Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach

Area of Science:

• Integrative medicine
• Biostatistics
• Preventive medicine

Background:

• Subhealth represents a critical transitional phase between optimal health and diagnosed disease.
• Quantifying subhealth manifestations and addressing the complexity of physical examination data pose significant challenges to effective intervention.
• Routine physical check-ups offer a window for detecting subhealth, necessitating systematic investigation of associated indicators.

Purpose of the Study:

• To systematically investigate physical examination indicators in the subhealthy population.
• To explore the interrelationships between various health indicators in individuals experiencing subhealth.
• To develop and validate a structural model for understanding subhealth indicators and their causal pathways.

Main Methods:

• Collected data from 878 subjects, encompassing basic information, Western medicine indicators, Traditional Chinese Medicine (TCM) inquiries, and sublingual vein (SV) characteristics.
• Employed statistical analyses using R software for comparative assessments.
• Utilized partial least squares-structural equation modelling (PLS-SEM) to analyze symptom distribution, identify symptom clusters, and establish a structural model of indicator relationships.

Main Results:

• Common subclinical symptoms included limb soreness (37.6%), fatigue (31.6%), shoulder and neck pain (30.5%), and dry eyes (29.2%).
• The subhealthy group exhibited paler sublingual vein (SV) redness compared to the healthy group (p < 0.001).
• Validated relationship pathways: routine blood tests to lipid metabolism (p < 0.001), lipid metabolism to obesity (p < 0.001), obesity to SV characteristics (p = 0.025), and liver function to SV characteristics (p = 0.027).

Conclusions:

• The study successfully established a directed acyclic relationship chain among physical examination indicators in the subhealthy population.
• The application of PLS-SEM provides a novel framework for exploring subhealth mechanisms and causal inference.
• Identifying typical subhealth symptoms and their underlying indicator relationships supports early management strategies, potentially preventing disease progression.