Changes in the levels of IL-1β, cortisol and chromogranin A in saliva of subjects with occupational fatigue

Fatigue in Inflammatory Joint Diseases

Fatigue is a prevalent symptom in various rheumatic diseases, such as rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis. It is characterised as a subjective, enduring feeling of generalised tiredness or exhaustion, impacting the patient's life quality and exacerbating disability. The fatigue nature is multifaceted, encompassing physiological, psychological, and social factors, and although the exact cause of inflammatory joint diseases is not fully understood, several factors are believed to contribute to its development. Despite high prevalence and importance, the symptom is often underestimated in clinical practice. Chronic inflammation, commonly associated with rheumatic diseases, has been proposed as a potential contributor to fatigue development. While current treatments effectively target inflammation and reduce disease activity, fatigue remains a persistent problem. Clinical evaluation of rheumatic diseases primarily relies on objective criteria, whereas fatigue, being a subjective symptom, is solely experienced and reported by the patient. Managing fatigue in inflammatory joint diseases involves a multifaceted approach. Identifying and comprehensively assessing the subjective components of fatigue in individual patients is crucial for effectively managing this symptom in everyday clinical practice.

Salivary cortisol, dehydroepiandrosterone, and chromogranin A levels in patients with gingivitis and periodontitis and a novel biomarker for psychological stress

Introduction

This study aimed to investigate the neuroendocrine responses based on cortisol, dehydroepiandrosterone (DHEA), cortisol/DHEA ratio, and chromogranin A levels, which reflect the activity of the hypothalamic-pituitary-adrenal axis, according to the presence or absence of psychological stress in patients with gingivitis and periodontitis compared to that in healthy controls.

Methods

In total, 117 patients (60 women, mean age: 36.29 ± 19.03 years) participated in this case-control study, comprising 32 healthy controls, 49 patients with gingivitis, and 36 patients with periodontitis. We investigated the presence of psychological stress and salivary characteristics, and analyzed the stress-related biomarkers of cortisol, DHEA, cortisol/DHEA ratio, and chromogranin A in the stimulated saliva.

Results

Salivary cortisol and chromogranin A levels increased with the severity of periodontal disease; their levels were the highest in the periodontitis group and were significantly higher in the following descending order: periodontitis, gingivitis, and healthy control groups (all values of p < 0.001). Additionally, the DHEA levels and cortisol/DHEA ratio were higher in the periodontitis group than those in the healthy control group (all values of p < 0.001). A multivariate logistic regression analysis revealed that the factors predicting above-average cortisol levels were periodontitis (odds ratio [OR] = 256.829; p < 0.001), women (OR = 6.365; p = 0.004), and psychological stress (OR = 6.036; p = 0.007); those predicting above-average cortisol/DHEA ratios were periodontitis (OR = 11.436; p < 0.001), psychological stress (OR = 3.977; p = 0.003), and women (OR = 2.890; p = 0.026). Thus, periodontitis and psychological stress were significant and strong predictors of above-average cortisol levels and cortisol/DHEA ratios. In the gingivitis group, salivary cortisol levels (r = 0.381, p = 0.007) and cortisol/DHEA ratios (r = 0.479, p < 0.001) were correlated with the presence of psychological stress. In the periodontitis group, increased cortisol/DHEA ratios (r = 0.412, p = 0.013) and lowered salivary buffer capacities (r = -0.334, p = 0.047) were correlated with the presence of psychological stress.

Conclusion

Periodontitis is a multifactorial disease resulting in inflammatory tissue destruction, which differs from gingivitis and a healthy state. Differences in stress-related neuroendocrine markers were revealed based on the severity of periodontal disease. The biomarkers that could be classified according to disease severity were salivary cortisol and chromogranin A levels. Above-average cortisol levels and cortisol/DHEA ratios are significant predictors of psychological stress in patients with gingivitis and periodontitis.

Wearable biosensors for human fatigue diagnosis: A review

Fatigue causes deleterious effects to physical and mental health of human being and may cause loss of lives. Therefore, the adverse effects of fatigue on individuals and the society are massive. With the ever-increasing frequency of overtraining among modern military and sports personnel, timely, portable and accurate fatigue diagnosis is essential to avoid fatigue-induced accidents. However, traditional detection methods require complex sample preparation and blood sampling processes, which cannot meet the timeliness and portability of fatigue diagnosis. With the development of flexible materials and biosensing technology, wearable biosensors have attracted increased attention to the researchers. Wearable biosensors collect biomarkers from noninvasive biofluids, such as sweat, saliva, and tears, followed by biosensing with the help of biosensing modules continuously and quantitatively. The detection signal can then be transmitted through wireless communication modules that constitute a method for real-time understanding of abnormality. Recent developments of wearable biosensors are focused on miniaturized wearable electrochemistry and optical biosensors for metabolites detection, of which, few have exhibited satisfactory results in medical diagnosis. However, detection performance limits the wide-range applicability of wearable fatigue diagnosis. In this article, the application of wearable biosensors in fatigue diagnosis has been discussed. In fact, exploration of the composition of different biofluids and their potential toward fatigue diagnosis have been discussed here for the very first time. Moreover, discussions regarding the current bottlenecks in wearable fatigue biosensors and the latest advancements in biochemical reaction and data communication modules have been incorporated herein. Finally, the main challenges and opportunities were discussed for wearable fatigue diagnosis in the future.