Imagine a detective trying to solve a mystery. They gather clues, analyze evidence, and make connections in order to piece together the truth. In a similar way, scientists conducted a study to unravel the factors that contribute to cognitive impairment in elderly individuals. They recruited over 3,000 participants and used cerebral magnetic resonance angiography (MRA) to collect data on blood pressure and other clinical risk factors. By applying artificial intelligence analysis to the MRA images, they discovered that low pulse differential pressure was associated with a lower risk of cognitive impairment, while a certain level of stenosis in the posterior cerebral arteries increased the risk. To make their insights even more accurate, the researchers combined the MRA findings with various blood pressure parameters using machine learning models… The results showed promising predictive capabilities for identifying cognitive decline risk in older adults. This research sheds light on important factors that could help healthcare providers proactively identify and address cognitive impairment in aging populations.

ObjectiveThis study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly.MethodsA total of 3221 patients (259 patients with CI and 2,962 subjects without CI) were recruited into this nested case-control study who underwent cerebral magnetic resonance angiography (MRA) from 2007 to 2021. All of the clinical data with MRA imaging were recorded followed by standardization processing blindly. The maximum stenosis score of the posterior circulatory artery, including the basilar artery, and bilateral posterior cerebral artery (PCA), was calculated by the cerebral MRA automatic quantitative analysis method. Logistic regression (LR) analysis was used to evaluate the relationship between risk factors and CI. Four machine learning approaches, including LR, decision tree (DT), random forest (RF), and support vector machine (SVM), employing 5-fold cross-validation were used to establish CI predictive models.ResultsAfter matching with age and gender, 208 CI patients and 208 control subjects were finalized the follow-up (3.46 ± 3.19 years) with mean age at 84.47 ± 6.50 years old. Pulse pressure (PP) in first tertile (<58 mmHg) (OR 0.588, 95% confidence interval (CI): 0.362–0.955) was associated with a decreased risk for CI, and ≥50% stenosis of the left PCA (OR 2.854, 95% CI: 1.387–5.872) was associated with an increased risk for CI after adjusting for body mass index, myocardial infarction, and stroke history. Based on the means of various blood pressure (BP) parameters, the performance of the LR, DT, RF and SVM models accurately predicted CI (AUC 0.740, 0.786, 0.762, and 0.753, respectively) after adding the stenosis score of posterior circulatory artery.ConclusionElderly with low pulse differential pressure may have lower risk for cognitive impairment. The hybrid model combined with the stenosis score of posterior circulatory artery, clinical indicators, and the means of various BP parameters can effectively predict the risk of CI in elderly individuals.

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