Tag: pubmed

Hum Reprod. 2025 Nov 24:deaf218. doi: 10.1093/humrep/deaf218. Online ahead of print.

ABSTRACT

STUDY QUESTION: Is male infertility independently associated with an increased risk of incident hypertension, ischemic and non-ischemic heart disease, diabetes, and/or cerebrovascular disease?

SUMMARY ANSWER: Fathers diagnosed with male infertility have a modestly increased risk of heart disease, diabetes, and hypertension compared with fertile fathers, after controlling for measured confounders; however, some important confounders remain inadequately measured.

WHAT IS KNOWN ALREADY: Cohort studies suggest that infertile men have an increased risk of incident cardiometabolic diseases, including diabetes, hypertension, heart disease, and cerebrovascular disease, although findings are mixed. The reasons for this association are unclear, but cardiometabolic conditions and male infertility share a wide range of shared etiological factors including age, chronic conditions such as obesity and obstructive sleep apnea, cancers and their treatments, environmental exposures such as pollution and pesticides, lifestyle factors such as smoking and cardiorespiratory fitness, autoimmune conditions such as lupus and Hashimoto’s thyroiditis, as well as congenital conditions such as cystic fibrosis and muscular dystrophy.

STUDY DESIGN, SIZE, DURATION: Our population-based cohort study included 445 909 men whose partner conceived a child between January 2009 and September 2016 in New South Wales (NSW), Australia. We excluded men with a diagnosis of infertility prior to 2009, men who were under the age of 14 at the time of the child’s conception, and men diagnosed with cardiometabolic conditions in the 6.5 years prior to their index date. The index date was the later of the date of the child’s conception or the date of the vasectomy for fertile men or the date of diagnosis of infertility for infertile men, i.e. the time when the exposure status was determined. From the index date, we followed participants for 5 years up until the latest available date of September 2021.

PARTICIPANTS/MATERIALS, SETTINGS, METHODS: The study was conducted in NSW, Australia. We determined infertility status by a diagnosis of male infertility in the Australian and New Zealand Assisted Reproduction Database, hospital records, or a record of fertility-related procedures. We assessed the following outcomes: incident hypertension, ischemic and non-ischemic heart disease, all heart disease, diabetes, and cerebrovascular disease. We calculated age-standardized prevalence rates at baseline. We mapped potential confounding pathways using directed acyclic graphs and controlled for measured confounders using inverse probability of treatment weighting and g-computation. We estimated adjusted marginal risk ratios (aRR) and adjusted marginal risk differences (aRD) using robust Poisson regression.

MAIN RESULTS AND THE ROLE OF CHANCE: The number of events and 5-year crude incidence rate for the outcomes were: hypertension (events: 17 433, fertile: 41.09 per 1000 population, infertile: 70.03 per 1000 population), all heart disease (events: 15 549, fertile: 36.44 per 1000 population, infertile: 59.88 per 1000 population), ischemic heart disease (events: 12 628 fertile: 29.24 per 1000 population, infertile: 47.1 per 1000 population), non-ischemic heart disease (events: 5183, fertile: 11.69 per 1000 population, infertile: 20.24 per 1000 population), cerebrovascular disease (events: 512, fertile: 1.14 per 1000 population, infertile: 1.78 per 1000 population) and diabetes (events: 7064, fertile: 16.05 per 1000 population, infertile: 27.59 per 1000 population). Compared with fertile men, men diagnosed with infertility demonstrated increased risk of incident disease for: hypertension aRR = 1.20 (95% CI 1.11-1.31, P < 0.001), aRD = 1.1% (95% CI: 0.6%-1.6%, P < 0.001); all heart disease aRR = 1.20 (95% CI 1.09-1.31, P < 0.001), aRD =0.9% (95% CI: 0.4%-1.4%, P < 0.001); non-ischemic heart disease aRR = 1.26 (95% CI 1.08-1.48, P = 0.004), aRD = 0.4% (95% CI: 0.1%-0.7%, P = 0.009); ischemic heart disease aRR = 1.13 (95% CI 1.02-1.25, P = 0.020), aRD = 0.4% (95% CI: 0.1%-0.7%, P = 0.028); and diabetes aRR = 1.28 (95% CI 1.12-1.46, P < 0.001), aRD 0.6% (0.2%-0.9%, P = 0.001). There was no significant difference in the incidence of cerebrovascular disease, aRR = 1.0 (95% CI 0.56-1.80, P = 0.996), aRD = 0.0% (95% CI: -0.1% to 0.1%, P = 0.996). These results remained consistent in sensitivity analyses, including an expanded exposure definition of infertility, a 10-year follow-up period, changing the outcomes of people who died in follow-up, and using an alternative index date.

LIMITATIONS, REASONS FOR CAUTION: The cohort includes men who fathered a child, so men who did not seek to, or were unable to, have a child, and men with poor access to the reproductive healthcare may not be included. This may generate selection effects, biasing the estimates toward the null. We were unable to adequately control for several confounders, including important lifestyle factors like smoking, diet, cardiorespiratory fitness, and alcohol intake, due to data limitations, which may bias estimates away from the null. It appears plausible that a combination of unmeasured and inadequately measured confounders may attenuate the observed estimates.

WIDER IMPLICATIONS OF THE FINDINGS: These findings suggest that male infertility may serve as an early indicator for a slightly heightened cardiometabolic risk, specifically relating to hypertension, diabetes, and various forms of heart disease. Our study is the largest on this topic, with extensive control for confounders. Our findings align with published research, indicating that men diagnosed with infertility have a slightly higher risk of incident diabetes, hypertension, and heart disease. From a public health perspective, fertility treatment may be an opportunity for earlier detection and intervention to help prevent the onset of cardiometabolic conditions in men diagnosed with infertility, particularly given that men generally have low rates of contact with the health system.

STUDY FUNDING/COMPETING INTEREST(S): The PhD candidacy of J.M. is supported by Medical Research Future Fund (MRFF) Emerging Priorities and Consumer Driven Research initiative: EPCD000007, 2020. M.K.O’B. and G.M.C. declare receiving payment to their institution by the same MRFF grant. G.M.C. reports receiving funding from an Australian MRFF grant paid to UNSW to support this work, and J.M. reports receiving PhD funding from the same MRFF grant. C.V. declares an unpaid role on Human Reproduction’s Editorial Board, and paid employment at the University of New South Wales (UNSW) until January 2023. The National Perinatal Epidemiology and Statistics Unit (NPESU), which belongs to UNSW, is custodian of the Australian and New Zealand Assisted Reproduction Database (ANZARD). Data from ANZARD were used in this study. G.M.C. also declares paid employment from UNSW. The remaining authors have nothing to declare.

TRIAL REGISTRATION NUMBER: N/A.

PMID:41285026 | DOI:10.1093/humrep/deaf218

JMIR Form Res. 2025 Nov 24;9:e80090. doi: 10.2196/80090.

ABSTRACT

BACKGROUND: Physical inactivity increases the risk of chronic disease and reduces life expectancy, yet adherence to physical activity (PA) guidelines remains low. SMS text messages are promising for promoting PA, but it is not clear what type of messaging is most effective. Messages with causal information, which explain why a recommendation is being made, may be more persuasive than messages containing only recommendations.

OBJECTIVE: This study aims to compare the effectiveness of causal versus noncausal SMS text messages for promoting PA in US adults.

METHODS: In this pilot study, we randomized US adults (n=28 in the analytic sample) aged 18-64 years to receive causal or noncausal SMS text messages roughly every other day for 2 weeks, following a 1-week baseline. PA was measured using Empatica wristbands during intervention and baseline periods, and the International Physical Activity Questionnaire – Short Form (IPAQ-SF) at baseline, postintervention, and 4 weeks later. The primary outcome was the change in mean metabolic equivalent of tasks (METs) per minute from baseline to intervention. The secondary outcomes were (1) PA differences on intervention and nonintervention days (mean METs/min), (2) changes in self-reported METs per week between surveyed periods, and (3) participant satisfaction. We used a linear mixed model to analyze our primary outcome, the Mann-Whitney U test and the chi-square test of independence to analyze quantitative secondary outcomes, and qualitative coding to analyze survey data.

RESULTS: The causal message group had a greater increase in mean METs per minute from baseline to intervention compared to the noncausal group with a moderate effect size (P=.01; Cohen d=0.54). In the causal group, PA was significantly higher on SMS text message days (mean 2.46, SD 0.12 METs/min) compared to nonmessage days (mean 2.25, SD 0.15 METs/min; P=.02), while there was no difference in the noncausal group (P=.54). No significant between-group difference was found in self-reported PA or satisfaction.

CONCLUSIONS: Causal information that links suggested PA to health outcomes can increase the effectiveness of SMS text messages promoting PA, indicating the value of incorporating causal information into intervention design. Our results provide further basis for just-in-time interventions, as activity was higher on message days. Further work is needed to better personalize message content and timing to maintain participant engagement.

PMID:41284987 | DOI:10.2196/80090

J Med Internet Res. 2025 Nov 24;27:e77155. doi: 10.2196/77155.

ABSTRACT

BACKGROUND: Osteoporosis is a prevalent skeletal disorder characterized by decreased bone mass and increased fracture risk; however, it frequently remains underdiagnosed due to limited health care resources and its asymptomatic progression. Deep learning (DL) provides a promising solution for automated screening using computed tomography (CT) scans, enabling earlier detection and improved management.

OBJECTIVE: This systematic review and meta-analysis aimed to investigate the diagnostic performance of DL models in diagnosing osteoporosis based on CT scans.

METHODS: This study was conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using articles extracted from PubMed, Scopus, Web of Science (Core), and Embase (Ovid). Studies involving adult participants who underwent CT and in which DL was applied for osteoporosis diagnosis were included. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool was used to estimate the risk of bias in each study. The confusion matrices from the included studies were extracted to summarize the diagnostic performance of DL models for osteoporosis. Within a bivariate random-effects framework, sensitivity and specificity were jointly synthesized to yield the summary estimates. Heterogeneity was quantified with Higgins I² statistics. Subgroup analyses were performed to explore potential sources of heterogeneity among the included studies.

RESULTS: This review included 24 studies, encompassing CT images from 29,808 participants. All studies used conventional CT scans and used DL-based architectures. Fifteen, 6, and 3 studies were assessed as having a low, uncertain, and high risk of bias, respectively. The meta-analysis included 20 studies. The pooled sensitivity and specificity were 0.88 (95% CI 0.85-0.91; I2=83.69%) and 0.94 (95% CI 0.91-0.96; I2=95.07%) for osteoporosis diagnosis; 0.81 (95% CI 0.76-0.85; I2=82.38%) and 0.92 (95% CI 0.90-0.94; I2=79.05%) for osteopenia identification; and 0.95 (95% CI 0.92-0.97; I2=98.28%) and 0.93 (95% CI 0.91-0.95; I2=94.93%) for normal case identification. The area under the curve of the DL models for identifying osteoporosis, osteopenia, and normal cases was 0.96 (95% CI 0.93-0.97), 0.94 (95% CI 0.92-0.96), and 0.98 (95% CI 0.96-0.99), respectively. Subgroup analyses revealed that models based on DenseNet variants (P<.01), multislice input (P<.01), 3D architecture (P<.01), and CT as the reference standard (P<.01) demonstrated superior diagnostic performance.

CONCLUSIONS: This study indicated that CT-based DL models achieve promising diagnostic performance for osteoporosis. However, substantial heterogeneity among the included studies, limited external validation, and incomplete end-to-end pipelines constrain the generalizability of the proposed models. Further research is warranted to support their clinical translation and standardized application.

PMID:41284986 | DOI:10.2196/77155

JMIR Form Res. 2025 Nov 24;9:e66931. doi: 10.2196/66931.

ABSTRACT

BACKGROUND: Breast cancer remains the most common cancer among women globally. Mammography is a key diagnostic modality; however, interpretation is increasingly challenged by rising imaging volumes, a global shortage of breast radiologists, and variability in reader experience. Artificial intelligence (AI) has been proposed as a potential adjunct to address these issues, particularly in settings with high breast density, such as Asian populations. This study aimed to evaluate the impact of AI assistance on mammographic diagnostic performance among resident and consultant radiologists in Singapore.

OBJECTIVE: To assess whether AI assistance improves diagnostic accuracy in mammographic breast cancer detection across radiologists with varying levels of experience.

METHODS: A multi-reader, multi-case study was conducted at the National University Hospital, Singapore, from May to August 2023. De-identified digital mammograms from 500 women (250 with cancer and 250 normal or benign) were interpreted by 17 radiologists (4 consultants, 4 senior residents, and 9 junior residents). Each radiologist read all cases over 2 reading sessions: one without AI assistance and another with AI assistance, separated by a 1-month washout period. The AI system (FxMammo) provided heatmaps and malignancy risk scores to support decision-making. Area under the curve of the receiver operating characteristic (AUROC) was used to evaluate diagnostic performance.

RESULTS: Among the 500 cases, 250 were malignant and 250 were non-malignant. Of the malignant cases, 16%(80/500) were ductal carcinoma in situ and 84%(420/500) were invasive cancers. Among non-malignant cases, 69.2%(346/500) were normal, 17.6%(88) benign, and 3.6%(18/500) possibly benign but stable on follow-up. Masses (54.4%, 272) and calcifications (10.8%, 54/500) were the most common findings in cancer cases. A majority of both malignant (66.8%, 334/500) and non-malignant (68%, 340/500) cases had heterogeneously or extremely dense breasts (BIRADS [Breast Imaging Reporting and Data System] categories C and D). The AI model achieved an AUROC of 0.93 (95% CI 0.91-0.95), slightly higher than consultant radiologists (AUROC 0.90, 95% CI 0.89-0.92; P=.21). With AI assistance, AUROC improved among junior residents (from 0.84 to 0.86; P=.38) and senior residents (from 0.85 to 0.88; P=.13), with senior residents approaching consultant-level performance (AUROC difference 0.02; P=.051). Diagnostic gains with AI were greatest in women with dense breasts and among less experienced radiologists. AI also improved inter-reader agreement and time efficiency, particularly in benign or normal cases.

CONCLUSIONS: This is the first study in Asia to evaluate AI assistance in mammography interpretation by radiologists of varying experience. AI significantly improved diagnostic performance and efficiency among residents, helping to narrow the experience-performance gap without compromising specificity. These findings suggest a role for AI in enhancing diagnostic consistency, improving workflow, and supporting training. Integration into clinical and educational settings may offer scalable benefits, though careful attention to threshold calibration, feedback loops, and real-world validation remains essential. Further studies in routine screening settings are needed to confirm generalizability and cost-effectiveness.

PMID:41284978 | DOI:10.2196/66931

JMIR Mhealth Uhealth. 2025 Nov 24;13:e73903. doi: 10.2196/73903.

ABSTRACT

BACKGROUND: Mobile health (mHealth) apps are believed to be an effective method to support family caregivers to better care for patients with stroke. This study’s purpose was to explore the status and the influencing factors of mHealth app use among family caregivers of patients with stroke via machine learning (ML) models.

OBJECTIVE: This study aimed to understand the status quo of mHealth app use among community family caregivers of patients with stroke and the factors influencing their use behavior. Six ML models were used to construct the classifier, and the Shapley Additive Explanations (SHAP) algorithm was introduced to interpret the best ML model.

METHODS: In this cross-sectional study, family carers of patients with stroke were recruited. Data on their basic profile and mHealth app use were obtained through face-to-face questionnaires. Hedonic motivation, usage habits, and other relevant information were additionally measured among app users. A total of 12 models were constructed using six ML algorithms. The top-performing logistic regression and random forest models were further analyzed with SHAP to interpret key influencing factors.

RESULTS: A total of 360 family caregivers of patients with stroke were included in this study from March 2023 to November 2023, of which 206 (57.2%) reported having used mHealth apps. Of the 6 ML models, the logistic regression model performed the best in terms of whether caregivers used the mHealth app, with an area under the receiver operating characteristic curve of 0.753 (95% CI 0.698-0.802), accuracy of 0.694 (95% CI 0.647-0.742), sensitivity of 0.748 (95% CI 0.688-0.806), and specificity of 0.623 (95% CI 0.547-0.698). SHAP analysis showed that the top 5 most influencing factors were educational level, age, the patient’s self-care ability, the relationship with the cared-for individual, and the duration of illness. The random forest model performed best in terms of use behavior with an area under the receiver operating characteristic curve of 0.773 (95% CI 0.725-0.818), accuracy of 0.602 (95% CI 0.534-0.665), sensitivity of 0.476 (95% CI 0.420-0.533), and specificity of 0.769 (95% CI 0.738-0.797). The SHAP analysis revealed that hedonic motivation, habits, occupation, convenience conditions, and effort expectations were the 5 most significant influencing factors.

CONCLUSIONS: The research results indicate that the software developers and policymakers of mHealth apps should take the abovementioned influencing factors into consideration when developing and promoting the software. We should focus on the older adults with lower educational levels, lower the threshold for software use, and provide more convenient conditions. By grasping the hedonistic tendencies and habitual usage characteristics of users, they can provide them with more concise and accurate health information, which will enhance the popularity and effectiveness of mHealth apps.

PMID:41284965 | DOI:10.2196/73903

Hepatol Commun. 2025 Nov 24;9(12):e0856. doi: 10.1097/HC9.0000000000000856. eCollection 2025 Dec 1.

ABSTRACT

BACKGROUND: Dietary interventions are key for managing metabolic dysfunction-associated steatotic liver disease (MASLD), yet optimal diets and the role of PNPLA3 in modulating response to diet remain unclear. We evaluated the efficacy of a Mediterranean diet (MD) versus a low-fat diet (LFD) on hepatic fat and fibrosis, assessing interactions with PNPLA3 genotype.

METHODS: Two hundred fifty adults with MASLD with BMI ≥25 kg/m2 were randomized to a 12-week moderately hypocaloric MD or LFD intervention. Individuals with excess alcohol intake and other etiologies of steatosis were excluded. Subjects were genotyped for PNLPA3 single-nucleotide polymorphism. Anthropometric measures, blood tests, and liver assessments [controlled attenuation parameter (CAP) and liver stiffness measurement (LSM)] were conducted at baseline and follow-up. Essential food items were provided, and adherence was tracked using validated questionnaires. The primary outcome was CAP, analyzed using linear mixed models adjusted for age and metabolic syndrome.

RESULTS: Both diets significantly reduced CAP, LSM, and body weight at follow-up, with no significant differences between groups. The mean difference between MD and LFD was -0.13 dB/m for CAP (p=0.976, 95% CI: -8.54, 8.28), -0.19 kPa for LSM (p=0.355, 95% CI: -0.58, 0.21), and 3.01 kg for weight (p=0.159, 95% CI: -7.21, 1.19). PNPLA3 genotype did not significantly interact with diet for CAP, LSM, or weight (p=0.286, p=0.464, p=0.622, respectively).

CONCLUSIONS: Weight reduction achieved by MD and LFD is similarly efficient in steatosis and fibrosis reduction, while PNPLA3 genotype does not affect the response to diet. Further studies investigating the impact of diet and nutrigenetics on liver-related outcomes are warranted.

PMID:41284948 | DOI:10.1097/HC9.0000000000000856

Integr Environ Assess Manag. 2025 Nov 24:vjaf175. doi: 10.1093/inteam/vjaf175. Online ahead of print.

ABSTRACT

This study investigates the environmental and economic factors driving pollution-related mortality in 37 Sub-Saharan African countries from 1990 to 2022. The analysis combines two complementary approaches. The first is a Panel Panel Quantile Autoregressive Distributed Lag (QARDL) model, which captures both short- and long-run relationships across different levels of the mortality distribution. The second is a Light Gradient Boosting Machine (LightGBM) model, a machine-learning method that detects nonlinear patterns and reveals interactions that may be missed by traditional statistical models. Together, these methods integrate structured econometric inference with flexible pattern recognition, offering a clearer and more reliable picture of how environmental and economic forces jointly shape mortality outcomes. The LightGBM partial dependence plots further confirm the Panel QARDL results, showing consistent directional effects across all variables. Fine particulate matter and consumer price index display the strongest nonlinear responses, while methane, health expenditures and Gross Domestic Product exhibit moderate but coherent patterns that reinforce the robustness of the findings. The results show that higher levels of fine particulate matter are consistently linked to increased mortality across all quantiles. Economic growth reduces mortality at higher quantiles of the distribution, where health burdens are most severe, indicating that stronger economies are better able to mitigate pollution-related deaths. Inflation exhibits a positive relationship with mortality, particularly at higher quantiles, indicating that rising prices can limit access to essential health services and increase vulnerability. Health spending, in contrast, reduces mortality, though its impact varies by both time horizon and income level. Overall, the findings highlight the importance of cleaner air, stable prices and stronger healthcare systems for reducing pollution-related mortality in Sub-Saharan Africa. The study provides policy-relevant insights for promoting health resilience under economic and environmental stress.

PMID:41284938 | DOI:10.1093/inteam/vjaf175

Genomics Proteomics Bioinformatics. 2025 Nov 24:qzaf111. doi: 10.1093/gpbjnl/qzaf111. Online ahead of print.

ABSTRACT

Depicting gene expression in a spatial context through spatial transcriptomics is beneficial for inferring cellular mechanisms. Identifying spatially variable genes is a crucial step in leveraging spatial transcriptome data to understand intricate spatial dynamics. In this study, we developed Spanve, a nonparametric statistical method for detecting spatially variable genes in large-scale spatial transcriptomics datasets by quantifying expression differences between each spot or cell and its local neighbors. This method offers a nonparametric approach for identifying spatial dependencies in gene expression without distributional assumptions. Compared with existing methods, Spanve yields fewer false positives, leading to more accurate identification of spatially variable genes. Furthermore, Spanve improves the performance of downstream spatial transcriptomics analyses including spatial domain detection and cell type deconvolution. These results show the broad application potential of Spanve in advancing our understanding of spatial gene expression patterns within complex tissue microenvironments. Spanve is publicly available at https://github.com/zjupgx/Spanve and https://ngdc.cncb.ac.cn/biocode/tool/BT7724.

PMID:41284930 | DOI:10.1093/gpbjnl/qzaf111

Int J Occup Saf Ergon. 2025 Nov 24:1-5. doi: 10.1080/10803548.2025.2586887. Online ahead of print.

ABSTRACT

Objectives. This study aimed to evaluate occupational safety perception and its influencing factors among students in the Department of Occupational Health and Safety at Sinop University, with a particular focus on the impact of socio-demographic variables and participation in occupational health and safety (OHS)-related training. Methods. A cross-sectional study was conducted with 154 OHS students in the 2022-2023 autumn term. Data from 128 students (83.1%) were collected using an informed consent form, a descriptive questionnaire and the occupational safety scale (OSS). The OSS is a 32-item Likert scale with a reliability of 0.75. Data analysis used SPSS version 25, including descriptive statistics, t tests, analysis of variance and Pearson correlation. Results. Participants had a mean age of 21.72 ± 1.33 years, 50.8% were male and 43% were second-year students. Most (73.4%) reported middle-income levels, and 66.4% had not received personal protective equipment (PPE) training. No significant correlation was found between socio-demographic factors and OHS perception scores (p > 0.05). However, students who received PPE training or participated in OHS activities had significantly higher perception scores (p = 0.018 and p = 0.002). Conclusion. OHS-related training, particularly in PPE and OHS activities, significantly improves safety perception. Expanding such training in educational settings can enhance future professionals’ safety awareness.

PMID:41284928 | DOI:10.1080/10803548.2025.2586887

Proc Natl Acad Sci U S A. 2025 Dec 2;122(48):e2500535122. doi: 10.1073/pnas.2500535122. Epub 2025 Nov 24.

ABSTRACT

Using data on millions of internal US migrants, we document that historical homicide rates follow migrants around the United States. Individuals born in historically safe states remain safer wherever they go, while individuals born in historically dangerous states face a greater risk, including from police violence. This pattern holds across demographic characteristics such as age, gender, and marital status, across migrant groups with different average levels of education, income, and even when comparing migrants from different states who reside in the same county. To help understand why, we conducted a large national survey that oversampled internal White US migrants. The results suggest this persistence may reflect a sociocultural adaptation to dangerous settings. Residents and migrants from historically unsafe states-mainly former frontier states and the deep South-see the world as more dangerous, react more forcefully in aggressive scenarios, value toughness, distrust law enforcement, and say they rely on self and family in violent situations. These adaptations may have kept them safe in historically dangerous states, but may increase their vulnerability to harm in safer states.

PMID:41284885 | DOI:10.1073/pnas.2500535122