BMJ open diabetes research & care · 2025
Latent class growth mixture modeling of HbA1C trajectories identifies individuals at high risk of developing complications of type 2 diabetes mellitus in the UK Biobank.
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Handley Dale, Gillett Alexandra C, Bala Renu, Tyrrell Jessica, Lewis Cathryn M
Аннотация
INTRODUCTION: Frequent glycated hemoglobin A1c (HbA1c) monitoring is recommended in individuals with type 2 diabetes mellitus (T2D). We aimed to identify distinct, long-term HbA1c trajectories following a T2D diagnosis and investigate how these glycemic control trajectories were associated with health-related traits and T2D complications.
RESEARCH DESIGN AND METHODS: A cohort of 12,435 unrelated individuals of European ancestry with T2D was extracted from the UK Biobank data linked to primary care records. Latent class growth mixture modeling was applied to identify classes with similar HbA1c trajectories over the 10 years following T2D diagnosis. Associations between HbA1c class membership and sociodemographic factors, biomarkers, polygenic scores, and T2D-related outcomes, were tested using logistic regression and Cox proportional hazards models.
RESULTS: Six HbA1c trajectory classes were identified. The largest class (76.8%) maintained low and stable HbA1c levels over time. Five additional smaller classes with distinct, but more variable, trajectories were found and were associated with younger age at T2D diagnosis, higher fasting glucose levels, higher random glucose levels, higher body mass index polygenic score and increased healthcare use before T2D diagnosis. Relative to the low and stable class, these five showed increased risks of T2D complications, including stroke (HR=1.55 (1.31-1.84)), kidney disease (HR=1.39 (1.27-1.53)), all-cause mortality (HR=1.36 (1.23-1.51)), and progression to combination therapy (HR=3.22 (3.04-3.41)) or insulin (HR=3.21 (2.89-3.55)).
CONCLUSION: Individuals with T2D who show higher and more variable HbA1c trajectories are at increased risk of developing T2D-related complications. Early identification of patients at risk, based on factors such as age at diagnosis and previous healthcare utilization could improve patient outcomes.
RESEARCH DESIGN AND METHODS: A cohort of 12,435 unrelated individuals of European ancestry with T2D was extracted from the UK Biobank data linked to primary care records. Latent class growth mixture modeling was applied to identify classes with similar HbA1c trajectories over the 10 years following T2D diagnosis. Associations between HbA1c class membership and sociodemographic factors, biomarkers, polygenic scores, and T2D-related outcomes, were tested using logistic regression and Cox proportional hazards models.
RESULTS: Six HbA1c trajectory classes were identified. The largest class (76.8%) maintained low and stable HbA1c levels over time. Five additional smaller classes with distinct, but more variable, trajectories were found and were associated with younger age at T2D diagnosis, higher fasting glucose levels, higher random glucose levels, higher body mass index polygenic score and increased healthcare use before T2D diagnosis. Relative to the low and stable class, these five showed increased risks of T2D complications, including stroke (HR=1.55 (1.31-1.84)), kidney disease (HR=1.39 (1.27-1.53)), all-cause mortality (HR=1.36 (1.23-1.51)), and progression to combination therapy (HR=3.22 (3.04-3.41)) or insulin (HR=3.21 (2.89-3.55)).
CONCLUSION: Individuals with T2D who show higher and more variable HbA1c trajectories are at increased risk of developing T2D-related complications. Early identification of patients at risk, based on factors such as age at diagnosis and previous healthcare utilization could improve patient outcomes.