Introduction: This scoping review mapped evidence on physical activity (including structured exercise) and sedentary behaviour interventions (interventions to reduce sedentary behaviour) in people living with both frailty and multiple long-term conditions (MLTCs) and their informal carers.
Methods: Ten databases and grey literature were searched from 2000 to October 2023. Two reviewers screened studies and one extracted data. Results were shared with three stakeholder groups (n = 21) in a consultation phase.
Results: After screening, 155 papers from 144 studies (1 ongoing) were retained. The majority were randomised controlled trials (86, 55%). Participants' mean age was 73 ± 12 years, and 73% were of White ethnicity. MLTC and frailty measurement varied widely. Most participants were pre-to-moderately frail. Physical health conditions predominated over mental health conditions.Interventions focused on structured exercise (83 studies, 60%) or combined interventions (55 studies, 39%). Two (1%) and one (0.7%) focused solely on habitual physical activity or sedentary behaviour. Adherence was 81% (interquartile range 62%-89%) with goal setting, monitoring and support important to adherence. Carers were only involved in 15 (11%) studies. Most interventions reported positive outcomes, primarily focusing on body functions and structures.
Conclusions: A modest volume of evidence exists on multicomponent structured exercise interventions, with less focus on habitual physical activity and sedentary behaviour. Interventions report largely positive effects, but an updated systematic review is required. The field could be advanced by more rigorous characterisation of MLTCs, socioeconomic status and ethnicity, increased informal carer involvement and further evaluation of habitual physical activity and sedentary behaviour interventions.
Background: Metabolomic scores based on age (MetaboAge) and mortality (MetaboHealth) are considered indicators of overall health, but their association with cognition in the general population is unknown. Therefore, the association between MetaboAge/MetaboHealth and level and decline in cognition was studied, as were differences between men and women.
Methods: Data of 2821 participants (50% women, age range 45-75) from the Doetinchem Cohort Study was used. MetaboAge and MetaboHealth were calculated from 1H-NMR metabolomics data at baseline. Cognitive domain scores (memory, flexibility and processing speed) and global cognitive functioning were available over a 10-year period. The association between MetaboAge/MetaboHealth and level of cognitive functioning was studied using linear regressions while for the association between MetaboAge/MetaboHealth and cognitive decline longitudinal linear mixed models were used. Analyses were adjusted for demographics and lifestyle factors.
Results: Higher MetaboAge, indicating poorer metabolomic ageing, was only associated with lower levels of processing speed in men. Higher MetaboHealth, indicating poorer immune-metabolic health, was associated with lower levels of cognitive functioning for all three domains and global cognitive functioning in both men and women. Only in men, MetaboHealth was also associated with 10-year decline in flexibility, processing speed and global cognition. Metabolites that contributed to the observed associations were in men mainly markers of protein metabolism, and in women mainly markers of lipid metabolism and inflammatory metabolites.
Conclusions: MetaboHealth, not MetaboAge, was associated with cognitive functioning independent of conventional risk factors. Individual metabolites affect cognitive functioning differently in men and women, suggesting sex-specific pathophysiological pathways underlying cognitive functioning.
Background: The rising prevalence of dementia necessitates identifying early neurobiological markers of dementia risk. Reduced cerebral white matter volume and flattening of the slope of the electrophysiological 1/f spectral power distribution provide neurobiological markers of brain ageing alongside cognitive decline. However, their association with modifiable dementia risk remains to be understood.
Methods: A cross-sectional sample of 98 healthy older adults (79 females, mean age = 65.44) underwent structural magnetic resonance imaging (sMRI), resting-state electroencephalography (EEG), cognitive assessments and dementia risk scoring using the CogDrisk framework. Univariate and multivariate linear regression models were conducted to investigate the relationships between modifiable dementia risk and sMRI brain volumes, the exponent of EEG 1/f spectral power, and cognition, whilst controlling for non-modifiable factors.
Results: Smaller global white matter volume (F(1,87) = 6.884, R2 = 0.073, P = .010), and not grey (F(1,87) = 0.540, R2 = 0.006, P = .468) or ventricle volume (F(1,87) = 0.087, R2 = 0.001, P = .769), was associated with higher modifiable dementia risk. A lower exponent, reflecting a flatter 1/f spectral power distribution, was associated with higher dementia risk at frontal (F(1,92) = 4.096, R2 = 0.043, P = .046) but not temporal regions. No significant associations were found between cognitive performance and dementia risk. In multivariate analyses, both white matter volume and the exponent of the 1/f spectral power distribution independently associated with dementia risk.
Conclusions: Structural and functional neurobiological markers of early brain ageing, but not cognitive function, are independently associated with modifiable dementia risk in healthy older adults.
Background: We aimed to examine whether current and lifetime night shift work is associated with accelerated biological ageing and the potential role of body mass index (BMI) in mediating the association.
Methods: Data were sourced from the UK Biobank cohort. This study included participants who reported detailed information on their current work schedule and had complete data to calculate PhenoAge. The outcome of interest was biological ageing, measured by PhenoAge acceleration. Multivariable linear regression models were conducted to test the relationship between night shift work and biological ageing. Mediation analyses were performed.
Results: Of the 182 064 participants included, the mean age was 52.6 years, and 51.1% were male. After adjustment for chronological age and sex, compared with day workers, shift workers without night shift, irregular night shift workers and permanent night shift workers were associated with 0.59-, 0.87- and 1.30-year increase in biological ageing, respectively (P for trend <.001). Considering the lifetime work schedule, participants who worked night shifts >10 years and participants who worked >8 night shifts each month showed increased biological age acceleration [>10 years: β = 0.54, 95% confidence interval (CI) 0.29-0.79; >8 times/month: β = 0.29, 95% CI 0.07-0.50]. The mediation analysis showed that BMI mediated the associations between night shift work and biological age acceleration by 36%-53%.
Conclusions: We showed that night shift work was associated with accelerated biological ageing. Our findings highlight the interventions on appropriate shift work schedules and weight management in night shift workers, which may slow the biological ageing process and ultimately reduce the burden of age-related diseases.
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