Tag: pubmed

BMC Psychiatry. 2025 Jul 29;25(1):735. doi: 10.1186/s12888-025-07181-9.

ABSTRACT

BACKGROUND: Sleep deprivation and overload have been associated with increased risks of both depression and mortality. However, no study has quantitatively compared the effects of objective and subjective sleep duration on mortality or examined the mediating role of depressive symptoms in these associations.

METHODS: Utilizing data from the NHANES 2011-2014, this study employed structural equation modeling (SEM) to explore the impact of depressive symptoms, measured by Patient Health Questionnaire (PHQ-9) scores, on the relationship between both objective and subjective sleep durations and all-cause mortality.

RESULTS: The study included 7838 participants, comprising 4392 women (55.96%) with a mean age of 46.51 (0.46) years. Over a median 6.83-year follow-up, 582 deaths occurred. The restricted cubic spline curves demonstrated a J-shaped relationship between objective sleep duration and the all-cause mortality risk, and a U-shaped relationship between subjective sleep duration and the all-cause mortality risk. SEM analysis revealed that when subjective sleep duration was < 7 h/day, the indirect effect of sleep duration on all-cause mortality was – 0.013 (P = 0.003), and the mediation proportion of PHQ-9 scores was 40.63%. When objective sleep duration ≥ 7 h/day, the indirect effect of sleep duration on all-cause mortality was 0.003 (P = 0.028), and the mediation proportion of PHQ-9 scores was 2.10%.

CONCLUSIONS: The study confirmed a J-shaped and a U-shaped correlation for objective and subjective sleep duration with mortality risk. Depressive symptoms significantly mediated the association between shorter subjective sleep duration and mortality. This suggests that there is a need to focus on the co-morbidity of subjective sleep deprivation and depression.

PMID:40730972 | DOI:10.1186/s12888-025-07181-9

BMC Cancer. 2025 Jul 29;25(1):1234. doi: 10.1186/s12885-025-14651-6.

ABSTRACT

BACKGROUND: Obesity is a known risk factor for breast cancer (BC), but conventional metrics such as body mass index (BMI) may insufficiently capture central adiposity. The weight-adjusted waist index (WWI) has emerged as a potentially superior anthropometric marker of central adiposity, as it provides a more accurate reflection of fat distribution around the abdomen compared to traditional measures such as BMI. This study aimed to investigate the association between WWI and BC prevalence using data from a nationally representative population in the United States.

METHODS: A total of 10,760 women aged over 20 years from the 2005-2018 National Health and Nutrition Examination Survey were included. Logistic regression was used to assess the association between WWI and BC prevalence. Multicollinearity was addressed using variance inflation factor diagnostics. Machine learning methods, including random forest and LASSO regression, were employed for variable selection and model comparison. The performance of the models was evaluated using ROC curves, calibration plots, and decision curve analysis.

RESULTS: In unadjusted models, WWI was significantly associated with BC (odds ratio (OR) = 1.56; 95% confidence interval (CI): 1.32-1.86). However, in the fully adjusted model, the association with BC was no longer statistically significant (OR = 0.98; 95% CI: 0.75-1.26). Machine learning models ranked WWI as one of the top predictors, with the random forest model retaining WWI as an important variable, while LASSO excluded it. Models based on variables selected by both LASSO and random forest, which included WWI, were built and assessed using ROC curve analysis. The random forest and LASSO models achieved AUCs of 0.795 and 0.79, respectively, demonstrating improved predictive performance. These findings suggest that while WWI may not serve as an independent predictor of BC, it may offer additional value when combined with other key covariates.

CONCLUSION: Although the WWI was related to BC prevalence before multivariable adjustment, it was not significantly linked to BC after adjustment. Given the cross-sectional design and the relatively small sample of BC cases (n = 326), the findings should be viewed with caution. Future research with larger prospective cohorts is needed to confirm these results and explore WWI’s role in BC risk stratification. Studies should also investigate whether WWI can serve as a reliable independent predictor of BC in future research, taking into account other factors that may influence the association.

PMID:40730969 | DOI:10.1186/s12885-025-14651-6

BMC Psychiatry. 2025 Jul 29;25(1):738. doi: 10.1186/s12888-025-07167-7.

ABSTRACT

BACKGROUND: Khat is a commonly used psychoactive substance in East Africa and the Middle East, with rising use among adolescents. While general prevalence has been studied, there is a lack of research on problematic khat use (PKU), a harmful pattern that leads to distress or impairment. Few studies employ consistent assessment tools to distinguish casual use from problematic use, thus limiting our understanding of its specific attributes and hindering effective prevention and intervention efforts.

OBJECTIVES: The aim of the study was to evaluate the prevalence of PKU and to identify factors that contribute to this issue among high school students.

METHODOLOGY: A cross-sectional study was conducted at Legambo High School, Northeast Ethiopia, from April 26 to June 10, 2023. A total of 947 participants were selected through systematic random sampling. PKU was assessed using the Problematic Khat Use Screening Test (PKUST-17). Data were entered into Epi-Data version 4.6 and exported to SPSS version 26 for analysis. Binary logistic regression was used to identify factors associated with PKU. Variables with a p-value < 0.25 in the bivariate analysis were included in the multivariate logistic regression model using the enter method. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were calculated, and a p-value < 0.05 was considered statistically significant.

RESULTS: This study found that 19.7% of participants had PKU, accounting for 46.5% (95% CI: 41.7-51.5) of students who used khat, with an overall khat use prevalence of 42.3% (95% CI: 38.3-44.5) among high school students. Factors associated with PKU included exposure to traumatic events (AOR = 3.1, 95% CI: 1.7-4.9), age < 20 years (AOR = 4.9, 95% CI: 2.1-11.6), age 20-24 years (AOR = 3.2, 95% CI: 1.4-7.1), poor social support (AOR = 2.2, 95% CI: 1.1-4.3), depression (AOR = 0.4, 95% CI: 0.2-0.8), paternal substance use (AOR = 0.4, 95% CI: 0.2-0.6), and satisfactory academic performance (AOR = 3.1, 95% CI: 1.4-6.7).

CONCLUSION: In this study, nearly one in five participants exhibited PKU, linked to exposure to traumatic event, poor social support, and low parental education, while strong academic performance was protective. The study highlights the need for school-based mental health programs and standardized diagnostic criteria for PKU. Prevention efforts should prioritize youth exposed to trauma, with limited support, and from low-education households.

PMID:40730968 | DOI:10.1186/s12888-025-07167-7

BMC Bioinformatics. 2025 Jul 29;26(1):196. doi: 10.1186/s12859-025-06235-9.

ABSTRACT

BACKGROUND: A key challenge in differential abundance analysis (DAA) of microbial sequencing data is that the counts for each sample are compositional, resulting in potentially biased comparisons of the absolute abundance across study groups. Normalization-based DAA methods rely on external normalization factors that account for compositionality by standardizing the counts onto a common numerical scale. However, existing normalization methods have struggled to maintain the false discovery rate in settings where the variance or compositional bias is large. This article proposes a novel framework for normalization that can reduce bias in DAA by re-conceptualizing normalization as a group-level task. We present two new normalization methods within the group-wise framework: group-wise relative log expression (G-RLE) and fold-truncated sum scaling (FTSS).

RESULTS: G-RLE and FTSS achieve higher statistical power for identifying differentially abundant taxa than existing methods in model-based and synthetic data simulation settings. The two novel methods also maintain the false discovery rate in challenging scenarios where existing methods suffer. The best results are obtained from using FTSS normalization with the DAA method MetagenomeSeq.

CONCLUSION: Compared with other methods for normalizing compositional sequence count data prior to DAA, the proposed group-level normalization frameworks offer more robust statistical inference. With a solid mathematical foundation, validated performance in numerical studies, and publicly available software, these new methods can help improve rigor and reproducibility in microbiome research.

PMID:40730965 | DOI:10.1186/s12859-025-06235-9

BMC Med Res Methodol. 2025 Jul 29;25(1):178. doi: 10.1186/s12874-025-02628-9.

ABSTRACT

BACKGROUND: Test-negative design (TND) studies are increasingly common in evaluating vaccine effectiveness (VE) for various infectious diseases. TND studies are susceptible to bias due to disease outcome misclassification caused by imperfect test sensitivity and specificity. Several bias correction methods have been proposed. However, sample size or power considerations for TND studies incorporating bias correction for such misclassification have not yet been investigated.

METHODS: We used Monte Carlo simulations to assess how bias correction affects the statistical power and sample size for VE estimation in TND studies. Simulations were conducted under varying levels of diagnostic test sensitivities (60%, 80%, and 95%). Bias correction was implemented using the multiple over-imputation method, which accounts for test misclassification through a parametric bootstrapping approach. Using a malaria vaccine as an example, we defined six vaccination status categories based on the time since receipt of the third or fourth vaccine dose. In the simulated target population, vaccination coverage was assumed to be low (< 10%) except for the group vaccinated more than 12 months after dose 4. We assumed relatively low VE (< 50%) against clinical malaria cases and a 30% malaria positivity rate among unvaccinated individuals presenting with malaria-related symptoms. Statistical power to detect VE was estimated for each vaccination status, both with and without bias correction.

RESULTS: Estimated VEs based on observed data were consistently underestimated across all vaccination status groups due to diagnostic misclassification. In contrast, bias-corrected estimates were approximately unbiased but displayed wider confidence intervals, with their precision decreasing at lower test sensitivities. Statistical power to detect VE declined substantially when diagnostic test sensitivity was low. For instance, at 80% sensitivity, only three vaccination status groups reached 80% power with a sample size of 10,000, whereas the same power was achieved with just 6,000 individuals under a perfect test.

CONCLUSIONS: Bias due to imperfect diagnostic testing can substantially reduce the power of TND studies. Power calculations should account for outcome misclassification and potential correction methods. Failure to do so may lead to underpowered studies and misleading VE estimates.

PMID:40730954 | DOI:10.1186/s12874-025-02628-9

Psychol Rep. 2025 Jul 29:332941251363909. doi: 10.1177/00332941251363909. Online ahead of print.

ABSTRACT

This study aimed to investigate the relationships between adult attachment patterns, emotion dysregulation, and pathological narcissism-both fragile and grandiose dimensions. Previous research has indicated that attachment styles and emotion regulation difficulties play significant roles in narcissism development; however, the combined effects of these variables on both dimensions of narcissism remain insufficiently explored. The sample consisted of 114 individuals aged at least 18 years who were selected using convenience sampling from Sakarya University’s student population. The gender distribution was balanced, with 57 (50%) female and 57 (50%) male participants. The Difficulties in Emotion Regulation Scale, Pathological Narcissism Inventory, Relationship Scales Questionnaire, and a Demographic Information Form were administered to participants. Data analysis was conducted using SPSS statistical software. Between-group differences were examined using t test analysis, relationships between variables were investigated using Pearson correlation analysis, and predictive capacity was determined using hierarchical regression analysis. The results revealed a significant negative relationship between fragile narcissism and secure attachment, while positive relationships were found between fragile narcissism and preoccupied attachment, dismissive attachment, and emotion dysregulation difficulties. No significant relationships were found between grandiose narcissism and attachment styles. When all independent variables were included in the regression analysis, they collectively explained 36% of the variance in fragile narcissism, with emotion dysregulation (β = .395) and preoccupied attachment (β = .287) emerging as the strongest predictors. These findings highlight the importance of addressing emotion regulation skills and insecure attachment patterns in therapeutic interventions for individuals with fragile narcissistic traits, which may contribute to more effective clinical approaches and psychological support strategies.

PMID:40729757 | DOI:10.1177/00332941251363909

IEEE J Biomed Health Inform. 2025 Jul 29;PP. doi: 10.1109/JBHI.2025.3593459. Online ahead of print.

ABSTRACT

Routine clinical assessments for Parkinson’s disease are essential instruments in both clinical practice and research that are often used to identify disease sub-types and monitor the progression of disease severity. However, each clinic has limited access to information and the quality of these assessments is often degraded by the amount of missing information recorded at the time of each visit. The main objective of this study is to evaluate the performance of Federated Learning (FL) algorithms for imputing missing clinical data, enhancing the quality of decentralized Parkinson’s disease assessments while maintaining data privacy. Specifically, we explore the impact of various aggregation strategies on the imputation of clinical data from 1,370 patients in the Parkinson Progression Marker Initiative (PPMI). Notably, the Cyclic Weight Transfer (CWT) algorithm stands out for its lower imputation errors. To validate this study, we conducted a downstream analysis using imputed data to predict symptoms progression. We observed that a FL-based approach yields superior model performance based on imputation errors, when compared to a traditional learning strategies. These improvements can achieve 37.7% and 31.5% lower mean imputation errors with low and moderate degree of missing scores in the training data, respectively. In addition, we achieved better classification scores with Random Forest models trained with imputed data from FL-based approaches, compared to traditional statistical methods, with improvements of 0.5% in PR-AUC, 0.6% in ROC-AUC, and 1.3% in F-1 score. These results highlight FL as a robust and secure solution for decentralized clinical data management, offering improved performance while preserving patient privacy.

PMID:40729715 | DOI:10.1109/JBHI.2025.3593459

Epidemiology. 2025 Sep 1;36(5):e21-e23. doi: 10.1097/EDE.0000000000001886. Epub 2025 Jul 29.

NO ABSTRACT

PMID:40729682 | DOI:10.1097/EDE.0000000000001886

JMIR Form Res. 2025 Jul 29;9:e71141. doi: 10.2196/71141.

ABSTRACT

BACKGROUND: Mental health problems are a barrier to the well-being of youth living with HIV. Many youth living with HIV in Nigeria face peculiar biopsychosocial vulnerabilities that predispose them to mental health problems including depression and substance use. In addition to improving treatment outcomes like medication adherence and linkage to care, peer engagement has shown some promise in improving the social and emotional well-being of this population. Mobile health (mHealth) interventions like SMS text messaging medication reminders may also contribute to better outcomes in youth living with HIV. Emerging evidence suggests that combination interventions may be more effective than single interventions in improving key HIV testing and treatment outcomes among youth in Nigeria.

OBJECTIVE: This study aims to explore the impact of Intensive Combination Approach to Rollback the Epidemic in Nigerian Adolescents (iCARE Nigeria) study-an mHealth and peer navigation intervention primarily aimed at medication adherence and viral suppression-on depressive symptoms and substance use among youth living with HIV in Nigeria.

METHODS: A single-arm clinical trial was conducted at the Infectious Disease Institute, College of Medicine, University of Ibadan, Nigeria- primarily to improve medication adherence and viral suppression among youth living with HIV attending its HIV clinic. The intervention combined peer navigation and daily, 2-way, text message medication reminders delivered over a period of 48 weeks. Participants were screened at baseline and follow-up visits (24 and 48 weeks) for depression and substance use using standardized measures. Paired t tests and McNemar tests were used to investigate the change in depressive symptoms and the change in the proportion of participants reporting substance use over time, respectively.

RESULTS: All 40 enrolled participants (n=20, 50% male; mean age 19.9 y, SD 2.5 y) completed baseline and follow-up visits at week 24, while 37 (92.5%) participants completed the week 48 visit. Compared with baseline, there were significantly fewer self-reported depressive symptoms observed at 48 weeks (mean 2.89 vs 2.08; t36=2.04, 95% CI 0.006-1.615) but not at 24 weeks (mean 2.89 vs 2.62; t36=0.47, 95% CI -0.74 to 1.44). There were fewer self-reports of substance use at weeks 24 and 48 when compared to baseline, but these were not statistically significant (odds ratio [OR] ∞, 95% CI 0.189-∞ and OR 3.0, 95% CI 0.24-157.49, respectively).

CONCLUSIONS: These findings suggest a statistically significant reduction in depressive symptoms among youth living with HIV over the 48-week intervention period that may be due to the iCARE Nigeria intervention. However, given limitations such as low levels of depressive symptoms at baseline, small sample size, and the lack of a control group, future studies such as the randomized stepped wedge evaluation of the iCARE intervention are needed to provide better insights into these exploratory findings.

PMID:40729632 | DOI:10.2196/71141

ACS Appl Mater Interfaces. 2025 Jul 29. doi: 10.1021/acsami.5c08450. Online ahead of print.

ABSTRACT

Pathogenic microbial infections pose a serious threat to human health and safety. Therefore, rapid detection and accurate identification of pathogenic microorganisms are critical for effective diagnosis and prevention. However, clinical testing often faces challenges such as processing large sample volumes and achieving a high detection efficiency. Here, we developed a series of sensing arrays based on cationic conjugated polymer/silver nanoparticle (CCP/Ag) composites, enabling fluorescence and colorimetric dual-signal readouts for microbial detection and identification. Five conjugated polymers with distinct optical and electronic properties were selected to construct a diverse sensor array: fluorenephenylene-based PFP, phenylenevinylene-based PPV, BODIPY-based PBF, thiophene-based PMNT, and phenylenevinylene-based oligomer OPV. These polymers bind to microbial surfaces through hydrophobic and electrostatic interactions, producing polymer-specific signal changes upon target recognition. The incorporation of silver nanoparticles regulates the interaction-induced responses by modulating local plasmonic effects, leading to changes in both the fluorescence and colorimetric signals. The resulting complex signals were then analyzed by using elastic net regression to distinguish different microbial samples and classify unknown ones. This dual-signal system supports rapid and high-throughput analysis, providing a reliable and straightforward strategy for microbial identification and improving the diagnostic efficiency.

PMID:40729613 | DOI:10.1021/acsami.5c08450