Associations between data-driven lifestyle profiles and cogn

Mounting evidence highlights the importance of combined modifiable lifestyle factors in reducing risk of cognitive decline and dementia. Several a priori additive scoring approaches have been established; however, limited research has employed advanced data-driven approaches to explore this association. This study aimed to examine the association between data-driven lifestyle profiles and cognitive function in community-dwelling Australian adults. A cross-sectional study of 4561 Australian adults (55.3% female, mean age 60.9 ± 11.3 years) was conducted. Questionnaires were used to collect self-reported data on diet, physical activity, sedentary time, smoking status, and alcohol consumption. Cognitive testing was undertaken to assess memory, processing speed, and vocabulary and verbal knowledge. Latent Profile Analysis (LPA) was conducted to identify subgroups characterised by similar patterns of lifestyle behaviours. The resultant subgroups, or profiles, were then used to further explore associations with cognitive function using linear regression models and an automatic Bolck, Croon & Hagenaars (BCH) approach. Three profiles were identified: (1) “Inactive, poor diet” (76.3%); (2) “Moderate activity, non-smokers” (18.7%); and (3) “Highly active, unhealthy drinkers” (5.0%). Profile 2 “Moderate activity, non-smokers” exhibited better processing speed than Profile 1 “Inactive, poor diet”. There was also some evidence to suggest Profile 3 “Highly active, unhealthy drinkers” exhibited poorer vocabulary and verbal knowledge compared to Profile 1 and poorer processing speed and memory scores compared to Profile 2. In this population of community-dwelling Australian adults, a sub-group characterised by moderate activity levels and higher rates of non-smoking had better cognitive function compared to two other identified sub-groups. This study demonstrates how LPA can be used to highlight sub-groups of a population that may be at increased risk of dementia and benefit most from lifestyle-based multidomain intervention strategies.

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Northern Territory , Australia , Germany , Monash , South Australia , United States , New Zealand , India , California , Canada , International Institute , Sri Lanka , Australian , America , Australians , Los Angeles , Muthen , National Health , Monash University , Deakin University Human Research Ethics Committee , World Health Organisation , Australia Survey , International Diabetes Institute , Research Council , Centre For Epidemiologic Studies Depression Scale , Cancer Council Of Victoria , Australian Diabetes , Lifestyle Study , Alfred Hospital , Food Frequency Questionnaire , Anti Cancer Council , Active Australia Survey , Active Australia Manual , Medical Research Council , Risk Reduction , Cognitive Decline , California Verbal Learning Test , Symbol Digit Modalities Test , Northern Europe , Southern Europe , Middle East , Pacific Islands , Aboriginal Australians , Torres Strait Islanders , Epidemiologic Studies Depression Scale , Dinamap Pro Series Monitor Model , Mplus Version , Akaike Information Criterion , Bayesian Information Criterion , Lo Mendell Rubin Likelihood Ratio ,