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Item Barriers to Screening: An Analysis of Factors Impacting Screening for Type 1 Diabetes Prevention Trials(Oxford University Press, 2023-01-11) Kinney, Mara; You, Lu; Sims, Emily K.; Wherrett, Diane; Schatz, Desmond; Lord, Sandra; Krischer, Jeffrey; Russell, William E.; Gottlieb, Peter A.; Libman, Ingrid; Buckner, Jane; DiMeglio, Linda A.; Herold, Kevan C.; Steck, Andrea K.; Pediatrics, School of MedicineContext: Participants with stage 1 or 2 type 1 diabetes (T1D) qualify for prevention trials, but factors involved in screening for such trials are largely unknown. Objective: To identify factors associated with screening for T1D prevention trials. Methods: This study included TrialNet Pathway to Prevention participants who were eligible for a prevention trial: oral insulin (TN-07, TN-20), teplizumab (TN-10), abatacept (TN-18), and oral hydroxychloroquine (TN-22). Univariate and multivariate logistic regression models were used to examine participant, site, and study factors at the time of prevention trial accrual. Results: Screening rates for trials were: 50% for TN-07 (584 screened/1172 eligible), 9% for TN-10 (106/1249), 24% for TN-18 (313/1285), 17% for TN-20 (113/667), and 28% for TN-22 (371/1336). Younger age and male sex were associated with higher screening rates for prevention trials overall and for oral therapies. Participants with an offspring with T1D showed lower rates of screening for all trials and oral drug trials compared with participants with other first-degree relatives as probands. Site factors, including larger monitoring volume and US site vs international site, were associated with higher prevention trial screening rates. Conclusions: Clear differences exist between participants who screen for prevention trials and those who do not screen and between the research sites involved in prevention trial screening. Participant age, sex, and relationship to proband are significantly associated with prevention trial screening in addition to key site factors. Identifying these factors can facilitate strategic recruitment planning to support rapid and successful enrollment into prevention trials.Item Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review(Springer Nature, 2023-10-05) Felton, Jamie L.; Griffin, Kurt J.; Oram, Richard A.; Speake, Cate; Long, S. Alice; Onengut-Gumuscu, Suna; Rich, Stephen S.; Monaco, Gabriela S. F.; Evans-Molina, Carmella; DiMeglio, Linda A.; Ismail, Heba M.; Steck, Andrea K.; Dabelea, Dana; Johnson, Randi K.; Urazbayeva, Marzhan; Gitelman, Stephen; Wentworth, John M.; Redondo, Maria J.; Sims, Emily K.; Pediatrics, School of MedicineBackground: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification. Methods: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument. Results: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings. Conclusions: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design.Item HLA-DRB1*15:01-DQA1*01:02-DQB1*06:02 Haplotype Protects Autoantibody-Positive Relatives From Type 1 Diabetes Throughout the Stages of Disease Progression(American Diabetes Association, 2016-04) Pugliese, Alberto; Boulware, David; Yu, Liping; Babu, Sunanda; Steck, Andrea K.; Becker, Dorothy; Rodriguez, Henry; DiMeglio, Linda; Evans-Molina, Carmella; Harrison, Leonard C.; Schatz, Desmond; Palmer, Jerry P.; Greenbaum, Carla; Eisenbarth, George S.; Sosenko, Jay M.; Medicine, School of MedicineThe HLA-DRB1*15:01-DQA1*01:02-DQB1*06:02 haplotype is linked to protection from the development of type 1 diabetes (T1D). However, it is not known at which stages in the natural history of T1D development this haplotype affords protection. We examined a cohort of 3,358 autoantibody-positive relatives of T1D patients in the Pathway to Prevention (PTP) Study of the Type 1 Diabetes TrialNet. The PTP study examines risk factors for T1D and disease progression in relatives. HLA typing revealed that 155 relatives carried this protective haplotype. A comparison with 60 autoantibody-negative relatives suggested protection from autoantibody development. Moreover, the relatives with DRB1*15:01-DQA1*01:02-DQB1*06:02 less frequently expressed autoantibodies associated with higher T1D risk, were less likely to have multiple autoantibodies at baseline, and rarely converted from single to multiple autoantibody positivity on follow-up. These relatives also had lower frequencies of metabolic abnormalities at baseline and exhibited no overall metabolic worsening on follow-up. Ultimately, they had a very low 5-year cumulative incidence of T1D. In conclusion, the protective influence of DRB1*15:01-DQA1*01:02-DQB1*06:02 spans from autoantibody development through all stages of progression, and relatives with this allele only rarely develop T1D.Item The Influence of Type 2 Diabetes–Associated Factors on Type 1 Diabetes(American Diabetes Association, 2019-08-01) Redondo, Maria J.; Evans-Molina, Carmella; Steck, Andrea K.; Atkinson, Mark A.; Sosenko, Jay; Pediatrics, School of MedicineCurrent efforts to prevent progression from islet autoimmunity to type 1 diabetes largely focus on immunomodulatory approaches. However, emerging data suggest that the development of diabetes in islet autoantibody–positive individuals may also involve factors such as obesity and genetic variants associated with type 2 diabetes, and the influence of these factors increases with age at diagnosis. Although these factors have been linked with metabolic outcomes, particularly through their impact on β-cell function and insulin sensitivity, growing evidence suggests that they might also interact with the immune system to amplify the autoimmune response. The presence of factors shared by both forms of diabetes contributes to disease heterogeneity and thus has important implications. Characteristics that are typically considered to be nonimmune should be incorporated into predictive algorithms that seek to identify at-risk individuals and into the designs of trials for disease prevention. The heterogeneity of diabetes also poses a challenge in diagnostic classification. Finally, after clinically diagnosing type 1 diabetes, addressing nonimmune elements may help to prevent further deterioration of β-cell function and thus improve clinical outcomes. This Perspectives in Care article highlights the role of type 2 diabetes–associated genetic factors (e.g., gene variants at transcription factor 7-like 2 [TCF7L2]) and obesity (via insulin resistance, inflammation, β-cell stress, or all three) in the pathogenesis of type 1 diabetes and their impacts on age at diagnosis. Recognizing that type 1 diabetes might result from the sum of effects from islet autoimmunity and type 2 diabetes–associated factors, their interactions, or both affects disease prediction, prevention, diagnosis, and treatment.Item Proinsulin:C-peptide ratio trajectories over time in relatives at increased risk of progression to type 1 diabetes(Elsevier, 2021-02-19) Triolo, Taylor M.; Pyle, Laura; Seligova, Sona; Yu, Liping; Simmons, Kimber; Gottlieb, Peter; Evans-Molina, Carmella; Steck, Andrea K.; Medicine, School of MedicineObjective: Biomarkers are needed to characterize heterogeneity within populations at risk for type 1 diabetes. The ratio of proinsulin to C-peptide (PI:C ratio), has been proposed as a biomarker of beta cell dysfunction and is associated with progression to type 1 diabetes. However, relationships between PI:C ratios and autoantibody type and number have not been examined. We sought to characterize PI:C ratios in multiple islet autoantibody positive, single autoantibody positive and autoantibody negative relatives of individuals with type 1 diabetes. Methods: We measured PI:C ratios and autoantibodies with both electrochemiluminescence (ECL) assays (ECL-IAA, ECL-GADA and ECL-IA2A) and radiobinding (RBA) assays (mIAA, GADA, IA2A and ZnT8A) in 98 relatives of individuals with type 1 diabetes followed in the TrialNet Pathway to Prevention Study at the Barbara Davis Center for a mean of 7.4 ± 4.1 years. Of these subjects, eight progressed to T1D, 31 were multiple autoantibody (Ab) positive, 37 were single Ab positive and 22 were Ab negative (by RBA). Results: In cross-sectional analyses, there were no significant differences in PI:C ratios between type 1 diabetes and/or multiple Ab positive subjects (4.16 ± 4.06) compared to single Ab positive subjects (4.08 ± 4.34) and negative Ab subjects (3.72 ± 3.78) (p = 0.92) overall or after adjusting for age, sex and BMI. Higher PI:C ratios were associated with mIAA titers (p = 0.03) and showed an association with ECL-IA2A titers (p = 0.09), but not with ECL-IAA, GADA, ECL-GADA, IA2A nor ZnT8A titers. In mixed-effects longitudinal models, the trajectories of PI:C ratio over time were significantly different between the Ab negative and multiple Ab positive/type 1 diabetes groups, after adjusting for sex, age, and BMI (p = 0.04). Conclusions: PI:C ratio trajectories increase over time in subjects who have multiple Ab or develop type 1 diabetes and may be a helpful biomarker to further characterize and stratify risk of progression to type 1 diabetes over time.Item Screening for Type 1 Diabetes in the General Population: A Status Report and Perspective(American Diabetes Association, 2022) Sims, Emily K.; Besser, Rachel E. J.; Dayan, Colin; Rasmussen, Cristy Geno; Greenbaum, Carla; Griffin, Kurt J.; Hagopian, William; Knip, Mikael; Long, Anna E.; Martin, Frank; Mathieu, Chantal; Rewers, Marian; Steck, Andrea K.; Wentworth, John M.; Rich, Stephen S.; Kordonouri, Olga; Ziegler, Anette-Gabriele; Herold, Kevan C.; NIDDK Type 1 Diabetes TrialNet Study Group; Pediatrics, School of MedicineMost screening programs to identify individuals at risk for type 1 diabetes have targeted relatives of people living with the disease to improve yield and feasibility. However, ∼90% of those who develop type 1 diabetes do not have a family history. Recent successes in disease-modifying therapies to impact the course of early-stage disease have ignited the consideration of the need for and feasibility of population screening to identify those at increased risk. Existing population screening programs rely on genetic or autoantibody screening, and these have yielded significant information about disease progression and approaches for timing for screening in clinical practice. At the March 2021 Type 1 Diabetes TrialNet Steering Committee meeting, a session was held in which ongoing efforts for screening in the general population were discussed. This report reviews the background of these efforts and the details of those programs. Additionally, we present hurdles that need to be addressed for successful implementation of population screening and provide initial recommendations for individuals with positive screens so that standardized guidelines for monitoring and follow-up can be established.Item Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine(Springer Nature, 2023) Tobias, Deirdre K.; Merino, Jordi; Ahmad, Abrar; Aiken, Catherine; Benham, Jamie L.; Bodhini, Dhanasekaran; Clark, Amy L.; Colclough, Kevin; Corcoy, Rosa; Cromer, Sara J.; Duan, Daisy; Felton, Jamie L.; Francis, Ellen C.; Gillard, Pieter; Gingras, Véronique; Gaillard, Romy; Haider, Eram; Hughes, Alice; Ikle, Jennifer M.; Jacobsen, Laura M.; Kahkoska, Anna R.; Kettunen, Jarno L. T.; Kreienkamp, Raymond J.; Lim, Lee-Ling; Männistö, Jonna M. E.; Massey, Robert; Mclennan, Niamh-Maire; Miller, Rachel G.; Morieri, Mario Luca; Most, Jasper; Naylor, Rochelle N.; Ozkan, Bige; Patel, Kashyap Amratlal; Pilla, Scott J.; Prystupa, Katsiaryna; Raghavan, Sridharan; Rooney, Mary R.; Schön, Martin; Semnani-Azad, Zhila; Sevilla-Gonzalez, Magdalena; Svalastoga, Pernille; Takele, Wubet Worku; Tam, Claudia Ha-Ting; Thuesen, Anne Cathrine B.; Tosur, Mustafa; Wallace, Amelia S.; Wang, Caroline C.; Wong, Jessie J.; Yamamoto, Jennifer M.; Young, Katherine; Amouyal, Chloé; Andersen, Mette K.; Bonham, Maxine P.; Chen, Mingling; Cheng, Feifei; Chikowore, Tinashe; Chivers, Sian C.; Clemmensen, Christoffer; Dabelea, Dana; Dawed, Adem Y.; Deutsch, Aaron J.; Dickens, Laura T.; DiMeglio, Linda A.; Dudenhöffer-Pfeifer, Monika; Evans-Molina, Carmella; Fernández-Balsells, María Mercè; Fitipaldi, Hugo; Fitzpatrick, Stephanie L.; Gitelman, Stephen E.; Goodarzi, Mark O.; Grieger, Jessica A.; Guasch-Ferré, Marta; Habibi, Nahal; Hansen, Torben; Huang, Chuiguo; Harris-Kawano, Arianna; Ismail, Heba M.; Hoag, Benjamin; Johnson, Randi K.; Jones, Angus G.; Koivula, Robert W.; Leong, Aaron; Leung, Gloria K. W.; Libman, Ingrid M.; Liu, Kai; Long, S. Alice; Lowe, William L., Jr.; Morton, Robert W.; Motala, Ayesha A.; Onengut-Gumuscu, Suna; Pankow, James S.; Pathirana, Maleesa; Pazmino, Sofia; Perez, Dianna; Petrie, John R.; Powe, Camille E.; Quinteros, Alejandra; Jain, Rashmi; Ray, Debashree; Ried-Larsen, Mathias; Saeed, Zeb; Santhakumar, Vanessa; Kanbour, Sarah; Sarkar, Sudipa; Monaco, Gabriela S. F.; Scholtens, Denise M.; Selvin, Elizabeth; Sheu, Wayne Huey-Herng; Speake, Cate; Stanislawski, Maggie A.; Steenackers, Nele; Steck, Andrea K.; Stefan, Norbert; Støy, Julie; Taylor, Rachael; Tye, Sok Cin; Ukke, Gebresilasea Gendisha; Urazbayeva, Marzhan; Van der Schueren, Bart; Vatier, Camille; Wentworth, John M.; Hannah, Wesley; White, Sara L.; Yu, Gechang; Zhang, Yingchai; Zhou, Shao J.; Beltrand, Jacques; Polak, Michel; Aukrust, Ingvild; de Franco, Elisa; Flanagan, Sarah E.; Maloney, Kristin A.; McGovern, Andrew; Molnes, Janne; Nakabuye, Mariam; Njølstad, Pål Rasmus; Pomares-Millan, Hugo; Provenzano, Michele; Saint-Martin, Cécile; Zhang, Cuilin; Zhu, Yeyi; Auh, Sungyoung; de Souza, Russell; Fawcett, Andrea J.; Gruber, Chandra; Mekonnen, Eskedar Getie; Mixter, Emily; Sherifali, Diana; Eckel, Robert H.; Nolan, John J.; Philipson, Louis H.; Brown, Rebecca J.; Billings, Liana K.; Boyle, Kristen; Costacou, Tina; Dennis, John M.; Florez, Jose C.; Gloyn, Anna L.; Gomez, Maria F.; Gottlieb, Peter A.; Greeley, Siri Atma W.; Griffin, Kurt; Hattersley, Andrew T.; Hirsch, Irl B.; Hivert, Marie-France; Hood, Korey K.; Josefson, Jami L.; Kwak, Soo Heon; Laffel, Lori M.; Lim, Siew S.; Loos, Ruth J. F.; Ma, Ronald C. W.; Mathieu, Chantal; Mathioudakis, Nestoras; Meigs, James B.; Misra, Shivani; Mohan, Viswanathan; Murphy, Rinki; Oram, Richard; Owen, Katharine R.; Ozanne, Susan E.; Pearson, Ewan R.; Perng, Wei; Pollin, Toni I.; Pop-Busui, Rodica; Pratley, Richard E.; Redman, Leanne M.; Redondo, Maria J.; Reynolds, Rebecca M.; Semple, Robert K.; Sherr, Jennifer L.; Sims, Emily K.; Sweeting, Arianne; Tuomi, Tiinamaija; Udler, Miriam S.; Vesco, Kimberly K.; Vilsbøll, Tina; Wagner, Robert; Rich, Stephen S.; Franks, Paul W.; Pediatrics, School of MedicinePrecision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.Item TCF7L2 Genetic Variants Do Not Influence Insulin Sensitivity or Secretion Indices in Autoantibody-Positive Individuals at Risk for Type 1 Diabetes(American Diabetes Association, 2021) Redondo, Maria J.; Warnock, Megan V.; Libman, Ingrid M.; Bocchino, Laura E.; Cuthbertson, David; Geyer, Susan; Pugliese, Alberto; Steck, Andrea K.; Evans-Molina, Carmella; Becker, Dorothy; Sosenko, Jay M.; Bacha, Fida; Type 1 Diabetes TrialNet Study Group; Medicine, School of MedicineObjective: We aimed to test whether type 2 diabetes (T2D)-associated TCF7L2 genetic variants affect insulin sensitivity or secretion in autoantibody-positive relatives at risk for type 1 diabetes (T1D). Research design and methods: We studied autoantibody-positive TrialNet Pathway to Prevention study participants (N = 1,061) (mean age 16.3 years) with TCF7L2 single nucleotide polymorphism (SNP) information and baseline oral glucose tolerance test (OGTT) to calculate indices of insulin sensitivity and secretion. With Bonferroni correction for multiple comparisons, P values < 0.0086 were considered statistically significant. Results: None, one, and two T2D-linked TCF7L2 alleles were present in 48.1%, 43.9%, and 8.0% of the participants, respectively. Insulin sensitivity (as reflected by 1/fasting insulin [1/IF]) decreased with increasing BMI z score and was lower in Hispanics. Insulin secretion (as measured by 30-min C-peptide index) positively correlated with age and BMI z score. Oral disposition index was negatively correlated with age, BMI z score, and Hispanic ethnicity. None of the indices were associated with TCF7L2 SNPs. In multivariable analysis models with age, BMI z score, ethnicity, sex, and TCF7L2 alleles as independent variables, C-peptide index increased with age, while BMI z score was associated with higher insulin secretion (C-peptide index), lower insulin sensitivity (1/IF), and lower disposition index; there was no significant effect of TCF7L2 SNPs on any of these indices. When restricting the analyses to participants with a normal OGTT (n = 743; 70%), the results were similar. Conclusions: In nondiabetic autoantibody-positive individuals, TCF7L2 SNPs were not related to insulin sensitivity or secretion indices after accounting for BMI z score, age, sex, and ethnicity.Item The Influence of Pubertal Development on Autoantibody Appearance and Progression to Type 1 Diabetes in the TEDDY Study(Oxford University Press, 2024-05-24) Warncke, Katharina; Tamura, Roy; Schatz, Desmond A.; Veijola, Riitta; Steck, Andrea K.; Akolkar, Beena; Hagopian, William; Krischer, Jeffrey P.; Lernmark, Åke; Rewers, Marian J.; Toppari, Jorma; McIndoe, Richard; Ziegler, Anette-G.; Vehik, Kendra; Haller, Michael J.; Elding Larsson, Helena; Pediatrics, School of MedicineContext: The 2 peaks of type 1 diabetes incidence occur during early childhood and puberty. Objective: We sought to better understand the relationship between puberty, islet autoimmunity, and type 1 diabetes. Methods: The relationships between puberty, islet autoimmunity, and progression to type 1 diabetes were investigated prospectively in children followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Onset of puberty was determined by subject self-assessment of Tanner stages. Associations between speed of pubertal progression, pubertal growth, weight gain, homeostasis model assessment of insulin resistance (HOMA-IR), islet autoimmunity, and progression to type 1 diabetes were assessed. The influence of individual factors was analyzed using Cox proportional hazard ratios. Results: Out of 5677 children who were still in the study at age 8 years, 95% reported at least 1 Tanner Stage score and were included in the study. Children at puberty (Tanner Stage ≥2) had a lower risk (HR 0.65, 95% CI 0.45-0.93; P = .019) for incident autoimmunity than prepubertal children (Tanner Stage 1). An increase of body mass index Z-score was associated with a higher risk (HR 2.88, 95% CI 1.61-5.15; P < .001) of incident insulin autoantibodies. In children with multiple autoantibodies, neither HOMA-IR nor rate of progression to Tanner Stage 4 were associated with progression to type 1 diabetes. Conclusion: Rapid weight gain during puberty is associated with development of islet autoimmunity. Puberty itself had no significant influence on the appearance of autoantibodies or type 1 diabetes. Further studies are needed to better understand the underlying mechanisms.Item The Transition From a Compensatory Increase to a Decrease in C-peptide During the Progression to Type 1 Diabetes and Its Relation to Risk(American Diabetes Association, 2022) Ismail, Heba M.; Cuthbertson, David; Gitelman, Stephen E.; Skyler, Jay S.; Steck, Andrea K.; Rodriguez, Henry; Atkinson, Mark; Nathan, Brandon M.; Redondo, Maria J.; Herold, Kevan C.; Evans-Molina, Carmella; DiMeglio, Linda A.; Sosenko, Jay; DPT-1 and TrialNet Study Groups; Pediatrics, School of MedicineObjective: To define the relationship between glucose and C-peptide during the progression to type 1 diabetes (T1D). Research design and methods: We longitudinally studied glucose and C-peptide response curves (GCRCs), area under curve (AUC) for glucose, and AUC C-peptide from oral glucose tolerance tests (OGTTs), and Index60 (which integrates OGTT glucose and C-peptide values) in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 72) and TrialNet Pathway to Prevention Study (TNPTP) (n = 82) participants who had OGTTs at baseline and follow-up time points before diagnosis. Results: Similar evolutions of GCRC configurations were evident between DPT-1 and TNPTP from baseline to 0.5 years prediagnosis. Whereas AUC glucose increased throughout from baseline to 0.5 years prediagnosis, AUC C-peptide increased from baseline until 1.5 years prediagnosis (DPT-1, P = 0.004; TNPTP, P = 0.012) and then decreased from 1.5 to 0.5 years prediagnosis (DPT-1, P = 0.017; TNPTP, P = 0.093). This change was mostly attributable to change in the late AUC C-peptide response (i.e., 60- to 120-min AUC C-peptide). Median Index60 values of DPT-1 (1.44) and TNPTP (1.05) progressors to T1D 1.5 years prediagnosis (time of transition from increasing to decreasing AUC C-peptide) were used as thresholds to identify individuals at high risk for T1D in the full cohort at baseline (5-year risk of 0.75-0.88 for those above thresholds). Conclusions: A transition from an increase to a decrease in AUC C-peptide ∼1.5 years prediagnosis was validated in two independent cohorts. The median Index60 value at that time point can be used as a pathophysiologic-based threshold for identifying individuals at high risk for T1D.