Environment International

We examined associations between a 15-component urinary biomarker mixture related to consumer product chemical exposure and wearable-derived circadian light exposure patterns in U.S. adults. Using National Health and Nutrition Examination Survey (NHANES) 2011-2014, we studied adults aged 20 years or older with valid wrist-worn ambient light data and urinary chemical biomarkers (N = 1,666). Eight circadian light metrics were derived from hour-level ActiGraph GT3X+ data. A standardized chemical burden index and quantile g-computation were used in survey-weighted linear regression adjusted for age, sex, race/ethnicity, poverty-income ratio, education, body mass index, cotinine, sleep duration, and season. Higher chemical burden was associated with greater morning light ({beta} = 0.54; 95% confidence interval [CI]: 0.14, 0.94), greater nighttime light ({beta} = 0.55; 95% CI: 0.21, 0.89), and earlier light centroid timing ({beta} = -1.37 hours; 95% CI: -2.14, -0.59) after false discovery rate (FDR) correction. Quantile g-computation confirmed these three outcomes. No sex modification was observed (all interaction P > .23). Higher consumer product chemical mixture burden co-occurred with an early-shifted circadian light exposure profile, consistent with shared behavioral, occupational, and environmental determinants.

Background: In the absence of high-resolution response data, exposure-response modelling often relies on aggregated low-frequency exposure data, leading to loss of high-resolution information. Mixed Data Sampling (MIDAS) from econometrics offers an alternative but is limited due to its inability to make high-resolution predictions, inflexible likelihoods and penalised nonlinear functions, and limited visualization options. We propose a mixed-frequency Distributed Lag Non-linear Model (mf-DLNM) which can eliminate the need to aggregate exposure data in environmental epidemiology and provide high resolution predictions for time series studies. Methods: We evaluated the inference and predictive performance of the mf-DLNM. To evaluate its ability to estimate exposure-response relationships, we applied mf-DLNM and same-frequency (sf)-DLNM using data from the West Midlands, UK. Additionally, we compared the predictive performance of mf-DLNM with sf-DLNM and MIDAS across nine regions of England. As MIDAS cannot predict at the resolution of the predictor (daily), we compared the predictive performance of mf-DLNM and MIDAS at weekly resolution. To test the model's ability to predict high temporal resolution risk (daily), we compared sf-DLNM (with access to daily mortality counts) with mf-DLNM (with access only to weekly mortality counts). Results: In the West Midlands example, mf-DLNM performed comparably to sf-DLNM in estimating daily risk of temperature on respiratory mortality. Furthermore, mf-DLNM and MIDAS exhibited similar performance for weekly predictions. For high-resolution predictions, mf-DLNM and sf-DLNM showed nearly similar performance, despite mf-DLNM having access only to low-resolution response data. Conclusion: This mixed-frequency approach in environmental epidemiology overcomes the limitations of predicting health risks using aggregated exposure data and provides estimates of high-resolution outcomes in the absence of high-frequency health outcome datasets.

Wastewater treatment plants (WWTPs) are known reservoirs of antibiotic resistance genes (ARGs). Non-antibiotic compounds such as antidepressants may further promote ARG acquisition through horizontal gene transfer (HGT). Desvenlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI) listed on the EU Surface Water Watch Lists, is among the most frequently detected antidepressants in WWTP effluents, yet its role in HGT has not been examined. Here, we detected desvenlafaxine at the highest concentrations among four antidepressants monitored across three municipal WWTPs in western New York. Using Acinetobacter baylyi ADP1 as a model recipient in natural transformation assays (n = 6), we found that desvenlafaxine significantly increased transformation frequency at 10 mg/L (1.74 {+/-} 0.33-fold) and 50 mg/L (1.49 {+/-} 0.19-fold; Padj < 0.05). Effects were independent of reactive oxygen species or membrane permeability stress, consistent with its very low toxicity (IC20 ~1353 mg/L). Instead, desvenlafaxine induced dose-dependent increases in membrane fluidity and shifts to less negative zeta potentials, suggesting that electrostatic interactions between its cationic amine group and the negatively charged membrane reduce surface repulsion and facilitate plasmid proximity during uptake. Non-targeted proteomics revealed a biphasic response: at 10 mg/L, competence-associated proteins (PilB, ComM) were upregulated and STRING analysis identified networks linked to membrane transport, transcriptional regulation, and envelope remodeling, while no connected network was recovered at 50 mg/L. Electron microscopy confirmed higher pili frequency at both doses. Together, these findings reveal an overlooked role of this non-antibiotic pharmaceutical in promoting ARG spread from wastewater environments.

Diagnosed diabetes disproportionately burdens socioeconomically disadvantaged populations in the United States, particularly Hispanic communities in the Texas-Mexico border region. Few studies have quantified whether geographic border-region status is independently associated with county-level diagnosed diabetes prevalence after accounting for lifestyle and food-environment factors. This cross-sectional ecological study examined 253 Texas counties using CDC PLACES 2025 health estimates and USDA Food Environment Atlas food-access data, including the 2015 county-level low-food-access measure. Border-region counties were defined using the official La Paz Agreement 32-county definition, which includes counties within 100 km of the US-Mexico boundary. Multiple linear regression with HC3 robust standard errors was used to estimate associations between border-region status, low food access, physical inactivity, and diagnosed diabetes prevalence. Variance inflation factor analysis assessed multicollinearity, and Global Moran's I tested spatial autocorrelation in diagnosed diabetes prevalence and OLS residuals. Border-region counties had 33% higher unadjusted mean diagnosed diabetes prevalence than non-border counties (16.1% vs. 12.1%). After adjustment, border-region status remained significantly associated with a 0.625 percentage-point higher diagnosed diabetes prevalence ({beta} = 0.625, 95% CI [0.357, 0.893], p < 0.001). Physical inactivity was the strongest independent predictor ({beta} = 0.404, 95% CI [0.391, 0.417], p < 0.001). The model explained 96.0% of county-level variance (R{superscript 2} = 0.960, N = 253), reflecting ecological associations among modeled county-level health indicators. Global Moran's I confirmed strong spatial clustering of diagnosed diabetes prevalence (I = 0.5734, p = 0.001), with reduced but significant residual spatial autocorrelation after OLS adjustment (I = 0.1696, p = 0.001). These findings suggest that border-region status is associated with elevated diagnosed diabetes prevalence beyond physical inactivity and low food access, supporting targeted public health investment in the Texas-Mexico border region

Background. Tobacco smoke exposure, quantified by serum cotinine, is associated with cardiovascular, metabolic, and sleep-related health risks. The relationship between biomarker-verified tobacco smoke exposure and objectively measured, free-living wrist-worn ambient light patterns has not been examined in a nationally representative U.S. adult sample. Methods. We analyzed NHANES 2011-2014 cross-sectional data from 6,937 adults aged >20 years with valid serum cotinine and wrist-worn Physical Activity Monitor (PAM) ambient light data. Seven light outcomes were modeled using survey-weighted linear regression with log2(cotinine+1) as the continuous exposure across four covariate adjustment levels. Benjamini-Hochberg false discovery rate (FDR) correction was applied across the 7 outcomes within each model. Results. In Model 2 (adjusted for age, sex, race/ethnicity, education, poverty-income ratio, BMI, and survey cycle; N = 6,350), higher serum cotinine was associated with significantly higher nighttime light (beta = +0.024, 95% CI: 0.010, 0.038; p-FDR = 0.014) and lower evening light (beta = -0.031, 95% CI: -0.055, -0.008; p-FDR = 0.042). In exploratory behavioral models without alcohol (Model 3a; N = 5,766), both nighttime and evening associations remained FDR-significant. After additional adjustment for alcohol, which substantially reduced the sample due to 37.6% missingness (Model 3b; N = 3,866), the nighttime association attenuated below the FDR threshold, while the evening association remained FDR-significant. Categorical analyses showed progressively higher nighttime light across cotinine groups, and a hypothesis-generating sex interaction was identified (p-interaction = 0.001). Conclusions. Higher serum cotinine concentrations were associated with higher nighttime and lower evening ambient light after sociodemographic adjustment. Attenuation after behavioral adjustment and the cross-sectional design preclude causal inference. Longitudinal studies with formal mediation analyses are needed to clarify the temporal ordering and mechanisms linking tobacco smoke exposure, smoking-related behaviors, and personal light-dark cycle patterns.

To understand the utility of healthcare facility-level wastewater surveillance (WWS) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is important to correlate wastewater SARS-CoV-2 RNA detection with the number of clinical infections. WWS for SARS-CoV-2 was performed at three skilled nursing facilities (SNFs) over 25 weeks. Electronegative membrane filtration (enMF) and Nanotrap(R) Magnetic Virus Particles (NP) virus concentration methods were compared. Extracts were tested by droplet digital polymerase chain reaction. Spearman's correlations ({rho}) between wastewater virus RNA concentrations and infection counts were calculated. From split wastewater samples, enMF recovered higher SARS-CoV-2 RNA concentrations than NP. Combining data from all facilities, the median concentrations were 53.0 versus 38.6 gc/100 mL for enMF and NP, respectively (p=0.001). Using enMF, correlations were moderate to strong at SNF A ({rho} ranged 0.67 to 0.86, all p-values <0.001). Weak to moderate correlations can be explained by the sampled manhole not representing the entire facility (SNF B, {rho} ranged 0.47 to 0.72, p-values ranged <0.001 to 0.12) and longitudinal data gaps from summer heat and equipment maintenance (SNF C, {rho} ranged 0.14 to 0.59, p-values ranged 0.52 to <0.01). WWS can be a valuable tool for tracking dynamics of SARS-CoV-2 infections in healthcare facilities.

To explore the safety of combined use of lidocaine/prilocaine aerosol and condoms of different materials, this study conducted compatibility tests between them. By observing changes in various physical properties of condom materials after exposure to the aerosol, the compatibility of different polymer materials with the aerosol was analyzed.The results showed that within 15 minutes of exposure to the aerosol, there was no significant difference in all physical properties of natural rubber latex condoms compared with the blank control group (P>0.05), indicating they can be used together. In contrast, obvious changes in physical properties of polyurethane condoms occurred within 5 minutes of exposure (P<0.05), and their performances failed to meet industrial application standards, so combined use is strictly prohibited.This study clarifies the compatibility differences between two mainstream condom materials and lidocaine/prilocaine aerosol, providing experimental evidence and theoretical references for rational matching in clinical and daily use as well as avoiding potential safety risks.

Background: With rising concerns about health impacts from climate change and environmental exposures, planetary health approaches are increasingly prominent, considering connections between human health and that of the natural environment. Naturopathy is an holistic traditional medicine system characterised by philosophies and practices rooted in nature that theoretically align with planetary health. However, it is unknown to what extent these philosophies translate into consideration of relevant factors during patient care. This study describes the perceptions and clinical behaviours of the global naturopathic workforce in addressing the health impacts of climate change and environmental pollutants. Methods: A cross-sectional online survey was administered to an international sample of naturopathic practitioners, recruited through communications from World Naturopathic Federation member organisations. The survey utilised the Climate Change Perceptions Scale, and asked participants about their perceptions of the health impacts of climate change and environmental pollutants. The survey also examined participant considerations of factors relating to climate change and environmental pollutants during clinical case assessment and prescribing of treatments. Data were descriptively analysed. Results: Of n=363 naturopathic practitioners who completed the survey, 88.7% agreed climate change is real, of whom the majority were concerned about impacts of climate change on their patients' health (89.1%). Almost all participants agreed that environmental pollutants harm human health (99.7%) and were concerned about impacts on their patients (99.5%). Climate-related health factors such as water intake (74.2%) and food security (72.9%) were frequently considered during patient assessment, while impacts of severe weather events (41.4%) were less commonly considered. Consideration of factors relating to environmental pollutants was more commonly reported, particularly for food quality (83.8%) and domestic/indoor sources of pollutants (73%). When formulating prescriptions, participants reported highly frequent consideration of all climate-related factors (73%-86.8%) and varied consideration of environmental pollutant exposures (54.4%-83.4%). Conclusions: The global naturopathic workforce demonstrates a high level of awareness and engagement with factors relating to health impacts of climate change and environmental pollutants, suggesting alignment with planetary health. While this engagement is evident in clinical behaviour, some gaps between awareness and application suggest a need for greater support to strengthen the naturopathic application of planetary and environmental health.

The gut microbiome has been implicated in alcohol use disorder (AUD), but its relationship to drinking intensity and treatment response remains poorly understood. We conducted a longitudinal multi-omics analysis of stool samples collected at baseline and endpoint (after 12 weeks) from 122 participants enrolled in a double-blind, placebo-controlled trial of dutasteride for AUD. Gut microbiome composition was characterized using 16S rRNA gene sequencing, and fecal metabolites were measured by LC-MS-based metabolomics. At baseline, drinking intensity was associated with increasingly lower microbial richness. Genera in the class Clostridia emerged as key microbial hubs associated with drinking intensity in an age- and sex-dependent manner. Drinking intensity promoted co-enrichment of [Ruminococcus] gnavus group and [Clostridium] inocuum group with amino acid catabolites, as well as the co-depletion of diverse Clostridia taxa and lipid metabolites. Dutasteride treatment and drinking reduction had minimal impact on gut microbiome composition. Random forest models integrating baseline clinical, microbiome, and metabolome data improved the classification of clinically meaningful drinking reduction compared to models using clinical data alone. These findings show that a coupled baseline gut microbiome-metabolome signature is associated with drinking intensity and future treatment response in AUD, highlighting the potential for multi-omics integration to inform precision treatment approaches.

While microbiome is increasingly recognized as crucial for human health, translating this knowledge into effective healthcare and preventive strategies remains challenging. Many studies focus on identifying changes in microbiome composition associated with disease and evaluating the potential of such disease-associated microbial profiles as biomarkers for disease diagnosis. Under the hypothesis that microbiome dysbiosis may reflect physiological alterations present long before disease onset, in this work, we analyse the potential of disease-specific microbial signatures not as a diagnostic tool when the disease is already present, but as a means of health assessment in the general population. Moreover, instead of trying to define a single health measure, we believe it is necessary to consider several ways in which the microbiome departs from health, according to different disease-related physiological changes. To evaluate our assumptions, we designed a two-stage study: the identification of disease-specific microbial signatures (discovery stage) and, subsequently, the study of their distribution in the general population to assess associations with general health (external validation stage). Specifically, in the discovery phase we characterized 16 disease-specific bacterial signatures from large public microbiome data using a compositional data analysis methodology. In the second phase, we quantified these microbial signatures in the Lifelines-DMP cohort, a large population-based cohort, and evaluated their association with self-reported health status. Results indicate that most disease-specific microbial signatures associate with health status, supporting our assumption that microbial composition can capture physiological alterations before disease onset, and highlighting the importance of considering multiple ways in which microbiome departs from a healthy state. These findings reaffirm the potential of microbial information as an additional tool in preventive medicine.

Artisanally produced embutidos are a culturally significant fermented meat product widely consumed in Puerto Rico, yet their microbiological safety remains largely uncharacterized. This preliminary study evaluated the effect of artisanal fermentation on microbial diversity and assessed the presence of potentially pathogenic and antimicrobial-resistant Proteobacteriain locally produced embutidos. Raw pork (locally sourced) and beef (commercially imported) were obtained from retail supermarkets and processed at a small-scale production facility under standard artisanal conditions. Surface sampling using RODAC contact plates on TSA, MAC, and SDA media was performed before and after fermentation. Fermentation reduced overall microbial diversity in both meat types. Two Gram-negative isolates recovered from pre-fermentation samples were characterized using selective and differential media (MAC, MSA, EMB) and the IMViC biochemical test series. A Salmonella specie was presumptively identified from imported beef, and an Enterobacter species from locally sourced pork. Kirby-Bauer disk diffusion testing revealed that the Salmonella isolate was resistant to five antibiotics (ampicillin, methicillin, penicillin, streptomycin, tetracycline) and showed intermediate susceptibility to chloramphenicol and gentamicin. The Enterobacter isolate was resistant to five antibiotics (ampicillin, methicillin, penicillin, streptomycin, tetracycline) and showed intermediate susceptibility to chloramphenicol and gentamicin. Both isolates met MDR criteria, highlighting the need for enhanced microbiological oversight of artisanally produced embutidos in Puerto Rico.

Polygenic risk scores (PRSs) can effectively identify individuals at risk across various health conditions, yet their association with the gut microbiome remains uncharacterized. We systematically analyzed associations between 4,794 PRSs covering 615 traits and diseases and the gut microbiome within the Estonian Microbiome Cohort (N > 2,500). Microbiome diversity was associated with 62 distinct PRSs across 10 traits, indicating that genetic predisposition is linked to significant alterations in the microbiome composition. At the species level, 282 associations were identified across 100 PRSs for 34 traits, with triglyceride measurements, glucose regulation, and chronotype measurement PRSs showing the strongest signals. Mediation analysis suggests that the microbiome is altered by physiological changes linked to genetic risk, but can also mediate this risk. These results help define early microbial biomarkers and explain inter-individual variability. The findings are accessible through the interactive Microbiome And Genomic Interaction (MAGI) Catalog to support future research.

Women's disproportionate responsibility for unpaid domestic and care work, including water collection, remains a barrier to gender equality globally and may constrain women's ability to engage in income-generating activities. We compared women's and men's time use in rural Kenya and Honduras and assessed whether women's time spent on water collection and income-generating activities differed between communities that had or had not received an improved water source from World Vision. We also examined the measurement of time-use agency among women and men. In-person surveys were conducted in July-August 2024 with 95 participants (48 women, 47 men) in six Kenyan communities and 102 participants (53 women, 49 men) in six Honduran communities. Surveys included a 24-hour time-use recall module and items on time-use agency. Analyses compared time use by gender and by community intervention status (improved vs. not yet improved water supply), and confirmatory factor analysis assessed the validity of the time-use agency measure. Women in both study sites spent substantially more time than men on unpaid domestic and care work activities, including cooking, cleaning, laundry, and caregiving. In Kenya, women also spent significantly more time collecting water. Men spent more time sleeping (Kenya), on paid work (Honduras), unpaid agricultural work (both settings), and traveling (both settings). Across both countries, there were no significant differences between intervention and comparison communities in women's time spent on water collection or income-generating activities. In Kenya, most respondents reported high influence over their time, and six items showed strong validity for measuring instrumental time-use agency. Women's time burdens remained high even in communities that had received improved water sources, including at the household level. Our results suggest that more transformative water infrastructure, combined with interventions that address gendered social norms, may be needed to meaningfully reduce women's domestic work burden and support their economic empowerment.

Childhood diarrheal disease remains a leading cause of morbidity and mortality among children under five years in sub-Saharan Africa, particularly in settings affected by inadequate sanitation, climate variability, malnutrition, and limited healthcare access. Conventional forecasting approaches are often constrained by sparse surveillance data, weak spatial representation, and limited incorporation of mechanistic disease dynamics. This study presents a Physics-Informed Multimodal Artificial Intelligence Digital Twin framework that integrates Physics-Informed Neural Networks, Graph Neural Networks, diffusion-reaction epidemiological modeling, multimodal fusion learning, and Digital Twin simulation to estimate and predict childhood diarrheal disease burden in Kenya, Somaliland, and Zimbabwe. Using public epidemiological, environmental, climate, sanitation, and synthetic proof-of-concept datasets, the framework modeled temporal disease dynamics, spatial transmission, pathogen-attributed burden, and outbreak trajectories while enforcing epidemiological consistency through physics-informed optimization. Results demonstrated robust forecasting performance, enhanced spatial transmission modeling, uncertainty-aware predictions, and realistic outbreak simulations across the three countries. Rotavirus, Shigella, and Cryptosporidium were identified as major contributors to modeled mortality burden, while unsafe water exposure, poor sanitation, malnutrition, and climate-sensitive transmission substantially increased disease risk. Compared with a Bayesian baseline model, the multimodal framework achieved superior nonlinear risk characterization, geospatial learning, and temporal prediction. These findings highlight the potential of scientific machine learning and digital twin systems for infectious disease surveillance, outbreak forecasting, climate-health analytics, and evidence-based public health decision-making in low-resource African settings. Keywords: Physics-Informed Neural Networks, Graph Neural Networks, Digital Twin, Childhood Diarrheal Disease, Epidemiology, Kenya, Somaliland, Zimbabwe, Scientific Machine Learning, Spatial Epidemiology, Multimodal Fusion

Background: Companies in the Health and Life Insurance space are increasingly turning to digital tools to promote healthier behaviours among their user base and reduce future health risks. This approach shifts insurers' role from passive underwriters to partners in health management. These tools, often smartphone or wearable-tracker-based, enable real-time monitoring of behaviours (such as physical activity or meditation), providing fruitful targets for behavioural change interventions. Gamification, a Behavioural Change Technique with rich theoretical backing, is increasingly used in this context; however, despite its theoretical promise, current evidence remains mixed, and makes it hard to disambiguate its effect compared to more isolated financial incentives, the extent to which initial effects may be sustained over time, and how such changes in behaviour potentially translate to downstream health risk reductions. Objective: This 9-month parallel-group, open-label Randomised Controlled Trial was designed to assess the causal impact of gamification in promoting health behaviours, independent of financial incentivisation. This was conducted in a real-world workplace setting, involving a cohort of participants using the YuLife Health and Wellbeing app, provided within an employer-sponsored group cover setting. Methods: For the purposes of the RCT, the app was adapted such that gamification features could be turned on or off in a controlled manner, and in-app rewards in the form of "YuCoin" were adjusted between treatment groups to account for the effect of financial incentives. Following a baseline phase involving acquisition of baseline step estimates and questionnaire data, 1,288 participants -- recruited from a number of companies partnered with YuLife, spanning various sectors -- were randomised to gamified versus non-gamified versions of the app using stratified block-randomisation, and evaluated at specific milestones over a 9-month period, to enable comparison of short-term to long-term outcomes. The primary outcomes assessed were absolute differences in mean daily step count and engagement with the YuLife app. The data were analysed using Linear Mixed-Effects Models (LMMs). Additionally, a Cox Proportional Hazards model fitted to UK Biobank data was used to map step differences directly onto downstream health risks, and reductions were evaluated using an LMM. Further secondary outcomes (such as smoking and alcohol consumption) were also evaluated using non-parametric statistics. Results: Compared with control, the gamified intervention was associated with greater mean daily steps throughout the study, with month / intervention interaction effects reaching one-sided 5% significance at months 3 ({beta}=473.84, p=0.027), 5 ({beta}=626.54, p=0.006), and 9 ({beta}=480.91, p=0.033). Additionally, strong seasonal effects were identified, with fewer steps in Autumn ({beta}{approx}-943.50, p<0.001) and Winter ({beta}{approx}-1,145.45, p<0.001) versus Summer; higher baseline activity was a strong predictor of later activity ({beta}{approx}0.85, p<0.001) and higher BMI was negatively associated with steps ({beta}{approx}-60.84 per unit, p<0.001). For app engagement, month / intervention interactions were positive and significant from Month 3 onwards (Month 3 {beta}=0.205, Month 5 {beta}=0.182, Month 7 {beta}=0.170, Month 9 {beta}=0.175, all p<0.001), effectively showing sustained engagement while main milestone terms indicated declines in the control arm. Sensitivity analyses demonstrated the potential for baseline step inflation due to novelty effects, motivating repeating the step count analyses under an alternative baseline definition; this showed similar results, but with interaction effects achieving one-sided significance over all study milestones. Predicted partial-hazard analyses showed progressively larger month / intervention reductions in hazard, reaching one-sided significance at months 5 (coef=-0.018, p=0.016) and 9 (coef=-0.026, p=0.002). No significant intervention effects were observed for other secondary outcomes (e.g. smoking, alcohol) following Bonferroni-Holm correction. Conclusions: Gamification elements can be an effective component in the context of digital interventions aiming to promote positive health behaviours, leading to improved engagement with the intervention and positive behavioural outcomes. Through progressive risk-reduction, even small but sustained improvements can be shown to meaningfully improve long-term health outcomes. Gamification is likely to add value to workplace health promotion initiatives, particularly for targeted short- to medium-term behavioural change interventions operating within a larger risk-management framework. Trial Pre-registration: https://osf.io/926pd

The 2026 Bundibugyo Ebola outbreak in eastern Democratic Republic of the Congo (DRC) has already generated international spread to Uganda, raising concerns about further regional and international dissemination. Using International Air Transport Association origin-destination passenger flows, we assessed relative exposure to Ebola virus disease importation into Europe under six outbreak expansion scenarios reflecting plausible pathways of geographical spread, including cross-border transmission and amplification in highly connected regional capitals. Relative exposure patterns remained largely unchanged under localized transmission in eastern DRC and border-spillover scenarios. Expansion into South Sudan generated a first structural increase in importation pressure to Europe through the connectivity associated with Juba, while hypothetical amplification in Kampala, Kigali, and Kinshasa substantially increased importation pressure and reshaped exposure patterns across Europe. Across all scenarios, France, Italy, and the United Kingdom remained among the most exposed countries. Mobility-informed scenario analyses support preparedness as the geography of the outbreak evolves.

Objective To develop clinically operational, population-representative risk-score models for detecting metabolic syndrome (MetS) in U.S. adults by incorporating the NHANES survey design. Research Design and Methods We analyzed 36,812 U.S. adults from NHANES 1988--2018. Seven models of increasing clinical complexity were trained and evaluated, ranging from basic demographics to full biochemical panels. We used a new deep-learning methodology for survey data with a predictive uncertainty quantification model. Results A model combining anthropometrics, vital signs and a basic lipid panel achieved an AUC of 0.923 at an estimated cost of 0.40 eur per individual. Adding diabetes-specific biomarkers, including fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), yielded only marginal improvements. Conclusions This low-cost population-representative screening tool for MetS may help identify at-risk individuals and support data-driven public health interventions.

Background: Chronic diseases and symptom syndromes often develop after prolonged biological changes that may precede formal diagnosis. RNA-based metatranscriptomics captures active microbial and human gene expression and may provide a functional layer for disease risk evaluation. To address this translational gap, we developed and validated a Disease Risk Score (DRS) framework that integrates metatranscriptome-derived pathway activity scores from stool, saliva, and blood samples, and evaluated its potential clinical utility as an adjunct risk-evaluation tool. Methods: DRS uses disease-specific sets of pathway activity scores derived from stool and saliva microbial functions, stool and saliva microbial taxa, and blood human gene expression. For each disease, 'not optimal' pathway scores are aggregated into a normalized cumulative odds ratio, or cOR, using score-level odds ratios, statistical significance, and literature-supported biological relevance derived from a Development Cohort of 22,369 individuals. A cOR [&ge;] 5 is defined as high risk. Performance is evaluated in an independent Validation Cohort of 15,908 individuals using self-reported diseases as the reference. Disease support requires both significant cOR separation between self-reported and not-reported (Cohen's d [&ge;] 0.2) and risk ratio enrichment of self-reported disease among individuals classified as high risk (95% CI of Risk Ratio > 1). Results: Of 20 initially evaluated diseases, 15 meet the prespecified validation criteria on the independent validation cohort: ADHD, anxiety, chronic fatigue syndrome, depression, GERD, hypertension, inflammatory bowel disease, IBS-C, IBS-D, insomnia, MASLD, obesity, obstructive sleep apnea, Sjogren's syndrome, and type 2 diabetes. Five selected clinical scenarios illustrate how DRS can support clinician-mediated decision making, including IBS subtype reclassification, improved diagnostic acceptance in IBS-D, personalized lifestyle counseling in MASLD and early type 2 diabetes, and diagnostic uncertainty in atypical GERD. Conclusions: DRS is a metatranscriptomics-based risk-stratification framework that aggregates active microbial and human pathway signals into interpretable disease-specific risk estimates across a wide range of disease conditions. Validation against self-reported disease labels in an independent cohort shows significant risk enrichment for each of 15 diseases. DRS is intended as an adjunct to clinical evaluation: a decision support tool in situations where routine care encounters uncertainty, delay, or low patient engagement. Future prospective studies using clinically adjudicated endpoints are needed to assess calibration and clinical outcomes.

Ultra-processed food (UPF) may contribute to increased energy intake and weight gain, but evidence synthesis from randomised controlled trials (RCT) is lacking. A pre-registered systematic review and meta-analysis of RCTs was conducted comparing UPF with less processed food (LPF) on energy intake and/or body weight in humans. Secondary analyses (meta-regression and sub-group) examined effects of UPF on appetite sensations, eating rate, palatability and considered the role of nutrient profile in explaining results. Ten eligible studies were included. UPF trial arms tended to have higher energy intake (standardised mean differences [SMDs]=0.18-0.44), but statistical significance varied between analytic models. Weight gain (SMD=0.65) and eating rate (SMD=0.96) were significantly greater in UPF trial arms. No significant differences in palatability, appetite sensations or energy intake later in the day were observed. Diets (UPF vs. LPF) used in trials were not matched for nutrient profile. Effects on energy intake varied if UPFs were higher (SMD=0.71) or similar (SMD=0.02) in energy density. Current RCTs are suggestive that UPFs may increase energy intake and body weight; however, results may be explained by energy density of foods used. Further research is needed to understand whether the level of processing impacts health outcomes independent to nutrient profile.

Background: Epic Cosmos is a relatively new centralized electronic health record dataset with high potential utility in perinatal epidemiologic research. Objectives: The study objectives were to develop replicable steps to create longitudinal, linked maternal-infant cohorts in Cosmos, assess completeness of key variables, evaluate potential selection bias with restrictions for longitudinal healthcare encounters, and provide an example epidemiologic analysis. Methods: We created maternal-infant cohorts by starting with live births during 2023-2024 recorded in the BirthFact data table and joining with additional data tables as needed. We selected and created variables for perinatal characteristics, common comorbidities, and routinely measured vital signs and laboratory values, and assessed variable completeness. We sequentially restricted the birth cohort for maternal-infant linkage and longitudinal healthcare from first-trimester prenatal care encounter through infant follow-up care within 12 weeks post-discharge from birth hospitalization. Finally, we conducted an example analysis of the association between high systolic blood pressure in the first trimester ([&ge;]140 mm Hg) and later onset of preeclampsia among those with chronic hypertension. Results: The total linked birth cohort included 2,624,186 pregnancies. Completeness was >90% for most variables assessed but was 77% for racial and ethnic group and 76% for body mass index at delivery. Characteristics of the cohort were similar to those reported for the entire United States birth population based on birth certificate data, including similar regional and racial-ethnic composition. Longitudinal cohort restriction requiring linked records from first trimester prenatal care through infant follow-up care reduced the cohort size to 509,148 pregnancies. However, restriction had minimal effects on cohort characteristics. In the example analysis, high systolic blood pressure was associated with increased risk of preeclampsia among those with chronic hypertension (aRR: 1.26; 95% CI: 1.22, 1.30). Conclusions: This study provides a rigorous and reproducible approach to creating longitudinal, linked maternal-infant cohorts in Epic Cosmos and the analytical findings suggest high data quality and representativeness.