Prediction of Type 2 Diabetes using genomics and proteomics: the HUNT Study
- 1HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim;
Abstract
Type 2 diabetes (T2D) is a multifactorial metabolic disorder with an increasing global prevalence and significant health burdens. Early identification of individuals at high risk is crucial for timely intervention and prevention of T2D and complications. This study aims to investigate the association between the blood plasma proteome and T2D, identify protein biomarkers associated with incident T2D, and explore the underlying biological pathways involved in the disease development in the Trondelag Health Study (HUNT).
The HUNT Study is a population-based cohort with four waves of enrolment beginning in 1984. Samples from HUNT3 were analysed with SomaScan including 3,221 using SomaScanv4.0 (HUNT-Soma5k, [~]5,000 proteins) and 2,181 on SomaScanv4.1 (HUNT-Soma7k, [~]7,000 proteins). We performed logistic regression and least absolute shrinkage and selection operator (LASSO) to generate the protein risk scores, and gene set enrichment analysis (GSEA) to identify potential biological pathways for incident T2D.
We identified 313 (308 proteins) and 142 (128 proteins) aptamers that are significantly associated with incident T2D, in HUNT-Soma5k and HUNT-Soma7k, respectively. In HUNT-Soma5k, MXRA8 has strongest association with decreased risk and PLXB2 has strongest association with increased risk; while IGFBP2 has strongest association with decreased risk and INHBC has strongest association with increased risk for incident T2D in HUNT-Soma7k. The most significant pathway from GO Biological Process is organic acid metabolic process in both datasets.
The associated proteins and biological pathways identified represent insights into the underlying molecular mechanism of T2D and may serve as potential biomarkers for risk prediction and prevention for T2D.
Article HighlightsType 2 diabetes (T2D) leads to substantial morbidity, including cardiovascular, renal, and neurological complications. Identifying individuals at elevated risk before clinical onset is essential, as early intervention can prevent progression from pre-diabetes and support timely lifestyle modification. In this study, we evaluated circulating proteins as predictors of future T2D using high-throughput, targeted proteomics. We identified hundreds of proteins associated with incident T2D, including 10 proteins not previously reported in other cohorts. These findings may enhance early risk prediction and inform more personalized prevention strategies.
Related
- Plasma proteomics identifies four candidate biomarkers associated with macrovascular complications in type 2 diabetes: evidence from a clinical cohort and an animal model
- A systematic review of prognostic multivariable risk prediction models for type 2 diabetes in prediabetic populations
- Prediction of Six-Month Glycemic Status in Type 2 Diabetes: Development and Internal Validation of a Multivariable Model
- Risk prediction models for macrovascular complications in type 2 diabetes mellitus: from traditional risk scores to multimodal intelligent prediction
- Comparative analysis of plasma pTau217, NfL and eight modifiable risk scores for 11-year dementia prediction in the HUNT Study
BibTeX
@misc{karinanur2026prediction,
title = {{Prediction of Type 2 Diabetes using genomics and proteomics: the HUNT Study}},
author = {F. Karinanur and B. O. Asvold and E. Almaas and G. F. Giskeodegard and C. Jonasson and W. Tang and J.-B. Hansen and I. S. Folling and K. Hveem and B. N. Wolford},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.06.26362374},
url = {https://www.medrxiv.org/content/10.64898/2026.09.06.26362374v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Prediction of Type 2 Diabetes using genomics and proteomics: the HUNT Study
AU - F. Karinanur
AU - B. O. Asvold
AU - E. Almaas
AU - G. F. Giskeodegard
AU - C. Jonasson
AU - W. Tang
AU - J.-B. Hansen
AU - I. S. Folling
AU - K. Hveem
AU - B. N. Wolford
PY - 2026
DA - 2026/09/06
PB - medRxiv
DO - 10.64898/2026.09.06.26362374
UR - https://www.medrxiv.org/content/10.64898/2026.09.06.26362374v1
AB - Type 2 diabetes (T2D) is a multifactorial metabolic disorder with an increasing global prevalence and significant health burdens. Early identification of individuals at high risk is crucial for timely intervention and prevention of T2D and complications. This study aims to investigate the association between the blood plasma proteome and T2D, identify protein biomarkers associated with incident T2D, and explore the underlying biological pathways involved in the disease development in the Trondelag Health Study (HUNT). The HUNT Study is a population-based cohort with four waves of enrolment beginning in 1984. Samples from HUNT3 were analysed with SomaScan including 3,221 using SomaScanv4.0 (HUNT-Soma5k, [~]5,000 proteins) and 2,181 on SomaScanv4.1 (HUNT-Soma7k, [~]7,000 proteins). We performed logistic regression and least absolute shrinkage and selection operator (LASSO) to generate the protein risk scores, and gene set enrichment analysis (GSEA) to identify potential biological pathways for incident T2D. We identified 313 (308 proteins) and 142 (128 proteins) aptamers that are significantly associated with incident T2D, in HUNT-Soma5k and HUNT-Soma7k, respectively. In HUNT-Soma5k, MXRA8 has strongest association with decreased risk and PLXB2 has strongest association with increased risk; while IGFBP2 has strongest association with decreased risk and INHBC has strongest association with increased risk for incident T2D in HUNT-Soma7k. The most significant pathway from GO Biological Process is organic acid metabolic process in both datasets. The associated proteins and biological pathways identified represent insights into the underlying molecular mechanism of T2D and may serve as potential biomarkers for risk prediction and prevention for T2D. Article HighlightsType 2 diabetes (T2D) leads to substantial morbidity, including cardiovascular, renal, and neurological complications. Identifying individuals at elevated risk before clinical onset is essential, as early intervention can prevent progression from pre-diabetes and support timely lifestyle modification. In this study, we evaluated circulating proteins as predictors of future T2D using high-throughput, targeted proteomics. We identified hundreds of proteins associated with incident T2D, including 10 proteins not previously reported in other cohorts. These findings may enhance early risk prediction and inform more personalized prevention strategies.
ER -