Key result
Suboptimal health status exhibited a significantly different synaptic network structure compared to optimal health, with 43 versus 58 non-zero edges (p=0.024) and fatigue as the central condition.
Why the study?
Connections between the 25 symptomatic items of the SHSQ-25 and their relevance in monitoring suboptimal health outcomes have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine.
Cross-Sectional (n=217)
Absolute Event Rate: 43% vs 58%
p-value: p=0.024
Network analysis of the SHSQ-25 reveals distinct connectivity patterns between optimal and suboptimal health status, highlighting fatigue as a central subclinical condition in suboptimal health.
Fatigue centrality in suboptimal health warrants monitoring; cross-sectional data leaves open predictive value for cardiovascular outcomes.
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: = 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: = 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.
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Afrifa‐Yamoah et al. (2023) conducted a cross-sectional in Suboptimal Health Status (n=217). Suboptimal health status vs. Optimal health status was evaluated on Network connectivity levels (non-zero edges) (p=0.024). Suboptimal health status exhibited a significantly different synaptic network structure compared to optimal health, with 43 versus 58 non-zero edges (p=0.024) and fatigue as the central condition.
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