Does Arterial Stiffness Predict Cardiovascular Disease in Older Adults With an Intellectual Disability?.

Abstract

Background: Arterial stiffness has been associated with an increased risk of cardiovascular disease (CVD) in some patient populations.

Objectives: The aims of this study were to investigate (1) whether there is an association between arterial stiffness, as measured by the Mobil-O-Graph, and risk for CVD in a population of individuals with intellectual disability and (2) whether arterial stiffness can predict the risk for CVD.

Methods: This cross-sectional study included 58 individuals who participated in wave 4 of the Intellectual Disability Supplement to the Irish Longitudinal Study on Aging (2019-2020). Statistical models were used to address the first aim, whereas machine learning models were used to improve the accuracy of risk predictions in the second aim.

Results: Sample characteristics were mean (SD) age of 60.69 (10.48) years, women (62.1%), mild/moderate level of intellectual disability (91.4%), living in community group homes (53.4%), overweight/obese (84.5%), high cholesterol (46.6%), alcohol consumption (48.3%), hypertension (25.9%), diabetes (17.24%), and smokers (3.4%). Mean (SD) pulse wave velocity (arterial stiffness measured by Mobil-O-Graph) was 8.776 (1.6) m/s. Cardiovascular disease risk categories, calculated using SCORE2, were low-to-moderate risk (44.8%), high risk (46.6%), and very high risk (8.6%). Using proportional odds logistic regression, significant associations were found between arterial stiffness, diabetes diagnosis, and CVD risk SCORE2 (P < .001). We also found the Mobil-O-Graph can predict risk of CVD, with prediction accuracy of the proportional odds logistic regression model approximately 60.12% (SE, 3.2%). Machine learning models, k-nearest neighbor, and random forest improved model predictions over and above proportional odds logistic regression at 75.85% and 77.7%, respectively.

Conclusions: Arterial stiffness, as measured by the noninvasive Mobil-O-Graph, can be used to predict risk of CVD in individuals with intellectual disabilities.