Does Arterial Stiffness Predict Cardiovascular Disease in Older Adults With an Intellectual Disability?
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".