Association of metabolic syndrome and its components with systemic lupus erythematosus

Association of metabolic syndrome and its components with systemic lupus erythematosus | 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 Association of metabolic syndrome and its components with systemic lupus erythematosus Zahra Bagheri-Hosseinabadi, Sahar Sadat Pourmirafzali, Mehdi kafi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4814887/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Systemic lupus erythematosus (SLE) patients might manifest an increased prevalence of Metabolic syndrome (MetS) components like insulin resistance, obesity, and dyslipidemia. Here we aimed to assess the interconnection between MetS components and SLE and attempted to divulge the potential contribution of MetS on SLE development. Methods We enrolled 200 cases with SLE whose diagnosis was accomplished by American College of Rheumatology (ACR) criteria. MetS diagnosed was accomplished through the International Diabetes Federation (IDF) criteria. Results The frequency of MetS among the SLE population was 28.5%. SLEDAI was not significantly different between SLE cases with and without MetS ( P = 0.3519). CRP level was significantly higher in the SLE cases with MetS compared to those without MetS ( P = 0.0412). BMI, FBS, WC (both in males and females), total cholesterol, TG, and LDL were significantly higher in SLE subjects with MetS compared to those without MetS. However, in both males and females, HDL level was significantly lower in SLE subjects with MetS compared to those without MetS. Obesity, CVD, T2D, dyslipidemia, and hypertension were more prevalent in MetS cases among the SLE population. Treatment with metformin (OR = 0.45, 95%CI: 0.21–0.96, P = 0.0398) and atorvastatin (OR = 0.46, 95%CI: 0.23–0.92, P = 0.0287) was associated significantly with a decreased risk of MetS in SLE patients Conclusions An increased prevalence of MetS in the SLE population was observed, while these patients did not have worsen disease severity. All conventional components of MetS were associated with MetS development in the SLE population. Systemic lupus erythematosus Metabolic syndrome Inflammation Diabetes Dyslipidemia Introduction Systemic Lupus Erythematosus (SLE), a chronic inflammatory disorder, arises from immune system dysregulation affecting various tissues in the body. SLE manifests with diverse clinical symptoms, categorized into cutaneous, systemic, drug-induced, and neonatal forms [ 1 – 3 ]. While SLE is reported worldwide, its prevalence is higher in specific populations, such as African Americans, Brazilians, and Swedes, with rates ranging from 0.7 to 8.7 per 100,000 individuals. However, Europe and Asia exhibit lower prevalence rates [ 4 ]. The occurrence of SLE varies with gender, peaking in the fourth decade of life, with subsequent fluctuations [ 5 ]. Clinical and prognostic signs also differ between genders, with alopecia, photosensitivity, and malar rash more common in females, while renal involvement is more prevalent in males [ 6 ]. The prevalence of SLE in females is higher during childbearing age, but a significant percentage is observed in older women [ 7 ]. For newly diagnosed SLE patients, the 5-year survival rate is approximately 90%, dropping to 80% for 15–20 years post-diagnosis [ 8 ]. The pathogenesis of SLE is intricate, disrupting a significant portion of the immune system [ 9 ]. Genetic predisposition, hormonal factors, and environmental elements such as sunlight, viruses, smoking, and certain medications contribute to immune system activation, leading to autoantibody production and immune complex deposition, causing tissue damage [ 10 – 12 ]. Metabolic Syndrome (MetS) is a systemic pro-inflammatory condition encompassing cardiovascular risk factors like blood lipid abnormalities, obesity, elevated fasting glucose, and high blood pressure [ 13 ]. MetS serves as a robust predictor for type 2 diabetes (T2D), stroke, cardiovascular diseases, and other illnesses [ 14 ]. It may induce insulin resistance through inflammatory cytokines like interleukin (IL)-6 and tumor necrosis factor (TNF)-α [ 15 ]. MetS is characterized by a cluster of cardiovascular risk factors that have been identified as a systemic pro-inflammatory condition [ 10 ]. This pro-inflammatory state is marked by the dysregulation of cytokines, adipokines, and other immune mediators, which collectively contribute to the perpetuation of chronic inflammation and development of inflammatory disorders [ 10 , 16 ]. SLE patients often exhibit an increased prevalence of MetS components, including central obesity, dyslipidemia, and insulin resistance [ 17 ]. The chronic inflammatory milieu in SLE, marked by the production of autoantibodies and immune complex deposition, may contribute to the development of metabolic abnormalities [ 18 ]. Conversely, the metabolic perturbations associated with MetS may exert an impact on the course of SLE [ 19 ]. Insulin resistance, a key feature of MetS, has been implicated in the pathogenesis of autoimmune diseases, potentially influencing the severity of SLE [ 20 ]. Additionally, the dyslipidemia characteristic of MetS may contribute to atherosclerosis [ 21 ], a complication that can further exacerbate cardiovascular manifestations in SLE patients. Given that MetS shares similar risk factors with cardiovascular diseases, the leading cause of mortality in lupus patients, the significance of the relationship between these conditions is doubled. Therefore, considering the overlap in risk factors between these diseases and the absence of studies regarding the prevalence of MetS and its components in lupus patients in Rafsanjan city, and further, given the importance of identifying lupus risk factors to reduce cardiovascular complications in these patients, the present study aims to determine the prevalence of MetS and its components in lupus patients attending the Rheumatology Clinic of Rafsanjan University of Medical Sciences. It is hoped that the results of this study can take a small step towards preventing and treating cardiovascular complications in lupus patients. Materials and methods Study subjects In this prospective investigation, we recruited 200 SLE patients with Iranian Fars ethnicity who visited the clinics of Ali Ibn Abi Talib Hospital, Rafsanjan University of Medical Sciences, Kerman, Iran, between September 2018 and August 2023. SLE was diagnosed as complied with the American College of Rheumatology (ACR) criteria [ 22 ]. The identification of MetS was conducted following the International Diabetes Federation (IDF) criteria [ 23 ]. As per the IDF definition, MetS was characterized by central obesity and a minimum of two criteria, including increased triglyceride (TG) levels (≥ 150 mg/dL), reduced high-density lipoprotein (HDL) cholesterol (< 40 mg/dL in men and < 50 mg/dL in women), hypertension (systolic blood pressure (BP) ≥ 130 or diastolic BP ≥ 85 mm Hg) or pre-existing hypertension, and fasting blood sugar (FBS) ≥ 100 mg/dL or a previous diagnosis of T2D [ 24 , 25 ]. Individuals with genetic disorders, infectious diseases, immunodeficiencies, cancers, pregnancy, breastfeeding, and other metabolic and endocrine disorders were excluded from the study. None of the participants had concurrent complications with other chronic conditions such as autoimmunity, cancer, liver disease, etc. Before obtaining 5 ml of peripheral blood samples for the analysis of biochemical components, all participants provided voluntary written consent to participate in the study. The research protocol received approval from the Ethics Committee of Rafsanjan University of Medical Sciences. Clinical examinations Waist circumference (WC) was assessed on a level plane at the midpoint between the lower rib margin and the iliac crest. According to the IDF guidelines, a WC below 80 cm indicates a decreased risk of T2D, hypertension, or coronary heart disease [ 23 ]. Systolic and diastolic blood pressure (BP) in millimeters of mercury (mmHg) were measured twice using a standard mercury sphygmomanometer after the participants rested in a seated position for 5 minutes. Hypertension was defined as BP ≥ 130 mmHg for systolic pressure or ≥ 85 mmHg for diastolic pressure, or current treatment for hypertension. Additionally, standing height was measured using a calibrated standard wall-mounted stadiometer, following World Health Organization (WHO) recommendations [ 26 ]. Weight (kg) was measured with participants wearing light clothing and being barefoot after an overnight fast, using a standard scale. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m 2 ). As per WHO criteria, a BMI value ≤ 18.5 kg/m2 was classified as underweight, 18.5–24.9 kg/m 2 as normal, 25-29.9 kg/m2 as overweight, and ≥ 30 indicated obesity [ 26 ]. Biochemical indices Subsequent to and overnight fasting, the serum levels of total cholesterol, TG, HDL, low-density lipoprotein (LDL), liver enzymes, creatinine, blood urine nitrogen (BUN), and FBS were determined exerting clinical biochemistry autoanalyzer BT3000 Plus (Biotecnica Instruments SPA, Italy) using commercial reagents (Pars Azmoon, Iran). Blood leukocytes were enumerated using Sysmex KX-21 hematology analyzer. ESR was measured using the automated kineticphotometric method (Automatic ESR analyzer, XC-A30, Caretium Medical Instruments, China). Inflammatory indices like CRP were measured by the Enzyme linked immunosorbent assay (ELISA) using commercial kits reagents (Pars Azmoon, Iran) and microplate reader (Stat Fax 4200, Awareness Technology Inc., UAS). Statistical analysis For data analysis, the Statistical Package for the Social Sciences (SPSS) software for Windows v. 23 (SPSS, Chicago, IL, USA) was used. The normality of the numerical variables was determined by the Kolmogorov–Smirnov test. The independent sample t -test was done to compare data with a normal distribution. In contrast, the non-normally distributed variables were analyzed via Mann-Whitney U test. The strength of association between nominal data with risk of MetS in SLE patients was determined via calculating the odds ratio (OR) and 95% confidence interval (CI). Data presentation was conducted by mean ± standard deviation (SD) and P values less than 0.05 were assumed as statistically significant. Results Demographics and characterization of the study subjects The clinical presentations and demographic data of the SLE patients are demonstrated in Table 1 . Study group was comprised of 200 SLE subjects, containing 6 (3%) males and 194 (97%) females. The frequency of MetS among the SLE population was detected to be 28.5% (57 MetS subjects). Table 1 Demographics and presentations of the SLE subjects. Characteristic SLE patients (N = 200) Gender; Male/ Female (N, %) 6 (3%)/ 194 (97%) MetS cases; Yes/No (N, %) 57 (28.5%)/143 (71.5%) Smoker/ Non-smoker (N, %) 13 (6.5%)/187 (93.5%) Familial history of CVD (Yes/No) 34 (17%)/ 166 (83%) Physical activity; Low/Moderate and High (N, %) 16 (8%), 184 (92%) Alcohol consumption; Yes/No (N, %) 2 (1%), 198 (99%) Residency; Urban/Rural (N, %) 9 (4.5%), 191 (95.5%) Marital status; Married/Single (N, %) 11 (5.5%)/ 189 (94.5%) Age (Year, mean ± SD) 43.5 ± 9.3 Duration of SLE (Year, mean ± SD) 13.5 ± 4.6 Systolic BP (mmHg, mean ± SD) 128.71 ± 15.3 Diastolic BP (mmHg, mean ± SD) 84.6 ± 6.9 WBC (cells/mm 3 , mean ± SD) 8423 ± 1945 ALP (IU/L, mean ± SD) 128.5 ± 21.4 AST (IU/L, mean ± SD) 25.3 ± 9.2 ALT (IU/L, mean ± SD) 32.5 ± 7.3 CRP (mg/L, mean ± SD) 3.6 ± 1.1 ESR (mm/h, mean ± SD) 19.3 ± 8.2 BMI (kg/m 2 , mean ± SD) 28.85 ± 3.81 WC-Male (cm, mean ± SD) 98.3 ± 13.51 WC-Female (cm, mean ± SD) 95.4 ± 12.30 Total cholesterol (mg/dl, mean ± SD) 184.6 ± 40.3 TG (mg/dl, mean ± SD) 189.13 ± 25.14 LDL (mg/dl, mean ± SD) 134.5 ± 15.8 HDL-Male (mg/dl, mean ± SD) 44.3 ± 11.2 HDL-Female (mg/dl, mean ± SD) 42.8 ± 10.6 Creatinine (mg/dl, mean ± SD) 1.78 ± 0.62 BUN (mg/dl, mean ± SD) 25.8 ± 12.5 FBS (mg/dl, mean ± SD) 98.3 ± 17.4 SLEDAI (mean ± SD) 16.2 ± 1.27 Anti-dsDNA antibody (IU/ml, mean ± SD) 36.8 ± 23.72 ANA (IU/ml, mean ± SD) 18.69 ± 12.35 Anti-Ro/SSA; Positive/Negaive 134 (67%), 66 (33%) SLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; WBC, White blood cell; CRP, C-reactive protein; ALP, Alkaline phosphatase; AST, Aspartate aminotransferase; ALT, Alanine aminotransferase; ESR, Erythrocyte sedimentation rate; CVD, Cardiovascular diseases; BMI, Body mass index; WC, Waist circumference; FBS, Fasting blood sugar; TG, Triglyceride; LDL, Low density lipoprotein; HDL, High density lipoprotein; BUN, Blood urea nitrogen; SD, Standard deviation; BP, Blood pressure; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; WC, Waist circumference; ANA, Antinuclear antibodies Comparison of SLE patients with and without MetS Characterization of the subjects Table 2 demonstrates the data of the SLE patients with and without MetS. Age of the subjects with and without MetS did not have significant difference (43.8 ± 9.4 vs . 43.2 ± 9.3 years; P = 0.2452). While familial history of CVD was seen in 15 (22.8%) SLE cases with MetS, 19 (14.7%) SLE subjects without Mets had familial history of CVD. It was observed that familial history of CVD was associated with a significant higher risk of MetS in the SLE patients (OR = 2.33; 95%CI: 1.08–4.99; P = 0.0295). It was detected that the number of cases with low physical activity was higher in the SLE cases with MetS in comparison to those without MetS (12.3% vs . 6.3%). Low physical activity was associated with an increased risk of MetS development in the SLE patients (OR = 3.64; 95%CI: 1.28–10.31; P = 0.0149). However, smoking, alcohol consumption, residency status, and marital status were not associated significantly with altered risk of SLE development (Table 2 ). Table 2 Comparison of the SLE patients with and without MetS and related association analyses. Characteristic SLE patients with MetS (N = 57) SLE patients without MetS (N = 143) OR (95% CI) P value Gender; Male/ Female (N, %) 2 (3.5%)/ 55 (96.5%) 4 (2.8%)/ 139 (97.2%) 1.26 (0.22–7.09) 0.7904 Smoker/ Non-smoker (N, %) 4 (7%)/53 (93%) 9 (6.3%)/ 134 (93.7%) 1.12 (0.33–3.80) 0.8514 Familial history of CVD (Yes/No) 15 (22.8%)/ 42 (77.2%) 19 (14.7%)/ 124 (85.3%) 2.33 (1.08–4.99) 0.0295 Physical activity; Low/Moderate and High (N, %) 9 (12.3%), 48 (87.7%) 7 (6.3%), 136 (93.7%) 3.64 (1.28–10.31) 0.0149 Alcohol consumption; Yes/No (N, %) 1 (1.7%), 56 (98.3%) 1 (0.7%), 142 (99.3%) 2.53 (0.15–41.24) 0.5132 Residency; Urban/Rural (N, %) 3 (5.26%), 54 (94.76%) 6 (4.20), 137 (95.80) 1.26 (0.30–5.25) 0.7429 Marital status; Married/Single (N, %) 4 (7.02%)/ 53 (92.98%) 7 (4.90%), 136 (95.10%) 1.46 (0.41–5.21) 0.5544 Age (Year, mean ± SD) 43.8 ± 9.4 43.2 ± 9.3 - 0.2452 Duration of SLE (Year, mean ± SD) 13.7 ± 4.6 13.3 ± 4.5 - 0.3211 Systolic BP (mmHg, mean ± SD) 128.8 ± 15.2 128.6 ± 15.3 - 0.3021 Diastolic BP (mmHg, mean ± SD) 84.1 ± 6.8 85.1 ± 6.9 - 0.6204 WBC (cells/mm 3 , mean ± SD) 8467 ± 1928 8379 ± 1939 - 0.2110 ALP (IU/L, mean ± SD) 129.5 ± 21.8 128.1 ± 20.9 - 0.6521 AST (IU/L, mean ± SD) 25.8 ± 9.5 24.8 ± 9.4 - 0.1980 ALT (IU/L, mean ± SD) 33.1 ± 7.2 31.9 ± 7.4 - 0.0844 CRP (mg/L, mean ± SD) 3.9 ± 1.12 3.3 ± 1.10 - 0.0412 ESR (mm/h, mean ± SD) 20.2 ± 8.3 18.4 ± 8.1 - 0.2541 BMI (kg/m 2 , mean ± SD) 31.5 ± 4.1 26.2 ± 3.3 - 0.0014 WC-Male (cm, mean ± SD) 105.1 ± 14.22 91.5 ± 13.12 - 0.0050 WC-Female (cm, mean ± SD) 98.6 ± 12.85 92.2 ± 12.13 - 0.0324 Total cholesterol (mg/dl, mean ± SD) 191.5 ± 41.3 177.7 ± 40.1 - 0.0031 TG (mg/dl, mean ± SD) 195.3 ± 16.2 182.9 ± 25.1 - 0.0080 LDL (mg/dl, mean ± SD) 142.3 ± 15.9 126.7 ± 15.2 - 0.0065 HDL-Male (mg/dl, mean ± SD) 40.2 ± 10.3 48.4 ± 11.9 - 0.0001 HDL-Female (mg/dl, mean ± SD) 38.9 ± 9.8 46.7 ± 10.1 0.0001 Creatinine (mg/dl, mean ± SD) 1.81 ± 0.65 1.75 ± 0.61 - 0.8411 BUN (mg/dl, mean ± SD) 25.9 ± 12.6 25.7 ± 12.5 - 0.1387 FBS (mg/dl, mean ± SD) 101.5 ± 17.6 95.1 ± 17.3 - 0.0021 SLEDAI (mean ± SD) 16.3 ± 1.30 16.1 ± 1.26 - 0.3519 Anti-dsDNA antibody (IU/ml, mean ± SD) 36.9 ± 26.5 36.7 ± 22.10 - 0.4782 ANA (IU/ml, mean ± SD) 19.11 ± 12.39 18.81 ± 12.34 - 0.6214 Anti-Ro/SSA antibody; Positive/Negaive 39 (68.4%), 18 (31.6%) 95 (66.4%), 48 (33.6%) 1.09 (0.56–2.11) 0.7873 SLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; WBC, White blood cell; CRP, C-reactive protein; ALP, Alkaline phosphatase; AST, Aspartate aminotransferase; ALT, Alanine aminotransferase; ESR, Erythrocyte sedimentation rate; CVD, Cardiovascular diseases; BMI, Body mass index; WC, Waist circumference; FBS, Fasting blood sugar; TG, Triglyceride; LDL, Low density lipoprotein; HDL, High density lipoprotein; BUN, Blood urea nitrogen; SD, Standard deviation; BP, Blood pressure; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; WC, Waist circumference; ANA, Antinuclear antibodies * Bold values show statistically significant comparisons. MetS components It was detected that Systolic and diastolic BP was not significantly different between SLE cases with and without MetS. BMI, FBS, WC-Male, WC-Female, total cholesterol, TG, and LDL were significantly higher in SLE subjects with MetS compared to those without MetS. However, in both male and female genders, HDL level was significantly lower in SLE subjects with MetS compared to those without MetS (Table 2 ). SLE specific characteristics SLEDAI was not significantly different between SLE cases with (16.3 ± 1.30) and without (16.1 ± 1.26) MetS ( P = 0.3519). Anti-dsDNA antibody did not show statistically significant difference between SLE cases with MetS (36.9 ± 26.5 IU/ml) and without (36.7 ± 22.10) MetS ( P = 0.4782). Furthermore, level of ANA was not significantly different between SLE cases with MetS (19.11 ± 12.39 IU/ml) and without (18.81 ± 12.34 IU/ml) MetS ( P = 0.6214). It was detected that 39 (68.4%) SLE cases with MetS and 95 (66.4%) cases without MetS were positive for Anti-Ro/SSA (OR = 1.09; 95%CI: 0.56–2.11; P = 0.7873) (Table 2 ). Blood leukocytes and inflammatory indices Count of blood leukocytes and ESR were not significantly different between SLE patients with and without MetS. However, CRP level was significantly higher in the SLE cases with MetS compared to those without MetS (3.9 ± 1.12 vs . 3.3 ± 1.10 mg/L; P = 0.0412) (Table 2 ). Liver enzymes Analysis indicated that levels of liver enzymes, including ALP, AST, and ALT was not significantly different between SLE cases with and without MetS (Table 2 ). Co-morbidities Obesity was seen in 38 (66.66%) SLE cases with MetS, while 28 (19.58%) cases without MetS had obesity. Hence, obesity was associated with increased risk of MetS in SLE patients (OR = 8.21, 95%CI: 4.12–16.35, P < 0.0001). CVD was observed in 18 (31.57%) SLE cases with MetS and 8 (5.59%) SLE subjects without MetS. Therefore, CVD was associated with increased risk of MetS in SLE patients (OR = 7.78, 95%CI: 3.14–19.26, P < 0.0001). T2D was detected in 14 (24.56%) SLE cases with MetS and 9 (6.29%) SLE patients without MetS. It was detected that presence of T2D was associated with an increased risk of MetS in SLE patients (OR = 4.84, 95%CI: 1.96–11.98, P = 0.0006). It was detected that 36 (63.15%) SLE cases with MetS had dyslipidemia, while 19 (13.28%) SLE cases without MetS has dyslipidemia. Thus, the analysis showed that dyslipidemia was associated with increased risk of MetS in SLE patients (OR = 11.18, 95%CI: 5.42–23.05, P < 0.0001). Hypertension was observed in 29 (50.87%) SLE cases with MetS and 39 (27.27%) SLE cases without MetS. Hence, hypertension was observed to increase the risk of MetS in SLE patients (OR = 2.76, 95%CI: 1.46–5.21, P = 0.0018). Kidney diseases and allergy were not associated with altered risk of MetS in the SLE population (Table 3 ). Table 3 Association of Co-morbidities in MetS subjects with SLE risk. Characteristic SLE patients with MetS (N = 57) SLE patients without MetS (N = 143) OR (95% CI) P value Obesity; Yes/No (N, %) 38 (66.66%)/19 (33.33%) 28 (19.58%)/ 115 (80.41%) 8.21 (4.12–16.35) < 0.0001 CVD; Yes/No (N, %) 18 (31.57%)/ 39 (68.43%) 8 (5.59%)/ 135 (94.41%) 7.78 (3.14–19.26) < 0.0001 T2D; Yes/No (N, %) 14 (24.56%)/ 43 (75.43%) 9 (6.29%)/ 134 (93.71%) 4.84 (1.96–11.98) 0.0006 Dyslipidemia; Yes/No (N, %) 36 (63.15%)/ 21 (36.85%) 19 (13.28%)/ 124 (86.72%) 11.18 (5.42–23.05) < 0.0001 Kidney diseases; Yes/No (N, %) 7 (12.28%)/ 50 (87.72%) 16 (11.18%)/ 127 (88.82%) 1.11 (0.43–2.86) 0.8271 Hypertension; Yes/No (N, %) 29 (50.87%)/ 28 (49.13%) 39 (27.27%)/ 104 (72.73%) 2.76 (1.46–5.21) 0.0018 Allergy; Yes/No (N, %) 8 (14.03%)/ 49 (85.97%) 19 (13.28%)/ 124 (86.72%) 1.06 (0.43–2.59) 0.8888 SLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; CVD, Cardiovascular diseases; T2D, Type 2 diabetes; OR, Odds ratio; CI, Confidence interval * Bold values show statistically significant comparisons. Drug treatment in SLE patients with and without MetS Analysis indicated that treatment with Metformin (OR = 0.45, 95%CI: 0.21–0.96, P = 0.0398) and Atorvastatin (OR = 0.46, 95%CI: 0.23–0.92, P = 0.0287) was associated significantly with a decreased risk of MetS in SLE patients. However, none of the other drugs used by the patients, including Alendronic acid, Aspirin, Azathioprine, Calcium + Vitamin D, Vitamin D3, Folic Acid, Omega-3, Celecoxib, Curcumin, Prednisolone, Hydroxychloroquine, Methotrexate were associated significantly with an altered risk of MetS in the SLE patients (Table 4 ). Table 4 Drug treatment in SLE patients with and without MetS. Characteristic SLE patients with MetS (N = 57) SLE patients without MetS (N = 143) OR (95% CI) P value Alendronic acid; Yes/No (N, %) 11 (19.29%)/46 (80.71%) 28 (19.58%)/ 115 (80.42%) 0.98 (0.45–2.13) 0.9637 Aspirin; Yes/No (N, %) 8 (14.03%)/ 49 (85.97%) 23 (16.08%)/ 120 (83.92%) 0.85 (0.35–2.03) 0.7180 Azathioprine; Yes/No (N, %) 24 (42.10%)/ 33 (57.90%) 66 (46.15%)/ 77 (53.85%) 0.84 (0.45–1.57) 0.6036 Calcium + Vitamin D; Yes/No (N, %) 9 (15.79%)/ 48 (84.21%) 25 (17.48%)/ 118 (82.52%) 0.88 (0.38–2.03) 0.7736 Vitamin D3; Yes/No (N, %) 10 (17.54%)/ 47 (82.46%) 35 (24.47%)/ 108 (75.53%) 0.65 (0.30–1.43) 0.2915 Folic Acid; Yes/No (N, %) 27 (47.36%)/ 30 (52.64%) 71 (49.65%)/ 72 (50.35%) 0.91 (0.49–1.68) 0.7708 Metformin; Yes/No (N, %) 11 (19.29%)/ 46 (80.71%) 49 (34.26%)/ 94 (65.74%) 0.45 (0.21–0.96) 0.0398 Omega-3; Yes/No (N, %) 5 (8.77%)/ 52 (91.22%) 28 (19.58%)/ 115 (80.42%) 0.39 (0.14–1.08) 0.0704 Atorvastatin; Yes/No (N, %) 14 (24.56%)/ 43 (75.44%) 59 (41.26%)/ 84 (58.74%) 0.46 (0.23–0.92) 0.0287 Celecoxib; Yes/No (N, %) 31 (54.38%)/ 28 (45.62%) 75 (52.44%)/ 68 (47.56%) 1.00 (0.54–1.84) 0.9902 Curcumin; Yes/No (N, %) 6 (10.52%)/ 51 (89.48%) 27 (18.88%)/ 116 (81.12%) 0.50 (0.19–1.29) 0.1565 Prednisolone; Yes/No (N, %) 52 (91.22%)/ 5 (8.78%) 128 (89.51%)/ 15 (10.49%) 1.21 (0.42–3.52) 0.7151 Hydroxychloroquine; Yes/No (N, %) 56 (98.24%)/ 1 (1.76%) 141 (98.60%)/ 2 (1.40%) 0.79 (0.07–8.93) 0.8521 Methotrexate; Yes/No (N, %) 26 (45.61%)/ 31 (54.39%) 64 (44.75%)/ 79 (55.25%) 1.03 (0.55–1.91) 0.9123 SLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; OR, Odds ratio; CI, Confidence interval * Bold values show statistically significant comparisons. Discussion Considering that MetS shares similar risk factors with cardiovascular diseases, the primary cause of mortality in lupus patients, the significance of the association between these conditions is heightened. Consequently, given the overlap in risk factors between these diseases and the lack of studies on the prevalence of MetS and its components in lupus patients in Rafsanjan city, and further recognizing the importance of identifying lupus risk factors to mitigate cardiovascular complications in these patients, this present study aims to ascertain the prevalence of MetS and its components in SLE patients. A bulk of studies have revelaed that the prevalence of MetS is higher in the SLE patients compared to the normal population [ 17 , 27 , 19 ]. Furthermore, a meta-analyssi in 2017 pooling 47 studies containing 8367 subjects indicated that prevalence of MetS in patients with SLE was 26% and the risk of MetS development in SLE patients was 1.88 times higher than the control population [ 28 ]. Compling with these observations, we detected that the frequency of MetS among the SLE population was 28.5%. It was previously reported that the prevalence of MetS in Iranian SLE patients and controls were 46.6% and 39.7%, respectively, based on IDF criteria to detect MetS [ 29 ]. Even though we detected that the prevalence of MetS was almost as high as the results obtained from meta-analysis [ 28 ], we did not expand our research to assess the normal population. But compared to the results from other Iranian population [ 29 ], the prevalnce of MetS in our study was lowere (28.5% versus 46.6%). The increased risk of MetS development in patients with SLE compared to the general population is a multifaceted phenomenon influenced by a combination of immunological, genetic, hormonal, and therapeutic factors. SLE is characterized by a dysregulated immune system, with chronic inflammation playing a central role. Persistent inflammation can contribute to insulin resistance, a key component of MetS. The proinflammatory cytokines and autoantibodies observed in SLE may directly interfere with insulin signaling pathways, predisposing patients to metabolic abnormalities [ 30 ]. Even, we recently indicated that MetS and its components were associated with a higher risk of COVID-19 infection (as an inflammatory setting) development and probably with aggravated symptoms in such patients [ 31 ]. Furthermore, we detected that level of CRP (as an indicator of systemic inflammation) was higher in SLE cases with MetS compared to those without MetS. On the other hand, there is evidence supporting a genetic basis for both SLE and MetS. Shared genetic susceptibility may underlie the increased co-occurrence of these conditions. Polymorphisms in genes related to immune function, lipid metabolism, and insulin signaling pathways may contribute to the higher prevalence of MetS in SLE patients [ 30 ]. Hormonal factors, particularly the female predominance in SLE [ 32 ], contribute to the increased risk of MetS. Estrogen, which has immunomodulatory effects, may influence metabolic homeostasis. Hormonal fluctuations, especially in the context of hormone replacement therapy or oral contraceptives, may impact insulin sensitivity and lipid metabolism in SLE patients. MetS is a cluster of interconnected metabolic abnormalities, including central obesity, dyslipidemia, hyperglycemia, and hypertension, collectively contributing to an increased risk of CVD. The implications of MetS in SLE introduce a complex interplay between immunological dysregulation, chronic inflammation, and metabolic disturbances. MetS amplifies the already heightened cardiovascular risk in SLE patients, where inflammation and immune system aberrations play pivotal roles in vascular damage. The coexistence of MetS in SLE individuals accentuates a proatherogenic milieu, predisposing them to accelerated atherosclerosis and early onset of CVD [ 33 , 17 ]. Our research also indicated that CVD was associated with MetS in SLE patients. The degree of SLE disease activity and severity may influence the risk of MetS. Higher disease activity, often requiring more intensive immunosuppressive therapies, is correlated with an increased likelihood of metabolic disturbances. The chronic nature of SLE and its impact on organ systems further contribute to this risk [ 30 ]. Nonetheless, we did not detect increased SLEDAI in SLE patients with MetS compared to those without MetS. Furthermore, SLE related autoantibodies, including anti-dsDNA antibody, ANA, and anti-Ro/SSA antibody were not increased in SLE patients with MetS compared to those without MetS. SLE is known for its heterogeneous clinical manifestations, with patients presenting a wide spectrum of disease phenotypes [ 34 ]. The variability in organ involvement and the fluctuating nature of disease activity might lead to diverse metabolic profiles among individuals, potentially masking a direct correlation between SLEDAI scores and MetS. Furthermore, the use of immunosuppressive medications, including corticosteroids and disease-modifying antirheumatic drugs (DMARDs), is common in the management of SLE. These medications may influence metabolic parameters independently of disease activity, and their differential effects on MetS risk should be considered. Almost all of the patients we studies were using prednisolone, hydroxychloroquine, and methotrexate; which might interfere with inflammatory state in the MetS subjects. A sedentary lifestyle in the lupus population can have significant implications for the development and management of MetS. Sedentary behavior is a known risk factor for MetS [ 35 ]. In the lupus population, where individuals may already face an elevated risk of MetS due to the nature of the autoimmune condition, a sedentary lifestyle can further contribute to the clustering of cardiovascular risk factors characteristic of MetS. Furthermore, sedentary behavior often correlates with weight gain and central adiposity, both of which are components of MetS. In individuals with lupus, obesity can exacerbate inflammation and contribute to insulin resistance, creating a conducive environment for the development of metabolic abnormalities. Additionally, sedentary lifestyles often contribute to dyslipidemia, characterized by elevated triglycerides and reduced HDL cholesterol. Lupus patients with MetS may face an intensified lipid profile disturbance, increasing the likelihood of atherosclerosis and cardiovascular events. We detected that subjects with a life style involving lower physical activity were more prone to develop MetS in the SLE patients. As such, obesity, dyslipidemia, and higher BMI were all associated with MetS risk in SLE population. Diabetes and MetS may have implications for the course of lupus itself. Chronic inflammation, a hallmark of lupus, can be influenced by metabolic factors [ 36 ]. The dysregulation of glucose metabolism and insulin resistance may contribute to an inflammatory milieu that could impact lupus disease activity [ 37 ]. Conversely, lupus-related inflammation might exacerbate insulin resistance and glucose dysregulation [ 38 ]. The management of diabetes in lupus patients with MetS requires careful consideration of medications. Certain antidiabetic medications or other medications commonly used in diabetes management may interact with immunosuppressive drugs prescribed for lupus. Collaborative care between rheumatologists and endocrinologists is essential to optimize treatment plans while minimizing potential drug interactions. Diabetes, especially when associated with MetS, can have systemic effects on various organs. Lupus patients may already be susceptible to organ involvement, and the metabolic disturbances from diabetes could exacerbate these vulnerabilities. Regular monitoring for complications related to both diabetes and lupus is crucial. It should be noted that diabetes, particularly when associated with metabolic abnormalities, can compromise the immune response and increase susceptibility to infections. Lupus patients already face immune dysregulation, and the additional impact of diabetes may heighten the risk of infectious complications. Lupus patients with both diabetes and MetS may face challenges in lifestyle management. Balancing dietary restrictions, physical activity, and medications for both conditions requires a comprehensive and individualized approach. Health education and support from healthcare providers are essential components of effective self-management. Our study found that the consumption of Metformin and Atorvastatin was associated with a reduced risk of MetS in patients with SLE. Metformin likely contributes to this reduction by improving insulin sensitivity, exerting anti-inflammatory effects, and aiding in weight management, which are crucial in countering the insulin resistance and chronic inflammation seen in SLE [ 39 ]. Atorvastatin, on the other hand, lowers LDL cholesterol and triglycerides while modestly increasing HDL cholesterol, thereby addressing dyslipidemia, a core component of MetS [ 40 ]. Additionally, atorvastatin's anti-inflammatory and endothelial function-enhancing properties further mitigate cardiovascular risks [ 41 , 42 ] associated with MetS. These findings underscore the potential of incorporating these medications into the comprehensive management of SLE patients to reduce their MetS risk and improve overall outcomes. While our research provides insights into MetS within the SLE population, the absence of a healthy normal population for comparison introduces limitations in terms of generalizability, causality determination, and the ability to assess relative risk. Future research endeavors could benefit from incorporating a healthy control group to enhance the robustness and applicability of the findings. In conclusion, our comprehensive analysis revealed a heightened prevalence of MetS within the SLE population. Intriguingly, despite the elevated occurrence of MetS, there was no concurrent exacerbation in the severity of the underlying lupus disease. The absence of a synergistic relationship between MetS prevalence and lupus disease severity prompts considerations about potential distinct pathways governing the two entities. While the etiological underpinnings of MetS in SLE remain intricate, these findings accentuate the need for nuanced therapeutic strategies that address both the autoimmune and metabolic dimensions of the disease. Examining the individual components integral to MetS elucidated distinctive associations within the SLE patients. Notably, conventional constituents of MetS, including obesity, dyslipidemia, hypertension, sedentary lifestyle, and higher BMI, exhibited significant correlations with the development of MetS in individuals with SLE. The coexistence of these metabolic factors in the context of lupus implicates a multifaceted relationship, wherein the immunological dysregulation inherent to SLE may intersect with metabolic perturbations, contributing to the observed prevalence of MetS. The heightened prevalence of obesity within the SLE population emerges as a notable contributor to MetS, aligning with broader epidemiological trends linking obesity to metabolic disturbances. Dyslipidemia, characterized by aberrations in lipid profiles, demonstrated a pronounced association with MetS in SLE patients, emphasizing the intricate metabolic landscape in this patients. The presence of hypertension, a cardinal component of MetS, further underscored the intricate cardiovascular implications within the SLE-MetS nexus. Our study not only contributes to the evolving understanding of MetS within the SLE landscape but also underscores the significance of personalized interventions targeting specific metabolic components. Further investigations into the mechanistic intricacies linking SLE and MetS will be instrumental in delineating targeted therapeutic avenues, offering a holistic approach to the comprehensive care of individuals navigating the complex intersection of autoimmune and metabolic challenges. Declarations Ethics approval and consent to participate The study protocol was approved from the local Ethical Review committee located in Rafsanjan University of Medical Sciences (Permission No. IR.RUMS.REC.1401.027) and written informed consent form was taken by all subjects. Research carried out here were in compliance with the Helsinki Declaration. The protocol of this study was approved by the Human Research Ethics Committee from the Rafsanjan University of Medical Sciences, Rafsanjna, Iran (Permission No. IR.RUMS.REC.1401.027). Written informed consent forms were obtained from patients and healthy controls before blood taking. Consent for publication Not applicable. Competing interests The authors declare that they have no conflict of interest to report. Funding This study was financially supported by the Rafsanjan University of Medical Sciences, Kerman, Iran. Data availablity statement Data are available by the corresponding author upon reasonable request. Authors' Contributions ZBH ; Performed the experiments, participated in manuscript preparation, and read the manuscript critically. SSP ; Performed the statistical analysis, participated in manuscript preparation, and read the manuscript critically. MK ; Performed the statistical analysis, participated in manuscript preparation, and read the manuscript critically. MAL ; Contributed in performing the experiments, participated in manuscript preparation and read the manuscript critically. MA ; Developed the main idea, examined the patients, take the financial support, participated in manuscript preparation and read the manuscript critically. 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Inflammopharmacology 30:369–383 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4814887","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345437634,"identity":"71c13736-13b0-49de-ad05-ea1716905304","order_by":0,"name":"Zahra Bagheri-Hosseinabadi","email":"","orcid":"","institution":"Rafsanjan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Bagheri-Hosseinabadi","suffix":""},{"id":345437635,"identity":"ae8b592b-1007-4387-ab11-55dee61e551f","order_by":1,"name":"Sahar Sadat Pourmirafzali","email":"","orcid":"","institution":"Rafsanjan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"Sadat","lastName":"Pourmirafzali","suffix":""},{"id":345437640,"identity":"da6e3a7c-8a3d-4f59-a177-a609dad2ce58","order_by":2,"name":"Mehdi kafi","email":"","orcid":"","institution":"Rafsanjan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"kafi","suffix":""},{"id":345437643,"identity":"382b770d-953c-42bb-965b-afbe06453eed","order_by":3,"name":"Mohammad Amin Lotfi","email":"","orcid":"","institution":"Ali-Ibn Abi-Talib Hospital, Rafsanjan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Amin","lastName":"Lotfi","suffix":""},{"id":345437646,"identity":"4cb4e3c4-09a3-4641-8ecb-171c2f3d2551","order_by":4,"name":"Mitra Abbasifard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDCCAwwMzIwNDAxszEAWDwODHFjwAVFa2BLAWozBggnEaGGAakkEsRnwaeG7fYDxc+EOO3s+NuZnH94wbEufH3b4IdAWOzndBuxaJM8lMEvPPJOc2MbGZjxzDsPt3I230wyAWpKNzQ5g12JwhoFBmreNOYFNvsGYmQekZXYCSMuBxG24tTD/5m2rt2djY/8M0pJuODv9AyEtbEBbDjO2sfGAbUmQl87Bb4vkGcY265lnjgP9wlPMOMfgtuEG6ZyCAwkGuP3Cd4b58O3CHdX28m3smxneVNyWl5+dvvnDhwo7OVxaGBjAkQJ3JxAdgDKIB/INBJWMglEwCkbBCAMA+59cfIPB4WwAAAAASUVORK5CYII=","orcid":"","institution":"Rafsanjan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mitra","middleName":"","lastName":"Abbasifard","suffix":""}],"badges":[],"createdAt":"2024-07-28 02:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4814887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4814887/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64188850,"identity":"e4efd281-6214-49b4-b831-cf0ecdfb15dc","added_by":"auto","created_at":"2024-09-09 17:20:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":831453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4814887/v1/d548e863-8af8-40e0-9e40-bd7c15a2224f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of metabolic syndrome and its components with systemic lupus erythematosus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystemic Lupus Erythematosus (SLE), a chronic inflammatory disorder, arises from immune system dysregulation affecting various tissues in the body. SLE manifests with diverse clinical symptoms, categorized into cutaneous, systemic, drug-induced, and neonatal forms [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While SLE is reported worldwide, its prevalence is higher in specific populations, such as African Americans, Brazilians, and Swedes, with rates ranging from 0.7 to 8.7 per 100,000 individuals. However, Europe and Asia exhibit lower prevalence rates [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The occurrence of SLE varies with gender, peaking in the fourth decade of life, with subsequent fluctuations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Clinical and prognostic signs also differ between genders, with alopecia, photosensitivity, and malar rash more common in females, while renal involvement is more prevalent in males [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The prevalence of SLE in females is higher during childbearing age, but a significant percentage is observed in older women [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For newly diagnosed SLE patients, the 5-year survival rate is approximately 90%, dropping to 80% for 15\u0026ndash;20 years post-diagnosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The pathogenesis of SLE is intricate, disrupting a significant portion of the immune system [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Genetic predisposition, hormonal factors, and environmental elements such as sunlight, viruses, smoking, and certain medications contribute to immune system activation, leading to autoantibody production and immune complex deposition, causing tissue damage [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolic Syndrome (MetS) is a systemic pro-inflammatory condition encompassing cardiovascular risk factors like blood lipid abnormalities, obesity, elevated fasting glucose, and high blood pressure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. MetS serves as a robust predictor for type 2 diabetes (T2D), stroke, cardiovascular diseases, and other illnesses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It may induce insulin resistance through inflammatory cytokines like interleukin (IL)-6 and tumor necrosis factor (TNF)-α [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. MetS is characterized by a cluster of cardiovascular risk factors that have been identified as a systemic pro-inflammatory condition [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This pro-inflammatory state is marked by the dysregulation of cytokines, adipokines, and other immune mediators, which collectively contribute to the perpetuation of chronic inflammation and development of inflammatory disorders [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSLE patients often exhibit an increased prevalence of MetS components, including central obesity, dyslipidemia, and insulin resistance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The chronic inflammatory milieu in SLE, marked by the production of autoantibodies and immune complex deposition, may contribute to the development of metabolic abnormalities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Conversely, the metabolic perturbations associated with MetS may exert an impact on the course of SLE [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Insulin resistance, a key feature of MetS, has been implicated in the pathogenesis of autoimmune diseases, potentially influencing the severity of SLE [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, the dyslipidemia characteristic of MetS may contribute to atherosclerosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], a complication that can further exacerbate cardiovascular manifestations in SLE patients.\u003c/p\u003e \u003cp\u003eGiven that MetS shares similar risk factors with cardiovascular diseases, the leading cause of mortality in lupus patients, the significance of the relationship between these conditions is doubled. Therefore, considering the overlap in risk factors between these diseases and the absence of studies regarding the prevalence of MetS and its components in lupus patients in Rafsanjan city, and further, given the importance of identifying lupus risk factors to reduce cardiovascular complications in these patients, the present study aims to determine the prevalence of MetS and its components in lupus patients attending the Rheumatology Clinic of Rafsanjan University of Medical Sciences. It is hoped that the results of this study can take a small step towards preventing and treating cardiovascular complications in lupus patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects\u003c/h2\u003e \u003cp\u003eIn this prospective investigation, we recruited 200 SLE patients with Iranian Fars ethnicity who visited the clinics of Ali Ibn Abi Talib Hospital, Rafsanjan University of Medical Sciences, Kerman, Iran, between September 2018 and August 2023. SLE was diagnosed as complied with the American College of Rheumatology (ACR) criteria [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The identification of MetS was conducted following the International Diabetes Federation (IDF) criteria [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As per the IDF definition, MetS was characterized by central obesity and a minimum of two criteria, including increased triglyceride (TG) levels (\u0026ge;\u0026thinsp;150 mg/dL), reduced high-density lipoprotein (HDL) cholesterol (\u0026lt;\u0026thinsp;40 mg/dL in men and \u0026lt;\u0026thinsp;50 mg/dL in women), hypertension (systolic blood pressure (BP)\u0026thinsp;\u0026ge;\u0026thinsp;130 or diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;85 mm Hg) or pre-existing hypertension, and fasting blood sugar (FBS)\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL or a previous diagnosis of T2D [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Individuals with genetic disorders, infectious diseases, immunodeficiencies, cancers, pregnancy, breastfeeding, and other metabolic and endocrine disorders were excluded from the study. None of the participants had concurrent complications with other chronic conditions such as autoimmunity, cancer, liver disease, etc. Before obtaining 5 ml of peripheral blood samples for the analysis of biochemical components, all participants provided voluntary written consent to participate in the study. The research protocol received approval from the Ethics Committee of Rafsanjan University of Medical Sciences.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eClinical examinations\u003c/h2\u003e \u003cp\u003eWaist circumference (WC) was assessed on a level plane at the midpoint between the lower rib margin and the iliac crest. According to the IDF guidelines, a WC below 80 cm indicates a decreased risk of T2D, hypertension, or coronary heart disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Systolic and diastolic blood pressure (BP) in millimeters of mercury (mmHg) were measured twice using a standard mercury sphygmomanometer after the participants rested in a seated position for 5 minutes. Hypertension was defined as BP\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg for systolic pressure or \u0026ge;\u0026thinsp;85 mmHg for diastolic pressure, or current treatment for hypertension. Additionally, standing height was measured using a calibrated standard wall-mounted stadiometer, following World Health Organization (WHO) recommendations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Weight (kg) was measured with participants wearing light clothing and being barefoot after an overnight fast, using a standard scale. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m\u003csup\u003e2\u003c/sup\u003e). As per WHO criteria, a BMI value\u0026thinsp;\u0026le;\u0026thinsp;18.5 kg/m2 was classified as underweight, 18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e as normal, 25-29.9 kg/m2 as overweight, and \u0026ge;\u0026thinsp;30 indicated obesity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical indices\u003c/h2\u003e \u003cp\u003eSubsequent to and overnight fasting, the serum levels of total cholesterol, TG, HDL, low-density lipoprotein (LDL), liver enzymes, creatinine, blood urine nitrogen (BUN), and FBS were determined exerting clinical biochemistry autoanalyzer BT3000 Plus (Biotecnica Instruments SPA, Italy) using commercial reagents (Pars Azmoon, Iran). Blood leukocytes were enumerated using Sysmex KX-21 hematology analyzer. ESR was measured using the automated kineticphotometric method (Automatic ESR analyzer, XC-A30, Caretium Medical Instruments, China). Inflammatory indices like CRP were measured by the Enzyme linked immunosorbent assay (ELISA) using commercial kits reagents (Pars Azmoon, Iran) and microplate reader (Stat Fax 4200, Awareness Technology Inc., UAS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor data analysis, the Statistical Package for the Social Sciences (SPSS) software for Windows v. 23 (SPSS, Chicago, IL, USA) was used. The normality of the numerical variables was determined by the Kolmogorov\u0026ndash;Smirnov test. The independent sample \u003cem\u003et\u003c/em\u003e-test was done to compare data with a normal distribution. In contrast, the non-normally distributed variables were analyzed via Mann-Whitney U test. The strength of association between nominal data with risk of MetS in SLE patients was determined via calculating the odds ratio (OR) and 95% confidence interval (CI). Data presentation was conducted by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and \u003cem\u003eP\u003c/em\u003e values less than 0.05 were assumed as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and characterization of the study subjects\u003c/h2\u003e \u003cp\u003eThe clinical presentations and demographic data of the SLE patients are demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Study group was comprised of 200 SLE subjects, containing 6 (3%) males and 194 (97%) females. The frequency of MetS among the SLE population was detected to be 28.5% (57 MetS subjects).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and presentations of the SLE subjects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLE patients (N\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender; Male/ Female (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3%)/ 194 (97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetS cases; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (28.5%)/143 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker/ Non-smoker (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (6.5%)/187 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamilial history of CVD (Yes/No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (17%)/ 166 (83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity; Low/Moderate and High (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (8%), 184 (92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1%), 198 (99%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidency; Urban/Rural (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (4.5%), 191 (95.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status; Married/Single (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (5.5%)/ 189 (94.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Year, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of SLE (Year, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.71\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (cells/mm\u003csup\u003e3\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8423\u0026thinsp;\u0026plusmn;\u0026thinsp;1945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC-Male (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC-Female (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184.6\u0026thinsp;\u0026plusmn;\u0026thinsp;40.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189.13\u0026thinsp;\u0026plusmn;\u0026thinsp;25.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Male (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Female (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLEDAI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-dsDNA antibody (IU/ml, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANA (IU/ml, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.69\u0026thinsp;\u0026plusmn;\u0026thinsp;12.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Ro/SSA; Positive/Negaive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (67%), 66 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; WBC, White blood cell; CRP, C-reactive protein; ALP, Alkaline phosphatase; AST, Aspartate aminotransferase; ALT, Alanine aminotransferase; ESR, Erythrocyte sedimentation rate; CVD, Cardiovascular diseases; BMI, Body mass index; WC, Waist circumference; FBS, Fasting blood sugar; TG, Triglyceride; LDL, Low density lipoprotein; HDL, High density lipoprotein; BUN, Blood urea nitrogen; SD, Standard deviation; BP, Blood pressure; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; WC, Waist circumference; ANA, Antinuclear antibodies\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparison of SLE patients with and without MetS\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eCharacterization of the subjects\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the data of the SLE patients with and without MetS. Age of the subjects with and without MetS did not have significant difference (43.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 \u003cem\u003evs\u003c/em\u003e. 43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 years; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2452). While familial history of CVD was seen in 15 (22.8%) SLE cases with MetS, 19 (14.7%) SLE subjects without Mets had familial history of CVD. It was observed that familial history of CVD was associated with a significant higher risk of MetS in the SLE patients (OR\u0026thinsp;=\u0026thinsp;2.33; 95%CI: 1.08\u0026ndash;4.99; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0295). It was detected that the number of cases with low physical activity was higher in the SLE cases with MetS in comparison to those without MetS (12.3% \u003cem\u003evs\u003c/em\u003e. 6.3%). Low physical activity was associated with an increased risk of MetS development in the SLE patients (OR\u0026thinsp;=\u0026thinsp;3.64; 95%CI: 1.28\u0026ndash;10.31; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0149). However, smoking, alcohol consumption, residency status, and marital status were not associated significantly with altered risk of SLE development (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the SLE patients with and without MetS and related association analyses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLE patients with MetS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLE patients without MetS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender; Male/ Female (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.5%)/ 55 (96.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.8%)/ 139 (97.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (0.22\u0026ndash;7.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker/ Non-smoker (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (7%)/53 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.3%)/ 134 (93.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.33\u0026ndash;3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamilial history of CVD (Yes/No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (22.8%)/ 42 (77.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (14.7%)/ 124 (85.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (1.08\u0026ndash;4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0295\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity; Low/Moderate and High (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (12.3%), 48 (87.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (6.3%), 136 (93.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.64 (1.28\u0026ndash;10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0149\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%), 56 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7%), 142 (99.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53 (0.15\u0026ndash;41.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidency; Urban/Rural (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5.26%), 54 (94.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4.20), 137 (95.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (0.30\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status; Married/Single (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (7.02%)/ 53 (92.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4.90%), 136 (95.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46 (0.41\u0026ndash;5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Year, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of SLE (Year, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (cells/mm\u003csup\u003e3\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8467\u0026thinsp;\u0026plusmn;\u0026thinsp;1928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8379\u0026thinsp;\u0026plusmn;\u0026thinsp;1939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0412\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC-Male (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0050\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC-Female (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0324\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191.5\u0026thinsp;\u0026plusmn;\u0026thinsp;41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.7\u0026thinsp;\u0026plusmn;\u0026thinsp;40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0065\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Male (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Female (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLEDAI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-dsDNA antibody (IU/ml, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANA (IU/ml, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.11\u0026thinsp;\u0026plusmn;\u0026thinsp;12.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.81\u0026thinsp;\u0026plusmn;\u0026thinsp;12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Ro/SSA antibody; Positive/Negaive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (68.4%), 18 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (66.4%), 48 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.56\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; WBC, White blood cell; CRP, C-reactive protein; ALP, Alkaline phosphatase; AST, Aspartate aminotransferase; ALT, Alanine aminotransferase; ESR, Erythrocyte sedimentation rate; CVD, Cardiovascular diseases; BMI, Body mass index; WC, Waist circumference; FBS, Fasting blood sugar; TG, Triglyceride; LDL, Low density lipoprotein; HDL, High density lipoprotein; BUN, Blood urea nitrogen; SD, Standard deviation; BP, Blood pressure; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; WC, Waist circumference; ANA, Antinuclear antibodies\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Bold values show statistically significant comparisons.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMetS components\u003c/h2\u003e \u003cp\u003eIt was detected that Systolic and diastolic BP was not significantly different between SLE cases with and without MetS. BMI, FBS, WC-Male, WC-Female, total cholesterol, TG, and LDL were significantly higher in SLE subjects with MetS compared to those without MetS. However, in both male and female genders, HDL level was significantly lower in SLE subjects with MetS compared to those without MetS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSLE specific characteristics\u003c/h2\u003e \u003cp\u003eSLEDAI was not significantly different between SLE cases with (16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30) and without (16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26) MetS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3519). Anti-dsDNA antibody did not show statistically significant difference between SLE cases with MetS (36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5 IU/ml) and without (36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.10) MetS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4782). Furthermore, level of ANA was not significantly different between SLE cases with MetS (19.11\u0026thinsp;\u0026plusmn;\u0026thinsp;12.39 IU/ml) and without (18.81\u0026thinsp;\u0026plusmn;\u0026thinsp;12.34 IU/ml) MetS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6214). It was detected that 39 (68.4%) SLE cases with MetS and 95 (66.4%) cases without MetS were positive for Anti-Ro/SSA (OR\u0026thinsp;=\u0026thinsp;1.09; 95%CI: 0.56\u0026ndash;2.11; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7873) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBlood leukocytes and inflammatory indices\u003c/h2\u003e \u003cp\u003eCount of blood leukocytes and ESR were not significantly different between SLE patients with and without MetS. However, CRP level was significantly higher in the SLE cases with MetS compared to those without MetS (3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12 \u003cem\u003evs\u003c/em\u003e. 3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10 mg/L; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0412) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLiver enzymes\u003c/h2\u003e \u003cp\u003eAnalysis indicated that levels of liver enzymes, including ALP, AST, and ALT was not significantly different between SLE cases with and without MetS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCo-morbidities\u003c/h2\u003e \u003cp\u003eObesity was seen in 38 (66.66%) SLE cases with MetS, while 28 (19.58%) cases without MetS had obesity. Hence, obesity was associated with increased risk of MetS in SLE patients (OR\u0026thinsp;=\u0026thinsp;8.21, 95%CI: 4.12\u0026ndash;16.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). CVD was observed in 18 (31.57%) SLE cases with MetS and 8 (5.59%) SLE subjects without MetS. Therefore, CVD was associated with increased risk of MetS in SLE patients (OR\u0026thinsp;=\u0026thinsp;7.78, 95%CI: 3.14\u0026ndash;19.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). T2D was detected in 14 (24.56%) SLE cases with MetS and 9 (6.29%) SLE patients without MetS. It was detected that presence of T2D was associated with an increased risk of MetS in SLE patients (OR\u0026thinsp;=\u0026thinsp;4.84, 95%CI: 1.96\u0026ndash;11.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006). It was detected that 36 (63.15%) SLE cases with MetS had dyslipidemia, while 19 (13.28%) SLE cases without MetS has dyslipidemia. Thus, the analysis showed that dyslipidemia was associated with increased risk of MetS in SLE patients (OR\u0026thinsp;=\u0026thinsp;11.18, 95%CI: 5.42\u0026ndash;23.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Hypertension was observed in 29 (50.87%) SLE cases with MetS and 39 (27.27%) SLE cases without MetS. Hence, hypertension was observed to increase the risk of MetS in SLE patients (OR\u0026thinsp;=\u0026thinsp;2.76, 95%CI: 1.46\u0026ndash;5.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0018). Kidney diseases and allergy were not associated with altered risk of MetS in the SLE population (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Co-morbidities in MetS subjects with SLE risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLE patients with MetS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLE patients without MetS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (66.66%)/19 (33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (19.58%)/ 115 (80.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.21 (4.12\u0026ndash;16.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (31.57%)/ 39 (68.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (5.59%)/ 135 (94.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.78 (3.14\u0026ndash;19.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (24.56%)/ 43 (75.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (6.29%)/ 134 (93.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.84 (1.96\u0026ndash;11.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (63.15%)/ 21 (36.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (13.28%)/ 124 (86.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.18 (5.42\u0026ndash;23.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney diseases; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (12.28%)/ 50 (87.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (11.18%)/ 127 (88.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.43\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (50.87%)/ 28 (49.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (27.27%)/ 104 (72.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.76 (1.46\u0026ndash;5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergy; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (14.03%)/ 49 (85.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (13.28%)/ 124 (86.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.43\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; CVD, Cardiovascular diseases; T2D, Type 2 diabetes; OR, Odds ratio; CI, Confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Bold values show statistically significant comparisons.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDrug treatment in SLE patients with and without MetS\u003c/h2\u003e \u003cp\u003eAnalysis indicated that treatment with Metformin (OR\u0026thinsp;=\u0026thinsp;0.45, 95%CI: 0.21\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0398) and Atorvastatin (OR\u0026thinsp;=\u0026thinsp;0.46, 95%CI: 0.23\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0287) was associated significantly with a decreased risk of MetS in SLE patients. However, none of the other drugs used by the patients, including Alendronic acid, Aspirin, Azathioprine, Calcium\u0026thinsp;+\u0026thinsp;Vitamin D, Vitamin D3, Folic Acid, Omega-3, Celecoxib, Curcumin, Prednisolone, Hydroxychloroquine, Methotrexate were associated significantly with an altered risk of MetS in the SLE patients (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrug treatment in SLE patients with and without MetS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLE patients with MetS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLE patients without MetS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlendronic acid; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (19.29%)/46 (80.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (19.58%)/ 115 (80.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.45\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (14.03%)/ 49 (85.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (16.08%)/ 120 (83.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.35\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzathioprine; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (42.10%)/ 33 (57.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (46.15%)/ 77 (53.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.45\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u0026thinsp;+\u0026thinsp;Vitamin D; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (15.79%)/ 48 (84.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (17.48%)/ 118 (82.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.38\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D3; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (17.54%)/ 47 (82.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (24.47%)/ 108 (75.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.30\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolic Acid; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (47.36%)/ 30 (52.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (49.65%)/ 72 (50.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.49\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (19.29%)/ 46 (80.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (34.26%)/ 94 (65.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45 (0.21\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0398\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega-3; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (8.77%)/ 52 (91.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (19.58%)/ 115 (80.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.14\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtorvastatin; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (24.56%)/ 43 (75.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (41.26%)/ 84 (58.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46 (0.23\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0287\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCelecoxib; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (54.38%)/ 28 (45.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75 (52.44%)/ 68 (47.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.54\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurcumin; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (10.52%)/ 51 (89.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (18.88%)/ 116 (81.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.19\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrednisolone; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (91.22%)/ 5 (8.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128 (89.51%)/ 15 (10.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21 (0.42\u0026ndash;3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxychloroquine; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (98.24%)/ 1 (1.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141 (98.60%)/ 2 (1.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.07\u0026ndash;8.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethotrexate; Yes/No (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (45.61%)/ 31 (54.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (44.75%)/ 79 (55.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.55\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSLE, Systemic lupus erythematosus; MetS, Metabolic syndrome; OR, Odds ratio; CI, Confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Bold values show statistically significant comparisons.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsidering that MetS shares similar risk factors with cardiovascular diseases, the primary cause of mortality in lupus patients, the significance of the association between these conditions is heightened. Consequently, given the overlap in risk factors between these diseases and the lack of studies on the prevalence of MetS and its components in lupus patients in Rafsanjan city, and further recognizing the importance of identifying lupus risk factors to mitigate cardiovascular complications in these patients, this present study aims to ascertain the prevalence of MetS and its components in SLE patients.\u003c/p\u003e \u003cp\u003eA bulk of studies have revelaed that the prevalence of MetS is higher in the SLE patients compared to the normal population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, a meta-analyssi in 2017 pooling 47 studies containing 8367 subjects indicated that prevalence of MetS in patients with SLE was 26% and the risk of MetS development in SLE patients was 1.88 times higher than the control population [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Compling with these observations, we detected that the frequency of MetS among the SLE population was 28.5%. It was previously reported that the prevalence of MetS in Iranian SLE patients and controls were 46.6% and 39.7%, respectively, based on IDF criteria to detect MetS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Even though we detected that the prevalence of MetS was almost as high as the results obtained from meta-analysis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we did not expand our research to assess the normal population. But compared to the results from other Iranian population [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the prevalnce of MetS in our study was lowere (28.5% versus 46.6%).\u003c/p\u003e \u003cp\u003eThe increased risk of MetS development in patients with SLE compared to the general population is a multifaceted phenomenon influenced by a combination of immunological, genetic, hormonal, and therapeutic factors. SLE is characterized by a dysregulated immune system, with chronic inflammation playing a central role. Persistent inflammation can contribute to insulin resistance, a key component of MetS. The proinflammatory cytokines and autoantibodies observed in SLE may directly interfere with insulin signaling pathways, predisposing patients to metabolic abnormalities [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Even, we recently indicated that MetS and its components were associated with a higher risk of COVID-19 infection (as an inflammatory setting) development and probably with aggravated symptoms in such patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, we detected that level of CRP (as an indicator of systemic inflammation) was higher in SLE cases with MetS compared to those without MetS. On the other hand, there is evidence supporting a genetic basis for both SLE and MetS. Shared genetic susceptibility may underlie the increased co-occurrence of these conditions. Polymorphisms in genes related to immune function, lipid metabolism, and insulin signaling pathways may contribute to the higher prevalence of MetS in SLE patients [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Hormonal factors, particularly the female predominance in SLE [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], contribute to the increased risk of MetS. Estrogen, which has immunomodulatory effects, may influence metabolic homeostasis. Hormonal fluctuations, especially in the context of hormone replacement therapy or oral contraceptives, may impact insulin sensitivity and lipid metabolism in SLE patients.\u003c/p\u003e \u003cp\u003eMetS is a cluster of interconnected metabolic abnormalities, including central obesity, dyslipidemia, hyperglycemia, and hypertension, collectively contributing to an increased risk of CVD. The implications of MetS in SLE introduce a complex interplay between immunological dysregulation, chronic inflammation, and metabolic disturbances. MetS amplifies the already heightened cardiovascular risk in SLE patients, where inflammation and immune system aberrations play pivotal roles in vascular damage. The coexistence of MetS in SLE individuals accentuates a proatherogenic milieu, predisposing them to accelerated atherosclerosis and early onset of CVD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our research also indicated that CVD was associated with MetS in SLE patients.\u003c/p\u003e \u003cp\u003eThe degree of SLE disease activity and severity may influence the risk of MetS. Higher disease activity, often requiring more intensive immunosuppressive therapies, is correlated with an increased likelihood of metabolic disturbances. The chronic nature of SLE and its impact on organ systems further contribute to this risk [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Nonetheless, we did not detect increased SLEDAI in SLE patients with MetS compared to those without MetS. Furthermore, SLE related autoantibodies, including anti-dsDNA antibody, ANA, and anti-Ro/SSA antibody were not increased in SLE patients with MetS compared to those without MetS. SLE is known for its heterogeneous clinical manifestations, with patients presenting a wide spectrum of disease phenotypes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The variability in organ involvement and the fluctuating nature of disease activity might lead to diverse metabolic profiles among individuals, potentially masking a direct correlation between SLEDAI scores and MetS. Furthermore, the use of immunosuppressive medications, including corticosteroids and disease-modifying antirheumatic drugs (DMARDs), is common in the management of SLE. These medications may influence metabolic parameters independently of disease activity, and their differential effects on MetS risk should be considered. Almost all of the patients we studies were using prednisolone, hydroxychloroquine, and methotrexate; which might interfere with inflammatory state in the MetS subjects.\u003c/p\u003e \u003cp\u003eA sedentary lifestyle in the lupus population can have significant implications for the development and management of MetS. Sedentary behavior is a known risk factor for MetS [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the lupus population, where individuals may already face an elevated risk of MetS due to the nature of the autoimmune condition, a sedentary lifestyle can further contribute to the clustering of cardiovascular risk factors characteristic of MetS. Furthermore, sedentary behavior often correlates with weight gain and central adiposity, both of which are components of MetS. In individuals with lupus, obesity can exacerbate inflammation and contribute to insulin resistance, creating a conducive environment for the development of metabolic abnormalities. Additionally, sedentary lifestyles often contribute to dyslipidemia, characterized by elevated triglycerides and reduced HDL cholesterol. Lupus patients with MetS may face an intensified lipid profile disturbance, increasing the likelihood of atherosclerosis and cardiovascular events. We detected that subjects with a life style involving lower physical activity were more prone to develop MetS in the SLE patients. As such, obesity, dyslipidemia, and higher BMI were all associated with MetS risk in SLE population.\u003c/p\u003e \u003cp\u003eDiabetes and MetS may have implications for the course of lupus itself. Chronic inflammation, a hallmark of lupus, can be influenced by metabolic factors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The dysregulation of glucose metabolism and insulin resistance may contribute to an inflammatory milieu that could impact lupus disease activity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Conversely, lupus-related inflammation might exacerbate insulin resistance and glucose dysregulation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The management of diabetes in lupus patients with MetS requires careful consideration of medications. Certain antidiabetic medications or other medications commonly used in diabetes management may interact with immunosuppressive drugs prescribed for lupus. Collaborative care between rheumatologists and endocrinologists is essential to optimize treatment plans while minimizing potential drug interactions. Diabetes, especially when associated with MetS, can have systemic effects on various organs. Lupus patients may already be susceptible to organ involvement, and the metabolic disturbances from diabetes could exacerbate these vulnerabilities. Regular monitoring for complications related to both diabetes and lupus is crucial. It should be noted that diabetes, particularly when associated with metabolic abnormalities, can compromise the immune response and increase susceptibility to infections. Lupus patients already face immune dysregulation, and the additional impact of diabetes may heighten the risk of infectious complications. Lupus patients with both diabetes and MetS may face challenges in lifestyle management. Balancing dietary restrictions, physical activity, and medications for both conditions requires a comprehensive and individualized approach. Health education and support from healthcare providers are essential components of effective self-management.\u003c/p\u003e \u003cp\u003eOur study found that the consumption of Metformin and Atorvastatin was associated with a reduced risk of MetS in patients with SLE. Metformin likely contributes to this reduction by improving insulin sensitivity, exerting anti-inflammatory effects, and aiding in weight management, which are crucial in countering the insulin resistance and chronic inflammation seen in SLE [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Atorvastatin, on the other hand, lowers LDL cholesterol and triglycerides while modestly increasing HDL cholesterol, thereby addressing dyslipidemia, a core component of MetS [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, atorvastatin's anti-inflammatory and endothelial function-enhancing properties further mitigate cardiovascular risks [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] associated with MetS. These findings underscore the potential of incorporating these medications into the comprehensive management of SLE patients to reduce their MetS risk and improve overall outcomes.\u003c/p\u003e \u003cp\u003eWhile our research provides insights into MetS within the SLE population, the absence of a healthy normal population for comparison introduces limitations in terms of generalizability, causality determination, and the ability to assess relative risk. Future research endeavors could benefit from incorporating a healthy control group to enhance the robustness and applicability of the findings.\u003c/p\u003e \u003cp\u003eIn conclusion, our comprehensive analysis revealed a heightened prevalence of MetS within the SLE population. Intriguingly, despite the elevated occurrence of MetS, there was no concurrent exacerbation in the severity of the underlying lupus disease. The absence of a synergistic relationship between MetS prevalence and lupus disease severity prompts considerations about potential distinct pathways governing the two entities. While the etiological underpinnings of MetS in SLE remain intricate, these findings accentuate the need for nuanced therapeutic strategies that address both the autoimmune and metabolic dimensions of the disease. Examining the individual components integral to MetS elucidated distinctive associations within the SLE patients. Notably, conventional constituents of MetS, including obesity, dyslipidemia, hypertension, sedentary lifestyle, and higher BMI, exhibited significant correlations with the development of MetS in individuals with SLE. The coexistence of these metabolic factors in the context of lupus implicates a multifaceted relationship, wherein the immunological dysregulation inherent to SLE may intersect with metabolic perturbations, contributing to the observed prevalence of MetS. The heightened prevalence of obesity within the SLE population emerges as a notable contributor to MetS, aligning with broader epidemiological trends linking obesity to metabolic disturbances. Dyslipidemia, characterized by aberrations in lipid profiles, demonstrated a pronounced association with MetS in SLE patients, emphasizing the intricate metabolic landscape in this patients. The presence of hypertension, a cardinal component of MetS, further underscored the intricate cardiovascular implications within the SLE-MetS nexus. Our study not only contributes to the evolving understanding of MetS within the SLE landscape but also underscores the significance of personalized interventions targeting specific metabolic components. Further investigations into the mechanistic intricacies linking SLE and MetS will be instrumental in delineating targeted therapeutic avenues, offering a holistic approach to the comprehensive care of individuals navigating the complex intersection of autoimmune and metabolic challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved from the local Ethical Review committee located in Rafsanjan University of Medical Sciences (Permission No. IR.RUMS.REC.1401.027) and written informed consent form was taken by all subjects.\u003c/p\u003e\n\u003cp\u003eResearch carried out here were in compliance with the Helsinki Declaration. The protocol of this study was approved by the Human Research Ethics Committee from the Rafsanjan University of Medical Sciences, Rafsanjna, Iran (Permission No. IR.RUMS.REC.1401.027). Written informed consent forms were obtained from patients and healthy controls before blood taking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by the Rafsanjan University of Medical Sciences, Kerman, Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availablity statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available by the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZBH\u003c/strong\u003e; Performed the experiments, participated in manuscript preparation, and read the manuscript critically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSSP\u003c/strong\u003e; Performed the statistical analysis, participated in manuscript preparation, and read the manuscript critically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMK\u003c/strong\u003e; Performed the statistical analysis, participated in manuscript preparation, and read the manuscript critically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAL\u003c/strong\u003e; Contributed in performing the experiments, participated in manuscript preparation and read the manuscript critically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMA\u003c/strong\u003e; Developed the main idea, examined the patients, take the financial support, participated in manuscript preparation and read the manuscript critically.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful of the patients and the healthy individuals for their participation in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCaielli S, Wan Z, Pascual V (2023) Systemic lupus erythematosus pathogenesis: interferon and beyond. Annual Review of Immunology 41:533\u0026ndash;560\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazar S, Kahlenberg JM (2023) Systemic lupus erythematosus: new diagnostic and therapeutic approaches. Annual review of medicine 74:339\u0026ndash;352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmadi M, Gharibi T, Dolati S, Rostamzadeh D, Aslani S, Baradaran B, Younesi V, Yousefi M (2017) Epigenetic modifications and epigenetic based medication implementations of autoimmune diseases. Biomedicine \u0026amp; Pharmacotherapy 87:596\u0026ndash;608\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFatoye F, Gebrye T, Mbada C (2022) Global and regional prevalence and incidence of systemic lupus erythematosus in low-and-middle income countries: a systematic review and meta-analysis. 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Inflammopharmacology 30:369\u0026ndash;383\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Systemic lupus erythematosus, Metabolic syndrome, Inflammation, Diabetes, Dyslipidemia","lastPublishedDoi":"10.21203/rs.3.rs-4814887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4814887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSystemic lupus erythematosus (SLE) patients might manifest an increased prevalence of Metabolic syndrome (MetS) components like insulin resistance, obesity, and dyslipidemia. Here we aimed to assess the interconnection between MetS components and SLE and attempted to divulge the potential contribution of MetS on SLE development.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe enrolled 200 cases with SLE whose diagnosis was accomplished by American College of Rheumatology (ACR) criteria. MetS diagnosed was accomplished through the International Diabetes Federation (IDF) criteria.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe frequency of MetS among the SLE population was 28.5%. SLEDAI was not significantly different between SLE cases with and without MetS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3519). CRP level was significantly higher in the SLE cases with MetS compared to those without MetS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0412). BMI, FBS, WC (both in males and females), total cholesterol, TG, and LDL were significantly higher in SLE subjects with MetS compared to those without MetS. However, in both males and females, HDL level was significantly lower in SLE subjects with MetS compared to those without MetS. Obesity, CVD, T2D, dyslipidemia, and hypertension were more prevalent in MetS cases among the SLE population. Treatment with metformin (OR\u0026thinsp;=\u0026thinsp;0.45, 95%CI: 0.21\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0398) and atorvastatin (OR\u0026thinsp;=\u0026thinsp;0.46, 95%CI: 0.23\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0287) was associated significantly with a decreased risk of MetS in SLE patients\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAn increased prevalence of MetS in the SLE population was observed, while these patients did not have worsen disease severity. All conventional components of MetS were associated with MetS development in the SLE population.\u003c/p\u003e","manuscriptTitle":"Association of metabolic syndrome and its components with systemic lupus erythematosus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 07:20:34","doi":"10.21203/rs.3.rs-4814887/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3c01a3b-003f-44ea-951b-912303c5cda5","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T17:12:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 07:20:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4814887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4814887","identity":"rs-4814887","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}