Establishment and validation of a predictive model for vascular Parkinsonism after multiple lacunar infarcts

doi:10.3969/j.issn.1006-7795.2026.03.026

Abstract

Abstract: Objective To explore the related risk factors of vascular Parkinsonism (VP) in patients with multiple lacunar infarction (MLI), and to evaluate the application value of hematological indicators and neuroimaging features in predicting the risk of VP. Methods A total of 169 patients with MLI admitted to Dunhua Hospital, Jilin Province from May 2021 to December 2024 were included. They were divided into the MLI group and the vascular Parkinsonism after multiple lacunar infarction (MLI-VP) group based on whether they developed VP. By using a random allocation method, the patients were divided into a model construction set (n=84) and an internal evaluation set (n=85). The clinical data and imaging features of the two groups were compared, and the risk factors for the occurrence of VP in MLI patients were screened. Then, a risk prediction model was constructed, and its performance and clinical application value were evaluated. Results The results showed that neutrophil count, high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), and basal ganglia involvement were significantly associated with the occurrence of MLI-VP (P<0.05). Neutrophil count (OR=1.730, 95%CI:1.046-3.105, P=0.047), decreased HDL-C (OR=0.166, 95%CI:0.034-0.581, P=0.011), elevated LDL-C (OR=5.884, 95%CI:1.520-31.075, P=0.019), and basal ganglia involvement (OR=3.941, 95%CI:1.019-15.732, P=0.046) were all independent risk factors for MLI-VP. Based on these variables, a nomogram was constructed, and the area under the ROC curve was 0.892 (95%CI：0.806-0.892). The calibration curve showed good fit (Hosmer-Lemeshow P>0.05), and the decision curve analysis confirmed the model's clinical utility. Conclusion Neutrophil count, HDL-C, LDL-C, and basal ganglia involvement are closely related to MLI-VP and are independent risk factors. Based on hematological indicators and imaging features, the prediction model shows good discriminative ability in this cohort, and it can be applied to early high-risk patients, providing a basis for implementing individualized precise intervention.

Key words: multiple lacunar cerebral infarction, vascular Parkinsonism, prediction model, risk factors, hematological indicators, neuroimaging features

CLC Number:

Gao Yan, Yang Xue, Wang Hua, Ning Yu, Wang Lingling. Establishment and validation of a predictive model for vascular Parkinsonism after multiple lacunar infarcts[J]. Journal of Capital Medical University, 2026, 47(3): 621-629.

References

［1］韩阅, 贾建平, 周爱红. 初次发病的腔隙性脑梗死患者的认知变化特点［J］. 首都医科大学学报, 2010, 31(2): 154-159.
［2］何冰怡, 林阳,秦莲花,等. 腔隙性脑梗死与颅内动脉钙化关系的研究进展［J］. 实用临床医药杂志, 2025, 29 (21): 138-143.
［3］米涛免, 孙永馨. 脑小血管病多样化临床表现的影像学基础［J］. 首都医科大学学报, 2013, 34(6): 860-867.
［4］Del Toro-Pérez C, Guevara-Sánchez E, Martínez-Sánchez P. Treatment of Vascular Parkinsonism: a systematic review［J］. Brain Sci. 2023;13(3):489.
［5］叶益超, 玉石, 佘亚军, 等. 血管性帕金森综合征的研究进展［J］. 中国卒中杂志, 2022, 17(4): 346-350.
［6］钟迪, 张舒婷, 吴波. 《中国急性缺血性脑卒中诊治指南2018》解读［J］. 中国现代神经疾病杂志, 2019, 19(11): 897-901.
［7］Zijlmans J C M, Daniel S E, Hughes A J, et al. Clinicopathological investigation of vascular parkinsonism, including clinical criteria for diagnosis［J］. Mov Disord, 2004, 19(6): 630-640.
［8］Joutel A, Chabriat H. Pathogenesis of white matter changes in cerebral small vessel diseases: beyond vessel-intrinsic mechanisms［J］. Clin Sci, 2017, 131(8): 635-651.
［9］Gupta D, Kuruvilla A. Vascular parkinsonism: what makes it different?［J］. Postgrad Med J, 2011, 87(1034): 829-836.
［10］Benjamin P, Lawrence A J, Lambert C, et al. Strategic lacunes and their relationship to cognitive impairment in cerebral small vessel disease［J］. Neuroimage Clin, 2014, 4: 828-837.
［11］Bloem B R, Okun M S, Klein C. Parkinson's disease［J］. Lancet, 2021, 397(10291): 2284-2303.
［12］Zhang Z, Wang Y, Wang J, et al. The role of peripheral inflammation-related biomarkers in distinguishing Parkinsons disease［J］. Parkinsonism Relat Disord, 2024, 123: 106102.
［13］Kim R, Kim H J, Kim A, et al. Peripheral blood inflammatory markers in early Parkinson's disease［J］. J Clin Neurosci, 2018, 58: 30-33.
［14］Santos-García D, De Deus Fonticoba T, Suárez Castro E, et al. High ultrasensitive serum C-reactive protein may be related to freezing of gait in Parkinson’s disease patients［J］. J Neural Transm, 2019, 126(12): 1599-1608.
［15］Akıl E, Bulut A, Kaplan I, et al. The increase of carcinoembryonic antigen (CEA), high-sensitivity C-reactive protein, and neutrophil/lymphocyte ratio in Parkinson’s disease［J］. Neurol Sci, 2015, 36(3): 423-428.
［16］Hosseini S, Shafiabadi N, Khanzadeh M, et al. Neutrophil to lymphocyte ratio in Parkinson's disease: a systematic review and meta-analysis［J］. BMC Neurol, 2023, 23(1): 333.
［17］Stolzenberg E, Berry D, Yang D, et al. A role for neuronal Alpha-synuclein in gastrointestinal immunity［J］. J Innate Immun, 2017, 9(5): 456-463.
［18］Kim R, Kang N, Byun K, et al. Prognostic significance of peripheral neutrophils and lymphocytes in early untreated Parkinson's disease: an 8-year follow-up study［J］. J Neurol Neurosurg Psychiatry, 2023, 94(12): 1040-1046.
［19］García-Sanz P, M F G Aerts J, Moratalla R. The role of cholesterol in α-synuclein and Lewy body pathology in GBA1 Parkinson's disease［J］. Mov Disord, 2021, 36(5): 1070-1085.
［20］Jiang Z, Xu X R, Gu X J, et al. Effects of higher serum lipid levels on the risk of Parkinson's disease: a systematic review and meta-analysis［J］. Front Neurol, 2020, 11: 597.
［21］Fu X X, Wang Y, He X F, et al. A systematic review and meta-analysis of serum cholesterol and triglyceride levels in patients with Parkinson's disease［J］. Lipids Health Dis, 2020, 19(1): 97.
［22］Fang F, Zhan Y Q, Hammar N, et al. Lipids, apolipoproteins, and the risk of Parkinson disease: a prospective cohort study and a mendelian randomization analysis［J］. Circ Res, 2019, 125(6): 643-652.
［23］Nam G E, Kim S M, Han K, et al. Metabolic syndrome and risk of Parkinson disease: a nationwide cohort study［J］. PLoS Med, 2018, 15(8): e1002640.
［24］Roh J H, Lee S, Yoon J H. Metabolic syndrome and Parkinson's disease incidence: a nationwide study using propensity score matching［J］. Metab Syndr Relat Disord, 2021, 19(1): 1-7.
［25］Park J H, Lee C W, Nam M J, et al. Association of high-density lipoprotein cholesterol variability and the risk of developing Parkinson disease［J］. Neurology, 2021, 96(10): e1391-e1401.
［26］Hong X, Guo W T, Li S S. Lower blood lipid level is associated with the occurrence of Parkinson's disease: a meta-analysis and systematic review［J］. Int J Clin Pract, 2022, 2022: 9773038.
［27］Sterling N W, Lichtenstein M, Lee E Y, et al. Higher plasma LDL-cholesterol is associated with preserved executive and fine motor functions in Parkinson's disease［J］. Aging Dis, 2016, 7(3): 237-245.
［28］陈彤, 王鲁宁, 王振福, 等. 低密度脂蛋白胆固醇水平与帕金森病的关系［J］. 解放军保健医学杂志, 2008, 10(1): 11-13.
［29］Reider-Groswasser I, Bornstein N M, Korczyn A D. Parkinsonism in patients with lucanar infarcts of the basal ganglia［J］. Eur Neurol, 1995, 35(1): 46-49.
［30］Jellinger K A. Vascular parkinsonism: an update［J］. J Neural Transm, 2025, 132(7): 899-919.
［31］Dunet V, Deverdun J, Charroud C, et al. Cognitive impairment and basal ganglia functional connectivity in vascular parkinsonism［J］. AJNR Am J Neuroradiol, 2016, 37(12): 2310-2316.

[1]	Xu Hanchi, Long Anhua, Wang Xuefei , Liu Liang. Risk factors and prediction model for contralateral hip fracture in elderly patients after hip fracture surgery [J]. Journal of Capital Medical University, 2026, 47(3): 612-620.
[2]	Zhao Youquan, Xiong Tianyu, Xie Ping, Niu Yinong. Prediction of adverse pathological features after radical prostatectomy for prostate cancer by clinical indicators and association with biochemical failure [J]. Journal of Capital Medical University, 2026, 47(2): 259-266.
[3]	Li Xiaojing, Shen Yi, Du Yangfan, Yao Mingyan, Yang Wei, Dong Song, Liu Hongzhou. Nomogram for predicting the risk of impaired fasting glucose in older adults [J]. Journal of Capital Medical University, 2026, 47(2): 353-359.
[4]	Fan Ruoying, Zheng Manqi, Zhang Qian, Guo Jing, Wang Anxin, Xia Xue, Li Jing, Xu Jiaming. Development and validation of a 10-year risk prediction model for cardiovascular diseases for the aerospace occupational population [J]. Journal of Capital Medical University, 2026, 47(1): 70-81.
[5]	Han Wenli, Zou Liying, Zheng Wei, Zhao Yue. Correlation between hemoglobin and gestational diabetes mellitus in early pregnancy [J]. Journal of Capital Medical University, 2026, 47(1): 82-86.
[6]	Liu Dawei, Cheng Peixia, Wang Qian, Wang Xiaonan, Zhu Huiping, Gao Qi. Comparative study of prediction models of antipsychotic extrapyramidal adverse reactions in adult inpatients with schizophrenia [J]. Journal of Capital Medical University, 2026, 47(1): 150-156.
[7]	Yang Shuyue, Liu Sifan, Ji Xu, Zhao Mengran, Zhang Zheng, Li Peng. Analysis on risk factors of adverse events after non-sedated esophagogastroduo-denoscopy [J]. Journal of Capital Medical University, 2025, 46(4): 676-681.
[8]	Song Jiugang, Wang Chunsaier, Zhang Qian, Li Peng, Zhang Shutian. Analysis of clinical characteristics of 470 cases of esophageal foreign body impaction by gastroscopy intervention in adults [J]. Journal of Capital Medical University, 2025, 46(3): 576-580.
[9]	Fan Yunpeng, Xiong Tianyu, Yang Kun, Liu Zhanliang, Jin Song, Xie Ping, Niu Yinong. Prediction model for extraprostatic extension of prostate based on MRI and clinical indicators [J]. Journal of Capital Medical University, 2025, 46(2): 243-251.
[10]	Qin Yunlong, Zhou Jin, Liu Meng, Luo Kaifa, Wang Anjing, Wang Di, Zhao Jin, Sun Shiren. Analysis of clinicopathological characteristics and prognosis of malignant hypertensive nephrosclerosis [J]. Journal of Capital Medical University, 2024, 45(5): 838-845.
[11]	Zhang Dakun, Zhang Hongbo, Dong Dexin, Gao Xiang, Zheng Han, Zhang Lang. Early diagnosis and treatment of urinary sepsis after ureteroscopic lithotripsy of upper urinary tract stones [J]. Journal of Capital Medical University, 2024, 45(5): 870-874.