Health‐Promoting Behaviour Patterns in Women With Gestational Diabetes Mellitus: A Latent Class Analysis and Association With Prenatal Depression

ABSTRACT

Background

Gestational diabetes mellitus (GDM) presents significant risks to both maternal and infant health, making adherence to health-promoting behaviours crucial for optimal maternal outcomes. Identifying distinct health behaviour patterns and understanding their association with prenatal depression can offer important insights for targeted interventions.

Objective

This study classifies health-promoting behaviour patterns among women with GDM and examines their association with prenatal depression to inform tailored interventions.

Design

A cross-sectional study with latent class analysis.

Method

A total of 570 women with GDM participated in this study. Data were collected through structured questionnaires, including the Health-Promoting Lifestyle Profile (HPLP) and the Edinburgh Postnatal Depression Scale (EPDS) to assess health-promoting behaviours and prenatal depression levels. Latent class analysis was used to identify distinct health-promoting behaviour patterns, while logistic regression was conducted to identify factors influencing behaviour classification. A comparative analysis of prenatal depression scores across different behaviour subgroups was also performed.

Result

Four distinct health-promoting behaviour patterns were identified: Comprehensive Health Promotion type, health neglect type, psychologically vulnerable type and lifestyle improvement needed type. Factors influencing behaviour patterns included region, education level, working hours, income, primiparity, adherence to prenatal check-ups and partner support. Significant differences in prenatal depression scores were observed across the behaviour patterns (p < 0.05).

Conclusion

This study reveals the heterogeneity in health-promoting behaviours among women with GDM and underscores the link between these behaviour patterns and prenatal depression. Targeted interventions addressing socio-economic and psychosocial factors can improve adherence to health-promoting behaviours and mitigate prenatal depression risks. Strengthening prenatal care adherence and encouraging partner involvement are effective strategies for improving maternal well-being in women with GDM.

Patient or Public Contribution

Participants were involved in providing data for this study through self-reports on health-promoting behaviours and prenatal depression. No other contributions from patients or the public were made.

Machine learning and single‐cell transcriptome profiling reveal regulation of fibroblast activation through THBS2/TGFβ1/P‐Smad2/3 signalling pathway in hypertrophic scar

Abstract

Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non-negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription-polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single-cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P-Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS.