Serum Makers And Metabonomics In Patients With Interstitial Lung Disease

Objective:1.To detect the serum levels of collagen I,KL-6,IL-1β,TNF-α,IGF-1 and GDF-15 in healthy individuals and patients with ILD,to explore the relationship between these indexes and the clinico-radiological subtypes of ILDs,and whether they can serve as differential biomarkers in ILDs.2.To investigate the differently expressed serum metabolites and metabolic pathways between different interstitial lung disease subgroups and healthy controls,so as to find out new biomarkers and metabolic pathways of ILD and to provide novel biological targets for the diagnosis and treatment of IPF.Methods:1.According to the inclusion criteria and exclusion criteria,the ILD patients admitted to our hospital from January 1,2021 to January 1,2022 were screened and signed with informed consent,and all of them had definite multidisciplinary diagnosis.During the same time interval,those who took health examination in the health management center of our hospital was selected as the healthy control group.According to the diagnosis,the patients were divided into idiopathic pulmonary fibrosis(IPF),autoimmue-associated interstitial lung disease(AI-ILD),other interstitial lung disease(ILD)patients and healthy control subgroups.Basing on radiological patterns,ILD patients were classified into typical UIP,probable UIP,interdeterminate UIP,non UIP subgroups,or fibrosing ILD/non fibrosing ILD subgroups.General clinical data of ILD patients and healthy controls were collected,including gender,age,onset,clinical manifestations,clinical biochemistry,lung function and other related data.Peripheral venous blood and serum samples were collected from the patients and healthy controls.Serum levels of type I collagen,KL-6,IL-1β,TNF-α,IGF-1 and GDF-15 were detected by enzyme linked immunosorbent assay(ELISA)in all patients and healthy controls.SPSS26.0 statistical software and R statistical software were used to perform T-test,variance analysis or nonparametric test method,according to different diagnoses,subgroup analysis was performed on the obtained data to compare differences.Pearson correlation analysis was used to analyze the correlation between serum biomarkers and other factors including lung function and serum metabolomics.ROC curve was drawn to analyze the diagnostic efficiency of each index,and the best critical value,sensitivity and specificity.2.The serum samples were analyzed by liquid chromatography-mass spectrometry(LC-MS)to obtain differential metabolites,metabolic maps and related data.R statistical software was used to calculate whether there are differential metabolites in IPF group and healthy control group,AI-ILD group and healthy control group and other ILD groups by using positive and negative ion flow charts and score charts of orthogonal partial least squares-discriminant analysis.And the potential biomarkers were identified through the S-plot diagram of OPLS-DA and the variable weight value VIP>1.ROC curve analysis of different metabolites was carried out by SPSS26.0 software to perform subgroup comparisions and to analyze the significance and value of novel candidate biomarkers in differential diagnosis.Based on KEGG database,the metabolic pathway enrichment analysis of differentially expressed metabolites was carried out to find out the main metabolic pathways.The correlation between the difference of core metabolism and the above six serum test indexes was analyzed.Results:1.General clinical data and clinical indicators: 25 cases of IPF,23 cases of AI-ILD,22 cases of others ILD and 20 cases of healthy control group were enrolled,respectively.The differences are statistically significant in age and sex among the four groups(P < 0.05).There are statistically significant differences among IPF group,AI-ILD group and other ILD groups in smoking history,expectoration symptoms,creatine kinase isoenzyme and carbon monoxide diffusion capacity(DLco)(P < 0.05).2.Serum metabolism-related indexes: The six serum indexes of ILD group,IPF group and AI-ILD group are higher than those of healthy control group,and the differences are statistically significant(P <0.05),but the difference between IPF group and AI-ILD group is not statistically significant(P>0.05).The serum levels of GDF-15 in IPF group and non-IPF ILD group are higher than that in healthy control group,with significant difference.The IPF group was higher than the non-IPF group,and the difference was statistically significant,but in the non-IPF subtype,the IPAF and CTD-ILD groups were compared and analyzed,and it was found that there was no significant difference in GDF-15.There was no difference in imaging of ILD patients.Serum levels of KL-6 and type I collagen were significantly different among different radiological subtypes,including typical UIP/probable UIP/interdeterminate UIP/non UIP and fibrosing ILD/non fibrosing ILD subgroups,which may serve as useful biomakers for differential diagnosis.Type I collagen was higher in UIP than CHP/AHP and other types,and it was statistically significant;KL-6was higher in UIP than other types.Type I collagen and IL-1β are negatively correlated with DLco.KL-6 is negatively correlated with the measured values of VC and DLco;IGF-1 is negatively correlated with FVC,VC and DLco(all P<0.05).GDF-15 is positively correlated with type I collagen,KL-6,IL-1β,TNF-α and IGF-1,and the difference is statistically significant(P < 0.05).ROC curve shows that the area under ROC curve(AUC)of six serum indexes in IPF group is greater than 0.90.In the AI-ILD group,the AUC of each detection index was greater than 0.8,of which the AUC of GDF-15 was 0.882,the cut-off value was342.077 pg/ml,the sensitivity was 85%,and the specificity was 83%;in other groups,these 6 detection indicators the area of ROC curve was between 0.60 and 0.80,among which IL-1β,IGF-1,GDF-15 and TNF-α had diagnostic value,and the other indicators had no diagnostic value,The only indexes to identify IPF from AI-ILD are type I collagen and KL-6;IPF was identified from other ILDs,and all the six detection indexes had diagnostic efficacy,among which the AUC of GDF-15 was 0.720,the cutoff value was 361.000pg/ml,the sensitivity was 59%,and the specificity was 88%.To identify AI-ILD from other ILDs,only IGF-1 has diagnostic value,but its diagnostic efficiency is low,and the rest has no statistical significance.To distinguish IPF from non-IPF,these six indexes have diagnostic value,but the AUC values of GDF-15 and TNF-α are between 0.60 and 0.70,so the diagnostic efficiency is low,and the diagnostic efficiency of other indexes is average.According to the analysis of ILD and healthy control group,these six biomarkers have good diagnostic efficacy.3.Metabolomics analysis: LC-MC total ion flow chart of serum samples and orthogonal partial least squares discriminant analysis(OPLS-DA)of serum samples shows the difference of ion peak intensity,and there were different metabolites among each group.The OPLS-DA modeln shows good quality evaluation and reliable data analysis.There are 195,113,108,182,126 and 172 differential metabolites between pairwise IPF group and healthy control group,IPF group and AI-ILD group,AI-ILD group and other ILD groups,others ILD groups and healthy control group respectively.There is a significant correlation between the differential metabolites of each ILD subgroup and the healthy control group.The most relevant metabolic pathways in IPF mainly include choline metabolic in cancer pathway,retrograde endocannabinoid signaling pathway and linoleic acid metabolic pathway.The metabolic pathways most related to AI-ILD and other ILD include choline metabolic in cancer pathway.Retrograde endocannabinoid signaling pathway and linoleic acid metabolic pathway may distinguish IPF from other ILD.Metabolites involved in retrograde endocannabinoid signaling pathway are L-Glutamate,PE(18:3(6Z,9Z,12Z)/P-18:0)and PC(18: 2(9z,12z)/20: 2(11z,14z)),respectively.There are three differential metabolites of linoleic acid pathway,namely 12,13-Ep OME,13S-HODE and PC(18:2(9Z,12Z)/20:2(11Z,14Z)).There are 34 kinds of differential metabolites in IPF group and healthy control group,and the two with the largest AUC was 3-sulfodeoxycholic acid and Gluten exorphin C,the AUC value was 0.996,the sensitivity was 100%,and the specificity was 96%.L-acetylcarnitine,5-hydroxydodecanoate and L-Glutamate are involved in significant enrichment pathways,among which the ROC curve area of L-Glutamate is the largest,with AUC value of 0.920,cutoff value of 40145.490,sensitivity of 80%,specificity of 96%,and participation in retrograde endocannabinoid signaling pathway.In IPF,L-Glutamate is positively correlated with type I collagen,KL-6,IGF-1,IL-1β,TNF-α and GDF-15,and the differences are statistically significant(P <0.05),with the highest correlation with IGF-1.Conclusion:In this study,the serum samples of IPF patients,AI-ILD patients,other interstitial lung diseases and healthy controls were analyzed by ELISA and LC-MS,and the following conclusions were drawn.1.The serum levels of type I collagen,KL-6,IL-1β,TNF-α,IGF-1 and GDF-15 are significantly different between ILD patients and healthy individuals or among ILD subgroups,which have potential diagnostic and pathogenic significence,and are correlated with lung function indexes,It may be related to the development of ILD and serve as an effective biomarker for ILD.2.There are differences in serum metabolites in different groups.The choline metabolic pathway in cancer may be an abnormal metabolic pathway common to ILD,and the retrograde endogenous cannabinoid signaling pathway and linoleic acid metabolic pathway may be abnormal metabolic pathways specific to IPF.Differential metabolites such as 3-sulfodeoxycholic acid helped to distinguish IPF from healthy controls.L-glutamic acid and other metabolites participated in the IPF enrichment pathway,and might be used as markers,and there was a positive correlation with GDF-15,suggesting that GDF-15 might be involved in the metabolic regulation of pulmonary fibrosis.