TY - JOUR
T1 - Longitudinal metabolite and protein trajectories prior to diabetes mellitus diagnosis in Danish blood donors
T2 - a nested case-control study
AU - Lundgaard, Agnete T
AU - Westergaard, David
AU - Röder, Timo
AU - Burgdorf, Kristoffer S
AU - Larsen, Margit H
AU - Schwinn, Michael
AU - Thørner, Lise W
AU - Sørensen, Erik
AU - Nielsen, Kaspar R
AU - Hjalgrim, Henrik
AU - Erikstrup, Christian
AU - Kjerulff, Bertram D
AU - Hindhede, Lotte
AU - Hansen, Thomas F
AU - Nyegaard, Mette
AU - Birney, Ewan
AU - Stefansson, Hreinn
AU - Stefánsson, Kári
AU - Pedersen, Ole B V
AU - Ostrowski, Sisse R
AU - Rossing, Peter
AU - Ullum, Henrik
AU - Mortensen, Laust H
AU - Vistisen, Dorte
AU - Banasik, Karina
AU - Brunak, Søren
AU - DBDS Genomic Consortium
N1 - © 2024. The Author(s).
PY - 2024/10
Y1 - 2024/10
N2 - AIMS/HYPOTHESIS: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes.METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.CONCLUSIONS/INTERPRETATION: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.
AB - AIMS/HYPOTHESIS: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes.METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.CONCLUSIONS/INTERPRETATION: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.
KW - Molecular biomarkers
KW - Multi-omics
KW - Polygenic risk scores
KW - Temporality
KW - Time-to-event prediction
KW - Type 1 diabetes mellitus
KW - Type 2 diabetes mellitus
KW - Diabetes Mellitus, Type 2/blood
KW - Blood Glucose/metabolism
KW - Humans
KW - Middle Aged
KW - Risk Factors
KW - Male
KW - Diabetes Mellitus, Type 1/blood
KW - Case-Control Studies
KW - Blood Donors
KW - Denmark/epidemiology
KW - Adult
KW - Biomarkers/blood
KW - Female
KW - Longitudinal Studies
UR - http://www.scopus.com/inward/record.url?scp=85200036310&partnerID=8YFLogxK
U2 - 10.1007/s00125-024-06231-3
DO - 10.1007/s00125-024-06231-3
M3 - Journal article
C2 - 39078488
SN - 0012-186X
VL - 67
SP - 2289
EP - 2303
JO - Diabetologia
JF - Diabetologia
IS - 10
ER -