Thyroid dysfunction and diabetes are reported to be associated with depression. However, their role in the suicide risk in patients with major depressive disorder (MDD) is unclear. The purpose of this study was to investigate and compare thyroid dysfunction and diabetes between suicide attempters and non-suicide attempters in a large sample of first-episode drug-naïve (FEND) MDD patients. A descriptive study was conducted on 1279 Chinese outpatients with a diagnosis of first-episode MDD. Their sociodemographic information, blood levels of thyroid hormones, glucose, lipids and body mass index (BMI) parameters were collected. The positive subscales of the positive and negative syndrome scale (PANSS), Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD) were measured for psychotic, anxiety and depressive symptoms. Our results showed that compared with non-suicide attempters (P<0.01), suicide attempters had statistically higher scores on HAMD, HAMA and PANSS psychotic symptoms, as well as higher thyroid stimulating hormone (TSH) serum levels, glucose, anti-thyroglobulin (A-TG), anti-thyroid peroxidase (A-TPO), total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), systolic blood pressure and diastolic blood pressure (all with P<0.001). Further logistic regression analysis indicated that suicide attempts were associated with MDD. These results revealed that TSH, A-TG, A-TPO, TC, TG and LDL-C may be promising biomarkers of suicide risk in MDD, implying the importance of regular assessment of blood glucose level and thyroid function parameters for suicide prevention, along with possible treatment for impaired thyroid function and diabetes for the suicide intervention in MDD patients. Such patients with abnormal blood sugar and TSH must undergo thorough screening for suicidal ideation.

Background: Digital health technologies are increasingly applied in diabetes care, enabling continuous monitoring, personalized support and remote interventions. Meanwhile, artificial intelligence (AI) is enhancing the precision and effectiveness of these tools. This study aims to map global research trends and thematic developments in AI-driven digital health technologies for diabetes management and to explore their future directions. Methods: We collected data from the Web of Science Core Collection, including articles and reviews published up to July 12, 2025, using CiteSpace, VOSviewer, and Microsoft Excel to analyze countries/regions, institutions, journals, references, authors, and keywords. Results: A total of 673 publications were included in the analysis. Global publications on AI-driven digital health technologies for diabetes increased steadily, with the USA leading in output. The University of London ranked as the most productive institution. Sensors and diabetes care were the most frequently published and cited journals in this field. Herrero P was among the most prolific authors. The most cited article was “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.” “diabetes” was the most frequently occurring keyword. Keyword cluster analysis identified 3 primary research hotspots: AI-enabled monitoring, digital health interventions, and AI-based diabetic retinopathy screening. Conclusions: This study summarizes the evolution of AI-driven digital health technologies in diabetes care. Although challenges remain in data security, standardization and validation, these technologies hold increasing potential for accurate diagnosis, real-time monitoring and personalized care.

Objective: Digital health technologies are increasingly used in chronic disease management, but their effective use requires adequate digital health literacy (DHL). Because an overall DHL score may mask heterogeneity across its multiple dimensions, this study aimed to identify latent profiles of DHL among young and middle-aged adults with diabetes and to examine factors associated with profile membership, thereby informing individualized health education and stratified health promotion strategies for diabetes management. Methods: Guided by the health ecology model, a cross-sectional study was conducted among 504 hospitalized patients with diabetes aged 18–59 years from two tertiary hospitals in Anhui Province, China, between June 2025 and March 2026. Data were collected using a general information questionnaire, the Digital Health Literacy Instrument, the Diabetes Self-Management Questionnaire, the Technophobia Scale, and the Social Support Rating Scale. Latent profile analysis (LPA) was used to identify DHL profiles. LASSO regression was applied to screen candidate variables, and multinomial logistic regression was used to examine factors associated with profile membership. Results: The mean DHLI score was 2.02 ± 0.61. LPA identified four profiles: limited DHL profile, basic application-low navigation profile, operational strength-limited critical appraisal profile, and high-level comprehensive DHL profile. Multinomial logistic regression showed that age, educational level, residence, complications, mobile health tool use, daily internet use, online health information seeking, diversity of information sources, technophobia, and social support were significantly associated with DHL profile membership ( p < 0.05). Conclusion: DHL among young and middle-aged adults with diabetes showed distinct profile characteristics in information access, appraisal, interaction, and privacy protection. These findings provide a basis for individualized health education and stratified health promotion strategies based on patients’ DHL profiles in diabetes management.

Background eHealth is emerging as a promising intervention for Parkinson's disease (PD) , yet there is currently no consensus on their impact on improving clinical management or on the most effective electronic health technology. Thus, we will conduct a systematic review and network meta-analysis to evaluate the most effective of various eHealth interventions in PD. Methods We will execute an expansive search across multiple databases, involving Cochrane Library, PubMed, Ovid, CINAHL, Scoups, Web of Science and Embase. This study will center on eHealth interventions related to PD and we will identify randomized controlled trials published in the English language that from the inception of these databases until 16 March 2025. Following initial title and abstract screening, key study elements including participates, interventions and outcomes will be gingerly extracted using eligible criteria. Then, quality of the literature will be evaluated using the Cochrane risk-of-bias tool. Direct comparisons between interventions will be analyzed through pairwise meta-analysis in STATA, with subsequent network meta-analysis employing this platform to establish symptom alleviation efficacy hierarchies among eHealth methods. Discussion This study will address a critical gap in Parkinson’s disease (PD) management by comprehensively evaluating the comparative effectiveness of eHealth interventions for delivering cognitive and motor function therapies, thereby advancing evidence-based strategies to optimize remote care for patients with this neurodegenerative condition. Systematic review registration The protocol of this systematic review has been registered in PROSPERO with the registration number CRD420250655984. Available at: https://www.crd.york.ac.uk/prospero/#searchadvanced.