The efficacy of islet autoantibody screening with or without genetic pre-screening strategies for the identification of presymptomatic type 1 diabetes

Early detection of type 1 diabetes, in its presymptomatic stage, offers significant clinical advantages, including treatment that can delay disease onset. Current screening focuses on identifying islet autoantibody positivity, with proposed optimal testing at ages 2, 6 and 10 years potentially achieving up to 80% sensitivity. However, challenges arise from participation rates and costs associated with multiple screenings. Genetic pre-screening has been suggested as a complementary strategy to target high-risk individuals prior to autoantibody testing, but its real-world benefits remain uncertain. Broad genetic selection strategies, based on family history, HLA typing or polygenic risk scores, can identify subsets of the population at elevated risk. However, these approaches face issues like low recall rates, socioeconomic biases and limited applicability across diverse ancestries. Additionally, the cost-effectiveness and infrastructure requirements of integrating genetic testing into routine healthcare remain significant hurdles. The combined use of genetic and autoantibody testing could improve predictive value, especially with innovations like point-of-care genetic testing. Yet, the ultimate success of any screening programme depends less on specific strategies and more on maximising public and healthcare-provider engagement, ensuring high participation, and addressing socioeconomic and demographic disparities. Digital-health infrastructure may play a crucial role in improving recall rates and maintaining follow-up adherence. In conclusion, while repeated islet autoantibody screening remains the most effective standalone approach, conducting genetic screening prior to islet autoantibody testing may be practical in certain contexts, provided that sufficient resources and equitable strategies are employed. Public engagement and robust infrastructure are essential to realising the full potential of early type 1 diabetes detection programmes.

Authors

Institutions

Publication Details

Journal
Diabetologia
Published
2025-03-19
DOI
https://doi.org/10.1007/s00125-025-06408-4
Citations
6
Primary Topic
Diabetes and associated disorders
Type
article
Field-Weighted Citation Impact
10.90

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The efficacy of islet autoantibody screening with or without genetic pre-screening strategies for the identification of presymptomatic type 1 diabetes

Zdenĕk Šumnı́k, Olga Kordonouri, Ezio E. Bonifacio, Flemming Pociot et al.
6 citations
Diabetologia
Diabetes and associated disorders
10.90
article

The efficacy of islet autoantibody screening with or without genetic pre-screening strategies for the identification of presymptomatic type 1 diabetes

Zdenĕk Šumnı́k, Olga Kordonouri, Ezio E. Bonifacio, Flemming Pociot, Dominik Ewald, Gita Gemulla, Jose Zapardiel‐Gonzalo, Vito Lampasona, Michael Hubmann, Anette‐Gabriele Ziegler, Mirjam Kohls, Agnieszka Szypowska, Parth Narendran, Przemysława Jarosz‐Chobot, Raquel Coelho
article en
6 citations

Abstract

Early detection of type 1 diabetes, in its presymptomatic stage, offers significant clinical advantages, including treatment that can delay disease onset. Current screening focuses on identifying islet autoantibody positivity, with proposed optimal testing at ages 2, 6 and 10 years potentially achieving up to 80% sensitivity. However, challenges arise from participation rates and costs associated with multiple screenings. Genetic pre-screening has been suggested as a complementary strategy to target high-risk individuals prior to autoantibody testing, but its real-world benefits remain uncertain. Broad genetic selection strategies, based on family history, HLA typing or polygenic risk scores, can identify subsets of the population at elevated risk. However, these approaches face issues like low recall rates, socioeconomic biases and limited applicability across diverse ancestries. Additionally, the cost-effectiveness and infrastructure requirements of integrating genetic testing into routine healthcare remain significant hurdles. The combined use of genetic and autoantibody testing could improve predictive value, especially with innovations like point-of-care genetic testing. Yet, the ultimate success of any screening programme depends less on specific strategies and more on maximising public and healthcare-provider engagement, ensuring high participation, and addressing socioeconomic and demographic disparities. Digital-health infrastructure may play a crucial role in improving recall rates and maintaining follow-up adherence. In conclusion, while repeated islet autoantibody screening remains the most effective standalone approach, conducting genetic screening prior to islet autoantibody testing may be practical in certain contexts, provided that sufficient resources and equitable strategies are employed. Public engagement and robust infrastructure are essential to realising the full potential of early type 1 diabetes detection programmes.

DiabetologiaVol. 68(6)
Steno Diabetes Centers (DK), Medical University of Silesia (PL), Charles University (CZ), Medical University of Warsaw (PL), Helmholtz Zentrum München (DE), Kinderkrankenhaus auf der Bult (DE), Associacao Protectora dos Diabeticos de Portugal (PT), IRCCS Ospedale San Raffaele (IT), University Hospital Carl Gustav Carus (DE), Institute of Diabetes Research, Technische Universität Dresden (DE), University of Birmingham (GB)
Leona M. and Harry B. Helmsley Charitable Trust, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, Innovative Health Initiative, European Commission, Bundesministerium für Bildung und Forschung, Technische Universität Dresden, HORIZON EUROPE Framework Programme
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Diabetes and associated disorders
10.90
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.