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Year : 2021  |  Volume : 38  |  Issue : 2  |  Page : 120-126

Investigation of the complex structure between the severity of alzheimer's disease and influencing factors using latent class cluster analysis

1 Department of Biostatistics and Medical Informatics, Mersin University, Mersin, Turkey
2 Department of Neurology, Tarsus State Hospital, Mersin, Turkey
3 Department of Neurology, Faculty of Medicine, Mersin University, Mersin, Turkey

Correspondence Address:
Didem Derici Yildirim
Department of Biostatistics and Medical Informatics, Faculty of Medicine, Mersin University, Ciftlikkoy, Campus, 33343, Mersin
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/NSN.NSN_92_20

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Objective: The cognition of Alzheimer's disease (AD) has a heterogeneous pattern. It is useful to obtain more information about specific subgroups of patients to prevent disease progression. For better identification of the population, we aimed to detect latent groups based on cognitive test scores using latent class (LC) cluster analysis and influencing factors of latent severity groups to assist practitioners in outpatient departments who have restricted time and instrumentation. Materials and Methods: Data for 630 patients with AD in the Mersin University Dementia Outpatient Unit were collected, and cognitive test scores, demographic variables, and other factors such as comorbidities and family history of dementia were obtained. Initially, LC cluster analysis was performed to distinguish subgroups considering clinical dementia scores, age, and sex as covariates. Second, univariate analysis was used to detect the relationship between latent subgroups and influencing factors. Finally, multinomial logistic regression was performed to identify the magnitude of risk for significant factors. Results: Four severity groups were defined as mild, moderate, severe, and very severe cases of AD, and severity was significantly related to educational level, hyperlipidemia, diabetes mellitus, and sarcopenia (P < 0.001, P = 0.001, P = 0.043, and P < 0.001, respectively). Family history also influenced severity (P = 0.024). Disease severity increased with decreased education levels. Family history predicted a 1.555-fold increase in the risk of being in the moderate group versus the mild group. Moreover, diabetes mellitus predicted a 3.690-fold increase of being in the very severe group versus the mild group. Conclusion: LC cluster analysis is effective for determining severity groups for AD, and study results will help prepare a guide for an optimum evaluation tool for the disease.

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