Association of Dietary Inflammatory Index, Diversity, and Micronutrient Adequacy with Rheumatoid Arthritis: A Matched Case-Control Study

Association of Dietary Inflammatory Index, Diversity, and Micronutrient Adequacy with Rheumatoid Arthritis: A Matched Case-Control Study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Association of Dietary Inflammatory Index, Diversity, and Micronutrient Adequacy with Rheumatoid Arthritis: A Matched Case-Control Study Hemant Mahajan , Janhavi Pawar , Sailaja Pothana , Ranga Srinivas K , Aditi Deshpande , B R Nikhita , Phani Kumar , Venkat Raji Reddy , Srikant Perugu , JJ Babu Geddam , Liza Rajasekhar , Devraj Parasannanavar doi: https://doi.org/10.1101/2025.11.18.25340542 Hemant Mahajan 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Janhavi Pawar 2 Sir Vithaldas Thackery College of Home Science , Mumbai, India . 400049 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sailaja Pothana 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ranga Srinivas K 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aditi Deshpande 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site B R Nikhita 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Phani Kumar 3 Nizam’s Institute of Medical Sciences , Hyderabad, Telangana, India . 500082 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Venkat Raji Reddy 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Srikant Perugu 4 Dr. B.R.K.R. Government Ayurvedic Medical College , S.R. Nagar, Erragadda, Hyderabad, Telangana, India . 500038 Find this author on Google Scholar Find this author on PubMed Search for this author on this site JJ Babu Geddam 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liza Rajasekhar 3 Nizam’s Institute of Medical Sciences , Hyderabad, Telangana, India . 500082 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Devraj Parasannanavar 1 Indian Council of Medical Research – National Institute of Nutrition , Hyderabad, India . 500007 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: jpdevraj26{at}gmail.com Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Background Rheumatoid Arthritis (RA), a chronic autoimmune disease, is influenced by genetic and environmental factors, including diet. This matched case &[ndash]control study examined the association of nutritional status (nutrient adequacy, dietary diversity score [DDS]) and the Dietary Inflammatory Index (DII) with RA onset and autoantibodies in a population from Hyderabad, India. Methodology A total of 301 RA patients and 314 age &[ndash] and sex &[ndash]matched healthy controls were recruited. Dietary intake was assessed using a validated 131 &[ndash]item food frequency questionnaire (FFQ). DII was used to measure dietary inflammation, while DDS and micronutrient adequacy probability assessed dietary quality. Autoantibodies (rheumatoid factor [RF] and anti &[ndash]CCP) were measured using ELISA. Multivariate regression models adjusted for age, sex, and BMI were used to examine associations, and population attributable fractions (PAFs) were estimated. Results RA cases had lower micronutrient adequacy (33% vs. 45%), lower median DDS (5.00 vs. 6.00), and higher median DII (0.84 vs. 0.26) compared to controls. Each one &[ndash]unit increase in DII increased RA likelihood by 42% [AOR: 1.42(1.21, 1.67)], while a one-unit increase in DDS reduced RA odds by 39% [AOR: 0.61(0.53, 0.70)]. A 1% increase in micronutrient adequacy reduced RA odds by 2% [AOR: 0.98(0.97, 0.99)]. Lower DDS and micronutrient inadequacy were linked to higher RF and anti &[ndash]CCP levels. The PAFs for higher DII, poor DDS, and nutrient inadequacy were 38.0%, 35.5%, and 39.9%, respectively. Conclusion Higher dietary diversity and micronutrient adequacy reduced RA likelihood, while higher DII increased RA likelihood, highlighting the role of diet in RA and autoimmunity. INTRODUCTION Rheumatoid arthritis (RA) is a systemic inflammatory autoimmune disorder with a global prevalence of approximately 1%, predominantly affecting women (3:1 ratio) and peaking between 30–50 years of age. RA is characterized by chronic synovial inflammation leading to joint damage, disability, and reduced life expectancy [ 1 ]. The pathogenesis of RA involves genetic and environmental factors, with the HLA-DRB1 gene being strongly associated with disease susceptibility [ 3 ]. Environmental triggers, including smoking, infections, and dietary factors, influence disease onset and progression through inflammatory mechanisms [ 4 ]. Dietary intake plays a critical role in modulating inflammation, a central process in RA pathophysiology [ 5 ]. Diets with anti-inflammatory properties, such as the Mediterranean diet, are associated with symptom mitigation, whereas pro-inflammatory dietary patterns, like the Western diet, exacerbate inflammation and disease progression [ 5 ]. Components of the Western diet, including saturated fats, trans fats, refined carbohydrates, and an imbalanced omega-3: omega-6 ratio, are associated with systemic inflammation and increased RA risk [ 6 , 7 ]. While dietary influences on inflammation in Western populations are well-documented, there is a paucity of research on the role of traditional Indian dietary patterns in RA-related inflammation. Macronutrients and micronutrients influence RA pathophysiology through their effects on inflammatory pathways [ 1 , 8 – 11 ]. High dietary fibre intake is inversely associated with inflammatory markers such as IL-6, TNF-α, and CRP, while high-protein intake is linked to elevated levels of inflammatory markers [ 10 ]. Micronutrients such as vitamins C, E, and B-complex, along with minerals like magnesium, zinc, and selenium, exhibit antioxidant and anti-inflammatory properties [ 1 , 9 ]. Vitamin D and calcium are essential for bone health and modulating immune responses [ 8 ]. Nutritional deficiencies, often observed in RA patients due to reduced energy intake and drug side effects, exacerbate inflammation and impair quality of life [ 11 ]. Comprehensive tools like the Dietary Diversity Score (DDS) assess overall dietary quality and predict micronutrient adequacy [ 12 ], while the Dietary Inflammatory Index (DII) quantifies the inflammatory potential of a diet [ 13 ]. Existing research, however, primarily focuses on isolated dietary components or nutrients. Very few studies and none from India have explored the role of DII on rheumatoid arthritis [ 14 – 16 ]. The combined effects of dietary adequacy, diversity, and inflammatory potential on RA-related inflammation and progression remain underexplored, particularly in the context of Indian diets. Despite the established role of diet in modulating inflammation, limited evidence exists on the dietary patterns, nutrient adequacy, and inflammatory potential of diets in RA patients in the Indian context. This study aims to evaluate nutritional adequacy, dietary diversity, and dietary inflammatory index among RA patients compared to healthy controls. By correlating these dietary factors with inflammatory markers, the study seeks to elucidate the relationship between diet quality, inflammatory potential, and RA pathogenesis. METHODOLOGY Study setting and population This case-control study was approved by the ethical committees of the National Institute of Nutrition (NIN IEC: 03/01/2014) and Nizam’s Institute of Medical Sciences (NIMS IEC: EC/NIMS/1665/2015, PBAC NO.1122/15). A total of 615 participants were included, comprising 301 newly diagnosed rheumatoid arthritis (RA) patients and 314 healthy controls, all aged between 30 and 55 years. RA patients were recruited based on the ACR-2010 criteria, confirmed by a rheumatologist, and classified as having definite RA if they met specific criteria, including the presence of synovitis in at least one joint and a total score of ≥6/10 from the ACR/EULAR domains. The control group was age- (±5 years) and sex-matched to the RA patients, and included individuals from schools, institutions, and private organizations in Hyderabad. Healthy controls were free from chronic conditions such as type 2 diabetes, hypertension, PCOD, severe anemia, thyroid dysfunction, autoimmune diseases, and were not pregnant or lactating. Both RA patients and controls were excluded if they had juvenile rheumatoid arthritis (JRA), seronegative spondylarthritis, inflammatory bowel disease, Celiac disease, or malabsorption illnesses. Additionally, individuals on antibiotics, probiotics, or immunosuppressive treatments were excluded. Participants were recruited between June 1, 2017, and November 30, 2019, and written informed consent was obtained from all subjects prior to their inclusion in the study. Anthropometric measurements and Dietary Assessment The body weight was determined to the nearest 0.1 kg, they were measured using a SECA scale, and standing height was measured using a stadiometer with a precision of 0.5 cm. Body mass index (BMI) was calculated for each individual, a value derived from a subject’s weight and height. BMI = Weight (kg) ÷ height (m 2 ). The dietary intake of Subjects was obtained by using a Semi - quantitative FFQ including 131 Dietary items divided into 13 Food Groups based on food classification according to the Indian Food Composition Tables (IFCT-2017). To minimize errors while assessing subjects’ dietary intake, the interview process started by asking the subjects to recall all the food items consumed before the onset of the disease, listing as every day, weekly, once a month, and yearly. Based on the 12 standardized cups (ranging from 30 ml to 1400 ml) each participant’s intake was converted into weight equivalents (mg or g). The amount of daily food intake was calculated from the FFQ according to the following formula: Quantity of raw food ingredients consumed = (Quantity of raw food ingredient cooked ÷ Total Quantity of cooked food) × Quantity of cooked food consumed. Estimation of Nutrient Adequacy The adequacy of macronutrient is assessed by following Acceptable Macronutrient Distribution Range (AMDR) approach, the percentages of energy obtained from each of the three macronutrients, including carbohydrate, fat, and protein were calculated based on per day intake compared to the AMDR ranges. The EAR was used because it offers a more accurate statistical estimate of the population’s nutritional needs, providing a better assessment of dietary intake in relation to health outcomes and assisting in identifying individuals whose intake may be deficient. Micronutrient adequacy was assessed using the probability of adequacy (PA) approach for 10 micronutrients, based on the Estimated Average Requirements (EAR). The standard deviation (SD) of nutrient requirements was derived using the coefficient of variation (CV) formula, calculated as SD = CV×EAR. The PA for micronutrients was calculated directly using the EAR values provided by ICMR-NIN. The PA for each of the micronutrients was determined using the “NORM.S. DIST” function in Microsoft Excel, representing the likelihood that an individual’s intake meets the nutrient requirement. The Mean Probability of Adequacy (MPA) was calculated as the average PA across all micronutrients, with an MPA below 0.5 indicating a prevalence of micronutrient inadequacy. Assessment of Dietary Diversity Dietary Diversity Score (DDS) is a measure used to evaluate how diverse and nutritious an individual’s diet is. It shows the range of different food groups consumed within a specific time frame. Higher DDS values indicate better nutrient intake and overall health outcomes. Based on recent dietary guidelines 2024, the diet is divided into 10 food groups: cereals and millets, pulses, vegetables, nuts and oil seeds, green leafy vegetables, fruits, dairy, roots and tubers, flesh food and spices, and condiments. A score of 1 was given if a food group was consumed, and 0 if it was not. Further, the overall DDS is calculated by summing all the scores of the food group. If the value is < 5 is a low DDS, and ≥ 5 is a high DDS. Assessment of Dietary Inflammatory Index (DII) Score The food frequency questionnaire (FFQ)-derived dietary data were utilized to compute the DII scores for each participant. A detailed explanation of the development and construct validation of the DII has been documented elsewhere. Following the approach described by Shivappa et al. [ 13 ], a counting algorithm was applied, based on an extensive literature review assessing the impact of diet on inflammatory biomarkers, including IL-4, TNF-α, IL-1β, IL-6, IL-10, and CRP. In this methodology, a total of 45 food parameters, encompassing both micronutrients and macronutrients, were assigned a score of (−1) for anti-inflammatory effects, (+1) for pro-inflammatory effects, or (0) if they had no impact on these inflammatory biomarkers. In this study, dietary data from 22 food parameters collected through the FFQ were available for DII calculation. These parameters included: energy, protein, carbohydrates, fats, MUFA, PUFA, trans fats, fiber, folic acid, β-carotene, iron, zinc, thiamine, riboflavin, vitamin B12, vitamin C, vitamin A, vitamin D3, vitamin B6, total cholesterol, magnesium, selenium. Each food constituent was assigned a DII score based on a global standard database. This score was calculated by subtracting the global mean from the reported intake and dividing by the standard deviation, resulting in a Z-score. To account for the right-skewed distribution commonly observed in dietary data, the Z-score was converted into a centered percentile score. Each food parameter’s centered percentile score was then multiplied by its respective food parameter-specific inflammatory effect score, as determined from literature reviews. The final DII score for each participant was obtained by summing all food parameter-specific DII scores, providing an overall measure of dietary inflammatory potential. Antibody Measurement Non fasting venous blood samples were collected from all recruited subjects, Serum and Plasma samples were separated and stored in aliquots at −80C until tested. Serum Rheumatoid Factor (RF) and Plasma anti-cyclic citrullinated peptide (Anti-CCP) levels were performed using ELISA technique as per manufacturer instructions (RF was detected by IgM RF ELISA, APSLABS India), values were interpreted in IU/ml. Anti-CCP antibodies were detected by ELISA using commercially available kits (DRG, Germany) and values were interpreted in U/mL. 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 analyzed using Pearson’s Chi-square test to assess differences between the two groups. To evaluate the independent associations of DDS, DII, and MPA with RA, we conducted separate unconditional logistic regression models for each dietary variable, adjusting for age, sex, and BMI. Unconditional logistic regression was chosen because complete one-to-one matching based on age and sex was not fully achieved. To further minimize the risk of residual confounding, we included age, sex, and BMI as covariates in all models, ensuring appropriate adjustment for these confounders. Multiple linear regression models adjusting for age, sex and BMI were used to assess the independent associations of DDS, DII, and MPA with Anti-CCP and RH antibodies. Additionally, we created the tertiles of DII and assessed its relationship with RA, Anti-CCP and RH antibodies using appropriate regression models. The population attributable fraction (PAF) was calculated for DII, DDS (DDS<5.0 vs. DDS ≥5.0), and MPA (MPA 1.0 and OR <1.0, we used Levin’s formula: [PAF= P exposed *(OR - 1) ÷ (1 + P exposed *(1-OR))] and [PAF= P exposed *(1 - OR) ÷ (1-P exposed *(1-OR))], respectively. We used Wald Method to calculate 95% CI of PAF. All p-values were two-tailed and a p-value less than 0.01 was considered significant to account for multiple testing. RESULTS Participant’s Characteristics The median age of the participants was 38 years (IQR: 30.0-45.0), with cases being significantly older than controls (40.0 vs. 34.0 years, p < 0.001). Females constituted the majority of the study population (88.6%), with a slightly higher proportion in controls (89.5%) than in cases (87.7%). The median BMI was lower among cases compared to controls (23.3 vs. 25.11 kg/m², p < 0.001). In terms of dietary habits, 83.6% of the participants were non-vegetarians, with a higher proportion among cases (85.7%) than controls (81.5%). The study also found that cases consumed lower daily levels of macronutrients, including protein (32.48 vs. 47.37 g), carbohydrates (155.50 vs. 226.03 g), fats (22.86 vs. 34.69 g), and fiber (8.97 vs. 14.17 g). Similarly, the median intake of micronutrients including thiamine, riboflavin, niacin, vitamin B6, folate, vitamin C, vitamin A, vitamin E, iron, zinc, selenium, magnesium, MUFA, and PUFA was lower among cases. Median energy, cholesterol, and trans-fat intake were also higher in cases compared to controls ( Table 1 ). View this table: View inline View popup Table 1. Demographic, body mass index and nutrient consumption distribution across participants Nutrient adequacy According to AMDR guidelines none of the groups consistently met the Macronutrient adequacy. In the RA group 31.6% had carbohydrate intakes within the AMDR, significantly lower than the 47.4% in the healthy controls (p <0.001). The protein adequacy was very low in both the groups, with 20.6% RA group meeting the AMDR compared to 27.1 % in the healthy controls (p < 0.001). Similarly, 16% of the RA group had fat intake within the AMDR compared to 24.2% of the healthy controls. Fibre intake was higher among cases compared to controls. Overall, the median probability of adequacy for iron, zinc, thiamine, riboflavin, vitamin B6 and vitamin B12 was nearly equal to zero in both groups. Additionally, for Iron, Zinc, Thiamine, Riboflavin, Vitamin B6, Niacin, the probability of adequacy was significantly lower among cases compared to controls. Whereas, for vitamin A and C, the probability of adequacy was significantly higher among cases compared to controls. Overall, the median of ‘mean probability of adequacy’ for vitamins and nutrients was significantly higher among controls compared to cases (45% vs 33%). Approximately, 37% controls were nutritionally adequate compared to 23% in cases ( Table 2 ). View this table: View inline View popup Download powerpoint Table 2. Acceptable Macronutrient Distribution Range and probability of Adequacy of micronutrients Dietary diversity and Dietary inflammation The median DDS was significantly lower for the RA group compared to healthy control (5.0 vs 6.0) (p-value <0.001). Approximately, 54.7% of cases had good DDS status compared to 77.4% in the healthy controls (p <0.001). DII scores of the participants ranged from – 4.33 (maximum anti-inflammatory score) to + 2.24 (most pro-inflammatory score). The median DII score was higher in the RA group in comparison to the control group (0.84 vs 0.26, p-value <0.001). Levels of anti-CCP and RH antibodies were 10 to 20-folds higher among cases compared to controls. ( Table 3 .0) View this table: View inline View popup Download powerpoint Table 3. Distribution of mean probability of adequacy of micronutrients, dietary score and dietary inflammation across participants After adjusting for age, sex and BMI, each one-unit increase in the DII was linked to a 42% increase in the odds of RA [AOR (95% CI): 1.42 (1.21, 1.67)]. The odds of RA were linearly increasing with increase in the tertiles of DII (p-trend <0.001). A one-unit increase in the DDS was associated with a 39% lower likelihood of RA [AOR (95% CI): 0.61 (0.53, 0.70)]. Low DDS status compared high DDS score was associated 2.63 times higher odds of RA [AOR (95% CI): 2.63 (1.80, 3.82)]. A 1% increase in the mean probability of adequacy was associated with a 2.0 % reduction in the odds of RA after controlling for age and sex [AOR (95% CI): 0.98 (0.97, 0.99)]. Participants with adequate MPA, compared to those with inadequate MPA, demonstrated 48% reduced odds of RA, independent of age and gender effects [AOR (95% CI): 0.52 (0.35, 0.76)]. The PAF (95% CI) for the DII (DII tertiles 2 and 3 vs. tertile 1), DDS (low vs. high), and MPA (MPA <0.5 vs. MPA ≥0.5) were 38.0% (19.0% - 57.0%), 35.5% (24.9% - 46.0%), and 39.9% (23.1% - 56.6%). This suggests that 38.0%, 35.5%, and 39.9% of cases could be attributed to higher DII, lower DDS and nutrient inadequacy (MPA <0.5) assuming causality. After adjusting for age, sex, and BMI, each one-unit increase in the DII was associated with 25.83 EU/ mL higher anti-CCP antibodies [β-coeff. (95% CI): 25.83 (1.03, 50.62)] and 36.23 U/mL higher RH antibodies [β-coeff. (95% CI): 36.23 (5.65, 66.81)]. Whereas, one unit in DDS score was associated with 38.05 EU/ mL lower anti-CCP antibodies [β-coeff. (95% CI): −38.05 (−55.78, −20.32)] and 40.82 U/mL lower RH antibodies [β-coeff. (95% CI): −40.82 (−62.79, −18.85)]. Low DDS status compared high DDS score was associated with 130.64 EU/ mL higher anti-CCP antibodies [β-coeff. (95% CI): 130.64 (69.50, 191.78)] and 72.72 U/mL higher RH antibodies [β-coeff. (95% CI): 72.72 (−3.64, 149.07)]. A 1% increase in the mean probability of adequacy was associated with 1.80 EU/ mL lower anti-CCP antibodies [β-coeff. (95% CI): −1.80 (−3.77, −0.14)] and 1.43 U/mL lower RH antibodies [β-coeff. (95% CI): −1.43 (−3.82, 0.95)]. Participants with adequate MPA, compared to those with inadequate MPA, demonstrated lesser 15.64 EU/ mL lower anti-CCP antibodies [β-coeff. (95% CI): −15.64 (−78.90, 47.62)] and 17.07 U/mL lower RH antibodies [β-coeff. (95% CI): −17.07 (−95.18, 61.03)]. View this table: View inline View popup Download powerpoint Table 4. Association of dietary inflammation, dietary score with rheumatoid arthritis and associated antibodies DISCUSSION This community-based matched case-control study examined the relationship between the dietary inflammation, diet diversity, micronutrient adequacy, and RA risk. The results indicate that RA cases had significantly higher DII scores, reflecting more pro-inflammatory diets, and lower DDS, indicating poor dietary diversity, compared to controls. Poor dietary diversity and micronutrient inadequacy were associated with increased RA risk and elevated antibodies, such as anti-CCP and RF antibodies. Specifically, each one-unit increase in DII was associated with a 42% increase in RA odds, while a one-unit increase in DDS was linked to a 39% reduction in RA odds. Furthermore, inadequate micronutrient adequacy (MPA) and poor DDS were strongly associated with higher levels of anti-CCP and RF antibodies, emphasizing the inflammatory and autoimmune impact of poor dietary quality. The PAF analyses suggest that 38.0%, 35.5%, and 40% of RA cases could be attributed to higher DII, lower DDS (DDS <5.0), and inadequate nutrients intake (probability of nutrient adequacy <50%), respectively, assuming causality in this study. These findings highlight the significant role of diet in the development and progression of RA. Our findings align with previous studies that underscore the role of diet in modulating inflammation and RA risk. Gao et al. [ 14 ] reported higher odds of RA in individuals consuming pro-inflammatory diets, with the highest DII tertile associated with elevated disease risk. Similarly, Nayebi [ 15 ] and Tandorost [ 17 ] observed that higher DII scores correlated with increased serum inflammatory markers (e.g., hs-CRP, TNF-α) and worse clinical outcomes (e.g., DAS-28 score). Evidence from Japan [ 18 ] and the UK Biobank [ 19 ] further supports the association between higher DII scores and increased RA activity and inflammation. In Japan, the TOMORROW study reported that RA patients had significantly higher energy-adjusted DII (E-DII) scores compared to controls, suggesting a more pro-inflammatory dietary pattern. While the E-DII score itself was not directly linked to significant changes in disease activity, a shift towards a more anti-inflammatory diet was associated with the maintenance of low disease activity over a six-year follow-up period. These findings highlight the potential role of dietary inflammation in modulating RA progression [ 18 ]. Similarly, data from the UK Biobank revealed a significant association between diet, inflammation (measured by C-reactive protein levels), and RA, suggesting that higher DII scores are linked to increased RA activity and inflammation [ 19 ]. Conversely, anti-inflammatory diets characterized by high fruit, vegetable, and omega-3 fatty acid intake have been shown to reduce inflammatory markers, disease activity, and symptom severity in RA patients [ 20 , 21 ]. Moreover, studies on dietary patterns indicate that adherence to anti-inflammatory diets, such as Mediterranean or vegan diets, can reduce systemic inflammation and RA disease activity [ 22 , 23 ]. For example, randomized controlled trials have demonstrated that omega-3 supplementation reduces RA-related inflammation, while diets high in sugar, red meat, and saturated fats exacerbate disease progression [ 24 ]. Our study expands on these findings by showing the specific impact of dietary inadequacy and low DDS on RA risk and antibody levels. Mechanisms Underlying DII and Rheumatoid Arthritis The relationship between DII and RA can be attributed to multiple biological mechanisms. Pro-inflammatory diets contribute to systemic inflammation by increasing the production of pro-inflammatory cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) [ 17 , 25 ]. These cytokines play a critical role in RA pathogenesis by driving synovial inflammation, joint destruction, and autoimmunity [ 2 ]. High DII scores, reflecting poor dietary quality, can also exacerbate oxidative stress and impair the body’s antioxidant defence systems, further promoting inflammatory cascades. Additionally, dietary patterns influence the gut microbiota, which plays a pivotal role in modulating immune responses [ 26 – 29 ]. Pro-inflammatory diets can disrupt gut microbial diversity, leading to dysbiosis and heightened immune activation [ 27 ]. This dysregulation can enhance the production of autoantibodies, such as anti-CCP and RF, which are hallmark features of RA. Studies have shown that RA patients exhibit decreased gut microbial diversity, correlating with elevated autoantibody levels [ 30 ]. Additionally, certain gut microbial species have been implicated in promoting autoantibody production and bone erosion in RA [ 31 ]. In contrast, anti-inflammatory diets rich in fiber, omega-3 fatty acids, and polyphenols support gut microbiota diversity, reduce inflammation, and suppress autoimmunity [ 21 , 26 ]. Micronutrient inadequacy, as observed in this study, further contributes to RA pathogenesis. Deficiencies in vitamins such as B6, C, and E and minerals like zinc and selenium impair immune regulation and antioxidant defences, leading to heightened inflammation and oxidative stress [ 32 , 33]. Adequate intake of these micronutrients is critical for maintaining immune homeostasis and mitigating RA-associated inflammation. Public Health Significance The findings of this study have important public health implications. RA is a chronic autoimmune disease with significant physical, emotional, and economic burdens. While pharmacological treatments are effective, they are often associated with adverse effects and high costs. Dietary interventions, on the other hand, offer a cost-effective, sustainable, and side-effect-free approach to reducing RA risk and managing disease activity. Promoting diets with low DII scores—rich in fruits, vegetables, whole grains, and omega-3 fatty acids—can serve as a preventive strategy against RA and other inflammatory diseases. Public health campaigns should emphasize the importance of dietary diversity and adequate micronutrient intake in reducing inflammation and supporting immune health. Additionally, integrating dietary counselling into RA management programs could improve patient outcomes by complementing pharmacological treatments with lifestyle modifications. This study highlights the critical role of diet in RA onset and progression. A pro-inflammatory diet and poor dietary quality are significant risk factors for RA, while anti-inflammatory diets and adequate nutrient intake may help mitigate disease risk and severity. Future research should focus on longitudinal studies and clinical trials to further validate these findings and develop targeted dietary interventions for RA prevention and management. Strengths and Limitations Our study has several strengths. The large sample size enabled a thorough evaluation of the effects of dietary exposure on RA, accounting for multiple potential confounders. We employed the DII, a validated algorithm that assesses the inflammatory potential of the overall diet, rather than focusing on individual components. The DII has been validated against various inflammatory markers across different populations, strengthening the reliability of our findings. Additionally, our assessment of micro- and macronutrients was conducted using a validated FFQ, tailored with locally collected recipes to accurately capture typical food consumption patterns in the study setting. The use of three complementary measures—DII, DDS, and nutrient adequacy—provides a comprehensive approach to evaluating both the quality and inflammatory potential of the diet, offering a robust and multifaceted understanding of their association with RA risk. Furthermore, we included diagnosed cases of RA along with anti-CCP and RF values, which further enhance the precision and relevance of our findings in understanding the relationship between diet and RA. Despite its strengths, our study has some limitations. First, the possibility of response bias in dietary data collection, which could lead to some misclassification of DII. While this cannot be entirely ruled out, the FFQ used was specifically validated to reflect the local dietary pattern and was administered by a trained interviewer to enhance accuracy. If misclassification did occur, it is likely non-differential, which would generally bias the association between DII and RA toward the null, potentially underestimating the true effect. Second, limitation of this study is its case-control design, which restricts our ability to establish a temporal relationship between dietary inflammation and RA. However, to minimize this limitation, we included only newly diagnosed RA cases, reducing the likelihood of significant dietary changes due to the disease. Additionally, dietary habits generally remain stable over short periods, further supporting the reliability of the dietary data. Moreover, the case-control design allowed for the efficient recruitment of a substantial number of cases and appropriately matched controls within a short timeframe, enhancing the study’s feasibility and statistical power. Nonetheless, longitudinal studies are needed to confirm the causal role of dietary inflammation in the onset and progression of RA. Third, although we controlled for age and BMI, we were unable to collect data on several sociodemographic and clinical characteristics. As a result, the possibility of residual confounding cannot be excluded. Fourth, this study was conducted among cases and controls residing in an urban region of southern India. Consequently, the findings may not be generalizable to other populations with differing dietary habits or sociodemographic profiles. Fifth, while the DII calculation ideally includes 45 parameters, we only had data for 22 food parameters. Of the 23 excluded parameters, 20 are categorized as anti-inflammatory, which may have skewed the DII scores toward being more pro-inflammatory. Notably, similar exclusions have been reported in other studies examining the association between DII and RA [ 14 , 15 ]. Sixth, we used only ten micronutrients to calculate micronutrient adequacy due to availability of CV for ten micronutrients only. Finally, we lacked information on genetic predispositions for RA, which could play a critical role in disease development and modulate the observed associations between dietary factors and RA. CONCLUSION This study highlights several differences in dietary patterns and nutrient intake between RA patients and healthy controls, which may have implications for disease management and prevention. While cases demonstrated lower intakes of macronutrients and certain micronutrients, they also exhibited a concerning deficiency in essential nutrients such as iron, zinc, and vitamins B6 and B12. Nutrient adequacy was notably lower among RA patients, with a significantly higher proportion of controls meeting recommended nutrient intake levels. Furthermore, the study underscores the potential role of dietary diversity and inflammation in RA. Participants with lower dietary diversity and higher inflammatory scores were more likely to have RA, while those with higher dietary diversity and higher nutrient adequacy demonstrated reduced odds of RA. These findings suggest that improving dietary quality and increasing nutrient adequacy may play a protective role in reducing RA risk and severity. Future interventions targeting dietary improvements, particularly in micronutrient intake and inflammation, may offer an additional approach to managing RA and mitigating its progression. Funding Source The author(s) received financial support from Department of Health Research, Ministry of Health and Family Welfare under Grant-in-Aid Scheme with project entitled “Correlation of Prakriti (ayurgenomics) with dietary patterns, gut microbe, HLA-DRB1 genes and disease severity in Rheumatoid arthritis patients”. Project Id-2014-0162, Sanction orders no. V 25011/142/2016-HR. Conflicts of Interest The authors do not have any conflict of interest. Data Availability Statement The raw data is available with Dr. Devraj Parasannanavar and would be made available to anyone upon written request with justification. Data Availability All data produced in the present study are available upon reasonable request to the Corresponding author. Footnotes Hemant Mahajan hemant.mahajan.84{at}gmail.com , Janhavi Pawar: n0213{at}svt.edu.in , Sailaja Pothana: shailaja_nin{at}yahoo.co.in , Ranga Srinivas K rangalt.ranga{at}gmail.com , Aditi Deshpande mail2aad{at}gmail.com , Nikhita B R nikhitab.r{at}gmail.com , Phani Kumar dphanimbbs{at}gmail.com , Venkat Raji Reddy gvenkatrajireddy1980{at}gmail.com , Srikant Perugu drperugu{at}gmail.com , JJ Babu Geddam geddambabuj{at}gmail.com , Liza Rajasekhar lizarajasekhar{at}gmail.com , Devraj Parasannanavar jpdevraj26{at}gmail.com References 1. ↵ Saag KG , Teng GG , Patkar NM , Anuntiyo J , Finney C , Curtis JR , Paulus HE , Mudano A , Pisu M , Elkins-Melton M , Outman R , Allison JJ , Suarez Almazor M , Bridges SL , Jr. , Chatham WW , Hochberg M , MacLean C , Mikuls T , Moreland LW , O’Dell J , Turkiewicz AM , Furst DE ( 2008 ) American College of Rheumatology 2008 recommendations for the use of nonbiologic and biologic disease-modifying antirheumatic drugs in rheumatoid arthritis . 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