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Cam-CAN

Publications and source records attributed to Cam-CAN.

3 recordsLinked to original sources

Age-related differences in self-reported sleep quality predict healthy ageing across multiple domains: a multi-modal cohort of 2406 adults

ObjectivesTo examine age related differences in self-reported sleep quality and their associations with health outcomes across four domains: Physical Health, Cognitive Health, Mental Health and Neural Health.\n\nSettingCam-CAN is a cohort study in East Anglia/England, which collected self-reported health and lifestyle questions as well as a range of objective measures from healthy adults.\n\nParticipants2406 healthy adults (age 18-98) answered questions about their sleep quality (Pittsburgh Sleep Quality Index) and measures of Physical, Cognitive, Mental, and Neural Health. A subset of 641 individuals provided measures of brain structure.\n\nMain outcome measuresPittsburgh Sleep Quality Index scores (PSQI) of sleep, and scores across tests within the four domains of health. Latent Class Analysis (LCA) is used to identify sleep types across the lifespan. Bayesian regressions quantify the presence, and absence, of relationships between sleep quality and health measures.\n\nResultsBetter sleep is generally associated with better health outcomes, strongly so for mental health, moderately for cognitive and physical health, but not for sleep quality and neural health. Latent Class Analysis identified four sleep types: Good sleepers (68.6%, most frequent in middle age), inefficient sleepers (13.05%, most frequent in old age), Delayed sleepers (9.76%, most frequent in young adults) and poor sleepers (8.6%, most frequent in old age). There is little evidence for interactions between sleep quality and age on health outcomes. Finally, we observe u-shaped associations between sleep duration and mental health (depression and anxiety) as well as self-reported general health, such that both short and long sleep were associated with poorer outcomes.\n\nConclusionsLifespan changes in sleep quality are multifaceted and not captured well by summary measures, but instead as partially independent symptoms that vary in prevalence across the lifespan. Better self-reported sleep is associated with better health outcomes, and the strength of these associations differs across health domains. Notably, we do observed associations between self-reported sleep quality and white matter.\n\nFundingBiotechnology and Biological Sciences Research Council (grant number BB/H008217/1). RAK is supported by the Wellcome Trust (grant number 107392/Z/15/Z and the UK Medical Research Council (MC-A060-5PR61).\n\nStrengths and limitations of this studyO_LIBroad phenotypic assessment of healthy ageing across multiple health domains\nC_LIO_LIAdvanced analytic techniques (i.e. Latent Class Analysis regression) allows new insights\nC_LIO_LIA uniquely large neuroimaging sample combined with Bayesian inference allows for quantification of evidence for the null hypothesis\nC_LIO_LISubjective sleep measures may have drawbacks in older samples\nC_LIO_LICross-sectional data precludes modelling of within subject changes\nC_LI

Neuroscience

Age-Related Delay in Visual and Auditory Evoked Responses is Mediated by White- and Gray-matter Differences

Slowing is a common feature of ageing, yet a direct relationship between neural slowing and brain atrophy is yet to be established in healthy humans. We combine magnetoencephalographic (MEG) measures of neural processing speed with magnetic resonance imaging (MRI) measures of white- and gray-matter in a large (N=617, 18-88yrs), population-derived cohort (www.cam-can.org) to investigate the relationship between age-related structural differences and VEF and AEF delay across two different tasks. Using a novel technique, we show that VEFs exhibit a constant delay, whereas AEPs exhibit delay that accumulates over time. Visual delay is mediated by white-matter microstructure in the optic radiation, presumably reflecting increased transmission time, whereas auditory delay is mediated by gray-matter differences in auditory cortex, presumably reflecting less efficient local processing. Our results demonstrate that age has dissociable effects on neural processing speed, and that these effects relate to different types of brain atrophy.

Neuroscience

A watershed model of individual differences in fluid intelligence

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.

Neuroscience