medRxiv : the preprint server for health sciences最新文献

{"title":"Quantify the Contribution of Modifiable Risk Factors for Progression of MGUS to Multiple Myeloma.","authors":"Mei Wang, Byron Sigel, Lawrence Liu, John H Huber, Mengmeng Ji, Martin W Schoen, Kristen M Sanfilippo, Theodore S Thomas, Graham A Colditz, Shi-Yi Wang, Su-Hsin Chang","doi":"10.1101/2025.04.21.25326164","DOIUrl":"10.1101/2025.04.21.25326164","url":null,"abstract":"<p><strong>Background: </strong>Multiple myeloma (MM), the most common plasma cell dyscrasia in the U.S., is preceded by an asymptomatic precursor monoclonal gammopathy of undetermined significance (MGUS). Although several risk factors for MGUS progression are known, their relative contributions remain unclear. Unlike other malignancies, such evidence is lacking for MM despite its high burden.</p><p><strong>Objective: </strong>To quantify contributions of modifiable risk factors to MGUS progression to MM to inform prevention.</p><p><strong>Design: </strong>Retrospective cohort study conducted from 1/1/2024-12/31/2024.</p><p><strong>Setting: </strong>Nationwide U.S. Veterans Health Administration (VHA).</p><p><strong>Participants: </strong>Patients with MGUS (IgG, IgA, or light chain) diagnosed from 10/1/1999-12/31/2023.</p><p><strong>Interventions: </strong>Modifiable risk factors including excess body mass index (BMI), chemical exposure, and comorbidities.</p><p><strong>Measurements: </strong>Excess body mass index was defined as BMI ≥25 kg/m2, chemical exposure was measured by prior exposure to Agent Orange, comorbidities were summarized using Charlson Comorbidity Index. Multivariable-adjusted population attributable fractions (aPAF) was calculated for each modifiable risk factor. The aPAF estimates the proportion of progression in patients diagnosed with MGUS that could have been prevented, if a given risk factor were absent.</p><p><strong>Results: </strong>The cohort included 35,073 MGUS patients (33,670 [96.0%] male and 23,218 [66.2%] White), of whom 2,895 (8.3%) progressed to MM. Median age at MGUS diagnosis was 71.8 (IQR: 64.4-78.6) years. Among all evaluated risk factors, excess BMI was the leading factor (Black: aPAF=27.1%, 95% CI 19.5-34.0%; White: 27.2%, 95% CI 20.3-33.4%; All: aPAF=27.1%, 95% CI: 22.1-31.9%).</p><p><strong>Limitations: </strong>Potential residual confounding, limited generalizability beyond the VHA population.</p><p><strong>Conclusion: </strong>Our study highlights the potential for weight management as a key strategy in reducing the risk of progression to MM in Black and White patients diagnosed with MGUS.</p><p><strong>Primary funding source: </strong>National Institutes of Health.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12045440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060044","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":"Clinical Validation and Machine Learning Optimization of MyCog: A Self-Administered Cognitive Screener for Primary Care Settings.","authors":"Stephanie Ruth Young, Yusuke Shono, Katherina Hauner, Elizabeth M Dworak, Maxwell Mansolf, Laura Curtis, Julia Yoshino Benavente, Stephanie Batio, Richard C Gershon, Michael S Wolf, Cindy J Nowinski","doi":"10.1101/2025.04.17.25325948","DOIUrl":"10.1101/2025.04.17.25325948","url":null,"abstract":"<p><strong>Background: </strong>Primary care presents an ideal opportunity for early detection of cognitive impairment, yet primary care clinics face barriers to cognitive screening. MyCog, an EHR-integrated tablet app that is self-administered during the rooming process of a primary care visit, streamlines the screening process to reduce barriers and encourage broader screening.</p><p><strong>Methods: </strong>We compared MyCog performance from 65 adults with diagnosed cognitive impairment to 80 cognitively normal adults, all aged 65+, recruited from clinical settings. We leveraged the consensus of five machine learning models (LASSO, Elastic Net, Random Forest, Bayesian Logistic Regression, and Gradient Boosting) to select consistently discriminative variables for the final detection algorithm. Performance was assessed at for two evidence-based thresholds (Youden's J and Top Left) with ROC AUC, sensitivity, specificity, and accuracy as the primary metrics.</p><p><strong>Results: </strong>All five models showed strong diagnostic performance, with ROC AUC values ranging from 0.839 to 0.876. The consensus modeling approach consistently identified the MyCog Picture Sequence Memory (PSM) exact match score and the MyCog Dimensional Change Card Sort (DCCS) overall rate-correct score as predictors of cognitive impairment. The final logistic regression achieved a robust AUC of 0.890. Depending on the cut point selected, sensitivity ranged from 0.723-0.785 (95% CI: 0.547-0.877), specificity 0.825-0.912 (95% CI: 0.716-0.954), accuracy 0.807-0.828 (95% CI: 0.731-0.869).</p><p><strong>Discussion: </strong>MyCog provides practical, accurate cognitive screening for primary care. The sub-7-minute self-administered assessment eliminates staffing requirements and automates evaluation, addressing screening barriers to facilitate earlier detection and improve clinical outcomes. The algorithm's robust performance and parsimony demonstrate clinical utility while maintaining diagnostic accuracy.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12047947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051178","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":"Multicenter Evaluation of Myocardial Flow Reserve as a Prognostic Marker for Mortality in ¹³N-Ammonia PET Myocardial Perfusion Imaging.","authors":"Giselle Ramirez, Valerie Builoff, Robert Jh Miller, Mark Lemley, Isabel Carvajal-Juarez, Erick Alexanderson, Thomas L Rosamond, Na Song, Mark I Travin, Leandro Slipczuk, Andrew J Einstein, Samuel Wopperer, Marcelo Di Carli, Panithaya Chareonthaitawee, Piotr Slomka","doi":"10.1101/2025.06.24.25330229","DOIUrl":"https://doi.org/10.1101/2025.06.24.25330229","url":null,"abstract":"<p><strong>Background: </strong>Myocardial flow reserve (MFR), measured by PET MPI, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of ¹³ N-ammonia PET MFR remains unconfirmed in larger multicenter cohorts of patients with suspected or known coronary artery disease (CAD).</p><p><strong>Methods: </strong>We considered patients from five sites in the REFINE PET registry who underwent ¹³ N-ammonia PET MPI for CAD. Clinical and imaging data were collected at the time of MPI. MFR was quantified as the ratio of stress to rest myocardial blood flow, using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA). The primary outcome was all-cause mortality (ACM). Survival analyses were performed using Kaplan-Meier and Cox regression models adjusted for clinical and imaging covariates.</p><p><strong>Results: </strong>In total, 6277 patients were included (mean age of 64 years, 56% male). Median follow-up time was 3.8 years. There were 1895 patients with MFR ≤2 and 4382 with MFR >2. Patients with MFR ≤2 had significantly higher mortality than those with MFR >2 (n=701 [37.0%] vs. n=537 [12.3%], respectively; p<0.001). Annualized ACM rates by MFR and SSS ranged from 1.7 to 11.6. In multivariable analysis, MFR ≤2 was independently associated with increased ACM in the overall population (HR 2.70, 95% CI 2.41-3.03, p<0.001), even among patients with no perfusion defects (HR 2.36, 95% CI 1.93-2.89; p<0.001). Mortality risk decreased across increasing MFR deciles ranging from HR 2.73 (95% CI 2.39-3.11) to HR 0.35 (95% CI 0.25-0.49).</p><p><strong>Conclusion: </strong>In this large multicenter cohort, MFR derived from ¹³ N-ammonia PET MPI is a strong, independent predictor of ACM, even in patients with normal perfusion. An MFR of ≤2.0 identifies elevated risk, while higher values are associated with improved survival. These findings support the routine integration of MFR to enhance risk stratification in patients with suspected or known CAD.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532308","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":"Diagnostic Performance of Universal versus Stratified Computer-Aided Detection Thresholds for Chest X-Ray-Based Tuberculosis Screening.","authors":"Joowhan Sung, Peter James Kitonsa, Annet Nalutaaya, David Isooba, Susan Birabwa, Keneth Ndyabayunga, Rogers Okura, Jonathan Magezi, Deborah Nantale, Ivan Mugabi, Violet Nakiiza, David W Dowdy, Achilles Katamba, Emily A Kendall","doi":"10.1101/2025.04.09.25325458","DOIUrl":"10.1101/2025.04.09.25325458","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Computer-aided detection (CAD) software analyzes chest X-rays for features suggestive of tuberculosis (TB) and provides a numeric abnormality score. However, estimates of CAD accuracy for TB screening are hindered by the lack of confirmatory data among people with lower CAD scores, including those without symptoms. Additionally, the appropriate CAD score thresholds for obtaining further testing may vary according to population and client characteristics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We screened for TB in Ugandan individuals aged ≥15 years using portable chest X-rays with CAD (qXR v3). Participants were offered screening regardless of their symptoms. Those with X-ray scores above a threshold of 0.1 (range, 0 - 1) were asked to provide sputum for Xpert Ultra testing. We estimated the diagnostic accuracy of CAD for detecting Xpert-positive TB when using the same threshold for all individuals (under different assumptions about TB prevalence among people with X-ray scores &lt;0.1), and compared this estimate to age- and/or sex-stratified approaches.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Of 52,835 participants screened for TB using CAD, 8,949 (16.9%) had X-ray scores ≥0.1. Of 7,219 participants with valid Xpert Ultra results, 382 (5.3%) were Xpert-positive, including 81 with trace results. Assuming 0.1% of participants with X-ray scores &lt;0.1 would have been Xpert-positive if tested, qXR had an estimated AUC of 0.920 (95% confidence interval 0.898-0.941) for Xpert-positive TB. Stratifying CAD thresholds according to age and sex improved accuracy; for example, at 96.1% specificity, estimated sensitivity was 75.0% for a universal threshold (of ≥0.65) versus 76.9% for thresholds stratified by age and sex (p=0.046).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;The accuracy of CAD for TB screening among all screening participants, including those without symptoms or abnormal chest X-rays, is higher than previously estimated. Stratifying CAD thresholds based on client characteristics such as age and sex could further improve accuracy, enabling a more effective and personalized approach to TB screening.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Funding: &lt;/strong&gt;National Institutes of Health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Research in context: &lt;/strong&gt;&lt;b&gt;Evidence before this study:&lt;/b&gt; The World Health Organization (WHO) has endorsed computer-aided detection (CAD) as a screening tool for tuberculosis (TB), but the appropriate CAD score that triggers further diagnostic evaluation for tuberculosis varies by population. The WHO recommends determining the appropriate CAD threshold for specific settings and population and considering unique thresholds for specific populations, including older age groups, among whom CAD may perform poorly. We performed a PubMed literature search for articles published until September 9, 2024, using the search terms \"tuberculosis\" AND (\"computer-aided detection\" OR \"computer aided detection\" OR \"CAD\" OR \"computer-aided reading\" OR \"computer aided reading\" ","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12036410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055851","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":"Evaluating the performance and potential bias of predictive models for detection of transthyretin cardiac amyloidosis.","authors":"Jonathan Hourmozdi, Nicholas Easton, Simon Benigeri, James D Thomas, Akhil Narang, David Ouyang, Grant Duffy, Ross Upton, Will Hawkes, Ashley Akerman, Ike Okwuosa, Adrienne Kline, Abel N Kho, Yuan Luo, Sanjiv J Shah, Faraz S Ahmad","doi":"10.1101/2024.10.09.24315202","DOIUrl":"10.1101/2024.10.09.24315202","url":null,"abstract":"<p><strong>Background: </strong>Delays in the diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) contribute to the significant morbidity of the condition, especially in the era of disease-modifying therapies. Screening for ATTR-CM with AI and other algorithms may improve timely diagnosis, but these algorithms have not been directly compared.</p><p><strong>Objectives: </strong>The aim of this study was to compare the performance of four algorithms for ATTR-CM detection in a heart failure population and assess the risk for harms due to model bias.</p><p><strong>Methods: </strong>We identified patients in an integrated health system from 2010-2022 with ATTR-CM and age- and sex-matched them to controls with heart failure to target 5% prevalence. We compared the performance of a claims-based random forest model (Huda et al. model), a regression-based score (Mayo ATTR-CM), and two deep learning echo models (EchoNet-LVH and EchoGo ^® Amyloidosis). We evaluated for bias using standard fairness metrics.</p><p><strong>Results: </strong>The analytical cohort included 176 confirmed cases of ATTR-CM and 3192 control patients with 79.2% self-identified as White and 9.0% as Black. The Huda et al. model performed poorly (AUC 0.49). Both deep learning echo models had a higher AUC when compared to the Mayo ATTR-CM Score (EchoNet-LVH 0.88; EchoGo Amyloidosis 0.92; Mayo ATTR-CM Score 0.79; DeLong P<0.001 for both). Bias auditing met fairness criteria for <i>equal opportunity</i> among patients who identified as Black.</p><p><strong>Conclusions: </strong>Deep learning, echo-based models to detect ATTR-CM demonstrated best overall discrimination when compared to two other models in external validation with low risk of harms due to racial bias.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12155028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144277244","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":"Characterizing Parkinson's Disease Clinical and Biomarker Interactions in REM Sleep Behavior Disorder.","authors":"Vijaya L Reddy, Samantha Esposito, Erika Renkl, Amine Benyakoub, Kara Mead, Camalene Chrysostoum, Sapna Patel, John P Seibyl, Yuan Huang, Brian B Koo, Jesse M Cedarbaum","doi":"10.1101/2025.05.16.25327469","DOIUrl":"10.1101/2025.05.16.25327469","url":null,"abstract":"<p><strong>Background: </strong>REM Sleep Behavior Disorder (RBD), marked by dream enactment due to loss of REM-related muscle atonia, is a prominent prodromal indicator of synucleinopathies, particularly Parkinson's Disease (PD).</p><p><strong>Objectives: </strong>This study aimed to investigate the interplay among key PD biomarkers- α-synuclein seed amplification assay (SAA), hyposmia, and dopamine transporter (DaT) SPECT imaging - in individuals with RBD. Additionally, we evaluated how phenoconversion events and Movement Disorder Society (MDS)-Prodromal PD probability scores relate to clinical symptoms and biomarker profiles in an incident RBD population.</p><p><strong>Methods: </strong>Participants with polysomnographically-confirmed RBD underwent comprehensive clinical and biomarker assessments. They were grouped along three non-exclusive biomarker-based axes (hyposmic vs. normosmic, SAA positive vs. SAA negative, and DaT positive vs. intermediate vs. negative) and two clinical outcome-based axes (high vs. intermediate/low MDS-Prodromal PD probability; phenoconverters vs. non-phenoconverters). Within each category, performance on various clinical assessments, the presence of other biomarkers, and clinical outcomes were evaluated.</p><p><strong>Results: </strong>Hyposmia was associated with reductions in striatal DaT binding and α-syn SAA positivity. MDS Prodromal PD Probability Scores, which incorporate DaT and olfactory function, predicted SAA positivity and phenoconversion. DaT positivity was much more common (80%) among phenoconverters (RBD-PC), than non-phenoconverters (10%). No significant motor or non-motor symptom differences were observed between the two groups at baseline, likely due to the small sample size.</p><p><strong>Conclusions: </strong>α-syn SAA positivity, DaT positivity, and hyposmia are highly associated with each other. MDS Prodromal PD Probability scores may be useful predictors of near-term progression, and thus as stratification factors in clinical research study design.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12132163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218036","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":"Human Applications of Transcranial Temporal Interference Stimulation: A Systematic Review.","authors":"Ilya Demchenko, Ishaan Tailor, Sina Chegini, Haochen Yu, Fatemeh Gholamali Nezhad, Alice Rueda, Anne Kever, Sridhar Krishnan, Abhishek Datta, Jed A Meltzer, Simon J Graham, Tom A Schweizer, Sumientra Rampersad, Edward S Boyden, Ines R Violante, Robert Chen, Andres M Lozano, Venkat Bhat","doi":"10.1101/2025.05.16.25327804","DOIUrl":"10.1101/2025.05.16.25327804","url":null,"abstract":"<p><strong>Background: </strong>Many neurological and psychiatric disorders involve dysregulation of subcortical structures. Transcranial temporal interference stimulation (tTIS) is a novel, non-invasive method developed to selectively modulate these regions and associated neural circuits.</p><p><strong>Methods: </strong>A systematic review was conducted to evaluate human applications of tTIS (PROSPERO ID: CRD42024559678). MEDLINE, Embase, APA PsycINFO, CENTRAL, ClinicalTrials.gov , and WHO ICTRP were searched up to December 12, 2024. Studies involving human applications of tTIS were eligible. Methodological quality was appraised using the NIH and modified Oxford Centre for Evidence-Based Medicine tools.</p><p><strong>Results: </strong>Forty-eight records were reviewed (20 published studies, 28 ongoing trials). Of published studies, 16 single-session and 4 multi-session studies assessed safety, mechanistic outcomes, or therapeutic effects of tTIS in 820 participants. Stimulation was most commonly delivered at beta (20 Hz) or gamma (30-130 Hz) envelope frequencies. Neuroimaging studies support target engagement of the motor cortex, basal ganglia, and hippocampus in humans, particularly when stimulation is paired with behavioural tasks. Preliminary clinical findings in small samples demonstrated acute symptom improvements in bradykinesia and tremor within 60 minutes following a single tTIS session in Parkinson's disease and essential tremor. Reported adverse events across studies were mild (e.g., tingling, itching). Emerging trials increasingly utilize multi-session protocols (2-40 sessions) and are extending tTIS to patients with neurological and psychiatric disorders, particularly epilepsy and depression.</p><p><strong>Conclusions: </strong>Phase 1 studies demonstrate that tTIS is safe, well-tolerated, and can engage deep brain targets in humans. Well-controlled Phase 2 trials are needed to assess its therapeutic potential in patient populations.</p><p><strong>Highlights: </strong>tTIS engages the motor cortex, basal ganglia, and hippocampus across human studies20 studies show tTIS is safe and well-tolerated in healthy and clinical cohortsOne tTIS session improves bradykinesia and tremor in Parkinsonism within 1 hourMulti-session trials now test tTIS in epilepsy, depression, and other disordersRobust Phase 2 trials are needed to study the efficacy of tTIS in patient populations.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12132165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218051","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":"Development and Validation of a Type 1 Diabetes Multi-Ancestry Polygenic Score.","authors":"Aaron J Deutsch, Andrew S Bell, Dominika A Michalek, Adam B Burkholder, Stella Nam, Raymond J Kreienkamp, Seth A Sharp, Alicia Huerta-Chagoya, Ravi Mandla, Ruth Nanjala, Yang Luo, Richard A Oram, Jose C Florez, Suna Onengut-Gumuscu, Stephen S Rich, Alison A Motsinger-Reif, Alisa K Manning, Josep M Mercader, Miriam S Udler","doi":"10.1101/2025.06.20.25329522","DOIUrl":"10.1101/2025.06.20.25329522","url":null,"abstract":"<p><strong>Objective: </strong>Polygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we developed a multi-ancestry polygenic score to accurately predict type 1 diabetes risk across diverse populations.</p><p><strong>Research design and methods: </strong>We used recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2_EUR and T1D GRS_AFR. We also developed an updated score (T1D MAPS2) that combines T1D GRS2_EUR and T1D MAPS.</p><p><strong>Results: </strong>Among individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2_EUR (0.82, <i>P</i> = 0.04) and T1D GRS_AFR (0.82, <i>P</i> = 0.007). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2_EUR (0.89 vs. 0.91, <i>P</i> = 0.02). However, T1D MAPS2 performed equivalently to T1D GRS2_EUR in European ancestry (0.91 vs. 0.91, <i>P</i> = 0.45) while still performing better in non-European ancestry (0.90 vs. 0.82, <i>P</i> = 0.04).</p><p><strong>Conclusions: </strong>A novel polygenic score improves type 1 diabetes risk prediction in non-European ancestry while maintaining high predictive power in European ancestry. These findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532254","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":"The association between adverse experiences throughout the life-course and risk of dementia in the English Longitudinal Study of Ageing.","authors":"Katherine Taylor, Laura D Howe, Rebecca E Lacey, David Carslake, Emma Anderson, Naaheed Mukadam","doi":"10.1101/2025.06.20.25329995","DOIUrl":"https://doi.org/10.1101/2025.06.20.25329995","url":null,"abstract":"<p><strong>Introduction: </strong>Studies investigating the association between adverse experiences across the life-course and dementia consider a narrow range of experiences and use sum scores, assuming each experience has the same impact on dementia risk. We considered the timing, type and cumulation of adverse experiences.</p><p><strong>Methods: </strong>The English Longitudinal Study of Ageing measured adverse experiences in a retrospective interview. Cox proportional hazard models were used to investigate associations between dementia and sum adversity scores, individual experiences, and broad categories adapted from existing frameworks.</p><p><strong>Results: </strong>Number of adult, but not total or childhood, adverse experiences was associated with dementia. Child abuse and adult economic hardship were associated with a 74% and 32% higher hazard of dementia respectively.</p><p><strong>Discussion: </strong>Adulthood adverse experiences associate with dementia in a cumulative risk manner. In childhood, only abuse was associated with dementia. Use of sum scores to summarise adverse experiences throughout the life-course may oversimplify associations with dementia.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532262","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":"Evaluating the accuracy of <i>Salmonella</i> Typhi Hemolysin E and lipopolysaccharide IgA to discriminate enteric fever from other febrile illnesses in South Asia.","authors":"Jessica C Seidman, Kristen Aiemjoy, Mehreen Adnan, Irum Fatima Dehraj, Junaid Iqbal, Khalid Iqbal, Seema Irfan, Nahidul Islam, Md Shakiul Kabir, Nishan Katuwal, Noshi Maria, Muhammad Ashraf Memon, Sira Jam Munira, Shiva Ram Naga, Sailesh Pradhan, Anik Sarkar, Rajeev Shrestha, Sony Shrestha, Syed Muktadir Al Sium, Krista Vaidya, Douglas Ezra Morrison, Alice S Carter, Senjuti Saha, Dipesh Tamrakar, Mohammad Tahir Yousafzai, Denise O Garrett, Stephen P Luby, Farah Naz Qamar, Samir Saha, Jason R Andrews, Richelle C Charles","doi":"10.1101/2025.06.20.25329792","DOIUrl":"https://doi.org/10.1101/2025.06.20.25329792","url":null,"abstract":"<p><p>Existing methods to identify patients infected with Salmonella enterica Typhi (S. Typhi) or Paratyphi are inadequately accurate, affordable, and efficient. We evaluated the discriminatory power of antibodies to S. Typhi hemolysin E (HlyE) and lipopolysaccharide (LPS) in Bangladesh, Nepal, and Pakistan. Plasma concentrations of anti-HlyE and LPS IgA were measured in blood culture-confirmed enteric fever cases and in febrile controls with laboratory-confirmed alternative etiology. Receiver operating characteristic analyses showed that combining anti-LPS and HlyE IgA distinguished enteric fever cases from other febrile illnesses with an area under the curve (AUC) of 0.93. Anti-LPS IgA alone performed nearly as well (AUC 0.92). In children under 5, the combination outperformed individual biomarkers (AUC 0.96 vs. 0.94 (HlyE), 0.93 (LPS)) and was most accurate in Bangladesh and Pakistan compared to Nepal. These findings support anti-HlyE and LPS IgA ELISA as an accurate method to identify enteric fever in endemic settings.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532298","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}