Evaluation of neopterin, oxidative stress, and immune system in silicosis

Introduction

Neopterin is considered an early biomarker of the cellular immune response. This low-molecular-mass compound belongs to the pteridine class and is a guanosine triphosphate metabolite produced by activated macrophages and dendritic cells after stimulation with interferon-gamma (IFN-γ) released from T-cells. Along with IFN-γ, tumor necrosis factor-alpha (TNF-α) also plays an important role in neopterin production [1]. It has been proposed that neopterin is not only an indication of oxidative stress arising from immunological activation but also contributes to oxidative stress by modifying the effects of reactive oxygen species (ROS) [2], 3]. An international group recognizes that serum neopterin levels can be used as an indicator of the impact of silica exposure and other occupational diseases [4].

Understanding the pathogenesis of silicosis is still incomplete, and there is limited data in humans [5]. In the early phase of inflammation in silica exposure, macrophages, mostly an M1 pro-inflammatory type, predominate. These macrophages secrete highly inflammatory cytokines such as TNF-α, interleukins (IL)-1α, IL-1β, IL-6, and ROS, which are necessary to eliminate silica particles. Since silica particles phagocytized by macrophages cannot be eliminated like infectious agents, they cause the continuation of inflammation and oxidative stress through persistent macrophage activation [6], [7], [8], [9]. Oxidative stress causes damage to the lungs, leading to an imbalance between oxidants (8-hydroxy-2-deoxyguanosine (8-OH-dG)) and antioxidants (glutathione reductase (GR), glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT)) in silicosis [10], [11], [12], [13]. In the late fibrosis stage of silicosis, M2 anti-inflammatory macrophages contribute to the resolution of inflammation, tissue repair, remodeling, immunosuppression, and fibrogenesis via anti-inflammatory cytokines [14], [15], [16]. Increased neopterin levels, closely associated with increased inflammation and oxidative stress, may lead to the onset of “immunodeficiency” with reduced T-cell response [17].

Clinical and functional changes in silicosis usually occur as the disease progresses. The identification of potential biomarkers for early detection of biological effects before these changes occur is crucial in terms of high-risk occupational exposures [18]. The objective of our study is to assess whether serum and urinary neopterin levels serve as biomarkers for the diagnosis and monitoring of silicosis. The study also aims to reveal the relationship between neopterin and the immune system (fibroblast growth factor (FGF), IFN-γ, TNF-α, IL-1β, and IL-1α) and oxidative stress (SOD, CAT, GPx, GR, and 8-OH-dG).

Materials and methods

Results

A total of 170 individuals with a mean age of 40.61 (±8.18, range: 18–72 years) were included in the study. Forty-seven were exposed to silica without silicosis (27.6 %), 53 (74.6 %) were diagnosed with simple silicosis, and 18 (25.4 %) were diagnosed with complicated silicosis. The demographic attributes of the study cohorts are outlined in Table 1. The percentages of forced expired volume (FEV1) and forced vital capacity (FVC) were markedly reduced in the silicosis group relative to the non-exposed group (p=0.001). The average diffusion capacity of carbon monoxide (DLCO) in the silicosis cohort was considerably lower than that in the exposure cohort (p=0.042).

The comparison of the serum and urinary neopterin levels and immunological and oxidative stress parameters of the study groups is shown in Table 2. Compared to those in the non-exposed group, the TNF-α, IFN-γ, and 8-OH-dG levels in the silicosis and exposure groups were significantly higher, and SOD level was lower. The FGF level in the exposure group was significantly higher than in the non-exposed group. GPx levels were lowest in the exposure group and significantly lower in the silicosis group compared to the non-exposed group. The IL-1α level was highest in the exposure group, followed by the silicosis group, with a statistically significant difference observed among all groups. Compared with the non-exposed group, GR levels were significantly higher in the silicosis and exposure groups.

While the serum neopterin levels were significantly different among study groups (p=0.003, Figure 1A), the urinary neopterin levels were not different (p=0.678, Figure 1B). The Bonferroni adjusted Dunn post-hoc test revealed the serum neopterin level in the silicosis group (19.48 (16.53–26.51)) significantly decreased compared to the exposure (27.72 (19.53–35.38), adj. p-value=0.008) and non-exposed (28.32 (19.26–37.14), adj. p-value=0.015) groups.

Figure 1:

Comparison of (A) serum neopterin and (B) urinary neopterin levels among the study groups. Each dot shows an individual neopterin level. p-Values were computed by the Kruskal-Wallis H test. *, **denote the statistically significant differences after multiple comparisons via the Dunn post-hoc test with Bonferroni adjustment.

The comparison of serum and urinary neopterin levels and immunological and oxidative stress parameters between simple and complicated silicosis is shown in Table 3. The TNF-α level was significantly higher in simple silicosis patients compared to complicated silicosis patients, while the GPx level was lower. We found no significant difference in FGF, IFN-γ, SOD, IL-1β, IL-1α, CAT, 8-OH-dG, GR, serum (Figure 2A), and urinary (Figure 2B) neopterin levels between simple and complicated silicosis patients. There was a significant and negative relationship between serum neopterin level and TNF-α (Spearman’s rho= −0.229, p=0.023) and duration of exposure (Spearman’s rho= −0.217, p=0.032).

Figure 2:

Comparison of (A) serum neopterin and (B) urinary neopterin levels between simple and complicated silicosis. Each dot shows an individual neopterin level. p-Values were computed by the Mann-Whitney U test (unpaired Wilcoxon test).

A ROC curve analysis for serum neopterin level as a biomarker for distinguishing silicosis from exposure showed that the AUC value was 0.680 (95 % CI, 0.578–0.771). The optimal cut-off value of serum neopterin was 23.2 ng/mL, with a sensitivity of 71.7 %, a specificity of 62.2 %, a PPV of 69.1 %, and an NPV of 65.1 % (Figure 3A). The ROC curve AUC to discriminate silicosis from non-exposed serum neopterin levels was 0.662 (95 % CI, 0.559–0.755). 23.2 ng/mL of serum neopterin level was determined as the optimal cut-off point, with a resulting sensitivity of 71.7 %, specificity of 63.6 %, PPV of 70.4 %, and NPV of 65.1 % (Figure 3B). Figure 3C shows a ROC curve that estimates the predictive capacity of the serum neopterin level in distinguishing silicosis from no-silicosis (including both exposure and non-exposed patients). The AUC value for silicosis diagnosis was 0.671 (95 % CI, 0.587–0.747). The optimal cut-off value of serum neopterin was 23.2 ng/mL, with a sensitivity of 71.7 %, a specificity of 62.9 %, a PPV of 53.5 %, and an NPV of 78.9 %.

Figure 3:

ROC curve analysis to distinguish patients with silicosis from (A) exposure, (B) non-exposed, and (C) no-silicosis (including both exposure and non-exposed patients).

To identify the risk factors associated with silicosis against individuals exposed to silica and non-exposed, multiple logistic regression analyses were conducted (Table 4), including the following variables with p<0.05 in the univariate analysis: age, duration of exposure (only for exposure), smoking status, FVC, FEV1, serum neopterin (≤23.2 vs. >23.2), IL-1α, and GR. Finally, the multiple logistic regression analysis showed that age, serum neopterin, and IL-1α were independent risk factors for the diagnosis of silicosis between patients with silicosis and those exposed to silica. Further, the multiple logistic regression analysis showed that age, FEV1 (%), serum neopterin, IL-1α, and GR were independent risk factors for the diagnosis of silicosis between patients with silicosis and those who were not exposed.

Discussion

Studies evaluating neopterin levels in silica exposure are very limited and have been conducted in small populations. In some of the studies, only exposure and healthy groups were compared [18], 20], 21], while in others, silicosis and healthy controls were measured [22], 23]. No study has compared serum and urine neopterin levels among silicosis patients, exposed individuals, and healthy control groups. Therefore, neopterin levels in studies have not been able to distinguish between people with silicosis and silica exposure. We know that not all people exposed to silica develop silicosis. In studies conducted on individuals exposed to silica [18], 20], 21], it is not known whether individuals with elevated neopterin develop silicosis during follow-up. Therefore, biomarkers measured at exposures may not provide accurate information on disease development. Some of the studies measured neopterin only in urine [21], 22], and some only in serum [23], 24], while only two studies evaluated neopterin levels in both urine and serum [18], 20]. In our study, we evaluated serum and urine neopterin levels, oxidative stress and inflammation parameters in patients with silicosis, a silica-exposed cohort and a control group. We also demonstrated the relationship of these parameters with clinical, radiological and functional variables.

A study on silicosis patients revealed that urinary neopterin levels differed from those of controls, and neopterin levels linked with disease severity. It was emphasized that increased neopterin levels are an indicator of cellular immunity in silica exposure [22]. In another study, serum and urine neopterin levels were elevated in the silica-exposed group (n=22) compared to the healthy controls (n=20). It was emphasized that neopterin levels have diagnostic value in silica exposure and can be utilized for diagnostic and monitoring purposes. Urinary neopterin levels had increased in relation to duration of work exposure [20].

Serum neopterin levels were found to be significantly higher in silicosis patients compared to the control group. Elevated neopterin levels in silicosis patients are highlighted as an early marker of cellular immune response activity and macrophage contribution to the disease’s pathogenesis, serving as diagnostic criteria alongside characteristic radiomorphological alterations [25]. A study conducted at an insulator manufacturing facility found that urine and serum neopterin levels were significantly higher in the group exposed to silica than in the unexposed group. Considering the sensitive and easy measurement of neopterin in biological fluids and the positive correlation between silica concentration in air and neopterin levels, more extensive studies in this field have been suggested. In a study conducted in glass sandblasting workers, it was emphasized that urinary neopterin could be evaluated as a biomarker of exposure to crystalline silica aerosols. However, no association was found between silica exposure and neopterin levels [21].

In our investigation, serum neopterin levels exhibited significant differences among the silicosis, exposure, and non-exposed group groups (respectively, 19.48 (16.53–26.51), 27.72 (19.53–35.38), 28.32 (19.26–37.14)) (p=0.003). No difference was found between the groups in terms of urine neopterin levels (p=0.678). In silicosis patients, serum neopterin levels were significantly lower than in both exposure and non-exposed groups. Serum neopterin levels were able to distinguish silicosis patients from exposure, non-exposed group, and non-silicosis (including both exposure and non-exposed) groups. The optimal cut-off value of serum neopterin was 23.2 ng/mL. Individuals with reduced serum neopterin levels (≤23.2 ng/mL) had a 4.4-fold higher risk of silicosis than the exposure group and a 26-fold higher risk than healthy individuals. Serum neopterin levels can predict the diagnosis of silicosis in 71 % of cases. Decreased neopterin levels in silicosis patients may indicate immunosuppression in the pathogenesis of the disease. While the increase in neopterin levels in silica exposure reflects the inflammatory response, the decrease in neopterin levels when silicosis develops suggests that it is a consequence of a reduced T-cell response. We recommend using serum neopterin levels as an early indicator to assess the risk of silicosis. When compared to silicosis and exposure groups, serum neopterin, age, and IL-1α levels were identified as risk factors for silicosis. In comparison to the silicosis and non-exposed group, we observed that GR and FEV1 (%) values heightened the probability of silicosis alongside these risk factors. The negative correlation between serum neopterin and duration of exposure has once again revealed the importance of neopterin in the evaluation of chronic exposure before the development of silicosis.

The differences in serum and urine neopterin levels of our patients may be due to the stability and sensitivity of serum and urine samples. Since serum neopterin levels tend to be more stable and sensitive, they may more accurately reflect the immune responses occurring in the body. Urine neopterin levels may fluctuate more depending on the hydration status of the individual and the time of sample collection [26], 27].

The relationship between neopterin levels and functional and radiologic parameters has not been comprehensively studied [18], 20], 21]. When Provake et al. divided silicosis into three groups: round, irregular small opacities, and large opacities, they found no difference in neopterin levels. The absence of a significant difference in neopterin levels among the three groups of silicosis patients indicates that macrophage activation is constant, while the radiological and morphological changes observed in the lungs are probably resulting from the additive impact of the activated cellular immune response [23]. In our study, serum and urine neopterin levels were found to be lower in simple silicosis compared to complicated silicosis, but the difference was not significant. There was no correlation between neopterin levels, the ILO profusion score, or functional parameters. There was no significant difference in neopterin levels between the silicosis categories. In the silicosis and exposure cohorts, FEV1 and FVC were reduced compared to the non-exposed group. Decreased FEV1 levels were a risk factor for the development of silicosis. DLCO, an early marker of interstitial lung diseases, was significantly lower in silicosis patients than in the exposure group.

A study on neopterin levels in transplant patients and those with infectious, inflammatory, and malignant diseases has demonstrated the important role of cellular immunity in these diseases [25]. Elevated neopterin formation and tryptophan catabolism may lead to “immunodeficiency” characterized by diminished T-cell responsiveness [17]. It is not known whether the individuals participating in the studies were questioned about conditions (e.g., autoimmune disease, cancer, infection) that may cause neopterin elevation [20], 28]. Therefore, attributing neopterin elevation solely to silica exposure may be questionable. Patients were included in our study after the exclusion of diseases that could cause neopterin elevation.

There are almost no studies evaluating neopterin, oxidative stress, and anti-inflammatory parameters together in silica exposure. Several recent studies have suggested that exposure to crystalline silica aerosols may affect immunological functions, including T lymphocytes, neutrophils, and immunoglobulins [20], 23]. A significant increase in IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-10, and TNF-α levels was found in ceramic workers with intense silica exposure, and it was emphasized that silica activates the immune system [13]. A study on cultures of cells sampled from rodent lung tissue showed that, after exposure to crystalline silica, IL-1α was rapidly released by alveolar macrophages, which stimulated IL-1β production and thus promoted lung inflammation [29]. Furthermore, the authors suggest that IL-1β biomarker levels can be used to assess the risk of silicosis among workers exposed to dust [30]. Studies have shown that TNF-α levels are elevated before the onset of clinical signs associated with silicosis, making TNF-α a valuable option for early diagnosis [31], 32]. A notable elevation in inflammation markers, such as TNF-α, FGF, IFN-γ, and IL-1α, was observed in the silicosis and exposure groups relative to the non-exposed group. The inflammatory response to silica was highest in the exposure group. In particular, the risk of silicosis increased 1.5-fold with increased IL-1α compared to healthy individuals. Neopterin and inflammatory markers tended to increase during the exposure period, while they tended to decrease with the effect of anti-inflammatory mechanisms when silicosis developed. Thus, neopterin may be considered a peripheral marker of respiratory epithelial damage that protects the airways against oxidative stress-induced inflammation.

Studies have shown that neopterin has great potential as a biomarker for early diagnosis of silicosis; nevertheless, measuring oxidative stress indicators is recommended for enhanced accuracy [26]. Glutathione (GSH) levels in red blood cells decrease in the initial stage of silicosis and increase in the later stages of the disease [33]; antioxidant levels in alveolar macrophages in the lung decrease in laboratory conditions following exposure to crystalline silica [34].

In another study, serum SOD, CAT, GPx, malondialdehyde (MDA), 8-OH-dG, IL-17, IL-23, and IL-27 levels were found to be higher in silicosis patients compared to the control group. It was emphasized that changes in inflammatory and oxidant/antioxidant systems may shed light on the mechanisms of silicosis [12]. Some studies have emphasized that plasma/serum MDA levels can be used as an indicator of lipid peroxidation, 8-OH-dG as a marker of oxidative stress, and glutathione levels as an indicator of antioxidant status in participants exposed to silica [11], 21], 35], 36].

In our study, 8-OH-dG, which is an indicator of oxidative DNA damage, was found to be increased in silicosis and exposure groups, while GPx and SOD, which are antioxidants among body defense mechanisms, were found to be low due to consumption. GR was higher in silica exposure (including both silicosis and exposure). We found a 2-fold increased risk of developing silicosis in individuals with elevated GR. The fact that serum neopterin levels are higher in silica-exposure patients than in silicosis patients supports oxidative stress, while lower levels in silicosis patients may indicate that neopterin contributes to antioxidant defense mechanisms in the body. When we compared simple and complicated silicosis patients, TNF-α among inflammatory markers was found to be high in simple silicosis patients, while GPx among antioxidants was found to be low. In the early period, defense mechanisms are activated along with the inflammatory response in silicosis patients.

The limitations of our study are that we were not able to perform a measurement analysis for the concentration of silica in the environment. Additionally, we were also unable to evaluate neopterin, immune, and oxidative parameters in bronchoalveolar lavage. If we had been able to perform these measurements, we could have evaluated the relationship between neopterin, immune, and oxidative parameters more clearly.

Conclusions

In summary, in our study, neopterin was able to differentiate silicosis from silica-exposed individuals who did not develop the disease, indicating that neopterin may be a biomarker that enables closer follow-up of exposed workers for silicosis in daily practice. Neopterin can be used to monitor the development and progression of the disease in periodic examinations in workplaces with silica exposure. Today, studies are needed to identify biomarkers for the development of disease from exposure. In light of these biomarkers, medical surveillance for early diagnosis in sectors with occupational exposures may contribute to reducing labor loss and disability rates.