Regional variation in serum ficolin levels and their association with disease activity and clinical manifestations in Systemic Lupus Erythematosus (SLE) patients from India

Regional variation in serum ficolin levels and their association with disease activity and clinical manifestations in Systemic Lupus Erythematosus (SLE) patients from India | 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 Research Article Regional variation in serum ficolin levels and their association with disease activity and clinical manifestations in Systemic Lupus Erythematosus (SLE) patients from India Kirti Rai, Ridi Khatri, Amrutha Jose, Deepak Upadhaya, Sukham Rishikanta Singh, and 37 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7357993/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Immunologic Research → Version 1 posted 10 You are reading this latest preprint version Abstract The lectin pathway, activated by ficolins, contributes to systemic lupus erythematosus (SLE) pathogenesis, but ficolin data remain inconsistent across populations. Present muti-centric cross-sectional study assessed serum ficolin-1, -2, and -3 levels and their associations with clinical features and disease activity among SLE patients from five Indian regions (Mumbai, Assam, Meghalaya, Manipur, and Nagaland). Serum levels of ficolin-1, ficolin-2, and ficolin-3 were measured using ELISA. Disease activity was assessed using the SELENA-SLEDAI score. Statistical analyses were performed using non-parametric tests, with p<0.05 considered significant. S erum ficolin levels differed significantly by region. Ficolin-1 levels were positively associated with lupus nephritis (r=0.247; p=0.040) in Manipur and musculoskeletal involvement (r=0.364; p=0.009) in Nagaland, while a negative correlation was noted with alopecia (r=-0.306; p=0.01) in Meghalaya. In Assam, ficolin-2 levels were significantly reduced in patients with rash (r=-0.267; p=0.011) and mucosal ulcers (r=-0.279; p=0.008), and ficolin-3 levels showed a negative correlation with musculoskeletal manifestations (r=-0.246; p=0.020). In Mumbai, ficolin-1 levels were positively associated with disease activity (r=0.139; p=0.018), and ficolin-3 levels correlated positively with anti-dsDNA autoantibodies (r=0.172; p=0.004). Conversely, ficolin-3 levels showed a negative correlation with anti-dsDNA (r=-0.470; p<0.001) in Assam. The present study demonstrated significant regional variations in ficolin levels among SLE patients across India. Association of ficolin-1 and ficolin-3 with specific organ involvement suggested their potential as possible disease biomarkers. These findings suggested the importance of considering regional and ethnic differences in SLE management and warranted further validation through larger, longitudinal studies. Systemic Lupus Erythematosus (SLE) Ficolins Regional variations Disease activity Clinical manifestations Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease characterized by the production of a wide range of autoantibodies and deposition of immune complexes, which leads to inflammation and organ damage. The clinical course of SLE varies significantly, ranging from mild mucocutaneous manifestations to severe organ threatening complications such as lupus nephritis (LN) with renal complications and neuropsychiatric involvement [ 1 ]. Despite significant advances in understanding SLE pathogenesis, its heterogeneity poses challenge in diagnosis and management [ 2 ]. These differences are particularly pronounced among the diverse ethnic and geographic populations, where genetic predisposition, environmental exposures, and socioeconomic factors influence disease phenotype and disease severity [ 3 ]. In SLE, the complement system is critical for clearing apoptotic cells as well as immune complex-mediated inflammation [ 4 – 6 ]. The lectin pathway of complement activation, that is activated by pattern-recognition molecules including ficolins and mannose-binding lectin (MBL), has emerged as an important contributor to SLE pathophysiology [ 7 , 8 ]. Ficolins, including ficolin-1 (M-ficolin), ficolin-2 (L-ficolin), and ficolin-3 (H-ficolin), are soluble proteins involved in pathogen recognition and immune modulation. Their ability to activate the complement cascade via MBL-associated serine proteases (MASPs) links innate immunity to the dysregulated inflammatory processes seen in SLE. Previous studies have observed altered levels of ficolins in SLE [ 6 , 7 , 9 ]; however, findings have been inconsistent. There is very limited information available among ethnically diverse population to understand regional variation in serum ficolin levels and their association with disease activity and clinical manifestations of SLE. The Northeast region (NE) of India is a distinct geographic and genetic landscape, comprising multiple indigenous ethnic populations with different socio-cultural backgrounds and healthcare access profiles [ 10 , 11 ]. Studies have suggested that the prevalence of LN and other severe SLE clinical manifestations may be disproportionately higher in this region compared to national estimates [ 10 ]. This also raise questions about the associated immunological signatures that may be responsible for regional variability in disease presentation. The present study aimed to assess the serum levels of ficolin-1, ficolin-2, and ficolin-3 in SLE patients from five geographically different regions of India, namely Mumbai, Assam, Meghalaya, Manipur, and Nagaland. The associations between ficolin levels, clinical manifestations and SLE disease activity were studied among ethnically diverse Indian populations. 2. Materials and methods 2.1 Study design and participants: A multi-centric, cross-sectional study was conducted between 2021–2023 involving clinically diagnosed SLE patients from five regions of India, namely Mumbai, Assam, Meghalaya, Manipur, and Nagaland. SLE patients were recruited from regional tertiary care centers and rheumatology clinics respectively. The Mumbai, SLE patients were enrolled from King Edward Memorial (KEM) Hospital, a major tertiary care centre. In Assam, SLE patients were enrolled from Guwahati Medical College (GMC). In Meghalaya, from the North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), in Manipur, from the Jawaharlal Nehru Institute of Medical Sciences (JNIMS); and in Nagaland, from the Naga Hospital Authority Kohima (NHAK) SLE patients were enrolled. All patients fulfilled the American College of Rheumatology (ACR) classification criteria for SLE [ 12 ]. Written informed consents were obtained from all participants, and this study was approved by Institutional Ethics Committees of all the participating centers. The study was conducted in adherence to the Declaration of Helsinki. The Safety of Estrogens in Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) score was used to evaluate the disease activity [ 13 ]. SLE patients with significant hyperlipidemia, diabetes mellitus, hypertension, and those who were pregnant, post-menopausal, or active smokers were not included in this study. Detailed demographic information, clinical history, and laboratory data were recorded using standardized case record forms and on a digital portal designed by Indian Council of Medical Research- National Institute of Immunohaematology (ICMR-NIIH), Mumbai for all the participants. Serum was separated and stored at -80℃ until tested 2.2 Serological analysis: Screening for anti-nuclear antibodies (ANA) and anti-double-stranded DNA (anti-dsDNA) antibodies was performed using indirect immunofluorescence (IFA). HEp-2 cells were used as the substrate for ANA detection, while Crithidia luciliae served as the substrate for identifying anti-dsDNA antibodies (EUROIMMUN, Germany). The ANA immunoblot profile was further analyzed using a LINEBlot assay (EUROIMMUN, Germany). Serum concentrations of complement components C3 and C4 were quantified by nephelometry using the MISPA-i3 system (Agappe Diagnostics, Kerala, India), with reference cut-off values of 80–180 mg% for C3 and 10–40 mg% for C4.Serum levels of ficolin-1, ficolin-2, and ficolin-3 were quantified using commercially available enzyme-linked immunosorbent assay (ELISA) (HK357, HK336, and HK340 respectively, Hycult Biotech, Netherlands), according to the manufacturer’s instructions. 2.3 Statistical analysis The distribution of continuous variables was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. As the majority of variables did not follow a normal (Gaussian) distribution, non-parametric statistical methods were employed. Categorical data are presented as frequencies and percentages, while continuous variables are summarized using medians with the first (Q 1 ) and third quartiles (Q 3 ). Group comparisons for continuous variables were conducted using the Kruskal-Wallis test followed by Dunn’s multiple comparisons test, and associations between categorical variables were analyzed using the chi-square test. Correlations were evaluated using Spearman’s rank correlation for continuous variables and point-biserial correlation for associations between continuous and binary variables. Missing ficolin values were imputed with median values if the percentage of missing data was less than 20%. A p-value < 0.05 was considered indicative of statistical significance. Data analysis was conducted using IBM SPSS Statistics, version 27 (IBM Corp, Armonk, NY, USA), R (version 4.45), and GraphPad Prism. 3. Results 3.1 Demographic and laboratory characteristics of SLE patients: The demographic and laboratory characteristics of SLE patients recruited from five different centres across India, namely Mumbai, Assam, Meghalaya, Manipur, and Nagaland is as summarised in Table 1 . The median age of enrolment was observed to be significantly different across centres (p < 0.001), with Assam having the youngest SLE patients (median: 23 years; Q 1 , Q 3 : 19, 32) and Manipur having the oldest (median: 37 years; Q 1 , Q 3 : 24, 45). Similarly, the age of disease onset was the lowest in Assam (median: 22 years; Q 1 , Q 3 : 16, 29), and highest in Nagaland (median: 32 years; Q 1 , Q 3 : 23, 40). A significant female predominance was observed in all centres except Nagaland, where 70% of patients were male (p < 0.001). Additionally, the duration of treatment was also observed to be significantly different across all centres (p < 0.001), where SLE patients from Manipur had the longest disease duration (median: 48.00 months; Q 1 , Q 3 : 16.75, 108.00) and Assam had the shortest disease duration (median: 0.00 months; Q 1 , Q 3 : 0.00, 12.00) for SLE patients receiving the treatment. Notably, SLEDAI score was the highest in Nagaland, indicating greater disease activity (median: 13.00; Q 1 , Q 3 : 10.00, 18.00). ANA positivity was observed to be consistently high across all regions, while anti-dsDNA positivity showed significant variation (p < 0.001). All SLE patients were anti-dsDNA positive in Nagaland (100%), followed by Mumbai (80.0%), Meghalaya (72.3%) and Assam (65.6%), and Manipur (58.6%). Complement measurements also revealed region-specific differences. It was observed that 56.0% SLE patients from Nagaland had low C3 levels (cut-off: 80–180 mg%), while 80.0% of those from Assam had low C4 levels (cut-off: 10–40 mg%). SLE patients from Mumbai had combined low C3 and C4 levels in 40.0% patients. 3.2 Variations in clinical characteristics of SLE patients across five regions: Figure 1 illustrates the regional distribution of key clinical characteristics among SLE patients from five different regions studied. Cutaneous manifestations, including rash and alopecia, were most prevalent in patients from Nagaland, affecting 56.0%, and 60.0% of SLE patients, respectively. In contrast, these manifestations were observed at comparatively lower frequencies in Meghalaya (23.1% and 12.3%) and Assam (25.6% and 14.4%). Similarly, mucosal ulcers were common in Meghalaya (38.5%) and Nagaland (36.0%), while less common in Manipur (25.7%) and Assam (23.3%). Haematological abnormalities such as thrombocytopenia (< 150,000 platelets/*10 9 /L) and leukopenia (< 4000 white blood cells/*10 9 /L) showed considerable variability. Thrombocytopenia was more prevalent in Mumbai (26.0%) and Nagaland (16.0%), while Assam (4.4%) and Manipur (1.4%) reported the lowest frequencies. Similarly, leukopenia was observed in SLE patients from Mumbai (15.4%) and Nagaland (14.0%), as compared to less frequent in Assam (1.1%). LN was the most frequently observed among patients from Assam (56.7%), Meghalaya (40.0%), and Mumbai (31.9%), whereas notably lower in Nagaland (24.0%), and Manipur (20.0%). Musculoskeletal manifestations were the most commonly reported in Nagaland (62.0%) and Manipur (40.0%), whereas lower frequencies were observed in SLE patients from Mumbai (17.2%), and Meghalaya (9.2%). Neuropsychiatric and serosal involvement (pleuritis, pericarditis) were generally low across all centers. 3.3 Autoantibody profile of SLE patients across five regions: The distribution of autoantibodies among SLE patients varied markedly across the five study regions (Fig. 2). Anti-dsDNA autoantibodies showed the highest prevalence in Nagaland, where all patients (100%) were positive. Higher frequencies were also observed in Mumbai (80.0%), Meghalaya (72.3%) and Assam (65.6%), and Manipur (58.6%). Anti-Ribosomal P (Rib-P) autoantibodies were most frequently detected in Meghalaya (44.6%) and Nagaland (38.0%), but were absent in patients from Manipur. Anti-histone autoantibodies were most common in Assam (53.3%) and Nagaland (48.0%), whereas less frequent in Meghalaya (24.6%) and Mumbai (23.5%). Anti-nucleosome autoantibodies showed higher positivity in Meghalaya (55.4%), Assam (53.3%) and Nagaland (42.0%) as compared to lower positivity in Manipur (31.4%) and Mumbai (29.5%). Anti-SSB autoantibodies were more frequently observed in Manipur (30.0%), Meghalaya (23.1%), and Nagaland (22.0%), whereas less frequently reported in Mumbai (9.1%) and Assam (8.9%). Anti-Ro-52 autoantibodies were most prevalent in patients from Nagaland (36.0%) and Assam (35.6%), while the lowest frequency was observed in Manipur (1.4%). Anti-SSA autoantibodies showed the highest positivity in Manipur (55.7%), followed by Nagaland (40.0%) and Assam (34.4%). Anti-Sm autoantibodies were relatively consistent across the regions, ranging from 22.9% in Manipur to 35.4% in Meghalaya. Anti-RNP/Sm autoantibodies were the most frequently observed in Assam (45.6%) and Mumbai (41.1%), and were the least common in SLE patients from Manipur (14.3%). 3.4 Serum levels of ficolins in SLE patients from different study regions: As shown in Fig. 3, serum levels of ficolins varied significantly among SLE patients across the study regions. The median ficolin-1 levels were significantly elevated in SLE patients from Meghalaya (median: 1527.10ng/ml; Q1, Q3: 424.73, 1746.10) as compared to Mumbai (p < 0.001), Assam (p < 0.001), Manipur (p = 0.006), and Nagaland (p < 0.001). In contrast, the lowest ficolin-1 levels were observed in Assam (median: 136.80ng/ml; Q1, Q3: 87.43, 270.20) as compared to Mumbai (p < 0.001), Meghalaya (p < 0.001), and Manipur (p < 0.001). Ficolin-2 levels were significantly elevated in SLE patients from Assam (median: 3896.00ng/ml; Q1, Q3: 3458.50, 4296.50) as compared to both Mumbai (p < 0.001) and Nagaland (p < 0.001). In contrast, SLE patients from Nagaland showed significantly lower ficolin-2 levels (median: 255.19ng/ml; Q1, Q3: 225.66, 283.37) as compared to Mumbai (p < 0.001), Assam (p < 0.001), and Meghalaya (p < 0.001). Ficolin-3 levels were significantly elevated in SLE patients from Nagaland (median: 66637.20 ng/ml; Q1, Q3: 66454.65, 70636.05) as compared to Mumbai (p < 0.001), Assam (p < 0.001), Meghalaya (p < 0.001), and Manipur (p < 0.001). Conversely, SLE patients from Meghalaya (median: 12040.00ng/ml; Q1, Q3: 4340.00, 25360.00) had significantly lower ficolin-3 levels as compared to SLE patients from Mumbai (p < 0.001), Assam (p = 0.001), Manipur (p < 0.001), and Nagaland (p < 0.001). 3.5 Association of ficolin levels with laboratory parameters and clinical manifestations across study regions: Figure 4 illustrates the correlations between ficolin levels, disease activity (SLEDAI), anti-dsDNA autoantibodies, complement components (C3 and C4), and clinical manifestations across five Indian cohorts. Among SLE patients from Mumbai, a strong positive inter correlation was noted among ficolin-1, -2 and − 3 levels. Ficolin-1 levels were positively correlated with disease activity (r = 0.139; p = 0.018), while ficolin-3 levels were positively correlated with anti-dsDNA autoantibodies (r = 0.172; p = 0.004). Ficolin-2 levels were significantly associated with the presence of alopecia (r = 0.124; p = 0.037). Among SLE patients from Assam, ficolin-1 levels were positively correlated with ficolin-2 (r = 0.277; p = 0.008), while ficolin-3 levels were negatively correlated with anti-dsDNA autoantibodies (r=-0.470; p < 0.001). SLE patients with rash and mucosal ulcers showed significantly low ficolin-2 levels as compared to those without these manifestations (r=-0.267; p = 0.011 and r=-0.279; p = 0.008, respectively). Similarly, patients with musculoskeletal manifestations showed significantly low ficolin-3 levels (r=-0.246; p = 0.020). Among SLE patients from Meghalaya displayed a negative correlation between ficolin-1 and ficolin-3 levels (r=-0.520; p < 0.001). Interestingly, alopecia was significantly associated with low ficolin-1 levels (r=-0.306; p = 0.01), while constitutional manifestations were significantly associated with elevated ficolin-2 levels (r = 0.253; p = 0.042). Among SLE patients from Manipur, ficolin-1 levels were positively correlated with C4 (r = 0.240; p = 0.045). Further, LN was associated with high ficolin-1 levels (r = 0.247; p = 0.040), while other manifestations were not statistically significant (p > 0.05). Among SLE patients from Nagaland, ficolin-3 levels were negatively correlated with C4 levels (r=-0.333; p = 0.018). Also, musculoskeletal involvement showed a significant positive correlation with ficolin-1 levels (r = 0.364; p = 0.009), while no statistically significant associations noted for ficolin-2 or ficolin-3 levels (p > 0.05). 4. Discussion The present multi-centric study demonstrated a comprehensive analysis of SLE patients from five geographically and ethnically distinct regions from India. This study revealed regional variations in ficolin levels among SLE patients. These findings suggested SLE heterogeneity across the Indian population, which could be driven by the complex interplay between genetic, environmental, and immunological factors. A significant regional variation in demographic and laboratory parameters was observed in the present study. Patients from Assam presented at a younger age, while those from Manipur and Nagaland were older, which may reflected delayed disease diagnosis, regional healthcare disparities, or variations in genetic susceptibility. Although SLE typically affects women, an unusual male predominance in Nagaland can possibly due to hormonal, genetic, and environmental factors [ 14 – 16 ]. Variations in treatment duration, SLEDAI scores, and complement levels across centres suggested differences in disease activity, healthcare access, or treatment practices. SLE heterogeneity was further reflected by significant regional variations in clinical manifestations across the five centers in India, which were consistent with global trends [ 17 , 18 ]. The variations observed in cutaneous and constitutional manifestations reflected differences in sunlight exposure, infections, genetic, and environmental factors. The high prevalence of LN in Assam suggested that renal involvement is a major feature in this region. This was similar to previous reports from Assam [ 19 – 21 ]. Given the high LN burden observed in Assam, periodic monitoring of renal indices could improve early diagnosis and outcome. Musculoskeletal manifestations were common in Nagaland and Manipur, and less in Assam (25.6%), as compared to 46.9% reported by Talukdar et al [ 22 ]. Further, SLE patients from Meghalaya showed lower musculoskeletal manifestations as compared with those reported by Barman et al [ 11 ]. Neuropsychiatric symptoms and serosal involvement were less common in our data, as compared to other reports from the Northeast (NE) region [ 11 , 22 ]. Autoantibody profile in the present multi-centric study had revealed notable regional differences. Anti-dsDNA positivity, a hallmark of SLE, showed high prevalence in all cohorts, which supports its established role as a key diagnostic marker and indicator of disease activity across diverse patient population [ 23 ]. Other autoantibodies also demonstrated distinct regional patterns. Anti-Rib-P antibody was the highest in Meghalaya and Nagaland, contrasting with complete absence in Manipur. Compared to earlier studies reported from Meghalaya and Assam, the present study showed higher frequencies [ 11 , 22 ]. Antibodies targeting chromatin, including anti-histone and anti-nucleosome antibodies, were particularly prominent in Assam and Meghalaya. Anti-SSB antibodies were more common in Manipur, while their occurrence in Assam and Meghalaya was similar to previous studies [ 11 , 22 ]. AntiSm antibody, a highly specific marker, was noted at similar frequencies as reported in earlier NE reports [ 11 , 22 ]. Anti-RNP/Sm antibody was more common in Assam and Mumbai, while less common in Meghalaya and Manipur, that suggested differences in mixed connective tissue–overlap serology. Overall, these findings suggested that the SLE patients from NE exhibited higher frequencies of anti-dsDNA, histone, and nucleosome autoantibodies than Western India (Mumbai) which indicated potentially more active SLE disease phenotype. The heterogeneity of autoantibody profile among SLE patients, which may have attributed from genetic diversity, environmental exposures, healthcare access, and treatment approaches. Ficolins are pattern-recognition molecules involved in the lectin pathway of complement activation, and their altered levels may influence immune complex clearance and inflammation [ 8 , 24 – 27 ]. One of the first region-specific insight into ficolin-1, -2, and − 3 ficolin levels was provided by this study among Indian SLE patients. Variations in ficolin levels suggested region-specific immune activation or differences in infection-driven immune modulation. Interestingly, ficolin-1 levels were positively associated with LN in Manipur and musculoskeletal involvement in Nagaland, indicated a potential role in tissue-specific/organ-specific inflammation. Tanha et al had reported the association between elevated ficolin-1 levels and histological subtypes of LN in Danish SLE patients [ 28 ]. These observations suggested that ficolin-1 may serve as a biomarker reflecting organ-specific disease activity. However, further studies, including longitudinal assessments with renal histopathology, are warranted to validate its specificity and determine its temporal association with disease activity and renal flares. The observed negative correlation between ficolin-1 and alopecia in Meghalaya suggested a possible link between reduced ficolin-1 levels and cutaneous manifestations. However, mechanistic studies are further needed to validate and explore this relationship. Ficolin-2 levels in patients with rash and mucosal ulcers in Assam were significantly reduced, suggesting a protective or regulatory role in modulating cutaneous and mucosal inflammation. The negative correlation between ficolin-3 levels and musculoskeletal manifestations in Assam supported a protective role in joint inflammation. However, Andersen et al reported no association between ficolin-3 levels and arthritis in a Danish SLE cohort [ 29 ]. Previous studies had reported either negative correlation [ 6 , 30 ] or no association [ 9 ] between ficolin-1 levels and SLE disease activity. In contrast, a positive association between ficolin-1 levels in Mumbai was noted in the present study, which suggested their potential role as an immunological indicator of SLE disease activity from this region. No significant association was observed between ficolin-2 levels and disease activity across all regions in the present study, which is consistent with the previous reports [ 6 , 9 , 30 , 31 ]. A positive correlation between ficolin-3 and anti-dsDNA autoantibodies was observed in SLE patients from Mumbai, while a negative correlation was observed in SLE patients from Assam. Troldborg et al had reported no significant correlation between ficolin-3 levels and anti-dsDNA autoantibodies [ 9 , 30 ]. The substantial inter-regional variation observed in different ficolin levels suggested a strong genetic or environmental modulation. The strength of this study lied in its multi centric design, which included diverse geographic, ethnic, and environmental backgrounds across five distinct Indian regions. This approach provided a comprehensive overview of the heterogeneity of clinical manifestations and immunological profile among Indian SLE patients. The simultaneous evaluation of clinical characteristics, autoantibody profiles, and novel innate immune recognition proteins, such as ficolins, had enhanced the understanding of disease heterogeneity. However, this study had certain limitations. Its cross-sectional study design limited its causal inferences between organ involvement, disease activity and ficolin levels. Variation in sample size across centres may have affected statistical power, particularly for less common clinical manifestations of SLE. Future longitudinal studies on a larger, more balanced cohorts, region-matched controls, and information of treatment details were therefore warranted to validate these findings. 5. Conclusions In conclusion, present multi-centric study demonstrated substantial regional heterogeneity among SLE patients across India. Differences in age at onset, sex distribution, patterns of organ involvement, autoantibody profiles, and circulating ficolin levels pointed to the combined influence of genetic background, environmental exposures, and healthcare access on disease phenotype within the country. Notably, ficolin-1 and ficolin-3 levels showed region- and organ-linked associations that further supported their potential as possible biomarkers of systemic inflammation and SLE disease activity. These findings suggested a need to validate these observations on a larger cohort for a region-specific tailored approach for SLE management in future. Declarations Acknowledgements: NA Author contributions: Kirti Rai: Data curation, Investigations, Methodology, and Writing- original draft. Ridi Khatri: Data curation, Formal analysis, Software, Validation, Writing- original draft, and writing- review and editing. Amrutha Jose: Formal analysis, Software, Validation, Visualization, and Writing- review and editing. Deepak Upadhaya: DatacurationandInvestigation. Sukham Rishikanta Singh: DatacurationandInvestigation. Kyntiewdor Lyting: DatacurationandInvestigation. Husulu: DatacurationandInvestigation. Harshada Konkar: DatacurationandInvestigation. Prashant Tapase: Software. Milind Nadkar: Validation. Anjali Rajadhyaksha: Validation. Pooja Jaiswal: Visualization. Swapnal Pawaskar: Investigation. Durga Chougule: Writing: review and editing. Ajanta Sharma: Validation. Lahari Saikia: Validation. Chiranjita Phukan: Validation. Anuradha Deori: Validation. Leena Talukdar: Validation. Supriya Laifangbam: Validation. Pukhrambam Vedanti Devi: Validation. Julie Leishangthem: Validation. Yengkhom Rameshwor Singh: Validation. W. Valarie Lyngdoh: Validation. Bhupen Barman: Validation. Monaliza Lyngdoh: Validation. Biswajit Dey: Validation. Sheryl Lanong: Investigation. Cleopatra Shadap: Investigation. Banraprbor Wankhar: Investigation. V. Khamo: Validation. Hutsulu: Investigation. K. Vanlalruati: Validation. Yopovinu Rhutso: Validation. Albert T. Pochury: Validation. Kejavisa Savino: Validation. C Longe Peter: Validation. Neimenuo Kuotsu: Validation. Neikhrietsonuo Kesiezie: Validation. Vijay Padwal: Software. Manisha Madkaikar: Validation and Supervision. Vandana Pradhan: Conceptualization, Funding acquisition, Validation, Supervision, and Writing: review and editing. Funding: This work was supported by the ICMR North East Task force (ICMR-NTF) (Project no. NTF-NER/NER-Cell/RBMCH-2020) Data availability: The datasets used and/or analyzed during the current study are available from the corresponding author upon request. Ethics approval and consent to participate: This study was performed in adherence to the principles of the Declaration of Helsinki. Approval was granted by the Institutional Ethics Committee for Research on Human Subjects, ICMR–National Institute of Immunohaematology (ICMR-NIIH), Mumbai, India (ICMR-NIIH/IEC/07/2020). 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Mod Rheumatol. 2012 Nov;22(6):899-902. https://doi.org/10.1007/s10165-012-0616-y Table Table 1: Demographic and laboratory characteristics of SLE patients from five regions in India ICMR-NIIH, Mumbai (n=285) GMC, Assam (n=90) NEIGRIHMS, Meghalaya (n=65) JNIMS, Manipur (n=70) NHAK, Nagaland (n=50) p-value Demographic characteristics Age at enrolment (years) 30.00 (23.00, 40.00) 23.00 (19.00, 32.00) 29.00 (21.00, 36.00) 37.00 (24.00, 45.00) 35.00 (25.00, 40.00) <0.001 Age of onset (years) 27.00 (20.00, 36.00) 22.00 (16.00, 29.00) 29.00 (24.00, 35.00) 26.00 (20.00, 37.00) 32.00 (23.00, 40.00) <0.001 Female 252 (88.4%) 88 (97.8%) 58 (89.2%) 66 (94.3%) 15 (30.0%) <0.001 Male 33 (11.6%) 2 (2.2%) 7 (10.8%) 4 (5.7%) 35 (70.00%) Duration of treatment (months) 6.00 (1.00, 36.00) 0.00 (0.00, 12.00) 12.00 (4.00, 36.00) 48.00 (16.75, 108.00) 3.50 (0.88, 26.00) <0.001 SLEDAI score 6.00 (3.00, 10.00) 6.00 (2.00, 10.00) 4.00 (2.00, 10.00) 4.00 (2.00, 6.00) 13.00 (10.00, 18.00) <0.001 Laboratory parameters ANA positivity 274 (96.1%) 88 (97.8%) 65 (100.0%) 70 (100.0%) 48 (96.0%) 0.228 Anti-dsDNA positivity 228 (80.0%) 59 (65.6%) 47 (72.3%) 41 (58.6%) 50 (100.0%) <0.001 Low antigenic C3 151 (53.0%) 34 (37.8%) 5 (7.7%) 3 (4.3%) 28 (56.0%) <0.001 Low antigenic C4 140 (49.1%) 72 (80.0%) 2 (3.1%) 5 (7.1%) 21 (42.0%) <0.001 Low antigenic C3 and C4 114 (40.0%) 24 (26.7%) 2 (3.1%) 0 (0.0%) 13 (26.0%) <0.001 Ficolin-1 (ng/mL) 335.00 (140.53, 766.50) 136.80 (87.43, 270.20) 1527.10 (424.73, 1746.10) 856.05 (646.75, 1007.35) 161.08 (143.34, 201.52) <0.001 Ficolin-2 (ng/mL) 2595.91 (1440.65, 4852.82) 3896.00 (3458.50, 4296.50) 3765.99 (2535.53, 5103.30) - 255.19 (225.66, 283.37) <0.001 Ficolin-3 (ng/mL) 23185.80 (8225.90, 72085.99) 25705.00 (13577.00, 34449.00) 12040.00 (4340.00, 25360.00) 37326.00 (31256.45, 58772.15) 66637.20 (66454.65, 70636.05) <0.001 Data are presented as median (Q 1 , Q 3 ). Comparisons between groups were performed using the Kruskal-Wallis test followed by Dunn’s multiple comparisons test for continuous variables and associations between categorical variables were analyzed using the chi-square test. p<0.05 was considered statistically significant. SLE: Systemic lupus erythematosus; ICMR-NIIH: Indian Council of Medical Research- National Institute of Immunohaematology; GMC: Guwahati Medical College; NEIGRIHMS: North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences; JNIMS: Jawaharlal Nehru Institute of Medical Sciences; NHAK: Naga Hospital Authority Kohima; SLEDAI score: SLE disease activity index score; ANA: Anti-nuclear antibodies. Additional Declarations No competing interests reported. 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Pochury","email":"","orcid":"","institution":"Naga Hospital Authority Kohima (NHAK)","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"T.","lastName":"Pochury","suffix":""},{"id":504890052,"identity":"409f39b6-c7ef-4f78-a479-1ec2c095cf92","order_by":35,"name":"Kejavisa Savino","email":"","orcid":"","institution":"Naga Hospital Authority Kohima (NHAK)","correspondingAuthor":false,"prefix":"","firstName":"Kejavisa","middleName":"","lastName":"Savino","suffix":""},{"id":504890053,"identity":"0b6f20f3-4dfc-4495-bb07-acf04f919bb2","order_by":36,"name":"C Longe Peter","email":"","orcid":"","institution":"Naga Hospital Authority Kohima (NHAK)","correspondingAuthor":false,"prefix":"","firstName":"C","middleName":"Longe","lastName":"Peter","suffix":""},{"id":504890054,"identity":"ac202b06-a22e-4c98-b52d-98a7ca7fa64c","order_by":37,"name":"Neimenuo Kuotsu","email":"","orcid":"","institution":"Naga Hospital Authority Kohima (NHAK)","correspondingAuthor":false,"prefix":"","firstName":"Neimenuo","middleName":"","lastName":"Kuotsu","suffix":""},{"id":504890055,"identity":"a5df04ad-f3c3-428e-b092-2abf22704f99","order_by":38,"name":"Neikhrietsonuo Kesiezie","email":"","orcid":"","institution":"Naga Hospital Authority Kohima (NHAK)","correspondingAuthor":false,"prefix":"","firstName":"Neikhrietsonuo","middleName":"","lastName":"Kesiezie","suffix":""},{"id":504890056,"identity":"7025bf24-5e2f-4812-8949-95e72e7884ea","order_by":39,"name":"Vijay Padwal","email":"","orcid":"","institution":"ICMR-National Institute of Immunohaematology","correspondingAuthor":false,"prefix":"","firstName":"Vijay","middleName":"","lastName":"Padwal","suffix":""},{"id":504890057,"identity":"bc15481e-a2bf-41c8-8de6-2a03369ba00a","order_by":40,"name":"Manisha Madkaikar","email":"","orcid":"","institution":"ICMR-National Institute of Immunohaematology","correspondingAuthor":false,"prefix":"","firstName":"Manisha","middleName":"","lastName":"Madkaikar","suffix":""},{"id":504890058,"identity":"4100bbf1-cb1b-474c-ace9-676c1cd05102","order_by":41,"name":"Vandana Pradhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYJACaRDBz958AEhJyBCvRbLnWAJICw/xWgxm5BiAaMJazKUPP7xdUHMncYNEzudXN2oseBjYDx/dgE+LZV+asfWMY88St/O83WadcwzoMJ60tBv4tBicYTCT5mE7bGzZnrvNOIcNqEWCx4yAFvZv0jz/DhsbHMh5ZpzzjygtPGbSvG2H5QxO5DA/zm0jQotlD0+xNW/fYTlgIJsx5/ZJ8LAR8os5D/vG2zzfDvMAo/Lx55xvdXL87IeP4XcYEptNAkziU46uhfkDIdWjYBSMglEwMgEAS1xFWQm7MpEAAAAASUVORK5CYII=","orcid":"","institution":"ICMR-National Institute of Immunohaematology","correspondingAuthor":true,"prefix":"","firstName":"Vandana","middleName":"","lastName":"Pradhan","suffix":""}],"badges":[],"createdAt":"2025-08-12 16:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7357993/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7357993/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12026-025-09735-1","type":"published","date":"2026-01-02T15:58:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90303819,"identity":"94c37d0d-925f-4943-a942-a4d69ada2768","added_by":"auto","created_at":"2025-09-01 09:09:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1102062,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of clinical manifestations in SLE patients across study regions\u003c/p\u003e\n\u003cp\u003eThe stacked bar chart illustrates the percentage of various clinical features observed\u003c/p\u003e\n\u003cp\u003ein SLE patients from five regions.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357993/v1/bcab0a0a151f8f6cab4b3815.jpg"},{"id":90304571,"identity":"890a6886-8d7e-41b0-9254-e0668eb58dd8","added_by":"auto","created_at":"2025-09-01 09:17:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":657135,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of autoantibodies in SLE patients across study regions\u003c/p\u003e\n\u003cp\u003eThe stacked bar chart illustrates the percentage of autoantibodies positivity observed\u003c/p\u003e\n\u003cp\u003ein SLE patients from five regions.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357993/v1/0aa51ef84be1457fdb2af5f4.jpg"},{"id":90303812,"identity":"aebf1d60-137a-4754-bf56-5542df180a93","added_by":"auto","created_at":"2025-09-01 09:09:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":423341,"visible":true,"origin":"","legend":"\u003cp\u003eRegional comparison of serum ficolin levels in SLE patients\u003c/p\u003e\n\u003cp\u003eData are presented as bar plots showing median and interquartile range (IQR) of ficolin-1, ficolin-2, and ficolin-3 among SLE patients across study regions. Group comparisons were performed using the Kruskal-Wallis test followed by Dunn’s multiple comparisons test. Statistical significance is indicated as: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357993/v1/b6d8cb6c14a2462375c16a1d.jpg"},{"id":90303292,"identity":"8728a663-c635-4d92-a758-d90d7456218e","added_by":"auto","created_at":"2025-09-01 09:09:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1752950,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap illustrating the relationships between ficolins (ficolin-1, ficolin-2, ficolin-3), SLEDAI scores, anti-dsDNA autoantibodies, complement proteins (C3, C4), and clinical manifestations across study regions: \u003cstrong\u003e(a)\u003c/strong\u003e ICMR-NIIH, Mumbai; \u003cstrong\u003e(b)\u003c/strong\u003e GMC, Assam; \u003cstrong\u003e(c)\u003c/strong\u003e NEIGRIHMS, Meghalaya; \u003cstrong\u003e(d)\u003c/strong\u003e JNIMS, Manipur; \u003cstrong\u003e(e)\u003c/strong\u003e NHAK, Nagaland\u003c/p\u003e\n\u003cp\u003eEach square represents either a Spearman’s correlation coefficient (for continuous variables) or a point-biserial correlation coefficient (for continuous vs. binary variables) between the variables indicated on the x- and y-axes. The color gradient indicates the strength and direction of correlation: green for positive, red for negative, and white for no correlation. Statistical significance is denoted as follows: \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 (*); \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01 (**); \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 (***).\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357993/v1/6ee0535c62ad6d090aa76c5a.jpg"},{"id":99545369,"identity":"e946bb67-338a-401e-a52e-9a11d3c41002","added_by":"auto","created_at":"2026-01-05 16:06:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5398998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7357993/v1/dc9d0ccc-b55f-4a8a-8ce5-4b6fb0c2881e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Regional variation in serum ficolin levels and their association with disease activity and clinical manifestations in Systemic Lupus Erythematosus (SLE) patients from India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSystemic Lupus Erythematosus (SLE) is a chronic autoimmune disease characterized by the production of a wide range of autoantibodies and deposition of immune complexes, which leads to inflammation and organ damage. The clinical course of SLE varies significantly, ranging from mild mucocutaneous manifestations to severe organ threatening complications such as lupus nephritis (LN) with renal complications and neuropsychiatric involvement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant advances in understanding SLE pathogenesis, its heterogeneity poses challenge in diagnosis and management [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These differences are particularly pronounced among the diverse ethnic and geographic populations, where genetic predisposition, environmental exposures, and socioeconomic factors influence disease phenotype and disease severity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn SLE, the complement system is critical for clearing apoptotic cells as well as immune complex-mediated inflammation [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The lectin pathway of complement activation, that is activated by pattern-recognition molecules including ficolins and mannose-binding lectin (MBL), has emerged as an important contributor to SLE pathophysiology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ficolins, including ficolin-1 (M-ficolin), ficolin-2 (L-ficolin), and ficolin-3 (H-ficolin), are soluble proteins involved in pathogen recognition and immune modulation. Their ability to activate the complement cascade via MBL-associated serine proteases (MASPs) links innate immunity to the dysregulated inflammatory processes seen in SLE. Previous studies have observed altered levels of ficolins in SLE [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; however, findings have been inconsistent. There is very limited information available among ethnically diverse population to understand regional variation in serum ficolin levels and their association with disease activity and clinical manifestations of SLE.\u003c/p\u003e\u003cp\u003eThe Northeast region (NE) of India is a distinct geographic and genetic landscape, comprising multiple indigenous ethnic populations with different socio-cultural backgrounds and healthcare access profiles [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies have suggested that the prevalence of LN and other severe SLE clinical manifestations may be disproportionately higher in this region compared to national estimates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This also raise questions about the associated immunological signatures that may be responsible for regional variability in disease presentation. The present study aimed to assess the serum levels of ficolin-1, ficolin-2, and ficolin-3 in SLE patients from five geographically different regions of India, namely Mumbai, Assam, Meghalaya, Manipur, and Nagaland. The associations between ficolin levels, clinical manifestations and SLE disease activity were studied among ethnically diverse Indian populations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and participants:\u003c/h2\u003e\u003cp\u003eA multi-centric, cross-sectional study was conducted between 2021\u0026ndash;2023 involving clinically diagnosed SLE patients from five regions of India, namely Mumbai, Assam, Meghalaya, Manipur, and Nagaland. SLE patients were recruited from regional tertiary care centers and rheumatology clinics respectively. The Mumbai, SLE patients were enrolled from King Edward Memorial (KEM) Hospital, a major tertiary care centre. In Assam, SLE patients were enrolled from Guwahati Medical College (GMC). In Meghalaya, from the North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), in Manipur, from the Jawaharlal Nehru Institute of Medical Sciences (JNIMS); and in Nagaland, from the Naga Hospital Authority Kohima (NHAK) SLE patients were enrolled. All patients fulfilled the American College of Rheumatology (ACR) classification criteria for SLE [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Written informed consents were obtained from all participants, and this study was approved by Institutional Ethics Committees of all the participating centers. The study was conducted in adherence to the Declaration of Helsinki. The Safety of Estrogens in Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) score was used to evaluate the disease activity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. SLE patients with significant hyperlipidemia, diabetes mellitus, hypertension, and those who were pregnant, post-menopausal, or active smokers were not included in this study. Detailed demographic information, clinical history, and laboratory data were recorded using standardized case record forms and on a digital portal designed by Indian Council of Medical Research- National Institute of Immunohaematology (ICMR-NIIH), Mumbai for all the participants. Serum was separated and stored at -80℃ until tested\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Serological analysis:\u003c/h2\u003e\u003cp\u003eScreening for anti-nuclear antibodies (ANA) and anti-double-stranded DNA (anti-dsDNA) antibodies was performed using indirect immunofluorescence (IFA). HEp-2 cells were used as the substrate for ANA detection, while \u003cem\u003eCrithidia luciliae\u003c/em\u003e served as the substrate for identifying anti-dsDNA antibodies (EUROIMMUN, Germany). The ANA immunoblot profile was further analyzed using a LINEBlot assay (EUROIMMUN, Germany). Serum concentrations of complement components C3 and C4 were quantified by nephelometry using the MISPA-i3 system (Agappe Diagnostics, Kerala, India), with reference cut-off values of 80\u0026ndash;180 mg% for C3 and 10\u0026ndash;40 mg% for C4.Serum levels of ficolin-1, ficolin-2, and ficolin-3 were quantified using commercially available enzyme-linked immunosorbent assay (ELISA) (HK357, HK336, and HK340 respectively, Hycult Biotech, Netherlands), according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\u003cp\u003eThe distribution of continuous variables was assessed using the Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests. As the majority of variables did not follow a normal (Gaussian) distribution, non-parametric statistical methods were employed. Categorical data are presented as frequencies and percentages, while continuous variables are summarized using medians with the first (Q\u003csub\u003e1\u003c/sub\u003e) and third quartiles (Q\u003csub\u003e3\u003c/sub\u003e). Group comparisons for continuous variables were conducted using the Kruskal-Wallis test followed by Dunn\u0026rsquo;s multiple comparisons test, and associations between categorical variables were analyzed using the chi-square test. Correlations were evaluated using Spearman\u0026rsquo;s rank correlation for continuous variables and point-biserial correlation for associations between continuous and binary variables. Missing ficolin values were imputed with median values if the percentage of missing data was less than 20%. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered indicative of statistical significance. Data analysis was conducted using IBM SPSS Statistics, version 27 (IBM Corp, Armonk, NY, USA), R (version 4.45), and GraphPad Prism.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1 Demographic and laboratory characteristics of SLE patients:\u003c/h2\u003e\n \u003cp\u003eThe demographic and laboratory characteristics of SLE patients recruited from five different centres across India, namely Mumbai, Assam, Meghalaya, Manipur, and Nagaland is as summarised in \u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e. The median age of enrolment was observed to be significantly different across centres (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Assam having the youngest SLE patients (median: 23 years; Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e: 19, 32) and Manipur having the oldest (median: 37 years; Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e: 24, 45). Similarly, the age of disease onset was the lowest in Assam (median: 22 years; Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e: 16, 29), and highest in Nagaland (median: 32 years; Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e: 23, 40). A significant female predominance was observed in all centres except Nagaland, where 70% of patients were male (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the duration of treatment was also observed to be significantly different across all centres (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), where SLE patients from Manipur had the longest disease duration (median: 48.00 months; Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e: 16.75, 108.00) and Assam had the shortest disease duration (median: 0.00 months; Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e: 0.00, 12.00) for SLE patients receiving the treatment. Notably, SLEDAI score was the highest in Nagaland, indicating greater disease activity (median: 13.00; Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e: 10.00, 18.00).\u003c/p\u003e\n \u003cp\u003eANA positivity was observed to be consistently high across all regions, while anti-dsDNA positivity showed significant variation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All SLE patients were anti-dsDNA positive in Nagaland (100%), followed by Mumbai (80.0%), Meghalaya (72.3%) and Assam (65.6%), and Manipur (58.6%). Complement measurements also revealed region-specific differences. It was observed that 56.0% SLE patients from Nagaland had low C3 levels (cut-off: 80\u0026ndash;180 mg%), while 80.0% of those from Assam had low C4 levels (cut-off: 10\u0026ndash;40 mg%). SLE patients from Mumbai had combined low C3 and C4 levels in 40.0% patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.2 Variations in clinical characteristics of SLE patients across five regions:\u003c/h2\u003e\n \u003cp\u003eFigure 1 illustrates the regional distribution of key clinical characteristics among SLE patients from five different regions studied. Cutaneous manifestations, including rash and alopecia, were most prevalent in patients from Nagaland, affecting 56.0%, and 60.0% of SLE patients, respectively. In contrast, these manifestations were observed at comparatively lower frequencies in Meghalaya (23.1% and 12.3%) and Assam (25.6% and 14.4%). Similarly, mucosal ulcers were common in Meghalaya (38.5%) and Nagaland (36.0%), while less common in Manipur (25.7%) and Assam (23.3%). Haematological abnormalities such as thrombocytopenia (\u0026lt;\u0026thinsp;150,000 platelets/*10\u003csup\u003e9\u003c/sup\u003e/L) and leukopenia (\u0026lt;\u0026thinsp;4000 white blood cells/*10\u003csup\u003e9\u003c/sup\u003e/L) showed considerable variability. Thrombocytopenia was more prevalent in Mumbai (26.0%) and Nagaland (16.0%), while Assam (4.4%) and Manipur (1.4%) reported the lowest frequencies. Similarly, leukopenia was observed in SLE patients from Mumbai (15.4%) and Nagaland (14.0%), as compared to less frequent in Assam (1.1%). LN was the most frequently observed among patients from Assam (56.7%), Meghalaya (40.0%), and Mumbai (31.9%), whereas notably lower in Nagaland (24.0%), and Manipur (20.0%). Musculoskeletal manifestations were the most commonly reported in Nagaland (62.0%) and Manipur (40.0%), whereas lower frequencies were observed in SLE patients from Mumbai (17.2%), and Meghalaya (9.2%). Neuropsychiatric and serosal involvement (pleuritis, pericarditis) were generally low across all centers.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.3 Autoantibody profile of SLE patients across five regions:\u003c/h2\u003e\n \u003cp\u003eThe distribution of autoantibodies among SLE patients varied markedly across the five study regions (Fig. 2). Anti-dsDNA autoantibodies showed the highest prevalence in Nagaland, where all patients (100%) were positive. Higher frequencies were also observed in Mumbai (80.0%), Meghalaya (72.3%) and Assam (65.6%), and Manipur (58.6%). Anti-Ribosomal P (Rib-P) autoantibodies were most frequently detected in Meghalaya (44.6%) and Nagaland (38.0%), but were absent in patients from Manipur. Anti-histone autoantibodies were most common in Assam (53.3%) and Nagaland (48.0%), whereas less frequent in Meghalaya (24.6%) and Mumbai (23.5%). Anti-nucleosome autoantibodies showed higher positivity in Meghalaya (55.4%), Assam (53.3%) and Nagaland (42.0%) as compared to lower positivity in Manipur (31.4%) and Mumbai (29.5%). Anti-SSB autoantibodies were more frequently observed in Manipur (30.0%), Meghalaya (23.1%), and Nagaland (22.0%), whereas less frequently reported in Mumbai (9.1%) and Assam (8.9%). Anti-Ro-52 autoantibodies were most prevalent in patients from Nagaland (36.0%) and Assam (35.6%), while the lowest frequency was observed in Manipur (1.4%). Anti-SSA autoantibodies showed the highest positivity in Manipur (55.7%), followed by Nagaland (40.0%) and Assam (34.4%). Anti-Sm autoantibodies were relatively consistent across the regions, ranging from 22.9% in Manipur to 35.4% in Meghalaya. Anti-RNP/Sm autoantibodies were the most frequently observed in Assam (45.6%) and Mumbai (41.1%), and were the least common in SLE patients from Manipur (14.3%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.4 Serum levels of ficolins in SLE patients from different study regions:\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig. 3, serum levels of ficolins varied significantly among SLE patients across the study regions. The median ficolin-1 levels were significantly elevated in SLE patients from Meghalaya (median: 1527.10ng/ml; Q1, Q3: 424.73, 1746.10) as compared to Mumbai (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Assam (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Manipur (p\u0026thinsp;=\u0026thinsp;0.006), and Nagaland (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the lowest ficolin-1 levels were observed in Assam (median: 136.80ng/ml; Q1, Q3: 87.43, 270.20) as compared to Mumbai (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Meghalaya (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Manipur (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ficolin-2 levels were significantly elevated in SLE patients from Assam (median: 3896.00ng/ml; Q1, Q3: 3458.50, 4296.50) as compared to both Mumbai (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Nagaland (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, SLE patients from Nagaland showed significantly lower ficolin-2 levels (median: 255.19ng/ml; Q1, Q3: 225.66, 283.37) as compared to Mumbai (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Assam (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Meghalaya (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ficolin-3 levels were significantly elevated in SLE patients from Nagaland (median: 66637.20 ng/ml; Q1, Q3: 66454.65, 70636.05) as compared to Mumbai (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Assam (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Meghalaya (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Manipur (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, SLE patients from Meghalaya (median: 12040.00ng/ml; Q1, Q3: 4340.00, 25360.00) had significantly lower ficolin-3 levels as compared to SLE patients from Mumbai (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Assam (p\u0026thinsp;=\u0026thinsp;0.001), Manipur (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Nagaland (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.5 Association of ficolin levels with laboratory parameters and clinical manifestations across study regions:\u003c/h2\u003e\n \u003cp\u003eFigure 4 illustrates the correlations between ficolin levels, disease activity (SLEDAI), anti-dsDNA autoantibodies, complement components (C3 and C4), and clinical manifestations across five Indian cohorts. Among SLE patients from Mumbai, a strong positive inter correlation was noted among ficolin-1, -2 and \u0026minus;\u0026thinsp;3 levels. Ficolin-1 levels were positively correlated with disease activity (r\u0026thinsp;=\u0026thinsp;0.139; p\u0026thinsp;=\u0026thinsp;0.018), while ficolin-3 levels were positively correlated with anti-dsDNA autoantibodies (r\u0026thinsp;=\u0026thinsp;0.172; p\u0026thinsp;=\u0026thinsp;0.004). Ficolin-2 levels were significantly associated with the presence of alopecia (r\u0026thinsp;=\u0026thinsp;0.124; p\u0026thinsp;=\u0026thinsp;0.037). Among SLE patients from Assam, ficolin-1 levels were positively correlated with ficolin-2 (r\u0026thinsp;=\u0026thinsp;0.277; p\u0026thinsp;=\u0026thinsp;0.008), while ficolin-3 levels were negatively correlated with anti-dsDNA autoantibodies (r=-0.470; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SLE patients with rash and mucosal ulcers showed significantly low ficolin-2 levels as compared to those without these manifestations (r=-0.267; p\u0026thinsp;=\u0026thinsp;0.011 and r=-0.279; p\u0026thinsp;=\u0026thinsp;0.008, respectively). Similarly, patients with musculoskeletal manifestations showed significantly low ficolin-3 levels (r=-0.246; p\u0026thinsp;=\u0026thinsp;0.020). Among SLE patients from Meghalaya displayed a negative correlation between ficolin-1 and ficolin-3 levels (r=-0.520; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, alopecia was significantly associated with low ficolin-1 levels (r=-0.306; p\u0026thinsp;=\u0026thinsp;0.01), while constitutional manifestations were significantly associated with elevated ficolin-2 levels (r\u0026thinsp;=\u0026thinsp;0.253; p\u0026thinsp;=\u0026thinsp;0.042). Among SLE patients from Manipur, ficolin-1 levels were positively correlated with C4 (r\u0026thinsp;=\u0026thinsp;0.240; p\u0026thinsp;=\u0026thinsp;0.045). Further, LN was associated with high ficolin-1 levels (r\u0026thinsp;=\u0026thinsp;0.247; p\u0026thinsp;=\u0026thinsp;0.040), while other manifestations were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among SLE patients from Nagaland, ficolin-3 levels were negatively correlated with C4 levels (r=-0.333; p\u0026thinsp;=\u0026thinsp;0.018). Also, musculoskeletal involvement showed a significant positive correlation with ficolin-1 levels (r\u0026thinsp;=\u0026thinsp;0.364; p\u0026thinsp;=\u0026thinsp;0.009), while no statistically significant associations noted for ficolin-2 or ficolin-3 levels (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present multi-centric study demonstrated a comprehensive analysis of SLE patients from five geographically and ethnically distinct regions from India. This study revealed regional variations in ficolin levels among SLE patients. These findings suggested SLE heterogeneity across the Indian population, which could be driven by the complex interplay between genetic, environmental, and immunological factors.\u003c/p\u003e\u003cp\u003e A significant regional variation in demographic and laboratory parameters was observed in the present study. Patients from Assam presented at a younger age, while those from Manipur and Nagaland were older, which may reflected delayed disease diagnosis, regional healthcare disparities, or variations in genetic susceptibility. Although SLE typically affects women, an unusual male predominance in Nagaland can possibly due to hormonal, genetic, and environmental factors [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Variations in treatment duration, SLEDAI scores, and complement levels across centres suggested differences in disease activity, healthcare access, or treatment practices.\u003c/p\u003e\u003cp\u003eSLE heterogeneity was further reflected by significant regional variations in clinical manifestations across the five centers in India, which were consistent with global trends [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The variations observed in cutaneous and constitutional manifestations reflected differences in sunlight exposure, infections, genetic, and environmental factors. The high prevalence of LN in Assam suggested that renal involvement is a major feature in this region. This was similar to previous reports from Assam [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Given the high LN burden observed in Assam, periodic monitoring of renal indices could improve early diagnosis and outcome. Musculoskeletal manifestations were common in Nagaland and Manipur, and less in Assam (25.6%), as compared to 46.9% reported by Talukdar et al [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Further, SLE patients from Meghalaya showed lower musculoskeletal manifestations as compared with those reported by Barman et al [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Neuropsychiatric symptoms and serosal involvement were less common in our data, as compared to other reports from the Northeast (NE) region [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAutoantibody profile in the present multi-centric study had revealed notable regional differences. Anti-dsDNA positivity, a hallmark of SLE, showed high prevalence in all cohorts, which supports its established role as a key diagnostic marker and indicator of disease activity across diverse patient population [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Other autoantibodies also demonstrated distinct regional patterns. Anti-Rib-P antibody was the highest in Meghalaya and Nagaland, contrasting with complete absence in Manipur. Compared to earlier studies reported from Meghalaya and Assam, the present study showed higher frequencies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Antibodies targeting chromatin, including anti-histone and anti-nucleosome antibodies, were particularly prominent in Assam and Meghalaya. Anti-SSB antibodies were more common in Manipur, while their occurrence in Assam and Meghalaya was similar to previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. AntiSm antibody, a highly specific marker, was noted at similar frequencies as reported in earlier NE reports [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Anti-RNP/Sm antibody was more common in Assam and Mumbai, while less common in Meghalaya and Manipur, that suggested differences in mixed connective tissue\u0026ndash;overlap serology. Overall, these findings suggested that the SLE patients from NE exhibited higher frequencies of anti-dsDNA, histone, and nucleosome autoantibodies than Western India (Mumbai) which indicated potentially more active SLE disease phenotype. The heterogeneity of autoantibody profile among SLE patients, which may have attributed from genetic diversity, environmental exposures, healthcare access, and treatment approaches.\u003c/p\u003e\u003cp\u003eFicolins are pattern-recognition molecules involved in the lectin pathway of complement activation, and their altered levels may influence immune complex clearance and inflammation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. One of the first region-specific insight into ficolin-1, -2, and \u0026minus;\u0026thinsp;3 ficolin levels was provided by this study among Indian SLE patients. Variations in ficolin levels suggested region-specific immune activation or differences in infection-driven immune modulation. Interestingly, ficolin-1 levels were positively associated with LN in Manipur and musculoskeletal involvement in Nagaland, indicated a potential role in tissue-specific/organ-specific inflammation. Tanha et al had reported the association between elevated ficolin-1 levels and histological subtypes of LN in Danish SLE patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These observations suggested that ficolin-1 may serve as a biomarker reflecting organ-specific disease activity. However, further studies, including longitudinal assessments with renal histopathology, are warranted to validate its specificity and determine its temporal association with disease activity and renal flares. The observed negative correlation between ficolin-1 and alopecia in Meghalaya suggested a possible link between reduced ficolin-1 levels and cutaneous manifestations. However, mechanistic studies are further needed to validate and explore this relationship. Ficolin-2 levels in patients with rash and mucosal ulcers in Assam were significantly reduced, suggesting a protective or regulatory role in modulating cutaneous and mucosal inflammation. The negative correlation between ficolin-3 levels and musculoskeletal manifestations in Assam supported a protective role in joint inflammation. However, Andersen et al reported no association between ficolin-3 levels and arthritis in a Danish SLE cohort [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies had reported either negative correlation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] or no association [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] between ficolin-1 levels and SLE disease activity. In contrast, a positive association between ficolin-1 levels in Mumbai was noted in the present study, which suggested their potential role as an immunological indicator of SLE disease activity from this region. No significant association was observed between ficolin-2 levels and disease activity across all regions in the present study, which is consistent with the previous reports [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A positive correlation between ficolin-3 and anti-dsDNA autoantibodies was observed in SLE patients from Mumbai, while a negative correlation was observed in SLE patients from Assam. Troldborg et al had reported no significant correlation between ficolin-3 levels and anti-dsDNA autoantibodies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The substantial inter-regional variation observed in different ficolin levels suggested a strong genetic or environmental modulation.\u003c/p\u003e\u003cp\u003eThe strength of this study lied in its multi centric design, which included diverse geographic, ethnic, and environmental backgrounds across five distinct Indian regions. This approach provided a comprehensive overview of the heterogeneity of clinical manifestations and immunological profile among Indian SLE patients. The simultaneous evaluation of clinical characteristics, autoantibody profiles, and novel innate immune recognition proteins, such as ficolins, had enhanced the understanding of disease heterogeneity. However, this study had certain limitations. Its cross-sectional study design limited its causal inferences between organ involvement, disease activity and ficolin levels. Variation in sample size across centres may have affected statistical power, particularly for less common clinical manifestations of SLE. Future longitudinal studies on a larger, more balanced cohorts, region-matched controls, and information of treatment details were therefore warranted to validate these findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, present multi-centric study demonstrated substantial regional heterogeneity among SLE patients across India. Differences in age at onset, sex distribution, patterns of organ involvement, autoantibody profiles, and circulating ficolin levels pointed to the combined influence of genetic background, environmental exposures, and healthcare access on disease phenotype within the country. Notably, ficolin-1 and ficolin-3 levels showed region- and organ-linked associations that further supported their potential as possible biomarkers of systemic inflammation and SLE disease activity. These findings suggested a need to validate these observations on a larger cohort for a region-specific tailored approach for SLE management in future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements: NA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: Kirti Rai:\u0026nbsp;\u003c/strong\u003eData curation, Investigations, Methodology, and Writing- original draft.\u003cstrong\u003e\u0026nbsp;Ridi Khatri:\u0026nbsp;\u003c/strong\u003eData curation, Formal analysis, Software, Validation, Writing- original draft, and writing- review and editing.\u003cstrong\u003e\u0026nbsp;Amrutha Jose:\u0026nbsp;\u003c/strong\u003eFormal analysis, Software, Validation, Visualization, and Writing- review and editing.\u003cstrong\u003e\u0026nbsp;Deepak Upadhaya:\u0026nbsp;\u003c/strong\u003eDatacurationandInvestigation.\u003cstrong\u003e\u0026nbsp;Sukham Rishikanta Singh:\u0026nbsp;\u003c/strong\u003eDatacurationandInvestigation.\u003cstrong\u003e\u0026nbsp;Kyntiewdor Lyting:\u0026nbsp;\u003c/strong\u003eDatacurationandInvestigation.\u003cstrong\u003e\u0026nbsp;Husulu:\u0026nbsp;\u003c/strong\u003eDatacurationandInvestigation.\u003cstrong\u003e\u0026nbsp;Harshada Konkar:\u0026nbsp;\u003c/strong\u003eDatacurationandInvestigation.\u003cstrong\u003e\u0026nbsp;Prashant Tapase:\u0026nbsp;\u003c/strong\u003eSoftware.\u003cstrong\u003e\u0026nbsp;Milind Nadkar:\u0026nbsp;\u003c/strong\u003eValidation.\u003cstrong\u003e\u0026nbsp;Anjali Rajadhyaksha:\u0026nbsp;\u003c/strong\u003eValidation.\u003cstrong\u003e\u0026nbsp;Pooja Jaiswal:\u0026nbsp;\u003c/strong\u003eVisualization.\u003cstrong\u003e\u0026nbsp;Swapnal Pawaskar:\u0026nbsp;\u003c/strong\u003eInvestigation.\u003cstrong\u003e\u0026nbsp;Durga Chougule:\u0026nbsp;\u003c/strong\u003eWriting: review and editing. \u003cstrong\u003eAjanta Sharma:\u0026nbsp;\u003c/strong\u003eValidation.\u003cstrong\u003e\u0026nbsp;Lahari Saikia:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eChiranjita Phukan:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eAnuradha Deori:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eLeena Talukdar:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eSupriya Laifangbam:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003ePukhrambam Vedanti Devi:\u0026nbsp;\u003c/strong\u003eValidation.\u003cstrong\u003e\u0026nbsp;Julie Leishangthem:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eYengkhom Rameshwor Singh:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eW. Valarie Lyngdoh:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eBhupen Barman:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eMonaliza Lyngdoh:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eBiswajit Dey:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eSheryl Lanong:\u0026nbsp;\u003c/strong\u003eInvestigation. \u003cstrong\u003eCleopatra Shadap:\u0026nbsp;\u003c/strong\u003eInvestigation.\u003cstrong\u003e\u0026nbsp;Banraprbor Wankhar:\u0026nbsp;\u003c/strong\u003eInvestigation.\u003cstrong\u003e\u0026nbsp;V. Khamo:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eHutsulu:\u0026nbsp;\u003c/strong\u003eInvestigation. \u003cstrong\u003eK. Vanlalruati:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eYopovinu Rhutso:\u003c/strong\u003e Validation. \u003cstrong\u003eAlbert T. Pochury:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eKejavisa Savino:\u003c/strong\u003e Validation. \u003cstrong\u003eC Longe Peter:\u0026nbsp;\u003c/strong\u003eValidation. \u003cstrong\u003eNeimenuo Kuotsu:\u0026nbsp;\u003c/strong\u003eValidation.\u003cstrong\u003e\u0026nbsp;Neikhrietsonuo Kesiezie:\u003c/strong\u003e Validation.\u003cstrong\u003e\u0026nbsp;Vijay Padwal:\u0026nbsp;\u003c/strong\u003eSoftware.\u003cstrong\u003e\u0026nbsp;Manisha Madkaikar:\u0026nbsp;\u003c/strong\u003eValidation and Supervision. \u003cstrong\u003eVandana Pradhan:\u0026nbsp;\u003c/strong\u003eConceptualization, Funding acquisition, Validation, Supervision, and Writing: review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the ICMR North East Task force (ICMR-NTF) (Project no. NTF-NER/NER-Cell/RBMCH-2020)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was performed in adherence to the principles of the Declaration of Helsinki. Approval was granted by the Institutional Ethics Committee for Research on Human Subjects, ICMR\u0026ndash;National Institute of Immunohaematology (ICMR-NIIH), Mumbai, India (ICMR-NIIH/IEC/07/2020). Written informed consent was taken from all participants prior to enrolment and publication of resulting data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest:\u003c/strong\u003e The authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRodr\u0026iacute;guez RD, Alarc\u0026oacute;n-Riquelme ME. Exploring the contribution of genetics on the clinical manifestations of systemic lupus erythematosus. Best Pract Res Clin Rheumatol. 2024 Dec;38(4):101971. https://doi.org/10.1016/j.berh.2024.101971\u003c/li\u003e\n\u003cli\u003eSu X, Yu H, Lei Q, Chen X, Tong Y, Zhang Z, Yang W, Guo Y, Lin L. Systemic lupus erythematosus: pathogenesis and targeted therapy. 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Front Pediatr. 2015 Jan 21;2:148. https://doi.org/10.3389/fped.2014.00148\u003c/li\u003e\n\u003cli\u003eTroldborg A, Thiel S, Jensen L, Hansen S, Laska MJ, Deleuran B, Jensenius JC, Stengaard-Pedersen K. Collectin liver 1 and collectin kidney 1 and other complement-associated pattern recognition molecules in systemic lupus erythematosus. Clin Exp Immunol. 2015 Nov;182(2):132-8. https://doi.org/10.1111/cei.12678\u003c/li\u003e\n\u003cli\u003eSharma M, Sangma BS. Lupus Nephritis in Northeastern India: Navigating the complex landscape of clinical challenges and emerging healthcare opportunities. Medical Research Archives. 2024 Feb 28;12(2). https://doi.org/10.18103/mra.v12i2.5123\u003c/li\u003e\n\u003cli\u003eBarman B, Lyngdoh WV, Bhattarcharya PK, Lanong S, Lyngdoh M, Jamil M, Tiewsoh I, Nune A. Clinical and Immunological Profile of Systemic Lupus Erythematosus: A 5-year Retrospective Analysis from Northeast India. J Assoc Physicians India. 2024 Mar;72(3):32-35. https://doi.org/10.59556/japi.72.0355\u003c/li\u003e\n\u003cli\u003eAringer M, Costenbader K, Daikh D, Brinks R, Mosca M, Ramsey-Goldman R, Smolen JS, Wofsy D, Boumpas DT, Kamen DL, Jayne D, Cervera R, Costedoat-Chalumeau N, Diamond B, Gladman DD, Hahn B, Hiepe F, Jacobsen S, Khanna D, Lerstr\u0026oslash;m K, Massarotti E, McCune J, Ruiz-Irastorza G, Sanchez-Guerrero J, Schneider M, Urowitz M, Bertsias G, Hoyer BF, Leuchten N, Tani C, Tedeschi SK, Touma Z, Schmajuk G, Anic B, Assan F, Chan TM, Clarke AE, Crow MK, Czirj\u0026aacute;k L, Doria A, Graninger W, Halda-Kiss B, Hasni S, Izmirly PM, Jung M, Kum\u0026aacute;novics G, Mariette X, Padjen I, Pego-Reigosa JM, Romero-Diaz J, R\u0026uacute;a-Figueroa Fern\u0026aacute;ndez \u0026Iacute;, Seror R, Stummvoll GH, Tanaka Y, Tektonidou MG, Vasconcelos C, Vital EM, Wallace DJ, Yavuz S, Meroni PL, Fritzler MJ, Naden R, D\u0026ouml;rner T, Johnson SR. 2019 European League Against Rheumatism/American College of Rheumatology Classification Criteria for Systemic Lupus Erythematosus. Arthritis Rheumatol. 2019 Sep;71(9):1400-1412. https://doi.org/10.1002/art.40930\u003c/li\u003e\n\u003cli\u003eBombardier C, Gladman DD, Urowitz MB, Caron D, Chang CH. Derivation of the SLEDAI. A disease activity index for lupus patients. The Committee on Prognosis Studies in SLE. Arthritis Rheum. 1992 Jun;35(6):630-40. https://doi.org/10.1002/art.1780350606\u003c/li\u003e\n\u003cli\u003eKim JW, Kim HA, Suh CH, Jung JY. Sex hormones affect the pathogenesis and clinical characteristics of systemic lupus erythematosus. Front Med (Lausanne). 2022 Aug 11;9:906475. https://doi.org/10.3389/fmed.2022.906475\u003c/li\u003e\n\u003cli\u003eChristou EAA, Banos A, Kosmara D, Bertsias GK, Boumpas DT. Sexual dimorphism in SLE: above and beyond sex hormones. Lupus. 2019 Jan;28(1):3-10. https://doi.org/10.1177/0961203318815768\u003c/li\u003e\n\u003cli\u003eSchwartzman-Morris J, Putterman C. Gender differences in the pathogenesis and outcome of lupus and of lupus nephritis. Clin Dev Immunol. 2012;2012:604892. https://doi.org/10.1155/2012/604892\u003c/li\u003e\n\u003cli\u003eBarber MRW, Drenkard C, Falasinnu T, Hoi A, Mak A, Kow NY, Svenungsson E, Peterson J, Clarke AE, Ramsey-Goldman R. Global epidemiology of systemic lupus erythematosus. Nat Rev Rheumatol. 2021 Sep;17(9):515-532. https://doi.org/10.1038/s41584-021-00668-1\u003c/li\u003e\n\u003cli\u003eFatoye F, Gebrye T, Mbada C. Global and regional prevalence and incidence of systemic lupus erythematosus in low-and-middle income countries: a systematic review and meta-analysis. Rheumatol Int. 2022 Dec;42(12):2097-2107. https://doi.org/10.1007/s00296-022-05183-4\u003c/li\u003e\n\u003cli\u003eAlam S, Parry M, Sharma M, Jeelani H, Mazumder M. Clinicopathological Features of Lupus Nephritis Patients in North-East India; A Single Center Retrospective Observational Study. Journal of Renal and Hepatic Disorders. 2022;6(1):1-6. https://doi.org/10.15586/jrenhep.v6i1.130\u003c/li\u003e\n\u003cli\u003eSharma M, Das HJ, Doley PK, Mahanta PJ. Clinical and histopathological profile of lupus nephritis and response to treatment with cyclophosphamide: A single center study. Saudi J Kidney Dis Transpl. 2019 Mar-Apr;30(2):501-507. https://doi.org/10.4103/1319-2442.256857\u003c/li\u003e\n\u003cli\u003eBhattacharya PK, Barman AK, Shinde SA. A clinical study of lupus nephritis in a tertiary care hospital in northeast India. Indian Journal of Medical Specialities. 2015 Oct 1;6(4):136-40. https://doi.org/10.1016/j.injms.2015.07.005\u003c/li\u003e\n\u003cli\u003eTalukdar D, Gogoi AP, Doley D, Marak RR, Kakati S, Pradhan V, Nadkarni AH, Baruah S. The clinical and immunological profiles of systemic lupus erythematosus patients from Assam, North-East India. Indian Journal of Rheumatology. 2020 Nov;15(3):181-6. https://doi.org/10.4103/injr.injr_37_20\u003c/li\u003e\n\u003cli\u003eRekvig OP. Anti-dsDNA antibodies as a classification criterion and a diagnostic marker for systemic lupus erythematosus: critical remarks. Clin Exp Immunol. 2015 Jan;179(1):5-10. https://doi.org/10.1111/cei.12296\u003c/li\u003e\n\u003cli\u003eAyano M, Horiuchi T. Complement as a Biomarker for Systemic Lupus Erythematosus. Biomolecules. 2023 Feb 15;13(2):367. https://doi.org/10.3390/biom13020367\u003c/li\u003e\n\u003cli\u003eWang P, Wu Q, Shuai ZW. Emerging role of ficolins in autoimmune diseases. Pharmacol Res. 2021 Jan;163:105266. https://doi.org/10.1016/j.phrs.2020.105266\u003c/li\u003e\n\u003cli\u003eBidula S, Sexton DW, Schelenz S. Ficolins and the Recognition of Pathogenic Microorganisms: An Overview of the Innate Immune Response and Contribution of Single Nucleotide Polymorphisms. J Immunol Res. 2019 Feb 5;2019:3205072. https://doi.org/10.1155/2019/3205072\u003c/li\u003e\n\u003cli\u003eKatayama M, Ota K, Nagi-Miura N, Ohno N, Yabuta N, Nojima H, Kumanogoh A, Hirano T. Ficolin-1 is a promising therapeutic target for autoimmune diseases. Int Immunol. 2019 Feb 6;31(1):23-32. https://doi.org/10.1093/intimm/dxy056\u003c/li\u003e\n\u003cli\u003eTanha N, Pilely K, Faurschou M, Garred P, Jacobsen S. Plasma ficolin levels and risk of nephritis in Danish patients with systemic lupus erythematosus. Clin Rheumatol. 2017 Feb;36(2):335-341. https://doi.org/10.1007/s10067-016-3508-2\u003c/li\u003e\n\u003cli\u003eAndersen T, Munthe-Fog L, Garred P, Jacobsen S. Serum levels of ficolin-3 (Hakata antigen) in patients with systemic lupus erythematosus. J Rheumatol. 2009 Apr;36(4):757-9. https://doi.org/10.3899/jrheum.080361\u003c/li\u003e\n\u003cli\u003eTroldborg A, Thiel S, Trendelenburg M, Friebus-Kardash J, Nehring J, Steffensen R, Hansen SWK, Laska MJ, Deleuran B, Jensenius JC, Voss A, Stengaard-Pedersen K. The Lectin Pathway of Complement Activation in Patients with Systemic Lupus Erythematosus. J Rheumatol. 2018 Aug;45(8):1136-1144. https://doi.org/10.3899/jrheum.171033\u003c/li\u003e\n\u003cli\u003eWatanabe H, Saito R, Asano T, Sato S, Iwadate H, Kobayashi H, Ohira H. Serum L-ficolin levels in patients with systemic lupus erythematosus. Mod Rheumatol. 2012 Nov;22(6):899-902. https://doi.org/10.1007/s10165-012-0616-y\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Demographic and laboratory characteristics of SLE patients from five regions in India\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1056\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICMR-NIIH, Mumbai\u003cbr\u003e\u0026nbsp;(n=285)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGMC, Assam\u003cbr\u003e\u0026nbsp;(n=90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEIGRIHMS, Meghalaya\u003cbr\u003e\u0026nbsp;(n=65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJNIMS, Manipur\u003cbr\u003e\u0026nbsp;(n=70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHAK, Nagaland\u003cbr\u003e\u0026nbsp;(n=50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 1056px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAge at enrolment (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e30.00 (23.00, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e23.00 (19.00, 32.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e29.00 (21.00, 36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e37.00 (24.00, 45.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e35.00 (25.00, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAge of onset (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e27.00 (20.00, 36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e22.00 (16.00, 29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e29.00 (24.00, 35.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e26.00 (20.00, 37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e32.00 (23.00, 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e252 (88.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e88 (97.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e58 (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e66 (94.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e15 (30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e33 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e2 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e7 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e35 (70.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eDuration of treatment (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.00 (1.00, 36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.00 (0.00, 12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e12.00 (4.00, 36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e48.00 (16.75, 108.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e3.50 (0.88, 26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eSLEDAI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.00 (3.00, 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e6.00 (2.00, 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e4.00 (2.00, 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.00 (2.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e13.00 (10.00, 18.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 1056px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eANA positivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e274 (96.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e88 (97.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e65 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e70 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e48 (96.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAnti-dsDNA positivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e228 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e59 (65.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e47 (72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e41 (58.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e50 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eLow antigenic C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e151 (53.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e34 (37.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e5 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e28 (56.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eLow antigenic C4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e140 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e72 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e2 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e21 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eLow antigenic C3 and C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e114 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e24 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e2 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e13 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eFicolin-1 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e335.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(140.53, 766.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e136.80\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(87.43, 270.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1527.10\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(424.73, 1746.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e856.05\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(646.75, 1007.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e161.08\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(143.34, 201.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eFicolin-2 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2595.91\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1440.65, 4852.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e3896.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(3458.50, 4296.50)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e3765.99\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2535.53, 5103.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e255.19\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(225.66, 283.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eFicolin-3 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e23185.80\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(8225.90, 72085.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e25705.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(13577.00, 34449.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e12040.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(4340.00, 25360.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e37326.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(31256.45, 58772.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e66637.20\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(66454.65, 70636.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as median (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e). Comparisons between groups were performed using the Kruskal-Wallis test followed by Dunn\u0026rsquo;s multiple comparisons test for continuous variables and\u0026nbsp;associations between categorical variables were analyzed using the chi-square test. \u003cem\u003ep\u0026lt;0.05\u003c/em\u003e was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eSLE: Systemic lupus erythematosus; ICMR-NIIH: Indian Council of Medical Research- National Institute of Immunohaematology; GMC: Guwahati Medical College; NEIGRIHMS: North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences; JNIMS: Jawaharlal Nehru Institute of Medical Sciences; NHAK: Naga Hospital Authority Kohima; SLEDAI score: SLE disease activity index score; ANA: Anti-nuclear antibodies.\u003c/p\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":"immunologic-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"imre","sideBox":"Learn more about [Immunologic Research](http://link.springer.com/journal/12026)","snPcode":"12026","submissionUrl":"https://submission.nature.com/new-submission/12026/3","title":"Immunologic Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Systemic Lupus Erythematosus (SLE), Ficolins, Regional variations, Disease activity, Clinical manifestations","lastPublishedDoi":"10.21203/rs.3.rs-7357993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7357993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe lectin pathway, activated by ficolins, contributes to systemic lupus erythematosus (SLE) pathogenesis, but ficolin data remain inconsistent across populations. Present muti-centric cross-sectional study assessed serum ficolin-1, -2, and -3 levels and their associations with clinical features and disease activity among SLE patients from five Indian regions (Mumbai, Assam, Meghalaya, Manipur, and Nagaland). Serum levels of ficolin-1, ficolin-2, and ficolin-3 were measured using ELISA. Disease activity was assessed using the SELENA-SLEDAI score. Statistical analyses were performed using non-parametric tests, with p\u0026lt;0.05 considered significant.\u003cstrong\u003eS\u003c/strong\u003eerum ficolin levels differed significantly by region. Ficolin-1 levels were positively associated with lupus nephritis (r=0.247; p=0.040) in Manipur and musculoskeletal involvement (r=0.364; p=0.009) in Nagaland, while a negative correlation was noted with alopecia (r=-0.306; p=0.01) in Meghalaya. In Assam, ficolin-2 levels were significantly reduced in patients with rash (r=-0.267; p=0.011) and mucosal ulcers (r=-0.279; p=0.008), and ficolin-3 levels showed a negative correlation with musculoskeletal manifestations (r=-0.246; p=0.020). In Mumbai, ficolin-1 levels were positively associated with disease activity (r=0.139; p=0.018), and ficolin-3 levels correlated positively with anti-dsDNA autoantibodies (r=0.172; p=0.004). Conversely, ficolin-3 levels showed a negative correlation with anti-dsDNA (r=-0.470; p\u0026lt;0.001) in Assam.\u003cstrong\u003e \u003c/strong\u003eThe present study demonstrated significant regional variations in ficolin levels among SLE patients across India. Association of ficolin-1 and ficolin-3 with specific organ involvement suggested their potential as possible disease biomarkers. These findings suggested the importance of considering regional and ethnic differences in SLE management and warranted further validation through larger, longitudinal studies.\u003c/p\u003e","manuscriptTitle":"Regional variation in serum ficolin levels and their association with disease activity and clinical manifestations in Systemic Lupus Erythematosus (SLE) patients from India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:09:20","doi":"10.21203/rs.3.rs-7357993/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-09T20:18:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T03:42:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96802214769345477402572274758899530869","date":"2025-08-27T03:23:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T19:55:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235556900570304832141394125327567698490","date":"2025-08-24T18:18:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145848643264613565196391319323085515243","date":"2025-08-20T03:20:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T21:11:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T05:59:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T05:59:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Immunologic Research","date":"2025-08-12T16:43:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"immunologic-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"imre","sideBox":"Learn more about [Immunologic Research](http://link.springer.com/journal/12026)","snPcode":"12026","submissionUrl":"https://submission.nature.com/new-submission/12026/3","title":"Immunologic Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"56fbfb5b-f3ed-4a0d-970d-7d174ff2977b","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:02:11+00:00","versionOfRecord":{"articleIdentity":"rs-7357993","link":"https://doi.org/10.1007/s12026-025-09735-1","journal":{"identity":"immunologic-research","isVorOnly":false,"title":"Immunologic Research"},"publishedOn":"2026-01-02 15:58:36","publishedOnDateReadable":"January 2nd, 2026"},"versionCreatedAt":"2025-09-01 09:09:20","video":"","vorDoi":"10.1007/s12026-025-09735-1","vorDoiUrl":"https://doi.org/10.1007/s12026-025-09735-1","workflowStages":[]},"version":"v1","identity":"rs-7357993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7357993","identity":"rs-7357993","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}