Prevalence of Type 1 Diabetes Mellitus across Antinuclear Antibody Patterns and Their Distribution in a Taiwanese Cohort

Prevalence of Type 1 Diabetes Mellitus across Antinuclear Antibody Patterns and Their Distribution in a Taiwanese Cohort | 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 Prevalence of Type 1 Diabetes Mellitus across Antinuclear Antibody Patterns and Their Distribution in a Taiwanese Cohort Tien-Ming Chan¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6687469/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background Type 1 Diabetes Mellitus (T1DM) is an autoimmune disease characterized by pancreatic β-cell destruction. While islet-specific autoantibodies are key markers, Antinuclear Antibodies (ANA) are also observed, but their relationship with specific ANA patterns, classified by the International Consensus on ANA Patterns (ICAP), in T1DM, especially in diverse populations like Taiwan, needs more investigation. This study aimed to determine the prevalence of T1DM across various pure ANA patterns and describe the distribution of these patterns among T1DM patients in a large Taiwanese cohort. Methods This cross-sectional study utilized de-identified data from the Chang Gung Research Database (CGRD) from January 2019 to September 2021. Patients undergoing ANA testing by indirect immunofluorescence (IIF) on HEp-2 cells were included. Individuals with inconsistent or mixed ANA patterns were excluded. T1DM diagnosis was based on International Classification of Diseases (ICD)-9-CM/ICD-10-CM codes, validated by endocrinologist records and further confirmed by catastrophic illness certificate (CIC) data for T1DM from Taiwan's National Health Insurance system. The prevalence of T1DM was calculated for each pure ANA pattern (AC-1 to AC-29), using the ANA-negative group (AC-0) as the reference. Odds ratios (ORs) with 95% confidence intervals (CIs) and Fisher’s exact test were used for statistical comparisons. Results From 38,572 initial patients, 35,763 with pure ANA patterns were analyzed (31,151 AC-0; 4,612 ANA-positive). The overall T1DM prevalence in the AC-0 group was 0.11% (34/31,151). Among ANA-positive patterns, only the AC-4 (Fine Speckled) pattern (n = 670) showed a significantly higher T1DM prevalence (0.60%, 4 cases; OR 5.50, 95% CI [1.75–17.26], p = 0.0082) compared to the AC-0 group. Other patterns such as AC-1 (Homogeneous, 0.18%), AC-3 (Centromere, 0.16%), and AC-5 (Large/Coarse speckled, 0.19%) also had T1DM cases, but these associations were not statistically significant. The overall ANA positivity rate was not significantly different between T1DM patients (19.05%, 8/42) and non-T1DM individuals (12.91%, 4612/35721) in this pure pattern cohort (p = 0.2360). Among the 8 ANA-positive T1DM patients, Speckled patterns (AC-4/AC-5) were predominant (6/8, 75.00%), followed by AC-1 (Homogeneous; 1/8, 12.50%) and AC-3 (Centromere; 1/8, 12.50%). All identified patterns in T1DM patients were nuclear. Conclusion In this large Taiwanese cohort, while overall ANA positivity was not significantly increased in T1DM patients, the pure AC-4 Speckled ANA pattern was associated with a significantly higher prevalence of T1DM compared to ANA-negative individuals. Speckled patterns were the most common ANA patterns observed among ANA-positive T1DM patients. These findings suggest a potential specific link between certain ANA patterns, particularly AC-4, and T1DM autoimmunity, warranting further investigation into the specific antigens involved and the clinical implications in diverse populations. Antinuclear Antibodies ANA patterns Type 1 Diabetes Mellitus Prevalence Distribution ICAP Autoimmunity Taiwan Figures Figure 1 Figure 2 1. Introduction Type 1 Diabetes Mellitus (T1DM) is a chronic autoimmune disease characterized by the immune-mediated destruction of pancreatic β-cells, leading to absolute insulin deficiency. 1 While islet-specific autoantibodies, such as those against glutamic acid decarboxylase 65 (GAD65), insulinoma-associated antigen 2 (IA-2), and zinc transporter 8 (ZnT8), are established markers for T1DM risk and diagnosis 2 , the presence of non-organ-specific autoantibodies, like Antinuclear Antibodies (ANA), is also frequently observed 3 , 4 . ANA, detected by indirect immunofluorescence (IIF) on HEp-2 cells, are hallmarks of systemic autoimmune rheumatic diseases (SARD) but can also be present in organ-specific autoimmune conditions, including T1DM. 5 The reported prevalence of ANA in T1DM patients varies widely across studies, ranging from 10% to over 50%, potentially influenced by differences in study populations, detection methods, titer cutoffs, and patient age. 3 , 4 Beyond mere positivity, the staining pattern observed during ANA testing provides additional clinical information, reflecting antibodies against different nuclear, cytoplasmic, or mitotic antigens. 6 The International Consensus on ANA Patterns (ICAP) provides a standardized nomenclature for these patterns (AC-1 to AC-29). 7 While certain patterns are strongly associated with specific SARD (e.g., AC-3 Centromere with systemic sclerosis (SSc), specific patterns in SLE) 6 , 8 , 9 , the clinical significance of specific ANA patterns in T1DM is less clear. Some studies have suggested potential associations between certain patterns (like homogeneous or speckled) and T1DM or related autoimmune comorbidities 4 , but data, especially using the detailed ICAP classification in large, specific ethnic populations, remain limited. Understanding the relationship between specific ANA patterns and T1DM could offer insights into shared autoimmune pathways or identify subgroups of T1DM patients with distinct immunological profiles or risks for polyautoimmunity. 3 , 10 T1DM frequently coexists with other autoimmune conditions, most notably autoimmune thyroid disease (AITD) and celiac disease. 4 , 11 – 13 Given the paucity of data from Taiwan, this study aimed to investigate two primary objectives within a large cohort from a major Taiwanese medical center: 1) To determine the prevalence of T1DM across the spectrum of pure ANA staining patterns defined by ICAP, and 2) To describe the distribution of these pure ANA patterns among patients diagnosed with T1DM. 2. Methods 2.1. Data Source and Study Design This cross-sectional study utilized de-identified electronic medical records from the Chang Gung Research Database (CGRD). CGRD encompasses data from seven hospital branches of the Chang Gung Memorial Health System in Taiwan. The study period was from January 1, 2019, to September 30, 2021. The study protocol was approved by the Institutional Review Board of Chang Gung Memorial Hospital (IRB No. 202101542B0), which waived the need for individual informed consent due to the de-identified nature of the data. 2.2. Study Population We initially identified all patients (N = 38,572) who underwent ANA testing during the study period. Patients with multiple ANA tests had only their first result included. We excluded patients with inconsistent results between tests (n = 2,401) and those exhibiting mixed ANA staining patterns (n = 408) to focus the analysis on the association with distinct, pure patterns. Patients tested solely for routine health examinations were also excluded. The final analytical cohort comprised 35,763 patients with a single, consistent, pure ANA staining pattern (or negative result). 2.3. Laboratory Measurements and ANA Classification All ANA tests were performed using IIF on HEp-2 cells with the AESKUSLIDES kit (Aesku Diagnostics, Wendelsheim, Germany) at the CAP-accredited Immunology Laboratory of Linkou Chang Gung Memorial Hospital. A titer ≧ 1:80 was considered positive. Staining patterns were meticulously reviewed and classified according to ICAP guidelines ( www.anapatterns.org ) into nuclear (AC-1 to AC-14, AC-29), cytoplasmic (AC-15 to AC-23), and mitotic (AC-24 to AC-28) patterns [7]. Pattern classification was independently confirmed by three experienced reviewers. Patients with negative results were classified as AC-0. 2.4. Identification of T1DM T1DM cases were identified using International Classification of Diseases (ICD) codes from the CGRD: ICD-9-CM codes 250.x1 or 250.x3, and ICD-10-CM codes E10.x. A diagnosis was considered valid if documented by a board-certified endocrinologist in at least two outpatient visits or at least one inpatient admission record during the study period. To further enhance diagnostic accuracy, all identified T1DM cases were additionally validated by confirming the issuance of a catastrophic illness certificate (CIC) for T1DM from Taiwan’s National Health Insurance (NHI) system, which requires fulfillment of standardized national diagnostic criteria for T1DM. 2.5. Statistical Analysis The primary outcome was the prevalence of T1DM within each pure ANA pattern category (AC-1 to AC-29) and the ANA-negative group (AC-0). Descriptive statistics (frequencies, percentages) were used. T1DM prevalence in each ANA-positive pattern group was compared to the ANA-negative (AC-0) reference group using univariate logistic regression to calculate Odds Ratios (ORs) and 95% Confidence Intervals (CIs). Fisher’s exact test was employed to calculate two-sided p-values for prevalence comparisons and comparisons of categorical variables (e.g., ANA positivity rates between T1DM and non-T1DM groups). Statistical significance was set at p < 0.05. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). 3. Results 3.1. Cohort Characteristics From the initial 38,572 patients tested for ANA, 35,763 met the inclusion criteria for the pure pattern analysis. This final cohort included 31,151 ANA-negative (AC-0) individuals and 4,612 ANA-positive individuals distributed across various pure ICAP patterns (Table 1 , Table 2 ). A total of 42 patients within this final cohort had a validated diagnosis of T1DM. Table 1 Prevalence of T1DM According to ANA Pure Staining Patterns Variable ANA (-) AC-1 AC-2 AC-3 AC-4 AC-5 AC-6 AC-7 AC-8 AC-9 AC-10 AC-11 AC-12 AC-13 AC-14 AC-29 AC-15 AC-16 AC-17 AC-18 AC-19 AC-20 AC-21 AC-22 AC-24 AC-25 AC-26 AC-27 AC-28 P-value (n = 31151) (n = 559) (n = 629) (n = 624) (n = 670) (n = 1050) (n = 38) (n = 83) (n = 194) (n = 103) (n = 93) (n = 22) (n = 31) (n = 6) (n = 7) (n = 5) (n = 9) (n = 25) (n = 1) (n = 53) (n = 100) (n = 42) (n = 200) (n = 23) (n = 7) (n = 8) (n = 22) (n = 6) (n = 2) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%) T1DM 34 (0.11%) 1 (0.18%) 0 (0.00%) 1 (0.16%) 4 (0.60%) 2 (0.19%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0.9646 Table 2 Prevalence of T1DM Across Different Pure ANA Staining Patterns Compared to ANA-negative Patients ANA Patterns Total N T1DM Cases Prevalence (%) Odds Ratio P-value AC-0 31151 34 0.11% 1 1 AC-1 559 1 0.18% 1.64 0.4636 AC-2 629 0 0.00% 0 1 AC-3 624 1 0.16% 1.47 0.5007 AC-4 670 4 0.60% 5.50 0.0082* AC-5 1050 2 0.19% 1.75 0.329 AC-6 38 0 0.00% 0 1 AC-7 83 0 0.00% 0 1 AC-8 194 0 0.00% 0 1 AC-9 103 0 0.00% 0 1 AC-10 93 0 0.00% 0 1 AC-11 22 0 0.00% 0 1 AC-12 31 0 0.00% 0 1 AC-13 6 0 0.00% 0 1 AC-14 7 0 0.00% 0 1 AC-29 5 0 0.00% 0 1 AC-15 9 0 0.00% 0 1 AC-16 25 0 0.00% 0 1 AC-17 1 0 0.00% 0 1 AC-18 53 0 0.00% 0 1 AC-19 100 0 0.00% 0 1 AC-20 42 0 0.00% 0 1 AC-21 200 0 0.00% 0 1 AC-22 23 0 0.00% 0 1 AC-24 7 0 0.00% 0 1 AC-25 8 0 0.00% 0 1 AC-26 22 0 0.00% 0 1 AC-27 6 0 0.00% 0 1 AC-28 2 0 0.00% 0 1 3.2. Prevalence of T1DM Among ANA Patterns The prevalence of T1DM in the ANA-negative (AC-0) reference group was 0.11% (34 out of 31,151 patients) (Table 1 , Table 2 ). Among the ANA-positive subgroups, T1DM prevalence varied (Table 1 , Table 2 , Fig. 1 ). The AC-4 (Fine speckled) pattern group (n = 670) exhibited the highest prevalence at 0.60% (4 T1DM cases), which was significantly higher than the AC-0 group (OR 5.50, 95% CI [1.75–17.26], p = 0.0082). T1DM cases were also identified in other common patterns: AC-1 (Homogeneous, n = 559) had 1 case (0.18%; OR 1.64, 95% CI [0.22–12.18], p = 0.4636), AC-3 (Centromere, n = 624) had 1 case (0.16%; OR 1.47, 95% CI [0.19–11.20], p = 0.5007), and AC-5 (Large/Coarse speckled, n = 1050) had 2 cases (0.19%; OR 1.75, 95% CI [0.42–7.31], p = 0.329). No T1DM cases were found in the AC-2 (Dense fine speckled) group or any other pure ANA pattern groups (AC-6 through AC-29). When comparing the overall T1DM prevalence across all pure ANA subtypes collectively against the ANA-negative group, no statistically significant difference was observed (p = 0.9646) (Table 1 ). 3.3. Distribution of ANA Patterns in T1DM Considering the initial cohort of 38,572 patients (including mixed/different patterns), 42 T1DM patients were identified. The overall ANA positivity rate in this initial T1DM group was 19.05% (8/42), which was not significantly different from the 19.07% (7347/38530) observed in non-T1DM subjects (p = 0.9973) (Table 3 ). Table 3 Comparison of ANA Positivity, Titers, and Demographic Characteristics in T1DM Patients: Inclusion of Different and Mixed ANA Patterns Single ANA patterns + Different / Mixed ANA patterns (N = 38572) non-T1DM T1DM P-value (n = 38530) (n = 42) n (%) n (%) Age (years, mean ± SD) 49.89 ± 16.29 48.38 ± 16.02 0.5481 Gender 0.2133 Female 25880 (67.17%) 32 (76.19%) Male 12650 (32.83%) 10 (23.81%) ANA 0.9973 Negative (-) 31183 (80.93%) 34 (80.95%) Positive (+) 7347 (19.07%) 8 (19.05%) ANA titer 0.8956 (-) Negative 31183 (80.93%) 34 (80.95%) (+) 1:80 2766 (7.18%) 4 (9.52%) (+) 1:160 2040 (5.29%) 1 (2.38%) (+) 1:320 337 (0.87%) 0 (0.00%) (+) 1:640 338 (0.88%) 1 (2.38%) (+) 1:1280 542 (1.41%) 1 (2.38%) (+) > 1:1280 1324 (3.44%) 1 (2.38%) Focusing on the final cohort of 35,763 patients with pure patterns, 8 of the 42 T1DM patients (19.05%) were ANA-positive, while 34 (80.95%) were ANA-negative (AC-0). The ANA positivity rate in T1DM patients (19.05%) was numerically higher but not statistically different from that in non-T1DM controls (12.91%, 4612/35721; p = 0.2360) within this pure pattern cohort (Table 4 ). Table 4 Distribution and Clinical Significance of Single ANA Patterns (AC-1 to AC-29) in Patients with T1DM non-T1DM T1DM P-value (n = 35721) (n = 42) n (%) n (%) Age (years, mean ± SD) 49.72 ± 16.31 48.38 ± 16.02 0.5952 Gender 0.1634 Female 23575 (66.00%) 32 (76.19%) Male 12146 (34.00%) 10 (23.81%) ANA 0.2360 Negative (-) 31109 (87.09%) 34 (80.95%) Positive (+) 4612 (12.91%) 8 (19.05%) ANA titer 0.5756 (-) Negative 31109 (87.09%) 34 (80.95%) (+) 1:80 1691 (4.73%) 4 (9.52%) (+) 1:160 1278 (3.58%) 1 (2.38%) (+) 1:320 228 (0.64%) 0 (0.00%) (+) 1:640 214 (0.60%) 1 (2.38%) (+) 1:1280 358 (1.00%) 1 (2.38%) (+) > 1:1280 843 (2.36%) 1 (2.38%) ANA patterns (Subgroup 1) 0.4179 AC-0 / ANA (-) 31109 (87.09%) 34 (80.95%) Nuclear (AC-1 to AC-14, AC-29) 4113 (11.51%) 8 (19.05%) Cytoplasmic (AC-15 to 23) 454 (1.27%) 0 (0.00%) Mitotic (AC-24 to 28) 45 (0.13%) 0 (0.00%) ANA patterns (Subgroup 2) 0.9218 AC-0 / ANA (-) 31109 (87.09%) 34 (80.95%) Homogeneous (AC-1) 559 (1.56%) 1 (2.38%) Dense fine speckled (AC-2) 630 (1.76%) 0 (0.00%) Centromere (AC-3) 623 (1.74%) 1 (2.38%) Speckled (AC-4, AC-5) 1717 (4.81%) 6 (14.29%) Discrete nuclear dots (AC-6, AC-7) 121 (0.34%) 0 (0.00%) Nucleolar (AC-8, AC-9, AC-10) 392 (1.10%) 0 (0.00%) Nuclear envelope (AC-11, AC-12) 53 (0.15%) 0 (0.00%) Pleomorphic (AC-13, AC-14) 13 (0.04%) 0 (0.00%) Fine grainy speckled (AC-29) 5 (0.01%) 0 (0.00%) Fibrillar (AC-15, AC-16, AC-17) 35 (0.10%) 0 (0.00%) Discrete dots (AC-18) 53 (0.15%) 0 (0.00%) Speckled (AC-19, AC-20) 143 (0.40%) 0 (0.00%) AMA (AC-21) 200 (0.56%) 0 (0.00%) Golgi (AC-22) 23 (0.06%) 0 (0.00%) Centrosome (AC-24) 7 (0.02%) 0 (0.00%) Spindle fibers (AC-25, AC-26) 30 (0.08%) 0 (0.00%) Intercellular bridge (AC-27) 6 (0.02%) 0 (0.00%) Mitotic chromosomal (AC-28) 2 (0.01%) 0 (0.00%) ANA patterns 0.9646 AC-0 / ANA (-) 31109 (87.09%) 34 (80.95%) AC-1 559 (1.56%) 1 (2.38%) AC-2 630 (1.76%) 0 (0.00%) AC-3 623 (1.74%) 1 (2.38%) AC-4 666 (1.86%) 4 (9.52%) AC-5 1051 (2.94%) 2 (4.76%) AC-6 38 (0.11%) 0 (0.00%) AC-7 83 (0.23%) 0 (0.00%) AC-8 195 (0.55%) 0 (0.00%) AC-9 104 (0.29%) 0 (0.00%) AC-10 93 (0.26%) 0 (0.00%) AC-11 22 (0.06%) 0 (0.00%) AC-12 31 (0.09%) 0 (0.00%) AC-13 6 (0.02%) 0 (0.00%) AC-14 7 (0.02%) 0 (0.00%) AC-15 9 (0.03%) 0 (0.00%) AC-16 25 (0.07%) 0 (0.00%) AC-17 1 (0.00%) 0 (0.00%) AC-18 53 (0.15%) 0 (0.00%) AC-19 101 (0.28%) 0 (0.00%) AC-20 42 (0.12%) 0 (0.00%) AC-21 200 (0.56%) 0 (0.00%) AC-22 23 (0.06%) 0 (0.00%) AC-24 7 (0.02%) 0 (0.00%) AC-25 8 (0.02%) 0 (0.00%) AC-26 22 (0.06%) 0 (0.00%) AC-27 6 (0.02%) 0 (0.00%) AC-28 2 (0.01%) 0 (0.00%) AC-29 5 (0.01%) 0 (0.00%) Among the 8 ANA-positive T1DM patients with pure patterns, the distribution was as follows (Table 4 , Fig. 2 ): Speckled patterns (AC-4 and AC-5) were the most common, found in 6 patients (75.00% of ANA-positive T1DM cases). Specifically, AC-4 accounted for 4 cases (50.00%) and AC-5 for 2 cases (25.00%). The Homogeneous pattern (AC-1) and Centromere pattern (AC-3) were each observed in 1 patient (12.50% each). All 8 positive cases displayed nuclear patterns; no cytoplasmic or mitotic patterns were observed in T1DM patients in this cohort. 4. Discussion This large cross-sectional study investigated the intricate relationship between specific ANA patterns, defined by ICAP, and T1DM in a Taiwanese population. Our primary findings reveal that while overall ANA positivity was not significantly elevated in T1DM patients compared to controls in our cohort, the AC-4 speckled pattern was associated with a significantly increased prevalence of T1DM. Furthermore, among the ANA-positive T1DM patients, Speckled patterns (AC-4/AC-5) were markedly predominant. The overall ANA positivity rate of 19.05% (at ≥ 1:80 titer) observed in our T1DM patients aligns with the lower-to-mid range reported in previous literature, which shows considerable variability (10% to over 50%). 4 Existing literature suggests that approximately one-quarter of adult T1DM patients in multiple studies test positive for ANAs, often with figures around 20–30% in Western cohorts; for instance, Heras et al. (2010) reported 27% ANA positivity in adult T1DM, and Ciechanowicz et al. (2016) found 24% in a Polish cohort. Our observed rate is notably lower than the 71% reported by Segni et al. 14 in pediatric patients with AITD using the same cutoff, suggesting potential differences based on age, ethnicity, or the specific underlying autoimmune condition. Our observed rate is closer to the background ANA positivity seen in some general hospital populations (e.g., 15.8% in a Turkish hospital cohort 15 and a similar rate reported in a general German population 16 ). The lack of a statistically significant difference in overall ANA positivity between our T1DM and non-T1DM groups (19.05% vs 12.91%) contrasts with some studies suggesting higher rates in T1DM and with observations from large phenome-wide studies where T1DM did not emerge as a top diagnosis associated with ANA. This potentially reflects the heterogeneity of control groups and ANA prevalence in the general population, as well as the notion that while a subset of T1DM patients may have ANA, the prevalence of T1DM among ANA carriers is generally very low. It also differs from findings in other autoimmune liver diseases like autoimmune hepatitis (AIH), where ANA and/or SMA are key diagnostic features. 17 , 18 The most striking finding of our study is the specific association between the AC-4 speckled pattern and an over five-fold increased odds of T1DM compared to ANA-negative individuals (OR 5.50, p = 0.0082). Speckled patterns (including large/coarse speckled AC-5 and fine speckled AC-4) are common ANA results, often considered relatively non-specific and frequently seen in various SARDs like SLE. 9 , 15 However, AC-4 represents a specific ICAP category characterized by fine speckles distributed throughout the nucleoplasm, sometimes larger and fewer than AC-5 (large/coarse speckled). 7 This distinct association between AC-4 and increased T1DM prevalence appears novel. It contrasts significantly with findings regarding the dense fine speckled (DFS) pattern (AC-2), often mediated by anti-DFS70/LEDGF antibodies. Studies by Mahler et al. [19] have shown that anti-DFS70 antibodies are paradoxically more prevalent in healthy individuals (8.9%) than in patients with SARD (like SLE, 2.8%) and may even argue against a SARD diagnosis when present in isolation. 19 , 20 The AC-2 pattern has also been termed the "pseudo-DFS" pattern when not associated with anti-DFS70. 22 Our finding that AC-4 (a speckled pattern, not AC-2/DFS) is associated with increased T1DM prevalence suggests either that the AC-4 pattern in our T1DM cohort reflects specific, yet unidentified, autoantigens relevant to T1DM pathogenesis or associated polyautoimmunity, distinct from those typically seen in SARD or represented by the DFS70 antigen. Further investigation, including antigen specificity testing (e.g., for Ro, La, Sm, RNP) in AC-4 positive T1DM patients, is crucial. While speckled ANAs can be common in T1DM, it has been noted that the prevalence of T1DM among all individuals with a speckled pattern is very small, as these patterns often indicate antibodies more typical of lupus or Sjögren's. Our study, by focusing on ICAP-defined AC-4, refines this observation for a specific speckled subtype in a Taiwanese cohort. Although T1DM cases were also observed with AC-1 (Homogeneous), AC-3 (Centromere), and AC-5 (Large/Coarse speckled) patterns, these associations did not reach statistical significance in our cohort, likely due to the small number of T1DM cases within these pattern groups. The presence of AC-1 and AC-5 is common but relatively non-specific. 6 , 15 AC-1 (Homogeneous) is frequently seen in SLE. 9 Some reports suggest the homogeneous pattern is often the most common among ANA-positive T1DM patients in Western cohorts (e.g., ~ 53% in Heras et al.), though in our study, speckled patterns predominated. The occurrence of AC-3 (anti-centromere) in one T1DM patient might reflect underlying polyautoimmunity, as this pattern is strongly linked to limited SSc 6 , 8 and shows slower progression of microvascular damage compared to other SSc patterns 22 , or it could be an incidental finding. Furthermore, nucleolar or centromere patterns are generally reported rarely in T1DM and would more likely point to another autoimmune condition. The lack of T1DM cases associated with the AC-2 (DFS) pattern in our cohort is consistent with the generally low prevalence of this pattern in SARD. 19 The distribution analysis among the 8 ANA-positive T1DM patients showed a clear predominance (75%) of Speckled patterns (AC-4 and AC-5). This contrasts with the overwhelming prevalence of the Homogeneous pattern (92%) reported by Segni et al. 14 in children with AITD. Furthermore, this speckled dominance differs from the characteristic patterns often seen in SARD, such as the high prevalence of AC-1 in SLE 9 or the specific associations of AC-3, AC-29, and nucleolar patterns (AC-8, 9, 10) with SSc subtypes. 8 , 22 Notably, nucleolar patterns (AC-8, 9, 10) have also been linked to an increased risk of cancer, particularly in SSc patients. 24 The distinct pattern distribution in our T1DM cohort suggests that the autoantibody profile in ANA-positive T1DM patients may differ significantly from that in AITD, SLE, or SSc. The absence of cytoplasmic (e.g., AC-21 AMA, seen in PBC 17 ) and mitotic patterns (e.g., AC-26 NuMA, AC-25 Spindle fibers) in our T1DM cohort is also noted, though conclusions are limited by the small sample size. Cytoplasmic ANA patterns are also generally considered uncommon in T1DM, although rare cases with AMA or ANCA can occur, sometimes in the context of polyglandular autoimmune syndromes. T1DM is well-known for its association with other autoimmune diseases, particularly AITD and celiac disease, occurring in up to 30% and 10% of patients, respectively, in some populations. 4 , 11 – 13 While our study did not find a significantly higher overall ANA positivity in T1DM, the specific association with the AC-4 pattern might delineate a subgroup with a particular autoimmune predisposition or reflect shared pathogenic pathways yet to be elucidated. It has been suggested that ANA positivity in T1DM might signify a distinct subset with heightened autoimmunity, potentially heralding latent development of a systemic autoimmune disease, and that screening T1DM patients for non-organ-specific autoantibodies could be useful for early detection of other autoimmune conditions. ANA positivity itself, even without overt SARD, can predate clinical disease onset by many years [5,24], and its presence in T1DM warrants awareness, although routine screening utility remains debated without specific clinical indications. 25 The potential development of autoimmune phenomena, including ANA and lupus-like syndromes, has also been noted following therapies like anti-TNF agents, although this is a distinct context from idiopathic autoimmunity. 26 A particularly relevant aspect is the potential for ethnic and regional differences in ANA prevalence in T1DM. Some literature suggests that Asian populations (e.g., East Asian) may have a lower prevalence of ANA positivity in T1DM compared to Caucasian populations. This could be attributed to genetic factors (different HLA associations) and a higher proportion of non-immune-mediated or autoantibody-negative diabetes (like fulminant T1DM, common in Japan) within what is clinically classified as T1DM in Asia. For instance, some studies report that only ~ 30–40% of adult-onset T1DM in China have any islet autoantibody, much lower than in Europeans, suggesting a generally lower autoimmunity burden which might extend to ANAs. Our study, conducted in a Taiwanese (East Asian) population, found an ANA positivity rate of 19.05%, which, while not directly compared to a non-Asian T1DM cohort within our study, appears consistent with the notion that ANA positivity might not be as high as some Western reports. However, it is also cautioned that direct comparative data are limited, and methodological differences (e.g., IIF vs. ELISA for ANA detection) can affect reported prevalence. For example, a study in Sudan using ELISA found 0% ANA positivity at T1DM onset, highlighting this methodological point. Our use of IIF, considered more sensitive, and finding a 19.05% rate, contributes valuable data from an Asian population. The predominance of speckled patterns (AC-4/AC-5) in our ANA-positive T1DM patients is an interesting finding that warrants comparison with pattern distributions in other Asian and non-Asian T1DM cohorts using standardized ICAP classification. 5. Limitations Although this study, based on a large Taiwanese medical database, provides initial insights into the association between ANA patterns and T1DM, several inherent limitations should be considered when interpreting the results. Firstly, this study employed a cross-sectional design, meaning we observed associations at a specific point in time. Consequently, it is not possible to establish a causal relationship between ANA patterns and T1DM. We can only describe their correlation and cannot determine which is the cause and which is the effect, or whether common underlying factors influence both. Secondly, the identification of T1DM cases primarily relied on ICD codes. Although we validated these with endocrinologist records to enhance accuracy, diagnoses based on coding may still carry a degree of misclassification risk, which could potentially affect the precision of the results. Furthermore, the data for this study were sourced from a single, large healthcare system in Taiwan (Chang Gung Memorial Health System). While the sample size is substantial, the generalizability of the findings to other regions in Taiwan or other ethnic populations may be limited, as patient characteristics and medical practices might differ across various healthcare systems or regions. Additionally, to ensure the purity and clarity of the ANA pattern analysis, this study excluded cases with inconsistent ANA test results or those exhibiting mixed patterns. While this exclusion criterion helps clarify the association between specific pure patterns and T1DM, it might also introduce selection bias, meaning the study results may not fully represent the situation for all ANA-positive individuals. More importantly, the number of ANA-positive T1DM patients in this study was relatively small (n = 8). This small sample size limits the statistical power, particularly when analyzing the distribution of different ANA patterns among T1DM patients and when investigating associations with less common ANA patterns. It might be difficult to detect weak but genuine associations, or the observed associations might lack stability. Lastly, this study could not incorporate data on T1DM-specific autoantibodies (such as GAD65, IA-2, etc.), nor did it have detailed clinical information on patients' other autoimmune comorbidities. The absence of these data restricted our ability to conduct a more in-depth analysis of polyautoimmunity in T1DM patients and made it challenging to comprehensively assess the role of ANA patterns within a complex autoimmune context. Future research should aim to overcome these limitations, for instance, by employing prospective cohort study designs to explore causal relationships, incorporating broader data sources to enhance the generalizability of results, and integrating more detailed autoantibody profiles and clinical phenotype data. This will allow for a more comprehensive elucidation of the precise significance of ANA patterns in the pathogenesis and clinical presentation of T1DM. 6. Conclusion This large Taiwanese cohort study found that while overall ANA positivity was not significantly increased in T1DM patients, the pure AC-4 Speckled ANA pattern was significantly associated with increased T1DM prevalence. Speckled patterns (AC-4/AC-5) predominated among ANA-positive T1DM individuals. These findings highlight the importance of specific ANA patterns, particularly AC-4, in T1DM, warranting further research to confirm this association and elucidate underlying immunological mechanisms. Declarations Ethics approval and consent to participate: The study protocol was approved by the Institutional Review Board of Chang Gung Memorial Hospital (IRB No. 202101542B0), which waived the need for individual informed consent due to the de-identified nature of the data. Consent for publication: Not applicable, as the manuscript does not contain any individual person’s data in any form. Availability of data and materials: Raw data were generated at Chang Gung Memorial Hospital at Linkou, Taiwan. The derived data supporting the findings of this study are available from the corresponding author upon reasonable request. Competing interests (Conflict of interest): The author declares that this research was conducted without any commercial or financial relationships that could be interpreted as potential conflicts of interest. Funding: This project was funded by the Center for Big Data Analytics and Statistics at Chang Gung Memorial Hospital, Linkou, and the Chang Gung Research Database (Project No.: CGRPG3N0061). Author's contributions: Tien-Ming Chan wrote the paper. Tien-Ming Chan acquired the clinical data and performed critical reviews. Tien-Ming Chan interpretated image reports. Tien-Ming Chan is the guarantor. Tien-Ming Chan read and approved the final manuscript. Acknowledgements: We are grateful to Jing-Yi Huang for providing valuable statistical support and extend our sincere thanks to Chang Gung Memorial Hospital for their generous assistance. This study was financially supported by the Center for Big Data Analytics and Statistics at Chang Gung Memorial Hospital, Linkou, along with the Chang Gung Research Database. Additionally, we recognize the commitment and exceptional clinical care provided by the healthcare professionals in the Department of Internal Medicine. References Atkinson MA, Eisenbarth GS, Michels AW (2014) Type 1 diabetes. Lancet 383(9911):69–82 Insel RA, Dunne JL, Atkinson MA, Chiang JL, Dabelea D, Gottlieb PA et al (2015) Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care 38(10):1964–1974 Kahaly GJ, Hansen MP (2016) Type 1 diabetes associated autoimmunity. Autoimmun Rev 15(7):644–648 Popoviciu MS, Kaka N, Sethi Y, Patel N, Chopra H, Cavalu S (2023) Type 1 Diabetes Mellitus and Autoimmune Diseases: A Critical Review of the Association and the Application of Personalized Medicine. J Pers Med 13(3):422 Agmon-Levin N, Damoiseaux J, Kallenberg C, Sack U, Witte T, Herold M et al (2014) International recommendations for the assessment of autoantibodies to cellular antigens referred to as anti-nuclear antibodies. Ann Rheum Dis 73(1):17–23 Damoiseaux J, Andrade LEC, Carballo OG, Conrad K, Francescantonio PLC, Fritzler MJ et al (2019) Clinical relevance of HEp-2 indirect immunofluorescent patterns: the International Consensus on ANA patterns (ICAP) perspective. Ann Rheum Dis 78(7):879–889 Chan EKL, Damoiseaux J, Carballo OG, Conrad K, de Melo Cruvinel W, Francescantonio PLC et al (2015) Report of the First International Consensus on Standardized Nomenclature of Antinuclear Antibody HEp-2 Cell Patterns 2014–2015. Front Immunol 6:412 Mierau R, Moinzadeh P, Riemekasten G, Melchers I, Meurer M, Reichenberger F et al (2011) Frequency of disease-associated and other nuclear autoantibodies in patients of the German network for systemic scleroderma: correlation with characteristic clinical features. Arthritis Res Ther 13(5):R172 Al-Mughales J (2022) Anti-Nuclear Antibodies Patterns in Patients With Systemic Lupus Erythematosus and Their Correlation With Other Diagnostic Immunological Parameters. Front Immunol 13:850759 Boelaert K, Newby PR, Simmonds MJ, Holder RL, Carr-Smith JD, Heward JM et al (2010) Prevalence and relative risk of other autoimmune diseases in subjects with autoimmune thyroid disease. Am J Med 123(2):183e1–183e9 Al-Hakami AM (2016) Pattern of thyroid, celiac, and anti-cyclic citrullinated peptide autoantibodies coexistence with type 1 diabetes mellitus in patients from Southwestern Saudi Arabia. Saudi Med J 37(4):386–391 Palma CC, Pavesi M, Nogueira VG, Clemente EL, Vasconcellos MF, Pereira LC Jr et al (2013) Prevalence of thyroid dysfunction in patients with diabetes mellitus. Diabetol Metab Syndr 5(1):58 Krzewska A, Ben-Skowronek I (2016) Effect of Associated Autoimmune Diseases on Type 1 Diabetes Mellitus Incidence and Metabolic Control in Children and Adolescents. Biomed Res Int 2016:6219730 Segni M, Pucarelli I, Truglia S, Turriziani I, Serafinelli C, Conti F (2014) High Prevalence of Antinuclear Antibodies in Children with Thyroid Autoimmunity. J Immunol Res 2014:150239 Mengeloglu Z, Tas T, Kocoglu E, Aktas G, Karabörk S (2014) Determination of anti-nuclear antibody pattern distribution and clinical relationship. Pak J Med Sci 30(2):380–383 Akmatov MK, Röber N, Ahrens W, Flesch-Janys D, Fricke J, Greiser H et al (2017) Anti-nuclear autoantibodies in the general German population: prevalence and lack of association with selected cardiovascular and metabolic disorders-findings of a multicenter population-based study. Arthritis Res Ther 19(1):127 Mack CL, Adams D, Assis DN, Kerkar N, Manns MP, Mayo MJ et al (2020) Diagnosis and Management of Autoimmune Hepatitis in Adults and Children: 2019 Practice Guidance and Guidelines From the American Association for the Study of Liver Diseases. Hepatology 72(2):671–722 Manns MP, Czaja AJ, Gorham JD, Krawitt EL, Mieli-Vergani G, Vergani D et al (2010) Diagnosis and management of autoimmune hepatitis. Hepatology 51(6):2193–2213 Mahler M, Parker T, Peebles CL, Andrade LE, Swart A, Carbone Y et al (2012) Anti-DFS70/LEDGF Antibodies Are More Prevalent in Healthy Individuals Compared to Patients with Systemic Autoimmune Rheumatic Diseases. J Rheumatol 39(11):2104–2110 Tebo AE (2017) Recent Approaches To Optimize Laboratory Assessment of Antinuclear Antibodies. Clin Vaccine Immunol 24(12):e00270–e00217 Infantino M, Bizzaro N, Grossi V, Manfredi M (2019) The long-awaited 'pseudo-DFS pattern'. Expert Rev Clin Immunol 15(5):445 Sulli A, Ruaro B, Smith V, Pizzorni C, Zampogna G, Gallo M et al (2013) Progression of Nailfold Microvascular Damage and Antinuclear Antibody Pattern in Systemic Sclerosis. J Rheumatol 40(5):634–639 Gauderon A, Roux-Lombard P, Spoerl D (2020) Antinuclear Antibodies With a Homogeneous and Speckled Immunofluorescence Pattern Are Associated With Lack of Cancer While Those With a Nucleolar Pattern With the Presence of Cancer. Front Med (Lausanne) 7:165 Arbuckle MR, McClain MT, Rubertone MV, Scofield RH, Dennis GJ, James JA et al (2003) Development of autoantibodies before the clinical onset of systemic lupus erythematosus. N Engl J Med 349(16):1526–1533 Van Hoovels L, Broeders S, Chan EKL, Andrade L, de Melo Cruvinel W, Damoiseaux J et al (2020) Current laboratory and clinical practices in reporting and interpreting anti-nuclear antibody indirect immunofluorescence (ANA IIF) patterns: results of an international survey. Autoimmun Highlights 11(1):17 Ramos-Casals M, Brito-Zerón P, Muñoz S, Soria N, Galiana D, Bertolaccini L et al (2007) Autoimmune diseases induced by TNF-targeted therapies: analysis of 233 cases. Med (Baltim) 86(4):242–251 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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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-6687469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468472524,"identity":"80234616-aeb0-49ef-a21b-49acbabea89c","order_by":0,"name":"Tien-Ming Chan¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACxgYgkQDE/CBeQgEpWiTBDANSrDM4ACaJUMnc3mO64eGOWjnj86sTPzwwYJDnFztAwGE9Z8xuJJ45bmx24+1mCaDDDGfOTiCgZUYOUEvbscRtN85uAGlJMLhNrJbNM85u/kGKlprEDfy924i0pedYGVDLAWOJG7zbLBIMJAj7xbC9edvNn211cvz9Zzff/FFhI88vTUhLAwcoLg4zMEiAVUrgVw4C8gzsD4BUHTDFHCCsehSMglEwCkYmAAB2/UuSWI/ZiAAAAABJRU5ErkJggg==","orcid":"","institution":"Chang Gung Memorial Hospital, Chang Gung University","correspondingAuthor":true,"prefix":"","firstName":"Tien-Ming","middleName":"","lastName":"Chan¹","suffix":""}],"badges":[],"createdAt":"2025-05-17 14:08:08","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6687469/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-6687469/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88045672,"identity":"2b098914-ce8c-461c-a675-06f196eea9e8","added_by":"auto","created_at":"2025-07-31 18:13:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27195,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of\u003c/p\u003e","description":"","filename":"f1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6687469/v2/0b18025cdb2f98cdcd4fabdd.jpg"},{"id":88045677,"identity":"2fb40a52-d346-4820-9d5c-85d4727679f8","added_by":"auto","created_at":"2025-07-31 18:13:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49910,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Single ANA Patterns in ANA-Positive T1DM Patients\u003c/p\u003e","description":"","filename":"f2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6687469/v2/23689707e3046f07fa017c70.jpg"},{"id":88047623,"identity":"83398bb5-e61c-4079-9562-0580519566d1","added_by":"auto","created_at":"2025-07-31 18:45:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1126958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6687469/v2/44e971b6-4d1a-4fd0-9ecb-009e27a8372b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Prevalence of Type 1 Diabetes Mellitus across Antinuclear Antibody Patterns and Their Distribution in a Taiwanese Cohort","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eType 1 Diabetes Mellitus (T1DM) is a chronic autoimmune disease characterized by the immune-mediated destruction of pancreatic β-cells, leading to absolute insulin deficiency.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e While islet-specific autoantibodies, such as those against glutamic acid decarboxylase 65 (GAD65), insulinoma-associated antigen 2 (IA-2), and zinc transporter 8 (ZnT8), are established markers for T1DM risk and diagnosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the presence of non-organ-specific autoantibodies, like Antinuclear Antibodies (ANA), is also frequently observed\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eANA, detected by indirect immunofluorescence (IIF) on HEp-2 cells, are hallmarks of systemic autoimmune rheumatic diseases (SARD) but can also be present in organ-specific autoimmune conditions, including T1DM.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The reported prevalence of ANA in T1DM patients varies widely across studies, ranging from 10% to over 50%, potentially influenced by differences in study populations, detection methods, titer cutoffs, and patient age.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eBeyond mere positivity, the staining pattern observed during ANA testing provides additional clinical information, reflecting antibodies against different nuclear, cytoplasmic, or mitotic antigens.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e The International Consensus on ANA Patterns (ICAP) provides a standardized nomenclature for these patterns (AC-1 to AC-29).\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e While certain patterns are strongly associated with specific SARD (e.g., AC-3 Centromere with systemic sclerosis (SSc), specific patterns in SLE)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, the clinical significance of specific ANA patterns in T1DM is less clear. Some studies have suggested potential associations between certain patterns (like homogeneous or speckled) and T1DM or related autoimmune comorbidities\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, but data, especially using the detailed ICAP classification in large, specific ethnic populations, remain limited.\u003c/p\u003e\u003cp\u003eUnderstanding the relationship between specific ANA patterns and T1DM could offer insights into shared autoimmune pathways or identify subgroups of T1DM patients with distinct immunological profiles or risks for polyautoimmunity.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e T1DM frequently coexists with other autoimmune conditions, most notably autoimmune thyroid disease (AITD) and celiac disease.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Given the paucity of data from Taiwan, this study aimed to investigate two primary objectives within a large cohort from a major Taiwanese medical center: 1) To determine the prevalence of T1DM across the spectrum of pure ANA staining patterns defined by ICAP, and 2) To describe the distribution of these pure ANA patterns among patients diagnosed with T1DM.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data Source and Study Design\u003c/h2\u003e\u003cp\u003eThis cross-sectional study utilized de-identified electronic medical records from the Chang Gung Research Database (CGRD). CGRD encompasses data from seven hospital branches of the Chang Gung Memorial Health System in Taiwan. The study period was from January 1, 2019, to September 30, 2021. The study protocol was approved by the Institutional Review Board of Chang Gung Memorial Hospital (IRB No. 202101542B0), which waived the need for individual informed consent due to the de-identified nature of the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Study Population\u003c/h2\u003e\u003cp\u003eWe initially identified all patients (N\u0026thinsp;=\u0026thinsp;38,572) who underwent ANA testing during the study period. Patients with multiple ANA tests had only their first result included. We excluded patients with inconsistent results between tests (n\u0026thinsp;=\u0026thinsp;2,401) and those exhibiting mixed ANA staining patterns (n\u0026thinsp;=\u0026thinsp;408) to focus the analysis on the association with distinct, pure patterns. Patients tested solely for routine health examinations were also excluded. The final analytical cohort comprised 35,763 patients with a single, consistent, pure ANA staining pattern (or negative result).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Laboratory Measurements and ANA Classification\u003c/h2\u003e\u003cp\u003eAll ANA tests were performed using IIF on HEp-2 cells with the AESKUSLIDES kit (Aesku Diagnostics, Wendelsheim, Germany) at the CAP-accredited Immunology Laboratory of Linkou Chang Gung Memorial Hospital. A titer\u0026thinsp;≧\u0026thinsp;1:80 was considered positive. Staining patterns were meticulously reviewed and classified according to ICAP guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.anapatterns.org\" target=\"_blank\"\u003ewww.anapatterns.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.anapatterns.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) into nuclear (AC-1 to AC-14, AC-29), cytoplasmic (AC-15 to AC-23), and mitotic (AC-24 to AC-28) patterns [7]. Pattern classification was independently confirmed by three experienced reviewers. Patients with negative results were classified as AC-0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Identification of T1DM\u003c/h2\u003e\u003cp\u003eT1DM cases were identified using International Classification of Diseases (ICD) codes from the CGRD: ICD-9-CM codes 250.x1 or 250.x3, and ICD-10-CM codes E10.x. A diagnosis was considered valid if documented by a board-certified endocrinologist in at least two outpatient visits or at least one inpatient admission record during the study period. To further enhance diagnostic accuracy, all identified T1DM cases were additionally validated by confirming the issuance of a catastrophic illness certificate (CIC) for T1DM from Taiwan\u0026rsquo;s National Health Insurance (NHI) system, which requires fulfillment of standardized national diagnostic criteria for T1DM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe primary outcome was the prevalence of T1DM within each pure ANA pattern category (AC-1 to AC-29) and the ANA-negative group (AC-0). Descriptive statistics (frequencies, percentages) were used. T1DM prevalence in each ANA-positive pattern group was compared to the ANA-negative (AC-0) reference group using univariate logistic regression to calculate Odds Ratios (ORs) and 95% Confidence Intervals (CIs). Fisher\u0026rsquo;s exact test was employed to calculate two-sided p-values for prevalence comparisons and comparisons of categorical variables (e.g., ANA positivity rates between T1DM and non-T1DM groups). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Cohort Characteristics\u003c/h2\u003e\u003cp\u003eFrom the initial 38,572 patients tested for ANA, 35,763 met the inclusion criteria for the pure pattern analysis. This final cohort included 31,151 ANA-negative (AC-0) individuals and 4,612 ANA-positive individuals distributed across various pure ICAP patterns (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 42 patients within this final cohort had a validated diagnosis of T1DM.\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\u003ePrevalence of T1DM According to ANA Pure Staining Patterns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"31\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c28\" colnum=\"28\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c29\" colnum=\"29\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c30\" colnum=\"30\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c31\" colnum=\"31\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANA (-)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAC-1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAC-2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAC-3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAC-4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAC-5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAC-6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAC-7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAC-8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAC-9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eAC-10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eAC-11\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eAC-12\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eAC-13\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eAC-14\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003eAC-29\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003eAC-15\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003eAC-16\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003eAC-17\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u003cp\u003eAC-18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c22\"\u003e\u003cp\u003eAC-19\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c23\"\u003e\u003cp\u003eAC-20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c24\"\u003e\u003cp\u003eAC-21\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c25\"\u003e\u003cp\u003eAC-22\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c26\"\u003e\u003cp\u003eAC-24\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c27\"\u003e\u003cp\u003eAC-25\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c28\"\u003e\u003cp\u003eAC-26\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c29\"\u003e\u003cp\u003eAC-27\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c30\"\u003e\u003cp\u003eAC-28\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c31\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;31151)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;559)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;629)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;624)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;670)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1050)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c22\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c23\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c24\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c25\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c26\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c27\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c28\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c29\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c30\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c22\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c23\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c24\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c25\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c26\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c27\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c28\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c29\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c30\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (0.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (0.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (0.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (0.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (0.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c24\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c26\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c27\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c28\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c29\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c30\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c31\"\u003e\u003cp\u003e0.9646\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\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\u003ePrevalence of T1DM Across Different Pure ANA Staining Patterns Compared to ANA-negative Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA Patterns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT1DM Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0082*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Prevalence of T1DM Among ANA Patterns\u003c/h2\u003e\u003cp\u003eThe prevalence of T1DM in the ANA-negative (AC-0) reference group was 0.11% (34 out of 31,151 patients) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the ANA-positive subgroups, T1DM prevalence varied (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The AC-4 (Fine speckled) pattern group (n\u0026thinsp;=\u0026thinsp;670) exhibited the highest prevalence at 0.60% (4 T1DM cases), which was significantly higher than the AC-0 group (OR 5.50, 95% CI [1.75\u0026ndash;17.26], p\u0026thinsp;=\u0026thinsp;0.0082). T1DM cases were also identified in other common patterns: AC-1 (Homogeneous, n\u0026thinsp;=\u0026thinsp;559) had 1 case (0.18%; OR 1.64, 95% CI [0.22\u0026ndash;12.18], p\u0026thinsp;=\u0026thinsp;0.4636), AC-3 (Centromere, n\u0026thinsp;=\u0026thinsp;624) had 1 case (0.16%; OR 1.47, 95% CI [0.19\u0026ndash;11.20], p\u0026thinsp;=\u0026thinsp;0.5007), and AC-5 (Large/Coarse speckled, n\u0026thinsp;=\u0026thinsp;1050) had 2 cases (0.19%; OR 1.75, 95% CI [0.42\u0026ndash;7.31], p\u0026thinsp;=\u0026thinsp;0.329). No T1DM cases were found in the AC-2 (Dense fine speckled) group or any other pure ANA pattern groups (AC-6 through AC-29). When comparing the overall T1DM prevalence across all pure ANA subtypes collectively against the ANA-negative group, no statistically significant difference was observed (p\u0026thinsp;=\u0026thinsp;0.9646) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Distribution of ANA Patterns in T1DM\u003c/h2\u003e\u003cp\u003eConsidering the initial cohort of 38,572 patients (including mixed/different patterns), 42 T1DM patients were identified. The overall ANA positivity rate in this initial T1DM group was 19.05% (8/42), which was not significantly different from the 19.07% (7347/38530) observed in non-T1DM subjects (p\u0026thinsp;=\u0026thinsp;0.9973) (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\u003eComparison of ANA Positivity, Titers, and Demographic Characteristics in T1DM Patients: Inclusion of Different and Mixed ANA Patterns\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSingle ANA patterns\u0026thinsp;+\u0026thinsp;Different / Mixed ANA patterns (N\u0026thinsp;=\u0026thinsp;38572)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-T1DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT1DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38530)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.89\u0026thinsp;\u0026plusmn;\u0026thinsp;16.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.38\u0026thinsp;\u0026plusmn;\u0026thinsp;16.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5481\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25880 (67.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (76.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12650 (32.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (23.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31183 (80.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7347 (19.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (19.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA titer\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8956\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(-) Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31183 (80.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2766 (7.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (9.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2040 (5.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e337 (0.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e338 (0.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e542 (1.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+)\u0026thinsp;\u0026gt;\u0026thinsp;1:1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1324 (3.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFocusing on the final cohort of 35,763 patients with pure patterns, 8 of the 42 T1DM patients (19.05%) were ANA-positive, while 34 (80.95%) were ANA-negative (AC-0). The ANA positivity rate in T1DM patients (19.05%) was numerically higher but not statistically different from that in non-T1DM controls (12.91%, 4612/35721; p\u0026thinsp;=\u0026thinsp;0.2360) within this pure pattern cohort (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution and Clinical Significance of Single ANA Patterns (AC-1 to AC-29) in Patients with T1DM\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003enon-T1DM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT1DM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;35721)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.72\u0026thinsp;\u0026plusmn;\u0026thinsp;16.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.38\u0026thinsp;\u0026plusmn;\u0026thinsp;16.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23575 (66.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (76.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12146 (34.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (23.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31109 (87.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive (+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4612 (12.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (19.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA titer\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(-) Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31109 (87.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1691 (4.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (9.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1278 (3.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e228 (0.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214 (0.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+) 1:1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e358 (1.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(+)\u0026thinsp;\u0026gt;\u0026thinsp;1:1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e843 (2.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA patterns (Subgroup 1)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-0 / ANA (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31109 (87.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNuclear (AC-1 to AC-14, AC-29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4113 (11.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (19.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCytoplasmic (AC-15 to 23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e454 (1.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMitotic (AC-24 to 28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (0.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA patterns (Subgroup 2)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-0 / ANA (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31109 (87.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomogeneous (AC-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (1.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDense fine speckled (AC-2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e630 (1.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentromere (AC-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e623 (1.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpeckled (AC-4, AC-5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1717 (4.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (14.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscrete nuclear dots (AC-6, AC-7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (0.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNucleolar (AC-8, AC-9, AC-10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e392 (1.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNuclear envelope (AC-11, AC-12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (0.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePleomorphic (AC-13, AC-14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (0.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFine grainy speckled (AC-29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibrillar (AC-15, AC-16, AC-17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (0.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscrete dots (AC-18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (0.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpeckled (AC-19, AC-20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143 (0.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMA (AC-21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (0.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGolgi (AC-22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (0.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentrosome (AC-24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpindle fibers (AC-25, AC-26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (0.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercellular bridge (AC-27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (0.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMitotic chromosomal (AC-28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANA patterns\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-0 / ANA (-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31109 (87.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (1.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e630 (1.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e623 (1.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e666 (1.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (9.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1051 (2.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (0.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (0.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (0.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (0.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (0.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (0.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (0.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (0.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (0.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (0.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (0.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (0.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (0.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (0.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (0.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (0.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (0.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (0.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong the 8 ANA-positive T1DM patients with pure patterns, the distribution was as follows (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e): Speckled patterns (AC-4 and AC-5) were the most common, found in 6 patients (75.00% of ANA-positive T1DM cases). Specifically, AC-4 accounted for 4 cases (50.00%) and AC-5 for 2 cases (25.00%). The Homogeneous pattern (AC-1) and Centromere pattern (AC-3) were each observed in 1 patient (12.50% each). All 8 positive cases displayed nuclear patterns; no cytoplasmic or mitotic patterns were observed in T1DM patients in this cohort.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis large cross-sectional study investigated the intricate relationship between specific ANA patterns, defined by ICAP, and T1DM in a Taiwanese population. Our primary findings reveal that while overall ANA positivity was not significantly elevated in T1DM patients compared to controls in our cohort, the AC-4 speckled pattern was associated with a significantly increased prevalence of T1DM. Furthermore, among the ANA-positive T1DM patients, Speckled patterns (AC-4/AC-5) were markedly predominant.\u003c/p\u003e\u003cp\u003eThe overall ANA positivity rate of 19.05% (at \u0026ge;\u0026thinsp;1:80 titer) observed in our T1DM patients aligns with the lower-to-mid range reported in previous literature, which shows considerable variability (10% to over 50%).\u003csup\u003e4\u003c/sup\u003e Existing literature suggests that approximately one-quarter of adult T1DM patients in multiple studies test positive for ANAs, often with figures around 20\u0026ndash;30% in Western cohorts; for instance, Heras et al. (2010) reported 27% ANA positivity in adult T1DM, and Ciechanowicz et al. (2016) found 24% in a Polish cohort. Our observed rate is notably lower than the 71% reported by Segni et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e in pediatric patients with AITD using the same cutoff, suggesting potential differences based on age, ethnicity, or the specific underlying autoimmune condition. Our observed rate is closer to the background ANA positivity seen in some general hospital populations (e.g., 15.8% in a Turkish hospital cohort \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and a similar rate reported in a general German population\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e). The lack of a statistically significant difference in overall ANA positivity between our T1DM and non-T1DM groups (19.05% vs 12.91%) contrasts with some studies suggesting higher rates in T1DM and with observations from large phenome-wide studies where T1DM did not emerge as a top diagnosis associated with ANA. This potentially reflects the heterogeneity of control groups and ANA prevalence in the general population, as well as the notion that while a subset of T1DM patients may have ANA, the prevalence of T1DM among ANA carriers is generally very low. It also differs from findings in other autoimmune liver diseases like autoimmune hepatitis (AIH), where ANA and/or SMA are key diagnostic features.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe most striking finding of our study is the specific association between the AC-4 speckled pattern and an over five-fold increased odds of T1DM compared to ANA-negative individuals (OR 5.50, p\u0026thinsp;=\u0026thinsp;0.0082). Speckled patterns (including large/coarse speckled AC-5 and fine speckled AC-4) are common ANA results, often considered relatively non-specific and frequently seen in various SARDs like SLE.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e However, AC-4 represents a specific ICAP category characterized by fine speckles distributed throughout the nucleoplasm, sometimes larger and fewer than AC-5 (large/coarse speckled).\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e This distinct association between AC-4 and increased T1DM prevalence appears novel. It contrasts significantly with findings regarding the dense fine speckled (DFS) pattern (AC-2), often mediated by anti-DFS70/LEDGF antibodies. Studies by Mahler et al. [19] have shown that anti-DFS70 antibodies are paradoxically more prevalent in healthy individuals (8.9%) than in patients with SARD (like SLE, 2.8%) and may even argue \u003cem\u003eagainst\u003c/em\u003e a SARD diagnosis when present in isolation.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The AC-2 pattern has also been termed the \"pseudo-DFS\" pattern when not associated with anti-DFS70.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Our finding that AC-4 (a speckled pattern, not AC-2/DFS) is associated with increased T1DM prevalence suggests either that the AC-4 pattern in our T1DM cohort reflects specific, yet unidentified, autoantigens relevant to T1DM pathogenesis or associated polyautoimmunity, distinct from those typically seen in SARD or represented by the DFS70 antigen. Further investigation, including antigen specificity testing (e.g., for Ro, La, Sm, RNP) in AC-4 positive T1DM patients, is crucial. While speckled ANAs can be common in T1DM, it has been noted that the prevalence of T1DM among all individuals with a speckled pattern is very small, as these patterns often indicate antibodies more typical of lupus or Sj\u0026ouml;gren's. Our study, by focusing on ICAP-defined AC-4, refines this observation for a specific speckled subtype in a Taiwanese cohort.\u003c/p\u003e\u003cp\u003eAlthough T1DM cases were also observed with AC-1 (Homogeneous), AC-3 (Centromere), and AC-5 (Large/Coarse speckled) patterns, these associations did not reach statistical significance in our cohort, likely due to the small number of T1DM cases within these pattern groups. The presence of AC-1 and AC-5 is common but relatively non-specific.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e AC-1 (Homogeneous) is frequently seen in SLE.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Some reports suggest the homogeneous pattern is often the most common among ANA-positive T1DM patients in Western cohorts (e.g., ~\u0026thinsp;53% in Heras et al.), though in our study, speckled patterns predominated. The occurrence of AC-3 (anti-centromere) in one T1DM patient might reflect underlying polyautoimmunity, as this pattern is strongly linked to limited SSc\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and shows slower progression of microvascular damage compared to other SSc patterns\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, or it could be an incidental finding. Furthermore, nucleolar or centromere patterns are generally reported rarely in T1DM and would more likely point to another autoimmune condition. The lack of T1DM cases associated with the AC-2 (DFS) pattern in our cohort is consistent with the generally low prevalence of this pattern in SARD.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe distribution analysis among the 8 ANA-positive T1DM patients showed a clear predominance (75%) of Speckled patterns (AC-4 and AC-5). This contrasts with the overwhelming prevalence of the Homogeneous pattern (92%) reported by Segni et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e in children with AITD. Furthermore, this speckled dominance differs from the characteristic patterns often seen in SARD, such as the high prevalence of AC-1 in SLE\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e or the specific associations of AC-3, AC-29, and nucleolar patterns (AC-8, 9, 10) with SSc subtypes.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Notably, nucleolar patterns (AC-8, 9, 10) have also been linked to an increased risk of cancer, particularly in SSc patients.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The distinct pattern distribution in our T1DM cohort suggests that the autoantibody profile in ANA-positive T1DM patients may differ significantly from that in AITD, SLE, or SSc. The absence of cytoplasmic (e.g., AC-21 AMA, seen in PBC\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e) and mitotic patterns (e.g., AC-26 NuMA, AC-25 Spindle fibers) in our T1DM cohort is also noted, though conclusions are limited by the small sample size. Cytoplasmic ANA patterns are also generally considered uncommon in T1DM, although rare cases with AMA or ANCA can occur, sometimes in the context of polyglandular autoimmune syndromes.\u003c/p\u003e\u003cp\u003eT1DM is well-known for its association with other autoimmune diseases, particularly AITD and celiac disease, occurring in up to 30% and 10% of patients, respectively, in some populations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e While our study did not find a significantly higher overall ANA positivity in T1DM, the specific association with the AC-4 pattern might delineate a subgroup with a particular autoimmune predisposition or reflect shared pathogenic pathways yet to be elucidated. It has been suggested that ANA positivity in T1DM might signify a distinct subset with heightened autoimmunity, potentially heralding latent development of a systemic autoimmune disease, and that screening T1DM patients for non-organ-specific autoantibodies could be useful for early detection of other autoimmune conditions. ANA positivity itself, even without overt SARD, can predate clinical disease onset by many years [5,24], and its presence in T1DM warrants awareness, although routine screening utility remains debated without specific clinical indications.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The potential development of autoimmune phenomena, including ANA and lupus-like syndromes, has also been noted following therapies like anti-TNF agents, although this is a distinct context from idiopathic autoimmunity.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA particularly relevant aspect is the potential for ethnic and regional differences in ANA prevalence in T1DM. Some literature suggests that Asian populations (e.g., East Asian) may have a lower prevalence of ANA positivity in T1DM compared to Caucasian populations. This could be attributed to genetic factors (different HLA associations) and a higher proportion of non-immune-mediated or autoantibody-negative diabetes (like fulminant T1DM, common in Japan) within what is clinically classified as T1DM in Asia. For instance, some studies report that only\u0026thinsp;~\u0026thinsp;30\u0026ndash;40% of adult-onset T1DM in China have any islet autoantibody, much lower than in Europeans, suggesting a generally lower autoimmunity burden which might extend to ANAs. Our study, conducted in a Taiwanese (East Asian) population, found an ANA positivity rate of 19.05%, which, while not directly compared to a non-Asian T1DM cohort within our study, appears consistent with the notion that ANA positivity might not be as high as some Western reports. However, it is also cautioned that direct comparative data are limited, and methodological differences (e.g., IIF vs. ELISA for ANA detection) can affect reported prevalence. For example, a study in Sudan using ELISA found 0% ANA positivity at T1DM onset, highlighting this methodological point. Our use of IIF, considered more sensitive, and finding a 19.05% rate, contributes valuable data from an Asian population. The predominance of speckled patterns (AC-4/AC-5) in our ANA-positive T1DM patients is an interesting finding that warrants comparison with pattern distributions in other Asian and non-Asian T1DM cohorts using standardized ICAP classification.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eAlthough this study, based on a large Taiwanese medical database, provides initial insights into the association between ANA patterns and T1DM, several inherent limitations should be considered when interpreting the results.\u003c/p\u003e\u003cp\u003eFirstly, this study employed a cross-sectional design, meaning we observed associations at a specific point in time. Consequently, it is not possible to establish a causal relationship between ANA patterns and T1DM. We can only describe their correlation and cannot determine which is the cause and which is the effect, or whether common underlying factors influence both.\u003c/p\u003e\u003cp\u003eSecondly, the identification of T1DM cases primarily relied on ICD codes. Although we validated these with endocrinologist records to enhance accuracy, diagnoses based on coding may still carry a degree of misclassification risk, which could potentially affect the precision of the results.\u003c/p\u003e\u003cp\u003eFurthermore, the data for this study were sourced from a single, large healthcare system in Taiwan (Chang Gung Memorial Health System). While the sample size is substantial, the generalizability of the findings to other regions in Taiwan or other ethnic populations may be limited, as patient characteristics and medical practices might differ across various healthcare systems or regions.\u003c/p\u003e\u003cp\u003eAdditionally, to ensure the purity and clarity of the ANA pattern analysis, this study excluded cases with inconsistent ANA test results or those exhibiting mixed patterns. While this exclusion criterion helps clarify the association between specific pure patterns and T1DM, it might also introduce selection bias, meaning the study results may not fully represent the situation for all ANA-positive individuals.\u003c/p\u003e\u003cp\u003eMore importantly, the number of ANA-positive T1DM patients in this study was relatively small (n\u0026thinsp;=\u0026thinsp;8). This small sample size limits the statistical power, particularly when analyzing the distribution of different ANA patterns among T1DM patients and when investigating associations with less common ANA patterns. It might be difficult to detect weak but genuine associations, or the observed associations might lack stability.\u003c/p\u003e\u003cp\u003eLastly, this study could not incorporate data on T1DM-specific autoantibodies (such as GAD65, IA-2, etc.), nor did it have detailed clinical information on patients' other autoimmune comorbidities. The absence of these data restricted our ability to conduct a more in-depth analysis of polyautoimmunity in T1DM patients and made it challenging to comprehensively assess the role of ANA patterns within a complex autoimmune context.\u003c/p\u003e\u003cp\u003eFuture research should aim to overcome these limitations, for instance, by employing prospective cohort study designs to explore causal relationships, incorporating broader data sources to enhance the generalizability of results, and integrating more detailed autoantibody profiles and clinical phenotype data. This will allow for a more comprehensive elucidation of the precise significance of ANA patterns in the pathogenesis and clinical presentation of T1DM.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis large Taiwanese cohort study found that while overall ANA positivity was not significantly increased in T1DM patients, the pure AC-4 Speckled ANA pattern was significantly associated with increased T1DM prevalence. Speckled patterns (AC-4/AC-5) predominated among ANA-positive T1DM individuals. These findings highlight the importance of specific ANA patterns, particularly AC-4, in T1DM, warranting further research to confirm this association and elucidate underlying immunological mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e The study protocol was approved by the Institutional Review Board of Chang Gung Memorial Hospital (IRB No. 202101542B0), which waived the need for individual informed consent due to the de-identified nature of the data.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable, as the manuscript does not contain any individual person\u0026rsquo;s data in any form.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e Raw data were generated at Chang Gung Memorial Hospital at Linkou, Taiwan. The derived data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests (Conflict of interest):\u003c/strong\u003e The author declares that this research was conducted without any commercial or financial relationships that could be interpreted as potential conflicts of interest.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This project was funded by the Center for Big Data Analytics and Statistics at Chang Gung Memorial Hospital, Linkou, and the Chang Gung Research Database (Project No.: CGRPG3N0061).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAuthor\u0026apos;s contributions:\u003c/strong\u003e Tien-Ming Chan wrote the paper. Tien-Ming Chan acquired the clinical data and performed critical reviews. Tien-Ming Chan interpretated image reports. Tien-Ming Chan is the guarantor. Tien-Ming Chan read and approved the final manuscript.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We are grateful to Jing-Yi Huang for providing valuable statistical support and extend our sincere thanks to Chang Gung Memorial Hospital for their generous assistance. This study was financially supported by the Center for Big Data Analytics and Statistics at Chang Gung Memorial Hospital, Linkou, along with the Chang Gung Research Database. Additionally, we recognize the commitment and exceptional clinical care provided by the healthcare professionals in the Department of Internal Medicine.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAtkinson MA, Eisenbarth GS, Michels AW (2014) Type 1 diabetes. Lancet 383(9911):69\u0026ndash;82\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInsel RA, Dunne JL, Atkinson MA, Chiang JL, Dabelea D, Gottlieb PA et al (2015) Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care 38(10):1964\u0026ndash;1974\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKahaly GJ, Hansen MP (2016) Type 1 diabetes associated autoimmunity. Autoimmun Rev 15(7):644\u0026ndash;648\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePopoviciu MS, Kaka N, Sethi Y, Patel N, Chopra H, Cavalu S (2023) Type 1 Diabetes Mellitus and Autoimmune Diseases: A Critical Review of the Association and the Application of Personalized Medicine. J Pers Med 13(3):422\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgmon-Levin N, Damoiseaux J, Kallenberg C, Sack U, Witte T, Herold M et al (2014) International recommendations for the assessment of autoantibodies to cellular antigens referred to as anti-nuclear antibodies. Ann Rheum Dis 73(1):17\u0026ndash;23\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDamoiseaux J, Andrade LEC, Carballo OG, Conrad K, Francescantonio PLC, Fritzler MJ et al (2019) Clinical relevance of HEp-2 indirect immunofluorescent patterns: the International Consensus on ANA patterns (ICAP) perspective. Ann Rheum Dis 78(7):879\u0026ndash;889\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan EKL, Damoiseaux J, Carballo OG, Conrad K, de Melo Cruvinel W, Francescantonio PLC et al (2015) Report of the First International Consensus on Standardized Nomenclature of Antinuclear Antibody HEp-2 Cell Patterns 2014\u0026ndash;2015. Front Immunol 6:412\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMierau R, Moinzadeh P, Riemekasten G, Melchers I, Meurer M, Reichenberger F et al (2011) Frequency of disease-associated and other nuclear autoantibodies in patients of the German network for systemic scleroderma: correlation with characteristic clinical features. Arthritis Res Ther 13(5):R172\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Mughales J (2022) Anti-Nuclear Antibodies Patterns in Patients With Systemic Lupus Erythematosus and Their Correlation With Other Diagnostic Immunological Parameters. Front Immunol 13:850759\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoelaert K, Newby PR, Simmonds MJ, Holder RL, Carr-Smith JD, Heward JM et al (2010) Prevalence and relative risk of other autoimmune diseases in subjects with autoimmune thyroid disease. Am J Med 123(2):183e1\u0026ndash;183e9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Hakami AM (2016) Pattern of thyroid, celiac, and anti-cyclic citrullinated peptide autoantibodies coexistence with type 1 diabetes mellitus in patients from Southwestern Saudi Arabia. Saudi Med J 37(4):386\u0026ndash;391\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalma CC, Pavesi M, Nogueira VG, Clemente EL, Vasconcellos MF, Pereira LC Jr et al (2013) Prevalence of thyroid dysfunction in patients with diabetes mellitus. Diabetol Metab Syndr 5(1):58\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrzewska A, Ben-Skowronek I (2016) Effect of Associated Autoimmune Diseases on Type 1 Diabetes Mellitus Incidence and Metabolic Control in Children and Adolescents. Biomed Res Int 2016:6219730\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSegni M, Pucarelli I, Truglia S, Turriziani I, Serafinelli C, Conti F (2014) High Prevalence of Antinuclear Antibodies in Children with Thyroid Autoimmunity. J Immunol Res 2014:150239\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMengeloglu Z, Tas T, Kocoglu E, Aktas G, Karab\u0026ouml;rk S (2014) Determination of anti-nuclear antibody pattern distribution and clinical relationship. Pak J Med Sci 30(2):380\u0026ndash;383\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkmatov MK, R\u0026ouml;ber N, Ahrens W, Flesch-Janys D, Fricke J, Greiser H et al (2017) Anti-nuclear autoantibodies in the general German population: prevalence and lack of association with selected cardiovascular and metabolic disorders-findings of a multicenter population-based study. Arthritis Res Ther 19(1):127\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMack CL, Adams D, Assis DN, Kerkar N, Manns MP, Mayo MJ et al (2020) Diagnosis and Management of Autoimmune Hepatitis in Adults and Children: 2019 Practice Guidance and Guidelines From the American Association for the Study of Liver Diseases. Hepatology 72(2):671\u0026ndash;722\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManns MP, Czaja AJ, Gorham JD, Krawitt EL, Mieli-Vergani G, Vergani D et al (2010) Diagnosis and management of autoimmune hepatitis. Hepatology 51(6):2193\u0026ndash;2213\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahler M, Parker T, Peebles CL, Andrade LE, Swart A, Carbone Y et al (2012) Anti-DFS70/LEDGF Antibodies Are More Prevalent in Healthy Individuals Compared to Patients with Systemic Autoimmune Rheumatic Diseases. J Rheumatol 39(11):2104\u0026ndash;2110\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTebo AE (2017) Recent Approaches To Optimize Laboratory Assessment of Antinuclear Antibodies. Clin Vaccine Immunol 24(12):e00270\u0026ndash;e00217\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInfantino M, Bizzaro N, Grossi V, Manfredi M (2019) The long-awaited 'pseudo-DFS pattern'. Expert Rev Clin Immunol 15(5):445\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSulli A, Ruaro B, Smith V, Pizzorni C, Zampogna G, Gallo M et al (2013) Progression of Nailfold Microvascular Damage and Antinuclear Antibody Pattern in Systemic Sclerosis. J Rheumatol 40(5):634\u0026ndash;639\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauderon A, Roux-Lombard P, Spoerl D (2020) Antinuclear Antibodies With a Homogeneous and Speckled Immunofluorescence Pattern Are Associated With Lack of Cancer While Those With a Nucleolar Pattern With the Presence of Cancer. Front Med (Lausanne) 7:165\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArbuckle MR, McClain MT, Rubertone MV, Scofield RH, Dennis GJ, James JA et al (2003) Development of autoantibodies before the clinical onset of systemic lupus erythematosus. N Engl J Med 349(16):1526\u0026ndash;1533\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Hoovels L, Broeders S, Chan EKL, Andrade L, de Melo Cruvinel W, Damoiseaux J et al (2020) Current laboratory and clinical practices in reporting and interpreting anti-nuclear antibody indirect immunofluorescence (ANA IIF) patterns: results of an international survey. Autoimmun Highlights 11(1):17\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamos-Casals M, Brito-Zer\u0026oacute;n P, Mu\u0026ntilde;oz S, Soria N, Galiana D, Bertolaccini L et al (2007) Autoimmune diseases induced by TNF-targeted therapies: analysis of 233 cases. Med (Baltim) 86(4):242\u0026ndash;251\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":"Antinuclear Antibodies, ANA patterns, Type 1 Diabetes Mellitus, Prevalence, Distribution, ICAP, Autoimmunity, Taiwan","lastPublishedDoi":"10.21203/rs.3.rs-6687469/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6687469/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eType 1 Diabetes Mellitus (T1DM) is an autoimmune disease characterized by pancreatic β-cell destruction. While islet-specific autoantibodies are key markers, Antinuclear Antibodies (ANA) are also observed, but their relationship with specific ANA patterns, classified by the International Consensus on ANA Patterns (ICAP), in T1DM, especially in diverse populations like Taiwan, needs more investigation. This study aimed to determine the prevalence of T1DM across various pure ANA patterns and describe the distribution of these patterns among T1DM patients in a large Taiwanese cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study utilized de-identified data from the Chang Gung Research Database (CGRD) from January 2019 to September 2021. Patients undergoing ANA testing by indirect immunofluorescence (IIF) on HEp-2 cells were included. Individuals with inconsistent or mixed ANA patterns were excluded. T1DM diagnosis was based on International Classification of Diseases (ICD)-9-CM/ICD-10-CM codes, validated by endocrinologist records and further confirmed by catastrophic illness certificate (CIC) data for T1DM from Taiwan's National Health Insurance system. The prevalence of T1DM was calculated for each pure ANA pattern (AC-1 to AC-29), using the ANA-negative group (AC-0) as the reference. Odds ratios (ORs) with 95% confidence intervals (CIs) and Fisher\u0026rsquo;s exact test were used for statistical comparisons.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom 38,572 initial patients, 35,763 with pure ANA patterns were analyzed (31,151 AC-0; 4,612 ANA-positive). The overall T1DM prevalence in the AC-0 group was 0.11% (34/31,151). Among ANA-positive patterns, only the AC-4 (Fine Speckled) pattern (n\u0026thinsp;=\u0026thinsp;670) showed a significantly higher T1DM prevalence (0.60%, 4 cases; OR 5.50, 95% CI [1.75\u0026ndash;17.26], p\u0026thinsp;=\u0026thinsp;0.0082) compared to the AC-0 group. Other patterns such as AC-1 (Homogeneous, 0.18%), AC-3 (Centromere, 0.16%), and AC-5 (Large/Coarse speckled, 0.19%) also had T1DM cases, but these associations were not statistically significant. The overall ANA positivity rate was not significantly different between T1DM patients (19.05%, 8/42) and non-T1DM individuals (12.91%, 4612/35721) in this pure pattern cohort (p\u0026thinsp;=\u0026thinsp;0.2360). Among the 8 ANA-positive T1DM patients, Speckled patterns (AC-4/AC-5) were predominant (6/8, 75.00%), followed by AC-1 (Homogeneous; 1/8, 12.50%) and AC-3 (Centromere; 1/8, 12.50%). All identified patterns in T1DM patients were nuclear.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn this large Taiwanese cohort, while overall ANA positivity was not significantly increased in T1DM patients, the pure AC-4 Speckled ANA pattern was associated with a significantly higher prevalence of T1DM compared to ANA-negative individuals. Speckled patterns were the most common ANA patterns observed among ANA-positive T1DM patients. These findings suggest a potential specific link between certain ANA patterns, particularly AC-4, and T1DM autoimmunity, warranting further investigation into the specific antigens involved and the clinical implications in diverse populations.\u003c/p\u003e","manuscriptTitle":"Prevalence of Type 1 Diabetes Mellitus across Antinuclear Antibody Patterns and Their Distribution in a Taiwanese Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-07-31 18:13:18","doi":"10.21203/rs.3.rs-6687469/v2","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}},{"code":1,"date":"2025-06-09 08:44:53","doi":"10.21203/rs.3.rs-6687469/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":"35557c26-41e8-4391-908f-0e03daeeb83a","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T16:11:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 18:13:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-6687469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6687469","identity":"rs-6687469","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}