Conceptualised psycho-medical footprint for health status outcomes and the potential impacts for early detection and prevention of chronic diseases in the context of 3P medicine

Abstract

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.

Keywords: Ghana; LASSO model; Network analysis; Predictive preventive personalised medicine (3PM); Suboptimal Health Status Questionnaire-25 (SHSQ-25); Synaptic structures.

Fig. 1

Schematic representation of the study design. The SHSQ-25 comprising of five health domains is a screening tool that can categorise individuals based on a median cut-off score. Individuals rate their health in the previous 3 months on a Likert scale, and their total SHS score is calculated. An SHS score lower and higher than the median cut-off value represents optimal or ideal and suboptimal health status, respectively

Fig. 2

Item by item response distribution for the optimal and suboptimal population cohorts

Fig. 3

Five-domain networks of SHSQ-25 for A) optimal population and B) suboptimal population with bootstrapped difference tests (α = 0.05) between edge weights in the estimated networks. Gray boxes indicate edges that do not differ significantly from one another, and black boxes represent edges that do differ significantly from one another. Blueish-shaded boxes on the main diagonal indicate positive connections between nodes; brownish-shaded boxes indicate negative connection, the darker the blue or brown the stronger the positive or negative correlation. The white shaded box indicates no connection between nodes

Fig. 4

Graphical LASSO network for A) optimal and B) suboptimal population cohorts, plotted with ordinal MDS configuration based on zero-order correlations for the 25 items of the SHSQ-25 questionnaire plotted as nodes. Green edges (i.e. connections) represent positive associations and red edges represent negative association. The thicker the connection, the stronger the association between nodes. Colour codes represent the 5 domains: FT Fatigue, MH Mental health, IS Immune system, DS Digestive system, CS Cardiovascular system

Fig. 5

Average correlations between centrality indices of A) optimal and B) suboptimal population networks sampled with samples dropped and the original sample. Bold lines indicate the means of the various centrality indices, and the areas indicate 95% confidence range. C) Centrality indices for the 25 items in the networks presented in Fig. 4, which are shown as standardised z-scores