ArticleHeliyon2024
Correlation analysis and predictive model construction of metabolic syndrome, complete blood count-derived inflammatory markers, and overall burden of cerebral small vessel disease.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Elevated monocyte-to-high-density lipoprotein ratio is associated with increased risk of cognitive impairment and severe cerebral small vessel disease burden.Frontiers in aging neuroscience · 2025Article
- Development of a clinical prediction model for inflammatory biomarkers and enlarged basal ganglia perivascular spaces using SHAP analysis: feature selection and model interpretation.Frontiers in neurology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The high burden of cerebral small vessel disease (CSVD) on neuroimaging is a significant risk factor for stroke, cognitive dysfunction, and emotional disorders. Currently, there is a lack of studies investigating the correlation between metabolic syndrome (MetS), complete blood count-derived inflammatory markers, and total CSVD burden. This study aims to evaluate the total CSVD imaging load using machine learning (ML) algorithms and to explore further the relationship between MetS, complete blood count-derived inflammatory markers, and CSVD load. Methods: We included CSVD patients from Xijing Hospital (2012-2022). Univariate and lasso regression analyses identified variables linked to CSVD neuroimaging burden. Six ML models predicted CSVD burden based on MetS and inflammatory markers. Model performance was evaluated using ROCauc, PRauc, DCA, and calibration curves. The SHAP method validated model interpretability. The best-performing model was selected to develop a web-based calculator using the Shiny package. Results: The Logistic regression model outperformed others in predicting CSVD burden. The model incorporated MetS, neutrophil-to-lymphocyte ratio (NLR), homocysteine (Hcy), age, smoking status, cystatin C (CysC), uric acid (UA), and prognostic nutritional index (PNI). Conclusion: MetS, NLR, Hcy and CSVD high load were positively correlated, and the Logistic regression model could accurately predict the total CSVD load degree.
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.