Population-Specific Reference Thresholds for Islet Autoantibodies in Bangladeshi Youth and Their Clinical Implications

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The aim of the study was to derive population-specific reference thresholds for antibodies against glutamic acid decarboxylase 65 (GAD65), tyrosine phosphatase-like insulinoma antigen 2 (IA2), and zinc transporter 8 (ZnT8) in a Bangladeshi normoglycemic cohort and to compare their application with manufacturer-recommended thresholds in youth-onset diabetes. Methods This cross-sectional study included a normoglycemic cohort (NGT, n = 148) and a youth-onset diabetes cohort (DM, n = 180) aged 10–34 years, recruited from the clinic-based screening program in Bangladesh Medical University, Dhaka, during 2023–2024. GAD65 and IA2 antibodies were measured by chemiluminescent immunoassay, and ZnT8 antibody by enzyme-linked immunosorbent assay. Study-derived thresholds were defined from the NGT cohort using the 99th percentile. In the diabetes cohort, antibody positivity rates and clinical and biochemical characteristics were compared using manufacturer-recommended and study-derived thresholds. Results Study-derived thresholds were 8.5 IU/mL for GAD65, 40.8 U/mL for ZnT8, and 3.2 U/mL for IA2 antibodies. Applying these thresholds to the diabetes cohort changed antibody positivity rates. GAD65 positivity increased from 5.0% by the manufacturer's threshold to 7.8% by the study-derived threshold. Similarly, IA2 positivity increased from 0% to 1.7%, whereas ZnT8 positivity decreased from 15.0% to 2.8%. Overall antibody positivity decreased from 18.9% to 11.7%. Under both threshold systems, antibody-positive participants had lower BMI, lower frequency of acanthosis nigricans, lower HOMA2-%B, lower C-peptide, and higher glycemic indices than antibody-negative participants. Conclusions Study-derived thresholds for islet autoantibodies differed from manufacturer-recommended thresholds and changed antibody positivity estimates. These findings support local calibration and contextual interpretation of autoantibody results in Bangladeshi youth-onset diabetes, alongside clinical and metabolic markers. Youth-onset diabetes Islet autoantibodies GAD65 ZnT8 Bangladesh Background The global rise in youth-onset diabetes presents a significant diagnostic challenge, particularly in South Asian populations where the classic clinical distinctions between Type 1 (T1D) and Type 2 diabetes (T2D) are often blurred [1]. The ambiguity stems from two divergent trends. First, the increasing burden of obesity and metabolic syndrome among young individuals means these metabolic features may coexist with T1D, masking the features of the underlying autoimmune process [2]. Conversely, T2D in South Asians frequently manifests at a younger age and at a lower body mass index (BMI) compared to Western people, mimicking the clinical presentation of T1D, leading to frequent misclassification [3]. Therefore, relying solely on clinical markers, such as age of onset, glycemic severity at presentation, obesity, or the presence of metabolic syndrome, is frequently insufficient to differentiate autoimmune-mediated insulin deficiency from insulin-resistant phenotypes. In this context, islet autoantibody (IAb) testing provides an objective immunological marker that can assist in diabetes classification. The autoimmune pathogenesis of diabetes involves a humoral immune response against several key pancreatic β-cell antigens. Among these, antibodies against glutamic acid decarboxylase 65 (GAD65), tyrosine phosphatase-like insulinoma antigen 2/islet antigen 2 (IA2), and zinc transporter 8 (ZnT8) are clinically most relevant [4]. Although these autoantibodies are not believed to be pathogenic, they serve as reliable clinical markers of β-cell autoimmunity [5]. GAD65 antibody is the primary antibody to measure in young adults, especially when the clinical onset is slower or latent [6]. Anti-IA2 and anti-ZnT8 further increase the overall diagnostic yield. Current guidelines suggest a sequential measurement of these IAbs [4]. Historically, the detection of IAbs relied on radioimmunoassays (RIAs) for decades. However, there are practical limitations of RIA, including the use of radioactive isotopes and short reagent shelf-life. As a result, there is a shift toward modern, non-isotopic platforms such as chemiluminescence immunometric assays (CLIA) and enzyme-linked immunosorbent assays (ELISA), which have demonstrated good diagnostic performance in IAb assays [7,8]. These advancements enable more precise quantification of low-titer antibodies, which is particularly relevant in adult-onset or latent autoimmune cases. Nevertheless, because antibody titers can vary significantly across assay platforms, it is vital that diagnostic thresholds are not merely adopted from older literature but instead validated for the specific technology and population in use [9]. This methodological shift supports the need for platform-specific reference thresholds to improve contextual interpretation of antibody results [10]. Despite the clinical utility of islet autoantibodies, their distribution among Bangladeshi youth remains insufficiently characterized. Most diagnostic thresholds currently used in clinical practice are derived from studies conducted on other ethnic groups. However, a growing body of evidence suggests significant ethnic variation exists in both the prevalence and the titers of these antibodies [11]. Furthermore, due to technical variability between assay platforms, a threshold established on one system may not be transferable to another [9]. In Bangladesh, locally validated reference thresholds for islet autoantibodies are not yet available, which may limit the contextual interpretation of antibody results in young people with diabetes. Without population-specific and assay-contextualized reference thresholds, interpretation of low-level antibody positivity may be uncertain, particularly in young people with overlapping metabolic and insulin-deficient features [12]. Against this background, the current study aimed to derive population-specific reference thresholds for GAD65, ZnT8, and IA2 antibodies in a young Bangladeshi normoglycemic cohort. A secondary objective was to compare the effect of manufacturer-recommended and study-derived thresholds on antibody positivity rates and to examine the clinical and biochemical characteristics associated with antibody positivity in a youth-onset diabetes cohort. Methods Study design and subjects This cross-sectional study was conducted at the Department of Endocrinology, Bangladesh Medical University (BMU), between 2023 and 2024. The study used two cohorts recruited from the clinic-based screening program of the 'Study on Obesity and Diabetes in Young (SODY)’ group at BMU. The first cohort was the normal glucose tolerance (NGT) cohort (n = 148), comprising individuals aged 10–34 years with confirmed NGT on a 75-g oral glucose tolerance test (OGTT). This group served as the reference for estimating population-specific thresholds for IAbs. The second was the youth-onset diabetes mellitus (DM) cohort (n = 180), which consisted of individuals aged 10–34 years diagnosed with diabetes. Participants with clinical features of endocrine disorders or a history of drug use that may interfere with glycemic status were excluded. Sample size The sample size for the NGT cohort was guided by the Clinical and Laboratory Standards Institute (CLSI EP28-A3c) guidelines for reference interval estimation [10], which recommend a minimum of 120 participants for non-parametric estimation of reference limits. Accordingly, 148 normoglycemic participants were included for deriving population-specific reference thresholds. The youth-onset diabetes cohort included 180 participants and was used to describe antibody positivity rates and to compare clinical and biochemical characteristics by antibody status. As this component was exploratory and descriptive, no separate formal sample size calculation was performed for this cohort. Study procedure Clinical data for the included participants were collected through face-to-face interviews using a structured case-record form (Supplementary file 1), along with anthropometric measurements and physical examinations. A fasting blood sample was collected from each participant to measure plasma glucose, lipid profile, and C-peptide. A post-prandial/post-glucose load blood sample was also collected for measurement of plasma glucose, glycated hemoglobin (HbA1c), and IAbs (anti-GAD65, anti-ZnT8, and anti-IA2). Clinical assessment Height and weight were measured using standard procedures, and BMI was calculated as kg/m 2 . Adolescents were classified based on Indian Academy of Pediatrics (IAP) growth chart BMI percentiles (underweight <5th ; healthy weight 5th -84th ; overweight 85th -94th ; obese ≥ 95th ), while young adult BMI was categorized according to Asian-specific cut-offs (underweight < 18.5 kg/m², healthy weight 18.5–22.9 kg/m², overweight 23-24.9 kg/m², and obesity ≥ 25 kg/m²) [13,14]. Central obesity was defined as a waist circumference (WC) ≥ 90 cm in males or ≥ 80 cm in females (or ≥ 70th percentile in adolescents) [15,16]. The waist-to-hip ratio (WHR) thresholds for central obesity were ≥ 0.9 for males and ≥ 0.85 for females; these were applied to both adults and adolescents [17]. A waist-to-height ratio (WHtR) ≥ 0.50 was considered abnormal [18]. Elevated blood pressure (BP) in adults was defined as systolic BP (SBP) ≥ 120 mmHg or diastolic BP (DBP) ≥ 80 mmHg, while in adolescents it was defined as BP ≥ 90th percentile for age, sex, and height [19,20]. Current use of antihypertensive drugs was also regarded as a criterion for elevated BP. Biochemical assessment Plasma glucose was measured using the glucose-oxidase method (BAS-150 TS Plus, Labomed, USA) and C-peptide by chemiluminescent immunometric assay (MAGLUMI 2000 Plus, Snibe, China). HbA1c was assessed by the high-performance liquid chromatography (HPLC) method with the Premier Hb9210 analyzer (Trinity Biotech, Bray, Ireland). Total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) were determined enzymatically using the ARCHITECT Plus ci8200 integrated system (Abbott Diagnostics, Abbott Park, IL, USA), and low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula. Dyslipidemia was classified according to the NCEP ATP III criteria [21]. Specific thresholds included TC ≥ 200/170 mg/dL (adults/adolescents), LDL-C ≥ 130/110 mg/dL (adults/adolescents), HDL-C ≤ 40 mg/dL (both), and TG ≥ 150 mg/dL (both). Current use of lipid-lowering therapy was also considered in the presence of dyslipidemia. Homeostasis model assessment 2 of β-cell function (HOMA2-%B) and insulin resistance (HOMA2-IR) were calculated using the HOMA2 computer-based model (HOMA Calculator, Diabetes Trials Unit, University of Oxford, UK). Analytic methods GAD65 and IA2 Abs were assessed by CLIA using the MAGLUMI 2000 Plus platform (Snibe, China). Quantitative determination of GAD65 Ab was performed within a measuring range of 1.0-280 IU/mL. The manufacturer-reported sensitivity and specificity were 73% and 96%, respectively. Our laboratory's internal quality control (IQC) measured a concentration of 32.2 IU/mL against a target of 31.7 IU/mL, with an intra-assay coefficient of variation (CV) of 3.6%. Anti-IA2 was measured within a range of 2.5–280 U/mL. Accuracy was verified via two-level IQC, yielding 21.3 U/mL (target: 20.1) and 53.3 U/mL (target: 52.7), with an intra-assay CV of 3.37%. Anti-ZnT8 was estimated using the ElisaRSR™ ZnT8 Ab™ ELISA kit (RSR Ltd., Cardiff, UK). This assay reported 72% sensitivity and 99% specificity, with a six-point calibration curve (0-200 U/mL) demonstrating high linearity across the analytical range used for quantification. The intra-assay CV was 4.39%. Statistical analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 26 (SPSS Inc., Chicago, IL, USA). Data were first assessed for normality using the Shapiro-Wilk test and visual inspection of histograms. Continuous variables with skewed distributions were reported as median (interquartile range [IQR]), and categorical variables as frequencies (percentages). Group differences were analyzed using the Mann-Whitney U test and Chi-square (or Fisher's exact) test. To define population-specific reference thresholds, autoantibody titers in the NGT cohort were analyzed after excluding extreme outliers (> 3× IQR). Due to the skewed distribution of titers, the 99th percentile was used to define the upper reference threshold, and the 95th and 97.5th percentiles were also reported descriptively. A two-tailed p-value < 0.05 was considered statistically significant. Ethical aspects The study received ethical approval from the Institutional Review Board of Bangladesh Medical University, Dhaka (Approval No. BSMMU/2023/6509). All procedures were conducted in full compliance with the ethical guidelines of the institutional research committee and the principles of the Declaration of Helsinki and its subsequent amendments. Written informed consent or assent was obtained from all participants before enrollment, with parental or guardian consent secured for minors. Results Baseline characteristics of the study participants The baseline characteristics of the two cohorts are shown and compared in Table 1. The cohorts were age-matched (p = 0.147), while the NGT cohort had a higher proportion of females compared to the diabetes cohort (79.1% vs 52.2%, p < 0.001). The diabetes cohort was characterized by a higher median BMI, a higher prevalence of elevated BP, and a positive family history of DM. Dyslipidemia was also significantly more prevalent in the diabetes cohort, particularly elevated triglycerides (70.6% vs 18.2%) and low HDL-C (66.7% vs 34.5%). The diabetes cohort also had higher HOMA2-IR and lower HOMA2-%B compared with the NGT cohort. Table 1: Baseline characteristics of the study cohorts (Young NGT, n=148; Young diabetes, n=180) Variable Young NGT cohort (n=148) Young diabetes cohort (n=180) p-value Age (years) 26.0 (24.0-29.8) 27.0 (22.3-29.0) 0.147 Female 117 (79.1%) 94 (52.2%) <0.001 Family history of DM 70 (47.3%) 127 (70.6%) <0.001 BMI (kg/m 2 ) 23.1 (20.6-26.0) 24.4 (22.2-28.3) 0.001 BMI categories Underweight 16 (10.8%) 9 (5.0%) Normal 52 (35.1%) 49 (27.2%) 0.041 Overweight 31 (20.9%) 41 (22.8%) Obese 49 (33.1%) 81 (45.0%) Central obesity By WC 90 (60.8%) 108 (60.0%) 0.881 By WHR 112 (75.7%) 138 (76.7%) 0.834 By WHtR 115 (77.7%) 146 (81.1%) 0.446 Blood pressure Elevated SBP 38 (25.7%) 97 (53.9%) <0.001 Elevated DBP 60 (40.5%) 100 (55.6%) 0.007 Glycemia and C-peptide Fasting PG (mmol/L) 4.9 (4.7-5.2) 11.2 (7.9-15.8) <0.001 PG after glucose load (mmol/L) 5.7 (5.0-6.5) 18.8 (14.3-24.0) <0.001 HbA1c (%) - 9.6 (7.7-12.0) - Fasting C-peptide (ng/mL) 2.3 (1.6-3.1) 3.6 (2.4-5.3) <0.001 Insulin indices HOMA2-%B 139.8 (97.8-173.6) 44.0 (24.4-101.9) <0.001 HOMA2-IR 1.7 (1.1-2.2) 3.9 (2.6-5.6) <0.001 Lipid profile Elevated TC 16 (10.8%) 65 (36.1%) <0.001 Elevated LDL-C 18 (12.2%) 37 (20.6%) 0.043 Low HDL-C 51 (34.5%) 120 (66.7%) <0.001 Elevated TG 27 (18.2%) 127 (70.6%) <0.001 Data are presented as median (interquartile range) for continuous variables and n (%) for categorical variables. P-values were calculated using the Mann-Whitney U test for continuous non-parametric variables, and the Chi-square test for categorical variables. NGT: Normal glucose tolerance; DM: Diabetes Mellitus; IQR: Interquartile Range; BMI: Body Mass Index; WC: Waist Circumference; WHR: Waist-to-Hip Ratio; WHtR: Waist-to-Height Ratio; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; PG: Plasma Glucose; TC: Total Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; TG: Triglycerides Distribution of islet autoantibody titers in the NGT cohort Autoantibody titers for GAD65, ZnT8, and IA2 in the NGT cohort exhibited a non-normal, skewed distribution. The median levels were 4.4 IU/mL for GAD65, 11.5 U/mL for ZnT8, and 2.5 U/mL for IA2 antibodies. Percentile-based reference limits were derived after excluding extreme outliers. The 99th percentile of the normoglycemic group was used to define the upper reference threshold for the antibodies. The resulting study-derived thresholds were: GAD65: 8.5 IU/mL, ZnT8: 40.8 U/mL, and IA2: 3.2 U/mL (Table 2 ). Table 2 Distribution of islet autoantibody levels in the young NGT cohort (n = 148) *Variable Median IQR (25th -75th ) Min-Max Skewness 95th percentile 97.5th percentile 99th percentile Anti-GAD65 (IU/mL) 4.4 0.9–5.9 0.2–8.7 − 0.084 7.6 8.2 8.5 Anti-ZnT8 (U/mL) 11.5 7.8–22.8 1.5–42.1 0.890 35.1 38.0 40.8 Anti-IA2 (U/mL) 2.5 2.0-2.5 2.0-3.8 − 0.036 2.5 2.8 3.2 Calculated after excluding extreme outliers (defined as > 3×IQR from the quartiles) NGT: Normal Glucose Tolerance; GAD65: Glutamic Acid Decarboxylase 65; ZnT8: Zinc Transporter 8; IA2: Islet antigen 2; IQR: Interquartile Range; Min: Minimum; Max: Maximum Effect of threshold selection on autoantibody positivity in the young diabetes cohort Autoantibody positivity rates in the young diabetes cohort varied depending on the thresholds applied (Table 3 ). Using manufacturer-recommended thresholds, overall antibody positivity was 18.9% (34/180). Application of study-derived thresholds reduced overall positivity to 11.7% (21/180). The direction and magnitude of change differed across antibodies. GAD65 positivity increased from 5.0% (9/180) to 7.8% (14/180) when the study-derived threshold was used. Similarly, IA2 positivity increased from 0% to 1.7% (3/180). In contrast, ZnT8 positivity decreased from 15.0% (27/180) to 2.8% (5/180) when the study-derived threshold was applied. Table 3 Comparison of manufacturer-recommended and study-derived reference thresholds and corresponding positivity rates in the young diabetes cohort (n = 180) Autoantibody Manufacturer’s reference thresholds Study-derived reference thresholds Positivity rate (Manufacturer’s threshold) Positivity rate (Study- derived threshold) Anti-GAD65 (IU/mL) 17.0 8.5 5.0% (9/180) 7.8% (14/180) Anti-ZnT8 (U/mL) 15.0 40.8 15.0% (27/180) 2.8% (5/180) Anti-IA2 (U/mL) 28.0 3.2 0% (0/180) 1.7% (3/180) Any antibody - - 18.9% (34/180) 11.7% (21/180) GAD65: Glutamic Acid Decarboxylase 65; ZnT8: Zinc Transporter 8; IA2: Islet antigen 2 Clinical and biochemical characteristics according to autoantibody status Clinical and biochemical characteristics of participants according to autoantibody status under manufacturer-recommended and study-derived thresholds are presented in Tables 4 and 5 . Across both threshold systems, there were no significant differences between antibody-positive and antibody-negative participants in age at onset, sex distribution, or family history of diabetes. Antibody-positive participants had lower BMI and a lower frequency of acanthosis nigricans under both manufacturer-recommended and study-derived thresholds. Measures of central obesity were similar between groups, except for WHtR, which was lower in antibody-positive participants only under study-derived thresholds. HOMA2-%B and C-peptide levels were lower among antibody-positive participants under both threshold systems, whereas HOMA2-IR did not differ significantly. Two-hour plasma glucose and HbA1c were significantly higher in the antibody-positive group under both definitions, while fasting plasma glucose was higher under study-derived thresholds and showed a borderline difference under manufacturer thresholds. Under manufacturer thresholds, antibody-positive participants had a higher frequency of elevated LDL-C and a lower frequency of elevated triglycerides. Under study-derived thresholds, differences in lipid parameters were not statistically significant. Table 4 Characteristics of participants with youth-onset diabetes according to islet autoantibody status using manufacturer-recommended thresholds (n = 180) Variable Antibodies negative (n = 146) One or more antibodies positive (n = 34) p-value Age at onset (yrs) 27.0 (23.0–29.0) 26.5 (20.8–30) 0.771 Female 77 (52.7%) 17 (50.0%) 0.773 Family history of DM 103 (70.5%) 24 (70.6%) 0.996 Acanthosis nigricans 73 (50.0%) 9 (26.5%) 0.013 BMI (kg/m 2 ) 24.6 (22.7–28.7) 23.6 (21.4–25.6) 0.038 BMI categories Underweight 7 (4.8%) 2 (5.9%) Normal 37 (25.3%) 12 (35.3%) 0.581 Overweight 33 (22.6%) 8 (23.5%) Obese 69 (47.3%) 12 (35.3%) Central obesity By WC 91 (62.3%) 17 (50.0%) 0.186 By WHR 113 (77.4%) 25 (73.5%) 0.631 By WHtR 120 (82.2%) 25 (73.5%) 0.250 Insulin indices HOMA2-%B 48.6 (26.6–109.0) 31.6 (20.5–58.2) 0.007 HOMA2-IR 4.0 (2.7–6.2) 3.5 (2.4–4.6) 0.066 Glycemia and C-peptide Fasting PG (mmol/L) 11.2 (7.4–15.7) 13.8 (9.8–16.2) 0.050 2h PG (mmol/L) 17.6 (13.4–23.4) 23.7 (19.0-25.3) 0.002 HbA1c (%) 9.4 (7.5–11.8) 10.8 (8.9–13.3) 0.030 C-peptide (ng/mL) 3.7 (2.5–5.5) 3.0 (2.3–4.2) 0.039 Lipid profile Elevated TC 49 (33.6%) 16 (47.1%) 0.140 Elevated LDL-C 24 (16.4%) 13 (38.2%) 0.005 Low HDL-C 102 (69.9%) 18 (52.9%) 0.059 Elevated TG 108 (74.0%) 19 (55.9%) 0.037 Data are presented as median (interquartile range) for continuous variables and n (%) for categorical variables. P-values were calculated using the Mann-Whitney U test for continuous non-parametric variables, and the Chi-square test or Fisher's exact test for categorical variables. Antibody positivity was defined as positivity for one or more of GAD65, IA2, or ZnT8 according to the manufacturer-recommended thresholds. BMI: Body Mass Index; WC: Waist Circumference; WHR: Waist-to-Hip Ratio; WHtR: Waist-to-Height Ratio; PG: Plasma Glucose; HbA1c: Glycated Hemoglobin; HOMA2-%B: Homeostatic Model Assessment 2 for Beta-cell function; HOMA2-IR: Homeostatic Model Assessment 2 for Insulin Resistance; TC: Total Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; TG: Triglycerides. Table 5 Characteristics of participants with youth-onset diabetes according to islet autoantibody status using study-derived thresholds (n = 180) Variable Antibodies negative (n = 159) One or more antibodies positive (n = 21) p-value Age at onset (yrs) 27.0 (23.0–29.0) 26.0 (20.5–29.0) 0.397 Female 82 (51.6%) 12 (57.1%) 0.631 Family history of DM 109 (68.6%) 18 (85.7%) 0.105 Acanthosis nigricans 79 (49.7%) 3 (14.3%) 0.002 BMI (kg/m 2 ) 24.5 (22.7–28.4) 22.4 (19.2–26.1) 0.040 BMI categories Underweight 7 (4.4%) 2 (9.5%) Normal 41 (25.8%) 8 (38.1%) 0.390 Overweight 38 (23.9%) 3 (14.3%) Obese 73 (45.9%) 8 (38.1%) Central obesity By WC 99 (62.3%) 9 (42.9%) 0.088 By WHR 125 (78.6%) 13 (61.9%) 0.089 By WHtR 133 (83.6%) 12 (57.1%) 0.004 Insulin indices HOMA2-%B 48.3 (26.5-106.1) 28.2 (17.0-40.4) 0.002 HOMA2-IR 3.9 (2.7-6.0) 3.1 (1.8–4.6) 0.055 Glycemia and C-peptide Fasting PG (mmol/L) 11.1 (7.6–15.3) 13.9 (11.5–17.1) 0.015 2h PG (mmol/L) 18.0 (13.6–23.5) 24.0 (18.5–26.6) 0.002 HbA1c (%) 9.4 (7.5–11.8) 11.2 (9.9–13.5) 0.002 C-peptide (ng/mL) 3.7 (2.5–5.5) 2.7 (1.9–3.5) 0.009 Lipid profile Elevated TC 58 (36.5%) 7 (33.3%) 0.778 Elevated LDL-C 32 (20.1%) 5 (23.8%) 0.695 Low HDL-C 109 (68.6%) 11 (52.4%) 0.140 Elevated TG 116 (73.0%) 11 (52.4%) 0.052 Data are presented as median (interquartile range) for continuous variables and n (%) for categorical variables. P-values were calculated using the Mann-Whitney U test for continuous non-parametric variables, and the Chi-square test or Fisher's exact test for categorical variables. Antibody positivity was defined as positivity for one or more of GAD65, IA2, or ZnT8 according to the study-derived thresholds. BMI: Body Mass Index; WC: Waist Circumference; WHR: Waist-to-Hip Ratio; WHtR: Waist-to-Height Ratio; PG: Plasma Glucose; HbA1c: Glycated Hemoglobin; HOMA2-%B: Homeostatic Model Assessment 2 for Beta-cell function; HOMA2-IR: Homeostatic Model Assessment 2 for Insulin Resistance; TC: Total Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; TG: Triglycerides. Discussion This study estimated population-specific reference thresholds for GAD65, IA2, and ZnT8 autoantibodies in a young Bangladeshi normoglycemic cohort and examined their application in youth-onset diabetes. The study-derived thresholds differed from manufacturer-recommended thresholds: GAD65 and IA2 thresholds were lower, whereas the ZnT8 threshold was higher. Application of these thresholds reduced overall autoantibody positivity in the young diabetes cohort, primarily due to a marked decrease in ZnT8 positivity. Studies investigating IAbs in Bangladesh have utilized different methods and thresholds, leading to inconsistent clinical interpretations. Several studies utilized ELISA-based assays to screen for GAD65 and IA2 and used manufacturers' recommended thresholds [22,23]. More recently, Mustafa et al. employed a chemiluminescence immunoassay but again relied on a predefined manufacturer-suggested threshold of 17 IU/mL for GAD65 [24]. Conversely, Shil et al. adopted a lower threshold of 5 IU/mL in a diabetes cohort with a high prevalence of metabolic syndrome [25]. While a lower threshold may increase sensitivity, its clinical interpretation remains uncertain without local validation and external confirmation of outcomes. The international literature supports the view that universal interpretive thresholds for IAbs may not be appropriate due to ethnic and geographic variability in antibody titers. The MAGLUMI platform's standard thresholds for GAD65 and IA2 were originally established based on a study including the Han Chinese population; however, clinical validation in other regions has yielded markedly different results. Ziobrowska-Bech et al. established upper reference limits of 5.1 IU/mL for GAD65 in a Northern European cohort, noting that the manufacturer's thresholds would have missed a substantial number of autoimmune cases [26]. Conversely, in Middle Eastern cohorts, even higher thresholds have been utilized, such as the 30 IU/mL cut-off for GAD65 reported by Zaboon et al. [27]. This discrepancy is further complicated by analytical shifts, in which fully automated chemiluminescence immunoassays, such as the MAGLUMI system, have been observed to overestimate IAb levels relative to conventional ELISA methods, necessitating method-specific recalibration [28]. For Anti-ZnT8, the international landscape is similarly fragmented. The manufacturer (RSR Ltd.) suggests a threshold of 15.0 U/mL, a standard adopted by several clinical studies [29,30]. However, population-specific research often mandates adjustment. In Japan, Kawasaki et al. established a lower threshold of 10.0 U/mL to optimize sensitivity in an East Asian cohort [31]. Again, Torn et al. recommended a threshold of 12.9 units/mL [32]. On the contrary, Grace et al. observed that ZnT8 thresholds must be substantially higher (up to 127 U/mL) in younger participants under 30 years of age to prevent false positives [33]. This age- and ethnicity-dependent variability suggests that using the standard 15.0 U/mL cutoff may increase the number of individuals classified as ZnT8-positive, although the clinical significance of this low-level positivity remains to be validated. The current study also observed a higher threshold than the manufacturer's recommendation. However, the current study-derived reference threshold should be viewed as a reference threshold requiring external validation rather than a definitive diagnostic cut-off. Published IA2 antibody reference threshold-related studies appear fewer than the corresponding GAD65 and ZnT8 reports, but available data also indicate assay- and population-related variability. In a radiobinding assay-based study, IA2 antibody threshold of ≥ 1.4 DK U/mL was suggested by using the 98th percentile of 500 control samples [34]. In a Han Chinese CLIA based reference-interval study, an IA2 antibody upper limit of 3.91 IU/mL was reported using the 95th percentile of 177 healthy volunteers [35]. In contrast, a Danish MAGLUMI CLIA-based study derived an upper reference limit of 11.5 U/mL in healthy adults and children showing a substantial discrepancy with the reference thresholds suggested by the manufacturer [26]. These differences reinforce that IA2 thresholds are not directly interchangeable across assay platforms, populations, and unit systems. Comparing antibody-positive and antibody-negative participants provided additional clinical context. Under both manufacturer-recommended and study-derived thresholds, antibody-positive participants had lower BMI, lower frequency of acanthosis nigricans, lower HOMA2-%B, lower C-peptide, and higher glycemic indices compared with antibody-negative participants. These findings are compatible with reduced β-cell reserve among antibody-positive individuals. However, antibody-positive and antibody-negative groups were not fully separable by clinical or biochemical features, emphasizing the heterogeneity of youth-onset diabetes in this population. The clinical implications of these findings should be interpreted cautiously. The study-derived thresholds reflect the distribution of antibody titers in a local normoglycemic cohort and provide population-specific reference limits for interpretation rather than diagnostic accuracy-derived cut-offs. In the absence of longitudinal outcomes, HLA risk markers, or a validated composite reference standard, these thresholds should not be used as standalone criteria for diabetes classification. Instead, they may assist contextual interpretation of antibody results when considered alongside clinical phenotype, C-peptide, and other relevant markers. The study has several strengths. It included a normoglycemic reference cohort with OGTT-confirmed NGT and used contemporary assay platforms for three clinically relevant islet autoantibodies. The inclusion of ZnT8 alongside GAD65 and IA2 provides broader immunological profiling than earlier local studies that relied mainly on GAD65. Several limitations should also be acknowledged. Although the normoglycemic cohort exceeded the minimum sample size for non-parametric reference threshold estimation, estimation of extreme percentiles may still be sensitive to sample size and outlying values. The cross-sectional nature of the data precludes tracking the longitudinal rate of β-cell decline and insulin requirement over time. The clinic-based design and sex imbalance in the normoglycemic cohort may limit generalizability. The study did not include a definitive external reference standard for autoimmune diabetes, such as HLA risk markers, genetic susceptibility data, longitudinal insulin requirement, or serial C-peptide assessment. Therefore, the proposed thresholds should be considered population-specific reference thresholds requiring external validation rather than definitive diagnostic cut-offs. Conclusions In conclusion, this study provides population-specific reference thresholds for GAD65, IA2, and ZnT8 autoantibodies in Bangladeshi youth and shows that applying these thresholds changes estimated antibody positivity in youth-onset diabetes. The findings support the need for local calibration and cautious interpretation of islet autoantibody results. Future multicenter and longitudinal studies incorporating clinical outcomes, C-peptide trajectories, ketosis history, and genetic susceptibility markers are needed to validate these thresholds and clarify their role in diabetes classification. Abbreviations BMU Bangladesh Medical University BMI Body mass index BP Blood pressure CLIA Chemiluminescence immunoassay CLSI Clinical and Laboratory Standards Institute CV Coefficient of variation DBP Diastolic blood pressure DKA Diabetic ketoacidosis DM Diabetes mellitus ELISA Enzyme-linked immunosorbent assay GAD65 Glutamic acid decarboxylase 65 HbA1c Glycated hemoglobin HDL-C High-density lipoprotein cholesterol HLA Human leukocyte antigen HOMA2-%B Homeostasis model assessment 2 of β-cell function HOMA2-IR Homeostasis model assessment 2 of insulin resistance IAb Islet autoantibody IAbs Islet autoantibodies IA2 / IA-2 Islet antigen 2 IAP Indian Academy of Pediatrics IQC Internal quality control IQR Interquartile range LDL-C Low-density lipoprotein cholesterol NCEP ATP III National Cholesterol Education Program Adult Treatment Panel III NGT Normal glucose tolerance OGTT Oral glucose tolerance test PG Plasma glucose RIA Radioimmunoassay SBP Systolic blood pressure SODY Study on Obesity and Diabetes in Young SPSS Statistical Package for the Social Sciences T1D Type 1 diabetes T2D Type 2 diabetes TC Total cholesterol TG Triglycerides WC Waist circumference WHR Waist-to-hip ratio WHtR Waist-to-height ratio ZnT8 Zinc transporter 8 Declarations Ethical approval and consent to participate The study received ethical approval from the Institutional Review Board of Bangladesh Medical University, Dhaka (Approval No. BSMMU/2023/6509). Written informed consent or assent was obtained from all participants before enrollment, with parental or guardian consent secured for minors. Competing interests: The authors declare no competing interests. Clinical trial number not applicable Consent for publication: Not applicable. Funding statement: Supported partially by a grant from Bangladesh Medical University (grant number BSMMU/2023/13314(6)]). Author Contribution Conceptualization: MH, NS, KKS, MAH; Data curation: MH, KKS, SBA, RHR; Formal analysis: MH, KKS, RHR; Funding acquisition: MH, NS, MAH; Investigation: MH, KKS, SBA; Methodology: MH, NS, KKS, MAH; Project administration: MAH; Supervision: MAH; Validation: MH, NS, MAH, Writing original draft: MH; Writing – review and editing: All authors. Acknowledgement We extend our sincere gratitude to all the participants and their families for their cooperation. Special thanks to the Study on Obesity and Diabetes of the Young (SODY) group members for their efforts in data collection. We also acknowledge the technical contributions of the laboratory staff, Md. Abdus Salam and Md. Nesar Uddin. Data Availability The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. References Tosur M, Huang X, Inglis AS, Aguirre RS, Redondo MJ. Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications. Sci Rep. 2024 Apr 17;14(1):8876. doi: 10.1038/s41598-024-58927-6. PMID: 38632329; PMCID: PMC11024140. Zaber AA, Islam US, Mahmud MA, Talukder MU, Bányai G. Childhood obesity in Bangladesh: an emerging public health crisis. Asian J Med Biol Res. 2026 Jan 3 ;12(1):5-15. doi: 10.3329/ajmbr.v12i1.84144 Misra A, Sattar N, Ghosh A, Nassar M, Jayawardena R, Gupta R. Type 2 diabetes in South Asians. 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Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017 Sep;140(3):e20171904. doi: 10.1542/peds.2017-1904. Epub 2017 Aug 21. Erratum in: Pediatrics. 2017 Dec;140(6):e20173035. doi: 10.1542/peds.2017-3035. Erratum in: Pediatrics. 2018 Sep;142(3):e20181739. doi: 10.1542/peds.2018-1739. PMID: 28827377. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002 Dec 17;106(25):3143-421. PMID: 12485966. Chowdhury AK, Sahi SR, Moniruzzaman M, Khan M. Islet cell and glutamic acid decarboxylase-65 autoantibodies in young diabetic patients attending in a General Hospital in Dhaka City. Bangladesh Med Res Counc Bull 2020; 46(2): 104-108. https://doi.org/10.3329/bmrcb.v46i2.49019  Zabeen B, Govender D, Hassan Z, Noble JA, Lane JA, Mack SJ, Atkinson MA, Azad K, Wasserfall CH, Ogle GD. Clinical features, biochemistry and HLA-DRB1 status in children and adolescents with diabetes in Dhaka, Bangladesh. Diabetes Res Clin Pract. 2019 Dec;158:107894. doi: 10.1016/j.diabres.2019.107894. Epub 2019 Oct 24. PMID: 31669629; PMCID: PMC6988504. Mustafa SJ, Prasad I, Afrooz F, Muhit MS. GAD65 autoantibody positivity and its association with clinical and biochemical parameters among young onset diabetes mellitus. J. Assoc. Clin. Endocrinol. Diabetol. Bangladesh. 2025, 3(1), 03-08. https://doi.org/10.3329/jacedb.v3i1.78615  Shil, K. K., Hasan, M., Sultana, N., Salam, S. B. A., Mostafa, S. N., & Hasanat, M. A. (2025). High Prevalence of Fatty Liver in Bangladeshi Adolescents and Young Adults with Type 2 Diabetes: Key Predictors and Screening Recommendations. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 18, 2019–2027. https://doi.org/10.2147/DMSO.S520806 Ziobrowska-Bech A, Winther-Larsen A, Kremke B, Parkner T, Soendersoe Knudsen C. Reference limits for GAD65 and IA-2 autoantibodies by chemiluminescence immunoassay in Northern European adults and children. Scand J Clin Lab Invest. 2019 Feb-Apr;79(1-2):123-125. doi: 10.1080/00365513.2019.1566566. Epub 2019 Feb 6. PMID: 30727763. Zaboon IA, Mansour AA, Haddad NS. Variables Associated with Persistence of C-Peptide Secretion among Patients with Type 1 Diabetes Mellitus. CHRISMED Journal of Health and Research 4(3):p 173-179, Jul–Sep 2017. | DOI: 10.4103/cjhr.cjhr_2_17 Danese E, Piona C, Rizza M, Tiziani E, Pighi L, Morotti E, Salvagno GL, Mattiuzzi C, Maffeis C, Lippi G. A Comparative Evaluation of the Chemiluminescence Immunoassay and ELISA for the Detection of Islet Autoantibodies in Type 1 Diabetes. Diagnostics (Basel). 2025 Jul 3;15(13):1695. doi: 10.3390/diagnostics15131695. PMID: 40647694; PMCID: PMC12249052. Hussein H, Ibrahim F, Sobngwi E, Gautier JF, Boudou P. Zinc transporter 8 autoantibodies assessment in daily practice. Clin Biochem. 2017 Jan;50(1-2):94-96. doi: 10.1016/j.clinbiochem.2016.06.008. Epub 2016 Jun 27. PMID: 27363941. Bost C, Jordan T, Magali D, Françoise F, Nicole F. Anti-ZnT8 autoantibodies: A new marker to be screened in patients with anti-adrenal antibodies. Clin Chim Acta. 2020 Dec;511:1-6. doi: 10.1016/j.cca.2020.09.019. Epub 2020 Sep 16. PMID: 32946793. Kawasaki E, Oikawa Y, Okada A, Kanatsuna N, Kawamura T, Kikuchi T, Terasaki J, Miura J, Ito Y, Hanafusa T. Zinc transporter 8 autoantibodies complement glutamic acid decarboxylase and insulinoma‐associated antigen‐2 autoantibodies in the identification and characterization of Japanese type 1 diabetes. Journal of Diabetes Investigation. 2020 Sep;11(5):1181-7. Törn C, Vaziri-Sani F, Ramelius A, Elding Larsson H, Ivarsson SA, Amoroso M, Furmaniak J, Powell M, Smith BR. Evaluation of the RSR 3 screen ICA™ and 2 screen ICA™ as screening assays for type 1 diabetes in Sweden. Acta Diabetol. 2022 Jun;59(6):773-781. doi: 10.1007/s00592-022-01856-5. Epub 2022 Feb 26. PMID: 35220476; PMCID: PMC9085662. Grace SL, Cooper A, Jones AG, McDonald TJ. Zinc transporter 8 autoantibody testing requires age-related cut-offs. BMJ Open Diabetes Res Care. 2021 Aug;9(1):e002296. doi: 10.1136/bmjdrc-2021-002296. PMID: 34348918; PMCID: PMC8340275. Grace SL, Bowden J, Walkey HC, Kaur A, Misra S, Shields BM, McKinley TJ, Oliver NS, McDonald TJ, Johnston DG, Jones AG, Patel KA. Islet Autoantibody Level Distribution in Type 1 Diabetes and Their Association With Genetic and Clinical Characteristics. J Clin Endocrinol Metab. 2022 Nov 25;107(12):e4341-e4349. doi: 10.1210/clinem/dgac507. PMID: 36073000; PMCID: PMC9693812. Fu Y, Zhang C, Gu Y, Ge S, Li J, Feng J, Zhang L, Liu W, Chen H. Establishing reference intervals for islet autoantibodies in Han Chinese type 1 diabetes. Scand J Clin Lab Invest. 2021 Dec;81(8):641-648. doi: 10.1080/00365513.2021.2001564. Epub 2021 Nov 15. PMID: 34779329. Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.png SupplementaryFile1CRF.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9542267","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634016321,"identity":"cb765b16-ceb9-491e-b46c-edd8cdfacc63","order_by":0,"name":"Mashfiqul Hasan","email":"data:image/png;base64,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","orcid":"","institution":"Bangladesh Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mashfiqul","middleName":"","lastName":"Hasan","suffix":""},{"id":634016322,"identity":"20951e8b-c327-425a-9b75-93b80011d570","order_by":1,"name":"Kishore Kumar Shil","email":"","orcid":"","institution":"Bangladesh Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kishore","middleName":"Kumar","lastName":"Shil","suffix":""},{"id":634016323,"identity":"e2d51885-131b-4689-9ea0-eba537f120d1","order_by":2,"name":"Sayad Bin Abdus-Salam","email":"","orcid":"","institution":"Bangladesh Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sayad","middleName":"Bin","lastName":"Abdus-Salam","suffix":""},{"id":634016324,"identity":"8b57453d-b0bd-4d97-87ff-a77f679d092a","order_by":3,"name":"Rifat Hossain Ratul","email":"","orcid":"","institution":"Bangladesh Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rifat","middleName":"Hossain","lastName":"Ratul","suffix":""},{"id":634016325,"identity":"d1a0be08-a637-4b9d-a2f2-bf58bc92aaa1","order_by":4,"name":"Nusrat Sultana","email":"","orcid":"","institution":"Bangladesh Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nusrat","middleName":"","lastName":"Sultana","suffix":""},{"id":634016326,"identity":"367e6687-dbc1-4fb1-ad67-39ea9d8d63d3","order_by":5,"name":"Muhammad Abul Hasanat","email":"","orcid":"","institution":"Bangladesh Medical University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Abul","lastName":"Hasanat","suffix":""}],"badges":[],"createdAt":"2026-04-27 13:10:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9542267/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9542267/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108491762,"identity":"f9420572-e98a-4d10-b99b-e56832661a83","added_by":"auto","created_at":"2026-05-05 09:55:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":479316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9542267/v1/ac4488cf-b145-4739-93dc-2390a67b46d7.pdf"},{"id":108481964,"identity":"dcd3a964-7453-4f1e-a223-b5ebcc393c94","added_by":"auto","created_at":"2026-05-05 08:10:56","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1582793,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-9542267/v1/630900cd3d8b8fb25d8fdab7.png"},{"id":108481959,"identity":"36c3d550-d342-486e-962d-d0649f9f916e","added_by":"auto","created_at":"2026-05-05 08:10:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31095,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1CRF.docx","url":"https://assets-eu.researchsquare.com/files/rs-9542267/v1/7a431929b455a72a6ab718c1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population-Specific Reference Thresholds for Islet Autoantibodies in Bangladeshi Youth and Their Clinical Implications","fulltext":[{"header":"Background","content":"\u003cp\u003eThe global rise in youth-onset diabetes presents a significant diagnostic challenge, particularly in South Asian populations where the classic clinical distinctions between Type 1 (T1D) and Type 2 diabetes (T2D) are often blurred [1]. The ambiguity stems from two divergent trends. First, the increasing burden of obesity and metabolic syndrome among young individuals means these metabolic features may coexist with T1D, masking the features of the underlying autoimmune process [2]. Conversely, T2D in South Asians frequently manifests at a younger age and at a lower body mass index (BMI) compared to Western people, mimicking the clinical presentation of T1D, leading to frequent misclassification [3]. Therefore, relying solely on clinical markers, such as age of onset, glycemic severity at presentation, obesity, or the presence of metabolic syndrome, is frequently insufficient to differentiate autoimmune-mediated insulin deficiency from insulin-resistant phenotypes. In this context, islet autoantibody (IAb) testing provides an objective immunological marker that can assist in diabetes classification.\u003c/p\u003e \u003cp\u003eThe autoimmune pathogenesis of diabetes involves a humoral immune response against several key pancreatic β-cell antigens. Among these, antibodies against glutamic acid decarboxylase 65 (GAD65), tyrosine phosphatase-like insulinoma antigen 2/islet antigen 2 (IA2), and zinc transporter 8 (ZnT8) are clinically most relevant [4]. Although these autoantibodies are not believed to be pathogenic, they serve as reliable clinical markers of β-cell autoimmunity [5]. GAD65 antibody is the primary antibody to measure in young adults, especially when the clinical onset is slower or latent [6]. Anti-IA2 and anti-ZnT8 further increase the overall diagnostic yield. Current guidelines suggest a sequential measurement of these IAbs [4].\u003c/p\u003e \u003cp\u003eHistorically, the detection of IAbs relied on radioimmunoassays (RIAs) for decades. However, there are practical limitations of RIA, including the use of radioactive isotopes and short reagent shelf-life. As a result, there is a shift toward modern, non-isotopic platforms such as chemiluminescence immunometric assays (CLIA) and enzyme-linked immunosorbent assays (ELISA), which have demonstrated good diagnostic performance in IAb assays [7,8]. These advancements enable more precise quantification of low-titer antibodies, which is particularly relevant in adult-onset or latent autoimmune cases. Nevertheless, because antibody titers can vary significantly across assay platforms, it is vital that diagnostic thresholds are not merely adopted from older literature but instead validated for the specific technology and population in use [9]. This methodological shift supports the need for platform-specific reference thresholds to improve contextual interpretation of antibody results [10].\u003c/p\u003e \u003cp\u003eDespite the clinical utility of islet autoantibodies, their distribution among Bangladeshi youth remains insufficiently characterized. Most diagnostic thresholds currently used in clinical practice are derived from studies conducted on other ethnic groups. However, a growing body of evidence suggests significant ethnic variation exists in both the prevalence and the titers of these antibodies [11]. Furthermore, due to technical variability between assay platforms, a threshold established on one system may not be transferable to another [9]. In Bangladesh, locally validated reference thresholds for islet autoantibodies are not yet available, which may limit the contextual interpretation of antibody results in young people with diabetes. Without population-specific and assay-contextualized reference thresholds, interpretation of low-level antibody positivity may be uncertain, particularly in young people with overlapping metabolic and insulin-deficient features [12]. Against this background, the current study aimed to derive population-specific reference thresholds for GAD65, ZnT8, and IA2 antibodies in a young Bangladeshi normoglycemic cohort. A secondary objective was to compare the effect of manufacturer-recommended and study-derived thresholds on antibody positivity rates and to examine the clinical and biochemical characteristics associated with antibody positivity in a youth-onset diabetes cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and subjects\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted at the Department of Endocrinology, Bangladesh Medical University (BMU), between 2023 and 2024. The study used two cohorts recruited from the clinic-based screening program of the 'Study on Obesity and Diabetes in Young (SODY)\u0026rsquo; group at BMU. The first cohort was the normal glucose tolerance (NGT) cohort (n\u0026thinsp;=\u0026thinsp;148), comprising individuals aged 10\u0026ndash;34 years with confirmed NGT on a 75-g oral glucose tolerance test (OGTT). This group served as the reference for estimating population-specific thresholds for IAbs. The second was the youth-onset diabetes mellitus (DM) cohort (n\u0026thinsp;=\u0026thinsp;180), which consisted of individuals aged 10\u0026ndash;34 years diagnosed with diabetes. Participants with clinical features of endocrine disorders or a history of drug use that may interfere with glycemic status were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe sample size for the NGT cohort was guided by the Clinical and Laboratory Standards Institute (CLSI EP28-A3c) guidelines for reference interval estimation [10], which recommend a minimum of 120 participants for non-parametric estimation of reference limits. Accordingly, 148 normoglycemic participants were included for deriving population-specific reference thresholds. The youth-onset diabetes cohort included 180 participants and was used to describe antibody positivity rates and to compare clinical and biochemical characteristics by antibody status. As this component was exploratory and descriptive, no separate formal sample size calculation was performed for this cohort.\u003c/p\u003e\n\u003ch3\u003eStudy procedure\u003c/h3\u003e\n\u003cp\u003eClinical data for the included participants were collected through face-to-face interviews using a structured case-record form (Supplementary file 1), along with anthropometric measurements and physical examinations. A fasting blood sample was collected from each participant to measure plasma glucose, lipid profile, and C-peptide. A post-prandial/post-glucose load blood sample was also collected for measurement of plasma glucose, glycated hemoglobin (HbA1c), and IAbs (anti-GAD65, anti-ZnT8, and anti-IA2).\u003c/p\u003e\n\u003ch3\u003eClinical assessment\u003c/h3\u003e\n\u003cp\u003eHeight and weight were measured using standard procedures, and BMI was calculated as kg/m\u003csup\u003e2\u003c/sup\u003e. Adolescents were classified based on Indian Academy of Pediatrics (IAP) growth chart BMI percentiles (underweight \u0026lt;5th ; healthy weight 5th -84th ; overweight 85th -94th ; obese \u0026ge;\u0026thinsp;95th ), while young adult BMI was categorized according to Asian-specific cut-offs (underweight\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;, healthy weight 18.5\u0026ndash;22.9 kg/m\u0026sup2;, overweight 23-24.9 kg/m\u0026sup2;, and obesity\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;) [13,14]. Central obesity was defined as a waist circumference (WC)\u0026thinsp;\u0026ge;\u0026thinsp;90 cm in males or \u0026ge;\u0026thinsp;80 cm in females (or \u0026ge;\u0026thinsp;70th percentile in adolescents) [15,16]. The waist-to-hip ratio (WHR) thresholds for central obesity were \u0026ge;\u0026thinsp;0.9 for males and \u0026ge;\u0026thinsp;0.85 for females; these were applied to both adults and adolescents [17]. A waist-to-height ratio (WHtR)\u0026thinsp;\u0026ge;\u0026thinsp;0.50 was considered abnormal [18]. Elevated blood pressure (BP) in adults was defined as systolic BP (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;120 mmHg or diastolic BP (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg, while in adolescents it was defined as BP \u0026ge;\u0026thinsp;90th percentile for age, sex, and height [19,20]. Current use of antihypertensive drugs was also regarded as a criterion for elevated BP.\u003c/p\u003e\n\u003ch3\u003eBiochemical assessment\u003c/h3\u003e\n\u003cp\u003ePlasma glucose was measured using the glucose-oxidase method (BAS-150 TS Plus, Labomed, USA) and C-peptide by chemiluminescent immunometric assay (MAGLUMI 2000 Plus, Snibe, China). HbA1c was assessed by the high-performance liquid chromatography (HPLC) method with the Premier Hb9210 analyzer (Trinity Biotech, Bray, Ireland). Total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) were determined enzymatically using the ARCHITECT Plus ci8200 integrated system (Abbott Diagnostics, Abbott Park, IL, USA), and low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula. Dyslipidemia was classified according to the NCEP ATP III criteria [21]. Specific thresholds included TC\u0026thinsp;\u0026ge;\u0026thinsp;200/170 mg/dL (adults/adolescents), LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;130/110 mg/dL (adults/adolescents), HDL-C\u0026thinsp;\u0026le;\u0026thinsp;40 mg/dL (both), and TG\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL (both). Current use of lipid-lowering therapy was also considered in the presence of dyslipidemia. Homeostasis model assessment 2 of β-cell function (HOMA2-%B) and insulin resistance (HOMA2-IR) were calculated using the HOMA2 computer-based model (HOMA Calculator, Diabetes Trials Unit, University of Oxford, UK).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalytic methods\u003c/h2\u003e \u003cp\u003eGAD65 and IA2 Abs were assessed by CLIA using the MAGLUMI 2000 Plus platform (Snibe, China). Quantitative determination of GAD65 Ab was performed within a measuring range of 1.0-280 IU/mL. The manufacturer-reported sensitivity and specificity were 73% and 96%, respectively. Our laboratory's internal quality control (IQC) measured a concentration of 32.2 IU/mL against a target of 31.7 IU/mL, with an intra-assay coefficient of variation (CV) of 3.6%. Anti-IA2 was measured within a range of 2.5\u0026ndash;280 U/mL. Accuracy was verified via two-level IQC, yielding 21.3 U/mL (target: 20.1) and 53.3 U/mL (target: 52.7), with an intra-assay CV of 3.37%. Anti-ZnT8 was estimated using the ElisaRSR\u0026trade; ZnT8 Ab\u0026trade; ELISA kit (RSR Ltd., Cardiff, UK). This assay reported 72% sensitivity and 99% specificity, with a six-point calibration curve (0-200 U/mL) demonstrating high linearity across the analytical range used for quantification. The intra-assay CV was 4.39%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using IBM SPSS Statistics for Windows, Version 26 (SPSS Inc., Chicago, IL, USA). Data were first assessed for normality using the Shapiro-Wilk test and visual inspection of histograms. Continuous variables with skewed distributions were reported as median (interquartile range [IQR]), and categorical variables as frequencies (percentages). Group differences were analyzed using the Mann-Whitney U test and Chi-square (or Fisher's exact) test. To define population-specific reference thresholds, autoantibody titers in the NGT cohort were analyzed after excluding extreme outliers (\u0026gt;\u0026thinsp;3\u0026times; IQR). Due to the skewed distribution of titers, the 99th percentile was used to define the upper reference threshold, and the 95th and 97.5th percentiles were also reported descriptively. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical aspects\u003c/h3\u003e\n\u003cp\u003eThe study received ethical approval from the Institutional Review Board of Bangladesh Medical University, Dhaka (Approval No. BSMMU/2023/6509). All procedures were conducted in full compliance with the ethical guidelines of the institutional research committee and the principles of the Declaration of Helsinki and its subsequent amendments. Written informed consent or assent was obtained from all participants before enrollment, with parental or guardian consent secured for minors.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study participants\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the two cohorts are shown and compared in Table\u0026nbsp;1. The cohorts were age-matched (p\u0026thinsp;=\u0026thinsp;0.147), while the NGT cohort had a higher proportion of females compared to the diabetes cohort (79.1% vs 52.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The diabetes cohort was characterized by a higher median BMI, a higher prevalence of elevated BP, and a positive family history of DM. Dyslipidemia was also significantly more prevalent in the diabetes cohort, particularly elevated triglycerides (70.6% vs 18.2%) and low HDL-C (66.7% vs 34.5%). The diabetes cohort also had higher HOMA2-IR and lower HOMA2-%B compared with the NGT cohort.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e \u003cstrong\u003eBaseline characteristics of the study cohorts (Young NGT, n=148; Young diabetes, n=180)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYoung NGT cohort (n=148)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYoung diabetes cohort (n=180)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e26.0 (24.0-29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e27.0 (22.3-29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e117 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e94 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e70 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e127 (70.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e23.1 (20.6-26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e24.4 (22.2-28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Underweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e16 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e9 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e52 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e49 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Overweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e31 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e41 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e49 (33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e81 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral obesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;By WC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e90 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e108 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;By WHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e112 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e138 (76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;By WHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e115 (77.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e146 (81.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Elevated SBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e38 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e97 (53.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Elevated DBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e60 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e100 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlycemia and C-peptide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fasting PG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e4.9 (4.7-5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e11.2 (7.9-15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;PG after glucose load \u0026nbsp; \u0026nbsp;\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e5.7 (5.0-6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e18.8 (14.3-24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;HbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e9.6 (7.7-12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fasting C-peptide (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e2.3 (1.6-3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e3.6 (2.4-5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsulin indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;HOMA2-%B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e139.8 (97.8-173.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e44.0 (24.4-101.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;HOMA2-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.7 (1.1-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e3.9 (2.6-5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipid profile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Elevated TC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e16 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e65 (36.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Elevated LDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e18 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e37 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e51 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e120 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.3299%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Elevated TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e27 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e127 (70.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as median (interquartile range) for continuous variables and n (%) for categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP-values were calculated using the Mann-Whitney U test for continuous non-parametric variables, and the Chi-square test for categorical variables.\u003c/p\u003e\n\u003cp\u003eNGT: Normal glucose tolerance; DM: Diabetes Mellitus; IQR: Interquartile Range; BMI: Body Mass Index; WC: Waist Circumference; WHR: Waist-to-Hip Ratio; WHtR: Waist-to-Height Ratio; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; PG: Plasma Glucose; TC: Total Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; TG: Triglycerides\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of islet autoantibody titers in the NGT cohort\u003c/h2\u003e \u003cp\u003eAutoantibody titers for GAD65, ZnT8, and IA2 in the NGT cohort exhibited a non-normal, skewed distribution. The median levels were 4.4 IU/mL for GAD65, 11.5 U/mL for ZnT8, and 2.5 U/mL for IA2 antibodies. Percentile-based reference limits were derived after excluding extreme outliers. The 99th percentile of the normoglycemic group was used to define the upper reference threshold for the antibodies. The resulting study-derived thresholds were: GAD65: 8.5 IU/mL, ZnT8: 40.8 U/mL, and IA2: 3.2 U/mL (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of islet autoantibody levels in the young NGT cohort (n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003cp\u003e(25th -75th )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin-Max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95th percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.5th percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99th percentile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-GAD65 (IU/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026ndash;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-ZnT8 (U/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8\u0026ndash;22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-IA2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(U/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0-2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0-3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCalculated after excluding extreme outliers (defined as \u0026gt;\u0026thinsp;3\u0026times;IQR from the quartiles)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNGT: Normal Glucose Tolerance; GAD65: Glutamic Acid Decarboxylase 65; ZnT8: Zinc Transporter 8; IA2: Islet antigen 2; IQR: Interquartile Range; Min: Minimum; Max: Maximum\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffect of threshold selection on autoantibody positivity in the young diabetes cohort\u003c/h2\u003e \u003cp\u003eAutoantibody positivity rates in the young diabetes cohort varied depending on the thresholds applied (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using manufacturer-recommended thresholds, overall antibody positivity was 18.9% (34/180). Application of study-derived thresholds reduced overall positivity to 11.7% (21/180). The direction and magnitude of change differed across antibodies. GAD65 positivity increased from 5.0% (9/180) to 7.8% (14/180) when the study-derived threshold was used. Similarly, IA2 positivity increased from 0% to 1.7% (3/180). In contrast, ZnT8 positivity decreased from 15.0% (27/180) to 2.8% (5/180) when the study-derived threshold was applied.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparison of manufacturer-recommended and study-derived reference thresholds and corresponding positivity rates in the young diabetes cohort (n\u0026thinsp;=\u0026thinsp;180)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoantibody\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManufacturer\u0026rsquo;s reference thresholds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy-derived reference thresholds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositivity rate (Manufacturer\u0026rsquo;s threshold)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositivity rate (Study- derived threshold)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-GAD65 (IU/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0% (9/180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.8% (14/180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-ZnT8 (U/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0% (27/180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8% (5/180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-IA2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(U/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0/180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7% (3/180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny antibody\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9% (34/180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.7% (21/180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eGAD65: Glutamic Acid Decarboxylase 65; ZnT8: Zinc Transporter 8; IA2: Islet antigen 2\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical and biochemical characteristics according to autoantibody status\u003c/h2\u003e \u003cp\u003eClinical and biochemical characteristics of participants according to autoantibody status under manufacturer-recommended and study-derived thresholds are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Across both threshold systems, there were no significant differences between antibody-positive and antibody-negative participants in age at onset, sex distribution, or family history of diabetes. Antibody-positive participants had lower BMI and a lower frequency of acanthosis nigricans under both manufacturer-recommended and study-derived thresholds. Measures of central obesity were similar between groups, except for WHtR, which was lower in antibody-positive participants only under study-derived thresholds. HOMA2-%B and C-peptide levels were lower among antibody-positive participants under both threshold systems, whereas HOMA2-IR did not differ significantly. Two-hour plasma glucose and HbA1c were significantly higher in the antibody-positive group under both definitions, while fasting plasma glucose was higher under study-derived thresholds and showed a borderline difference under manufacturer thresholds. Under manufacturer thresholds, antibody-positive participants had a higher frequency of elevated LDL-C and a lower frequency of elevated triglycerides. Under study-derived thresholds, differences in lipid parameters were not statistically significant.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants with youth-onset diabetes according to islet autoantibody status using manufacturer-recommended thresholds (n\u0026thinsp;=\u0026thinsp;180)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntibodies negative (n\u0026thinsp;=\u0026thinsp;146)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne or more antibodies positive (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003e\u003cb\u003eAge at onset\u003c/b\u003e (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.0 (23.0\u0026ndash;29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.5 (20.8\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcanthosis nigricans\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.6 (22.7\u0026ndash;28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.6 (21.4\u0026ndash;25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (5.9%)\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\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (23.5%)\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\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (35.3%)\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\u003cb\u003eCentral obesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120 (82.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA2-%B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.6 (26.6\u0026ndash;109.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.6 (20.5\u0026ndash;58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA2-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 (2.7\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5 (2.4\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycemia and C-peptide\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting PG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.2 (7.4\u0026ndash;15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.8 (9.8\u0026ndash;16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2h PG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.6 (13.4\u0026ndash;23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.7 (19.0-25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.4 (7.5\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.8 (8.9\u0026ndash;13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-peptide (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7 (2.5\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.3\u0026ndash;4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid profile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated LDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (69.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108 (74.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (55.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as median (interquartile range) for continuous variables and n (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eP-values were calculated using the Mann-Whitney U test for continuous non-parametric variables, and the Chi-square test or Fisher's exact test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAntibody positivity was defined as positivity for one or more of GAD65, IA2, or ZnT8 according to the manufacturer-recommended thresholds.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI: Body Mass Index; WC: Waist Circumference; WHR: Waist-to-Hip Ratio; WHtR: Waist-to-Height Ratio; PG: Plasma Glucose; HbA1c: Glycated Hemoglobin; HOMA2-%B: Homeostatic Model Assessment 2 for Beta-cell function; HOMA2-IR: Homeostatic Model Assessment 2 for Insulin Resistance; TC: Total Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; TG: Triglycerides.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants with youth-onset diabetes according to islet autoantibody status using study-derived thresholds (n\u0026thinsp;=\u0026thinsp;180)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntibodies negative (n\u0026thinsp;=\u0026thinsp;159)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne or more antibodies positive (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003e\u003cb\u003eAge at onset\u003c/b\u003e (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.0 (23.0\u0026ndash;29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0 (20.5\u0026ndash;29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcanthosis nigricans\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79 (49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.5 (22.7\u0026ndash;28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.4 (19.2\u0026ndash;26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (9.5%)\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\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (14.3%)\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\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (38.1%)\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\u003cb\u003eCentral obesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133 (83.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA2-%B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.3 (26.5-106.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2 (17.0-40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA2-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9 (2.7-6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.1 (1.8\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycemia and C-peptide\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting PG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.1 (7.6\u0026ndash;15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.9 (11.5\u0026ndash;17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2h PG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.0 (13.6\u0026ndash;23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.0 (18.5\u0026ndash;26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.4 (7.5\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2 (9.9\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-peptide (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7 (2.5\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7 (1.9\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid profile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated LDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116 (73.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.052\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as median (interquartile range) for continuous variables and n (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eP-values were calculated using the Mann-Whitney U test for continuous non-parametric variables, and the Chi-square test or Fisher's exact test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAntibody positivity was defined as positivity for one or more of GAD65, IA2, or ZnT8 according to the study-derived thresholds.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI: Body Mass Index; WC: Waist Circumference; WHR: Waist-to-Hip Ratio; WHtR: Waist-to-Height Ratio; PG: Plasma Glucose; HbA1c: Glycated Hemoglobin; HOMA2-%B: Homeostatic Model Assessment 2 for Beta-cell function; HOMA2-IR: Homeostatic Model Assessment 2 for Insulin Resistance; TC: Total Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; HDL-C: High-Density Lipoprotein Cholesterol; TG: Triglycerides.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study estimated population-specific reference thresholds for GAD65, IA2, and ZnT8 autoantibodies in a young Bangladeshi normoglycemic cohort and examined their application in youth-onset diabetes. The study-derived thresholds differed from manufacturer-recommended thresholds: GAD65 and IA2 thresholds were lower, whereas the ZnT8 threshold was higher. Application of these thresholds reduced overall autoantibody positivity in the young diabetes cohort, primarily due to a marked decrease in ZnT8 positivity.\u003c/p\u003e \u003cp\u003eStudies investigating IAbs in Bangladesh have utilized different methods and thresholds, leading to inconsistent clinical interpretations. Several studies utilized ELISA-based assays to screen for GAD65 and IA2 and used manufacturers' recommended thresholds [22,23]. More recently, Mustafa et al. employed a chemiluminescence immunoassay but again relied on a predefined manufacturer-suggested threshold of 17 IU/mL for GAD65 [24]. Conversely, Shil et al. adopted a lower threshold of 5 IU/mL in a diabetes cohort with a high prevalence of metabolic syndrome [25]. While a lower threshold may increase sensitivity, its clinical interpretation remains uncertain without local validation and external confirmation of outcomes.\u003c/p\u003e \u003cp\u003eThe international literature supports the view that universal interpretive thresholds for IAbs may not be appropriate due to ethnic and geographic variability in antibody titers. The MAGLUMI platform's standard thresholds for GAD65 and IA2 were originally established based on a study including the Han Chinese population; however, clinical validation in other regions has yielded markedly different results. Ziobrowska-Bech et al. established upper reference limits of 5.1 IU/mL for GAD65 in a Northern European cohort, noting that the manufacturer's thresholds would have missed a substantial number of autoimmune cases [26]. Conversely, in Middle Eastern cohorts, even higher thresholds have been utilized, such as the 30 IU/mL cut-off for GAD65 reported by Zaboon et al. [27]. This discrepancy is further complicated by analytical shifts, in which fully automated chemiluminescence immunoassays, such as the MAGLUMI system, have been observed to overestimate IAb levels relative to conventional ELISA methods, necessitating method-specific recalibration [28].\u003c/p\u003e \u003cp\u003eFor Anti-ZnT8, the international landscape is similarly fragmented. The manufacturer (RSR Ltd.) suggests a threshold of 15.0 U/mL, a standard adopted by several clinical studies [29,30]. However, population-specific research often mandates adjustment. In Japan, Kawasaki et al. established a lower threshold of 10.0 U/mL to optimize sensitivity in an East Asian cohort [31]. Again, Torn et al. recommended a threshold of 12.9 units/mL [32]. On the contrary, Grace et al. observed that ZnT8 thresholds must be substantially higher (up to 127 U/mL) in younger participants under 30 years of age to prevent false positives [33]. This age- and ethnicity-dependent variability suggests that using the standard 15.0 U/mL cutoff may increase the number of individuals classified as ZnT8-positive, although the clinical significance of this low-level positivity remains to be validated. The current study also observed a higher threshold than the manufacturer's recommendation. However, the current study-derived reference threshold should be viewed as a reference threshold requiring external validation rather than a definitive diagnostic cut-off.\u003c/p\u003e \u003cp\u003ePublished IA2 antibody reference threshold-related studies appear fewer than the corresponding GAD65 and ZnT8 reports, but available data also indicate assay- and population-related variability. In a radiobinding assay-based study, IA2 antibody threshold of \u0026ge;\u0026thinsp;1.4 DK U/mL was suggested by using the 98th percentile of 500 control samples [34]. In a Han Chinese CLIA based reference-interval study, an IA2 antibody upper limit of 3.91 IU/mL was reported using the 95th percentile of 177 healthy volunteers [35]. In contrast, a Danish MAGLUMI CLIA-based study derived an upper reference limit of 11.5 U/mL in healthy adults and children showing a substantial discrepancy with the reference thresholds suggested by the manufacturer [26]. These differences reinforce that IA2 thresholds are not directly interchangeable across assay platforms, populations, and unit systems.\u003c/p\u003e \u003cp\u003eComparing antibody-positive and antibody-negative participants provided additional clinical context. Under both manufacturer-recommended and study-derived thresholds, antibody-positive participants had lower BMI, lower frequency of acanthosis nigricans, lower HOMA2-%B, lower C-peptide, and higher glycemic indices compared with antibody-negative participants. These findings are compatible with reduced β-cell reserve among antibody-positive individuals. However, antibody-positive and antibody-negative groups were not fully separable by clinical or biochemical features, emphasizing the heterogeneity of youth-onset diabetes in this population.\u003c/p\u003e \u003cp\u003eThe clinical implications of these findings should be interpreted cautiously. The study-derived thresholds reflect the distribution of antibody titers in a local normoglycemic cohort and provide population-specific reference limits for interpretation rather than diagnostic accuracy-derived cut-offs. In the absence of longitudinal outcomes, HLA risk markers, or a validated composite reference standard, these thresholds should not be used as standalone criteria for diabetes classification. Instead, they may assist contextual interpretation of antibody results when considered alongside clinical phenotype, C-peptide, and other relevant markers.\u003c/p\u003e \u003cp\u003eThe study has several strengths. It included a normoglycemic reference cohort with OGTT-confirmed NGT and used contemporary assay platforms for three clinically relevant islet autoantibodies. The inclusion of ZnT8 alongside GAD65 and IA2 provides broader immunological profiling than earlier local studies that relied mainly on GAD65. Several limitations should also be acknowledged. Although the normoglycemic cohort exceeded the minimum sample size for non-parametric reference threshold estimation, estimation of extreme percentiles may still be sensitive to sample size and outlying values. The cross-sectional nature of the data precludes tracking the longitudinal rate of β-cell decline and insulin requirement over time. The clinic-based design and sex imbalance in the normoglycemic cohort may limit generalizability. The study did not include a definitive external reference standard for autoimmune diabetes, such as HLA risk markers, genetic susceptibility data, longitudinal insulin requirement, or serial C-peptide assessment. Therefore, the proposed thresholds should be considered population-specific reference thresholds requiring external validation rather than definitive diagnostic cut-offs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study provides population-specific reference thresholds for GAD65, IA2, and ZnT8 autoantibodies in Bangladeshi youth and shows that applying these thresholds changes estimated antibody positivity in youth-onset diabetes. The findings support the need for local calibration and cautious interpretation of islet autoantibody results. Future multicenter and longitudinal studies incorporating clinical outcomes, C-peptide trajectories, ketosis history, and genetic susceptibility markers are needed to validate these thresholds and clarify their role in diabetes classification.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBangladesh Medical University\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChemiluminescence immunoassay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical and Laboratory Standards Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDKA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetic ketoacidosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnzyme-linked immunosorbent assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAD65\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlutamic acid decarboxylase 65\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman leukocyte antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHOMA2-%B\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeostasis model assessment 2 of β-cell function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHOMA2-IR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeostasis model assessment 2 of insulin resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIAb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIslet autoantibody\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIAbs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIslet autoantibodies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIA2 / IA-2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIslet antigen 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndian Academy of Pediatrics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternal quality control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCEP ATP III\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Cholesterol Education Program Adult Treatment Panel III\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormal glucose tolerance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOGTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral glucose tolerance test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlasma glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadioimmunoassay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSODY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStudy on Obesity and Diabetes in Young\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 1 diabetes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 diabetes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHtR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist-to-height ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZnT8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZinc transporter 8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe study received ethical approval from the Institutional Review Board of Bangladesh Medical University, Dhaka (Approval No. BSMMU/2023/6509). Written informed consent or assent was obtained from all participants before enrollment, with parental or guardian consent secured for minors.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement:\u003c/h2\u003e \u003cp\u003eSupported partially by a grant from Bangladesh Medical University (grant number BSMMU/2023/13314(6)]).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: MH, NS, KKS, MAH; Data curation: MH, KKS, SBA, RHR; Formal analysis: MH, KKS, RHR; Funding acquisition: MH, NS, MAH; Investigation: MH, KKS, SBA; Methodology: MH, NS, KKS, MAH; Project administration: MAH; Supervision: MAH; Validation: MH, NS, MAH, Writing original draft: MH; Writing \u0026ndash; review and editing: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our sincere gratitude to all the participants and their families for their cooperation. Special thanks to the Study on Obesity and Diabetes of the Young (SODY) group members for their efforts in data collection. We also acknowledge the technical contributions of the laboratory staff, Md. Abdus Salam and Md. Nesar Uddin.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTosur M, Huang X, Inglis AS, Aguirre RS, Redondo MJ. Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications. Sci Rep. 2024 Apr 17;14(1):8876. doi: 10.1038/s41598-024-58927-6. PMID: 38632329; PMCID: PMC11024140. \u003c/li\u003e\n\u003cli\u003eZaber AA, Islam US, Mahmud MA, Talukder MU, B\u0026aacute;nyai G. Childhood obesity in Bangladesh: an emerging public health crisis. Asian J Med Biol Res. 2026 Jan 3 ;12(1):5-15. doi: 10.3329/ajmbr.v12i1.84144\u003c/li\u003e\n\u003cli\u003eMisra A, Sattar N, Ghosh A, Nassar M, Jayawardena R, Gupta R. Type 2 diabetes in South Asians. BMJ. 2025 Aug 12;390:e079801. doi: 10.1136/bmj-2024-079801. PMID: 40796264.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2025. Diabetes Care. 2025 Jan 1;48(1 Suppl 1):S27-S49. doi: 10.2337/dc25-S002. PMID: 39651986; PMCID: PMC11635041.\u003c/li\u003e\n\u003cli\u003eWinter WE, Pittman DL, Jialal I. Practical Clinical Applications of Islet Autoantibody Testing in Type 1 Diabetes. J Appl Lab Med. 2022 Jan 5;7(1):197-205. doi: 10.1093/jalm/jfab113. PMID: 34996067.\u003c/li\u003e\n\u003cli\u003eHolt RIG, DeVries JH, Hess-Fischl A, Hirsch IB, Kirkman MS, Klupa T, Ludwig B, N\u0026oslash;rgaard K, Pettus J, Renard E, Skyler JS, Snoek FJ, Weinstock RS, Peters AL. 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PMID: 40647694; PMCID: PMC12249052.\u003c/li\u003e\n\u003cli\u003eMarzinotto I, Pittman DL, Achenbach P, Akolkar B, Wasserfall CH, Lampasona V, Long AE. Interlaboratory Evaluation of Multiplex Autoantibody Assay Performance in the Islet Autoantibody Standardization Program 2024 Workshop. Diabetes Care. 2026 Jan 23:dc251326. doi: 10.2337/dc25-1326. Epub ahead of print. PMID: 41575380.\u003c/li\u003e\n\u003cli\u003eCLSI. Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline\u0026mdash;Third Edition. CLSI document EP28-A3c. Wayne, PA: Clinical and Laboratory Standards Institute; 2008.\u003c/li\u003e\n\u003cli\u003eOng YH, Koh WCA, Ng ML, Tam ZY, Lim SC, Boehm BO; Adult-Onset Autoimmune Diabetes Mellitus Consortium (ADAMS). Glutamic acid decarboxylase and islet antigen 2 antibody profiles in people with adult-onset diabetes mellitus: a comparison between mixed ethnic populations in Singapore and Germany. Diabet Med. 2017 Aug;34(8):1145-1153. doi: 10.1111/dme.13358. Epub 2017 May 22. PMID: 28370329; PMCID: PMC5575487.\u003c/li\u003e\n\u003cli\u003eAbdraimova A, Besan\u0026ccedil;on S, Portocarrero J, Ramaiya K, Dunganova A, Ewen M, Hogerzeil H, Lazo-Porras M, Laing R, Lepeska M, Nchimbi H, Sidib\u0026eacute; A, Swai A, Tenorio-Mucha J, Yudkin JS, Zafra-Tanaka JH, Zurdinova A, Beran D. Management of type 1 diabetes in low- and middle-income countries: Comparative health system assessments in Kyrgyzstan, Mali, Peru and Tanzania. Diabet Med. 2022 Aug;39(8):e14891. doi: 10.1111/dme.14891. Epub 2022 Jun 6. PMID: 35621029; PMCID: PMC9543552.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention (US). Child and teen BMI categories [Internet]. Atlanta (GA): Centers for Disease Control and Prevention; [cited 2025 Jun 27]. Available from: https://www.cdc.gov/bmi/child-teen-calculator/bmi-categories.html\u003c/li\u003e\n\u003cli\u003eMisra A, Shrivastava U. Obesity and dyslipidemia in South Asians. Nutrients. 2013 Jul 16;5(7):2708-33. doi: 10.3390/nu5072708. PMID: 23863826; PMCID: PMC3738996.\u003c/li\u003e\n\u003cli\u003eSiddiquee T, Bhowmik B, Munir SB, Nasrin H, Moreira NCDV, Mahmood S, Islam T, Mahmud F, Haque I, Khan S, Mahtab H, Azad Khan AK. Optimal cut-off points for waist circumference in the definition of metabolic syndrome: a cross-sectional study in rural Bangladesh. BMJ Open. 2025 Mar 25;15(3):e093159. doi: 10.1136/bmjopen-2024-093159. PMID: 40132836; PMCID: PMC11938217.\u003c/li\u003e\n\u003cli\u003eKhadilkar A, Ekbote V, Chiplonkar S, Khadilkar V, Kajale N, Kulkarni S, Parthasarathy L, Arya A, Bhattacharya A, Agarwal S. Waist circumference percentiles in 2-18 year old Indian children. J Pediatr. 2014 Jun;164(6):1358-62.e2. doi: 10.1016/j.jpeds.2014.02.018. Epub 2014 Mar 18. PMID: 24655536.\u003c/li\u003e\n\u003cli\u003eSruthi KG, John SM, David SM. Assessment of obesity in the Indian setting: A clinical review. Clin Epidemiol Glob Health. 2023;23:101348. doi: 10.1016/j.cegh.2023.101348.\u003c/li\u003e\n\u003cli\u003eYoo EG. Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Korean J Pediatr. 2016 Nov;59(11):425-431. doi: 10.3345/kjp.2016.59.11.425. Epub 2016 Nov 18. PMID: 27895689; PMCID: PMC5118501.\u003c/li\u003e\n\u003cli\u003eMcEvoy JW, McCarthy CP, Bruno RM, Brouwers S, Canavan MD, Ceconi C, Christodorescu RM, Daskalopoulou SS, Ferro CJ, Gerdts E, Hanssen H, Harris J, Lauder L, McManus RJ, Molloy GJ, Rahimi K, Regitz-Zagrosek V, Rossi GP, Sandset EC, Scheenaerts B, Staessen JA, Uchmanowicz I, Volterrani M, Touyz RM; ESC Scientific Document Group. 2024 ESC Guidelines for the management of elevated blood pressure and hypertension. Eur Heart J. 2024 Oct 7;45(38):3912-4018. doi: 10.1093/eurheartj/ehae178. Erratum in: Eur Heart J. 2025 Apr 7;46(14):1300. doi: 10.1093/eurheartj/ehaf031. Erratum in: Eur Heart J. 2025 Dec 1;46(45):4949. doi: 10.1093/eurheartj/ehaf659. PMID: 39210715.\u003c/li\u003e\n\u003cli\u003eFlynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, de Ferranti SD, Dionne JM, Falkner B, Flinn SK, Gidding SS, Goodwin C, Leu MG, Powers ME, Rea C, Samuels J, Simasek M, Thaker VV, Urbina EM; SUBCOMMITTEE ON SCREENING AND MANAGEMENT OF HIGH BLOOD PRESSURE IN CHILDREN. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017 Sep;140(3):e20171904. doi: 10.1542/peds.2017-1904. Epub 2017 Aug 21. Erratum in: Pediatrics. 2017 Dec;140(6):e20173035. doi: 10.1542/peds.2017-3035. Erratum in: Pediatrics. 2018 Sep;142(3):e20181739. doi: 10.1542/peds.2018-1739. PMID: 28827377.\u003c/li\u003e\n\u003cli\u003eNational Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002 Dec 17;106(25):3143-421. PMID: 12485966.\u003c/li\u003e\n\u003cli\u003eChowdhury AK, Sahi SR, Moniruzzaman M, Khan M. Islet cell and glutamic acid decarboxylase-65 autoantibodies in young diabetic patients attending in a General Hospital in Dhaka City. Bangladesh Med Res Counc Bull 2020; 46(2): 104-108. https://doi.org/10.3329/bmrcb.v46i2.49019\u0026amp;nbsp;\u003c/li\u003e\n\u003cli\u003eZabeen B, Govender D, Hassan Z, Noble JA, Lane JA, Mack SJ, Atkinson MA, Azad K, Wasserfall CH, Ogle GD. Clinical features, biochemistry and HLA-DRB1 status in children and adolescents with diabetes in Dhaka, Bangladesh. Diabetes Res Clin Pract. 2019 Dec;158:107894. doi: 10.1016/j.diabres.2019.107894. Epub 2019 Oct 24. PMID: 31669629; PMCID: PMC6988504.\u003c/li\u003e\n\u003cli\u003eMustafa SJ, Prasad I, Afrooz F, Muhit MS. GAD65 autoantibody positivity and its association with clinical and biochemical parameters among young onset diabetes mellitus. J. Assoc. Clin. Endocrinol. Diabetol. Bangladesh. 2025, 3(1), 03-08. https://doi.org/10.3329/jacedb.v3i1.78615\u0026amp;nbsp;\u003c/li\u003e\n\u003cli\u003eShil, K. K., Hasan, M., Sultana, N., Salam, S. B. A., Mostafa, S. N., \u0026amp; Hasanat, M. A. (2025). High Prevalence of Fatty Liver in Bangladeshi Adolescents and Young Adults with Type 2 Diabetes: Key Predictors and Screening Recommendations. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 18, 2019\u0026ndash;2027. https://doi.org/10.2147/DMSO.S520806\u003c/li\u003e\n\u003cli\u003eZiobrowska-Bech A, Winther-Larsen A, Kremke B, Parkner T, Soendersoe Knudsen C. Reference limits for GAD65 and IA-2 autoantibodies by chemiluminescence immunoassay in Northern European adults and children. Scand J Clin Lab Invest. 2019 Feb-Apr;79(1-2):123-125. doi: 10.1080/00365513.2019.1566566. Epub 2019 Feb 6. PMID: 30727763.\u003c/li\u003e\n\u003cli\u003eZaboon IA, Mansour AA, Haddad NS. Variables Associated with Persistence of C-Peptide Secretion among Patients with Type 1 Diabetes Mellitus. CHRISMED Journal of Health and Research 4(3):p 173-179, Jul\u0026ndash;Sep 2017. | DOI: 10.4103/cjhr.cjhr_2_17 \u003c/li\u003e\n\u003cli\u003eDanese E, Piona C, Rizza M, Tiziani E, Pighi L, Morotti E, Salvagno GL, Mattiuzzi C, Maffeis C, Lippi G. A Comparative Evaluation of the Chemiluminescence Immunoassay and ELISA for the Detection of Islet Autoantibodies in Type 1 Diabetes. Diagnostics (Basel). 2025 Jul 3;15(13):1695. doi: 10.3390/diagnostics15131695. PMID: 40647694; PMCID: PMC12249052.\u003c/li\u003e\n\u003cli\u003eHussein H, Ibrahim F, Sobngwi E, Gautier JF, Boudou P. Zinc transporter 8 autoantibodies assessment in daily practice. 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Evaluation of the RSR 3 screen ICA\u0026trade; and 2 screen ICA\u0026trade; as screening assays for type 1 diabetes in Sweden. Acta Diabetol. 2022 Jun;59(6):773-781. doi: 10.1007/s00592-022-01856-5. Epub 2022 Feb 26. PMID: 35220476; PMCID: PMC9085662.\u003c/li\u003e\n\u003cli\u003eGrace SL, Cooper A, Jones AG, McDonald TJ. Zinc transporter 8 autoantibody testing requires age-related cut-offs. BMJ Open Diabetes Res Care. 2021 Aug;9(1):e002296. doi: 10.1136/bmjdrc-2021-002296. PMID: 34348918; PMCID: PMC8340275.\u003c/li\u003e\n\u003cli\u003eGrace SL, Bowden J, Walkey HC, Kaur A, Misra S, Shields BM, McKinley TJ, Oliver NS, McDonald TJ, Johnston DG, Jones AG, Patel KA. Islet Autoantibody Level Distribution in Type 1 Diabetes and Their Association With Genetic and Clinical Characteristics. J Clin Endocrinol Metab. 2022 Nov 25;107(12):e4341-e4349. doi: 10.1210/clinem/dgac507. PMID: 36073000; PMCID: PMC9693812.\u003c/li\u003e\n\u003cli\u003eFu Y, Zhang C, Gu Y, Ge S, Li J, Feng J, Zhang L, Liu W, Chen H. Establishing reference intervals for islet autoantibodies in Han Chinese type 1 diabetes. Scand J Clin Lab Invest. 2021 Dec;81(8):641-648. doi: 10.1080/00365513.2021.2001564. Epub 2021 Nov 15. PMID: 34779329.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Youth-onset diabetes, Islet autoantibodies, GAD65, ZnT8, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-9542267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9542267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough islet autoantibodies are clinically useful markers of β-cell autoimmunity, their distribution and interpretive thresholds in Bangladeshi youth remain poorly defined. The aim of the study was to derive population-specific reference thresholds for antibodies against glutamic acid decarboxylase 65 (GAD65), tyrosine phosphatase-like insulinoma antigen 2 (IA2), and zinc transporter 8 (ZnT8) in a Bangladeshi normoglycemic cohort and to compare their application with manufacturer-recommended thresholds in youth-onset diabetes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included a normoglycemic cohort (NGT, n\u0026thinsp;=\u0026thinsp;148) and a youth-onset diabetes cohort (DM, n\u0026thinsp;=\u0026thinsp;180) aged 10\u0026ndash;34 years, recruited from the clinic-based screening program in Bangladesh Medical University, Dhaka, during 2023\u0026ndash;2024. GAD65 and IA2 antibodies were measured by chemiluminescent immunoassay, and ZnT8 antibody by enzyme-linked immunosorbent assay. Study-derived thresholds were defined from the NGT cohort using the 99th percentile. In the diabetes cohort, antibody positivity rates and clinical and biochemical characteristics were compared using manufacturer-recommended and study-derived thresholds.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eStudy-derived thresholds were 8.5 IU/mL for GAD65, 40.8 U/mL for ZnT8, and 3.2 U/mL for IA2 antibodies. Applying these thresholds to the diabetes cohort changed antibody positivity rates. GAD65 positivity increased from 5.0% by the manufacturer's threshold to 7.8% by the study-derived threshold. Similarly, IA2 positivity increased from 0% to 1.7%, whereas ZnT8 positivity decreased from 15.0% to 2.8%. Overall antibody positivity decreased from 18.9% to 11.7%. Under both threshold systems, antibody-positive participants had lower BMI, lower frequency of acanthosis nigricans, lower HOMA2-%B, lower C-peptide, and higher glycemic indices than antibody-negative participants.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eStudy-derived thresholds for islet autoantibodies differed from manufacturer-recommended thresholds and changed antibody positivity estimates. These findings support local calibration and contextual interpretation of autoantibody results in Bangladeshi youth-onset diabetes, alongside clinical and metabolic markers.\u003c/p\u003e","manuscriptTitle":"Population-Specific Reference Thresholds for Islet Autoantibodies in Bangladeshi Youth and Their Clinical Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 08:09:59","doi":"10.21203/rs.3.rs-9542267/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":"a08642be-b127-4f90-9762-38aac1af2c81","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvited","content":"","date":"2026-04-30T07:00:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T18:01:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2026-04-29T16:52:35+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T08:10:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 08:09:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9542267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9542267","identity":"rs-9542267","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}