Coronary plaque burden assessed by coronary CT angiography in individuals with type 1 diabetes and healthy controls

Figures

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  Fig. 1. AI-QCT analysis of a female 51 year-old participant with a diabetes duration of 32 years. Shown are straightened reformatted CCTA images of the right coronary artery). On the right-hand image, AI-QCT additions show lumen boundaries (yellow lines), lumen-plaque borders (pink lines), non-calcified plaque (yellow area), calcified plaque (blue area)

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  Fig. 2. Boxplots of coronary plaque burden in healthy controls and type 1 diabetes participants. Shown are boxplots of the different plaque volumes for both the control group as well as the individuals with type 1 diabetes. CPV, percent calcified plaque volume; NCPV, percent non-calcified plaque volume; PAV, percent atheroma volume

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  Fig. 3. Associations between CGM-metrics and coronary plaque burden. Shown are the relative increases derived beta coefficients from linear regression models of time in range (A), time above range (B), time below range (C), and glucose CV (D) with the different plaque volumes (log2 transformed) as outcome variable. Models were adjusted for clinical risk factors (age, sex, systolic blood pressure, body mass index, LDL-C, triglycerides, smoking history, disease duration, presence of diabetic retinopathy, and family history of CVD). P.p, percentage point; TIR, time in rage; glucose CV, glucose coefficient of variation

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  Fig. 4. Associations between cumulative glycemic exposure (HbA1c mmol/mol*years) and coronary plaque burden in the CGM cohort. Shown are the relative increases derived beta coefficients from linear regression model of HbA1c with the different plaque volumes (log2 transformed) as outcome variable. Models were adjusted for clinical risk factors (age, sex, systolic blood pressure, body mass index, LDL-C, triglycerides, smoking history, disease duration, presence of diabetic retinopathy, and family history of CVD)

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  Fig. 5. Associations between low-density lipoprotein cholesterol (LDL-C) and coronary plaque burden in the CGM cohort. Shown are the relative increases derived beta coefficients from linear regression model of LDL-C with the different plaque volumes (log2 transformed) as outcome variable. Models were adjusted for clinical risk factor (age, sex, systolic blood pressure, body mass index, triglycerides, smoking history, disease duration, presence of diabetic retinopathy, and family history of CVD)