Background White matter hyperintensity (WMH), a critical early biomarker in cerebrovascular/neurodegenerative diseases, has traditionally been studied via global volume or subjective scoring, which overlooks its spatial heterogeneity, leading to conflicting risk factor conclusions. Recent neuroimaging advances enable “subtype resolution” research, but standardized assessments remain lacking. This study evaluates WMH risk factor spatial variability and constructs a risk stratification model to support precision prevention. Methods This study retrospectively enrolled inpatients and outpatients aged ≥40 years median 70.0, (59.0–77.0) who underwent head MRI examinations due to neurological symptoms or suspected cerebrovascular disease between January 2023 and December 2024.excluding those with imaging contraindications, intracranial masses, or technical artifacts. Data included demographics (age, sex), medical history (hypertension, diabetes), and lab markers (creatinine, cystatin C). FLAIR MRI (3.0 T United Imaging uMR780) was used to acquire images. WMH volume and Fazekas scores were automatically quantified via the United Imaging AI module (UAI. OCR, R001) and validated by two senior neuroradiologists. Stratification included semi-quantitative Fazekas scoring (PWMH:periventricular WMH, DWMH:deep WMH) and anatomical segmentation (4 subregions: ventricular, periventricular, DWMH, juxtacortical). Statistical methods included Mann–Whitney U and chi-square tests for group comparisons, binary logistic regression for risk factors of moderate–severe WMH (Fazekas2-3), and multiple linear regression for volume associations ( p 0.05 significant). Results Compared with absent or mild WMH (Fazekas 0–1), Group comparisons revealed that advanced age, hypertension, and abnormal renal function markers creatinine, cystatin C, β2-microglobulin (β2-MG) were common risk factors for moderate–severe WMH (all p 0.0001). The prevalence of coronary heart disease was higher in the moderate–severe PWMH group than in the absent or mild group (22.9% vs. 12.3%, p = 0.001). In contrast, the moderate-to-severe DWMH group exhibited higher rates of smoking (40.3% vs. 30.2%), alcohol consumption (35.6% vs. 26.1%), and diabetes (47.0% vs. 34.8%) compared with the absent or mild group, while the prevalence of hyperlipidemia was lower (42.95% vs. 52.43%, p = 0.04). Multivariate models revealed that moderate–severe PWMH driven by age (OR = 1.09/year), hypertension (OR = 2.92), creatinine (OR = 2.07); moderate–severe DWMH by age (OR = 1.034/year), hypertension (OR = 2.10), smoking (OR = 1.98), diabetes (OR = 1.55), β2-MG (OR = 1.79). Cys-C (OR = 0.52) and hyperlipidemia (OR = 0.66) inversely associated with moderate–severe PWMH and moderate–severe DWMH, respectively ( p 0.05). Linear regression analysis demonstrated that age and hypertension strongly affected PWMH volume ( β = 0.236–3.618); diabetes expanded periventricular lesions ( β = 3.073); coronary heart disease and creatinine increased juxtacortical WMH ( β = 0.232–0.280); and hyperlipidemia was inversely correlated with DWMH ( β = −0.783) and juxtacortical WMH ( β = −0.194) (all p 0.05). Conclusion WMH exhibits spatial heterogeneity with distinct mechanisms: PWMH associates with coronary/renal issues; DWMH with smoking/diabetes. Spatial classification optimizes risk stratification, guiding subtype-specific interventions and individualized prevention for cerebral small vessel disease.
Li et al. (Tue,) studied this question.