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Publication Information
Public Release Type
Journal
Publication Year
2023
Affiliation
aDepartment of Statistics and Data Science, The University of Texas at Austin, Austin, TX, USA, parast@austin.utexa.edu
bDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, lutian@stanford.edu
cDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA, tcai@hsph.harvard.edu
dDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
eDepartment of Medicine, Stanford University, School of Medicine, Palo Alto, CA, USA, lathap@stanford.edu
Authors
Cai T, Palaniappan L, Parast L, Tian L
Abstract
Aims
To measure and compare four individual surrogate markers: change from baseline to 1-year after randomization in HbA1c, fasting plasma glucose, TyG index and HOMA-IR, in terms of their ability to explain a treatment effect on reducing the risk of Type 2 diabetes mellitus at 2, 3, and 4 years after treatment initiation.
Methods
Study participants were from the Diabetes Prevention Program (DPP) study, randomly assigned to either a lifestyle intervention (N=1,023) or placebo (N=1,030). The surrogate markers were measured at baseline and 1 year, and diabetes incidence was examined at 2, 3, and 4 years post-randomization. Surrogacy was evaluated using a robust model-free estimate of the proportion of treatment effect explained(PTE) by the surrogate marker.
Results
Across all time points, change in fasting glucose and HOMA-IR explained higher proportions of the treatment effect than the TyG index or HbA1c. For example, at 2 years, glucose explained the highest (80.l%) proportion of the treatment effect followed by HOMA-IR (77.7%) and HbA1c (74.6%); the TyG index explained the smallest (70.3%) proportion.
Conclusions
These data suggest that of the four examined surrogate markers, glucose and HOMA-IR were the superior surrogate markers in terms of PTE, compared to HbA1c and the TyG index.