{"id":{"repo_id":"exeter","oai_identifier":"oai:figshare.com:article/31820374"},"canonical_url":"https://search.dev.ndltd.org/etd/exeter/oai:figshare.com:article/31820374","repository":{"repo_id":"exeter","name":"University of Exeter","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"Multimodal Prediction of Type 1 Diabetes","abstract":"Type 1 diabetes often presents abruptly with life-threatening symptoms; however, its autoimmune pathogenesis typically unfolds over many years. After autoimmunity has been established, the rate of progression to clinical disease varies markedly between individuals, creating both an opportunity and a challenge. Early identification can reduce diabetic ketoacidosis at diagnosis and enable the use of preventative therapies; however, disease heterogeneity limits the accuracy and transportability of risk estimates used to guide screening, follow-up, and treatment decisions across settings and age groups. This thesis aims to investigate how demographic, immunologic, metabolic, and genetic factors jointly explain variation in progression from islet autoimmunity to clinical type 1 diabetes, and how this knowledge can inform screening and precision risk stratification. Using longitudinal data from two major type 1 diabetes studies with distinct screening strategies, this work evaluates prediction model transportability, characterises age- and sex-related variation in disease progression, and refines metabolic staging in adults. First, a multivariable risk prediction model combining genetic risk, autoantibody measures, age, and family history is developed and externally evaluated across cohorts. While key predictors are shown to be consistent, accurate risk estimation requires recalibration to the target screening context. This model is translated into a web-based risk calculator to support individualised risk communication and clinical or research decision-making. Next, sex differences in progression are examined, demonstrating that sex-related risk is not constant but varies with age and autoimmune status, with evidence of a developmental shift in progression trajectories around early adolescence. Differences between adult and paediatric presymptomatic type 1 diabetes are then investigated within the TrialNet cohort, revealing distinct immunologic and genetic profiles and slower average progression in adults, while showing convergence in progression rates once dysglycaemia and multiple autoantibodies are present. Finally, HbA1c is evaluated as a marker of metabolic progression in autoantibody-positive adults, demonstrating that age-related increases in HbA1c inflate dysglycaemia classification without necessarily reflecting increased risk of type 1 diabetes. These findings collectively show that presymptomatic type 1 diabetes progression is shaped by interacting biological and demographic factors, and that identical biomarkers or thresholds do not carry uniform meaning across the life course. This thesis advances a framework for contextual interpretation of individual risk in type 1 diabetes screening and supports the development of more precise, equitable, and clinically meaningful early identification strategies. Future implementation will require broader external validation, increased ancestral diversity in study populations, and prospective evaluation of risk tools within real-world screening pathways.<p></p>","abstract_html":"Type 1 diabetes often presents abruptly with life-threatening symptoms; however, its autoimmune pathogenesis typically unfolds over many years. After autoimmunity has been established, the rate of progression to clinical disease varies markedly between individuals, creating both an opportunity and a challenge. Early identification can reduce diabetic ketoacidosis at diagnosis and enable the use of preventative therapies; however, disease heterogeneity limits the accuracy and transportability of risk estimates used to guide screening, follow-up, and treatment decisions across settings and age groups. This thesis aims to investigate how demographic, immunologic, metabolic, and genetic factors jointly explain variation in progression from islet autoimmunity to clinical type 1 diabetes, and how this knowledge can inform screening and precision risk stratification. Using longitudinal data from two major type 1 diabetes studies with distinct screening strategies, this work evaluates prediction model transportability, characterises age- and sex-related variation in disease progression, and refines metabolic staging in adults. First, a multivariable risk prediction model combining genetic risk, autoantibody measures, age, and family history is developed and externally evaluated across cohorts. While key predictors are shown to be consistent, accurate risk estimation requires recalibration to the target screening context. This model is translated into a web-based risk calculator to support individualised risk communication and clinical or research decision-making. Next, sex differences in progression are examined, demonstrating that sex-related risk is not constant but varies with age and autoimmune status, with evidence of a developmental shift in progression trajectories around early adolescence. Differences between adult and paediatric presymptomatic type 1 diabetes are then investigated within the TrialNet cohort, revealing distinct immunologic and genetic profiles and slower average progression in adults, while showing convergence in progression rates once dysglycaemia and multiple autoantibodies are present. Finally, HbA1c is evaluated as a marker of metabolic progression in autoantibody-positive adults, demonstrating that age-related increases in HbA1c inflate dysglycaemia classification without necessarily reflecting increased risk of type 1 diabetes. These findings collectively show that presymptomatic type 1 diabetes progression is shaped by interacting biological and demographic factors, and that identical biomarkers or thresholds do not carry uniform meaning across the life course. This thesis advances a framework for contextual interpretation of individual risk in type 1 diabetes screening and supports the development of more precise, equitable, and clinically meaningful early identification strategies. Future implementation will require broader external validation, increased ancestral diversity in study populations, and prospective evaluation of risk tools within real-world screening pathways.&lt;p&gt;&lt;/p&gt;","abstract_has_math":false,"creators":["Erin Templeman (21043883)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-02-27T00:00:00Z","date_published":"2026-02-27T00:00:00Z","updated_at":"2026-07-27T19:33:53Z","subjects":["prediction","type 1 diabetes","autoantibodies","genetics","progression","risk","autoimmune","autoimmunity"],"languages":[],"rights":["All rights reserved","Open Access after 2027-09-23"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10779/exe.31820374.v1"],"render_values":[{"text":"10779/exe.31820374.v1","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Erin Templeman (21043883)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-02-27T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/Multimodal_Prediction_of_Type_1_Diabetes/31820374"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["prediction","type 1 diabetes","autoantibodies","genetics","progression","risk","autoimmune","autoimmunity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["All rights reserved","Open Access after 2027-09-23"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10779/exe.31820374.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Type 1 diabetes often presents abruptly with life-threatening symptoms; however, its autoimmune pathogenesis typically unfolds over many years. After autoimmunity has been established, the rate of progression to clinical disease varies markedly between individuals, creating both an opportunity and a challenge. Early identification can reduce diabetic ketoacidosis at diagnosis and enable the use of preventative therapies; however, disease heterogeneity limits the accuracy and transportability of risk estimates used to guide screening, follow-up, and treatment decisions across settings and age groups. This thesis aims to investigate how demographic, immunologic, metabolic, and genetic factors jointly explain variation in progression from islet autoimmunity to clinical type 1 diabetes, and how this knowledge can inform screening and precision risk stratification. Using longitudinal data from two major type 1 diabetes studies with distinct screening strategies, this work evaluates prediction model transportability, characterises age- and sex-related variation in disease progression, and refines metabolic staging in adults. First, a multivariable risk prediction model combining genetic risk, autoantibody measures, age, and family history is developed and externally evaluated across cohorts. While key predictors are shown to be consistent, accurate risk estimation requires recalibration to the target screening context. This model is translated into a web-based risk calculator to support individualised risk communication and clinical or research decision-making. Next, sex differences in progression are examined, demonstrating that sex-related risk is not constant but varies with age and autoimmune status, with evidence of a developmental shift in progression trajectories around early adolescence. Differences between adult and paediatric presymptomatic type 1 diabetes are then investigated within the TrialNet cohort, revealing distinct immunologic and genetic profiles and slower average progression in adults, while showing convergence in progression rates once dysglycaemia and multiple autoantibodies are present. Finally, HbA1c is evaluated as a marker of metabolic progression in autoantibody-positive adults, demonstrating that age-related increases in HbA1c inflate dysglycaemia classification without necessarily reflecting increased risk of type 1 diabetes. These findings collectively show that presymptomatic type 1 diabetes progression is shaped by interacting biological and demographic factors, and that identical biomarkers or thresholds do not carry uniform meaning across the life course. This thesis advances a framework for contextual interpretation of individual risk in type 1 diabetes screening and supports the development of more precise, equitable, and clinically meaningful early identification strategies. Future implementation will require broader external validation, increased ancestral diversity in study populations, and prospective evaluation of risk tools within real-world screening pathways.<p></p>"]},{"key":"dc:title","label":"Title","values":["Multimodal Prediction of Type 1 Diabetes"]}]}],"canonical_facts":{"dc:creator":["Erin Templeman (21043883)"],"dc:date":["2026-02-27T00:00:00Z"],"dc:description":["Type 1 diabetes often presents abruptly with life-threatening symptoms; however, its autoimmune pathogenesis typically unfolds over many years. After autoimmunity has been established, the rate of progression to clinical disease varies markedly between individuals, creating both an opportunity and a challenge. Early identification can reduce diabetic ketoacidosis at diagnosis and enable the use of preventative therapies; however, disease heterogeneity limits the accuracy and transportability of risk estimates used to guide screening, follow-up, and treatment decisions across settings and age groups. This thesis aims to investigate how demographic, immunologic, metabolic, and genetic factors jointly explain variation in progression from islet autoimmunity to clinical type 1 diabetes, and how this knowledge can inform screening and precision risk stratification. Using longitudinal data from two major type 1 diabetes studies with distinct screening strategies, this work evaluates prediction model transportability, characterises age- and sex-related variation in disease progression, and refines metabolic staging in adults. First, a multivariable risk prediction model combining genetic risk, autoantibody measures, age, and family history is developed and externally evaluated across cohorts. While key predictors are shown to be consistent, accurate risk estimation requires recalibration to the target screening context. This model is translated into a web-based risk calculator to support individualised risk communication and clinical or research decision-making. Next, sex differences in progression are examined, demonstrating that sex-related risk is not constant but varies with age and autoimmune status, with evidence of a developmental shift in progression trajectories around early adolescence. Differences between adult and paediatric presymptomatic type 1 diabetes are then investigated within the TrialNet cohort, revealing distinct immunologic and genetic profiles and slower average progression in adults, while showing convergence in progression rates once dysglycaemia and multiple autoantibodies are present. Finally, HbA1c is evaluated as a marker of metabolic progression in autoantibody-positive adults, demonstrating that age-related increases in HbA1c inflate dysglycaemia classification without necessarily reflecting increased risk of type 1 diabetes. These findings collectively show that presymptomatic type 1 diabetes progression is shaped by interacting biological and demographic factors, and that identical biomarkers or thresholds do not carry uniform meaning across the life course. This thesis advances a framework for contextual interpretation of individual risk in type 1 diabetes screening and supports the development of more precise, equitable, and clinically meaningful early identification strategies. Future implementation will require broader external validation, increased ancestral diversity in study populations, and prospective evaluation of risk tools within real-world screening pathways.<p></p>"],"dc:identifier":["10779/exe.31820374.v1"],"dc:relation":["https://figshare.com/articles/thesis/Multimodal_Prediction_of_Type_1_Diabetes/31820374"],"dc:rights":["All rights reserved","Open Access after 2027-09-23"],"dc:subject":["prediction","type 1 diabetes","autoantibodies","genetics","progression","risk","autoimmune","autoimmunity"],"dc:title":["Multimodal Prediction of Type 1 Diabetes"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:33:53Z"}