基于MRI定量参数构建老年帕金森患者认知功能障碍的风险预测模型

doi:10.3969/j.issn.1003-9198.2026.04.007

摘要/Abstract

摘要： 目的基于MRI定量参数探讨老年PD患者认知功能障碍的风险因素。方法采用回顾性研究设计,选取2021年9月至2025年1月期间西安国际医学中心医院收治的150例老年PD患者,根据是否合并认知功能障碍将其分为观察组(n=52)和对照组(n=98)。收集并比较2组患者MRI参数、一般临床资料以及实验室指标,采用logistic回归分析影响老年PD患者发生认知功能障碍的因素。采用ROC曲线、Calibration曲线评估模型的预测价值。结果与对照组比较,观察组黑质表观扩散系数(ADC)、H-Y分级升高,病程延长,黑质各向异性分数(FA)、UA水平降低(P＜0.05)。Logistic回归分析结果显示,黑质ADC(OR=2.192)、黑质FA(OR=0.464)、病程(OR=1.893)、H-Y分级(OR=1.923)和UA(OR=0.488)是老年PD患者发生认知功能障碍的独立影响因素(P＜0.05)。基于影响因素构建PD患者发生认知功能障碍的风险预测模型,ROC曲线结果显示,该模型预测PD患者发生认知功能障碍的AUC为0.910(95%CI:0.852～0.950),敏感度为90.54%,特异度为83.65%,约登指数为0.729。Calibration曲线显示,该预测模型的拟合度好(Hosmer-Lemeshow χ²=0.825,P=0.437)。结论黑质ADC、FA、病程、H-Y分级及UA是老年PD患者发生认知功能障碍的影响因素,基于影响因素构建的老年PD患者认知功能障碍风险预测模型具有良好的评估价值。

关键词: 帕金森病, 老年人, 磁共振成像, 认知功能障碍, 风险预测模型

Abstract: Objective To explore the risk factors of cognitive dysfunction in the elderly patients with Parkinson’s disease (PD) based on quantitative parameters of magnetic resonance imaging (MRI). Methods A retrospective study was conducted, enrolling 150 elderly PD patients admitted to Xi’an International Medical Center Hospital from September 2021 to January 2025. These patients were divided into an observation group (n=52) and a control group (n=98) based on the occurrence of cognitive dysfunction. The MRI parameters, general clinical data, and laboratory indicators of the two groups were collected and compared. Logistic regression analysis was used to investigate the factors influencing the occurrence of cognitive dysfunction in elderly PD patients. ROC curve and calibration curve were used to verify the model. Results Compared with the control group, the observation group showed increased apparent diffusion coefficient (ADC) and H-Y grading, prolonged disease course, decreased gray matter fractional anisotropy (FA) and uric acid (UA) levels (P<0.05). Logistic regression analysis revealed that substantia nigra ADC (OR=2.192), substantia nigra FA (OR=0.464), disease duration (OR=1.893), H-Y grading (OR=1.923), and UA (OR=0.488) were independent predictors of cognitive dysfunction in elderly PD patients (P<0.05). A risk prediction model for cognitive dysfunction in PD patients was constructed based on the influencing factors. The ROC curve results showed that the area under the curve (AUC) of this prediction model for cognitive dysfunction in PD patients was 0.910 (95%CI: 0.852-0.950), with a sensitivity of 90.54% and a specificity of 83.65%, and the Youden index was 0.729. The calibration curve demonstrated that the predictive model exhibited good fit (Hosmer-Lemeshow χ²=0.83, P=0.437). Conclusions The substantia nigra ADC, FA, disease duration, H-Y grading, and UA levels are influencing factors for cognitive dysfunction in elderly PD patients. The risk prediction model for cognitive dysfunction constructed based on these factors in elderly PD patients, demonstrates significant assessment value.

Key words: Parkinson’s disease, aged, magnetic resonance imaging, cognitive dysfunction, risk prediction model

中图分类号:

R 741

鲁爽, 李侠, 任倩萌. 基于MRI定量参数构建老年帕金森患者认知功能障碍的风险预测模型[J]. 实用老年医学, 2026, 40(4): 362-366.

LU Shuang, LI Xia, REN Qianmeng. Risk prediction model of cognitive dysfunction in elderly patients with Parkinson’s disease based on quantitative MRI parameters[J]. Practical Geriatrics, 2026, 40(4): 362-366.

参考文献

[1] LANGESKOV-CHRISTENSEN M, FRANZÉN E, GRØNDAHL HVID L, et al. Exercise as medicine in Parkinson’s disease[J]. J Neurol Neurosurg Psychiatry, 2024, 95(11):1077-1088.
[2] GALLAGHER J, GOCHANOUR C, CASPELL-GARCIA C, et al. Long-term dementia risk in parkinson disease[J]. Neurology, 2024, 103(5):e209699.
[3] KUMARAN S P, GURRAM S L, VISWAMITRA S, et al. Utility of DTI (diffusion tensor imaging) metrics to study microstructural changes of trigeminal nerve in patients with trigeminal neuralgia (TN)[J]. Neurol India, 2022, 70(1):270-274.
[4] 郭龙军, 梁长华. 磁共振弥散张量成像在帕金森病诊断与定量分析中的应用价值[J]. 中风与神经疾病杂志, 2020, 37(10):900-903.
[5] HÖGLINGER G, German Parkinsin’s Guidelines Committee, TRENKWALDER C. Diagnosis and treatment of Parkinson’s disease (guideline of the German Society for Neurology)[J]. Neurol Res Pract, 2024, 6(1):30.
[6] YUAN X F, ZHANG Y, ZHANG Y, et al. Reliability and validity of the Brief Assessmentof Impaired Cognition (Chinese version) for stroke patients[J]. Appl Neuropsychol Adult, 2023, 30(1):27-33.
[7] MORRIS H R, SPILLANTINI M G, SUE C M, et al. The pathogenesis of Parkinson’s disease[J]. Lancet, 2024, 403(10423):293-304.
[8] 孙婷, 朱建兵, 马清, 等. MRI检查对帕金森病患者认知功能障碍继发痴呆的预测价值研究[J]. 中国医学装备, 2024, 21(12):83-87.
[9] TANTIK PAK A, NACAR DOGAN S, SENGUL Y. Structural integrity of corpus callosum in patients with migraine:a diffusion tensor imaging study[J]. Acta Neurol Belg, 2023, 123(2):385-390.
[10] 耿磊, 王锐, 张照婷, 等. 弥散张量成像对帕金森病前驱期的诊断价值[J]. 实用医学杂志, 2023, 39(21):2843-2849.
[11] BECERRA-LAPARRA I, CORTEZ-CONRADIS D, GARCIA-LAZARO H G, et al. Radial diffusivity is the best global biomarker able to discriminate healthy elders, mild cognitive impairment, and Alzheimer’s disease:a diagnostic study of DTI-derived data[J]. Neurol India, 2020, 68(2):427-434.
[12] NIGRO P, CHIAPPINIELLO A, SIMONI S, et al. Changes of olfactory tract in Parkinson’s disease:a DTI tractography study[J]. Neuroradiology, 2021, 63(2):235-242.
[13] BOHNEN N I, ALBIN R L, MÜLLER M L T M, et al. Frequency of cholinergic and caudate nucleus dopaminergic deficits across the predemented cognitive spectrum of Parkinson disease and evidence of interaction effects[J]. JAMA Neurol, 2015, 72(2):194-200.
[14] KARIMI B, SILWAL P, BOOTH S, et al. Schizophrenia-associated LRRTM1 regulates cognitive behavior through controlling synaptic function in the mediodorsal thalamus[J]. Mol Psychiatry, 2021, 26(11):6912-6925.
[15] AARSLAND D, BATZU L, HALLIDAY G M, et al. Parkinson disease-associated cognitive impairment[J]. Nat Rev Dis Primers, 2021, 7(1):47.
[16] LI L, TANG S, HAO B, et al. Early detection and management of cognitive impairment in Parkinson’s disease:a predictive model approach[J]. Brain Behav, 2025, 15(3):e70423.
[17] 杨一帆, 高峰, 柳华, 等. 老年帕金森病病人外周血ApoA1/HDL、血尿酸水平与认知功能的关系[J]. 中西医结合心脑血管病杂志, 2023, 21(13):2474-2478.
[18] MIJAILOVIC N R, VESIC K, BOROVCANIN M M. The influence of serum uric acid on the brain and cognitive dysfunction[J]. Front Psychiatry, 2022, 13:828476.
[19] 王菲, 张娟利. 应用血清RANTES、MIF构建帕金森病并发认知功能障碍预测模型的价值分析[J]. 中风与神经疾病杂志, 2023, 40(6):544-548.