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PubMed2026

Statistical test to compare the linkage model and the admixture model based on central limit results.

In the Admixture Model, the probability that an individual carries a certain allele at a specific marker depends on the allele frequencies in K ancestral populations and the proportion of the individual's genome originating from these populations. The markers are assumed to be independent. The Linkage Model is a Hidden Markov Model that extends the Admixture Model by incorporating linkage between neighboring loci. We prove consistency and asymptotic normality of maximum likelihood estimators for the ancestry of individuals in the Linkage Model, complementing earlier results by (Pfaff et al., 2004; Pfaffelhuber and Rohde, 2022; Heinzel, 2025) for the Admixture Model. These results are used to prove that a statistical test that allows for model selection between the Admixture Model and the Linkage Model is an asymptotic level-α-test. Finally, we demonstrate the practical relevance of our results by applying the test to real-world data from The 1000 Genomes Project Consortium (2015).

Genetic Linkage↗
PubMed2026

Care Navigation for Methamphetamine Use Disorder: A Randomized Clinical Trial.

IMPORTANCE: Stimulant-involved deaths continue to increase in the US, and methamphetamine use remains a weighty public health concern. Treating methamphetamine use disorders is complicated. Contingency management has demonstrated the best effectiveness but is not widely implemented. OBJECTIVE: To examine the effectiveness of dedicated care navigation in linking patients to treatment. DESIGN, SETTING, AND PARTICIPANTS: This prospective randomized clinical trial was conducted at an integrated safety-net health system in Denver, Colorado, between April 10, 2023, and December 31, 2024. Eligible participants were 18 years or older who had a methamphetamine-related encounter in an acute care setting; those with involuntary treatment holds, substance treatment in past 90 days or actively seeking treatment, and inability to provide consent were excluded. Participants completed baseline, 30-day, and 90-day study visits. INTERVENTION: Dedicated care navigation, incorporating contingency management principles, with a focus on addressing health-related social needs. MAIN OUTCOMES AND MEASURES: Linkage to treatment within 30 and 90 days of enrollment defined as a composite measure of at least 1 of the following: electronic health record data indicating a visit at the health system's substance treatment clinic, a behavioral health encounter at an outpatient clinic, temporary residential treatment, or self-reported treatment on the 30- and/or 90-day follow-up survey. RESULTS: Of 192 participants enrolled in the Beginning Early and Assertive Treatment for Methamphetamine Use trial, 156 (81.3%) were male, and the median age was 39 (IQR, 31-47) years. Most participants were unstably housed (163 [84.9%]), not currently employed (158 [82.3%]), and without regular access to a working phone (94 [49.0%]). Of the 96 participants randomized to the intervention, 60 (62.5%) engaged in 2 or more navigation sessions, 45 (46.9%) completed the 30-day study visit, and 47 (49.0%) completed the 90-day study visit compared with 44 (46.3%) and 37 (38.5%), respectively, of the 96 randomized to the control arm. No statistically significant differences in treatment linkage were observed at 30 days (24 participants [25.0%] in both arms; risk ratio, 1.00 [95% CI, 0.61-1.63]) or 90 days post enrollment, (32 [33.3%] in intervention vs 24 [25.0%] in control arms; risk ratio, 1.33 [95% CI, 0.85-2.09]). CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, integrating principles of contingency management into the intervention may have increased engagement with a dedicated care navigator but did not increase likelihood of linkage to treatment for methamphetamine use disorder. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06033365.

Humans↗
PubMed2026

An introductory practical guide to secondary data analysis in pediatric urology.

INTRODUCTION: Secondary data analysis (SDA) has become an increasingly important approach in pediatric urology, enabling the study of long-term outcomes, care variation, and disparities in populations with chronic or congenital urologic conditions. With the growing availability of large datasets, a structured approach to designing and conducting SDA studies is increasingly relevant. OBJECTIVES: To provide an introductory, practical guide to SDA in pediatric urology by (1) summarizing commonly used data sources with representative studies, (2) outlining a stepwise approach to designing and executing SDA studies, and (3) highlighting key methodological considerations, limitations, and opportunities for future work. STUDY DESIGN: Narrative review of existing literature and commonly used datasets relevant to pediatric urology, including administrative claims, hospital encounter databases, clinical registries, electronic health record networks, and population-based surveys. RESULTS: Data sources differ in scope, clinical granularity, longitudinal follow-up, and representativeness, and each is suited to specific research questions. We present a practical workflow for SDA, including dataset selection, cohort definition, and analytic planning. Linkage across datasets can provide a more comprehensive view of care patterns and outcomes, although feasibility is influenced by legal, technical, and data-quality constraints. DISCUSSION: SDA enables population-level analyses and the study of rare conditions that are challenging to evaluate through single-center or prospective designs. However, careful cohort definition, feasibility assessment, and awareness of data limitations are essential to ensure validity and interpretability. CONCLUSION: SDA provides a scalable, cost-efficient framework for generating meaningful evidence in pediatric urology. Continued efforts to harmonize data elements, improve linkage infrastructure, and support cross-institution collaboration will enhance the quality and impact of future research. This article provides a practical framework and examples to support the design and execution of SDA studies.

Humans↗
PubMed2026

Carboxyl group number and acidity of organic acids regulate structural reorganization and low glycemic index in cassava pyrodextrins via molecular interactions.

Transforming high-glycemic cassava starch into functional dietary fiber via pyrodextrinization is a promising way to valorize tuber crops, yet the molecular mechanisms catalyzed by organic acids with different carboxyl numbers and acidity remain unclear. This study investigates how carboxyl number and acidity of acetic acid (AA), tartaric acid (TA), and citric acid (CA) affect structural reorganization and low glycemic properties of cassava pyrodextrins. Compared with AA, TA, and CA with stronger acidity and more carboxyl groups promoted more extensive hydrolysis, transglycosylation, repolymerization, and esterification. These changes increased indigestible glycosidic linkages and the branching degree, while reducing molecular weight. Molecular docking confirmed stronger hydrogen-bonding interactions between TA/CA and starch chains. Furthermore, TA- and CA-catalyzed pyrodextrins exhibited superior anti-digestive properties with resistant starch up to 54.26% and an estimated glycemic index as low as 42.46, highlighting the critical role of carboxyl numbers and acidities in modulating the functionality of pyrodextrins.

Manihot↗
PubMed2026

Molecular characterization of 16 MAPK genes in silver carp (Hypophthalmichthys molitrix) and the differences of their mRNA expression between Qiandao Lake and Taihu Lake.

Mitogen-activated protein kinase (MAPK), a serine-threonine protein kinase, is involved in a variety of stress-induced responses and also plays an important regulatory role in cell metabolism. In the study the open reading frames (ORFs) of 16 MAPK genes in silver carp (Hypophthalmichthys molitrix) were obtained and verified, with the evaluations of their taxonomy, structures, conserved motifs, and evolutionary linkages. And the expression patterns of these genes in the silver carp from Qiandao Lake and Taihu Lake were explored for better understanding the response of MAPK genes to different water environment. MAPK genes of silver carp were divided into three subfamilies, including extracellular signal-regulated kinase (ERK) subfamily, p38 subfamily and C-Jun N-terminal kinase (JNK) subfamily. All these genes possessed similar structures and conserved motifs of MAPK family. Realtime qPCR revealed that the expression patterns of 10 MAPK genes (ScMAPK1, ScMAPK3, ScMAPK4, ScMAPK7, ScMAPK15, ScMAPK8a, ScMAPK8b, ScMAPK9, ScMAPK10 and ScMAPK11) in head kidney, spleen and gill of silver carp in Taihu Lake and Qiandao Lake were different. These findings provide a basis for further research on the function of MAPK in silver carp.

Animals↗
PubMed2026

Effectiveness of peer recovery support services for substance use disorders: A systematic review of healthcare utilization, behavioral health, and engagement outcomes.

BACKGROUND: Peer recovery support services (PRS) delivered by individuals with lived experience of substance use, are increasingly incorporated into substance use disorder (SUD) care systems to improve care engagement, reduce acute care use, and support recovery. However, existing systematic reviews have focused on substance use outcomes, with limited attention to healthcare utilization, psychosocial functioning, and outcomes across settings, and populations. METHODS: This systematic review, registered in PROSPERO (CRD42023469279), synthesized peer-reviewed studies from 2003 to 2026 evaluating PRS for individuals with alcohol or drug-related SUD. Using MEDLINE, Embase, PsycINFO, and CINAHL, the review included 53 studies primarily conducted in high-income countries that reported quantitative outcomes across substance use, healthcare utilization, behavioral health, and treatment engagement. Risk of bias was assessed using Cochrane RoB 2, ROBINS-I, and ROBINS-E tools. RESULTS: Overall, evidence was most favorable for selected treatment-linkage and engagement outcomes, whereas findings for substance use, emergency department use, hospitalization, overdose, and mortality were inconsistent. Uncontrolled longitudinal studies frequently reported improvements in depression and anxiety, but no randomized trials evaluated these outcomes, limiting causal inference. Exploratory cross-study patterns suggested that sustained navigation, practical assistance, and repeated peer contact were more often present in programs reporting favorable outcomes; however, these components were not independently evaluated. Substantial heterogeneity, frequent multicomponent interventions, high risk of bias in many nonrandomized studies, and limited long-term and economic data constrain conclusions. CONCLUSIONS: Findings support the promise of PRS while underscoring the need for more rigorous comparative studies, cost-effectiveness data, and further research in low- and middle-income countries.

Humans↗
PubMed2026

Linking women leaving jail to medications for opioid use disorder: Costs to implement pre-release telehealth and peer navigation services.

AIMS: Telehealth and peer navigation are feasible strategies for connecting women in the criminal-legal system with medications for opioid use disorder (MOUD), yet implementation costs are not well understood. This study conducted a microcosting analysis of two interventions for women leaving jail in Kentucky: pre-release, PreTreatment Telehealth with a MOUD provider (TH-Only) and PreTreatment Telehealth combined with peer navigation (TH+PN) through the Justice Community Opioid Innovation Network (JCOIN). METHODS: From the provider perspective, we estimated total start-up costs, total intervention costs, and average cost per participant. Women participating in the clinical trial were randomly assigned to TH-Only (n=299) or TH+PN (n=301). Start-up costs were incurred primarily in 2019 - 2020; intervention costs represent expenses in 2021 - 2023. Cost data were collected from study and agency financial records and interviews with research staff and analyzed using Microsoft Excel (version 16.90.2). RESULTS: Start-up costs were $36,320, comprising planning, meetings, travel, and supplies. The total cost of TH-Only was $60,767, representing 259 telehealth sessions with an average duration of 47 minutes. Total cost of TH+PN was $472,148 based on 270 telehealth sessions (48 minutes), 268 peer navigation (PN) sessions (30 minutes), and 12 weeks of PN support post-release per participant. Average cost per TH-Only participant was $235 and per TH+PN participant was $1,760. CONCLUSIONS: Telehealth may be a relatively low-cost approach for jails lacking on-site MOUD services. Although more costly, combining telehealth with PN may add value by supporting service continuity and facilitating linkage to treatment during the jail to community transition.

Humans↗
PubMed2026

Dissemination of blaKPC-3-harbouring Klebsiella pneumoniae across ST48 and ST628 in multiple healthcare facilities in the Republic of Korea.

Klebsiella pneumoniae carbapenemase-3 (KPC-3) remains rare in South Korea, where KPC-2 is the dominant carbapenemase, making the repeated detection of a concentrated blaKPC-3 signal over five years notable. We performed genomic analyses of blaKPC-3-harbouring K. pneumoniae from a regional healthcare network. Two chromosomally distinct lineages with concordant capsule loci (ST628/KL15 and ST48/KL62) presented multidrug-resistant phenotypes, and the virulence-associated loci were confined to ST48. Single-nucleotide polymorphism (SNP) analyses revealed near-clonal relatedness within lineages, with 0-38 pairwise SNPs among ST628 isolates and 8 SNPs between the two ST48 isolates. Core-genome multilocus sequence typing (cgMLST) supported this structure, as ST628 isolates were assigned to complex type 19149 with 0-7 allelic differences, and ST48 isolates were assigned to complex type 19150 with 5 allelic differences. These patterns support vertical spread via clonal expansion across multiple facilities. Despite substantial chromosomal separation, most isolates carried the same IncFII(K) plasmid backbone and blaKPC-3, and they were nearly indistinguishable from a plasmid previously reported in South Korea. One isolate carried blaKPC-3 on a distinct multireplicon IncFIB(K)/IncFII(K) plasmid, indicating that the signal was not confined to a single plasmid backbone. In both plasmids, blaKPC-3 was embedded within Tn4401b. These findings indicate that a rare blaKPC-3 genotype can persist regionally through sustained clonal dissemination and that cross-lineage linkage is compatible with past horizontal transfer involving a conserved plasmid. These findings underscore the need for subtype-resolved, regionally coordinated genomic surveillance in connected healthcare networks to detect uncommon carbapenemase variants early.

Klebsiella pneumoniae↗
PubMed2026

The Childhood Cancer and Leukemia International Consortium (CLIC): Expanding global collaboration in pediatric cancer etiology research.

Childhood cancers are rare, but incidence has risen modestly in countries with robust registration, partly reflecting improved diagnosis. In high-income countries, cancer is the leading cause of disease-related death in children. Marked inequities in incidence, survival, and research capacity underscore the need for large-scale collaboration to identify environmental, genetic, and contextual determinants of risk. The Childhood Cancer and Leukemia International Consortium (CLIC) was established in 2007 to study the etiology of childhood leukemia and later expanded in 2019 to include other childhood cancers, principally solid tumors. CLIC pools harmonized, individual-level data from case-control and cohort studies, obtained through interviews, record linkage (insurance claims, registries), or geographic information systems, and integrates germline genomic data where available. Membership has grown from 13 studies in 9 countries to 57 studies in 21 countries; recruitment spans the early 1960s to the present and encompasses approximately 150,000 cases across all tumor types and 300,000 controls with clinical, demographic, and exposure data, centralized via harmonized data dictionaries at the Data Coordination Center, established in 2014 at the International Agency for Research on Cancer, and supported by a secure analysis platform. Pooled analyses across diverse populations have implicated parental age, prenatal vitamin or folic acid use, mode of delivery, fetal growth, selected congenital anomalies, occupational or household exposures (e.g., pesticides), paternal smoking, and markers of early-life immune modulation (e.g., breastfeeding, daycare attendance) in leukemia risk, informing carcinogen evaluation and prevention. The integration of genetic ancestry and germline susceptibility data is clarifying ancestry-related differences in leukemia biology and outcomes, while confirming risk loci with population-specific effects. CLIC is now adding polygenic risk scores and exposomic data to refine etiologic subtyping and identify modifiable pathways, while broadening representation from underserved regions through partnership-building and capacity-strengthening.

Humans↗
PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗
PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗
PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5​ concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗
PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗
PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗
PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗
PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗