Elsevier

Neurobiology of Aging

Volume 32, Issue 3, March 2011, Pages 434-442
Neurobiology of Aging

A Multiple Indicators Multiple Causes (MIMIC) model of Behavioural and Psychological Symptoms in Dementia (BPSD)

https://doi.org/10.1016/j.neurobiolaging.2009.03.005Get rights and content

Abstract

Introduction

Although there is evidence for distinct behavioural sub-phenotypes in Alzheimer's disease (AD), their inter-relationships and the effect of clinical variables on their expression have been little investigated.

Methods

We have analysed a sample of 1850 probable AD patients from the UK and Greece with 10 item Neuropsychiatric Inventory (NPI) data. We applied a Multiple Indicators Multiple Causes (MIMIC) approach to investigate the effect of MMSE, disease duration, gender, age and age of onset on the structure of a four-factor model consisting of “psychosis”, “moods”, “agitation” and “behavioural dyscontrol”.

Results

Specific clinical variables predicted the expression of individual factors. When the inter-relationship of factors is modelled, some previously significant associations are lost. For example, lower MMSE scores predict psychosis, agitation and behavioural dyscontrol factors, but psychosis and mood predict the agitation factor. Taking these associations into account MMSE scores did not predict agitation.

Conclusions

The complexity of the inter-relations between symptoms, factors and clinical variables is efficiently captured by this MIMIC model.

Introduction

Behavioural and Psychological Symptoms in Dementia (BPSD) commonly occur in patients with Alzheimer's disease (AD). As many as 80% of patients with AD have one or more symptom of BPSD as measured using scales such as the Neuropsychiatric Inventory (NPI) (Craig et al., 2005, Drevets and Rubin, 1989, Finkel, 1996, Rosen and Zubenko, 1991). BPSD are strongly associated with more severe functional and cognitive decline (Craig et al., 2005, Cummings, 2000, Stern et al., 1994) and result in carer stress, premature institutionalisation, as well as increased social and economic cost (Donaldson et al., 1998, Steele et al., 1990).

However, little is known about the heterogeneity of BPSD observed in clinical practice. Several reports have addressed biological, clinical and demographic correlates associated with individual BPSD, but with little consistency. BPSD are diverse and symptoms fluctuate with time and it is therefore difficult to study their interactions. Some behavioural symptoms in AD tend to occur together suggesting that distinct behavioural sub-phenotypes exist. For example two large recent studies have identified, using exploratory factor analysis techniques, four sets of symptoms (latent variables or factors) that occur together (Aalten et al., 2007, Hollingworth et al., 2006). Such ‘sub-phenotypes’ may have distinct neurobiological correlates. If the molecular pathways responsible could be identified then this might lead to novel treatment strategies for BPSD, since related symptoms could respond to the same drugs (Aalten et al., 2003). This is important as treatments currently used for the management of BPSD have poor efficacy and serious side effects (Madhusoodanan et al., 2007).

Although a number of studies have examined the effect of clinical variables on individual BPSD (Aalten et al., 2005a, Craig et al., 2005, Eustace et al., 2002, Mega et al., 1996, Piccininni et al., 2005, Selbaek et al., 2007, Spalletta et al., 2004) only a 2-year longitudinal study (Aalten et al., 2005b) and a cross-sectional study (Hollingworth et al., 2006) have examined the effect of clinical variables on behavioural sub-phenotypes. In addition, although behavioural sub-phenotypes in dementia co-occur and influence each other, only Aalten et al. (2005b) has addressed their inter-relationships. As latent variables have no scale and are represented through indicator variables, in this case behavioural symptoms, it is subsequently difficult to assess the overall effect of covariates or their inter-relationship using standard χ2 difference tests. There is therefore a need for more systematic statistical approaches to investigate these complex associations.

The aim of this analysis was to extend previous studies of BPSD and to generate a model which describes the effects of covariates on latent variables and the inter-relationships of latent variables. We have utilised three independent datasets comprising over 1800 probable AD patients (n = 1850) and used Multiple Indicators Multiple Causes (MIMIC) modelling, a special case of Structural Equation Modelling (SEM). MIMIC models provide a better insight into the correlations between symptoms, latent variables and covariates. They have the advantage of not only allowing the simultaneous detection of associations between the covariates and latent variables but also the detection of direct associations between covariates and symptoms, after controlling for the presence of latent variables. Although MIMIC models have been successfully applied in geriatric research (Gallo et al., 1994, Mast, 2004, Mast, 2005) and psychiatric studies (Agrawal and Lynskey, 2007, Chung et al., 2005, Gomez and Vance, 2008) they have not been previously applied to BPSD studies.

Section snippets

Subject cohorts

We used three independently ascertained cohorts: the UK/Ireland cohort comprising 957 participants from the Medical Research Council Genetic Resource for Late-onset AD, a cohort of 348 participants from Queen's University Belfast (the Northern Irish Cohort) and a cohort from Greece with 545 participants from Thessaloniki (total number of patients: 1850). All individuals were unrelated white European, recruited through secondary care services and diagnosed with probable AD in accordance with the

Results

The key demographic characteristics of the 1850 patients are presented on Table 1.

Conclusions

This study has extended previous studies on the factor structure of BPSD and proposed a systematic way to investigate the nature of behavioural sub-phenotypes in AD. To our knowledge, all the published studies classifying BPSD in AD have used an exploratory approach such as Principal Components Analysis (Aalten et al., 2003, Aalten et al., 2007, Cook et al., 2003, Frisoni et al., 1999, Fuh et al., 2001, Gauthier et al., 2005, Harwood et al., 1998, Herrmann et al., 2005, Hollingworth et al., 2006

Conflict of interest statement

There are no actual or potential conflicts of interest related to the work described in this paper, either by the authors or authors’ institutions. Petroula Proitsi is an Alzheimer's Research Trust Post-Doctoral Fellow. DC Rubinsztein is a Wellcome Trust Senior Clinical Fellow.

Acknowledgements

We are grateful for funding from the Alzheimer's Research Trust, the MRC Centre for Neurodegeneration Research, the NIHR BRC Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, KCL, the Alzheimer's Society, Stewart Bequest and Ulster Garden Villages.

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