Modifiable Risk Factors for dementia, awareness of, and barriers to, brain health behaviours

Abstract

Introduction: Dementia is common and in many societies, underdiagnosed. The commonest cause of dementia globally is Alzheimer’s disease. Dementia prevalence is increasing globally. It is estimated that the number of people living with dementia globally will increase from 57.4 million people in 2019 to 152.8 million in 2050. Whilst pharmacological agents for treating dementia are limited, recent years have seen an increasing focus on drug trials for Alzheimer’s disease. As of January 2023, there were 141 agents in 187 clinical trials for Alzheimer’s Disease. A handful of disease modifying agents are now at advanced stage of clinical development. In addition to disease modification, an increasing body of literature supports the potential for dementia prevention. It is now recognised that 12 modifiable risk factors account for 40% of dementias globally. The life-course model of brain health highlights that it is never too early and never too late to consider reducing one’s dementia risk. Whilst evidence supports the benefits of so called ‘brain health behaviours’, the adoption of such habits is not widespread. Knowledge regarding dementia risk factors is variable and barriers to brain health behaviours numerous. In this context, the aims of this project were as follows: 1) to examine prevalence of modifiable risk factors for dementia amongst the study cohort, 2) to investigate awareness of specific modifiable risk factors for dementia, 3) to investigate whether exposure to dementia risk factors varies between groups, 4) to investigate whether awareness for modifiable risk factors varies between groups, 5) to assess barriers to risk reduction behaviours, 6) to investigate the distribution of barriers across sociodemographic factors. Methods: 1. A cross-sectional survey was distributed online among an Irish non-patient population. Inclusion criteria comprised those aged ≥50 years old. Those who had a history of dementia, or who had worked in the healthcare sector were excluded. A pilot version of the survey was undertaken in January 2022 and the final survey was subsequently administered in February 2022. 2. The survey was adapted from the Lifestyle Barriers for Cognitive Health Questionnaire and captured the following information: 1) Sociodemographic factors; 2) Exposure to, as well as knowledge of modifiable risk factors for dementia; 3) Barriers to brain health behaviours; 4) Participants' perceptions regarding potential for dementia prevention, and risk reduction (Likert scale). 3. Analyses were conducted using StataMP 17.0 for Mac or IBM SPSS Statistics software for Windows (version 29). Frequency counts and percentages were used to show the sociodemographic characteristics and rates of exposure to modifiable risk. To investigate differences in awareness levels for modifiable risk factors across groups, 2-way mixed ANOVAs with Greenhouse-Geisser correction were performed. Binary logistic regression models were used to investigate the associations between exposure to modifiable risk factors for dementia, and sociodemographic factors. Binary logistic regression analyses were adjusted for potential confounding factors such as household income and educational attainment, as well as for sex and age depending on the predictor variable used in the model. 4. Z-tests with a Bonferroni correction for multiple comparisons were utilised to examine distribution of barriers between groups. A two-step cluster analysis was used to classify participants in distinct groups based on age, gender, education and household income. The distance measure was log-likelihood and the clustering criterion was Schwartz’s Bayesian Criterion. Results: Sample characteristics: The study population comprised 551 participants (50.3% male; 49.6% female). Mean age was 59.7 years, with the majority of the sample ranging between 50-59 years of age (54.3%). 98.9% of respondents were of white ethnicity. Most participants were educated to secondary school level or higher (98.2%), were employed or self-employed (52.2%), and cohabited with one or more persons (74.4%). Exposure to modifiable risk factors: Modifiable risk factors for dementia were prevalent with the commonest exposures as follows: being overweight (60.6%), having a lack of social engagement (54.9%), physical inactivity (42.7%), hypertension (36.7%), and self-assessed poor sleep quality (33.5%). Impact of sociodemographic factors on exposure to modifiable risk factor exposure: Male gender was significantly associated with an increased likelihood to report multiple risk factors, namely excess alcohol consumption (AOR 3.127, CI 1.953 - 5.007), smoking (AOR 1.618, CI 1.065 - 2.457), diabetes (AOR 2.141, CI 1.097 - 4.176) and low mental stimulation (AOR 1.796, CI 1.171 - 2.754). With increasing age, participants were less likely to report multiple risk factors. Relative to the secondary school education group, participants educated to undergraduate and postgraduate level were significantly less likely to report smoking (AOR 0.468, CI 0.296-0.738; AOR 0.199, CI 0.076-0.520, respectively). Awareness of modifiable risk factors for dementia: Across the total sample, head injury (90.9%, n = 500), low mental stimulation (85.3%, n = 469) and excess alcohol consumption (77.8%, n = 428) were the three most commonly recognised modifiable risk factors for dementia. Hearing impairment had the poorest recognition (34.7%, n = 191). Impact of sociodemographic actors on awareness of modifiable risk factors for dementia: Awareness of low mental stimulation as a dementia risk factor was higher in the female group (p=.05). Two-way mixed ANOVA with Greenhouse-Geisser correction did not demonstrate a significant interaction effect between risk factors and age on awareness F(21.49,5879.15) = 1.16, p = 0.279. With regard to education, awareness was significantly greater in both university groups (undergraduate and postgraduate) relative to the secondary education group for multiple risk factors, namely diabetes, air pollution, sleep, diet, low social interaction, and low mental stimulation (p=.01). Awareness of hearing loss, hypertension, and depression were significantly greater in the university postgraduate group when compared to the secondary school group (p=.05). Barrier prevalence: Various practical barriers, and lack of motivation were the most prevalently cited barriers across multiple risk factors. Practical barriers were most commonly reported, with at least one practical barrier reported by 84.2% of the study population (n = 464). One or more emotional barriers were reported by 83.5% of the population (n = 460), motivational barriers by 62.6% (n = 345), and social barriers by 48.8% (n = 269). Knowledge barriers were least commonly reported with 34.7% of participants reporting one or more knowledge barriers (n = 191). Association between sociodemographic factors, risk factor exposure, and barriers to individual risk factors: Multiple individual effects were noted between sociodemographic factors and exposure to individual barriers. Cluster analysis revealed six clusters. Multiple risk factors disproportionately impact those of lower educational status (clusters A, C, and E). Secondary educated males (cluster E) were more likely to report exposure to excess alcohol when compared to other clusters, with the exception of university educated males (cluster F). They were also more likely to report smoking when compared to other clusters, with the exception of secondary educated females (cluster A). Secondary educated females (cluster A) were more likely to report poor sleep when compared to older males (cluster B), Secondary-educated, higher income (cluster C), and Uni-educated males (cluster F). Discussion: Modifiable risk factors for dementia were common among the study cohort. The distribution of exposure to modifiable risk factors for dementia was unequal across gender, age and education groups. Awareness levels vary considerably across risk factors, gender, and education level. A majority of the study cohort were unaware of the significance of certain risk factors. In this study, clusters defined by lower socio-economic status were disproportionately impacted by multiple risk factors, and several barriers were unequally distributed across socioeconomic groups. Our study highlights the individualised nature of risk factor profiles, and barriers to brain health behaviours, thereby underlining the utility of devising personalised risk reduction plans. These findings highlight the importance of individual risk profiling and tailoring interventions towards an individual’s specific needs. Nonetheless, individualised assessment and primary prevention in the context of specialist brain health clinics is unlikely to be impactful on a population level in the short to medium term. Novel health promotion and risk reduction approaches must be prioritised with public health interventions, rooted in the sociocultural contexts of their intended recipients, targeting relevant barriers to risk reduction behaviours.