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Estimating risk factor progression equations for the UKPDS Outcomes Model 2 (UKPDS 90)
Author(s) -
Leal Jose,
Alva Maria,
Gregory Vanessa,
Hayes Alison,
Mihaylova Borislava,
Gray Alastair M.,
Holman Rury R.,
Clarke Philip
Publication year - 2021
Publication title -
diabetic medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.474
H-Index - 145
eISSN - 1464-5491
pISSN - 0742-3071
DOI - 10.1111/dme.14656
Subject(s) - medicine , albuminuria , risk factor , atrial fibrillation , creatinine , cardiology , renal function
Objectives To estimate 13 equations that predict clinically plausible risk factor time paths to inform the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model version 2 (UKPDS‐OM2). Methods Data from 5102 UKPDS participants from the 20‐year trial, and the 4031 survivors with 10 years further post‐trial follow‐up, were used to derive equations for the time paths of 13 clinical risk factors: HbA 1c , systolic blood pressure, LDL‐cholesterol, HDL‐cholesterol, BMI, micro‐ or macro‐albuminuria, creatinine, heart rate, white blood cell count, haemoglobin, estimated glomerular filter rate , atrial fibrillation and peripheral vascular disease (PVD). The incidence of events and death predicted by the UKPDS‐OM2 when informed by the new risk factor equations was compared with the observed cumulative rates up to 25 years. Results The new equations were based on 24 years of follow‐up and up to 65,252 person‐years of data. Women were associated with higher values of all continuous risk factors except for haemoglobin. Older age and higher BMI at diagnosis were associated with higher rates of PVD (HR 1.06 and 1.02), atrial fibrillation (HR 1.10 and 1.08) and micro‐ or macro‐albuminuria (HR 1.01 and 1.18). Smoking was associated with higher rates of developing PVD (HR 2.38) and micro‐ and macro‐albuminuria (HR 1.39). The UKPDS‐OM2, informed by the new risk factor equations, predicted event rates for complications and death consistent with those observed. Conclusions The new equations allow risk factor time paths beyond observed data, which should improve modelling of long‐term health outcomes for people with type 2 diabetes when using the UKPDS‐OM2 or other models.