A data‐driven examination of clustering of apathy and depression symptoms in people with dementia

Abstract

AbstractBackgroundApathy is one of the most common Neuropsychiatric Symptom (NPS) and is associated with poor clinical outcomes. Apathy remains under‐researched and its medical classification remains unclear, potentially associated with the fact that apathy is commonly a comorbidity with depression. Research that helps define the apathy phenotype is urgently needed, particularly for clinical and biomarker studies.MethodUsing latent class analysis (LCA) we analysed data from the KCL Care Home Research Network study with 349 participants (an observational study of people living with dementia in care homes). The aim was to understand how apathy and depression phenotypes cluster together. Apathy and depression were defined on the basis of item G (Apathy) and D (Depression) of the NPI‐NH scale, combining the 7 apathy subquestions with the 8 depression subquestions, rated as yes or no. Predictors of class membership were then analysed, including other NPS, demographics, medication and Clinical Dementia Rating (CDR).ResultThe LCA analysis showed a 4‐cluster group which was considered the best model: No symptoms, predominantly depression, apathy/depression, and a predominantly apathy group. Those in the apathy/depression class were more likely to experience psychosis symptoms compared to the no symptoms class, whilst those in the predominantly apathy class were more likely to have a higher CDR score compared to the no symptoms class.ConclusionUsing a data driven method, we show distinct clustering of apathy and depression symptoms in this cohort. There was evidence that these clusters have different clinical associations which may help inform diagnostic categories for research studies and clinical practice. Potentially, the four subtypes may be used in future studies to further evaluate their utility in biomarker studies in dementia.