Search

Background: As cognitive decline and dementia‐related diseases are currently incurable, identifying modifiable and preventative factors is imperative. Thus, we aimed to investigate the associations between modifiable lifestyle factors and cognitive function in a population‐based sample of nondemented men. Methods: Data for 537 men were drawn from the ongoing, population‐based, Geelong Osteoporosis Study (GOS). The CogState Brief Battery (CBB) was utilised to determine cognitive function (overall cognitive function [OCF], psychomotor function, attention, working memory, and visual learning). Demographic and lifestyle factors (diet, smoking, physical activity [sport, work/home, and leisure time], alcohol use, and body mass index [BMI]) were self‐reported and documented across three waves of data collection. Generalised estimating equation (GEE) models were used to determine associations between the time‐updating exposures of interest and each of the cognitive domains. Results: Age was associated with OCF (η2 = 0.298), psychomotor function (η2 = 0.163), attention (η2 = 0.210), working memory (η2 = 0.215), and visual learning (η2 = 0.097). Playing sport was associated with all aspects of cognition: OCF (η2 = 0.014), psychomotor function (η2 = 0.007), attention (η2 = 0.004), working memory (η2 = 0.013), and visual learning (η2 = 0.004), and being physically active during leisure time was associated with visual learning (η2 = 0.008). Being physically active at work and home, smoking, diet quality, energy consumed per day, alcohol intake, and BMI were not associated with any of the cognitive domains. Conclusion: In this study of nondemented men, elements of a healthy lifestyle such as playing sport and being physically active during leisure time were associated with better cognition. Further studies into whether these factors, amongst others, prevent the onset and progression of dementia are needed. [ABSTRACT FROM AUTHOR]

Background: A minimally invasive blood-based assessment of cognitive function could be a promising screening strategy to identify high-risk groups for the incidence of Alzheimer's disease. Methods: The study included 448 cognitively unimpaired men (mean age 64.1 years) drawn from the Geelong Osteoporosis Study. A targeted mass spectrometry-based proteomic assay was performed to measure the abundance levels of 269 plasma proteins followed by linear regression analyses adjusted for age and APOE ε4 carrier status to identify the biomarkers related to overall cognitive function. Furthermore, two-way interactions were conducted to see whether Alzheimer's disease-linked genetic variants or health conditions modify the association between biomarkers and cognitive function. Results: Ten plasma proteins showed an association with overall cognitive function. This association was modified by allelic variants in genes ABCA7, CLU, BDNF and MS4A6A that have been previously linked to Alzheimer's disease. Modifiable health conditions such as mood disorders and poor bone health, which are postulated to be risk factors for Alzheimer's disease, also impacted the relationship observed between protein marker levels and cognition. In addition to the univariate analyses, an 11-feature multianalyte model was created using the least absolute shrinkage and selection operator regression that identified 10 protein features and age associated with cognitive function. Conclusions: Overall, the present study revealed plasma protein candidates that may contribute to the development of a blood-based screening test for identifying early cognitive changes. This study also highlights the importance of considering other risk factors in elucidating the relationship between biomarkers and cognition, an area that remains largely unexplored. [ABSTRACT FROM AUTHOR]

We aimed to investigate cross-sectional associations between skeletal muscle density, a proxy measure for fatty infiltration into muscle, and cognition. Contributions from body fat mass, systemic inflammation and lifestyle were explored, as these factors have been identified in both muscle and cognitive deterioration. For 281 men (60–95 year) from the Geelong Osteoporosis Study, radial and tibial muscle density were measured using peripheral quantitative computed tomography. Body fat and appendicular lean mass were measured using dual-energy X-ray absorptiometry. Cognitive function was assessed for psychomotor function (DET), visual identification/attention (IDN), visual learning (OCL) and working memory (OBK) (CogState Brief Battery). Composite scores signified overall cognitive function (OCF). Higher scores represent poorer performance except for OCL and OCF. Regression analyses examined associations between muscle density and cognition; potential confounders included age, muscle cross-sectional area (CSA), body composition, lifestyle and serum markers of inflammation. Negative associations with age were evident for muscle density, all cognitive domains and OCF. Muscle density at both sites was positively associated with DET, OCL and OCF. After adjustment for age, the association persisted for DET (radius: B = − 0.006, p = 0.02; tibia: B = − 0.003, p = 0.04) and OCL (radius B = + 0.004, p = 0.02; tibia: B = + 0.005, p < 0.001). At the radius, further adjustment for serum TNF-α explained the association between muscle density (B = − 0.002, p = 0.66) and DET. Education and physical activity contributed to the model for radial muscle density and DET. There were no contributions from muscle CSA, appendicular lean mass, body fat mass, other markers of inflammation or other potential confounders. At the tibia, the association between muscle density and DET (B = − 0.003, p = 0.04) was independent of TNF-α. There was an age-adjusted association between muscle density and OCL at both sites (radius: B = + 0.004, p = 0.02; tibia: B = + 0.005, p < 0.001). None of the potential confounders contributed to the models. Muscle density was associated with cognitive function in the DET and OCL domains. However, there was little evidence that this was explained by inflammation or body fat mass. No associations were identified between muscle density and IDN or OBK. [ABSTRACT FROM AUTHOR]

Sarcopenia is the loss of skeletal muscle mass and function with advancing age. It involves both complex genetic and modifiable risk factors, such as lack of exercise, malnutrition and reduced neurological drive. Cognitive decline refers to diminished or impaired mental and/or intellectual functioning. Contracting skeletal muscle is a major source of neurotrophic factors, including brain-derived neurotrophic factor, which regulate synapses in the brain. Furthermore, skeletal muscle activity has important immune and redox effects that modify brain function and reduce muscle catabolism. The identification of common risk factors and underlying mechanisms for sarcopenia and cognition may allow the development of targeted interventions that slow or reverse sarcopenia and also certain forms of cognitive decline. However, the links between cognition and skeletal muscle have not been elucidated fully. This review provides a critical appraisal of the literature on the relationship between skeletal muscle health and cognition. The literature suggests that sarcopenia and cognitive decline share pathophysiological pathways. Ageing plays a role in both skeletal muscle deterioration and cognitive decline. Furthermore, lifestyle risk factors, such as physical inactivity, poor diet and smoking, are common to both disorders, so their potential role in the muscle–brain relationship warrants investigation. [ABSTRACT FROM AUTHOR]

Psychiatric symptomatology and medications used in their treatment may be modifiable risk factors associated with cognitive function, although findings from population-based studies spanning the full adult age range are lacking. This study aimed to investigate associations between psychiatric symptomatology, psychotropic medication use and cognitive function in a population-based sample of men. Data for 537 men were drawn from the Geelong Osteoporosis Study. Cognitive function (psychomotor function, attention, working memory and visual learning) was determined using the Cog-State Brief Battery. Current depressive and anxiety symptomatology was determined using the Hospital Anxiety and Depression Scale, and psychotropic medication use was self-reported. Linear regression models were developed to determine associations between psychiatric symptomatology and psychotropic medication use with each cognitive measure. Depressive symptomatology was associated with lower overall cognitive function (b-0.037 ± 0.010, η2 = 0.025, p < 0.001), psychomotor function (b 0.006 ± 0.002, η2 = 0.028 p < 0.001) and attention (b 0.004 ± 0.001, η2 = 0.021, p < 0.001), whereas psychotropic use was associated with lower overall cognitive function (b − 0.174 ± 0.075, η2 = 0.010, p = 0.021), attention (b 0.017 ± 0.008, η2 = 0.008, p = 0.038 and working memory (b 0.031 ± 0.012, η2 = 0.010, p = 0.010). Anticonvulsant use was associated with lower overall cognitive function (b − 0.723 ± 0.172, η2 = 0.032, p < 0.001), attention (b 0.065 ± 0.018, η2 = 0.029, p < 0.001) and working memory (b 0.088 ± 0.026, η2 = 0.022, p < 0.001). All relationships were found to have a small effect. There were no significant associations between anxiety symptomatology and antidepressant and anxiolytic use with any of the cognitive domains. Depressive symptomatology and anticonvulsant use were associated with lower cognitive function. Understanding the underlying mechanisms involved in these relationships can advance knowledge on the heterogeneity in cognitive ageing and aid in prevention initiatives. • What is the primary question addressed by this study? What are the associations between depressive and anxiety symptoms, and psychotropic medication use with cognitive function? • What is the main finding of this study? Depressive symptoms and the use of psychotropics, in particular anticonvulsants were associated with lower cognitive functioning. • What is the meaning of the finding? Our study highlights the importance of mental wellbeing in cognitive health, and the need for further research between these exposures on cognition. [ABSTRACT FROM AUTHOR]

Background: Certain age‐related and medical factors have been associated with cognitive dysfunction; however, less is known regarding social determinants of health. The current study aimed to investigate associations between social determinants of health and cognitive function in a population‐based sample of men without dementia. Methods: Data were drawn from the ongoing Geelong Osteoporosis Study (n = 536). Cognitive function was determined using the Cog‐State Brief Battery. Area‐based socioeconomic status (SES) was determined using the Index of Relative Socioeconomic Advantage and Disadvantage, marital status by self‐report, and social support by the Multidimensional Scale of Perceived Social Support, which considers family, friends, and significant others. Results: Belonging to a higher SES group, being in a relationship (married/de‐facto) and perceived social support from a significant other and friends were each associated with better overall cognitive function. In regard to the specific cognitive domains, higher SES was associated with better psychomotor function and visual learning, being in a relationship was associated with better working memory, and perceived social support from a significant other was associated with better attention and working memory, with perceived social support from friends associated with better psychomotor function. There were no associations detected between social support from family and any of the cognitive domains. Conclusion: Higher SES, being in a relationship, and greater perceived social support from a significant other and friends were associated with better cognitive function. Further studies identifying underlying mechanisms linking social factors with cognition are needed to establish prevention strategies and enhance cognitive health. The current study investigated the relationship between socioeconomic status, relationship status and social support and their association with both overall and specific domains of cognitive function in a population‐based sample of men without dementia. [ABSTRACT FROM AUTHOR]