Metabolic alterations in vitamin D deficient systemic lupus erythematosus patients

Metabolic alterations in vitamin D deficient systemic lupus erythematosus patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolic alterations in vitamin D deficient systemic lupus erythematosus patients Yunxia Yan, Fangyuan Yu, Qi Li, Xuebing Feng, Linyu Geng, Lingyun Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3861907/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Vitamin D deficiency is increasingly common in systemic lupus erythematosus (SLE) patients and is associated with the disease activity and proteinuria. Recently, alterations in metabolism have been recognized as key regulators of SLE pathogenesis. Our objective was to identify changes in the serum metabolome of SLE with vitamin D deficiency. Methods : In this study, we applied untargeted metabolomics to serum samples obtained from a cross-sectional cohort of age- and sex-matched SLE patients, with or without vitamin D deficiency. Subsequently, we performed metabolomics profiling analysis, including principal component analysis, student’s t test, fold change analysis, volcano plot analysis, cluster analysis, Spearman’s correlation analysis, KEGG enrichment analysis, regulatory network analysis and receiver operating characteristic (ROC) analysis, to identify 52 significantly altered metabolites in vitamin D deficient SLE patients. The area under the curve (AUC) from ROC analyses was calculated to assess the diagnostic potential of each candidate metabolite biomarker. Results: Lipids accounted for 66.67% of the differential metabolites in the serum, highlighted the disruption of lipid metabolism. The 52 differential metabolites were mapped to 27 metabolic pathways, with fat digestion and absorption, as well as lipid metabolism, emerging as the most significant pathways. The AUC of (S)-Oleuropeic acid and 2-Hydroxylinolenic acid during ROC analysis were 0.867 and 0.833, respectively, indicating their promising diagnostic potential. Conclusions: In conclusion, our results revealed vitamin D deficiency alters SLE metabolome, impacting lipid metabolism, and thrown insights into the pathogenesis and diagnosis of SLE. Biological sciences/Immunology/Autoimmunity Health sciences/Biomarkers/Diagnostic markers Vitamin D lipids metabolism systemic lupus erythematosus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by production of pathogenic antibodies leading to tissue damage in multiple organs, including, but not limited to, the skin, kidney, blood and central nervous system 1 . The pathogenesis of SLE remains unclear, however, it has been reported that genetic susceptibility, epigenetics and environmental factors involve in the pathogenesis of SLE 2 . While previous research has primarily focused on genomic, transcriptomic, and proteomic changes in SLE, recent attention has turned to the role of metabolomics in autoimmune diseases. These metabolomics studies provide novel insights into the underling mechanisms of SLE 3 . For example, both glycolysis and mitochondrial oxidative metabolism were elevated in CD4 + T cells from lupus-prone mice and SLE patients, and targeting specific metabolic pathways could normalize T cell metabolism and has shown potential for reversing disease biomarkers 4 . Moreover, lipid metabolism has emerged as a crucial aspect of SLE pathogenesis. CD4 + T cells from SLE patients showed an aberrant profile of lipid raft-associated glycosphingolipids, which was closely related to T cell receptor activation 5 . Inhibiting the synthesis of these glycosphingolipids has been shown to decrease the production of anti-dsDNA antibody by activated B cells 6 . Additionally, dysregulated mammalian target of rapamycin (mTOR) activation has been observed in CD4 + T cells from SLE patients and lupus-prone mice, leading to altered glycolysis, fatty acid synthesis, mRNA translation, lipid synthesis, sense amino acids and growth factors 7 – 8 . Vitamin D, a well-known regulator immune regulation, has been implicated in the pathogenesis of SLE. It can regulate the functions and differentiation of antigen-presenting cells (APCs). In addition, vitamin D is shown to downregulate the expression of toll-like receptors on the monocytes and inhibit the production of proinflammatory cytokines. Also, vitamin D inhibits the proliferation of T lymphocytes and regulates the differentiation of subgroups of T cells. Moreover, it induces the apoptosis of activated B cells and plasma cells 9 . SLE patients had lower vitamin D levels than healthy controls, which inversely correlated with disease activity in SLE 10 . Further, lower serum levels of vitamin D were associated with renal disease as well as the degree of proteinuria. SLE patients with lupus nephritis (LN) had significantly lower levels of vitamin D than those with inactive SLE and SLE without LN 11 . Vitamin D and vitamin D receptor (VDR) have been shown to modulate the metabolic activity of immune cells. VDR’s tasks in the control of metabolism involves regulating genes mediating energy metabolism, as well as in the catabolism of lipophilic intra-cellular molecules 12 . It regulates the metabolic pathways involved in energy production, such as glycolysis and oxidative phosphorylation, in immune cells like T cells, B cells, monocytes/macrophages and dendritic cells. These metabolic alterations impact cell activation, differentiation, and cytokine production, ultimately might affecting the immune response in autoimmune diseases 13 – 14 . Recently, vitamin D had multiple effects on lipid metabolism through its actions on nuclear hormone receptors, such as peroxisome proliferator-actiated receptor γ (PPARγ) and liver X receptor (LXR), including changing the phospholipid content of cells 15 , and vitamin D supplementation reduces markers of oxidative stress and positively affects many other metabolic markers in multiple sclerosis 16 , and other populations 17 , however, it is not clear whether and how serum vitamin D regulates metabolism in SLE patients. In our study, we performed metabolomics studies in patients with SLE, comparing those with low serum vitamin D levels to those with normal level. Our findings shed light on the metabolic differences associated with vitamin D deficiency and emphasize the crucial role of vitamin D in regulating lipid metabolism. These insights contribute to our understanding of the pathological mechanisms underlying SLE, potentially paving the way for novel diagnostic strategies. Materials and methods Study population Thirty SLE patients, aged-matched from 14 to 69 years (mean 38.90 ± 2.33 years), were enrolled in the study. All fulfilled the 1997 American College of Rheumatology classification criteria 18 . Current SLE disease activity was measured using the SLE Disease Activity Index 2000 (SLEDAI-2K) 19 . The patients’ characteristics, including age, gender, routine blood test results, kidney and liver biochemistry test results, lipid levels, serum ferritin levels, serum vitamin D values, immunoglobin, serum complement, and urinary protein results were recorded. All participants were given written consent to the study approved by the Ethics Committee of the Drum Tower Clinical Medical School of Nanjing Medical University. Sample Preparation for Metabolomics Study The serum of SLE patients were collected and transferred to -80℃ for long term storage. 100 µL serum was thoroughly mixed with 400 µL of cold methanol acetonitrile (v/v, 1:1) via vortexing. And then the mixture was processed with sonication for 1 h in ice baths. The mixture was then incubated at -20°C for 1 h, and centrifuged at 4°C for 20 minutes with a speed of 14, 000 g. The supernatants were then harvested and dried under vacuum liquid chromatography-mass spectrometry (LC-MS) analysis 20 . LC-MS Metabolomics Data Acquisition Metabolomics profiling was analyzed using a UPLC-ESI-Q-Orbitrap-MS system (UHPLC, Shimadzu Nexera X2 LC-30AD, Shimadzu, Japan) coupled with Q-Exactive Plus (Thermo Scientific, San Jose, USA). For liquid chromatography (LC) separation, samples were analyzed using a ACQUITY UPLC® HSS T3 column (2.1×100 mm, 1.8µm)(Waters, Milford, MA, USA). The flow rate was 0.3 mL/min and the mobile phase contained: A: 0.1% FA in water and B: 100% acetonitrile (ACN). The gradient was 0% buffer B for 2 min and was linearly increase to 48% in 4 min, and then up to 100% in4 min and maintained for 2 min, and then decreased to 0% buffer B in 0.1 min, with 3 min re-equilibration period employed. The electrospray ionization (ESI) with positive-mode and negative mode were applied for MS data acquisition separately. The HESI source conditions were set as follows: Spray Voltage: 3.8kv (positive) and 3.2kv (negative); Capillary Temperature: 320 ℃; Sheath Gas (nitrogen) flow: 30 arb (arbitrary units); Aux Gas flow: 5 arb; Probe Heater Temp: 350 ℃; S-Lens RF Level: 50. The instrument was set to acquire over the m/z range 70-1050 Da for full MS. The full MS scans were acquired at a resolution of 70,000 at m/z 200, and 17,500 at m/z 200 for MS/MS scan. The maximum injection time was set to for 100 ms for MS and 50 ms for MS/MS. The isolation window for MS2 was set to 2 m/z and the normalized collision energy (stepped) was set as 20, 30 and 40 for fragmentation 20 . LC-MS Metabolomics Data Analysis The raw MS data were processed using MS-DIAL for peak alignment, retention time correction and peak area extraction. The metabolites were identified by accuracy mass (mass tolerance < 10 ppm) and MS/MS data (mass tolerance < 0.02Da) which were matched with HMDB, massbank and other public databases and our self-built metabolite standard library. In the extracted-ion features, only the variables having more than 50% of the nonzero measurement values in at least one group were kept. R (version:4.0.3) and R packages were used for all multivariate data analyses and modeling. Data were mean-centered using Pareto scaling. To understand the difference of metabolomics profile between SLE patients with low serum vitamin D level and normal serum vitamin D level, principal component analysis (PCA) was carried out. At the same time, the discriminating metabolites were obtained using Fold change analysis and two-tailed Student’s t test on the normalized raw data at univariate analysis level. Further volcano plot analysis with fold-change (FC) > = 1.5 or FC<=1.5 and p <0.05 were considered to be statistically significant metabolites. On the other side, the identified differential metabolites were used to perform cluster analyses with R package. The Spearman’s correlation was used to describe the relationships between different metabolites. The ROC analysis was carried out to evaluate each candidate metabolite biomarker. The AUC from ROC analyses was computed using the SPSS software package (version 24, IBM) 20 . KEGG enrichment analysis To identify the perturbed biological pathways, the differential metabolite data were performed KEGG pathway analysis using KEGG database ( http://www.kegg.jp ). KEGG enrichment analyses were carried out with the Fisher’s exact test, and FDR correction for multiple testing was performed. Enriched KEGG pathways were nominally statistically significant at the p < 0.05 level 20 . Statistics The clinical data of SLE patients were summarized as the mean ± standard error (mean ± SEM). To compare clinical data of two groups, the Student’s t -test was performed. All statistical analyses were performed using GraphPad Prism 9 software (Graph-Pad, San Diego, CA, USA). Results Characteristics of SLE Patients In this study, a total of 31 SLE patients were included, and patients were divided into Low vitamin D SLE (VD1, serum vitamin D20ng/ml, n = 10) groups, based on their serum vitamin D levels 21 . The demographics and clinical characteristics of SLE patients were shown in Table 1 . There were no significant differences in routine blood test results, kidney and liver biochemistry, lipid levels, serum ferritin levels, immunoglobin, or serum complement between the two groups. However, VD1 SLE patients tended to have higher SLE disease activity index (SLEDAI) scores and proteinuria than VD2 SLE patients. Serum metabolites profiling in SLE patients with or without serum vitamin D deficiency To uncover the metabolome differences between SLE patients with low serum vitamin D level and normal serum vitamin D level, we performed liquid chromatography-mass spectrometry (LC-MS) analysis and identified a total of 1599 metabolites (Fig. 1 A). Using volcano plot analysis, we selected 52 significantly altered metabolites based on fold change (FC) > = 1.5 or FC<=-1.5 and p-values<0.05 (Fig. 1 B). Among these, etofylline was up-regulated in VD1 SLE patients, while the remaining 51 metabolites were all downregulated in VD1 SLE patients, predominantly comprising lipids (Fig. 1 C). The differential metabolites screened in metabolomics mainly included lipid and lipid-like molecules, benzenoids, organic acids and derivatives, organic oxygen compounds and organoheterocyclic compounds. Lipid and lipid-like molecules accounted for a major proportion (66.67%) of metabolites identified (Fig. 1 D). Specifically, the differential lipid and lipid-like molecules mainly included fatty acyls (41.18%), prenol lipids (29.41%), steroids and steroid derivatives (14.71%), glycerophospholipids (8.82%) and sphingolipids (5.88%) (Fig. 1 E). These findings suggest that lipid metabolism is significantly altered in SLE patients with low vitamin D levels. Correlations between differential metabolites We analyzed the linear correlations between these significant differential metabolites and screened out significant correlations with absolute correlation coefficient > 0.9( |r| >0.9). There were 19 pairs of strong correlations between these differential metabolites in total. Interestingly, 12 of these pairs involved correlations between lipids and lipids, especially fatty acyls, while 5 pairs of them were between lipids and 2-Hydroxy-4,5',8a'-trimethyl-1'-oxo-4-vinyloctahydro-1'H-spiro[cyclopentane-1,2'-naphthalene]-5'-carboxylic acid (Fig. 2 A and 2 B). Moreover, they were all positive associated. Their strong positive association suggested that these metabolites may participate in the same metabolic pathway, or biological process. KEGG pathways of SLE patients with or without serum vitamin D deficiency A total of 52 differential metabolites were submitted to the KEGG, and they were matched to 27 metabolic pathways. Among these pathways, lipid metabolism and digestive system pathway were significant secondary classific pathways (Fig. 3 A). In terms of the primary classific pathways, fat digestion and absorption ( P = 4.75E-05), pathways in cancer( P = 2.82E-04) and vitamin digestion and absorption ( P = 4.46E-04) were significant (Fig. 3 B). Further pathway impact analyses revealed that sphingolipid metabolism, glycerolipid metabolism, glycerophospholipid metabolism and primary bile acid biosynthesis were all significant pathways and they all belong to lipid metabolism pathway (Fig. 3 C, D), indicating the important role of lipid metabolism in SLE patients with serum vitamin D deficiency. Regulatory network analysis of differential metabolites and pathways We searched the screened differential metabolites in the KEGG database for their corresponding pathways and performed regulatory network analysis. The most important differential metabolites were cholesterol (C00187), Lysophosphatidic acid (18:2) (C00681) and spectral match to Lyso-Sphingomyelin from NIST14 (C00550). The mainly associated pathways were vitamin digestion and absorption (hsa04977), neuroactive ligand-receptor interaction (hsa04080), basal cell carcinoma (hsa05217), fat digestion and absorption (hsa04975), regulation of lipolysis in adipocytes (hsa04923), sphingolipid metabolism (hsa00600) and glycerophospholipid metabolism (hsa00564) (Fig. 4 ). These results suggest that vitamin D-altered metabolites may involve in lipid digestion, absorption and metabolism, as well as cellular interaction in SLE patients with different vitamin D levels. Diagnostic potential of differential metabolites To explore the diagnostic potential of the differential metabolites in predicting vitamin D levels and diagnosing SLE, we further compared relative abundance of these differential metabolites. The result showed that totally 9 metabolites were significantly decreased in VD1 SLE patients, while Etofylline was significantly increased in VD1 SLE patients (Fig. 5 A). Then we performed classical univariate receiver operating characteristic (ROC) curve analyses to generate ROC curve, to calculate area under the curve (AUC) and their 95% confidence intervals. The ROC analysis showed that the AUC of (S)-Oleuropeic acid was 0.867 and 2-Hydroxylinolenic acid was 0.833(Fig. 5 B), which might have promising diagnostic potential as biomarkers for predicting vitamin D levels and diagnosing SLE. Discussion In recent years, metabolomics has emerged as a valuable tool for studying the underlying pathological mechanisms of SLE. Lipid metabolism, including anabolism and catabolism, is essential for almost every aspect of cellular functioning 22 . Vitamin D, on the other hand, has been shown to be involved in autoimmune diseases 9 . It is well-established that SLE patients often have insufficiency of vitamin D levels and lower serum levels of vitamin D were associated with proteinuria 10 , 11 , 23 , 24 . Our study investigated the serum metabolome differences between SLE patients with normal vitamin D levels and those with vitamin D deficiency using LC-MS analysis. We identified a total of 52 differentially distributed metabolites, with 34 of them being lipid and lipid-like molecules. Interestingly, all these lipids were downregulated in SLE patients with vitamin D deficiency. These results indicated a strong association between vitamin D and lipid metabolism of SLE patients. Among the altered metabolites, 14 differential metabolites were fatty acyls, which can promote angiogenesis and reduce inflammation. A total of 10 differential metabolites were prenol lipids, which can regulate the progression of aging-related diseases, diabetes and inflammation and as regulator of bone health and cardiovascular homeostasis. A total of 5 differential metabolites were sterol lipids, which is the component of membrane lipids and can regulate T cell function as hormones and signaling molecules. A total of 3 differential metabolites were glycerophospholipids, which is the structural component of membrane and can affect membrane fluidity. The other 2 differential metabolites were sphingolipids, which is an important component of the cell membrane and can induce lipotoxicity and inflammation and regulate cell death 25 . Current evidence suggested that lipids and lipid metabolites played an important role in the pathogenesis of SLE. Lipid metabolism was considered as an important facilitator of T cell differentiation. Lipid biosynthesis, including cholesterol and fatty acids, was critical for the proliferation and differentiation of T cells, especially Th17 cells 26 . Inhibiting FA synthesis in memory CD4 + T cells of SLE patients decreased interferon (IFN)-γ production and increased Foxp3 expression in T-bet + Foxp3 + cells. Fatty acid synthesis inhibitors may improve the pathological status by correcting Th1 subset imbalance and overproduction of IFN-γ in SLE 27 . Fatty acids could modulate the process of macrophage differentiation 28 . Targeting receptors associated lipid metabolism, such as peroxisome proliferator-activated receptor γ (PPARγ), 12/15-Lipoxygenase (12/15-LO), sphingosine-1-phosphate (S1P) and liver X receptors (LXRs), was able to affect differentiation of monocytes and production of proinflammatory cytokines, which were related to TLR7 and TLR9 expression 29 – 32 . Furthermore, we observed strong associations between the differential metabolites, particularly the lipids, suggesting that these metabolites may participate in the same metabolic pathway, or biological process. We speculated that the downregulated lipids in vitamin D deficient SLE patients may affect the proliferation, differentiation and production of proinflammatory cytokines of lymphocytes and innate immune cells and participated in the pathogenesis of SLE. The fatty acid (FA) and cholesterol metabolic pathways are the most important pathways in lipid metabolism. Lipid synthesis is critical for the cell and determine cell survival 33 . Lipid degradation is also essential to sustain life. Lipolytic products and intermediates play a vital role in cellular signaling 34 . Our results showed that fat digestion and absorption, sphingolipid metabolism, glycerolipid metabolism, glycerophospholipid metabolism and primary bile acid biosynthesis pathways played a crucial part in regulating differential metabolites of vitamin D deficient SLE patients. These data shed light on the probable signal pathways by which these altered metabolites participated in the development of SLE. Metabolomics studies have shown promise in identifying potential biomarkers for SLE diagnosis and disease activity assessment. Li et al. collected serum samples from 17 SLE and 17 healthy and performed metabolic profiles by high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). They found that metabolites such as ceramide, trimethylamine N-oxide, xanthine were significantly elevated in the serum of active systemic lupus erythematosus patients, while acylcarnitine, caffeine, hydrocortisone, itaconic acid and serotonin were down-regulated 35 . Zhang et al. performed metabolomic and lipidomic profiles analysis of 133 SLE patients and 30 HCs. They successfully found that a combination of DHEAS, Benzoic acid, 2-Methylbutyrylglycine, and FA (20:1) could distinguish SLE from HC. Lysophosphatidylcholine (18:0), phosphatidylcholine (18:3/18:3) and phosphatidylethanolamine (16:0/22:4) could be combined to differentiate inactive SLE and active SLE. Moreover, they could identify biomarkers associated with different organ involvement in SLE patient, including sonly kidney involvement, skin involvement, blood system involvement, and multisystem involvement respectively 36 . Yan et al. performed SLE fecal metabolome analysis and suggested that L-tryptophan was positively correlated with the SLEDAI-2K 37 . In this study, we identified two altered lipids, (S)-Oleuropeic acid(AUC = 0.867) and 2-Hydroxylinolenic acid(AUC = 0.833), as potential biomarkers in vitamin D deficient SLE patients. Recent researches have shed light on the potential mechanisms by which vitamin D participates in the pathogenesis of SLE. VDR generally skews immune cells towards anti-inflammatory states 38 . Vitamin D has been shown to decrease neutrophil extracellular traps (NETs) and prevent endothelial damage in cultured neutrophiles derived from SLE patients, compared to controls, revealing the possibility of Vitamin D as supplementary therapy for SLE patients with hypovitamin D to prevent endothelial damage 39 . Liu, et al revealed that 1,25-(OH)2D3/VDR facilitated the recovery of SLE by downregulating Skp2 and upregulating p27 expression 40 . New scientific approaches targeting VitD/VDR signaling at the cellular metabolic level may provide a better comprehension of its role in SLE progression. However, there is no related report till now. Our study indicated the metabolic interactions between VitD and SLE, providing new insights into the mechanism of SLE. The clinical manifestations of SLE patients are highly heterogeneous, involving various symptoms in the gastrointestinal tract, skin, kidney, blood, nerves, and other organs. Approximately 60–70% of SLE patients followed a relapsing-remitting course, with the rest 30–40% divided equally between prolonged remission and persistently active disease 41 . Due to the multiorgan involvement and the varied disease activity, disease activity measurements are necessary to guide therapy. The most widely used instruments include SLEDAI 19 , British Isles Lupus Activity Group (BILAG) index 42 , Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR DI; SDI) 43 and other tools 44 . Promising recent studies have used soluble mediator levels to assess clinical disease activity and predict imminent flare in SLE, including innate and adaptive cytokines, chemokines, as well as soluble receptors and tumor necrosis factor superfamily members 45 . However, current tools are still not available to fully measure disease activity of SLE patients with different subtypes. Given the different clinical manifestations of SLE and the pathological differences between active and inactive disease, metabolomics may be an important platform for discovering different clinical phenotypic biomarkers for SLE 36 . Notably, SLE patients with vitamin D deficiency could represent an important subgroup. The metabolomic profiling in our study have the potential to unravel unique metabolic signatures associated with different clinical phenotypes in SLE. These metabolites could serve as specific biomarkers to better stratify patients, identify disease activity, and predict outcomes. Furthermore, the discovery of differential metabolites in SLE patients with vitamin D deficiency may shed light on the underlying mechanisms linking vitamin D status and disease pathogenesis in SLE. In conclusion, our study revealed the global metabolic changes in SLE patients with vitamin D deficiency, illustrating the extensive association between vitamin D and lipid metabolism in these patients. Further investigations are needed to further explore the underlying mechanism by which vitamin D regulates the lipid metabolism and affects immune cells. This study provides a rational basis for a better understanding of the pathogenetic mechanism underlying SLE and may pave the way for the development of targeted therapeutic approaches. Table 1 Demographics and clinical characteristics of SLE patients VD1 (n = 21) VD2 (n = 10) P value Age(Years) 40.48 ± 3.10 35.60 ± 3.13 0.33 Male/female 3/18 3/7 Disease duration (years) 5.47 ± 1.78 6.51 ± 2.52 0.75 White blood cells (10 9 /L) 5.50 ± 0.39 6.45 ± 0.85 0.25 Hemoglobin (g/L) 97.90 ± 5.98 102.20 ± 9.94 0.70 Platelet(10 9 /L) 212.90 ± 22.20 219.50 ± 26.14 0.86 SLEDAI-2K 10.52 ± 1.19 7.20 ± 1.83 0.13 Albumin (g/L) 32.02 ± 1.26 36.47 ± 1.51 0.04 ALT(U/L) 26.23 ± 5.53 27.22 ± 6.28 0.91 AST(U/L) 25.00 ± 4.85 20.79 ± 2.67 0.57 BUN (mmol/L) 7.72 ± 0.96 6.62 ± 0.51 0.68 Creatinine(umol/L) 81.71 ± 15.52 57.30 ± 5.36 0.30 Cholesterol(mmol/L) 4.85 ± 0.37 4.68 ± 0.57 0.80 LDL (mmol/L) 2.71 ± 0.37 2.60 ± 0.37 0.84 HDL(mmol/L) 1.36 ± 0.13 1.30 ± 0.37 0.79 ESR(mm/h) 40.81 ± 7.13 35.86 ± 12.90 0.73 Urinary total protein/creatinine(mg/g) 2.07 ± 0.67 0.89 ± 0.38 0.24 24hour Urinary protein(mg) 2587 ± 907.6 688.7 ± 389.2 0.35 Ferritin(ng/ml) 515.00 ± 167.20 227.60 ± 67.33 0.26 Complement 3 (g/L) 0.71 ± 0.06 0.74 ± 0.09 0.79 Complement 4 (g/L) 0.13 ± 0.02 0.14 ± 0.04 0.78 IgA(g/L) 2.44 ± 0.32 2.64 ± 0.58 0.75 IgM(g/L) 1.32 ± 0.32 1.11 ± 0.21 0.65 IgG(g/L) 13.41 ± 1.42 15.53 ± 3.37 0.50 IgE(g/L) 66.20 ± 23.07 189.4 ± 91.67 0.10 SLEDAI-2K, SLE disease activity index 2000; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; LDL, low density lipoprotein; HDL, high density lipoprotein; ESR, erythrocyte sedimentation rate; IgA, immunoglobulin A; IgM, immunoglobulin M; IgG, immunoglobulin G; IgE, immunoglobulin E. Declarations Funding: The work was supported by Nanjing Medical Science and technique Development Foundation (JQX20004), and funding for Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (2021-LCYJ-PY-16). Ethical rule: All participants gave their written Informed consent approved by the Ethics Committee of the Drum Tower Clinical Medical School of Nanjing Medical University in accordance with the Declaration of Helsinki. Acknowledgements: The authors acknowledge Hong Yu for assistance in analysing the data. Competing Interests: The authors declare no conflicts of interest. Authors’ Contributions: Linyu Geng, Lingyun Sun designed the study. Yunxia Yan collected the data, performed the experiments and performed the statistical analysis. Yunxia Yan, Fangyuan Yu and Qi Li analysed the data. Yunxia Yan, Linyu Geng and Xuebing Feng wrote the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Data availability: The datasets generated during and/or analyzed during the current study are not publicly available out of concern for patient privacy, as data from medical records is considered sensitive, but de-identified datasets are available from the corresponding author on reasonable request. References S. Bernatsky, J. F. Boivin, L. Joseph, et al. Mortality in systemic lupus erythematosus. Arthritis Rheum 2006; 54(8): 2550-2557. https://doi.org/10.1002/art.21955. M. A. Ameer, H. Chaudhry, J. Mushtaq, et al. An Overview of Systemic Lupus Erythematosus (SLE) Pathogenesis, Classification, and Management. Cureus 2022; 14(10): e30330. https://doi.org/10.7759/cureus.30330. T. Zhang and C. Mohan. Caution in studying and interpreting the lupus metabolome. Arthritis Res Ther 2020; 22(1): 172. https://doi.org/10.1186/s13075-020-02264-2. Y. Yin, S. C. Choi, Z. Xu, et al. 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Memarian: Immunomodulation in systemic lupus erythematosus: induction of M2 population in monocyte-derived macrophages by pioglitazone. Lupus 2017; 26(12): 1318-1327. https://doi.org/10.1177/0961203317701842. S. Uderhardt, M. Herrmann, O. V. Oskolkova, et al. 12/15-lipoxygenase orchestrates the clearance of apoptotic cells and maintains immunologic tolerance. Immunity 2012; 36(5): 834-846. https://doi.org/10.1016/j.immuni.2012.03.010. A. Cartier and T. Hla. Sphingosine 1-phosphate: Lipid signaling in pathology and therapy. Science 2019; 366(6463). https://doi.org/10.1126/science.aar5551. H. A. Kim, W. Y. Baek, M. H. Han, et al. The Liver X Receptor Is Upregulated in Monocyte-Derived Macrophages and Modulates Inflammatory Cytokines Based on LXRalpha Polymorphism. Mediators Inflamm 2019; 6217548. https://doi.org/10.1155/2019/6217548. D. Howie, A. Ten Bokum, A. S. Necula, et al. The Role of Lipid Metabolism in T Lymphocyte Differentiation and Survival. Front Immunol 2017; 8: 1949. https://doi.org/10.3389/fimmu.2017.01949. R. Zechner, R. Zimmermann, T. O. Eichmann, et al. FAT SIGNALS--lipases and lipolysis in lipid metabolism and signaling. Cell Metab 2012; 15(3): 279-291. https://doi.org/10.1016/j.cmet.2011.12.018. Y. Li, L. Liang, X. Deng, et al. Lipidomic and metabolomic profiling reveals novel candidate biomarkers in active systemic lupus erythematosus. Int J Clin Exp Pathol 2019; 12(3): 857-866. W. Zhang, H. Zhao, P. Du, et al. Integration of metabolomics and lipidomics reveals serum biomarkers for systemic lupus erythematosus with different organs involvement. Clin Immunol 2022; 241: 109057. https://doi.org/10.1016/j.clim.2022.109057. R. Yan, H. Jiang, S. Gu, et al. Fecal Metabolites Were Altered, Identified as Biomarkers and Correlated With Disease Activity in Patients With Systemic Lupus Erythematosus in a GC-MS-Based Metabolomics Study. Front Immunol 2020; 11: 2138. https://doi.org/10.3389/fimmu.2020.02138. C. Y. Yang, P. S. Leung, I. E. Adamopoulos, et al. The implication of vitamin D and autoimmunity: a comprehensive review. Clin Rev Allergy Immunol 2013; 45(2): 217-226. https://doi.org/10.1007/s12016-013-8361-3. K. Handono, Y. O. Sidarta, B. A. Pradana, et al. Vitamin D prevents endothelial damage induced by increased neutrophil extracellular traps formation in patients with systemic lupus erythematosus. Acta Med Indones 2014; 46(3): 189-198. D. Liu, Y. X. Fang, X. Wu, et al. 1,25-(OH)(2)D(3)/Vitamin D receptor alleviates systemic lupus erythematosus by downregulating Skp2 and upregulating p27. Cell Commun Signal 2019; 17(1): 163. https://doi.org/10.1186/s12964-019-0488-2. A. Fanouriakis, N. Tziolos, G. Bertsias, et al. Update omicronn the diagnosis and management of systemic lupus erythematosus. Ann Rheum Dis 2021; 80(1): 14-25. https://doi.org/10.1136/annrheumdis-2020-218272. D. A. Isenberg, A. Rahman, E. Allen, et al. BILAG 2004. Development and initial validation of an updated version of the British Isles Lupus Assessment Group's disease activity index for patients with systemic lupus erythematosus. Rheumatology (Oxford) 2005; 44(7): 902-906. https://doi.org/10.1093/rheumatology/keh624. M. Petri, A. M. Orbai, G. S. Alarcon, et al. Derivation and validation of the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum 2012; 64(8): 2677-2686. https://doi.org/10.1002/art.34473. J. Mikdashi and O. Nived. Measuring disease activity in adults with systemic lupus erythematosus: the challenges of administrative burden and responsiveness to patient concerns in clinical research. Arthritis Res Ther 2015; 17(1): 183. https://doi.org/10.1186/s13075-015-0702-6. A. Thanou, E. Jupe, M. Purushothaman, et al. Clinical disease activity and flare in SLE: Current concepts and novel biomarkers. J Autoimmun 2021; 119: 102615. https://doi.org/10.1016/j.jaut.2021.102615. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Mar, 2024 Reviews received at journal 28 Feb, 2024 Reviewers agreed at journal 27 Feb, 2024 Reviews received at journal 25 Feb, 2024 Reviews received at journal 21 Feb, 2024 Reviewers agreed at journal 21 Feb, 2024 Reviewers invited by journal 20 Feb, 2024 Editor assigned by journal 07 Feb, 2024 Editor invited by journal 19 Jan, 2024 Submission checks completed at journal 19 Jan, 2024 First submitted to journal 13 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3861907","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267999643,"identity":"caab63dc-bab6-477d-b33c-7746f5e279ab","order_by":0,"name":"Yunxia Yan","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunxia","middleName":"","lastName":"Yan","suffix":""},{"id":267999644,"identity":"6066d6f9-4d47-46b3-82a5-bb50df479445","order_by":1,"name":"Fangyuan Yu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Fangyuan","middleName":"","lastName":"Yu","suffix":""},{"id":267999645,"identity":"e88eb78f-d437-446e-802d-ae06fe528b56","order_by":2,"name":"Qi Li","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Li","suffix":""},{"id":267999646,"identity":"397a553d-8729-41ed-857c-bdd4b1dc095e","order_by":3,"name":"Xuebing Feng","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xuebing","middleName":"","lastName":"Feng","suffix":""},{"id":267999647,"identity":"6c59fcc4-d386-4c57-931c-43e0f687233f","order_by":4,"name":"Linyu Geng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYLCCBwZAgpn54OM/FRJy8kRpSQBpYWdLNuA5Y2Fs2ECUFhDBz2MmwdtWkchwgIBqeffewy8SCu4kbjjMY2wgOU8igbGB+eGjG3i0GJ45l2aRYPAMqIWt8IHhNok8dgY2Y+McfFpm5JgZJBgcBmph3myQuE2imLGBh00ar5b5b2BaGMwkDs6RSGw4QECLvASP8QOIFhYzycYGIrQY8OSYAQP5sPHMw2zJxgzHJIwNmwn4Rb79jPGHD38Oy/adP3zwMUNNnZw8e/PDx3htOcDAJgGkHRvgQsx4lINtaWBg/gCk7QmoGwWjYBSMgpEMAE6jUE0ru9qaAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"Linyu","middleName":"","lastName":"Geng","suffix":""},{"id":267999648,"identity":"0f487f0c-6478-4e6c-b0e4-249b555fb716","order_by":5,"name":"Lingyun Sun","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-01-14 03:14:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3861907/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3861907/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-67588-4","type":"published","date":"2024-08-14T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49978049,"identity":"2de87a14-dd60-4942-8ddd-ecf5970852cd","added_by":"auto","created_at":"2024-01-22 14:58:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2197341,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e \u003cstrong\u003eClassified bubble plot. \u003c/strong\u003eThe horizontal coordinate represents the number interval of differential metabolites, and the vertical coordinate represents the logarithmic transformation of FC. Each circle represents a differential metabolite, and the larger the circle, the higher the difference significance. Different colors indicate a primary class, where the differentiated metabolites of the same class are arranged together. \u003cstrong\u003eB. Volcano Plot.\u003c/strong\u003e Metabolites with FC \u0026gt;=1.5 or FC＜＝-1.5 and \u003cem\u003ep\u003c/em\u003e＜0.05 were considered as statistically significant metabolites. The vertical dashed line analysis indicated log2(1/1.5) and log2(1.5). The red dots represent up-regulated metabolites, and the blue dots represent down-regulated metabolites. \u003cstrong\u003eC. Complex heatmap for significantly differential metabolites. \u003c/strong\u003eThe abundance of each metabolite is normalized by Z score normalization. Metabolite ontology, \u003cem\u003ep\u003c/em\u003e value and FC value are shown in the left of the heatmap. \u003cstrong\u003eD. Classified pie chart of HMDB super class. \u003c/strong\u003eThe numbers and proportions of differential metabolites are shown. \u003cstrong\u003eE. Classified pie chart of HMDB class. \u003c/strong\u003eThe numbers and proportions of differential metabolites are shown.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3861907/v1/099703619e1096ce25de83bf.jpg"},{"id":49978495,"identity":"9a300fa8-79d1-4a2a-adff-2aabd270ec7f","added_by":"auto","created_at":"2024-01-22 15:06:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2277073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Correlation coefficient matrix heatmap. \u003c/strong\u003ePositive correlation is shown in red, while negative correlation is shown in blue. The larger the proportion of coloring intervals, the stronger the correlation. \u003cstrong\u003eB. Circos plot.\u003c/strong\u003e From outside to inside, there are metabolite names, HMDB classification of metabolites, log2(fold change), \u003cem\u003ep\u003c/em\u003e-value, and correlation lines.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3861907/v1/a94e99574fbb17ef5915ad4b.jpg"},{"id":49978048,"identity":"0c4d588d-50cf-4026-bb7b-ee465a76a473","added_by":"auto","created_at":"2024-01-22 14:58:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1157134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic pathway analysis of differential metabolites using MetaboAnalyst 4.0 based on the KEGG. A. Secondary classific significant pathway bubble plot. B. Significant pathway bubble plot. \u003c/strong\u003eThe significance of pathways in each class decreases from top to bottom. Size of circle represents the number of differential metabolites annotated into the pathway. Color of circle represents the corrected \u003cem\u003eP\u003c/em\u003e value. (M, Metabolism; G, Genetic Information Processing; E, Environmental Information Processing; C, Cellular Processes; O, Organismal Systems; H, Human Diseases; D, Drug Development.) \u003cstrong\u003eC. Pathway impact plot. \u003c/strong\u003eThe importance is calculated by Betweenness centrality method. \u003cstrong\u003eD. Pathway impact plot. \u003c/strong\u003eThe importance is calculated by Out degree centrality method.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3861907/v1/51bfa5b717388389a3b9334c.jpg"},{"id":49978496,"identity":"b56f9a18-7855-456c-991c-b1d3dc800ada","added_by":"auto","created_at":"2024-01-22 15:06:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2141274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory network analysis of differential metabolites. \u003c/strong\u003eRed circle represents metabolic pathway. Purple circle represents modular information. Yellow circle represents enzyme. Green circle represents background compound of a metabolic pathway. Blue circle represents chemical reaction. Green square represents differential metabolite.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3861907/v1/59ba3e9b3d2c9beec4d653ff.jpg"},{"id":49978051,"identity":"8296a075-255b-4c07-99ea-0538da491ce9","added_by":"auto","created_at":"2024-01-22 14:58:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1805888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic potential of differential metabolites. A. Relative presentations of differential metabolites in VD1 SLE patients and VD2 SLE patients. B. ROC curves of the important altered metabolites.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3861907/v1/e9644b59e43f14b2cc75c4be.jpg"},{"id":63071010,"identity":"62d144d1-e556-450a-bacf-cf11134187b2","added_by":"auto","created_at":"2024-08-22 20:02:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10301070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3861907/v1/4e40bc34-4b61-4c30-9f60-071ce13af7ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic alterations in vitamin D deficient systemic lupus erythematosus patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by production of pathogenic antibodies leading to tissue damage in multiple organs, including, but not limited to, the skin, kidney, blood and central nervous system\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The pathogenesis of SLE remains unclear, however, it has been reported that genetic susceptibility, epigenetics and environmental factors involve in the pathogenesis of SLE\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile previous research has primarily focused on genomic, transcriptomic, and proteomic changes in SLE, recent attention has turned to the role of metabolomics in autoimmune diseases. These metabolomics studies provide novel insights into the underling mechanisms of SLE\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For example, both glycolysis and mitochondrial oxidative metabolism were elevated in CD4\u003csup\u003e+\u003c/sup\u003e T cells from lupus-prone mice and SLE patients, and targeting specific metabolic pathways could normalize T cell metabolism and has shown potential for reversing disease biomarkers\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Moreover, lipid metabolism has emerged as a crucial aspect of SLE pathogenesis. CD4\u003csup\u003e+\u003c/sup\u003e T cells from SLE patients showed an aberrant profile of lipid raft-associated glycosphingolipids, which was closely related to T cell receptor activation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Inhibiting the synthesis of these glycosphingolipids has been shown to decrease the production of anti-dsDNA antibody by activated B cells\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additionally, dysregulated mammalian target of rapamycin (mTOR) activation has been observed in CD4\u003csup\u003e+\u003c/sup\u003e T cells from SLE patients and lupus-prone mice, leading to altered glycolysis, fatty acid synthesis, mRNA translation, lipid synthesis, sense amino acids and growth factors\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVitamin D, a well-known regulator immune regulation, has been implicated in the pathogenesis of SLE. It can regulate the functions and differentiation of antigen-presenting cells (APCs). In addition, vitamin D is shown to downregulate the expression of toll-like receptors on the monocytes and inhibit the production of proinflammatory cytokines. Also, vitamin D inhibits the proliferation of T lymphocytes and regulates the differentiation of subgroups of T cells. Moreover, it induces the apoptosis of activated B cells and plasma cells\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. SLE patients had lower vitamin D levels than healthy controls, which inversely correlated with disease activity in SLE\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Further, lower serum levels of vitamin D were associated with renal disease as well as the degree of proteinuria. SLE patients with lupus nephritis (LN) had significantly lower levels of vitamin D than those with inactive SLE and SLE without LN\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVitamin D and vitamin D receptor (VDR) have been shown to modulate the metabolic activity of immune cells. VDR\u0026rsquo;s tasks in the control of metabolism involves regulating genes mediating energy metabolism, as well as in the catabolism of lipophilic intra-cellular molecules\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. It regulates the metabolic pathways involved in energy production, such as glycolysis and oxidative phosphorylation, in immune cells like T cells, B cells, monocytes/macrophages and dendritic cells. These metabolic alterations impact cell activation, differentiation, and cytokine production, ultimately might affecting the immune response in autoimmune diseases\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Recently, vitamin D had multiple effects on lipid metabolism through its actions on nuclear hormone receptors, such as peroxisome proliferator-actiated receptor γ (PPARγ) and liver X receptor (LXR), including changing the phospholipid content of cells\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and vitamin D supplementation reduces markers of oxidative stress and positively affects many other metabolic markers in multiple sclerosis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and other populations\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, however, it is not clear whether and how serum vitamin D regulates metabolism in SLE patients.\u003c/p\u003e \u003cp\u003eIn our study, we performed metabolomics studies in patients with SLE, comparing those with low serum vitamin D levels to those with normal level. Our findings shed light on the metabolic differences associated with vitamin D deficiency and emphasize the crucial role of vitamin D in regulating lipid metabolism. These insights contribute to our understanding of the pathological mechanisms underlying SLE, potentially paving the way for novel diagnostic strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThirty SLE patients, aged-matched from 14 to 69 years (mean 38.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33 years), were enrolled in the study. All fulfilled the 1997 American College of Rheumatology classification criteria\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Current SLE disease activity was measured using the SLE Disease Activity Index 2000 (SLEDAI-2K)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The patients\u0026rsquo; characteristics, including age, gender, routine blood test results, kidney and liver biochemistry test results, lipid levels, serum ferritin levels, serum vitamin D values, immunoglobin, serum complement, and urinary protein results were recorded. All participants were given written consent to the study approved by the Ethics Committee of the Drum Tower Clinical Medical School of Nanjing Medical University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample Preparation for Metabolomics Study\u003c/h2\u003e \u003cp\u003eThe serum of SLE patients were collected and transferred to -80℃ for long term storage. 100 \u0026micro;L serum was thoroughly mixed with 400 \u0026micro;L of cold methanol acetonitrile (v/v, 1:1) via vortexing. And then the mixture was processed with sonication for 1 h in ice baths. The mixture was then incubated at -20\u0026deg;C for 1 h, and centrifuged at 4\u0026deg;C for 20 minutes with a speed of 14, 000 g. The supernatants were then harvested and dried under vacuum liquid chromatography-mass spectrometry (LC-MS) analysis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS Metabolomics Data Acquisition\u003c/h2\u003e \u003cp\u003eMetabolomics profiling was analyzed using a UPLC-ESI-Q-Orbitrap-MS system (UHPLC, Shimadzu Nexera X2 LC-30AD, Shimadzu, Japan) coupled with Q-Exactive Plus (Thermo Scientific, San Jose, USA). For liquid chromatography (LC) separation, samples were analyzed using a ACQUITY UPLC\u0026reg; HSS T3 column (2.1\u0026times;100 mm, 1.8\u0026micro;m)(Waters, Milford, MA, USA). The flow rate was 0.3 mL/min and the mobile phase contained: A: 0.1% FA in water and B: 100% acetonitrile (ACN). The gradient was 0% buffer B for 2 min and was linearly increase to 48% in 4 min, and then up to 100% in4 min and maintained for 2 min, and then decreased to 0% buffer B in 0.1 min, with 3 min re-equilibration period employed. The electrospray ionization (ESI) with positive-mode and negative mode were applied for MS data acquisition separately. The HESI source conditions were set as follows: Spray Voltage: 3.8kv (positive) and 3.2kv (negative); Capillary Temperature: 320 ℃; Sheath Gas (nitrogen) flow: 30 arb (arbitrary units); Aux Gas flow: 5 arb; Probe Heater Temp: 350 ℃; S-Lens RF Level: 50. The instrument was set to acquire over the m/z range 70-1050 Da for full MS. The full MS scans were acquired at a resolution of 70,000 at m/z 200, and 17,500 at m/z 200 for MS/MS scan. The maximum injection time was set to for 100 ms for MS and 50 ms for MS/MS. The isolation window for MS2 was set to 2 m/z and the normalized collision energy (stepped) was set as 20, 30 and 40 for fragmentation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS Metabolomics Data Analysis\u003c/h2\u003e \u003cp\u003eThe raw MS data were processed using MS-DIAL for peak alignment, retention time correction and peak area extraction. The metabolites were identified by accuracy mass (mass tolerance\u0026thinsp;\u0026lt;\u0026thinsp;10 ppm) and MS/MS data (mass tolerance\u0026thinsp;\u0026lt;\u0026thinsp;0.02Da) which were matched with HMDB, massbank and other public databases and our self-built metabolite standard library. In the extracted-ion features, only the variables having more than 50% of the nonzero measurement values in at least one group were kept.\u003c/p\u003e \u003cp\u003eR (version:4.0.3) and R packages were used for all multivariate data analyses and modeling. Data were mean-centered using Pareto scaling. To understand the difference of metabolomics profile between SLE patients with low serum vitamin D level and normal serum vitamin D level, principal component analysis (PCA) was carried out. At the same time, the discriminating metabolites were obtained using Fold change analysis and two-tailed Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test on the normalized raw data at univariate analysis level. Further volcano plot analysis with fold-change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.5 or FC\u0026lt;=1.5 and \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 were considered to be statistically significant metabolites. On the other side, the identified differential metabolites were used to perform cluster analyses with R package. The Spearman\u0026rsquo;s correlation was used to describe the relationships between different metabolites. The ROC analysis was carried out to evaluate each candidate metabolite biomarker. The AUC from ROC analyses was computed using the SPSS software package (version 24, IBM)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eKEGG enrichment analysis\u003c/h2\u003e \u003cp\u003eTo identify the perturbed biological pathways, the differential metabolite data were performed KEGG pathway analysis using KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KEGG enrichment analyses were carried out with the Fisher\u0026rsquo;s exact test, and FDR correction for multiple testing was performed. Enriched KEGG pathways were nominally statistically significant at the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eThe clinical data of SLE patients were summarized as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM). To compare clinical data of two groups, the Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was performed. All statistical analyses were performed using GraphPad Prism 9 software (Graph-Pad, San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of SLE Patients\u003c/h2\u003e \u003cp\u003eIn this study, a total of 31 SLE patients were included, and patients were divided into Low vitamin D SLE (VD1, serum vitamin D\u0026lt;20ng/ml, n\u0026thinsp;=\u0026thinsp;21) and Normal vitamin D SLE (VD2, serum vitamin D\u0026gt;20ng/ml, n\u0026thinsp;=\u0026thinsp;10) groups, based on their serum vitamin D levels\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The demographics and clinical characteristics of SLE patients were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences in routine blood test results, kidney and liver biochemistry, lipid levels, serum ferritin levels, immunoglobin, or serum complement between the two groups. However, VD1 SLE patients tended to have higher SLE disease activity index (SLEDAI) scores and proteinuria than VD2 SLE patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSerum metabolites profiling in SLE patients with or without serum vitamin D deficiency\u003c/h2\u003e \u003cp\u003eTo uncover the metabolome differences between SLE patients with low serum vitamin D level and normal serum vitamin D level, we performed liquid chromatography-mass spectrometry (LC-MS) analysis and identified a total of 1599 metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Using volcano plot analysis, we selected 52 significantly altered metabolites based on fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.5 or FC\u0026lt;=-1.5 and p-values\u0026lt;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Among these, etofylline was up-regulated in VD1 SLE patients, while the remaining 51 metabolites were all downregulated in VD1 SLE patients, predominantly comprising lipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The differential metabolites screened in metabolomics mainly included lipid and lipid-like molecules, benzenoids, organic acids and derivatives, organic oxygen compounds and organoheterocyclic compounds. Lipid and lipid-like molecules accounted for a major proportion (66.67%) of metabolites identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Specifically, the differential lipid and lipid-like molecules mainly included fatty acyls (41.18%), prenol lipids (29.41%), steroids and steroid derivatives (14.71%), glycerophospholipids (8.82%) and sphingolipids (5.88%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These findings suggest that lipid metabolism is significantly altered in SLE patients with low vitamin D levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between differential metabolites\u003c/h2\u003e \u003cp\u003eWe analyzed the linear correlations between these significant differential metabolites and screened out significant correlations with absolute correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.9(\u003cem\u003e|r|\u003c/em\u003e\u0026gt;0.9). There were 19 pairs of strong correlations between these differential metabolites in total. Interestingly, 12 of these pairs involved correlations between lipids and lipids, especially fatty acyls, while 5 pairs of them were between lipids and 2-Hydroxy-4,5',8a'-trimethyl-1'-oxo-4-vinyloctahydro-1'H-spiro[cyclopentane-1,2'-naphthalene]-5'-carboxylic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, they were all positive associated. Their strong positive association suggested that these metabolites may participate in the same metabolic pathway, or biological process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eKEGG pathways of SLE patients with or without serum vitamin D deficiency\u003c/h2\u003e \u003cp\u003eA total of 52 differential metabolites were submitted to the KEGG, and they were matched to 27 metabolic pathways. Among these pathways, lipid metabolism and digestive system pathway were significant secondary classific pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In terms of the primary classific pathways, fat digestion and absorption (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.75E-05), pathways in cancer(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.82E-04) and vitamin digestion and absorption (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.46E-04) were significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Further pathway impact analyses revealed that sphingolipid metabolism, glycerolipid metabolism, glycerophospholipid metabolism and primary bile acid biosynthesis were all significant pathways and they all belong to lipid metabolism pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D), indicating the important role of lipid metabolism in SLE patients with serum vitamin D deficiency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory network analysis of differential metabolites and pathways\u003c/h2\u003e \u003cp\u003eWe searched the screened differential metabolites in the KEGG database for their corresponding pathways and performed regulatory network analysis. The most important differential metabolites were cholesterol (C00187), Lysophosphatidic acid (18:2) (C00681) and spectral match to Lyso-Sphingomyelin from NIST14 (C00550). The mainly associated pathways were vitamin digestion and absorption (hsa04977), neuroactive ligand-receptor interaction (hsa04080), basal cell carcinoma (hsa05217), fat digestion and absorption (hsa04975), regulation of lipolysis in adipocytes (hsa04923), sphingolipid metabolism (hsa00600) and glycerophospholipid metabolism (hsa00564) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results suggest that vitamin D-altered metabolites may involve in lipid digestion, absorption and metabolism, as well as cellular interaction in SLE patients with different vitamin D levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic potential of differential metabolites\u003c/h2\u003e \u003cp\u003eTo explore the diagnostic potential of the differential metabolites in predicting vitamin D levels and diagnosing SLE, we further compared relative abundance of these differential metabolites. The result showed that totally 9 metabolites were significantly decreased in VD1 SLE patients, while Etofylline was significantly increased in VD1 SLE patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Then we performed classical univariate receiver operating characteristic (ROC) curve analyses to generate ROC curve, to calculate area under the curve (AUC) and their 95% confidence intervals. The ROC analysis showed that the AUC of (S)-Oleuropeic acid was 0.867 and 2-Hydroxylinolenic acid was 0.833(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), which might have promising diagnostic potential as biomarkers for predicting vitamin D levels and diagnosing SLE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, metabolomics has emerged as a valuable tool for studying the underlying pathological mechanisms of SLE. Lipid metabolism, including anabolism and catabolism, is essential for almost every aspect of cellular functioning\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Vitamin D, on the other hand, has been shown to be involved in autoimmune diseases\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. It is well-established that SLE patients often have insufficiency of vitamin D levels and lower serum levels of vitamin D were associated with proteinuria\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Our study investigated the serum metabolome differences between SLE patients with normal vitamin D levels and those with vitamin D deficiency using LC-MS analysis. We identified a total of 52 differentially distributed metabolites, with 34 of them being lipid and lipid-like molecules. Interestingly, all these lipids were downregulated in SLE patients with vitamin D deficiency. These results indicated a strong association between vitamin D and lipid metabolism of SLE patients.\u003c/p\u003e \u003cp\u003eAmong the altered metabolites, 14 differential metabolites were fatty acyls, which can promote angiogenesis and reduce inflammation. A total of 10 differential metabolites were prenol lipids, which can regulate the progression of aging-related diseases, diabetes and inflammation and as regulator of bone health and cardiovascular homeostasis. A total of 5 differential metabolites were sterol lipids, which is the component of membrane lipids and can regulate T cell function as hormones and signaling molecules. A total of 3 differential metabolites were glycerophospholipids, which is the structural component of membrane and can affect membrane fluidity. The other 2 differential metabolites were sphingolipids, which is an important component of the cell membrane and can induce lipotoxicity and inflammation and regulate cell death\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Current evidence suggested that lipids and lipid metabolites played an important role in the pathogenesis of SLE. Lipid metabolism was considered as an important facilitator of T cell differentiation. Lipid biosynthesis, including cholesterol and fatty acids, was critical for the proliferation and differentiation of T cells, especially Th17 cells\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Inhibiting FA synthesis in memory CD4\u003csup\u003e+\u003c/sup\u003eT cells of SLE patients decreased interferon (IFN)-γ production and increased Foxp3 expression in T-bet\u003csup\u003e+\u003c/sup\u003eFoxp3\u003csup\u003e+\u003c/sup\u003e cells. Fatty acid synthesis inhibitors may improve the pathological status by correcting Th1 subset imbalance and overproduction of IFN-γ in SLE\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Fatty acids could modulate the process of macrophage differentiation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Targeting receptors associated lipid metabolism, such as peroxisome proliferator-activated receptor γ (PPARγ), 12/15-Lipoxygenase (12/15-LO), sphingosine-1-phosphate (S1P) and liver X receptors (LXRs), was able to affect differentiation of monocytes and production of proinflammatory cytokines, which were related to TLR7 and TLR9 expression\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Furthermore, we observed strong associations between the differential metabolites, particularly the lipids, suggesting that these metabolites may participate in the same metabolic pathway, or biological process. We speculated that the downregulated lipids in vitamin D deficient SLE patients may affect the proliferation, differentiation and production of proinflammatory cytokines of lymphocytes and innate immune cells and participated in the pathogenesis of SLE.\u003c/p\u003e \u003cp\u003eThe fatty acid (FA) and cholesterol metabolic pathways are the most important pathways in lipid metabolism. Lipid synthesis is critical for the cell and determine cell survival\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Lipid degradation is also essential to sustain life. Lipolytic products and intermediates play a vital role in cellular signaling\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Our results showed that fat digestion and absorption, sphingolipid metabolism, glycerolipid metabolism, glycerophospholipid metabolism and primary bile acid biosynthesis pathways played a crucial part in regulating differential metabolites of vitamin D deficient SLE patients. These data shed light on the probable signal pathways by which these altered metabolites participated in the development of SLE.\u003c/p\u003e \u003cp\u003eMetabolomics studies have shown promise in identifying potential biomarkers for SLE diagnosis and disease activity assessment. Li et al. collected serum samples from 17 SLE and 17 healthy and performed metabolic profiles by high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). They found that metabolites such as ceramide, trimethylamine N-oxide, xanthine were significantly elevated in the serum of active systemic lupus erythematosus patients, while acylcarnitine, caffeine, hydrocortisone, itaconic acid and serotonin were down-regulated\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Zhang et al. performed metabolomic and lipidomic profiles analysis of 133 SLE patients and 30 HCs. They successfully found that a combination of DHEAS, Benzoic acid, 2-Methylbutyrylglycine, and FA (20:1) could distinguish SLE from HC. Lysophosphatidylcholine (18:0), phosphatidylcholine (18:3/18:3) and phosphatidylethanolamine (16:0/22:4) could be combined to differentiate inactive SLE and active SLE. Moreover, they could identify biomarkers associated with different organ involvement in SLE patient, including sonly kidney involvement, skin involvement, blood system involvement, and multisystem involvement respectively\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Yan et al. performed SLE fecal metabolome analysis and suggested that L-tryptophan was positively correlated with the SLEDAI-2K\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In this study, we identified two altered lipids, (S)-Oleuropeic acid(AUC\u0026thinsp;=\u0026thinsp;0.867) and 2-Hydroxylinolenic acid(AUC\u0026thinsp;=\u0026thinsp;0.833), as potential biomarkers in vitamin D deficient SLE patients.\u003c/p\u003e \u003cp\u003eRecent researches have shed light on the potential mechanisms by which vitamin D participates in the pathogenesis of SLE. VDR generally skews immune cells towards anti-inflammatory states\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Vitamin D has been shown to decrease neutrophil extracellular traps (NETs) and prevent endothelial damage in cultured neutrophiles derived from SLE patients, compared to controls, revealing the possibility of Vitamin D as supplementary therapy for SLE patients with hypovitamin D to prevent endothelial damage\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Liu, et al revealed that 1,25-(OH)2D3/VDR facilitated the recovery of SLE by downregulating Skp2 and upregulating p27 expression\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. New scientific approaches targeting VitD/VDR signaling at the cellular metabolic level may provide a better comprehension of its role in SLE progression. However, there is no related report till now. Our study indicated the metabolic interactions between VitD and SLE, providing new insights into the mechanism of SLE.\u003c/p\u003e \u003cp\u003eThe clinical manifestations of SLE patients are highly heterogeneous, involving various symptoms in the gastrointestinal tract, skin, kidney, blood, nerves, and other organs. Approximately 60\u0026ndash;70% of SLE patients followed a relapsing-remitting course, with the rest 30\u0026ndash;40% divided equally between prolonged remission and persistently active disease\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Due to the multiorgan involvement and the varied disease activity, disease activity measurements are necessary to guide therapy. The most widely used instruments include SLEDAI\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, British Isles Lupus Activity Group (BILAG) index\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR DI; SDI)\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and other tools\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Promising recent studies have used soluble mediator levels to assess clinical disease activity and predict imminent flare in SLE, including innate and adaptive cytokines, chemokines, as well as soluble receptors and tumor necrosis factor superfamily members\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. However, current tools are still not available to fully measure disease activity of SLE patients with different subtypes. Given the different clinical manifestations of SLE and the pathological differences between active and inactive disease, metabolomics may be an important platform for discovering different clinical phenotypic biomarkers for SLE\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Notably, SLE patients with vitamin D deficiency could represent an important subgroup. The metabolomic profiling in our study have the potential to unravel unique metabolic signatures associated with different clinical phenotypes in SLE. These metabolites could serve as specific biomarkers to better stratify patients, identify disease activity, and predict outcomes. Furthermore, the discovery of differential metabolites in SLE patients with vitamin D deficiency may shed light on the underlying mechanisms linking vitamin D status and disease pathogenesis in SLE.\u003c/p\u003e \u003cp\u003eIn conclusion, our study revealed the global metabolic changes in SLE patients with vitamin D deficiency, illustrating the extensive association between vitamin D and lipid metabolism in these patients. Further investigations are needed to further explore the underlying mechanism by which vitamin D regulates the lipid metabolism and affects immune cells. This study provides a rational basis for a better understanding of the pathogenetic mechanism underlying SLE and may pave the way for the development of targeted therapeutic approaches.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinical characteristics of SLE patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVD1 (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVD2 (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale/female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.90\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212.90\u0026thinsp;\u0026plusmn;\u0026thinsp;22.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219.50\u0026thinsp;\u0026plusmn;\u0026thinsp;26.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLEDAI-2K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.23\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.71\u0026thinsp;\u0026plusmn;\u0026thinsp;15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR(mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.86\u0026thinsp;\u0026plusmn;\u0026thinsp;12.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary total protein/creatinine(mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24hour Urinary protein(mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2587\u0026thinsp;\u0026plusmn;\u0026thinsp;907.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e688.7\u0026thinsp;\u0026plusmn;\u0026thinsp;389.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerritin(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e515.00\u0026thinsp;\u0026plusmn;\u0026thinsp;167.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227.60\u0026thinsp;\u0026plusmn;\u0026thinsp;67.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplement 3 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplement 4 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgA(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgM(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgE(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.20\u0026thinsp;\u0026plusmn;\u0026thinsp;23.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189.4\u0026thinsp;\u0026plusmn;\u0026thinsp;91.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSLEDAI-2K, SLE disease activity index 2000; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; LDL, low density lipoprotein; HDL, high density lipoprotein; ESR, erythrocyte sedimentation rate; IgA, immunoglobulin A; IgM, immunoglobulin M; IgG, immunoglobulin G; IgE, immunoglobulin E.\u003c/em\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe work was supported by Nanjing Medical Science and technique Development Foundation (JQX20004), and funding for Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (2021-LCYJ-PY-16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical rule:\u0026nbsp;\u003c/strong\u003eAll participants gave their written Informed consent approved by the Ethics Committee of the Drum Tower Clinical Medical School of Nanjing Medical University in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors acknowledge Hong Yu for assistance in analysing the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eLinyu Geng, Lingyun Sun designed the study. Yunxia Yan collected the data, performed the experiments and performed the statistical analysis. Yunxia Yan, Fangyuan Yu and Qi Li analysed the data. Yunxia Yan, Linyu Geng and Xuebing Feng wrote the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analyzed during the current study are not publicly available out of concern for patient privacy, as data from medical records is considered sensitive, but de-identified datasets are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS. Bernatsky, J. F. Boivin, L. Joseph, et al. Mortality in systemic lupus erythematosus. Arthritis Rheum 2006; 54(8): 2550-2557. https://doi.org/10.1002/art.21955.\u003c/li\u003e\n\u003cli\u003eM. A. Ameer, H. 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Clinical disease activity and flare in SLE: Current concepts and novel biomarkers. J Autoimmun 2021; 119: 102615. https://doi.org/10.1016/j.jaut.2021.102615.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vitamin D, lipids, metabolism, systemic lupus erythematosus","lastPublishedDoi":"10.21203/rs.3.rs-3861907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3861907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eVitamin D deficiency is increasingly common in systemic lupus erythematosus (SLE) patients and is associated with the disease activity and proteinuria. Recently, alterations in metabolism have been recognized as key regulators of SLE pathogenesis. Our objective was to identify changes in the serum metabolome of SLE with vitamin D deficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: In this study, we applied untargeted metabolomics to serum samples obtained from a cross-sectional cohort of age- and sex-matched SLE patients, with or without vitamin D deficiency. Subsequently, we performed metabolomics profiling analysis, including principal component analysis, student’s t test, fold change analysis, volcano plot analysis, cluster analysis, Spearman’s correlation analysis, KEGG enrichment analysis, regulatory network analysis and receiver operating characteristic (ROC) analysis, to identify 52 significantly altered metabolites in vitamin D deficient SLE patients. The area under the curve (AUC) from ROC analyses was calculated to assess the diagnostic potential of each candidate metabolite biomarker.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eLipids accounted for 66.67% of the differential metabolites in the serum, highlighted the disruption of lipid metabolism. The 52 differential metabolites were mapped to 27 metabolic pathways, with fat digestion and absorption, as well as lipid metabolism, emerging as the most significant pathways. The AUC of (S)-Oleuropeic acid and 2-Hydroxylinolenic acid during ROC analysis were 0.867 and 0.833, respectively, indicating their promising diagnostic potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIn conclusion, our results revealed vitamin D deficiency alters SLE metabolome, impacting lipid metabolism, and thrown insights into the pathogenesis and diagnosis of SLE.\u003c/p\u003e","manuscriptTitle":"Metabolic alterations in vitamin D deficient systemic lupus erythematosus patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 14:58:05","doi":"10.21203/rs.3.rs-3861907/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-13T10:07:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-28T22:42:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2d4ee7d4-6b30-4379-96f0-0dd48cb1002a","date":"2024-02-27T13:35:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-25T12:44:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-21T17:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226d89d3-00ce-457e-972c-d8959643a307","date":"2024-02-21T12:47:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-20T09:07:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-07T23:22:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-19T08:07:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-19T08:00:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-14T03:00:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b61c1b3-b676-4e02-94d4-3f90ce34fa6b","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28239501,"name":"Biological sciences/Immunology/Autoimmunity"},{"id":28239502,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2024-08-22T19:30:59+00:00","versionOfRecord":{"articleIdentity":"rs-3861907","link":"https://doi.org/10.1038/s41598-024-67588-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-14 15:57:16","publishedOnDateReadable":"August 14th, 2024"},"versionCreatedAt":"2024-01-22 14:58:05","video":"","vorDoi":"10.1038/s41598-024-67588-4","vorDoiUrl":"https://doi.org/10.1038/s41598-024-67588-4","workflowStages":[]},"version":"v1","identity":"rs-3861907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3861907","identity":"rs-3861907","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}