ABSTRACTObjectives:In this large population study, we set out to examine the profile of mild behavioral impairment (MBI) by using the Mild Behavioral Impairment Checklist (MBI-C) and to explore its factor structure when employed as a self-reported and informant-rated tool.Design:This was a population-based cohort study.Setting:Participants were recruited from the Platform for Research Online to Investigate Genetics and Cognition in Aging study (https://www.protect-exeter.org.uk).Participants:A total of 5,742 participant-informant dyads participated in the study.Measurements:Both participants and informants completed the MBI-C. The factor structure of the MBI-C was evaluated by exploratory factor analysis.Results:The most common MBI-C items, as rated by self-reported and informants, related to affective dysregulation (mood/anxiety symptoms), being present in 34% and 38% of the sample, respectively. The least common items were those relating to abnormal thoughts and perception (psychotic symptoms) (present in 3% and 6% of the sample, respectively). Only weak correlations were observed between self-reported and informant-reported MBI-C responses. Exploratory factor analysis for both sets of respondent answers indicated that a five-factor solution for the MBI-C was appropriate, reflecting the hypothesized structure of the MBI-C.Conclusion:This is the largest and most detailed report on the frequency of MBI symptoms in a nondementia sample. The full spectrum of MBI symptoms was present in our sample, whether rated by self-reported or informant report. However, we show that the MBI-C performs differently in self-reported versus informant-reported situations, which may have important implications for the use of the questionnaire in clinic and research.

Background The neuropsychiatric syndrome mild behavioral impairment (MBI) describes an at‐risk state for dementia and may be a useful screening tool for sample enrichment. We hypothesized that stratifying a cognitively normal sample on MBI status would enhance the association between genetic risk for Alzheimer's disease (AD) and cognition. Methods Data from 4458 participants over age 50 without dementia was analyzed. A cognitive composite score was constructed and the MBI Checklist was used to stratify those with MBI and those without. Polygenic scores for AD were generated using summary statistics from the IGAP study. Results AD genetic risk was associated with worse cognition in the MBI group but not in the no MBI group (MBI: β = –0.09, 95% confidence interval: –0.13 to –0.03, P = 0.002, R 2 = 0.003). The strongest association was in those with more severe MBI aged ≥65. Conclusions MBI is an important feature of aging; screening on MBI may be a useful sample enrichment strategy for clinical research.

Background A questionnaire assessing awareness of positive and negative age-related changes (AARC gains and losses) was developed in the US and Germany. We validated the short form of the measure (AARC-10 SF) and the cognitive functioning subscale from the 50-item version of the AARC (AARC-50) questionnaire in the UK population aged 50 and over. Methods Data from 9410 participants (Mean (SD) age = 65.9 (7.1)) in the PROTECT cohort were used to explore and confirm the psychometric properties of the AARC measures including: validity of the factor structure; reliability; measurement invariance across men and women, individuals with and without a university degree, and in middle age, early old age, and advanced old age; and convergent validity with measures of self-perception of aging and mental, physical, and cognitive health. We explored the relationship between demographic variables (age, sex, marital status, employment, and university education) and AARC. Results We confirmed the two-factor structure (gains and losses) of the AARC-10 SF and the AARC-50 cognitive functioning subscale. Both scales showed good reliability and good convergent validity for AARC losses, but weak convergent validity for AARC gains. For both scales metric invariance was held for the two subgroups defined by education level and age. For the AARC-50 subscale, but not for the AARC-10 SF, strong invariance was also held for the two subgroups defined by sex. Age, sex, marital status, employment, and university education predicted AARC gains and losses. Conclusions The AARC-10 SF and AARC-50 cognitive functioning subscale identify UK individuals who perceive age-related changes in their mental, physical, and cognitive health.

Objectives: Evidence linking subjective concerns about cognition with poorer objective cognitive performance is limited by reliance on unidimensional measures of self-perceptions of aging (SPA). We used the awareness of age-related change (AARC) construct to assess self-perception of both positive and negative age-related changes (AARC gains and losses). We tested whether AARC has greater utility in linking self-perceptions to objective cognition compared to well-established measures of self-perceptions of cognition and aging. We examined the associations of AARC with objective cognition, several psychological variables, and engagement in cognitive training. Design: Cross-sectional observational study. Participants: The sample comprised 6056 cognitively healthy participants (mean [SD] age = 66.0 [7.0] years); divided into subgroups representing middle, early old, and advanced old age. Measurements: We used an online cognitive battery and measures of global AARC, AARC specific to the cognitive domain, subjective cognitive change, attitudes toward own aging (ATOA), subjective age (SA), depression, anxiety, self-rated health (SRH). Results: Scores on the AARC measures showed stronger associations with objective cognition compared to other measures of self-perceptions of cognition and aging. Higher AARC gains were associated with poorer cognition in middle and early old age. Higher AARC losses and poorer cognition were associated across all subgroups. Higher AARC losses were associated with greater depression and anxiety, more negative SPA, poorer SRH, but not with engagement in cognitive training. Conclusions: Assessing both positive and negative self-perceptions of cognition and aging is important when linking self-perceptions to cognitive functioning. Objective cognition is one of the many variables – alongside psychological variables – related to perceived cognitive losses.

Introduction Late‐life onset psychosis is associated with faster progression to dementia in cognitively normal people, but little is known about its relationship with cognitive impairment in advance of dementia. Methods Clinical and genetic data from 2750 people ≥50 years of age without dementia were analyzed. Incident cognitive impairment was operationalized using the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) and psychosis was rated using the Mild Behavioral Impairment Checklist (henceforth MBI‐psychosis). The whole sample was analyzed before stratification on apolipoprotein E ( APOE ) ε4 status. Results In Cox proportional hazards models, MBI‐psychosis had a higher hazard for cognitive impairment relative to the No Psychosis group (hazard ratio [HR]: 3.6, 95% confidence interval [CI]: 2.2–6, p < 0.0001). The hazard for MBI‐psychosis was higher in APOE ε4 carriers and there was an interaction between the two (HR for interaction: 3.4, 95% CI: 1.2–9.8, p = 0.02). Discussion Psychosis assessment in the MBI framework is associated with incident cognitive impairment in advance of dementia. These symptoms may be particularly important in the context of APOE genotype.