From compounds to mechanism: unveiling the therapeutic action of Rudijinniuding on herpes zoster via integrated UPLC-Q/TOF-MS, network pharmacology, and molecular dynamics analyses.

With the rapid development of the social economy, global environmental changes, population aging, and the emergence of novel diseases, fatigue has emerged as a critical public health challenge under the influence of multiple interacting factors. A large-scale global study involving 623,624 participants revealed that 16.4% of the population currently experiences fatigue, with chronic fatigue being particularly detrimental, as it persists despite rest and leads to severe consequences. It reduces work efficiency by up to 75%, and exacerbates underlying diseases, including an increased risk of Hashimoto’s thyroiditis (10-20%) and immune dysfunction in ME/chronic fatigue syndrome patients (CFS) patients (25% showing deficiencies in total immunoglobulins or immunoglobulin subclasses, 15% lacking mannose-binding lectin, and 25% demonstrating polyclonal immunoglobulin proliferation), and is associated with elevates mortality risk (CFS patients show an all-cause standardized mortality ratio of 1.14; 95% CI = 0.65–1.85). Furthermore, it severely diminishes quality of life, leaving one-third of patients bedridden, and imposes substantial economic burdens, with annual costs averaging $20,000 per case. Occupational fatigue among specific groups, such as drivers (contributing to 10-40% of fatigue-related traffic accidents), firefighters (10–20% suffering duty-related injuries due to impaired balance) and healthcare workers (who faces a 2.3-fold higher risk of adverse events or medication errors when fatigued) poses severe threats to productivity and public safety, potentially resulting in irreversible loss of life and economic damage. Fatigue arises from complex interactions among environmental, behavioral, genetic, and disease-related factors, with existing research identifying multiple contributors, including circadian rhythm disruption (manifested through reduced light exposure, irregular activity patterns, delayed melatonin secretion, and disrupted body temperature rhythms), overcrowded spaces, environmental stressors (54.9% of CFS patients report light sensitivity that exacerbates fatigue, while 62.8% experience noise-induced fatigue aggravation), occupational pressures (77.3% of healthcare workers developed burnout during the COVID-19 pandemic), and sleep deprivation (sleep disruption and non-restorative sleep affect 95% of ME/CFS patients, with unstable non-rapid eye movement sleep being linked to fatigue symptoms). Genetic factors, including candidate genes such as , and , have also been associated with fatigue. While these findings provide a basis for intervention strategies, most existing research relies on cross-sectional surveys, retrospective studies, or qualitative interviews, which are limited by confounding factors and reverse causality. For example, it remains unclear whether cortisol reduction in CFS patients is a cause or consequence of the condition. Similarly, while indoor environmental factors like temperature, humidity, light, and noise are known to exacerbate fatigue in healthcare settings, their relative contributions remain undefined, which hinders precise interventions. These knowledge gaps underscore the need for Mendelian randomization (MR) analysis, an emerging causal inference method that leverages genetic variants as instrumental variables (IVs) to investigate the causal relationships between exposures (e.g., biomarkers, behaviors, or environmental factors) and health outcomes. By mimicking the logic of randomized controlled trials (RCTs), MR addresses challenges inherent in traditional observational studies. This investigation specifically aims to elucidate genetically validated causal relationships between modifiable behavioral preference factors and fatigue susceptibility using genetic variants as instrumental variables. Our MR analysis utilized GWAS summary statistics from the Neale Lab and MRC-IEU database, encompassing comprehensive environmental and behavioral- related measures: internal microenvironment (including pro-inflammatory factors [TNF-α, IL-1β, IL-6, IL-8], anti-inflammatory factors [IL-4, IL-10, TGFβ], immunoregulatory factors [IL-2, IFN-γ], stress markers [cortisol, C-reaction protein], chemokines, and metabolites [lactate, reactive oxygen species, glucose, ketones]); indoor environment (temperature, light, humidity, noise, and microbial exposures [bacteria, mold, and dust mites]); natural environment (climate zones [tropical, temperate, polar], weather patterns [precipitation, wind speed, UV index, atmospheric pressure], seasonal variations [photoperiod, diurnal temperature variation], and air quality [PM, PM, NO, SO, VOCs, CO concentration]). After screening, a total of 32 GWAS databases were included in our study (, Supplemental Digital Content 1). Through comprehensive evaluation of potential determinants, our MR study ultimately identified three key exposure-outcome pairs that consistently satisfied the core MR assumptions based on available stastistical tests, alth