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Nucleotide metabolic abnormalities in post-COVID-19 condition and type 2 diabetes mellitus patients and their association with endocrine dysfunction

  • Yalei Fan , Xiaomin Xie EMAIL logo , Guirong Bai , Wenrui Ji , Yanting He , Li Zhang , Haiyan Zhou , Ling Li , Huan Li and Dan Qiang
Published/Copyright: October 7, 2025

Abstract

COVID-19 virus infection can cause disorders of the endocrine system. The aim of this study was to characterize the alterations of nucleotide metabolomic patterns in patients with post-COVID-19 condition (PCC). The study population included 18 patients with PCC alone (PCC-A), 31 patients with PCC combined with type 2 diabetes mellitus (PCC-DM), 20 healthy volunteers (HV), and 20 patients with type 2 diabetes mellitus (DM). Ultraperformance liquid chromatography–mass spectrometry was conducted on plasma metabolites. A total of 116, 178, and 163 differential metabolites were identified in PCC-DM vs PCC-A, PCC-A vs HV groups, and PCC-DM vs DM groups, respectively. Adenine was significantly down-regulated, and purine, thymine, and uracil were significantly up-regulated in the PCC-A group compared with the HV group, and the same results were observed in the PCC-DM group compared with the DM group. Differential metabolites were mainly involved in nucleotide metabolism, especially pyrimidine metabolism in PCC patients. After the arginine stimulation test, cortisol and adrenocorticotropic hormone secretion were reduced in PCC patients. In conclusion, the nucleotide de novo synthesis pathway and the remedial synthesis pathway are seriously damaged in patients with PCC-A, especially in patients with PCC-DM, which leads to the disorder and imbalance of the body cell metabolism pathway.

1 Introduction

Since the global epidemic of coronavirus disease 2019 (COVID-19) began in 2019, some patients have a series of long-term clinical symptoms involving multiple systems and organs of the body after treatment, that is, long COVID, also called post-COVID-19 condition (PCC) [1]. PCC is defined as 3 months after COVID-19 infection; at least 10% of patients develop long-term symptoms, including fatigue, intermittent headache, cough, dyspnea, decreased smell, and muscle pain, and may also have memory loss, sleep disturbance, chest pain or tightness, heart palpitations, depression and anxiety, nausea, diarrhea, and rash. These symptoms reflect the chronic damage of multi-system organs, not only damaging the nervous system, digestive system, reproductive system, and motor system, but also causing endocrine system disorders [2,3,4,5]. Currently, the main hypotheses for the pathogenesis of PCC include changes in the immune system, chronic inflammation, endothelial dysfunction, microthrombosis, mitochondrial dysfunction, metabolic abnormalities, activation of chronic viral infection, microbiome dysregulation, and unresolved tissue damage [6]. PCC affects the quality of life of patients, increases the cost of social health care, and causes a huge burden to families and society. Therefore, it is very important to study the long-term health status and dynamic changes of patients with PCC.

COVID-19 is closely associated with endocrine function and can affect the endocrine functions of the pituitary gland, thyroid, adrenal glands, gonads, and pancreas [7]. Studies have confirmed that COVID-19 can invade many parts of the brain, including the hypothalamus and pituitary gland, and the COVID-19 genome is present in the cerebrospinal fluid [8]. Adrenocorticotropic hormone (ACTH) and cortisol (F) exert a crucial role in the hypothalamic–pituitary–adrenal axis [9]. ACTH secretion is significantly decreased in COVID-19 patients compared to healthy individuals [10]. ACTH and growth hormone decreased in the anterior pituitary cells of SARS patients [11]. In addition, the levels of F were discovered to be diminished in COVID-19 patients compared with the controls [12]. The mechanisms by which COVID-19 affects endocrine function include direct viral damage, endothelial dysfunction secondary to SARS-CoV-2-induced endotheliitis, and immune-mediated organ damage caused by uncontrolled cytokine release [13]. SARS-CoV-2 could induce endothelial dysfunction via regulating ACE2, AXL, and L-SIGN expression [14]. A recent study has demonstrated that SARS-CoV-2 led to enhanced caspase-3 cleavage and apoptotic cell death in endothelial cells [15]. The cytokine storm triggered by severe COVID-19 (e.g., IL-6, IL-1β, TNF-α, and MCP-1) was a good predictor of the severity of COVID-19, and it also intensified endothelial cell damage [16]. However, the underlying mechanism of endocrine hormone disruption in PCC patients is not fully understood.

In recent years, metabolomics has emerged as a vital field. It delves deeply into metabolic processes and excavates potential biomarkers. As a result, it has become a powerful tool for determining metabolic perturbations across a wide range of diseases [17,18]. The metabolomic techniques mainly include liquid chromatography–mass spectrometry (LC–MS), gas chromatography–mass spectrometry (GC–MS), and ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) [19,20,21]. López‑Hernández et al. found that patients with long COVID-19 exhibited mitochondrial dysfunction, redox state imbalance, and lipid metabolism dysregulation through LC–MS/MS analysis [22]. Saito et al. discovered that amino acid metabolomic abnormalities occurred in long COVID patients by LC–MS analysis [23]. Metabolomic studies in PCC patients have highlighted alterations in lipid metabolism, amino acid metabolism, and the tricarboxylic acid cycle [22,24,25]. Currently, the persistence of SARS-CoV-2 was significantly associated with PCC [26]. The latest research indicates that the proteins expressed by SARS-CoV-2 are involved in cellular nucleotide metabolism [27]. However, it remains unclear whether patients with PCC exhibited abnormal nucleotide metabolism and whether such disturbances are associated with endocrine dysfunction.

In this study, we deciphered the differential metabolites and nucleotide metabolism in the subjects with or without PCC using UPLC–MS/MS. We also investigated the relationship between differential metabolites and ACTH and F in patients with PCC to explore new mechanisms of endocrine hormone disorders in PCC patients. This study will provide potential biomarkers and therapeutic targets for PCC patients.

2 Methods

2.1 Research objects

A total of 49 PCC patients aged 18–75 years old, including 28 females and 21 males, were enrolled in the Department of Endocrinology, the Second Affiliated Hospital of Ningxia Medical University from September 2023 to November 2023. Among the PCC patients, 18 patients were diagnosed with PCC alone (PCC-A) and 31 patients were diagnosed with PCC combined with type 2 diabetes mellitus (PCC-DM). The control group included 40 subjects aged 18–75 years old, including 20 females and 20 males, who had not been infected with SARS-CoV-2 in the medical examination center of our hospital from September to November 2021. Among the 40 controls, 20 cases were healthy volunteers (HV) and 20 cases were type 2 diabetes mellitus (DM). The diagnostic criteria for PCC and DM patients are as follows: all patients with DM met the 2019 American ADA diagnostic criteria [28] and all patients with PCC met the PCC diagnostic criteria [29]. PCC-DM patients meet the above two diagnostic criteria. Exclusion criteria are as follows: (a) patients with type 1 diabetes and other specific types of diabetes; (b) patients with acute and chronic complications of diabetes; (c) patients with chronic kidney or liver disease or cancer; (d) patients with secondary hypertension; (e) patients with anemia and history of severe cardiovascular or cerebrovascular diseases or tumors; and (f) the woman is not pregnant or lactating.

2.2 Blood index detection

Peripheral venous blood was collected after fasting 8–12 h. Fasting blood glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), creatinine (Cr), and uric acid (UA) were measured using the automatic biochemical analyzer AU5821. Plasma cortisol (F) and ACTH were detected by electrochemiluminescence assay using the Cobas 6000 automatic biochemical immunoanalyzer. The difference of F and ACTH at 0, 30, 60, 90, and 120 min after intravenous infusion of arginine was calculated. Plasma samples from all subjects were stored in a −80°C refrigerator for subsequent metabolomics.

2.3 Arginine excitation test

Fasting blood samples were collected from PCC patients at 8:00 am and preserved in an ice bath. PCC patients were given an intravenous infusion of arginine at a dosage of 0.5 g/kg (the maximum dosage was 30 g), and the infusion was finished within 30 min. Blood samples were taken before and after arginine injection at –30 (fasting), 0 (the end of arginine infusion), 30, 60, 90, and 120 min and stored in an ice bath. F and ACTH were detected at all time periods.

2.4 Metabolites extraction

Plasma samples were subjected to vacuum freeze-drying in a lyophilizer, followed by grinding into powder using a grinder. A weight of 100 mg of powder was then dissolved in 1.2 mL of 70% methanol extract. The mixture was vortexed once every 30 min, with each vortexing session lasting 30 s, for a total of six times. The sample was subsequently refrigerated at 4°C overnight. After centrifugation, the supernatant was carefully aspirated, filtered through a microporous filter membrane, and stored in an injection bottle for subsequent UPLC–MS/MS analysis.

2.5 UPLC conditions

Chromatographic separation was conducted on a column Agilent SB-C18 (2.1 mm × 100 mm, 1.8 µm). The mobile phase included 0.1% formic acid in ultra-pure water (phase A) and acetonitrile (phase B). Elution for phase B started at 5%, increased to 95% within 9 min, then returned to 5% in 10 min, and balanced at 5% for 14 min. A total of 4 μL samples was injected into the detector and separated on a column maintained at 40°C with a flow rate of 0.35 mL/min.

2.6 LC–MS/MS analysis

Linear ion trap and triple quadrupole (QQQ) scans were obtained on a triple quadrupole-linear ion trap mass spectrometer (QTRAP), AB6500 QTRAP LC–MS/MS System. This system is equipped with an ESI Turbo Ion-Spray interface and is capable of operating in both positive and negative ion modes and controlled by Analyst 1.6.3 software (Sciex). The ESI source operation parameters were set as follows: the source temperature was set to 550°C, and the ion spray voltage was set to 5,500 V for the positive mode and −4,500 V for the negative mode. The nebulizer gas (GSI), heater gas (GSII), and curtain gas were set at 50, 60, and 25 psi, respectively. Collision-induced ionization parameter was set to high. The instrument was tuned and calibrated with 10 and 100 μmol/L polypropylene glycol solution in QQQ and LIT modes, respectively. The QQQ scan uses the multiple reaction monitoring (MRM) mode and sets the collision gas (nitrogen) to medium. A specific set of MRM ion pairs is monitored at each period based on the metabolites elution during each period. Based on a self-constructed database, substance identification is carried out according to secondary spectral information. During analysis, isotopic signals, duplicate signals containing K+ ions, Na+ ions, NH 4 + ions, as well as duplicate signals that are fragments of larger molecular weight substances, are removed. Metabolite quantification was accomplished using the MRM mode of a triple quadrupole mass spectrometer. In the MRM mode, the quadrupole first screened for the precursor ions of the target substances, excluding ions corresponding to other molecular weights to initially eliminate interference. After ionization induced by the impact chamber, the precursor ions were fractured to form many fragments, and then, a characteristic fragment ion was selected by filtering through the triple quadrupole. After obtaining the metabolite mass spectrometry data from different samples, the peak areas of all substance mass spectrometry peaks were integrated, and the peak integrations of the same metabolite across different samples were corrected. The mass spectrometry data were processed using the software Analyst 1.6.3.

Principal component analysis (PCA) and Spearman correlation analysis were employed to assess the repeatability of samples within each group, as well as the quality control samples, ensuring the reliability and consistency of the data. The identified compounds were cross-referenced with the KEGG, HMDB, and Lipidmaps databases to obtain classification and pathway information. A t-test was conducted to determine the significance of the differences for each compound. Metabolites were listed in Tables S1–S3. Based on the orthogonal partial least squares discriminant analysis (OPLS-DA) results, the variable importance in projection (VIP) from the obtained multivariate OPLS-DA model can be used for an initial screening of differential metabolites. Differential metabolites were screened based on the criteria of fold change ≥1, p-value <0.05, and VIP ≥1. Fisher’s exact test was utilized to compute p-values based on the method of Benjamini and Hochberg for multiple testing corrections. Pathway analysis was conducted by utilizing the KEGG pathway database through the MetaboAnalyst 5.0 online software.

2.7 Statistical analysis

SPSS 26.0 (IBM, USA) software was used for statistical analysis, and the data were expressed as mean ± standard deviation. A t-test was used for comparison between the two groups. Correlations were analyzed by Pearson correlation analysis. The difference of p < 0.05 was considered statistically significant.

  1. Ethical approval: This study was approved by the Ethics Committee of The Second Affiliated Hospital of Ningxia Medical University.

  2. Informed consent: All subjects received informed consent.

3 Results

3.1 The biochemical and hormonal level changes in PCC patients

The biochemical and hormonal level changes in patients with PCC-A and patients with PCC-DM were analyzed. There was no significant difference in weakness, edema, hypomnesis, muscular soreness, sleep disorders, depression/anxiety, erythra, headache, cough, chest distress, palpitation, nausea, constipation, diarrhea, and hyposmia/hypogeusia between PCC patients and patients with PCC-DM (Table 1). Subsequently, the changes in biochemical indexes and hormone levels were analyzed. Compared with PCC patients, the levels of FBG and Hb1c in patients with PCC-DM were significantly increased (Table 2). However, no significant difference was observed in BMI, ALT, AST, TP, ALB, TG, TC, LDL, Cr, and UA levels between the two groups (Table 2). F and ATCH levels were detected at baseline, 0, 30, 60, and 90 min after the arginine stimulation test. As shown in Table 3, F and ATCH levels were gradually decreased as time went on in patients with PCC-A and patients with PCC-DM. There was no significant difference found in F level and ACTH level at any time between the two groups. Overall, ACTH and F cannot secrete normally after stress in PCC patients.

Table 1

Main clinical manifestations of PCC patients

PCC-A (N = 18) PCC-DM (N = 31) χ 2 P
Weakness 8/18 (44.4%) 12/31 (38.7%) 0.155 0.694
Edema 2/18 (11.1%) 5/31 (16.1%) 0.234 0.628
Hypomnesis 9/18 (50.0%) 14/31 (45.2%) 0.107 0.744
Muscular soreness 9/18 (50.0%) 14/31 (45.2%) 0.107 0.744
Sleep disorders 9/18 (50.0%) 14/31 (45.2%) 0.107 0.744
Depression/anxiety 10/18 (55.5%) 14/31 (45.2%) 0.492 0.483
Erythra 0/18 (0.0%) 1/31 (3.2%) 0.593 0.441
Headache 3/18 (16.7%) 6/31 (19.4%) 0.055 0.815
Cough 2/18 (11.1%) 2/31 (6.5%) 0.330 0.566
Chest distress 1/18 (5.5%) 6/31 (19.4%) 1.771 0.183
Palpitation 1/18 (5.5%) 2/31 (6.5%) 0.02 0.900
Nausea 2/18 (11.1%) 3/31 (9.7%) 0.03 0.873
Constipation 1/18 (5.5%) 3/31 (9.7%) 0.258 0.611
Diarrhea 0/18 0/31
Hyposmia/Hypogeusia 0/18 0/31

PCC-A: PCC alone. PCC-DM: PCC combined with type 2 diabetes mellitus.

Table 2

Biochemical changes of PCC patients

PCC-A (N = 18) PCC-DM (N = 31) T P
BMI (kg/m2) 25.83 ± 3.69 25.29 ± 3.85 0.475 0.637
FBG (mmol/L) 4.96 ± 0.62 7.59 ± 3.18 −3.456 0.001
Hb1c (%) 5.45 ± 0.31 8.26 ± 2.33 −5.064 0.000
ALT (U/L) 19.42 ± 11.29 25.81 ± 19 −1.296 0.201
AST (U/L) 19.74 ± 5.75 22.03 ± 9.47 −0.93 0.357
TP (g/L) 67.6 ± 5.07 66.68 ± 7.73 0.451 0.654
ALB (g/L) 40.99 ± 1.77 40.14 ± 5.43 0.64 0.526
TG (mmol/L) 1.4 ± 0.68 2.13 ± 1.81 −1.647 0.106
TC (mmol/L) 4.19 ± 1.05 4.48 ± 1.24 −0.836 0.407
LDL (mmol/L) 2.08 ± 0.62 2.34 ± 0.77 −1.212 0.232
Cr (umol/L) 61.68 ± 8.45 66.18 ± 14.74 −1.184 0.242
UA (mmol/L) 346.87 ± 92.88 337.39 ± 103.48 0.321 0.75

PCC-A: PCC alone. PCC-DM: PCC combined with type 2 diabetes mellitus. BMI: body mass index. FBG: fasting blood glucose. Hb1c: hoglobin1c. ALT: alanine transaminase. AST: aspartate aminotransferase. TP: total protein. ALB: albumin. TG: triglyceride. TC: total choiesterol. LDL: low-density lipoprotein. Cr: creatinine. UA: uric acid.

Table 3

Hormone level changes of PCC patients undergoing the arginine stimulation test

PCC-A (N = 18) PCC-DM (N = 31) T P
F baseline (nmol/L) 2.46 ± 0.27 2.47 ± 0.38 −0.07 0.944
F 0 min (nmol/L) 2.46 ± 0.23 2.4 ± 0.35 0.584 0.562
F 30 min (nmol/L) 2.43 ± 0.18 2.35 ± 0.35 0.903 0.371
F 60 min (nmol/L) 2.39 ± 0.18 2.31 ± 0.34 0.981 0.332
F 90 min (nmol/L) 2.33 ± 0.23 2.29 ± 0.33 0.377 0.708
F 0 min growth value (nmol/L) −0.01 ± 0.19 −0.07 ± 0.13 1.37 0.177
F 30 min growth value (nmol/L) −0.03 ± 0.22 −0.12 ± 0.14 1.695 0.097
F 60 min growth value (nmol/L) −0.07 ± 0.25 −0.16 ± 0.15 1.595 0.117
F 90 min growth value (nmol/L) −0.14 ± 0.27 −0.18 ± 0.16 0.661 0.512
ACTH baseline (nmol/L) 0.73 ± 0.28 0.71 ± 0.29 0.314 0.755
ACTH 0 min (nmol/L) 0.76 ± 0.34 0.59 ± 0.3 1.724 0.091
ACTH 30 min (nmol/L) 0.64 ± 0.27 0.52 ± 0.28 1.487 0.144
ACTH 60 min (nmol/L) 0.58 ± 0.3 0.52 ± 0.23 0.878 0.384
ACTH 90 min (nmol/L) 0.44 ± 0.25 0.53 ± 0.21 −1.463 0.15
ACTH 0 min growth value (nmol/L) 0.02 ± 0.28 −0.11 ± 0.21 1.923 0.061
ACTH 30 min growth value (nmol/L) −0.09 ± 0.31 −0.18 ± 0.23 1.214 0.231
ACTH 60 min growth value (nmol/L) −0.15 ± 0.42 −0.19 ± 0.24 0.422 0.675
ACTH 90 min growth value (nmol/L) −0.3 ± 0.34 −0.17 ± 0.22 −1.541 0.13

PCC-A: PCC alone. PCC-DM: PCC combined with type 2 diabetes mellitus.

3.2 Metabolomics multivariate analysis in PCC patients with DM

PCA and OPLS-DA were utilized to evaluate the difference in plasma metabolites in B vs A. The correlation analysis showed that the correlation coefficients within groups were higher than those between groups, indicating that the identified differential metabolites are reliable (Figure 1a). The PCA score plot showed that the percentage of principal components 1 and 2 were 16.47 and 6.71%, respectively (Figure 1b). OPLS-DA analysis revealed a significant separation between the PCC-A and PCC-DM groups of plasma samples (Figure 1c). Additionally, the model exhibited a satisfactory fit, as indicated by R2Y = 0.817 and Q2Y = 0.418 (Figure 1c). The OPLS-DA permutation validation revealed that the original R2 and Q2 parameters for the two groups surpassed the respective values post-Y-axis permutation, signifying that the model exhibits an appropriate fit (Figure 1d). Similarly, PCA and OPLS-DA were conducted in PCC-A vs HV and PCC-DM vs DM. The PCA score plot revealed a clear distinction in the metabolic profiles between the PCC-A and HV groups, as well as between the PCC-DM and DM groups (Figure 2a and b). Concurrently, the OPLS-DA model demonstrated a significant isolation between the PCC-A and HV groups as well as the PCC-A and DM groups of plasma samples (Figure 2c and d). The model exhibited a robust fit, with R2Y = 0.999 and Q2Y = 0.988 in PCC-A vs HV groups, and R2Y = 0.999 and Q2Y = 0.99 in PCC-DM vs DM groups. The OPLS-DA permutation test confirmed that the original R2 and Q2 values for PCC – PCC-A vs HV groups and PCC-DM vs DM groups exceeded those obtained after Y-axis permutation (Figure 2e and f). Overall, the metabolic profiles of PCC patients exhibit significant alterations.

Figure 1 
                  Multivariate analysis of metabolites from patients with PCC-A and patients with PCC-DM. (a) Correlation analysis between samples. (b) PCA score plot based on the metabolites in PCC-DM and PCC-A groups. (c) OPLS-DA plot based on the metabolites in PCC-DM and PCC-A groups. (d) OPLS-DA permutation test in PCC-DM and PCC-A groups. PCC-A: PCC alone. PCC-DM: PCC combined with type 2 diabetes mellitus.
Figure 1

Multivariate analysis of metabolites from patients with PCC-A and patients with PCC-DM. (a) Correlation analysis between samples. (b) PCA score plot based on the metabolites in PCC-DM and PCC-A groups. (c) OPLS-DA plot based on the metabolites in PCC-DM and PCC-A groups. (d) OPLS-DA permutation test in PCC-DM and PCC-A groups. PCC-A: PCC alone. PCC-DM: PCC combined with type 2 diabetes mellitus.

Figure 2 
                  Multivariate analysis of metabolites from patients with PCC-A, patients with PCC-DM, and corresponding controls. (a and b) PCA score plot based on the metabolites in PCC-A vs HV groups and PCC-DM vs DM groups. (c and d) OPLS-DA plot based on the metabolites in PCC-A vs HV groups and PCC-DM vs DM groups. (e and f) OPLS-DA permutation test in PCC-A vs HV groups and PCC-DM vs DM groups. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.
Figure 2

Multivariate analysis of metabolites from patients with PCC-A, patients with PCC-DM, and corresponding controls. (a and b) PCA score plot based on the metabolites in PCC-A vs HV groups and PCC-DM vs DM groups. (c and d) OPLS-DA plot based on the metabolites in PCC-A vs HV groups and PCC-DM vs DM groups. (e and f) OPLS-DA permutation test in PCC-A vs HV groups and PCC-DM vs DM groups. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.

3.3 Identification of differential nucleic acid metabolites

Differential metabolites were identified based on the criteria of VIP greater than 1 and p-values less than 0.05. Compared to the group PCC-A, group PCC-DM identified 116 differential metabolites, comprising 29 upregulated and 87 downregulated metabolites (Figure 3a). Figure 3b illustrates the top 10 upregulated and downregulated metabolites in the PCC-DM group as compared to the PCC-A group. Hypaphorine, uric acid, (±)7(8)-DiHDPE(A), and diethyl-malonate were significantly decreased, whereas 7-ketodeoxycholic acid was remarkably increased in the PCC-DM group compared with the PCC-A group. Additionally, a total of 178 and 163 differential metabolites were identified in PCC-A vs HV groups and PCC-DM vs DM groups, respectively (Figure 4a). Compared with the HV group, 155 metabolites were upregulated in the PCC-A group, including l-cystine and uracil, while 23 were downregulated, including l-tyrosine, l-phenylalanine, and adenine (Table S4). Compared with the DM group, 132 metabolites were upregulated in the PCC-DM group, including l-glutamine, 6-O-methylguanine, 1-methylguanine, and uracil, while 31 were downregulated, including adenine and l-tyrosine (Table S5). The heatmap revealed a distinct classification of the metabolomes between the PCC-A and HV groups, as well as between the PCC-DM and DM groups (Figure 4b). Compared with the HV group, 1-methylhistidine, l-tyrosine, and adenine were remarkably decreased, whereas 5-acrylamide and sebacate were obviously increased in the PCC-A group (Figure 4c). Compared with the DM group, l-tyrosine and porphobilinogen were remarkably decreased, whereas 7-methylguanine, sebacate, and pyruvic acid were obviously increased in the PCC-DM group (Figure 4c).

Figure 3 
                  Identification of differential metabolites between PCC-A and PCC-DM. (a) The volcano plot of the plasma metabolomics between the PCC-DM and PCC-A groups. (b) The top 10 upregulated and downregulated metabolites in the PCC-DM group compared with the PCC-A group. PCC-A: PCC alone. PCC-DM: PCC combined with type 2 diabetes mellitus.
Figure 3

Identification of differential metabolites between PCC-A and PCC-DM. (a) The volcano plot of the plasma metabolomics between the PCC-DM and PCC-A groups. (b) The top 10 upregulated and downregulated metabolites in the PCC-DM group compared with the PCC-A group. PCC-A: PCC alone. PCC-DM: PCC combined with type 2 diabetes mellitus.

Figure 4 
                  Identification of differential metabolites between PCC-A, PCC-DM, and corresponding controls. (a) The volcano plot of the plasma metabolomics in PCC-A vs HV groups and PCC-DM vs DM groups. (b) Heatmap of differential metabolites of PCC-A vs HV groups and PCC-DM vs DM groups. (c) The top 10 upregulated and downregulated metabolites in PCC-A vs HV groups and PCC-DM vs DM groups. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.
Figure 4

Identification of differential metabolites between PCC-A, PCC-DM, and corresponding controls. (a) The volcano plot of the plasma metabolomics in PCC-A vs HV groups and PCC-DM vs DM groups. (b) Heatmap of differential metabolites of PCC-A vs HV groups and PCC-DM vs DM groups. (c) The top 10 upregulated and downregulated metabolites in PCC-A vs HV groups and PCC-DM vs DM groups. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.

Subsequently, the common differential metabolites between the PCC-A vs HV group and the PCC-DM vs DM group were analyzed. As shown in Figure 5a, 136 common differential metabolites were identified between the PCC-A vs HV group and the PCC-DM vs DM group. Among these metabolites, we identified 11 common differential metabolites related to nucleotide metabolism between the PCC-A vs HV group and the PCC-DM vs DM group (Figure 5b). The expression of 2-methylguanosine, purine, thymine, and uracil was remarkably enhanced, while adenine was diminished in the PCC-A group compared with the HV group and in the PCC-DM group compared with the DM group (Figure 5c and d). Overall, abnormal nucleotide metabolism occurred in PCC patients.

Figure 5 
                  Analysis of differential metabolites related to nucleotide metabolism. (a) Venn diagram of common differential metabolites between PCC-A vs HV groups and PCC-DM vs DM groups. (b) Venn diagram of common differential metabolites related to nucleotide metabolism between PCC-A vs HV groups and PCC-DM vs DM groups. (c) The expression of metabolites related to nucleotide metabolism in PCC-A and HV groups. (d) The expression of metabolites related to nucleotide metabolism in PCC-DM and DM groups.
Figure 5

Analysis of differential metabolites related to nucleotide metabolism. (a) Venn diagram of common differential metabolites between PCC-A vs HV groups and PCC-DM vs DM groups. (b) Venn diagram of common differential metabolites related to nucleotide metabolism between PCC-A vs HV groups and PCC-DM vs DM groups. (c) The expression of metabolites related to nucleotide metabolism in PCC-A and HV groups. (d) The expression of metabolites related to nucleotide metabolism in PCC-DM and DM groups.

3.4 Analysis of pathways associated with nucleotide metabolism

To elucidate the metabolic characteristics of PCC patients, we conducted a metabolic pathway enrichment analysis. Differential metabolites between PCC-DM and PCC-A groups were mainly enriched in glycine, serine, and threonine metabolism, Carbohydrate digestion and absorption, cysteine and methionine metabolism, and ABC transporters (Figure S1). Differential metabolites between PCC-A and HV groups were mainly enriched in tyrosine metabolism, pyrimidine metabolism, and nicotinate and nicotinamide metabolism (Figure 6a). Differential metabolites between the PCC-DM and DM groups were mainly enriched in tyrosine metabolism, cysteine and methionine metabolism, and glycine and pyrimidine metabolism (Figure 6b). As shown in Figure 6c, uracil and thymine were involved in pyrimidine metabolism. Given the involvement of metabolites from both the PCC-A vs HV and PCC-DM vs DM groups in pyrimidine metabolism, we speculate that patients with PCC may experience disorders of nucleotide metabolism.

Figure 6 
                  Metabolic pathway enrichment analysis. Metabolic pathway enrichment analysis plot revealed remarkably changed pathways in PCC-A vs HV groups (a) and PCC-DM vs DM groups (b). (c) Pyrimidine metabolism. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.
Figure 6

Metabolic pathway enrichment analysis. Metabolic pathway enrichment analysis plot revealed remarkably changed pathways in PCC-A vs HV groups (a) and PCC-DM vs DM groups (b). (c) Pyrimidine metabolism. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.

3.5 The relationship between F and ACTH levels and nucleic acid metabolites

To determine the relationship between metabolic abnormalities and ACTH and F levels after stress in PCC patients, we analyzed the correlation between nucleic acid metabolites (adenine, uracil, 6-O-methylguanine, 7-methylguanine, 1-methylguanine, and 2-deoxyribose 1-phosphate) and F and ACTH levels. The expression of adenine was positively correlated with the growth value of F secretion at 90 min after arginine stimulation test (Table 4). However, there was no correlation between F and ACTH growth values and uracil, 6-O-methylguanine, 7-methylguanine, 1-methylguanine, and 2-deoxyribose 1-phosphate.

Table 4

The relationship between F and ACTH levels and nucleic acid metabolites in PCC patients undergoing the arginine stimulation test

Metabolites F 0 min growth value (nmol/L) F 30 min growth value (nmol/L) F 60 min growth value (nmol/L) F 90 min growth value (nmol/L) ACTH 0 min growth value (pmol/L) ACTH 30 min growth value (pmol/L) ACTH 60 min growth value (pmol/L) ACTH 90 min growth value (pmol/L)
Adenine r 0.162 0.178 0.274 0.328 0.177 0.207 0.256 0.238
p 0.265 0.221 0.057 0.022 0.224 0.153 0.076 0.099
Uracil r −0.033 −0.074 −0.041 0.027 −0.184 −0.077 0.02 0.067
p 0.822 0.615 0.778 0.852 0.205 0.598 0.89 0.646
6-O-Methylguanine r −0.156 −0.043 0.016 0.068 −0.126 0.041 0.071 0.125
p 0.284 0.772 0.913 0.643 0.387 0.78 0.628 0.394
7-Methylguanine r −0.156 −0.043 0.016 0.068 −0.126 0.041 0.071 0.125
p 0.284 0.772 0.913 0.643 0.387 0.78 0.628 0.394
1-Methylguanine r −0.156 −0.043 0.016 0.068 −0.126 0.041 0.071 0.125
p 0.284 0.772 0.913 0.643 0.387 0.78 0.628 0.394
2-Deoxyribose 1-phosphate r −0.018 −0.018 −0.03 −0.051 −0.033 −0.145 −0.139 −0.201
P 0.903 0.902 0.838 0.73 0.824 0.319 0.34 0.166

3.6 Biomarkers identification

Receiver operating characteristic (ROC) curve analysis was used to identify biomarkers in PCC patients. Uracil and thymine, which are related to pyrimidine metabolism, were analyzed. The results showed that the area under the ROC curves (AUCs) for uracil and thymine in PCC-A vs HV groups were 0.986 and 1, respectively (Figure 7a). The AUCs for uracil and thymine in PCC-DM vs DM groups were 0.971 and 1, respectively (Figure 7b). These results indicated that uracil and thymine had the potential to be biomarkers for PCC patients.

Figure 7 
                  ROC curve analysis of the diagnostic values of metabolites in PCC patients. (a) Biomarkers identified in PCC-A vs HV groups. (b) Biomarkers identified in the PCC-DM vs DM groups. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.
Figure 7

ROC curve analysis of the diagnostic values of metabolites in PCC patients. (a) Biomarkers identified in PCC-A vs HV groups. (b) Biomarkers identified in the PCC-DM vs DM groups. PCC-A: PCC alone. HV: healthy volunteer. PCC-DM: PCC combined with type 2 diabetes mellitus. DM: type 2 diabetes mellitus.

4 Discussion

PCC patients had symptoms such as fatigue, chest tightness, joint muscle pain, fatigue, sleep disorders, and psychological abnormalities [30], but routine and biochemical examinations showed no obvious abnormalities. We highly suspected that there was an internal environment disorder at this time. We used UPLC–MS/MS to analyze differential metabolites in PCC patients. Compared with the PCC-A group, 29 differential metabolites were up-regulated and 87 differential metabolites were down-regulated in the PCC-DM group. Further comparison of metabolites with HV and DM patients showed that PCC patients mainly had abnormal nucleotide metabolism.

Nucleotides, including purine nucleotides and pyrimidine nucleotides, are active precursors of nucleic acids and participate in many biological processes in the body as mediators [31]. Nucleotides are mainly synthesized by the body cells themselves, and there are two ways of synthesis: de novo synthesis and salvage pathway [32]. The de novo synthesis mainly occurs in the liver, and aspartate and glutamine are the main sources of purine and pyrimidine bases [33]. In this study, adenine was significantly down-regulated and uracil was significantly up-regulated in patients with PCC alone compared with normal controls. Compared to patients with DM alone, 6-O-methylguanine, 1-methylguanine, 7-methylguanine, and uracil were significantly up-regulated, and adenine was significantly down-regulated in patients with PCC-DM. These findings indicate that PCC patients exhibit abnormal nucleotide metabolism.

The main raw material of the salvage pathway is the free purine/pyrimidine base produced by nucleotide degradation. Because the human brain lacks the enzyme system of de novo synthesis of purine nucleotides, the salvage pathway can only be used. Adenine can assist in the synthesis of DNA and RNA and is a key regulatory factor in the maintenance of physiological processes [34,35]. When NAD+ levels are reduced, aging, diabetes, cancer, neurodegeneration, and cardiovascular disease are significantly increased [36]. In this study, abnormal de novo synthesis was found in PCC patients, and adenine expression was further reduced in patients with PCC-DM, and uric acid was significantly reduced. These results suggest that both de novo nucleotide synthesis and remedial synthesis pathways are seriously damaged in PCC patients, especially in patients with PCC-DM.

The diagnosis and treatment of PCC remain challenging, and there is a lack of biomarkers for early recognition and intervention. In recent years, significant progress has been made in the study of biomarkers for PCC patients. Gu et al. identified 23 potential biomarkers that could influence the long-term consequences of COVID-19, which could help identify patients with PCC at high risk [37]. Cervia-Hasler et al. discovered that patients with PCC showed increased markers of hemolysis, tissue damage, platelet activation, and monocyte–platelet aggregates and confirmed complement and thromboinflammatory proteins as PCC biomarkers [38]. In this study, we preliminarily identified uracil and thymine as potential biomarkers for PCC patients using ROC curves. Uracil and thymine are important components of nucleotides involved in the synthesis and metabolism of DNA and RNA. In patients with PCC, abnormal levels of these two substances may reflect disruption of nucleotide metabolism in the body. However, it is important to note that current research on the role of uracil and thymine in PCC is still in its preliminary stages. Future studies require larger-scale clinical trials to validate these findings and to further explore their interactions with other pathological mechanisms of PCC. Additionally, the expression of these biomarkers may vary among different patient groups, highlighting the need for personalized diagnosis and treatment strategies as a crucial direction for future research.

Studies have confirmed that COVID-19 can invade many parts of the brain, including the hypothalamus and pituitary gland [8]. Among patients who underwent low-dose ACTH stimulation tests 3 months after COVID-19 infection, 16.2% showed insufficient F response [39,40]. In this study, we performed arginine stimulation tests on PCC patients, and PCC patients showed a decreased trend of ACTH and F, which may be related to multiple endocrine axis dysfunction caused by the COVID-19 virus attack on the ventromedial nucleus of the hypothalamus. The hypothalamus–pituitary–adrenal (HPA) axis is composed of the hypothalamus, pituitary gland, and adrenal gland and is the core of homeostasis, stress response, energy metabolism, and neuropsychiatric function [41]. F is a hormone secreted by the adrenal gland, which plays an important role in the regulation of metabolism, immunity, and blood pressure [42]. The decrease of F may lead to fatigue, low blood pressure, low blood sugar, and decreased immunity [43]. The deficiency of ACTH and F can be life-threatening under stress conditions (such as infection, surgery, and trauma) [44]. For patients with PCC, a direct consequence of low F levels is a decrease in the brain’s ability to suppress inflammation [45]. When the patient encounters the stressor again, the inflammatory response of the brain may become extremely intense and even lead to repeated episodes of symptoms. Normally, low cortisol levels should be compensated for by an increase in corticotropin produced by the pituitary gland. However, no such compensatory increase was observed in patients with PCC, indicating dysfunction of the HPA axis [46]. However, the underlying mechanisms of ACTH and F changes remain to be further investigated. Furthermore, we found that adenine was positively correlated with F growth value at 90 min after the arginine stimulation test. Together, there may be a complex interaction between ACTH and F levels and the disturbance of nucleotide metabolism in patients with PCC. Future studies need to further clarify the specific mechanism of this relationship to provide new ideas and targets for the diagnosis and treatment of PCC.

This study has some limitations in exploring the abnormal nucleotide metabolism and its relationship with endocrine function in patients with PCC. First, due to the limited scope of the metabolite panel, we may not have covered all important nucleotide metabolites. Future research needs to expand the scope of the metabolite panel to include a more diverse range of nucleotide metabolites in order to obtain more comprehensive and accurate research conclusions. Second, this study primarily relied on metabolomic analysis and hormone level analysis, failing to deeply investigate the complex interactions between nucleotide metabolism and the endocrine system. Finally, as PCC is an emerging disease and related research is still in its infancy, this study may not have fully leveraged and integrated existing knowledge, potentially leading to gaps in understanding. Therefore, future research needs to expand the sample size, employ interdisciplinary approaches, and consider more potential influencing factors to further uncover the nature of the relationship between nucleotide metabolism abnormalities and the endocrine system in patients with PCC.

In summary, the de novo synthesis and remedial synthesis pathways in patients with PCC, especially in patients with PCC-DM, are seriously damaged, leading to the disorder of the body’s cell metabolic pathways and the imbalance of cell metabolism. The abnormal secretion of ACTH and F after stress may be an important reason for the dysfunction of nucleotide metabolism.

Abbreviations

ACTH

adrenocorticotropic hormone

ALB

albumin

ALT

alanine aminotransferase

AST

aspartate aminotransferase

COVID-19

coronavirus disease 2019

Cr

creatinine

DM

diabetes mellitus

F

cortisol

GC–MS

gas chromatography–mass spectrometry

GSI

nebulizer gas

GSII

heater gas

HDL

high-density lipoprotein

HV

healthy volunteers

LC–MS

liquid chromatography–mass spectrometry

LDL

low-density lipoprotein

OPLS-DA

orthogonal partial least squares discriminant analysis

PCC

post-COVID-19 condition

PCC-A

PCC alone

PCC-DM

PCC combined with type 2 diabetes mellitus

TC

total cholesterol

TG

triglyceride

TP

total protein

UA

uric acid

UPLC–MS/MS

ultraperformance liquid chromatography–mass spectrometry

VIP

variable importance in projection


# These authors contributed equally to this work.


Acknowledgments

Not applicable.

  1. Funding information: This study was supported by the Key Research and Development Program of the Autonomous Region in 2024 (2024BEG02015).

  2. Author contributions: All authors contributed to the study conception and design. The original draft was written by Yalei Fan and revised by Xiaomin Xie. Data collection and investigation were performed by Guirong Bai, Wenrui Ji, and Yanting He. Li Zhang, Haiyan Zhou, and Ling Li were responsible for data analysis and visualization. Validation was performed by Huan Li and Dan Qiang. All authors read and approved the final manuscript.

  3. Conflict of interest: The authors declare that there is no conflict of interest.

  4. Data availability statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

[1] Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601–15.10.1038/s41591-021-01283-zSearch in Google Scholar PubMed PubMed Central

[2] Koc HC, Xiao J, Liu W, Li Y, Chen G. Long COVID and its management. Int J Biol Sci. 2022;18(12):4768–80.10.7150/ijbs.75056Search in Google Scholar PubMed PubMed Central

[3] Lechuga GC, Morel CM, De-Simone SG. Hematological alterations associated with long COVID-19. Front Physiol. 2023;14:1203472.10.3389/fphys.2023.1203472Search in Google Scholar PubMed PubMed Central

[4] Davis HE, Assaf GS, McCorkell L, Wei H, Low RJ, Re’em Y, et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine. 2021;38:101019.10.1016/j.eclinm.2021.101019Search in Google Scholar PubMed PubMed Central

[5] Dinis Teixeira JP, Santos M, Soares P, Azevedo L, Barbosa P, Boas AV, et al. LOCUS (LOng Covid-Understanding Symptoms, events and use of services in Portugal): A three-component study protocol. PLoS One. 2023;18(4):e0285051.10.1371/journal.pone.0285051Search in Google Scholar PubMed PubMed Central

[6] Liu Y, Gu X, Li H, Zhang H, Xu J. Mechanisms of long COVID: An updated review. Chin Med J Pulmo Crit Care Med. 2023;1(4):231–40.10.1016/j.pccm.2023.10.003Search in Google Scholar PubMed PubMed Central

[7] Clarke SA, Abbara A, Dhillo WS. Impact of COVID-19 on the endocrine system: A mini-review. Endocrinology. 2022;163(1):bqab203.10.1210/endocr/bqab203Search in Google Scholar PubMed PubMed Central

[8] Zhou L, Zhang M, Wang J, Gao J. Sars-Cov-2: Underestimated damage to nervous system. Travel Med Infect Dis. 2020;36:101642.10.1016/j.tmaid.2020.101642Search in Google Scholar PubMed PubMed Central

[9] Lightman SL, Birnie MT, Conway-Campbell BL. Dynamics of ACTH and cortisol secretion and implications for disease. Endocr Rev. 2020;41(3):bnaa002.10.1210/endrev/bnaa002Search in Google Scholar PubMed PubMed Central

[10] Gu WT, Zhou F, Xie WQ, Wang S, Yao H, Liu YT, et al. A potential impact of SARS-CoV-2 on pituitary glands and pituitary neuroendocrine tumors. Endocrine. 2021;72(2):340–8.10.1007/s12020-021-02697-ySearch in Google Scholar PubMed PubMed Central

[11] Ding Y, He L, Zhang Q, Huang Z, Che X, Hou J, et al. Organ distribution of severe acute respiratory syndrome (SARS) associated coronavirus (SARS-CoV) in SARS patients: implications for pathogenesis and virus transmission pathways. J Pathol. 2004;203(2):622–30.10.1002/path.1560Search in Google Scholar PubMed PubMed Central

[12] Tomo S, Banerjee M, Karli S, Purohit P, Mitra P, Sharma P, et al. Assessment of DHEAS, cortisol, and DHEAS/cortisol ratio in patients with COVID-19: A pilot study. Hormones. 2022;21(3):515–8.10.1007/s42000-022-00382-xSearch in Google Scholar PubMed PubMed Central

[13] Esmaeilzadeh A, Elahi R, Siahmansouri A, Maleki AJ, Moradi A. Endocrine and metabolic complications of COVID-19: Lessons learned and future prospects. J Mol Endocrinol. 2022;69(3):R125–50.10.1530/JME-22-0036Search in Google Scholar PubMed

[14] Xu SW, Ilyas I, Weng JP. Endothelial dysfunction in COVID-19: An overview of evidence, biomarkers, mechanisms and potential therapies. Acta Pharmacol Sin. 2023;44(4):695–709.10.1038/s41401-022-00998-0Search in Google Scholar PubMed PubMed Central

[15] Motta CS, Torices S, da Rosa BG, Marcos AC, Alvarez-Rosa L, Siqueira M, et al. Human brain microvascular endothelial cells exposure to SARS-CoV-2 leads to inflammatory activation through NF-kappaB non-canonical pathway and mitochondrial remodeling. Viruses. 2023;15(3):745.10.3390/v15030745Search in Google Scholar PubMed PubMed Central

[16] Huang P, Zuo Q, Li Y, Oduro PK, Tan F, Wang Y, et al. A vicious cycle: In severe and critically ill covid-19 patients. Front Immunol. 2022;13:930673.10.3389/fimmu.2022.930673Search in Google Scholar PubMed PubMed Central

[17] Chang KH, Cheng ML, Tang HY, Huang CY, Wu YR, Chen CM. Alternations of metabolic profile and kynurenine metabolism in the plasma of Parkinson’s disease. Mol Neurobiol. 2018;55(8):6319–28.10.1007/s12035-017-0845-3Search in Google Scholar PubMed

[18] Adant I, Bird M, Decru B, Windmolders P, Wallays M, de Witte P, et al. Pyruvate and uridine rescue the metabolic profile of OXPHOS dysfunction. Mol Metab. 2022;63:101537.10.1016/j.molmet.2022.101537Search in Google Scholar PubMed PubMed Central

[19] Cui L, Lu H, Lee YH. Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases. Mass Spectrom Rev. 2018;37(6):772–92.10.1002/mas.21562Search in Google Scholar PubMed

[20] Zeki OC, Eylem CC, Recber T, Kir S, Nemutlu E. Integration of GC-MS and LC-MS for untargeted metabolomics profiling. J Pharm Biomed Anal. 2020;190:113509.10.1016/j.jpba.2020.113509Search in Google Scholar PubMed

[21] Sheng Y, Meng G, Zhang M, Chen X, Chai X, Yu H, et al. Dan-shen Yin promotes bile acid metabolism and excretion to prevent atherosclerosis via activating FXR/BSEP signaling pathway. J Ethnopharmacol. 2024;330:118209.10.1016/j.jep.2024.118209Search in Google Scholar PubMed

[22] Lopez-Hernandez Y, Monarrez-Espino J, Lopez DAG, Zheng J, Borrego JC, Torres-Calzada C, et al. The plasma metabolome of long COVID patients two years after infection. Sci Rep. 2023;13(1):12420.10.1038/s41598-023-39049-xSearch in Google Scholar PubMed PubMed Central

[23] Saito S, Shahbaz S, Luo X, Osman M, Redmond D, Cohen Tervaert JW, et al. Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome. Front Immunol. 2024;15:1341843.10.3389/fimmu.2024.1341843Search in Google Scholar PubMed PubMed Central

[24] Dogan HO, Senol O, Bolat S, Yildiz SN, Buyuktuna SA, Sariismailoglu R, et al. Understanding the pathophysiological changes via untargeted metabolomics in COVID-19 patients. J Med Virol. 2021;93(4):2340–9.10.1002/jmv.26716Search in Google Scholar PubMed

[25] Barberis E, Timo S, Amede E, Vanella VV, Puricelli C, Cappellano G, et al. Large-scale plasma analysis revealed new mechanisms and molecules associated with the host response to SARS-CoV-2. Int J Mol Sci. 2020;21(22):8623.10.3390/ijms21228623Search in Google Scholar PubMed PubMed Central

[26] Zuo W, He D, Liang C, Du S, Hua Z, Nie Q, et al. The persistence of SARS-CoV-2 in tissues and its association with long COVID symptoms: A cross-sectional cohort study in China. Lancet Infect Dis. 2024;24(8):845–55.10.1016/S1473-3099(24)00171-3Search in Google Scholar PubMed

[27] Gioia U, Tavella S, Martinez-Orellana P, Cicio G, Colliva A, Ceccon M, et al. SARS-CoV-2 infection induces DNA damage, through CHK1 degradation and impaired 53BP1 recruitment, and cellular senescence. Nat Cell Biol. 2023;25(4):550–64.10.1038/s41556-023-01096-xSearch in Google Scholar PubMed PubMed Central

[28] American Diabetes A. 2. Classification and diagnosis of diabetes: Standards of medical care in diabetes-2019. Diabetes care. 2019;42(Suppl 1):S13–28.10.2337/dc19-S002Search in Google Scholar PubMed

[29] Thaweethai T, Jolley SE, Karlson EW, Levitan EB, Levy B, McComsey GA, et al. Development of a definition of postacute sequelae of SARS-CoV-2 infection. JAMA. 2023;329(22):1934–46.10.1001/jama.2023.15712Search in Google Scholar PubMed

[30] Yong SJ. Long COVID or post-COVID-19 syndrome: Putative pathophysiology, risk factors, and treatments. Infect Dis. 2021;53(10):737–54.10.1080/23744235.2021.1924397Search in Google Scholar PubMed PubMed Central

[31] Kamatani N, Jinnah HA, Hennekam RCM, van Kuilenburg ABP. 6 - Purine and pyrimidine metabolism. In: Pyeritz RE, Korf BR, Grody WW, editors. Emery and Rimoin’s principles and practice of medical genetics and genomics. (Seventh Edition) USA: Academic Press; 2021. p. 183–234.10.1016/B978-0-12-812535-9.00006-6Search in Google Scholar

[32] Fernandez-Lucas J. Biotechnological and biomedical applications of enzymes involved in the synthesis of nucleosides and nucleotides. Biomolecules. 2021;11(8):1147.10.3390/biom11081147Search in Google Scholar PubMed PubMed Central

[33] Villa E, Ali ES, Sahu U, Ben-Sahra I. Cancer cells tune the signaling pathways to empower de novo synthesis of nucleotides. Cancers. 2019;11(5):688.10.3390/cancers11050688Search in Google Scholar PubMed PubMed Central

[34] Verdin E. NAD(+) in aging, metabolism, and neurodegeneration. Science. 2015;350(6265):1208–13.10.1126/science.aac4854Search in Google Scholar PubMed

[35] Covarrubias AJ, Perrone R, Grozio A, Verdin E. NAD(+) metabolism and its roles in cellular processes during ageing. Nat Rev Mol Cell Biol. 2021;22(2):119–41.10.1038/s41580-020-00313-xSearch in Google Scholar PubMed PubMed Central

[36] Fang EF, Lautrup S, Hou Y, Demarest TG, Croteau DL, Mattson MP, et al. NAD(+) in Aging: Molecular mechanisms and translational implications. Trends Mol Med. 2017;23(10):899–916.10.1016/j.molmed.2017.08.001Search in Google Scholar PubMed PubMed Central

[37] Gu X, Wang S, Zhang W, Li C, Guo L, Wang Z, et al. Probing long COVID through a proteomic lens: A comprehensive two-year longitudinal cohort study of hospitalised survivors. EBioMedicine. 2023;98:104851.10.1016/j.ebiom.2023.104851Search in Google Scholar PubMed PubMed Central

[38] Cervia-Hasler C, Bruningk SC, Hoch T, Fan B, Muzio G, Thompson RC, et al. Persistent complement dysregulation with signs of thromboinflammation in active Long Covid. Science. 2024;383(6680):eadg7942.10.1126/science.adg7942Search in Google Scholar PubMed

[39] Urhan E, Karaca Z, Unuvar GK, Gundogan K, Unluhizarci K. Investigation of pituitary functions after acute coronavirus disease 2019. Endocr J. 2022;69(6):649–58.10.1507/endocrj.EJ21-0531Search in Google Scholar PubMed

[40] Hamazaki K, Nishigaki T, Kuramoto N, Oh K, Konishi H. Secondary adrenal insufficiency after COVID-19 diagnosed by insulin tolerance test and corticotropin-releasing hormone test. Cureus. 2022;14(3):e23021.10.7759/cureus.23021Search in Google Scholar PubMed PubMed Central

[41] Miller WL. The hypothalamic-pituitary-adrenal axis: A brief history. Horm Res Paediatr. 2018;89(4):212–23.10.1159/000487755Search in Google Scholar PubMed

[42] Katsu Y, Baker ME. Subchapter 123D - Cortisol. In: Ando H, Ukena K, Nagata S, editors. Handbook of hormones. 2nd edn. San Diego: Academic Press; 2021. p. 947–9.10.1016/B978-0-12-820649-2.00261-8Search in Google Scholar

[43] Hahner S, Ross RJ, Arlt W, Bancos I, Burger-Stritt S, Torpy DJ, et al. Adrenal insufficiency. Nat Rev Dis Primers. 2021;7(1):19.10.1038/s41572-021-00252-7Search in Google Scholar PubMed

[44] Martin-Grace J, Dineen R, Sherlock M, Thompson CJ. Adrenal insufficiency: Physiology, clinical presentation and diagnostic challenges. Clin Chim Acta; Int J Clin Chem. 2020;505:78–91.10.1016/j.cca.2020.01.029Search in Google Scholar PubMed

[45] Frank MG, Ball JB, Hopkins S, Kelley T, Kuzma AJ, Thompson RS, et al. SARS-CoV-2 S1 subunit produces a protracted priming of the neuroinflammatory, physiological, and behavioral responses to a remote immune challenge: A role for corticosteroids. Brain Behav Immun. 2024;121:87–103.10.1016/j.bbi.2024.07.034Search in Google Scholar PubMed

[46] Klein J, Wood J, Jaycox JR, Dhodapkar RM, Lu P, Gehlhausen JR, et al. Distinguishing features of long COVID identified through immune profiling. Nature. 2023;623(7985):139–48.10.1038/s41586-023-06651-ySearch in Google Scholar PubMed PubMed Central

Received: 2024-11-22
Revised: 2025-04-15
Accepted: 2025-05-20
Published Online: 2025-10-07

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  74. Comprehensive biomedicine assessment of Apteranthes tuberculata extracts: Phytochemical analysis and multifaceted pharmacological evaluation in animal models
  75. Relation of time in range to severity of coronary artery disease in patients with type 2 diabetes: A cross-sectional study
  76. Dopamine attenuates ethanol-induced neuronal apoptosis by stimulating electrical activity in the developing rat retina
  77. Correlation between albumin levels during the third trimester and the risk of postpartum levator ani muscle rupture
  78. Factors associated with maternal attention and distraction during breastfeeding and childcare: A cross-sectional study in the west of Iran
  79. Mechanisms of hesperetin in treating metabolic dysfunction-associated steatosis liver disease via network pharmacology and in vitro experiments
  80. The law on oncological oblivion in the Italian and European context: How to best uphold the cancer patients’ rights to privacy and self-determination?
  81. The prognostic value of the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and prognostic nutritional index for survival in patients with colorectal cancer
  82. Factors affecting the measurements of peripheral oxygen saturation values in healthy young adults
  83. Comparison and correlations between findings of hysteroscopy and vaginal color Doppler ultrasonography for detection of uterine abnormalities in patients with recurrent implantation failure
  84. The effects of different types of RAGT on balance function in stroke patients with low levels of independent walking in a convalescent rehabilitation hospital
  85. Causal relationship between asthma and ankylosing spondylitis: A bidirectional two-sample univariable and multivariable Mendelian randomization study
  86. Correlations of health literacy with individuals’ understanding and use of medications in Southern Taiwan
  87. Correlation of serum calprotectin with outcome of acute cerebral infarction
  88. Comparison of computed tomography and guided bronchoscopy in the diagnosis of pulmonary nodules: A systematic review and meta-analysis
  89. Curdione protects vascular endothelial cells and atherosclerosis via the regulation of DNMT1-mediated ERBB4 promoter methylation
  90. The identification of novel missense variant in ChAT gene in a patient with gestational diabetes denotes plausible genetic association
  91. Molecular genotyping of multi-system rare blood types in foreign blood donors based on DNA sequencing and its clinical significance
  92. Exploring the role of succinyl carnitine in the association between CD39⁺ CD4⁺ T cell and ulcerative colitis: A Mendelian randomization study
  93. Dexmedetomidine suppresses microglial activation in postoperative cognitive dysfunction via the mmu-miRNA-125/TRAF6 signaling axis
  94. Analysis of serum metabolomics in patients with different types of chronic heart failure
  95. Diagnostic value of hematological parameters in the early diagnosis of acute cholecystitis
  96. Pachymaran alleviates fat accumulation, hepatocyte degeneration, and injury in mice with nonalcoholic fatty liver disease
  97. Decrease in CD4 and CD8 lymphocytes are predictors of severe clinical picture and unfavorable outcome of the disease in patients with COVID-19
  98. METTL3 blocked the progression of diabetic retinopathy through m6A-modified SOX2
  99. The predictive significance of anti-RO-52 antibody in patients with interstitial pneumonia after treatment of malignant tumors
  100. Exploring cerebrospinal fluid metabolites, cognitive function, and brain atrophy: Insights from Mendelian randomization
  101. Development and validation of potential molecular subtypes and signatures of ocular sarcoidosis based on autophagy-related gene analysis
  102. Widespread venous thrombosis: Unveiling a complex case of Behçet’s disease with a literature perspective
  103. Uterine fibroid embolization: An analysis of clinical outcomes and impact on patients’ quality of life
  104. Discovery of lipid metabolism-related diagnostic biomarkers and construction of diagnostic model in steroid-induced osteonecrosis of femoral head
  105. Serum-derived exomiR-188-3p is a promising novel biomarker for early-stage ovarian cancer
  106. Enhancing chronic back pain management: A comparative study of ultrasound–MRI fusion guidance for paravertebral nerve block
  107. Peptide CCAT1-70aa promotes hepatocellular carcinoma proliferation and invasion via the MAPK/ERK pathway
  108. Electroacupuncture-induced reduction of myocardial ischemia–reperfusion injury via FTO-dependent m6A methylation modulation
  109. Hemorrhoids and cardiovascular disease: A bidirectional Mendelian randomization study
  110. Cell-free adipose extract inhibits hypertrophic scar formation through collagen remodeling and antiangiogenesis
  111. HALP score in Demodex blepharitis: A case–control study
  112. Assessment of SOX2 performance as a marker for circulating cancer stem-like cells (CCSCs) identification in advanced breast cancer patients using CytoTrack system
  113. Risk and prognosis for brain metastasis in primary metastatic cervical cancer patients: A population-based study
  114. Comparison of the two intestinal anastomosis methods in pediatric patients
  115. Factors influencing hematological toxicity and adverse effects of perioperative hyperthermic intraperitoneal vs intraperitoneal chemotherapy in gastrointestinal cancer
  116. Endotoxin tolerance inhibits NLRP3 inflammasome activation in macrophages of septic mice by restoring autophagic flux through TRIM26
  117. Lateral transperitoneal laparoscopic adrenalectomy: A single-centre experience of 21 procedures
  118. Petunidin attenuates lipopolysaccharide-induced retinal microglia inflammatory response in diabetic retinopathy by targeting OGT/NF-κB/LCN2 axis
  119. Procalcitonin and C-reactive protein as biomarkers for diagnosing and assessing the severity of acute cholecystitis
  120. Factors determining the number of sessions in successful extracorporeal shock wave lithotripsy patients
  121. Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery
  122. Inhibition of ATG7 promotes orthodontic tooth movement by regulating the RANKL/OPG ratio under compression force
  123. A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer
  124. Glutathione attenuates sepsis-associated encephalopathy via dual modulation of NF-κB and PKA/CREB pathways
  125. FAHD1 prevents neuronal ferroptosis by modulating R-loop and the cGAS–STING pathway
  126. Association of placenta weight and morphology with term low birth weight: A case–control study
  127. Investigation of the pathogenic variants induced Sjogren’s syndrome in Turkish population
  128. Nucleotide metabolic abnormalities in post-COVID-19 condition and type 2 diabetes mellitus patients and their association with endocrine dysfunction
  129. TGF-β–Smad2/3 signaling in high-altitude pulmonary hypertension in rats: Role and mechanisms via macrophage M2 polarization
  130. Ultrasound-guided unilateral versus bilateral erector spinae plane block for postoperative analgesia of patients undergoing laparoscopic cholecystectomy
  131. Profiling gut microbiome dynamics in subacute thyroiditis: Implications for pathogenesis, diagnosis, and treatment
  132. Delta neutrophil index, CRP/albumin ratio, procalcitonin, immature granulocytes, and HALP score in acute appendicitis: Best performing biomarker?
  133. Anticancer activity mechanism of novelly synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative on colon cancer cells
  134. H2valdien3 arrests the cell cycle and induces apoptosis of gastric cancer
  135. Prognostic relevance of PRSS2 and its immune correlates in papillary thyroid carcinoma
  136. Association of SGLT2 inhibition with psychiatric disorders: A Mendelian randomization study
  137. Motivational interviewing for alcohol use reduction in Thai patients
  138. Luteolin alleviates oxygen-glucose deprivation/reoxygenation-induced neuron injury by regulating NLRP3/IL-1β signaling
  139. Polyphyllin II inhibits thyroid cancer cell growth by simultaneously inhibiting glycolysis and oxidative phosphorylation
  140. Relationship between the expression of copper death promoting factor SLC31A1 in papillary thyroid carcinoma and clinicopathological indicators and prognosis
  141. CSF2 polarized neutrophils and invaded renal cancer cells in vitro influence
  142. Proton pump inhibitors-induced thrombocytopenia: A systematic literature analysis of case reports
  143. The current status and influence factors of research ability among community nurses: A sequential qualitative–quantitative study
  144. OKAIN: A comprehensive oncology knowledge base for the interpretation of clinically actionable alterations
  145. The relationship between serum CA50, CA242, and SAA levels and clinical pathological characteristics and prognosis in patients with pancreatic cancer
  146. Identification and external validation of a prognostic signature based on hypoxia–glycolysis-related genes for kidney renal clear cell carcinoma
  147. Engineered RBC-derived nanovesicles functionalized with tumor-targeting ligands: A comparative study on breast cancer targeting efficiency and biocompatibility
  148. Relationship of resting echocardiography combined with serum micronutrients to the severity of low-gradient severe aortic stenosis
  149. Effect of vibration on pain during subcutaneous heparin injection: A randomized, single-blind, placebo-controlled trial
  150. The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
  151. Comparing biofeedback device vs diaphragmatic breathing for bloating relief: A randomized controlled trial
  152. Serum uric acid to albumin ratio and C-reactive protein as predictive biomarkers for chronic total occlusion and coronary collateral circulation quality
  153. Multiple organ scoring systems for predicting in-hospital mortality of sepsis patients in the intensive care unit
  154. Single-cell RNA sequencing data analysis of the inner ear in gentamicin-treated mice via intraperitoneal injection
  155. Suppression of cathepsin B attenuates myocardial injury via limiting cardiomyocyte apoptosis
  156. Review Articles
  157. The effects of enhanced external counter-pulsation on post-acute sequelae of COVID-19: A narrative review
  158. Diabetes-related cognitive impairment: Mechanisms, symptoms, and treatments
  159. Microscopic changes and gross morphology of placenta in women affected by gestational diabetes mellitus in dietary treatment: A systematic review
  160. Review of mechanisms and frontier applications in IL-17A-induced hypertension
  161. Research progress on the correlation between islet amyloid peptides and type 2 diabetes mellitus
  162. The safety and efficacy of BCG combined with mitomycin C compared with BCG monotherapy in patients with non-muscle-invasive bladder cancer: A systematic review and meta-analysis
  163. The application of augmented reality in robotic general surgery: A mini-review
  164. The effect of Greek mountain tea extract and wheat germ extract on peripheral blood flow and eicosanoid metabolism in mammals
  165. Neurogasobiology of migraine: Carbon monoxide, hydrogen sulfide, and nitric oxide as emerging pathophysiological trinacrium relevant to nociception regulation
  166. Plant polyphenols, terpenes, and terpenoids in oral health
  167. Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective
  168. End-of-life in cancer patients: Medicolegal implications and ethical challenges in Europe
  169. The maternal factors during pregnancy for intrauterine growth retardation: An umbrella review
  170. Intra-abdominal hypertension/abdominal compartment syndrome of pediatric patients in critical care settings
  171. PI3K/Akt pathway and neuroinflammation in sepsis-associated encephalopathy
  172. Screening of Group B Streptococcus in pregnancy: A systematic review for the laboratory detection
  173. Giant borderline ovarian tumours – review of the literature
  174. Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making – A systematic review
  175. Cholera epidemiology analysis through the experience of the 1973 Naples epidemic
  176. Risk factors of frailty/sarcopenia in community older adults: Meta-analysis
  177. Supplement strategies for infertility in overweight women: Evidence and legal insights
  178. Scurvy, a not obsolete disorder: Clinical report in eight young children and literature review
  179. A meta-analysis of the effects of DBS on cognitive function in patients with advanced PD
  180. Protective role of selenium in sepsis: Mechanisms and potential therapeutic strategies
  181. Strategies for hyperkalemia management in dialysis patients: A systematic review
  182. C-reactive protein-to-albumin ratio in peripheral artery disease
  183. Case Reports
  184. Delayed graft function after renal transplantation
  185. Semaglutide treatment for type 2 diabetes in a patient with chronic myeloid leukemia: A case report and review of the literature
  186. Diverse electrophysiological demyelinating features in a late-onset glycogen storage disease type IIIa case
  187. Giant right atrial hemangioma presenting with ascites: A case report
  188. Laser excision of a large granular cell tumor of the vocal cord with subglottic extension: A case report
  189. EsoFLIP-assisted dilation for dysphagia in systemic sclerosis: Highlighting the role of multimodal esophageal evaluation
  190. Molecular hydrogen-rhodiola as an adjuvant therapy for ischemic stroke in internal carotid artery occlusion: A case report
  191. Coronary artery anomalies: A case of the “malignant” left coronary artery and its surgical management
  192. Rapid Communication
  193. Biological properties of valve materials using RGD and EC
  194. A single oral administration of flavanols enhances short-term memory in mice along with increased brain-derived neurotrophic factor
  195. Letter to the Editor
  196. Role of enhanced external counterpulsation in long COVID
  197. Expression of Concern
  198. Expression of concern “A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma”
  199. Expression of concern “Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway”
  200. Expression of concern “circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8”
  201. Corrigendum
  202. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  203. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  204. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  205. Retraction
  206. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  207. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  208. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
  209. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
  210. Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer
  211. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  212. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  213. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  214. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  215. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  216. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  217. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  218. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  219. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  220. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  221. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  222. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  223. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  224. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  225. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  226. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  227. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  228. A review of brain research on T2DM-related cognitive dysfunction
  229. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  230. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  231. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
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