Menegon Federico, De Marchi Fabiola, Aprile Davide, Zanelli Iacopo, Decaroli Greta, Comi Cristoforo, Tondo Giacomo
Neurology Unit, Department of Translational Medicine, Maggiore della Carità Hospital, University of Piemonte Orientale, 28100 Novara, Italy.
Neurology Unit, Department of Translational Medicine, Sant'Andrea Hospital, University of Piemonte Orientale, Corso Abbiate 21, 13100 Vercelli, Italy.
Biomedicines. 2024 Jul 26;12(8):1675. doi: 10.3390/biomedicines12081675.
The conversion from mild cognitive impairment (MCI) to dementia is influenced by several factors, including comorbid conditions such as metabolic and vascular diseases. Understanding the impact of these comorbidities can help in the disease management of patients with a higher risk of progressing to dementia, improving outcomes. In the current study, we aimed to analyze data from a large cohort of MCI (n = 188) by principal component analysis (PCA) and cluster analysis (CA) to classify patients into distinct groups based on their comorbidity profile and to predict the risk of conversion to dementia. From our analysis, four clusters emerged. CA showed a significantly higher rate of disease progression for Cluster 1, which was predominantly characterized by extremely high obesity and diabetes compared to other clusters. In contrast, Cluster 3, which was defined by a lower prevalence of all comorbidities, had a lower conversion rate. Cluster 2, mainly including subjects with traumatic brain injuries, showed the lowest rate of conversion. Lastly, Cluster 4, including a high load of hearing loss and depression, showed an intermediate risk of conversion. This study underscores the significant impact of specific comorbidity profiles on the progression from MCI to dementia, highlighting the need for targeted interventions and management strategies for individuals with these comorbidity profiles to potentially delay or prevent the onset of dementia.
从轻度认知障碍（MCI）转变为痴呆症受到多种因素的影响，包括代谢和血管疾病等合并症。了解这些合并症的影响有助于对有更高进展为痴呆症风险的患者进行疾病管理，改善预后。在本研究中，我们旨在通过主成分分析（PCA）和聚类分析（CA）对一大群MCI患者（n = 188）的数据进行分析，以便根据他们的合并症情况将患者分为不同的组，并预测转变为痴呆症的风险。通过我们的分析，出现了四个聚类。聚类分析显示，聚类1的疾病进展率显著更高，其主要特征是与其他聚类相比存在极高的肥胖和糖尿病。相比之下，由所有合并症患病率较低所定义的聚类3，其转化率较低。主要包括患有创伤性脑损伤的受试者的聚类2，显示出最低的转化率。最后，包括高负荷听力损失和抑郁症的聚类4，显示出中等的转化风险。本研究强调了特定合并症情况对从MCI进展为痴呆症的重大影响，突出了针对具有这些合并症情况的个体采取有针对性的干预措施和管理策略以潜在延缓或预防痴呆症发作的必要性。
