A Mendelian Randomization and Disease Severity Study

European Journal of Clinical Nutrition (2021) 75:1368–1382 https://doi.org/10.1038/s41430-021-00913-6

ARTICLE

Nutrition in acute and chronic diseases Food groups associated with immune-mediated inﬂammatory diseases: a Mendelian randomization and disease severity study

1 1 2,3 4 2 Antonio Julià ● Sergio H. Martínez-Mateu ● Eugeni Domènech ● Juan D. Cañete ● Carlos Ferrándiz ● 5 3,6 7 6 8 Jesús Tornero ● Javier P. Gisbert ● Antonio Fernández-Nebro ● Esteban Daudén ● Manuel Barreiro-de Acosta ● 9 10 11 12 Carolina Pérez ● Rubén Queiró ● Francisco Javier López-Longo ● José Luís Sánchez Carazo ● 13 10 14 15 16 Juan Luís Mendoza ● Mercedes Alpéri ● Carlos Montilla ● José Javier Pérez Venegas ● Fernando Muñoz ● 6 1 1 1 Santos Castañeda ● Adrià Aterido ● María López Lasanta ● Sara Marsal ● for the IMID Consortium

Received: 26 July 2020 / Revised: 17 March 2021 / Accepted: 29 March 2021 / Published online: 23 April 2021 © The Author(s), under exclusive licence to Springer Nature Limited 2021

Abstract Background/Objectives Immune-mediated inﬂammatory diseases (IMIDs) are prevalent diseases. There is, however, a lack of understanding of the link between diet and IMIDs, how much dietary patterns vary between them and if there are food groups associated with a worsening of the disease. n =

1234567890();,: 1234567890();,: Subjects/Methods To answer these questions we analyzed a nation-wide cohort of 11,308 patients from six prevalent IMIDs and 2050 healthy controls. We compared their weekly intake of the major food categories, and used a Mendelian randomization approach to determine which dietary changes are caused by disease. Within each IMID, we analyzed the association between food frequency and disease severity. Results After quality control, n = 11,230 recruited individuals were used in this study. We found that diet is profoundly altered in all IMIDs: at least three food categories are significantly altered in each disease (P < 0.05). Inflammatory bowel diseases showed the largest differences compared to controls (n ≥ 8 categories, P < 0.05). Mendelian randomization analysis supported that some of these dietary changes, like vegetable reduction in Crohn’s Disease (P = 2.5 × 10−10, OR(95% CI) = 0.73(0.65, 0.80)), are caused by the disease. Except for Psoriatic Arthritis and Systemic Lupus Erythematosus, we have found ≥2foodgroupssignificantly associated with disease severity in the other IMIDs (P < 0.05). Conclusions This cross-disease study demonstrates that prevalent IMIDs are associated to a significant change in the normal dietary patterns. This variation is highly disease-specific and, in some cases, it is caused by the disease itself. Severity in IMIDs is also associated with specific food groups. The results of this study underscore the importance of studying diet in IMIDs.

Introduction Members of the IMID Consortium are listed below Acknowledgements. Immune-mediated inflammatory diseases (IMIDs) are a Supplementary information The online version contains group of pathologies characterized by the dysregulation of supplementary material available at https://doi.org/10.1038/s41430- immune pathways leading to inflammation, organ damage 021-00913-6. and multiple comorbidities [1]. Rheumatoid arthritis (RA), * Antonio Julià psoriatic arthritis (PsA), psoriasis (PS), systemic lupus ery- [email protected] thematosus (SLE), Crohn’s disease (CD) and ulcerative * Sara Marsal colitis (UC) are among the most prevalent IMIDs, collec- [email protected] tively affecting ~4% of the population [2]. They are complex diseases caused by an interplay of genetic and environmental Extended author information available on the last page of the article factors. In the last 15 years, genome-wide association studies (GWAS) have been highly successful at characterizing the genetic basis of IMIDs [3]. One of the key findings of Food groups associated with immune-mediated inflammatory diseases: a Mendelian randomization and. . . 1369

GWAS is that IMIDs tend to share genetic risk factors [4]. significant female sex predominance in RA and SLE Much less is known, however, on the environmental factors cohorts (78% and 93%, respectively). This is very close to that influence IMIDs. Despite nutrition is a major environ- the commonly reported 3:1 and 9:1 ratio, respectively. mental modifier, there is a lack of well-powered cross-sec- Other features that were found to be differential among tional studies characterizing the dietary patterns of IMIDs IMIDs, are less well known or new. This is likely due to the and their association to disease severity. lack of previous cross-IMID comparative studies. For There is increasing evidence that recent changes in dietary example, PsA patients showed the highest rate of high habits could be behind the increased prevalence of certain physical activity (28%) and UC the lowest levels (15%) inflammatory diseases like type 1 diabetes or asthma [5]. Also, among the six IMIDs. The prevalence of obesity among recent studies are demonstrating how nutrition can play an PsA and PS patients was 26.5% and 25.2% respectively, the essential role in the regulation of the immune system activity, highest of all cohorts, including healthy individuals. PsA either through the direct provision of specific compounds [6] and, particularly, RA had the highest mean age (52.3 ± or by the modulation of the activity of the intestinal micro- 13.2) and 60.7 ± 12.7) years, respectively), and CD patients biome [7, 8]. For these reasons, there is an increasing interest were the youngest on average (41.9 ± 13.5). The rate of high to characterize the dietary patterns of inflammatory diseases educational status for RA patients was the lowest (33%). [5]. To date however, there is a lack of large population-based Table 2 shows the unadjusted dietary habits for each of studies evaluating the association of food with IMIDs. the six IMID cohorts. A high heterogeneity was found Changes in dietary factors could either reflect habits that across IMIDs, with some food items being consumed contributed to disease risk or changes induced by the disease similarly between diseases and other food groups showing [9]. Mendelian randomization (MR) analysis is an epide- notable differences. Eggs were consumed rather similarly miological tool that can be very useful to discern what is the across all IMIDs, with differences smaller than 4%. On the most probable causal relationship in these cases [10]. other hand, fruit consumption showed differences as large Through the use of MR, we here provide evidence on which as 31% (i.e., between CD and RA patients). All IMID altered food habits are caused by the disease. The food fre- patients systematically showed lower rates of consumption quency changes that cannot be explained as caused by IMIDs of stimulant and alcoholic beverages compared to healthy are of interest because they might be contributors to disease individuals. While only 28% of the healthy individuals worsening. Identifying these dietary factors could be very reported to be abstinent, the lowest abstinence rate observed helpful to design simple preventive and interventional stra- among IMIDs was 43% in PS patients. The highest rates of tegies to improve the outcome of patients. To this end, we alcohol abstention were found in RA (72%) and SLE have also performed an association analysis between the (78%). The lowest levels of consumption of tea/coffee were different food types and disease severity, as defined for each found in IBDs, with 38% (CD) and 34% (UC) of non- IMID. Using this approach, we have found several foods that drinkers, compared to 15% of the control cohort. are correlated with an increase of disease activity levels. Table 3 shows the association results between the six Together, the simultaneous analysis of six prevalent diseases IMIDs and the 13 food categories adjusted by the two using different analytical approaches shows that diet is models (Model 1: adjusted for potential confounders, Model important in IMIDs, and we provide associations that may be 2: adjusted for potential confounders and mediators). At the in future used to improve the health status of patients suf- disease-level, all IMIDs showed a significant association to fering from these chronic diseases. at least three of the 13 food items in the two regression models considered. The largest diet alteration was found in the two IBDs, with 9 and 8 food items consumed at dif- Results ferent rates in CD and UC compared to healthy controls, respectively. RA patients showed changes in 4 food items, The baseline epidemiological characteristics of the study while SLE, PS and PSA showed dietary changes in 3 items. population are shown in Table 1. Healthy individuals had a At the food-level, 11 categories were consumed differently similar mean age to IMID patients (49.6 years, although in at least one IMID. Only eggs and fish showed no sig- with a narrower distribution, SD = 7.0), lower prevalence of nificant difference in any disease group against controls. In underweight (0.2%), higher prevalence of overweight this case-control analysis, two disease-specific food asso- (47.3%), and elevated rates of high physical activity (46%) ciations were identified. Both occurred in CD and included and high educational status (70% of individuals having a reduced ingestion of dairy products (P = 0.002) and a completed tertiary education). The baseline characteristics drastic decrease in vegetable consumption (P <2×10−16). between the six IMIDs were more related among them- Three food items were found to be similarly changed selves than against controls. Some features, however, are between two IMIDs: legume consumption was reduced in distinctive of each disease. For example, there was a UC and CD (P = 2×10−6 and P = 6×10−5, respectively) 1370 A. Julià et al.

Table 1 Baseline epidemiological characteristics of the study population stratiﬁed by disease type. HC (N = 1968) PSA (N = 1378) RA (N = 1945) UC (N = 1407) CD (N = 1946) SLE (N = 695) PS (N = 1891)

Age (years) Mean (SD) 49.55 (7.0) 52.28 (13.2) 60.68 (12.7) 48.68 (14.5) 41.87 (13.5) 45.76 (13.7) 48.04 (15.8) Gender Female, n (%) 812 (41) 645 (48) 1506 (78) 637 (45) 969 (50) 643 (93) 807 (43) Male, n (%) 1155 (59) 711 (52) 435 (22) 767 (55) 967 (50) 47 (7) 1080 (57) Body mass index (kg/m2) Mean (SD) 27.04 (3.95) 27.61 (4.84) 26.37 (4.68) 25.68 (4.21) 24.54 (4.34) 25.38 (5.03) 27.06 (4.98) <18, n (%) 3 (0.2) 11 (0.9) 32 (1.7) 22 (1.6) 93 (5.0) 20 (3.1) 31 (1.7) 18–25, n (%) 615 (32.8) 403 (31.4) 782 (41.6) 618 (45.6) 1033 (55.0) 340 (52.2) 658 (35.6) >25–30, n (%) 888 (47.3) 531 (41.3) 696 (37.0) 513 (37.8) 555 (29.6) 178 (27.3) 692 (37.5) >30, n (%) 371 (19.8) 340 (26.5) 371 (19.7) 203 (15.0) 197 (10.5) 113 (17.4) 465 (25.2) Physical activitya Higher, n (%) 886 (46) 380 (28) 354 (19) 206 (15) 365 (20) 156 (23) 392 (21) Lower, n (%) 1047 (54) 955 (72) 1547 (81) 1125 (85) 1468 (80) 529 (77) 1459 (79) Educational levelb Higher, n (%) 1323 (70) 605 (47) 628 (33) 757 (55) 1184 (62) 364 (53) 981 (54) Lower, n (%) 564 (30) 674 (53) 1253 (67) 626 (45) 727 (38) 321 (47) 844 (46) Season of the year Spring, n (%) 465 (30) 491 (37) 556 (30) 351 (25) 422 (22) 201 (29) 541 (29) Summer, n (%) 456 (29) 162 (12) 288 (15) 257 (19) 355 (19) 172 (25) 378 (20) Autumn, n (%) 356 (23) 202 (15) 467 (25) 462 (33) 652 (34) 160 (23) 422 (22) Winter, n (%) 288 (18) 481 (36) 563 (30) 315 (23) 486 (25) 150 (22) 538 (29) Region, n (%) Cataluña/Baleares 828 (42.1) 539 (39.1) 761 (39.1) 463 (32.9) 624 (32.1) 74 (10.6) 490 (25.9) Madrid 422 (21.4) 202 (14.7) 345 (17.7) 162 (11.5) 280 (14.4) 228 (32.8) 708 (37.4) Andalucia 186 (9.5) 124 (9.0) 299 (15.4) 222 (15.8) 275 (14.1) 233 (33.5) 237 (12.5) Galicia/Asturias 93 (4.7) 319 (23.1) 427 (22.0) 132 (9.4) 161 (8.3) 7 (1.0) 4 (0.2) Leon/Rioja 112 (5.7) 147 (10.7) 25 (1.3) 244 (17.3) 240 (12.3) 7 (1.0) 262 (13.9) Valencia/Murcia 277 (14.1) 29 (2.1) 10 (0.5) 159 (11.3) 336 (17.3) 60 (8.6) 147 (7.8) Mancha/Extremadura 2 (0.1) 8 (0.6) 68 (3.5) 21 (1.5) 22 (1.1) 15 (2.2) 39 (2.1) PVasco/Navarra/ 48 (2.4) 10 (0.7) 10 (0.5) 4 (0.3) 8 (0.4) 71 (10.2) 4 (0.2) Cantabria Currently smoking (cigarettes/day) 0, n (%) 1522 (77.3) 1047 (76.0) 1581 (81.3) 1164 (82.7) 1226 (63.0) 494 (71.1) 1127 (59.6) 1–10, n (%) 219 (11.1) 164 (11.9) 167 (8.6) 239 (17.0) 710 (36.5) 197 (28.3) 310 (16.4) >10–20, n (%) 144 (7.3) 116 (8.4) 137 (7.0) 3 (0.2) 9 (0.5) 3 (0.4) 313 (16.6) >20, n (%) 83 (4.2) 51 (3.7) 60 (3.1) 1 (0.1) 1 (0.1) 1 (0.1) 141 (7.5) Smoking before diagnostic No, n (%) 1522 (77) 840 (64) 1399 (72) 1058 (75) 803 (41) 419 (60) 917 (50) Yes, n (%) 446 (23) 464 (36) 546 (28) 349 (25) 1143 (59) 276 (40) 910 (50) HC healthy controls, PSA psoriatic arthritis, RA rheumatoid arthritis, UC ulcerative colitis, CD Crohn’s disease, SLE systemic lupus erythematosus, PS psoriasis. aHigher: doing regular exercise during leisure or at work, Lower: otherwise. bHigher: having completed university-level studies, Lower: otherwise. while rice/pasta was very significantly increased in these two levels (P = 0.01 and P = 3×10−6, respectively). UC, CD and −11 −9 IBDs (PCD = 2×10 and PUC = 6×10 ), and RA and UC PS shared a common increase in bread and/or grain weekly −10 patients showed a significant increase in fruit consumption consumption compared to healthy controls (PUC = 5×10 , Food groups associated with immune-mediated inflammatory diseases: a Mendelian randomization and. . . 1371

Table 2 Unadjusted dietary habits of the six IMID cohorts and healthy controls. HC (N = 1968) PSA (N = 1378) RA (N = 1945) UC (N = 1407) CD (N = 1946) SLE (N = 695) PS (N = 1891)

Eggs (d/w)a 0, n (%) 110 (6) 86 (6) 120 (6) 88 (6) 131 (7) 30 (4) 149 (8) 1–2, n (%) 1439 (74) 1041 (76) 1449 (75) 1027 (74) 1387 (72) 510 (74) 1345 (72) 3–5, n (%) 357 (18) 216 (16) 333 (17) 249 (18) 383 (20) 138 (20) 330 (18) 6–7, n (%) 34 (2) 18 (1) 21 (1) 31 (2) 30 (2) 11 (2) 52 (3) Fish (d/w) 0, n (%) 77 (4) 84 (6) 67 (3) 54 (4) 172 (9) 31 (5) 130 (7) 1–2, n (%) 1098 (56) 748 (55) 987 (51) 800 (57) 1117 (58) 362 (53) 1039 (55) 3–5, n (%) 720 (37) 468 (34) 788 (41) 474 (34) 567 (29) 256 (37) 624 (33) 6–7, n (%) 54 (3) 66 (5) 82 (4) 66 (5) 77 (4) 39 (6) 83 (4) Meat (d/w) 0, n (%) 35 (2) 27 (2) 71 (4) 17 (1) 29 (1) 18 (3) 46 (2) 1–2, n (%) 693 (35) 556 (41) 998 (52) 400 (29) 494 (25) 255 (37) 691 (37) 3–5, n (%) 1074 (55) 644 (47) 743 (38) 739 (53) 1055 (54) 355 (51) 908 (48) 6–7, n (%) 154 (8) 139 (10) 121 (6) 242 (17) 363 (19) 65 (9) 234 (12) Processed meats (d/w) 0, n (%) 323 (17) 382 (28) 678 (35) 321 (23) 392 (21) 174 (26) 381 (21) 1–2, n (%) 944 (49) 567 (42) 877 (46) 553 (40) 759 (40) 332 (49) 822 (45) 3–5, n (%) 472 (25) 268 (20) 256 (13) 302 (22) 467 (24) 102 (15) 360 (20) 6–7, n (%) 172 (9) 130 (10) 106 (6) 214 (15) 293 (15) 71 (10) 281 (15) Bread/Wholegrain (d/w) 0, n (%) 62 (3) 51 (4) 61 (3) 43 (3) 65 (3) 36 (5) 73 (4) 1–2, n (%) 221 (11) 107 (8) 154 (8) 87 (6) 176 (9) 77 (11) 175 (9) 3–5, n (%) 242 (12) 144 (11) 137 (7) 97 (7) 204 (11) 65 (9) 188 (10) 6–7, n (%) 1422 (73) 1057 (78) 1579 (82) 1168 (84) 1488 (77) 514 (74) 1443 (77) Rice/Pasta/Potatoes (d/w) 0, n (%) 43 (2) 53 (4) 57 (3) 17 (1) 30 (2) 30 (4) 48 (3) 1–2, n (%) 761 (39) 546 (40) 729 (38) 387 (28) 529 (27) 290 (42) 760 (41) 3–5, n (%) 876 (45) 538 (40) 796 (41) 638 (46) 904 (47) 313 (45) 772 (41) 6–7, n (%) 263 (14) 220 (16) 356 (18) 356 (25) 467 (24) 57 (8) 296 (16) Legumes (d/w) 0, n (%) 212 (11) 150 (11) 142 (7) 245 (18) 488 (25) 82 (12) 200 (11) 1–2, n (%) 1333 (69) 874 (64) 1369 (71) 941 (68) 1132 (59) 460 (66) 1302 (70) 3–5, n (%) 355 (18) 280 (21) 353 (18) 185 (13) 270 (14) 135 (20) 312 (17) 6–7, n (%) 44 (2) 57 (4) 64 (3) 21 (2) 37 (2) 15 (2) 54 (3) Vegetables (d/w) 0, n (%) 37 (2) 35 (3) 42 (2) 89 (6) 252 (13) 30 (4) 85 (5) 1–2, n (%) 507 (26) 405 (30) 472 (24) 412 (30) 782 (41) 198 (29) 615 (33) 3–5, n (%) 757 (39) 476 (35) 681 (35) 498 (36) 559 (29) 243 (35) 638 (34) 6–7, n (%) 651 (33) 449 (33) 736 (38) 395 (28) 336 (17) 218 (32) 542 (29) Fruit (d/w) 0, n (%) 63 (3) 62 (5) 39 (2) 79 (6) 217 (11) 42 (6) 150 (8) 1–2, n (%) 274 (14) 183 (13) 174 (9) 187 (13) 419 (22) 107 (15) 358 (19) 3–5, n (%) 415 (21) 259 (19) 231 (12) 187 (13) 405 (21) 118 (17) 320 (17) 6–7, n (%) 1203 (62) 863 (63) 1495 (77) 948 (68) 891 (46) 426 (61) 1053 (56) 1372 A. Julià et al.

Table 2 (continued) HC (N = 1968) PSA (N = 1378) RA (N = 1945) UC (N = 1407) CD (N = 1946) SLE (N = 695) PS (N = 1891)

Sweet (d/w) 0, n (%) 395 (20) 305 (22) 479 (25) 275 (20) 311 (16) 118 (17) 419 (22) 1–2, n (%) 760 (39) 423 (31) 685 (35) 400 (29) 571 (30) 228 (33) 579 (31) 3–5, n (%) 396 (20) 255 (19) 300 (16) 239 (17) 392 (20) 150 (22) 327 (17) 6–7, n (%) 390 (20) 374 (28) 468 (24) 481 (34) 652 (34) 189 (28) 551 (29) Alcoholb Abstainer, n (%) 519 (28) 712 (54) 1376 (72) 728 (53) 1036 (54) 510 (75) 795 (43) Sometimes, n (%) 514 (27) 241 (18) 166 (9) 278 (20) 505 (27) 100 (15) 507 (27) Daily, n (%) 848 (45) 365 (28) 360 (19) 369 (27) 363 (19) 66 (10) 542 (29) Tea/coffeec 0, n (%) 289 (15) 309 (22) 484 (25) 536 (38) 671 (34) 202 (29) 481 (25) 1–2, n (%) 1168 (59) 772 (56) 1125 (58) 680 (48) 982 (50) 386 (56) 1064 (56) >2, n (%) 510 (26) 297 (22) 336 (17) 191 (14) 293 (15) 107 (15) 346 (18) aThe rates of consumption for all foods are in days per week (d/w). bAbstainer: never drinks alcohol beverages, Sometimes: only drinks during the weekends, Daily: drinks beer, wine or spirits during the week. cNumber of tea or coffee cups per day.

−5 PCD = 4×10 and PPS = 0.01). Meat consumption showed observed in the case-control analysis (i.e., high and low, an opposite behavior in IBD compared to the two arthritis: respectively) was also found to be caused by each disease while meat ingestion was significantly higher in the diseases (PUC = 0.02 and PRA = 0.0001). Fruit consumption was also involving the gut (PCD = 0.002 and PUC = 0.001), it was associated using MR analysis, revealing an association with reduced in the diseases affecting the joints (PRA = 0.0003 and the reduction in frequency in CD and PS (PCD = 0.0001 and PPSA = 0.02). The more commonly shared dietary changes in PPS = 0.012). IMIDs were the consumption of tea/coffee and alcoholic The association between disease activity levels and food beverages. A major prevalence of alcohol abstinence was consumption within each IMID is shown in Table 5. The −12 −16 found in all six diseases (PUC = 4.6 × 10 , PCD <2×10 , two alternative association models (M1: adjustment for −13 −14 −16 PPS = 8.5 × 10 , PPSA = 2.8 × 10 , PRA <2×10 ,and confounders, and M2: adjustment for confounders and −16 PSLE = 3.3 × 10 ). Stimulant beverages are also avoided mediators) were also used to evaluate this association. At −16 −16 more frequently in IMIDs (PUC <2×10 , PCD <2×10 , the disease-level, an increase in disease severity with food −5 −5 −4 PPS = 1.1 × 10 , PRA = 2.6 × 10 ,andPSLE = 2.2 × 10 ) consumption was found in the two IBDs, RA and PS. with the only exception of PsA, where there was no sig- Conversely, dietary variation was not associated with an nificant difference compared to controls. increase in inflammatory status in PsA or SLE. Among the To help discern which food frequency patterns are IMIDs showing an association between food frequency and caused by the presence of the disease we performed a activity, CD showed the larger number of food item asso- Mendelian randomization (MR) analysis. Table 4 shows the ciations (n = 5), followed by UC (n = 3), RA (n = 2) and MR results for the 13 food items. At the disease-level, all PS (n = 2). At the food-level, 8 of the 13 food types were IMIDs but PSA showed significant associations. Both CD associated with disease activity in one or more IMIDs. Of and UC showed the largest number of food group asso- these, associations were predominantly disease-specific. ciations (n = 5), followed by PS (n = 4), and RA and SLE Active CD patients were found to consume less dairy and (n = 2). At the food-level, associations caused by the dis- less vegetables than inactive patients (P = 0.03 and P = ease were found for all food groups except for legume 1.7 × 10−5, respectively). Higher fish consumption was consumption. Abstinence from alcohol and stimulating associated with high disease activity in UC patients (P = drink beverages was found to be associated in all IMIDs 0.048). RA patients with high disease activity showed a −8 with the exception of PsA (PUC = 0.004, PCD = 9.7 × 10 , highly significant reduction in alcohol consumption (P = −7 −11 −5 PPS = 0.002, PRA = 2.2 × 10 , and PSLE = 3.6 × 10 ). At 6.7 × 10 ). Active PS patients were found to consume the disease-specific level, a strong association with vege- more rice/pasta than patients with low activity (P = 0.003). table reduction was found in CD (P = 2.5 × 10−10). The Shared dietary changes associated with disease activity development of either IBD was associated with an increase were found between the two IBDs and between CD and in sweets in the diet (PCD = 0.009 and PUC = 0.007). Meat RA. In the former, fruit consumption was markedly reduced consumption frequency disparity between UC and RA in patients with an active disease (PCD = 0.00014 and PUC odgop soitdwt muemdae in immune-mediated with associated groups Food Table 3 Comparison of dietary habits between the six IMIDs and healthy controls. Eggs ProcMeats Dairy BreadGrain RicePasta Fish Meat Vegetables Legumes Fruit Sweet Alcohol TeaCoffee

PsA Mod 1 0.76 0.91 0.98 1.09 0.81 0.94 0.78(0.66, 1.02 1.05 1.06 1.26(1.06, 0.47(0.39, 0.77 (0.62, 0.94) (0.76, 1.1) (0.8, 1.19) (0.9, 1.32) (0.68, 0.96) (0.79, 1.11) 0.93)* (0.85, 1.23) (0.86, 1.29) (0.89, 1.26) 1.48)* 0.57)*** (0.62, 0.95) Mod 2 0.78 0.91 0.97 1.08 0.82 1 0.77(0.65, 1.05 1.08 1.06 1.26(1.07, 0.52(0.43, 0.8 (0.63, 0.97) (0.75, 1.09) (0.8, 1.18) (0.89, 1.31) (0.69, 0.97) (0.85, 1.18) 0.91)* (0.88, 1.26) (0.88, 1.31) (0.89, 1.26) 1.49)* 0.62)*** (0.65, 0.99) RA Mod 1 1 0.78 1.19 1.29(1.07, 1.17(1, 1.37) 0.97 0.71(0.61, 0.97 0.91 1.31(1.1, 1.13 0.33(0.27, 0.63(0.52, (0.82, 1.22) (0.65, 0.94) (0.98, 1.45) 1.56)* (0.82, 1.13) 0.84)** (0.81, 1.16) (0.75, 1.11) 1.56)* (0.96, 1.33) 0.4)*** 0.77)*** Mod 2 1 0.79 1.2 1.28 1.17 1.01 0.7(0.6, 1.01 0.92 1.33(1.11, 1.12 0.37(0.3, 0.7(0.57, (0.82, 1.22) (0.66, 0.95) (0.99, 1.47) (1.06, 1.56) (0.99, 1.38) (0.86, 1.19) 0.83)** (0.84, 1.2) (0.76, 1.12) 1.58*) (0.95, 1.31) 0.45)*** 0.85)* UC fl Mod 1 0.89 1.2 0.84 1.91(1.57, 1.74(1.48, 1.04 1.37(1.16, 0.83 0.59(0.48, 1.53(1.3, 1.57(1.34, 1373 0.52(0.44, 0.39(0.32, . . and. randomization Mendelian a diseases: ammatory (0.73, 1.08) (1.01, 1.42) (0.7, 1.01) 2.33)*** 2.06)*** (0.89, 1.22) 1.62)* (0.7, 0.98) 0.72)*** 1.81)*** 1.84)*** 0.62)*** 0.47)*** Mod 2 0.89 1.2(1, 1.42) 0.83 1.85(1.51, 1.76(1.49, 1.07 1.32(1.11, 0.88 0.58(0.47, 1.54(1.29, 1.6(1.36, 0.56(0.47, 0.42(0.34, (0.73, 1.09) (0.68, 1) 2.27)*** 2.09)*** (0.9, 1.26) 1.57)* (0.74, 1.05) 0.72)*** 1.83)*** 1.88)*** 0.68)*** 0.51)*** CD Mod 1 0.93 1.13 0.74(0.62, 1.51(1.26, 1.76(1.5, 0.99 1.34(1.14, 0.46(0.39, 0.64(0.53, 0.9 1.69(1.45, 0.4(0.33, 0.42(0.35, (0.77, 1.13) (0.96, 1.33) 0.88)* 1.81)*** 2.07)*** (0.85, 1.16) 1.58)* 0.55)*** 0.79)*** (0.76, 1.05) 1.97)*** 0.48)*** 0.51)*** Mod 2 0.92 1.16 0.74(0.62, 1.53(1.28, 1.71(1.45, 1.01 1.32(1.12, 0.46(0.39, 0.67(0.55, 0.88 1.7(1.46, 0.46(0.38, 0.5(0.41, (0.76, 1.12) (0.99, 1.38) 0.89)* 1.84)*** 2.01)*** (0.86, 1.19) 1.56)* 0.55)*** 0.81)** (0.75, 1.03) 1.99)*** 0.55)*** 0.6)*** SLE Mod 1 0.94 0.93 0.8 1.21 1.17 1.11 1.03 0.82 0.86 1.2 1.41(1.15, 0.27(0.2, 0.6(0.47, (0.74, 1.2) (0.74, 1.17) (0.63, 1.01) (0.96, 1.53) (0.95, 1.43) (0.91, 1.36) (0.83, 1.26) (0.66, 1.02) (0.67, 1.1) (0.97, 1.48) 1.73)* 0.37)*** 0.76)** Mod 2 0.94 0.94 0.81 1.18 1.15 1.11 1.01 0.81 0.86 1.18 1.41(1.15, 0.3(0.22, 0.67(0.52, (0.73, 1.2) (0.75, 1.19) (0.64, 1.03) (0.93, 1.49) (0.94, 1.41) (0.9, 1.36) (0.82, 1.24) (0.65, 1.02) (0.67, 1.11) (0.95, 1.46) 1.73)* 0.42)*** 0.851)* PS Mod 1 0.98 0.99 0.9 1.32(1.11, 1.12 1.06 0.89 0.88 0.83 1.02 1.21 0.53(0.44, 0.63(0.52, (0.81, 1.18) (0.84, 1.16) (0.75, 1.07) 1.58)* (0.96, 1.31) (0.91, 1.24) (0.76, 1.04) (0.74, 1.03) (0.69, 1.01) (0.87, 1.19) (1.04, 1.41) 0.63)*** 0.77)*** Mod 2 0.97 0.99 0.91 1.35(1.13, 1.09 1.11 0.87 0.92 0.83 1.08 1.22 0.58(0.49, 0.68(0.56, (0.8, 1.17) (0.84, 1.17) (0.76, 1.09) 1.61)* (0.93, 1.27) (0.95, 1.3) (0.74, 1.02) (0.78, 1.09) (0.69, 1.01) (0.92, 1.27) (1.05, 1.42) 0.7)*** 0.83)** Association analysis – Odds Ratio (95% Confidence Interval) – of the dietary habits comparing each IMID group against controls. Mod 1: Association model adjusted for age, sex, smoking status at diagnosis, geographical region, season of the year and highest educational level. Mod 2: Association model aadjusted for: age, sex, current smoking status, geographical region, season of the year, highest educational level and physical activity. Significance level: *P < 0.05; **P < 0.001; ***P < 0.0001. 1374 A. Julià et al.

= 2.7 × 10−5), and in the latter, tea/coffee was found to be reduced in RA and CD with more severe features (PRA = 0.006 and PCD = 0.026). Finally, an opposite consumption 0.89 (0.78, 1.01) 0.88 (0.78, 0.99)* 0.81 (0.72, 0.9)** 0.89 (0.79, 0.99) 0.76 (0.68, 0.85)*** 0.83 (0.67, 1.03) pattern was found for legumes: while CD and UC patients with high disease activity reduce its consumption (PCD = 0.027 and PUC = 0.02), PS patients with a more severe disease consumed it more frequently (PCD = 0.009). Eggs, 0.75 (0.67, 0.83)*** 0.72 (0.66, 0.79)*** 0.76 (0.69, 0.84)*** 0.87 (0.79, 0.94)* 0.87 (0.79, 0.95)* 0.89 (0.74, 1.07) processed meat, bread/grain, meat and sweets were not associated to the presence of more severe symptoms in any of the six IMID cohorts. (0.90, 1.12) 1.01 (0.92, 1.10) 1.13 (1.03, 1.24)* 0.99 (0.90, 1.09) 1.14 (1.04, 1.25)* 0.99 (0.83, 1.16) 1.01 Discussion

Immune-mediated inflammatory diseases are a prevalent 1.03 (0.94, 1.13) 0.83 (0.75, 0.91)** 0.89 (0.82, 0.98)* 1.10 (0.99, 1.22) 0.98 (0.82, 1.16) 1.07 (0.96, 1.20) group of common disorders caused by the interplay of genetic and environmental factors. While a large fraction of the genetic component has been identified in the last years, very little is known on the relevance of a key environ- 0.93 (0.84, 1.04) 0.93 (0.83, 1.05) 0.92 (0.83, 1.02) 0.90 (0.79, 1.02) 0.96 (0.79, 1.16) 1.03 (0.91, 1.17) mental factor like the diet. Here we provide a simultaneous analysis of the food habits of six of the most prevalent IMIDs, comparing them to healthy individuals. Using a nation-wide cohort, we find that diet is profoundly altered 1.01 (0.92, 1.11) 0.73 (0.65, 0.80)*** 0.93 (0.84, 1.03) 0.98 (0.89, 1.09) 0.93 (0.76, 1.11) 1.03 (0.92, 1.17) in all IMIDs. With a Mendelian randomization analysis, we have identified which food habits are likely caused by the disease and differentiate them from those that could be contributing to the disease. To provide additional evidence 0.98 (0.89, 1.07) 1.08 (0.98, 1.19) 0.96 (0.88, 1.04) 1.13 (1.02, 1.25)* 0.98 (0.83, 1.15) 0.81 (0.73, 0.90)** of the latter, we performed an association between the diet and severity, and found evidence that the eating frequency of particular food items are associated with disease symp- tom aggravation. 1.07 (0.98, 1.18) 0.92 (0.84, 1.01)* 0.91 (0.83, 1.00) 1.03 (0.94, 1.14)* 0.98 (0.82, 1.16) 1.02 (0.92, 1.12) Eleven out of the 13 food groups were consumed differently from controls in at least one IMID. Applying a Mendelian randomization analysis, several of these differences can be ascribed to the presence of the disease. Among of the dietary habits with disease severity within each IMID. 1.00 (0.91, 1.10) 1.11 (1.01, 1.22)* 1.00 (0.92, 1.09) 1.17 (1.06, 1.30)* 0.93 (0.80, 1.09) 1.00 (0.90, 1.10)

– these, the increase in the abstinence from alcoholic drinks is common to all six IMIDs. For some of the diseases, like RA [11], IBD [12] and SLE [13] there is previous evidence supporting this reduction in comparison to controls. This is 0.97 (0.87, 1.07) 0.98 (0.88, 1.09) 0.93 (0.85, 1.03) 1.22 (1.08, 1.37)* 0.86 (0.71, 1.04) 1.02 (0.91, 1.15) < 0.0001. fi P dence Interval) the rst time that the relationship between this dietary habit fi and IMIDs has been tested using a MR framework. Our results support that the reduction in alcohol consumption is fl (95% BootstrapCI) 0.88 (0.79, 0.99)* 0.99 (0.89, 1.11) 1.00 (0.91, 1.10) 1.06 (0.95, 1.19) 0.94 (0.77, 1.15) 1.01 (0.88, 1.16) largely due to the presence of the in ammatory disease. < 0.001; *** a

P However, our results also show that this drastic reduction in alcohol intake in IMIDs is correlated with changes in disease activity. In fact, we found the evidence of the contrary Odds Ratio (95% Con < 0.05; ** 0.93 (0.84, 1.03) 1.09 (0.99, 1.20) 1.03 (0.94, 1.14) 1.08 (0.98, 1.19) 0.95 (0.7, 1.1) 0.84 (0.74, 0.94)* in RA. In this IMID we found that patients with higher – P levels of disease activity consumed less alcohol than patients with lower inﬂammatory and pain markers.

cient from the Mendelian Randomization analysis with the ratio method. Although this result might seem contradictory given the ﬁ Mendelian randomization analysis for dietary habits on the six IMIDs. (0.84, 1.06) Eggs(0.84, 1.06) ProcMeats Dairy BreadGrain RicePasta Fish Meat Vegetables Legumes Fruit Sweet Alcohol TeaCoffee (0.88, 1.09) (0.88, 1.12) (0.73, 1.11) (0.88, 1.13) associated toxicity, there is previous epidemiological evi- cance level: * ﬁ dence showing that alcohol consumption in inversely Beta coef SLE 0.94 CD 0.94 PS 0.98 Table 4 a IMID Diet categories, Estimation UC 0.99 Association analysis PSA 0.89 Signi RA 1.00 associated with disease severity in RA [11]. In line with our odgop soitdwt muemdae in immune-mediated with associated groups Food Table 5 Association of dietary habits with disease severity in IMIDs. Eggs ProcMeats Dairy BreadGrain RicePasta Fish Meat Vegetables Legumes Fruit Sweet Alcohol TeaCoffee

PSA, M1 1.1 0.96 1.05 1.13 0.97 1.04 0.96 1.05 0.88 1.09 1.05 0.86 1.02 (0.94, 1.29) (0.84, 1.11) (0.91, 1.22) (0.97, 1.31) (0.86, 1.09) (0.91, 1.17) (0.84, 1.08) (0.92, 1.19) (0.77, 1.02) (0.96, 1.24) (0.93, 1.19) (0.74, 1) (0.87, 1.19) PSA, M2 1.11 0.98 1.03 1.13 1 1.06 0.97 1.06 0.9 1.08 1.02 0.88 1 (0.95, 1.3) (0.85, 1.14) (0.89, 1.19) (0.98, 1.31) (0.89, 1.13) (0.94, 1.2) (0.85, 1.1) (0.93, 1.21) (0.78, 1.04) (0.94, 1.23) (0.91, 1.16) (0.76, 1.01) (0.86, 1.16) RA, M1 1.09(1, 0.96 1.08 0.98 1.04 0.96 1 1 0.98 0.99 1 0.83(0.76, 0.9(0.84, 1.18)* (0.88, 1.04) (0.99, 1.19) (0.9, 1.06) (0.97, 1.11) (0.9, 1.03) (0.93, 1.06) (0.93, 1.07) (0.91, 1.06) (0.91, 1.07) (0.93, 1.07) 0.91)*** 0.97)* RA, M2 1.08 0.96 1.08 0.98 1.04 0.96 0.99 1 0.97 0.98 1 0.83(0.76, 0.88(0.82, (0.99, 1.17) (0.88, 1.04) (0.98, 1.18) (0.9, 1.06) (0.97, 1.11) (0.9, 1.02) (0.92, 1.06) (0.93, 1.08) (0.89, 1.05) (0.91, 1.07) (0.93, 1.07) 0.91)** 0.95)* UC, M1 1.02 0.99 0.95 1 1.02 1.05(1, 0.95(0.9, 1) 0.96 0.91(0.84, 0.89(0.84, 0.99 1 1 (0.95, 1.08) (0.94, 1.04) (0.9, 1.01) (0.93, 1.07) (0.96, 1.08) 1.11)* (0.91, 1.01) 0.99)* 0.94)*** (0.94, 1.04) (0.95, 1.07) (0.95, 1.05) UC, M2 1.02 0.97 0.97 0.99 1.01 1.06(1, 0.94(0.88, 0.96 0.89(0.81, 0.89(0.84, 0.99 1.02 0.98

(0.96, 1.09) (0.92, 1.03) (0.91, 1.02) (0.93, 1.07) (0.95, 1.07) 1.12)* 0.99)* (0.91, 1.02) 0.97)* 0.94)*** (0.94, 1.04) (0.96, 1.08) (0.93, 1.04) fl maoydsae:aMneinrnoiainad 1375 . . and. randomization Mendelian a diseases: ammatory CD, M1 0.96 1.03 0.95(0.91, 0.98 1.04 0.99 1.01 0.9(0.86, 0.93(0.87, 0.92(0.87, 1 0.98 0.95(0.91, (0.91, 1.01) (0.99, 1.07) 1)* (0.93, 1.03) (0.99, 1.09) (0.95, 1.04) (0.96, 1.06) 0.95)*** 0.99)* 0.96)*** (0.96, 1.05) (0.92, 1.04) 0.99)* CD, M2 0.96 1.03 0.95(0.9, 1 1.05(1, 1.1) 1 1 0.9(0.85, 0.93(0.88, 0.93(0.89, 1.01 0.99 0.94(0.9, (0.91, 1.02) (0.98, 1.07) 0.99)* (0.95, 1.06) (0.95, 1.05) (0.95, 1.06) 0.94)*** 0.99)* 0.98)*** (0.96, 1.05) (0.93, 1.05) 0.99)* SLE, M1 0.96 0.99 0.99 0.98 0.99 1 1 0.99 0.97 1 1 1.04 1.01 (0.91, 1) (0.95, 1.03) (0.95, 1.03) (0.94, 1.02) (0.95, 1.02) (0.96, 1.03) (0.97, 1.04) (0.96, 1.03) (0.92, 1.01) (0.96, 1.03) (0.97, 1.04) (0.98, 1.11) (0.97, 1.05) SLE, M2 0.95(0.9, 1) 0.99 1 0.98 0.99 1 1 0.99 0.96 1 1.01 1.05 1.02 (0.95, 1.03) (0.96, 1.04) (0.95, 1.02) (0.95, 1.02) (0.96, 1.04) (0.97, 1.04) (0.96, 1.03) (0.92, 1.01) (0.96, 1.03) (0.97, 1.04) (0.99, 1.12) (0.98, 1.06) PS, M1 1 1.01 0.99 1 1.03(1.01, 0.99 1 0.98 1.03(1.01, 0.98 1.01 1 0.99 (0.98, 1.02) (0.99, 1.03) (0.97, 1.01) (0.98, 1.03) 1.05)* (0.97, 1.01) (0.98, 1.02) (0.97, 1) 1.05)* (0.96, 1) (0.99, 1.02) (0.98, 1.02) (0.97, 1.01) PS, M2 1 1.01 0.99 1.01 1.03(1.01, 0.99 1 0.99 1.03(1.01, 0.98 1 1 1 (0.98, 1.02) (0.99, 1.03) (0.97, 1.01) (0.98, 1.03) 1.05)* (0.97, 1.01) (0.98, 1.02) (0.97, 1) 1.05)* (0.96, 1) (0.99, 1.02) (0.98, 1.02) (0.98, 1.02) Association analysis – Odds Ratio (95% Confidence Interval) – of the dietary habits with disease severity within each IMID. M1: Adjusted for: age, sex, smoking status at diagnosis, geographical region, season of the year and highest education level. M2: Adjusted for: age, sex, current smoking status, geographical region, season of the year, highest education level and physical activity. Significance level: *P < 0.05; **P < 0.001; ***P < 0.0001. 1376 A. Julià et al.

findings, recent experimental evidence demonstrates that doesn’t support a negative impact on UC. Vegetables pro- ethanol consumption is able to mitigate autoimmune vide many essential nutrients like vitamins, and therefore, a arthritis, and that is done by targeting key immunological reduction should be adequately justified by evidence which, processes associated with RA etiology [14]. in the case of UC, is missing. Our MR analysis also provides evidence of disease- Conversely, in UC we identified an increased con- specific dietary changes. The increase of meat consumption sumption of fruits compared to healthy individuals, which in the two IBDs is a clear example. To our knowledge, it is was also significant in the MR analysis. This increase of the first time that this change in food frequency has been fruit consumption in the diet was clearly not observed in described for these two diseases. Red meat in particular, CD, in neither the case-control nor the MR analyses. To our carries high amounts of tryptophan which has been asso- knowledge, there is no previous evidence reporting this ciated with gut homeostasis [5]. UC patients with higher specific dietary behavior in UC. Furthermore, this result is disease activity were also found to have a reduced ingestion in apparent contradiction with what would be expected of meat, further supporting this potential protective effect of according to the dietary recommendations from different meat in IBD. In RA, we found an opposite pattern to that of clinical nutrition societies, where fruit consumption reduc- IBD, with patients eating lower quantities of meat. In this tion is advised [16, 17]. However, when analyzing the case, rather than being due to a specific nutritional aspect of association of this food group with disease severity, both this food, it could be explained by a specific functional IBDs showed a significant decrease in fruit consumption feature of RA. In this IMID, inflammation tends to occur in when the disease is more active (Table 3). Therefore, this the hands (in our RA cohort >98% of patients had joint result suggests that, when inflammatory symptoms appear erosions in hands), and is generally expressed as a sym- in both IBDs, patients tend to follow the suggested metric arthritis that causes inability to perform more stren- recommendations and reduce fruit consumption. However, uous manual tasks like as cutting meat [15]. this result does not explain the significant increase in fruit The MR analysis also showed that the development of consumption of UC patients globally compared to controls. CD is responsible for the drastic reduction in vegetable Compared to vegetables, fruit fibers tend to be more fer- consumption observed in these patients. This highly sig- mentable and have a greater impact reducing transit time nificant dietary change, however, was not observed in UC, [18]. An increase in fruit consumption therefore could have the other IMID targeting the gut. While both IBDs involve been acquired by UC as a strategy to minimize the impact of the colon mucosa, only in CD inflammation occurs in other digested food on the inflamed mucosa. sections of the gastrointestinal tract, which tends to be the The dietary changes in IMIDs that were found to be ileal section. This dietary reduction could be heavily influ- significantly different from controls but were not associated enced by medical and dietary specialists. Many dietetic in the MR analysis could be indicative of food groups that associations recommend patients from both IBDs to reduce contribute to the disease. In this group of dietary associa- their vegetable intake to facilitate digestion [16], particu- tions, we found that IBD patients consumed higher quan- larly when the inflammatory symptoms worsen. Our data, tities of rice and pasta than healthy individuals. There is however, indicates that this dietary change only occurs in previous evidence that starchy foods could be a risk factor CD patients and not in UC patients. Also, CD patients with for IBD [19]. Analysis of fecal matter of CD patients has a more active disease were found to consume lower levels shown that they have a major reduction of microbiota of vegetables compared to CD patients with milder or no involved in the fermentation of resistant starch [20]. In PS, symptoms. The reason for this difference could be due to we found a higher consumption of bread and grain com- the digestive process of vegetables and how it negatively pared to controls. Antibodies against gluten, anti-gliadin impacts inflammation at specific regions of the gut. In IgA antibodies, have been found to be augmented in PS particular, this highly significant association in CD supports patients compared to controls, even in the absence of celiac that vegetables in the diet would affect the inflammation in disease or non-celiac gluten sensitivity [21, 22]. In a pre- the ileum but not the colon. Supporting this hypothesis, we vious study in the Scandinavian population, the prevalence also found that CD patients with strictures -a narrowing of of anti-gliadin antibodies was found to be 16% in PS the wall of the small intestine that is induced by inflam- patients, while the positivity in the general population was mation- consume less vegetables that patients without only 1% [23]. Both PS and celiac disease are inflammatory this obstruction (OR(CI) = 0.79 (0.64–0.98), P = 0.038). diseases that tend to co-occur, and GWAS have shown that Together, our results suggest that dietary recommendations there is genetic risk overlap between them [24]. Our results involving vegetable consumption should distinguish provide evidence in favor of a common etiological factor for between CD and UC. While our data clearly supports that these two diseases. Gut permeability and inflammation high vegetable consumption is detrimental in CD and that is associated with celiac disease should therefore be con- should be reduced in patients with more active disease, it templated as a potential causal mechanism in PS etiology. Food groups associated with immune-mediated inflammatory diseases: a Mendelian randomization and. . . 1377

We have also found several dietary habits in IMIDs that associated with the frequency of the different food groups are not different from healthy controls but are significant prevented to test this causal association. However, the identi- when comparing patients with different levels of disease fication of foods associated with a worsening on disease severity. This result shows the importance of incorporating activity is a powerful alternative strategy. If these findings are the disease activity in the analysis of the diet, particularly in a validated in a controlled clinical trial, they could provide a group of diseases where patients fluctuate in their lifetime simple and inexpensive way to reduce disease severity between periods of highly damaging flares and periods of in IMIDs. much milder inflammation. In our study, PS patients with Our study provides a better understanding of the relation- more severe disease (represented by a larger skin inflamma- ship between diet and immune-mediated inflammatory dis- tory involvement) showed an increase in the weekly con- eases. We show that this group of common diseases have sumption of legumes and rice and pasta. Based on this result, dietary patterns that are different to that of healthy individuals. both food groups could be contributing to the increase in Changes in the frequency of food are not restricted to the disease activity. As described previously, the high presence of diseases that affect the digestive system, CD and UC, but also anti-gliadin antibodies in PS patients promote a subclinical occur in IMIDs that target other organs and tissues. Mendelian gut inflammatory process when pasta is increased in the diet. randomization allowed to identify which changes are caused This activation of the immune system could have harmful by the disease and differentiate from those that could be effects that could extend beyond the gut and, in this case, involved in the disease pathology. The identification of food increase the level of inflammatory activation in the skin. frequency habits that correlate with disease severity levels, Legumes are highly abundant in lectins, a type of glycopro- provides powerful additional evidence in this direction. In the teins that are known to bind strongly to the surface of epi- forthcoming years, the study of diet should be prioritized if we dermal cells [25], including keratinocytes and subsets of want to fully understand the complexity of IMIDs. epidermal Langerhans cells [26]. Experimental studies have shown that the epidermis from PS patients has a lower capacity to bind to lectins, and that this could be due to the Methods previous occupancy in this cells with lectins from the diet [27]. According to these results, pasta and legumes are two Study population types of food that promote worsening of symptoms in PS. Studying these associations in depth could help to reveal The patients and controls of this study were recruited on a relevant pathogenic mechanisms for this IMID. multicenter collaborative project by the Immune-Mediated The observational nature of the present study involves Inflammatory Diseases Consortium (IMIDC) [30]. The limitations. Self-report of food intakes involves the generation IMIDC is a nation-wide network of clinical, biology and of noisier statistical estimates [28]. However, the use of a large epidemiological researchers in Spain focused on the study of nation-wide cohort of >13,000 individuals is a strong measure IMIDs. In the recruitment of the IMID patients a total of 73 to improve the statistical power of the results [29]. The university hospital clinical departments participated, including simultaneous analysis of six IMIDs is a unique feature of this rheumatology (for RA, PsA, and SLE), dermatology (for PS study, allowing to directly evaluate the reproducibility of and PsA) and gastroenterology departments (for UC and CD). dietary factor associations as well as demonstrating that dis- Simultaneously, a control group of healthy subjects form the eases with a closer pathology (e.g., CD and UC, PS and PSA) same Spanish regions were also recruited. have also similar dietary patterns. To further increase the All the patients included in this cross-sectional cohort ful- robustness of our results, we performed an exhaustive search filled the consensus diagnosis criteria of each IMID (Supple- and adjustment for potential confounders. Dietary habits can mental Methods). The healthy group subjects were recruited be affected by many epidemiological features, and it is from blood donors attending to the same university hospitals. essential to reduce their impact in the study. Here, we used Healthy subjects having first- or second-order relatives affec- common epidemiological variables like age, sex and smoking ted with an IMID were excluded from the study. All patients status, but also we controlled for geographical location of the and controls were over 18 years old at the time of recruitment, individuals, the season of the year, the educational level and, were born in Spain and had also all four grandparents born in in the case of IMID patients, the number of years since Spain. The data were collected from June 2007 to December diagnosis. All these variables can have an important impact in 2012. The sample size at the end of the recruitment period the dietary habits of individuals but are rarely incorporated consisted of 13,358 subjects, including 2282 RA, 2277 PS, jointly in dietary studies. Finally, in this study the MR fra- 1481 PsA, 1070 SLE, 1723 UC and 2475 CD patients and mework could be used to determine the causality of IMIDs 2050 healthy controls (Supplementary Fig. 1). over dietary changes, but the contrary could not be directly Epidemiological variables considered in the study included analyzed. The lack of genetic variants -instrumental variables- age, sex, number of years since diagnosis, smoking status (at 1378 A. Julià et al. diagnosis and present), place of residence, season of the year, of the food item responses (excluding stimulating and alco- and educational level. Place of residence was categorized at holic beverages). For this objective, frequency categories were the province level (50 total) and education was summarized as numerically coded as 0, 1.5, 4 and 6.5 days/week. The first having completed university-level studies (yes/no). Biometric three components (i.e., eigenvalues >1) were evaluated for the variables weight and height were also measured on the same presence of outliers or possible confounders. This analysis day the diet was assessed, as well as the level of physical revealed a group of n = 354 consecutive participants from the activity (i.e., doing regular exercise during leisure or at work, same center that showed a significant deviation in the first yes/no). Informed consent was obtained from all participants, principal component (Supplementary Fig. 2, P <1e−127). In and protocols were reviewed and approved by local institu- order to avoid the risk of confounding, this outlying group of tional review boards. This study was conducted in accordance participants was excluded from downstream analyses. with the Helsinki Declaration of 1975 as revised in 1983. Diet categories were dichotomized into high- and low- consumption levels in order to perform the posterior analyses Assessment of disease severity using logistic regression models. Dichotomization was done as follows: food categories where more than 50% of the Simultaneous to dietary assessment, disease severity of sample had the maximum level of consumption (“6or7 IMID patients was recorded at the day of visit to the hos- times a week”, i.e., dairy, bread/grain and fruits), this level pital by the clinician. For each disease, established severity was coded as “1” (high) and the remaining consumption scores were measured: the Disease Activity Score for 28 levels as “0” (low). The rest of food categories had “1or joints (DAS28) for RA and PsA [31], the Psoriasis Area and 2 days per week” or “3–5 days per week” as their mode. For Severity Index (PASI) for PS [32], the British Isles Lupus these variables, the four levels of consumption were dichot- Assessment Group (BILAG) for SLE [33], the Harvey- omized aggregating the two superior (high) and the two Bradshaw Index for CD [34] and the Lichtiger Score for UC inferior (low) levels. Regarding stimulating beverage and [35]. All the physicians participating in the study were alcohol consumption, variables were also binarized, dividing trained to follow the same criteria. individuals into abstinent or drinkers. In the case of alcohol, week-end only drinkers were considered as abstinent. Dietary assessment Association testing with IMIDs was performed using logistic regression analyses with the binarized diet variables The participants completed an FFQ during their visit to the as the outcome. To examine the differences between IMID hospital. The FFQ included 11 major food groups: fruits, patients and the healthy group, an indicator variable for vegetables (excluding legumes), meat, processed meat, fish, disease type was introduced as predictor, with the healthy eggs, dairy products, bread and/or wholegrain, rice and/or group as the reference level. Relevant adjustment covariates pasta and/or potatoes, sweet products (e.g., pastry, marma- were also included in the logistic model as predictors. Wald lades) and legumes. For each food category, participants tests of the coefficients were used to test for significant indicated their average frequency of consumption over the differences against the control group on the log-odds scale. previous week by selecting between four different cate- Multiple-test adjustment using Dunnett’s method and gories: “0 times a week”, “1 or 2 times a week”, “3–5 times adjusted estimates of the rates of high consumption for each a week” and “6 or 7 times a week”. Stimulating beverage cohort were determined with the emmeans R package. drinking habits were measured by asking for the average To test for association with disease severity, the previous number of cups of tea or coffee consumed per day. Finally, regression models were modified by adding an interaction alcohol drinking habits were classified into three categories: term between disease type and disease activity and by “daily” when respondents reported drinking beer, wine or excluding the healthy control cohort. Main effects were con- spirits during the week, “sometimes” when they only drank sidered for disease type but not for disease activity, so that occasionally on weekends, and ‘abstinent’ otherwise. disease activity effects were nested within disease type. Wald tests of the coefficients corresponding to the interactions were Statistical analyses used to test for significant trends in the rate of high consumption when changing from low disease activity to high Quality control analysis was performed on the dietary, epi- disease activity. demiological and clinical data. 1143 individuals showed high level of missingness (>50%) along the different questionnaires Adjustment for nondietary exposures and were excluded from the study. Since the remaining missing proportions were less than 5% for each variable, an All the regressions for the food items were performed using available-case analysis was performed. Quality control of two adjustment models. In Model 1 we adjusted for all dietary habits was performed by principal component analysis potential confounders. As confounders, we considered those Food groups associated with immune-mediated inflammatory diseases: a Mendelian randomization and. . . 1379 variables that were likely to influence and not be influenced In order to test the causal hypothesis that IMIDs cause the by dietary habits and, simultaneously, were associated with observed changes in the diet, we performed a Mendelian at least one of the IMIDs. Age, sex and smoking status at randomization (MR) analysis. GWAS data was available for diagnosis were included since they are known risk factors 7554 subjects (Supplementary Fig. 1). For this objective we for IMIDs [36] and likely to be related with dietary habits. used the genetic risk scores for each disease as the instru- In this model we also included geographical region, season mental variables (IV). Genetic risk scores were calculated of the year and highest educational level as covariates. In following recently described methodology (Supplementary Model 2, we adjusted for both for potential confounders as Methods) [39]. The complete list of genetic variants included well as for potential mediators. We replaced smoking status per each IMID are listed in Supplementary Table 1. The ratio at diagnosis for the present smoking status and additionally method with bootstrap was then used to obtain empirical adjusted for the current physical activity habits. These confidence intervals and variance of the ratio estimator [40]. covariates represent plausible mechanisms by which the The beta coefficients of the ratio method were obtained by disease might be indirectly affecting eating habits. logistic regression for both the IV-outcome and the IV- The same two-model approach was applied to study exposure. The 10 first principal components of genetic var- associations between disease activity and diet by including iation were calculated using the EIGENSTRAT method [41] an interaction term between disease activity and the disease and included in the models to account for potential popula- factor. In this case, the control cohort was excluded and the tion stratification. P-values were obtained by approximating model was additionally adjusted for the number of years the distribution of the ratio estimator by a normal distribution since diagnosis. In most IMIDs chronic exposure to the and applying a Wald-like test. disease aggravates the severity of the disease and, conse- quently, it was considered a potential confounder. Acknowledgements The IMID Consortium includes the following: Exploratory analysis of the different covariables showed Eduardo Fonseca, Jesús Rodríguez, Patricia Carreira, Valle García, non-linear relationships between age and BMI with the rates José A. Pinto-Tasende, Lluís Puig, Elena Ricart, Francisco Blanco, of high vs. low food consumptions (Supplementary Fig. 3). Jordi Gratacós, Ricardo Blanco, Víctor Martínez Taboada, Emilia Fernández, Isidoro González, Fernando Gomollón García, Raimon For this reason, age and BMI were converted into catego- Sanmartí, Ana Gutiérrez, Àlex Olivé, José Luís López Estebaranz, rical variables. Age was divided into six age ranges Esther García-Planella, Juan Carlos Torre-Alonso, José Luis Andreu, according to quantiles (i.e., years 18–34, 35–42, 43–48, David Moreno Ramírez, Benjamín Fernández, Mª Ángeles Aguirre 49–56, 57–63 and 63–92) and BMI was categorized into Zamorano, Pablo de la Cueva, Pilar Nos Mateu, Paloma Vela, Fran- ‘ ’ ‘ ’ ‘ ’ ‘ ’ cisco Vanaclocha, Héctor Corominas, Santiago Muñoz, Joan Miquel Underweight , Normal , Overweight and Obese , Nolla, Enrique Herrera, Carlos González, José Luis Marenco de la according to the WHO classification [37]. In the case of age, Fuente, Maribel Vera, Alba Erra, Daniel Roig, Antonio Zea, María we also included the continuous predictor since both ver- Esteve, Carlos Tomás, Pedro Zarco, José María Pego, Cristina Saro, sions appeared to be independent significant factors for Antonio González, Mercedes Freire, Alicia García, Elvira Díez, Georgina Salvador, César Díaz-Torne, Simón Sánchez, Alfredo most of the outcomes. Willisch Domínguez, José Antonio Mosquera, Julio Ramírez, Esther In order to incorporate geography as a covariate, we used Rodríguez Almaraz, Núria Palau, Raül Tortosa, Mireia López, Andrea the most detailed residence information we had from the Pluma, Adrià Aterido. We would like to thank Dr Eduard Cabré for cohort individuals. Here it was represented by provinces; stimulating discussions.

Spain is divided into 50 different provinces that range from 17 18 2 2 IMID Consortium Eduardo Fonseca , Jesús Rodríguez , Patricia 1090 km (Guipúzcoa) to 21,766 km (Badajoz). we Carreira19, Valle García20, José A. Pinto-Tasende21, Lluís Puig22, approached the issue of having regions with relatively small Elena Ricart23, Francisco Blanco24, Jordi Gratacós25, Ricardo sample size by aggregating individuals into larger regions. Blanco26, Víctor Martínez Taboada26, Emilia Fernández27, Pablo Unamuno27, Isidoro González28, Fernando Gomollón García29, Rai- Sensitivity analyses were performed to see whether alter- 30 31 32 ﬁ mon Sanmartí , Ana Gutiérrez , Àlex Olivé , José Luís López native region de nitions had a relevant impact on the con- Estebaranz33, Esther García-Planella34, Juan Carlos Torre-Alonso35, clusions. We found that using an eight-region variable José Luis Andreu36, David Moreno Ramírez37, Benjamín Fernández38, (resulting from aggregating neighboring provinces), the Mª Ángeles Aguirre Zamorano39, Pablo de la Cueva40, Pilar Nos 41 42 43 results were not qualitatively affected. For the analyses Mateu , Paloma Vela , Francisco Vanaclocha , Héctor Cor- omines44, Santiago Muñoz45, Joan Miquel Nolla46, Enrique Herrera47, performed in present study we therefore used this more Carlos González48, José Luis Marenco de la Fuente49, Maribel Vera50, parsimonious version. Alba Erra51, Daniel Roig52, Antonio Zea53, María Esteve Comas54, Bivariate analyses with the baseline variables were per- Carles Tomàs55, Pedro Zarco56, José María Pego57, Cristina Saro58, 59 60 61 62 formed using ANOVA for age and chi-square tests for Antonio González , Mercedes Freire , Alicia García , Elvira Díez , Georgina Salvador63, César Díaz64, Simón Sánchez65, Alfredo Willisch categorical variables. All the analyses were performed with Dominguez66, José Antonio Mosquera67, Julio Ramírez68, Esther R software, version 3.4.4 [38]. The level of signiﬁcance was Rodríguez Almaraz69, Núria Palau51, Raül Tortosa51, Mireia López51, set at a two-sided p value of 0.05. Andrea Pluma51, Adrià Aterido51 1380 A. Julià et al.

17Dermatology Department, Complejo Hospitalario Universitario A de Ourense, Ourense, Spain; 67Rheumatology Department, Complejo Coruña, A Coruña, Spain; 18Rheumatology Department, Hospital Hospitalario Universitario de Pontevedra, Pontevedra, Spain; 68Rheu- Universitari de Bellvitge, Barcelona, Spain; 19Rheumatology Depart- matology Department, Hospital Clínic de Barcelona, Barcelona, Spain; ment, Hospital Universitario 12 de Octubre, Madrid, Spain; 20Gas- 69Rheumatology Department, Hospital Universitario 12 de Octubre, troenterology Department, Hospital Universitario Reina Sofía, Madrid, Spain Córdoba, Spain; 21Rheumatology Department, Complejo Hospitalario 22 Universitario A Coruña, A Coruña, Spain; Dermatology Department, Author contributions AJ designed the study, conceived, designed and 23 Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Gastro- analyzed data and wrote the manuscript; SH performed data curation enterology Department, Fundació Clínic per a la Recerca Biomèdica, and statistical analysis; ED, JDC, CF, JT, JPG, AFN, ED, MBA, CP, 24 Barcelona, Spain; Rheumatology Department, Complejo Hospita- RQ, FJLL, JLSC, JLM, MA, CM, JJPV, FM, SC and MLL contributed 25 lario Universitario A Coruña, A Coruña, Spain; Rheumatology to patient recruitment, clinical data collection and analysis and Department, Hospital Universitari Parc Taulí, Sabadell, Spain; manuscript revision; AA contributed to genetic data analysis; SM 26 Rheumatology Department, Hospital Universitario Marqués de designed the study, coordinated clinical data collection and analysis, 27 Valdecilla, Santander, Spain; Dermatology Department, Hospital and co-wrote the manuscript. Universitario de Salamanca, Salamanca, Spain; 28Rheumatology Department, Hospital Universitario de La Princesa, Madrid, Spain; Funding This work was supported by the Spanish Ministry of Econ- 29Gastroenterology Department, Hospital Clínico Universitario omy and Competitiveness grants (IPT-010000-2010-36, PSE-010000- Lozano Blesa, Zaragoza, Spain; 30Rheumatology Department, Fun- 2006-6, and PI12/01362). dació Clínic per a la Recerca Biomèdica, Barcelona, Spain; 31Gas- troenterology Department, Hospital General Universitario de Alicante, Alicante, Spain; 32Rheumatology Department, Hospital Universitari Compliance with ethical standards Germans Trias i Pujol, Badalona, Spain; 33Dermatology Department, 34 Hospital Universitario Fundación Alcorcón, Madrid, Spain; Gastro- Conflict of interest The authors declare no competing interests. enterology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; 35Rheumatology Department, Hospital Monte Nar- Publisher’s note Springer Nature remains neutral with regard to anco, Oviedo, Spain; 36Rheumatology Department, Hospital Uni- jurisdictional claims in published maps and institutional affiliations. versitario Puerta de Hierro, Madrid, Spain; 37Dermatology Department, Hospital Universitario Virgen Macarena, Sevilla, Spain; 38Rheumatology Department, Hospital Clínico San Carlos, References Madrid, Spain; 39Rheumatology Department, Hospital Universitario Reina Sofia, Córdoba, Spain; 40Dermatology Department, Hospital Universitario Infanta Leonor, Madrid, Spain; 41Gastroenterology 1. 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Afﬁliations

1 Rheumatology Research Group, Vall d’Hebron Hospital Research 4 Hospital Clínic de Barcelona and IDIBAPS, Barcelona, Spain Institute, Barcelona, Spain 5 Hospital Universitario de Guadalajara, Guadalajara, Spain 2 Hospital Universitari Germans Trias i Pujol, Badalona, Spain 6 Hospital Universitario de La Princesa and IIS-IP, Madrid, Spain 3 CIBERehd, Madrid, Spain 1382 A. Julià et al.

7 UGC Reumatología, Instituto de Investigación Biomédica 13 Hospital Clínico San Carlos, Madrid, Spain (IBIMA), Hospital Regional Universitario de Málaga, Universidad 14 de Málaga, Málaga, Spain Hospital Virgen de la Vega, Salamanca, Spain 15 8 Hospital Clínico Universitario de Santiago, Santiago de Hospital Universitario Virgen Macarena, Sevilla, Spain Compostela, Spain 16 Complejo Asistencial Universitario de León, León, Spain 9 Hospital del Mar, Barcelona, Spain

10 Hospital Universitario Central de Asturias, Oviedo, Spain

11 Hospital General Universitario Gregorio Marañón, Madrid, Spain

12 Consorcio Hospital General Universitario de Valencia, Valencia, Spain