>Objective: </strong>Catastrophic antiphospholipid syndrome (CAPS), characterized by widespread thrombosis and multi-organ failure, is associated with high morbidity and mortality. We previously established complement activation as a pathogenic driver of CAPS and identified rare germline variants in complement-regulatory genes including Complement Receptor 1 (<i>CR1</i>) in 50% of CAPS.</p><p><strong>Methods: </strong>We quantified CR1 expression by flow cytometry across hematopoietic cell types. CRISPR/Cas9 genome editing of TF-1 (erythroleukemia) cells was performed to generate <i>CR1</i> \"knock-out\" and \"knock-in\" lines with patient-specific <i>CR1</i> variants. Multiomics analysis was performed to investigate the role of methylation in CR1 expression in patients with reduced CR1 expression. Functional impact of low CR1 expression was assessed by complement-mediated cell killing using modified Ham (mHam) assay, cell-bound complement degradation products through flow cytometry and circulatory immune complexes (CIC) in serum samples through ELISA.</p><p><strong>Results: </strong>CR1 expression in erythrocytes was markedly reduced on CAPS erythrocytes (n=9, 21.80%) compared to healthy controls (HC; n=32, 82.40%), with promoter hypermethylation emerging as a plausible epigenetic mechanism for CR1 downregulation. A novel germline variant (<i>CR1-</i>V2125L; <i>rs202148801</i>) mitigated CR1 expression and increased complement-mediated cell death of knock-in cell lines. Erythrocytes from the patient with the <i>CR1-</i>V2125L variant had low CR1 expression. Levels of CIC, which are bound and cleared by CR1 on erythrocytes, were higher in acute CAPS (n=3, 25.55 μg Eq/ml) than healthy controls (n=3, 7.445 μg Eq/ml). Five patients were treated with C5 inhibition which mitigated thrombosis.</p><p><strong>Conclusion: </strong>Genetic or epigenetic-mediated CR1 deficiency is a potential hallmark of CAPS and predicts response to C5 inhibition.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction.","authors":"Leonardo Cobuccio, Arnor I Sigurdsson, Kajsa-Lotta Georgii Hellberg, Morten Dybdahl Krebs, Jonas Meisner, Thomas Werge, Michael E Benros, Andrew J Schork, Simon Rasmussen","doi":"10.1101/2025.05.05.25326794","DOIUrl":"10.1101/2025.05.05.25326794","url":null,"abstract":"<p><p>Polygenic scores (PGSs) have emerged as promising tools for predicting complex traits from genetic data, however, their predictive performance for psychiatric disorders remains limited and the added value of deep learning (DL) over linear models is underexplored. In this study, we compared our DL model, Genome-Local-Net (GLN), with the linear model bigstatsr in predicting five psychiatric disorders-ADHD, ASD, BIP, MDD, and SCZ-using individual-level genotype data. We further assessed whether combining these <i>internal</i> (individual-based) PGSs with <i>external</i> (GWAS-derived) PGSs and family genetic risk scores (FGRSs) could improve prediction additively or synergistically. While GLN and bigstatsr performed similarly in-sample, GLN showed better generalization on an out-of-sample replication set for ADHD, ASD, and MDD, with an average AUROC gain of 0.026. Integrating <i>internal</i>, <i>external</i>, and family-based scores significantly improved ADHD prediction, though DL-based integration provided no consistent advantage over logistic models. These findings suggest that while DL may enhance generalizability for specific psychiatric traits, linear models remain competitive and effective for genetic risk prediction.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Target Trial Emulation using Distributed Observational Data for Treatment Effect Estimation.","authors":"Haoyang Li, Chengxi Zang, Zhenxing Xu, Weishen Pan, Suraj Rajendran, Yong Chen, Fei Wang","doi":"10.1101/2025.05.02.25326905","DOIUrl":"10.1101/2025.05.02.25326905","url":null,"abstract":"<p><p>Target trial emulation (TTE) aims to estimate treatment effects by simulating randomized controlled trials using real-world observational data. Applying TTE across distributed datasets shows great promise in improving generalizability and power but is always infeasible due to privacy and data-sharing constraints. Here we propose a Federated Learning-based TTE framework, FL-TTE, that enables TTE across multiple sites without sharing patient-level data. FL-TTE incorporates federated protocol design, federated inverse probability of treatment weighting, and a federated Cox proportional hazards model to estimate time-to-event outcomes across heterogeneous data. We validated FL-TTE by emulating Sepsis trials using eICU and MIMIC-IV data from 192 hospitals, and Alzheimer's trials using INSIGHT Network across five New York City health systems. FL-TTE produced less biased estimates than traditional meta-analysis methods when compared to pooled results and is theoretically supported. Our FL-TTE enables federated treatment effect estimation across distributed and heterogeneous data in a privacy-preserved way.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic Mapping and Automated Segmentation of Human Brainstem White Matter Bundles.","authors":"Mark D Olchanyi, David R Schreier, Jian Li, Chiara Maffei, Annabel Sorby-Adams, Hannah C Kinney, Brian C Healy, Holly J Freeman, Jared Shless, Christophe Destrieux, Henry Tregidgo, Juan Eugenio Iglesias, Emery N Brown, Brian L Edlow","doi":"10.1101/2025.05.01.25326687","DOIUrl":"10.1101/2025.05.01.25326687","url":null,"abstract":"<p><p>Brainstem white matter bundles are essential conduits for neural signaling involved in modulation of vital functions ranging from homeostasis to human consciousness. Their architecture forms the anatomic basis for brainstem connectomics, subcortical mesoscale circuit models, and deep brain navigation tools. However, their small size and complex morphology compared to cerebral white matter structures makes mapping and segmentation challenging in neuroimaging. This results in a near absence of automated brainstem white matter tracing methods. We leverage diffusion MRI tractography to create BrainStem Bundle Tool (BSBT), which segments eight key white matter bundles in the rostral brainstem. BSBT performs automated segmentation on a custom probabilistic fiber map generated from tractography with a convolutional neural network architecture tailored for detection of small structures. We demonstrate BSBTs robustness across diffusion MRI acquisition protocols through validation on healthy subject <i>in vivo</i> scans and <i>ex vivo</i> scans of brain specimens with corresponding histology. Using BSBT, we reveal distinct brainstem white matter bundle alterations in Alzheimer's disease, Parkinson's disease, and acute traumatic brain injury cohorts through tract-based analysis and classification tasks. Finally, we provide proof-of-principle evidence supporting the prognostic utility of BSBT in a longitudinal analysis of coma recovery. BSBT creates opportunities to automatically map brainstem white matter in large imaging cohorts and investigate its role in a broad spectrum of neurological disorders.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Racial and Ethnic Reporting and Representation in Phase III Alzheimer's Disease Clinical Trials in the US.","authors":"Zhuoer Lin, Ruochen Sun, Joseph S Ross, Kien Lau, Sophia Stumpf, Xi Chen","doi":"10.1101/2025.05.03.25326933","DOIUrl":"10.1101/2025.05.03.25326933","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) disproportionately affects racial and ethnic minoritized populations in the United States, yet these groups remain markedly underrepresented in clinical research. Phase III clinical trials are critical for informing regulatory decision and treatment guidelines, but the extent to which they report and include racial and ethnic diverse participants in the US context has not been systematically assessed.</p><p><strong>Methods: </strong>We conducted a comprehensive retrospective review of all US-based Phase III AD clinical trials from 1997 to 2023 using the Trialtrove database, cross-referenced with PubMed, ClinicalTrials.gov, and other public sources. We analyzed long-term trends in the reporting and representation of racial and ethnic groups across the longest observation period to date.</p><p><strong>Results: </strong>Of 88 identified trials, 71 (80.7%) had published data. Nearly half (49.3%) did not report any race or ethnicity information. Among those that did, most focused on White patients, with limited and inconsistent reporting for racial and ethnic minoritized groups. Median enrollment was 0.9% for Asian or Pacific Islander, 4.5% for Black (ethnicity unspecified), 7.2% for Black (non-Hispanic), 5.2% for Hispanic, and 0.4% for Native American participants, compared to nearly 90% for White participants. Only 4.2% of trials conducted subgroup analysis by race or ethnicity, and none reported detailed outcome differences. Terminology varied widely and no trials acknowledged underrepresentation or proposed corrective strategies. Notably, these patterns showed little to no improvement over time.</p><p><strong>Conclusions and implications: </strong>Racial and ethnic minoritized populations remain consistently underreported and underrepresented in Phase III AD trials in the US, limiting the generalizability of findings and risking the exacerbation of health inequities. Improving equity in AD research will require standardized reporting, inclusive recruitment practices, and intentional efforts to engage underrepresented communities.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}