PLOS digital health最新文献

{"title":"Transparency, accountability, and scholarly integrity: A systematic narrative review of artificial intelligence governance frameworks in healthcare publication.","authors":"Amina Silva, Bruna Canever, Monica Lino, Vanessa Silva E Silva","doi":"10.1371/journal.pdig.0001783","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001783","url":null,"abstract":"<p><p>The integration of artificial intelligence (AI) in healthcare research and scholarly publication has produced a rapidly expanding but fragmented ecosystem of reporting guidelines, governance frameworks, editorial policies, and regulatory instruments, imposing significant burdens on researchers, reviewers, and editors. This systematic narrative review identified, stratified, and synthesized governance frameworks and reporting standards addressing transparent and ethical AI use in healthcare publications, and characterized the governance trajectory from predictive model standards to generative AI oversight. Fourteen Boolean search strings were executed in PubMed (MEDLINE) covering 1 January 2018-19 May 2026, supplemented by manual searching of the EQUATOR Network guideline library. Records were deduplicated and prioritized using a relevance-scoring algorithm. Of 30,276 deduplicated records, 888 progressed to human screening; 100 publications were included, comprising 69 governance instruments and policies classified across four evidence tiers and 31 contextual evidence sources. Included sources spanned six thematic groups: predictive and diagnostic AI frameworks (G1); sociotechnical and lifecycle governance (G2); generative AI and LLM-specific standards (G3); specialty-specific extensions (G4); surgical reporting updates (G5); and editorial and publication policy (G6). Analysis identified four consecutive phases of governance development from 2018 to 2026, characterized by a shift from model performance transparency toward comprehensive sociotechnical accountability. Three governance tensions emerged: the trade-off between framework specificity and universal applicability; the gap between disclosure-focused standards and lifecycle governance; and the risk of epistemic conflation, whereby heterogeneous instruments are perceived as carrying equivalent authority when treated as interchangeable. AI governance in healthcare publications remains fragmented and epistemically uneven. Advancing the field requires harmonization across evidence tiers, adaptive updating mechanisms, and lifecycle-oriented governance frameworks.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001783"},"PeriodicalIF":7.7,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The world's oldest living culture leads the way with interpretable generative AI: Aboriginal women shaping the future of perinatal mental health services.","authors":"Guanjin Wang, Hachem Bennamoun, Wai Hang Kwok, Rehan Raza, Ferdous Sohel, Bridgette Kelly, Trish Ratajczak, Corinne Reid, Rhonda Marriott, Roz Walker, Jayne Kotz, Tracy Reibel","doi":"10.1371/journal.pdig.0001709","DOIUrl":"10.1371/journal.pdig.0001709","url":null,"abstract":"<p><p>Despite their strengths, Aboriginal mothers experience significant health disparities, including perinatal mental health challenges influenced by social, historical, and cultural determinants. Routine risk screening informs follow-up care, yet conventional approaches often fail to address cultural considerations. The Baby Coming You Ready (BCYR) program provides a strengths-based, culturally adapted digital assessment designed to enhance perinatal mental health care. However, some clinicians continue to rely primarily on conventional risk scores, which can neglect broader contextual circumstances influencing Aboriginal mothers, including cultural strengths and protective factors, thereby limiting the program's full impact. This exploratory model-development study examined whether generative AI data augmentation combined with an intrinsically interpretable machine-learning model could support culturally contextualised prediction of perinatal psychological distress among Aboriginal mothers. We analysed deidentified BCYR pilot data from 293 Aboriginal mothers across six Western Australian sites. After excluding five records with incomplete outcome data, the analytic dataset included 288 participants. Models were evaluated using repeated internal train-test splits, with synthetic data used only for training augmentation and performance assessed on held-out real test data. The highest internal performance was observed for the TVAE-augmented Explainable Boosting Machine at an augmentation ratio of 1.0, with an accuracy of 0.867, an F1 score of 0.817, and an AUC of 0.934. Model explanations identified self-blame, loneliness, family pride, day-to-day management, sleep, and related contextual factors as influential contributors to model-estimated risk. These explanations are intended as prompts for culturally safe discussion and clinical reflection. Findings suggest that interpretable generative AI may have potential to support culturally responsive perinatal mental health assessment. However, broader community-governed external validation and prospective clinical workflow evaluation are required before clinical implementation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001709"},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198149","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":"Contribution of artificial intelligence research to the development of inclusive applications for people with disabilities: A scoping review (2015-2025).","authors":"Soutongnoma Safiata Kaboré, Azadeh Bayani, Léandre Parfait Epoh Ewane, Davllyn Santos Oliveira Dos Anjos, Jean Noel Nikiéma, Muriel Mac-Seing","doi":"10.1371/journal.pdig.0001749","DOIUrl":"10.1371/journal.pdig.0001749","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly transforming healthcare. Yet people with disabilities (PWD), representing 16% of the global population, remain disproportionately excluded from these advances. This exclusion is driven by algorithmic bias, non-representative datasets, and limited involvement of PWD in the design, development, and evaluation of AI applications. This scoping review examines the extent to which AI research in health contributes to the development of inclusive technologies for PWD, focusing on equity considerations. It identifies current trends, methodological gaps, and opportunities for socially responsive innovation. We conducted a systematic search across MEDLINE, Embase, Web of Science, and CINAHL for the 2015-2025 period, following Arksey and O'Malley's five-stage scoping review methodology and the PRISMA-ScR guidelines. Data were extracted and synthesized thematically using the Population-Concept-Context approach. Of 4,233 references retrieved, 93 studies were included. Most studies originated from high-income countries, particularly in North America (45.2%) and Europe (26.9%). AI applications addressed motor, sensory, cognitive, communication-related, and psychosocial disabilities, with emphasis on prediction/diagnosis, assistive technologies, and personalized care. While about 60% of studies referenced equity, only 12 (12.9%) implemented concrete measures (e.g., adapting tools for vulnerable groups or ensuring sample diversity), and only six (6.5%) actively involved PWD through co-design or user validation. AI holds significant potential to enhance autonomy, access, and quality of life for PWD. Realizing this promise will require moving beyond technical optimization to embrace inclusive design, participatory methods, and equity-driven frameworks aligned with responsible AI development and use.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001749"},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198167","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":"Assessment of change in drug-induced parkinsonism in daily-life gait using vision-based machine learning.","authors":"Benjamin Filtjens, Vida Adeli, Soroush Mehraban, Andrea Sabo, Babak Taati, Andrea Iaboni","doi":"10.1371/journal.pdig.0001780","DOIUrl":"10.1371/journal.pdig.0001780","url":null,"abstract":"<p><p>Drug-induced parkinsonism is a potentially reversible movement disorder that may be subtle and difficult to monitor in residential dementia care. Vision-based machine learning analysis of gait may provide unobtrusive longitudinal monitoring around medication changes. We conducted a two-center cohort study of 61 residents with dementia receiving psychotropic medication. Hallway gait sequences were recorded using ceiling-mounted cameras and converted to three-dimensional body pose. A TabPFN classifier estimated UPDRS-III item 29 gait severity for each walk. We evaluated associations between acute antipsychotic (AP) and antidepressant (AD) dose reduction/cessation (≥50% dose reduction or cessation, 0-21 days after change) and daily mean predicted gait severity using linear mixed-effects models in ever-exposed participants, with complementary equal-weight paired analyses among participants observed in both medication states. In mixed-effects models, acute AP dose reduction/cessation was associated with lower predicted gait severity (β=-0.127, p = 0.022), whereas AD dose reduction/cessation was not (β=-0.060, p = 0.210). Equal-weight paired analyses found no statistically significant participant-average within-person change among paired participants (AP mean change = 0.031, 95% CI [-0.114, 0.176], p = 0.653; AD mean change =-0.021, 95% CI [-0.098, 0.056], p = 0.575), consistent with heterogeneous individual responses. Vision-based monitoring captured within-participant gait variation around psychotropic dose-reduction periods, but findings are associative. Larger prospective studies with documented reasons for medication changes are needed before using such monitoring to guide medication decisions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001780"},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197924","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":"Prevalent knowledge omissions: An unseen risk of LLMs in patient-friendly adaptation of medical literature.","authors":"Mei Chen, Shibin Wang","doi":"10.1371/journal.pdig.0001739","DOIUrl":"10.1371/journal.pdig.0001739","url":null,"abstract":"<p><p>Large language models (LLMs) can produce patient-friendly adaptations, such as simplifying specialized medical literature for readers with a middle school education. However, any distortion of knowledge during this process may have serious consequences. Current evaluations overlook such risks. In this study, we proposes a knowledge triplet comparison (KTC) method to assess knowledge distortion by systematically quantifying the retention and alteration of medical knowledge before and after text adaptation. Using the Chemical-Disease Relations dataset, this study evaluates the performance of six LLMs across eight adaptation scenarios (e.g., adjusting reading level, modifying text length) and draws the following key conclusions: First, from a quantitative perspective, knowledge distortion is prevalent in all adaptation scenarios, with the models retaining only approximately half of the original medical knowledge. Additionally, about 10% more knowledge is added. Second, the distribution of knowledge distortion follows a clear categorical distribution pattern, with etiology and pathological mechanisms, as well as complications and risk factors, being the most frequently distortion types. Third, targeted corrections through prompt engineering have no significant impact on the quantity and categorical distribution patterns of knowledge distortion, indicating that knowledge distortion is persistent and regular. This study shows that, in addition to the more obvious risk of hallucinations caused by a small amount of added knowledge, patient-friendly rewrites by LLMs also pose a significant risk of subtle yet potentially serious knowledge omissions. Furthermore, this research reveals the regularity of knowledge distortion, laying the groundwork for further investigation into the patterns of knowledge distortion and the development of solutions to this problem.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001739"},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198106","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":"Identifying facilitators of and barriers to digital health literacy in pediatric rheumatic disease: A scoping review.","authors":"Craig Eling, Alan M Rosenberg, Jennifer N Stinson, Maryam Mehtar, Jasmin Bhawra, Roona Sinha, Donna Goodridge","doi":"10.1371/journal.pdig.0001777","DOIUrl":"10.1371/journal.pdig.0001777","url":null,"abstract":"<p><p>Digital health (DH) technologies have the potential to benefit pediatric patients with rheumatic diseases and their caregivers. Digital health literacy (DHL) refers to the knowledge and skills necessary to use technology and the internet to manage and communicate about health. The aim of this scoping review is to identify facilitators of and barriers to DHL and map them to the Capability, Opportunity, and Motivation Model of Behavior (COM-B Model) for children and adolescents with rheumatic diseases and their primary caregivers. We searched MEDLINE, CINAHL, Embase, Web of Science, and ProQuest for English full-text primary research studies. Facilitators of and barriers to DHL identified were codified and mapped to the COM-B Model. This review adhered to the JBI scoping review framework and conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-Scoping Review guidelines. Of the 5789 unique records, 44 met inclusion criteria. All the included studies did not explicitly address the concept and impact of DHL. However, the studies identified promoting and detracting factors that matched the definition of DHL. Thus, these were coded as facilitators of and barriers to DHL. The COM-B Model domain of psychological capability emerged as a major determinant of DHL. The COM-B Model domain of psychological capability emerged as a prominent source of facilitators of and barriers to DHL. The design, function, language, and presentation of DH crafted to meet the developmental and educational needs of the user were essential to improving DHL. Likewise, providers fostering quality communication and answering medical and technical questions arising from DH use fostered DHL in the domain of physical opportunity. Addressing DHL requires consideration across clinical, administrative, design, and research contexts. Recognizing low DHL and enacting educational interventions are essential to empowering digital health users. Trial Registration Open Science Framework (https://osf.io/g4jbh/).</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001777"},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198087","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":"Demographic subgroup-aware analysis of blood pressure predictive models.","authors":"Blessing Kolawole, Sinuo Fan, Yingjie Lao, Valencia J Koomson","doi":"10.1371/journal.pdig.0001687","DOIUrl":"10.1371/journal.pdig.0001687","url":null,"abstract":"<p><p>Blood pressure (BP) is an important cardiovascular health indicator, usually monitored non-invasively with a sphygmomanometer, which can be bulky and inconvenient for real-time monitoring. Blood pressure estimation using photoplethysmography (PPG) waveform analysis offers a promising alternative for intermittent or continuous cuff-less BP monitoring. However, changes in vascularization due to individual physiology and age can directly affect the morphology of the PPG waveform. Thus, prediction accuracy requires a demographic-aware approach. To address these challenges, we trained three conventional ML models for BP prediction and conducted a subgroup-aware analysis to evaluate model performance across individual and intersectional demographic groups. Additionally, we show that signal quality assessment is a pivotal step in the signal-processing pipeline before feature extraction to mitigate external perturbations (e.g., motion artifacts) in the PPG waveform. We achieve a mean absolute error (MAE) of 3.36 mmHg for systolic blood pressure (SBP) and 1.76 mmHg for diastolic BP (DBP) with models trained when signal quality assessment is incorporated.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001687"},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198124","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":"Perceptions, attitudes, and concerns of underrepresented groups regarding the use of big data in children and young people's healthcare research - A systematic literature review.","authors":"Charlotte Burdge, Anne-Marie Burn, Lily Zhang, Sasha Menon, Karolina Kaminska, Sophie Wallace-Hanlon, Maria Fischer, Camille Evans, Peter Fonagy, Anna Wiedemann, Jessica Young, Anna Moore, Joanna K Anderson","doi":"10.1371/journal.pdig.0001434","DOIUrl":"10.1371/journal.pdig.0001434","url":null,"abstract":"<p><p>Children and young people (CYP), and those from underserved groups, remain underrepresented in biomedical research. Understanding their perspectives on large-scale, data-driven healthcare research is essential to ensuring that such approaches are acceptable, ethically conducted, and equitable. This review is the first to systematically examine the perceptions, attitudes, and concerns of underrepresented groups regarding the use of big data in CYP healthcare research. Seven bibliographic databases, ASSIA, CINAHL, EMBASE, MEDLINE, PsycINFO, Scopus, and Web of Science, were searched alongside grey literature sources. A mixed-methods systematic review design was used. Included studies captured the views of CYP, particularly those from underserved groups, as well as their parents or carers, acting as proxy decision-makers, regarding the use of big data in paediatric healthcare research. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT), and findings were integrated through narrative synthesis. Seventeen studies were included, capturing perspectives from 6,261 participants, including at least 4,710 CYP and 1,443 parents and carers; one study did not report participant numbers separately by group. Underrepresented groups expressed broadly favourable, but conditional, support for the use of big data in paediatric healthcare research. Willingness to share data was shaped by participant characteristics, lived experience, and trust in institutions. Perceived benefits included societal and public health gains, the opportunity to empower others with similar experiences, and the advancement of scientific progress. Concerns centred on privacy, the risk of re-identification, data misuse, commercial exploitation, and limited transparency in data governance. Conditions for acceptable data use included robust anonymisation, independent ethical oversight, and developmentally appropriate consent processes. Underrepresented groups hold broadly supportive, but conditional, views on the use of big data in paediatric healthcare research. Embedding inclusive governance practices and engaging these communities in research design will be essential to ensuring that data-driven paediatric research is both ethical and equitable.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001434"},"PeriodicalIF":7.7,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13671531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198163","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 ethical and regulatory framework of medical artificial intelligence (AI) system's behavioral performance for image-based diagnostics: An ethical evaluation of the International and European Union Scope.","authors":"Nina Parchmann, Marcin Orzechowski, Daniel Braun, Florian Steger","doi":"10.1371/journal.pdig.0001781","DOIUrl":"10.1371/journal.pdig.0001781","url":null,"abstract":"<p><p>The use of Artificial Intelligence (AI) for image-based diagnostics is rising rapidly and enables new opportunities for improving clinical diagnostics through high precision and a quick identification of anomalies. However, a technology characterized by a high variability of image data and the opaqueness of AI models within patient care raises ethical challenges and requires protection by an appropriate ethical and regulatory framework. To identify ethical challenges of the ethical and regulatory framework of the behavioral performance of medical AI systems for image-based diagnostics on the international and European Union level, we searched and screened documents in April and May 2025. The documents were thematically evaluated before conducting an ethical analysis. We identified regulations and ethical recommendations that could be divided into five thematic categories: The (i) data basis is regulated by data standardization, data assessment, and data quality. (ii) Reliability considers assessments, the intended performance, robustness, accuracy, and compliance, whereas the (iii) comprehensibility centers on explainability, understandability, and traceability. The (iv) disclosure of the system considers transparency, operational disclosure, and collaborations, and the (v) usability is regulated on resources and competency. This framework could support the autonomy, beneficence, non-maleficence, and (social) justice, however allows a wide range of compliant technical measures that vary in effectiveness. The insufficient technical feasibility to provide security measures against malicious data alteration as well as the dynamic development of attack and defense mechanisms severely challenge the maintenance of a compliant safe and secure diagnostic AI system. Thereby, the autonomy, beneficence, non-maleficence, and (social) justice could be jeopardized. In conclusion, AI image-based diagnostic systems are regulated by a similar ethical and regulatory framework as general medical AI systems. To protect data and the intended performance, sufficient protection through a regulatory framework is necessary that responds to the challenges of image-based AI diagnostic systems.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001781"},"PeriodicalIF":7.7,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186884","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":"Allostatic load, genetic susceptibility, and lung cancer risk: Evidence from a prospective cohort and integrative proteomic analyses.","authors":"Fan Yang, Wei Wang, Fangyi Chen, Zhichao Wang, Yifan Wang, Tianchen Qi, Zhazheng He, Shuang Liang, Qingyin Bu, Xirui Qiu, Dong Jiaqing, Shuai Ren, Juncheng Dai, Xiaoxiao Wang","doi":"10.1371/journal.pdig.0001766","DOIUrl":"10.1371/journal.pdig.0001766","url":null,"abstract":"<p><p>Allostatic load (AL), a composite measure of multisystem dysregulation associated with chronic stress, has been linked to lung cancer risk. However, its joint effects with genetic susceptibility and the biological pathways underlying this relationship remain unclear. We conducted a prospective cohort study of 336,945 UK Biobank participants to assess associations between AL and lung cancer incidence and its histological subtypes. A polygenic risk score was constructed to evaluate the joint associations of AL and genetic susceptibility. We further assessed whether AL provided incremental risk stratification beyond established low-dose computed tomography (LDCT) screening eligibility frameworks and integrated plasma proteomic data using machine learning, enrichment, and mediation analyses. We found that higher AL was associated with an increased risk of lung cancer (per-unit HR = 1.073, 95% CI: 1.053-1.094; Low vs. Normal HR = 1.284, 95% CI: 1.065-1.547; High vs. Normal HR = 1.629, 95% CI: 1.347-1.971). Subtype analyses revealed the strongest effect for small-cell lung cancer (SCLC), with significant associations also observed for lung adenocarcinoma and squamous cell carcinoma. High AL combined with high genetic risk was associated with a 136.9% increased risk (HR = 2.369, 95% CI: 1.565-3.583), without evidence of interaction. AL provided additional risk discrimination beyond established LDCT screening eligibility criteria. Proteomic analyses identified 111 AL-associated proteins and 34 proteins with potential mediation signal, including HGF, RARRES2, and GDF15, implicating inflammatory, metabolic, and growth factor-related processes as biological correlates of the AL-lung cancer association. In conclusion, higher AL was independently associated with lung cancer risk, particularly SCLC. Integrative proteomic analyses provided exploratory evidence for biological pathways that may link cumulative physiological dysregulation to lung carcinogenesis. Incorporating AL, as a composite biomarker reflecting cumulative physiological burden, into risk models may improve risk stratification and help identify individuals at elevated risk.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001766"},"PeriodicalIF":7.7,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186821","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}