Assessment of Nutrient Adequacy, Dietary Diversity Score, and Dietary Inflammatory Index in Individuals with Systemic Lupus Erythematosus (SLE)

Assessment of Nutrient Adequacy, Dietary Diversity Score, and Dietary Inflammatory Index in Individuals with Systemic Lupus Erythematosus (SLE) | 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 Assessment of Nutrient Adequacy, Dietary Diversity Score, and Dietary Inflammatory Index in Individuals with Systemic Lupus Erythematosus (SLE) Soni Priya valeru, Uroosa Fathima, Hemant Mahajan, B.R Nikhita, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8153117/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) is a chronic autoimmune disorder primarily affecting women of reproductive age and non-white populations. It arises from genetic and environmental interactions, leading to inflammation, tissue damage, and multi-organ involvement. Evidence suggests that poor nutrient intake, low dietary diversity, and pro-inflammatory diets influence disease activity, yet their combined impact on SLE risk remains unclear. This study aimed to assess the association between nutrient adequacy, dietary diversity, and the inflammatory potential of diet with SLE risk. Methodology: A case-control study included 400 women (150 SLE cases, 250 age-matched healthy controls) aged 15–45 years. Dietary intake was assessed using a validated semi-quantitative FFQ. Nutrient adequacy was evaluated via AMDR and the Probability of Adequacy for ten key micronutrients. Dietary Diversity Score (DDS) was calculated across ten food groups, and the Dietary Inflammatory Index (DII) was derived from 21 pro- and anti-inflammatory dietary parameters. Multivariate logistic regression examined associations of DDS, DII, and nutrient adequacy with SLE risk, adjusting for age, gender and BMI. Results SLE cases consumed significantly less energy (865 vs 1681 kcal), protein (24 vs 47 g), fat (23 vs 34 g), and carbohydrates (128 vs 223 g) than controls (all p < 0.001). Fewer than 6% of cases met AMDR targets, and their mean probability of adequacy for micronutrients was ninefold lower, with notable deficiencies in B-complex vitamins (especially B12), zinc, iron, and folate. Their diets were also less diverse (Dietary Diversity Score, p < 0.001) and more pro-inflammatory (Dietary Inflammatory Index). Each one-unit increase in DII raised SLE odds by 27%, while a 1% increase in MPA reduced odds by 14%. Conclusion Women with SLE exhibited dietary patterns that were micronutrient-deficient, less diverse, and highly pro-inflammatory. A higher Dietary Inflammatory Index (DII) score independently predicted the presence of SLE. These findings highlight the critical role of dietary assessment and targeted nutritional interventions as adjuncts in the clinical management of SLE. Systemic Lupus Erythematosus Nutrient Adequacy Dietary Diversity Score Dietary Inflammatory Index Autoimmunity Inflammation Figures Figure 1 Figure 2 1. INTRODUCTION Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that affects multiple organ systems, characterized by immune dysregulation, autoantibody production, and widespread inflammation, with a global prevalence of approximately 43.7 cases per 100,000 individuals ( 1 ). The disease primarily affects women of reproductive age, with a female-to-male ratio of 9:1, and is more common among non-white ethnic groups who tend to experience earlier onset and more severe manifestations, particularly involving the kidneys, skin, and nervous system ( 2 , 3 ). The pathogenesis of SLE results from a complex interplay of genetic susceptibility, environmental triggers, and epigenetic modifications ( 4 ), leading to the loss of immune tolerance, persistent inflammation, and immune complex deposition, accompanied by autoreactive B and T cell activation with a predominance of Th2-mediated responses. Nutrition plays a critical role in modulating inflammatory and immune processes in SLE patients. Diets rich in refined carbohydrates and free sugars are associated with greater inflammation, disease activity, and dyslipidaemia, whereas fibre-rich, low-glycaemic diets help improve metabolic health and reduce symptom severity. Adequate protein intake may protect genetically susceptible individuals ( 5 ). In contrast, excessive fat intake, especially from high-fat diets adversely affects immune function, and obesity further aggravates inflammation through altered leptin and adiponectin levels, often exacerbated by vitamin D deficiency ( 6 ). Micronutrients also play a pivotal role in SLE pathophysiology by modulating immune and inflammatory pathways. Vitamin D supports regulatory T-cell development and suppresses pro-inflammatory cytokines; its deficiency correlates with increased disease activity and fatigue ( 7 ). B-complex vitamins influence DNA methylation and immune gene expression, with deficiencies potentially inducing disease flares via epigenetic dysregulation ( 8 ). Vitamin A helps maintain Th17/Treg balance and improves immune markers (C3, C4) ( 9 ), while antioxidant vitamins such as C and E, along with selenium, reduce oxidative stress and limit tissue damage ( 18 , 26 ). Key minerals like zinc further support T-cell regulation and inflammatory control ( 10 ). An imbalance in nutritional status and daily dietary intake has been identified as a risk factor for the exacerbation of autoimmune diseases, including SLE, by activating inflammatory pathways that upregulate cytokines such as tumour necrosis factor-α (TNF-α) and interleukin-6 (IL-6). Studies have consistently reported that individuals with SLE tend to exhibit poorer nutritional status and lower nutrient intake compared to the general population ( 11 ). Dietary patterns further play a pivotal role in modulating inflammation and immune responses in SLE. Pro-inflammatory diets like the Western diet worsen oxidative stress, disrupt gut microbiota, and trigger inflammatory pathways ( 12 ). In contrast, anti-inflammatory diets such as the Mediterranean diet are linked to lower disease activity and better immune regulation ( 13 ). Emerging data also support the DASH diet’s efficacy in reducing hypertension and renal complications, both of which are pivotal concerns in SLE management ( 14 ) Therefore, these previous findings support the role of nutrients in the immune response modulation and highlight the potential contribution of an adequate nutritional and dietary status in the prognosis and development of comorbidities that modify the disease course and survival in SLE. Another tool, the Dietary Inflammatory Index (DII), measures a diet's inflammatory potential; high scores (pro-inflammatory diets) are associated with increased disease activity, while low scores (anti-inflammatory diets) may reduce symptoms. Additionally, the Diet Diversity Score (DDS), reflecting food group variety, serves as a proxy for diet quality, with low DDS linked to poor nutritional status in SLE ( 15 ). While existing research predominantly focuses on isolated effects of nutrient deficiencies and pro-inflammatory diets, integrated assessments of their collective impact on SLE risk remain limited. The present study seeks to address this gap by examining the assessment of dietary diversity, nutrient adequacy, and the inflammatory potential of the diet, as measured by the DII, in individuals with SLE. 2. METHODOLOGY 2.1 Study Design and Participant Recruitment This research employed an exploratory, cross-sectional, case-control design with a total of 400 participants, including 150 individuals newly diagnosed with SLE and 250 healthy controls. The study protocol received ethical clearance from the Institutional Ethics Committee (IEC) of the Indian Council of Medical Research (ICMR-RA) (Ref No-3/1/2/162/2019). All participants provided written informed consent before enrolment. Participant recruitment was conducted within the Department of Rheumatology at KIMS Hospital, under the direct supervision of qualified rheumatologists. Eligibility criteria included female patients diagnosed with systemic lupus erythematosus (SLE), aged between 15 and 45 years, who met at least four of the revised American College of Rheumatology (ACR) classification criteria ( 16 ). The control group consisted of 250 participants, age- and sex-matched healthy individuals without a history of autoimmune diseases. Individuals with chronic metabolic disorders known to influence nutritional or inflammatory status were excluded. Furthermore, pregnant or lactating women were not included due to physiological variations that may confound dietary and immune parameters. 2.2 Anthropometric Measurements Anthropometric measurements, including height and weight, were recorded using a stadiometer and SECA weighing scale, respectively. Body weight was measured to the nearest 0.1 kg and height to 0.5 cm. Body Mass Index (BMI) was then calculated as weight (kg)/height (m²). 2.3 Assessment of Dietary Intake A semi-quantitative Food Frequency Questionnaire (FFQ) was employed to obtain detailed information regarding participants’ demographic characteristics, dietary intake, and frequency of consumption of various food items over the preceding year. The FFQ comprised 131 food items and was structured to evaluate long-term dietary patterns. These food items were systematically categorized into 13 food groups in accordance with the Indian Food Composition Tables 2017 (IFCT). The interview commenced with an open recall of food items using standardized utensils, typically consumed daily, weekly, and monthly consumption patterns with particular attention to the estimation of portion sizes. Individual daily food intake was calculated using the following formula: Quantity of raw food ingredient consumed = (Quantity of total raw ingredient used in cooking / Total quantity of cooked food prepared) × Quantity of cooked food consumed individually. Subsequent estimations of nutrient and total energy intake were derived using the IFCT database. The FFQ was thus designed to capture both the quantity and frequency of food consumption. 2.4 Estimation of Nutrient Adequacy Macronutrient adequacy was evaluated using the Acceptable Macronutrient Distribution Range (AMDR) framework. This approach involves calculating the percentage of total daily energy intake derived from each of the three primary macronutrients, i.e., carbohydrates, fats, and proteins, and comparing these values against the established AMDR ranges. The Estimated Average Requirement (EAR) was employed in this context as it provides a statistically robust estimate of the nutrient needs of a population. Micronutrient adequacy was assessed using the Probability of Adequacy (PA) method for ten selected micronutrients, based on EAR values. The standard deviation (SD) of nutrient requirements was estimated using the coefficient of variation (CV) formula, expressed as SD = CV × EAR. PA values were calculated using the EAR values provided by the Indian Council of Medical Research–National Institute of Nutrition (ICMR–NIN). The Mean Probability of Adequacy (MPA) was subsequently derived as the average of the individual PA values across all assessed micronutrients. An MPA value below 0.5 was considered indicative of a high prevalence of micronutrient inadequacy within the population. 2.5 Assessment of Dietary Diversity The Dietary Diversity Score (DDS) is a validated tool employed to assess the variety and nutritional adequacy of an individual's diet. It reflects the number of distinct food groups consumed over a defined reference period, thereby serving as a proxy indicator for micronutrient sufficiency and overall dietary quality. According to the ICMR-NIN 2024 dietary guidelines for Indians, the diet is categorized into ten food groups: ( 1 ) cereals and millets, ( 2 ) pulses, ( 3 ) vegetables, ( 4 ) nuts and oilseeds, fats and oils ( 5 ) green leafy vegetables, ( 6 ) fruits, ( 7 ) dairy products, ( 8 ) roots and tubers, ( 9 ) flesh foods, and ( 10 ) spices and condiments. For scoring, the consumption of any food item from a given group was assigned a value of 1, while non-consumption was assigned a value of 0. The total DDS was obtained by summing the scores across all food groups, yielding a possible range of 0 to 10. A DDS of less than 5 was indicative of low dietary diversity, whereas a score of 5 or higher signified high dietary diversity. 2.6 Assessment of DII Score Dietary data obtained from the FFQ were utilized to compute the Dietary Inflammatory Index (DII) scores. According to the approach ( 17 ), the algorithm assigns an inflammatory effect score to a total of 45 dietary parameters—comprising both macro- and micronutrients—based on their reported influence on inflammation: -1 for anti-inflammatory, + 1 for pro-inflammatory, and 0 for no effect. In the present study, FFQ data were available for 21 of these parameters, which were consequently used in DII computation. These parameters included: energy, protein, carbohydrate, total fat, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), dietary fibre, folic acid, β-carotene, iron, zinc, thiamine, riboflavin, vitamin B12, vitamin C, vitamin A, vitamin D3, vitamin B6, total cholesterol, magnesium, and selenium. Individual exposure levels were standardized against global means by calculating a Z-score, which involved subtracting the global mean intake from the individual's reported intake and dividing the result by the global standard deviation. To mitigate the impact of right-skewed distributions often seen in dietary data, the Z-scores were converted to centred percentile scores. These scores were subsequently multiplied by the literature-derived inflammatory effect score specific to each food parameter, resulting in a parameter-specific DII score. The final DII score for each participant was obtained by summing all parameter-specific DII scores. 2.7 Statistical Analysis Continuous data were expressed as median and interquartile range (IQR) and compared between cases and controls using the Mann–Whitney U test. Categorical data were presented as frequency and percentage and analysed using Pearson’s Chi-square test to assess differences between the two groups. To evaluate the independent associations of DDS, DII, and MPA with SLE, separate unconditional logistic regression models were performed for each dietary variable, adjusting for age, sex, and BMI. Unconditional logistic regression was selected due to incomplete one-to-one matching based on age and sex. To further reduce the potential for residual confounding, all models included age, sex, and BMI as covariates. All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant to account for multiple comparisons. 3. RESULTS 3.1 Participant’s Characteristics A total of 400 participants were enrolled in this case-control study, comprising 150 patients diagnosed with systemic lupus erythematosus (SLE) and 250 age- and sex-matched healthy controls. The mean age of the study population was 32.42 years (SD ± 9.06), with SLE cases being significantly younger than the control group (29.89 ± 9.05 vs. 33.93 ± 8.75 years, p < 0.001). The average BMI was significantly higher among SLE cases compared to controls (26.63 ± 5.02 vs. 24.92 ± 3.49 kg/m², p < 0.001). (Table 1 ) Table 1 Demographic, Body Mass Index and Nutrient Consumption Distribution Across Participants Variables Overall (n = 400) Control (n = 250) SLE cases (n = 150) p-value Age in years, mean (SD) 32.42 (9.06) 33.93 (8.75) 29.89 (9.05) < 0.001 BMI (kg/m 2 ), mean (SD) 25.56 (4.21) 24.92 (3.49) 26.63 (5.02) < 0.001 Macronutrient Intake Energy in kcal, median (IQR) 1329.04 (880.41, 1868.09) 1680.69 (1322.51, 2197.34) 865.15 (659.54, 1076.52) < 0.001 Protein in grams, median (IQR) 32.37 (21.66, 53.76) 46.69 (27.83, 64.21) 24.38 (17.82, 30.90) < 0.001 Carbohydrate in grams, median (IQR) 156.85 (103.93, 278.90) 223.05 (112.3, 349.42) 127.54 (96.35, 160.08) < 0.001 Fats in grams, median (IQR) 28.65 (17.76, 45.78) 34.13 (18.85, 54.40) 22.66 (15.52, 34.20) < 0.001 Fibre in grams, median (IQR) 14.33 (9.69, 21.17) 14.09 (8.66, 20.53) 14.62 (11.13,21.52) 0.082 Micronutrient Intake Iron in mg, median (IQR) 7.42 (4.89,12.08) 10.07 (6.32,15.09) 5.28 (3.79, 6.81) < 0.001 Zinc in mg, median (IQR) 4.61 (2.87, 7.19) 5.67 (3.54, 8.38) 3.29 (2.49, 4.87) < 0.001 Thiamine in mg, median (IQR) 0.65 (0.43,1.04) 0.88 (0.51, 1.24) 0.48 (0.35, 0.66) < 0.001 Riboflavin in mg, median (IQR) 0.35 (0.19, 0.63) 0.55 (0.35, 0.81) 0.17 (0.12, 0.24) < 0.001 Niacin in mg, median (IQR) 5.99 (3.70, 10.73) 9.33 (5.61, 13.68) 4.01 (2.99, 5.20) < 0.001 Vitamin B12 in mcg, median (IQR) 0 (0, 0.21) 0.16 (0, 0.49) 0 (0, 0) < 0.001 Vitamin B6 in mg, median (IQR) 0.56 (0.33, 0.86) 0.64 (0.42, 0.92) 0.40 (0.22, 0.67) < 0.001 Vitamin C in mg, median (IQR) 75.29 (40.90, 130.58) 98.66 (60.87, 143.92) 49.08 (26.31, 80.14) < 0.001 Vitamin A in mcg, median (IQR) 268.52 (124.73, 605.74) 424.95 (196.18, 830.55) 158.44 (100.28, 255.25) < 0.001 Folate in mcg, median (IQR) 161.12 (87.33, 260.27) 211.77 (148.99, 338.54) 87.36 (63.76, 123.33) < 0.001 Total cholesterol in mg, median (IQR) 64.86 (29.24, 177.09) 93.37 (29.36, 204.98) 60.85 (28.08, 91.28) < 0.001 Selenium in mcg, median (IQR) 13.66 (6.15, 24.66) 13.66 (5.60, 24.77) 13.70 (6.72, 23.57) < 0.001 Vitamin E in mg, median (IQR) 0.10 (0.04, 0.46) 0.05 (0.02, 0.10) 0.63 (0.36, 1.22) 0.640 Magnesium in mg, median (IQR) 120.41 (48.69, 206.09) 161.67 (102.14, 236.48) 47.34 (23.81, 97.32) < 0.001 MUFA in mg, median (IQR) 654.30 (323.62, 1335.05) 568.15 (279.38, 1167.27) 893.72 (460.80, 1760.42) < 0.001 PUFA in mg, median (IQR) 920.08 (540.16, 1473.19) 969.94 (571.48,1463.60) 837.56 (520.35, 1526.50) 0.645 IQR, interquartile range; BMI, body mass index; mg, milligram; mcg, microgram; MUFA, mono-unsaturated fatty acids; PUFA, polyunsaturated fatty acids. 3.2 Dietary Intake Profile and Nutrient Adequacy Across all major macronutrients, SLE cases reported significantly reduced dietary intakes in comparison to healthy controls. Median energy intake among SLE cases was 865.15 kcal (IQR: 659.54, 1076.52), considerably lower than that of the control group (1680.69 kcal; IQR: 1322.51, 2197.34; p < 0.001). Protein consumption was also markedly deficient in SLE participants (24.38 g; IQR: 17.82, 30.90), less than half the intake observed in controls (46.69 g; IQR: 27.83, 64.21; p < 0.001). Similar patterns were observed for carbohydrate (127.54 g vs. 223.05 g) and fat intake (22.66 g vs. 34.13 g), both statistically significant (p < 0.001). Fiber intake was not significantly different between groups. Assessment against the Acceptable Macronutrient Distribution Range (AMDR) revealed pronounced inadequacies in the SLE group. Only 0.7% of SLE participants met the protein adequacy criteria versus 27.2% of controls (p < 0.001). Adequate fat and carbohydrate intakes were observed in just 6.0% and 5.3% of SLE cases, compared to 24.8% and 48.0% among controls, respectively (p < 0.001). Micronutrient adequacy, assessed via the Probability of Adequacy method for essential micronutrients, showed pronounced insufficiency across multiple vitamins, especially B-complex vitamins and minerals in SLE cases. Thiamine intake among SLE cases was 0.48 mg (IQR: 0.35–0.66), significantly lower than the 0.88 mg (IQR: 0.51–1.24) observed in controls ( p < 0.001). Riboflavin intake was also reduced in the SLE cases (0.17 mg; IQR: 0.12–0.24) relative to controls (0.55 mg; IQR: 0.35–0.81; p < 0.001), as was niacin (4.01 mg; IQR: 2.99–5.20 vs. 9.33 mg; IQR: 5.61–13.68; p < 0.001). and Vitamin B6 (0.40 mg; IQR: 0.22–0.67 vs. 0.64 mg; IQR: 0.42–0.92; p < 0.001). Notably, vitamin B12 intake was virtually absent among SLE cases (median: 0 mcg), while controls reported a median intake of 0.16 mcg (IQR: 0–0.49; p < 0.001). Other vitamins followed a similar trend. Vitamin C intake in the SLE cases was significantly lower (49.08 mg; IQR: 26.31–80.14) than in controls (98.66 mg; IQR: 60.87–143.92; p < 0.001), as was vitamin A (158.44 mcg; IQR: 100.28–255.25 vs. 424.95 mcg; IQR: 196.18–830.55; p < 0.001). Folate intake was also substantially reduced among SLE cases (87.36 mcg; IQR: 63.76–123.33) compared to the control group (211.77 mcg; IQR: 148.99–338.54; p < 0.001). In terms of mineral intake, both iron and zinc were significantly reduced in the SLE group. Median iron intake in the SLE cases was 5.28 mg (IQR: 3.79–6.81), significantly lower than in the control group (10.07 mg; IQR: 6.32–15.09; p < 0.001). Similarly, zinc intake was reduced in SLE patients (3.29 mg; IQR: 2.49–4.87) compared to controls (5.67 mg; IQR: 3.54–8.38; p < 0.001). Table 2 Acceptable Macronutrient Distribution Range and probability of Adequacy of micronutrients Overall (n = 400) Control (n = 250) SLE cases (n = 150) p-value Acceptable Macronutrient Distribution Range Protein AMDR (Adequate), n(%) 69 (17.2) 68 (27.2) 1 (0.7) < 0.001 Fat AMDR (Adequate), n(%) 71 (17.75) 62 (24.80) 9 (6.00) < 0.001 Carbohydrate AMDR (Adequate), n(%) 128 (32.00) 120 (48.00) 8 (5.33) < 0.001 Energy status (Adequate), n(%) 133 (33.42) 128 (51.61) 5 (3.33) < 0.001 Fiber consumption (in % RDA), median (IQR) 56.36 (37.61, 83.40) 56.38 (34.66, 82.15) 55.83 (40.71 84.27) 0.454 Probability of Adequacy of Micronutrients Iron, median (IQR) 0.00 (5.60, 0.97) 0.53 (0.00, 0.99) 1.99 ( 3.56, 1.74) < 0.001 Zinc, median (IQR) 1.35 (1.50, 0.00) 7.61 (5.52, 0.05) 1.06 (5.3, 1.26) < 0.001 Thiamine, median (IQR) 4.07 (1.01, 1.17) 5.08 (1.62, 2.03) 8.22 (1.89, 0.00) < 0.001 Riboflavin, median (IQR) 8.01 (5.71, 2.23) 1.75 (2.56, 2.14) 1.79 (1.20, 6.65) < 0.001 Niacin, median (IQR) 0.09 (0.00, 0.99) 0.99( 0.14, 1) 0.00 (2.34, 0.00) < 0.001 Vitamin B12, median (IQR) 7.62 (7.62, 6.27) 7.62 (7.62, 7.62) 4.06 (7.62,1.02) 0.259 Vitamin B6, median (IQR) 4.24 (1.35, 3.55) 4.14 (4.98, 5.59) 1.06 (3.63,1.98) < 0.001 Vitamin C, median (IQR) 0.99 (0.00, 1) 1 (0.87, 1) 0.14 (6.68, 0.99) < 0.001 Vitamin A, median (IQR) 0.05 (0.00, 0.99) 0.67 (0.00, 1) 0.00 (0.00, 0.03) < 0.001 Folate, median (IQR) 1 (7.75, 1) 1 ( 1 , 1 ) 3.57 (1.04, 3.96) < 0.001 AMDR, Acceptable Macronutrient Distribution Range; IQR, interquartile range; MPA, mean probability of adequacy; n, frequency; %, percentage; RDA, recommended daily allowance; Diet quality and inflammatory potential Dietary quality was poor among SLE cases compared to healthy controls. The overall MPA was significantly lower in SLE patients (5.05%; IQR: 0.02–10.29) compared to controls (44.28%; IQR: 27.68–55.55; p < 0.001). While 37.6% of controls achieved an adequate MPA (≥ 50%), only 0.7% of SLE cases met this threshold. Similarly, the DDS was significantly lower in the SLE group (median: 5.0; IQR: 5.0–6.0) than in controls (median: 6.0; IQR: 4.0–7.0; p < 0.001). Although the majority of both groups met the minimum DDS threshold (≥ 5), the narrower interquartile range and lower median suggest reduced variety and potential qualitative limitations in food group intake. Conversely, the DII was significantly higher in SLE patients (median: 1.18; IQR: 0.13–1.85) versus controls (median: 0.48; IQR: − 0.62–1.74; p < 0.001). This indicates that SLE patients tend to consume more pro-inflammatory dietary components, indicating a substantially more pro-inflammatory dietary pattern in the cases group. Associations Between Dietary Indicators and SLE Unconditional logistic regression analysis revealed a significant association between dietary inflammatory potential and SLE status. After adjusting for age, sex, and BMI, each one-unit increase in the Dietary Inflammatory Index (DII) was associated with a 27% higher likelihood of having SLE (AOR: 1.27; 95% CI: 1.08–1.48; p = 0.003). Stratified analysis by DII tertiles showed that individuals in the second and third tertiles had 2.82-fold and 1.91-fold increased odds of SLE, respectively, compared to those in the lowest tertile (all p < 0.05), indicating a positive, though not strictly monotonic, trend. Conversely, higher micronutrient adequacy, as measured by the MPA, was associated with a reduced likelihood of SLE. Each 1% increase in MPA was linked to a 14% decrease in SLE odds (AOR: 0.86; 95% CI: 0.84–0.89; p < 0.001). Participants with adequate MPA (≥ 0.5) had markedly lower odds of SLE (AOR: 0.01; 95% CI: 0.002–0.09; p < 0.001), although this estimate may be influenced by sparse data, as only one SLE case had MPA value above the adequacy threshold. DDS, on the other hand, was not independently associated with SLE status in the multivariable model (AOR: 1.12; 95% CI: 0.96–1.32; p = 0.159), although a tendency toward lower DDS among SLE cases remained evident. Table 3 Distribution of mean probability of adequacy of micronutrients, dietary score and dietary inflammation across participants Overall (n = 400) Control (n = 250) SLE cases (n = 150) p-value MPA in %, median (IQR) 26.30 (9.70, 49.11) 44.28 (27.68, 55.55) 5.05 (0.02, 10.29) < 0.001 MPA status (Adequate), n(%) 95 (23.8) 94 (37.6) 1 (0.7) < 0.001 DDS, median (IQR) 6.0 (5.0, 7.0) 6.0 (4.0, 7.0) 5.0 (5.0, 6.0) < 0.001 DDS status (High), n (%) 308 (77.0) 197 (78.8) 111 (74.0) 0.163 DII, median (IQR) 0.82 (-0.32, 1.80) 0.48 (-0.62, 1.74) 1.18 (0.13, 1.85) < 0.001 DDS, dietary diversity score; DDS score ≥ 5 is considered as High ; IQR, interquartile range; n, frequency; %, percentage; MPA, mean probability of adequacy. Table 4 Association of dietary inflammation, mean probability of nutrient adequacy and diet diversity score with SLE Variables Unadjusted OR (95% CI) p-value AOR (95% CI) p-value DII (per unit change) 1.30 (1.13, 1.51) < 0.001 1.27 (1.08, 1.48) 0.003 DII Tertiles* Tertile 1 1.00 - 1.00 - Tertile 2 2.42 (1.44, 4.06) 0.001 2.82 (1.62, 4.91) < 0.001 Tertile 3 2.14 (1.27, 3.60) 0.004 1.91 (1.09, 3.33) 0.023 DDS (per unit change) 1.11 (0.95, 1.28) 0.192 1.12 (0.96, 1.32) 0.159 DDS status ( Low vs. High ) 1.31 (0.81, 2.10) 0.270 1.32 (0.80, 2.19) 0.280 MPA in % (per 1% change) 0.87 (0.84, 0.89) < 0.001 0.86 (0.84, 0.89) < 0.001 MPA status (≥ 0.5 vs. <0.5) 0.01 (0.001, 0.08) < 0.001 0.01 (0.002, 0.09) < 0.001 AOR, adjusted odds ratio; CI, confidence interval; DII, dietary inflammatory index; DDS, dietary diversity score; IQR, interquartile range; n, frequency; %; MPA, mean probability of adequacy; DDS score < 5 is considered as Low ; MPA ≥ 0.5 considered as nutrient adequacy; Regression models were adjusted for age, sex and body mass index; *p-trend across the tertiles of DII < 0.01. 4. DISCUSSION Autoimmune diseases have increased in their prevalence in Western countries in the past few decades. SLE is a chronic autoimmune disease marked by immune dysregulation, inflammation, and oxidative stress. Alongside genetic and environmental factors, diet plays a key role in modulating disease risk and activity ( 18 ). Diet affects immune tolerance, cytokine expression, epigenetic modifications, and gut microbiota composition, all of which are relevant to autoimmune disease activity and flare severity ( 19 – 21 ). Three interrelated dietary components, nutrient adequacy, dietary diversity, and the DII, offer a comprehensive view of diet quality and its inflammatory potential. In our study, we found that SLE cases occurred predominantly in younger individuals, with a mean age of 29.89 ± 9.05 years. This finding is consistent with existing literature, which identifies SLE as a disease that most frequently affects individuals in their 20s and 30s, particularly women of reproductive age ( 22 , 23 ). This early onset has been associated with higher disease activity scores ( 24 ). Additionally, our study found that the average BMI was significantly higher among SLE patients compared to healthy controls, highlighting a trend toward increased weight in individuals with SLE. This observation is consistent with several studies that have demonstrated a higher prevalence of obesity within the SLE population and its association with adverse health outcomes. Evidence from a cohort of 105 patients showed that 39% were either overweight or obese, and this was associated with greater disease-related complications and increased cumulative organ damage ( 25 ). The same study also noted associations between elevated BMI and disrupted sleep patterns, leading to increased cortisol levels and fat accumulation. ( 26 ). The study also revealed that individuals with SLE experience significant nutritional inadequacies, particularly with respect to macronutrient and micronutrient intake. SLE participants had significantly lower energy, protein, and carbohydrate intake compared to the control group. A similar pattern of inadequacy of caloric consumption was significantly lower in SLE cases, despite a greater proportion of individuals in this group being overweight compared to controls. This apparent paradox aligns with existing research, which suggests that factors beyond caloric intake, such as reduced physical activity due to disease-related fatigue, increased cortisol levels, and glucocorticoid therapy that may alter glucose and lipid metabolism and alterations may contribute to weight gain in this population ( 27 , 28 ). Deficiencies were more prominent for B-complex vitamins (B6, B12, folate), vitamin A, and vitamin C , all of which are crucial for immune regulation, antioxidant defense, and DNA methylation. In the SLE group, the folate intake was almost 60% less than that in controls, and vitamin B₁₂ intake was negligible. These two vitamins, in particular, serve as methyl donors in one-carbon metabolism, influencing DNA synthesis, repair, and methylation. Deficiencies in these vitamins can result in hyper-homocysteinemia and epigenetic dysregulation, both of which are implicated in increased oxidative stress and immune dysfunction, which are hallmarks of SLE pathogenesis ( 29 , 30 ). Our findings are in line with the study conducted on a Brazilian cohort of lupus sufferers, which showed an inadequate intake of iron and vitamin B12 ( 40 ). Vitamin A, often regarded as a key modulator of immune homeostasis, was also significantly less in SLE cases. Reduced vitamin A levels have been linked to a shift toward pro-inflammatory Th17 dominance and diminished regulatory T cell function. This imbalance contributes to disease progression in SLE ( 31 ). Mineral intake was inadequate, with significantly lower iron and zinc levels observed in the SLE group. These results are consistent with earlier studies indicating that SLE patients often exhibit low intake and serum levels of these minerals ( 32 , 33 ). Iron is essential for oxygen transport and immune cell proliferation. However, in the context of chronic inflammation, such as in SLE, iron metabolism is frequently dysregulated ( 34 ). Zinc deficiency has been shown to impair T-cell maturation and increase pro-inflammatory cytokine activity via NF-κB signalling, all of which are relevant to SLE pathophysiology. It was found that MPA was substantially lower among participants with SLE compared to healthy controls. Importantly, each 1% increase in MPA was associated with a 14% reduction in the odds of having SLE, highlighting the potential protective role of adequate micronutrient intake against disease development, which may contribute to immune dysregulation, oxidative stress, and inflammation, all central to SLE pathogenesis and disease progression ( 35 ). The study provides further support for the growing body of evidence linking dietary patterns with SLE susceptibility and disease activity. It was observed that participants with SLE exhibited significantly higher DII scores compared to healthy controls, indicating a predominance of pro-inflammatory dietary habits within this group. This observation is consistent with earlier research suggesting that higher DII scores are associated with increased systemic inflammation and immune dysregulation, which are hallmarks of SLE pathogenesis (79–81). Elevated DII scores have been linked to increased circulating levels of pro-inflammatory cytokines such as IL-6 and TNF-α, both of which are key mediators in the pathogenesis of SLE ( 36 ). In addition, diets high in DII scores have been associated with increased risk of SLE-related complications, including cardiovascular disease ( 37 ). In contrast, diets with lower DII exhibit immunomodulatory effects and are associated with lower disease activity and reduced risk of flare-ups ( 38 , 39 ). Importantly, our results extend this evidence by demonstrating that the association between DII and SLE status remains significant even after adjusting for nutrient adequacy and dietary diversity. Each one-unit increase in DII was associated with a 27% higher likelihood of having SLE, suggesting its independent role in mediating disease risk. Strengths of this study include its large sample size, rigorous dietary assessment using validated tools, and use of clinically confirmed SLE diagnoses. The simultaneous evaluation of MPA, DDS, and DII in a single analytical framework is a methodological strength. Limitations include the case-control design (limiting causal inference), self-reported dietary data , and a partial DII score (based on 21 of 45 parameters), which may affect accuracy. Residual confounding from unmeasured factors (e.g., disease duration, medication use, physical activity) is also possible. Conclusion Individuals with SLE showed markedly lower intake of key micronutrients, especially B-complex vitamins, Vitamin A, Vitamin C, and minerals (zinc, iron). Lower Mean Probability of Adequacy and Dietary Diversity Scores, along with higher Dietary Inflammatory Index values, indicate a pro-inflammatory and nutritionally inadequate diet that may contribute to SLE onset and progression. 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Nutrients, 10. https://doi.org/10.3390/nu10101531 Phillips C, Shivappa N, Hébert J, Perry I (2018) Dietary Inflammatory Index and Biomarkers of Lipoprotein Metabolism, Inflammation and Glucose Homeostasis in Adults. Nutrients , 10 García-Arellano A, Ramallal R, Ruíz-Canela M, Salas-Salvadó J, Corella D, Shivappa N, Schröder H, Hébert J, Ros E, Gómez-Garcia E, Estruch R, Lapetra J, Arós F, Fiol M, Serra-Majem L, Pintó X, Babio N, González J, Fitó M, Martínez J, Martínez-González M, Investigators T (2015) Dietary Inflammatory Index and Incidence of Cardiovascular Disease in the PREDIMED Study. Nutrients 7:4124–4138 Wang B, Wang H, Huang J, Zhao T (2024) Association between Unsaturated Fatty Acid-Type Diet and Systemic Lupus Erythematosus: A Systematic Review with Meta-Analyses. Nutrients, 16 Pocovi-Gerardino G, Correa-Rodríguez M, Callejas-Rubio J, Ríos-Fernandez R, Martín-Amada M, Cruz-Caparrós M, Rueda-Medina B, Ortego-Centeno N (2020) Beneficial effect of Mediterranean diet on disease activity and cardiovascular risk in systemic lupus erythematosus patients: a cross-sectional study. Rheumatology Borges MC, De Santos MMD, Telles F, Lanna RW, C. C. D., Correia MIT (2012) Nutritional status and food intake in patients with systemic lupus erythematosus. Nutrition 28(11–12):1098–1103. https://doi.org/10.1016/j.nut.2012.01.015 Additional Declarations The authors declare no competing interests. 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. 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2","display":"","copyAsset":false,"role":"figure","size":153840,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methodology section.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8153117/v1/d14f21f1985de2146a1e6e4f.png"},{"id":96452785,"identity":"be7f5255-af22-4fa3-8449-a1f4826ab206","added_by":"auto","created_at":"2025-11-21 09:44:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1269798,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8153117/v1/41ea5b72-40bd-4504-b8fc-a22c584bbef5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessment of Nutrient Adequacy, Dietary Diversity Score, and Dietary Inflammatory Index in Individuals with Systemic Lupus Erythematosus (SLE)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSystemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that affects multiple organ systems, characterized by immune dysregulation, autoantibody production, and widespread inflammation, with a global prevalence of approximately 43.7 cases per 100,000 individuals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The disease primarily affects women of reproductive age, with a female-to-male ratio of 9:1, and is more common among non-white ethnic groups who tend to experience earlier onset and more severe manifestations, particularly involving the kidneys, skin, and nervous system (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The pathogenesis of SLE results from a complex interplay of genetic susceptibility, environmental triggers, and epigenetic modifications (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), leading to the loss of immune tolerance, persistent inflammation, and immune complex deposition, accompanied by autoreactive B and T cell activation with a predominance of Th2-mediated responses.\u003c/p\u003e\u003cp\u003eNutrition plays a critical role in modulating inflammatory and immune processes in SLE patients. Diets rich in refined carbohydrates and free sugars are associated with greater inflammation, disease activity, and dyslipidaemia, whereas fibre-rich, low-glycaemic diets help improve metabolic health and reduce symptom severity. Adequate protein intake may protect genetically susceptible individuals (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In contrast, excessive fat intake, especially from high-fat diets adversely affects immune function, and obesity further aggravates inflammation through altered leptin and adiponectin levels, often exacerbated by vitamin D deficiency (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). \u003cb\u003eMicronutrients\u003c/b\u003e also play a pivotal role in SLE pathophysiology by modulating immune and inflammatory pathways. Vitamin D supports regulatory T-cell development and suppresses pro-inflammatory cytokines; its deficiency correlates with increased disease activity and fatigue (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). B-complex vitamins influence DNA methylation and immune gene expression, with deficiencies potentially inducing disease flares via epigenetic dysregulation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Vitamin A helps maintain Th17/Treg balance and improves immune markers (C3, C4) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), while antioxidant vitamins such as C and E, along with selenium, reduce oxidative stress and limit tissue damage (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Key minerals like zinc further support T-cell regulation and inflammatory control (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn imbalance in nutritional status and daily dietary intake has been identified as a \u003cb\u003erisk factor\u003c/b\u003e for the exacerbation of autoimmune diseases, including SLE, by activating inflammatory pathways that upregulate cytokines such as tumour necrosis factor-α (TNF-α) and interleukin-6 (IL-6). Studies have consistently reported that individuals with SLE tend to exhibit poorer nutritional status and lower nutrient intake compared to the general population (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Dietary patterns further play a pivotal role in modulating inflammation and immune responses in SLE. Pro-inflammatory diets like the Western diet worsen oxidative stress, disrupt gut microbiota, and trigger inflammatory pathways (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In contrast, anti-inflammatory diets such as the Mediterranean diet are linked to lower disease activity and better immune regulation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Emerging data also support the DASH diet\u0026rsquo;s efficacy in reducing hypertension and renal complications, both of which are pivotal concerns in SLE management (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTherefore, these previous findings support the role of nutrients in the immune response modulation and highlight the potential contribution of an adequate nutritional and dietary status in the prognosis and development of comorbidities that modify the disease course and survival in SLE.\u003c/p\u003e\u003cp\u003eAnother tool, the Dietary Inflammatory Index (DII), measures a diet's inflammatory potential; high scores (pro-inflammatory diets) are associated with increased disease activity, while low scores (anti-inflammatory diets) may reduce symptoms. Additionally, the Diet Diversity Score (DDS), reflecting food group variety, serves as a proxy for diet quality, with low DDS linked to poor nutritional status in SLE (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). While existing research predominantly focuses on isolated effects of nutrient deficiencies and pro-inflammatory diets, integrated assessments of their collective impact on SLE risk remain limited. The present study seeks to address this gap by examining the assessment of dietary diversity, nutrient adequacy, and the inflammatory potential of the diet, as measured by the DII, in individuals with SLE.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Participant Recruitment\u003c/h2\u003e\u003cp\u003eThis research employed an exploratory, cross-sectional, case-control design with a total of 400 participants, including 150 individuals newly diagnosed with SLE and 250 healthy controls. The study protocol received ethical clearance from the Institutional Ethics Committee (IEC) of the Indian Council of Medical Research (ICMR-RA) (Ref No-3/1/2/162/2019). All participants provided written informed consent before enrolment. Participant recruitment was conducted within the Department of Rheumatology at KIMS Hospital, under the direct supervision of qualified rheumatologists. Eligibility criteria included female patients diagnosed with systemic lupus erythematosus (SLE), aged between 15 and 45 years, who met at least four of the revised American College of Rheumatology (ACR) classification criteria (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The control group consisted of 250 participants, age- and sex-matched healthy individuals without a history of autoimmune diseases. Individuals with chronic metabolic disorders known to influence nutritional or inflammatory status were excluded. Furthermore, pregnant or lactating women were not included due to physiological variations that may confound dietary and immune parameters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Anthropometric Measurements\u003c/h2\u003e\u003cp\u003eAnthropometric measurements, including height and weight, were recorded using a stadiometer and SECA weighing scale, respectively. Body weight was measured to the nearest 0.1 kg and height to 0.5 cm. Body Mass Index (BMI) was then calculated as weight (kg)/height (m\u0026sup2;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Assessment of Dietary Intake\u003c/h2\u003e\u003cp\u003eA semi-quantitative Food Frequency Questionnaire (FFQ) was employed to obtain detailed information regarding participants\u0026rsquo; demographic characteristics, dietary intake, and frequency of consumption of various food items over the preceding year. The FFQ comprised 131 food items and was structured to evaluate long-term dietary patterns. These food items were systematically categorized into 13 food groups in accordance with the Indian Food Composition Tables\u0026nbsp;2017 (IFCT). The interview commenced with an open recall of food items using standardized utensils, typically consumed daily, weekly, and monthly consumption patterns with particular attention to the estimation of portion sizes. Individual daily food intake was calculated using the following formula: Quantity of raw food ingredient consumed = (Quantity of total raw ingredient used in cooking / Total quantity of cooked food prepared) \u0026times; Quantity of cooked food consumed individually. Subsequent estimations of nutrient and total energy intake were derived using the IFCT database. The FFQ was thus designed to capture both the quantity and frequency of food consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Estimation of Nutrient Adequacy\u003c/h2\u003e\u003cp\u003eMacronutrient adequacy was evaluated using the Acceptable Macronutrient Distribution Range (AMDR) framework. This approach involves calculating the percentage of total daily energy intake derived from each of the three primary macronutrients, i.e., carbohydrates, fats, and proteins, and comparing these values against the established AMDR ranges. The Estimated Average Requirement (EAR) was employed in this context as it provides a statistically robust estimate of the nutrient needs of a population.\u003c/p\u003e\u003cp\u003eMicronutrient adequacy was assessed using the Probability of Adequacy (PA) method for ten selected micronutrients, based on EAR values. The standard deviation (SD) of nutrient requirements was estimated using the coefficient of variation (CV) formula, expressed as SD\u0026thinsp;=\u0026thinsp;CV \u0026times; EAR. PA values were calculated using the EAR values provided by the Indian Council of Medical Research\u0026ndash;National Institute of Nutrition (ICMR\u0026ndash;NIN). The Mean Probability of Adequacy (MPA) was subsequently derived as the average of the individual PA values across all assessed micronutrients. An MPA value below 0.5 was considered indicative of a high prevalence of micronutrient inadequacy within the population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Assessment of Dietary Diversity\u003c/h2\u003e\u003cp\u003eThe Dietary Diversity Score (DDS) is a validated tool employed to assess the variety and nutritional adequacy of an individual's diet. It reflects the number of distinct food groups consumed over a defined reference period, thereby serving as a proxy indicator for micronutrient sufficiency and overall dietary quality. According to the ICMR-NIN 2024 dietary guidelines for Indians, the diet is categorized into ten food groups: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) cereals and millets, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) pulses, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) vegetables, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) nuts and oilseeds, fats and oils (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) green leafy vegetables, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) fruits, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) dairy products, (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) roots and tubers, (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) flesh foods, and (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) spices and condiments. For scoring, the consumption of any food item from a given group was assigned a value of 1, while non-consumption was assigned a value of 0. The total DDS was obtained by summing the scores across all food groups, yielding a possible range of 0 to 10. A DDS of less than 5 was indicative of low dietary diversity, whereas a score of 5 or higher signified high dietary diversity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Assessment of DII Score\u003c/h2\u003e\u003cp\u003eDietary data obtained from the FFQ were utilized to compute the Dietary Inflammatory Index (DII) scores. According to the approach (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), the algorithm assigns an inflammatory effect score to a total of 45 dietary parameters\u0026mdash;comprising both macro- and micronutrients\u0026mdash;based on their reported influence on inflammation: -1 for anti-inflammatory, +\u0026thinsp;1 for pro-inflammatory, and 0 for no effect. In the present study, FFQ data were available for 21 of these parameters, which were consequently used in DII computation. These parameters included: energy, protein, carbohydrate, total fat, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), dietary fibre, folic acid, β-carotene, iron, zinc, thiamine, riboflavin, vitamin B12, vitamin C, vitamin A, vitamin D3, vitamin B6, total cholesterol, magnesium, and selenium.\u003c/p\u003e\u003cp\u003eIndividual exposure levels were standardized against global means by calculating a Z-score, which involved subtracting the global mean intake from the individual's reported intake and dividing the result by the global standard deviation. To mitigate the impact of right-skewed distributions often seen in dietary data, the Z-scores were converted to centred percentile scores. These scores were subsequently multiplied by the literature-derived inflammatory effect score specific to each food parameter, resulting in a parameter-specific DII score. The final DII score for each participant was obtained by summing all parameter-specific DII scores.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous data were expressed as median and interquartile range (IQR) and compared between cases and controls using the Mann\u0026ndash;Whitney U test. Categorical data were presented as frequency and percentage and analysed using Pearson\u0026rsquo;s Chi-square test to assess differences between the two groups. To evaluate the independent associations of DDS, DII, and MPA with SLE, separate unconditional logistic regression models were performed for each dietary variable, adjusting for age, sex, and BMI. Unconditional logistic regression was selected due to incomplete one-to-one matching based on age and sex. To further reduce the potential for residual confounding, all models included age, sex, and BMI as covariates. All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant to account for multiple comparisons.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Participant\u0026rsquo;s Characteristics\u003c/h2\u003e\u003cp\u003eA total of 400 participants were enrolled in this case-control study, comprising 150 patients diagnosed with systemic lupus erythematosus (SLE) and 250 age- and sex-matched healthy controls. The mean age of the study population was 32.42 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;9.06), with SLE cases being significantly younger than the control group (29.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05 vs. 33.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average BMI was significantly higher among SLE cases compared to controls (26.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02 vs. 24.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49 kg/m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\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\u003eDemographic, Body Mass Index and Nutrient Consumption Distribution Across Participants\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=\"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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSLE cases\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge in years, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.42 (9.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.93 (8.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.89 (9.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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 (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.56 (4.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.92 (3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.63 (5.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMacronutrient Intake\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy in kcal, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1329.04 (880.41, 1868.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1680.69 (1322.51, 2197.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e865.15 (659.54, 1076.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein in grams, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.37 (21.66, 53.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.69 (27.83, 64.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.38 (17.82, 30.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbohydrate in grams, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156.85 (103.93, 278.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e223.05 (112.3, 349.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127.54 (96.35, 160.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFats in grams, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.65 (17.76, 45.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.13 (18.85, 54.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.66 (15.52, 34.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibre in grams, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.33 (9.69, 21.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.09 (8.66, 20.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.62 (11.13,21.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMicronutrient Intake\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.42 (4.89,12.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.07 (6.32,15.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.28 (3.79, 6.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZinc in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.61 (2.87, 7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.67 (3.54, 8.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.29 (2.49, 4.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThiamine in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65 (0.43,1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88 (0.51, 1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48 (0.35, 0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiboflavin in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.35 (0.19, 0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55 (0.35, 0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17 (0.12, 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNiacin in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.99 (3.70, 10.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.33 (5.61, 13.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.01 (2.99, 5.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B12 in mcg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0, 0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16 (0, 0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0, 0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B6 in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56 (0.33, 0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.64 (0.42, 0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40 (0.22, 0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin C in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.29 (40.90, 130.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.66 (60.87, 143.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.08 (26.31, 80.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin A in mcg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e268.52 (124.73, 605.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e424.95 (196.18, 830.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e158.44 (100.28, 255.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFolate in mcg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e161.12 (87.33, 260.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e211.77 (148.99, 338.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.36 (63.76, 123.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.86 (29.24, 177.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.37 (29.36, 204.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.85 (28.08, 91.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelenium in mcg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.66 (6.15, 24.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.66 (5.60, 24.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.70 (6.72, 23.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin E in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10 (0.04, 0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05 (0.02, 0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.36, 1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagnesium in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120.41 (48.69, 206.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161.67 (102.14, 236.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.34 (23.81, 97.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMUFA in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e654.30 (323.62, 1335.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e568.15 (279.38, 1167.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e893.72 (460.80, 1760.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePUFA in mg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e920.08 (540.16, 1473.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e969.94 (571.48,1463.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e837.56 (520.35, 1526.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIQR, interquartile range; BMI, body mass index; mg, milligram; mcg, microgram; MUFA, mono-unsaturated fatty acids; PUFA, polyunsaturated fatty acids.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Dietary Intake Profile and Nutrient Adequacy\u003c/h2\u003e\u003cp\u003eAcross all major macronutrients, SLE cases reported significantly reduced dietary intakes in comparison to healthy controls. Median energy intake among SLE cases was 865.15 kcal (IQR: 659.54, 1076.52), considerably lower than that of the control group (1680.69 kcal; IQR: 1322.51, 2197.34; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Protein consumption was also markedly deficient in SLE participants (24.38 g; IQR: 17.82, 30.90), less than half the intake observed in controls (46.69 g; IQR: 27.83, 64.21; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar patterns were observed for carbohydrate (127.54 g vs. 223.05 g) and fat intake (22.66 g vs. 34.13 g), both statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Fiber intake was not significantly different between groups.\u003c/p\u003e\u003cp\u003eAssessment against the \u003cb\u003eAcceptable Macronutrient Distribution Range (AMDR)\u003c/b\u003e revealed pronounced inadequacies in the SLE group. Only \u003cb\u003e0.7%\u003c/b\u003e of SLE participants met the protein adequacy criteria versus \u003cb\u003e27.2%\u003c/b\u003e of controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Adequate fat and carbohydrate intakes were observed in just \u003cb\u003e6.0%\u003c/b\u003e and \u003cb\u003e5.3%\u003c/b\u003e of SLE cases, compared to \u003cb\u003e24.8%\u003c/b\u003e and \u003cb\u003e48.0%\u003c/b\u003e among controls, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eMicronutrient adequacy, assessed via the Probability of Adequacy method for essential micronutrients, showed pronounced insufficiency across multiple vitamins, especially B-complex vitamins and minerals in SLE cases. Thiamine intake among SLE cases was 0.48 mg (IQR: 0.35\u0026ndash;0.66), significantly lower than the 0.88 mg (IQR: 0.51\u0026ndash;1.24) observed in controls (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Riboflavin intake was also reduced in the SLE cases (0.17 mg; IQR: 0.12\u0026ndash;0.24) relative to controls (0.55 mg; IQR: 0.35\u0026ndash;0.81; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was niacin (4.01 mg; IQR: 2.99\u0026ndash;5.20 vs. 9.33 mg; IQR: 5.61\u0026ndash;13.68; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). and Vitamin B6 (0.40 mg; IQR: 0.22\u0026ndash;0.67 vs. 0.64 mg; IQR: 0.42\u0026ndash;0.92; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, vitamin B12 intake was virtually absent among SLE cases (median: 0 mcg), while controls reported a median intake of 0.16 mcg (IQR: 0\u0026ndash;0.49; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other vitamins followed a similar trend. Vitamin C intake in the SLE cases was significantly lower (49.08 mg; IQR: 26.31\u0026ndash;80.14) than in controls (98.66 mg; IQR: 60.87\u0026ndash;143.92; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was vitamin A (158.44 mcg; IQR: 100.28\u0026ndash;255.25 vs. 424.95 mcg; IQR: 196.18\u0026ndash;830.55; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Folate intake was also substantially reduced among SLE cases (87.36 mcg; IQR: 63.76\u0026ndash;123.33) compared to the control group (211.77 mcg; IQR: 148.99\u0026ndash;338.54; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn terms of mineral intake, both iron and zinc were significantly reduced in the SLE group. Median iron intake in the SLE cases was 5.28 mg (IQR: 3.79\u0026ndash;6.81), significantly lower than in the control group (10.07 mg; IQR: 6.32\u0026ndash;15.09; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, zinc intake was reduced in SLE patients (3.29 mg; IQR: 2.49\u0026ndash;4.87) compared to controls (5.67 mg; IQR: 3.54\u0026ndash;8.38; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eAcceptable Macronutrient Distribution Range and probability of Adequacy of micronutrients\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSLE cases\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcceptable Macronutrient Distribution Range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein AMDR (Adequate), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFat AMDR (Adequate), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71 (17.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (24.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbohydrate AMDR (Adequate), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e128 (32.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (48.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (5.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy status (Adequate), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e133 (33.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (51.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (3.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFiber consumption (in % RDA), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56.36 (37.61, 83.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.38 (34.66, 82.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.83 (40.71 84.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProbability of Adequacy of Micronutrients\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (5.60, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53 (0.00, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.99 ( 3.56, 1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZinc, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35 (1.50, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.61 (5.52, 0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06 (5.3, 1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThiamine, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.07 (1.01, 1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.08 (1.62, 2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.22 (1.89, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiboflavin, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.01 (5.71, 2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.75 (2.56, 2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.79 (1.20, 6.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNiacin, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.09 (0.00, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99( 0.14, 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (2.34, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B12, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.62 (7.62, 6.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.62 (7.62, 7.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.06 (7.62,1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B6, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.24 (1.35, 3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.14 (4.98, 5.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06 (3.63,1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin C, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99 (0.00, 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.87, 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14 (6.68, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin A, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05 (0.00, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67 (0.00, 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (0.00, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFolate, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (7.75, 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.57 (1.04, 3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAMDR, Acceptable Macronutrient Distribution Range; IQR, interquartile range; MPA, mean probability of adequacy; n, frequency; %, percentage; RDA, recommended daily allowance;\u003c/p\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eDiet quality and inflammatory potential\u003c/span\u003e\u003c/p\u003e\u003cp\u003eDietary quality was poor among SLE cases compared to healthy controls. The overall MPA was significantly lower in SLE patients (5.05%; IQR: 0.02\u0026ndash;10.29) compared to controls (44.28%; IQR: 27.68\u0026ndash;55.55; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While 37.6% of controls achieved an adequate MPA (\u0026ge;\u0026thinsp;50%), only 0.7% of SLE cases met this threshold. Similarly, the DDS was significantly lower in the SLE group (median: 5.0; IQR: 5.0\u0026ndash;6.0) than in controls (median: 6.0; IQR: 4.0\u0026ndash;7.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the majority of both groups met the minimum DDS threshold (\u0026ge;\u0026thinsp;5), the narrower interquartile range and lower median suggest reduced variety and potential qualitative limitations in food group intake. Conversely, the DII was significantly higher in SLE patients (median: 1.18; IQR: 0.13\u0026ndash;1.85) versus controls (median: 0.48; IQR: \u0026minus;\u0026thinsp;0.62\u0026ndash;1.74; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that SLE patients tend to consume more pro-inflammatory dietary components, indicating a substantially more pro-inflammatory dietary pattern in the cases group.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAssociations Between Dietary Indicators and SLE\u003c/span\u003e\u003c/p\u003e\u003cp\u003eUnconditional logistic regression analysis revealed a significant association between dietary inflammatory potential and SLE status. After adjusting for age, sex, and BMI, each one-unit increase in the Dietary Inflammatory Index (DII) was associated with a 27% higher likelihood of having SLE (AOR: 1.27; 95% CI: 1.08\u0026ndash;1.48; p\u0026thinsp;=\u0026thinsp;0.003). Stratified analysis by DII tertiles showed that individuals in the second and third tertiles had 2.82-fold and 1.91-fold increased odds of SLE, respectively, compared to those in the lowest tertile (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a positive, though not strictly monotonic, trend. Conversely, higher micronutrient adequacy, as measured by the MPA, was associated with a reduced likelihood of SLE. Each 1% increase in MPA was linked to a 14% decrease in SLE odds (AOR: 0.86; 95% CI: 0.84\u0026ndash;0.89; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants with adequate MPA (\u0026ge;\u0026thinsp;0.5) had markedly lower odds of SLE (AOR: 0.01; 95% CI: 0.002\u0026ndash;0.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although this estimate may be influenced by sparse data, as only one SLE case had MPA value above the adequacy threshold. DDS, on the other hand, was not independently associated with SLE status in the multivariable model (AOR: 1.12; 95% CI: 0.96\u0026ndash;1.32; p\u0026thinsp;=\u0026thinsp;0.159), although a tendency toward lower DDS among SLE cases remained evident.\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\u003eDistribution of mean probability of adequacy of micronutrients, dietary score and dietary inflammation across participants\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSLE cases\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPA in %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.30 (9.70, 49.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.28 (27.68, 55.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.05 (0.02, 10.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPA status (Adequate), n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDDS, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.0 (5.0, 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0 (5.0, 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDDS status (High), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e308 (77.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e197 (78.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e111 (74.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDII, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82 (-0.32, 1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.48 (-0.62, 1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18 (0.13, 1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDDS, dietary diversity score; DDS score\u0026thinsp;\u0026ge;\u0026thinsp;5 is considered as \u003cem\u003eHigh\u003c/em\u003e; IQR, interquartile range; n, frequency; %, percentage; MPA, mean probability of adequacy.\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\u003eAssociation of dietary inflammation, mean probability of nutrient adequacy and diet diversity score with SLE\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnadjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDII (per unit change)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30 (1.13, 1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.27 (1.08, 1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDII Tertiles*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.42 (1.44, 4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.82 (1.62, 4.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.14 (1.27, 3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.91 (1.09, 3.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDDS (per unit change)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11 (0.95, 1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12 (0.96, 1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDDS status (\u003cem\u003eLow\u003c/em\u003e vs. \u003cem\u003eHigh\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.31 (0.81, 2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32 (0.80, 2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMPA in % (per 1% change)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87 (0.84, 0.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86 (0.84, 0.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMPA status (\u0026ge;\u0026thinsp;0.5 vs. \u0026lt;0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01 (0.001, 0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01 (0.002, 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAOR, adjusted odds ratio; CI, confidence interval; DII, dietary inflammatory index; DDS, dietary diversity score; IQR, interquartile range; n, frequency; %; MPA, mean probability of adequacy;\u003c/p\u003e\u003cp\u003eDDS score\u0026thinsp;\u0026lt;\u0026thinsp;5 is considered as \u003cem\u003eLow\u003c/em\u003e; MPA\u0026thinsp;\u0026ge;\u0026thinsp;0.5 considered as nutrient adequacy;\u003c/p\u003e\u003cp\u003eRegression models were adjusted for age, sex and body mass index; *p-trend across the tertiles of DII\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eAutoimmune diseases have increased in their prevalence in Western countries in the past few decades. SLE is a chronic autoimmune disease marked by immune dysregulation, inflammation, and oxidative stress. Alongside genetic and environmental factors, diet plays a key role in modulating disease risk and activity (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Diet affects immune tolerance, cytokine expression, epigenetic modifications, and gut microbiota composition, all of which are relevant to autoimmune disease activity and flare severity (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Three interrelated dietary components, nutrient adequacy, dietary diversity, and the DII, offer a comprehensive view of diet quality and its inflammatory potential.\u003c/p\u003e\u003cp\u003eIn our study, we found that SLE cases occurred predominantly in younger individuals, with a mean age of 29.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05 years. This finding is consistent with existing literature, which identifies SLE as a disease that most frequently affects individuals in their 20s and 30s, particularly women of reproductive age (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This early onset has been associated with higher disease activity scores (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, our study found that the average BMI was significantly higher among SLE patients compared to healthy controls, highlighting a trend toward increased weight in individuals with SLE. This observation is consistent with several studies that have demonstrated a higher prevalence of obesity within the SLE population and its association with adverse health outcomes. Evidence from a cohort of 105 patients showed that 39% were either overweight or obese, and this was associated with greater disease-related complications and increased cumulative organ damage (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The same study also noted associations between elevated BMI and disrupted sleep patterns, leading to increased cortisol levels and fat accumulation. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study also revealed that individuals with SLE experience significant nutritional inadequacies, particularly with respect to macronutrient and micronutrient intake. SLE participants had significantly lower energy, protein, and carbohydrate intake compared to the control group. A similar pattern of inadequacy of caloric consumption was significantly lower in SLE cases, despite a greater proportion of individuals in this group being overweight compared to controls. This apparent paradox aligns with existing research, which suggests that factors beyond caloric intake, such as reduced physical activity due to disease-related fatigue, increased cortisol levels, and glucocorticoid therapy that may alter glucose and lipid metabolism and alterations may contribute to weight gain in this population (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDeficiencies were more prominent for \u003cb\u003eB-complex vitamins (B6, B12, folate), vitamin A, and vitamin C\u003c/b\u003e, all of which are crucial for immune regulation, antioxidant defense, and DNA methylation. In the SLE group, the folate intake was almost 60% less than that in controls, and vitamin B₁₂ intake was negligible. These two vitamins, in particular, serve as methyl donors in one-carbon metabolism, influencing DNA synthesis, repair, and methylation. Deficiencies in these vitamins can result in hyper-homocysteinemia and epigenetic dysregulation, both of which are implicated in increased oxidative stress and immune dysfunction, which are hallmarks of SLE pathogenesis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Our findings are in line with the study conducted on a Brazilian cohort of lupus sufferers, which showed an inadequate intake of iron and vitamin B12 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Vitamin A, often regarded as a key modulator of immune homeostasis, was also significantly less in SLE cases. Reduced vitamin A levels have been linked to a shift toward pro-inflammatory Th17 dominance and diminished regulatory T cell function. This imbalance contributes to disease progression in SLE (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMineral intake was inadequate, with significantly lower iron and zinc levels observed in the SLE group. These results are consistent with earlier studies indicating that SLE patients often exhibit low intake and serum levels of these minerals (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Iron is essential for oxygen transport and immune cell proliferation. However, in the context of chronic inflammation, such as in SLE, iron metabolism is frequently dysregulated (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Zinc deficiency has been shown to impair T-cell maturation and increase pro-inflammatory cytokine activity via NF-κB signalling, all of which are relevant to SLE pathophysiology.\u003c/p\u003e\u003cp\u003eIt was found that MPA was substantially lower among participants with SLE compared to healthy controls. Importantly, each 1% increase in MPA was associated with a 14% reduction in the odds of having SLE, highlighting the potential protective role of adequate micronutrient intake against disease development, which may contribute to immune dysregulation, oxidative stress, and inflammation, all central to SLE pathogenesis and disease progression (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study provides further support for the growing body of evidence linking dietary patterns with SLE susceptibility and disease activity. It was observed that participants with SLE exhibited significantly higher DII scores compared to healthy controls, indicating a predominance of pro-inflammatory dietary habits within this group. This observation is consistent with earlier research suggesting that higher DII scores are associated with increased systemic inflammation and immune dysregulation, which are hallmarks of SLE pathogenesis (79\u0026ndash;81). Elevated DII scores have been linked to increased circulating levels of pro-inflammatory cytokines such as IL-6 and TNF-α, both of which are key mediators in the pathogenesis of SLE (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In addition, diets high in DII scores have been associated with increased risk of SLE-related complications, including cardiovascular disease (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In contrast, diets with lower DII exhibit immunomodulatory effects and are associated with lower disease activity and reduced risk of flare-ups (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Importantly, our results extend this evidence by demonstrating that the association between DII and SLE status remains significant even after adjusting for nutrient adequacy and dietary diversity. Each one-unit increase in DII was associated with a 27% higher likelihood of having SLE, suggesting its independent role in mediating disease risk.\u003c/p\u003e\u003cp\u003eStrengths of this study include its large sample size, rigorous dietary assessment using validated tools, and use of clinically confirmed SLE diagnoses. The simultaneous evaluation of MPA, DDS, and DII in a single analytical framework is a methodological strength. Limitations include the \u003cb\u003ecase-control design\u003c/b\u003e (limiting causal inference), \u003cb\u003eself-reported dietary data\u003c/b\u003e, and a \u003cb\u003epartial DII score\u003c/b\u003e (based on 21 of 45 parameters), which may affect accuracy. Residual confounding from unmeasured factors (e.g., disease duration, medication use, physical activity) is also possible.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIndividuals with SLE showed markedly lower intake of key micronutrients, especially B-complex vitamins, Vitamin A, Vitamin C, and minerals (zinc, iron). Lower Mean Probability of Adequacy and Dietary Diversity Scores, along with higher Dietary Inflammatory Index values, indicate a pro-inflammatory and nutritionally inadequate diet that may contribute to SLE onset and progression. Future studies should employ longitudinal and interventional designs to clarify causal pathways and assess the benefits of micronutrient-replete, anti-inflammatory dietary interventions. Research integrating diet with genetic, microbial, and socioeconomic determinants is warranted to develop culturally tailored, cost-effective nutrition strategies for SLE prevention and management.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTian J, Zhang D, Yao X, Huang Y, Lu Q (2023) Global epidemiology of systemic lupus erythematosus: a comprehensive systematic analysis and modelling study. Ann Rheum Dis 82(3):351\u0026ndash;356\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePons-Estel GJ, Ugarte-Gil MF, Alarc\u0026oacute;n GS (2017) Epidemiology of systemic lupus erythematosus. 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Nutrition 28(11\u0026ndash;12):1098\u0026ndash;1103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nut.2012.01.015\u003c/span\u003e\u003cspan address=\"10.1016/j.nut.2012.01.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Institute of Nutrition","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, Nutrient Adequacy, Dietary Diversity Score, Dietary Inflammatory Index, Autoimmunity, Inflammation","lastPublishedDoi":"10.21203/rs.3.rs-8153117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8153117/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) is a chronic autoimmune disorder primarily affecting women of reproductive age and non-white populations. It arises from genetic and environmental interactions, leading to inflammation, tissue damage, and multi-organ involvement. Evidence suggests that poor nutrient intake, low dietary diversity, and pro-inflammatory diets influence disease activity, yet their combined impact on SLE risk remains unclear. This study aimed to assess the association between nutrient adequacy, dietary diversity, and the inflammatory potential of diet with SLE risk.\u003c/p\u003e\u003ch2\u003eMethodology:\u003c/h2\u003e\u003cp\u003eA case-control study included 400 women (150 SLE cases, 250 age-matched healthy controls) aged 15\u0026ndash;45 years. Dietary intake was assessed using a validated semi-quantitative FFQ. Nutrient adequacy was evaluated via AMDR and the Probability of Adequacy for ten key micronutrients. Dietary Diversity Score (DDS) was calculated across ten food groups, and the Dietary Inflammatory Index (DII) was derived from 21 pro- and anti-inflammatory dietary parameters. Multivariate logistic regression examined associations of DDS, DII, and nutrient adequacy with SLE risk, adjusting for age, gender and BMI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSLE cases consumed significantly less energy (865 vs 1681 kcal), protein (24 vs 47 g), fat (23 vs 34 g), and carbohydrates (128 vs 223 g) than controls (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Fewer than 6% of cases met AMDR targets, and their mean probability of adequacy for micronutrients was ninefold lower, with notable deficiencies in B-complex vitamins (especially B12), zinc, iron, and folate. Their diets were also less diverse (Dietary Diversity Score, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and more pro-inflammatory (Dietary Inflammatory Index). Each one-unit increase in DII raised SLE odds by 27%, while a 1% increase in MPA reduced odds by 14%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eWomen with SLE exhibited dietary patterns that were micronutrient-deficient, less diverse, and highly pro-inflammatory. A higher Dietary Inflammatory Index (DII) score independently predicted the presence of SLE. These findings highlight the critical role of dietary assessment and targeted nutritional interventions as adjuncts in the clinical management of SLE.\u003c/p\u003e","manuscriptTitle":"Assessment of Nutrient Adequacy, Dietary Diversity Score, and Dietary Inflammatory Index in Individuals with Systemic Lupus Erythematosus (SLE)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 08:20:00","doi":"10.21203/rs.3.rs-8153117/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":"0046e26e-b380-4d89-b834-5b9bb365ee31","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-20T08:20:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 08:20:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8153117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8153117","identity":"rs-8153117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}