Correlation between concentrations of NAMPT and NMNAT1 and the risk of upper respiratory infections in the island reef

Abstract

BACKGROUND

The frequent occurrence of respiratory diseases in the island reef environment of the navy severely affects the health of personnel and the combat effectiveness of the troops. Current common screening methods can only indicate whether there is an infection with pathogenic microorganisms but not the degree of disease progression. Therefore, it is necessary to identify simple-to-operate and cost-effective methods that indicate the degree of disease progression, based on traditional screening methods.

AIM

To explores correlation between serum concentrations of nicotinamide phosphoribosyltransferase (NAMPT), nicotinamide nucleotide adenylyltransferase 1 (NMNAT1), and the risk of upper respiratory infections in the island reef envirsonment.

METHODS

A total of 600 cases of upper respiratory infections among naval officers and soldiers were included. Types of infection were confirmed through sputum culture combined with multiplex polymerase chain reaction. The serum concentrations of NAMPT and NMNAT1 were measured using ELISA, and infection severity was assessed using the pneumonia severity index (PSI). Statistical analysis was performed using nonparametric tests and Spearman correlation analysis.

RESULTS

The serum concentrations of NAMPT and NMNAT1 in high-risk group patients with PSI were significantly lower than those in the medium and low-risk groups (P < 0.05), and the concentrations increased in a stepwise manner with disease progression. However, within the same risk group, the differences in concentrations of NAMPT and NMNAT1 among patients infected with different pathogens were not significant (P > 0.05).

CONCLUSION

Concentrations of NAMPT and NMNAT1 are closely related to severity of upper respiratory infections, and their common regulatory mechanisms provide new directions for development of broad-spectrum anti-infection strategies.

Key Words: Island reef environment; Respiratory pathogenic microorganisms; NAMPT; NMNAT1; Upper respiratory tract infection biomarkers

Core Tip: This study investigated the correlation between five common respiratory pathogens in the ship environment (influenza H1N1 virus, influenza B virus, Streptococcus pneumoniae, adenovirus, and severe acute respiratory syndrome coronavirus 2) and the severity of upper respiratory tract infections in patients, as well as serum concentrations of nicotinamide phosphoribosyltransferase (NAMPT) and nicotinamide nucleotide adenylyltransferase 1 (NMNAT1). We analyzed the feasibility of using NAMPT and NMNAT1 concentrations as potential biomarkers for assessing the severity of upper respiratory tract infections, providing a basis for clinical diagnosis and treatment of personnel stationed on ships and islands.

INTRODUCTION

The maritime island reef environment is characterized by high temperatures, high humidity, high salinity, and strong winds throughout the year. Soldiers stationed to safeguard the seas carry out their missions under these conditions. The confined spaces of ship cabins and living quarters on the islands lead to poor ventilation, which results in the prolonged presence of viruses and bacteria through droplets and aerosols, significantly increasing the risk of respiratory disease transmission. Additionally, the dense population and the long-term shared use of living facilities substantially raise the probability of cross-infection. Such environments often face shortages of medical resources (including specialized equipment, insufficient pharmaceuticals, and inadequate isolation conditions), making it difficult to provide timely treatment for severe cases and increasing the likelihood of clustered outbreaks. Statistics indicate that respiratory infections, orthopedic diseases, injuries, skin diseases, and digestive diseases rank among the top five causes of morbidity in the island reef forces. Respiratory infections are one of the primary causes affecting the safety of military personnel, and lower respiratory tract infections are the fourth leading cause of death globally, particularly in closed environments like ships, which are more prone to outbreaks[1]. However, hospitals in such environments are usually restricted by conditions that make it difficult to maintain and safeguard chest computed tomography equipment, and there is a lack of conditions for the diagnosis and treatment of respiratory diseases, with limited reported data. The common screening methods currently available include quantitative polymerase chain reaction (qPCR) testing and colloidal gold antigen test strips, but these methods only indicate the presence of infectious pathogens but not disease progression. Therefore, it is necessary to select simple and cost-effective indicators and detection methods that indicate disease deterioration, based on traditional screening methods, to understand the incidence of common respiratory diseases among soldiers stationed on islands and reefs. This will provide a basis for adopting targeted prevention and treatment measures, reducing noncombat casualties, and ensuring the combat effectiveness of the island and reef troops[2,3].

Most research has focused on the host response to pathogenic microorganisms in specialized immune cells, but the respiratory epithelium represents the first line of defense against upper respiratory infections. It forms a mechanical barrier, produces a viscous mucus composed of glycoproteins to encapsulate bacteria, and secretes various cytokines and chemokines to recruit immune cells to the site of infection. Aprianto et al[4] analyzed the immediate transcriptional response of alveolar epithelial cells infected with Streptococcus pneumoniae (S. pneumoniae) within 4 h of infection, demonstrating widespread dysregulation of gene expression, particularly the suppression of genes involved in the immune response.

In 2023, research conducted by Klabunde et al[5] indicated that nicotinamide adenine dinucleotide (NAD) metabolism was a key regulatory factor in bacterial respiratory epithelial infections. Dysregulation of the NAD salvage pathway caused a reduction in NAD production following infections in cell line models, primary human lung tissue, and in rodents. The knockout of NAD salvage enzymes [nicotinamide phosphoribosyltransferase (NAMPT) and nicotinamide nucleotide adenylyltransferase 1 (NMNAT1)] increased bacterial replication[4]. The levels of NAMPT and NMNAT1 in serum may serve as potential targets for the detection and treatment of upper respiratory infections. However, no relevant clinical studies have been reported. The common pathogenic microorganisms in the reef environment include influenza H1N1 virus, influenza B virus, S. pneumoniae, adenovirus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infections with these pathogens typically result in a significant increase in interleukin (IL)-6 and tumor necrosis factor (TNF)-α levels, which are closely related to the severity of influenza symptoms. S. pneumoniae is one of the main pathogens causing community-acquired pneumonia, and it is typically accompanied by significant release of cytokines, such as TNF-α[6]. Adenoviruses are common pathogens that cause upper respiratory infections, pneumonia, and conjunctivitis in children and immunocompromised populations, and can also lead to severe infections in adults. During infection, adenovirus rapidly replicates within the epithelial cells of the respiratory tract, resulting in necrosis and release of a large number of proinflammatory cytokines (such as IL-1β, IL-6, and TNF-α) and interferon-γ. Although these cytokines assist in recruiting immune cells and suppressing viral spread, excessive secretion may exacerbate local tissue inflammation and even trigger systemic inflammatory response syndrome, which can progress to acute respiratory distress syndrome or multiple organ dysfunction syndrome in severe cases. Coronavirus disease 2019 (COVID-19) can lead to a wide range of clinical manifestations, from mild to severe, with elderly individuals and those with underlying conditions being more susceptible to developing severe pneumonia or systemic complications. Following viral infection of the epithelial cells in the respiratory tract, the innate immune response is activated, leading to the massive release of proinflammatory factors and chemokines (such as CXCL10) including IL-6, TNF-α, and IL-1β, culminating in a cytokine storm. While this response can facilitate viral clearance, uncontrolled inflammation may damage alveolar structures, resulting in pulmonary edema, fibrosis, and thrombosis, with potential involvement of other organs such as the heart and kidneys. Additionally, the ability of the virus to directly damage endothelial cells exacerbates the risk of multiple organ failure, making it a core mechanism for high mortality rates in severe cases[5,7]. In the enclosed environment of naval islands and reefs, the transmission and infection of these common respiratory pathogens pose a significant threat to the health of the crew. Therefore, the screening of serum markers that influence the development of infections caused by common pathogens in the closed environment of naval islands and reefs, as well as the impact of NAMPT and NMNAT1 concentrations in the treatment of upper respiratory infections and serum detection, is important.

This study investigated the correlation between five common respiratory pathogenic microorganisms (influenza H1N1 virus, influenza B virus, S. pneumoniae, adenovirus, and SARS-CoV-2) in the environment of warships and the severity of upper respiratory infections in patients, as well as the concentrations of NAMPT and NMNAT1 in serum. It analyzed the feasibility of using NAMPT and NMNAT1 concentrations as potential biomarkers for assessing the severity of upper respiratory infections. The findings will provide a basis for the clinical diagnosis and treatment of upper respiratory infections in military personnel stationed on ships and islands.

MATERIALS AND METHODS

Study design

The subjects of this study were 600 soldiers diagnosed with upper respiratory infections at a naval training base in Shanghai, from February 1, 2023 to January 30, 2024. Pathogenic bacteria were identified through sputum cultures, and a multiplex PCR detection kit was used to screen sputum for infections from the following five respiratory pathogens: Influenza H1N1 virus, Influenza B virus, S. pneumoniae, adenovirus, and SARS-CoV-2. Individuals infected with each pathogen were identified. All participants signed informed consent forms and met the specified inclusion and exclusion criteria.

Inclusion criteria

(1) Within 7 days, new onset of cough with sputum, worsening cough, increased sputum volume, purulent sputum, and/or elevated body temperature (> 37.3 °C); (2) Chest X-ray suggesting pulmonary infiltrates; and (3) Sputum culture confirming the presence of identifiable pathogens.

Exclusion criteria

(1) Noninfectious pulmonary inflammation; (2) Coexisting thyroid diseases; (3) Malignant tumors; (4) Inability to tolerate bronchoscopy; (5) Infections at other sites; (6) Autoimmune diseases or any immunosuppressive treatment lasting > 3 months; (7) Chemotherapy within 1 month prior to infection; (8) Chronic liver disease or cirrhosis; (9) Acquired immunodeficiency syndrome; (10) Sputum culture and mNGS failing to identify any clear pathogens; and (11) Received antiviral or antimicrobial treatment.

Sputum samples

For patients who can spontaneously expectorate, they should adequately rinse their mouths with warm water after waking up and before eating, and then cough the sputum into a sterile sputum collection container. For patients with poor ability to expectorate, a bronchoscope can be inserted into the airway to select a deeper area where more sputum is present, followed by attaching a sputum collection device and immediately using a negative pressure suction of 50-100 mmHg to collect the sputum. The collected sputum should be sent to the microbiology laboratory of the hospital.

Sputum samples were used for qPCR analysis to confirm the presence of microbial infection. The participants were instructed to rinse their mouth before sputum collection, take a deep breath, and cough to collect deeper sputum. The sputum was spat into a sterile container that was sealed immediately. Samples were stored at 4 °C and promptly sent to the laboratory for qPCR analysis. All samples strictly adhered to aseptic protocols to avoid contamination and ensure the accuracy of the analysis.

DNA extraction and qPCR

DNA was extracted using a commercial DNA extraction kit. The process included the following steps. Sample lysis: Lysis buffer and proteinase K were added to lyse the cells and release DNA. Removal of impurities: Proteins, lipids, and other impurities were removed through multiple washes. DNA elution: Purified DNA was eluted using elution buffer and collected in a new sterile tube. DNA quality and concentration assessment: The concentration and purity of DNA (OD260/OD280 ratio) were measured using a NanoDrop or Qubit fluorometer. A multiplex qPCR gene detection kit was developed by our institution. The specific steps were as follows. The qPCR reaction system was prepared and 1 µL of DNA extracted from pathogens cultured in sputum was added. qPCR cycling conditions: Initial denaturation: 95 °C for 2 minutes; cycling reaction (40-45 cycles); denaturation: 95 °C for 15 seconds; annealing/extension: 60 °C for 30 seconds. At the end of each cycle, the intensity of the fluorescent signal was recorded. The software of the fluorometric qPCR instrument automatically plotted the standard curve, and quantification of the target gene copy number in the samples was based on the standard curve. Infection status was confirmed based on the results of specific gene detection for different pathogenic microorganisms, and comprehensive analysis was conducted in conjunction with the cytokine concentration detection. All experimental procedures were conducted under sterile conditions to avoid sample contamination. After the experiment, it was essential to promptly clean the workbench and equipment to ensure a clean and safe experimental environment.

ELISA

The samples were placed in a -10 °C refrigerated box and detected using ELISA.

Clinical symptoms and infection severity (pneumonia severity index)

To assess the impact of different microbial infections on clinical symptoms and the severity of patient conditions, we utilized the pneumonia severity index (PSI). The PSI is a validated tool used to evaluate the prognosis and severity of illness in patients with community-acquired pneumonia. The PSI score considers various clinical variables, including the demographic characteristics of the patient, comorbidities, signs, and laboratory results. The PSI scoring method was as follows: Demographic characteristics include: Age (years), sex (male or female), residence (such as whether hospitalized), comorbidities, tumors, liver disease, heart failure, cerebrovascular disease, and kidney disease. Characteristic signs included: Pulse (beats/minute), systolic blood pressure (mmHg), respiratory rate (breaths/minute), body temperature, and consciousness state (e.g., altered consciousness). Laboratory and imaging results included: Blood pH, partial pressure of oxygen (PaO₂) or oxygen saturation (SaO₂), blood urea nitrogen levels, sodium and glucose concentrations, hematocrit, and chest X-ray imaging. Each variable was assigned a specific score, with higher total scores indicating greater severity of illness. Based on the total score, patients were categorized into low risk (≤ 90 points), medium risk (91-130 points), and high risk (> 130 points). At the time of patient enrollment, all necessary clinical information was recorded and relevant laboratory tests and imaging studies were performed. The total PSI score for each patient was calculated, and patients were classified according to their scores. In data analysis, the PSI score was used to evaluate the severity of different microbial infections and is correlated with the concentrations of cytokines in bronchoalveolar lavage fluid to explore the relationship between cytokine levels and disease severity.

Statistical analysis

The results were statistically analyzed using the nonparametric Kruskal-Wallis test and the Mann-Whitney U test. The statistical relationships between variables were assessed using the Spearman rank correlation coefficient. All statistical analyses were performed using the Statistica v6.0 software package (Statsoft Inc., Tulsa, OK, United States).

RESULTS

Patient characteristics

After data organization, 600 cases were included, comprising 551 males and 49 females, with an age range of 22-41 years, and a median age of 26 years.

A multiplex polymerase chain reaction (PCR) method was used to detect respiratory pathogens in 600 military personnel at a naval training base in Shanghai. There were 15 cases of influenza H1N1 virus infection, 12 of influenza B virus infection, five of S. pneumoniae infection, 49 of adenovirus infection, and 12 of SARS-CoV-2 infection. Confirmation of the PCR results ensured that each case was an infection caused by a specific pathogen.

Clinical symptoms and infection severity (PSI score)

In the assessment of symptoms and severity of the disease, the PSI scoring system was used. Pneumonia patients were classified into low risk (≤ 90 points), medium risk (91-130 points), and high risk (> 130 points) based on their PSI scores. The results indicated that individuals infected with the influenza A virus predominantly fell into the low-risk group, with a total of 10 cases. Similarly, those infected with the influenza B virus mainly belonged to the low-risk group, also totaling 10 cases. Among the five cases of pneumococcal infection, three were categorized as medium risk, one as low risk and one as high risk, reflecting severe infection. Additionally, eight cases of adenovirus infection exhibited pneumonia symptoms, while one case of COVID-19 was diagnosed with severe pneumonia. These findings highlight the differences in severity of infection associated with different microbial pathogens, particularly noting that bacterial infections often lead to more severe clinical manifestations (Table 1).

Table 1 Clinical symptoms and severity of illness (pneumonia severity index score).

Pathogenic microorganisms	Clinical symptoms and severity of illness (PSI score)
	Low risk (≤ 90 points)	Medium risk (91-130 points)	High risk (> 130 points)
Influenza H1N1 virus	10	4	1
Influenza B virus	10	2	0
Streptococcus pneumoniae	1	3	1
Adenovirus	37	8	4
SARS-CoV-2	9	2	1

PSI: Pneumonia severity index; SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2.

Comparison of NAMPT and NMNAT1 concentration among different risk levels of pneumonia according to PSI

Comparison of NAMPT and NMNAT1 concentrations among the low-, medium-, and high-risk groups revealed significant differences in serum levels of NAMPT and NMNAT1 (P < 0.05). Concentrations of NAMPT and NMNAT1 in the high-risk group were lower than those in the medium- and low-risk groups (P < 0.05). Concentrations of NAMPT and NMNAT1 in the medium-risk group were lower than those in the low-risk group (P < 0.05) (Table 2).

Table 2 Comparison of nicotinamide phosphoribosyltransferase and nicotinamide nucleotide adenylyltransferase 1 concentrations among different pneumonia severity index scores, n (%).

	PSI classification
	Low risk	Medium risk	High risk
NAMPT (pg/mL) (IQR)	35.29 (34.68)2,3	27.22 (26.23)1,3	21.19 (21.71)1,2
NMNAT1 (pg/mL) (IQR)	42.49 (44.50)2,3	26.19 (26.52)1,3	18.24 (18.77)1,2

¹Indicates statistical significance when compared with the low-risk group.

²Indicates statistical significance when compared with the medium-risk group.

³Indicates statistical significance when compared with the high-risk group.

PSI: Pneumonia severity index; IQR: Interquartile range; NAMPT: Nicotinamide phosphoribosyltransferase; NMNAT1: Nicotinamide nucleotide adenylyltransferase 1.

Comparison of NAMPT and NMNAT1 concentrations in infections caused by different pathogens

There were 23 types of infections identified. Due to a reduced number of cases for some pathogens, the analysis focused on patients with common isolated infections including 15 infections with influenza H1N1 virus, 12 with influenza B virus, five with S. pneumoniae, 49 with adenovirus, and 12 with SARS-CoV-2. Comparison revealed that serum concentrations of NAMPT and NMNAT1 among these five groups of commonly occurring pathogenic microorganisms showed no significant differences among low-risk, medium-risk, and high-risk patients (P > 0.05) (Tables 3, 4, and 5).

Table 3 Comparison of concentrations of nicotinamide phosphoribosyltransferase and nicotinamide nucleotide adenylyltransferase 1 in low-risk patients with five common pathogenic microorganisms, n (%).

	Influenza H1N1 virus	Low risk (≤ 90 points)			SARS-CoV-2
		Influenza B virus	Streptococcus pneumoniae	Adenovirus
NAMPT (pg/mL) (IQR)	35.33 (35.41)	36.42 (36.43)	35.44 (35.45)	36.17 (38.19)	34.31 (34.39)
NMNAT1 (pg/mL) (IQR)	43.21 (42.95)	45.42 (45.09)	43.15 (42.35)	44.26 (44.41)	42.33 (41.91)

SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2; NAMPT: Nicotinamide phosphoribosyltransferase; NMNAT1: Nicotinamide nucleotide adenylyltransferase 1; IQR: Interquartile range.

Table 4 Comparison of nicotinamide phosphoribosyltransferase and nicotinamide nucleotide adenylyltransferase 1 concentrations in medium-risk patients with five common pathogenic microorganisms, n (%).

	Influenza H1N1 virus	PSI (scores 91-130)			SARS-CoV-2
		Influenza B virus	Streptococcus pneumoniae	Adenovirus
NAMPT (pg/mL) (IQR)	27.32 (26.53)	27.4 (27.90)	25.31 (25.02)	26.29 (25.27)	26.19 (25.82)
NMNAT1 (pg/mL) (IQR)	27.22 (27.21)	26.4 (26.52)	25.19 (24.77)	26.49 (26.13)	25.22 (23.97)

SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2; NAMPT: Nicotinamide phosphoribosyltransferase; NMNAT1: Nicotinamide nucleotide adenylyltransferase 1; IQR: Interquartile range; PSI: Pneumonia severity index.

Table 5 Comparison of nicotinamide phosphoribosyltransferase and nicotinamide nucleotide adenylyltransferase 1 concentrations in high-risk patients with five common pathogenic microorganisms, n (%).

	Influenza H1N1 virus	PSI (scores > 130)			SARS-CoV-2
		Influenza B virus	Streptococcus pneumoniae	Adenovirus
NAMPT (pg/mL) (IQR)	21.32 (19.66)	22.42 (21.56)	20.31 (18.91)	23.29 (21.92)	23.19 (22.25)
NMNAT1 (pg/mL) (IQR)	18.22 (19.10)	19.46 (20.08)	15.19 (17.79)	20.49 (18.62)	20.22 (20.54)

SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2; NAMPT: Nicotinamide phosphoribosyltransferase; NMNAT1: Nicotinamide nucleotide adenylyltransferase 1; IQR: Interquartile range; PSI: Pneumonia severity index.

DISCUSSION

The environment of military islands and reefs, characterized by high population density and limited air circulation, has become a high-incidence area for respiratory tract infections; particularly the spread of pathogens such as influenza H1N1 virus, influenza B virus, S. pneumoniae, adenovirus, and SARS-CoV-2, which often leads to rapid outbreaks. Klabunde et al[5] have indicated that NAD metabolism is a key regulatory factor in bacterial respiratory epithelial infections. In cell line models, primary human lung tissues, and in vivo infections in rodents, dysregulation of the NAD salvage pathway results in reduced NAD production. The knockout of NAD salvage enzymes (NAMPT and NMNAT1) increases bacterial replication[4]. Serum levels of NAMPT and NMNAT1 may serve as potential targets for the detection and treatment of upper respiratory infections. However, relevant clinical studies reporting these findings are yet to be seen. The host NAD metabolic pathway, particularly the enzymes NAMPT and NMNAT1, plays a crucial role in regulating the immune response to infections, yet the dynamic changes during clinical infections and their potential as biomarkers remain unclear. This study focused on five common respiratory pathogens affecting military personnel in island environments, exploring the correlation between the severity of illness post-infection and the serum concentrations of NAMPT and NMNAT1. It investigated the impact of infections on the serum levels of NAMPT and NMNAT1, providing new insights for infection prevention and control in confined environments.

NAMPT is a multifunctional enzyme that has previously reported cytokine functions in the proinflammatory process[8]. At the same time, NMNAT1 has been confirmed to participate in the infection process, demonstrating the direct antimicrobial effect of NAD. Treatment with the NAD precursor NR can increase the intracellular concentration of NAD both in vitro and in vivo[9-11]. NR and NAM treatment can promote the production of cellular NAD, thereby inhibiting the replication of mouse herpes virus in vitro, which is related to the energy demands of antiviral cellular mechanisms. During viral infection, NAMPT is typically upregulated in a JAK/STAT-dependent manner. This results in increased NAD biosynthesis to meet the energy requirements of antiviral enzyme mechanisms[12]. To evade this defense mechanism, the virus has synthesized a large structural domain that counteracts the function of PARP, which consumes NAD, by hydrolyzing the ADP-ribosylation sites of host proteins[13,14]. The direct interference of the virus on NAMPT expression is believed to be mediated by the induction of miRNA expression, thereby inhibiting the translation of NAMPT[15,16], The possible mechanism involves the upregulation of MARylating PARPs, which induces the gene expression of enzymes responsible for NAD synthesis in streptococcus pneumoniae[17]. During infection, bacteria have been shown to utilize host-derived NAD for their own energy metabolism[18-20]. The report indicates that the biosynthesis of NAD specific to S. pneumoniae is downregulated through inhibition of NMNAT1 gene expression mediated by pneumolysin. Prior to bacterial infection, the biosynthesis of NAD can be reduced by knocking out NAMPT or NMNAT1. Both knockouts result in decreased NAD production in epithelial cells and increased bacterial replication. This study involves 600 cases of upper respiratory infections among naval officers, identifying the pathogen infection types through sputum culture combined with multiplex PCR. The concentrations of serum NAMPT and NMNAT1 were measured using ELISA, and disease severity was assessed using the PSI scoring system. Through stratified analysis, differences in biomarkers among different pathogen infection groups and between different PSI risk groups were compared. The results indicate a relationship between disease severity and NAMPT and NMNAT1 levels, with patients in the high-risk PSI group exhibiting significantly lower serum concentrations of NAMPT and NMNAT1 compared to those in the medium- and low-risk groups. Concentrations of NAMPT and NMNAT1 increased in a stepwise manner with worsening conditions, suggesting a close correlation between the two and severity of infection. While S. pneumoniae infections predominantly occurred in the high-risk group (78.46%), within the low-risk group, the differences in NAMPT and NMNAT1 concentrations among the five pathogen infection types were not significant. In the medium- and high-risk groups, the differences in concentrations of NAMPT and NMNAT1 among the five types of pathogenic infections were not significant; however, due to the small sample size, it is necessary to further expand the sample size for verification. This suggests that different pathogens may influence NAD metabolism through common mechanisms, such as the inflammatory cascade, rather than relying on pathogen-specific pathways.

This study revealed the dynamic changes in NAMPT and NMNAT1 in closed-environment respiratory infections. Consistent with the foundational research of Schmeck et al[5], an imbalance in NAD metabolism was significantly associated with the severity of infection. The elevation of NAMPT and NMNAT1 in high-risk groups may represent a compensatory response of the host to pathogen invasion, aimed at maintaining immune cell function by enhancing NAD biosynthesis. However, there is a lack of concentration differences among different pathogen infection groups, suggesting that NAMPT and NMNAT1 are more likely to serve as universal biomarkers for disease progression rather than pathogen-specific indicators. The regulatory mechanisms of NAD metabolism differ between viral and bacterial infections: Viral infections may upregulate NAMPT via the JAK/STAT pathway to support antiviral energy demands, while bacterial infections exacerbate metabolic imbalance by hijacking host NAD or inhibiting the expression of recovery enzymes. This difference in mechanisms may explain why the type of pathogen does not affect biomarker concentrations within the same risk group, but further molecular mechanism studies are needed for validation.

CONCLUSION

This study explored the correlation between five common respiratory pathogens (influenza H1N1 virus, influenza B virus, S. pneumoniae, adenovirus, and SARS-CoV-2) in the ship island environment and the severity of upper respiratory infections in patients, as well as serum concentrations of NAMPT and NMNAT1. It analyzed the feasibility of these as potential biomarkers for assessing the severity of upper respiratory infections. The findings provide a basis for clinical diagnosis and treatment of upper respiratory infections among personnel in the ship island reef environment. The results indicate a significant correlation between NAMPT and NMNAT1 concentrations and the severity of upper respiratory infections. These can be quickly assessed using colloidal gold or rapid ELISA tests, applicable in scenarios with limited medical resources, such as ship island reef environments, to enhance the health of military personnel and improve the combat capability of the armed forces. Although no intergroup concentration differences were observed due to infections by different pathogens, their common regulatory mechanisms offer new directions for developing broad-spectrum anti-infection strategies, particularly relevant in closed environments like ships.

ACKNOWLEDGEMENTS

We are grateful to all participants, especially the study members. We are also obliged to the sample testers, who performed the measurements.