Abstract

   This study investigated the alterations in serum metabolic profile in
   systemic lupus erythematosus (SLE) patients with increased levels of
   serum ferritin. 52 SLE patients were divided into two groups based on
   their ferritin levels. The metabolomic profile was identified using
   non-targeted metabolomics technology (UHPLC-MS/MS), and analyzed by
   Principal Component Analysis (PCA), Orthogonal Partial Least Squares
   Discrimination Analysis (OPLS-DA), ROC analysis, and pathway analysis.
   Results showed that SLE patients with high ferritin levels had
   increased hematologic involvement and elevated levels of inflammatory
   markers, including procalcitonin (PCT), alanine transaminase (ALT), and
   aspartate transaminase (AST). Additionally, there was decreased levels
   of albumin and CD4^+ T cell counts. A distinct metabolic profile was
   found in the high-ferritin SLE group, with significant changes in
   metabolites and metabolic pathways. Potential correlations between
   differential metabolites and clinical features were identified,
   including associations with PCT, interleukin-6 (IL-6), erythrocyte
   sedimentation rate (ESR), C-reactive protein (CRP), albumin, ALT, AST,
   immunoglobulin G (IgG), and CD3^+CD4^+ T cell. The findings confirm
   elevated serum ferritin is associated with hematology involvement and
   offer insights into the pathology and targeted therapeutic strategies
   of SLE.

Supplementary Information

   The online version contains supplementary material available at
   10.1038/s41598-025-06170-y.

   Keywords: Systemic lupus erythematosus, Serum ferritin, Metabolomics

   Subject terms: Rheumatology, Rheumatic diseases

Introduction

   Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune
   disorder with an elusive etiology, showcasing diverse clinical
   manifestations that span from mild skin erythema and joint pain to
   severe complications such as end-stage renal disease and
   cerebrovascular accidents^[42]1. While established indices like SLE
   disease activity index 2000 (SLEDAI-2K), British Isles Lupus Assessment
   Group’s disease activity index (BLIAG) and SLE Responder Index 4
   (SRI-4) offer reliable assessments of SLE activity, the identification
   of novel markers remains imperative for a comprehensive understanding
   of the disease state, early diagnosis, prognostic assessment, and
   targeted therapeutic intervention^[43]2–[44]5.

   Ferritin, recognized as both an iron storage protein and an acute phase
   response protein (APR), plays a pivotal role in cellular iron
   homeostasis, the level of which is regulated by intracellular iron
   status and the serum pro-inflammatory factors like IL-6 and tumor
   necrosis factor-α (TNF-α). Serum ferritin levels reflect the iron
   status of macrophages and may be derived primarily from macrophage
   secretion^[45]6. Animal experiments have demonstrated that ferritin
   induces systemic inflammation and triggers neutrophil extracellular
   trap (NET) formation, exacerbating systemic inflammation in diseases
   like adult Still’s disease in a macrophage scavenger receptor 1 (Msr1)
   -dependent manner^[46]7. Several studies have reported significantly
   elevated serum ferritin levels in active SLE patients, hinting at its
   potential role as a distinguishing marker that was more specific for
   SLE activity than conventional marker^[47]8–[48]12. Moreover,
   associations between elevated serum ferritin and adverse outcomes, such
   as anemia, premature delivery in mid-pregnancy SLE patients, and even
   macrophage activation syndrome in SLE^[49]13–[50]15, have been noted,
   suggesting that the elevated serum ferritin may not only be a
   consequence of inflammatory activity, but also as a predictor for poor
   disease prognosis or an indicator of an adverse disease state in SLE
   patients^[51]14,[52]16. However, the precise mechanistic underpinnings
   of elevated serum ferritin in SLE pathology remain elusive.

   The studies of metabolomics in SLE are increasingly intensive and have
   made great progress. For instance, studies have revealed that
   dysregulated tryptophan metabolism, elevated ceramides levels, and
   aberrant glucose metabolism are associated with the onset, lupus
   manifestations and disease activity prediction^[53]17–[54]21. Moreover,
   metabolomic-based studies have discerned that metabolomic signatures
   can predict the progression of atherosclerosis in a subset of patients
   with juvenile-onset SLE (JSLE) with relevance for clinical trial
   stratification^[55]22. Additionally, taurine metabolism has been
   implicated in SLE pathogenesis by enhancing pDC-mediated type I IFN
   production^[56]23. A multi-omics analysis has pinpointed high
   ApoB:ApoA1 ratios as potential biomarkers indicative of heightened
   cardiometabolic risk and worse clinical outcomes in JSLE^[57]24.
   Another multi-omics study presented an imbalance in lipid metabolism,
   especially in sphingolipid metabolism, accompanied with dysregulated
   apolipoproteins, which are seen to contribute to SLE disease
   activity^[58]25. These studies demonstrate the great potential of
   metabolomics in unraveling the complexities of of systemic lupus
   erythematosus. However, in certain subgroups of SLE, such as those with
   elevated serum ferritin levels, challenges remain in identifying
   metabolic abnormalities associated with disease progression, activity,
   and clinical manifestations. Further research is warranted to elucidate
   the underlying etiologies and enhance our understanding of the
   condition.

   In this study, we explore the changes of serum metabolic profiles in
   SLE patients with elevated serum ferritin. By identifying key metabolic
   pathways and biomarkers, we can gain a better understanding of the
   complexity of the disease and serve as a foundation for the development
   of personalized treatment strategies for this subgroup.

Method

Patients

   We recruited 52 SLE patients who visited Nanjing Drum Tower Hospital
   between May 2022 and March 2023. All participants met the 1997 revised
   diagnostic criteria for SLE set by the American College of Rheumatology
   (ACR)^[59]26. Those with a history of active malignancy, New York Heart
   Association functional class III-IV, severe infections, liver failure,
   end-stage renal disease, Adult Still disease, or positive test results
   for HIV and other pathogenic microorganisms were excluded. Informed
   consent was obtained from all SLE patients. Demographic and clinical
   profiles of the patients are summarized in Table [60]1. Based on serum
   ferritin level, the patients were divided into 2 groups: the non-high
   ferritin group (F1 Group, serum ferritin ≤ 322 ng/ml for male or serum
   ferritin ≤ 291 ng/ml for female) and the high ferritin group (F2 Group,
   serum ferritin > 322 ng/ml for male or serum ferritin > 291 ng/ml for
   female). The study was approved by the ethics committee of Nanjing Drum
   Tower Hospital.

Table 1.

   Clinical characteristics of participants.
   Characteristic F1 N = 32 F2 N = 20 P
   Ferritin (ng/ml) 82.45 [19.60, 182.55] 742.35 [489.05, 1143.17]
    < 0.001
   Age (years) 38.69 ± 14.24 46.75 ± 13.87 0.05
   Gender (%)
    Male 2 (6.25) 4 (20.00) 0.189
    Female 30 (93.75) 16 (80.00)
   SLEDAI 8.00 [3.75, 12.00] 8.50 [4.75, 12.25] 0.631
   CRP (mg/L) 3.30 [2.00, 12.45] 5.85 [3.08, 22.60] 0.122
   ESR (mm/h) 30.00 [13.00, 77.00] 52.50 [33.50, 83.75] 0.103
   PCT (ng/ml) 0.05 [0.02, 0.12] 0.16 [0.04, 0.49] 0.101
   Normal (%) 25 (100.00) 10 (71.43) 0.012
   Increase (%) 0 (0.00) 4 (28.57)
   IL-6 (pg/ml) 2.61 [1.32, 5.96] 9.12 [2.06, 37.92] 0.085
    Normal (%) 17 (73.91) 6 (42.86) 0.124
    Increase (%) 6 (26.09) 8 (57.14)
   WBC (10^9/L) 5.35 [3.42, 6.43] 3.85 [2.88, 6.80] 0.333
   HB (g/L) 96.56 ± 29.75 96.00 ± 19.79 0.941
   PLT (10^9/L) 184.50 [140.00, 250.25] 193.00 [109.50, 227.75] 0.492
   BUN (mmol/L) 6.05 [5.23, 8.15] 6.75 [5.25, 9.83] 0.408
   SCr (umol/l) 60.50 [44.00, 97.28] 58.00 [46.50, 91.00] 0.97
   Urinary total protein/Creatinine (g/g) 0.33 [0.09, 1.38] 0.75 [0.32,
   2.37] 0.053
   24 h Urinary protein (mg) 583.00 [328.00, 3218.00] 610.00 [302.50,
   1793.00] 0.882
   Albumin (g/L) 35.29 ± 5.20 31.02 ± 3.86 0.003
   ALT (U/L) 15.05 [8.55, 19.23] 25.35 [19.08, 34.75] 0.001
   AST (U/L) 16.50 [12.63, 20.97] 23.40 [20.00, 33.47] 0.002
   Cholesterol (mmol/l) 4.53 [3.70, 5.03] 4.56 [4.11, 5.26] 0.543
   LDL (mmol/l) 2.30 [1.81, 2.99] 2.54 [1.74, 3.13] 0.714
   HDL (mmol/l) 1.37 ± 0.45 1.25 ± 0.61 0.447
   IgA (g/L) 1.83 [1.36, 2.57] 2.38 [1.52, 3.54] 0.237
   IgG (g/L) 10.50 [7.90, 13.35] 15.15 [10.72, 17.22] 0.054
   IgM (g/L) 0.79 [0.55, 1.20] 1.00 [0.43, 1.39] 0.561
   IgE (IU/ml) 25.00 [11.50, 107.50] 35.00 [15.25, 143.50] 0.5
   C3 (g/L) 0.81 ± 0.27 0.73 ± 0.28 0.312
   C4 (g/L) 0.18 [0.07, 0.26] 0.13 [0.04, 0.18] 0.474
   Total Lymphocyte (10^9/l) 1.10 [0.60, 1.70] 0.65 [0.50, 1.08] 0.053
   B Lymphocyte (10^9/l) 0.13 [0.04, 0.18] 0.05 [0.02, 0.10] 0.123
   CD3^+T cell (10^9/l) 0.90 [0.48, 1.41] 0.55 [0.39, 0.88] 0.096
   CD3^+CD4^+T cell (10^9/l) 0.38 [0.26, 0.60] 0.20 [0.16, 0.34] 0.01
   CD3^+CD8^+ T cell (10^9/l) 0.39 [0.21, 0.66] 0.29 [0.22, 0.51] 0.654
   NK cell (10^9/l) 0.07 [0.05, 0.11] 0.04 [0.03, 0.08] 0.068
   Anti-dsDNA antibody (IU/l) 170.65 [8.88, 405.52] 81.89 [10.00, 691.03]
   0.805
    Positive (%) 16 (59.26) 6 (46.15) 0.659
    Negative (%) 11 (40.74) 7 (53.85)
   LN (%)
    No 13 (40.62) 11 (55.00) 0.468
    Yes 19 (59.38) 9 (45.00)
   Hematologic involvement (%)
    No 9 (47.37) 1 (7.69) 0.024
    Yes 10 (52.63) 12 (92.31)
   Serositis (%)
    No 3 (23.08) 1 (9.09) 0.596
    Yes 10 (76.92) 10 (90.91)
   [61]Open in a new tab

   Values were presented as mean ± SD, median (interquartile range), or
   number (percentage). SLEDAI systemic lupus erythematosus disease
   activity index, WBC white blood cell, HB hemoglobin, PLT platelet, ALT
   alanine transaminase, AST aspartate transaminase, BUN blood urea
   nitrogen, SCr serum creatinine, LDL low-density lipoprotein, HDL high
   density lipoprotein, CRP C-reactive protein, ESR erythrocyte
   sedimentation rate, IgA immunoglobulin A, IgM immunoglobulin M, IgG
   immunoglobulin G, IgE immunoglobulin E, C3 completement 3, C4
   completement 4, PCT procalcitonin, IL-6 interleukin-6. LN,
   hospitalization diagnosed lupus nephritis; Hematologic involvement,
   leukopenia or autoimmune hemolytic anemia (AIHA) or thrombocytopenia;
   Serositis, pleural effusion or pericardial effusion.

Sample preparation

   We collected peripheral venous blood samples from the participants
   after overnight fasting using yellow vacuum blood collection tubes.
   Around 300 μl of serum was preserved in EP tubes and stored at − 80 °C
   for further analysis. Thaw the samples at 4 °C, vortex well, take
   100 μl of each sample into an EP tube, add 400 μl pre-cooled pure
   methanol, vortex well, ultrasonic for 20 min in an ice bath, static for
   1 h at − 20 °C, centrifuge at 16,000 g for 20 min at 4 °C, and then
   collect the supernatant to be dried up in a high-speed vacuum
   concentration centrifuge. For mass spectrometry, 100 μl of
   methanol–water solution (1:1, v/v) is added for re-dissolution, and the
   supernatant is centrifuged at 20,000 g for 15 min at 4 °C.

LC–MS/MS analysis

Chromatographic separation

   Samples were placed in an autosampler at 4 °C throughout the analysis
   using a SHIMADZU-LC30 ultra-high performance liquid chromatography
   (UHPLC) system and an ACQUITY UPLC^® HSS T3 (2.1 × 100 mm, 1.8 µm)
   (Waters, Milford, MA, USA) column. The injection volume was 4 μl, the
   column temperature was 40 °C and the flow rate was 0.3 mL/min; the
   chromatographic mobile phases were A: 0.1% formic acid aqueous
   solution, and B: acetonitrile; and the chromatographic gradient elution
   program was as follows: 0–2 min, 0 B; 2–6 min, B varied linearly from 0
   to 48%; 6–10 min, B varied linearly from 48 to 100%; 10–12 min, B was
   maintained at 100%; 12–12.1 min, B changed linearly from 100 to 0%, and
   12.1-15 min, B was maintained at 0%.

Mass spectrometry acquisition

   Each sample was detected by electrospray ionization (ESI) in positive
   (+) and negative (−) modes, respectively. After the samples were
   separated by UPLC, we performed mass spectrometry analysis with a QE
   Plus mass spectrometer (Thermo Scientific), and used a HESI source for
   ionization with the following ionization conditions: Spray Voltage:
   3.8kv (+) and 3.2kv (−); Capillary Temperature: 320 (±); Sheath Gas: 30
   (±); Aux Gas: 5 (±) Probe Heater Temp: 350 (±); S-Lens RF Level: 50.
   The mass spectrometry acquisition settings were as follows:

   MS acquisition time: 15 min, parent ion scan range: 70–1050 m/z,
   primary MS resolution: 70,000 @ m/z 200, AGC target: 3e6, primary
   Maximum IT: 100 ms. Secondary mass spectrometry analysis was acquired
   as follows: the 10 highest-intensity parent ions was triggered after
   each full scan (MS2 scan), secondary mass resolution: 17,500 @ m/z 200,
   AGC target: 1e5, secondary Maximum IT: 50 ms, MS2 Activation Type: HCD,
   Isolation window: 2 m/z, normalized collision energy (Stepped): 20, 30,
   40.

Data preprocessing

   The raw data were processed by MSDIAL software for peak alignment,
   retention time correction and peak area extraction. We used precise
   mass number matching (mass tolerance < 10 ppm) and secondary spectrum
   matching (mass tolerance < 0.01 Da) to identify the metabolite
   structures, and matched the metabolites with Human Metabolome Database
   (HMDB) and self-built metabolite standard library (BP-DB).

   For the extracted data, ion peaks with > 50% missing values were
   removed from the group and not involved in the subsequent statistical
   analysis; the positive and negative ion data were normalized by the
   total peak area, and the positive and negative ion peaks were
   integrated and applied to the Python software for pattern recognition,
   and then pre-processed by Unit variance scaling (UV) for the subsequent
   data analysis.

Data analysis

   For the clinical baseline data, R Studio (version 4.3.2) was used for
   statistical analysis: the Shapiro–wilk test was used for normality, for
   continuous variables with normal distribution, mean and standard
   deviation were used, and t-test was used for comparison between groups;
   for continuous variables with non-normal distribution, median and
   quartile were used, and Mann–Whitney U-test was used for comparison
   between groups; and for categorical variables, number and percentage
   were used, and Chi-square test or Fisher exact test was used for
   comparison between groups. Spearman’s rank correlation analysis was
   used for correlation analysis, and GraphPad Prism 9 was used to draw
   the heat map of correlation analysis.

   For the comparison of metabolite expression differences, t-test was
   used for univariate analysis, and fold change (FC) was performed to
   compare the relative expression; PCA and OPLS-DA were used for
   multivariate analysis; OPLS-DA is a supervised discriminant analysis
   statistical method based on PCA, which utilizes partial least squares
   regression to establish the relationship between metabolite expression
   and sample category, and to realize the prediction of sample category.
   Variable important in projection (VIP) is the variable weight value of
   the OPLS-DA model variables, which can be used to measure the influence
   strength and explanatory power of the accumulation differences of each
   metabolite on the categorical discrimination of each group of samples.
   The permutation test was used to assess whether the OPLS-DA model was
   overfitted. In this study, we determined the differential metabolites
   based on P < 0.05 and VIP > 1. Also, ROC analysis was performed to
   assess the diagnostic value of metabolites and the KEGG enrichment
   analysis of differential metabolites was performed to identify the
   perturbed biological pathways. 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. In this study,
   P < 0.05 was considered statistically significant.

Result

Clinical characteristics of participations

   As depicted in Table [62]1, the average age of the participants in the
   two groups was 38.69 ± 14.24 versus 46.75 ± 13.87 years (F1 vs. F2).
   Compared to the F1 group, the F2 group showed lower levels of C3 and C4
   and higher levels of PCT, IL-6, urinary protein/creatinine and
   immunoglobulin, although not entirely statistically significant.
   Additionally, the F2 group demonstrated significantly lower serum
   albumin levels, CD4^+ lymphocyte counts, as well as higher levels of
   ALT and AST. Both groups displayed average hemoglobin (HB) levels below
   the lower limit of the normal range (female: 115–150 g/L, male:
   130–175 g/L) and the F2 group exhibited a greater extent of hematologic
   involvement than the F1 group. There is no significant difference in
   SLEDAI and anti-dsDNA antibodies between the two groups. Among the 32
   SLE patients with confirmed hematological system involvement, those
   with hematologic involvement exhibited higher serum ferritin levels
   (Table [63]2).

Table 2.

   Serum ferritin levels and hemocytes counts by hematologic involvement
   in the SLE patients.
   Characteristic Hematologic involvement (No, N = 10) Hematologic
   involvement (Yes, N = 22) P
   Ferritin (ng/ml) 82.45 [56.30, 93.53] 411.60 [186.60, 768.12] 0.008
   WBC (10^9/L) 5.85 [4.77, 6.88] 2.95 [2.35, 4.12] 0.006
   Hb (g/L) 127.50 [120.00, 137.50] 88.50 [67.25, 96.75]  < 0.001
   PLT (10^9/L) 214.50 [160.25, 252.75] 136.00 [82.00, 223.50] 0.064
   [64]Open in a new tab

   The hematologic involvement refers to leukopenia (WBC < 3.5 × 10⁹/L),
   autoimmune hemolytic anemia (AIHA), or thrombocytopenia
   (PLT < 125 × 10⁹/L).

Untargeted metabolic alterations in SLE patients with elevated ferritin
levels

   The PCA analysis (Fig. [65]1A) showed the quality control (QC) samples
   were close clustering in both positive and negative mixing modes,
   indicating excellent reproducibility of the experiment. The OPLS-DA
   score plot (Fig. [66]1B) effectively distinguished SLE patients with
   high ferritin levels from those with non-high ferritin levels,
   highlighting differences in serum metabolite profiles between the two
   groups. The permutation test verified that the OPLS-DA model was not
   overfit, with R2Y = 0.914cum and Q2 = 0.233cum. 128 differential
   metabolites were identified by t test and OPLS-DA (P < 0.05, VIP > 1).
   (Fig. [67]2A–C). These differential metabolites predominantly belonged
   to categories such as lipids and lipid-like molecules, organic acids
   and derivatives, organoheterocyclic compounds, benzenoids, organic
   oxygen compounds (Fig. [68]2D). Pearson correlation analysis revealed
   the close associations between most of these differential metabolites
   (Fig. [69]2E). Based on further ROC analysis, we selected six
   significantly differential metabolites with AUC > 0.7 and VIP > 2.5
   (Table [70]3): N-Lactoylvaline, Succinic acid,
   3-Hydroxyisovalerylcarnitine, 1-Carboxyethylphenylalanine,
   N-Lactoylleucine and N-lactoyl-Tyrosine.

Fig. 1.

   [71]Fig. 1
   [72]Open in a new tab

   Identification of serum metabolic profiles in SLE patients between the
   F1 group and the F2 group. (A) Unsupervised PCA model; (B) Supervised
   OPLS-DA model.

Fig. 2.

   Fig. 2
   [73]Open in a new tab

   Identification of differential metabolites in SLE patients between the
   F1 group and the F2 group. (A) Volcano Plot of metabolites between the
   two groups. The red dots represent up-regulated metabolites, the blue
   dots represent down-regulated metabolites. (B) Scatter plot of the mean
   expression values of metabolites. (C) Hierarchical clustering results
   of differential metabolites. (D) Pie chart of HMBD Super Class
   classification of differential metabolites. (E) Heatmap for correlation
   analysis of differential metabolites.

Table 3.

   Significantly different metabolites between the F1 and F2 groups.
   Metabolite name P FC VIP AUC Class
   3-Hydroxyisovalerylcarnitine 0.002 2.10 2.89 0.769 Fatty Acyls
   N-lactoyl-Tyrosine 0.001 2.37 2.77 0.742 Carboxylic acids and
   derivatives
   N-Lactoylvaline  < 0.001 1.90 2.66 0.781 Carboxylic acids and
   derivatives
   1-Carboxyethylphenylalanine  < 0.001 2.19 2.62 0.769 Carboxylic acids
   and derivatives
   Succinic acid  < 0.001 1.61 2.58 0.770 Carboxylic acids and derivatives
   N-Lactoylleucine  < 0.001 1.89 2.55 0.761 Carboxylic acids and
   derivatives
   [74]Open in a new tab

   FC refers to fold change, VIP refers to variable important in
   projection, and AUC refers to area under the curve.

Pathways analysis of the differential metabolites

   We performed KEGG pathway enrichment analysis on the differential
   metabolites and identified significant enrichment in 10 second class
   pathways and 18 third class pathways (Fig. [75]3A–B), amino acid
   metabolism was the most significantly enriched second KEGG pathway, and
   arginine biosynthesis emerged as the most prominent pathway
   (Fig. [76]3C–D). In another, we studied the overall metabolic changes
   using the abundance of differential metabolites (Fig. [77]3E), found
   the F2 group exhibited upward trends in those pathways such as
   tryptophan and carbon metabolism and downward trends in those pathways
   such as aminoacyl-tRNA biosynthesis, pyrimidine metabolism and protein
   digestion and absorption.

Fig. 3.

   [78]Fig. 3
   [79]Open in a new tab

   Pathway analysis. (A) Bubble map of the enriched Significant classify
   KEGG pathway. (B) Bubble map of the enriched Secondary classify KEGG
   pathway. Environmental Information Processing: E, Genetic Information
   Processing: G, Human Diseases: H, Metabolism: M, Organismal Systems: O.
   (C) Pathway Impact plot of differential metabolites calculated by Out
   degree centrality method. (D) Pathway Impact plot of differential
   metabolites calculated by Betweenness centrality method. (E)
   Differential abundance score plot for differential metabolites.

Correlation analysis of clinical features and significantly different
metabolites

   We conducted Spearman’s rank correlation analysis among the clinical
   features and the six significantly different metabolites
   (Fig. [80]4A–B). In addition to the positive correlations with the six
   different metabolites, serum ferritin was positively correlated with
   age, ESR, CRP, ALT, AST and IgG, while negatively correlated with
   albumin, total lymphocyte count, CD3^+ T cell count, CD3^+CD4^+ T cell
   count and NK cell count. The six significantly different metabolite
   showed significantly positive correlations each other, except for
   N-lactoyl-Tyrosine with succinate acid. They showed significant
   correlations with specific clinical features, for instance,
   1-Carboxyethylphenylalanine and 3-Hydroxyisovalerylcarnitine were
   positively correlated with PCT, IL-6, ESR and CRP; N-Lactoyl-Tyrosine,
   1-Carboxyethylphenylalanine and 3-Hydroxyisovalerylcarnitine were
   negatively correlated with albumin levels. All of these metabolites,
   except for N-Lactoyl-Tyrosine, showed a significant positive
   correlation with ALT or AST. Moreover, 3-Hydroxyisovalerylcarnitine
   exhibited a positive correlation with serum BUN and Scr levels. These
   findings highlight the associations between differential metabolites
   and clinical features, suggesting their potential roles in the
   progression of SLE with elevated serum ferritin.

Fig. 4.

   [81]Fig. 4
   [82]Open in a new tab

   Heat maps of correlation analysis of clinical characteristics with
   significantly differential metabolites. (A) Heat maps of relative
   coefficient of the correlation analysis; (B) Heat maps of P value of
   the correlation analysis. ***P < 0.001; **P < 0.01; *P < 0.05; NS,
   P ≥ 0.05.

Discussions

   It is noteworthy that approximately 15–30% of SLE patients develop
   cytokine storm-mediated hyperinflammation, distinct from classic
   autoantibody-driven pathology^[83]27,[84]28. These cases feature
   inflammasome activation, Toll-Like receptor pathways stimulation, and
   metabolic reprogramming^[85]29,[86]30. Ferritin, which can be produced
   in large quantities in response to cytokine stimulation, is now
   receiving increasing attention for its role in SLE^[87]12,[88]16. Our
   study reveals that SLE patients with hyperferritinemia demonstrate
   distinct metabolic perturbations, particularly in arginine
   biosynthesis, lipid metabolism pathways and organic acid metabolism
   pathways, which may underlie ferritin’s potential pathogenic role in
   SLE beyond being an acute-phase reactant. However, the specific
   mechanism still needs further exploration.

   Among the metabolic pathways analyzed, arginine biosynthesis was found
   to be the most dominant pathway, in which the high-ferritin group (F2
   Group) presented an increase of citrulline. Previous study in MRL mice
   has shown elevated citrulline levels are associated with preferential
   propagation and overproduction of IgG in γ-committed B
   lymphocytes^[89]31, which to some extent is proved by our result that a
   positive correlation between citrulline and IgG (Supplementary
   Fig. [90]1).

   Previously, most phosphatidylcholines are deregulated in SLE
   patients^[91]32,[92]33, and in our study, three downregulated
   phosphatidylcholines were identified in the F2 group: LysoPC (18:0),
   LysoPC (16:0), and LysoPC (P-16:0), which correlated negatively with
   serum ferritin levels (Supplementary Fig. [93]2),which aligns with
   emerging evidence that phospholipid metabolism is crucial to SLE
   immunoregulation. LysoPCs are precursors of anti-inflammatory mediators
   and modulate macrophage polarization^[94]34–[95]38. Their reduction in
   high-ferritin SLE patients may favor a pro-inflammatory M1 phenotype,
   exacerbating tissue damage.

   Sixteen differentially expressed fatty acids were identified, three of
   which were derivatives of the polyunsaturated omega-6-long-chain fatty
   acid lineolic acid, namely 8,12-Octadecadienoic acid, alpha-Eleostearic
   acid, and Avenoleic acid, they were all shown to be down-regulated in
   the F2 group. As omega-6 fatty acids may be involved in
   immunomodulation and inflammation regulation, which are precursors of
   both pro-inflammatory and pro-resolving mediators^[96]39–[97]43, their
   depletion may skew the immune response toward chronic inflammation.

   Of the 34 differentially expressed organic acids and their derivatives
   identified, 25 were expressed at elevated levels in the F2 group, and
   five of these metabolites were significantly different between the two
   groups, including Succinic acid, N-Lactoylvaline, N-Lactoylleucine and
   N-lactoyl-Tyrosine. Accumulation of succinate may play a role in SLE
   patients with elevated serum ferritin, by providing B cell help and
   promoting pro-inflammatory cytokine production^[98]44,[99]45.
   N-Lactoylvaline, N-Lactoylleucine and N-lactoyl-Tyrosine are classified
   as N-lactoyl amino acids, synthesized intracellularly via
   CNDP2-mediated reversed protein hydrolysis of lactate and amino acids,
   and the plasma levels of which depends on lactate and amino
   acids^[100]46. Additionally, there was a significant increase in the
   phenylalanine derivative 1-Carboxyethylphenylalanine levels, strongly
   correlated with the levels of these three N-lactoyl amino acids.
   However, the corresponding serum valine, leucine, tyrosine, and
   phenylalanine levels were not significantly altered in these patients
   (Supplementary Table [101]1). Since all N-lactate amino acids are
   synthesized intracellularly, our findings provide a new perspective for
   studying SLE patients with elevated serum ferritin, suggesting a
   potential focus on alterations in lactate metabolism and intracellular
   amino acid metabolism.

   3-Hydroxyisovalerylcarnitine increased in the high-ferritin SLE
   patients significantly, which derived from the leucine alternative
   catabolic pathway and has been identified as an indicator of biotin
   deficiency^[102]47,[103]48, as well as associated with worse clinical
   scores in heart failure with preserved ejection fraction^[104]49, might
   also play a role in the pathogenesis of SLE patients with elevated
   serum ferritin.

   In addition to the arginine biosynthesis pathway, our findings suggest
   correlations between elevated serum ferritin levels in SLE patients and
   tryptophan metabolism, carbon metabolism, aminoacyl-tRNA biosynthesis
   and pyrimidine metabolism. Altered tryptophan catabolism contributes to
   autoimmunity in lupus-susceptible mice and may play an important role
   in SLE development^[105]50–[106]52. Carbon metabolism is responsible
   for redox equilibrium and provides substrates for acetylation and
   methylation, and regulate epigenetic modifications of immune cells such
   as macrophage^[107]53. Alteration of one-carbon metabolic pathway
   influences epigenetic of MHC2TA and RFC1, thus contributing to
   phenotypic heterogeneity of SLE^[108]54. Previous study shows
   dysregulated aminoacyl-tRNA biosynthesis in SLE patients^[109]51.
   Pyrimidine metabolism is a key driver of effector functions in CD4^+T
   cells and Th1^+ cells^[110]55,[111]56. Together, the disturbances in
   those metabolism pathways were likely related to the underlying
   processes of high-ferritin SLE patients.

   Limitations of this study are in the following: the sample size is
   small and needs for more robust evidence. Serum metabolite profiles
   alone cannot fully represent the body’s metabolic status, emphasizing
   the need for future investigations at tissue and cellular levels.

   In conclusion, we find that SLE patients with elevated serum ferritin
   exhibit more hematologic involvement, and our results present a unique
   serum metabolic profile in patients with elevated serum ferritin in
   SLE, particularly disrupted arginine biosynthesis, lipid pathways and
   organic acid metabolism pathways. These findings provide valuable
   insights to further understand the role of elevated ferritin in SLE.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [112]Supplementary Material 1^ (27.7MB, docx)

Acknowledgements