ArticleJournal of Korean medical science2026
Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning.
Article in Journal of Korean medical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThe rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk of early cardiovascular complications, is concerning. Brachial-ankle pulse wave velocity (baPWV) and carotid intima-media thickness (cIMT) are noninvasive markers of arterial stiffness and atherosclerosis. They serve as important markers for cardiovascular risk assessment. This study aimed to apply machine learning models to identify high-risk individuals who may benefit from early and targeted cardiovascular screening.
methodsThis retrospective study included 129 patients with youth-onset T2DM who underwent baPWV and cIMT measurements between January 2018 and July 2024. High-risk groups were defined as having values of ≥ mean + 1 standard deviation for baPWV (arterial stiffness) and cIMT (atherosclerosis). Clinical predictors were evaluated using linear, logistic regression, and machine learning analyses. Multiple machine learning models were trained using oversampling and cross-validation techniques to enhance prediction performance.
resultsAmong the models tested, the gradient boosting model with adaptive synthetic sampling oversampling achieved the best performance in predicting both arterial stiffness (accuracy 0.81) and atherosclerosis prediction (accuracy 0.92). Age and hypertension were consistently identified as the most important factors for arterial stiffness. For atherosclerosis risk, traditional analysis identified dyslipidemia, male sex, and duration of illness as relevant factors; machine learning more clearly emphasized low-density lipoprotein cholesterol and triglyceride levels as key predictors of increased cIMT.
conclusionHypertension and age were consistent predictors of arterial stiffness, while atherosclerosis risk factors were further clarified with lipid parameters by machine learning analysis. These findings suggest that conventional and machine learning analyses offer complementary strengths. Their combined use may enable earlier to detect nuanced cardiovascular risk patterns and support early and targeted vascular screening in youth-onset T2DM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.