The Role of β-cell Autoantibodies in Prediction of Type 1 Diabetes Mellitus in Children

The Role of β-cell Autoantibodies in Prediction of Type 1 Diabetes Mellitus in Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of β-cell Autoantibodies in Prediction of Type 1 Diabetes Mellitus in Children Kseniya Korneva, Dmitry Chichevatov, Leonid Strongin, Vladimir Zagainov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6238174/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The study is aimed at assessing the possibility of type 1 diabetes mellitus (T1DM) prediction on the basis of autoantibodies concentrations and their dynamics. Antibodies against glutamic acid decarboxylase (ab_gad), tyrosine phosphatase (ab_ia2), and zinc transporter 8 (ab_znt8) were assayed. Methods Regression modeling was applied to repeated measured longitudinal data from a total of 517 participants: 314 children with T1DM and 203 healthy siblings. Results Among healthy siblings the high risk of T1DM was associated with 1) high baseline concentration of all three antibodies, 2) significant and rapid decrease in ab_gad and ab_ia2, 3) maintenance of high ab_znt8 levels. Conclusions Mathematical modeling of ab_gad, ab_ia2 и ab_znt8 concentration dynamics has potential benefits for stratifying risk groups and prediction of T1DM in healthy siblings. type 1 diabetes mellitus autoantibodies glutamic acid decarboxylase tyrosine phosphatase zinc transporter 8 prediction Figures Figure 1 Figure 2 Figure 3 Background Recently, curative approaches to type 1 diabetes mellitus (T1DM) have been transformed from hormone-replacement therapy to disease-modifying one. Genetic predisposition to T1DM incorporated with environmental considerations triggers autoimmune destruction of the islet cells, which is marked by specific serum autoantibodies (AABs) to different proteins of β-cells. Since the first AAB was identified in 1974, investigations of autoimmune disorders responsible for T1DM have been ongoing. At the present time five AABs are well-known reliable benchmarks of the early disease onset. These are autoantibodies against islet cells, insulin (ab_ins), glutamic acid decarboxylase (ab_gad), tyrosine phosphatase (ab_ia2), zinc transporter 8 (ab_znt8) [ 1 ]. As a rule, AABs appear several months or years before and persist up to the disease manifestation. T1DM is known to have consecutive steps in the course [ 2 ]. The first stage is featured by onset of autoimmune aggression against β-cells associated with increased concentrations of at least two AABs on the background of normoglycemia. Ab_gad and ab_ins emerge first. Ab_ins reach the peak of seroconversion by the age of 2, whilst ab_gad by 3–5 years. Ab_ia2 and ab_znt8 are registered at the later stage [ 3 ]. The second stage of T1DM starts with dysglycemia which can be diagnosed by the oral glucose tolerance test. The third stage is already represented by classical symptoms of T1DM. 5-year and 10-year risk of T1DM at stages 2 vs 3 are estimated as 44% and 70%. The lifetime risk is 50% and 100% respectively [ 4 – 5 ]. Efficient therapy aimed at prevention or deceleration of T1DM is possible only at stages 1 or 2 when there is a consistent residual pool of intact β-cells. New current curative methods which affect the course of the disease are expected to be more prominent. Since registration of the monoclonal antibody teplizumab the capabilities of the immune-modulating therapy have become more evident [ 6 ]. As per these reasons, T1DM must be diagnosed at its earliest preclinical stages when prophylaxis treatment may improve prognosis and the quality of life considerably as well as prevent long-lasting complications. We aim to assess the possibility of T1DM prediction on the basis of AAB concentrations and their longitudinal changes. Methods The present study was designed and reported according to STROBE Statement – checklist of items that should be included in reports of observational studies. Study design This was a prospective longitudinal cohort study performed in three Regional Children’s hospitals of Nizhny Novgorod, the Chuvash Republic and the Republic of Mari El. The study included patients aged 0–18 years hospitalized with confirmed newly diagnosed T1DM between 2017 and 2020 in one of the three hospitals. One hundred and sixty-six participants with newly diagnosed T1DM had one or more healthy siblings who was bone to the same parents. Inpatient baseline blood samples were collected within 1–2 days of admission to the hospital whilst in healthy siblings within 1–2 weeks on an outpatient basis. Subsequently, the blood tests were performed at 3 months intervals up to 12 months in the group T1DM and annually in the group of healthy siblings. The delivery of blood samples from local clinics was carried out with adherence to necessary transportation conditions to an independent centralized laboratory certified according to European standards. All children’s legal guardians signed informed consent. Participants A total of 517 participants were enrolled in the study. Three hundred and fourteen of them had first-diagnosed T1DM and 203 were healthy siblings. Age ranged from 3.0 to 341.4 months, with mean age of 109.4 ± 64.3 months, 94.6% of those observed were under 18 years old, 28 (5.4%) were over 18 years old as elder healthy siblings. Among the participants the proportion of females was 42.0% (217) and males 58.0% (300). There were 400 people (77.4%) from Nizhny Novgorod region, 92 (17.8%) from the Chuvash Republic, and 25 (4.8%) from the Republic of Mari El. Six of 203 initially healthy siblings got sick during the observation. All participants were divided into 3 groups. Group 1 (n = 314) incorporated patients who had T1DM at the time of recruiting (group ‘DM’, diabetic patients). Group 2 (n = 197) consisted of simultaneously recruited siblings who did not have T1DM until the end of the study (group ‘no_DM’, healthy). Group 3 (n = 6) was represented by siblings simultaneously included in the study and developing T1DM during follow-up (group ‘got_sick’, diseased). Variables The time-dependent AAB concentrations were the key estimated outcomes. We assessed how baseline AAB concentrations depended on the particular group of participants. Hence, the groups defined above regarded as levels of the nominal predictor. AAB concentrations dynamics were estimated as well. Therefore, time of measurement served as a predictor in the separate models. Data sources/measurements Since there were children under 1 year of age, the latter was measured in months. Disease free survival (DFS) registered in a standard manner since the date of. The observation period ranged from 3.4 to 400.1 months. The median of the follow-up was 213.0 months. U/mL was a unit of AAB concentrations. Reference range of AAB concentrations: ab_gad < 4 U/mL, ab_ia2 < 8 U/mL, ab_znt8 < 15 U/mL. The repeated AAB concentrations measurements were performed every 3 months within the first year in the group 1 and annually up to 24 months in the group 2, starting with 0 month (the baseline level, the point when the participant was enrolled into the study). Due to repeated measurements the primary data set had longitudinal (person-period) format with the “PatientID” variable. Laboratory testing. The study participants underwent blood tests for the autoantibodies against glutamic acid decarboxylase (ab_gad), tyrosine phosphatase (ab_ia2), and zinc transporter 8 (ab_znt8). The serum levels of AABs were determined by enzyme-linked immunosorbent assay (ELISA) on Tecan Sunrise absorbance microplate reader (Tecan Austria GmbH, Austria). The following test systems were used: antibodies to glutamic acid decarboxylase (kit «Anti-GAD/IA2 Pool ELISA (IgG)», EUROIMMUN, Medizinische Labordiagnostika AG, Germany), antibodies to tyrosine phosphatase (kit «Medizym anti-IA2») and to zinc transporter 8 (kit «Medizym anti-ZnT8») from MEDIPAN GmbH, Germany. Statistical methods All statistical calculations were implemented using the freely available R programming language (v. 4.4.1) and the integrated developer environment RStudio 2024.09.1 Build 394 © 2009–2024 Posit Software, PBC. Descriptive statistics and Kruskal-Wallis test were estimated applying basic built-in libraries of the RSudio. Values of p < 0.05 were considered significant when testing hypotheses. The main outcomes (AAB concentrations) were modeled by Generalized Linear Regression Models. We applied Bayesian math on the basis of the brms software library (Bayesian Regression Models using ‘Stan’, v. 2.21.0, https://github.com/paul-buerkner/brms ). Since the AAB levels might take only any positive values, a gamma probability density function with a logarithmic link-function was chosen for modeling the distribution of the dependent variable. While all regression models assumed individual variability (random-effects models), they were multilevel with individual intercept. Bayesian framework implied interval assessment of variance. Additionally we used bayestestR ( https://easystats.github.io/bayestestR/ ) and ggplot2 ( https://ggplot2.tidyverse.org , https://github.com/tidyverse/ggplot2 ) packages. All figures were created on the basis of the latter one. As there were missing data throughout the repeated measurements, such empty rows were removed automatically during computer calculations. Results Participant characteristics and descriptive statistics are displayed in Table 1 . Table 1 Participant characteristics Group 1 Group 2 Group 3 Participants, n 314 197 6 Sex, males (%) 182 (58.0) 114 (58.0) 4 (66.7) Age (months), Q25-median-Q75 63.3-105.0-141.0 52.0-103.0-181.0 36.5-78.5-85.3 Age (months), range 3.0-251.0 6.0-341.0 16.0-184.0 Ab_gad/ab_znt8/ab_ia2 concentrations, median (number of missing values) month 0 (baseline) 134.6/216.6/191.2(0) 1.4/5.0/5.8(0) 437.0/188.0/415.0(0) month 3 84.3/79.7/294.35(254) 3.8/2.6/2.7(195) NA/NA/NA(6) month 6 139.8/191.3/262.5(260) NA/NA/NA(197) 345.4/601.6/498.9(3) month 9 135.2/81.0/354.3(282) NA/NA/NA(197) 32.6/168.4/143.7(4) month 12 59.6/3.7/48.3(266) 1.2/1.4/1.4(138) 70.5/240.0/202.3(4) month 24 NA/NA/NA(313) 40.1/0.22/0.46(191) NA/NA/NA(6) Baseline AABs concentrations were estimated in 517 patients. These absolute values were as follows (Q25-median-Q75): 1.20–1.40–2.00 U/mL for ab_gad, 3.70–5.00–6.90 U/mL for ab_znt8, and 5.00–5.80–7.10 U/mL for ab_ia2 in the group 2. The analogous estimations in the groups of diseased ones (groups 1 and 3) were 35.18–135.40–379.28 U/mL for ab_gad, 11.53–214.45–483.85 U/mL for ab_znt8, and 7.40–193.15–440.00 U/mL for ab_ia2. In groups 1 and 3 the level of ab_gad was from 3.57 (Q95) to 204.55 (Q80) times, the level of ab_znt8 was from 2.48 (Q20) to 192.65 (Q95) times, and the level of ab_ia2 was from 1.38 (Q10) to 250.97 (Q95) times higher than in group 2. Differences in the baseline concentrations in groups 1 and 3 compared to group 2 were significant: Kruskal-Wallis test, χ 2 = 166,04; df = 1; p = 0,000 for ab_ia2, χ 2 = 228,01; df = 1; p = 0,000 for ab_gad, and χ 2 = 192,73; df = 1; p = 0,000 ab_znt8. Distribution of the baseline level is presented in Fig. 1 . The histograms exhibit diverse distributions in groups with or without T1DM. In the group 2 this distribution was closer to symmetric whereas in the group 3 it was evidently asymmetric or multimodal sometimes. Dependence of baseline levels of AABs on the particular group was the first point of interest. Three regression models were applied. The baseline AAB levels were the response variables and the study group was the only nominal predictor. The results of the models 1–3 calculations are presented in Table 2 . Table 2 Fragments of the regression summary tables (regression coefficients only) a Family: gamma Links: µ = “log” , shape = “identity” Factor Contrasts: coding “sum” Models 1–3. Formula: AAB ~ 1 + Group + (1 | PatientID) Estimate Est. Error Lower 95% CI Upper 95% CI ab_gad_Intercept 4.14 0.27 3.62 4.67 ab_gad_β_Group_1 (“DM”) 0.97 0.27 0.45 1.48 ab_gad_β_Group_3 (“got_sick”) 1.77 0.52 0.77 2.77 ab_znt8_Intercept 4.67 0.26 4.18 5.21 ab_znt8_β_Group_1 (“DM”) 1.14 0.25 0.62 1.62 ab_znt8_β_Group_3 (“got_sick”) 1.46 0.49 0.57 2.49 ab_ia2_Intercept 4.80 0.25 4.31 5.32 ab_ia2_β_Group_1 (“DM”) 0.90 0.26 0.36 1.38 ab_ia2_β_Group_3 (“got_sick”) 1.74 0.50 0.81 2.76 Models 4–6 (group 1). Formula: AAB ~ 1 + Time + (1| PatientID) ab_gad_Intercept 5.37 0.09 5.19 5.55 ab_gad_β_Time – 0.02 0.02 – 0.06 0.01 ab_znt8_Intercept 5.37 0.14 5.09 5.66 ab_znt8_β_Time – 0.08 0.02 – 0.12 – 0.04 ab_ia2_Intercept 5.41 0.15 5.13 5.70 ab_ia2_β_Time – 0.03 0.02 – 0.07 0.02 Models 7–9 (group 2). Formula: AAB ~ 1 + Time + (1| PatientID) ab_gad_Intercept 1.70 0.18 1.35 2.08 ab_gad_β_Time 0.03 0.02 – 0.01 0.08 ab_znt8_Intercept 2.06 0.13 1.83 2.31 ab_znt8_β_Time – 0.04 0.02 – 0.08 0.00 ab_ia2_Intercept 2.25 0.14 1.99 2.53 ab_ia2_β_Time – 0.01 0.02 – 0.05 0.04 Models 10–12 (group 3). Formula: AAB ~ 1 + Time + (1| PatientID) ab_gad_Intercept 5.41 0.93 3.58 7.28 ab_gad_β_Time – 0.11 0.04 – 0.20 – 0.02 ab_znt8_Intercept 5.19 1.05 3.10 7.29 ab_znt8_β_Time – 0.03 0.07 – 0.19 0.10 ab_ia2_Intercept 6.03 0.88 4.34 7.86 ab_ia2_β_Time – 0.11 0.07 – 0.25 0.03 a – time was counted in months. According to the factor contrast coding and link function, the baseline levels of AABs were calculated by groups using regression equations and displayed in Table 3 . Table 3 Baseline autoantibody levels by study groups b Estimate Est. Error CI.Lower CI.Upper ab_gad Intercept 62,80 1,31 37,48 107,22 Group 1 (“DM”) 166,36 1,13 131,85 209,74 Group 2 (“no_DM”) 4,04 1,15 3,04 5,35 Group 3 (“got_sick”) 368,43 2,17 85,44 1710,72 ab_znt8 Intercept 107,12 1,29 65,67 183,94 Group 1 (“DM”) 335,50 1,14 258,40 431,17 Group 2 (“no_DM”) 7,96 1,13 6,25 10,17 Group 3 (“got_sick”) 460,06 2,08 119,63 2176,51 ab_ia2 Intercept 121,51 1,29 74,77 204,68 Group 1 (“DM”) 299,03 1,13 235,68 376,20 Group 2 (“no_DM”) 8,68 1,13 6,78 11,19 Group 3 (“got_sick”) 690,98 2,10 170,93 3235,40 b – U/mL is a unit of AAB concentrations This information is visualized in Fig. 2 . Either Table 3 or Fig. 2 expose identical regularity of the baseline levels for three AABs. The maximum mean value was observed in the group 3 (“got sick” group). Since there were only 6 observations in this group the respective variance was expected to be the highest. The mean value in group 1 (“DM” group) was slightly lower than in group 3, but comparable. The baseline AAB concentration in group 2 (“no DM” group) was not only significantly lower, but even the confidence interval did not overlap with the confidence intervals of the other groups. Dynamics of circulating AAB concentrations in these three groups was the next aspect analyzed in the present study. The problem was implemented similarly, using regression models in which time of repeated measurements of antibody concentrations played a role of a predictor. The results are presented in Table 2 and in Fig. 3 . As shown in Table 2 , in group 1 time had a negative effect in all three models, the AAB concentration decreased over time. The effect was statistically significant only for ab_znt8. However, we can speak about a significant or at least substantial effect size in relation to other AABs, because the probability of the negative regression coefficient (the probability of direction, PD) was 92.85% for ab_gad, and 87.90% for ab_ia2. The visual marginal time effect in group 1 for three AABs is shown in Fig. 3 (top row). In group 2 there was a multi-directional AAB concentration dynamics. The level of ab_gad increased over time. This effect was not statistically significant, but it was quite large (the probability of positive values of the regression coefficient, PD, was 92.0%). The level of ab_znt8 decreased significantly (PD = 96.4% for negative values of the regression coefficient). Finally, the level of ab_ia2 did not change over time (the probability of positive and negative values of the regression coefficient was almost equal, PD = 65.05%). The visual marginal time effect in group 2 is shown in Fig. 3 (middle row). In group 3 the effect of concentration measurement time was different compared to groups 1 and 2. Concentrations of ab_gad and ab_ia2 dropped rapidly. For ab_gad, the influence of time was statistically significant. The effect size for ab_ia2 was pronounced, negative, and almost significant with a high probability (PD = 94.6% for the regression coefficient). In contrast, the level of ab_znt8 hardly changed over time, the effect size was small and the probability of its opposite value was high (PD = 69.35%). Visual information is shown in Fig. 5 (bottom row). Despite the wide variance, the principal trends of AAB concentrations dynamics are clearly traced in Fig. 3 . The rate of change in AAB concentration depending on repeated measurement time was a point of investigation. Since the response variable (concentration) non-linearly depended on the predictor (time), the concentration change rate varied over time and was not a constant in different groups for different AABs. Several values were calculated, they were as follows (1st month value : 36th month value, U/mL/month): for ab_gad [– 4.25 : – 2.11] in group 1, [0.17 : 0.48] in group 2, [– 23.29 : – 0.50] in group 3, for ab_znt8 [– 16.52 : – 1.00] in group 1, [– 0.31 : − 0.08] in group 2, [– 5.30 : – 1.86] in group 3, for ab_ia2 [– 6.61 : – 2.31] in group 1, [– 0.09 : – 0.07] in group 2, [– 43.30 : – 0.92] in group 3. Discussion Assessment of ab_gad, ab_znt8, ab_ia2 baseline and longitudinal data in different groups of children revealed particular features that may be clinically relevant for predicting high risk T1DM in healthy siblings. Just a simple look at the distributions of the row data (Fig. 1 ) showed that the concentrations of these three antibodies represented similar systematic patterns. In the group 2 the initial concentration of all AABs was extremely low and had a relatively symmetric distribution with narrow variance. The outliers were rare. In contrast, in groups 1 and 3 where T1DM developed, the baseline values were several times to several orders higher than in group 2. Concentration distributions were extremely asymmetric and had a fairly long «right tail» containing many high and extreme values. This phenomenon was reflected throughout mathematical modeling when dependence of AAB concentrations on the membership in the certain group was estimated. And in this case all three AABs exhibited almost identical tendencies which are well-represented in Table 2 and Fig. 2 . In group 2, the confidence intervals of the means are at least an order of magnitude below the lower limits of similar confidence intervals in groups 1 and 3. Thus, the models clearly distinguished between healthy participants and those who had T1DM. This phenomenon allowed applying the AAB baseline concentration as a reliable predictor which primarily determines the diabetes low-risk group. Several studies have shown that the risk of T1DM depends on the titer and diversity of circulating AABs. The highest risk is associated with two or more persistent AABs, and high titers reflect the intensity of the current autoimmune disorder and are associated with faster disease progression [ 7 ]. Indeed, the risk of T1DM development is directly related to the number of positive (i.e. higher than reference values) AABs. 10-year risk of T1DM development is 70.0% in children with two or more positive AABs, 14.5% with one AAB, and only 0.4% without positive AABs [ 4 ]. The dynamics of AAB concentrations was peculiar in different groups. In group 1 the concentration of ab_gad and ab_ia2 decreased smoothly and statistically insignificantly, although the probability of the negative regression coefficient was clearly dominant. The concentration of ab_znt8 in this group declined quite rapidly and significantly. The concentration ab_gad in group 2 rose slowly, the concentration of the ab_ia2 was almost constant, and the concentration of ab_znt8 dropped substantially and significantly. The possible increase of ab_gad level in this group should be hardly considered a negative symptom: the growth was so slow, starting from an extremely low level, that the concentration could reach the lower limit of the confidence interval of the baseline concentration in group 3 approximately only after 90 months (7.5 years). This effect should not be completely ignored, but the time interval is large enough for observation and re-measurement. In group 3, AAB concentration dynamics was exactly the opposite to groups 1 and 2. Concentrations of ab_gad and ab_ia2 decreased steadily and fairly rapidly, while the concentration of ab_znt8 remained almost stable with a very slight tendency to decrease. Our results are consistent with other studies. The combination of ab_ia2 and ab_znt8 is associated with the highest relative risk for T1DM development while emerging of the third and fourth AAB increases it slightly [ 8 ]. In one of the studies authors assessed the current level of AABs in patients with newly diagnosed T1DM compared to those with long-lasting T1DM. The rate of ab_gad persistency stayed substantial at the levels of 80% vs 83% while the rate of ab_ia2 and ab_znt8 decreased from 66–27% and from 63–27% respectively. Possibly, ab_ia2 and ab_znt8 play a major role in prognosis for T1DM being markers of current autoimmune β-cell disorder whereas ab_gad may persist for a long time reflecting indolent progression of T1DM in children. It is a frequently determined AAB in adults suffering T1DM [ 9 – 10 ]. Thus, the modeling of the initial level of three AABs and their concentration dynamics proved to be quite successful, allowing us to draw a kind of «serological portrait» of each group quite clearly and thus, consistently discriminate between high and low risk patients for T1DM. High risk may be associated with 1) high base concentration of all three AABs, 2) significant and rapid decrease in ab_gad and ab_ia2, 3) persistent high ab_znt8 level. Repeated assays of several AABs have an edge over one of tests and enhances the accuracy of predictive risk assessment. Given age, sex, genetics, environmental factors, the natural course of preclinical T1DM may vary displaying ambiguous AAB profiles, which should be taken into consideration [ 11 ]. Limitations Undoubtedly, the low number of observations in group 3 (n = 6) and multiple missing values caused the high variance and uncertainty of estimates. Nevertheless, the obvious size of the effects and the application of a Bayesian mathematical approach, more resistant to the phenomenon of small samples, allowed us to make more or less certain conclusions. The accuracy of all models was checked using specific programming tests. Generalizability Patients like those of group 3 are believed to be of the greatest interest. But, taking into account the whole large cohort of 517 participants and long follow-up, such patients are not expected to be multiple. We encountered only 6 children developing T1DM by the end of the study period, hence, this is very low rate for prompt recruiting an appropriate study group. So, it was quite problematic to confirm the validity of the models using our own additional data. Nevertheless, we tried to create “shiny application” on the basis of R programming language. It is freely available on https://dach-md.shinyapps.io/DMPs/ . Certainly, it is only a sketch rather than complete software. However, it calculates values applying regression models of the present study. We believe such an approach may help us to check generalizability of models using multiple external data. Conclusions Mathematical approaches to model complex longitudinal ab_gad, ab_ia2 and ab_znt8 profiles have potential utility for stratification of AAB positive healthy siblings and offer new opportunities to prediction of T1DM. Abbreviations AAB autoantibody AABs autoantibodies Ab_gad antibodies against glutamic acid decarboxylase Ab_ia2 antibodies against tyrosine phosphatase AB_znt8 antibodies against zinc transporter 8 T1DM type 1 diabetes mellitus SD standard deviation PD probability of direction Declarations Ethics approval and consent to participate . The study was approved by the ethical committee of Privolzhsky Research Medical University. Informed consent was obtained from the parents or legal guardians of the trial participants before enrollment in the study. Consent for publication. Not applicable. Availability of data and materials. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests. The authors declare that they have no competing interests. Funding. The founders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author’s contributions. The individual contributions of the authors are as follows: Conception and design: Korneva K., Strongin L., and Zagainov V.; Collection and assembly of data: Korneva K. and Chichevatov D.; Statistical analysis: Chichevatov D.; Manuscript drafting: Korneva K. and Chichevatov D.; Revisions of the manuscript: Korneva K., Chichevatov D., Strongin L., and Zagainov V. All authors reviewed the manuscript. Acknowledgments. Not applicable. Author’s information . Korneva Ksenia: MD, Associate Professor, Department of Endocrinology and Internal Medicine, Privolzhsky Research Medical University, Nizhny Novgorod, Russia. Phone: +79036026668. E-mail: [email protected] ORCID: 0000-0003-3293-4636. Chichevatov Dmitry: MD, Professor, Department of Surgery, Penza State University, Penza, Russia. Phone: +79273663585 E-mail: [email protected] ORCID: 0000-0001-6436-3386 Strongin Leonid: MD, professor of the Department of Endocrinology and Internal Medicine, Privolzhsky Research Medical University, Nizhny Novgorod, Russia. ORCID: 0000-0003-2645-2729 Zagainov Vladimir: MD, Professor, Head of the Department of Faculty Surgery and Transplantology, Privolzhsky Research Medical University, Nizhny Novgorod, Russia. ORCID: 0000-0002-5769-0378 References O'Donovan AJ, Gorelik S, Nally LM. Shifting the paradigm of type 1 diabetes: a narrative review of disease modifying therapies. Front Endocrinol (Lausanne). 2024;15:1477101. 10.3389/fendo.2024.1477101 . Insel RA, Dunne JL, Atkinson MA, Chiang JL, Dabelea D, Gottlieb PA, et al. Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care. 2015;38:1964–74. 10.2337/dc15-1419 . Vehik K, Bonifacio E, Lernmark Å, Yu L, Williams A, Schatz D, et al. TEDDY Study Group. Hierarchical Order of Distinct Autoantibody Spreading and Progression to Type 1 Diabetes in the TEDDY Study. Diabetes Care. 2020;43:2066–73. 10.2337/dc19-2547 . Ziegler AG, Rewers M, Simell O, Simell T, Lempainen J, Steck A, et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013;309:2473–9. 10.1001/jama.2013.6285 . Krischer JP, Type 1 Diabetes TrialNet Study Group. The use of intermediate endpoints in the design of type 1 diabetes prevention trials. Diabetologia. 2013;56:1919–24. 10.1007/s00125-013-2960-7 . Epub 2013 Jun 7. Quinn LM, Swaby R, Tatovic D, Narendran P, Besser REJ, Dayan CM. What does the licensing of teplizumab mean for diabetes care? Diabetes Obes Metab. 2023;25:2051–57. 10.1111/dom.15071 . Achenbach P, Warncke K, Reiter J, Naserke HE, Williams AJ, Bingley PJ, et al. Stratification of type 1 diabetes risk on the basis of islet autoantibody characteristics. Diabetes. 2004;53:384–92. 10.2337/diabetes.53.2.384 . Podichetty JT, Lang P, O'Doherty IM, David SE, Muse RN, Karpen SR, et al. Clin Pharmacol Ther. 2022;111:1133–41. 10.1002/cpt.2559 . Type-1 Diabetes Consortium (T1DC). Leveraging Real-World Data for EMA Qualification of a Model-Based Biomarker Tool to Optimize Type-1 Diabetes Prevention Studies. Kawasaki E. Anti-Islet Autoantibodies in Type 1 Diabetes. Int J Mol Sci. 2023;24:10012. 10.3390/ijms241210012 . Endesfelder D, Zu Castell W, Bonifacio E, Rewers M, Hagopian WA, She JX, et al. TEDDY Study Group. Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children. Diabetes. 2019;68:119–30. 10.2337/db18-0594 . Bonifacio E, Achenbach P. Birth and coming of age of islet autoantibodies. Clin Exp Immunol. 2019;198:294–305. 10.1111/cei.13360 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6238174","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435197500,"identity":"e9dd9aa3-baf4-438d-85d1-944fd827e430","order_by":0,"name":"Kseniya Korneva","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kseniya","middleName":"","lastName":"Korneva","suffix":""},{"id":435197501,"identity":"35fba9fb-fd7a-45b2-ad48-3cc69e5809d9","order_by":1,"name":"Dmitry Chichevatov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3NParCQBDA8VkGYrOrhc2KhVcwjZWSs4RAbF9pYZGwsGn2AAG/bpFeFlLlAMJaaO8DIb0YIdXjsdHOYv/VDMyPAXC5vjFOEmxHcrmvALzeu4QCoJ9XDcFO0py2xBsyCe1qabARaf2z0pOgp2bI9sWkj+DV1PbkfBTjvNK+otXsuiuMLxFwYyNTHkpkMiaKx8vpb2HIi2A3ecRBQ2LOtiZ4kyTzUPGoHLHEhJ2En0IxpuU8UpXWfl6aSCIRZGshg3x5remaL7IsTS/3tVkcMqHhZiH/RZIPgcvlcrn+9gS1L0TsjfZnzwAAAABJRU5ErkJggg==","orcid":"","institution":"Penza State University","correspondingAuthor":true,"prefix":"","firstName":"Dmitry","middleName":"","lastName":"Chichevatov","suffix":""},{"id":435197502,"identity":"aec03924-d682-4c3a-bf61-143e0834c970","order_by":2,"name":"Leonid Strongin","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Leonid","middleName":"","lastName":"Strongin","suffix":""},{"id":435197503,"identity":"524a0097-9c09-4f8a-8920-ef8fa1ae0662","order_by":3,"name":"Vladimir Zagainov","email":"","orcid":"","institution":"Privolzhsky Research Medical University","correspondingAuthor":false,"prefix":"","firstName":"Vladimir","middleName":"","lastName":"Zagainov","suffix":""}],"badges":[],"createdAt":"2025-03-16 14:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6238174/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6238174/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79663623,"identity":"2cb1dc63-d4a1-4166-a3b7-a6d0386453de","added_by":"auto","created_at":"2025-04-01 09:55:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152284,"visible":true,"origin":"","legend":"\u003cp\u003eThe histograms of the baseline levels of three circulating AABs. The ОХ axis (AAB concentration) is limited by values referred to Q85. U/mL is a unit of measure of AAB concentration.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6238174/v1/feb2f0756b3e4f45963bcbb9.png"},{"id":79664736,"identity":"eac7d4e6-e08b-4880-a827-dfdb458f7fcf","added_by":"auto","created_at":"2025-04-01 10:03:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74913,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline AAB levels by study groups.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6238174/v1/ee97316db64c75492a8199bc.png"},{"id":79663632,"identity":"5de32360-a097-4ff6-baf9-f4f62d6aa9bf","added_by":"auto","created_at":"2025-04-01 09:55:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":370221,"visible":true,"origin":"","legend":"\u003cp\u003eDynamics of AAB concentrations by study groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6238174/v1/75548eed96c574e4ee38447a.png"},{"id":85219963,"identity":"7c82c69d-3bff-47d3-a3d5-0db8390cf677","added_by":"auto","created_at":"2025-06-23 14:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1133487,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6238174/v1/86531ba1-cc17-4d8c-9803-a4841246f43d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of β-cell Autoantibodies in Prediction of Type 1 Diabetes Mellitus in Children","fulltext":[{"header":"Background","content":"\u003cp\u003eRecently, curative approaches to type 1 diabetes mellitus (T1DM) have been transformed from hormone-replacement therapy to disease-modifying one. Genetic predisposition to T1DM incorporated with environmental considerations triggers autoimmune destruction of the islet cells, which is marked by specific serum autoantibodies (AABs) to different proteins of β-cells. Since the first AAB was identified in 1974, investigations of autoimmune disorders responsible for T1DM have been ongoing. At the present time five AABs are well-known reliable benchmarks of the early disease onset. These are autoantibodies against islet cells, insulin (ab_ins), glutamic acid decarboxylase (ab_gad), tyrosine phosphatase (ab_ia2), zinc transporter 8 (ab_znt8) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a rule, AABs appear several months or years before and persist up to the disease manifestation.\u003c/p\u003e \u003cp\u003eT1DM is known to have consecutive steps in the course [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The first stage is featured by onset of autoimmune aggression against β-cells associated with increased concentrations of at least two AABs on the background of normoglycemia. Ab_gad and ab_ins emerge first. Ab_ins reach the peak of seroconversion by the age of 2, whilst ab_gad by 3\u0026ndash;5 years. Ab_ia2 and ab_znt8 are registered at the later stage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The second stage of T1DM starts with dysglycemia which can be diagnosed by the oral glucose tolerance test. The third stage is already represented by classical symptoms of T1DM. 5-year and 10-year risk of T1DM at stages 2 vs 3 are estimated as 44% and 70%. The lifetime risk is 50% and 100% respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEfficient therapy aimed at prevention or deceleration of T1DM is possible only at stages 1 or 2 when there is a consistent residual pool of intact β-cells. New current curative methods which affect the course of the disease are expected to be more prominent. Since registration of the monoclonal antibody teplizumab the capabilities of the immune-modulating therapy have become more evident [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As per these reasons, T1DM must be diagnosed at its earliest preclinical stages when prophylaxis treatment may improve prognosis and the quality of life considerably as well as prevent long-lasting complications.\u003c/p\u003e \u003cp\u003eWe aim to assess the possibility of T1DM prediction on the basis of AAB concentrations and their longitudinal changes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe present study was designed and reported according to STROBE Statement \u0026ndash; checklist of items that should be included in reports of observational studies.\u003c/p\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003e This was a prospective longitudinal cohort study performed in three Regional Children\u0026rsquo;s hospitals of Nizhny Novgorod, the Chuvash Republic and the Republic of Mari El. The study included patients aged 0\u0026ndash;18 years hospitalized with confirmed newly diagnosed T1DM between 2017 and 2020 in one of the three hospitals. One hundred and sixty-six participants with newly diagnosed T1DM had one or more healthy siblings who was bone to the same parents. Inpatient baseline blood samples were collected within 1\u0026ndash;2 days of admission to the hospital whilst in healthy siblings within 1\u0026ndash;2 weeks on an outpatient basis. Subsequently, the blood tests were performed at 3 months intervals up to 12 months in the group T1DM and annually in the group of healthy siblings. The delivery of blood samples from local clinics was carried out with adherence to necessary transportation conditions to an independent centralized laboratory certified according to European standards. All children\u0026rsquo;s legal guardians signed informed consent.\u003c/p\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eA total of 517 participants were enrolled in the study. Three hundred and fourteen of them had first-diagnosed T1DM and 203 were healthy siblings. Age ranged from 3.0 to 341.4 months, with mean age of 109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;64.3 months, 94.6% of those observed were under 18 years old, 28 (5.4%) were over 18 years old as elder healthy siblings. Among the participants the proportion of females was 42.0% (217) and males 58.0% (300). There were 400 people (77.4%) from Nizhny Novgorod region, 92 (17.8%) from the Chuvash Republic, and 25 (4.8%) from the Republic of Mari El. Six of 203 initially healthy siblings got sick during the observation.\u003c/p\u003e \u003cp\u003eAll participants were divided into 3 groups. Group 1 (n\u0026thinsp;=\u0026thinsp;314) incorporated patients who had T1DM at the time of recruiting (group \u0026lsquo;DM\u0026rsquo;, diabetic patients). Group 2 (n\u0026thinsp;=\u0026thinsp;197) consisted of simultaneously recruited siblings who did not have T1DM until the end of the study (group \u0026lsquo;no_DM\u0026rsquo;, healthy). Group 3 (n\u0026thinsp;=\u0026thinsp;6) was represented by siblings simultaneously included in the study and developing T1DM during follow-up (group \u0026lsquo;got_sick\u0026rsquo;, diseased).\u003c/p\u003e \u003cp\u003eVariables\u003c/p\u003e \u003cp\u003eThe time-dependent AAB concentrations were the key estimated outcomes. We assessed how baseline AAB concentrations depended on the particular group of participants. Hence, the groups defined above regarded as levels of the nominal predictor. AAB concentrations dynamics were estimated as well. Therefore, time of measurement served as a predictor in the separate models.\u003c/p\u003e \u003cp\u003eData sources/measurements\u003c/p\u003e \u003cp\u003eSince there were children under 1 year of age, the latter was measured in months. Disease free survival (DFS) registered in a standard manner since the date of. The observation period ranged from 3.4 to 400.1 months. The median of the follow-up was 213.0 months. U/mL was a unit of AAB concentrations. Reference range of AAB concentrations: ab_gad\u0026thinsp;\u0026lt;\u0026thinsp;4 U/mL, ab_ia2\u0026thinsp;\u0026lt;\u0026thinsp;8 U/mL, ab_znt8\u0026thinsp;\u0026lt;\u0026thinsp;15 U/mL. The repeated AAB concentrations measurements were performed every 3 months within the first year in the group 1 and annually up to 24 months in the group 2, starting with 0 month (the baseline level, the point when the participant was enrolled into the study). Due to repeated measurements the primary data set had longitudinal (person-period) format with the \u0026ldquo;PatientID\u0026rdquo; variable.\u003c/p\u003e \u003cp\u003eLaboratory testing.\u003c/p\u003e \u003cp\u003eThe study participants underwent blood tests for the autoantibodies against glutamic acid decarboxylase (ab_gad), tyrosine phosphatase (ab_ia2), and zinc transporter 8 (ab_znt8). The serum levels of AABs were determined by enzyme-linked immunosorbent assay (ELISA) on Tecan Sunrise absorbance microplate reader (Tecan Austria GmbH, Austria). The following test systems were used: antibodies to glutamic acid decarboxylase (kit \u0026laquo;Anti-GAD/IA2 Pool ELISA (IgG)\u0026raquo;, EUROIMMUN, Medizinische Labordiagnostika AG, Germany), antibodies to tyrosine phosphatase (kit \u0026laquo;Medizym anti-IA2\u0026raquo;) and to zinc transporter 8 (kit \u0026laquo;Medizym anti-ZnT8\u0026raquo;) from MEDIPAN GmbH, Germany.\u003c/p\u003e \u003cp\u003eStatistical methods\u003c/p\u003e \u003cp\u003eAll statistical calculations were implemented using the freely available R programming language (v. 4.4.1) and the integrated developer environment RStudio 2024.09.1 Build 394 \u0026copy; 2009\u0026ndash;2024 Posit Software, PBC. Descriptive statistics and Kruskal-Wallis test were estimated applying basic built-in libraries of the RSudio. Values of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant when testing hypotheses. The main outcomes (AAB concentrations) were modeled by Generalized Linear Regression Models. We applied Bayesian math on the basis of the brms software library (Bayesian Regression Models using \u0026lsquo;Stan\u0026rsquo;, v. 2.21.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/paul-buerkner/brms\u003c/span\u003e\u003cspan address=\"https://github.com/paul-buerkner/brms\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Since the AAB levels might take only any positive values, a gamma probability density function with a logarithmic link-function was chosen for modeling the distribution of the dependent variable. While all regression models assumed individual variability (random-effects models), they were multilevel with individual intercept. Bayesian framework implied interval assessment of variance. Additionally we used bayestestR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://easystats.github.io/bayestestR/\u003c/span\u003e\u003cspan address=\"https://easystats.github.io/bayestestR/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ggplot2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ggplot2.tidyverse.org\u003c/span\u003e\u003cspan address=\"https://ggplot2.tidyverse.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tidyverse/ggplot2\u003c/span\u003e\u003cspan address=\"https://github.com/tidyverse/ggplot2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) packages. All figures were created on the basis of the latter one. As there were missing data throughout the repeated measurements, such empty rows were removed automatically during computer calculations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics and descriptive statistics are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, males (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (months),\u003c/p\u003e \u003cp\u003eQ25-median-Q75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.3-105.0-141.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.0-103.0-181.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5-78.5-85.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (months), range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0-251.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0-341.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0-184.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAb_gad/ab_znt8/ab_ia2 concentrations, median (number of missing values)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonth 0 (baseline)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.6/216.6/191.2(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4/5.0/5.8(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437.0/188.0/415.0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonth 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.3/79.7/294.35(254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8/2.6/2.7(195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA/NA/NA(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonth 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.8/191.3/262.5(260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA/NA/NA(197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e345.4/601.6/498.9(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonth 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135.2/81.0/354.3(282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA/NA/NA(197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.6/168.4/143.7(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonth 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.6/3.7/48.3(266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2/1.4/1.4(138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.5/240.0/202.3(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonth 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA/NA/NA(313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1/0.22/0.46(191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA/NA/NA(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline AABs concentrations were estimated in 517 patients. These absolute values were as follows (Q25-median-Q75): 1.20\u0026ndash;1.40\u0026ndash;2.00 U/mL for ab_gad, 3.70\u0026ndash;5.00\u0026ndash;6.90 U/mL for ab_znt8, and 5.00\u0026ndash;5.80\u0026ndash;7.10 U/mL for ab_ia2 in the group 2. The analogous estimations in the groups of diseased ones (groups 1 and 3) were 35.18\u0026ndash;135.40\u0026ndash;379.28 U/mL for ab_gad, 11.53\u0026ndash;214.45\u0026ndash;483.85 U/mL for ab_znt8, and 7.40\u0026ndash;193.15\u0026ndash;440.00 U/mL for ab_ia2. In groups 1 and 3 the level of ab_gad was from 3.57 (Q95) to 204.55 (Q80) times, the level of ab_znt8 was from 2.48 (Q20) to 192.65 (Q95) times, and the level of ab_ia2 was from 1.38 (Q10) to 250.97 (Q95) times higher than in group 2. Differences in the baseline concentrations in groups 1 and 3 compared to group 2 were significant: Kruskal-Wallis test, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;166,04; df\u0026thinsp;=\u0026thinsp;1; p\u0026thinsp;=\u0026thinsp;0,000 for ab_ia2, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;228,01; df\u0026thinsp;=\u0026thinsp;1; p\u0026thinsp;=\u0026thinsp;0,000 for ab_gad, and χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;192,73; df\u0026thinsp;=\u0026thinsp;1; p\u0026thinsp;=\u0026thinsp;0,000 ab_znt8. Distribution of the baseline level is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe histograms exhibit diverse distributions in groups with or without T1DM. In the group 2 this distribution was closer to symmetric whereas in the group 3 it was evidently asymmetric or multimodal sometimes.\u003c/p\u003e \u003cp\u003eDependence of baseline levels of AABs on the particular group was the first point of interest. Three regression models were applied. The baseline AAB levels were the response variables and the study group was the only nominal predictor. The results of the models 1\u0026ndash;3 calculations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFragments of the regression summary tables (regression coefficients only)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFamily: gamma\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLinks: \u0026micro; = \u003cb\u003e\u0026ldquo;log\u0026rdquo;\u003c/b\u003e, shape = \u0026ldquo;identity\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFactor Contrasts: coding \u0026ldquo;sum\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModels 1\u0026ndash;3. Formula: AAB\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Group + (1 | PatientID)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEst. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower 95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper 95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_β_Group_1 (\u0026ldquo;DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_β_Group_3 (\u0026ldquo;got_sick\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_β_Group_1 (\u0026ldquo;DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_β_Group_3 (\u0026ldquo;got_sick\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_β_Group_1 (\u0026ldquo;DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_β_Group_3 (\u0026ldquo;got_sick\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModels 4\u0026ndash;6 (group 1). Formula: AAB\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Time + (1| PatientID)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash; 0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModels 7\u0026ndash;9 (group 2). Formula: AAB\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Time + (1| PatientID)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModels 10\u0026ndash;12 (group 3). Formula: AAB\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Time + (1| PatientID)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash; 0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2_β_Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash; 0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash; 0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea \u0026ndash; time was counted in months.\u003c/p\u003e \u003cp\u003eAccording to the factor contrast coding and link function, the baseline levels of AABs were calculated by groups using regression equations and displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline autoantibody levels by study groups\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEst. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI.Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI.Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_gad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107,22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 (\u0026ldquo;DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e209,74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 2 (\u0026ldquo;no_DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 3 (\u0026ldquo;got_sick\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e368,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1710,72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_znt8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e183,94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 (\u0026ldquo;DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e335,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e258,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e431,17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 2 (\u0026ldquo;no_DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 3 (\u0026ldquo;got_sick\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e460,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2176,51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab_ia2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e204,68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 (\u0026ldquo;DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e299,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e235,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e376,20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 2 (\u0026ldquo;no_DM\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 3 (\u0026ldquo;got_sick\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e690,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3235,40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eb \u0026ndash; U/mL is a unit of AAB concentrations\u003c/p\u003e \u003cp\u003eThis information is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEither Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e or Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e expose identical regularity of the baseline levels for three AABs. The maximum mean value was observed in the group 3 (\u0026ldquo;got sick\u0026rdquo; group). Since there were only 6 observations in this group the respective variance was expected to be the highest. The mean value in group 1 (\u0026ldquo;DM\u0026rdquo; group) was slightly lower than in group 3, but comparable. The baseline AAB concentration in group 2 (\u0026ldquo;no DM\u0026rdquo; group) was not only significantly lower, but even the confidence interval did not overlap with the confidence intervals of the other groups.\u003c/p\u003e \u003cp\u003eDynamics of circulating AAB concentrations in these three groups was the next aspect analyzed in the present study. The problem was implemented similarly, using regression models in which time of repeated measurements of antibody concentrations played a role of a predictor. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in group 1 time had a negative effect in all three models, the AAB concentration decreased over time. The effect was statistically significant only for ab_znt8. However, we can speak about a significant or at least substantial effect size in relation to other AABs, because the probability of the negative regression coefficient (the probability of direction, PD) was 92.85% for ab_gad, and 87.90% for ab_ia2. The visual marginal time effect in group 1 for three AABs is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (top row).\u003c/p\u003e \u003cp\u003eIn group 2 there was a multi-directional AAB concentration dynamics. The level of ab_gad increased over time. This effect was not statistically significant, but it was quite large (the probability of positive values of the regression coefficient, PD, was 92.0%). The level of ab_znt8 decreased significantly (PD\u0026thinsp;=\u0026thinsp;96.4% for negative values of the regression coefficient). Finally, the level of ab_ia2 did not change over time (the probability of positive and negative values of the regression coefficient was almost equal, PD\u0026thinsp;=\u0026thinsp;65.05%). The visual marginal time effect in group 2 is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (middle row).\u003c/p\u003e \u003cp\u003eIn group 3 the effect of concentration measurement time was different compared to groups 1 and 2. Concentrations of ab_gad and ab_ia2 dropped rapidly. For ab_gad, the influence of time was statistically significant. The effect size for ab_ia2 was pronounced, negative, and almost significant with a high probability (PD\u0026thinsp;=\u0026thinsp;94.6% for the regression coefficient). In contrast, the level of ab_znt8 hardly changed over time, the effect size was small and the probability of its opposite value was high (PD\u0026thinsp;=\u0026thinsp;69.35%). Visual information is shown in Fig.\u0026nbsp;5 (bottom row). Despite the wide variance, the principal trends of AAB concentrations dynamics are clearly traced in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe rate of change in AAB concentration depending on repeated measurement time was a point of investigation. Since the response variable (concentration) non-linearly depended on the predictor (time), the concentration change rate varied over time and was not a constant in different groups for different AABs. Several values were calculated, they were as follows (1st month value : 36th month value, U/mL/month): for ab_gad [\u0026ndash; 4.25 : \u0026ndash; 2.11] in group 1, [0.17 : 0.48] in group 2, [\u0026ndash; 23.29 : \u0026ndash; 0.50] in group 3, for ab_znt8 [\u0026ndash; 16.52 : \u0026ndash; 1.00] in group 1, [\u0026ndash; 0.31 : \u0026minus;\u0026thinsp;0.08] in group 2, [\u0026ndash; 5.30 : \u0026ndash; 1.86] in group 3, for ab_ia2 [\u0026ndash; 6.61 : \u0026ndash; 2.31] in group 1, [\u0026ndash; 0.09 : \u0026ndash; 0.07] in group 2, [\u0026ndash; 43.30 : \u0026ndash; 0.92] in group 3.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAssessment of ab_gad, ab_znt8, ab_ia2 baseline and longitudinal data in different groups of children revealed particular features that may be clinically relevant for predicting high risk T1DM in healthy siblings. Just a simple look at the distributions of the row data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed that the concentrations of these three antibodies represented similar systematic patterns. In the group 2 the initial concentration of all AABs was extremely low and had a relatively symmetric distribution with narrow variance. The outliers were rare. In contrast, in groups 1 and 3 where T1DM developed, the baseline values were several times to several orders higher than in group 2. Concentration distributions were extremely asymmetric and had a fairly long \u0026laquo;right tail\u0026raquo; containing many high and extreme values.\u003c/p\u003e \u003cp\u003eThis phenomenon was reflected throughout mathematical modeling when dependence of AAB concentrations on the membership in the certain group was estimated. And in this case all three AABs exhibited almost identical tendencies which are well-represented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In group 2, the confidence intervals of the means are at least an order of magnitude below the lower limits of similar confidence intervals in groups 1 and 3. Thus, the models clearly distinguished between healthy participants and those who had T1DM. This phenomenon allowed applying the AAB baseline concentration as a reliable predictor which primarily determines the diabetes low-risk group. Several studies have shown that the risk of T1DM depends on the titer and diversity of circulating AABs. The highest risk is associated with two or more persistent AABs, and high titers reflect the intensity of the current autoimmune disorder and are associated with faster disease progression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Indeed, the risk of T1DM development is directly related to the number of positive (i.e. higher than reference values) AABs. 10-year risk of T1DM development is 70.0% in children with two or more positive AABs, 14.5% with one AAB, and only 0.4% without positive AABs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe dynamics of AAB concentrations was peculiar in different groups. In group 1 the concentration of ab_gad and ab_ia2 decreased smoothly and statistically insignificantly, although the probability of the negative regression coefficient was clearly dominant. The concentration of ab_znt8 in this group declined quite rapidly and significantly. The concentration ab_gad in group 2 rose slowly, the concentration of the ab_ia2 was almost constant, and the concentration of ab_znt8 dropped substantially and significantly. The possible increase of ab_gad level in this group should be hardly considered a negative symptom: the growth was so slow, starting from an extremely low level, that the concentration could reach the lower limit of the confidence interval of the baseline concentration in group 3 approximately only after 90 months (7.5 years). This effect should not be completely ignored, but the time interval is large enough for observation and re-measurement. In group 3, AAB concentration dynamics was exactly the opposite to groups 1 and 2. Concentrations of ab_gad and ab_ia2 decreased steadily and fairly rapidly, while the concentration of ab_znt8 remained almost stable with a very slight tendency to decrease.\u003c/p\u003e \u003cp\u003eOur results are consistent with other studies. The combination of ab_ia2 and ab_znt8 is associated with the highest relative risk for T1DM development while emerging of the third and fourth AAB increases it slightly [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In one of the studies authors assessed the current level of AABs in patients with newly diagnosed T1DM compared to those with long-lasting T1DM. The rate of ab_gad persistency stayed substantial at the levels of 80% vs 83% while the rate of ab_ia2 and ab_znt8 decreased from 66\u0026ndash;27% and from 63\u0026ndash;27% respectively. Possibly, ab_ia2 and ab_znt8 play a major role in prognosis for T1DM being markers of current autoimmune β-cell disorder whereas ab_gad may persist for a long time reflecting indolent progression of T1DM in children. It is a frequently determined AAB in adults suffering T1DM [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, the modeling of the initial level of three AABs and their concentration dynamics proved to be quite successful, allowing us to draw a kind of \u0026laquo;serological portrait\u0026raquo; of each group quite clearly and thus, consistently discriminate between high and low risk patients for T1DM. High risk may be associated with 1) high base concentration of all three AABs, 2) significant and rapid decrease in ab_gad and ab_ia2, 3) persistent high ab_znt8 level. Repeated assays of several AABs have an edge over one of tests and enhances the accuracy of predictive risk assessment. Given age, sex, genetics, environmental factors, the natural course of preclinical T1DM may vary displaying ambiguous AAB profiles, which should be taken into consideration [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eUndoubtedly, the low number of observations in group 3 (n\u0026thinsp;=\u0026thinsp;6) and multiple missing values caused the high variance and uncertainty of estimates. Nevertheless, the obvious size of the effects and the application of a Bayesian mathematical approach, more resistant to the phenomenon of small samples, allowed us to make more or less certain conclusions. The accuracy of all models was checked using specific programming tests.\u003c/p\u003e \u003cp\u003eGeneralizability\u003c/p\u003e \u003cp\u003ePatients like those of group 3 are believed to be of the greatest interest. But, taking into account the whole large cohort of 517 participants and long follow-up, such patients are not expected to be multiple. We encountered only 6 children developing T1DM by the end of the study period, hence, this is very low rate for prompt recruiting an appropriate study group. So, it was quite problematic to confirm the validity of the models using our own additional data. Nevertheless, we tried to create \u0026ldquo;shiny application\u0026rdquo; on the basis of R programming language. It is freely available on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dach-md.shinyapps.io/DMPs/\u003c/span\u003e\u003cspan address=\"https://dach-md.shinyapps.io/DMPs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Certainly, it is only a sketch rather than complete software. However, it calculates values applying regression models of the present study. We believe such an approach may help us to check generalizability of models using multiple external data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMathematical approaches to model complex longitudinal ab_gad, ab_ia2 and ab_znt8 profiles have potential utility for stratification of AAB positive healthy siblings and offer new opportunities to prediction of T1DM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eautoantibody\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAABs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eautoantibodies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAb_gad\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantibodies against glutamic acid decarboxylase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAb_ia2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantibodies against tyrosine phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAB_znt8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantibodies against zinc transporter 8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etype 1 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprobability of direction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethical committee of Privolzhsky Research Medical University. Informed consent was obtained from the parents or legal guardians of the trial participants before enrollment in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe founders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions.\u0026nbsp;\u003c/strong\u003eThe individual contributions of the authors are as follows: Conception and design:\u0026nbsp;Korneva K.,\u0026nbsp;Strongin L., and\u0026nbsp;Zagainov V.; Collection and assembly of data:\u0026nbsp;Korneva K.\u0026nbsp;and\u0026nbsp;Chichevatov D.; Statistical analysis:\u0026nbsp;Chichevatov D.; Manuscript drafting:\u0026nbsp;Korneva K.\u0026nbsp;and\u0026nbsp;Chichevatov D.; Revisions of the manuscript:\u0026nbsp;Korneva K.,\u0026nbsp;Chichevatov D., Strongin L., and\u0026nbsp;Zagainov V.\u0026nbsp;All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s information\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eKorneva Ksenia: MD, Associate Professor, Department of Endocrinology and Internal Medicine,\u0026nbsp;Privolzhsky Research Medical University, Nizhny Novgorod, Russia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhone: +79036026668.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eORCID:\u0026nbsp;0000-0003-3293-4636.\u003c/p\u003e\n\u003cp\u003eChichevatov Dmitry: MD, Professor, Department of Surgery, Penza State University, Penza, Russia.\u003c/p\u003e\n\u003cp\u003ePhone: +79273663585\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eORCID:\u0026nbsp;0000-0001-6436-3386\u003c/p\u003e\n\u003cp\u003eStrongin Leonid: MD, professor of the Department of Endocrinology and Internal Medicine,\u0026nbsp;Privolzhsky Research Medical University, Nizhny Novgorod, Russia.\u003c/p\u003e\n\u003cp\u003eORCID:\u0026nbsp;0000-0003-2645-2729\u003c/p\u003e\n\u003cp\u003eZagainov Vladimir: MD, Professor, Head of the Department of Faculty Surgery and Transplantology,\u0026nbsp;Privolzhsky\u0026nbsp;Research Medical University, Nizhny Novgorod, Russia.\u003c/p\u003e\n\u003cp\u003eORCID: 0000-0002-5769-0378\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eO'Donovan AJ, Gorelik S, Nally LM. Shifting the paradigm of type 1 diabetes: a narrative review of disease modifying therapies. Front Endocrinol (Lausanne). 2024;15:1477101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2024.1477101\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1477101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInsel RA, Dunne JL, Atkinson MA, Chiang JL, Dabelea D, Gottlieb PA, et al. Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care. 2015;38:1964\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc15-1419\u003c/span\u003e\u003cspan address=\"10.2337/dc15-1419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVehik K, Bonifacio E, Lernmark \u0026Aring;, Yu L, Williams A, Schatz D, et al. TEDDY Study Group. Hierarchical Order of Distinct Autoantibody Spreading and Progression to Type 1 Diabetes in the TEDDY Study. Diabetes Care. 2020;43:2066\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc19-2547\u003c/span\u003e\u003cspan address=\"10.2337/dc19-2547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiegler AG, Rewers M, Simell O, Simell T, Lempainen J, Steck A, et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013;309:2473\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2013.6285\u003c/span\u003e\u003cspan address=\"10.1001/jama.2013.6285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrischer JP, Type 1 Diabetes TrialNet Study Group. The use of intermediate endpoints in the design of type 1 diabetes prevention trials. Diabetologia. 2013;56:1919\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00125-013-2960-7\u003c/span\u003e\u003cspan address=\"10.1007/s00125-013-2960-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2013 Jun 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuinn LM, Swaby R, Tatovic D, Narendran P, Besser REJ, Dayan CM. What does the licensing of teplizumab mean for diabetes care? Diabetes Obes Metab. 2023;25:2051\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/dom.15071\u003c/span\u003e\u003cspan address=\"10.1111/dom.15071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAchenbach P, Warncke K, Reiter J, Naserke HE, Williams AJ, Bingley PJ, et al. Stratification of type 1 diabetes risk on the basis of islet autoantibody characteristics. Diabetes. 2004;53:384\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diabetes.53.2.384\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.53.2.384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePodichetty JT, Lang P, O'Doherty IM, David SE, Muse RN, Karpen SR, et al. Clin Pharmacol Ther. 2022;111:1133\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cpt.2559\u003c/span\u003e\u003cspan address=\"10.1002/cpt.2559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Type-1 Diabetes Consortium (T1DC). Leveraging Real-World Data for EMA Qualification of a Model-Based Biomarker Tool to Optimize Type-1 Diabetes Prevention Studies.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawasaki E. Anti-Islet Autoantibodies in Type 1 Diabetes. Int J Mol Sci. 2023;24:10012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241210012\u003c/span\u003e\u003cspan address=\"10.3390/ijms241210012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndesfelder D, Zu Castell W, Bonifacio E, Rewers M, Hagopian WA, She JX, et al. TEDDY Study Group. Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children. Diabetes. 2019;68:119\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/db18-0594\u003c/span\u003e\u003cspan address=\"10.2337/db18-0594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonifacio E, Achenbach P. Birth and coming of age of islet autoantibodies. Clin Exp Immunol. 2019;198:294\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/cei.13360\u003c/span\u003e\u003cspan address=\"10.1111/cei.13360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"type 1 diabetes mellitus, autoantibodies, glutamic acid decarboxylase, tyrosine phosphatase, zinc transporter 8, prediction","lastPublishedDoi":"10.21203/rs.3.rs-6238174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6238174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe study is aimed at assessing the possibility of type 1 diabetes mellitus (T1DM) prediction on the basis of autoantibodies concentrations and their dynamics. Antibodies against glutamic acid decarboxylase (ab_gad), tyrosine phosphatase (ab_ia2), and zinc transporter 8 (ab_znt8) were assayed.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRegression modeling was applied to repeated measured longitudinal data from a total of 517 participants: 314 children with T1DM and 203 healthy siblings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong healthy siblings the high risk of T1DM was associated with 1) high baseline concentration of all three antibodies, 2) significant and rapid decrease in ab_gad and ab_ia2, 3) maintenance of high ab_znt8 levels.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMathematical modeling of ab_gad, ab_ia2 и ab_znt8 concentration dynamics has potential benefits for stratifying risk groups and prediction of T1DM in healthy siblings.\u003c/p\u003e","manuscriptTitle":"The Role of β-cell Autoantibodies in Prediction of Type 1 Diabetes Mellitus in Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 09:55:07","doi":"10.21203/rs.3.rs-6238174/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc70f23b-00e8-4b57-9e13-1c4de34c96cc","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T13:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 09:55:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6238174","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6238174","identity":"rs-6238174","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}