Phenome-wide causal proteomics enhance systemic lupus erythematosus flare prediction: A study in Asian populations

• URL: http://arxiv.org/abs/2411.11915v1
• Date: Mon, 18 Nov 2024 01:50:36 GMT
• Title: Phenome-wide causal proteomics enhance systemic lupus erythematosus flare prediction: A study in Asian populations
• Authors: Liying Chen, Ou Deng, Ting Fang, Mei Chen, Xvfeng Zhang, Ruichen Cong, Dingqi Lu, Runrun Zhang, Qun Jin, Xinchang Wang,
• Abstract summary: Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares. This study aimed to develop a novel lupus-based risk prediction model specifically for Asian SLE populations.
• Score: 8.003585790309332
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares. This study aimed to develop a novel proteomics-based risk prediction model specifically for Asian SLE populations to enhance personalized disease management and early intervention. Methods: A longitudinal cohort study was conducted over 48 weeks, including 139 SLE patients monitored every 12 weeks. Patients were classified into flare (n = 53) and non-flare (n = 86) groups. Baseline plasma samples underwent data-independent acquisition (DIA) proteomics analysis, and phenome-wide Mendelian randomization (PheWAS) was performed to evaluate causal relationships between proteins and clinical predictors. Logistic regression (LR) and random forest (RF) models were used to integrate proteomic and clinical data for flare risk prediction. Results: Five proteins (SAA1, B4GALT5, GIT2, NAA15, and RPIA) were significantly associated with SLE Disease Activity Index-2K (SLEDAI-2K) scores and 1-year flare risk, implicating key pathways such as B-cell receptor signaling and platelet degranulation. SAA1 demonstrated causal effects on flare-related clinical markers, including hemoglobin and red blood cell counts. A combined model integrating clinical and proteomic data achieved the highest predictive accuracy (AUC = 0.769), surpassing individual models. SAA1 was highlighted as a priority biomarker for rapid flare discrimination. Conclusion: The integration of proteomic and clinical data significantly improves flare prediction in Asian SLE patients. The identification of key proteins and their causal relationships with flare-related clinical markers provides valuable insights for proactive SLE management and personalized therapeutic approaches.

Related papers

• Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency [52.50039435394964]
  We systematically evaluate foundation models for regression-based tasks.<n>We extract patch-level features from whole slide images (WSI) using five state-of-the-art foundation models.<n>Models are trained to predict continuous HRD scores based on these extracted features across breast, endometrial, and lung cancer cohorts.
  arXiv  Detail & Related papers  (2026-01-29T14:06:50Z)
• IMILIA: interpretable multiple instance learning for inflammation prediction in IBD from H&E whole slide images [2.5182612992285844]
  We introduce IMILIA, an end-to-end framework for prediction of inflammation presence in IBD digitized slides stained with hematoxylin and eosin (H&E)<n> IMILIA is composed of an inflammation prediction module, and an interpretability module, divided in two blocks: HistoPLUS, for cell instance detection, and EpiSeg, for epithelium segmentation.<n>The interpretability module yields biologically consistent insights: tiles with higher predicted scores show increased densities of immune cells, whereas lower-scored tiles predominantly contain normal epithelial cells.
  arXiv  Detail & Related papers  (2025-12-15T15:36:09Z)
• Interpretable Machine Learning Model for Early Prediction of Acute Kidney Injury in Critically Ill Patients with Cirrhosis: A Retrospective Study [3.5626691568652507]
  Cirrhosis is a progressive liver disease with high mortality and frequent complications.<n>Many predictive tools lack accuracy, interpretability, and alignment with intensive care unit (ICU)<n>This study developed an interpretable machine learning model for early AKI prediction in critically ill patients with cirrhosis.
  arXiv  Detail & Related papers  (2025-08-13T23:03:28Z)
• SurgeryLSTM: A Time-Aware Neural Model for Accurate and Explainable Length of Stay Prediction After Spine Surgery [44.119171920037196]
  We develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery.<n>We compare traditional ML models with our developed model, SurgeryLSTM, a masked bidirectional long short-term memory (BiLSTM) with an attention.<n>Performance was evaluated using the coefficient of determination (R2) and key predictors were identified using explainable AI.
  arXiv  Detail & Related papers  (2025-07-15T01:18:28Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images [2.23127246021293]
  Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies.<n>We present an attention-based multiple instance learning framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides.
  arXiv  Detail & Related papers  (2025-05-20T02:06:34Z)
• Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome [0.0]
  The aim of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS) A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. The protein-protein interaction network was analyzed with String software.
  arXiv  Detail & Related papers  (2025-02-25T15:46:41Z)
• Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach [7.212939068975618]
  We utilized data about 10,326 CKD patients, combining their clinical and claims information from 2009 to 2018. A 24-month observation window was identified as optimal for balancing early detection and prediction accuracy. The 2021 eGFR equation improved prediction accuracy and reduced racial bias, notably for African American patients.
  arXiv  Detail & Related papers  (2024-10-02T03:21:01Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data [42.96821770394798]
  TACCO is a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data. We conduct experiments on the public MIMIC-III dataset and Emory internal CRADLE dataset over the downstream clinical tasks of phenotype classification and cardiovascular risk prediction. In-depth model analysis, clustering results analysis, and clinical case studies further validate the improved utilities and insightful interpretations delivered by TACCO.
  arXiv  Detail & Related papers  (2024-06-14T14:18:38Z)
• Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial [0.6087644423424302]
  We present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. Our model produces a visual vascular network which is the basis of the model's prediction. Our approach offers insights into angiogenesis biology and AA treatment response.
  arXiv  Detail & Related papers  (2024-05-28T16:21:20Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia [7.152311859951986]
  This study evaluated the prediction power of laboratory biomarkers to optimize the predictive model for bacteremia. A total of 218 patients with 48 cases of true bacteremia were analyzed in this research.
  arXiv  Detail & Related papers  (2023-11-14T06:44:26Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability [5.9854349801427285]
  Hyperkalemia is a potentially life-threatening condition that can lead to fatal arrhythmias. We developed predictive models to identify intensive care unit (ICU) patients at risk of developing hyperkalemia. Our models were able to predict hyperkalemia with an AUC of (i) 0.79, 0.81, 0.81 and (ii) 0.81, 0.85, 0.85 for LR, RF, and XGBoost respectively.
  arXiv  Detail & Related papers  (2021-01-16T12:35:27Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.