Short-term exposure to ambient air pollution, gut microbiota, and pro-atherosclerotic responses in healthy adults: A prospective panel study.

The InAdvance Study addresses the critical need for comprehensive data and biospecimen collection from individuals at elevated risk for cancer. Despite the existence of numerous biobanks for patients with cancer and individuals at average risk for cancer, a gap persists in resources targeting individuals with an elevated risk for cancer. The InAdvance Study is longitudinally collecting biospecimens and health data from individuals with a range of cancer risk factors to facilitate collaborative research in cancer early detection and interception. Patient populations include those with precancerous or precursor lesions (e.g., oral precursor lesions and Barrett's esophagus), hereditary cancer risk, personal or family history of cancer, or a history of exposures associated with cancer risk. Additionally, we are enrolling patients receiving multicancer early detection (MCED) tests, family members of elevated risk participants, and controls. Key goals include optimized biospecimen collection through standardized procedures, participant enrollment and engagement through an online platform, and standardized scalable procedures. Data are compiled into a central repository, and researchers can request access through a streamlined data request process. Since April 2023, 1,415 participants have enrolled in the study. In total, 817 (58%) completed the baseline survey, and 816 (58%) gave a baseline blood sample. The InAdvance Study's comprehensive approach provides a framework for basic, population, and translational studies to elucidate the molecular and environmental factors that drive cancer development. Comprehensive data and biospecimen biorepositories are critical to informing strategies for early detection, risk stratification, early interception for risk mitigation, and improved outcomes for higher-risk populations.

Enhancing prediabetes and diabetes detection through a machine learning-enabled self-assessment approach.

To assess whether HL7 FHIR Consent, as currently specified and deployed, is sufficient to support verifiable, regulation-aligned consent governance in distributed and cross-organisational health data sharing. We conducted a qualitative critical analysis of FHIR Consent informed by (i) peer-reviewed implementation literature, (ii) national-scale consent exchange initiatives, and (iii) accountability requirements under GDPR and the European Health Data Space (EHDS). The analysis is organized into four dimensions: semantic interpretability, consent lifecycle management, runtime enforcement, and cross-organisational trust/auditability. FHIR Consent provides an interoperable representation of authorisation intent, but large-scale deployments remain limited by (1) non-canonical semantics across implementations, (2) lack of standardized lifecycle versioning and cross-organisational revocation propagation, (3) heterogeneous translation of declarative consent into enforceable access control, and (4) limited capability for independent verification of consent provenance and historical integrity across institutional boundaries. We derive an architecture pattern that separates (a) standards-based consent representation (FHIR Consent), (b) local policy interpretation/enforcement, and (c) cross-organisational integrity verification. Cryptographic integrity anchoring is discussed as a complementary mechanism for tamper-evident verification of off-chain consent artifacts and lifecycle events, without externalizing consent semantics or personal data.

This study aims to investigate the association between triglyceride-glucose (TyG) index and risk of CVD using explainable survival analysis method based on 2011 to 2020 China Health and Retirement Longitudinal Study (CHARLS) data. We enrolled 7,721 participants in a prospective cohort. A Lasso Cox model was implemented to select covariates for constructing the multivariate Cox regression model. Restricted cubic splines (RCS) analysis was performed to explore dose-response relationship. In addition, we utilized explainable machine learning methods to analyse the Cox model. During 9-year follow-up, 1,895 (24.5%) participants developed CVD. Nine variables including TyG index, age, BMI, WC, SBP, history of hypertension, liver disease and kidney disease and antihypertensive medication were retained to establish the Cox model. HRs (95% CIs) for CVD were 1.21 (1.06-1.39), 1.26 (1.10-1.44), and 1.22 (1.06-1.40) for Q2 to Q4 groups compared with Q1. Result of RCS showed that the Q1 group had lowest risk. Time-dependent feature importance analysis showed that age and history of hypertension were two most important risk factors based on Brier score and C/D AUC. All variables in the coxph model gain importance over time. Routine measurement of the TyG index may aid in the early detection and risk stratification of CVD in the aging population.

Background: Previous research has predominantly focused on the relationship between individual nutrients and asthma. However, diet involves complex interactions among its various components. Dietary pattern analysis can more accurately reflect the impact of overall diet on disease. Methods: Based on the 2011-2020 National Health and Nutrition Examination Survey data base, this study included 7216 children ages 3-17 years. Principal component analysis was used to extract dietary features, and K-means clustering was used to identify typical dietary patterns. Multivariate weighted logistic regression was used to analyze the association between dietary patterns and the risk of asthma. Subgroup analyses were also conducted. Furthermore, ordinal logistic regression was used to explore the relationship between dietary patterns and asthma exacerbation levels. Results: Two primary dietary patterns were identified: a "refined high red meat and high sugar diet pattern" (characterized by high intake of refined grains, red meat, processed meats, solid fats, and added sugars) and a "fruits and vegetables enrichment-balanced diet pattern" (characterized by a balanced diet with adequate intake of fruits and vegetables). After adjusting for covariates, children who followed the refined high red meat and high sugar diet pattern had a 15% higher risk of asthma compared with those who followed the fruits and vegetables enrichment-balanced diet pattern (odds ratio [OR] 1.15; p = 0.04). Notably, in the 3-5-year-old subgroup, the fruits and vegetables enrichment-balanced diet pattern was associated with a 54% reduction in asthma risk (OR 0.46). However, no significant association was found between dietary patterns and asthma exacerbation severity levels (p > 0.05). Conclusion: The refined high red meat and high sugar diet pattern is a modifiable risk factor for childhood asthma, whereas the fruits and vegetables enrichment-balanced diet pattern seems to have a protective effect. Early childhood (ages 3-5 years) may be a critical period for dietary intervention. Analysis of these findings suggests that modifying dietary patterns could be an important strategy for the primary prevention of childhood asthma.

This cross-sectional observational study analyzed patterns of tooth loss across different population groups using National Health and Nutrition Examination Survey (NHANES) data from 2009 to 2018, aiming to identify the teeth most at risk and the key contributing factors. Data from 24,945 adults aged 20 y and older were analyzed. Tooth presence/absence was modeled in relation to age, income, education, smoking history, and glycemic status. To improve robustness, missing covariate data were imputed using k-nearest neighbors, and survey weights were applied in regression analyses to account for NHANES's complex sampling design. A 2-step analytic approach was used: (1) survey-weighted quasi-Poisson regression estimated associations with total tooth count and (2) unweighted generalized estimating equations identified explanatory variables associated with individual tooth loss, accounting for intraoral clustering. Dental implants were excluded. Tooth-level models suggested distinct intraoral and demographic patterns. Males retained more posterior teeth but lost more mandibular incisors than females did. Older adults (≥60 y) exhibited markedly higher loss in mandibular anterior and premolar regions. High-income and high-education participants retained more teeth overall but were more likely to lack first premolars, likely reflecting orthodontic extractions. Smoking and diabetes were associated with anterior tooth loss, especially in visible, functionally important regions. Glycemic control showed a clear gradient, with low-normal HbA1c levels protective in posterior sites. Tooth-level modeling suggested anatomically and socially patterned vulnerabilities often masked in whole-mouth analyses. Findings underscore the need for site-specific prevention strategies that address biological risk and structural inequity, advancing more equitable approaches to oral health care and policy.Knowledge Transfer Statement:This study identified which specific teeth are most at risk of loss across age, health, and socioeconomic groups. Understanding these tooth-level patterns can help clinicians tailor preventive care to vulnerable patients, guide patient education, and inform public health strategies that target oral health disparities. These findings support more precise, equitable dental care to help people keep their natural teeth longer.

Zopiclone was recently identified as a frequent contributor to fatal drug-intoxications in Sweden. However, characteristics of misuse and factors associated with prescribed use remain insufficiently described. Alprazolam has a more established misuse profile. The aim of the study was to describe and compare characteristics of fatal intoxications positive for zopiclone or alprazolam, focusing on prescription status and factors associated with prescribed drug use. Fatal intoxications between 2012 and 2020 with zopiclone and/or alprazolam in blood were identified using the Swedish Cause of Death Register and the Swedish forensic medicine database. Prescriptions within 1 year before death, and health and sociodemographic data were obtained from national registers. Among 1270 zopiclone cases, 84% were prescribed users compared to 22% of 1532 alprazolam cases. For both substances, prescribed use was associated with female sex (zopiclone: aOR = 1.68, 95% CI = 1.22-2.30; alprazolam: aOR = 2.04, 95% CI = 1.54-2.71), suicide relative to accidental intoxication (zopiclone: aOR = 1.61, 95% CI = 1.11-2.33; alprazolam: aOR = 3.21, 95% CI = 2.16-4.75) and age ≥ 46 years (zopiclone: aOR = 2.26, 95% CI = 1.45-3.54; alprazolam: aOR = 3.20, 95% CI = 2.29-4.46). Psychiatry-contact and previous self-harm were associated with prescribed zopiclone use, but not alprazolam. Fatal intoxications involving zopiclone differ from alprazolam. While factors associated with prescribed use were often shared, substance-specific preventative strategies could be beneficial.

Effect of the COVID-19 pandemic on initiations of opioid agonist therapy in Ontario: A population-based interrupted time series analysis.

Architectural and translational perspectives on clinical decision support systems for rare disease diagnosis: a scoping review.

This study explores patient motivations and preferences for sharing medical data with researchers using the iAgree platform. We examine how study characteristics, including data type requested and data-sharing arrangements, influence consent decisions, and assess the role of demographic factors, privacy concerns, and perceived benefits in shaping data-sharing behavior. We conducted a mixed-methods study with 527 US adults (≥18 years) recruited via advisory boards, social media, clinics, and newsletters. Participants completed 3 of 4 simulated studies on iAgree, each varying by data elements requested and data-sharing scope. Participants provided consent and data-sharing decisions and completed a post-simulation survey capturing demographics, data-sharing motivations, privacy concerns, and patient activation. We used logistic regressions to examine associations between demographics, privacy concerns, and patient activation and: (1) consent status and (2) willingness to share particular data elements. Finally, we applied thematic analysis to open-ended responses. Consent status did not significantly vary by data type or study design. However, participants citing altruism, personal benefit, and patient solidarity were more likely to share data. Higher privacy concerns were linked to lower willingness to share family health and mental health information. Participants with higher patient activation were also less likely to share data. Demographic factors were not significantly associated with consent or willingness to share data, countering common assumptions about disparities in sharing preferences. Altruism and perceived benefit drive willingness to share health data, while privacy concerns and patient activation may reduce it, emphasizing the need for patient-centered, transparent consent models.

Opioid agonist treatment and risk of mortality in French primary care: a nationwide, retrospective cohort study.

Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions requires consistent engagement in self-care behaviours such as healthy eating, physical activity, and medication adherence. However, behavioural interventions often lack personalisation, limiting their impact, whereas digital twin (DT) systems, which use digital technologies to generate real-time representations of individuals, offer the potential to support people through adaptive and patient-centred approaches by integrating health data to personalise and optimise self-care strategies. A systematic review was conducted to synthesise and evaluate the use of DT for supporting self-care behaviours among adults with diet-related chronic conditions METHODS: Five electronic databases (PubMed, Web of Science, Embase, CINAHL, and IEEE Xplore) were searched from inception to 31 July 2025. Studies including adults with diet-related chronic conditions, using DT interventions, and reporting changes in self-care maintenance, monitoring, or management were eligible. Of 3,685 records identified, four studies (N=2,662) met the inclusion criteria, all focusing on type 2 diabetes. All four studies used continuous glucose monitoring (CGM), and three additionally used wearable devices and dietary logs integrated with AI-driven DT platforms to provide personalised feedback and recommendations, often alongside human coaching. Two retrospective studies found substantial reductions in antidiabetic medication use, with one reporting a 74% reduction over one year including discontinuation of insulin in 94% of baseline insulin users, and large class‑specific declines, such as sulfonylureas (-99%) and DPP‑4 inhibitors (-88%), with many participants maintaining HbA1c<7% on no therapy or metformin monotherapy, and the other showing that 42.8% of participants eliminated all diabetes medications within 90days while maintaining glycaemic control (via a staged medication‑reduction framework). One randomised controlled trial found that 94% of participants discontinued all T2D medications and 72.7% achieved diabetes remission after one year, accompanied by increased daily steps, improved sleep, and reduced sedentary time (with only 4.7% remaining on metformin alone at one year). Another study found that personalised insulin infusion guided by a DT over 14days reduced insulin doses by 14-29% and increased time in the target glucose range to 86-97% (personal insulin pump data over a 14‑day collection period) CONCLUSIONS: DT-based interventions demonstrated potential to enhance multi-behavioural self-care and clinical outcomes in adults with type 2 diabetes. Evidence is currently limited to diabetes, highlighting the need for studies in other diet-related chronic conditions and standardised assessment of self-care behaviours.

Effectiveness of nirsevimab immunisation after birth versus RSVpreF maternal vaccination in preventing RSV-related hospitalisations in infants: a population-based retrospective cohort study.

Congenital lung malformations (CLM) are developmental anomalies that may predispose to respiratory morbidity. Their association with asthma remains unclear, largely due to small series and the lack of population-based controls. To evaluate asthma prevalence and related outcomes in children with CLM compared to population controls. We conducted a population-based case-control study linking CLM patients from a surgical registry to provincial health data. Each CLM patient was linked to up to ten date-of-birth-matched controls. Asthma was defined by ICD-9/10 codes from age ≥5 years. Outcomes included asthma prevalence, time to first diagnosis, recurrent medical encounters, and pharmacotherapy patterns. Analyses yielded risk ratios (RR), adjusted hazard ratios (aHR), and adjusted incidence rate ratios (aIRR). Forty-nine CLM cases and 486 controls were included. Asthma was diagnosed in 19 (38.8%) CLM cases versus 76 (15.6%) controls (RR = 2.48, 95% CI [1.65-3.73]; p < 0.001). The RR did not differ between surgically and conservatively managed CLM cases (RR = 2.87, 95% CI [0.45-18.32]; p = 0.234). CLM cases had a threefold higher hazard of asthma (aHR = 3.24, 95% CI [1.84-5.71]; p < 0.001). Female cases were particularly affected (aHR = 3.92, 95% CI [1.98-7.74]; p < 0.001), with no difference observed in males (aHR = 1.98, 95% CI [0.90-4.37]; p = 0.092). Annually, CLM patients accumulated 71% more asthma-related encounters (aIRR = 1.71, 95% CI [1.28-2.25]; p < 0.001). Children with CLM are at increased risk of asthma, especially females, with earlier onset and more frequent encounters. These findings highlight the need for long-term respiratory follow-up and objective validation of asthma diagnoses in this population.

Implementation of addiction consult services and bridge clinics in the HEALing communities study in Massachusetts.

Cystic fibrosis (CF) is a rare, multisystem genetic disease requiring early diagnosis and lifelong multidisciplinary follow-up. In many middle-income countries, including Colombia, national CF registries are absent, and epidemiological information largely relies on administrative health data. Although such data do not reflect true clinical prevalence, they enable systematic monitoring of service-based prevalence and diagnostic practice patterns, offering critical insights for health system planning. We conducted a nationwide, observational study using the Colombian Individual Registry of Provision of Health Services (RIPS) from 2015 to 2024. Children and adolescents aged 0-19 years with CF diagnoses (ICD-10 E84.0-E84.9) were identified. Annual crude and age-standardized administrative prevalence rates were estimated using national population projections. Diagnostic test utilization (spirometry, chest CT, pancreatic elastase, genetic testing) was quantified per 100 encounters. Temporal trends were analyzed using LOESS smoothing with 95% confidence intervals. The age-standardized national administrative prevalence of CF-related healthcare encounters was 5.64 per 100,000. Administrative prevalence was highest in children aged 0-4 years and declined progressively with age. The contributory insurance regime showed the highest service-based prevalence, suggesting disparities in diagnostic access. Temporal patterns revealed a peak in 2018-2019 followed by a decline after 2020, coinciding with COVID-19-related disruptions in pediatric care. Diagnostic utilization was markedly low: spirometry was performed in fewer than 6 tests per 100 encounters annually, chest CT use was sporadic, and genetic testing was minimal before 2018, increasing slightly thereafter. Administrative prevalence of CF in Colombia has remained stable over a decade but is accompanied by underutilization of essential diagnostic procedures and inequities across insurance groups. Strengthening diagnostic capacity, standardizing monitoring practices, and improving early detection are urgent priorities. This study provides a scalable framework for CF surveillance in countries lacking national registries.

The factors influencing the oral microbiome during childhood and adolescence remain under-explored at the population level. Furthermore, details on how the oral microbiome differs with age, varies between individuals of different ethnicities, or is associated with socioeconomic factors and diet in children and adolescents are almost entirely unknown. Saliva samples and detailed demographic, health, diet and socioeconomic data were collected from children and adolescents that attended the Ontario Science Centre (Toronto, Canada) and were enrolled in the Spit for Science cohort. We characterised the bacterial microbiota of 4812 samples using 16S rRNA gene sequencing to make this the largest population cohort of the paediatric oral microbiome to date. Exploration of limited participant genotyping information and more than 50 variables encompassing the demographics, health, diet, socioeconomic status and living environment of participants revealed that almost all of the investigated variables were associated with overall community structure and/or the abundance of specific bacterial genera. However, most of these associations were modest (R2 < 0.01) and the correlation between genetic relatedness and salivary bacteriome similarity was weak (R2 = -0.014), while the strongest determinants of oral bacteriome composition were shared family/household environment (R2 = 0.61 in the subset of participants from multi-child households), age (R2 = 0.014) and ethnicity (R2 = 0.01). We show that older children and adolescents have higher richness but lower evenness than younger children, suggesting that their oral bacteriome changes as they are exposed to more influences outside the home, and that the oral bacteriome is more consistent with more core taxa among children and adolescents than adults. We also find that diet variables related to the frequency of sugar consumption have the largest impact on the oral bacteriome of children and adolescents, and that microbial differences attributed to ethnicity and diet are likely intertwined. This study provides an atlas of the demographic, health and lifestyle factors that are associated with the salivary bacteriome of children and adolescents. These findings highlight the complex interplay between social, environmental, and biological factors in shaping the developing oral microbiome and underscore the importance of inclusive, demographically diverse cohorts in microbiome research. This presents a reference for the variables that are important to account for in paediatric oral microbiome studies. Video Abstract.

To compare the 2-year incidence of (1) revision due to periprosthetic joint infection (PJI) and (2) all-cause revision after primary knee arthroplasty (KA) in patients with/without obesity and to compare the microbial profiles in early (≤ 90 days) and late (91-730 days) PJI. A nationwide, register-based cohort of patients undergoing primary total or unicompartmental KA for osteoarthritis in Denmark (2011-2023). Patients were stratified by body mass index (BMI), with obesity defined as BMI ≥ 30 kg/m2. Cox proportional hazards models estimated hazard ratios (HRs) with corresponding two-sided 95% confidence intervals (95% CIs) for PJI and all-cause revision within 2 years. Analyses included crude and propensity score-adjusted models based on health and socioeconomic data. Microbial profiles were compared using absolute risk difference (ARD) with 95% CIs in the early and late post-operative periods. Among 111,117 eligible patients, 41,306 had obesity (mean BMI: 34.6) and 59,993 did not (mean BMI: 26.1). Patients with obesity were slightly younger and had higher use of glucose-lowering medication. Patients with obesity had a higher risk of PJI revision than those without obesity (482 [1.2%] vs. 559 [0.9%]; crude HR: 1.25 [1.11-1.42], and propensity-adjusted HR: 1.30, 95% CI: 1.14-1.48). The adjusted risk of all-cause revision was comparable (1494 [3.6%] vs. 1893 [3.2%]; HR: 1.05, 95% CI: 0.98-1.13). Within 90 days, patients with obesity had more polymicrobial infections (62 [18.5%] vs. 43 [11.3%]; ARD: 7.2%, 95% CI: 2.0%-12.5%) and more non-staphylococcal gram-positive organisms (69 [21%] vs. 54 [14%]; ARD: 6.3%, 95% CI: 0.8%-12%). Staphylococcus aureus was the most frequent pathogen in both groups (127 [38%] vs. 136 [36%]; ARD: 2.0%, 95% CI: -5.1% to 9.1%). Obesity was associated with an increased 2-year risk of PJI after KA; however, the risk of all-cause revision was comparable to that in patients without obesity. Within 90 days, patients with obesity exhibited a higher incidence of polymicrobial infections and non-staphylococcal gram-positive organisms. Level II, prognostic studies-investigating natural history and evaluating the effect of a patient characteristic.

Systematic collection of social determinants of health (SDoH) data remains inconsistent across healthcare settings, despite its critical impact on patient outcomes. Large language model (LLM)-powered chatbots offer promise for scalable SDoH data collection, but rigorous, feasible evaluation methods for patient-facing applications are lacking. To describe an efficient, iterative, multidisciplinary approach for developing and evaluating a patient-facing SDoH chatbot using synthetic data and case simulation, with the goal of optimizing both chatbot performance and evaluation rubric prior to clinical deployment. A 10-criterion evaluation rubric was adapted from established healthcare artificial intelligence (AI) frameworks and applied to 27 synthetic clinical scenarios representing diverse SDoH profiles. Scenarios were role-played by a licensed clinical social worker (RJK, and chatbot-patient interactions were rated by three members of the research team that were multidisciplinary experts - a social worker(RJK), nurse practitioner (HC), and physician (AMM). Quantitative analysis used percent agreement and Fleiss' κ to characterize chatbot performance and rater consensus, with percent agreement selected due to high prevalence of ceiling effects in several domains. Qualitative analysis synthesized rater feedback to guide iterative refinement of both chatbot prompts and rubric domains. Across 27 simulated cases, the chatbot received high proportions of positive ratings for accurate interpretation (% agreement = 0.98; 95% CI, 0.91-0.99), communication quality and cultural sensitivity (% agreement = 0.99; 95% CI, 0.93-1.00), and appropriately adaptive questioning (% agreement = 0.99; 95% CI, 0.93-1.00). Lower performance was observed in domain focus and completeness (% agreement = 0.51; 95% CI, 0.40-0.61), completeness of data capture (% agreement = 0.59; 95% CI, 0.48-0.69; Fleiss' κ=0.18), and safety (% agreement = 0.69; 95% CI, 0.58-0.78; Fleiss' κ=-0.04), prompting targeted adaptations. Qualitative feedback highlighted the importance of distinguishing screening from clinical interviewing capabilities and informed the refinement of the rubric, including clarifying the definition of safety to focus on recognition of physical and mental health emergencies. This study describes a formative feasibility approach for iterative refinement of a patient-facing SDoH chatbot and its evaluation rubric using synthetic case simulation. Future work will include independent external raters, patient stakeholders, repeated scenario testing, and prospective clinical evaluation.