Temporal dynamics of the gut microbiome preceding celiac disease in genetically at-risk children

Temporal dynamics of the gut microbiome preceding celiac disease in genetically at-risk children | 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 Temporal dynamics of the gut microbiome preceding celiac disease in genetically at-risk children Angelica P. Ahrens , Kristian Lynch , Heikki Hyöty , Richard E. Lloyd , Joseph Petrosino , View ORCID Profile Eric W. Triplett , Daniel Agardh doi: https://doi.org/10.1101/2025.05.29.25328357 Angelica P. Ahrens 1 Microbiology and Cell Science Dept., University of Florida , Gainesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristian Lynch 2 Health Informatics Institute, University of South Florida , Tampa, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Heikki Hyöty 3 Faculty of Medicine and Health Technology, Tampere University , Tampere, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard E. Lloyd 4 Molecular Virology & Microbiology Dept., Baylor College of Medicine , Houston, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joseph Petrosino 4 Molecular Virology & Microbiology Dept., Baylor College of Medicine , Houston, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric W. Triplett 1 Microbiology and Cell Science Dept., University of Florida , Gainesville, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eric W. Triplett For correspondence: ewt{at}ufl.edu Daniel Agardh 5 Unit of Celiac Disease and Diabetes, Clinical Sciences Dept., Lund Univ. , Malmö, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF Abstract Longitudinal study of the microbial dysbiosis preceding celiac disease (CD) is needed, particularly in the first several years of life. Within the Environmental Determinants of Diabetes in the Young (TEDDY) multi-national prospective cohort study, a case-cohort study of 306 CD cases (i.e., seroconverting by 48 months of age), with controls matched 2:1 by site, gender, and time of birth, was assessed. Temporal microbiome case-control dynamics were modelled by 16S rRNA analysis of monthly sequential stool samples taken from age three months up to age four (or until the development of CD). Significant differences were identified across time, including key taxa that break down gluten and influence inflammation, all before the development of autoantibodies. Key bacterial associations with environmental factors such as diet were assessed using detailed longitudinal nutrient intake and diary data, along with genetic variants conferring high CD risk. INTRODUCTION Celiac disease (CD) is a chronic immune-mediated disorder triggered by gluten ingestion in genetically predisposed individuals carrying the HLA-DQ2 and/or HLA-DQ8 haplotypes 1 . While genetic susceptibility is required for disease development, environmental factors play a key role in modulating immune responses to gluten and influencing disease onset 2 . Among these, dietary composition and gut microbiota have emerged as critical determinants of CD risk 3 . Epidemiological studies suggest that the quantity of gluten introduction may influence CD risk. Data from The Environmental Determinants of Diabetes in the Young (TEDDY) Study 4 indicates that higher gluten intake during the first years of life is associated with an increased risk of CD autoimmunity and progression to clinical disease 5 . However, the mechanisms underlying this association remain unclear since exposure to other environmental factors also contribute to the disease risk 6 . Emerging evidence suggests that dietary fiber intake may reduce CD risk, possibly by shaping gut microbiota composition and promoting immune tolerance 7 . Fiber fermentation by gut microbes leads to the production of short-chain fatty acids (SCFAs), such as butyrate, which exert immunomodulatory effects and maintain epithelial integrity 8 . As with other autoimmune diseases such as T1D, the microbiome has been postulated to have a role in CD pathogenesis 9 . At the phylum level, decreases in the abundance of Firmicutes species and increases in Proteobacteria species have been detected in both children and adults with active CD 10 , 11 . The Proteobacteria phylum includes several immuno-stimulatory taxa and has been associated with flares in other inflammatory diseases, such as Inflammatory Bowel Disease (IBD). Further studies have demonstrated that decreases in anti-inflammatory bacteria such as Bifidobacterium and increases in the proportion of Bacteroides and Escherichia coli have also been associated with active CD 12 , 13 . Other work suggests that the normal gut microbiome may be protective in reducing inflammation associated with gluten ingestion, and that certain microbial constituents may be involved in the development of CD 14 . Other studies have reported distinct microbial signatures in individuals with CD, including a reduction in anti-inflammatory taxa ( Bifidobacterium and Faecalibacterium prausnitzii ) and an expansion of pro-inflammatory species ( Proteobacteria ) 15 , 16 . Importantly, microbial dysbiosis has been observed in genetically at-risk infants before the onset of CD 17 . Some gut bacteria, including Lactobacillus and Bifidobacterium , can metabolize gluten peptides and potentially reduce their immunogenicity, whereas others, such as Pseudomonas aeruginosa , may enhance gluten toxicity by modifying peptide digestion 18 , 19 . These findings support a complex interplay between diet, microbial metabolism, and immune activation in CD pathogenesis. Although there is increasing evidence of strong linkage between human gut microbial communities and autoimmune disorders. Several reports indicate linkage of both viruses and microbiota with CD development 1 – 9 . In children at genetic risk, gastrointestinal infections increase the risk of CDA. HLA genotype, infant gluten consumption, breastfeeding, and rotavirus vaccination modify this risk, suggesting that a complex series of interactions between infectious agents, diet, and genetics promote this disease 10 . Even among those who carry the highest genetic susceptibility for CD 11 (e.g., HLA-DR3-DQ2 homozygous individuals) and are exposed to gluten, only a minority will develop CD, suggesting that other factors must play critical roles. However, prior studies linking gut microbiome and virome with autoimmune diseases are limited in scale and scope and fail to provide consistent or persuasive linkages to specific etiologic agents or conclusions that certain bacteria or viruses are linked. In addition, very few prospective studies have evaluated the role of bacteria and viruses already before CDA and those that have are small in sample size focused on CD and those that have are small in sample size. In this case-cohort study, nested in the prospective TEDDY birth cohort, we investigate the relationship between early-life dietary patterns, gut microbiome composition, and the risk of developing CD. By analyzing a longitudinal, genetically at-risk cohort, we aim to identify microbial and dietary factors that could play a role prior providing novel insights into CD etiology. RESULTS This TEDDY ancillary study represents the largest multinational prospective investigation of the CD microbiome to date, with quarterly stool sampling from 3 to 48 months and longitudinal CDA assessments, confirmed by biopsy-proven CD diagnosis. For this study’s case-control cohort, a total of 137 CD cases and 232 randomly chosen controls were included. For each case, sample collection ceased once CDA was detected ( Fig. 1a ), while controls were sampled continuously until 48 months. The median age at seroconversion was 28.3 months (IQR: 21.8-36.7). CD cases were further stratified by age at seroconversion: early (≤24 months, median 18.5), mid (24 to ≤36 months; median 29.2), and late (36 to ≤48 months; median 42.0) seroconversion, respectively ( Table 1 ). The number of stool samples from CD cases and number of CD cases available for this study as a function of the age in months at time of CDA seroconversion ( Fig. 1b ). View this table: View inline View popup Download powerpoint Table 1. Number of celiac disease (CD) cases by time of seroconversion to CD autoimmunity (CDA). Download figure Open in new tab Figure 1. Number of stool samples from CD cases and number of CD cases available for this study as a function of the age in months at time of CDA seroconversion. DNA was extracted from 8,473 stool samples and sequenced using Illumina targeting the V4 region of the 16S rRNA gene, identifying 1,325 bacterial operational taxonomic units (OTUs), clustered at 97% similarity. To minimize the impact of extreme values, abundances within the 99.9% percentile were retained, removing only the top 0.1%. This approach ensures that rare or low-abundance bacteria are not excluded, maximizing ability to detect subtle but potentially meaningful differences. Longitudinal sampling reveals CD microbiome differences preceding seroconversion To detect microbial abundance differences, the global case-control cohort (all cases combined) was first analyzed, with subsequent stratified analysis based on the child’s age at seroconversion (≤24 months; “early seroconverters”; 24-36 months: “mid seroconverters”; 36-48 months: “late seroconverters”). Significant microbiome differences were observed in the critical period just before seroconversion regardless of time of birth ( Fig. 2a-e ). This was observed while accounting for expected microbiome shifts during early childhood, key confounders of CD risk and microbiome composition were balanced using propensity score matching across future cases and controls. Download figure Open in new tab Figure 2. Taxonomic differences up to 48 months of age (a-d) Significant increases in genus-level relative abundance betweens in controls compared to D cases over the first four years of life, separated by timepoint. The relative abundance of specific taxa across time is shown in cases (red) and controls (blue). Vertical lines above the timelines on the x-axis represent specific timepoints where cases and controls differed significantly with an adjusted p-value of <0.01 (yellow) or <0.05 (green). (e) Relative abundance of Lacticaseibacillus and Phascolarctobacterium , six months before seroconversion. Propensity score matching was applied to adjust for a variety of factors (sex, maternal education, parity, age at gluten introduction, breastfeeding duration, and age at stool collection). Stool samples collected between 3 and 18 months of age were pooled. For cases, samples collected six months prior to seroconversion were analyzed, with a random two age-matched controls randomly selected for each case. After matching, no significant differences remained between groups for any of these factors: sex (p=0.645), maternal education (p=0.933), parity (p=1.0), age at gluten introduction (p=0.659), total breastfeeding duration (p=0.403), or duration of exclusive breastfeeding (p=0.332). All stool samples collected in these individuals between 3 and 18 months of age were pooled. For future cases, samples collected six months prior to the visit at which seroconversion were then moved forward for analysis. For each case, two age-matched controls were randomly selected. Bacterial genera significantly higher in controls across all periods prior to seroconversion included Faecalibacterium ( Fig. 2a ), Bacteroides ( Fig. 2b ), Bifidobacterium ( Fig. 2c ), and Akkermansia ( Fig. 2d ). Lacticaseibacillus and Phascolarctobacterium are depleted in cases six months before seroconversion, independent of child age and key confounders. Two of the strongest genus-level differences in abundance ( Fig. 2e ) were identified in Lacticaseibacillus (l2FC = 2.8, q = 1.78e-7) and Phascolarctobacterium (l2FC = 4.3, q = 2.83e-14). Only 7.9% of cases (at 6 months prior to CDA) had at least 0.1% Lacticaseibacillus , compared to 21.9% of controls. At this same threshold of abundance, Phascolarctobacterium was also half as prevalent in CD cases. Cases with early seroconversion of celiac disease autoimmunity (CDA) In the CDA-phase analysis of all early seroconverters (CDA 13-24 months), several OTU and genus-level differences were noted between case and control children across time. For example, OTU1138 Collinsella , likely Collinesella aerofaciens , based on sequencing alignment with NCBI BLAST, spiked higher in cases just before and persisted within the window of seroconversion ( Fig. 3b ). Erysipelotrichaceae UCG 003 , (d) Ruminococcus , (e) Enterococcus , and (f) Turicibacter across time. Download figure Open in new tab Figure 3. Cases with early seroconversion of celiac disease autoimmunity (CDA) Dysbiosis that occurs at 6 months of age, comparing controls and CD cases seroconverting between 13-24 months of age (early seroconverters), (a) with red dots and labels corresponding to taxa significant after FDR correction. Key differential microbial abundancdifferences between early seroconverters (CDA ≤24 months) and controls, including (b) Collinsella OTU1138, (c) Erysipelotrichaceae UCG 003 , (d) Ruminococcus , (e) Enterococcus , and (f) Turicibacter across time. Vertical lines above timelines on the x-axis represent specific timepoints where cases and controls differed significantly with an adjusted p-value of <0.01 (yellow) or <0.05 (green) (b-f). Cases with mid and late seroconversion of celiac disease autoimmunity (CDA) During the mid seroconversion time frames, Faecalibacterium and certain Clostridia are significantly higher in controls and cases, respectively ( Figs. 4 ). At both mid and late seroconversion time periods, there are several time points where Bifidobacterium and Faecalibacterium are significantly higher than controls ( Figs. 5 ) while others were higher in cases ( Fig. 6 ). At the late seroconversion period, certain Bacteroides are higher at specific time points in controls compared to cases. ( Fig. 5 ) Download figure Open in new tab Figure 4. Cases with mid-seroconversion of celiac disease autoimmunity (CDA) Key microbial abundance differences across time between (a-d) controls and CD cases who seroconverted between 24-36 months (mid seroconverters) across time. Vertical lines above the timelines on the x-axis represent specific timepoints where cases and controls differed significantly with an adjusted p-value of <0.01 (yellow) or <0.05 (green). Download figure Open in new tab Figure 5. Cases with late seroconversion of celiac disease autoimmunity (CDA) Key microbial abundance differences (a-c) across time higher in controls than CD cases who seroconverted between 36-48 months (late seroconverters) across time. Vertical lines above the timelines on the x-axis represent specific timepoints where cases and controls differed significantly with an adjusted p-value of <0.01 (yellow) or <0.05 (green). Download figure Open in new tab Figure 6. Cases with late seroconversion of celiac disease autoimmunity (CDA) Key microbial abundance differences (a-d) across time higher in CD case than in controls in those who seroconverted between 36-48 months (late seroconverters) across time. Vertical lines above the timelines on the x-axis represent specific timepoints where cases and controls differed significantly with an adjusted p-value of <0.01 (yellow) or <0.05 (green). DISCUSSION Across the entire seroconversion period, CD cases were significantly deficient in important bacteria bacterial genera that contribute to gut health including Bifidobacterium, Akkermansia , and Faecalibacterium. Bifidobacterium produces antioxidants and converts lactate to acetate 20 , 21 . Bifidobacterium has also been shown in cross sectional studies to be in lower relative abundance in subjects with celiac disease 22 , 23 . As a result, strains in this bacterial genus are being tested as probiotics to reduce celiac disease symptoms 24 – 26 . (Alternatives to a gluten-free diet are necessary as this diet changes alone do not necessarily resolve all issues 27 . Akkermansia is commonly associated with gut health compared to subjects with a variety of gut ailments 28 including celiac disease 29 . Faecalibacterium similarly is considered anti-inflammatory and has been associated with reduced coronary artery disease 30 and inflammatory bowel disease 31 as well as playing a modulatory role in immune responses 32 . Here, Bacteroides was also higher in controls. Some beneficial strains of Bacteroides have been described as promising probiotics to reduce gut leakiness 33 and have been shown to increase in children on a gluten-free diet 34 . The significantly lower prevalence of Lacticaseibacillus and Phascolarctobacterium in cases six months prior to seroconversion was also striking. Strains of Lacticaseibacillus are well characterized for their probiotic, antioxidant, and anti-inflammatory properties 35 , 36 . Phascolarctobacterium strains are asaccharolytic 37 of this genus was recently discovered to adjust innate immunity and reduce obesity in a mouse model 38 . Given these characteristics, it is not surprising that Phascolarctobacterium would be more common in those consuming less gluten and avoiding CD. The gut dysbiosis in those who seroconvert have an abundance of bacteria more highly associated with cases than controls. This lack of protective bacteria may be contributing to the early seroconversion in the first two years of life. These harmful bacteria include Collinsella, Enterococcus , Erysipelotrichaceae, Ruminococcus , and Turicibacter. Collinsella is considered pro-inflammatory and associated low fiber intake, obesity, and type 2 diabetes 39 , 40 . Likely Collinsella aerofaciens , based on sequencing alignment by NCBI BLAST, this species is known to produce a pH-responsive lipid immunogen that induces the production of proinflammatory markers, TLR2 and TNF-α, both associated with CD 41 . In the analysis of mid- and late seroconverters, OTU0192 Bacteroides was significantly lower across time, from age five months well into the second year of life. Bacteroides can stimulate the immune system and enhance phagocytosis of macrophages 42 . Important to its fitness are mucins. The polysaccharide A (PSA) produced by Bacteroides fragilis contributes to T-cell responses and has shown protective properties in mouse models of colitis, correcting immune deficiencies and preventing inflammation 43 , necessary for inducing CD4 + and CD8 + T cells that control inflammation via IL-10 44 . Enterococcus strains are well known for their antimicrobial resistance 45 which can lead to their higher abundance when a child is treated for a disease caused by bacteria. Unclassified bacteria in the family Erysipelotrichaceae possess GalNAc catabolism pathway and degrades N-acetylgalactosamine, an inmportant component of mucin, a protein required for gut integrity 46 . Ruminococcus is abundant in inflammatory bowel disease 47 and Chron’s disease 48 . And like Erysipelotrichaceae, Ruminococcus strains can degrade mucin 49 . Turicibacter strains can promote ROS-associated apoptosis 50 . Clostridia were found to be higher in cases that were mid-seroconverters. In a large population study, subjects with CD were twice as likely to have a Clostridium difficile infection as those without CD 51 . Our interpretation of these results is that gut dysbiosis associated with future early childhood CDA and CD can occur very early in life. The severity of the dysbiosis appears to be associated with the timing of serconversion ( Fig. 7 ). Those who seroconvert early have an abundance of bacteria that can lead to inflammation and lessened gut integrity. Late seroconversion appears to be more associated with the reduced abundance of beneficial bacteria rather than harmful bacteria. Notably, these beneficial bacteria were consistently more abundant in controls throughout the first four years of life. Download figure Open in new tab Figure 7. Model for the role of gut bacteria in the development of the inflammatory environment contributing to celiac disease. FUNDING STATEMENT This work was funded by NIH with award number R01DK124581. Proposal title was Gut Viral and Bacterial Associations with Celiac Disease in the TEDDY Cohort. This project was led by Dr. Richard Lloyd, Baylor College of Medicine, 2020-2025. RESOURCE AVAILABILITY Materials Availability This study did not generate new unique reagents. Data and Code Availability Processed microbiome data derived from human samples will be deposited at the time of peer-reviewed publication likely using DRYAD. Approvals from Institutional Review Boards Institutional Review Boards of Colorado’s Colorado Multiple Institutional Review Board, Georgia’s Medical College of Georgia Human Assurance Committee (2004-2010)/Georgia Health Sciences University Human Assurance Committee (2011-2012)/Georgia Regents University Institutional Review Board (2013-2016)/Augusta University Institutional Review Board (2017-present), Florida’s University of Florida Health Center Institutional Review Board, Washington state’s Washington State Institutional Review Board (2004-2012)/Western Institutional Review Board (2013-2019)/WCG IRB (2020-present), and European Ethics Committee Boards of Finland’s Ethics Committee of the Hospital District of Southwest Finland, Germany’s Bayerischen Landesärztekammer (Bavarian Medical Association) Ethics Committee, Sweden’s Regional Ethics Board in Lund, Section 2 (2004-2012)/Lund University Committee for Continuing Ethical Review (2013-present) gave ethical approval for this work. METHODS A celiac case control cohort was established as an ancillary study in TEDDY, comprising pure controls (without type 1 diabetes), controls who developed CDA before 48 months but did not develop CD before four years of age, and case CDA who developed CDA before 48 months and eventually CD or had high titer in persistent sample. Controls were followed for a maximum of 48 months in this investigation. CD Case-Cohort Design To combine the flexible advantages of a cohort study with the efficiency of a nested case-control (NCC), a Celiac Case-Cohort has been designed to examine virome and microbiome content of 18,200 stool samples on the subsequent risk of Celiac Disease Autoimmunity (CDA) (i.e. persistent tissue transglutaminase autoantibodies tTGA) leading to a biopsy proven celiac disease (CD) diagnosis (CDA-to-CD). The original Celiac TEDDY cohort consisted of 6555 enrolled children carrying HLA high risk haplo-genotypes homozygous or heterozygous for DR3-DQ2 and DR4-DQ8. Children were screened for CDA and islet autoantibodies (IA) before 4 years of age any children developing IA were excluded (n=423, 6132 remaining). The number of CDA-to-CD cases before 4 years of age was 306/6132 (5%) children with a median (interquartile range) age at CDA onset of 2.3 (1.8 – 3.0) years. An additional 398/6132 children developed CDA and not CD. These children were treated in analysis as a competing CDA group with a low risk for CD in childhood. The case-cohort design differs from an NCC study in that a sub-cohort is selected from the original cohort to use as a representative population. All children in the sub-cohort, including any cases chosen by chance, will be weighted in all regression analysis to represent the original Celiac cohort. (case-cohort Ref to add) A random sample of 561/6132 children (overall sampling fraction = 9.2%) were chosen from the original Celiac TEDDY cohort, and 37/561 CDA-to-CD cases were chosen by chance to serve as time varying controls until before seroconversion. Non CDA-to-CD cases were followed till censoring (dropout or age 48 months, N= 423) or until seroconversion for the competing CDA group that had no subsequent CD diagnosis (n=41).. A statistical power analysis was performed to detect significant hazard ratios (HR) with a 9.1% sub-cohort sampling fraction and a 5% CDA-to-CD case incidence proportion assuming a 1% type-1 error rate (i.e. p-value <0.01 is significant). Disease outcome measurements The definitions of the two outcomes in this work, celiac disease autoimmunity (CDA) and celiac disease, were described previously 52 . Trans-tissue glutaminase IgA and IgG autoantibodies (tTG) were determined in two laboratories and the data harmonized as described previously 52 . Stool DNA extraction, 16S rRNA sequencing and analysis DNA was extracted from 8,473 stools samples using the PowerMag Microbiome DNA isolation kit. Amplication of the V4 region of the 16S rRNA gene and 16S rRNA sequencing using Illumina sequencing (1×150 bp reads) 53 . Classification of reads, statistical analysis, and visualization of the 16S rRNA data was done across time was done using DESeq2 54 which includes false discovery rate adjustments. Confounders considered in the analysis included sex, material education, parity gluten and breastfeeding. To account for differences in sequencing depth across samples, using DESeq 54 , we applied a median-of-ratios normalization, which estimates size factors for each feature by dividing each sample’s counts by the geometric mean across all samples. A generalized linear model was fitted for each taxon, modeling the count data using a negative binomial distribution. Estimating dispersions for each feature, biological and technical variability can be accounted for. To improve the reliability of fold change estimates, empirical Bayes shrinkage was employed, especially useful for low-abundance taxa. Wald tests were used to assess statistical significance, and multiple testing correction was applied using the Benjamini-Hochberg procedure to control the false discovery rate (FDR). REFERENCES 1. ↵ Lebwohl , B. , Sanders , D.S. , and Green , P.H.R. ( 2018 ). Coeliac disease . Lancet 391 : 70 – 81 . doi: 10.1016/S0140-6736(17)31796-8 . OpenUrl CrossRef PubMed 2. ↵ Kuja-Halkola , R. , Lebwohl , B. , Halfvarson , J. , Wijmenga , C. , Magnusson , P.K.E. , and Ludvigsson , J.F. ( 2016 ). Heritability of non-HLA genetics in coeliac disease: a population-based study in 107 000 twins . Gut 65 : 1793 – 1798 . doi: 10.1136/gutjnl-2016-311713 . 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