The rising prevalence of chronic diseases demands a more effective healthcare management approach to overcome the constraints of existing health monitoring devices, including user intrusiveness and fragmented data. This perspective proposes the peri-body perception space, as a human-centric framework for chronic disease management. Conceived as a human-centric intelligent environment, it enables continuous, non-intrusive acquisition of multi-dimensional physiological and behavioral data by integrating contact-based and non-contact sensing. The framework features four characteristics: boundary plasticity, multi-sensory integration, functional adaptability, and social extensibility. For instance, when a patient moves from hospital to home, the system supports boundary plasticity through seamless transition from hospital-grade to home-based devices. It achieves multi-sensory integration through joint vital sign tracking by wearable and ambient sensors, enables functional adaptability by dynamically adjusting alert thresholds based on the patient’s personal baseline, and demonstrates social extensibility by sharing data across care settings. This framework directly serves the three goals of predictive, preventive and personalized medicine (PPPM). It turns these concepts into practical actions through continuous non-intrusive monitoring and adaptive data analysis. Built on a closed-loop system, this framework starts with human-centered, non-intrusive continuous health monitoring and leverages advanced signal processing and artificial intelligence to enable a transition from multi-modal data fusion to early risk warning. We also critically examine the existing challenges in hardware-software integration, multi-modal data fusion, and human-machine interaction within the peri-body perception space. Looking ahead, the integration of flexible sensing devices and cross-scenario privacy protection, adaptive modeling and intervention simulation, and human-machine interaction and personalized feedback will enable more autonomous, precise, and context-aware chronic disease management, marking a significant step forward in healthcare.

This paper reviewed the epidemiology,comorbidity mechanisms,risk factors, prediction models,and prevention and control strategies for cardiovascular and cerebrovascular comorbidity in cancer survivors.The aim is to provide insights for the co⁃management,co⁃prevention,and co⁃treatment of these comorbidity in cancer survivors,thereby supporting the enhancement of chronic disease prevention and control effectiveness under the Healthy China strategy.

Background:Despite extraordinary progress in global tobacco control and prevention, smoking, secondhand smoke (SHS) and chewing tobacco continue to profoundly impact public health, affecting males and females differently. Existing studies have not systematically characterized sex disparities in disease burden attributable to these three tobacco risks within a unified framework, nor have they quantified the contributions of demographic and exposure-related drivers to these disparities or projected their future trajectories. We aimed to assess patterns of sex disparity in smoking-, SHS-, and chewing tobacco-attributable disease burden from 1990 to 2023, decompose the drivers of these disparities, and project sex-specific burden to 2050 under alternative scenarios. Methods:We used data from the Global Burden of Disease Study (GBD) 2023, including sex-specific estimates of disability-adjusted life years (DALYs) and the age-standardized DALY rates (ASDR) attributable to smoking, SHS, and chewing tobacco. Sex disparities were examined across geographic, temporal, and age dimensions. Decomposition analysis was applied to quantify the contributions of population growth, population aging, change in risk exposure, and change in risk-deleted burden to sex-specific DALY changes between 1990 and 2023. Sex ratios were derived from ASDR and their 95% confidence intervals (CI) were estimated using the delta method. Future sex disparities were projected to 2050 under four counterfactual tobacco exposure scenarios. Findings:Over the past three decades, the tobacco-attributable disease burden among males has consistently exceeded that among females. In 2023, the overall smoking-attributable ASDR remained substantially higher in males than in females (3146.9 [95% uncertainty interval 2591.8-3703.8] vs. 507.8 [361.0-677.1] per 100,000). The all-cause ASDR attributable to SHS was slightly higher in males than in females (540.7 [434.3-672.9] vs. 506.6 [402.8-612.5] per 100,000). For chewing tobacco, the attributable burden was also markedly greater in males (95.3 [61.3-144.1] vs. 47.8 [25.4-78.5] per 100,000). Decomposition analysis showed that population growth and aging were the predominant drivers of increasing absolute DALYs in both sexes, with consistently larger effects among males, while change in risk exposure contributed differently to sex-specific burden trajectories across the three tobacco risks. Among GBD regions, the sex ratio of smoking-attributable ASDR of all causes was notably lowest in Australasia and high-income North America. The sex ratio of SHS-attributable all-cause ASDR increased from below 1.0 in 1990 to above 1.0 in 2023 across several GBD regions like high-income Asia Pacific and Andean Latin America. Globally, the sex ratio of overall ASDR attributable to chewing tobacco has remained stable, from 1990 (ratio: 1.9 95% CI 1.0-4.0) to 2023 (ratio: 2.0 95% CI 1.0-4.0). Among age groups, smoking-attributable DALY rates increased with age, peaking in the 75+ group in 2023, and the burden among males always exceeded that among females in all age brackets. Compared to the sex ratios of DALY rates attributable to smoking risk in the same regions, the fluctuations in disease burden attributable to SHS and chewing tobacco between sexes across age groups were less pronounced in 2023. Projections suggested that sex disparities would persist to 2050, with alternative tobacco control scenarios yielding divergent patterns of future burden. Interpretation:While smoking- and SHS-attributable ASDRs have declined and chewing tobacco-attributable ASDR has remained stable, sex disparities have varied by time, region, and life stage. These findings may inform the integration of sex-specific perspectives into tobacco control strategies, which could strengthen the implementation of the Framework Convention on Tobacco Control and help mitigate the future burden of tobacco use. Funding:National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project.

Health assessment instruments are essential for individual health monitoring, yet most existing tools rely on periodic surveys and lack the capacity to integrate large-scale digital health data. With the increasing availability of regional health big data, there is a need to establish comprehensive indicator resources that can inform the development of validated instruments tailored to local health contexts. This study aimed to develop and validate a health assessment item bank based on regional health big data, providing a structured foundation for subsequent instrument development. A sequential mixed-methods design was adopted. First, semi-structured interviews were conducted with experts from diverse health-related disciplines to identify key health categories and subcategories. A preliminary conceptual framework was produced through a directed qualitative content analysis process. Second, data elements were extracted from the national Regional Health Information Platform Interaction Standard (WS/T 790) of China and related standards. These data elements were vectorized using a Chinese pre-trained RoBERTa model, clustered with the DBSCAN algorithm, and matched to the conceptual framework through cosine similarity. Finally, experts reviewed and rated the semantic matching results by scoring the matching relevance from 1 to 5, agreement among experts were evaluated with the Fleiss’ Kappa analysis, and consensus discussions were conducted to refine the item pool and ensure content validity. The resulting item bank comprised 430 indicators distributed across five main categories and 17 subcategories. The main categories are Physiological health, Psychological health, Health behaviors, Social health, and Environment and healthcare services. Expert review yielded high agreement (mean score = 4.95/5.00; Fleiss’ Kappa = 0.626), supporting the adequacy of content validity. The Social support and Health service subcategories contained no mapped indicator, highlighting areas requiring integration of additional data sources. This study established a comprehensive and validated item bank for health assessment based on regional health big data, offering a structured foundation for future development, calibration, and psychometric testing of health assessment instruments. This work contributes to advancing data-driven, context-specific approaches for monitoring health at the individual level.

Cardiometabolic diseases are characterized by long disease courses, substantial inter-individual heterogeneity, and complex interactions among multiple risk factors, posing significant challenges to long-term management and precision interventions. Digital twin technology, which constructs individualized digital representations of patients, has demonstrated unique potential in precision disease management and clinical decision support. However, there remains a gap in the systematic synthesis of its applications in cardiometabolic disease management. This scoping review aimed to systematically outline the core application scenarios of digital twin technology in cardiometabolic disease management, summarize the integrated data sources and key technical approaches used to construct and operate digital twins, identify current technical, ethical, and clinical challenges, and outline future research directions. A scoping review was conducted on November 29, 2025. Relevant studies on the application of digital twin technology in cardiometabolic disease management were systematically searched, screened, and selected. Data were extracted and synthesized with a focus on application scenarios, data sources, modeling approaches, implementation stages, and reported challenges. Of the 5596 articles identified, 32 met inclusion criteria after screening. Digital twin applications in cardiometabolic diseases primarily focused on individualized risk prediction and stratification, optimization of treatment and intervention strategies, and clinical decision support. Integrated data sources mainly included physiological and metabolic data, electronic data of clinical documents, medical imaging, and behavioral and lifestyle-related data. Data-driven models were the most commonly used approach, followed by mechanistic models, hybrid models, multi-scale or multi-physics models, and simulation-based optimization methods. Most studies remained at the proof-of-concept or early implementation stage. Major challenges included limited data availability and integration, difficulties in model personalization and validation, ethical and privacy concerns, and insufficient integration into clinical and care workflows. Digital twin technology shows significant promise for precision management of cardiometabolic diseases. Existing studies indicate that digital twins are primarily applied in individualized risk assessment, optimization of treatment strategies, disease progression simulation, and long-term management support. However, prevailing research remains predominantly data-driven and is not yet fully aligned with sustained disease management and routine care processes. Future efforts should emphasize mechanistic and hybrid modeling approaches, conduct multicenter prospective validations and randomized controlled trials, and promote interoperable and privacy-preserving system deployment to support the sustainable implementation of digital twins in cardiometabolic disease management.

Individual health monitoring and evaluation instruments help reflect specific health status, enhance people’s acceptance and adherence to treatments, and reduce disease burdens. However, measurement instruments for individual health have not been adequately explored in community settings. This study is to scope the existing evidence with two aims: (1) to identify the individual health measurement instruments for the general population, (2) to synthesize thematic categories that are used to construct the instruments and the way that these instruments are combined in actual usage. This scoping review followed the Joanna Briggs Institute methodological framework and adhered to the PRISMA-ScR reporting guideline. Seven electronic databases (PubMed, Embase, Web of Science, Cochrane Library, Scopus, CNKI, and WanFang) were searched from their inception to 15 November 2025. Studies were included if they involved adults from the general population, applied individual health monitoring or evaluation instruments in community or home-based settings, and were peer-reviewed primary or secondary research published in English or Chinese. Studies targeting disease-specific populations or clinical therapeutic interventions were excluded. A double screening process of titles, abstracts, and full text was applied. The search initially identified 51,269 records, of which 257 studies met the eligibility criteria, yielding 310 individual health measurement instruments. Among these instruments, 53 were related to Health / Quality of Life, 158 focused on Physical Health, 47 on Mental Health, 21 on Health Behaviors, 16 on Healthcare Environment, and 15 on Social Health. The instruments were further categorized as Generic instruments, Mixed instruments, Self-developed instruments, and Specific Indicators, according to how they were used in the studies to generate the final health outcomes. This scoping review maps individual-level health measurement instruments used in community settings, synthesizing six health domains and multiple instrument types. These findings highlight priorities for developing more balanced and policy-relevant systems for individual health monitoring. A shift from generic to mixed instruments is found, with physical indicators dominating while mental, behavioral, environmental, and social dimensions remain underrepresented. Emerging digital data offer opportunities for more continuous assessment, but challenges in interoperability, privacy, and equity need to be addressed before further progress can be made. 10.17605/OSF.IO/4PS5C (OSF), 4 October 2023.

Prediabetes is the earliest identifiable stage of glycemic dysregulation, and its progression can be delayed by effective control of risk factors. Currently, various risk factors for the progression from prediabetes to type 2 diabetes mellitus (T2DM) need to be further summarized. This systematic evaluation of the risk factors for the progression of prediabetes to type 2 diabetes mellitus provides a theoretical basis for early recognition and intervention. The meta-analysis identifies the Fatty Liver Index as a significant risk factor [OR = 6.14, 95

Aims: This study aimed to develop and validate machine learning-based risk prediction models for ischemic stroke-diabetes mellitus (IS-DM) comorbidity using routinely available clinical data, and to compare the performance of traditional logistic regression with backpropagation neural networks (BPNN). ​ Methods Health records of 16,406 community-dwelling adults from Beijing, China, we analyzed. From 41 initial candidate predictors across five categories, seven optimal predictors were selected through ​univariate analysis​ followed by ​multivariate analysis. The dataset was randomly split into training (70%) and validation (30%) sets. We developed prediction models using both logistic regression and BPNN approaches, with model performance evaluated through confusion matrix, AUC, and 10-fold cross-validation. ​ Results The single-hidden-layer BPNN model with three hidden nodes demonstrated superior predictive performance, achieving an AUC of 0.921 (95% CI: 0.92-0.93), outperforming logistic regression. Key predictors included age, marital status, fasting glucose, HbA1c, systolic blood pressure, serum creatinine, and serum sodium. However, the BPNN required significantly more computational resources. ​ Conclusion Machine learning approaches, particularly BPNN, can effectively predict IS-DM comorbidity risk using routine clinical parameters. These models could enhance early comorbidity detection in community settings and inform targeted prevention strategies. Despite it predictive efficacy, the computational demands of BPNN should be considered for clinical implementation.

As global populations become increasingly aged, existing elderly care models are proving insufficient. The development and application of nursing robots have shown potential in addressing the challenges of elder care in aging societies. This perspective outlines current state and potential applications of nursing robots in promoting healthy aging. Given this background, a networked intelligent elderly care model for nursing robots, which integrates technologies such as big data, artificial intelligence, the Internet of Things, and nursing robotics, is proposed. This model would synergistically combine elderly health monitoring, capability assessment, and intelligent allocation functions to revolutionize global elderly care practices and promote healthy aging.

Background Nurses have been at the forefront of the battle against the COVID-19 pandemic, facing extended work hours and heightened stress, predisposing them to psychological distress. This study aims to investigate the prevalence and correlates of severe anxiety among frontline nurses in China during and after the COVID-19 pandemic. Methods A large-scale multi-center survey was conducted from November to December 2022 and from April to July 2023. Data were collected using online surveys, covering demographic characteristics, job-related factors, anxiety, depression, and sleep disorders. Statistical analyses, including chi-square tests, t-tests, and logistic regression, were performed to assess the incidence and factors influencing severe anxiety. Results The study included 816 nurses during the pandemic and 763 nurses after the pandemic. The prevalence of severe anxiety during the pandemic (52.3%) was significantly higher than after the pandemic (8.0%). Factors such as nursing title, night shift frequency, educational level, exercise frequency, COVID-19 infection status, economic pressure, and work pressure showed significant differences between the two periods. Binary logistic regression revealed associations between severe anxiety and factors such as night shift frequency, COVID-19 infection status, nursing title, depression, and sleep disorders. Receiver Operating Characteristic analysis demonstrated good predictive value for severe anxiety. Conclusion The study underscores the importance of understanding and addressing severe anxiety among frontline nurses during and after the COVID-19 pandemic. Future research should delve into long-term psychological effects and implement effective intervention measures to support nurses' mental health.

Precision management of chronic diseases is crucial for improving patient quality of life and alleviating global health burdens. Advancements at the intersection of medicine and engineering, particularly through artificial intelligence (AI), have driven significant progress in precision care. From the perspective of the full life span management of chronic diseases, we focus on medicine-engineering crossover for monitoring chronic diseases, developing and implementing precision care plans, and evaluating care outcomes. Through an in-depth discussion, we address key issues such as AI's potential to enable precision care and the challenges associated with its implementation, including data accuracy, privacy concerns, and clinical adoption. Emphasizing the importance of nurses embracing new technologies and interdisciplinary collaboration, this paper highlights how technological innovation can improve chronic disease management, particularly by enhancing care efficiency and personalizing health interventions. We aim to support the development of integrated healthcare solutions that improve patient outcomes in chronic disease management.

Objectives This study aimed to develop and validate a stroke risk prediction model based on machine learning (ML) and regional healthcare big data, and determine whether it may improve the prediction performance compared with the conventional Logistic Regression (LR) model. Methods This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou (CHERRY) (2015–2021). We included adults aged 18–75 from the platform who had established records before 2015. Individuals with pre-existing stroke, key data absence, or excessive missingness (>30 %) were excluded. Data on demographic, clinical measures, lifestyle factors, comorbidities, and family history of stroke were collected. Variable selection was performed in two stages: an initial screening via univariate analysis, followed by a prioritization of variables based on clinical relevance and actionability, with a focus on those that are modifiable. Stroke prediction models were developed using LR and four ML algorithms: Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Back Propagation Neural Network (BPNN). The dataset was split 7:3 for training and validation sets. Performance was assessed using receiver operating characteristic (ROC) curves, calibration, and confusion matrices, and the cutoff value was determined by Youden’s index to classify risk groups. Results The study cohort comprised 92,172 participants with 436 incident stroke cases (incidence rate: 474/100,000 person-years). Ultimately, 13 predictor variables were included. RF achieved the highest accuracy (0.935), precision (0.923), sensitivity (recall: 0.947), and F1 score (0.935). Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR, with training/validation area under the curve (AUC)s of 0.777/0.779 (LR), 0.921/0.918 (BPNN), 0.988/0.980 (RF), 0.980/0.955 (DT), and 0.962/0.958 (XGBoost). Calibration analysis revealed a better fit for DT, LR and BPNN compared to RF and XGBoost model. Based on the optimal performance of the RF model, the ranking of factors in descending order of importance was: hypertension, age, diabetes, systolic blood pressure, waist, high-density lipoprotein Cholesterol, fasting blood glucose, physical activity, BMI, low-density lipoprotein cholesterol, total cholesterol, dietary habits, and family history of stroke. Using Youden’s index as the optimal cutoff, the RF model stratified individuals into high-risk (>0.789) and low-risk (≤0.789) groups with robust discrimination. Conclusions The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model, providing an effective tool for risk stratification in primary stroke prevention in community settings.

With the development of digital technologies, advancements in remote technologies, virtual reality, and large language models are fostering revolutionary progress in nursing education. In this paper, we review how digital technologies can enable nursing education to reach underserved regions and populations and how nursing education can delve into the internal spaces of the human body that are not visible to the naked eye. Digital technologies can enhance both the efficiency of instructors in classroom teaching and student management and promote greater student independence. The promotion of digital technologies in the field of nursing education needs to address challenges related to fairness, privacy, and ethics. A review of these digital technologies can help better understand and promote the future development of nursing education.

To clarify the concept of health from a health promotion perspective that promotes positive health outcomes. Rodgers’ evolutionary concept analysis. Engaging in an extensive and meticulous review of scholarly literature, we delved into the repositories of China National Knowledge Infrastructure, Wanfang Database, PubMed, Web of Science, and Embase databases, spanning the period from January 2000 to February 2024. A total of 62 papers were analyzed, revealing 6 core attributes essential to understand the status of health: complexity, multidimensionality, dynamism, continuity, objectivity, and subjectivity. These attributes are further elucidated across 5 dimensions: physical, mental, social, moral and environmental health, with each bearing significant implications. The antecedents of health are shaped by medical models, disease spectra, and cultural contexts, collectively influencing individuals, healthcare systems, and social environment. The analysis of the concept of health provides comprehensive understanding of the fundamental attributes, thereby establishing a solid theoretical foundation for healthcare professionals. Incorporating the perspective of health promotion can further clarify the connotation of health, providing practical “health standards” for medical and health practice. This approach can guide future nursing practice to focus on improving patients’ coping ability, maintenance ability, and rehabilitation ability.

BackgroundEarly-onset type 2 diabetes (T2DM), diagnosed before age 40, progresses rapidly and has a higher risk of complications compared to late-onset T2DM. Its global prevalence is rising, but the underlying risk factors are insufficiently understood.ObjectiveThis systematic review and meta-analysis aimed to evaluate risk factors associated with early-onset T2DM to support clinical decision-making and inform preventive strategies.Methods37 studies (cohort, case-control, cross-sectional) were identified from PubMed, Web of Science, and Embase up to October 31, 2024. Data were analyzed using STATA 17.0, and pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Subgroup analyses were performed by region, study type, and sample size.ResultsCompared with normoglycemic individuals, early-onset T2DM was strongly associated with family history of diabetes (OR = 4.54, 95%CI: 2.31-8.90), high BMI (OR = 2.87, 95%CI: 2.22-3.80), maternal gestational diabetes (OR = 3.01, 95%CI: 2.44-3.72), and elevated fasting glucose (OR = 8.73, 95%CI: 4.91-16.92). Subgroup and sensitivity analyses confirmed the robustness of these findings despite persistent heterogeneity. In comparisons with late-onset T2DM, family history (OR = 2.90), male sex (OR = 1.57), and BMI (OR = 1.12 per unit) remained significant risk factors.ConclusionEarly-onset T2DM is shaped by familial, metabolic, and lifestyle determinants. Incorporating these factors into early screening and intervention programs, particularly lifestyle modification in young high-risk populations, is essential to reduce disease burden and delay progression.

Background Chronic conditions often co-occur in specific disease patterns. Certain chronic diseases contribute to incident frailty or cognitive impairment (CI), but the associations of multimorbidity patterns and the order of frailty and CI occurrence remain unclear. Objectives To determine multimorbidity patterns amongst older adults and their associations with the order of frailty and CI occurrence. Design Prospective cohort study. Methods Using data from National Health and Aging Trends Study, 7522 community-dwelling participants were included and followed up for four years. Latent class analysis was conducted to identify multimorbidity patterns with clinical meaningfulness. Fine and Grey competing risks models were used to examine the associations between multimorbidity patterns and different orders of frailty and CI occurrence (frailty-first, CI-first, frailty-CI co-occurrence). Results Four multimorbidity patterns were identified: cardiometabolic, osteoarticular, cancer-dominated and psychiatric/multisystem pattern. Compared to non-multimorbidity, all four multimorbidity patterns were associated with a higher risk of developing frailty-first, but not developing CI-first. Specifically, the psychiatric/multisystem pattern had the highest risk of developing frailty-first ( Sub-distribution hazard ratios [SHR] = 3.74, 95% confidence intervals = 2.96, 4.71), followed by osteoarticular pattern (SHR = 2.53, 95% CI = 1.98, 3.22) and cardiometabolic pattern (SHR =2.41, 95% confidence intervals = 1.96, 2.98). In addition, only participants from psychiatric/multisystem and cardiometabolic pattern showed a higher risk of frailty-CI co-occurrence. Conclusions Our findings highlight the etiological heterogeneity between physical frailty and CI. Clinician should be aware of multimorbidity clusters and thus provide more effective strategies for comorbid older adults to prevent the onset of these two geriatric syndromes.

ObjectiveTo construct a transformation risk prediction model for young and middle⁃aged prediabetic patients based on the data from the health information platform of Yinzhou district of Ningbo city Zhejiang province.MethodsA retrospective cohort study design was adopted.Combined with literature review and expert recommendations,24 predictors were included.LASSO regression was applied for variable screening.The prediction model was constructed by using Logistic regression and four machine learning algorithms.ResultsA total of 7 379 patients were included,among whom 731 cases(9.91%) transformed into type 2 diabetes.The random forest model performed relatively well.It had a good discriminatory ability.However,its accuracy was moderate.ConclusionsThe transformation risk prediction model for young and middle⁃aged prediabetic patients based on random forests has good discriminatory ability.It could provide a reference for optimizing the precise management of young and middle⁃aged prediabetic patients.

Objective To systematically review the current status and influencing factors of psychological resilience in stroke patients and to provide a theoretical basis for future personalized rehabilitation support and psychological interventions.Method This systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. A comprehensive search of databases including PubMed, Web of Science, Medline, PsycINFO, CINAHL, Cochrane Library, CNKI, VIP, CMB, and WANGFANG was conducted from inception until November 22, 2023, resulting in the retrieval of 2099 studies. Literature screening and data extraction were performed by two independent evaluators based on pre-defined inclusion and exclusion criteria, and meta-analysis was performed using Review Manager 5.4 software.Results The final review included 23 studies. The results showed that self-efficacy, hope, confrontation coping, avoidance coping, functional independence, quality of life, and social support were positively associated with psychological resilience. Conversely, anxiety, depression, and resignation coping were negatively associated with psychological resilience.Conclusions Patients with stroke have a low level of psychological resilience, which was influenced by a variety of factors. However, longitudinal and large sample studies are needed to further confirm these findings. These results should be integrated into clinical practice for early assessment and targeted intervention in psychological resilience to assist patients in coping with the rehabilitation process and life changes after a stroke.

Purpose Vitamins and polyunsaturated fatty acids (PUFAs) have been studied extensively as safe and manageable nutrient interventions for mild cognitive impairment (MCI). The purpose of the current meta-analysis was to examine the effects of vitamins and PUFAs on cognition and to compare the effects of single and multiple nutrient subgroups in patients with MCI. Methods Randomized controlled trials (RCTs) written in English and Chinese were retrieved from eight databases, namely, PubMed, CENTRAL, Embase, CINAHL, Web of Science, SinoMed, CNKI, and Wanfang Data, from their respective dates of inception until 16 July 2023. The quality of the included studies was assessed using the Cochrane Risk of Bias Tool 2.0. Meta-analyses were performed to determine the standardized mean differences (SMDs) in global cognitive function, memory function, attention, visuospatial skills, executive function, and processing speed between the supplement and control groups using 95% confidence intervals (CI) and I 2 . Prospero registration number: CRD42021292360. Results Sixteen RCTs that studied different types of vitamins and PUFAs were included. The meta-analysis revealed that vitamins affected global cognitive function (SMD = 0.58, 95% CI = [0.20, 0.96], P = 0.003), memory function (SMD = 2.55, 95% CI = [1.01, 4.09], P = 0.001), and attention (SMD = 3.14, 95% CI = [1.00, 5.28], P = 0.004) in patients with MCI, and PUFAs showed effects on memory function (SMD = 0.65, 95% CI = [0.32, 0.99], P < 0.001) and attention (SMD = 2.98, 95% CI = [2.11, 3.84], P < 0.001). Single vitamin B (folic acid [FA]: SMD = 1.21, 95% CI = [0.87, 1.55]) supplementation may be more effective than multiple nutrients (FA and vitamin B12: SMD = 0.71, 95% CI = [0.41, 1.01]; and FA combined with docosahexaenoic acid [DHA]: SMD = 0.58, 95% CI = [0.34, 0.83]) in global cognitive function. Conclusions FA, vitamin B6, vitamin B12, and vitamin D may improve global cognitive function, memory function, and attention in patients with MCI. Eicosapentaenoic acid (EPA) and DHA may improve memory function and attention. We also noted that FA may exert a greater effect than a vitamin B combination (FA and vitamin B12) or the combination of FA and DHA. However, because of the low evidence-based intensity, further trials are necessary to confirm these findings.

This study investigated gender differences in health-risk behaviour patterns among young adults and assessed the associations of anxiety and depression with these patterns. A cross-sectional survey was conducted with 1740 young Chinese adults aged 18-24 years. Latent class analysis (LCA) and multinomial logistic regression were conducted to identify the clusters of health-risk behaviours and their associations with anxiety and depression. Three common patterns were found for both genders: physical inactivity, substance use, and insufficient fruit intake (5.7% for males [M] and 11.6% for females [F]); a sedentary lifestyle only (48.4% for M and 48.9% for F); and a sedentary lifestyle, substance use, and an unhealthy diet (7.6% for M and 20.0% for F). Additionally, two additional unique patterns were found: physical inactivity and unhealthy diet in males (38.3%) and physical inactivity and insufficient fruit intake in females (19.6%). Sociodemographic variables exert different effects on health-risk behaviour patterns as a function of gender. Lower anxiety levels (odds ratio [OR]: 0.892; 95% confidence interval [CI]: 0.823-0.966) and greater depression levels (OR: 1.074; 95% CI: 1.008-1.143) were associated with a sedentary lifestyle, substance use, and unhealthy diet class only in female young adults compared with a sedentary-only class. These findings underscore the need for the implementation of targeted interventions based on gender differences.