RESUMEN
Background: The Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns. Methods: The cross-sectional study involved healthy Ghanaian participants (n = 217; aged 30-80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations. Results: We observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks (p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions. Conclusions: We have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage. Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00344-2.
RESUMEN
Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. The multivariate integrative framework, DIABLO was employed for the statistical analysis. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters were observed based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis [Formula: see text]. Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.
